analysing the effects of anthropogenic activities on …...- 2 - analysing the effects of...

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Murdoch University, Western Australia

2013

Analysing the effects of anthropogenic

activities on two aquatic ecosystems in

Western Australia and identifying

sustainable policies for ecosystem-

based management

Thesis submitted for the degree of

Doctor of Philosophy

Sarah Fretzer

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Analysing the effects of anthropogenic activities on two aquatic

ecosystems in Western Australia and identifying sustainable policies for

ecosystem-based management

Submitted by

Sarah Fretzer

Diplom in Marine Biology, University Bremen, Germany, 2006

Diplomvorprüfung in Biology, University Regensburg, Germany, 2003

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This thesis is presented for the degree of Doctor of Philosophy of

Murdoch University, Western Australia, 2013

I declare that this thesis is my own account of my research and

contains as its main content work that has not previously been

submitted for a degree at any tertiary education institution

______________________

Sarah Fretzer

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Abstract

Anthropogenic impacts such as fishing and eutrophication are significant challenges

to the sustainable management of aquatic ecosystems. This study used two

ecosystem modelling techniques to investigate the effects of fishing and

eutrophication on aquatic ecosystems in Western Australia.

Firstly, a qualitative modelling technique called ‘loop analysis’ or ‘qualitative

modelling’ was used to characterise the dynamics of the seagrass ecosystem in Shark

Bay, Western Australia (Chapter 2). A qualitative model based on differential

equations, was developed to represent the dynamics of the seagrass ecosystem,

particularly interactions among tiger sharks, megafauna (e.g. dugongs), and

megafauna prey (Fig. 2.2). Although the model structure generated some uncertainty

about model predictions and model stability, it was possible to assess the stability of

the model and to determine the response signs of model variables by applying data

and magnifying loops.

Qualitative modelling analyses indicated a strong top-down control by tiger sharks

and suggested that this controlling effect occurred in four stages. A step-by-step

increase in tiger sharks (States 1 and 2) led to a habitat shift by the megafauna out of

seagrass meadows and into safer, deeper channel habitat. A step-by-step decrease in

tiger shark numbers led to the megafauna returning to seagrass meadows, leading to

a decrease of megafauna prey in this habitat (steps 3 and 4).

Thus, tiger sharks influenced the use of seagrass habitats by megafauna species

through direct and behaviourally mediated impacts. Further, megafauna responses

to tiger shark predation risk established alternating predation pressure on different

prey groups within seagrass habitats. Curiously, despite the fact that only some

megafauna species (e.g. dugongs) are major components of diet of the adult tiger

sharks, the perceived predation risk created by the high abundance of of tiger sharks

in summer appears sufficient to cause megafauna species to leave (or under-utilise)

feeding habitats in seagrass meadows. Thus, the modelling results suggest that the

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abundance of tiger sharks exerts an important top-down, regulatory influence on the

other ecosystem components.

This regulatory system has the potential to become imbalanced if there is a decrease

in the abundance of adult tiger sharks in Shark Bay, as has occurred tiger shark

populations in other areas worldwide. Targeting of tiger sharks by fishers in the

waters of Northern Australia and Indonesia has increased steadily during the last

years and impact the tiger shark stock in Shark Bay if, as has been hypothesized,

there is single common stock. A qualitative trophic model suggested that the

activities of recreational fishermen within Shark Bay reduce prey availability for

juvenile tiger sharks, an impact which might adversely affect the tiger shark

population and, thus, the dynamics of this seagrass ecosystem.

Evidence of the ecological importance of tiger sharks and the potential impact of a

population decline emphasises the need to sustain the tiger shark population in

Shark Bay.

In the second part of this study, a quantitative modelling technique, Ecopath with

Ecosim and Ecospace, was applied to the ecosystem of the Peel-Harvey Estuary,

Western Australia. A key impact on this ecosystem is the the Dawesville Channel, an

artificial entrance channel was constructed in the mid-1990s to increase the flushing

and reduce nutrient concentrations in the estuary.

Ecopath was used to analyse the impact of the Dawesville Channel on the estuarine

ecosystem. A large dataset was collected for model development, a process that

uncovered significant data gaps (e.g. missing data on detritus pool and dietary

information and indicated important areas for further research (Chapter 3).

Two identical Ecopath models (comprising 30 living functional groups) were

otherwise developed for the Peel-Harvey Estuary to describe the state of the

ecosystem before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville

Channel (Chapter 4). Modelling found that, in addition to changes in the community

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structure of plants, fish and invertebrates, the entire ecosystem of the Peel-Harvey

Estuary has declined drastically in total biomass since the opening of the Dawesville

Channel, as has the biomass at each trophic level and in the size of flows between

the functional groups. Changes in flows and transfer efficiencies suggested a change

in the functioning of the ecosystem in which consumption has become a more

important and more efficient flow since the opening of the channel. Analysis of

network and system statistics indicated that food web structure had also changed,

with more linkages in the ‘post DC’ model and thus a more web-like structure than in

the ‘pre DC’ model. Modelling also identified changes in cycling processes and

suggested that the ecosystem in the ‘post DC’ model was not able to keep carbon

within the system, even though: (i) the food web has developed more linkages and

(ii) with less primary production and less cycling, the size of the ecosystem has

decreased drastically since the channel opening.

Overall, the results of the Ecopath modelling indicated that the Dawesville Channel

has markedly impacted the features, functioning and services of the Peel-Harvey

Estuary (Chapter 4). Several indices were applied that suggested that both the ‘pre

DC’ and the ‘post DC’ models were highly immature. Ecopath was also applied to

investigate the impact of the Dawesville Channel on ecosystem services. Ecopath

modelling indicated that all ecosystem services had declined, such as provisioning

services (catches), regulating services (CO2-Fixation) and supporting services

(nutrient cycling, primary production and biodiversity). Unfortunately, it was not

possible to locate data relating to cultural services (tourism) for the ‘pre DC’ model.

To support the reliability of the Ecopath and Ecosim predictions, model uncertainty

and the sensitivity of the parameter settings were assessed in detail (Chapter 5).

Overall, the results of this analysis indicated that the parameter settings for the ‘pre

DC and ‘post DC’ models were robust and did not lead to uncertainties regarding

modelling results and predictions. However, the vulnerability settings are crucial for

Ecosim and Ecospace and need to be treated with caution.

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Ecosim was applied to identify: 1) the impact and effectiveness of the selective

reduction of different primary producers and 2) the impacts of fishing on target and

non-target species on the ecosystem model (Chapter 6). The application of Ecosim

requires fitting a model to time series data; for this study, the sourcing and fitting of

time-series data indicated the importance and uncertainty of vulnerability settings.

Three categories of vulnerabilities were identified: (a) vulnerabilities that did not

have any effect on time series fitting (category 1); (b) interactions in which the

lowest sums of squares occurred at low vulnerability settings (v=1 or 2, category 2);

and settings that had a drastic impact on model fitting (category 3).

The Ecosim simulations indicated that fishing affected almost all functional groups in

the model, not just the target species. The recreational fishing sector also had a very

strong impact on many functional groups, particularly Blue Swimmer Crabs and other

invertebrate groups like bivalves and gastropods. The commercial fishing sector

affected functional groups less than the recreational sector, but affected a range of

estuarine fish groups including non-target fish species. Thus, the results of this study

suggest that it may be not advisable to close those fleets completely as some aspects

of the estuary ecosystem appear to benefit from increasing fishing pressure. Some

fish groups and some target species responded positively to the closure of certain

fleets, while others – particularly waterbirds and other top predators – did not (Table

6.8). Ecosim analyses highlighted the need for more data to ensure sustainable

management, but suggested that the coexistence of fleets might be a better solution

for sustaining catches and group biomasses in the future.

Ecosim modelling indicated that selective plant removal is a reasonable management

tool for this estuary. However, nutrient reduction and, thus, the permanent

reduction of microscopic algae appears to be more ecologically and economically

worthwhile (Fig. 6.12). Removing aquatic plant groups showed no significant long-

term change in biomasses and the magnitude of short-term effects was much higher

than for long-term effects. The Ecosim simulations demonstrated that only a

permanent reduction in microscopic algae led to a reduction in total biomass.

Reducing phytoplankton might be worthwhile because, although blooms of

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Nodularia spumigena no longer occur in the estuary because the salinities are too

high (Huber, 1985), the estuary now contains several phytoplankton species (e.g.

Heterosigma akashiwo) that cause blooms in other ecosystems (Guiry & Guiry,

2010). While the effects of phytoplankton blooms on the ecosystem depend on the

size (i.e. the biomass) of the bloom, even blooms that only double the biomass of

microscopic algae can have drastic long-term effects. This study supports the

conclusion that a reduction in phytoplankton through management of nutrient input

in the estuarine catchment represents the only ecological and economical

management scenario that provides long-term sustainability for this ecosystem

(Chapter 6).

Ecospace modelling represents biomass dynamics over two-dimensional space and

time. For this study, a model with fours habitats (shallow mud, deep sand, rocks and

plant habitat) was developed. By applying Ecospace, the effects of reducing plant

habitat and the effectiveness of two Marine Protected Areas were investigated, with

specific consideration of waterbirds (Chapter 7). The Ecospace simulations suggested

that waterbirds and piscivorous waterbirds were impacted by fishing and would

benefit slightly from an introduction of a MPA, in particular a MPA at Point Grey.

Further, the results of this Ecospace scenario indicated that waterbirds would profit

from the reduction of plant habitat, whereas piscivorous waterbirds showed a small

decline in biomass after removal of aquatic plants. Under the current fishing effort,

the total biomass of the system and of the fish community increased. Thus, while the

major prey groups of piscivorous waterbirds increased in biomass, but piscivorous

waterbirds did not benefit from increased prey biomasses in the model, presumably

because of the competition for fish. Ecospace modelling indicated that the catches

would also increase drastically and, thus, that piscivorous waterbirds were in direct

competition with the fishing sectors and other piscivorous predators (e.g. dolphins

and sharks) and were out-competed for fish. The modelling suggest that the

sustainable management of the fishing sectors is essential for bird conservation.

A MPA at Peel Inlet led to lower catches under the current fishing scenario and

catches declined even further under lower fishing effort. In contrast, after

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introducing a MPA at Point Grey, the total catch only declined when the fishing

effort was lessened. The Ecospace simulations indicated that an MPA at Point Grey

increased the biomasses of functional groups and target species and also raised the

total biomass of the system; however, these effects strongly depend on fisheries

management (Chapter 7).

Overall, the qualitative and quantitative modelling methods applied in this study

improved our understanding of the dynamics and functioning of the Shark Bay and

Peel-Harvey ecosystems (Chapter 8). Both approaches produced robust and reliable

results. If precise quantitative predictions are required for a management scenario,

Ecopath with Ecosim is the appropriate method to choose, as this approach can

deliver detailed changes in biomass and catches. In contrast, qualitative modelling

only indicates the direction of change, which might not always satisfy management

needs. However, qualitative models are the ideal method when management

decisions have to be made fast and when a detailed data set of the ecosystem is not

available.

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Acknowledgements

Special thanks go to Martin Fretzer and Andreas Steinbrecher for their support

through all these years!

I would like to thank Hugh Finn very much for his support! Hugh is the only person at

Murdoch University who ever read my thesis, the only one willing to help me and

provide guidance. If he had been my supervisor, my PhD would be a completely

different story. His advice and help are greatly appreciated.

Furthermore, I would like to thank all the people who supported the Peel-Harvey

Ecosystem Modelling Project: Simon Allen, Helen Astill, Steve Blake, David Blockley,

Anne Brearly, Natasha Coen, Peter Coulson, Jeffrey Dambacher, David Fairclough,

Dan Gaughan, Jenny Hale, Norman Hall, Christopher Hallett, Alex Hesp, Karen

Hillman, Steeg Hoeksema, Felicity Horn, Matthew Hourston, Jim Lane, Paul Lavery,

Rod Lenanton, Neil Loneragan, Hector Lozano, Arthur McComb, Sarah Metcalf, Brett

Molony, Kane Moyle, Karen Olkowski, Eric Paling, Margaret Platell, Bob Pond,

Damien Postma, Ian Potter, Frank Prokop, Peter Rogers, Tom Rose, Cecily Scutt,

Jenny Shaw, Spencer Shute, Luke Twomey, Fiona Valesini, John Watts, Michelle

Wildsmith, Brent Wise, Lauren Veale and Christian Summit.

Very special thanks go to Villy Christensen and Carl Walters for their scientific advice

and their quick email responses! I have to thank the Ewe team at University of British

Columbia, Sherman Lai, Jeroen Steenbeek and Joe B., for helping me out whenever I

had issues with the Ecopath software.

Furthermore, I would like to thank Michael Heithaus and Aaron Wirsing for their

feedback and suggestions, when I was modelling the dynamics of the seagrass

ecosystem in Shark Bay.

My studies were funded by Murdoch University and by the Western Australian

Science Institution (WAMSI).

Thank you very much!

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Table of contents Abstract ............................................................................................................................ 4

Acknowledgements ........................................................................................................ 10

Chapter 1 – Introduction ................................................................................................. 22

Chapter 2 – The seagrass ecosystem of Shark Bay - the role of tiger sharks and the impacts of fishing ............................................................................................................ 28

2.1 Introduction ....................................................................................................... 28

2.2 The Shark Bay ecosystem: tiger shark – megafauna interaction ....................... 29

2.3 Use of qualitative modelling to interactions in biotic communities ................. 34

2.4 Materials and Methods ...................................................................................... 34

Part I - Modelling the dynamics of the seagrass ecosystem ................................ 35

2.4.1 Differential equations and interaction coefficients .................................... 36

2.4.2 Problems with model structure .................................................................. 45

2.4.3 Analysis of submodels ................................................................................. 46

2.4.4 Calculating stability and response signs by magnifying interaction coefficients ........................................................................................................... 56

Part II - Modelling trophic interactions and the impacts of fishing on the seagrass ecosystem .............................................................................................. 90

2.4.5 Modelling trophic interactions ................................................................... 90

2.5 Results ................................................................................................................ 92

2.5.1 The dynamics of the seagrass ecosystem in Shark Bay .............................. 92

2.5.2 The impact of fishing on the seagrass ecosystem in Shark Bay .................. 95

2.6 Discussion ........................................................................................................... 97

Chapter 3 - Ecopath models of the Peel-Harvey Estuary, Western Australia, before and after the opening of an artificial entrance channel, the Dawesville Channel - Data analysis and parameter input .................................................................................. 99

3.1 Introduction ................................................................................................. 99

3.1.1 History of the Peel-Harvey Estuary ........................................................ 99

3.1.2 Aims of this Chapter ............................................................................. 102

3.1.3 The Ecopath model .............................................................................. 103

3.2 Model structure ......................................................................................... 105

3.3 Parameter input ......................................................................................... 109

3.3.1 Dolphins (Functional group 1) ............................................................. 109

3.3.2 Birds (Functional groups 2 and 3) ........................................................ 112

3.3.3 Sharks (Functional group 4) ................................................................. 116

3.3.4 Fish (Functional groups 5 to 17) .......................................................... 118

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3.3.5 Bivalves (Functional group 18) ............................................................. 133

3.3.6 Gastropods (Functional group 19) ....................................................... 135

3.3.7 Western King Prawn (Functional group 20) ......................................... 137

3.3.8 Blue Swimmer Crab (Functional group 21) .......................................... 139

3.3.9 Crustaceans (Functional group 22) ...................................................... 141

3.3.10 Worms (Functional group 23) .............................................................. 144

3.3.11 Zooplankton (Functional group 24) ..................................................... 147

3.3.12 Microscopic algae (Functional group 25) ............................................. 148

3.3.13 Plants (Functional groups 26 to 30) ..................................................... 152

3.3.14 Detritus (Functional group 31) ............................................................. 155

3.4 Data gaps and data pedigree ..................................................................... 156

3.5 Discussion ................................................................................................... 160

Chapter 4 – The impact of an artificial entrance channel on the ecosystem of the Peel-Harvey Estuary, Western Australia ........................................................................ 162

4.1 Introduction ............................................................................................... 162

4.2 Materials and Methods .................................................................................... 167

4.2.1 Analysis ..................................................................................................... 167

4.2.2 Balancing of the models ....................................................................... 170

4.3 Results ........................................................................................................ 172

4.3.1 Comparisons of basic model parameters ................................................. 172

4.3.2 Comparisons of network & system statistics ............................................ 195

4.3.3 Ecosystem services.................................................................................... 206

4.3.4 Model maturity ......................................................................................... 207

4.4 Discussion ......................................................................................................... 208

4.4.1 Comparison of basic model parameters ................................................... 208

4.4.2 Comparisons of network & system statistics and ecosystem maturity.... 211

4.4.3 Ecosystem services.................................................................................... 215

4.4.4 Conclusion ................................................................................................. 216

Chapter 5 – Sensitivity analysis and model stability ....................................................... 218

5.1 Introduction ............................................................................................... 218

5.2 Methodology .............................................................................................. 220

5.2.1 Ecopath sensitivity analysis ........................................................................ 220

5.2.2 Ecosim model stability analysis .................................................................. 220

5.3 Results & Discussion .................................................................................. 221

5.3.1 Ecopath sensitivity analysis .................................................................. 221

5.3.2 Ecosim analysis..................................................................................... 226

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Chapter 6 – The effects of the selective reduction of primary producers and the impacts of fishing on the ecosystem of the Peel-Harvey Estuary .................................... 240

6.1 Introduction ..................................................................................................... 240


6.2.1 Ecosim master equation ........................................................................... 241

6.2.2 Tuning the model ...................................................................................... 242

6.3 Results & Discussion ........................................................................................ 255

6.4 General Conclusions......................................................................................... 281

Chapter 7 – Use of Ecospace as an evaluation method to identify an effective Marine Protected Area and to assess the effects of plant habitat reduction through dredging on the ecosystem of the Peel-Harvey Estuary, with emphasis on the impacts on waterbirds .................................................................................................. 285

7.1 Introduction ............................................................................................................ 285


7.3 Results .............................................................................................................. 299

7.4 Discussion ......................................................................................................... 321

Chapter 8 – General Discussion ..................................................................................... 327

I. Modelling techniques ......................................................................................... 327

II. Considerations for Ecosystem Management ..................................................... 332

References .................................................................................................................... 337

Appendix ...................................................................................................................... 360

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List of Figures: Page Figure 2.1 Map showing the location of Shark Bay in Australia (a), the gulfs with

the location of the study area (*) in the eastern gulf (b) and the study area (c) in Shark Bay with the transects over shallow in light grey and deeper habitat in dark grey (used with permission from Michael Heithaus, Florida International University)

33

Figure 2.2 A+B

Qualitative model showing the interaction linkages between tiger sharks, different megafauna subpopulations (M2, M1sg, M1ch, M*) and the prey of the latter (Psg, Pch) in two different habitats (i.e. the seagrass and the channel habitat) in times of low (A) and high (B) tiger shark abundance. The model presented in Figure 2.2A is hereafter referred to as ‘Model 1’ and the model presented in Figure 2.2B as ‘Model 2’. The overall model is referred to as the ‘Overall Model’.

44

Figure 2.3 Submodel 1 A+B: Qualitative model showing the interaction linkages between tiger sharks, two megafauna subpopulations in the seagrass (Msg) and in the channel habitat (Mch) and the prey of the latter (Psg, Pch) in two different habitats (i.e. the seagrass and the channel habitat).

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Figure 2.4 Submodel 2 A+B: Qualitative model showing the interaction linkages between tiger sharks, the megafauna populations (M) and the prey of the latter (Psg, Pch) in two different habitats, such as the seagrass and the channel habitat.

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Figure. 2.5 Different states of Figure 2.2 A: Qualitative model showing the response signs of the system in two different stages after an increase (state A.1) and a decrease (state A.2) in the tiger shark variable in model Fig. 2.2 A.

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Figure 2.6 Different states of Fig. 2.2 B: Qualitative model showing the response signs of the system in two different stages after an increase (state B.1) and a decrease (state B.2) in the tiger shark variable in model Fig. 2.2 B.

89

Figure 2.7 “trophic interactions and impact of fisheries” – a model of the ontogenetic shift in the diet of tiger sharks and the effects of fishing activities on different functional groups within the Shark Bay seagrass ecosystem.

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Figure 2.8 The four dynamic states of Figure 2.2: Qualitative models showing the dynamics of the modelled seagrass ecosystem in four states after positive and negative press perturbations on the tiger shark variables in Fig. 2.2 A and B.

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Figure 3.1 Map of the Peel-Harvey Estuary in Western Australia 103

Figure 4.1 Comparison of the trophic level of each functional group before (‘pre DC’) and after (‘post DC’) the Dawesville Channel opening

175

Figure 4.2 Biomass per trophic level before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.3 Percentages of total biomass for phytoplankton, zooplankton and non-planktivore groups before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.4 Composition of total primary production, presenting biomass estimates in tkm-2 for aquatic plants (functional groups 26 to 30) and phytoplankton

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Figure 4.5 Community structure of aquatic plants presented in percentage of total plant biomass for the time period before (pre DC) and after (post DC) the opening of the Dawesville Channel in the Peel-Harvey Estuary

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Figure 4.6 Biomass estimates in tkm-2 for target (functional groups 20 and 21) and 179

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non-target (functional groups 18, 19, 22 and 23) invertebrate groups before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

Figure 4.7 Biomass estimates in tkm-2 for commercially important target species before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.8 Biomass estimates in tkm-2 for target species, non-target species, marine and estuarine fish groups before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.9 Community structure of teleost fish presented in percentage of total fish biomass before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel in the Peel-Harvey Estuary

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Figure 4.10 Omnivory index (y-axis) for each predator group (x-axis) before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.11 Net (P/A) and gross (P/Q) food conversion efficiencies (y-axis) for each predator group (group 1 to 24, x-axis) before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.12 Respiration/Assimilation ratio (y-axis) for each consumer group (group 1 to 24, x-axis) before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.13 % change in Respiration/Biomass ratio (y-axis) for each consumer group (group 1 to 24, x-axis) before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.14 % of total predation mortality caused by the main (>1 %) predator groups before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.15 % of predation mortality caused by the main (>1 %) vertebrate predator groups (groups 1 to 17) before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.16 % of predation mortality on main (>1 %) prey groups before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.17 Consumption (in %) of fish, invertebrates and primary producers by the vertebrate predator groups, before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.18 % change in main dietary components (fish, invertebrates and primary producers) of vertebrate predator groups (y-axis), before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.19 Estimates of fishing mortalities F for commercially important target species before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.20 Estimates of fishing mortality F and predation mortality for the commercially most important target species, the Blue Swimmer Crab

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Figure 4.21 Quantity of fish (groups 5 to 17), Western King Prawns (group 20) and Blue Swimmer Crabs (group 21) consumed by predators and fished (in t/km²), before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

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Figure 4.22 Mixed trophic impact of functional groups and fishing fleets in the ‘pre DC’ model

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Figure 4.23 Mixed trophic impact of the functional groups and fishing fleets in the ‘post DC’ model

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Figure 4.24 Niche overlap plots of the ‘pre DC’ (top) and ‘post DC’ Ecopath models 198

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presenting prey overlap index (y-axis) and predator overlap index (x-axis)

Figure 4.25 A+B

Flow diagrams of the ‘pre DC’ (top) and ‘post DC’ Ecopath models presenting the functional groups ordered by trophic levels with box size exhibiting relative group biomasses

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Figure 4.26 Lindeman spines of the ‘pre DC’ (top) and ‘post DC’ Ecopath models presenting the flows, as well as biomasses and total system throughput (TST in %) for each trophic level and the detritus pool

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Figure 4.27 % change in throughput (sum of all flows) per trophic level 202

Figure 4.28 Structure of flows as percentage of throuput (sum of all flows) per trophic level

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Figure 5.1 Ecosim scenario in the ‘pre DC’ model run over 50 years without disturbance (A) and with a 50% decrease in total fishing effort between year 5 and 10 (B)

228

Figure 5.2 Impact of fishing closure of all gears on the functional groups in the ‘pre DC’ model (A) and post DC model (B)

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Figure 5.3 A Impact of different vulnerability settings (1, 3, 10) on the relative biomasses of the functional groups of the ‘pre DC’ model

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Figure 5.3 B Impact of different vulnerability settings (50, 100, 150) on the relative biomasses of the functional groups of the ‘pre DC’ model

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Figure 5.4 Ecosim scenario in the ‘post DC’ model run over 50 years without disturbance (A) and with a 50% decrease in total fishing effort between year 5 and 10 (B)

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Figure 5.5 A Impact of different vulnerability settings (1, 3, 10) on the relative biomasses of the functional groups of the ‘post DC’ model

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Figure 5.5 B Impact of different vulnerability settings (50, 100, 150) on the relative biomasses of the functional groups of the ‘post DC’ model

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Figure 5.6 % change in relative biomasses between default value (0.5) and adjusted feeding time factors for different vulnerability settings in a scenario that is run over 50 years

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Figure 6.1 Relative weight of defined plant groups that function as trophic mediator in the ‘post DC’ model

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Figure 6.2 Shape of long-term forcing function generated for Cladophora montagneana and microscopic algae exhibiting the massive decline in biomasses in times of the Dawesville Channel opening

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Figure 6.3 Time series data showing the catch per unit effort in kg per number of vessels for the most important target species in the Peel-Harvey Estuary from 1976 to 2007

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Figure 6.4 Time series data showing the fishing effort in number of vessels for different gears used by the commercial fishery in the Peel-Harvey Estuary from 1976 to 2007

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Figure 6.5 Time series data generated for Blue Swimmer crab and Aldrichetta forsteri

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Figure 6.6 Time series data generated for Mugil cephalus and Arripis georgianus 249

Figure 6.7 Sums of squares of varying vulnerability settings of the predators marine carnivorous (group 6) and marine detritivorous (group 8) fish, presenting the pattern of category 2 interactions

258

Figure 6.8 Sums of squares of varying vulnerability settings of the predator groups crustaceans, worms and zooplankton, showing interactions that had drastic impact on the fitting procedure

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Figure 6.9 Tuning results for the functional groups Blue Swimmer Crabs, Mugil 260

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cephalus, Aldrichetta forsteri and Arripis georgianus showing the generated historical data points (dot points) and the simulated time pattern (solid line) of the ‘post DC’ Ecosim model

Figure 6.10 Long-term effect (after 10 years) of 1 year (dark grey) and 3 year (light grey) selective algal harvesting (algae, macrophytes, Chaetomorpha linum, Cladophora montagneana and seagrass) on the biomasses of all functional groups and on total biomass

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Figure 6.11 Short-term effect (year after harvesting ended) of 1 year (dark grey) and 3 year (light grey) selective algal harvesting (algae, macrophytes, Chaetomorpha linum, Cladophora montagneana and seagrass) on the biomasses of all functional groups and on total biomass

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Figure 6.12 Long-term effect (10 years after harvesting) of reducing microscopic algae by 10% or 50% biomass on all other functional groups and effect of a permanent 10% reduction in phytoplankton biomass

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Figure 6.13 Short-term and long-term effects of phytoplankton blooms, caused by an increase in biomass of microscopic algae by factor 2 and by factor 10

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Figure 7.1 Basemap of the Peel-Harvey Estuary showing the assumed two levels of relative primary production with highest productivity closest to the discharging river entrances (the river mouths of the Serpentine and Murray Rivers in the north-east of the estuary and of the Harvey River in the south).

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Figure 7.2 Basemap of the Peel-Harvey Estuary showing the current distribution of the four habitats

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Figure 7.3 Basemap of the Peel-Harvey Estuary showing the hypothesized habitat distribution after the reduction of plant habitat through dredging in the north-eastern part of Peel Inlet.

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Figure 7.4 Basemap of the Peel-Harvey Estuary showing the hypothesized Marine Protected Area (MPA) at the eastern part of Peel Inlet

297

Figure 7.5 Basemap of the Peel-Harvey Estuary showing the hypothesized Marine Protected Area (MPA) at the Point Grey peninsula

298

Figure 7.6 Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary (basemap Fig. 7.2, Scenario shown in Table 7.3 under current fishing scenario with F = 1)

302

Figure 7.7 Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario where the commercial fishing sector was closed (scenario based on basemap Fig. 7.2, scenario shown in Table 7.3 no commercial fishing)

303

Figure 7.8 Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario where the recreational fishing sector was closed (scenario based on basemap Fig. 7.2, scenario shown in Table 7.3 no recreational fishing)

304

Figure 7.9 Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario where aquatic plants in the Peel Inlet are removed through dredging (scenario based on basemap Fig. 7.3, scenario shown in Table 7.4 run with settings presented in Table 7.1)

306

Figure 7.10 Effects of plant removal and, consequently, reduction of plant habitat, presented as percentage (%) change in biomass for each functional group

307

Figure 7.11 Biomass distributions predicted by Ecospace for the Peel-Harvey 310

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Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area in the Peel Inlet (scenario based on basemap Fig. 7.4)

Figure 7.12 Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area at Point Grey

311

Figure 7.13 Percentage (%) change in biomass for each functional group after introduction of a MPA at the eastern Peel Inlet (Fig. 7.4, grey bars) or at Point Grey (Fig. 7.5, black bars)

312

Figure 7.14 Percentage (%) change in catch exhibited for the different commercial fishing gears and the recreational fishing sector after introduction of a MPA at the eastern Peel Inlet (Fig. 7.4, grey bars) or at Point Grey (Fig. 7.5, black bars)

313

Figure 7.15 Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area in the Peel Inlet after closing the recreational fishing sector

314

Figure 7.16 Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area in the Peel Inlet after closing the commercial fishing sector

315

Figure 7.17 Percentage (%) change in biomass exhibited for the different functional groups after introduction of a MPA at Peel Inlet under the current fishing scenario (black bars) and under the scenarios of closure of commercial (grey bars) and recreational (light grey bars) fishing sectors.

316

Figure 7.18 Percentage (%) change in catch exhibited for the different commercial fishing gears and the recreational fishing sector after introduction of a MPA at the eastern Peel Inlet under the current fishing scenario (black bars) and under the scenarios of closure of the commercial (grey bars) and recreational (light grey bars) fishing sectors.

317

Figure 7.19 Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area at Point Grey after closing the recreational fishing sector

318

Figure 7.20 Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area at Point Grey after closing the commercial fishing sector

319

Figure 7.21 Percentage (%) change in biomass exhibited for the different functional groups after introduction of a MPA at Point Grey under the current fishing scenario (black bars) and under the scenarios of closure of commercial (grey bars) and recreational (light grey bars) fishing sectors.

320

Figure 7.22 Percentage (%) change in catch exhibited for the different commercial fishing gears and the recreational fishing sector after introduction of a MPA at Point Grey under the current fishing scenario (black bars) and under the scenarios of closure of commercial (grey bars) and recreational (light grey bars) fishing sectors.

321

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List of Tables: Page Table 2.1 Relative abundance estimates of the megafauna subpopulations. 57

Table 2.2 Estimated magnitudes of the interaction coefficients αi,j calculated according to the assumptions described in section 2.3.1.4.

60

Table 2.3: Absolute values of magnified feedback cycles to estimate the strength of negative versus positive loops for determining the stability of model Fig. 2.2B

62

Table 2.4 Determining the response sign of the tiger shark variable in model Fig. 2.2 A

63

Table 2.5 Determining the response signs of the M2 variable in model Fig. 2.2 A 65

Table 2.6 Determining the response signs of M1sg variable in model Fig. 2.2 A 67

Table 2.7 Determining the response sign of the M1ch variable in model Fig. 2.2 A 68

Table 2.8 Determining the response sign of the M* variable in model Fig. 2.2 A 69

Table 2.9 Determining the response sign of the Psg variable in model Fig. 2.2 A 70

Table 2.10 Determining the response sign of the Pch variable in model Fig. 2.2 A 72

Table 2.11 Determining the response sign of the tiger shark variable in model Fig. 2.2 B

76

Table 2.12 Determining the response sign of the M2 variable in model Fig. 2.2 B 78

Table 2.13 Determining the response sign of the M1sg variable in model Fig. 2.2 B 79

Table 2.14 Determining the response sign of the M1ch variable in model Fig. 2.2 B 81

Table 2.15 Determining the response sign of the M* variable in model Fig. 2.2 B 83

Table 2.16 Determining the response sign of the Psg variable in model Fig. 2.2 B 84

Table 2.17 Determining the response sign of the Pch variable in model Fig. 2.2 B 86

Table 2.18 Press perturbations (pp) performed in Figure 2.7 and the system’s response described by listing the response signs of the different model components and exhibiting the components with the highest probability that the response sign is correct (p = 1, based on 95% bound on proportion of correct sign (Hosack et al., 2008)).

96

Table 3. 1 Functional groups of the ‘pre DC’ and ‘post DC’ Ecopath models of the Peel-Harvey Estuary

107

Table 3.2 Parameter input of the functional group ‘dolphins’ 111

Table 3.3 Diet entry of the functional group ‘dolphins’ 112

Table 3.4 List of most numerous bird species considered for the ‘pre DC’ and ‘post DC’ Ecopath models in the functional groups ‘waterbirds’ and ‘piscivorous waterbirds’

113

Table 3.5 Parameter input for the functional groups ‘waterbirds’ and ‘piscivorous waterbirds’ for both Ecopath models of the Peel-Harvey Estuary

113

Table 3.6 Dietary composition of the two functional bird groups estimated for both Ecopath models

115

Table 3.7 Literature cited for quantitative dietary information of different bird species, which was used to estimate the dietary composition described in Table 3.6

116

Table 3.8 Values of the functional group ‘sharks’ for the ‘pre DC’ and ‘post DC’ Ecopath models

117

Table 3.9 Dietary consumption of the functional group ‘sharks’ 118

Table 3.10 List of species of the functional fish groups that were recorded in the pre and post DC period and the list of biomass estimates for each species in tkm-2

119

Table 3.11 Estimated catch (in tkm-2) of the functional fish groups that were 123

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estimated for different fishing gears in the pre (1980) and post (2000) DC period

Table 3.12 Mortality estimates for the functional fish groups for the ‘pre DC’ and ‘post DC’ Ecopath models

124

Table 3.13 Natural mortality M and Q/B ratio of each fish species that were recorded in the pre and post DC period.

126

Table 3.14 Data input for the functional fish groups for the ‘pre DC’ and ‘post DC’ Ecopath models

129

Table 3.15 Reference list for the dietary information that was used for the functional fish groups in the ‘pre’ and ‘post DC’ Peel-Harvey Ecopath models.

130

Table 3.16 Dietary matrix of fish for the ‘pre DC’ Ecopath model 132

Table 3.17 Dietary matrix of fish for the ‘post DC’ Ecopath model 132

Table 3.18 Species list and their abundances in numbers per 0.1m-2 (Rose, 1994; Wildsmith, 2007) and parameter input for the functional group ‘bivalves’

133

Table 3.19 Species list and their abundances in numbers per 0.1m-2 (Rose, 1994; Wildsmith, 2007) and parameter input for the functional group ‘gastropods’

135

Table 3.20 Diet entry of the functional group ‘gastropods’ 137

Table 3.21 Parameter input for the functional group ‘Western King Prawn’ 138

Table 3.22 Diet entry of the functional group ‘Western King Prawn’ 139

Table 3.23 Parameter input for the functional group ‘Blue Swimmer Crabs’ 140

Table 3.24 Diet entry of the functional group ‘Blue Swimmer Crab’ 141

Table 3.25 Constituents of the functional groups ‘zooplankton’ and ‘crustaceans’ adopted from Table 6.1., Platell and Hall (2006)

141

Table 3.26 Abundances of species (no/ 0.1m²) of the functional group ‘crustaceans’ that were recorded for the ‘pre DC’ and ‘post DC’ period in the Peel-Harvey Estuary

142

Table 3.27 Parameter input for the functional group ‘crustaceans’ 144

Table 3.28 Abundances (in numbers per 0.1m-2) of recorded species that are listed for the functional group ’worms’, according to (Rose, 1994; Wildsmith, 2007)

145

Table 3.29 Parameter input for the functional group ‘worms’ 146

Table 3.30 Data input for the functional group ‘zooplankton’ 148

Table 3.31 Phytoplankton growth rates adapted from the literature for the Ecopath models of the Peel-Harvey Estuary

150

Table 3.32 Parameter input of the functional group ‘microscopic algae’ 151

Table 3.33 Parameter input for aquatic plants (functional groups 26 to 30) 153

Table 3.34 Model input and sources for the functional groups of the ‘pre DC’ and ‘post DC’ models in the Peel-Harvey Estuary

156

Table 3.35 Ecopath criteria for the pedigree assignment 158

Table 3.36 Pedigree indices for each parameter for the ‘pre DC’ and ‘post DC’ Ecopath models

159

Table 4.1 Terms and concepts used 167

Table 4.2 Basic parameters of the balanced ‚pre DC‘ and ‘post DC’ models of the Peel-Harvey Estuary

173

Table 4.3 System indices of the ‚pre DC‘ and ‚post DC‘ Ecopath models of the Peel-Harvey Estuary

204

Table 4.4 Ecosystem services of the Peel-Harvey Estuary 207

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Table 5.1 Sensitivity of estimated EE of selected functional groups of 50% change in input parameters of other functional groups in the ‘pre DC’ model (only sensitivities bigger than 10% are shown)

222

Table 5.2 Sensitivity of estimated EE of selected functional groups of 50% change in input parameters of other functional groups in the ‘post DC’ model (only sensitivities bigger than 10% are shown)

223

Table 6.1 Parameter input for Ecosim procedure for the functional groups of predators in the ‘post DC’ Ecopath model

243

Table 6.2 Vulnerability settings for each predator-prey interaction identified in the tuning process of the ‘post DC‘ Ecopath model

251

Table 6.3 List of adjusted P/B values that were reduced to a minimum value, which is the lowest value without causing model to lose balance, in the fitting procedure for the functional groups of the ‘post DC’ model

253

Table 6.4 Three categories of vulnerability settings identified in the tuning process of the ‘post DC‘ Ecopath model. Vulnerabilities were varied from 1 to 10 000.

256

Table 6.5 Development of the different functional groups without any perturbations in primary production or fishing pressure in an Ecosim scenario run over 15 years

263

Table 6.6 Changes in biomass of total catch (in %) after a 50% increase and 50% decrease of fishing effort of the recreational (rec.), beach seine, gill net and crab trap fleets; only the effort of one fleet is changed, while the other fleets in the Ecosim scenario operate with current effort (F=1) and these results here present changes in catch estimates after a time period of 10 years

277

Table 6.7 Changes in biomass (in %) of all functional groups as response to a 50% increase and 50% decrease of fishing effort of the recreational (rec), beach seine, gill net and crab trap fleets; only the effort of one fleet is changed, while the other fleets in the Ecosim scenario operate with current effort (F=1). The results present changes in biomass after a time period of 10 years

278

Table 6.8 Effects of closure of different fishing fleets on the biomasses of all functional groups; only one fleet is closed in these Ecosim scenarios, while the other fleets operate with current effort (F=1)

280

Table 7.1 Ecospace settings for the different functional groups 291

Table 7.2 Assignment of the fishing sectors/ gears to the four habitats (deep sand, shallow mud, plant cover and rock) defined for the Peel-Harvey Estuary; the commercial sector operating with nets (beach seine, gill nets), crab traps and ‘other’ fishing gears

292

Table 7.3 % change in biomass predicted by Ecospace for a scenario (Fig. 7.2) under current fishing effort and with no commercial or recreational fishing effort in the Peel-Harvey Estuary

301

Table 7.4 Biomass estimates (in t/km²) for each functional group in an Ecospace scenario where plant habitat is reduced in Peel Inlet (Fig. 7.9).

308

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Chapter 1

Introduction

Human well-being depends on healthy ecosystems (Millenium Ecosystem

Assessment, 2005). However, human activities also heavily impact aquatic

ecosystems, with adverse impacts including habitat destruction and species

extinction (IUCN, World Conservation Union, 2008). Complex ecological systems like

estuaries are typically characterised by large numbers of species and a broad range

of habitats and environmental factors, all of which are inter-linked in complex ways

(Okey & Mahmoudi, 2002; Patricio & Marques, 2006). Species diversity is essential

for the functioning of ecosystems (Chapinn III et al., 1997) and, if native species are

lost, introduced or invasive species cannot compensate their loss (Worm et al.,

2006).

Aquatic ecosystems and their biodiversity are affected by overfishing (Ortiz & Wollf,

2002; Pauly, 1998; Pauly & Christensen, 1995; Smith et al., 2011) and eutrophication

(Bradby, 1997; Brando et al., 2004; Cruz-Escalona et al., 2007). Historically, economic

interests have tended to prevail over the sustainable management and conservation

of aquatic ecosystems. Further, management has often been based on single species.

In fisheries management, for example, the populations of target species have been

the primary concern and the management bodies have focused on managing these

populations in the interest of stakeholders (Simpfendorfer, 2000; Walker, 1998).

However, the decline of a species that is not of economical interest (e.g. tiger sharks)

could adversely impact on an ecosystem (coral reef) and, thus, on other species that

are of great economic importance (tuna) (Stevens 2000). Even if only one species of

an ecosystem is of economic interest, a variety of factors need to be taken into

account for sustainable management and, the more species that are of interest, the

greater the need to sustainably manage their ecosystem (Williams, 1998).

The need to consider ecosystem-based approaches for managing fish stocks

sustainably has become apparent in recent years (Babcock et al., 2005; Link, 2002;

- 23 -

Pikitch et al., 2004; Smith et al., 2011; Smith et al., 2007). Ecosystem models are one

tool that could be used for ecosystem-based fisheries management, e.g. a trophic

mass-balance model like Ecopath (Babcock et al., 2005; Pauly et al., 2000; Zabel et

al., 2003). Ecopath, Ecosim and Ecospace have been applied worldwide to improve:

(1) our understanding of ecosystems in, for example, the Pacific (Pauly et al., 1996),

the North Sea (Christensen, 1995; Mackinson & Daskalov, 2007), Florida Shelf (Okey

& Mahmoudi, 2002), Newfoundland (Bundy et al., 2000), Western Australia (Lozano-

Montez et al., 2011), and Brazil (Wolff, 2000) and (2) management efforts aimed at

(e.g.) fishing sustainably (Lozano-Montes et al., 2012; Ortiz & Wollf, 2002; Smith et

al., 2011), managing ecosystems, e.g. management of reserves or marine parks

(Lozano-Montez et al., 2011; Ortiz et al., 2009) or dealing with eutrophication

(Barausse et al., 2007; Brando et al., 2004; Cruz-Escalona et al., 2007).

Estuaries, for example, are often impacted by eutrophication, e.g. estuaries in France

(Rybarczyk & Elkaim, 2003; Rybarczyk et al., 2003), in Italy (Brando et al., 2004), in

Portugal (Patricio & Marques, 2006), in Taiwan (Lin et al., 2007) or in Mexico (Cruz-

Escalona et al., 2007). Ecosystem modelling was applied to improve our

understanding of these estuarine ecosystems and to assess the management

strategy of the estuary (Brando et al., 2004).

Some human impacts, particularly fishing and eutrophication, present major

challenges for the sustainable management of aquatic ecosystems such as estuaries

and nearshore systems. Ecosystem modelling has been helpful in assessing

appropriate management strategies for fishing (Eisenack & Kropp, 2001; Zeller &

Reinert, 2004) and for eutrophic systems (Brando et al., 2004).

In this thesis, two aquatic ecosystems are investigated using ecosystem modelling to

assess: (1) how human activities affect ecosystem functioning and (2) how those

systems might be managed sustainably. The thesis focuses on two study areas in

Western Australia, one of which is in near pristine condition (Shark Bay) and the

other of which is heavily impacted by anthropogenic activities (Peel-Harvey Estuary).

Both study areas are of great economical and ecological importance.

- 24 -

Investigations into the Shark Bay ecosystem

The first part of the thesis (Chapter 2) considers the ecosystem of Shark Bay,

Western Australia. Shark Bay, a World Heritage Area, is a large embayment with

extensive seagrass meadows (Francesconi & Clayton, 1996; Marsh, 1990; Walker,

1989; Wells, 1985). Field studies have determined that tiger sharks are the main

predator of this ecosystem (Dill, 2003; Heithaus, 2001, 2002a; Wirsing, 2007a). Tiger

sharks are found from the tropics to warm temperature waters virtually world-wide

(Randall, 1992) and outside of Shark Bay, the species is affected by fishing (Baum et

al., 2003; McAuley, 2006; White, 2007). Heithaus et al. (2008) hypothesized that

tiger sharks might be an important factor for the functioning and stability of the

seagrass ecosystem in Shark Bay by behaviourally impacting other species of this

ecosystem. Thus, it is possible that over-fishing of tiger sharks could have an adverse

effect on the Shark bay ecosystem.

The effects of fishing on the Shark Bay ecosystem may be more complex than just

direct takes of tiger sharks. Shark Bay is partly trawled by the commercial fishing

sector targeting prawns and scallops (Kangas, 2006; Kangas et al., 2006). The area

also attracts many recreational fishers from Western Australia, who travel to Shark

Bay to fish (Francesconi & Clayton, 1996).

This thesis addresses several questions relating to the sustainable management of

the Shark Bay ecosystem:

How do these fishing activities impact the ecosystem?;

What is the role of tiger sharks for the Shark Bay ecosystem?;

What are the consequences of a decline in tiger sharks for this seagrass-

based ecosystem?; and

If the system is impacted by humans, how can the ecosystem of Shark Bay be

managed sustainably?

To address these questions, this study uses qualitative modelling or ‘loop analysis’.

This tool is used because behavioural effects are difficult to quantify and, thus, it is

not feasible to apply a quantitative modelling tool to these questions. Loop analysis

- 25 -

only focuses on the sign of interaction between the different variables in the model,

which can be positive (i.e. enhancing the other variable) or negative (i.e. decreasing

the other variable) (Dambacher, 2001; Dambacher et al., 2007; Dambacher et al.,

2002; Dambacher et al., 2003a; Dambacher et al., 1999; Dambacher et al., 2003b;

Dambacher & Ramos-Jiliberto, 2007; Dambacher & Rossignol, 2001; Hosack et al.,

2008; Levins, 1974, 1975, 1998; May, 1972, 1973). As all interaction strengths in loop

analysis are either 1 or 0, the analysis is highly simplified, which enables the

assessment of quantifiable and non-quantifiable variables (Ortiz & Wolff, 2002). For

this reason, this technique is ideal for data poor systems where it is desirable to

improve our understanding of ecosystem processes and to exploring different

management scenarios, and to consider ‘what if’ questions (Dambacher, 2001;

Levins, 1998; May, 1972, 1973; Puccia & Levins, 1985).

Investigations into the Peel-Harvey ecosystem

In the second part of this thesis, Ecopath with Ecosim is applied to model the

biomass fluxes in the Peel-Harvey Estuary, which has been studied extensively over

the last few decades and therefore a large data set is available for quantitative

modelling. The Peel-Harvey Estuary is located about 80 km south of Perth, Western

Australia (Hale & Butcher, 2007). The estuary is heavily impacted by eutrophication

(Birch, 1980, 1982; Huber, 1980, 1985, 1986; Jakowyna, 2000; Lavery & McComb,

1991; Lukatelich & McComb, 1986a, b; McAuliffe et al., 1998; McComb, 1992, 1998;

McComb & Lukatelich, 1990), caused by agricultural activities in the catchment area

and urban nutrient run-off (Bradby, 1997; Hale & Butcher, 2007). The high level of

nutrients in the water body caused massive algal blooms (Huber, 1980, 1985, 1986;

Jakowyna, 2000) and extensive macroalgal growth (Wilson et al., 1999), which led to

the opening of an artificial entrance channel in 1994, the Dawesville Channel

(Bradby, 1997; Hale & Butcher, 2007; Peel Inlet Management Authority, 1990, 1994).

The estuary is heavily fished by the commercial and recreational fishing sectors, with

the recreational sector exceeding the commercial sector in catch (Malseed &

Sumner, 2001) and the number of commercial vessels decreasing steadily over the

last decades. The Peel-Harvey Estuary is of great economic importance (e.g. it has a

large crab fishery: de Lestang et al., 2000, 2003; Kangas, 2000; Malseed & Sumner,

- 26 -

2001) and is also listed as RAMSAR Site (RAMSAR Convention in Ramsar, Iran 1971:

Convention on Wetlands of International Importance, see also Chapter 7), due to its

importance to waterbirds (Bamford & Rutherford, 2007; Hale & Butcher, 2007;

Ninox-Wildlife-Consulting, 1990; Rogers et al., 2010). To improve our understanding

and help enhance the sustainable management of the Peel-Harvey Estuary, a

quantitative ecosystem modelling technique was applied in this study.

“Ecopath with Ecosim” is a quantitative modelling technique that is based on a food

web and also considers temporal (Ecosim) and spatial (Ecospace) dynamics of the

ecosystem, as well as the effects of fishing activities (Christensen et al., 2005). A

large dataset is required for this modelling tool, including biomass values,

productivity and mortality estimates, and catch rates, as well as data on

consumption rates and dietary composition for each species that is included in the

ecosystem model (Christensen et al., 2005; Kavanagh et al., 2004).

An Ecopath model describes the biomass fluxes between the different species or

functional groups of the model (Christensen et al., 2004, 2005). By using Ecosim, the

steady-state Ecopath model can be dynamically changed over time, enabling the

user to investigate the effects of environmental drivers, such as temperature and

rainfall on the steady-state ecosystem, as well as the effects of varying fishing

pressures (Christensen et al., 2005; Pauly et al., 2000).

For the Peel-Harvey Estuary, this modelling process is undertaken in three steps: 1)

using Ecopath to model the biomass fluxes in the estuary before and after the

opening of a large channel opening in 1994; 2) identifying the impact of fishing and

primary producers by using Ecosim; and 3) using Ecospace to investigate the effects

of increasing urbanisation on estuarine habitats and the effects of implementing a

protected area with particular reference to waterbirds. These three steps represent

the key aims for the latter part of the thesis (Chapters 3 to 7).

- 27 -

Overall aims of thesis

The overall aims of this study are to evaluate the suitability of the two ecosystem

modelling tools for management purposes and to discuss the advantages and

limitations of applying these tools to investigate ecological scenarios and

management options for the Shark Bay ecosystem and for the Peel-Harvey

ecosystem. These aims are considered within the context of the individual chapters

and in a Conclusion in Chapter 8.

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Chapter 2

The seagrass ecosystem of Shark Bay - the role of tiger sharks and the impacts of fishing

2.1 Introduction

Tiger shark (Galeocerdo cuvier) populations are declining in many areas around the

world (Simpfendorfer, 2009). For example, in the Northwest Atlantic, tiger shark

catch rates declined by over 65% between 1986 and 2000 (Baum et al., 2003). Tiger

sharks are also captured in fisheries in waters off northern Australia, with catches

increasing from 37 t in 2001/02 to 81 t in 2004/05 (McAuley, 2006). Tiger sharks are

also caught in nearby Indonesian waters (White, 2007), which are well within the

observed ranging distances for sharks tagged in field studies at Shark Bay, Western

Australia (Heithaus et al., 2007b). Individual sharks can move over large (ie hundreds

of kilometres) ranges and easily could leave ‘no-take’ areas such as the shark fishing

exclusion zone that encompasses Shark Bay (Heithaus et al., 2007b).

As tiger sharks are apex predators in the Shark Bay ecosystem, predicting the

potential consequences of fishery-induced decline of the tiger shark population

inhabiting the bay is important for management of this World Heritage area. How a

decline in tiger sharks could affect the various temperate and tropical ecosystems

with which this species is associated is not well understood, although such a decline

could have substantial effects on the diversity and stability of those ecosystems.

Stevens (2000) examined predictions from a quantitative Ecopath model and

suggested that declines in tiger shark populations were likely to lead to the depletion

of other fish species, such as tuna.

Objectives

The objectives of this study are to: (1) develop a basic modelling framework for the

dynamics and drivers of the seagrass ecosystem of Shark Bay, particularly the role of

tiger sharks and (2) investigate how these dynamics might be affected by fishing

- 29 -

activities within and outside of Shark Bay by comparing model predictions with

empirical data.

It is hypothesized that (1) tiger sharks are an important driver of the seagrass

ecosystem in Shark Bay and (2) fishing fleets in and outside of Shark Bay affect tiger

sharks and thus, fishing activities have the potential to alter ecosystem stability.

The first part of this chapter uses a qualitative modelling technique and assesses

whether this approach is capable of incorporating the behavioural and condition-

dependent effects that characterise the dynamics of the seagrass ecosystem in Shark

Bay. The second part of this chapter applies loop analysis to investigate the

importance of trophic interactions and the impact of fishing.

The overall aim of this chapter are to: (1) identify important areas for further

research (e.g. dealing with behavioural interactions) and (2) improve the scientific

basis for sustainable management of the Shark Bay ecosystem, by applying a

qualitative modelling approach.

2.2 The Shark Bay ecosystem: tiger shark – megafauna interaction

Ecosystem description

Shark Bay is a large embayment located about 800 km north of Perth, Western

Australia ((Francesconi & Clayton, 1996). The marine habitat encompasses an area of

13 000 km² (Francesconi & Clayton, 1996) that is divided up into inner and outer

Shark Bay (Fig. 2.1). The outer area of Shark Bay has oceanic characteristics, in

contrast to the inner area where salinities and temperatures range widely

(Francesconi & Clayton, 1996; Smith & Atkinson, 1983). The inner Shark Bay area is

divided by the Peron Peninsula into the eastern and western Gulf (Francesconi &

Clayton, 1996; Smith & Atkinson, 1983) and is dominated by extensive seagrass

meadows (Walker, 1990; Walker, 1976; Wells, 1985). Shark Bay has a semi-arid to

- 30 -

arid climate (Francesconi & Clayton, 1996) with water temperatures in the oceanic

areas ranging from 20.9 ºC to 26 ºC (Marsh, 1990).

Seagrass is the major primary producer in the ecosystem and also serves as shelter

and nursery area for many species. The seagrass in Shark Bay is dominated by one

species, Amphibolis antarctica, which forms extensive monospecific seagrass

meadows (Walker, 1989, 1990) and also provides refuge habitat for juvenile fish and

general habitat for the abundant populations of sea snakes in Shark Bay (Walker,

1990). This seagrass species reaches its maximum production rate in summer when

light intensity is high (Walker, 1989). Some other seagrass species occur as well, such

as Posidonia australis, but with minor importance compared to A. antarctica. The

primary producers, such as seagrass, algae and phytoplankton are mainly consumed

by zooplankton, invertebrates and fishes, as well as herbivorous megafauna such as

dugongs and adult green turtles.

The productivity of the seagrass meadows greatly exceeds that of the sandy,

unvegetated areas that lie between the seagrass beds (Wells, 1985). These sand

areas serve as habitat for molluscs, crustaceans, polychaetes and bryozoans, but

with a far lower density compared to the seagrass areas (Wells, 1985). In general,

the number of fish species in sandy areas is much lower than in seagrass beds

(Travers & Potter, 2002). Under predatory pressure, some fish species such as

whitings (Sillago sp.) or Black Snapper (Lethrinus laticaudis) move from sand to

nearby seagrass for shelter (Kerford, 2005).

Biology of tiger sharks

Tiger sharks are the apex predator in Shark Bay. This species is found from the

tropics to warm temperate waters virtually world-wide (Randall, 1992) and is notable

for its propensity to consume large-bodied taxa including sea turtles, marine

mammals, and other elasmobranchs (Lowe et al., 1996; Simpfendorfer, 1992;

Simpfendorfer et al., 2001). Small sharks (<165 cm fork length, FL) feed primarily on

teleosts, invertebrates and sea snakes (Simpfendorfer et al., 2001). Large tiger sharks

(>260 cm FL) mainly consume turtles, birds and elasmobranchs (Simpfendorfer et al.,

- 31 -

2001). Simpfendorfer et al. (2001) found that dugong occurred in the diet of all size

classes of tiger sharks in Western Australia, while dolphins were a minor component

in the diet of medium-sized sharks (165-260cm TL) only.

Individual tiger sharks move over large home ranges and may disperse across ocean

basins occasionally (Heithaus et al., 2007b; Holland et al., 1999). In Shark Bay, the

abundance and habitat use of tiger sharks is linked to prey availability and water

temperature (Dill et al., 2003; Heithaus, 2001; Heithaus et al., 2002a; Heithaus et al.,

2001; Wirsing, 2007a). Tiger sharks are abundant in Shark Bay during summer

(Heithaus, 2001) and prefer prey-rich shallow seagrass beds, especially the edges

(Heithaus et al., 2002a, 2006).

Megafauna of Shark Bay

Dugongs and adult green turtles are the herbivore megafauna in Shark Bay. During

winter the dugongs migrate to the western oceanic areas of Shark Bay, which are

warmer (Anderson, 1981) and leave the study area which is located in the eastern

Gulf of Shark Bay (Fig. 1). Dugongs feed on seagrass and the seagrass species

Amphibolis antarctica is their major food source in Shark Bay (Wirsing et al., 2007).

Adult green turtles (Chelonia mydas) feed primarily on seagrass and algae and

establish and maintain grazing plots within seagrass meadows (Bjorndal, 1997). In

Shark Bay, the population consists mainly of large, adult individuals (Heithaus et al.,

2005).

The seagrass meadows in Shark Bay serve also as foraging ground for loggerhead

turtles, Caretta caretta. This species feeds mainly on benthic and infaunal

invertebrates, but also on plant material (Bjorndal, 1997; Heithaus et al., 2002b;

Wirsing et al., 2004).

Other major piscivorous groups in the ecosystem are dolphins, elasmobranchs, sea

snakes, and pied cormorants. The majority of the dolphins occurring in Shark Bay are

Indo-Pacific bottlenose dolphins (Tursiops aduncus) (Heithaus et al., 2002). This

species primarily feeds on teleost and cephalopods (Amir et al., 2005). In Shark Bay,

the occurrence and abundance of elasmobranchs may be linked to tiger shark

abundance, as Heithaus (2001) notes that other species of large sharks, such as

- 32 -

mako sharks (Isurus oxyrinchus) and dusky sharks (Carcharhinus obscurus), were

caught outside the period of peak abundance for tiger sharks between November to

March. Pied cormorant (Phalocrocorax varius) is a piscivorous bird that is a common

resident in Shark Bay (Storr, 1990).

Interactions between tiger sharks & megafauna

Temporal and spatial variation in tiger shark abundance influences the habitat use of

the megafauna in Shark Bay (Wirsing et al., 2007). When sharks are scarce, dugongs,

dolphins, pied cormorants and green turtles tend to be distributed relative to their

prey; in contrast, a large portion of individuals shift to the edges of the seagrass

meadows or safer deep habitats when sharks are abundant (Heithaus et al. 2005a,

2005b, 2007a, 2007b, Wirsing et al., 2007b). This behaviour reflects the “food-safety

trade-off” (Wirsing et al., 2007, p.1031) of the megafauna, which is driven by

“perceived predation risk” also referred to as “fear factor” (Wirsing et al., 2007,

p.1031).

Feeding behaviour is particularly impacted by predation pressure from tiger sharks,

with dugongs and other species leaving the shallow feeding grounds and moving into

deeper, safer patches during summer when the abundance of large tiger sharks is

high (Heithaus et al., 2005, 2007a; Wirsing et al., 2007).

Loggerhead turtles avoid shallow habitats when the abundance of tiger sharks is

high, only some male individuals take the higher predation risk and stay in the

favoured feeding grounds (Heithaus et al., 2005). This results in a higher rate of

shark-inflicted injuries for male loggerhead turtles (Heithaus et al., 2005).

Like turtles, sea snakes exhibit a habitat shift that avoids predators (Kerford, 2005).

For example, the bar bellied sea snake, Hydrophis elegans, moves from sand into the

seagrass when predators are near (Kerford, 2005). This species does not use seagrass

for foraging, but for refuge from tiger sharks and for resting (Kerford, 2005).

Pied cormorants are generally distributed across the seagrass habitats in Shark Bay in

proportion to the abundance of fish (Heithaus, 2005). The birds change the use of

their habitat, however, when tiger sharks are highly abundant in summer (Heithaus,

- 33 -

2005). Under high predation pressure, cormorants avoid shallow habitats and prefer

deeper areas (Heithaus, 2005).

Figure 2.1: Map showing: (a) the location of Shark Bay, Western Australia; (b) the eastern and western gulfs within the interior of the bay (the * shows the location of Monkey Mia, a well-known tourist site); and (c) the location of belt transects used in megafauna research conducted by Michael Heithaus and other researchers. In figure (c), the shallow, seagrass-dominated areas are in light grey and the deeper, sand-silt habitat is in dark grey. The figures are used with permission from Michael Heithaus, Florida International University, USA.

- 34 -

2.3 Use of qualitative modelling to interactions in biotic communities

Qualitative modelling allows modellers to represent the relationship between

different aspects of a biotic community by identifying the sign of their connection.

Levins’ qualitative analysis is based on the equivalence between signed digraphs and

the community matrix of the described ecological community (Levins, 1974, 1975,

1998). This matrix represents the interactions within members of the community at a

local point of equilibrium and consists of the coefficients αi,j (Levins, 1975), which

represent the effects of species j on species i. These effects might lead to an increase

(+αi,j), or a decrease (-αi,j) in, or have no effect on, species i (αi,j = 0).

The matrix elements can be directly transformed into a graphical description, the

signed digraph (Fig. 2.2). Here, each variable is displayed as a vertex of the graph

(circle or arrow) and the interactions (αi,j) are exhibited as the lines or linkages

between the variables. The linkages can be positive ( ) or negative ( ),

depending on the sign of interaction (+1, -1, 0) (Dambacher et al., 2001a, 2007a,

1999). Self-effect is presented by a line that connects a variable to itself. This

graphical modelling technique may also be used to describe indirect effects, but

these must be transformed into direct effects for the community matrix (Dambacher

et al., 2007b).

Calculating the feedback associated with the community matrix is necessary for

determining the stability of a system and also for determining the possible effects of

a perturbation on the described system (Dambacher et al., 2001a, 2007a, 2002,

2003a, b, 2007b, 2001b; Levins, 1974, 1998). In general, the response prediction is

based on to the cancellation or summation of feedback cycles (Dambacher, 2001a,

2002, 2003a, 2007b, 2001b; Levins, 1974, 1998). To assess uncertainty in the

response sign, Hosack et al. (2008) presented a framework that combines signed

digraphs with a Bayesian belief network and used this approach to produce

conditional probability tables that incorporate the effects of feedback on the model’s

response due to a perturbation (Hosack, 2008).

- 35 -

For more details about the mathematical background, refer to studies on qualitative

models using loop analysis in the literature (Dambacher, 2001; Dambacher et al.,

2002; Dambacher et al., 2003a; Dambacher & Ramos-Jiliberto, 2007; Dambacher &

Rossignol, 2001; Levins, 1974, 1975, 1998; May, 1972, 1973).

2.4 Materials and Methods

Part I - Modelling the dynamics of the seagrass ecosystem

A highly-simplified model of the Shark Bay seagrass ecosystem was constructed to

explore the influence of tiger sharks on other megafauna, and on the prey and

seagrass species consumed by the latter group. For this, a simple predator-prey

model was developed relating: (1) tiger sharks (ts) to their megafauna prey i.e.

dolphins, elasmobranchs, turtles, dugongs and pied cormorants) and (2) the

megafauna prey species to their prey species (ts megafauna prey).

Megafauna “prey” included the seagrass species consumed by the herbivore

megafauna. This simple model was enhanced to allow for the distribution of

megafauna between: (i) feeding areas in which they are exposed to predation by

tiger sharks and (ii) other areas in which they are not exposed to such predation.

Two types of prey were considered for the qualitative model: (1) the prey group

consumed by the megafauna within the seagrass habitat (Psg) and (2) the prey group

consumed in the channel habitat (Pch). In the state-dependent modelling present in

Heithaus et al. (2007a), the authors divided megafauna populations into three

subpopulations on the basis of the individual body condition: (i) and (ii) those

individuals in very good condition inhabiting the seagrass habitat (M1 sg) and

migrating to the channel habitat under high predatory pressure from tiger sharks

(M1 ch) and (iii) those individuals in fair or poor condition (M2). The modelling in this

chapter includes a fourth megafauna subpopulation category (M*) which applies

when there is no knowledge about the likely condition of individuals but some

- 36 -

individuals are assumed to be present in less productive sandy channels (e.g.

megafauna moving through the area via the channels).

2.4.1 Differential equations and interaction coefficients

To analyse the complex interactions, behavioural impacts, and migration of these

populations, differential equations were generated for each population. Then, from

these equations, the interaction coefficients and signed digraphs were derived for

the community matrix (Dambacher & Ramos-Jiliberto, 2007; Levins, 1974, 1998;

Puccia & Levins, 1985). This modelling approach focused on the dynamics of the

behavioural interactions and predator-prey relationships over a short period of time

– the austral summer, when tiger shark abundance peaks in Shark Bay. The growth

rates of the different populations through birth processes were therefore not

considered, as those demographic processes take place on a different time scale

than the behaviourally-induced dynamics described by the differential equations.

The differential equations were developed for each of the populations and the

partial derivatives were computed to derive each interaction coefficient αi,j according

to the procedure in Dambacher & Ramos-Jiliberto (2007). The interaction

coefficients represented the entries of the community matrix for the signed

digraphs.

ts (tiger sharks):

2, tststs

ts Nadt

dN

tsts

ts

ts

ts

tsts aN

dtN

dN

,,

, thus the sign in the community matrix is (-1)

- 37 -

M2 (megafauna subpopulation in fair and poor body condition):

sgP

sgP

MMsgMsgMsgMMtsMtsMMMM

M

K

NrecNconNconNNaNa

dt

dN1

22,111,22,2

2

22,2

2

= – limiting self-effect on NM2 – loss through predation by Nts + increase through NM1sg individuals that decrease in body condition – decrease as individuals of NM2 recover and shift to NM1sg by consuming prey NPsg

2

2

11,2

2,2

2

2

2

2,2M

sgMsgMM

MM

M

M

M

MMN

Ncona

N

dtN

dN


tsM

ts

M

M

tsM aN

dtN

dN

,2

2

2

,2


sgP

MsgM

sgP

M

M

sgPMK

reccon

N

dtN

dN

2,12

2

,2


2

1,2

1

2

2

1,2M

sgMM

sgM

M

M

sgMMN

con

N

dtN

dN

, thus the sign in the community matrix is (+1)

M1 sg (megafauna subpopulation in very good and good body condition inhabiting the seagrass habitat):

ts

ts

ts

chMchMsgMts

ts

ts

sgMsgMchM

sgP

sgP

MMsgMsgMsgMMsgMsgMsgM

sgM

NK

fNmN

K

fNm

K

NrecNconNconNa

dt

dN

11

1

11,111,1

22,111,2

2

11,1

1

= – limiting self-effect on NM1sg - decrease through NM1sg individuals that decrease in body condition and shift to NM2 + increase as individuals of NM2 recover and shift to NM1sg by consuming prey NPsg – loss through migration to NM1ch pool, which is enhanced by tiger shark presence + increase through migration from NM1ch pool, which is suppressed by tiger shark presence

- 38 -

2

1

11,1

2

1

22,1

1,1

1

1

1

1,1

11

sgM

ts

tstschMchMsgM

sgM

sgP

sgP

MMsgM

sgMsgM

sgM

sgM

sgM

sgMsgMN

K

NfNm

N

K

NrecNcon

aN

dtN

dN


sgM

sgP

sgP

MsgM

M

sgM

sgM

MsgMN

K

Nreccon

N

dtN

dN

1

2,1

2

1

1

2,1

1


sgPsgM

MMsgM

sgP

sgM

sgM

sgPsgMKN

recNcon

N

dtN

dN

1

22,11

1

,1


ts

tsts

sgM

chMsgM

chM

sgM

sgM

chMsgMK

Nf

N

m

N

dtN

dN

11

1,1

1

1

1

1,1

, the sign of this interaction coefficient is ambiguous. The migration rate from the subpopulation NM1ch to NM1sg is heavily impacted by the product of tiger shark numbers Nts and the fear factor fts. In times of high tiger shark abundance, the migration will stop and the interaction coefficient will be zero and it will be positive under low impact from tiger sharks.

, thus the sign in the community matrix is (-1) M1 ch (megafauna subpopulation in good body condition migrating into the channel habitat):

chMchMMMMchM

ts

ts

ts

chMchMsgMts

ts

ts

sgMsgMchMchMchMchMchM

NconNcon

NK

fNmN

K

fNmNa

dt

dN

11*,**,1

11,111,1

2

11,1

1 11

ts

ts

ts

ts

ts

tsK

N

Nfm

K

fm

N

dtN

dN

1sgM

1chM

1chM,1sgM

1sgM,1chM1sgM

1sgM

,1sgM

- 39 -

= – limiting self-effect on NM1ch + increase through migration from NM1sg to NM1ch pool, which is enhanced by tiger shark presence – loss through migration from NM1ch to NM1sg pool, which is suppressed by tiger shark presence + increase as individuals of NM* shift to NM1ch in the channel habitat – loss through shift from NM1ch to NM* pool in the channel habitat


ts

ts

ts

chM

sgMchM

sgM

chM

chM

sgMchM NK

f

N

m

N

dtN

dN

11

1,1

1

1

1

1,1


chM

MchM

M

chM

chM

MchMN

con

N

dtN

dN

1

*,1

*

1

1

*,1


ts

tschMsgM

ts

tssgMchM

chM

sgM

ts

chM

chM

tschMK

fm

K

fm

N

N

N

dtN

dN

1,11,1

1

11

1

,1

, thus the sign in the community matrix is (+1) M* (megafauna in the channel):

chMchMMMMchMMMMM NconNconNa

dt

dN11*,**,1

2***,

*

= – limiting self-effect on NM* – loss through individuals of NM* that shift to the NM1ch pool in the channel habitat increase through shift from NM1ch to NM* pool in the channel habitat

2*

11*,

**,

*

*

*

**,

M

chMchMM

MM

M

M

M

MMN

Ncona

N

dtN

dN


2

1

**,1

2

1

11,1

1,1

1

1

1

1,1

1

chM

MMchM

chM

ts

tstssgMsgMchM

chMchM

chM

chM

chM

chMchMN

Ncon

N

K

NfNm

aN

dtN

dN

- 40 -

*

1*,

1

*

*

1*,

M

chMM

chM

M

M

chMMN

con

N

dtN

dN


Psg (prey in seagrass):

= – limiting self-effect on NPsg – loss through consumption by NM2 – loss through consumption by NM1sg, which is suppressed by the presence of tiger sharks – loss through consumption by NM1ch, which is suppressed by the presence of tiger sharks – loss through migration to the NPch prey pool in the channel habitat, which is suppressed by the presence of megafauna subpopulations NM1ch and NM* + increase through migration from the NPch prey pool in the channel habitat, which is enhanced by the presence of megafauna subpopulations NM1ch and NM*



2,

2

2, MsgP

M

sgP

sgP

MsgP aN

dtN

dN


*

1*1

1*

,

*

1*1

1*

,11,

11,22,2

,

1

11

1

M

chMM

M

chM

chMM

M

chPchPsgP

M

chMM

M

chM

chMM

M

sgPsgPchPts

ts

ts

sgPchMchMsgP

ts

ts

ts

sgPsgMsgMsgPsgPMMsgPsgPsgPsgP

sgP

NKK

fN

KK

fNm

NKK

fN

KK

fNmN

K

fNNa

NK

fNNaNNaNa

dt

dN

*

1*1

1*2,,, 1 M

chMM

M

chM

chMM

M

sgP

chP

chPsgPsgPsgP

sgP

sgP

sgP

sgPsgP NKK

fN

KK

f

N

Nma

N

dtN

dN

ts

tschMchMsgP

ts

tssgMsgMsgP

ts

sgP

sgP

tssgPK

fNa

K

fNa

N

dtN

dN

11,11,

,

- 41 -

, the sign of this interaction coefficient is ambiguous. The consumption of prey NPsg by NM1sg reduces the prey group and thus, the effect is represented by a negative term. However, this is reduced through the impact of the fear factor fts and the correlating number of tiger sharks Nts. Thus, the impact of tiger sharks can outweigh the negative effect of consuming NPsg, when the fear factor (i.e. perceived predation risk) is strong enough to prevent NM1sg from consuming prey in the seagrass meadows. For this reason, the sign of this interaction coefficient is negative under low impact from tiger sharks and zero under high impact from tiger sharks.

, the sign of this interaction is ambiguous in regard to the ecosystem in Shark Bay. The first term determines the sign of this predator-prey interaction. In times of high tiger shark abundance, the positive term including fear factor fts outweighs the consumption term –aPsg,M1ch. The migration rates between the populations NPsg and NPch are also driven by a fear factor fm. However, these rates are rather small in magnitude compared to the predation linkages affecting NPsg. As, the prey group in the channels, NPch, is very small compared to the seagrass meadows, NPsg, the migration rates cannot outweigh the predation loss and thus these terms are negligible for determining the overall sign of this interaction. Thus, the sign of this coefficient is negative in times of low tiger shark abundance and zero in times of high tiger shark abundance.



ts

ts

ts

sgMsgP

sgM

sgP

sgP

sgMsgP NK

fa

N

dtN

dN

11,

1

1,

chMMsgP

mchPchPsgP

chMM

msgPchP

ts

ts

ts

chMsgP

chM

sgP

sgP

chMsgPKKN

fNm

KK

fmN

K

fa

N

dtN

dN

1*

,

1*

,

1,

1

1, 1

chMMP

mchPchPP

chMM

mPchP

M

P

P

MPKKN

fNm

KK

fm

N

dtN

dN

1*sg

,sg

1*

sg,

*

sg

sg

*,sg

*

1*1

1*

,, 11

M

chMM

M

chM

chMM

M

sgPchPsgP

chP

sgP

sgP

chPsgP NKK

fN

KK

f

Nm

N

dtN

dN

- 42 -

Pch (prey in channels):

= – limiting self-effect on NPch – loss through consumption by NM* – loss through consumption by NM1sg, which is enhanced by the presence of tiger sharks – loss through consumption by NM1ch, which is enhanced by the presence of tiger sharks + increase through migration from NPsg to the NPch prey pool in the channel habitat, which is suppressed by the presence of megafauna subpopulations NM1ch and NM* - loss through migration from the NPch prey pool in the channel habitat into the NPsg pool in the seagrass, which is enhanced by the presence of megafauna subpopulations NM1ch and NM*




*

1*1

1*

,

*

1*1

1*

,14,

11,**,2

,

1

11

1

M

chMM

M

chM

chMM

M

chPchPsgP

M

chMM

M

chM

chMM

M

sgPsgPchPts

ts

ts

chMchPchP

ts

ts

ts

chPsgMsgMchPchPMMchPchPchPchPchP

NKK

fN

KK

fNm

NKK

fN

KK

fNmN

K

fNNa

NK

fNNaNNaNa

dt

dN

*

1*1

1*2

,

,, 1 M

chMM

M

chM

chMM

M

chP

sgPsgPchP

chPchP

chP

chP

chP

chPchP NKK

fN

KK

f

N

Nma

N

dtN

dN

chMM

mchPsgP

chMMchP

msgPsgPchP

MchP

M

chP

chP

MchPKK

fm

KKN

fNma

N

dtN

dN

1*

,

1*

,

*,

*

*,

ts

ts

ts

sgMchP

sgM

chP

chP

sgMchP NK

fa

N

dtN

dN

11,

1

1,

- 43 -

, this interaction coefficient is very small in magnitude and its sign is ambiguous. The number of prey that will leave the seagrass and migrate into the channels is rather low. Also the prey group of NPch is rather small in abundance compared to the prey group in the seagrass meadows. This small migration rate will be close to zero under high impact of fear factor fM, which is correlated to the numbers of megafauna in the channels NM1ch and NM*. Thus, the migration rate can be turned off in times of high predatory pressure. Thus, the sign in the community matrix is zero in times of high tiger shark abundance, which increases fM, and the sign is positive in times of low tiger shark abundance.


, thus the sign in the community matrix is (-1) Parameter explanation: N: number of individuals of a population, a: rate of interaction between two variables, coni,j: rate of individuals that shift in body condition from subpopulation j to i, rec: recovery in body condition by consuming prey, mi,j: migration rate describing the flow between the different habitats from subpopulation j to i, fts: “fear factor“ (perceived predation risk) presented by tiger sharks, which depends directly on the abundance of tiger sharks, fM: “fear

factor“ (perceived predation risk) presented by megafauna subpopulations through predatory pressure on the prey group in the channel habitat, which leads to the escape of prey into the seagrass meadows, K: carrying capacity

Note that the magnitudes of fear factors, fm and fts, cannot be quantified and might

differ between populations and in time.

The interaction coefficients derived from the partial derivatives resulted in two

qualitative models. The first model described the dynamics of the system in times of

low tiger shark abundance and of a negligible magnitude of the fear factors (fm and

*

1*1

1*

,, 11

M

chMM

M

chM

chMM

M

chPsgPchP

sgP

chP

chP

sgPchP NKK

fN

KK

f

Nm

N

dtN

dN

chMM

mchPsgP

chMMchP

msgPsgPchP

ts

ts

ts

chMchP

chM

chP

chP

chMchPKK

fm

KKN

fNmN

K

fa

N

dtN

dN

1*

,

1*

,

1,

1

1, 1

ts

tschM

chMchP

ts

tssgM

sgMchP

ts

chP

chP

tschPK

fNa

K

fNa

N

dtN

dN

1

1,

1

1,,

- 44 -

fats, Fig. 2.2 A). Under high impact of tiger sharks, the importance of the fear factors

increased dramatically, which led to a switch off of predation and migration links, as

the megafauna migrated to (and only consumed prey within) the safe channel

habitat. The prey group (Pch) respond by escaping into the seagrass (Fig. 2.2 B); for

example, some fish species such as whitings (Sillago sp.) or Black Snapper (Lethrinus

laticaudis) that belong to the variable Pch move from sand to nearby seagrass for

shelter if they are under predatory pressure (Kerford, 2005).

Consequently, the dynamics described by Figure 2.2 were divided into two different

stages, i.e low and high abundances of tiger sharks resulting from a change in the

sign of interaction coefficients.

Psg Pch

M1sgM2

ts

M*M1ch

A

Psg Pch

M1sgM2

ts

M*M1ch

B

seagrass channel

seagrass channel

- 45 -

Figure 2.2 A+B: Qualitative model showing the interaction linkages between tiger sharks, different megafauna subpopulations (M2, M1sg, M1ch, M*) and the prey of the latter (Psg, Pch) in two different habitats (i.e. the seagrass and the channel habitat) in times of low (A) and high (B) tiger shark abundance. The model presented in Figure 2.2A is hereafter referred to as ‘Model 1’ and the model presented in Figure 2.2B as ‘Model 2’. The overall model is referred to as the ‘Overall Model’.

2.4.2 Problems with model structure

The qualitative model (shown in Figure 2.2) consisted of a megafauna variable that

was split into four different subpopulations that are positively linked to each other.

Two prey groups in the seagrass and the channel habitat were also positively linked

to each other. These variables were all self-regulated. The determinant of a system

consisting of two self-regulated variables that are positively linked to each other is

zero, as the two negative self-regulating links are cancelled out by the two positive

links that connect the variables. A determinant that equals zero is critical, as the

mathematical system cannot be solved explicitly and this determinant implies zero

feedback. Zero feedback has the consequence that, whatever press perturbation is

performed on one of those variables, that is, the variable is increased or decreased in

the model, the press perturbation will remain without impact and the system is not

responding, as the response or feedback of the variable is zero. Feedback “denotes

that an action or activity initiated by someone or something sets in motion activities

or responses by others which then affect the original source of the activity” (Puccia &

Levins, 1985, p.17). Feedback depends on the loops of the qualitative model and

“positive feedback occurs when an increase in one variable (the initiator) causes

other variables to change in such a way as to increase itself (the initiator) further”

(Puccia & Levins, 1985, p.17) and, conversely, negative feedback leads to a decline in

the variable. If feedback is zero, caused by a determinant that equals zero, the

variable is not increasing or decreasing after a press perturbation.

The analysis of the Overall Model was difficult, as many response signs were

ambiguous due to positive and negative loops that outweighed each other. The

ambiguity was caused by model structure. Thus, two attempts were made to solve

the problem of sign uncertainty. Firstly, the Overall Model was simplified in smaller

- 46 -

submodels and, from the response signs of these submodels, insight was obtained

into the response signs of the Overall Model. However, the larger model had a larger

and a different complementary feedback system than the submodels. This was

critical, as the complementary feedback system determines the response sign of a

variable. To deal with the issue of the response signs, a second attempt was made to

calculate the magnitudes of the interaction coefficients, which were based on

estimates derived from published studies on Shark Bay. By calculating the magnitude

of negative versus positive loops, it was assumed that the stronger effect was more

likely to determine the response sign.

2.4.3 Analysis of submodels

Two submodels were analysed to clarify the response signs of the Overall Model. In

the first submodel the megafauna subpopulations were combined to two variables

and in the second submodel all megafauna subpopulations were combined to a

single variable to analyse the response signs of the two prey groups.

Submodel 1

In the first submodel, the megafauna subpopulations were combined in regard to

their habitat location. M2 and M1sg were combined to the node Msg, which describes

the dynamics of the megafauna in the seagrass habitat. The megafauna

subpopulations in the channel (M1ch and M*) were combined to the variable Mch.

The prey variables remained unchanged.

The differential equations and correlating interaction coefficients were as follows:

ts (tiger sharks):

2, tststs

ts Nadt

dN

- 47 -


Msg (subpopulation in the seagrass habitat):

2

,

2

,

,,

1

sgM

ts

ts

ts

chMchMsgM

sgM

chMchMsgM

sgMsgM

sgM

sgM

sgM

sgMsgMN

NK

fNm

N

Ncona

N

dtN

dN





Mch (megafauna subpopulation in the channel):

tsts

ts

ts

ts

tsts aN

dtN

dN

,,

ts

ts

sgM

chM

chMsgM

ts

ts

sgMchMtssgM

ts

sgM

sgM

tssgMK

f

N

Nm

K

fma

N

dtN

dN

,,,,

sgM

ts

ts

ts

chMsgM

sgM

chMsgM

chM

sgM

sgM

chMsgMN

NK

fm

N

con

N

dtN

dN

1,,

,

sgP

sgMchM

sgP

sgM

sgM

sgPsgMK

reccon

N

dtN

dN

,

,

ts

ts

ts

chMchMsgMts

ts

ts

sgMsgMchM

sgP

sgP

sgMsgMchMchMchMsgMtssgMtssgMsgMsgMsgM

sgM

NK

fNmN

K

fNm

K

NrecNconNconNNaNa

dt

dN

11

1

,,

,,,2

,

- 48 -

, thus the sign in the community matrix is (-1) , thus the sign in the community matrix is (+1)



Psg (prey in the seagrass habitat):

2

,

2

,

,,

11

chM

ts

ts

ts

sgMsgMchM

chM

sgP

sgP

sgMsgMchM

chMchM

chM

chM

chM

chMchMN

NK

fNm

N

K

NrecNcon

aN

dtN

dN

ts

ts

chMsgM

tschM

tssgM

sgMchM

ts

chM

chM

tschMK

fm

KN

fNm

N

dtN

dN

,,,

chM

ts

ts

ts

sgMchM

chM

sgP

sgP

sgMchM

sgM

chM

chM

sgMchMN

NK

fm

N

K

Nreccon

N

dtN

dN

11,,

,

sgPchM

sgMsgMchM

sgP

chM

chM

sgPchMKN

recNcon

N

dtN

dN

,

,

ts

ts

ts

chMchMsgMts

ts

ts

sgMsgMchM

sgP

sgP

sgMsgMchMchMchMsgMchMchMchMchM

NK

fNmN

K

fNm

K

NrecNconNconNa

dt

dN

11

1

,,

,,2

,

- 49 -


sgMsgP

sgM

sgP

sgP

sgMsgP aN

dtN

dN

,,


chMsgP

mchP

chPsgP

chM

m

sgPchPts

ts

ts

chMsgP

chM

sgP

sgP

chMsgPKN

fNm

K

fmN

K

fa

N

dtN

dN

,,,, 1

, thus the sign in the community matrix is (-1) or zero

The sign of this interaction coefficient is ambiguous. Under high impact of the fear factor (perceived predation risk) caused by the megafauna in the channels, prey will escape from the channels into the seagrass meadows. Under this circumstance, the positive effect on Psg by prey migration will outweigh the negative effect caused by the consumption of prey by the megafauna Mch and thus, the interaction coefficient will be zero.


, thus the sign in the community matrix is (+1) Pch (prey in the channel habitat):

2

,

,,

1

sgP

chM

chM

m

chPchPsgP

sgPsgP

sgP

sgP

sgP

sgPsgPN

NK

fNm

aN

dtN

dN

chM

chM

m

sgP

chPsgP

chP

sgP

sgP

chPsgP NK

f

N

m

N

dtN

dN

1,

,

ts

ts

chMchMsgP

ts

ts

sgMsgMsgP

ts

sgP

sgP

tssgPK

fNa

K

fNa

N

dtN

dN

,,,

chM

chM

m

chPchPsgPchM

chM

m

sgPsgPchP

ts

ts

ts

sgPchMchMsgPsgPsgMsgMsgPsgPsgPsgP

sgP

NK

fNmN

K

fNm

NK

fNNaNNaNa

dt

dN

11

1

,,

,,2

,

- 50 -

2

,

,,

1

chP

chM

chM

m

sgPsgPchP

chPchP

chP

chP

chP

chPchPN

NK

fNm

aN

dtN

dN


ts

ts

ts

sgMchP

sgM

chP

chP

sgMchP NK

fa

N

dtN

dN

1,,



, thus the sign in the community matrix is (+1) or zero. The sign of this interaction coefficient is ambiguous, as under high impact of the fear factor, the migration of prey from seagrass into the channels will stop and thus, the interaction coefficient becomes zero.


The interaction coefficients resulted in a submodel with two states (the signed

digraphs are shown in Figure 2.3). The two states of the submodel derived from

chM

m

chPsgP

chMchP

msgP

sgPchPts

ts

ts

chMchP

chM

chP

chP

chMchPK

fm

KN

fNmN

K

fa

N

dtN

dN

,,,, 1

chM

chM

m

chP

sgPchP

sgP

chP

chP

sgPchP NK

f

N

m

N

dtN

dN

1,

,

ts

ts

chMchMchP

ts

ts

sgMsgMchP

ts

chP

chP

tschPK

fNa

K

fNa

N

dtN

dN

,,,

chM

chM

m

chPchPsgPchM

chM

m

sgPsgPchP

ts

ts

ts

chPchMchMchPts

ts

ts

chPsgMsgMchPchPchPchPchP

NK

fNmN

K

fNm

NK

fNNaN

K

fNNaNa

dt

dN

11

11

,,

,,2

,

- 51 -

ambiguous signs of the interaction coefficients, which were based on a high and low

impact of fear factors (perceived predation risk). The fear factors fts and fm are

caused by tiger sharks and the megafauna (respectively); the magnitudes of these

factors are unknown and may vary in strength depending on the populations they

are impacting. It was not necessary to distinguish four different states with all

combinations of different signs of interaction coefficients, as the factors rose at the

same time. When the fear factor (perceived predation risk) caused by tiger sharks on

the megafauna (fts) increased, the megafauna escaped into the channels, where they

elicited a fear response in their prey (fm). When tiger shark abundance decreased,

the habitat shift of the megafauna was reversed and both fear factors decreased at

the same time. Thus, it was essential to distinguish two different states depending

on high and low impact of fear factors (Fig. 2.3).

Psg Pch

Msg

ts

Mch

1.A

Psg Pch

Msg

ts

Mch

1.B

seagrass channel

seagrass channel

- 52 -

Figure 2.3: Submodel 1 A+B: Qualitative model showing the interaction linkages between tiger sharks, two megafauna subpopulations in the seagrass (Msg) and in the channel habitat (Mch) and the prey of the latter (Psg, Pch) in two different habitats (i.e. the seagrass and the channel habitat). Fig. 1.A shows the linkages in times of low tiger shark abundance and Fig.1.B in times of high tiger shark abundance and high impact of fear factors (fts and fm).

The analysis of Submodel 1 showed that both stages were unstable as the feedback

at the highest levels was ambiguous, as positive and negative feedback cycles

outweighed each other (Fig. 2.3). Further, a positive input to the tiger shark variable

in both stages A and B of Submodel 1 led to a zero response sign in all five variables.

Thus, the sign of each of the response signs remained uncertain and Submodel 1 was

unable to provide any clarity in regard to the response signs of the Overall Model.

Submodel 2

To gain insight in the response signs of the two prey variables, the megafauna

subpopulations were further combined to a single variable, which led to Submodel 2.

The differential equations and correlating interaction coefficients were as follows:

ts (tiger sharks):

tsts

ts

ts

ts

tsts aN

dtN

dN

,,


M (megafauna population in the seagrass and the channel habitat):

MM

M

M

M

MM aN

dtN

dN

,,


2, tststs

ts Nadt

dN

tsMtsMMMMM NNaNa

dt

dN,

2,

- 53 -

tsM

ts

M

M

tsM aN

dtN

dN

,,


Psg (prey in the seagrass habitat):

M

M

m

chPchPsgP

M

M

m

sgPsgPchPts

ts

ts

sgPMMsgPsgPsgPsgP

sgP

NK

fNm

NK

fNmN

K

fNNaNa

dt

dN

1

11

,

,,2

,

2

,

,,

1

sgP

M

M

m

chPchPsgP

sgPsgP

sgP

sgP

sgP

sgPsgPN

NK

fNm

aN

dtN

dN


, thus the sign in the community matrix is (-1) or zero

M

M

m

sgP

chPsgP

chP

sgP

sgP

chPsgP NK

f

N

m

N

dtN

dN

1,

,


ts

tsMMsgP

ts

sgP

sgP

tssgPK

fNa

N

dtN

dN

,,


Pch (prey in the channel habitat):


MsgP

mchP

chPsgP

M

m

sgPchPts

ts

tsMsgP

M

sgP

sgP

MsgPKN

fNm

K

fmN

K

fa

N

dtN

dN

,,,, 1

2

,

,,

1

chP

M

M

m

sgPsgPchP

chPchP

chP

chP

chP

chPchPN

NK

fNm

aN

dtN

dN

M

M

m

chPchPsgP

M

M

m

sgPsgPchPts

ts

ts

chPMMchPchPchPchPchP

NK

fNm

NK

fNmN

K

fNNaNa

dt

dN

1

11

,

,,2

,

- 54 -


, thus the sign in the community matrix is (+1) or zero

, thus the sign in the community matrix is (-1) The interaction coefficients resulted in a second submodel with two states (the

signed digraphs are shown in Figure 2.4).

Psg Pch

M

ts2.A

seagrass

channel

MsgP

mchP

M

m

chPsgP

MsgP

mchP

sgPchPts

ts

tsMchP

M

chP

chP

MchPKN

fN

K

fm

KN

fNmN

K

fa

N

dtN

dN

,,,, 1

M

M

m

chP

sgPchP

sgP

chP

chP

sgPchP NK

f

N

m

N

dtN

dN

1,

,

ts

tsMMchP

ts

chP

chP

tschPK

fNa

N

dtN

dN

,,

- 55 -

Psg Pch

M

ts2.B

Figure 2.4: Submodel 2 A+B: Qualitative model showing the interaction linkages between tiger sharks, the megafauna populations (M) and the prey of the latter (Psg, Pch) in two different habitats (i.e. the seagrass and the channel habitat). Fig. 2.A shows the linkages in times of low tiger shark abundance and Fig.2.B in times of high tiger shark abundance and high impact of fear factors (fts and fm).

The analysis of the second submodel provided some insight into the behaviour of the

prey variables in response to an increase in the tiger shark variable (Fig. 2.4).

The Submodel 2.A was unstable, as the feedback of the overall system (which is the

feedback that includes all variables and linkages in the model) could not be

determined explicitly as this feedback was zero. The overall feedback was zero due

to negative and positive feedback cycles outweighing each other. An increase in the

tiger shark variable in Figure 2.4A led to zero response signs of the tiger shark and

megafauna variables, which was caused by an equal number of positive and negative

feedback cycles that determined those signs. Both prey variables Psg and Pch

increased in response to an increase in tiger sharks in Figure 2.4 A., as three positive

feedback cycles and one negative loop lead to an overall positive sign. The

probability that the signs of the prey variables was correct was p = 0.87, based on

average proportion of correct sign (Hosack et al., 2008).

In contrast to Submodel 2.A, Submodel 2.B was stable and an increase in tiger sharks

led to a positive response sign of the tiger shark variable (Fig. 2.4). However, the

megafauna variable M decreased, Psg increased, and the sign of Pch was ambiguous.

seagrass

channel

- 56 -

The probability that the sign of Psg was correct was low (p = 0.77), based on average

proportion of correct sign (Hosack et al., 2008).

Discussion of the analysis of submodels

Sign indeterminacy still persisted in Submodel 1 and the response signs in Submodel

2 also remained ambiguous (Figs 2.3 and 2.4). Submodel 2 indicated that the

behaviour of the response signs of the prey variables was due to an input in the tiger

shark variable (Fig. 2.4). However, the results of this analysis were not significant

with rather low probabilities of correct signs. In Submodel 2.B, there was no direct

link between the megafauna variable and Psg, in contrast to four linkages between

Psg and the different megafauna subpopulations in Model 2. Consequently, the

response signs of all variables in Submodel 1 and, in particular, the response signs of

the prey groups in Submodel 2 due to an increase in the tiger shark variable, could

not be adapted for Models 1 and 2.

Conclusion

The attempt to gain insight into the response signs of a qualitative model through

the investigation of simplified submodels was unsuccessful. The problem of model

structure couldn’t be solved in the submodels and, thus, sign indeterminacy

remained.

2.4.4 Calculating stability and response signs by magnifying interaction coefficients

A. Conceptial foundation for this analysis

The second attempt to analyse stability and the response signs of Overall Model

focused on the model itself and, thus, no simplifications were made in relation to the

model structure. Analysing a smaller submodel means that the response sign of a

variable is determined by a much smaller subsystem. For example, in a model with

four variables (e.g. Submodel 2), the response sign of one variable is determined by

the remaining three variables and their interactions. Adapting the response sign

from this smaller model with four variables to a larger model with seven variables

- 57 -

(such as in Overall Model) means that the linkages of three variables are set equal to

a subsystem of six variables, which is problematic, as these systems of

complementary feedback are different in size and structure.

For this reason, the results of this second analysis are likely to be more reliable, as

the response signs of the Overall Model were determined by magnifying and

calculating its interaction strengths without changing the model structure.

B. Assumptions for determining the magnitude of loops

It was assumed that in times of low tiger shark abundances, the fear factors were

negligible, thus:

fats = fM = 0

However, in times of high tiger shark abundance, it was assumed that the fear

factors were important. Thus, as the fear factors were correlated with the number of

predators, it was assumed that:

fts = 2*Nts and fm = 2*NM

, with M = Mch or M* or NM = 0.3, which represents the mean value of the proportion

of Mch and M* at high tiger shark abundance (Table 2.1).

It is very difficult to estimate the magnitude of the interaction coefficients when

specific data are not available, particularly in regard to the magnitude of self-

regulation. Thus, it was assumed that all interaction rates were equal in magnitude

aij = β

, which represented a valid assumption for this method, as in loop analysis all

interaction coefficients are either one or zero in magnitude. However, as some data

were available on the system, this knowledge was applied to determine rough

magnitudes of the interactions.

Furthermore, it was assumed that:

K = 1 for any ratio N / K.

To estimate the magnitude of the loops (Table 2.2), data were applied from

publications on the ecosystem (Table 2.1). Using the data from Heithaus et

al.(Heithaus, 2005; Heithaus et al., 2007a) and Wirsing (2007), rough estimates for

- 58 -

abundances in relation to the total population of megafauna were made (Table 2.1).

Unfortunately, the abundances had to be converted to proportions, as data

expressed in numbers of individuals were not available for each species for every

subpopulation.

Table 2.1: Relative abundance estimates of the megafauna subpopulations. #: estimates are based on Fig. 3 of a study on loggerhead and green turtles (Heithaus et al., 2007a), ##: Fig. 2, (Heithaus, 2005) gives a rough estimate for M*, which is based on a study on pied cormorants, but the value was adapted for other species of megafauna as they pass through the channels presenting a similar behaviour, ###: Fig. 5 indicates an increase by 0.3 of deep habitat residuals of dugongs when shark catch rates increase, which is adapted for the table by increasing the megafauna in the channel habitat by 0.3 in times of high tiger shark abundance. The total populations do not equal 1, as the values represent relative indices of abundance rather than absolute measures of abundance. The model also does not consider migration into and out of the study area, which affects (e.g.) dugongs (Wirsing, 2007a, b; Wirsing et al., 2007), turtles (Heithaus et al., 2002b; Heithaus et al., 2005; Heithaus et al., 2007a; Wirsing et al., 2004) and pied cormorants (Heithaus, 2005).

Seagrass channel

Ts abundance M2 M1sg M1ch M*

Low 0.25 # 0.36 # 0.18 # 0.1 ##

High 0.23 # 0.15 # 0.48 ### 0.1 ##

Heithaus et al. (2005) reported that shark catches in Shark Bay increased from 0 to

0.12 sharks per hour in times of high shark abundance. Figure 4 (entitled “Seasonal

changes in the relative abundance of shark size classes”) in Heithaus (2001, p.30)

described the proportion of monthly catches according to the size classes. The figure

showed that the proportion of mature tiger sharks increased during summer to 0.7

and decreased in colder months to 0.1 of the total catch. Thus, it was possible to

extrapolate the abundance of tiger sharks in proportions in the following manner:

Nts low = 0.1

Nts high = 0.7

The results in Heithaus et al. (2005) also allowed for the calculation of a rough

estimate for the ratio of the prey abundance, on the basis that about eight times

higher biomass was found in shallow habitats than in deep habitats. This led to the

assumption:

NPsg:NPch= 8:1, or NPsg = 0.88 and NPch = 0.12.

- 59 -

It was assumed further that a recovery rate was much smaller than the rate of

shifting individuals to a worse body condition. Also, the rate of shifting to a worse

body condition was assumed to be higher than shifting to a better body condition.

This ratio was estimated using the information in Figure 4 of Heithaus et al. (2002b,

p. 232), which gave a rough estimate of the proportion of unclassed wounded turtles

as 0.07. This value was adapted as an estimation for the percentage of megafauna

shifting to the subpopulation with a worse body condition.

conM1ch,M* = conM2,M1sg = 0.07

The recovery, which led to the shift of an individual to the subpopulation with the

better body condition, was estimated by

conM*,M1ch = conM1sg,M2 = 0.1*0.07 = 0.007

which implied a recovery rate (rec) of 0.1 in the differential equations.

These calculations did not apply for the subpopulations M1sg and M1ch, as they

represented one population in very good body condition (M1) that migrated

between seagrass (M1sg) and channels (M1ch), but were equal in body condition.

The migration rates reflected a very dynamic shift in habitat, driven by predator

avoidance. As the strength of the migration rates could not be estimated from data,

the rates remained as parameters in the calculations of magnitude; this issue must

be discussed when sign determinacy remained difficult. However, to ease the

calculations of magnitude, it was assumed that all migration rates were equal in

strength:

mM1sg,M1ch = mM1ch,M1sg = mPch,Psg = mPsg,Pch = m

, which indicated that: (i) the rate of migrating into another habitat to avoid

predators was equal for both prey groups and megafauna subpopulations and (ii) the

rate did not differ between habitats. However, while this assumption was unlikely to

be correct, there were no quantitative data available to estimate the migration rates,

meaning it was necessary to calculate the rough magnitude of the loops in the

qualitative models.

- 60 -

The interaction coefficients were estimated according to the assumptions above. The

results are summarized in Table A2. To provide a rough estimate of the magnitude of

the interaction coefficients, values bigger than 1 were rounded to the next integer

and values smaller than 1 were rounded to the next decimal; likewise, values that

were equal to or above 5 were rounded up and values below 5 rounded down. In

order to adapt the magnified interaction coefficients to the feedback cycles

calculated by Maple, the subscripts of the interaction coefficients were converted as

follows:

ts = 1, M2 = 2, M1sg = 3, M1ch = 4, M* = 5, Psg = 6 and Pch = 7

Table 2.2: Estimated magnitudes of the interaction coefficients αi,j calculated according to the assumptions described in section 2.4.4.B Estimates for the magnitude of the interaction coefficients

Model Fig. 3.3A Model Fig. 3.3B

1,1

1,2

2,2 4.0 2.0

3,2 0.3 0.3

6,2 01.0 01.0

1,3 0 m6

2,3 0.02 0.1

3,3 m2.0 2

4,3 m3 0

6,3 001.0 001.0

1,4 2 m2

3,4 m6 m11

4,4 3.0m11 04.0m

5,4 0.4 0.1

4,5 0.1 0.1

5,5 1.0 2.0

- 61 -

1,6 0

2,6

3,6 0

4,6 m3.0

5,6 0 m3.0

6,6 m2.0 m2.0

7,6 m m

1,7 0

3,7 2

4,7 m32

5,7 m3

6,7 m8 m7

7,7 m61 m9

C. Model stability

For a qualitative model to be stable, it has to meet the criteria of Hurwitz

(Dambacher, 2001; Dambacher et al., 2003b; Hurwitz, 1895; May, 1972, 1973).

Meeting the first criterion (i) ensures that feedback is negative at each level. To meet

the second criterion (ii) for stability, the overall feedback must be smaller than the

product of feedback at lower levels and “Criterion ii fails due to relative weakness in

low-level feedback” (Dambacher et al., 2003b, p. 878). Models that fail to meet the

second criterion usually do so because of model structure. Such models are classified

as “Class ΙΙ models” and are “prone to fail criterion ii independently of criterion

i”(Dambacher et al., 2003b, p. 885). Dambacher et al. (2003b, p. 887) also observed

that such Class II models “generally have a significant proportion of single links

(amensal or commensal) within them, are less selfregulated, and are often less

pyramidal in trophic structure”. Models 1 and 2 were both classified as Class II

models.

D. Model stability of Model 1

- 62 -

The structure of Model 1 caused problems in terms of determining stability and the

response sign of its variables. Model 1 met the first Hurwitz criterion, as each level of

feedback was negative. Depending on its parameter space, it is possible that Model 1

failed the second Hurwitz criterion, as the weighted Determinant wD6 was negative.

However, it met the condition that the overall feedback should be less than the

product of feedback at lower levels (Dambacher et al., 2003b); this occurred when

only feedback levels were compared that produce equal length of loops, as

suggested in Dambacher et al. (2003b, p. 879):

F1F6 > F7

F2F5 > F7

F3F4 > F7

Despite the fact that the weighted determinant for this model was negative, it was

likely that Model 1 was stable, as the feedback at lower levels exceeded the overall

feedback of the modelled system.

E. Model stability of Model 2

The Model 2 was also classified as a “Class ΙΙ model”. However, Model 2 had zero

overall feedback, meaning that at the highest feedback level (which incorporated

every variable and link in the model) positive and negative loops outweighed each

other. To ensure that the modelled system met the first Hurwitz criterion, data were

applied (Tables 2.1 and 2.2) and the strength of the different loops was magnified.

The results of this analysis (shown in Table 2.3) showed that the negative loops

clearly outweighed the positive ones and, thus, that the overall feedback of the

system was negative as soon as the strength of loops was magnified by applying

knowledge of the system.

Table 2.3: Absolute values of magnified feedback cycles to estimate the strength of negative versus positive loops for determining the stability of Model 2

Sum of absolute values of negative loops Sum of absolute values of positive loops

- 63 -

)2.001.0(

)21.0(

)511201.0(

)27101.0(

)11241(

)102(

32

232

332

42

52

6

7

mm

mmm

mmm

mm

mm

m

)02.002.0(

)1.02.004.0(

)3.0004.0(

)1.003.0(

)1.01.0(

)01.0(

32

232

32

42

5

6

mm

mmm

mm

mm

m

The weighted determinant wD6 was positive in Model 2, but was close to zero with a

value of 0.0000027, indicating that this model was more likely to pass the second

criterion of stability than Model 1. Inspection of the feedback loops according to the

stability analysis of model Fig. 2.2 B, clearly showed that the feedback at lower levels

exceeded the overall feedback of the system. Thus, even if the structure of Model 2

made it very difficult to determine the stability of the model by applying calculations

of wD6, it was likely that Model 2 was stable based on the results when date were

applied.

F. Response signs of the Overall Model

Two press perturbations had to be considered for the Overall Model: (i) an increase

and (ii) a decrease in the tiger shark variable. To analyse the response signs, the

interaction strengths were magnified according to the assumptions made in Tables

2.1 and 2.2.

G. Determining the response signs of Model 1

Model 1 described the dynamics of the system during periods of low tiger shark

abundance and negligible impact from fear factors. Two press perturbations were

performed in this model: a decrease and an increase in the tiger shark variable. The

design of the model was such that if a variable had a positive response sign due to an

increase in tiger sharks, it would possess the opposite response sign when tiger

sharks were decreased. For this reason, the different response signs of the variables

in Model 1 only needed to be analysed for a positive press perturbation on the tiger

- 64 -

shark variable. The results could automatically be reversed for a negative

perturbation on the tiger shark variable.

After applying the estimates of magnitude from Table 2.2, some feedback cycles

dropped out and did not need to be analysed in Model 1, as the estimated

magnitude of some interaction coefficients was zero, e.g. α3,1, α6,1, α6,5 and α7,1.

Table 2.4: Determining the response sign of the tiger shark variable in Model 1

negative loops:

5,57,66,22,33,44,7 222 0001.0001.0 mm

4,45,57,66,22,33,7

m

mmmm

000002.0

0001.000004.0001.00001.0 22323

3,22,35,44,57,66,7 2002.0 m

2,23,34,45,57,66,7

432432

242322333242

202.04023.0

887229688

mmmmmm

mmmmmm

2,25,44,57,66,33,7 mm 00002.000004.0 2

5,56,22,33,44,67,7 222 001.001.0 mm

5,44,56,33,22,67,7 m001.000001.0 2

7,66,22,33,44,55,7 20001.0 m

4,45,56,22,33,67,7

m

m

001.0

01.000002.00002.0 223

3,34,45,56,22,67,7

32

232222

3233445

1.0

01.08121.00001.0

7002.001.001.0

mm

mmmm

mmm

2,25,44,56,33,67,7 mm 001.0002.000002.000004.0 223

2,23,35,44,56,67,7

3232

2222334

2.004.02.0

33004.0304.004.0

mmmmm

mmm

2,24,33,45,56,67,7

443

2422333242

182161134

21610981818

mmm

mmmmm

3,22,34,45,56,67,7

32

2222334

302.0

73.00003.0004.001.0

mmm

mmm

positive loops:

2,27,66,33,44,55,7 222 0004.0001.0 mm

5,44,57,66,22,33,7 m00001.0

2,23,35,44,57,66,7 323222 1.003.03.03.03.0 mmmmm

2,24,33,45,57,66,7 4424 58144144 mmm

- 65 -

3,22,34,45,57,66,7 233222 003.01.004.01.0 mmmmm

2,25,57,66,33,44,7 22232 0004.001.001.0 mmm

2,24,45,57,66,33,7

222

23234

0004.000001.001.0

0003.0001.0001.0001.0

mmm

mmmm

4,45,56,33,22,67,7

22

223234

2.0

01.000001.002.00001.00003.0

m

mm

5,44,56,22,33,67,7 m001.000001.0 2

2,25,56,33,44,67,7 22223234 04.00004.001.001.0 mmmmmm

3,35,44,56,22,67,7 2223 02.001.002.00001.00004.0 mmm

4,33,45,56,22,67,7 3232232 1202.02.0 mmmm

2,24,45,56,33,67,7

32

23222232

33445

01.00004.0

1.01.001.000001.01.0

02.00002.002.0001.0001.0

mm

mmmm

mm

3,22,35,44,56,67,7 22 002.001.00002.0 mm

2,23,34,45,56,67,7

432

4322423

22223332

334244556

503.0

654962.0132483

1334002.0815803

331.07559873

mmm

mmmmmm

mmmm

mmmm


432

432

2432

332

42

5

201.0

)2561141202.0(

)304153153004.0(

)11943351.0(

)2606.0(

)01.0(

mmm

mmmm

mmmm

mmm

mm

432

432

2432

332

42

5

6

631.0

)2095262.00004.0(

)2764951354002.0(

)815804331.0(

)755981(

)731(

mmm

mmmm

mmmm

mmm

mm

m

The analysis of magnitudes showed that the positive loops were more numerous and

also stronger in magnitude (Table 2.4). Consequently, the positive feedback loops

outweighed the negative ones and an increase of tiger sharks in Model 1 led to a

positive response sign of the tiger shark variable. Furthermore, a negative press

perturbation on the tiger shark variable in Model 1 led to the reverse response sign,

i.e. a decrease in tiger sharks in this model (Table 2.4).

- 66 -

Table 2.5: Determining the response signs of the M2 variable in Model 1

negative loops

7,66,75,54,33,21,4 3310 mm

7,66,35,74,53,21,4 m0001.0

7,76,35,54,63,21,4 mm 01.01.00001.0001.0 223

7,66,35,54,73,21,4 mm 0001.0001.0 2

3,34,45,56,67,71,2

43

243223

22233323

34244556

112

1.013210214815

20712274567

01.013642.012

mm

mmmmmm

mmmmm

mmm

3,35,44,57,66,71,2 3222 3.01.03.0 mmm

4,33,45,57,66,71,2 424 14144 mm

7,66,33,44,55,71,2 22001.0 m

5,56,33,44,67,71,2 223234 1.0001.001.0 mmmm

5,57,66,33,44,71,2 2232 001.001.0 mm

4,45,56,33,67,71,2

22232

33445

1.0002.0

03.000003.01.00004.0001.0

mmm

mm

4,45,57,66,33,71,2

22

23234

001.0

00003.001.00004.0001.0

m

mmmm

positive loops

6,67,75,54,33,21,4 mm 01.01.00001.0001.0 223

7,66,25,74,53,31,4 2002.00004.0002.0 mmm

7,76,25,54,63,31,4

2

2222334

1.002.0

3.00004.001.002.0

mm

mmm

7,66,25,54,73,31,4 22223 002.00004.002.001.002.0 mmmmm

7,76,25,54,33,61,4 22223 601.01.0 mmmm

7,66,25,54,33,71,4 222 01.01.0 mm

3,34,45,57,66,71,2

432

242322333242

112

882829698

mmm

mmmmmm

3,35,44,56,67,71,2

3

2222334

4.0

1.024.0201.004.0

m

mmmm

4,33,45,56,67,71,2

4

2423333242

24

2161321098218

m

mmmmm

- 67 -

5,44,56,33,67,71,2 23 002.000004.0 m

5,44,57,66,33,71,2 200004.0 m


432

432

24

32

332

42

5

6

1131.0

)14611214(

)144

815208120001.0(

)27476701.0(

)13642.0(

)121(

mmm

mmmm

m

mmm

mmm

mm

m

32

432

24

32

332

42

2002.00004.0

)3538202.0(

)304

1601330004.0(

)119411502.0(

)261.0(

mmm

mmmm

m

mmm

mmm

m

The analysis of magnitudes showed that the negative loops were more numerous

and also stronger in magnitude (Table 2.5). The negative cycles outweighed the

positive ones by far, as they reached levels of power six and five, compared to level

four, which represented the highest level of power that was reached by the positive

cycles (Table 2.5). However, the negative cycles were also stronger in the lower

levels of power (Table 2.5). Consequently, an increase of tiger sharks in Model 1 led

to a negative response sign of the M2 variable. Furthermore, a negative press

perturbation on the tiger shark variable in Model 1 led to the reverse response sign,

i.e. an increase in the M2 subpopulation in this model.

Table 2.6: Determining the response signs of M1sg variable in Model 1

negative loops

7,66,75,54,32,21,4 3323 24848 mmm

7,66,35,74,52,21,4 mm 0001.00002.0 2

7,76,35,54,62,21,4

m

mm

01.0

01.00001.01.0001.0002.0 22334

7,66,35,54,72,21,4 mmm 0001.0002.0002.0 23

7,76,25,54,32,61,4 22223 601.01.0 mmmm

4,45,56,67,72,31,2

3223222

3233445

2.002.02204.0

14001.001.002.0

mmmmm

mmm

5,44,57,66,72,31,2 201.0 m

7,76,35,44,52,61,2 23 002.000004.0 m

- 68 -

positive loops

6,67,75,54,32,21,4

33223

2223234

6721272

3662.036666

mmmm

mmmmm

7,66,25,74,52,31,4 m00004.0

7,76,25,54,62,31,4 mm 002.002.000004.00004.0 223

7,66,25,54,72,31,4 mm 00004.00004.0 2

4,45,57,66,72,31,2 32232232 2.001.021.02.0 mmmmm

5,44,56,67,72,31,2 223 01.01.0001.0 mm

7,76,35,54,42,61,2

22232

33445

1.0002.0

03.000003.01.00004.0001.0

mmm

mm


3

32

232

32

4

5

2

)4801.0(

)5081.00001.0(

)14002.0(

)01.0(

)02.0(

m

mm

mmm

mm

m

3

32

232

32

4

5

6

)7212002.0(

)743663.000004.0(

)3676001.0(

)60004.0(

)001.0(

m

mmm

mmm

mm

m

The analysis of magnitudes showed that the negative loops were stronger at the

highest level of power (Table 2.6). However, the positive cycles outweighed the

negative ones at every other level of power (Table 2.6). Thus, a positive press

perturbation on the tiger shark variable led to a positive response sign, i.e. an

increase in the M1sg population in Model 1. A negative press perturbation on the

tiger shark variable in this model led to the opposite response sign, i.e. a decrease in

M1sg.

Table 2.7: Determining the response sign of the M1ch variable in Model 1

negative loops

6,67,75,53,42,31,2 3232234 1.061.0 mmmmm

2,23,35,57,66,71,4

3

232232232 1.0164161916

m

mmmmmm

3,22,35,56,67,71,4 22223 1.0601.01.0 mmmm

5,56,22,33,67,71,4 mm 0004.002.000004.00004.0 223

5,57,66,22,33,71,4 mm 00004.00004.0 2

3,35,56,22,67,71,4

2

2222334

1.002.0

4.00004.001.002.0

mm

mmm

- 69 -

positive loops

7,66,75,53,42,31,2 323 1.0 mm

7,76,35,53,42,61,2 223234 1.0001.001.0 mmmm

2,23,35,56,67,71,4

32

322222

3233445

103

2631872402.0

1221354.012422

mm

mmmmm

mmm

2,25,56,33,67,71,4

m

mm

004.0

1.00001.01.0002.0002.0 22334

2,25,57,66,33,71,4 mmm 0001.0002.0002.0 23

3,22,35,57,66,71,4 22 01.01.0 mm

5,56,33,22,67,71,4 mm 001.01.00001.0001.0 223


32

32

232

32

4

2.0

)165(

)1721601.0(

)1671.0(

)1.002.0(

mm

mmm

mmm

mm

m

32

32

232

32

4

5

103

)2631(

)872402.0(

)1231354.0(

)1242(

)2(

mm

mmm

mmm

mm

m


also stronger in magnitude, as the positive cycles outweighed the negative ones at

every level of power (Table 2.7). Thus, a positive press perturbation on the tiger

shark variable led to a positive response sign, i.e. an increase in the M1ch population

in Model 1. A negative press perturbation on the tiger shark variable in this model

led to the opposite response sign, i.e. a decrease in M1ch.

Table 2.8: Determining the response sign of the M* variable in Model 1

negative loops

6,67,74,53,42,31,2 3223 1.001.0 mmm

7,66,74,53,32,21,4 323222 2.0222 mmmmm

6,67,74,53,22,31,4 22 01.01.0001.0 mm

7,76,24,53,62,31,4 m002.000004.0 2

7,66,24,53,72,31,4 m00004.0

7,76,24,53,32,61,4 223 1.002.01.0002.0 mmm

- 70 -

positive loops

7,66,74,53,42,31,2 31.0 m

7,76,34,53,42,61,2 223 1.0001.0 mm

6,67,74,53,32,21,4

32222

22334

2.0715

1202.0122.02.0

mmmmm

mm

7,76,34,53,62,21,4 mm 01.001.00001.00002.0 223

7,66,34,53,72,21,4 mm 0001.00002.0 2

7,66,74,53,22,31,4 201.0 m

7,76,34,53,22,61,4 m004.00001.0 2


32

32

22

3

2.0

)221.0(

)31.0001.0(

)01.0002.0(

mm

mmm

mm

m

32

32

22

3

4

2.0

)27(

)151202.0(

)122.0(

)2.0(

mm

mmm

mm

m


also stronger in magnitude, as the positive cycles outweighed the negative ones at

the higher levels of power (Table 2.8). Thus, a positive press perturbation on the

tiger shark variable led to a positive response sign, i.e. an increase in the M*

population in Model 1. A negative press perturbation on the tiger shark variable in

this model led to the opposite response sign, i.e. a decrease in M*.

Table 2.9: Determining the response sign of the Psg variable in Model 1

negative loops

5,44,57,73,62,31,2 23 1.0001.0 m

7,65,44,53,72,31,2 2001.0 m

3,35,44,57,72,61,2 222334 2301.004.0 mmm

4,33,45,57,72,61,2 23333242 1081098218 mmmm

5,77,64,53,32,21,4 22223 1.001.02.02.02.0 mmmmm

5,57,74,63,32,21,4

222

2233445

4248

2402.01344.012222

mmm

mmm

7,65,54,73,32,21,4

2

2223234

1.002.0

3.0222

mm

mmmmm

5,57,74,33,62,21,4 22223234 121582.036636 mmmmmm

7,65,54,33,72,21,4 22232 2.066 mmm

- 71 -

5,57,74,33,22,61,4 22223 121222.02 mmmm

positive loops

7,65,54,73,42,31,2 2232 01.01.0 mm

5,77,64,53,42,31,2 2201.0 m

5,57,74,63,42,31,2 223234 601.01.0 mmmm

4,45,57,73,62,31,2

22232

33445

03.011

4.0001.001.002.0

mmm

mm

7,65,54,43,72,31,2 2223234 03.002.02.001.002.0 mmmmm

3,34,45,57,72,61,2

2322233323

34244556

661267119512

01.0743702.073

mmmmmm

mmm

5,77,64,53,22,31,4 m001.0

5,57,74,63,22,31,4 mm 1.0001.001.0 223

7,65,54,73,22,31,4 mm 001.001.0 2


)282(

)1083372602.0(

)10983761444.0(

)181302(

)2(

2

232

332

42

5

mm

mmm

mmm

mm

)1.0(

)66142001.0(

)6712121202.0(

)743712.0(

)731(

232

332

42

5

6

m

mmm

mmm

mm

m

The analysis of magnitudes showed that the positive loops were less numerous, but

stronger in magnitude, as the positive cycles outweighed the negative ones at the

three highest levels of power (Table 2.9). The negative cycles dominated the three

lower levels of power, but their impact was not strong enough to determine the

response sign (Table 2.9). Thus, a positive press perturbation on the tiger shark

variable led to a positive response sign, i.e. an increase in the Psg population in

Model 1. A negative press perturbation on the tiger shark variable in this model led

to the opposite response sign, i.e. a decrease in Psg.

- 72 -

Table 2.10: Determining the response sign of the Pch variable in Model 1

negative loops

5,56,64,33,72,21,4 mmmmmm 04.04.03.036 2223234

6,75,44,53,62,31,2 201.0 m

5,44,56,63,72,31,2 23 0002.0001.0 m

6,75,44,53,32,61,2 2223 3.03.0 mmm

6,75,54,33,42,61,2 2333 14144 mm

5,76,34,53,42,61,2 3001.0 m

5,56,34,73,42,61,2 34 001.001.0 mm

5,56,34,43,72,61,2 33445 001.000003.001.00004.0001.0 mm

5,76,64,53,32,21,4

2

2222334

02.0004.0

04.01.002.02.01.02.0

mm

mmm

6,75,54,63,32,21,4 22223234 1.082161116 mmmmmmm

5,56,64,73,32,21,4

mmm

mmm

02.0004.02.02.0

1.04.024.0222222

23233445

6,75,54,33,62,21,4 22232 24848 mmm

5,76,34,53,62,21,4 23 0001.00002.0

5,56,34,73,62,21,4 234 0001.0002.0002.0

5,56,24,63,72,31,4 23 00004.00004.0

6,75,54,33,22,61,4 22214 mm

5,76,34,53,22,61,4 20001.0

5,56,34,73,22,61,4 23 0001.0001.0

positive loops

5,56,24,73,62,31,4 23 00004.00004.0

5,76,64,53,42,31,2 223 002.001.0 mm

6,75,54,63,42,31,2 2232 1.0 mm

5,56,64,73,42,31,2 223234 002.002.001.01.0 mmmm

6,75,54,43,62,31,2 23234 01.021.02.0 mmmm

- 73 -

4,45,56,63,72,31,2

222323

3445

004.00001.004.002.0

001.02.001.002.0

mmmm

m

6,75,54,43,32,61,2

2322233

3234245

1121.088

2829698

mmmm

mmmmm

5,46,34,53,72,61,2 300004.0

5,56,34,63,72,21,4 234 0001.0002.0002.0

5,76,64,53,22,31,4 m0002.0001.0 2

6,75,54,63,22,31,4 mm 01.01.0 2

5,56,64,73,22,31,4 mm 0002.0002.0001.001.0 223

5,76,24,53,62,31,4 200004.0

5,76,24,53,32,61,4 223 002.00004.0002.0 m

5,56,24,73,32,61,4

2

2334

002.0

0004.002.001.002.0

m

m

5,56,24,33,72,61,4 23 01.01.0 mm


)42.0004.0(

)147141.0(

)14465161(

)242(

)2(

2

232

332

4

5

mm

mmm

mmm

m

)01.0(

)1122.0002.0(

)8831203.0(

)961003.0(

)8(

232

332

42

5

m

mmm

mmm

mm

m

An increase in the tiger shark variable led to a change in the Pch variable. However,

the response sign of the Pch variable was difficult to determine (Table 2.10). The

analysis of magnitudes showed that the positive loops were stronger in magnitude at

the two highest levels of power, whereas the negative loops were dominant at the

remaining levels of power (Table 2.10). The response sign of this variable depended

on the values of β and m. For β ≤ 1.6 and 0 < m < 1, the response sign was always

negative. It was assumed that β could not exceed 1, as on a maximum level of

impact, the interaction coefficient would affect the whole and not only a fraction of

the population. Consequently, an increase in the tiger shark variable would lead to a

negative response sign in the Pch variable and thus, a decrease in the prey variable in

the channels. This argument was supported by the results of the analysis of the

adjoint matrix when data was not applied and only the signs of interactions (e.g. 0,

- 74 -

+1 and -1) were considered. In Model 1, a positive press perturbation on the tiger

shark variable led to a negative response sign of the adjoint, as the 44 negative

cycles outweighed the 40 positive loops. The dominance of negative loops also

remained when data was applied, as the 18 negative loops outweighed the 16

positive ones (Table 2.10).

For these reasons, a positive press perturbation on the tiger shark variable led to a

negative response sign, i.e. a decrease in the Pch population in Model 1. A negative

press perturbation on the tiger shark variable in this model led to the opposite

response sign, e.g. an increase in Pch.

H. Results of the response sign analysis in Model 1

Two different scenarios needed to be considered in Model 1 – the response of the

system after an increase or a decrease of the tiger shark abundance (Fig. 2.5). The

response signs of the different variables are shown in the figures below, where state

A.1 shows the response sign after a positive input to the tiger shark variable and

state A.2 after a negative press perturbation to this variable.

- 75 -

Psg

+

Pch

-

M1sg

+

M2

-

ts

+

M*+

M1ch

+

State A.1

Psg

-

Pch

+

M1sg

-M2

+

ts

-

M*

-M1ch

-

State A.2

Figure 2.5.Different states of Figure 2.2 A: Qualitative model showing the response signs of the system in two different stages after an increase (state A.1) and a decrease (state A.2) in the tiger shark variable in Model 1. The sign “–“ indicates a decrease of the variable and “+” an increase in the different variables, such as tiger sharks (ts), different megafauna subpopulations (M2, M1sg, M1ch, M*) and the prey of the latter (Psg, Pch) in two different habitats (i.e. the seagrass and the channel habitats).

I. Determining the response signs of Model 2

Model 2 described the dynamics of the system during periods of high tiger shark

abundance and significant impact from fear factors. Two press perturbations are

performed in this model: (i) an increase and (ii) a decrease in the tiger shark variable.

If a variable had a positive response sign due to an increase in tiger sharks, it would

seagrass channel

seagrass channel

- 76 -

possess the opposite response sign when tiger sharks were decreased. For this

reason, and according to the analysis performed in Model 1, the different response

signs of the variables in Model 2 only needed to be analysed for a positive press

perturbation on the tiger shark variable. The results could be automatically reversed

for a negative perturbation on the tiger shark variable.

The estimates of magnitude from Table 2.2 were applied and, because of the high

impact of the fear factors, no feedback cycle equalled zero in Model 2.

Table 2.11: Determining the response sign of the tiger shark variable in Model 2

negative loops

2,23,35,44,56,67,7

22

2222334

01.004.004.0

02.02.00004.01.002.001.0

mmm

mmm

2,26,33,44,55,67,7

mmmm 001.0003.00001.00003.0 222

2,25,44,57,66,33,7

mm 000001.000002.0 2

3,22,34,45,56,67,7

3232

2222334

01.0001.01.01.0003.0

3.01.00003.03.001.003.0

mmmmm

mmm

7,66,22,33,44,55,7

32 003.0001.0 mm

5,57,66,22,33,44,7

33222 01.003.0004.002.0 mmmm

4,45,57,66,22,33,7

mm

mmm

00002.00004.0

002.00004.0002.02

2223

5,44,56,33,22,67,7

m00003.0000003.0 2

3,34,45,56,22,67,7

22222

3233445

04.0002.02.01.00002.0

1.02.001.01.002.001.0

mmmm

mmm

positive loops

2,23,34,45,56,67,7

323223

22223332

334244556

2.0001.02004.04

80004.0224

81.01122102

mmmmmm

mmmm

mmmm

2,27,66,33,44,55,7

33222 001.0003.00002.0001.0 mmmm

2,25,57,66,33,44,7

33

2232232

001.001.0

001.003.001.002.0

mm

mmmm

- 77 -

2,24,45,57,66,33,7

222

23234

0001.0000004.00001.0

0001.0002.0001.0002.0

mmm

mmmm

3,22,35,44,56,67,7

22 001.0003.00003.0 mm

6,22,33,44,55,67,7

32 003.00003.0 mm

5,44,57,66,22,33,7

m00002.0

4,45,56,33,22,67,7

222

22334

001.0000003.0003.0

001.00000003.0003.00001.00003.0

mmm

mm

3,35,44,56,22,67,7

mm 02.001.0002.0001.0 223


32

32

22

32

4

5

02.001.0001.0

)1.02.01.0(

)4.001.0(

)1.004.0(

)1.01.0(

)01.0(

mmm

mmm

mm

mm

m

32

32

232

332

42

5

6

2.0002.0

)203.0(

)48003.0(

)22481.0(

)11221(

)102(

mm

mmm

mmm

mmm

mm

m

Despite the fact that positive and negative loops were equal in numbers, the analysis

of magnitudes showed that the positive loops were stronger in magnitude, as the

positive cycles outweighed the negative ones at each level of power (Table 2.11).

Thus, a positive press perturbation on the tiger shark variable led to a positive

response sign of the variable itself, i.e. an increase in the ts population in Model 2. A

negative press perturbation on the tiger shark variable in this model led to the

opposite response sign, i.e. a decrease in ts.

- 78 -

Table 2.12: Determining the response sign of the M2 variable in Model 2

negative loops

3,34,45,56,67,71,2

2

2322223332

334244556

1.0004.0

4.01.00004.024

01.0114104.0

mm

mmmmm

mmmm

7,66,33,44,55,71,2 322 003.0001.0 mm

5,57,66,33,44,71,2 3232232 01.003.0004.002.0 mmmm

4,45,57,66,33,71,2

222

3234

0004.000002.0

002.00004.0002.0

mm

mmm

4,45,56,67,73,21,3

43432

232223234

04.0452.0

22402.0204.02

mmmmm

mmmmmm

7,66,25,74,53,41,3 43 3.01.0 mm

7,66,25,54,73,41,3 44323 34.02 mmmm

7,66,25,54,43,71,3 32232232 02.0001.01.002.01.0 mmmmm

7,66,35,74,53,21,4 32 0003.00001.0 mm

7,66,35,54,73,21,4 33222 003.00002.0002.0 mmmm

7,76,25,64,53,31,4 33222 02.001.0002.0001.0 mmmm

7,76,35,44,53,21,6 m00003.0000003.0 2

7,76,25,54,43,31,6

22222

3233445

04.0002.02.01.00002.0

1.02.001.01.002.001.0

mmmm

mmm

7,66,35,54,43,21,7 2223 0003.00001.00003.0 mmm

7,66,25,44,53,31,7 mm 0002.00001.0 2

Positive loops

3,35,44,56,67,71,2

222

2334

04.002.0

2.01.002.001.0

mm

mm

6,33,44,55,67,71,2 22 003.00003.0 mm

5,44,57,66,33,71,2 200002.0 m

5,44,56,67,73,21,3 322 04.02.002.0 mmm

7,76,25,64,53,41,3 43 2.002.0 mm

7,66,25,44,53,71,3 2001.0 m

7,76,35,64,53,21,4 32 0002.000002.0 mm

7,66,25,74,53,31,4 33222 01.001.0004.0002.0 mmmm

7,66,25,54,73,31,4

33

2232232

04.01.0

01.01.01.004.0

mm

mmmm

7,76,35,54,43,21,6

2222

2334

001.000003.0003.0001.0

000003.0003.00001.00003.0

mmmm

m

7,76,25,44,53,31,6 mm 002.0001.00002.00001.0 223

- 79 -

7,66,35,44,53,21,7 m000003.0

7,66,25,54,43,31,7

222

23234

04.00002.002.0

004.001.002.001.0

mmm

mmmm


43

432

232

332

42

5

6

23.0

)763.0003.0(

)2562.0001.0(

)224202.0(

)1161(

)104.0(

mm

mmmm

mmm

mmm

mm

m

43

32

232

32

4

2.01.0

)1.03.0002.0(

)1.01.02.00002.0(

)1.01.002.0(

)01.001.0(

mm

mmm

mmm

mm

m

The analysis of magnitudes showed that the negative loops were more numerous

and also stronger in magnitude, as they outweighed the positive loops at each level

of power (Table 2.12). Thus, a positive press perturbation on the tiger shark variable

led to a negative response sign of the M2 variable, i.e. a decrease in the megafauna

subpopulation M2 in Model 2. A negative press perturbation on the tiger shark

variable in this model led to the opposite response sign, i.e. an increase in M2.

Table 2.13: Determining the response sign of the M1sg variable in Model 2

negative loops

4,45,56,67,72,31,2

3223222

3233445

04.002.02.02.001.0

2.0001.002.01.0

mmmmm

mmm

7,76,35,44,52,61,2 23 0001.000001.0 m

2,24,45,56,67,71,3

3243

22423222

333234245

02.025

002.0112261.0

55246026

mmmm

mmmmmm

mmmmmm

5,44,56,22,67,71,3 22 01.0001.0 mm

7,66,35,74,52,21,4

3

2322

0002.0

00004.0001.00002.0

m

mmm

7,66,35,54,72,21,4

33

2232232

0004.0004.0

0002.001.0002.0004.0

mm

mmmm

7,76,25,64,52,31,4 32 001.00001.0 mm

7,76,35,44,52,21,6 mm 00002.00001.0000002.000001.0 223

- 80 -

7,76,25,54,42,31,6

222

22334

002.00001.001.0

003.000001.001.00002.0001.0

mmm

mm

7,66,35,54,42,21,7

222

23234

00004.0000002.00004.0

0001.0001.00004.0001.0

mmm

mmmm

7,66,25,44,52,31,7 m0001.0

positive loops

5,44,56,67,72,31,2 223 002.001.0001.0 mm

7,76,35,54,42,61,2

222

323445

002.00001.0

01.0002.001.00002.0001.0

mm

mmm

2,25,44,56,67,71,3 3322223 04.02.02.002.01.0 mmmmmm

4,45,56,22,67,71,3

32

232223234

2.001.0

2.0001.002.01.0

mm

mmmmmm

7,76,35,64,52,21,4 33222 0002.0001.000002.00001.0 mmmm

7,66,25,74,52,31,4 32 001.00002.0 mm

7,66,25,54,72,31,4 33222 002.001.0001.0004.0 mmmm

7,76,35,54,42,21,6

222

2232

33445

0004.000002.0004.0

001.0000002.001.0

004.00001.001.00004.0001.0

mmm

mm

mm

7,76,25,44,52,31,6 m001.000001.0 2

7,66,35,44,52,21,7 mm 000002.000001.0 2

7,66,25,54,42,31,7

2

2223

0002.0

00001.0001.00002.0001.0

m

mmmm

Sum of absolute values of negative loops

Sum of absolute values of positive loops

32

432

2432

332

42

5

02.0

)25002.0(

)112261.000001.0(

)5525001.0(

)60302.0(

)61.0(

mm

mmmm

mmmm

mmm

mm

m

3

32

232

32

4

5

04.0

)4.02.00001.0(

)03.000001.0(

)1.0001.0(

)1.0001.0(

)002.0(

m

mmm

mmm

mm

m

- 81 -

Despite the fact that positive and negative loops were equal in numbers, the analysis

of magnitudes showed that the negative loops were stronger in magnitude, as they

outweighed the positive loops at each level of power (Table 2.13). Thus, a positive

press perturbation on the tiger shark variable led to a negative response sign of the

M1sg variable, i.e. a decrease in the megafauna subpopulation M1sg in Model 2. A

negative press perturbation on the tiger shark variable in this model led to the

opposite response sign, i.e. an increase in M1sg.

Table 2.14: Determining the response sign of the M1ch variable in Model 2

negative loops:

5,56,67,73,42,31,2 323223234 4.02292.0 mmmmmm

5,56,67,73,42,21,3

4432423

22333242

76626132264

35942666

mmmmm

mmmm

3,22,35,56,67,71,4

232

2223

04.02.02.0

004.002.01.0

mmm

mmmm

5,57,66,22,33,71,4 222 001.0004.0 mm

3,35,56,22,67,71,4

222

23234

1.04.0

01.02.004.002.0

mm

mmmm

7,76,25,53,42,31,6 22223 02.01.0002.001.0 mmmm

7,66,35,53,42,21,7 22232 0004.0004.001.0 mmm

Positive loops:

7,76,35,53,42,61,2 223 01.0001.0 mm

7,76,25,53,42,61,3 3232232 292.0 mmmm

2,23,35,56,67,71,4

33223222

333234245

3.0329162.0

44421852

mmmmmm

mmmmmm

2,25,57,66,33,71,4 22232 0002.0002.0004.0 mmm

5,56,33,22,67,71,4 22223 002.001.00002.0001.0 mmmm

7,76,35,53,42,21,6

222

23234

004.004.0

0004.01.0004.001.0

mm

mmmm

7,66,25,53,42,31,7 222 002.001.0 mm


- 82 -

42

432

2432

332

42

704.0

)66273.0004.0(

)132266703.0(

)594354.0(

)66(

mm

mmmm

mmmm

mmm

mm

3

32

232

332

42

5

3.0

)52(

)18162.0(

)4452(

)185(

)2(

m

mm

mmm

mmm

mm

m

Despite the fact that positive and negative loops were equal in numbers, the positive

loops were stronger in magnitude at the highest level of power and thus, the

response sign of the M1ch variable was positive after a positive press perturbation on

the tiger shark variable (Table 2.14). The positive loops were stronger at the highest

level of power, the loops were about equal in magnitude at power level four and the

negative loops were stronger at every other level of power (Table 2.14). The impact

of the negative loops was strong enough to determine the response sign for any

value m > 0.1.The parameter m described the migration of prey and the megafauna

into another habitat to avoid predators. The exact value for m was unknown and, as

mentioned in section 3.3.2, it was unlikely that the value of m was equal for the

different variables and different habitats. Thus, m alone could not be used for

determining the response sign. Consequently, the number of positive versus

negative loops and the power of these loops were the only measures that could be

relied on to determine the sign of response of a variable. For this reason, an increase

in tiger shark abundance in Model 2 led to an increase in the M1ch variable. On the

other hand, a decrease in the tiger shark variable in Model 2 led to a decrease in the

M1ch variable.

- 83 -

Table 2.15: Determining the response sign of the M* variable in Model 2

negative loops

6,67,74,53,42,31,2 3223 2.01.0 mmm

6,67,74,53,42,21,3 443232232 149637 mmmmmm

6,67,74,53,22,31,4 322 02.01.001.0 mmm

7,66,24,53,72,31,4 20004.0 m

7,76,24,53,32,61,4 22223 04.0004.0002.0 mmmm

7,76,24,53,42,31,6 22 01.0001.0 mm

7,66,33,44,52,21,7 222 01.0001.0 mm

Positive loops

7,76,34,53,42,61,2 223 01.0001.0 mm

7,76,24,53,42,61,3 3221.0 mm

6,67,74,53,32,21,4

3322322

23234

2.04.04

1.024.02.0

mmmmm

mmmm

7,66,34,53,72,21,4 222 0001.00004.0 mm

7,76,34,53,22,61,4 22 001.00001.0 mm

7,76,34,53,42,21,6 22223 002.001.00002.0001.0 mmmm

7,66,22,33,44,51,7 2001.0 m


43

432

232

32

02.0

)1492.0(

)63201.0(

)71.0(

mm

mmm

mmm

mm

3

32

232

32

4

2.0

)2(

)4.041.0(

)24.0(

)2.0(

m

mm

mmm

mm

m

Despite the fact that positive and negative loops were equal in numbers and the

positive loops were stronger in magnitude at the highest level of power, the negative

loops were stronger at every other level of power (Table 2.15). The impact of the

negative loops was strong enough to determine the response sign for any value m >

0.09. However, m alone could not used for determining the response sign and the

number of positive versus negative loops and the power of these loops were the only

measures that could be relied upon to determine the sign of response of a variable.

- 84 -

Thus, it was concluded that an increase in tiger shark abundance in Model 2 led to an

increase in the M* variable. On the other hand, a decrease in the tiger shark variable

in Model 2 led to a decrease in the M* variable.

Table 2.16: Determining the response sign of the Psg variable in Model 2

negative loops

2,23,35,44,57,71,6

m

mm

04.0004.0

2.002.01.001.0 2233

3,22,34,45,57,71,6

2

222233

1.001.00003.0

3.01.001.03.003.0

mm

mmm

7,65,54,42,23,31,7

2

2223234245

1.0004.0

1.022

mm

mmmmmmm

7,65,44,53,22,31,7 m0003.0

7,75,64,53,42,31,2 322 3.003.0 mm

7,65,44,53,72,31,2 2002.0 m

5,44,57,73,32,61,2 2334 2.01.002.001.0 mm

7,75,64,53,42,21,3 44323 4184.02 mmmm

7,65,44,53,72,21,3 222 02.01.0 mm

5,44,57,73,22,61,3 22 2.002.0 mm

5,77,64,52,23,31,4 332232233 4.04.02.0 mmmmmm

7,65,54,72,23,31,4

3

22322333242

6

4.0144684

m

mmmmmm

7,75,64,53,22,31,4 32 02.0002.0 mm

Positive loops

3,22,35,44,57,71,6 m003.00003.0 2

7,65,44,52,23,31,7 mmm 004.002.001.0 23

7,65,54,43,22,31,7 22223 01.00003.003.001.003.0 mmmmm

5,77,64,53,42,31,2 322 3.01.0 mm

7,65,54,73,42,31,2 3232232 34.02 mmmm

7,65,54,43,72,31,2

2

223234

002.0

04.02.004.02.0

m

mmmm

4,45,57,73,32,61,2

222323

34244556

42.0205

02.09224.0102

mmmm

mmm

5,77,64,53,42,21,3 44323 7217 mmmm

7,65,54,73,42,21,3

44

3242333

13105

519852132

mm

mmmm

- 85 -

7,65,54,43,72,21,3

3223

22333242

4.002.04

12412

mmm

mmmm

4,45,57,73,22,61,3

32

232223234

42.0

18402.0204.02

mm

mmmmmm

7,75,64,52,23,31,4 332232232 4.0204.02.01.0 mmmmmm

5,77,64,53,22,31,4 32 03.001.0 mm

7,65,54,73,22,31,4 33222 1.03.004.02.0 mmmm

2,23,34,45,57,71,6

2

2222323

34244556

004.080004.02210

1.0924102

m

mmmmm

mmm



43

432

232

332

42

5

4

)18821.0004.0(

)17603.0(

)61021.0(

)5201.0(

)(

mm

mmmm

mmm

mmm

mm

m

43

432

2432

332

42

5

6

20

)1261401.0(

)1983518001.0(

)14468151.0(

)30481(

)204(

2

mm

mmmm

mmmm

mmm

mm

m


also stronger in magnitude, as they outweighed the positive loops at each level of

power (Table 2.16). Thus, a positive press perturbation on the tiger shark variable led

to a positive response sign of the Psg variable, i.e. an increase in the prey variable

within the seagrass habitat in Model 2. A negative press perturbation on the tiger

shark variable in this model led to the opposite response sign, i.e. a decrease in Psg.

- 86 -

Table 2.17: Determining the response sign of the Pch variable in Model 2

negative loops

3,35,44,56,22,61,7 23 0002.00001.0

2,23,34,45,56,61,7

2

222232

334244556

02.0

001.02.01.0004.0

1.02.022

m

mmmm

mmmm

3,22,35,44,56,61,7 m0001.00003.0 2

6,22,33,44,55,61,7 20003.0 m

4,45,56,33,22,61,7

2

2334

0001.0

000003.00003.00001.00003.0

m

m

5,76,34,53,22,61,4 22 00003.000001.0 mm

5,56,34,73,22,61,4 22223 001.0003.00004.0002.0 mmmm

5,76,34,53,42,21,6 22223 001.0003.00002.0001.0 mmmm

5,56,34,73,42,21,6

2

2223234

001.0

01.0001.003.001.002.0

m

mmmmm

5,56,34,43,72,21,6

22

33445

0001.0000004.0

001.00001.0002.0001.0002.0

m

mm

5,46,24,53,72,31,6 200002.0

5,44,56,63,72,31,2 23 0004.0002.0 m

5,76,34,53,42,61,2 223 003.0001.0 mm

5,56,34,73,42,61,2 223234 01.003.0004.002.0 mmmm

5,56,34,43,72,61,2

3

3445

0004.0

0002.0002.00004.0002.0

m

m

5,44,56,63,72,21,3 22223 004.002.002.01.0 mmmm

5,76,24,53,42,61,3 322 3.01.0 mm

5,56,24,73,42,61,3 3232232 34.02 mmmm

5,56,24,43,72,61,3 2223234 02.0001.01.002.01.0 mmmmm

5,76,64,53,32,21,4

2222

22334

1.04.02.02.0

1.04.02.0

mmmm

mm

5,56,64,73,32,21,4

33223222

333234245

2.02564.0

21546104

mmmmmm

mmmmmm

5,66,34,53,72,21,4 222 00002.00001.0 mm

Positive loops

2,23,35,44,56,61,7

m

mm

001.0

002.0002.0002.002.001.0 223

2,26,33,44,55,61,7 222 0001.00003.0 mm

- 87 -

3,22,34,45,56,61,7

222

22334

001.000004.001.0

01.00002.004.001.003.0

mmm

mm

5,44,56,33,22,61,7 2000003.0

3,34,45,56,22,61,7

2

233445

004.0

0002.002.001.001.002.001.0

m

mm

5,76,24,53,42,31,6 22 003.0001.0 mm

5,56,64,73,22,31,4

m

mmm

01.0

1.01.001.03.004.02.0 2223

5,66,24,53,72,31,4 20001.0 m

5,76,24,53,32,61,4 22223 01.001.0004.0002.0 mmmm

5,56,24,73,32,61,4

2

2223234

04.0

2.002.01.01.004.0

m

mmmmm

5,46,34,53,72,21,6 23 000004.000002.0

5,56,24,73,42,31,6 22223 01.003.0004.002.0 mmmm

5,56,24,43,72,31,6

2

2334

0004.0

00002.0002.00004.0002.0

m

m

5,76,64,53,42,31,2 3223 1.03.01.0 mmm

5,56,64,73,42,31,2 323223234 1.034.02 mmmmmm

4,45,56,63,72,31,2

222

3233445

01.00004.0

04.01.0002.02.004.02.0

mm

mmm

5,46,34,53,72,61,2 300002.0

5,76,64,53,42,21,3 443232232 73217 mmmmmm

5,56,64,73,42,21,3

4432423

22333242

22094092

524453132

mmmmm

mmmm

4,45,56,63,72,21,3

3223222

333234245

2.001.004.0

2512612

mmmmm

mmmmmm

5,46,24,53,72,61,3 2001.0 m

5,76,64,53,22,31,4 322 01.003.001.0 mmm



- 88 -

32

32

232

332

42

5

6

2.01.0

)32.0(

)8721.0(

)21861(

)6121(

)52(

mm

mmm

mmm

mmm

mm

m

43

432

2432

332

42

5

301.001.0

)27122.01.001.0(

)4011594.01.0(

)2266822.0(

)14481.0(

)122.0(

mmm

mmmm

mmmm

mmm

mm

m

Despite the fact that positive and negative loops were equal in numbers and the

negative loops were stronger in magnitude at the highest level of power, the positive

loops were stronger at every other level of power (Table 2.17). The impact of the

positive loops was strong enough to determine the response sign for any value m >

0.14. However, m alone could not be used for determining the response sign and the

number of positive versus negative loops and the power of these loops were the only

measures that could be relied on to determine the sign of response of a variable

(Table 2.17). Thus, it was concluded that an increase in tiger shark abundance in

Model 2 led to a decrease in the Pch variable. On the other hand, a decrease in the

tiger shark variable in model Fig. 2.2B led to an increase in the Pch variable.

J. Results of the response sign analysis in Model 2

Like in Model 1, two different scenarios needed to be considered in Model 2 -- the

response of the system after an increase or a decrease of the tiger shark abundance.

The response signs of the different variables are shown in Figure 2.6, where state B.1

shows the response sign after a positive input to the tiger shark variable and state

B.2 after a negative press perturbation to this variable.

- 89 -

Psg

+

Pch

-

M1sg

-

M2

-

ts

+

M*+

M1ch

+

State B.1

Psg

-

Pch

+

M1sg

+

M2

+

ts

-

M*

-M1ch

-

State B.2

Figure 2.6: Different states of Fig. 2.2 B: Qualitative model showing the response signs of the system in two different stages after an increase (state B.1) and a decrease (state B.2) in the tiger shark variable in model Fig. 2.2 B. The sign “–“ represents a decrease of the variable and “+” an increase in the different variables, i.e. tiger sharks (ts), different megafauna subpopulations (M2, M1sg, M1ch, M*) and the prey of the latter (Psg, Pch) in two different habitats (i.e. the seagrass and the channel habitats).

seagrass channel

seagrass channel

- 90 -

Part II - Modelling trophic interactions and the impacts of fishing on the seagrass ecosystem

2.4.5 Modelling trophic interactions

The trophic interactions involving tiger sharks as apex predator and the effects of

fishing activities on the tiger shark population in Shark Bay were investigated in the

“diet + fishery model” presented in Figure 2.7. This model was based only upon

direct effects that were incorporated into the model according to the literature and

expert knowledge (Michael Heithaus, Florida International University, USA and

Norman Hall, Murdoch University, Western Australia). Thus, no differential

equations were derived for this model.

The model design reflected what is known about the ecology of tiger sharks in

seagrass ecosystems like Shark Bay. Tiger sharks, like many large sharks, show an

ontogenetic shift in diet. Adult tiger sharks mainly feed on turtles, dugongs, pied

cormorants, elasmobranchs and sea snakes (Fig. 2.7, ats agr, logt, dug, pc, elas,

dol, ssn). In addition to this direct trophic interaction, these prey groups are also

impacted by a behaviourally-induced indirect effect of tiger sharks, which has a

positive effect on seagrass, invertebrates and fish (Fig. 2.7, ats dug, agr, logt, pc,

elas, dol; ats sg, inv, fish). Juvenile tiger sharks mainly consume fish,

invertebrates, sea snakes, pied cormorants and elasmobranchs (Fig. 2.7, jts fish,

inv, ssn, pc, elas) and do not appear to have a behavioural impact on their prey

groups. The adult and juvenile tiger shark populations are positively linked to each

other (Fig. 2.7, ats jts). However, the deletion of the positive recruitment link

from juvenile to adult tiger sharks had no consequences for the stability of the

model or for response signs in the model predictions.

The model design also reflected certain assumptions about the effect of fishing on

tiger sharks and other species. The first assumption was that the Northern Shark

Fishery mainly affects large, adult tiger sharks that (assuming the hypothesis of a

common tiger shark stock is true) leave the protected areas of Shark Bay (Fig. 2.7, nsf

- 91 -

ats). The second assumption was that the recreational fishing sector mainly

targets fish (Fig. 2.7, rc fish). The third assumption was that the commercial

fishery in Shark Bay takes invertebrates, fish and sea snakes as by-catch (Fig. 2.7, sbcf

fish, inv, sbcf ssn ) and discards most of the by-catch (Fig. 2.7, sbcf dis).

Tiger sharks, sea birds and elasmobranchs are also known to feed on the discards

(Fig. 2.7, ats, jts, elas dis). The final assumption was that the commercial fishing

sector operating in Shark Bay has a negative impact on seagrass (Francesconi &

Clayton, 1996; Kangas et al., 2006) (Fig. 2.7, sbcf sg).

inv

ssndolelaspclogtagr

jts

ats

dug

sg fish

nsf

dis

sbcf

rc

Figure 2.7: “trophic interactions and impact of fisheries” – a model of the ontogenetic shift in the diet of tiger sharks and the effects of fishing activities on different functional groups within the Shark Bay seagrass ecosystem. ats: adult tiger sharks, jts: juvenile tiger sharks, dug: dugongs, agr: adult green turtles, logt: loggerhead turtles, pc: pied cormorant, elas: elasmobranchs, dol: dolphins, ssn: sea snakes, inv: invertebrates, sg: seagrass, fish: teleost fish, nsf: Northern Shark Fishery, sbcf: Shark Bay Commercial Fishery, dis: discards, rc: recreational fishery

- 92 -

2.5 Results

2.5.1 The dynamics of the seagrass ecosystem in Shark Bay

The structure of the Overall Model was characterised by several populations that

were positively linked to each other and the splitting of populations. These attributes

complicated the analysis of model stability and press perturbations. As a

consequence, data and knowledge of the system had to be applied to perform these

analyses by magnifying the interaction strengths (Tables 2.1 and 2.2). Model stability

was uncertain but, due to the calculation of loops (Table 2.3), it was more likely that

the Overall Model was stable, as the feedback at lower levels was stronger than the

overall feedback of the system and each feedback level was negative (see pages 35

and 36).

The investigations into the Overall Model suggested that the dynamics of the

seagrass ecosystem involving tiger sharks, several megafauna populations, and the

prey of the megafauna populations, were structured in four different stages (Fig.

2.8). In general, an increase in tiger sharks led to a habitat shift by the megafauna

out of the seagrass meadows and into the safe, deeper channel habitat. A decrease

in tiger shark numbers allowed the megafauna to return to the seagrass meadows,

leading to a decrease of the megafauna prey in this habitat.

In the first state (Fig. 2.8), a slight increase in the abundance of tiger shark (which

was equivalent to a positive press perturbation on the tiger shark variable in Model

1) the M2 subpopulation decreased, whereas the megafauna subpopulations that

also consumed prey in the channels increased (M1sg, M1ch, M*). The prey in the

seagrass habitat increased, whereas the prey variable in the channels decreased due

to increased predation pressure by the megafauna.

A further increase in tiger shark abundance led to the dynamics described in state 2

(Fig. 2.8) (which was equivalent to a positive press perturbation on the tiger shark

variable in Model 2). In this state, all megafauna subpopulations associated with the

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seagrass meadows (e.g. M2 and M1sg) decreased and the subpopulations in the

channels showed a further increase. Thus, the increased predation pressure by an

increase of megafauna in the channels led to a further decrease of the prey in this

habitat; in contrast, easing predation pressure on the prey in the seagrass led to a

further increase of this prey group.

States 3 and 4 were marked by a step-by-step decline in tiger shark numbers (Fig.

2.8). In state 3 (which was equivalent to a negative press perturbation on the tiger

shark variable in Model 2) a decrease of tiger shark abundance led to a return of

megafauna in the seagrass habitat and hence, a decrease of the prey in this habitat.

Consequently, the megafauna subpopulations in the channel decreased and the

megafauna prey in the channels increased because of easing predation mortality.

The further decrease in tiger shark numbers exhibited in state 4 (Fig. 2.8) (which was

equivalent to a negative press perturbation on the tiger shark variable in Model 1)

led to an increase in M2 (which represented the megafauna subpopulation in poor

body condition and also megafauna that only consumed prey in the seagrass

meadows). All megafauna subpopulations associated with the channel habitat

through consumption of channel-based prey decreased while their prey groups in

the channel increased. The prey group in the seagrass habitat decreased.

- 94 -

Psg

+

Pch

-

M1sg

+

M2

-

ts

+

M*+

M1ch

+

State 1

Psg

+

Pch

-

M1sg

-

M2

-

ts

+

M*+

M1ch

+

State 2

Psg

-

Pch

+

M1sg

+

M2

+

ts

-

M*

-M1ch

-

State 3

seagrass channel

seagrass channel

seagrass channel

- 95 -

Psg

-

Pch

+

M1sg

-M2

+

ts

-

M*

-M1ch

-

State 4

Figure 2.8: The four dynamic states of Figure 2.2: Qualitative models showing the dynamics of the modelled seagrass ecosystem in four states after positive and negative press perturbations on the tiger shark variables in Fig. 2.2 A and B. The sign “–“ represents a decrease of the variable and “+” an increase in the different variables, i.e. tiger sharks (ts), different megafauna subpopulations (M2, M1sg, M1ch, M*), and the megafauna prey (Psg, Pch) in two different habitats (i.e. the seagrass and the channel habitats).

2.5.2 The impact of fishing on the seagrass ecosystem in Shark Bay

The model “trophic interactions and impact of fisheries” had a high potential for

being stable (Fig. 2.7). The complexity of the model reflected the components of the

megafauna group that appeared in the diets of either adult or juvenile tiger sharks or

both. The dietary components of the megafauna group in the model were restricted

to three major functional groups: seagrass, invertebrates, and fish (Fig. 2.7). Deletion

of the positive recruitment-link from juvenile to adult tiger sharks in the model (Fig.

2.7) had no consequences for the stability or for response signs in the model

predictions. An increasing complexity of the seagrass community in the model led,

however, to instability and to high ambiguity in regard to the response signs.

A decrease of adult tiger sharks in this model led to an increase in their prey

(dugongs, turtles, cormorants, elasmobranchs, dolphins and sea snakes). The

decrease in the tiger shark variables (ats, jts) was accompanied by a decrease in

seagrass, invertebrates, fish and all three fishing sectors (Table 2.1, a).

seagrass channel

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The press perturbations of increasing the Northern Shark Fishery, the Shark Bay

commercial fishing sector, or the recreational fishing sector in Shark Bay produced a

clear negative response of the tiger shark populations (Fig. 2.7, Table 2.18 b,c and d).

A decrease of the tiger shark population was accompanied by an increase of their

prey groups (Table 2.18 b, c). In particular, an increase in the recreational fishing

sector in Shark Bay (Table 2.18 d) led to a decrease in piscivorous groups in model

Figure 2.7, which also included the juvenile tiger shark population. The commercial

fishing sectors in and outside of Shark Bay also decreased.

Table 2.18: Press perturbations (pp) performed in Figure 2.7 and the system’s response described by listing the response signs of the different model components and exhibiting the components with the highest probability that the response sign is correct (p = 1, based on 95% bound on proportion of correct sign (Hosack et al., 2008)). ats: adult tiger sharks, jts: juvenile tiger sharks, elas: elasmobranchs, dol: dolphins, pc: pied cormorant, agr: adult green turtles, logt: loggerhead turtles, ssn: sea snake, fish: teleost fish, inv: invertebrates, sg: seagrass, nsf: Northern Shark Fishery, sbcf: Shark Bay Commercial Fishery, dis: discards, rc: recreational fishery

Manipulation Fig. 2.7

a b C d

pp response

decrease ats increase sf increase sbcf increase rc

+

dug, agr, logt,

pc, elas, dol,

ssn

nsf, dug, agr,

logt, pc, elas,

dol, ssn

sbcf, dis, pc,

elas

dug, agr, logt,

elas, ssn, sg,

inv, rc

-

ats, jts, pc, sg,

inv, fish, rc,

sbcf, nsf

ats, jts, sg, inv,

fish, rc, sbcf

ats, jts, dug,

agr, logt, dol,

ssn, sg, inv,

fish, nsf, rc

ats, jts, pc, dol,

fish, sbcf, nsf

0 dis dis dis

p = 1 all, but dis, sg

and jts (p =

0.96)

all, but dis, sg

and jts (p =

0.96)

all, but dis, sg

and jts (p =

0.96)

all, but dis, sg

and jts (p =

0.96)

- 97 -

2.6 Discussion

Migration and behaviour-induced habitat shift have received little attention using

qualitative modelling techniques, so this study presents several novel applications

for this approach.

During model construction, problems with model structure emerged, such as

negative and positive loops outweighing each other, which caused ambiguous

response signs in the Overall Model (Fig. 2.2) and uncertainty of model stability. This

problem is caused by model structure. As the analysis of submodels indicated (Figs.

2.3 and 2.4), it was not possible to change the model structure in a way that

overcame the problem of negative and positive loops outweighing each other.

Quantifying loops (Tables 2.1 and 2.2) helped to assess model stability (Table 2.3)

and also clarify response signs (Tables 2.4 to 2.17). By applying a quantification of

loops (Tables 2.1 and 2.2) it was possible to develop a qualitative model that

represents the dynamics of the seagrass ecosystem in Shark Bay (Figs 2.2 and 2.8).

Previous field ecological studies have suggested that predation risk from tiger sharks

is an important influence on megafauna populations in the seagrass ecosystem in

Shark Bay and that a decline in tiger shark numbers could alter the dynamics of this

ecosystem (Heithaus et al., 2007a; Wirsing et al., 2007). This study developed

qualitative models grounded in empirical knowledge of the ecosystem to test these

hypotheses. The model outcomes provide support for the hypotheses. The model

analyses (Fig. 2.2 and Fig. 2.7) suggested that tiger sharks drive and control the

dynamics of this seagrass ecosystem. A decrease in tiger shark numbers affected the

dynamics of the whole ecosystem described by the different states in the Overall

Model (see Fig. 2.8). The modelling results indicate that predation risk from tiger

sharks influences the migration and habitat use of the megafauna.

A decline in the tiger shark population in Shark Bay would result in the dynamics

described by the state with lowest impact of tiger sharks (i.e. state 4 in Fig. 2.8). If

this state occurs, the modelling suggests that the alternating predation pressure on

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the prey groups in the seagrass meadows would cease and a more constant

predation pressure would lead to a decline in seagrass-based prey groups and the

seagrass. This outcome could alter the equilibrium state of this seagrass ecosystem.

The importance of large sharks for the dynamics of ecosystems, such as a coral reef

ecosystem in Hawaii, has already been described in an ecosystem model (Stevens,

2000). However, Stevens et al. (2000) used a quantitative ecosystem modelling

approach (Ecopath with Ecosim) which described the biomass fluxes between

different trophic levels. In the models presented here, Ecopath would account for

the predator-prey relationships between the megafauna subpopulations and their

prey and the only interaction involving tiger sharks would be the predator-prey link

between M2 and ts. Thus, a quantitative Ecopath model describing the biomass

fluxes of the seagrass ecosystem in Shark Bay would only account for a small fraction

of the interactions likely to structure and drive this ecosystem. Specifically, a

quantitative Ecopath model would not be able to incorporate the behavioural effects

described by qualitative modelling.

However, there are some limitations with the qualitative modelling approach. For

example, qualitative modelling cannot define magnitudes. It is important to

determine the extent to which the tiger shark population in Shark Bay is affected by

fishing activities. In particular, there is a need to determine the magnitude of the

fishing effects on prey availability for juvenile tiger sharks and to investigate whether

fishing effects adversely influence the recruitment of juvenile sharks to the adult

stock in Shark Bay, even if the recruitment link indicated no impact (based on the

signs of the responses predicted by the model in Fig. 2.7).

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Chapter 3

Ecopath models of the Peel-Harvey Estuary, Western Australia, before and after the opening of an artificial entrance channel, the Dawesville Channel

Data analysis and parameter input

3.1 Introduction

3.1.1 History of the Peel-Harvey Estuary

Environmental Characteristics

The Peel-Harvey Estuary, located 80 km south of Perth, Western Australia, comprises

two water-bodies – the Peel Inlet (75 km2) and the Harvey Estuary (56 km2) – and

covers a total area of 131 km2(Hale & Butcher, 2007). The Peel Inlet and Harvey

Estuary are joined by a narrow channel through the Point Grey Sill. The Peel-Harvey

Estuary has a maximum depth of 2.5 m and an average depth of 0.5 m (Hale &

Butcher, 2007). The estuary is connected to the Indian Ocean via a natural entrance

channel at the northern edge of the Peel Inlet and an artificial entrance channel, the

Dawesville Channel, located along the northern part of the Harvey Estuary (Peel Inlet

Management Authority, 1994). The catchment of the Peel-Harvey Estuary has a

cumulative area of 11 930 km2 and includes three rivers which discharge into the

estuary (the Serpentine River, Murray and Harvey Rivers) (Jakowyna, 2000).

Environmental Challenges

The condition of the catchment presents several environmental challenges for the

Peel-Harvey Estuary. Approximately 75% of the catchment area is cleared of natural

vegetation for agricultural purposes, leading to loss of riparian and marshland

habitat (Jakowyna, 2000). In addition to land clearing, the catchment of the Peel-

Harvey Estuary has also been extensively modified through the construction of

drains used to make the land suitable for agricultural use (e.g. dairy, beef, orchard,

and vegetable farming (Birch, 1982; Bradby, 1997). The soils of the catchment are

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mostly sand, loams and clays and tend to be infertile with low nutrient binding

capacity, characteristics that make the application of fertilisers essential for

agriculture (Jakowyna, 2000). However, large amounts of the nutrients applied as

fertiliser entered groundwater systems and were flushed into the estuary (Birch,

1980), causing extensive macrophyte growth and algal blooms (Lukatelich, 1986,

1989; Lukatelich & McComb, 1986a, b; McComb, 1992, 1998). Drifting macrophytes,

in particular Cladophora montagneana, accumulated on beaches and caused an

unpleasant smell; this affected surrounding neighbourhoods to the extent that

removal of the macrophytes was required (Gordon et al., 1981; Gordon & McComb,

1989; Lavery et al., 1999; Lavery et al., 1991; Lavery & McComb, 1991a, b). Toxic

blooms of the green algae Nodularia spumigena caused fish kills and also affected

human health (Huber, 1980, 1985, 1986; Potter et al., 1983a). The algal blooms and

extensive macrophyte growth became a nuisance to the commercial fishing sector,

which was of great economic importance to the region during the 1970s and 1980s

(Bradby, 1997).

In the 1970s, the condition of the estuary attracted sufficient public concern that an

extensive study of the estuary was initiated (Peel Inlet Management Authority,

Mandurah). This study investigated different aspects of the ecosystem, e.g. nutrient

input (Birch, 1980), important macrophyte and phytoplankton species (Gordon et al.,

1981; Huber, 1980), invertebrates (Wells et al., 1980) and key environmental factors,

such as hydrology (Black & Rosher, 1980). The fish fauna was also studied extensively

at the same time (Loneragan et al., 1986, 1987; Potter et al., 1983a; Potter et al.,

1983b).

The scientific outcomes from this study, allowed management authorities to develop

a management action plan aimed at reducing nutrient inputs and increasing flushing

of the estuary in order to reduce nutrient levels in the water-body and thereby

decrease primary production (Peel Inlet Management Authority, 1990, 1994). The

research also emphasised the need to reduce the quantities of fertiliser applied and

the value of natural vegetation as a means for reducing nutrient inputs (Bradby,

1997; Pond, 2005).

- 101 -

To increase flushing, an artificial entrance channel – the Dawesville Channel – was

constructed. When construction finished in 1994, it was expected that the channel

would flush about 10% of the estuary’s volume per day (Peel Inlet Management

Authority, 1994). The channel was also expected to increase salinity within the

estuary and reduce environmental differences between wet and dry seasons (Peel

Inlet Management Authority, 1994). Improvements in water quality were

anticipated, particularly increases in dissolved oxygen, reductions in the duration of

stratification and decreases in turbidity (Peel Inlet Management Authority, 1994).

Hydrodynamic modelling suggested increases in water levels and tidal effects,

factors that on-going management would need to address (Peel Inlet Management

Authority, 1994).

Monitoring of the estuary since 2004 indicates that the Dawesville Channel delivered

the predicted changes in water quality and reductions in primary production (Hale &

Butcher, 2007). After the channel opened, extensive blooms of Nodularia spumigena

did not occur in the estuary, as this species does not tolerate high salinities (Huber,

1985). The channel also impacted on the ecosystem of the Peel-Harvey Estuary and

its surrounding natural vegetation. Species composition and dominance have

changed in many aquatic communities, with many marine species occurring that had

not previously been recorded in the estuary, including several macrophytes (Wilson

et al., 1999), fish (Young & Potter, 2003b) and invertebrates (Wildsmith, 2007).

Catchment vegetation has been affected by the increased penetration of salt water

into the lower Harvey River, which has led to the decline of riverine trees in the area

(Gibson, 2001).

The Peel-Harvey Estuary is of great importance to waterbirds. It is considered an

important breeding site for many species and forms a major part of the Peel-

Yalgorup wetland system, which ‘comprises the most important area for waterbirds

in south-western Australia’ (Hale & Butcher, 2007). Since June 1990, The Peel-

Yalgorup wetland system has been recognised as ‘Wetland of International

Importance’ by the Ramsar Convention on Wetlands (Hale & Butcher, 2007). The

impact of the Dawesville Channel on the different waterbird species around the

- 102 -

Peel-Harvey Estuary remains unknown because of a lack of data and substantial

variations in bird abundances (Hale & Butcher, 2007).

In contrast to the opening of the artificial entrance channel, other management

actions appear to have been less effective (or to have been poorly implemented, as

the nutrient input from the catchment in 2007 was still as high as in the 1980s (Hale

& Butcher, 2007). Furthermore, increasing urbanisation and canal development

means that urban nutrient run-off is a growing concern, as urban groundwater

contributes to the nitrogen compounds in the estuary (Sewell, 1982). Climate

change is another major environmental challenge for the estuary, with decreasing

rainfall and rising temperatures affecting both the terrestrial and aquatic

components of the estuary. Fishing also causes pressure on the estuarine ecosystem

with recreational fishing pressure slowly replacing the commercial fishing sector,

which has decreased substantially due to political pressure over the last few decades

(Bradby, 1997; Malseed & Sumner, 2001). For example, the recreational catch of

blue swimmer crabs in 1998 exceeded the commercial catch by a factor of four

(Malseed & Sumner, 2001). These pressures make ecosystem based management an

important strategy for the sustainability of the estuary in the future, particularly to

maintain the estuary’s biological diversity and ecological integrity.

3.1.2 Aims of this Chapter

This chapter surveys the scientific literature to gather relevant information to

establish parameters for the functional groups to be used in Ecopath modelling of

the Peel-Harvey Estuary in subsequent chapters. The chapter includes a brief

description of Ecopath modelling and an outline of the model to be used for the

Peel-Harvey Estuary. Information for the parameter input is then presented for 31

functional groups. The chapter concludes with a discussion of data gaps and data

pedigree.

- 103 -

Figure 3.1: Map of the Peel-Harvey Estuary in Western Australia

3.1.3 The Ecopath model

Ecopath is a quantitative modelling technique that describes the biomass flows

between functional groups (Christensen et al., 2005). A functional group can consist

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of a single species or a population, a taxonomic family or several taxa, for example,

both single species (e.g. Indo-Pacific bottlenose dolphins, Tursiops aduncus) and

broad taxonomic groupings (e.g. Gastropods) form functional groups in the model of

the Peel-Harvey Estuary.

The input of each functional group i is assumed to equal its output for a defined

period of time (1), with production equalling the sum of predation and non-

predation mortalities, net migration and accumulated biomass (Christensen et al.,

2005). The equation for this model is:

(1)

where for each functional group i the parameters of the equation are defined as

follows: Pi is the total production rate, Yi is the total fishery catch rate, Bi is the

biomass, M2i is the predation mortality rate, Ei is the net migration rate, BAi is the

biomass accumulation rate and Pi · (1-EEi) accounts for ‘other mortality’ rate

(Christensen et al., 2005).

For performing the underlying matrix calculations in Ecopath, equation (1) needs to

be re-expressed for each functional group i (Christensen et al., 2005):

0)/()/(1

iii

n

jjijjiii BAEYDCBQBEEBPB (2)

where Bi is the biomass of the prey i and Bj the biomass of the predator group j,

(P/B)i is the production/biomass ratio for the functional group i

EEi is the ecotrophic efficiency, which describes the proportion of the

production that is used in the system

(Q/B)j is the consumption/ biomass ratio for predator j,

DCij is the fraction that prey i contributes to the diet of predator j

Yi is the total fishery catch rate of group i

Ei is the net migration rate

)1(2 iiiiiiii EEPBAEMBYP

- 105 -

BAi is the biomass accumulation rate and

n is the number of functional groups

The data employed in the model consist of a set of basic parameters for each

functional group for a defined period of time: e.g. biomass (B) (t km-2); production/

biomass ratio (P/B) (year-1), - which equals total mortality Z ); consumption/ biomass

ratio (Q/B) (year-1); and ecotrophic efficiency EE (Christensen et al., 2005). The

model also contains certain assumptions, e.g. the P/B ratio must equal total

mortality (Z).

When one of these basic parameters is not available for a functional group, further

data entry is required so that Ecopath can estimate the missing basic parameter.

Additional data that are required are net migration, dietary composition, catches,

biomass accumulation rate and assimilation rate (Christensen et al., 2005).

3.2 Model structure

The data used to construct an ecosystem model are the empirical foundation on

which the validity of the model and the reliability of its predictions depend. Thus, to

fully appreciate the structure of the ecosystem models this thesis develops to

describe the Peel-Harvey Estuary, it is essential to: (1) describe the data used for

each functional group in the model; (2) identify any gaps in those data; and (3)

document the ways in which these gaps are resolved.

It is common for ecosystem studies to have gaps in data for some functional groups

(Okey & Mahmoudi, 2002). Such gaps are filled by drawing upon data from studies in

other, similar systems, while acknowledging that the pedigree of such data is

considerably less than that which would accompany data obtained from a study

undertaken in the system for which the ecosystem model is being constructed (Okey

& Mahmoudi, 2002).

This chapter therefore documents the data employed, as well the data gaps, within

two Ecopath models of the Peel-Harvey Estuary, Western Australia. The first of these

- 106 -

models describes the biomass fluxes of this estuarine ecosystem before the opening

of an artificial entrance channel, the Dawesville Channel, in 1994 (‘pre DC’ Ecopath

model), based on data from the 1970s and 1980s. The second model describes those

fluxes after the opening of the Dawesville Channel (‘post DC’ Ecopath model), with

data collected in scientific studies between 1995 and 2007.

The boundaries for both models are restricted to the basins of the Peel-Inlet and

Harvey Estuary and to the two entrance channels (the Mandurah and the Dawesville

Channel). The rivers discharging into the estuary are not included in these

quantitative models, as there is a paucity of data available for the rivers.

The two Ecopath models, and the results of analyses undertaken using these models,

are further described in Chapters 5, 6, 7 and 8.

Functional Groups

A brief description of each of the functional groups used in the ‘pre DC’ and ‘post DC’

Ecopath models is provided in Table 3.1. In general, species were grouped according

to taxonomic criteria (e.g. species belonging to the same taxonomic group) or, in

some cases, according to their ecological importance (e.g. the more dominant

macrophyte species like Cladophora montagneana). Dietary information also

influenced the grouping of faunal species. Birds, for example, were grouped

according to their diets. Fish were grouped according to their life cycles, their diet, or

their importance for fisheries, with some target species placed in their own

functional group (e.g. Australian Herring). The data available for the main

invertebrate target species (Blue Swimmer Crabs and prawns) were sufficient to

place them into their own functional group.

- 107 -

Table 3.1: Functional groups of the ‘pre DC’ and ‘post DC’ Ecopath models of the Peel-Harvey Estuary

Functional group Description

1 dolphins population of Indo-Pacific Bottlenose Dolphin (Tursiops aduncus)

2 waterbirds

waterbirds feeding on invertebrates and plant material, e.g. waders, ducks, swans, etc.

3 piscivorous waterbirds piscivorous waterbirds, e.g. pelicans

4 sharks

juvenile sharks entering the estuary, e.g. Heterodontus portusjacksoni and Mustelus antarcticus

5 marine omnivorous fish marine fish entering the estuary temporarily, exhibiting omnivory

6 marine carnivorous fish marine fish entering the estuary temporarily, exhibiting carnivory

7 marine herbivorous fish marine fish entering the estuary temporarily, exhibiting herbivory

8 marine detritivorous fish marine fish entering the estuary temporarily, exhibiting detritivory

9 estuarine omnivorous

fish estuarine fish populations , exhibiting omnivory

10 estuarine carnivorous

fish estuarine fish populations , exhibiting carnivory

11 estuarine herbivorous

fish estuarine fish populations , exibiting herbivory

12 estuarine detritivorous

fish estuarine fish populations , exhibiting detritivory

13 whiting fish belonging to family Sillagonidae

14 Arripis georgianus Australian Herring, target species in the Peel-Harvey Estuary

15 Aldrichetta forsteri Yellow-eye Mullet, target species

16 Mugil cephalus Sea Mullet, target species

17 Torquigener

pleurogramma common blowfish occurring in the estuary in very high numbers, nuisance to fishermen

18 bivalves small bivalves, Arthritica semen being most abundant species

19 gastropods small snails

20 Western King Prawn Penaeus (Melicertus) latisulcatus, target prawn

- 108 -

species in the estuary

21 Blue Swimmer Crab Portunus pelagicus, main target species in the estuary

22 crustaceans mainly amphipods and other benthic crustaceans

23 worms mainly polychaetes, but also nematodes, sipunculans, platyelminthes

24 zooplankton zooplankton and ichthyoplankton

25 microscopic algae phytoplankton and benthic microalgae

26 algae red and brown algae

27 macrophytes Enteromorpha sp. and Ulva sp. and other Chlorophyta

28 Chaetomorpha linum green macroalgae

29 Cladophora

montagneana green macroalgae

30 seagrass Halophila sp., Ruppia sp.

31 detritus sediment detritus, no data on water column detritus available

For each functional group, the following parameters were used for Ecopath

modelling: biomass (B), production per unit of biomass (P/B) and consumption per

unit of biomass (Q/B), as well as data on the dietary composition and catch rates.

Scientific studies undertaken in the Peel-Harvey have general focused on gaining

ecological information about floral and faunal communities, and have tended to

analyse abundances rather than biomasses (Lukatelich & McComb, 1986a; Potter et

al., 1983a; Wildsmith, 2007; Young & Potter, 2003b). In general, abundances were

converted to biomass parameters for the Ecopath models by multiplying the

abundance with an average weight for the species adopted from the literature, for

example the abundances of bivalves in Table 3.18 (Rose, 1994; Wildsmith, 2007)

were multiplied with the average body weight for each species that were recorded

by Rose in 1994.

In the literature, dietary composition usually presents prey items according to the

occurrence or volume of prey in the predator’s diet. In the dietary matrices of

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Ecopath, the predator will exhibit the dietary composition according to the

functional groups defined for the model. All dietary components are entered as

fractions of the total diet of the predator. For this reason, it is appropriate to directly

adapt this data for the dietary parameter input in Ecopath.

Production values for primary producers were adapted from the literature. The

production/ biomass ratio for non-primary producers equals total mortality and was

calculated for the functional groups by applying growth parameters and body

characteristics, asymptotic lengths and biomasses.

Few scientific studies explore how much prey is consumed by a predator. While

consumption can be calculated depending on body size, these parameters were

unavailable for different groups. Thus, these parameters were adopted from other

models.

3.3 Parameter input 3.3.1 Dolphins (Functional group 1) Indo-Pacific Bottlenose Dolphins (Tursiops aduncus) occur within the Peel-Harvey

Estuary (Groom & Coughran, 2012). However, no studies have been undertaken to

determine the abundance of dolphins within the estuary. Data on the diet and food

consumption of this dolphin population are also not available. It was therefore

necessary to obtain estimates of the missing parameters for this functional group by

extrapolating data from other ecosystems.

The abundance of dolphins within the estuary has not been studied yet. However, 46

individuals have been identified in in the area around the Mandurah Channel (James

Raeside, Murdoch University, unpublished data). The abundance of dolphins for the

entire estuary is estimated with 75 for the ‘post DC’ and 50 individuals for the ‘pre

DC’ models (pers. comm. Hugh Finn Murdoch University). From these abundances,

the biomass estimates for the functional group ‘dolphins’ were derived assuming the

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population structure is similar to those in other areas of Western Australia (Groom &

Coughran, 2012), namely 28% adult individuals, 39% sub-adults and 33% calves

(Table 1 in (Groom & Coughran, 2012). By applying growth curves from Bottlenose

Dolphins in captivity (Cheal & Gales, 1992), the overall biomass for the functional

group ‘dolphins’ for the entire estuary was estimated with 0,039 t km-² for the ‘pre

DC’ model and 0,059 t km-² for the ‘post DC’ model (Table 3.2).

A model life table for bottlenose dolphins was developed in Florida, which estimated

the natural mortality of the population with 9.8% (Stolen & Barlow, 2006). For the

West Florida Shelf model, the mortality of cetaceans was estimated with 0.1 year-1

(Okey & Mahmoudi, 2002). The balanced Ecopath models of the Newfoundland-

Labrador Shelf (Bundy et al., 2000), Gulf of Mexico (Vidal-Hernandez, 2000) and the

Jurien Bay Marine Park in Western Australia (Lozano-Montez et al., 2011) also

presented a P/B ratio of 0.1 year-1 for dolphins, whereas the P/B ratio for dolphins in

the middle Gulf of California was only slightly higher to the Jurien Bay model with

0.16 year-1 (del Monte-Luna et al., 2007). The mortality rate of dolphins hasn’t been

studied in the Peel-Harvey Estuary. It is assumed that the mortality of dolphins in the

Peel-Harvey Estuary is similar to the mortality in other estuaries, e.g. the Indian River

Lagoon in Florida (Stolen & Barlow, 2006). Stolen & Barlow studied the mortality of

Tursiops truncatus in detail and the overall annual mortality rate was estimated with

9.8% (Stolen & Barlow, 2006), an estimate which is also applied in this study (Table

3.2).

Since the Dawesville Channel opened, and with increasing canal development in the

Peel-Harvey Estuary, areas of deeper habitat are more available for the dolphin

population. However, the dolphins are also known to occur in very shallow water

and, for this reason, it is assumed for both Ecopath models that the habitat area of

the dolphin population covers the entire Peel-Harvey Estuary (pers. comm. Simon

Allen, Murdoch University).

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Table 3.2: Parameter input of the functional group ‘dolphins’

Parameter ‘pre DC’ ‘post DC’

Biomass (t/ km²) 0.0395 0.059

P/B (year -1) 0.098 0.098

Q/B (year -1) 5.61 5.61

The composition of the diets of the dolphins of the Peel-Harvey Estuary, which is

required for the diet matrix used by the Ecopath models, was adapted from other

aquatic systems, such as Sarasota Bay, Florida (Barros & Wells, 1998) and Port

Lincoln, South Australia (Kemper & Gibbs, 2001). The dolphins in those systems fed

mainly on fish but also on cephalopods. Cephalopods have not been reported for any

fish or invertebrate study on the Peel-Harvey Estuary. Thus, it is concluded that, if

they are present in the system, they occur only in such small numbers that they can

be ignored in the Ecopath models for the Peel-Harvey Estuary. The diets of dolphins

in Florida and South Australia were quite similar. The main fish families listed as

dolphin prey in both of the above studies are Carangidae, Gerreidae, Clupeidae,

Mugilidae and Sparidae. These fish families accounted for 77% of the dolphin prey in

Sarasota Bay, Florida (Barros & Wells, 1998) and were also consumed by dolphins in

South Australia (Kemper & Gibbs, 2001). Consequently, these teleost fish are also

regarded as the main prey groups in the Peel-Harvey Estuary, accounting for 75% of

the dolphin diet in this estuary. These dietary studies list prey items, but do not

specify biomasses or volumes of the dolphin prey. The dolphin prey was

standardized according to the functional groups in this Ecopath approach (Table 3.3),

using a study on marine mammals as guidance (Pauly et al., 1998).

Isopods and crustaceans that were present in small amounts in the diets of dolphins

in South Australia (Kemper & Gibbs, 2001). This finding is consistent with

observations made in estuaries in Western Australia that dolphins interact with crabs

and prawns, these species are not considered to be a major component of the

dolphins’ diet (pers. comm., Simon Allen, Murdoch University). The diet of dolphins

in the Peel-Harvey Estuary (Table 3.3) is similar to the dietary composition of

dolphins in Florida (Okey & Mahmoudi, 2002).

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The diet matrix entry for the dolphin group is presented in Table 3.3. It is assumed

that the dolphin population in the Peel-Harvey Estuary is a resident rather than

migrating population and thus, only a small amount of food (10%) is consumed

outside of the estuary (pers. comm., Simon Allen, Murdoch University).

Table 3.3: Diet entry of the functional group ‘dolphins’

Fish group % occurence

Carnivore marine fish 41.25

Omnivore marine fish 7.5

Omnivore estuarine fish 7.5

Detritivore estuarine fish 3.75

Aldrichetta forsteri 7.5

Mugil cephalus 7.5

3.3.2 Birds (Functional groups 2 and 3)

The birds occurring around the Peel-Harvey Estuary are divided into two functional

groups, i.e. ‘piscivorous waterbirds’ and ‘waterbirds’, such as the waders and

invertebrate-eating birds that use the wetlands of the Peel-Harvey Estuary. The Peel-

Harvey Estuary is part of a RAMSAR site (which also includes lakes near the estuary)

and is highly significant for resident and migratory bird species in Western Australia

(Hale & Butcher, 2007). Numerous (86) species of waterbirds have been recorded in

the Peel-Harvey Estuary (Department of Conservation and Land Management, Perth,

Western Australia, 1990,

http://www.naturebase.net/pdf/national_parks/wetlands/fact_sheets/peel-

yelgorup1.doc), of which the most common species within each functional group are

listed in Table 3.4.

http://www.naturebase.net/pdf/national_parks/wetlands/fact_sheets/peel-yelgorup1.doc

http://www.naturebase.net/pdf/national_parks/wetlands/fact_sheets/peel-yelgorup1.doc

- 113 -

Table 3.4: List of most numerous bird species considered for the ‘pre DC’ and ‘post DC’ Ecopath models in the functional groups ‘waterbirds’ and ‘piscivorous waterbirds’

Functional Group Species Scientific name

waterbirds Australian Shelduck Tadorna todornoides

Grey Teal Anas gracilis

Bar-tailed Godwit Limosa lapponica

Red Knot Calidris cannutus

Sharp tailed Sandpiper Calidris acuminate

Curlew Sandpiper Calidris ferruginea

Banded Stilt Cladorhynchus leucocephalus

Black Swan Cygnus atratus

Pacific Black Duck Anas superciliosus

Australian Shoveler Anas rhynchotis

Black-winged Stilt Himantopus himantopus

Common Greenshank Tringa nebularia

Blue-billed Duck Oxyura lobata

Eurasian Coot Fulica atra

Hoary-headed Grebe Poliocephalus poliocephalus

Pink-eared Duck Malacorhyncus membranaceus

piscivorous waterbirds

Little Pied Cormorant Phalacrocorax malanoleucos

Pied Cormorant Phalacrocorax varius

Little Black Cormorant Phalacrocorax sulcirostris

Australian Pelican Pelecanus conspicillatus

White -faced Heron Egretta navaehollandiae

Bird numbers were recorded during both time periods used in the Ecopath models

(Bowman & Bamford, 2008; Lane et al., 2002; Ninox-Wildlife-Consulting, 1990).

These abundances of birds were used to obtain the biomass estimates listed in Table

3.5. To obtain biomass estimates, the abundances were multiplied with average

individual body weights gained from the literature and from an online database

provided by the Australian Museum (http://www.birdsinbackyards.net).

Table 3.5: Parameter input for the functional groups ‘waterbirds’ and ‘piscivorous waterbirds’ for both Ecopath models of the Peel-Harvey Estuary

Functional group

Biomass (t/km2) ‘pre DC’

Biomass (t/km2)

‘post DC’

P/ B (year-1) ‘pre DC‘

P/ B

(year-1) ‘post DC‘

Q/B (year-1)

Habitat area (fraction)

waterbirds 0.508 0.058 0.09 0.28 55.63 32.50%


0.182

0.178

0.09

0.28

12.56

100%

http://www.birdsinbackyards.net/

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An Ecopath model of Hong Kong waters (Pitcher et al., 2002) presented a seabird

mortality rate of 0.06 year-1, which is similar to the mortality rate of birds in the Bay

of Somme, France with 0.074 year-1 (Rybarczyk et al., 2003), whereas the mortality

of seabirds in the North Sea was estimated to be much higher with 0.28 year-1

(Mackinson & Daskalov, 2007). The mortality of waders and diving seabirds in the

temperate Jurien Bay Marine Park in Western Australia was estimated to be less

than 10% of the population of these functional groups (Lozano-Montez et al., 2011).

Although the mortality rate of birds in the Peel-Harvey Estuary hasn’t been studied

yet, (Rogers et al., 2010) suggest that bird numbers in the estuary have decreased by

one third compared to numbers recorded in 1980s. These findings indicate that

mortality rates might have changed over the last decades. There are several

plausible causes for the decrease in bird numbers, such as climate change, lack of

prey, habitat loss or increased mortality rates in winter territories of migrating birds.

However, also the area around the Peel-Harvey Estuary has changed recently, as a

new highway was constructed that is now passing the estuary and there is an

increase in development sites in the Mandurah area. These factors might have led to

a slight increase in bird mortality around the estuary. For this reason, the mortality

estimate of the pre DC Ecopath model is smaller, 0.09 year-1 (Lozano-Montez et al.,

2011), than the parameter of the post DC model, 0.28 year-1 (Mackinson & Daskalov,

2007) presented in Table 3.5. However, these parameter settings are marked with a

low pedigree (Table 3.36) and are still far smaller than mortality estimates of other

Ecopath models that present mortality rates of approx 5 year-1 (Barausse et al., 2007;

Okey et al., 2004).

The Q/B values for the different bird species (Table 3.5) were adopted from a study

in Sweden, where consumption of waterbirds was estimated (Nilson & Nilson, 1976).

In Jurien Bay, the consumption of waders was estimated with a Q/B ratio of 40 and a

ratio of 45 for diving birds (Lozano-Montez et al., 2011), compared to a high Q/B

ratio for seabirds of 216.56 in the North Sea (Mackinson & Daskalov, 2007) and a

small Q/B estimate in the Bay of Somme with a ratio of 6.146 (Rybarczyk et al., 2003)

and a ratio of 0.53 in the Seine Estuary, France (Rybarczyk & Elkaim, 2003). The Q/B

values used in this Ecopath approach (Table 3.5) is in line with consumption

estimates used for waterbirds in other Ecopath models. The area of habitat that is

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used by the birds differs between the functional groups. It is assumed that the

piscivorous bird species fish for prey throughout the whole estuary, whereas waders

or small invertebrate-eating birds feed mainly in the very shallow waters (<30 cm

water depth) of the estuary, which according to bathymetric maps (Hale & Butcher,

2007) is assumed to equal about 32.5% of the total estuarine area.

No quantitative studies exploring the dietary composition and consumption of birds

in the Peel-Harvey Estuary have yet been reported. For this reason, data on bird

diets had to be adopted from other ecosystems. For most species, diet information

could be obtained from an online database provided by the Australian Museum

(http://www.birdsinbackyards.net). However, this database lacked detailed data on

dietary composition. The input for the ‘diet composition’ matrix for both Ecopath

models is presented in Table 3.6 and references to the sources of these data are

listed in Table 3.7. If no quantitative dietary information was available from the

literature, rough estimates were gained from the online database

(http://www.birdsinbackyards.net).

Table 3.6: Dietary composition of the two functional bird groups estimated for both Ecopath models. Note the prey group ‘fish’ consists of 13 different fish groups, such as ‘carnivore marine fish’, etc. that are detailed in section 3.3.4, but which are combined here to simplify the description of the dietary assumptions made for both bird groups. Predator group Prey group: waterbirds piscivorous waterbirds

(% Volume) (% Volume)

Fish 0.13 62.42

Bivalves 4.61 0.16

Gastropods 10.76 0.16

Prawns 3.89 5.56

Blue Swimmer Crab 3.89 0

Crustaceans 10.29 17.02

Polychaetes 11.76 0

Zooplankton 6.94 0

Microscopic algae 1.67 0

Algae 2.22 0

Macrophytes 1.67 0

C.linum 10.29 0

C. montagneana 11.76 0

Seagrass 6.94 0



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Table 3.7: Literature cited for quantitative dietary information of different bird species, which was used to estimate the dietary composition described in Table 3.6

Bird species Reference

Malacorhyncus membranaceus (Martin et al., 2007)

Calidris ruficollis (Dann, 1999)

Calidris acuminate (Dann, 1999)

Phalacrocorax malanoleucos (Miller, 1979)

Phalacrocorax varius (Barquete et al., 2008)

Phalacrocorax sulcirostris (Miller, 1979)

Egretta navaehollandiae (Lo, 1991)

3.3.3 Sharks (Functional group 4)

Two species of sharks were reported in the studies of fish within the Peel-Harvey

Estuary in 1979 (Potter et al., 1983b) and 1996 (Young & Potter, 2003b): Mustelus

antarcticus (Gummy Shark) and Heterodontus portusjacksoni (Port Jackson Shark).

The sharks caught were juveniles and their abundance and biomasses were recorded

in these studies. No sharks were recorded in a recent study ((Valesini et al., 2009);

however, this was possibly due to the fact that sampling took place in shallow waters

close to the shore and not in the deeper basins and canals (Valesini et al., 2009). It is

assumed that juvenile sharks still enter the Peel-Harvey Estuary and thus, sharks are

included as a functional group in both Ecopath models. The biomass values for the

shark functional group (Table 3.8) were adopted from the two fish studies that took

place in years prior to and following the opening of the Dawesville Channel (Potter et

al., 1983b; Young & Potter, 2003b). The abundances, as well as the total biomasses

for each species in the samples were recorded for both time periods (Potter et al.,

1983b; Young & Potter, 2003b). The biomass estimates were calculated by dividing

the biomass of the samples by the sampled area and thus, the estimates were

converted into the standard unit t/km2 for the Ecopath parameter input thereby

assuming a 100% efficiency of the fishing gear.

The average natural mortality for Mustelus antarcticus of 0.2 was adopted from the

literature for the shark groups in both Ecopath models (Compagno, 1984). This

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parameter is smaller than P/B ratios used in other models, for example a ratio of

0.41 in Florida (Okey & Mahmoudi, 2002) or a ratio of 0.32 for small coastal sharks in

Jurien Bay Marine Park (Lozano-Montez et al., 2011). However, juvenile sharks do

not have to face any (or few) predators in the shallow waters of the Peel-Harvey

Estuary and thus, it seems more likely that the mortality estimate adopted from the

literature is appropriate for these Ecopath models. Also, sharks are not listed as

target species and are only caught by the recreational fishermen in the Peel-Harvey

in small numbers and catches were too insignificant to be recorded explicitly

(Malseed & Sumner, 2001). It is assumed that the natural mortality of the sharks in

the Peel-Harvey exceeds fishing mortality by far and thus, the total mortality will

equal the natural mortality (Table 3.8).

The habitat area for this functional group is estimated to be about 2/3rd of the

estuarine area (Table 3.8), as sharks were mainly caught in deeper waters (Potter et

al., 1983b; Young & Potter, 2003b) and were not caught in 2005-07 when only

“nearshore shallow waters (<2m)” were sampled (Valesini et al., 2009).

Table 3.8: Values of the functional group ‘sharks’ for the ‘pre DC’ and

‘post DC’ Ecopath models

Parameter: ‘pre DC’ ‘post DC’

Biomass (t/ km²) 0.0163 0.00027

P/B (year -1) 0.2 0.2

Q/B (year -1) 2.5 2.5

Habitat area (fraction in %) 66 66

No study has been undertaken to study the consumption of these shark species in

the Peel-Harvey Estuary and no data were found in the literature showing

consumption rates. For these Ecopath model (Table 3.8), a consumption estimate

was adopted from a mass-balanced model of the North Sea, which shows a Q/B ratio

of 2.5 for juvenile sharks (Mackinson & Daskalov, 2007). This parameter for juvenile

sharks seems more appropriate here than higher Q/B ratios that are used in other

Ecopath models, for example the Florida Shelf model (Okey & Mahmoudi, 2002),

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which presents a Q/B ratio of 3.29 for coastal sharks or the Ecopath model of the

nearby Jurien Bay Marine Park that presents a Q/B ratio of 10.4 for small coastal

sharks. The diet of Heterodontus portusjacksoni, seems to be similar

(www.Fishbase.org) to the dietary composition of Mustelus antarcticus

(Simpfendorfer, 2001). For this reason, the dietary information of M.antarcticus is

used for these Ecopath models (Table 3.9).

Table 3.9: Dietary consumption of the functional group ‘sharks’

Prey group % of diet

Worms 0.2

Crabs 20.8

Prawns 12.8

Crustaceans 3.7

Gastropods 0.1

Teleost 50

3.3.4 Fish (Functional groups 5 to 17)

This section explains the process by which fish species were placed into different

functional groups. It also provides the mathematical information for how the three

input parameters (biomass, mortality and consumption per biomass) were calculated

for fish.

The fish species that were recorded for the Peel-Harvey Estuary were grouped

according to their life cycle categories (marine or estuarine) and their diet. As there

are few dietary data for fish from the Peel-Harvey Estuary, advice as to diet was

obtained from an expert in diets of Western Australian fish species (pers. comm.,

Margaret Platell, Murdoch University) (Table 3.10). Numerous studies have been

undertaken on the fish communities of the Peel-Harvey Estuary (Loneragan et al.,

1986, 1987; Potter et al., 1983a; Potter et al., 1983b; Young & Potter, 2003b). The

species list for the different fish groups and the data used for extrapolating the

http://www.fishbase.org/

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biomass estimates for the Ecopath models are based on studies undertaken in: (a)

1979/80 (Potter et al., 1983b) and (b) 2005-07 (Valesini et al., 2009). Grouping fish

species according to their size was considered, but was rejected because this would

have required a grouping strategy to divide the larger species into juveniles and

adults, as they would be part of at least two size groups. Data for biomass, P/B and

Q/B ratios (as well as the diets for the different size groups in case of an ontogenetic

shift in diet) are not available for this estuary. Grouping by size, as well as life history

and diet, would have required the adopting of further data from other models, other

estuarine ecosystems, and other species, thereby lowering the pedigree of the Peel-

Harvey Ecopath models. For this reason, size was not included in the criteria for

classifying the fish species into functional groups.

Fish species were divided into: (1) eight different dietary groups; (2) four single-

species groups comprising commercially important finfish and crustaceans; and (3) a

single-species group for the common blowfish Torquigener pleurogramma (a species

of specific interest to fishers and fishery managers).

The abundance of T. pleurogramma has increased substantially since the opening of

the Dawesville Channel in 1994, becoming the most abundant species shortly after

the channel opening (Young & Potter, 2003b). A recent study found that T.

pleurogramma abundance had declined somewhat, although the species is still

considered a ‘plague’ (Valesini et al., 2009). To facilitate investigation of factors

influencing the abundance of this species, it was treated a single species group.

Table 3.10: List of species of the functional fish groups that were recorded in the pre and post DC period and the list of biomass estimates for each species in tkm-2

species recorded in Biomass (in t km-2)

Species pre DC post pre DC post DC

Marine omnivorous fish: functional group 5

Acanthaluteres brownii X 0.00004

Ammotretis elongata X X 0.00052 0.00433

Atherinomorus ogilbyi X X 0.03036 0.06152

Brachaluteres jacksonianus X 0.00002

Eubalichthys mosaicus X 0.00001

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Haletta semifasciata X X 0.00848 0.00546

Meuschenia freycineti X X 0.00104 0.00003

Microcanthus strigatus X 0.00001

Rhabdosargus sarba X X 0.08583 0.00672

Scobinichthys granulatus X X 0.00139 0.00006

Group parameter 0.12767 0.07816

Marine carnivorous fish: functional group 6

Argyrosomus hololepidotus X 2.14753

Arripis truttaceus X X 0.00004 0.00153

Callogobius depressus X 0.00022

Contusus brevicaudus X X 0.27583 0.001

Cristiceps australis X X 0.00001 0.00017

Dotalabrus alleni X 0.00004

Elops machnata X 0.03648

Enoplosus armatus X 0.00012

Filicampus tigris X 0.00002

Galaxias maculates X 0.00001

Gerres subfasciatus X X 1.90549 0.04145

Gonorynchus greyi X 0.00005

Gymnapistes marmoratus X X 0.02076 0.00326

Halichoeres brownfieldi X 0.00001

Helcogramma decurrens X 0.0000024

Hyperlophus vittatus X X 0.00912 0.16445

Lesueurina platycephala X 0.00001

Neoodax sp. X X 0.00004

Papillogobius punctatus X 0.00657

Parapercis haackei X 0.00004

Platycephalus laevigatus X 0.00038

Pomatomus saltatrix X X 0.90855 0.00022

Pseudocaranx dentex X X 0.03368 0.00012

Pseudocaranx wrightii X 0.00115

Pseudorhombus jenynsii X X 0.14999 0.02107

Sardinops sagax X 0.0001

Scorpis georgianus X X 0.00004 0.000003

Spratelloides robustus X X 0.0001 0.00633

Stigmatopora argus X 0.00103

Trachurus novaezelandiae X 0.20076

Upeneus tragula X 0.00004

Urocampus carinirostris X 0.00002

Group parameter 5,68908 0,24875

Marine herbivorous fish: functional group 7

Acanthaluteres spilomelanurus X 0.00004

Acanthaluteres vittiger X 0.00001

Arrhamphus sclerolepis X 0.00005

Hyporhamphus melanochir X X 0.10926 0.00048

Monacanthus chinensis X 0.00007

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Pelates sexlineatus X X 1.93878 0.03368


Marine detritivorous fish: functional group 8

Omobranchus germaini X 0.000003

Petroscirtes breviceps X 0.00002

Group parameter 0.000023

Estuarine omnivorous fish: functional group 9

Acanthopagrus butcheri X X 0.042 0.038

Amniataba caudavittata X X 1.090 0.004

Craterocephalus mugiloides X X 0.001 0.012

Craterocephalus pauciradiatus X X 0,00001 0.028

Pseudogobius olorum X X 0.003 0.011


Estuarine carnivorous fish: functional group 10

Afurcagobius suppositus X X 0.001 0.003

Apogon rueppellii X X 0.155 0.024

Arenigobius bifrenatus X X 0.003 0.002

Atherinosoma elongata X X 0.016 0.247

Cnidoglanis macrocephalus X X 5.711 0.00001

Edelia vittata X 0.00004

Engraulis australis X X 0.018 0,0001

Favonigobius lateralis X X 0.018 0.033

Galaxias occidentalis X 0.00002

Gambusia holbrooki X 0.0001

Leptatherina presbyteroides X X 0.001 0.041

Leptatherina wallacei X X 0.003 0.018

Platycephalus speculator X 0.001

Siphamia cephalotes X X 0.0001 0.000006


Estuarine herbivorous fish: functional group 11

Hyporhamphus regularis X X 0.021 0.0007

Estuarine detritivorous fish: functional group 12

Nematalosa vlaminghi X X 5.997 0.010

Whiting: functional group 13

Sillago bassensis X 0.015

Sillago burrus X X 0.008 0.005

Sillaginodes punctata X X 0.050 0.001

Sillago schomburgkii X X 0.661 0.040

Sillago vittata X 0.001


Arripis georgianus: functional group 14

Arripis georgianus X X 0.136 0.012

Aldrichetta forsteri: functional group 15

Aldrichetta forsteri X X 19.562 0.085

Mugil cephalus: functional group 16

Mugil cephalus X X 31.580 0.037

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Torquigener pleurogramma: functional group 17

Torquigener pleurogramma X X 0.080 1.773

The following process was used to derive the biomass estimates for all fish groups.

Firstly, abundance estimates were obtained for both time periods using information

reported in 1979-81 (Potter et al., 1983b) and 2005-07 (unpublished data, F.

Valesini, Murdoch University). Secondly, the biomass parameters were calculated by

dividing the biomass by the sampling area. This allowed the parameters to be

converted into the standard unit t/km2 for the Ecopath parameter input, thereby

assuming a 100% efficiency of the fishing gear (Table 3.10). The biomass values for

the ‘post DC’ Ecopath model were derived from samples in the “nearshore shallow

waters (<2m)” (Valesini et al., 2009). This lowered the pedigree for these biomass

estimates and therefore these parameters were changed first in the balancing

process. As no fish were recorded for the group ‘marine detritivorous fish’ for the

‘pre DC’ Ecopath model, the biomass was set to a very low value, as it cannot be

concluded that this functional group was completely absent before the Dawesville

Channel was opened (Table 3.10).

Thirdly, mortality was assessed. The P/B parameter equals total mortality Z, which

summarizes natural mortality M and fishing mortality F (Christensen et al., 2005).The

mortalities for each functional fish group were estimated using length data for

estimating natural mortality M (Potter et al., 1983b) and catch for estimating fishing

mortality F (Department of Fisheries, Perth, WA).

The Peel-Harvey Estuary is fished by a commercial and a recreational fishing sector.

The main target species with the estuary is the Blue Swimmer Crab (Portunus

pelagicus), but prawns and fish are also targeted. The commercial fishing sector has

changed the gear that is used within the estuary from trawling to mainly crab traps

and gill nets. The catch and effort of the commercial fishing sector has been

recorded in the Peel-Harvey Estuary for several decades (Department of Fisheries,

Perth, Western Australia). For the Ecopath models described here, the catch of the

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years 1980 and 2000 have been adapted for the ‘pre DC’ and ‘post DC’ Ecopath

models respectively (Table 3.11).

Table 3.11: Estimated catch (in tkm-2) of the functional fish groups that were estimated for different fishing gears in the pre (1980) and post (2000) DC period

Catch (in t km-2) Beach seine Gill net Recreational

Functional group

pre post pre post pre post

5 0.141 0.001 0.103 0.002 0.008 0.008

6 0.528 0.012 0.387 0.059 0.031 0.031

7 0.035 0.0003 0.026 0.002 0.002 0.002

8 0.0001 0.0003 0.00001 0.002 0.002 0.002

9 0.07 0.0005 0.052 0.002 0.004 0.004

10 0.246 0.009 0.181 0.071 0.01 0.01

11 0.035 0.0003 0.026 0.002 0.002 0.002

12 0.035 0.0003 0.026 0.002 0.002 0.002

13 0.027 0.057 0.211 0.059 0.01 0.01

14 0.00001 0.036 0.00001 0.018 0.033 0.033

15 0.723 0.215 1.66 0.067 0 0

16 0.321 0.319 1.105 0.029 0 0

17 0 0 0 0 0.001 0.001

Detailed information for the recreational catch in the Peel-Harvey Estuary is not

available. The catch is not recorded on a yearly basis and is difficult to estimate, as

fishing licences have never been required in Western Australia. Creel surveys were

undertaken in 1998-99 (Malseed & Sumner, 2001) and in 2008. The data of the later

survey is not yet available for the ‘post DC’ model. The recreational fishing sector

poses a much higher pressure on fish and crab stocks than the commercial, which is

continuously decreasing in boat numbers (Malseed & Sumner, 2001). The ratio of

commercial versus recreational catch of Blue Swimmer Crabs is approximately 1:4

(pers. comm., D. Johnston, Department of Fisheries, Perth, Western Australia;

(Malseed & Sumner, 2001). However, it is likely that the recreational fishing effort is

still increasing, as the population around the estuary is growing. But, this hypothesis

still needs to be investigated.

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The catch in annual biomass for the ‘pre DC’ and ‘post DC’ Ecopath models is derived

from catch rates that were provided by Department of Fisheries, Perth, Western

Australia and by the survey on the recreational fishing sector in 1998-99 (Malseed &

Sumner, 2001). As, no recreational catch data are available for the period before the

Dawesville Channel was opened, the survey data are applied for both Ecopath

models (Table 3.11).

Fishing mortality (F) was estimated (Table 3.12) according to the equation:

Catch ≈ Biomass * Fishing mortality

Catch data (in kg) provided by the Department of Fisheries, Perth, Western Australia

presented single species catch rates, as well as catch for ‘fish, other’. These

undetermined catches were divided between the multispecies functional groups

according to the numbers of possible target species, thus assuming that small fish

species of the families Gobiidea and Atherinidae are unlikely to be kept (Table 3.12).

To calculate fishing mortality F, catch was divided by biomass estimates listed in the

literature (Potter et al., 1983b; Young & Potter, 2003b).

Table 3.12: Mortality estimates for the functional fish groups

pre DC post DC

Functional groups M F Z M F Z

5 marine omnivorous fish 1.552 0.523 2.075 1.448 0.014 1.462

6 marine carnivorous fish 1.808 0.116 1.924 2.008 0.369 2.377

7 marine herbivorous fish 0.811 0.238 1.049 0.763 0.011 0.774

8 marine detritivorous fish 1.939 0.007 1.946 1.939 0.042 1.981

9 estuarine omnivorous fish 1.806 0.018 1.824 2.742 0.007 2.514

10 estuarine carnivorous fish 2.001 0.074 2.075 1.935 0.257 2.192

11 estuarine herbivorous fish 1.134 0.153 1.287 1.072 0.043 1.115

12 estuarine detritivorous fish 0.866 0.011 0.877 0.819 0.023 0.842

13 whiting 1.165 0.293 1.458 0.892 0.272 1.164

14 Arripis georgianus 0.853 0.111 0.964 0.806 0.707 1.513

15 Aldrichetta forsteri 0.852 0.122 0.695 0.542 0.344 0.886

16 Mugil cephalus 0.421 0.045 0.466 0.398 0.975 1.373

17 Torquigener pleurogramma 0.798 0.011 0.809 0.755 0.001 0.755

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For determining natural mortality M, the maximum length recorded in the estuary

for each fish species was converted into asymptotic length (Pauly, 1984) according

to:

LL 95.0max

If no length data were recorded in the Peel-Harvey (Potter et al., 1983b), length data

were used from other estuaries in Western Australia (Hyndes et al., 1997) or, if no

local source was available, from FishBase (www.FishBase.org, Table 3.13). The fish

lengths reported for the Peel-Harvey Estuary are generally smaller than in other sites

and much smaller than the maximum lengths reported in FishBase. However, it

should be emphasised that the fish lengths recorded in the Peel-Harvey Estuary were

derived from an extensive study using gill nets, beach seine and otter trawl (Potter et

al., 1983b). Further, data analysis clearly demonstrated that the Peel-Harvey Estuary

is used as a nursery area, as 0+ and 1+ year classes were caught (Potter et al.,

1983b). These factors account for the fish lengths being smaller than in other areas

and smaller than maximum lengths recorded in FishBase (Table 3.12). This logic of

preferring data with a local provenance also applies to the possibility of using the

maximum size of the fish in the region, not the maximum size caught. Using length

data from the region belongs to the pedigree category “same species, similar

system”. In contrast, length data from the Peel-Harvey Estuary belongs to the

highest pedigree category: “same species, same system”. There is no reasonable

argument that explains why length data from the Peel-Harvey Estuary should not be

applied here. Ignoring data from the Peel-Harvey Estuary lowers the pedigree and,

thus, the quality of the Ecopath models.


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Table 3.13: Natural mortality M and Q/B ratio of each fish species that were recorded in the pre and post DC period. Group parameters were derived by weighting each species parameter by abundance. The length data (Lmax) for each species was derived from * Potter et al., 1983b, **Hyndes et al. 1997 or from Fish Base (www.FishBase.org).

M Q/B

Species L∞ in mm pre DC post DC pre DC post DC


Acanthaluteres brownii *100.00 2.025 6.661

Ammotretis elongata *147.37 1.464 1.384 6.322 6.174

Atherinomorus ogilbyi *129.47 1.631 1.542 6.500 6.348

Brachaluteres jacksonianus 105,26 1.834 6.696

Eubalichthys mosaicus 631,58 0.433 4.801

Haletta semifasciata 305,26 0.796 0.752 5.225 5.103

Meuschenia freycineti 631,58 0.433 0.409 3.790 3.702

Microcanthus strigatus 168,42 1.234 5.774

Rhabdosargus sarba *293.68 0.822 0.777 5.061 4.943

Scobinichthys granulatus *181.05 1,23 1.165 6.299 6.153

group parameters weighted by abundance

1.552 1.448 6.369 6.213


Argyrosomus hololepidotus *702.11 0.396 4.204

Arripis truttaceus *30.53 5.466 5.168 13.577 13.259

Callogobius depressus *93.68 2.138 6.111

Contusus brevicaudus *238.95 0.977 0.924 5.537 5.407

Cristiceps australis 189,47 1.186 1.121 5.650 5.518

Dotalabrus alleni 89,47 2.101 7.171

Elops machnata *487.37 0.538 5.287

Enoplosus armatus 526,32 0.477 4.208

Filicampus tigris 311,58 0.740 6.704

Galaxias maculates 200 1.072 5.817

Gerres subfasciatus *207.37 1.100 1.040 5.628 5.497

Gonorynchus greyi 526,32 0.477 4.749

Gymnapistes marmoratus *137.89 1.547 1.463 5.813 5.677

Halichoeres brownfieldi 157,89 1.306 6.020

Helcogramma decurrens 47,37 3.578 8.343

Hyperlophus vittatus *93.68 2.138 2.022 7.344 7.172

Lesueurina platycephala 115,79 1.693 6.410

Neoodax sp. *201.05 1.129 1.067 6.500 6.348

Papillogobius punctatus 152,63 1.344 5.755

Parapercis haackei 105,26 1.834 6.592

Platycephalus laevigatus *218.95 1.051 6.069

Pomatomus saltatrix *208.42 1.095 1.035 6.030 5.889

Pseudocaranx dentex *281.05 0.853 0.806 5.417 5.291

Pseudocaranx wrightii 736,84 0.360 4.493


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Pseudorhombus jenynsii *395.79 0.640 0.605 4.655 4.546

Sardinops sagax *106.32 1.924 6.982

Scorpis georgianus *56.84 3.249 3.071 8.868 8.661

Spratelloides robustus *84.21 2.338 2.211 8.003 7.816

Stigmatopora argus 267,37 0.841 6.754

Trachurus novaezelandiae *289.47 0.832 5.352

Upeneus tragula 315,79 0.731

Urocampus carinirostris 68,42 2.630 9.699

group parameters weighted by abundance

1.808 2.008 6.785 7.154


Acanthaluteres spilomelanurus

147,37 1.384 10.799

Acanthaluteres vittiger 368,42 0.643 8.506

Arrhamphus sclerolepis 378,95 0.628 9.956

Hyporhamphus melanochir *397.89 0.637 0.603 10.081 9.845

Monacanthus chinensis 400 0.600 8.695

Pelates sexlineatus *297.89 0.812 0.768 8.104 7.915

group parameters* weighted by abundance

0.811 0.763 8.120 8.053

Marine detritivorous fish: functional group 8

Omobranchus germaini 84,21 2.211 10.984

Petroscirtes breviceps 117,89 1.668 10.329

group parameters (weighted by abundance) 1.939 10.656


Acanthopagrus butcheri *386.32 0.653 0.618 4.702 4.592

Amniataba caudavittata *241.05 0.970 0.917 8.340 7.242

Craterocephalus mugiloides *47.37 3.784 3.578 5.858 5.721

Craterocephalus pauciradiatus

73,68 2.614 2.472 7.416 7.822

Pseudogobius olorum *67.37 2.818 2.664 8.010 8.145

group parameters* weighted by abundance

1.806 2.742 6.756 7.557


Afurcagobius suppositus *77.89 2.496 2.360 7.280 7.110

Apogon rueppellii *100.00 2.025 1.914 6.824 6.665

Arenigobius bifrenatus *152.63 1.421 1.344 6.146 6.002

Atherinosoma elongata *109.47 1.877 1.775 6.291 6.144

Cnidoglanis macrocephalus *722.11 0.387 0.366 4.118 4.021

Edelia vittata *21.05 7.459 10.888

Engraulis australis *136.84 1.557 1.472 7.156 6.988

Favonigobius lateralis *93.68 2.138 2.022 6.565 6.412

Galaxias occidentalis 171,58 1.219 6.042

Gambusia holbrooki 36,84 4.415 9.458

Leptatherina presbyteroides *98.95 2.043 1.931 6.952 6.789

Leptatherina wallacei *57.89 3.199 3.025 8.690 8.487

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Platycephalus speculator 218 0.994 5.928

Siphamia cephalotes *50.53 3.585 3.390 8.551 8.351

group parameters (weighted by abundance)

2.001 1.935 6.675 6.482


Hyporhamphus regularis *200 1.134 1.072 9.875 9.644

Estuarine detritivorous fish: functional group 12

Nematalosa vlaminghi *275.79 0.866 0.819 7.343 7.171


Sillago bassensis *124.21 1.689 7.042

Sillago burrus **235.79 0.988 0.934 5.905 5.767

Sillaginodes punctata *220 1.047 0.990 5.409 5.283

Sillago schomburgkii *308.42 0.789 0.746 5.233 5.111

Sillago vittata **253,68 0.878 5.644

group parameters (weighted by abundance)

1.165 0.892 5.988 5.396

Arripis georgianus: functional group 14

Arripis georgianus *281.05 0.853 0.806 6.517 6.364

Aldrichetta forsteri: functional group 15

Aldrichetta forsteri *541.58 0.852 0.542 4.975 4.858

Mugil cephalus: functional group 16

Mugil cephalus *652.63 0.421 0.398 4.767 4.651

Torquigener pleurogramma: functional group 17

Torquigener pleurogramma *304.21 0.798 0.755 5.200 5.079

The asymptotic lengths for each species were used to estimate natural mortality M

according based on an equation provided by Dr. Norman Hall (pers.comm., Murdoch

University):

TLM ln840745.0ln83685.0328061.0ln

where L∞ is the asymptotic length in cm and T symbolizes the water temperature in

degrees Celsius (°C).

For calculating the total natural mortality for a multispecies group, the single

mortality values were weighted by abundance (Table 3.12). As no biomass data is

available for the functional group ‘marine detritivorous fish’ for the ‘pre DC’ model,

the natural mortality value was adopted from the later period (Table 3.13). The

water temperature was adopted from the literature for all parameter calculations

and the mean annual water temperature for the ‘pre DC’ period was estimated with

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15.5°C (based on samples taken in 1980/81) and 14.5°C for the ‘post DC’ (based on

samples taken in 1995-98) period (de Lestang et al., 2003).

The Q/B ratios (Table 3.13) were estimated according to:

dhATWBQ 398.0532.0083.0'965.1log204.0964.7/log

where W∞ is the asymptotic weight (in g), T’ is the mean annual temperature of the

water body, expressed as T’= 1000/Kelvin, A is the aspect ratio of the caudal fin

(according to A = H2/s, whereas H is height of caudal fin, s is the surface area of

caudal fin), h for herbivores (h = 1, d = 0), d for detritivores (d = 1, h = 0) and

carnivores are identified by default (h = 0, d = 0) (Palomares & Pauly, 1998).

Length data were converted into weight data via length-weight relationships

from the literature (Table 3.13) or Fishbase (FishBase, 2006). The Q/B values for

multispecies fish groups were calculated by weighting the value for each species by

its abundance (Table 3.13).

Table 3.14: Data input for the functional fish groups for the ‘pre DC’ and ‘post DC’ Ecopath models

pre DC post DC

B P/B Q/B B P/B Q/B

Functional groups (tkm-²) (year

-1) (year

-1) (tkm

-²) (year

-1) (year

-1)

5 marine omnivorous fish 0.128 2.075 6.369 0.078 1.462 6.213

6 marine carnivorous fish 5.689 1.924 6.785 0.249 2.377 7.154

7 marine herbivorous fish 2.048 1.049 8.120 0.034 0.774 8.053

8 marine detritivorous fish 0.0001 1.946 10.656 0.00002 1.981 10.656

9 estuarine omnivorous fish 1.135 1.824 6.756 0.093 2.514 7.557

10 estuarine carnivorous fish 5.926 2.075 6.675 0.369 2.192 6.482

11 estuarine herbivorous fish 0.021 1.287 9.875 0.0007 1.115 9.644

12 estuarine detritivorous fish 5.997 0.877 7.343 0.01 0.842 7.171

13 Whiting 0.734 1.458 5.988 0.046 1.164 5.396

14 Arripis georgianus 0.136 0.964 6.517 0.012 1.513 6.364

15 Aldrichetta forsteri 19.526 0.695 4.975 0.085 0.886 4.858

16 Mugil cephalus 31.580 0.466 4.767 0.037 1.373 4.651

17 T. pleurogramma 0.080 0.809 5.200 1.773 0.755 5.079

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The P/B values of the fish groups range from 0.466 to 2.514 and Q/B range from

4.651 to 10.656 (Table 3.14). The range of these parameters equals the parameter

input of other models (Bundy et al., 2000; Cruz-Escalona et al., 2007).

Only one study on the diets of fish is available for the Peel-Harvey Estuary (Geijsel,

1983). Data for most of the fish species were adopted from other estuaries or

marine areas in Western Australia (Table 3.15). When no data were available,

information from FishBase was applied to gain the dietary information for each

functional group (FishBase, 2006) (Table 3.15).

Table 3.15: Reference list for the dietary information that was used for the functional fish groups in the ‘pre’ and ‘post DC’ Peel-Harvey Ecopath models. For all the fish species that are listed in Table 3.10, but which are not recorded in this table, the dietary information was adopted from FishBase (www.FishBase.org)

Species Reference – fish diet


Ammotretis elongate (Hourston et al., 2004)

Atherinomorus ogilbyi (Prince et al., 1982)

Brachaluteres jacksonianus (Stewart, 1998)

Haletta semifasciata (MacArthur & Hyndes, 2007)

Scobinichthys granulatus (Stewart, 1998)


Argyrosomus hololepidotus (Lasiak & McLachlan, 1987)

Gerres subfasciatus (Linke et al., 2001)

Hyperlophus vittatus (Goh, 1992)

Lesueurina platycephala (Hourston et al., 2004)

Neoodax sp. (MacArthur & Hyndes, 2007)

Platycephalus laevigatus (Platell & Hall, 2006)

Pseudocaranx wrightii (Platell, 2001)

Pseudorhombus jenynsii (Platell & Hall, 2006)

Stigmatopora argus (Kendrick & Hyndes, 2005)

Upeneus tragula (Linke et al., 2001)


Acanthaluteres spilomelanurus (Stewart, 1998)

Arrhamphus sclerolepis (Waltham & Connolly, 2006)

Hyporhamphus melanochir (Robertson & Klumpp, 1983)

Pelates sexlineatus (Shaw, 1986)

Marine detritivorous fish: functional group 8 FishBase



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Acanthopagrus butcheri (Sarre, 1999)

Amniataba caudavittata (Wallace, 1979)

Craterocephalus mugiloides (Prince et al., 1982)

Pseudogobius olorum (Platell & Hall, 2006)


Afurcagobius suppositus (Platell & Hall, 2006)

Apogon rueppellii (Platell & Hall, 2006)

Atherinosoma elongate (Prince et al., 1982)

Cnidoglanis macrocephalus (Platell & Hall, 2006)

Edelia vittata (Pen, 1990)

Favonigobius lateralis (Platell & Hall, 2006)

Galaxias occidentalis (Pen, 1990)

Leptatherina presbyteroides (Prince et al., 1982)

Leptatherina wallacei (Prince et al., 1982)

Platycephalus speculator (Platell & Hall, 2006)


Hyporhamphus regularis (Tibbetts & Carseldine, 2005)

Estuarine detritivorous fish: group 12 FishBase


Sillago bassensis (Hyndes et al., 1997)

Sillago burrus (Hyndes et al., 1997)

Sillaginodes punctata (Hyndes et al., 1997)

Sillago schomburgkii (Hyndes et al., 1997)

Sillago vittata (Hyndes et al., 1997)

Arripis georgianus: functional group 14 (Platell & Hall, 2006)

Aldrichetta forsteri: functional group 15 (Platell & Hall, 2006)

Mugil cephalus: functional group 16 (Platell & Hall, 2006)

Torquigener pleurogramma: functional group 17 (Potter, 1988)

The dietary data for multi-species functional groups were averaged and were not

weighted by abundances of each species, as in some cases only qualitative data were

available (Tables 3.16 and 3.17). If qualitative dietary data were used, it was

assumed that each dietary component contributed equally to the total biomass of

the predator’s diet.

The dietary information for each functional group was entered to the diet matrices

in Ecopath, where the biomass consumed by the functional group represented in

each column (predator) comprises of prey of the functional group represented in

each row (Tables 3.16 and 3.17).

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Table 3.16: Dietary matrix of fish for the ‘pre DC’ Ecopath model; data entry is converted so that all components of a group add up to 1 (according to Ecopath)

Prey group:

Predator groups

5 6 7 8 9 10 11 12 13 14 15 16 17

5 0.0001 0.0132 0 0 0 0 0 0 0.001 0 0 0 0

6 0 0.0132 0 0 0 0.002 0 0 0.001 0 0 0 0

7 0 0.0132 0 0 0 0 0 0 0.001 0 0 0 0

8 0 0.0132 0 0 0 0.001 0 0 0.001 0 0 0 0

9 0 0.0132 0 0 0 0.001 0 0 0.001 0 0 0 0

10 0 0.0132 0 0 0.0002 0.003 0 0 0.001 0 0 0 0

11 0 0.0132 0 0 0 0 0 0 0.001 0 0 0 0

12 0 0.0132 0 0 0 0 0 0 0.001 0 0 0 0

13 0 0.0132 0 0 0 0 0 0 0.001 0 0 0 0

14 0 0.0132 0 0 0 0.005 0 0 0.001 0 0 0 0

15 0 0.0132 0 0 0 0 0 0 0.001 0 0 0 0

16 0 0.0132 0 0 0 0 0 0 0.001 0 0 0 0

17 0 0,0132 0 0 0 0 0 0 0,001 0 0 0 0

18 0.126 0.052 0.025 0.0025 0.253 0.068 0 0 0.069 0.001 0.044 0 0.479

19 0.026 0.032 0.025 0.0025 0.013 0.005 0.111 0 0.011 0 0 0 0.044

20 0.002 0.026 0 0.0025 0 0.02 0 0 0 0 0 0 0

21 0.002 0.011 0 0.0025 0 0.02 0 0 0 0 0 0 0.026

22 0.162 0.171 0.063 0.0025 0.242 0.281 0 0 0.32 0.461 0 0 0

23 0.092 0.133 0.014 0.0025 0.07 0.118 0 0 0.366 0.095 0.179 0 0.163

24 0.098 0.287 0.071 0 0.241 0.303 0.111 0.15 0.087 0.002 0.2 0.064 0.213

25 0.013 0.043 0 0.0125 0.09 0.035 0 0 0 0 0.049 0.291 0

26 0.024 0.004 0.076 0.0688 0.03 0 0 0.04 0.002 0 0.099 0 0

27 0.011 0 0.076 0.0685 0 0 0 0.04 0.007 0.007 0.124 0 0

28 0.011 0 0.076 0.0685 0 0 0 0.04 0.002 0 0.006 0 0

29 0.011 0 0.076 0.0685 0 0 0 0.04 0.002 0 0.103 0 0

30 0.287 0.003 0.422 0.0685 0.001 0.001 0.778 0.04 0.007 0 0 0 0

31 0.006 0 0 0.625 0.046 0.072 0 0.5 0 0.001 0.025 0.227 0

Table 3.17: Dietary matrix of fish for the ‘post DC’ Ecopath model; data entry is converted so that all components of a group add up to 1 (according to Ecopath)

Predator groups

Prey group:

5 6 7 8 9 10 11 12 13 14 15 16 17

5 0.0001 0.0047 0 0 0 0.002 0 0 0.001 0 0 0 0

6 0 0.0048 0 0 0 0.004 0 0 0.001 0 0 0 0

7 0 0.0047 0 0 0 0.002 0 0 0.001 0 0 0 0

8 0 0.0086 0 0 0 0.003 0 0 0.001 0 0 0 0

9 0 0.0149 0 0 0 0.005 0 0 0.001 0 0 0 0

10 0 0.0047 0 0 0.00025 0.002 0 0 0.001 0 0 0 0

11 0 0.0047 0 0 0 0.002 0 0 0.001 0 0 0 0

12 0 0.0049 0 0 0 0.002 0 0 0.001 0 0 0 0

13 0 0.0049 0 0 0 0.002 0 0 0.001 0 0 0 0

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14 0 0.0047 0 0 0 0.006 0 0 0.001 0 0 0 0

15 0 0.0047 0 0 0 0.002 0 0 0.001 0 0 0 0

16 0 0.0047 0 0 0 0.002 0 0 0.001 0 0 0 0

17 0 0,0047 0 0 0 0,001 0 0 0,001 0 0 0 0

18 0.14 0.0384 0.0085 0.0025 0.316 0.069 0 0 0.066 0.001 0.044 0 0.479

19 0.047 0.0435 0.027 0.0025 0.01625 0.006 0.111 0 0.009 0 0 0 0.044

20 0.009 0.0253 0 0.0025 0 0.015 0 0 0 0 0 0 0

21 0.009 0.0399 0 0.0025 0 0.015 0 0 0.009 0 0.001 0 0.026

22 0.167 0.2288 0.033 0.0025 0.2773 0.276 0 0 0.292 0.461 0 0 0

23 0.105 0.0845 0.0248 0.0025 0.0748 0.134 0 0 0.397 0.095 0.179 0 0.163

24 0.098 0.2567 0.0452 0 0.1635 0.232 0.111 0.15 0.108 0.002 0.2 0.064 0.213

25 0.013 0.0248 0 0.0125 0.05625 0.027 0 0 0 0 0.049 0.291 0

26 0.032 0.0018 0.0875 0.0688 0.039 0.0001 0 0.04 0.003 0 0.099 0 0

27 0.011 0.0007 0.0735 0.0685 0 0.0001 0 0.04 0.007 0.007 0.124 0 0

28 0.011 0.0007 0.0735 0.0685 0 0 0 0.04 0.003 0 0.006 0 0

29 0.011 0.0007 0.0735 0.0685 0 0 0 0.04 0.003 0 0.103 0 0

30 0.131 0.0012 0.418 0.0685 0.0013 0.003 0.778 0.04 0.007 0 0 0 0

31 0.006 0 0 0.625 0.0325 0.055 0 0.5 0 0.001 0.025 0.227 0

3.3.5 Bivalves (Functional group 18)

Only a small number of bivalves have been recorded in the Peel-Harvey Estuary to

date, with Arthritica semen being the most abundant species (Rose, 1994; Wells &

Threlfall, 1982; Wildsmith, 2007). Biomass values for the most abundant species

were calculated by multiplying abundances with average individual biomasses (Table

3.18) that were recorded before and after the Dawesville Channel opening (Rose,

1994; Wildsmith, 2007).

Table 3.18: Species list and their abundances in numbers per 0.1m-2 (Rose, 1994; Wildsmith, 2007) and parameter input for the functional group ‘bivalves’

Species list and abundances pre DC post DC

Anticorbula amara 3.65

Arthritica semen 305.83 16.25

Donax sp. 0.21

Mysella sp. 0.83

Sanguinolaria biradiata 1.25

Tellina deltoidalis 0.21 0.73

Xenostrobus securis 19.06

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Parameter input

Biomass (t/ km²) 219.36 11.249

P/B (year -1) 3.94 3.94

Q/B (year -1) 10.63 10.63

Arthritica semen has been the most abundant bivalve species in the benthic macro-

invertebrate studies conducted in the 1990s (Rose, 1994) and more recently

(Wildsmith, 2007). This species was studied in detail in the Peel Inlet in 1977-1980

and its production was estimated as 4.1 g m-2 year-1 and its P/B ratio as 3.94 (Wells &

Threlfall, 1982). The parameter input of this functional group is only based on data

available for the most abundant species, A.semen. This value is employed for the

functional group ‘bivalve’ for both Ecopath models.

A. semen is known to be a ‘deposit feeder’ and ‘suspension feeder’, which feeds

mainly on detritus and small planktonic organisms (Rose, 1994; Wells & Threlfall,

1982; Wildsmith, 2007). For this reason, the dietary components of bivalves for the

Ecopath models are assumed to be detritus (33%), phytoplankton (33%) and

zooplankton (33%). These assumptions are reasonable, as the dietary components of

bivalves for the West Florida Shelf Ecopath model are similar (Okey & Mahmoudi,

2002).

The Q/B ratio for bivalves was estimated with 10.63 for the Laguna Alvarado Ecopath

model in the Gulf of Mexico (Cruz-Escalona et al., 2007), which is similar to the Q/B

ratio of 12.5 for benthic invertebrates in the Newfoundland Labrador Shelf model

(Bundy et al., 2000). Also, the Q/B ratio for the functional group ‘macrobenthos’

(Q/B = 13 year-1) in the North Sea has the same order of magnitude (Christensen,

1995). Compared to those rather low Q/B values, the West Florida Shelf Ecopath

model presented a Q/B parameter for bivalves of 23 (Okey & Mahmoudi, 2002). This

parameter was adopted from an Ecopath model of the Northeastern Pacific (Pauly et

al., 1996), where it was used for the functional group ‘macrobenthos’ and S.

Guenette (see page 66 in(Pauly et al., 1996) estimated this parameter based on the

consumption of sea urchins and abalone. The low Q/B ratio of 10.63 for Laguna

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Alvarado seems more appropriate here, as environmental conditions are more

similar to the conditions in the Peel-Harvey Estuary than an open ocean or a shelf

model. For this reason, a Q/B ratio of 10.63 was adopted for these Ecopath models

(Cruz-Escalona et al., 2007).

3.3.6 Gastropods (Functional group 19)

As with bivalves, only a small number of gastropod species were recorded in benthic

studies in the Peel-Harvey Estuary by Rose (1994) and Wildsmith (2007). A total of 22

gastropod species were recorded in a study in 1977-79 that investigated the biology

of molluscs in the Peel-Harvey Estuary, as well as in the rivers discharging into the

estuary, such as the Serpentine, the Murray and the Harvey River (Wells et al., 1980).

However, the data provided by Rose (1994) and Wildsmith (2007) are employed here

to estimate biomass parameters, as the effect of the Dawesville Channel on the

benthic assemblages of the Peel-Harvey Estuary was more clearly revealed by

duplication in 2003/04 of the sampling regime, sampling sites and methodology used

in 1986/87. The most abundant species with 48.3% of total molluscs collected in

1977-79, Hydrococcus brazieri, was not listed in any later benthic study in the Peel-

Harvey Estuary (Rose, 1994; Wildsmith, 2007). Biomass estimates for this species for

the ‘pre DC’ model (0.2g m-2 year-1) were derived from a detailed study of two

molluscs species in the Peel-Harvey Estuary (Wells & Threlfall, 1982). For all other

gastropod species, biomass estimates were derived from multiplying the recorded

abundances (Table 3.19,(Rose, 1994; Wildsmith, 2007) with averaged individual

biomasses (Rose, 1994).

Table 3.19: Species list and their abundances in numbers per 0.1m-2 (Rose, 1994; Wildsmith, 2007) and parameter input for the functional group ‘gastropods’ Species list and abundances pre DC post DC

Assiminea sp. 0.1

Nassarius sp. 0.73

Nodilittorina sp. 1.04

Hydrococcus brazieri, (Wells & Threlfall, 1982) mean annual biomass:

0.2

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Parameter input

Biomass (t/ km²) 0.21 0.177

P/B (year -1) 2.5 2.5

Q/B (year -1) 15.97 15.97

Data on the production of gastropods of the Peel-Harvey Estuary are only available

for Hydrococcus brazieri (Wells & Threlfall, 1982). An extensive search of the

literature did not uncover data on the specific growth rates for the species listed in

the estuary (Table 3.19). The P/B ratio for the functional group ‘gastropods’ in both

Ecopath models is assumed to be equal to the P/B ratio of Hydrococcus brazieri,

which is recorded as 2.5 for the Peel-Harvey estuarine system (Table 5 in(Wells &

Threlfall, 1982).

The consumption/biomass ratio of the functional group ‘echinoderms and large

gastropods’ of the Florida Ecopath model was estimated with 3.7 year-1 (Okey &

Mahmoudi, 2002), whereas the Jurien Bay Ecopath model presented a value of 14

for small gastropods, as well as large gastropods (Lozano-Montez et al., 2011). Also

the Ecopath model of Laguna Alvarado presented a Q/B ratio of a similar magnitude,

such as 15.97 year-1 (Cruz-Escalona et al., 2007). Weeks Bay in Alabama, USA, is an

estuary that shows a geographic structure similar to the Peel-Harvey Estuary

(Althauser, 2003). For this estuarine Ecopath model, the Q/B values ranged

seasonally from 7.908 to 16 (Althauser, 2003). Apparently, Q/B parameters for

coastal ecosystems are assumed to be higher than the parameter used for the

Florida Shelf model (Okey & Mahmoudi, 2002). For this reason, the Q/B ratio for

gastropods from the Laguna Alvarado Ecopath model (Cruz-Escalona et al., 2007)

was adopted for this Ecopath approach (Table 3.19).

It has been noted that gastropods in the Peel-Harvey Estuary are most likely

herbivorous ‘deposit feeder’ (Rose, 1994). The dietary composition that is needed

for the diet matrix in both Ecopath models had to be adopted from another Ecopath

model (Table 3.20), where the components of the diet were more detailed, such as

the West Florida Shelf model (Okey & Mahmoudi, 2002).

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Table 3.20: Diet entry of the functional group ‘gastropods’ (Okey & Mahmoudi, 2002)

Functional group % of diet

Algae 0.3

Macrophytes 0.1

Chaetomorpha linum 0.1

Cladophora montagneana 0.1

Microscopic algae 0.1

Seagrass 0.05

Detritus 0.25

3.3.7 Western King Prawn (Functional group 20)

Western King Prawns (Penaeus (former Melicertus) latisulcatus) and Western School

Prawns (Metapeneus dalli) are the most important prawn species in the Peel-Harvey

Estuary, as they are targeted by commercial and recreational fishers. The biology of

the Western King Prawn, which is the more important of the two species, was

studied in this estuary by Potter et al. (1991). The prawn population was studied in

the Peel-Harvey Estuary in 1986-88 and again in 1995-97, shortly after the

Dawesville Channel was opened. Length–frequency data (unpublished data, Ian

Potter, Murdoch University) were used to estimate the biomass parameters for the

functional groups in both the ‘pre DC’ and ‘post DC’ Ecopath models. The length data

were converted into weights by applying the mean length-weight relationship

provided by Potter et al. (1991). The biomass estimates of the samples were entered

in the standard unit t km-2, assuming the parameters gained from the sampled

habitat also account for the abundances and biomasses of prawns in the remaining

water body of the estuary (Table 3.21).

For estimating the P/B ratio (Table 3.21), the fishing mortality (Fpre= 4.49, Fpost= 0.9)

was estimated according to section 3.3.4 with a total annual catch estimate of

17.148 t for the pre DC model and 3.668 t for the post DC model (Department of

Fisheries, Perth, WA,(Malseed & Sumner, 2001). The natural mortality of prawns has

not been studied in the estuary and thus, the parameter (M = 1.25) for this species

was adopted from a matrix model that investigated mortalities in Gulf St Vincent,

South Australia (Tanner, 2003). The parameters used here are of the same

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magnitude as the P/B values used in other coastal estuarine Ecopath models, such as

Laguna Alvarado (Cruz-Escalona et al., 2007) and Weeks Bay (Althauser, 2003).

The Q/B value for the functional group was adopted from other Ecopath models. The

annual Q/B value for Penaeid shrimp for Weeks Bay was estimated with 6.84 year-1

(Althauser, 2003). The Q/B value reported for adult shrimps from other areas were

much higher with 19.2 year-1 (Okey & Mahmoudi, 2002). However, the prawns use

the estuary as a nursery and migrate out of the estuary, when freshwater discharge,

salinity and temperature changed from February to July (Potter et al., 1991) and

thus, the adult prawns are not feeding within the estuary all year round. For this

reason, a Q/B ratio of 19.2 for adult shrimp (Okey & Mahmoudi, 2002) is likely to be

too high for these models here. For this reason, the parameter from the estuarine

Ecopath model of Weeks Bay (Althauser, 2003) was adopted for the prawn

population in the Peel-Harvey Estuary (Table 3.21).

Table 3.21: Parameter input for the functional group ‘Western King Prawn’

pre DC post DC

Biomass (t/ km²) 0.0289 0.031

P/B (year -1) 5.74 2.15

Q/B (year -1) 6.84 6.84

Dietary data are adopted from a similar species of the same family, the white prawn

Penaeus merguiensis, for which the diets of juveniles and adult prawns have been

analysed in detail (Chong & Sasekumar, 1981). The dietary components were

adapted for the Western King Prawns according to the functional groups defined for

both Ecopath models applying the mean percentage of juvenile and adult diets for

the Peel-Harvey models (Table 3.22).

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Table 3.22: Diet entry of the functional group ‘Western King Prawn’

Functional group juvenile prawns

(%Vol.) adult prawns

(%Vol.) Peel-Harvey

model

Crustaceans 0 23.155 11.57

Detritus 45.654 12.63 29.14

Gastropods 6.522 0 3.26

Crabs 8.696 0 4.34

Prawns 0 42.1 21.05

Macrophyte 6.522 0 3.26

Microscopic algae 13.044 0 6.52

Small teleosts 0 12.63 6.32

Zooplankton 13.044 0 6.52

3.3.8 Blue Swimmer Crab (Functional group 21)

The Blue Swimmer Crab, Portunus pelagicus, is the main target species in the Peel-

Harvey Estuary for both the commercial and the recreational fishing sector. The

biology of this species has been studied in detail and even the effects of the artificial

entrance channel on the biological characteristics of P. pelagicus have been

investigated (de Lestang et al., 2003; de Lestang et al., 2000; Kangas, 2000). The

parameter values estimated for the Ecopath models are based on the results

reported for these studies (de Lestang et al., 2003; de Lestang et al., 2000). Data on

crab abundances for both life stages were converted into biomass estimates (Table

3.23) using a length-weight relationship (and assuming a sex ratio of 1:1 in both life

stages) (Kangas, 2000). The carapace width (in cm) was converted into weight (in g)

according to:

female crabs: CWW log260.356.2log00001.0log

male crabs: CWW log056.397.5log00001.0log

The P/B ratio, which equals total mortality, was calculated as described for fish

groups in section 3.3.4. The fishing mortality F was estimated using catch rates

provided by the Department of Fisheries, Perth, WA (Table 3.23). The natural

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mortality M was calculated as described in section 3.3.4 with the equation having

been adapted for Cancer sp. in northern Chile, according to

TKCWM ln4631.0ln654341.0ln27955.00152.0log

where CW∞ is the asymptotic carapace width in cm and T is the annual water

temperature in °C (Wolff & Soto, 1992). This equation was used to estimate natural

mortality for the group of crabs in the Peel-Harvey Estuary.

The Q/B parameter had to be adopted from another Ecopath model, as no data on

feeding rate of Blue Swimmer Crabs is available for the Peel-Harvey Estuary. The

estuarine Ecopath model of Weeks Bay, USA presented a mean annual Q/B ratio of

3.179 for blue crabs (Althauser, 2003). This value is rather low compared to Q/B

estimates of an estuary in Taiwan (Q/B = 14,(Lin et al., 2007), Laguna Alvarado (Q/B =

8.55,(Cruz-Escalona et al., 2007), Jurien Bay (Q/B = 8.5,(Lozano-Montez et al., 2011)

and the Caete Mangrove Estuary in Brazil (Q/B = 22,(Wolff, 2000). To obtain an

appropriate P/Q value (P/Q ranges from 0.1 to 0.3 for most groups,(Kavanagh et al.,

2004) for the Blue Swimmer Crab population in these models, a Q/B ratio of 14 year-

1 (Lin et al., 2007) is applied here (Table 3.23).

Dietary information is available for juvenile and adult Blue Swimmer Crabs in the

Peel-Harvey Estuary (de Lestang et al., 2000) and the mean value of their dietary

composition is used according to the functional groups of these Ecopath models

(Table 3.24). All macrophyte groups (i.e. the groups of seagrass, macrophytes, algae,

C. montagneana and C. linum) are assumed to get equally consumed by crabs and

each group is consumed by juvenile crabs contributing 0.6% Volume to their diet and

Table 3.23 - Parameter input for the functional group ‘Blue Swimmer Crabs’

pre DC post DC

Biomass (t/ km²) 3.477 1.460

P/B (year -1) 3.658 3.351

Q/B (year -1) 14 14

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by adult crabs contributing 1.6% Vol. (Table 3.24). Each of the fish groups contributes

to the diet of juvenile and adult crabs (Table 3.24).

Table 3.24: Diet entry of the functional group ‘Blue Swimmer Crab’

Functional groups juvenile crabs adult crabs Peel-Harvey model

(% diet) (% diet)

Bivalves 29,2 22,1 25,65

Crustaceans 43,9 16,8 30,35

Detritus 1,4 4,2 2,8

Gastropods 5,6 1,8 3,7

Plant material 0,6 1,6 1,1

Worms 14,2 38,4 26,3

Fish 0,1 0,7 0,4

3.3.9 Crustaceans (Functional group 22)

To avoid overlapping of the functional groups ‘crustaceans’ and ‘zooplankton’, the

species were grouped based on their taxonomy (Table 3.25).

Table 3.25: Constituents of the functional groups ‘zooplankton’ and ‘crustaceans’ adopted from Table 6.1., Platell and Hall (2006) Functional group Constituents

zooplankton Class Cladoceral

Class Conchostraca

Class Copepoda

Class Notostraca

Class Ostracoda

Class Malocostraca

Class Ascidiacea

Class Tunicata

crustaceans Order Amphipoda

Order Cumacea

Order Isopoda

Order Tanaidacea

Order Leptostraca

Class Pycnogonida

Class Mysidacea

Class Caridea

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Class Palinuridae

Class Portunidae

Class Penaidae

The biomass estimates for the functional group ‘crustaceans’ for the ‘pre DC’ and

‘post DC’ Ecopath models are based on two studies of macro-invertebrate

assemblages that were undertaken in 1986/87 (Rose, 1994) and 2003/04 (Wildsmith,

2007).

The abundances of crustaceans (Table 3.26) were converted into biomass estimates

using individual weights that were provided for the most abundant species in

1986/87 (Rose, 1994). If no average individual biomass was available for a species,

the average weight of the group from that study (Rose, 1994) was applied to gain the

biomass estimates (Table 3.27). The data for the functional group ‘crustaceans’ is

only based on benthic studies that took place in very shallow (0.5 m) waters (Rose,

1994; Wildsmith, 2007). The number of species and, thus, the biomass of this group

might change significantly if a study would be undertaken that investigated the

water column as well.

Table 3.26: Abundances of species (no/ 0.1m²) of the functional group ‘crustaceans’ that were recorded for the ‘pre DC’ and ‘post DC’ period in the Peel-Harvey Estuary Species pre DC post DC

Allorchestes compressa 0.63 1.15

Caprellid sp. 0.21 1.77

Corophium minor 463.13 276.15

Eusirid sp. 1.77

Exoediceroides sp. 0.21

Gastrosaccus sp. 0.94 4.69

Grandidierella propodentata 109.58 53.02

Halicarcinus bedfordi 0.1

Melita matilda 6.04

Melita zeylanica 30.94

Palaeomonetes australis 0.1

Paracorophium excavatum 12.4 0.1

Paranthurid sp. 2.81

Paratanytarsus grimmii 11.56

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Penaeid sp 0.1

Pseudocerceis reticulates 0.52

Syncassidina aestuaria 1.46

Talitrid sp. 0.1

Tanais dulongii 262.08 17.19

Tethygenia elanora 78.96

The abundance of benthic invertebrates in the Peel-Harvey Estuary has been

investigated on two occasions, i.e. in 1986/87 (Rose, 1994) and in 2003/04

(Wildsmith, 2007), using an identical sampling regime. It is unclear how abundant

the crustacean species (Table 3.26) are in other areas of the estuary. For this

modelling approach however, it is assumed that all species are equally abundant

across the whole estuary.

The values used of the production and consumption of crustaceans had to be

adopted from other Ecopath models. For the balanced Caete Mangrove Estuary, the

P/B and Q/B ratios for the functional group ‘small benthos’ are recorded with 12 and

50 year-1 (Wolff, 2000). The estuarine Ecopath model of Weeks Bay, USA presents an

annual P/B ratio of 8.088 and an annual Q/B ratio of 12.398 (Althauser, 2003). A P/B

ratio of 8 is also used in the Ecopath model of Laguna Alvarado (Cruz-Escalona et al.,

2007) and thus, the estuarine annual P/B ratio (Althauser, 2003) seems to be

appropriate for the models of the Peel-Harvey Estuary. The Q/B ratios for small

crustaceans varies greatly in the literature, with values from 12.398 (Althauser,

2003), 20 (Christensen, 1995), 27.14 (Okey & Mahmoudi, 2002), 27.36 (Cruz-

Escalona et al., 2007) up to 50 (Wolff, 2000).The P/Q value for this functional group

is generally between 0.1 and 0.3 (Kavanagh et al., 2004). The seasonal Ecopath

models of Weeks Bay showed a seasonal range in P/Q for the group ‘zoobenthos’

from 0.99 in winter to 0.47 in summer (Althauser, 2003). As these values are beyond

the common parameter range for crustaceans (Kavanagh et al., 2004), the Q/B value

from this estuarine Ecopath model is not applied here, but the Q/B of the West

Florida Shelf model (Okey & Mahmoudi, 2002), which leads to a P/Q value that is

- 144 -

more appropriate for this functional group ‘custaceans’ in the Peel-Harvey Ecopath

model (Table 3.27).

Table 3.27: Parameter input for the functional group ‘crustaceans’ pre DC post DC

Biomass (t km-2) 364.179 173.616

P/B (year-1) 8.088 8.088

Q/B (/year-1) 27.14 27.14

The crustaceans in the Peel-Harvey Estuary are known to be ‘deposit’ and

‘suspension feeders’ (Wildsmith, 2007). In other estuaries, Corophium volutator

(Gerold & Hughes, 1994) and Gastrosaccus brevifissura mainly feed on microscopic

algae (Kibirige et al., 2003). No data were found for any other genus of the recorded

crustacean species. However, it cannot be concluded that all species are herbivorous

suspension feeders. Thus, based on the literature, it is assumed from the literature

that the dominant dietary component of the functional group ‘crustaceans’ are

microscopic algae (50%). However, two dietary components -- detritus (25%) and

zooplankton (25%) -- also need to be considered as minor parts of the crustacean

diet (Wildsmith, 2007).

3.3.10 Worms (Functional group 23)

The functional group’ worms’ mainly consists of polychaete species (Table 3.28). In

recent years however, the composition of worm species in the sediments of the

Peel-Harvey Estuary has been changing (Wildsmith, 2007). The most common phyla

are Polychaeta, Nematoda, Sipuncula and Platyhelminthes (Wildsmith, 2007), which

are combined as one functional group ‘worms’ for both Ecopath models of the Peel-

Harvey Estuary (Table 3.28).

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Table 3.28: Abundances (in numbers per 0.1m-2) of recorded species that are listed for the functional group ’worms’, according to (Rose, 1994; Wildsmith, 2007)

species pre DC post DC

Ampharetid sp. 0.21

Armandia sp. 0.31

Australonereis ehlersii 10.63 29.69

Boccardiella limnicola 66.15

Brania sp. 0.63

Capitella spp. 1462.4 247.81

Capitomastus sp. 1.25

Carazziella victoriensis 18.33

Ceratonereis aequisetis 283.33 109.38

Cirriformia filigera 10.42

Diopatra sp. 2 0.21

Exogone sp. 0.31

Exogonella sp. 0.1

Ficopomatus enigmatus 14.69

Heteromastus sp. 40.21

Leitoscoloplos normalis 15 39.17

Lumbrinereid sp. 0.1

Malacoceros sp. 6.46

Maldanis sp. 0.21

Marphysa sanguinea 0.31

Mediomastus sp. 3.23

Nanereis sp. 0.1

Nematode sp. 0.31

Nemertean sp. 11.35 0.63

Nephtys graverii 4.17

Oligochaete spp. 15.94

Paratanytarsus grimmii 11.56

Phyllodoce sp. 2.29

Polydora socialis 23.02 7.19

Prionospio cirrifera 15.63

Pseudopolydora kempi 0.83

Pseudopolydora sp. 0.1

Sabellid sp. 0.42

Sipunculan sp. 0.42

Sphaerosyllis sp. 1.25

Spio sp. 0.31

Terrebellid sp. 0.1

Turbellarian sp. 0.21 0.1

The abundances were used to produce biomass estimates for the Peel-Harvey

Estuary (Table 3.29) by multiplying the abundance by the mean body weight (Rose,

1994). No data have been available on the production and consumption of worms

- 146 -

and in particular polychaetes in the Peel-Harvey Estuary. For this reason, the P/B and

Q/B ratios (Table 3.29) had to be adopted from another model. For the functional

group ‘annelids’ of the Florida West Shelf Ecopath model, the P/B ratio was

estimated with 4.6 and Q/B with 15.9 year-1 (Okey & Mahmoudi, 2002). As input

data for the Newfoundland-Labrador Shelf Ecopath model a P/B ratio of 2.0 and a

Q/B of 22.2 year -1 were used for the group ‘polychaetes’ (Bundy et al., 2000). These

ratios are rather small compared to the parameters of the coastal Laguna Alvarado

Ecopath model, that applied a P/B ratio of 6.22 and a Q/B ratio of 26.5 for the

functional group ‘worms’ (Cruz-Escalona et al., 2007). Also an estuarine Ecopath

model of northern Taiwan used high ratios for P/B and Q/B, such as 8.10 and 57

respectively (Lin et al., 2007). The parameter input for polychaetes in the Pearl River

Ecopath model presented a P/B ratio of 4.928 and 19.712 (Duan et al., 2009).

Apparently, these ratios differ between Shelf and coastal Ecopath models. Like the

Peel-Harvey Estuary, Laguna Alvarado is a shallow estuarine lagoon that is connected

to the Gulf of Mexico via a small entrance channel (Cruz-Escalona et al., 2007). As

the characteristics of both estuaries are similar, the P/B and Q/B ratios for the

functional group ‘worms’ are adopted from the Laguna Alvarado Ecopath model

(Table 3.29).

Table 3.29: Parameter input for the functional group ‘worms’

pre DC post DC

Biomass (t km-2) 78.31 40.739

P/B (year-1) 6.22 6.22

Q/B (year-1) 26.5 26.5

All species (Table 3.28) were described as ‘deposit’ and ‘suspension feeders’

(Wildsmith, 2007); however, no detailed data on the consumption of ‘worms’ are

available for the Peel-Harvey Estuary. It is assumed that ‘worms’ in the Peel-Harvey

Estuary mainly feed on small organisms, such as detritus (33%) and small plankton

organisms (zooplankton 33%, microscopic algae 33%).

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3.3.11 Zooplankton (Functional group 24)

Several studies of species within the functional group ‘zooplankton’ have been

undertaken in the Peel-Harvey Estuary (e.g. Rose, 1994 in 1986/87). The

ichthyoplankton in both the Dawesville and the Mandurah entrance channel were

studied in 1997 (Young & Potter, 2003a). No study of the ichthyoplankton has been

reported since the opening of the Dawesville Channel. Whether the species

composition and abundance of zooplankton and ichthyoplankton within the Peel-

Harvey Estuary has changed since the Rose (1994) remains unclear. With the data

available (Rose, 1994; Young & Potter, 2003a), rough estimates of zooplankton

biomasses were extrapolated with the ‘post DC’ biomass only including

ichtyoplankton (Table 3.30). The mean abundances were multiplied by averaged

individual weights recorded for the dominant species to gain the biomass estimates

for the Ecopath models (Rose, 1994).

The P/B and Q/B ratios for the functional group ‘zooplankton’ were adapted from

the the estuarine Ecopath model of Laguna Alvarado, Mexico (Cruz-Escalona et al.,

2007) due to the paucity of data from the Peel-Harvey Estuary. However, the Laguna

Alvarado Ecopath model presents P/B and Q/B ratios for zooplankton that are similar

to the estuarine Ecopath model of Weeks Bay (Althauser, 2003). Those two Ecopath

models present estuaries that consist of a large basin and that are connected to the

ocean via a small channel (Althauser, 2003; Cruz-Escalona et al., 2007), like the Peel-

Harvey Estuary (Hale & Butcher, 2007). The P/B and Q/B ratios for zooplankton in

these two estuarine Ecopath models differ from the parameters for this functional

group that are used in other Ecopath models, (Lozano-Montez et al., 2011; Okey &

Mahmoudi, 2002; Wolff, 2000). However, due to environmental similarities, the

parameters of Laguna Alvarado seem to be appropriate for this Ecopath approach

(Table 3.30).

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Table 3.30: Data input for the functional group ‘zooplankton’

pre DC post DC

Biomass (t km-2) 3990.3 7.991

P/B (year-1) 20.20 20.20

Q/B (year-1) 87.8 87.8

Similar to the West Florida Shelf Ecopath model (Okey & Mahmoudi, 2002), it is

assumed for the Ecopath models of the Peel-Harvey Estuary that zooplankton

species feed on other zooplankton organisms (25%), microscopic algae (50%) and

detritus (25%).

3.3.12 Microscopic algae (Functional group 25)

The functional group of microscopic algae includes phytoplankton in the water

column as well as benthic microalgae. The data available for this functional group are

rather limited. Due to massive blooms of the toxic blue-green algae Nodularia

spumigena, studies on cyanobacteria were undertaken in the Peel-Harvey Estuary in

the late 1980s (Huber, 1985, 1986; Lukatelich, 1986; Lukatelich & McComb, 1986b).

The importance of benthic phytoplankton for this estuary was also investigated

during that time through measurements of benthic chlorophyll a concentrations

(Lukatelich & McComb, 1986a). The biomass estimate of the phytoplankton group

for the ‘pre DC’ Ecopath model was based on a study on microalgae by Lukatelich

(1986a); that study presented chlorophyll concentrations for the sediment as well as

the water column. Since 2001, the phytoplankton community has been sampled

regularly by the Department of Water, Perth, in the Water Information (WIN)

database – discrete sample data (2001-2008). The dataset listed cell numbers for

these diatoms: Chlorophyta, Chrysopthyta, Cryptophyta, Cyanophyta, Dictyophyta,

Dinophyta, Euglenophyta, Haptophyta and Prasinophyta. Some of the most common

genera and species are, for example, Nitzschia sp., Gyrodinium sp., Skeletonema sp.,

Anabaena sp., Heterocapsa sp. and Heterosigma akashiwo. However, the cells were

not counted for the levels of genera or species and thus, chlorophyll a

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concentrations were used to estimate the phytoplankton biomass in the ‘post DC’

Ecopath model. Since the opening of the Dawesville Channel, only water samples

have been collected to measure chlorophyll a concentrations and thus, a biomass

estimate for benthic chlorophyll a is not available. It should be noted that the

biomass value for this functional group might be underestimating the biomass of

phytoplankton in the estuary due to insufficient data. The biomass value for the

‘post DC’ Ecopath model is based on an average annual chlorophyll a concentration

of 0.41 mg per litre, which was extrapolated from the chlorophyll a measurements of

the WIN database.

For the assessment of phytoplankton biomass, chlorophyll a concentrations were

converted into biomass by applying the ratio of organic carbon to chlorophyll a (C/

chl a). Applying an incorrect C/chl a ratio can lead to an underestimation of organic

Carbon (de Jonge & Colijn, 1994). However, no study on this ratio has been

undertaken in the Peel-Harvey Estuary. Thus, a ratio of 46.8 is adopted from the Ems

estuary (de Jonge & Colijn, 1994). This estuarine ratio is more suitable for the Peel-

Harvey Estuary than other ratios from the literature, for example the ratio of 50

from a tropical lagoon (Garrigue, 1998). Still, it is noted that the biomass of

phytoplankton might be underestimated due to the adoptation of this value from a

different estuary.

The organic carbon was converted into wet weight by applying a ratio of 1:9 (Pauly &

Christensen, 1995). A conversion ratio based on data from the Peel-Harvey Estuary is

not available.

The biomass was calculated for an estimated volume of the estuary of 196 500 litre,

by estimating an area of 131 km² and a depth of 1.5 m (Hale & Butcher, 2007).

However, the Nodularia blooms mainly occurred in the Harvey Estuary and this

species caused the high biomass values of this functional group in the ‘pre DC’ model

(Hale & Butcher, 2007). For this reason, it is more realistic to reduce the habitat of

this functional group to 50% of the total estuarine body in the ‘pre DC’ model. This

value is reasonable, as it includes the smaller habitat of the Harvey Estuary (approx.

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40% of total area) and also recognises the small biomass fraction of phytoplankton

occurring in the Peel Inlet.

The production of the phytoplankton could only be estimated from the literature by

employing data that are similar to the environmental conditions of the Peel-Harvey

Estuary. No studies on phytoplankton growth in the Peel-Harvey Estuary have been

reported, other than those focused on Nodularia spumigena which was studied in

detail in the 1980s (Huber, 1980, 1985, 1986; Lukatelich, 1986; Lukatelich &

McComb, 1986b).

For the ‘pre DC’ Ecopath model, phytoplankton growth was mainly determined by

the growth of Nodularia spumigena and some diatom blooms occurred as well.

(Lukatelich & McComb, 1986b). For this reason, the production of this functional

group was determined by the growth rate of Nodularia spumigena (Table 3.31).

The growth of phytoplankton in the ‘post DC’ Ecopath model was based on a range

of species. The average of their growth rates is used for the P/B value of this

functional group (Table 3.31).

Table 3.31: Phytoplankton growth rates adapted from the literature for the Ecopath models of the Peel-Harvey Estuary

Phytoplankton species divisions/ day Reference

Anabaena sp. 0.96 (Moisander et al., 2002)

Anabaenopsis sp. 0.96 (Moisander et al., 2002)

Chaetoceros sp. 0.25 (Riegmann et al., 1996)

Diatom (average growth rate) 0.222 (Riegmann et al., 1996)

Gymnodinium sp. 0.33 (Riegmann et al., 1996)

Gyrodinium sp. 0.161 (Nielsen & Tonseth, 1991)

Heterocapsa sp. 0.324 (Riegmann et al., 1996)

Heterosigma akashiwo 0.78 (Zhang, 2006)

Heterosigma sp. 0.36 (Riegmann et al., 1996)

Nitzschia sp. 1.82 (Nicklisch et al., 2008)

Nodularia sp. 0.68 (Moisander et al., 2002)

Oscillatoria sp. 0.033 (Feuillade, 1987)

Skeletonema sp. 2.41 (Langdon, 1988)

Thalassiosira sp. 0.0134 (Langdon, 1988)

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The phytoplankton studies that were undertaken in the late 1980s cover the whole

area of the Peel-Harvey Estuary and encompassed the entire water column, as well

as the top layers of sediment (Huber, 1980, 1985, 1986; Lukatelich, 1986; Lukatelich

& McComb, 1986a, b). For this reason, the data quality is very high. In the ‘post DC’

period, the sampling regime is only based on three sites in the Peel Inlet and three

sites in the Harvey Estuary. However, no sediment samples were taken to measure

benthic chlorophyll, which has had major impact on the biomass values of the ‘pre

DC models’. For this reason, the quality of the biomass parameter used for the ‘post

DC’ model is lower than the biomass parameter in the ‘pre DC’ Ecopath model.

Consequently, the parameter with low quality will be adjusted first during the

balancing process (Table 3.32).

Table 3.32: Parameter input of the functional group ‘microscopic algae’

pre DC post DC

Biomass (t km-2) 10360.09 0.0343

P (year-1) 231.483 254.977

The production estimate applied here is similar to parameters used in other models,

e.g. the Danshuei River Estuary (Lin et al., 2007). However, the biomass estimates

for the ‘pre DC’ model are extraordinary high compared to other Ecopath models

that present phytoplankton biomass values of 8.26 tkm-2 (Laguna Alvarado, (Cruz-

Escalona et al., 2007), 7.782 tkm-2 (Weeks Bay,(Althauser, 2003), 22.069 tkm-2 (Pearl

River Estuary,(Duan et al., 2009) or 54.78 tkm-2 (microphytobenthos plus

phytoplankton) for the West Florida Shelf Ecopath model (Okey & Mahmoudi, 2002).

Blooms of Nodularia spumigena were mainly responsible for the extremely high

biomass value in the ‘pre DC’ period (Lukatelich & McComb, 1986b) and the blooms

turned the water into a green soup with zero visibility (Bradby, 1997; Hale &

Butcher, 2007). After the Dawesville Channel opening and changes in water

conditions, the Nodularia blooms disappeared in the Peel-Harvey Estuary, as the

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species cannot tolerate high salinities (Huber, 1985); their disappearance explains

the decline in phytoplankton biomass for the ‘post DC’ period.

3.3.13 Plants (Functional groups 26 to 30)

The parameter inputs for the functional groups 26 to 30 are based on a single report

(Wilson et al., 1999), which described the biomasses of these groups before and

after the opening of the Dawesville Channel. Only the biomass value for the group

Cladophora in the ‘pre DC’ Ecopath model had to be adopted from a different study

which provided biomass values (Lukatelich, 1989).

The grouping of the species used in the literature (Wilson et al., 1999) was applied

here with minor simplifications. The most important species of green macroalgae in

the Peel-Harvey Estuary were Cladophora montagneana, Chaetomorpha linum,

Enteromorpha spp. and Ulva spp. The first two species were separated into single

species groups and the last two genera were combined to the functional group

‘macrophytes’, which also includes other Chlorophyta species such as Caulerpa spp.

(Wilson et al., 1999).

Data for the P/B values are not available from the Peel-Harvey Estuary for some

groups and had to be adapted from the literature, with references listed in the

following text. Production parameters from the Peel-Harvey Estuary are rated with a

higher pedigree, as well as parameters from similar estuaries, such as the Swan

Estuary.

Algae

The functional group ‘algae’ consisted of red (Rhodophyta) and brown (Phaeophyta)

algae, which are abundant in the Peel-Harvey Estuary with Rhodophyta being the

dominant group (Wilson et al., 1999). The habitat fraction covered by this functional

group was estimated from SYMAPS (Wilson et al., 1999); it has increased since the

Dawesville Channel opening (Table 3.33). The production of algae (Gracillaria cornea,

Rhodophyta) was studied in detail under laboratory cultures (Navarro-Angulo &

Robledo, 1999). The mean growth rate of 0.14 d-1 was applied to estimate the annual

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production of this algal species and this production estimate was divided by the

functional biomass in the experiments resulting in a P/B ratio of 34.07 (Table 3.33).

In La Paz Bay, Mexico (Arreguin-Sanchez et al., 2007), the P/B of macro algae was

slightly smaller with 20.142 year-1 and also Weeks Bay presents a smaller P/B ratio of

9.373 year-1 (Althauser, 2003). The P/B value applied here represents the production

of algal species that occur in the Peel-Harvey Estuary. The production values

presented in other Ecopath models do not list the species of macroalgae and thus,

these values might not be directly comparable to this approach.

Table 3.33: Parameter input for aquatic plants (functional groups 26 to 30)

Functional Group

B (tkm-2) pre post

Habitat in % pre post

P/B (year-1) pre post

Algae 33.42 7.471 62.4 84.3 34.07 34.07

Macrophytes 146.202 21.627 77.9 23.3 64.42 67.56

Chaetomorpha linum

243.14 34.294 85 83.3 73 73

Cladophora montagneana

1.246 0.573 50 15 2.15 2.15

Seagrass 49.966 15.606 71.3 71.3 4.17 4.17

Macrophytes

The biomass values for the functional group ‘macrophytes’ (Table 3.33) have

declined drastically since the opening of the Dawesville Channel, as well as the

habitat that is covered by this group (Wilson et al., 1999). This functional group is

dominated by Ulva sp., which constitutes approximately 70% of total macrophyte

biomass (Wilson et al., 1999). The primary production for Enteromorpha was studied

in Roscoff Aber Bay, France and estimated the net primary production with 16 gC m-2

year-1 for an area of 0.67 km2 (Hubas & Davoult, 2006). This primary production

estimate was extrapolated for the unit area covered by this functional group in the

Peel-Harvey Estuary; to obtain P/B, the production estimate was divided by the

biomass estimates of Enteromorpha sp. in the Peel-Harvey Estuary for the ‘pre DC’

and ‘post DC’ periods. The growth rate of Ulva lactuca was measured in field

experiments and, with an initial biomass of 1 g fresh weight, Ulva lactuca showed a

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growth rate of 0.656d-1 (Pedersen & Borum, 1996). For the Peel-Harvey Estuary, the

growth rate of Ulva rigida was studied and the results presented a slightly smaller

mean growth rate of 0.25 d-1 (Lavery & McComb, 1991b). This daily production

estimated was extrapolated to obtain an annual Production/Biomass estimate of

91.25 for Ulva spp. This P/B ratio was multiplied by 0.7, as Ulva spp. contributes only

70% to the total macrophyte biomass. The total P/B estimate for this functional

group for the ‘pre DC’ and ‘post DC’ Ecopath model is listed in Table 3.33.

Chaetomorpha linum

Also the biomass values for the functional group ‘Chaetomorpha linum’ have

declined after the Dawesville Channel opening (Table 3.33). The habitat fraction

which is covered by the functional group was estimated from SYMAPS (Wilson et al.,

1999) and only changed slightly. Apparently, the density of this species has

decreased and there is a habitat partitioning among aquatic plants.

The production of Chaetomorpha linum was studied in the Peel-Harvey Estuary and

presented a growth rate of 0.2d-1 (Lavery & McComb, 1991b), which leads to a P/B

ratio of 73 year-1 (Table 3.33).


The biomass values for the functional group ‘Cladophora montagneana’ are listed in

Table 3.33 and show a decrease of biomass of 50% from XX to XX. The habitat

fraction covered by Cladophora montagneana has also changed drastically (Wilson et

al., 1999). This species has been studied in detail in the Peel-Harvey Estuary (Gordon

et al., 1981; Gordon & McComb, 1989). The study presented an annual net

production ratio of 560 g m-2 year-1 for a Cladophora mat in the estuary, produced by

a functional mat biomass of 260 g m-2 (Gordon & McComb, 1989). This results in a

P/B ratio of 2.15 year-1 for Cladophora montagneana in the Peel-Harvey Estuary

(Table 3.33).

Seagrass

Halophila spp. and Ruppia sp. are the most dominating seagrass species, which are

included in the Ecopath models in the functional group ‘seagrass’ (Wilson et al.,

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1999). The biomass values for the functional group ‘seagrass’ have decreasedsince

the opening of the Dawesville Channel (Wilson et al., 1999). The production of

Halophila ovalis was studied in the nearby Swan Canning Estuary (Hillman et al.,

1995). The production was estimated with 500 g C m-2 year-1, that were produced by

a functional biomass of 120 g m-2 in the Swan Canning Estuary (Hillman et al., 1995).

Thus, a P/B ratio of 4.17 is applied for the functional group ‘seagrass’ for this

Ecopath approach (Table 3.33).

For the Jurien Bay model, the P/B ratio of seagrass was estimated with 7.3 year-1

(Lozano-Montez et al., 2011) and the Ecopath model of Laguna Alvarado (Cruz-

Escalona et al., 2007) presented a P/B of 14.92 year-1 for the Wigeongrass (Ruppia

maritima). These estimates are slightly greater than the P7B ratio applied here.

However, the production estimate presented by Hillman et al. (1995) in the nearby

Swan Canning Estuary is more likely to represent the production of seagrass in the

Peel-Harvey Estuary, as environmental conditions are similar. For this reason, a P/B

ratio of 4.17 year-1 is adopted for this modelling approach.

3.3.14 Detritus (Functional group 31)

No detailed study on detritus has been reported for the Peel-Harvey Estuary. Only

one thesis listed the contend of particulate organic matter (POM) in the sediments of

the Peel-Harvey Estuary when studying the diet of a detritivore mullet (Geijsel,

1983).

Based on this study, the size of the detritus pool for the ‘pre DC’ Ecopath model was

estimated with 20.544 tkm-2 and this value was also adopted for the ‘post DC’

Ecopath model.

It is possible to estimate detritus according to (Pauly et al., 1993):

EPPD 101010 log863.0log954.041.2log

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where D is the detritus standing stock (in g C m-2), PP is the primary production (in g

C m-2 year-1) and E is the eutrophic depth in m. However, this equation is not applied

here, as the maximum depth in the Peel-Harvey Estuary is only 2.5m and thus, the

detritus pool would be 7.85g C m-2, which is far smaller than the POM recorded for

this estuary in the literature (Geijsel, 1983).

3.4 Data gaps and data pedigree

In some cases, data were not available for basic parameters (e.g. consumption (Q) or

production (P)), or for different species or functional groups in the Peel-Harvey

Estuary. Where no data were available, the parameters were adopted from the

literature or from other Ecopath models. Table 3.34 gives an overview of the

references that were applied for the different functional groups in this Ecopath

approach.

Table 3.34: Model input and sources for functional groups of the ‘pre DC’ and ‘post DC’ models in the Peel-Harvey Estuary Functional group

References

Dolphins (group 1)

B: (Okey & Mahmoudi, 2002), P/B: (Stolen & Barlow, 2006), Q/B (Tristes & Pauly, 1998), diets: (Okey & Mahmoudi, 2002)

Waterbirds, piscivorous waterbirds (groups 2 & 3)

B: (Bowman & Bamford, 2008; Lane et al., 2002; Ninox-Wildlife-Consulting, 1990), P/B: (Lozano-Montez et al., 2011; Mackinson & Daskalov, 2007), Q/B: (Nilson & Nilson, 1976), diets: (Barquete et al., 2008; Dann, 1999; Lo, 1991; Martin et al., 2007; Miller, 1979)

Sharks (group 4)

B: (Potter et al., 1983b; Young & Potter, 2003b), P/B: (Compagno, 1984), Q/B: (Mackinson & Daskalov, 2007), diets: (Simpfendorfer, 2001)

Fish (groups 5 to 17)

B: (Potter et al., 1983b; Valesini et al., 2009; Young & Potter, 2003b), P/B:(Malseed & Sumner, 2001; Potter et al., 1983b), diets: (Goh, 1992; Hourston et al., 2004; Hyndes et al., 1997; Kendrick & Hyndes, 2005; Lasiak & McLachlan, 1987; Linke et al., 2001; MacArthur & Hyndes, 2007; Pen, 1990; Platell & Hall, 2006; Platell, 2001; Potter, 1988; Prince et al., 1982; Robertson & Klumpp, 1983; Sarre, 1999; Shaw, 1986; Stewart, 1998; Tibbetts & Carseldine, 2005; Wallace, 1979; Waltham & Connolly, 2006)

Bivalves (group 18)

B & diets: (Rose, 1994; Wells & Threlfall, 1982; Wildsmith, 2007), P/B: (Wells & Threlfall, 1982), Q/B: (Cruz-Escalona et al., 2007)

Gastropods (group 19)

B: (Rose, 1994; Wells & Threlfall, 1982; Wildsmith, 2007), P/B: (Wells & Threlfall, 1982), Q/B: (Cruz-Escalona et al., 2007), diets: (Okey & Mahmoudi, 2002)

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Western King Prawns (group 20)

B:(Potter et al., 1991), P/B: (Department of Fisheries, Perth, WA,(Malseed & Sumner, 2001; Tanner, 2003), Q/B: (Althauser, 2003), diets: (Chong & Sasekumar, 1981)

Blue Swimmer Crab (group 21)

B & diets: (de Lestang et al., 2003; de Lestang et al., 2000; Kangas, 2000), P/B: (Department of Fisheries, Perth, WA,(Malseed & Sumner, 2001), Q/B: (Lin et al., 2007)

Crustaceans (group 22)

B: (Rose, 1994; Wildsmith, 2007), P/B: (Althauser, 2003), Q/B: (Okey & Mahmoudi, 2002), diets: (Wildsmith, 2007)

Worms (group 23)

B: (Rose, 1994; Wildsmith, 2007), P/B & Q/B: (Cruz-Escalona et al., 2007), diets: (Wildsmith, 2007)

Zooplankton (group 24)

B: (Rose, 1994; Young & Potter, 2003a), P/B & Q/B: (Cruz-Escalona et al., 2007), diets: (Okey & Mahmoudi, 2002)

Microscopic algae (group 25)

B: (de Jonge & Colijn, 1994; Huber, 1985, 1986; Lukatelich, 1986; Lukatelich & McComb, 1986b), P/B: (Langdon, 1988; Moisander et al., 2002; Nicklisch et al., 2008; Nielsen & Tonseth, 1991; Riegmann et al., 1996; Zhang et al., 2006)

Aquatic plants (groups 26 to 30)

B: (Wilson et al., 1999), P/B: (Navarro-Angulo & Robledo, 1999) (Hubas & Davoult, 2006,(Gordon et al., 1981; Gordon & McComb, 1989; Hillman et al., 1995; Lavery & McComb, 1991b)

Detritus (group 31)

(Geijsel, 1983)

If input parameters are adopted from other models, the quality of the data input is

very low. To assess the quality of data, the pedigree index P is applied in Ecopath

(Christensen et al., 2005). This index expresses the quality of the parameter input for

each parameter B, P/B, Q/B and also diet and catch. The value of P ranges from 0 for

non-local data to 1 for local data with high sampling precision. P is calculated

according to (Christensen et al., 2005):

n

i

ij

n

IP

1

where Iij is the pedigree index value for each of the n living functional groups i and

parameter j represents B, P/B, Q/B, Y or diet. Ecopath defines different criteria for

the pedigree assigned for each parameter (Table 3.35).

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Table 3.35: Ecopath criteria for the pedigree assignment of each parameter and the diets of each functional group

Pedigree value Quality criteria

1

local study with high precision, same group/ species, quantitative detailed diet composition study

0.8

similar system with high precision, same group/ species

0.7

locally based, low precision study, quantitative, but limited diet composition study

0.6

similar species/ group, study based on a similar system

0.5

empirical relationship (P/B, Q/B), qualitative diet composition study

0.4

approximate or indirect method

0.2

parameter from other model

0 estimated by Ecopath

The pedigree indices for the Ecopath models of the Peel-Harvey Estuary are 0.79 for

the ‘pre DC’ model and 0.76 for the ‘post DC’ model (Table 3.36).

These pedigree indices (PI) indicate that the quality of data applied here is quite

presentable compared to other Ecopath models, such as Laguna Alvarado with PI =

0.5, PI = 0.657 (Gartner, 2010) or Jurien Bay PI = 0.72 (Lozano-Montez et al., 2011).

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Table 3.36: Pedigree indices for each parameter for the ‘pre DC’ and ‘post DC’ Ecopath models

pre DC post DC Group: B P/B Q/B Diet B P/B Q/B Diet

1 0.4 0.6 0.6 0.7 0.4 0.6 0.6 0.7

2 0.7 0.2 0.6 0.7 0.7 0.2 0.6 0.7

3 0.7 0.2 0.6 0.7 0.7 0.2 0.6 0.7

4 1 0.8 0.2 0.7 1 0.8 0.2 0.7

5 1 1 1 0.7 0.7 1 1 0.7

6 1 1 1 0.7 0.7 1 1 0.7

7 1 1 1 0.7 0.7 1 1 0.7

8 0 0.8 0.8 0.7 0.7 1 1 0.7

9 1 1 1 0.7 0.7 1 1 0.7

10 1 1 1 0.7 0.7 1 1 0.7

11 1 1 1 1 0.7 1 1 1

12 1 1 1 0.7 0.7 1 1 0.7

13 1 1 1 1 0.7 1 1 1

14 1 1 1 1 0.7 1 1 1

15 1 1 1 1 0.7 1 1 1

16 1 1 1 1 0.7 1 1 1

17 1 1 1 1 0.7 1 1 1

18 1 1 0.2 0.5 1 1 0.2 0.5

19 1 1 0.2 0.7 1 1 0.2 0.7

20 0.7 1 0.2 0.7 0.7 1 0.2 0.7

21 1 1 0.2 1 1 1 0.2 1

22 1 0.2 0.2 0.2 1 0.2 0.2 0.2

23 1 0.2 0.2 0.2 1 0.2 0.2 0.2

24 0.7 0.2 0.2 0.2 0.7 0.2 0.2 0.2

25 1 0.8 -- -- 0.7 0.8 -- --

26 1 0.8 -- -- 1 0.8 -- --

27 1 1 -- -- 1 1 -- --

28 1 1 -- -- 1 1 -- --

29 1 1 -- -- 1 1 -- --

30 1 0.8 -- -- 1 0.8 -- --

31 -- -- -- -- -- -- -- --

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3.5 Discussion

Many data gaps became apparent during this modelling exercise, with biological

data and data on detritus being the most important gaps. Many studies in the Peel-

Harvey Estuary over the last decades have investigated: fish communities (Loneragan

et al., 1986, 1987; Potter et al., 1983a; Potter et al., 1983b; Young & Potter, 2003b);

invertebrates; macrophytes (Lukatelich, 1989; Lukatelich & McComb, 1986a; Wilson

et al., 1999); and birds (Lane et al., 2002; Ninox-Wildlife-Consulting, 1990). However,

studies of the biology of single species are rare; extensive species-specific

information is only available for a few species within the Peel-Harvey Estuary, e.g.

Nodularia (Huber, 1980, 1985, 1986), Cladophora (Gordon et al., 1981; Gordon &

McComb, 1989), and the bivalve Arthritica semen (Wells & Threlfall, 1982). Not

surprisingly, the most well-studied species tend to be pest species (e.g. Nodularia) or

species of commercial value (e.g. Blue Swimmer Crabs).

Data gaps were filled by adapting data from other ecosystems, mostly estuaries in

Western Australia, but also from other Ecopath models. The trophic interactions in

the Peel-Harvey Estuary are mostly unknown; while it was assumed that these

interactions were similar to those of food webs in other estuaries, this hypothesis

still needs to be confirmed.

The opening of an artificial entrance channel had a major impact on the

environmental conditions in the estuary, e.g. salinity, water level and water resident

times(Hale & Butcher, 2007). Thus, the opening may have altered not only species

compositions and abundances, but also trophic interactions.

It is well-known that massive algal blooms (Huber, 1980, 1985, 1986; Lukatelich,

1986; Lukatelich & McComb, 1986b) have occured – and continue to occur – in the

estuary and these lead to fish kills and impacts on benthic invertebrates (Potter et

al., 1983a). An overload in nutrients from agricultural and urban run-off caused

eutrophication and resulted in massive macroalgal growth, such that harvesting of

macroalgaes on beaches became necessary (Jakowyna, 2000; Lavery et al., 1999;

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Lavery et al., 1991; Lavery & McComb, 1991a, b; Lukatelich, 1989). The detritus pool

is likely to be very large, but this has not yet been studied.

The lack of these data affects the pedigree and the quality of the ‘pre DC’ and ‘post

DC’ Ecopath models. However, the high quality of the biomass studies suggests that

the Ecopath models are able to describe the impact of an artificial entrance channel

on the biomass fluxes of the Peel-Harvey Estuary. Nonetheless, closing these data

gaps would improve our understanding of trophic interactions and the impact of

environmental factors on this estuarine ecosystem and improve the quality of future

Ecopath models of the Peel-Harvey Estuary.

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Chapter 4

The impact of an artificial entrance channel on the ecosystem of the Peel-Harvey Estuary, Western Australia

4.1 Introduction Investigating ecosystem change using Ecopath models

When investigating ecosystems that have experienced massive change, multiple

models may be needed to represent the ecosystem before, during, and after the

change (Christensen et al., 2005). Chapter 3 reviewed the drastic impact that the

Dawesville Channel has had on the ecosystem of the Peel-Harvey Estuary. Physio-

chemical conditions have changed, as well as the abundance, distribution, and

diversity of estuarine biota (Wilson et al., 1999; Young & Potter, 2003; Hale &

Butcher, 2007).

Given such systemic change, a single model is unlikely to fully describe and assess

the changes in the structure and functioning of the estuarine ecosystem caused by

the opening of the Dawesville Channel. However, the availability of biological and

environment data for the Peel-Harvey Estuary makes it possible to develop and

compare two Ecopath models that describe the ecosystem before (‘pre DC’) and

after (‘post DC’) the Dawesville Channel opening. If the scope and design of the ‘post

DC’ Ecopath model are identical to those of the ‘pre DC’ model, comparisons can be

made of the state of estuary ecosystem before and after the construction of the

Dawesville Channel. These comparisons can be made using information for basic

ecosystem parameters, network analysis and system statistics; and measures of

ecosystem services.

Chapter aims

This chapter investigates the impact of the Dawesville Channel by comparing three

aspects of the ‘pre DC’ and ‘post DC’ Ecopath models: (1) basic model parameters;

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(2) network and system statistics; and (3) ecosystem services. The fourth aim is to

assess the maturity of the models.

I. Ecosystem parameters

Ecopath allows information for various ecosystem parameters to be used to identify

one possible equilibrium state for the model (Christensen et al., 2005). This

equilibrium state provides a snap-shot of the model and its underlying ecosystem,

with the structure of the food web represented with different trophic levels and the

efficiency of the different functional groups indicated through information on diet

(omnivory index), metabolism (food conversion efficiency, respiration) and mortality.

This chapter compares six basic model parameters for the ‘pre DC’ and ‘post DC’

Ecopath models:

(a) trophic levels;

(b) biomass & community structure;

(c) omnivory Index;

(d) food conversion efficiency;

(e) respiration;

(f) mortality (P/B ratio); and

(g) dietary composition.

II. Network analysis & system statistics

Ecopath also supports network analysis and system statistics functions, which are

useful in understanding the functioning of the modelled system, the interactions

between its functional groups (mixed trophic impact routine, niche overlap), and the

structure of flows. In addition to flow structure, the system indices also indicate the

cycling indices and path lengths of the model.

This chapter compares four categories of network & systems statistics for the ‘pre

DC’ and ‘post DC’ Ecopath models:

(a) mixed trophic impact (MTI) routine;

(b) niche overlap;

- 164 -

(c) throughput & flow; and

(d) system indices.

III. Ecosystem services

From 2001 to 2005 the United Nations conducted the Millenium Ecosystem

Assessment (MEA) conducted to assess the condition and health of the world’s

ecosystems (see http://www.millenniumassessment.org). The goal of the MEA was

to investigate the consequences of ecosystem change for the well-being of the

human populations. Analyses of ecosystem services were a key scientific tool for the

MEA. Ecosystem services are defined as “the benefits people obtain from

ecosystems” (Millenium Ecosystem Assessment, 2005, p. V). These benefits include

provisioning, regulating and cultural services that directly affect people and

supporting services needed to maintain the other services

The term ‘supporting services’ is used to describe the core ecosystem attributes that

must function in order for the ecosystem to provide all other ecosystem services

such as provisioning services, regulating services, and cultural services (Millenium

Ecosystem Assessment, 2005). Supporting services, which include primary

production, nutrient cycling, and biodiversity, are typically difficult to measure. This

study makes a novel contribution to the Ecosystem Services literature by using

Ecopath system statistics to estimate and quantify these services. To my knowledge,

this approach has not been done before. The analysis presented in this chapter will

demonstrate that Ecopath is a valuable tool for quantifying ecosystem service

provision.

This chapter compares the provision of four ecosystem services for the ‘pre DC’ and

‘post DC’ Ecopath models:

(a) provisioning services;

(b) regulating services;

(c) cultural services; and

(d) supporting services.

http://www.millenniumassessment.org/

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This chapter estimates the supporting services of nutrient cycling and primary

production for the time period before and after the Dawesville Channel opening. As

the biodiversity of an ecosystem is an important supporting service, the species

diversity that supports the ecosystem services of the Peel-Harvey Estuary is also

investigated by comparing the species recorded for the ‘pre DC’ and ‘post DC’

models.

In the Peel-Harvey Estuary, fisheries catches are the key provisioning service

delivered by the estuary ecosystem. Although commercial catches have decreased

since the channel opening, there is uncertainty regarding about the extent of

recreational catches. By comparing the ‘pre DC’ and ‘post DC’ models, a change in

catches and, thus, a change in this provisioning service can be investigated.

Regulating services include climate and water regulation (Millenium Ecosystem

Assessment, 2005). As the ecosystem service ‘water regulation’ or ‘water

purification’ is difficult to quantify for the Peel-Harvey Estuary, only the climate

regulating service is analysed here by assessing carbon fixation of primary producers.

Cultural services provided by the Peel-Harvey Estuary include the estuary-based

tourism industry. This service is assessed here by analysing the value of the industry

in the area, such as the profit and number of jobs.

IV. Model maturity

Network and cycling analyses can be used to evaluate the maturity of the model.

Ecosystem maturity is synonymous with ecosystem development and, by analysing

the maturity of a system, it is possible to assess whether a system has already

reached a stable state or how the system can be expected to develop further

(Christensen, 1995). Model or ecosystem maturity is also assumed to be correlated

with ecosystem efficiency (Baird et al., 1991) and, for this reason, several indices are

applied to assess the maturity of the ecosystem and the efficiency of the ecosystem

services that the system provides.

- 166 -

There is a dispute over the most appropriate method for assessing the maturity of a

system. On one hand, Christensen (1995) recommended assessing the maturity of an

ecosystem by evaluating several indices said to be linked to system maturity, such as

the ratios of primary production/ respiration, primary production/ biomass, and

biomass/ system throughput, as well as -net community yield, which is the

difference between primary production and respiration. In mature systems, the

primary production will be similar to the level of respiration (Christensen, 1995). In

regard to primary production/ biomass ratio, the biomasses of an ecosystem are

expected to accumulate as the ecosystem matures (Christensen, 1995). The net

community yield (also called net system production) will decrease as systems mature

(Christensen, 1995).

On the other hand, Baird et al. (1991) suggested applying indices that are not linked

with the size of an ecosystem because indices based on ecosystem size will limit or

preclude comparisons between ecosystems of different scales. The ratios proposed

by Christensen (1995) for the assessment of ecosystem maturity are linked to

biomass and system throughput, and thus the size of the ecosystem influences the

maturity indices. To avoid this problem, Baird et al. (1991) recommended using only

dimensionless indices for inter-system comparisons, such as FCI and Ai/Ci.

Ecosystem maturity is also affected by cycling processes, as cycling affects the

system overheads (Christensen & Pauly, 1993). The flow indices (e.g. ascendency,

overhead and capacity) are linked with the total system throughput (Christensen et

al., 2005).

Ascendency describes the “combined attributes of system size and trophic

organization” (Baird et al., 1991, p. 16) and is calculated as the product of total

system throughput and average mutual information, which is the probability

describing where a unit of carbon or energy will next flow to (Christensen et al.,

2005). Ascendency is limited by an upper bound that is described as development

capacity and the difference between those two indices is called overhead, which

reflects the “system’s strength in reserve from which it can draw to meet

- 167 -

unexpected perturbations” (Ulanowicz & Norden, 1990. p. 435). Ascendency,

overhead and capacity can be each segregated into input flows, internal transfers,

and losses via respiration and export flow (Ulanowicz & Norden, 1990). To

investigate the internal development of a system, the internal capacity needs to be

analysed and, in particular, how this capacity is divided between internal ascendency

and internal overhead (Ulanowicz & Norden, 1990).

This chapter examines the maturity of the ‘pre DC’ and ‘post DC’ models in a

comparative context.


4.2.1 Analysis

For the model analyses, parameters and indices were calculated and analysed

according to the guidelines and assumptions described in Christensen et al., (2005).

Table 4.1 provides a list of the terms and concepts used in the analyses in this

chapter.

Table 4.1: Terms and concepts used (following Christensen et al. 2005).

I. Basic Ecopath model parameters

Gross food conversion ratio: This efficiency is determined by

production/consumption ratio for each functional group.

Net food conversion efficiency: This efficiency is defined as the

production/assimilation ratio.

Omnivory index (OI): The Omnivory Index is calculated as the variance of trophic

levels of the prey groups that are consumed by a predator. A large index indicates

that the predator feeds on many trophic levels, whereas an index of zero indicates a

specialist.

P/B ratio: The P/B ratio equals total mortality Z and consists of the following

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components:

P/B = predation mortality + catches + net migration + biomass accumulation + other mortality

Respiration: All assimilated food ends up as production or respiration. The

respiration/assimilation ratio must not exceed 1, as respiration cannot be higher

than assimilation. The respiration/biomass ratio expresses the activity of the

functional group.

Respiration/biomass ratio: This ratio reflects the activity of the functional group.

Dietary composition and consumption: To analyse change in dietary composition

and consumption, prey groups are categorised into three prey components: fish,

invertebrates and primary producers

II. Network & System Statistics

Ascendancy: Ascendancy represents a measure of the average mutual information in

a system, scaled by system throughput. The measure is derived from information

theory. This mutual information reduces the uncertainty about where a unity of

energy will next flow to from its known location. The ascendency, overheads and

capacity can all be divided up into imports, internal flow, exports and respiration.

Connectance index: This index determines the number of food links in the system

relative to the number of possible links.

Development capacity: Development capacity is the upper limit of ascendancy.

Finn’s Cycling index: This index represents the fraction of the ecosystem throughput

that is recycled (Finn, 1980).

Flow Diagram: The Lindeman spines show the flow composition between the

different trophic levels. Each trophic level is represented as a box that is connected

via consumption; flows to detritus, export and respiration are also shown.

Mixed Trophic Impact routine (MTI): This routine allows the direct and indirect

interactions between the functional groups in a model to be assessed. MTI describes

the effect of a small increase in the biomass of one functional group (<10%) on all

other model components, thus allowing for all the biomasses and trophic

interactions within the system to be considered.

Niche overlap: The niche overlap plot presents the prey overlap index and the

- 169 -

predator overlap index. The closer these indices are to 1, the greater the overlap. An

index close to zero demonstrates that there is no overlap between two functional

groups. An index close to zero demonstrates that there is no overlap between two

functional groups.

Path length: Path length was defined this way:

Path length = Total system throughput / ( ∑ Export = .∑ Respiration)

The diversity of flows and recycling is expected to increase with maturity and thus

also path length (Christensen et al., 2005).

Predatory cycling index: Cycles that include detritus groups were eliminated from

this index.

System overhead: System overhead is the difference between the ascendency and

capacity of a system. The overhead on imports and internal flows may be seen as

measure of system stability.

System throughput: System throughput describes the sum of all flows in a system (in

tkm-2 year-1).

System throughput = total consumption + total export + total respiration + total flows of detritus

III. Ecosystem Services (Millenium Ecosystem Assessment, 2005)

Provisioning services: The food supplied by an ecosystem; fishery catches are used

in this study.

Regulating services: These include climate regulating services, such as CO2-fixation,

which is used in this study.

Cultural services: The tourism industry in the Peel-Harvey estuarine area is used in

this study.

Supporting services: There are three main supporting services: primary production,

nutrient cycling and biodiversity.

IV. Ecosystem & Model Maturity

Maturity: development state of an ecosystem

A/C (ascendency/ capacity) ratio: fraction of possible organization that is actually

realized (Baird et al., 1991)

- 170 -

Ai/Ci (ascendencyinternal / capacityinternal) ratio: measure of system maturity or

efficiency that describes the internal organization of the system (Baird et al., 1991)

Ecosystem Services

For analysing ecosystem services, the carbon fixation of primary producers was

determined according to Muraoka (2004):

C-absorption (t C year-1

) = total area (km2) * standing stock (g cm

-2) * P/B * carbon content (in %).

The estimate for the carbon content of microscopic algae was 27% (Sicko-Goad et

al., 1984). Estimates for the carbon content of algae (Gevaert et al., 2001) and

seagrass (Hillman et al., 1995) were quite similar (27.7 and 27.9%, respectively).

Estimates for the functional groups macrophytes (23.1%), Chaetomorpha linum

(18.4%) and Cladophora sp. (17.7%) also presented smaller carbon contents

(LaPointe et al., 1992). Based on these values, the carbon fixation of the primary

producers was estimated based on the P/B ratio and standing stocks provided by the

balanced ecosystem models. All other ecosystem services investigated in this study

are based on parameters of the balanced Ecopath models and on the literature.

4.2.2 Balancing of the models

Both Ecopath models were balanced manually according to the pedigree of the

parameter input (shown in Table 3.36 for each functional group).

Pre-DC model

In the ‘pre DC’ model, ten out of the 30 living functional groups were out of balance

in the first round of the balancing process, including Cladophora montagneana,

microscopic algae and zooplankton, Western King Prawns, gastropods, and six fish

groups (Torquigener pleurogramma, Arripis georgianus, estuarine herbivorous fish,

and marine detritivorous and omnivorous fish).

- 171 -

Model parameters were adjusted, changing the parameter of one unbalanced group

by 5% in each balancing process, to reach an equilibrium point, which describes a

state where all ecotrophic efficiencies (i.e. the proportion of the production of a

functional group that is utilised in the system: Christensen et al., 2005) are below 1.

However, some P/Q ratios were unreasonably high and thus, the ratio indicated that

production far exceeded consumption. As the Q/B values for most groups were

drawn from the literature, these parameters were adjusted step by step until the

P/Q ratio was below 0.5. All parameter ratios complied with the guidelines of

Ecopath and all respiration values were positive (Christensen et al., 2005). The

unbalanced groups were adjusted one by one, starting with groups of the lower

trophic levels. The flows to the detrital pool were adjusted based on the assumption

that migrating species only contribute partially to the detritus pool of the estuary;

thus, these values were lowered from 100% flow into detritus to 80% for migrating

fish species (Western King Prawn and Blue Swimmer Crabs) and 50% for other

migrating groups.

Post-DC model

In the ‘post DC’ model, 15 out of 30 living functional groups were out of balance in

the first round of the balancing process with ecotrophic efficiencies exceeding one,

including Cladophora montagnean, microscopic algae, zooplankton, Western King

Prawns, gastropods, Arripis georginus, Aldrichetta forsteri, Mugil cephalus, whiting,

herbivorous and detritivorous estuarine fish, the marine fish groups with

omnivorous, herbivorous and detritivorous diets and sharks.

The model parameters were adjusted manually to reach an equilibrium point

(Christensen et al., 2005). Parameters with low pedigree index were changed first

(Mackinson & Daskalov, 2007), according to the pedigree indices presented in Table

3.36. As with the ‘pre DC’ model, the flows to the detritus pool were adjusted based

on the assumption that migrating species only contribute partially to the detritus

pool of the estuary; thus, these values were lowered to 80% for migrating fish and

invertebrate species and 50% for other migrating groups.

- 172 -

After the models were balanced, the respiration/assimilation was checked to ensure

that this ratio did not exceed 1 (Christensen et al., 2005). However, the R/A ratios for

four functional groups (dolphins, waterbirds, piscivorous waterbirds and sharks)

were well above 1. For this reason, the P/B and Q/B ratios needed further

adjustment, as well as the GS (Unassimilated/consumption) setting. The GS default

value of 0.2 had to be lowered to 0.01 for both waterbird groups, to 0.03 for

dolphins and 0.1 for sharks to achieve balancing.

4.3 Results

4.3.1 Comparisons of basic model parameters

A. Trophic levels

Based on trophic interactions, four trophic levels were identified for the Peel-Harvey

estuarine ecosystem models, with dolphins and sharks being the top predator

groups (Table 4.2). Some functional groups showed a slightly lower trophic level

after the channel opening (Fig. 4.1; Table 4.2). Of these, zooplankton exhibited the

biggest change in trophic level, decreasing from 2.33 to 2.05.

In the ‘pre DC’ model, the mean trophic level (± 1 SD) for fish was 2.79 ± 0.54 and

2.57 ± 0.47 for invertebrate groups (functional groups 18 to 23). In the ‘post DC’

model, the mean trophic level (± 1 SD) for fish was slightly lower (2.71 ± 0.52) (Table

4.2). The mean trophic level for invertebrates in the ‘post DC’ model (2.46 ± 0.45)

was also slightly lower to the ‘pre DC’ model.

- 173 -

Table 4.2: Basic parameters of the balanced ‘pre DC‘ and ‘post DC’ models of the Peel-Harvey Estuary TL: trophic level, B: biomass in tkm

-², P/B: production per biomass (year

-1), Q/B: consumption

per biomass (year-1

), EE: ecotrophic efficiency, P/Q: production/consumption ratio, which equals gross efficiency (year

-1)

Group name TL B P/B Q/B EE P/Q

1 dolphins pre 4.05 0.040 0.147 4.6 - 0.03

post 3.98 0.059 0.147 4.6 - 0.03

2 waterbirds pre 2.83 0.165 0.234 20.75 - 0.01

post 2.72 0.0189 0.28 20.75 - 0.01

3 piscivorous waterbirds

pre 3.7 0.182 0.280 12.56 - 0.02

post 3.62 0.178 0.28 12.56 - 0.02

4 sharks pre 3.83 0.011 0.240 2.25 0.04 0.11

post 3.77 0.0009 0.24 2.25 0.47 0.11

5 marine omnivorous fish

pre 2.8 0.768 1.556 6.369 0.93 0.24

post 2.88 0.195 1.462 6.213 0.9 0.24

6 marine carnivorous fish

pre 3.38 5.689 1.924 6.785 0.18 0.28

post 3.28 0.273 1.901 7.154 0.85 0.27

7 marine herbivorous fish

pre 2.28 2.048 1.049 8.12 0.42 0.13

post 2.16 0.238 0.851 8.053 0.9 0.11

8 marine detritivorous fish

pre 2.02 0.753 1.176 8.528 0.99 0.14

post 2.02 0.14 1.981 9.594 0.86 0.21

9 estuarine omnivorous fish

pre 3.14 1.135 1.824 6.756 0.49 0.27

post 3.08 0.164 1.824 7.557 0.93 0.24

10 estuarine carnivorous fish

pre 3.24 5.926 2.075 6.675 0.11 0.31

post 3.15 0.369 1.867 6.482 0.46 0.29

11 estuarine herbivorous fish

pre 2.26 0.903 1.029 7.9 0.97 0.13

post 2.18 0.214 1.029 7.715 0.91 0.13

12 estuarine detritivorous fish

pre 2.24 5.997 0.877 7.343 0.21 0.12

post 2.11 0.291 0.842 7.171 0.78 0.12

13 Whiting pre 3.36 0.734 1.458 5.988 0.81 0.24

post 3.22 0.397 1.164 5.396 0.76 0.22

14 Arripis georgianus

pre 3.33 3.010 0.915 6.842 0.99 0.13

post 3.2 0.221 1.513 6.364 0.96 0.24

15 Aldrichetta forsteri

pre 2.72 19.526 0.695 4.975 0.2 0.14

post 2.6 0.637 0.93 4.615 0.91 0.2

16 Mugil cephalus pre 2.15 31.580 0.466 4.767 0.08 0.1

post 2.09 0.647 0.98 4.77 0.95 0.21

17 Torquigener pleurogramma

pre 3.42 1.080 0.809 5.2 0.96 0.16

post 3.26 1.773 0.755 5.079 0.1 0.15

18 bivalves pre 2.44 219.36 3.940 10.63 0.03 0.37

post 2.27 11.249 3.546 13.28 0.27 0.27

19 gastropods pre 2 3.780 4.375 17.57 0.4 0.25

- 174 -

post 2 0.637 4.375 17.56 0.8 0.25

20 Western King Prawn

pre 2.92 1.993 5.740 19.84 0.91 0.29

post 2.82 0.93 4.73 19.8 0.92 0.24

21 Blue Swimmer Crab

pre 3.34 3.477 3.658 14 0.43 0.26

post 3.2 1.46 3.351 14 0.8 0.24

22 crustaceans pre 2.33 364.179 8.088 27.14 0.02 0.3

post 2.21 173.62 8.088 27.14 0.01 0.3

23 worms pre 2.44 78.310 6.22 26.5 0.09 0.23

post 2.28 40.739 6.22 26.5 0.04 0.23

24 zooplankton pre 2.33 3990.3 25.25 87.8 0.91 0.29

post 2.05 57.3 25.56 84.2 0.96 0.3

25 microscopic algae

pre 1 10360 61.60 - 0.28 -

post 1 39.4 184.8 - 0.36 -

26 algae pre 1 21.0 34.07 - 0.05 -

post 1 6.298 34.07 - 0.02 -

27 macrophytes pre 1 114.0 64.420 - 0.004 -

post 1 5.039 67.56 - 0.01 -

28 Chaetomorpha linum

pre 1 207.0 73.0 - 0.001 -

post 1 28.567 73 - 0.001 -

29 Cladophora montagneana

pre 1 5 3.87 - 0.48 -

post 1 0.198 3.87 - 0.74 -

30 seagrass pre 1 36 4.17 - 0.14 -

post 1 11.127 4.17 - 0.07 -

31 detritus pre 1 20.544 - - 0.16 -

post 1 20.544 - - 0.23 -

- 175 -

Figure 4.1: Comparison of the trophic level of each functional group in the ‘pre DC’ and ‘post DC’ models.

1

2

3 4

5

6

7

8

9 10

11 12

13 14

15

16

17

18

19

20

21

22 23

24

25 26 27 28 29 30 31

1

2

3 4

5

6

7 8

9 10

11 12

13 14

15

16

17

18

19

20

21

22 23

24

25 26 27 28 29 30 31 1

2

3

4

Trophic levels

pre DC post DC

- 176 -

B. Biomass and community structure

In the ‘pre DC’ model, biomass values ranged from 0.011 t km-2 for sharks to 10 360 t

km-2 for microscopic algae (Table 4.2). In the ‘post DC’ model, the biomass

parameters ranged from 0.001 t km-2 per year for sharks to 173.616 t km-2 per year

for crustaceans (Table 4.2).

There were marked differences in several aspects of the ‘pre’ and ‘post’ Ecopath

models in terms of biomass and community structure. Firstly, biomasses declined in

each of the four trophic levels, with the largest decline (>99%) occurring at the first

trophic level (Fig. 4.2).

Figure 4.2: Biomass per trophic level before (pre DC) and after (post DC) the opening of the Dawesville Channel in the Peel-Harvey Estuary.

Secondly, the functional groups comprising the planktivores represented 93% of the

total biomass in the ‘pre DC’ model, but only 25% of total biomass in the ‘post DC’

period (Fig.4.3). Thirdly, while the biomass of phytoplankton (microscopic algae)

decreased, the total biomass of other primary producers also declined (Table 4.2).

0

2000

4000

6000

8000

10000

12000

I II III IV

10762

4515

219 7 111 257 34 1

Tota

l bio

mas

s (t

/km

²)

Biomass per trophic level (t/km²)

pre DC post DC

- 177 -

Figure 4.3: Percentages of total biomass for zooplankton, phytoplankton (microscopic algae)and non-planktivore groups before (pre DC) and after (post DC) the opening of the Dawesville Channel in the Peel-Harvey Estuary.

Fourthly, the composition of primary producers changed (Figs. 4.4 and 4.5). Aquatic

plants were more important for the primary production of the estuarine ecosystem,

contributing almost 60% to primary production in the ‘post DC’ model, compared to

less than 5% in the ‘pre DC’ model (Fig. 4.5). Fifthly, the community structure of

aquatic plants changed, with seagrass and algae almost doubling their percentages

of plant biomass, whereas the contributions of the functional groups macrophytes

and Cladophora montagneana declined (Fig. 4.5).

Figure 4.4: Composition of total primary production, presenting biomass estimates in tkm-2 for aquatic plants (functional groups 26 to 30) and phytoplankton before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

67%

26%

7%

pre DC phytoplankton

zooplankton

non-planktivore

10%

15%

75%

post DC

0%

20%

40%

60%

80%

100%

pre DC post DC

382

51

10.360 39

Primary production

aquatic plants phytoplankton

- 178 -

Figure 4.5: Community structure of aquatic plants presented in percentage of total plant biomass for the time period before (pre DC) and after (post DC) the opening of the Dawesville Channel in the Peel-Harvey Estuary

Sixthly, the biomass estimates for invertebrates and fish declined following the

opening of the Dawesville Channel (Table 4.2). The biomass of important target

invertebrate species, like prawns and crabs, declined by more than half (Fig. 4.6).

While the biomass estimates of whiting remained relatively stable, the biomasses of

all other target species declined drastically, in particular A. forsteri and M. cephalus

(Fig. 4.7). The decline in biomass was not restricted to target species, but also

affected non-target fish groups, which decreased by 84% (Fig. 4.8). Estuarine and

marine fish groups have each decreased by more than 90% in biomass (Fig. 4.8).

algae 6%

macrophytes 30%

Chaetomorpha linum 54%


1%

seagrass 9%

pre DC

algae 12%

macrophytes 10%

Chaetomorpha linum 56%


0.4%

seagrass 22%

post DC

- 179 -

Figure 4.6: Biomass estimates in tkm-2 for target (functional groups 20 and 21) and non-target (functional groups 18, 19, 22 and 23) invertebrate groups before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

Figure 4.7: Biomass estimates in tkm-2 for commercially important target species before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

0

500

1000

1500

2000

2500

3000

3500

4000

4500

pre DC post DC

5 2

4.074

226

Biomass of invertebrates (t/km²)

target

non-target

0

5

10

15

20

25

30

35

Whiting Arripisgeorgianus

Aldrichettaforsteri

Mugilcephalus

WesternKing Prawn

BlueSwimmer

Crab

0,7 3,0

19,5

31,6

2,0 3,5

0,4 0,2 0,6 0,6 0,9 1,5

Bio

mas

s (t

/km

²)

Biomass of target species

pre post

- 180 -

Figure 4.8: Biomass estimates in tkm-2 for target species, non-target species, marine and estuarine fish groups before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

Aldrichetta forsteri (functional group 15) and Mugil cephalus (functional group 16)

represented 65% of fish biomass in the ‘pre DC’ model, but declined to 23% of fish

biomass in the ‘post DC’ model. Mugil cephalus was the most dominant species

before the channel opening and accounted for 40% of total fish biomass in the ‘pre

DC’ model (Fig. 4.9). In contrast, Torquigener pleurogramma (functional group 17)

was the most dominant species in the ‘post DC’ model, representing 32% of total fish

biomass.

Several groups that accounted for 1% in fish biomass in the ‘pre DC’ model increased

in biomass, including omnivorous and detritivorous marine fish (functional groups 5

0

20

40

60

80

pre DC post DC

54,9

1,9

24,3

3,7

Biomass of fish (t/km²)

non-target

target

0

20

40

60

80

pre DC post DC

14,0 1,0

65,2

4,5

marine

estuarine

- 181 -

and 8), whiting (functional group 13), and Torquigener pleurogramma. In contrast,

estuarine fish biomasses (groups 9 to 12) decreased from 13.9 t km-2 to 1.0 t km-2

(Table 4.2). Nonetheless, estuarine fish groups (groups 9 to 12) represented about

18% of total fish biomass in both the ‘pre DC’ and ‘post DC’ Ecopath model (Fig. 4.9).

Figure 4.9: Community structure of teleost fish presented in percentage of total fish biomass before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel in the Peel-Harvey Estuary Fish groups: 5: marine omnivorous fish, 6: marine carnivorous fish, 7: marine herbivorous fish, 8:

marine detritivorous fish, 9: estuarine omnivorous fish, 10: estuarine carnivorous fish, 11: estuarine

5 1%

6 7%

7 3%

8 1%

9 1% 10

7%

11 1%

12 8%

13 1%

14 4%

15 25%

16 40%

17 1%

pre DC

5 3%

6 5% 7

4% 8 3% 9

3% 10 7%

11 4%

12 5%

13 7% 14

4% 15

11%

16 12%

17 32%

post DC

- 182 -

herbivorous fish, 12: estuarine detrivorous fish, 13: whiting, 14: Arripis georgianus, 15: Aldrichetta

forsteri, 16: Mugil cephalus, 17: Torquigener pleurogramma

C. Omnivory index

The omnivory indices for most functional groups changed (Fig. 4.10), indicating that

the trophic levels of the predators’ prey groups also changed. The functional group

‘gastropods’ had the lowest omnivory index, which increased from 0.0004 in the ‘pre

DC’ model to 0.008 in the ‘post DC’ model. This functional group is the most

specialized group in the Peel-Harvey Estuary; it is likely to be a herbivorous deposit

feeder and to only feed at the first trophic level (Rose, 1994; chapter 3). The

omnivory indices for the top predator groups (groups 1, 3 and 4) showed little

change, whereas the indices for the estuarine fish groups (groups 9 to 12) changed

between 23 and 57% (Fig. 4.10). Marine carnivorous fish had the most drastic change

in omnivory index of all fish groups, increasing from 0.21 to 0.47 (Fig. 4.10).

Figure 4.10: Omnivory index (y-axis) for each predator group (group 1 to 24, x-axis) before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine

omnivorous fish, 6: marine carnivorous fish, 7: marine herbivorous fish, 8: marine detritivorous fish, 9:

estuarine omnivorous fish, 10: estuarine carnivorous fish, 11: estuarine herbivorous fish, 12: estuarine

detrivorous fish, 13: whiting, 14: Arripis georgianus, 15: Aldrichetta forsteri, 16: Mugil cephalus, 17:

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Omnivory index for each functional predator group

pre DC post DC

- 183 -

Torquigener pleurogramma, 18: bivalves, 19: gastropods, 20: Western King Prawn, 21: Blue Swimmer

Crab, 22: crustaceans, 23: worms, 24: zooplankton

D. Food conversion efficiency

Analysis of food conversion efficiencies found that net food conversion efficiency

(P/A) exceeded gross food conversion efficiency (P/Q) for each functional group (Fig.

4.11), which is an acceptable fact for ecosystems described by Ecopath models

(Christensen et al., 2005). In general, the gross food efficiency was below 0.3 for all

functional groups (Fig. 4.11). The food conversion efficiencies are lowest for the top

predators and highest for the second trophic level of the ecosystem (Fig. 4.11). The

fish groups show a great variance in food conversion efficiencies, with detritivorous

(groups 8 and 12) and herbivorous (groups 7 and 11) fish groups having the lowest

food conversion efficiencies (Fig. 4.11). The food conversion efficiencies for Arripis

georgianus (group 14), Aldrichetta forsteri (group 15) and Mugil cephalus (group 16)

increased substantially after the opening of the Dawesville Channel, whereas the

efficiency of gastropods (group 19) decreased.

Figure 4.11: Net (P/A) and gross (P/Q) food conversion efficiencies (y-axis) for each predator

group (group 1 to 24, x-axis) before (‘pre DC’) and after (‘post DC’) the opening of the

Dawesville Channel

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Food conversion efficiency for each predator group

P/A pre DC P/Q pre DC P/A post DC P/Q post DC

- 184 -

E. Respiration

In general, the respiration/assimilation (R/A) ratios ranged from 0.53 to 0.99 in the

‘pre DC’ model and from 0.62 to 0.99 in the ‘post DC’ model. This ratio was highest

for zooplankton (45 in the ‘pre DC’ model and 41 in the ‘post DC model) and lowest

for sharks (a ratio of 2 in both models). The R/A ratio of the top predator groups was

close to 1. In contrast, the invertebrate groups (groups 18 to 24) and the omnivorous

and carnivorous estuarine fish (groups 9 and 10) had the lowest R/A ratio (Fig. 4.12).

There were marked changes in R/A ratios for some groups. The R/A ratio for Arripis

georgianus (group 14), Aldrichetta forsteri (group 15) and Mugil cephalus (group 16)

decreased after the opening of the Dawesville Channel, whereas the R/A of bivalves

(group 18) increased from 0.53 to 0.66 (Fig. 4.12). Bivalves had the greatest

percentage change (an increase of 55%). The R/A ratios for the fish groups whiting

(group 13), Arripis georgianus (group 14), Aldrichetta forsteri (group 15) and Mugil

cephalus (group 16) decreased up to 20%.

Figure 4.12: Respiration/Assimilation ratio (y-axis) for each consumer group (group 1 to 24, x-axis) before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine


0,5

0,55

0,6

0,65

0,7

0,75

0,8

0,85

0,9

0,95

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Respiration/Assimilation ratio for each consumer group

pre DC post DC

- 185 -





Figure 4.13: % change in Respiration/Biomass ratio (y-axis) for each consumer group (group 1 to 24, x-axis) before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine






F. Mortality (P/B ratio)

Analysis of predation mortalities indicates that the main predator and main prey

groups in the ecosystem changed following the Dawesville Channel opening. In the

‘pre DC’ model, zooplankton caused 67% of total predation mortality, followed by

Western King Prawns with 11% (Fig. 4.14). In contrast, zooplankton and crustaceans

were each responsible for 37% of total predation mortality in the ‘post DC’ model

(Fig. 4.14). The predation mortality caused by vertebrate predators was about 16 %

in the ‘pre DC’ model and 6% in the ‘post DC’ model (Fig. 4.14).

-30

-20

-10

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

% change in respiration/biomass ratio for each consumer group

- 186 -

Marine omnivorous fish were the most important predator in the ‘pre DC’ model,

causing 48% of predation mortality of all vertebrate predators combined (Fig. 4.15).

In the ‘post DC’ model, the marine carnivorous fish were less influential (only 8% of

predation mortality). Piscivorous waterbirds are the predator group in the ‘post DC’

model, causing the highest predation mortality of all vertebrate predator groups

(46%) (Fig. 4.15).

Zooplankton and microscopic algae had highest predation mortalities in the ‘pre DC’

and ‘post DC’ model and therefore appear to be the main prey groups in the

ecosystem (Fig. 4.16).

- 187 -

Figure 4.14: % of total predation mortality caused by the main (>1 %) predator groups before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine






3 2%

6 9%

10 2% 15

3% 19 3%

20 11%

21 1%

22 2%

24 67%

pre DC

3 5%

17 1%

19 2% 20

9% 21 2%

22 37%

23 7%

24 37%

post DC

- 188 -

Figure 4.15: % of predation mortality caused by the main (>1 %) vertebrate predator groups (groups 1 to 17) before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine






0%

2 2%

3 9%

0%

5 1%

6 48%

7 5%

8 1%

9 1%

10 9%

11 3%

12 3%

13 1%

0% 15

16%

0%

17 1%

pre DC

1 7%

2 2%

3 46%

0%

5 2%

6 8%

7 5%

8 3%

9 1%

10 4%

11 4% 12

2%

13 3%

0%

15 3%

0%

17 11%

post DC

- 189 -

Figure 4.16: % of predation mortality on main (>1 %) prey groups before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine





Crab, 22: crustaceans, 23: worms, 24: zooplankton, 25: microscopic algae, 26: algae, 27: macrophytes,

28 Chaetomorpha linum, 29: Cladophora montagneana, 30: seagrass

5 2%

8 2%

9 1%

11 2%

13 2%

17 1% 19

3% 20 9%

21 1% 23

1%

24 39%

25 30%

26 3%

29 3%

30 1%

pre DC

5 1%

6 1%

8 2%

9 1%

19 3% 20

4%

24 23%

25 62%

29 3%

post DC

- 190 -

G. Dietary composition

The dietary components of vertebrate predator groups did not change much

between the ‘pre DC’ and ‘post DC’ models. Notable changes occurred marine

omnivorous (group 5) and the estuarine detritivorous group (group 12), which

showed clear changes in their consumed prey classes (Fig. 4.17). Marine and

estuarine carnivorous (groups 6 and 10) fish and whiting (group 13) also showed

drastic changes in their dietary composition (Fig. 4.18).

Figure 4.17: Consumption (in %) of fish, invertebrates and primary producers by the vertebrate predator groups, before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine


0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

% o

f d

ieta

ry c

om

po

siti

on

pre DC fish invertebrates primary producers

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

% o

f d

ieta

ry c

om

po

siti

on

predator groups

post DC fish invertebrates primary producers

- 191 -





Figure 4.18: % change in main dietary components (fish, invertebrates and primary producers) of vertebrate predator groups (y-axis), before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel; presenting groups of Figure 4.17 with great changes in dietary composition, group 10 (estuarine carnivorous fish) shows an increase of 260% in fish consumption, which is not presented here to maintain the current scale of the graph Functional groups: 2: waterbirds, 4: sharks, 5: marine omnivorous fish, 6: marine carnivorous fish, 7:

marine herbivorous fish, 8: marine detritivorous fish, 9: estuarine omnivorous fish, 10: estuarine

carnivorous fish, 11: estuarine herbivorous fish, 12: estuarine detrivorous fish, 13: whiting, 15:

Aldrichetta forsteri

-55 -35 -15 5 25 45 65

2

4

5

6

7

8

9

10

11

12

13

15

pre

dat

or

gro

up

s

% change in dietary composition

Fish Invertebrates primary producers

- 192 -

Fishing mortalities for target species changed between the two time periods (Fig.

4.19). The main target species in the estuary, the Blue Swimmer Crab, experienced

the highest fishing mortality of all target species (Fig. 4.19), with fishing mortality far

exceeding predation mortality (Fig. 4.20). In the ‘pre DC’ model, the biomass (in t km-

2) of Blue Swimmer Crabs consumed in the food web equalled the crab biomass that

was fished (Fig. 4.21). In the ‘post DC’ model, however, the crab biomass that was

fished was three times higher than the amount of Blue Swimmer Crabs that was

consumed (Fig. 4.21). In general, the biomass of fish that was fished has decreased

since the channel opening (Fig. 4.21).

- 193 -

Figure 4.19: Estimates of fishing mortalities F for commercially important target species before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

Figure 4.20: Estimates of fishing mortality (F) and predation mortality for the commercially most important target species, the Blue Swimmer Crab, before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel.

0

0,5

1

1,5

2

Whiting Arripisgeorgianus

Aldrichettaforsteri

Mugilcephalus

Western KingPrawn

BlueSwimmer

Crab

0,051

0,562

0,094 0,011 0,065

0,678

0,317 0,394 0,459

0,554

0,030

1,861

Fishing mortalities

pre post

0

0,5

1

1,5

2

F Pred

Blue Swimmer Crab

pre post

- 194 -

Figure 4.21: Quantity of fish (groups 5 to 17), Western King Prawns (group 20) and Blue Swimmer Crabs (group 21) consumed by predators and fished (in t/km²), before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel

0

2

4

6

8

10

12

5 6 7 8 9 10 11 12 13 14 15 16 17 20 21

t/km

² pre DC consumed fished

0

1

2

3

4

5 6 7 8 9 10 11 12 13 14 15 16 17 20 21

t/km

²

functional groups

post DC consumed fished

- 195 -

4.3.2 Comparisons of network & system statistics

A. Mixed Trophic Impact (MTI) routine

As every trophic interaction between functional groups (and fishing fleets) was

greater than zero, the Mixed Trophic Impact routine plots in Figures 4.22 and 4.23

present only the interactions with highest impact. Notable interactions included:

In the ‘pre DC’ model, marine carnivorous fish impacted heavily on all other

fish groups, whereas in the ‘post DC’ model, piscivorous waterbirds had the

strongest negative impact on fish (Figures 4.22 and 4.23).

In the ‘pre DC’ model, seagrass had a positive impact on herbivorous fish,

whereas the trophic impact of other primary producers seems to be

negligible (Fig. 4.22).

There was a strong negative trophic impact between planktonic groups in

both models (Figs. 4.22 and 4.23).

The recreational fishing sector had a strong negative impact on sharks and

Blue Swimmer Crabs. The sector also, through indirect effects, negatively

impacted invertebrate groups such as bivalves and gastropods (Figures 4.22

and 4.23).

In both models, the trophic impact of the recreational fishing fleet was

greater than the impact of all other fishing fleets (Figures 4.22 and 4.23). T

Commercial gill net fishing in the ‘pre DC’ model affected a large number of

functional groups and also had a strong negative effect on A. georgianus.

However, the impact of this fishery was smaller that that of the recreational

fishery (Figures 4.22 and 4.23).

B. Niche Overlap

Only the indices above 0.8 are presented in Figure 4.24. In both the ‘pre DC’ and

‘post DC’ models, bivalves and worms (groups 18 and 23) showed a strong prey

overlap, whereas detritivorous fish (groups 8, 12), carnivorous fish (groups 6, 10) and

herbivorous fish (groups 7, 11) showed a strong prey and predator overlap (Fig.

4.24).

- 196 -

Figure 4.22: Mixed trophic impact of functional groups and fishing fleets in the ‘pre DC’ model

- 197 -

Figure 4.23: Mixed trophic impact of the functional groups and fishing fleets in the ‘post DC’ model

- 198 -

Figure 4.24: Niche overlap plots of the ‘pre DC’ (top) and ‘post DC’ Ecopath models presenting prey overlap index (y-axis) and predator overlap index (x-axis)

- 199 -

C. Throughput & Flow

The flow diagrams are different for the ‘pre DC’ and ‘post DC’ models, indicating that

the flow structure of the ecosystem changed (Fig. 4.25). Most functional groups

decreased in biomass, particularly at the first and second trophic level. Biomasses

decreased at every trophic level, and thus all flows also decreased (Fig. 4.26).

In the ‘pre DC’ model, the primary producers accounted for 43% of total system

throughput (TST) (Fig. 4.26). However, in the ‘post DC’ model, the first trophic level

only accounted for 31% of TST, while the three remaining trophic levels increased

their contribution to TST (Fig. 4.26).

The transfer efficiencies between the second & third trophic levels, and between the

third & fourth trophic levels, were similar (1.5% and 1.2%, respectively) (Fig. 4.26). In

the ‘pre DC’ model, the transfer efficiencies at lower trophic levels had almost the

same magnitude as the higher trophic levels (Fig. 4.26). In the ‘post DC’ model, the

transfer efficiency between the second and third level increased substantially to

8.7% (Fig. 4.26). The efficiency then decreased between the next trophic levels to

1.2%. The total transfer efficiency increased from 2.4% in the ‘pre DC’ model to 4.8%

in the ‘post DC’ Ecopath model, with values between 3 to 7% for the latter model.

The sum of flows (i.e. system throughput) decreased at each trophic level between

the ‘pre DC’ and ‘post DC’ models (Fig. 4.27). The throughput decreased by more

than 90% at the first trophic level and by more than 70% at the other trophic levels.

The composition of flows changed at each trophic level (Figure 4.28). At the first

trophic level, export (-1%) and flow to detritus (-4%) decreased. The flow of

consumption increased at the lower trophic levels. Respiration increased by 2 to 5%

at the third and fourth trophic levels, whereas the flows to detritus have decreased

by 4% (in percentage of the total system throughput) at the higher trophic levels (Fig.

4.28).

- 200 -

Figure 4.25A: Flow diagrams of the ‘pre DC’ (top) Ecopath model presenting the functional groups ordered by trophic levels with box size exhibiting relative group biomasses.

- 201 -

Figure 4.25B: Flow diagrams of the ‘post DC’ Ecopath model presenting the functional groups ordered by trophic levels with box size exhibiting relative group biomasses.

- 202 -

Figure 4.26: Lindeman spines of the ‘pre DC’ (top) and ‘post DC’ Ecopath models presenting the flows, as well as biomasses and total system throughput (TST in %) for each trophic level and the detritus pool.

Figure 4.27: Percentage (%) change in throughput (sum of all flows) per trophic level.

-100

-80

-60

-40

-20

0

I II III IV

%change in throughput per trophic level

- 203 -

Figure 4.28: Structure of flows as percentage of throuput (sum of all flows) per trophic level before (‘pre DC’) and after (‘post DC’) the opening of the Dawesville Channel. The outer ring presents the first trophic level, the inner ring presents the top-predator groups of the fourth trophic level.

5% 3%

28%

64%

1%

48% 51%

1%

31%

68%

22%

39%

39%

pre DC

Consumption by predators

Export

Flow to detritus

Respiration

5% 6%

20%

69%

1%

46%

53%

9%

37% 54%

25%

40%

35%

post DC

- 204 -

D. System Indices

Analyses of system indices indicated that the size of estuary ecosystem declined

substantially after the opening of the Dawesville Channel (Table 4.3). System

throughput declined approximately 98% and there were also dramatic declines in

production, net primary production and biomass. The ‘post DC’ model included a

lower ratio of production over respiration and a higher biomass/ total system

throughput ratio. The trophic level of catch and total biomass of catches decreased,

indicating an easing of the commercial fishing pressure on the ecosystem (Table 4.3).

Table 4.3: System indices of the ‘pre DC‘ and ‘post DC‘ Ecopath models of the Peel-Harvey Estuary

Parameter pre DC post DC %

change Units

System statistics Sum of all consumption 365243 10851 -97 t/km²/year Sum of all exports 474538 8627 -98 t/km²/year Sum of all respiratory flows 187060 5504 -97 t/km²/year Sum of all flows into detritus 566169 11267 -98 t/km²/year Total system throughput 1593010 36249 -98 t/km²/year Sum of all production 766644 13149 -98 Mean trophic level of the catch 3.08 3.05 -1 Gross efficiency (catch/net p.p.) 0.00001 0.0004 2877 t/km²/year Calculated total net primary production 661483 9969 -98 t/km²/year Total primary production/total respiration 3.54 1.81 -49 t/km²/year Net system production 474422 4464 -99 t/km²/year Total primary production/total biomass 42.72 26.07 -39 Total biomass/total throughput 0.010 0.011 9 Total biomass (excluding detritus) 15482.87 382.38 -98 t/km² Total catches 8.56 3.84 -55 t/km²/year Connectance Index 0.30 0.32 5 System Omnivory Index 0.31 0.28 -12

Cycling indices Throughput cycled (excluding detritus) 87596 606 -99 t/km²/year Predatory cycling index (without detritus) 19.01 4.43 -77 % of total throughput

throughput Throughput cycled (including detritus) 129289 2164 -98 t/km²/year Finn's cycling index 8.12 5.97 -26 % of total throughput Finn's mean path length 2.408 2.565 7 Finn's straight-through path length (without

1.994 2.38 19 without detritus Finn's straight-through path length 2.212 2.412 9 with detritus

Flow indices Ascendency: total 1584603 42029 -97 flowbits Ascendency: Import (Aim) 1362 7257 433 flowbits Ascendency: Internal flow (Ai) 477536 10658 -98 flowbits Ascendency: Export (Ae) 708170 14527 -98 flowbits Ascendency: Respiration (AR) 397527 9587 -98 flowbits Overhead: total 2717688 100741 -96 flowbits

- 205 -

Overhead: Import 490 11924 2334 flowbits Overhead: Internal flow 2357928 71318 -97 flowbits Overhead: Export 121095 3394 -97 flowbits Overhead: Respiration 238176 14105 -94 flowbits Capacity: total 4302285 142769 -97 flowbits Capacity: Import (Cim) 1852 19182 936 flowbits Capacity: Internal flow (Ci) 2835464 81976 -97 flowbits Capacity: Export (Ce) 829265 17921 -98 flowbits Capacity: Respiration (CR) 635704 23691 -96 flowbits Aim/Cim 0.74 0.38 -49 Ai/Ci 0.17 0.13 -24 Ae/Ce 0.85 0.81 -5 AR/CR 0.63 0.40 -37 A/C 0.37 0.29 -22

The connectance index of the Peel-Harvey Estuary increased slightly (Table 4.3),

suggesting a small change in food web structure in the Peel-Harvey estuarine

ecosystem with more linkages in the ‘post DC’ model and thus, a more web-like

structure than in the ‘pre DC’ model. On the other hand, the system’s omnivory

index decreased slightly (Table 4.3), indicating a specialization in diet at which a

smaller number of trophic levels is consumed by the predator groups.

Overall, the ‘post DC’ food web contained more linkages, but with the consumers

focused more on prey groups of similar trophic levels (Table 4.3). The path lengths in

the food web also increased (Table 4.3), meaning that flow in the ‘post DC’ model

passed through a larger number of nodes or groups before it was exported. Even the

straight-through path lengths (which are due to flow passing straight through the

system: Finn, 1980) increased, with flow passing through 1.99 groups in the ‘pre DC’

model and 2.38 groups in the ‘post DC’ model. If detritus is included, the straight-

through path length increased from 2.212 to 2.412 groups before being exported.

This suggests that the food web in the ‘post DC’ model had more linkages between

the functional groups and that the paths connecting these groups were also longer

(thereby also connecting a larger number of groups).

However, system throughput was less cycled in the ‘post DC’ model (Table 4.3). In

the ‘pre DC’ model, 8.12% of total system throughput was cycled in the system and

19% of the throughput flowing through predator groups was cycled. In the ‘post DC’

- 206 -

model, the primary production is lower, the path lengths are longer and linkages

between functional groups are more numerous, but the carbon is now cycled less

than before.

In the ‘pre DC’ model, the internal overhead constituted 83% of internal capacity,

which means that internal ascendency was only 17% (Table 4.3). In the ‘post DC’

model, the internal ascendency exhibited 13% of internal capacity (Table 4.3). The

internal capacities represented approximately 60% of total capacities in both models

(Table 4.3). The import capacity in the ‘pre DC’ model was small and mostly

represented by internal ascendency, whereas the import capacity in the ‘post DC’

model was ten times higher and mostly consisted of import overhead (Table 4.3).

The overhead on imports increased drastically in the ‘post DC’ model and, similarly,

the overhead on exports increased from 15% of export capacity to 19% of export

capacity. The most drastic increase in overhead took place in respiration with an

increase from 37% in the ‘pre DC’ to 60% of respiration capacity in the ‘post DC’

model (Table 4.3). Overall, the flows of respiration and export decreased drastically

(Table 4.3).

The flows of import showed a decrease in Aim/Cim by almost 50% (Table 4.3). Both

models present low Ai/Ci ratios with 0.17 in the ‘pre DC’ and 0.13 in the ‘post DC’

model (Table 4.3).

4.3.3 Ecosystem services

The analysis of the climate regulating service shows that over 17 million tonnes of

carbon were absorbed by primary producers in the ‘pre DC’ model, compared to less

than 300 000 tonnes in the ‘post DC’ model (Table 4.4).

The cultural service that the Peel-Harvey Estuary provides is the tourism industry,

which are based on 2010 estimates has an annual value of approximately $234

- 207 -

million and offers 1200 jobs in the region (Mandurah, 2011). Unfortunately, no

economic data was found for the ‘pre DC’ period.

The supporting services of nutrient cycling and primary production declined after the

Dawesville Channel opening (Table 4.4). Biodiversity was also affected, with 21

species no longer present in the ‘post DC model’ period and 67 new species entering

the estuary since the Dawesville Channel opening (see tables 3.10 – 3.28

in Chapter 3). Thus, the species diversity that supports the ecosystem services of the

Peel-Harvey Estuary has changed (Table 4.4)

Table 4.4: Ecosystem services of the Peel-Harvey Estuary

Ecosystem services

pre DC post DC

Provisioning services

Catches (t/year) 1121 503

Regulating services

Climate regulation: CO2-Fixation (t CO2 year-1)

17 702 136

285 218

Cultural services

Tourism: annual value Industry employment (Number of jobs)

No data $234 million

1200

Supporting services

Nutrient cycling: Throughput cycled, excluding detritus (t/km²/year) Primary production (t/km²)

87596

680534

607

10395

Biodiversity 21 species only recorded in ‘pre DC’ model, 67 new species in ‘post DC’ model

4.3.4 Model maturity

The primary production/ respiration ratio decreased from 3.54 in the ‘pre DC’ model

to 1.81 in the ‘post DC’ model (Table 4.3). Primary production/ total biomass ratio

also decreased drastically (39% decrease), as did net system production (99%

decrease) (Table 4.3). The total biomass/ system throughput ratio has increased

slightly, but wass very low in both models (0.01 and 0.011, respectively) (Table 4.3).

- 208 -

Ai/Ci ratios were low and decreased by 24% in the ‘post DC’ model (Table 4.3). The

FCI indices also decrease by 26%; however, the FCI indices of both of the Peel-Harvey

Ecopath models had high values (8.12 and 5.97, respectively).

4.4 Discussion

4.4.1 Comparison of basic model parameters

Primary production & respiration

The Dawesville Channel was constructed as part of a management strategy to reduce

phytoplankton blooms and macroalgal growth (Peel Inlet Management Authority,

1990, 1994). The drastic decrease in primary production since the opening of the

artificial entrance channel indicates that this aim was substantially achieved (Tables

4.2 and 4.3, Figs. 4.2 - 4.5).

The primary production in the Peel-Harvey Estuary was very high (Table 4. 3), but

more important the ratio of system primary production/ respiration (PP/R) was too

high in the ‘pre DC’ model, as the ratio should range from 0.8 to 3.2 (Christensen &

Pauly, 1993). If the primary production/respiration ratio is out of range, another

ratio needs to be investigated, the total exports/ total system throughput ratio

(Christensen & Pauly, 1993). If this ratio exceeds 0.3, it indicates that the respiration

parameters in the model are probably inappropriate, as respiration is linked to

consumption and egestion, which is difficult to quantify (Christensen & Pauly, 1993).

The higher egestion, the lower respiration and of course, the higher egestion, the

higher the production of the detritus pool (Christensen & Pauly, 1993). In Ecopath,

the export of detritus pool is the difference between flow into and flow out of the

detritus pool and the “export of detritus is the only important export flow of

practically all models“ (Christensen & Pauly, 1993). Thus, the total system

throughput should be at least three times total export, otherwise the respiration and

assimilation parameters should be re-examined in the model (Christensen & Pauly,

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1993). Here, the total export/ system throughput ratio was 0.298 for the ‘pre DC’

and 0.237 for the ‘post DC’ PHES model, so the parameters were not alarming.

Furthermore, the omission of bacterial activity in the models might have also caused

the high PP/R ratio, as more detritus was actually consumed in the system and not

exported (Christensen & Pauly, 1993). In the Peel-Harvey Estuary, bacterial activity is

important (Huber, 1980), but was not included as a single functional group in the

model due to the paucity of data. However, the high bacterial activity in the

ecosystem, in particular of cyanobacteria (Huber, 1980), might explain the high PP/R

ratio in the ‘pre DC’ PHES model, which turned to a normal range after the

Dawesville Channel opening (Table 4.3).

Biomasses and metabolism

Substantial changes occurred in the biomasses and consumptions of functional

groups, which illustrated that the functioning of the ecosystem was altered by the

Dawesville Channel opening (Table 4.2). The change in biomasses of some functional

groups, for example fish groups (Table 4.2), can be explained by a change in

environmental conditions and also by a change in species composition. Estuarine fish

species have decreased, that includes for example the species of Cobbler,

Cnidoglanis macrocephalus, and marine species have become more dominant, like

Torquigener pleurogramma, (Fig. 4.6 and 4.12) in the Peel-Harvey Estuary due to the

Dawesville Channel opening. A change in species composition due to the artificial

entrance channel has already been reported for the fish community in the Peel-

Harvey Estuary (Young & Potter, 2003). In this study, the change in community

structure has been analysed based on the comparison of two estuarine ecosystem

models.

Besides changes in community structure, also changes in flows have occurred and

consumption has become a more important and more efficient flow in the

ecosystem (Table 4.3, Figs. 4.17 and 4.18). The food conversion efficiencies

demonstrate that consumption is 3 to 10 times higher than production. In general,

fish show lower P/Q ratios than macroinvertebrate taxa (Fig. 4.11), which is in line

with results from other Ecopath models (Gartner, 2010). Furthermore herbivorous

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fish show lowest production efficiencies (P/A). A low production efficiency (P/A) for

herbivorous populations has also been shown for terrestrial invertebrate populations

(Humphreys, 1979). Early studies indicated that there is no correlation between net

food conversion efficiency (P/A) and body weight and body temperature, as there

was no significant difference found between poikilothermic and homoeothermic

animals (Humphreys, 1979). The analysis of R/B reveals that a change in activity

levels (Fig. 4.13) may have caused a change in food conversion efficiency, in

particular for fish groups whiting (group 13), Arripis georgianus (group 14),

Aldrichetta forsteri (group 15) and Mugil cephalus (group 16). Why then do these

fishes show a lower activity after the Dawesville Channel opening? The MTI plot

reveals that the trophic impacts between these fish groups have increased, whereby

these fish species negatively impact each other (Figures 4.22 and 4.23). There seems

to be an increase in competition, as the MTI has increased (Figures 4.22 and 4.23)

and the dietary composition of these fish is similar (Fig. 4.17). However, there is no

apparent prey overlap between these functional fish groups (Fig. 4.24). Thus, the

only explanation for a lower activity level is a decrease in predation pressure. In fact,

the MTI plots reveal that the trophic impact has changed, as piscivorous waterbirds

have become more influential in the ecosystem, particularly with regard to the fish

groups (Figures 4.22 and 4.23). The results demonstrate that the Dawesville Channel

has markedly impacted species composition and dominance in faunal (Fig. 4.12) and

floral (Fig. 4.13) communities (Wildsmith, 2007; Wilson et al., 1999).

Impact of fishing

In the ‘pre DC’ model fishing pressure was far greater than the predation pressure

for some fish groups (Fig. 4.21). However, this changed after the Dawesville Channel

opening (Fig. 4.21). A decrease in catch and the trophic level of catch assume easing

fishing pressure (Table 4.3). The fishing data used for both models consists of historic

catch data for the commercial fishery and a single study on the recreational fishing

sector in 1998 (Malseed & Sumner, 2001). Due to a lack of data, this recreational

data set was applied for the ‘pre DC’ and ‘post DC’ Ecopath models and thus it was

assumed that the recreational fishing pressure was constant for the time period

before and after the opening of the Dawesville Channel. Consequently, even if the

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modelling results suggest a decrease in total fishing pressure (Table 4.3), these

results can be interpreted as a decrease in commercial fishing rather than a decrease

in the overall fishing rate. A decrease in the trophic level of catch indicates that the

main target species at the second and third trophic levels, in particular the Blue

Swimmer Crab, has become more important to the fishing sectors (Tables 4.1, 4.2,

and Figs. 4.20 – 4.24). The analyses of fishing mortalities reveal that target species

are still heavily affected by fishing (Figs. 4.19 and 4.20) and thus, more data on the

recreational fishing sector is necessary to determine whether the overall fishing

pressure has actually decreased.

4.4.2 Comparisons of network & system statistics and ecosystem maturity

Food web structure

The results of this study clearly demonstrated that the structure of the food web has

changed since the Dawesville Channel opening, even though the data available were

very poor. Both Ecopath models were based on the same underlying food web

(Chapter 3). Due to a lack of dietary data that ideally should have been collected for

each species or functional group before and after the opening of the Dawesville

Channel, it is not possible to determine if the channel has impacted the food web of

the Peel-Harvey Estuary, but a change in the indices of connectance and system

omnivory indicates that a change in food web structure might have occurred (Table

4.3). The connectance index determines the number of food links in the system

relative to the number of possible links, but the index is correlated with the number

of groups in the system and the number of groups are responsible for a small

proportion (1/4) of the variability of the connectance in the system (Christensen &

Pauly, 1993). Thus, it is difficult to compare the connectance indices of ecosystem

models that differ in the number of functional groups. However, the connectance

index in the Peel-Harvey Estuary has increased and thus, the number of links in this

estuarine food web has increased since the Dawesville Channel opening (Table 4.3).

Thus, the results indicated a small change in food web structure in the Peel-Harvey

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estuarine ecosystem with more linkages in the ‘post DC’ model and thus, a more

web-like structure than in the ‘pre DC’ model (Table 4.3).

On the other hand, the system’s omnivory index decreased slightly; this suggests

that the variance in the trophic levels of consumer prey groups decreased slightly

and thus that the consumers grew more specialized and began to consume prey

groups with similar trophic levels (Table 4.3). The change in omnivory indices

indicates a change in food web structure that mainly affects the functional fish

groups, but not the top predators of the ecosystem (Fig. 4.10).

Ecosystem size and transfer efficiencies

The whole ecosystem has declined in size with the system throughput having

decreased by nearly 99% (Table 4.3). Despite the decline in biomasses and flows

(Table 4.3), the efficiency of transferring biomass from one trophic level to the next

has improved. The improvement in the transfer efficiencies since the Dawesville

Channel opening means that these values are now in line with efficiencies identified

for temperate systems (Christensen & Pauly, 1993).

Transfer efficiencies at lower trophic levels are usually higher than at higher levels

(Christensen & Pauly, 1993). However, in the ‘pre DC’ model, these transfer

efficiencies were similar (Fig. 4.26). The total transfer efficiency increased from 2.4%

in the ‘pre DC’ model to 4.8% in the ‘post DC’ Ecopath model, with values between 3

to 7% for the latter model, which are normal for temperate ecosystems, rivers and

fjords (Christensen & Pauly, 1993).

Cycling processes

One reason for the decline in ecosystem size was less cycling within the system,

which was revealed by the systems statistics. Cycling is linked to the size of the

ecosystem, the total system throughput, as the cycling index (FCI) describes the

fraction of total system throughput that is cycled within the ecosystem (Finn, 1980).

However, system throughput was less cycled in the ‘post DC’ model (Table 4.3). In

the ‘pre DC’ model, 8.12% of total system throughput was cycled in the system and

19% of the throughput flowing through predator groups was cycled. The predatory

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cycling index describes a cycling index that omits the detritus pool (Christensen et

al., 2005). The large size of the ‘pre DC’ ecosystem was caused by high primary

production and high cycling indices, thus the ecosystem produced a lot of carbon

and cycled the carbon within the system, making it longer available for predator

groups before the matter is finally exported (Table 4.3). In the ‘post DC’ model, the

primary production is lower, the path lengths are longer and linkages between

functional groups are more numerous, but the carbon is now cycled less than before.

Thus, we have more connections in the food web, but less cycles, which indicates

that the new paths have to reach from top to bottom of the food web. The

ecosystem in the ‘post DC’ model is not able to keep carbon within the system, even

though the food web has developed more linkages and, with less primary production

and less cycling, the size of the ecosystem has decreased drastically since the

Dawesville Channel opening.

Ecosystem maturity

Several indices of the system statistics were analysed to determine the maturity of

the Peel-Harvey estuarine ecosystem models (Table 4.3). The maturity analysis

indicated that the ‘pre DC’ and ‘post DC’ ecosystem models are highly immature.

Christensen (1995) recommended assessing the maturity of an ecosystem by

evaluating several indices, e.g. the ratios of primary production/ respiration, primary

production/ biomass, biomass/ system throughput, and the net community yield.

Analyses of Christensen’s indices indictaed that the ‘pre DC’ Peel-Harvey model is

less mature because, in mature systems, the primary production will be similar to

the level of respiration (Christensen, 1995), which was not the case for the ‘pre DC’

model. In relation to the primary production/ biomass ratio, the biomasses of an

ecosystem are expected to accumulate as the ecosystem matures (Christensen,

1995) and thus, the ‘pre DC’ Peel-Harvey model represented an immature system

(Table 4.3). For the biomass/ system throughput ratio, both of the Peel-Harvey

models were highly immature, exhibiting only very small ratios. The net community

yield (or net system production) decreases as systems mature and again, the Peel-

Harvey models were highly immature, showing yields that exceeded the net

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productions of the other ecosystems by far, e.g. Laguna Alvarado in Mexico (Cruz-

Escalona et al., 2007).

Maturity levels are difficult to compare between ecosystems. One reason is that the

ratios determined by Christensen (1995) for the assessment of ecosystem maturity

are linked to biomass and system throughput, so the size of the ecosystem

influences the maturity indices. For this reason, (Baird et al., 1991) recommended to

use only dimensionless indices for inter-system comparisons, such as FCI and Ai/Ci.

To perform the maturity analysis suggested by Baird et al. (1991), the flow indices

ascendency, capacity and overhead had to be examined first (Table 4.3). The import

capacity in the ‘pre DC’ model was small, whereas the import capacity in the ‘post

DC’ model is ten times higher and mostly comprises of import overhead (Table 4.3).

This indicates that imports have become more important to the system and import

ascendency is still evolving in the ‘post DC’ model, as import overhead is very large

(Table 4.3). The overhead on imports has increased drastically, as has the overhead

on exports, which has increased from 15% of export capacity to 19% of export

capacity in the ‘post DC’ model and the respiration capacity has increased drastically

in the ‘post DC’ model (Table 4.3). As overhead embodies the “inefficiencies in

operation and ambiguities in structure” (Baird et al., 1991, p. 19), there is room for

the ecosystem to become more efficient and organised.

Overall, the flows of respiration and export have decreased drastically, which

indicates that only small amounts of biomass are lost to the system (Table 4.3). The

A/C ratios, which represent “the fraction of possible organization that is actually

realized” (Baird et al., 1991, p. 17) suggest that only export flows are well organized

in both models (Table 4.3) The flows of import were well organized in the ‘pre DC’

model, but this has decreased drastically after the Dawesville Channel opening

(Table 4.3). In a developed and stable ecosystem, the system tends to internalize

most of its activity (Baird et al., 1991) and, in this case, the Ai/Ci ratio would be close

to one, as only these indices represent internal exchanges. The Ai/Ci ratio should be

rather large for a highly mature ecosystem, which possesses a significant internal

stability (Baird et al., 1991). However, the Ai/Ci ratios for the Peel-Harvey Estuary are

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low. This indicates that the ecosystem is unstable and immature. If maturity is

understood to reflect a “long-term adaptation to abiotic and biotic conditions” (Baird

et al., 1991, p. 26), then the ecosystem states in both models lack such adaptation

(Table 4.3). Furthermore, there is an inverse relation between Ai/Ci and FCI (Baird et

al., 1991). While Ai/Ci represents the system efficiency or maturity, FCI can function

as a stress indicator. Stressed ecosystems trend to show a higher FCI index, which is

apparent for this ecosystem, in particular in the ‘pre DC’ model (Table 4.3).

Thus, it can be concluded that the Peel-Harvey ecosystem models are highly

immature models lacking internal stability (Table 4.3). The very low Ai/Ci ratios

(Table 4.3) demonstrate that both ecosystems are poorly organized and do not

possess internal stability (Baird et al., 1991). The internal flow characteristics do not

differ between the ‘pre DC’ and ‘post DC’ models; however, the import and export

flow characteristics have shifted (Table 4.3).

4.4.3 Ecosystem services

The decline of the Peel-Harvey estuarine ecosystem also affects the services

provided by the ecosystem. The human population in the area now benefits less

from the provisioning services supplied by the estuary (Table 4.4). Biodiversity is also

linked with ecosystem services and a loss of species richness negatively affects

ecosystem services and stability. This trend may be reversible in some situations, and

recovery of biodiversity may allow ecosystems to recuperate their services (Worm et

al., 2006).

However, this recuperation cannot be expected in the Peel-Harvey Estuary, as the

environmental conditions have shifted closer to that of a marine embayment (Hale &

Butcher, 2007), making it difficult for estuarine species to recolonise and survive.

Twenty-one species recorded in the Peel-Harvey Estuary during the ‘pre DC’ period

(Table 4.4) have not been discovered after the Dawesville Channel opening, even

with the identical sampling regime in both periods (Rose, 1994; Valesini et al., 2009;

Wildsmith, 2007; Young, 2003). It is possible that these species withdrew from the

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estuarine basin and now inhabit the rivers discharging into the estuary, which were

not analysed in this study.

New species have invaded the Peel-Harvey Estuary, leading to a higher number of

species in the ‘post DC’ model (Table 4.4). However, an increased number of species

invasions cannot compensate for the loss of native biodiversity and ecosystem

services (Worm et al., 2006). In this study, a decline in native biodiversity is

consistent with a decrease in ecosystem services for the estuary (Table 4.4). The

Peel-Harvey Estuary also appears no longer able to provide a climate regulation

service like in the ‘pre DC’ model, a loss of service which relates to the decrease in

plant and phytoplankton biomass (Tables 4.3 and 4.4). The supporting services of

nutrient cycling and primary production have also declined after the Dawesville

Channel opening (Table 4.4).

4.4.4 Conclusion

Overall, the Dawesville Channel has affected the size, functioning and services of the

Peel-Harvey estuarine ecosystem (Tables 4.3 and 4.4), and has also changed the

species composition and community structure, e.g. the communities of macrophytes

(Wilson et al., 1999), fish (Young & Potter, 2003b), and invertebrates (Wildsmith,

2007). These results draw into question the long-term effectiveness of an artificial

entrance channel as management tool. Certainly, the Dawesville Channel has

eliminated the Nodularia blooms and decreased the primary productivity in the

system 1980s (Hale & Butcher, 2007), as predicted by the managers and authorities

(Peel Inlet Management Authority, 1994), but at what ecological cost?

If the two models of Peel-Harvey are compared to another system, such as the

Orbetello Lagoon (Brando et al., 2004), it is possible to compare the consequences of

different management actions, such as construction of artificial entrance channel

and selective algal harvesting (Brando et al., 2004). In the Orbetello Lagoon, these

management scenarios resulted in the decrease of production, biomasses, flows and

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ecosystem sizes (Brando et al., 2004). However, cycling indices such as the A/C and

Ai/Ci ratio and the information index have remained stable despite perturbations

caused by algal harvesting, indicating that the Orbetello Lagoon has maintained its

topological structure and cycling characteristics (Brando et al., 2004). In contrast, the

construction of an artificial entrance channel the Peel-Harvey Estuary has had a

drastic impact on the ecosystem of changing every index and parameter in the

system (Table 4.3). The Dawesville Channel has decreased the size of the estuarine

ecosystem, changed the structure of the food web, cycling indices, and diminished

the maturity level and stability of the ecosystem even further (Table 4.3).

The results for the system indices suggest that the ecosystem of the Peel-Harvey

Estuary may have shifted to a new equilibrium with lower biomasses than before.

More research needs to be done to test this hypothesis – in particular, a monitoring

program for abundances and biomasses and dietary studies to fill the data gaps

listed in chapter 3.

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Chapter 5

Sensitivity analysis and model stability

5.1 Introduction

Sensitivity Analysis

The balanced Ecopath model represents one possible equilibrium state of the

modelled ecosystem (Christensen et al., 2005). Given the number of other possible

equilibrium states, it is necessary to investigate the reliability of the equilibrium state

in the balanced model. This can be done by performing a sensitivity analysis that

assesses the robustness of the parameters and the consequences of changing the

parameters slightly (Gartner, 2010; Kavanagh et al., 2004; Lees & Mackinson, 2007;

Mackinson & Daskalov, 2007).

This chapter conducts a sensitivity analysis for the model parameters of the ‘pre DC’

and ‘post DC’ Ecopath models through a two-part process. In the first part, a

sensitivity analysis routine in Ecopath is run for both the ‘pre DC’ and the ‘post DC’

balanced Ecopath models to identify sensitive parameters. The sensitivity analysis in

Ecopath identifies sensitive parameters in the balanced Ecopath model. This analysis

works by changing an input parameter and estimating the response of the remaining

parameters in the model (Christensen et al., 2005).

In the second part, the modelling program Ecosim is used to test model stability,

following the approach described in Mackinson & Daskalov (2007) for the North Sea

ecosystem. Ecosim allows users to evaluate the stability of a model when it is moved

away from the equilibrium state that is described by Ecopath (Mackinson &

Daskalov, 2007).

Ecosim

Ecosim is based on the coupling of differential equations, which are derived from the

Ecopath master equation (Christensen et al., 2005)

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iiiiij

ijj

jiii BeFMIQQg

dt

dB)(

where dBi/dt equals the rate of change in the biomass of group i,

gi is the growth efficiency (production/consumption ratio) ,

Ii is the immigration rate,

Mi is the non-predation natural mortality rate,

Fi is the fishing mortality rate,

ei is the emigration rate,

Qij is the fraction that prey i contributes to the diet of predator j.

In Ecosim, behavioural factors are added to model analysis, such as feeding rates and

vulnerability settings. Food consumption is controlled by the time spent feeding and

predation risk. Leaving aside the influence of predation risk, a basic ecological

assumption is that animals will spend less foraging if food availability is high. Thus, it

is important to determine how much prey is available. To account for the influence

of predation risk, the biomass, Bi, is divided into a vulnerable and an invulnerable

component in Ecosim (Christensen et al., 2005). Only the vulnerable fraction of prey

biomass is available to predation.

The vulnerability settings in Ecosim “represent the factor that a large increase in

predator biomass will cause in predation mortality for a given prey” (Christensen et

al., 2005, p. 85). These vulnerabilities are assigned to each predator-prey

relationship during the process of model calibration. A low vulnerability setting

indicates that increased predator biomass will not result in an appreciable increase

in predation mortality (Christensen et al., 2005). In contrast, a high vulnerability

indicates that the predator biomass controls how much prey is consumed, e.g. if

predator biomass is tripled, predation mortality also triples at the same time

(Christensen et al., 2005).

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5.2 Methodology

5.2.1 Ecopath sensitivity analysis

In the Ecopath sensitivity routine, the input parameter is changed and then the

effect of this disturbance on all other parameters in the model is recorded. In

general, the most drastic change in input parameters is 50% (Lees & Mackinson,

2007; Mackinson & Daskalov, 2007). This study used a 50% change setting was used

as the maximum change setting for sensitivity analysis, in order to conduct a rigorous

analysis of model robustness in the sensitivity routine. Initial analyses found that

some parameters did not show any effect even at a 50% change setting, while others

showed changes less than a 5% change setting. To investigate the parameters with

highest sensitivity, this analysis focused on parameters showing changes at greater

than a 10% change setting (Table 5.1). The sensitivity analysis routine was performed

for both (pre-DC and post-DC) Ecopath models.

5.2.2 Ecosim model stability analysis

This analysis was performed according to the stability analysis in the North Sea

model, which presented three Ecosim scenarios for stability analysis (Mackinson &

Daskalov, 2007). Parameters (e.g. vulnerability and feeding rates) were set to default

values and three scenarios were run. The scenarios and the justifications for using

them are explained below.

Scenario 1: small disturbance in fishing effort of all fleets (scenario run 50 years)

Following Mackinson & Daskalov (2007), this scenario, effort reduced by 50% in

years 6 to 10 and then the scenario continued under the current fishing effort until

year 50. This scenario allows the model performance at the beginning to be

compared to the results after the disturbance. It allows the user to determine how

long the model needs to return to the pre-perturbation status (i.e. the situation in

years 1-5).

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Scenario 2: closure of all fishing fleets (scenario run for 70 years)

This scenario is usually run for a long period of time, in order to investigate the long-

term stability of the model. Following the approach used in the North Sea model

(Mackinson & Daskalov, 2007), a duration of 70 years was used for this scenario. The

70 year simulation period of this scenario improves the likelihood of detecting any

trophic interactions that would affect the long-term stability of the Peel-Harvey

models

Scenario 3: varying feeding time factors & vulnerabilities (scenario run for 50 years)

In Scenario 3, the Ecosim group parameters were varied. The feeding time factors

were set to a default value (0.5) or changed according to values presented in the

literature (Mackinson & Daskalov, 2007). Following the North Sea Ecosim approach,

the feeding time factor was initially set to the default value of 0.5 and then the time

factor was set to: 0.1 for top predators (trophic level > 3.5 for this model: Tables 4.1

and 4.2); 0.5 for piscivorous fish and seabirds (trophic level between 2.5 and 3.5); 1

for low trophic levels (trophic level < 2.5); and 0 to zooplankton and sessile groups

(e.g. bivalves) (Mackinson & Daskalov, 2007).

The vulnerability settings were also changed. Vulnerabilities were varied for all

groups simultaneously and values were initially set to the default value 2 and then

changed to 1 for all groups. Vulnerabilities were set to 3, 6, and 10, as well as to

values of 50, 100 and 150 to check if changes occur at very high vulnerability

settings.

5.3 Results & Discussion

5.3.1 Ecopath sensitivity analysis

The sensitivity analysis is described in detail for a 50% change in input parameters in

both Ecopath models. In the ‘pre DC’ model, a 50% change in biomass and Q/B

parameters of marine carnivorous fish affected the ecotrophic efficiency (EE) of

almost all remaining fish groups in the model (Table 5.1). A 50% change in input

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parameter of marine carnivorous fish altered the ecotrophic efficiency of T.

pleurogramma by 30%, the biggest change of all affected functional groups (Table

5.1). In contrast to marine carnivorous fish, a change in the input parameters of

Western King Prawns, Blue Swimmer Crabs, Aldrichetta forsteri and estuarine

carnivorous fish affected the ecotropohic efficiencies of smaller number of functional

groups, with the changes also occurring at a smaller scale (Table 5.1).

Table 5.1: Sensitivity of estimated ecotrophic efficiencies (EE, in %) of selected functional groups of 50% change in input parameters of other functional groups in the ‘pre DC’ model (only sensitivities bigger than 10% are shown) B: Biomass, Q/B: consumption/biomass, EE: ecotrophic efficiency

Change in input

parameter

Group Input

parameter Group Estimated parameter -50% 50%

marine carnivorous fish

B marine omnivorous fish EE -23 23 B marine herbivorous fish EE -28 28

B marine detritivorous fish EE -29 29 B estuarine omnivorous fish EE -25 25 B estuarine carnivorous fish EE -18 18 B estuarine herbivorous fish EE -28 28 B estuarine detritivorous fish EE -23 23 B whiting EE -29 29 B M. cephalus EE -21 21 B T. pleurogramma EE -30 30 Q/B marine omnivorous fish EE -23 23 Q/B marine herbivorous fish EE -28 28 Q/B marine detritivorous fish EE -29 29 Q/B estuarine omnivorous fish EE -25 25 Q/B estuarine carnivorous fish EE -18 18 Q/B estuarine herbivorous fish EE -28 28 Q/B estuarine detritivorous fish EE -23 23 Q/B whiting EE -29 29 Q/B M. cephalus EE -21 21 Q/B T. pleurogramma EE -30 30

estuarine carnivorous fish

B crustaceans EE -10 10 Q/B crustaceans EE -10 10

A. forsteri B worms EE -18 18 Q/B worms EE -18 18

Western King Prawn B marine herbivorous fish EE -10 10 B marine detritivorous fish EE -11 11

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B estuarine herbivorous fish EE -10 10 B whiting EE -11 11 B T. pleurogramma EE -11 11 B Blue Swimmer Crab EE -15 15 Q/B marine herbivorous fish EE -10 10 Q/B marine detritivorous fish EE -11 11 Q/B estuarine herbivorous fish EE -10 10 Q/B whiting EE -11 11 Q/B T. pleurogramma EE -11 11 Q/B Blue Swimmer Crab EE -15 15

Blue Swimmer Crab B bivalves EE -23 23 B gastropods EE -13 13 B crustaceans EE -14 14 B worms EE -13 13 Q/B bivalves EE -22 22 Q/B gastropods EE -13 13 Q/B crustaceans EE -14 14 Q/B worms EE -13 13

In the ‘post DC’ model, the ecotrophic efficiency of marine fish groups changed after

a 50% change in biomass and Q/B parameters of dolphins and piscivorous waterbirds

(Table 5.2). As in the ‘pre DC’ model, functional groups of fish and invertebrates

were also sensitive to changes in biomass and Q/B of Blue Swimmer Crabs and

Western King Prawns (Table 5.2).

Table 5.2: Sensitivity of estimated EE (in %) of selected functional groups of 50% change in input parameters of other functional groups in the ‘post DC’ model (only sensitivities bigger than 10% are shown)

Change in input

parameter

Group Input

parameter Group Estimated parameter -50% 50%

dolphins B marine carnivorous fish EE -13 13

Q/B marine carnivorous fish EE -13 13


B marine omnivorous fish EE -22 22

B marine carnivorous fish EE -13 13

B marine herbivorous fish EE -22 22

B marine detritivorous fish EE -23 23

B estuarine omnivorous fish EE -20 20

B estuarine carnivorous fish EE -18 18

B estuarine herbivorous EE -21 21

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fish

B estuarine detritivorous fish EE -22 22

B Whiting EE -16 16

B A. georgianus EE -17 17

B A. forsteri EE -10 10

B T. pleurogramma EE -41 41

Q/B marine omnivorous fish EE -22 22


Q/B marine herbivorous fish EE -22 22

Q/B marine detritivorous fish EE -23 23

Q/B estuarine omnivorous fish EE -20 20

Q/B estuarine carnivorous fish EE -18 18

Q/B estuarine herbivorous fish EE -21 21

Q/B estuarine detritivorous fish EE -22 22

Q/B Whiting EE -16 16

Q/B A. georgianus EE -17 17

Q/B A. forsteri EE -10 10

Q/B T. pleurogramma EE -41 41

T. pleurogramma B bivalves EE -20 20

Q/B bivalves EE -20 20

Western King Prawn B

marine omnivorous fish EE -18 18

B marine carnivorous fish EE -10 10

B marine herbivorous fish EE -23 23

B marine detritivorous fish EE -19 19

B estuarine omnivorous fish EE -16 16

B estuarine carnivorous fish EE -14 14

B estuarine herbivorous fish EE -21 21

B estuarine detritivorous fish EE -22 22

B Whiting EE -13 13

B A. georgianus EE -14 14

B gastropods EE -13 13

B Blue Swimmer Crab EE -10 10

Q/B marine omnivorous fish EE -18 18


Q/B marine herbivorous fish EE -23 23

- 225 -

Q/B marine detritivorous fish EE -19 19

Q/B estuarine omnivorous fish EE -16 16

Q/B estuarine carnivorous fish EE -14 14

Q/B estuarine herbivorous fish EE -21 21

Q/B estuarine detritivorous fish EE -22 22

Q/B Whiting EE -13 13

Q/B A. georgianus EE -14 14

Q/B gastropods EE -13 13

Q/B Blue Swimmer Crab EE -10 10

Blue Swimmer Crab B bivalves EE -25 25

B gastropods EE -17 17

B crustaceans EE -26 26

B worms EE -25 25

Q/B bivalves EE -17 17

Q/B gastropods EE -26 26

Q/B crustaceans EE -29 29

Q/B worms EE -21 21

crustaceans B zooplankton EE -21 21

Q/B zooplankton EE -13 13

The changes in estimated parameters can be interpreted as possible biotic

interactions between functional groups, e.g. predation or competition (Mackinson &

Daskalov, 2007). The sensitivity analysis indicates that the functional groups

responded to changes in the input parameters of marine carnivorous fish and

piscivorous waterbirds, which were identified as most important predator groups in

both the ‘pre DC’ and ‘post DC’ models (see Fig. 4.15, chapter 4).

A comparison of the parameter sensitivities to the results of the MTI routine

conducted in chapter 4 (Figs. 4.22 and 4.23) also suggests that trophic relationships

between functional groups were highly sensitive to a parameter change in predator

groups (Tables 5.1 and 5.2). However, it should be noted that even a 50% change in

input parameters only caused a rather small change in the ecotrophic efficiencies of

fish and invertebrate functional groups (Tables 5.1 and 5.2).

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The balanced Ecopath model represents one possible equilibrium state of the ‘pre

DC’ and ‘post DC’ model. This state was achieved by adjusting the input parameters

to achieve mass balance (see Chapter 3). Uncertainty in parameter fitting was

reduced as much as possible in the balancing process, with the balancing procedure

following the data pedigree assignment so that parameters with low pedigree were

changed first (Table 3.36). However, regardless of the precautions implemented,

model uncertainties inevitably arise because of factors relating to sampling and data

gathering (see discussion of these issues for this study in Chapter 3), emphasising the

need to recognise that “we do not know enough to make perfect models“

(Christensen & Walters, 2000, p. 95).

Nonetheless, it should be emphasised that the most rigorous form of sensitivity

analysis was performed here and that this analysis did not bring the models out of

balance or cause significant responses in the functional groups (Tables 5.1 and 5.2).

Thus, the sensitivity analysis indicated that the balanced ‘pre DC’ and ‘post DC’

models were robust to changes in input parameters.

5.3.2 Ecosim analysis

The ‘pre DC’ Ecopath model

Scenario 1 (fishing effort reduced by 50% in years 5 to 10 - scenario run for 50 years):

In the original model scenario, the relative biomasses reached a stable equilibrium

point after ten years, after which the functional groups remained stable (Fig. 5.1 A).

To examine their persistence, the total fishing effort was decreased for a period of

five years, starting in year 5 (Fig. 5.1B). The results of Scenario 1 were similar to that

of the original model and the graphs of the two simulations did not show any

notable differences, with only some groups changing slightly in response to the

change in fishing effort (Fig. 5.1). Dolphins and estuarine herbivorous fish responded

to the change by decreasing in relative biomass by less than 0.00001%, whereas

waterbirds, sharks, marine herbivorous fish, estuarine omnivorous fish, whiting and

worms increased in relative biomass by less than 0.0005%. Thus, the functional

- 227 -

groups presented a high persistence and long-term stability after a small change in

fishing effort (Fig. 5.1).

Scenario 2 (closure of all fishing fleets - scenario run for 70 years):

The results for Scenario 2 showed a similar trajectory as the original model (Fig. 5.2),

with the scenario presented in Fig. 5.1 A. This suggests that the impact of fishing on

the ecosystem is rather small. The biomasses of functional groups changed within

the first years of the scenario and there were no significant differences in relative

biomasses between the original model scenario and Scenario 2 after 70 years.

Scenario 3 (varying feeding time factors & vulnerabilities - scenario run for 50 years):

In the ‘pre DC’ model, adjusting feeding time factors did not notably affect the

relative biomasses of the functional groups. Only Aldrichetta forsteri showed a slight

decrease of 1.4% and sharks a small increase in relative biomass of 1.9% after 50

years. The results of the third test trial presented similar graphs for default feeding

time factors and for the settings defined by Mackinson & Daskalov (2007), which

were explained in section 5.2.2. For this reason, figures with the default settings for

the third test trial are not presented here.

- 228 -

Figure 5.1: Ecosim scenario in the ‘pre DC’ model run over 50 years without disturbance (A) and with a 50% decrease in total fishing effort between year 5 and 10 (B)

A

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Figure 5.2: Impact of fishing closure of all gears on the functional groups in the ‘pre DC’ model (A) and post DC model (B)

The vulnerability settings had a big impact on the functional groups in the model

(Fig. 5.3 A & B). Only model stability was analysed in this chapter – a more detailed

analysis of vulnerability settings is presented in chapter 6. Thus, the analyses in this

chapter only investigated if the models were stable, not why certain functional

groups changed over time. The results of the third trial showed that certain groups

responded to changes in vulnerabilities. For example, the relative biomass of Arripis

A

10 20 30 40 50 60 70 years

10 20 30 40 50 60 70 years

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georgianus increased and, further, the higher the vulnerability setting, the more

drastic was the increase (Fig. 5.3 A). The relative biomasses of dolphins (top black

line, Fig. 5.3A) and piscivorous waterbirds (top green line, Fig. 5.3 A) showed a

similar trend (Fig. 5.3 A). When the vulnerabilities of all functional groups were set to

high values, the system no longer reached a stable state after 50 years (Fig. 5.B).

Some groups show considerable fluctuations (Fig. 5.3 B) and, at vulnerabilities of 50,

the fluctuation of Arripis georgianus showed a periodic fluctuation (top red line, Fig.

5.3, V = 50) that looked similar to Lotka-Volterra dynamics (see further analysis and

discussion in chapter 6).

Overall, the results demonstrated that the ‘pre DC’ model is robust and stable.

However, vulnerability settings must be defined with caution, as the system showed

drastic fluctuations at higher settings (Fig. 5.3 B).

- 231 -

Figure 5.3 A: Impact of different vulnerability settings (1, 3, 10) on the relative biomasses of the functional groups of the ‘pre DC’ model

V = 1

V = 3

10 20 30 40 50

years

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V = 10

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years

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Figure 5.3 B: Impact of different vulnerability settings (50, 100, 150) on the relative biomasses of the functional groups of the ‘pre DC’ model

V = 50

V = 100

V = 150

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The ‘post DC’ model

Scenario 1 (fishing effort reduced by 50% in years 5 to 10 - scenario run for 50 years):

Similar to the ‘pre DC’ model analysis, the ‘post DC’ analysis for Scenario 1 found that

the relative biomasses reached a stable equilibrium point after twenty years, after

which the functional groups remained stable (Fig. 5.4). However, in contrast to the

‘pre DC’ model in which Arripis georgianus showed the highest relative biomasses,

sharks had the highest relative biomass in the ‘post DC’ model, followed by Mugil

cephalus and dolphins (Fig. 5.4). In the ‘post DC’ model, the biomass of Arripis

georgianus remained pretty stable with only a slight increase from 0.262 tkm-2 to

0.274 tkm-2, whereas the biomass of sharks tripled and the biomass of Mugil

cephalus and dolphins doubled over 50 years (Fig. 5.4). Thus, the ‘post DC’ model

reached an equilibrium state that differed from the ‘pre DC’ model (Figs. 5.1, 5.2 and

5.4).

Scenario 2 (closure of all fishing fleets - scenario run for 70 years):

As for the ‘pre DC’ model, the total fishing effort was decreased by 50% for a period

of five years to examine the persistence of the ‘post DC’ system (Fig. 5.4B). The

results of these two scenarios were similar and the graphs did not show any

differences (Fig. 5.4). After 50 years none of the functional groups had changed

notably due to the change in fishing effort (Fig. 5.4 B). Overall, the functional groups

showed high persistence and long-term stability after a small change in fishing

pressure (Fig. 5.4). Even after an extended (70 years) closure of fishing fleets (Fig. 5.2

B), the ecosystem model reached the same state of equilibrium as in the scenario

without any changes or disturbances (Figure 5.4 A). The state of equilibrium reached

in scenario 1 and 2 was similar, even though the fishing pressure differed

significantly between these scenarios.

- 234 -

Figure 5.4: Ecosim scenario in the ‘post DC’ model run over 50 years without disturbance (A) and with a 50% decrease in total fishing effort between year 5 and 10 (B)

Scenario 3 (varying feeding time factors & vulnerabilities - scenario run for 50 years):

When the relative biomasses in the ‘post DC’ model were compared between

adjusted feeding time factors and a default value (0.5), there was a change in

biomasses after 50 years across the various vulnerability settings. At a vulnerability

setting of 1, only sharks showed a 30% change in relative biomass. However, for

vulnerability settings of 3 and 10, some changes were drastic. This suggests that an

adjustment of feeding time factors that considers the predation risk that a functional

group experience is essential for the ‘post DC’ Ecopath model (Fig. 5.6).

B

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- 235 -

Changes in response to vulnerability settings were also notable (Fig. 5.5 A and B).

Sharks were the group with the highest relative biomasses in all vulnerability settings

and at small vulnerabilities (Fig. 5.5 A), with the system reaching an equilibrium state

after 30 years. The higher the vulnerability setting, the higher the increase in shark

biomass, which approached a maximum after approximately 30 years and then

declined to a lower level (Fig. 5.5 B). The biomasses of Mugil cephalus also peaked at

30 years and then returned to a lower level, although this group did not reach the

high biomass values of sharks (Fig. 5.5 B).

These findings indicate that the Ecosim parameter settings in the ‘post DC’ model

need to be defined carefully, as they will affect the results of any Ecosim scenario

(Figs. 5.5 and 5.6). At default settings, the ‘post DC’ model reached a state of

equilibrium after a short period of time (Fig. 5.4). The ‘post DC’ model also did not

change in response to the closure of fishing or a reduction in fishing pressure (Fig.

5.2 B and 5.4 B).

- 236 -

Figure 5.5 A: Impact of different vulnerability settings (1, 3, 10) on the relative biomasses of the functional groups of the ‘post DC’ model;

V = 3

V = 1

10 20 30 40 50

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Figure 5.5 B: Impact of different vulnerability settings (50, 100, 150) on the relative biomasses of the functional groups of the ‘post DC’ model

V = 50

V = 100

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Figure 5.6: % change in relative biomasses between default value (0.5) and adjusted feeding time factors for different vulnerability settings in a scenario that is run over 50 years

-100 -50 0 50 100

dolphins

waterbirds


sharks

marine omnivorous fish


marine herbivorous fish

marine detritivorous fish

estuarine omnivorous fish


estuarine herbivorous fish

estuarine detritivorous fish

Whiting

Arripis georgianus


Mugil cephalus

Torquigener pleurogramma

bivalves

grastropods

Western King Prawn

Blue Swimmer Crab

crustaceans

worms

zooplankton

microscopic algae

algae

macrophytes

Chaetomorpha linum


seagrass

detritus

% change in relative biomasses due to changes in feeding time factors

v = 2 v = 3 v = 10

- 239 -

5.4 Conclusion

The analyses in this chapter indicate that the ‘pre DC’ and ‘post DC’ models are

robust and stable. Both models demonstrated robustness regarding their input

parameters, as a 50% change in input only affected the ecotrophic efficiency of a

small number of functional groups and changes were minor. These findings enhance

the credibility of model results in chapter 4 and suggest that, even if some

parameters are open to criticism, changing these parameters will not drastically

affect other functional groups or the overall system.

The stability of both models was analysed using approaches well-established in the

literature (Gartner, 2010; Kavanagh et al., 2004; Lees & Mackinson, 2007; Mackinson

& Daskalov, 2007). Both models demonstrated stability by approaching states of

equilibrium. The models also reached a stable equilibrium state after disturbances.

The trials testing effects of feeding time factors and vulnerability settings showed

that these parameters have to be carefully defined, as they are able to impact

Ecosim scenarios, particularly scenarios run in the ‘post DC’ model. These settings

are further discussed in chapter 6, where Ecosim scenarios are performed in the

‘post DC’ model. In contrast, there are no further analyses of the ‘pre DC’ model in

subsequent chapters. Ecosim and Ecospace were not performed for the ‘pre DC’

model because of a paucity of historic long-term data.

- 240 -

Chapter 6

The effects of the selective reduction of primary producers and the impacts of fishing on the ecosystem of the Peel-Harvey Estuary

6.1 Introduction Chapter 4 described the drastic changes the Peel-Harvey estuarine ecosystem

experienced after the opening of the Dawesville Channel. The opening of the

channel altered the dynamics of the ecosystem so significantly that attributes such

as species composition, environmental conditions, and ecosystem services all shifted

markedly – and probably irreversibly – from the previous equilibrium state (which

represented by the ‘pre DC’ model).

Given this outcome, it is reasonable to consider whether other management

approaches like nutrient reduction or selected algal harvesting might be more

appropriate and more sustainable for the Peel-Harvey Estuary in the future.

Comparisons with other ecosystems, such as the Orbetello Lagoon in Italy, also

support the value of considering management measures other than artificial

entrance channels (Brando et al., 2004).

There are several other management approaches that could be considered.

Modifying primary production is an obvious approach as primary productivity is an

important forcing factor in ecosystems and also drives changes in fish abundance in

ecosystems (Mackinson et al., 2009). Primary producers could be partly removed

from the ecosystem, e.g. by selective algal harvesting (Brando et al., 2004). Selective

harvesting must be applied carefully, as stakeholders will only accept this biological

measure if it does not impact adversely on economically important functional groups

in the ecosystem. Interventions involving harvesting may also be costly and

logistically difficult to implement.

- 241 -

Alternatively, nutrient management could be implemented. Nutrients are an

important driver of phytoplankton densities in the Peel-Harvey Estuary and nutrient

concentrations in the estuary remain high, despite the presence of the entrance

channel (Hale & Butcher, 2007). Thus, phytoplankton blooms remain a present and

future threat for ecosystem health.

To assess the utility of nutrient management or other phytoplankton-based

measures, several questions must be investigated: (1) what is the impact of

phytoplankton on other functional groups?; (2) how effective is a limitation of

phytoplankton biomass through nutrient reduction?; and (3) what are the

consequences of phytoplankton reduction and phytoplankton blooms for the

estuarine ecosystem model? Answers to these questions will help us assess the most

important question -- how can this estuary be managed sustainably?

This chapter tries to answer these questions by investigating: 1) the impact and

effectiveness of the selective reduction of different primary producers and 2) the

impacts of fishing on target and non-target species on the ecosystem model using

Ecosim simulations. Specifically, the Ecosim simulations are used to investigate how

the catches and different functional groups would respond to decreases or increases

in fishing effort and how the biomass of different groups would change in response

to the closure of specific fishing fleets or the ban of certain fishing gears.


6.2.1 Ecosim master equation

Through Ecosim, dynamical simulations of the mass-balanced Ecopath model over a

defined time period can be run to investigate alternative fishing policies (Christensen

et al., 2005; see discussion in Chapter 5, section 5.3.1).

- 242 -

The consumption rates in Ecosim, Qij, are based on the ‘foraging arena’ concept,

which states that not all individuals are equally vulnerable to predation. The biomass

(Bi) is divided into a vulnerable and an invulnerable component (Christensen et al.,

2005). These vulnerabilities are assigned to each predator-prey relationship during

the process of model calibration. The set of differential equations is solved in Ecosim

using an Adams-Bashford integration routine (Christensen et al., 2005).

6.2.2 Tuning the model

This section describes the major steps for tuning the Ecosim model: (1) parameter

settings; (2) forcing function; (3) time series dataset; (4) model fitting procedure; and

(5) Ecosim scenarios.

Parameter settings

The parameter settings for the different functional groups were defined in the

following manner. The maximum relative feeding time was set to default value of 2,

and the density dependent catchability, which is represented by Qmax/Q0 ratio

(default = 1) and the QBmax/QB0 ratio (default = 1000) were set to default values

(Christensen et al., 2005; Mackinson et al., 2009).

The feeding time adjustment rates were defined for the functional groups. High

adjustment rates were allocated to top predators, as their feeding time is likely to be

constant and not impacted by any predators outside the Peel-Harvey Estuary (Table

6.1). For the lower trophic levels and invertebrates, feeding rates were set to 0.5 and

1, as they are likely to show strong behavioural responses and fast feeding time

changes depending on prey availability and also predation risk (Table 6.1). For sessile

invertebrates, like bivalves and for zooplankton, the feeding rates were set to 0, as

their feeding behaviour was assumed to remain constant (Table 6.1). The predator

effect on the feeding time of zooplankton was also set to zero, which assumes that

zooplankton organisms do not stop feeding or show a faster feeding rate when

predators are near (Table 6.1). As fish respond to predation risk and this affects their

- 243 -

feeding behaviour, the predator effect for fish was set to 1 (Mackinson & Daskalov,

2007). The effect of predators on the feeding time of benthic invertebrates was

assumed to be less strong as the effect on fish (Mackinson & Daskalov, 2007) and

thus, this effect was set to 0.5 (Table 6.1). For top predators, the predator effect was

set to zero on the basis that the top predators in the ecosystem are not affected by

other predators (Table 6.1).

Table 6.1: Parameter input for Ecosim procedure for the functional groups of predators in the ‘post DC’ Ecopath model

Group name Feeding time

adjust rate [0,1] Predator effect on feeding time [0,1]

1 dolphins 0,1 0

2 waterbirds 0,5 0

3 piscivorous waterbirds 0,1 0

4 sharks 0,1 0

5 marine omnivorous fish 0,5 1

6 marine carnivorous fish 0,5 1

7 marine herbivorous fish 1 1

8 marine detritivorous fish 1 1

9 estuarine omnivorous fish 0,5 1

10 estuarine carnivorous fish 0,5 1

11 estuarine herbivorous fish 1 1

12 estuarine detritivorous fish 1 1

13 Whiting 0,5 1

14 Arripis georgianus 0,5 1

15 Aldrichetta forsteri 0,5 1

16 Mugil cephalus 1 1

17 Torquigener pleurogramma 0,5 1

18 bivalves 0 0,5

19 grastropods 1 0,5

20 Western King Prawn 0,5 0,5

21 Blue Swimmer Crab 0,5 0,5

22 crustaceans 1 0,5

23 worms 1 0,5

24 zooplankton 0 0

The trophic mediation function was applied in this Ecosim approach (Christensen et

al., 2005). This function implies that a third group affected the predator-prey

relationship between two functional groups by providing protection or shelter. A

- 244 -

negative, linear mediation function was used to represent this mediating effect. In

the Peel-Harvey Estuary, potential mediators are: (1) aquatic plants, which provide

cover for prey groups and (2) phytoplankton blooms, which reduce water clarity and

thereby also the search efficiency of visual predators on small prey groups

(Christensen et al., 2005). The phytoplankton density in the estuarine water basin

has decreased drastically since the Dawesville Channel opening (see Chapter 3).

Thus, the relative weighted impact of the phytoplankton functional group was set to

a small level of 0.1, while seagrass was given a relative weighted impact of 1 as the

structure of seagrass meadows allows small organisms to readily hide and find

shelter (Wells, 1985). The weighted trophic mediation of other plant groups was set

to 0.5, except for Cladophora montagneana which was set to 0.3 because its ball-like

shape doesn’t provide much shelter or cover (Fig. 6.1). This mediating effect was

taken into consideration for all vertebrate predator groups, as well as Western King

Prawns and Blue Swimmer Crabs.

Figure 6.1: Relative weight of defined plant groups that function as trophic mediator in the ‘post DC’ model

Forcing function

Since the Dawesville Channel opening, the biomass of phytoplankton has declined

substantially because the main species causing phytoplankton blooms, Nodularia

spumigena, cannot tolerate high salinities (Huber, 1985). The biomass of Cladophora

montagneana also collapsed because of a storm event that destroyed the nutrient

supply of the algal mats (Gordon et al., 1981; Hale & Butcher, 2007; Lavery &

- 245 -

McComb, 1991). In both cases the shape of the function that describes the biomass

collapse is similar and was applied as forcing function to these two primary

producers (Fig. 6.2).

Figure 6.2: Shape of long-term forcing function generated for Cladophora montagneana and microscopic algae exhibiting the massive decline in biomasses in times of the Dawesville Channel opening

Time series dataset

During the tuning process, the Ecopath model is fitted to time series data, such as

the relative biomass of major target species in the Peel-Harvey Estuary. Ecopath

models that are adjusted to time series data can track biomass changes that have

occurred in the past and, thus, the tuning process is a tool to explore model

performance (Christensen et al., 2005). For this model, the catch per unit effort (in

kg catch per number of vessels) of the commercial fishing sector was provided by the

Department of Fisheries for different target species, e.g. Blue Swimmer Crabs, Arripis

georgianus, Mugil cephalus and Aldrichetta forsteri. The CPUE data shows great

variations for each species over the last decades (Fig. 6.3). The variation in CPUE is

accompanied by a marked decline in the number of commercial fishing vessels from

about fifty vessels using gill nets in the 1970s to less than ten vessels in 2007 (Fig.

6.4). Since Dawesville Channel opened, the commercial catch only forms a small

proportion of the total catch. For example, the ratio of the recreational versus

commercial catch of Blue Swimmer crabs was 4:1 in 1998 (Malseed & Sumner, 2001).

shape of forcing function

years

tota

l b

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The tuning process based on the CPUE time series data was unsuccessful, as it was

not possible to track biomass changes that occurred in the past in the Ecosim model

using only the commercial dataset (Figs. 6.3 and 6.4). This failure could be explained

in two ways. Firstly, the ‘post DC’ model failed in the tuning process. This suggests

that the ‘post DC’ system is not able to replicate past (i.e. pre-DC) biomass changes.

This explanation is quite reasonable, as the historic biomass trends are based on

estuarine biotic and abiotic conditions in the Peel-Harvey Estuary before the

Dawesville channel opening and the ‘post DC’ model represents a different, far more

marine-based system. Secondly, problems with the dataset may account for the

failure in the tuning process. The commercial dataset may constitute ‘bad trend

data’ (Christensen et al., 2005) because it fails to adequately capture the changes in

CPUE for all fisheries over the last few decades. This seems likely, given that the

impact of the recreational fishery has become significant (Malseed & Sumner, 2001)

and recreational catches are not included in the long-term dataset provided by the

Department of Fisheries.

It is therefore reasonable to conclude that the historic commercial dataset does not

fully reflect the changes that occurred in the estuarine ecosystem over the last few

decades. ‘Bad trend data’ is a common problem for analyses involving time series

data and should be considered in the fitting process for Ecosim models, given the risk

that poor trend data might generate misleading results, as seems to be the case in

this instance (Christensen et al., 2005). Nonetheless, despite these issues, it is still

generally useful to undertake analyses based on historic datasets because, in the

absence of information on density dependence, time series data represent the best

way to tune models (Villy Christensen, University of British Columbia, pers. comm.,

exchange of emails September 2010).

As the tuning process is essential for Ecosim simulations and for the reliability of

Ecosim predictions, several measures were taken to deal with problem of ‘bad trend

data’. In particular, a better quality dataset was created, which better reflected the

biomass changes that occurred in the ecosystem over the last decades, as also

recreational fishing was considered. This dataset was created by estimating the

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recreational catch based on the human population in the area of the Peel-Harvey

Estuary – this allowed both fishing sectors, commercial and recreational, to be

included. While imperfect, this approach does improve the quality of the trend data

for the fitting procedure. The creation of the new trend dataset is described in

further detail below.

Figure 6.3: Time series data showing the catch per unit effort in kg per number of vessels for the most important target species in the Peel-Harvey Estuary from 1976 to 2007

Figure 6.4: Time series data showing the fishing effort in number of vessels for different gears used by the commercial fishery in the Peel-Harvey Estuary from 1976 to 2007

0

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no

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Beach Seine Crab trap Gill net

- 248 -

As total catch data that describes the commercial and the recreational fishing sectors

in the Peel-Harvey Estuary were not available, it was not possible to generate a

historical dataset based on a Virtual Population Analysis (VPA). However, it was

possible to generate historical data by linking the recreational catch to the

population living in the area of the Peel-Harvey Estuary. Two assumptions underlie

the logic for this estimation: 1) the fishing behaviour of the recreational fishermen

has not changed over time and 2) the fraction (%) of the total population or citizens

that actively fishes has remained constant. The uncertainty in this generated

historical dataset is unavoidably high because: (a) many of the recreational

fishermen fishing in the Peel-Harvey Estuary are not locals, but visitors from the

Perth area (Malseed & Sumner, 2001) and (b) fishing regulations have also changed

over the last decades. Although it is important to recognise consider the potential

influence of these factors, they are not sufficient to undermine the usefulness of

estimating the recreational catch.

The population of Mandurah in 1998 (c. 35 500 people with an annual growth rate of

6.5%: Australian Bureau of Statistics, 2010) was used as the basis for estimating the

recreational catch, as this year was surveyed and provides the only available data

point for the recreational catch in the estuary (Malseed & Sumner, 2001). The

generated historical dataset includes the commercial catch data and also the

estimated recreational catch for the four target species: Blue Swimmer crab,

Aldrichetta forsteri, Mugil cephalus and Arripis georgianus (Figs. 6.5 and 6.6).

- 249 -

Figure 6.5: Time series data generated for Blue Swimmer crab and Aldrichetta forsteri by summarizing commercial catch and estimates of recreational catch. The recreational catch is estimated by linking the recreational catch surveyed in 1998 (Malseed & Sumner, 2001) to the human population living around the estuary in Mandurah, applying an annual population growth of 6.5% (ASB, 2010).

Figure 6.6: Time series data generated for Mugil cephalus and Arripis georgianus by summarizing commercial catch and estimates of recreational catch. The recreational catch is estimated by linking the recreational catch surveyed in 1998 (Malseed & Sumner, 2001) to the human population living around the estuary in Mandurah, applying an annual population growth of 6.5% (ASB, 2010).

0.00

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- 250 -

Model fitting procedure

By applying the reference data for four target species (Figures 6.5 and 6.6), the

tuning was proceeded according to the ‘iterative, stepwise procedure for model

fitting’ described in Christensen et al. (2005). Vulnerability represents the degree to

which “a large increase in predator biomass will cause an increase in predation

mortality for a given prey” (Christensen et al., 2005, p. 85). The vulnerability settings

are very important parameters in the tuning process, as the process is based on the

search for vulnerabilities that provide better fits for the model (see also Chapter 5.1,

(Christensen et al., 2005). For this reason, these settings were analysed in detail for

each predator/prey interaction. Each vulnerability setting was varied from 1 to 10

000 to identify the setting that showed the best goodness of fit using the approach

described in Christensen et al. (2005). Christensen et al. (2005, p.81) define the

goodness of fit measure as:

the weighted sum of squared deviations (SS) of log biomasses from log predicted biomasses, scaled in the case of relative abundance data by the maximum likelihood estimate of the relative abundance scaling factor q in the equation y = q * B”, where y is the relative abundance and B is the absolute abundance.

The aim of the model fitting procedure is to identify the vulnerability setting with the

best fit, which is the vulnerability where the predicted biomasses are closest to the

biomasses in the historic dataset. This tuning process is based on varying the

vulnerabilities for each predator/prey interaction and using fishing effort data or

alternatively fishing mortality data as driving factors (Christensen et al., 2005). In this

modelling process, the vulnerabilities represent the degree to which an increase in

predator biomass will cause an increase in predation mortality for a given

predator/prey relationship (Christensen et al., 2005) and, thus, the vulnerabilities

can reach very high values (Table 6.2).

- 251 -

Table 6.2: Vulnerability settings for each predator-prey interaction identified in the tuning process of the ‘post DC‘ Ecopath model Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine omnivorous fish, 6: marine carnivorous fish, 7: marine

herbivorous fish, 8: marine detritivorous fish, 9: estuarine omnivorous fish, 10: estuarine carnivorous fish, 11: estuarine herbivorous fish, 12:

estuarine detrivorous fish, 13: whiting, 14: Arripis georgianus, 15: Aldrichetta forsteri, 16: Mugil cephalus, 17: Torquigener pleurogramma, 18:

bivalves, 19: gastropods, 20: Western King Prawn, 21: Blue Swimmer Crab, 22: crustaceans, 23: worms, 24: zooplankton, 25: microscopic algae, 26:

algae, 27: macrophytes, 28 Chaetomorpha linum, 29: Cladophora montagneana, 30: seagrass, 31: detritus

Predator groups:

Prey 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

5 1 5000 70 2 1 2 13 2 2 2

6 1 2000 1 2 15 2 2 2 12

7 2 1 2 9 11 2 2 1

8 400 1 1 2 23 1 1 30

9 1 2 45 1.1 60 10 2 7 2

10 1 46 1.7 1 1 1 1 1 1

11 2 1 1 2 8 2 28 2

12 1 2 2 1 2 2 2 2 6

13 2 28 1.1 1 15 45 1 500

14 1 3 1 1 5500 150 30 60

15 1 2500 170 5 30 2000

40 11 1

16 2 5000 130 25 1 1 1 2 1

17 100 2000 5 1.7 1 1 1 29

18 1 25 150 1 2 1 2 2000 55 2 2 2000

2

19 5000 450 1.7 2 20 2 700 2 20 250 1 2 4 1

20 2 1.1 18 400 2 1 150 1

- 252 -

21 1 1 10 1 1 3000 80 1 900 5

22 2 3 1.1 1 1 2 1 2 60 5 11 4 8

23 19 1.7 20 2 1 1 2 500 6 1 2 2 3

24 95 1.1 1 2 1 25 5 1 1 1 2 1 3 8 5 1 11 2

25 2 2 2 1 2 6 1 1 90 5 15 2 1 4500

26 10 2 2 2 50 2 2 1 2 2 4 2

27 4 2 2 2 50 2 9 2 1 2 2 9 2

28 400 2 2 2 110 1 2 2 4 2

29 650 2 2 35 600 15 75 2 5 2

30 450 2 2 4 5 2 2 90 2 1 2 2

31 2 3000 2 6 1 1 3 2 2 3 6 1 1 20 46

- 253 -

To identify these final vulnerability settings (Table 6.2), the procedure had to be

repeated three times, until the fitting was successful and the curves showed an

acceptable fit to time series data (Fig. 6.9). Christensen et al., 2005 suggest P/B

settings are critical for the fitting procedure and warn against the risk of Ecopath

inputs [including P/B (i.e. Z) values] that are set far too high, thereby causing “low

sensitivity of a functional group to changes in mortality agents” (Christensen et al.,

2005, p. 89). To achieve fitting, the P/B settings were analysed and, in cases where

the total mortality greatly exceeded the sum of fishing and natural mortality in the

Ecosim ‘group plots’, the P/B values were adjusted to an appropriate P/B value

without affecting the balance of the Ecopath model (Table 6.3). In most cases (Table

6.3), the rate of ‘other’ mortality exceeded predation and fishing mortality rates by

far and this mortality rate was lowered to an appropriate level, depending on how

much biomass of a functional group was consumed, fished or went to the detritus

pool. After adjusting the P/B values, Ecosim was run and the new total mortality

estimate was checked; this was done by examining the adjusted P/B setting (Table

6.3) and its partitioning among factors (fishing, predation and ‘other’ mortality rates)

to see if these values were reasonable (Christensen et al., 2005). The adjusted P/B

ratios in Table 6.3 present the total mortality that was appropriate to maintain all

energy flows in the model.

Table 6.3: List of adjusted P/B values that were reduced to a minimum value, which is the lowest value without causing model to lose balance, in the fitting procedure for the functional groups of the ‘post DC’ model Functional group P/B Adjusted P/B

6 marine carnivorous fish 1.901 1.801

10 estuarine carnivorous fish 1.867 1.067

18 bivalves 3.546 1.546

19 gastropods 4.375 3.975

20 Western King Prawn 4.730 4.530

22 crustaceans 8.088 2.088

23 worms 6.220 1.220

24 zooplankton 25.56 25.06

25 microscopic algae 184.8 84.8

26 algae 34.07 3.07

27 macrophytes 67.56 2.56

28 Chaetomorpha linum 73 3

29 Cladophora montagneana 3.870 3.370

30 seagrass 4.170 1.170

- 254 -

Ecosim scenarios

The long-term forcing function generated for Cladophora montagneana and

microscopic algae (Fig. 6.2) was not applied to evaluate future scenarios, on the

assumption that these functional groups will not show a similar change of biomass in

the future, given that it is highly unlikely that environmental conditions within the

estuary will change again as drastically as they did following the opening of the

Dawesville channel.

In order to analyse the impact of primary producers and the impact of fishing,

Ecosim was run for 15 years without any perturbations. The results of these runs

provided the basis for the analysis of all other Ecosim scenarios performed in this

study.

To investigate the effect of the selective reduction of primary producers, three

Ecosim scenarios were performed: (1) Ecosim is run for 10 years without any changes

to primary producers and (2) each functional plant group (functional groups 26 to

30) is reduced by 10% in (a) a short (1 year) and (b) a long (3 years) pulse

perturbation. The results of the perturbation scenarios are presented immediately

after the perturbations and ten years later.

To investigate the impact of microscopic algae on other functional groups, this group

was decreased by 10% and by 50% over three years. A scenario with a permanent

decrease in phytoplankton biomass of 10% over a period of 10 years was also

analysed. The impact of phytoplankton on the system was investigated further as the

nutrient concentrations remain high enough to cause phytoplankton blooms (Hale &

Butcher, 2007); thus, two scenarios were performed with an increase in biomass by a

factor of 2 and a factor of 10.

The effect of fishing on the functional groups of the ecosystem was investigated by

reducing and increasing each fishing fleet by 50% and by a total closure of the fishing

sector. The effect of a complete fishing closure was compared to a status quo

- 255 -

scenario with the current fishing effort of the underlying ‘post DC’ Ecopath model.

All scenarios were run for ten years.

6.3 Results & Discussion

Vulnerability settings

The vulnerability settings were ranged from 1 to 10 000 to identify the setting with

the lowest sum of squared deviations (Table 6.2). By analysing these data, three

main categories of vulnerability settings were identified (Table 6.4).

Firstly, a large number of predator/prey interactions were robust to changes in

vulnerability settings, as the sums of squares and the curve fitting did not react to a

change in vulnerabilities in a range from 1 to 1000 (Category 2 in Table 6.4). These

interactions were analysed regarding the importance of the prey in the predators’

diet (see chapter 3). However, no obvious pattern was found that could explain the

robustness against variations in vulnerability settings (Table 6.4).

Secondly, some interactions presented the lowest sums of squares at vulnerability

settings of 1 and 2 and then, the sums increased with higher settings (Category 2 in

Table 6.4). Most predator/prey interactions of marine carnivorous and detritivorous

fish (groups 6 and 8) followed this pattern (Fig. 6.7).

Thirdly, a small number of vulnerability settings had a drastic impact on the curve

fitting procedure (Category 3 in Table 6.4). This category included the predator/prey

interactions of crustaceans/microscopic algae, worms/zooplankton and

zooplankton/detritus (Fig. 6.8). Setting the vulnerability of the crustaceans/

microscopic algae interaction to 100 resulted in oscillations in the model, for

example the functional groups Blue Swimmer Crab and Arripis georgianus presented

oscillating curves and the fit to time series data worsened, which was confirmed by

an increase in the sum of squares (Fig. 6.8).

- 256 -

Table 6.4: Three categories of vulnerability settings identified in the tuning process of the ‘post DC‘ Ecopath model. Vulnerabilities were varied from 1 to 10 000. Category 1: no changes in sums of squares Category 2: settings of 1 and 2 show the lowest sums of squares Category 3: vulnerability settings have drastic impact on sums of squares and on curve fitting results Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine omnivorous fish, 6: marine carnivorous fish, 7: marine herbivorous fish, 8: marine detritivorous fish, 9: estuarine omnivorous fish, 10: estuarine carnivorous fish, 11: estuarine herbivorous fish, 12: estuarine detrivorous fish, 13: whiting, 14: Arripis georgianus, 15: Aldrichetta forsteri, 16: Mugil cephalus, 17: Torquigener pleurogramma, 18: bivalves, 19: gastropods, 20: Western King Prawn, 21: Blue Swimmer Crab, 22: crustaceans, 23: worms, 24: zooplankton, 25: microscopic algae, 26: algae, 27: macrophytes, 28 Chaetomorpha linum, 29: Cladophora montagneana, 30: seagrass, 31: detritus

Predator groups:

Prey: 2 3 4 5 6 7 8 9 10 12 13 14 15 18 20 21 22 23 24

5 1 1 1 1

6 1 1 1

7 1 1 1 2

8 3 3

9 1 1 1

10 3 2

11 1

12 1 1

13 2

15 3 2

16 3 2 1 2

17 2 2

18 2 1 2

19 1 1 2

- 257 -

20 1 2

21 2 2

22 3 2 1 2 3

23 3 2 2 3 3

24 2 1 2 2 3 3

25 1 1 2 1 3 3 3

26 1 1 1 1 1 1 1

27 1 1 1 1 1

28 1 1 1 2 1

29 1 1 1 1

30 1 1 1 1 1

31 1 3 1 2 3 3 3 3

- 258 -

Figure 6.7: Sums of squares of varying vulnerability settings of the predators marine carnivorous (group 6) and marine detritivorous (group 8) fish, presenting the pattern of category 2 interactions

Functional prey groups: 21: Blue Swimmer Crab, 22: crustaceans, 23: worms, 24: zooplankton, 25: microscopic algae

26

26,2

26,4

26,6

26,8

27

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27,4

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1 2 100 1000

Sum

s o

f sq

uar

es

vulnerability setting

category 2 predator/prey interactions

6/21 6/22 6/24 8/23 8/25

- 259 -

Figure 6.8: Sums of squares of varying vulnerability settings of the predator groups crustaceans, worms and zooplankton, showing interactions that had drastic impact on the fitting procedure

10

15

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Sum

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are

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Category 3 interactions

crustaceans/ microscopic algae worms/zooplankton

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Sum

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Category 3: zooplankton/ detritus interaction

- 260 -

Model fitting procedure – target species using CPUE data

For the Blue Swimmer Crab, the fitted curve presented a good fit to the catches in

the 1980s, but the historical reference data simulated higher catches for the 1990s

and 2000s (Fig. 6.9). The model simulations are preferable, as it is likely that the crab

catch that was generated by the reference dataset was too high. For example, the

predicted catch probably did not adequately reflect the effect of fishing regulations

like maximum crab lengths. Nonetheless, given that the data available are so poor,

this observation is speculative.

Figure 6.9: Tuning results for the functional groups Blue Swimmer Crabs, Mugil cephalus, Aldrichetta forsteri and Arripis georgianus showing the generated historical data points (dot points) and the simulated time pattern (solid line) of the ‘post DC’ Ecosim model

For Arippis georgianus, the fitted curve presented slightly higher catches in the

1970s than the reference data and lower simulated catches in the 1990s and 2000s.

The tuning results illustrated a rather constant catch for Arripis georgianus with a

- 261 -

minimum relative biomass of 0.01 and a maximum of 0.05 t km-2 (Fig. 6.9). This trend

was consistent with the commercial CPUE data (Fig. 6.3). The impact of the

recreational fishing sector was not as high for this species as for the Blue Swimmer

Crab (see Chapter 3). Thus, it is likely that the model simulations were reflecting the

actual catches over time, but more data are needed to examine this hypothesis.

The fitting results presented very good fits for the curves of Mugil cephalus and

Aldrichetta forsteri (Fig. 6.9). The model was able to track the changes in biomasses

of these two functional groups over the last decades.

Primary production scenarios

Baseline Scenario: The baseline Ecosim scenario assessed how the functional groups

would develop over a period of 15 years without any perturbations (Table 6.5). Some

groups increased drastically, such as the top predator groups (groups 1 to 4), marine

detritivorous and carnivorous fish and invertebrate groups, such as Blue Swimmer

Crabs, Western King Prawns, crustaceans and worms (Table 6.5). However, aside

from microscopic algae, all primary producer groups decreased in biomass (Table

6.5). There was a collapse of Cladophora montagneana after six years (Table 6.5).

The total biomass of all functional groups combined increased steadily (Table 6.5).

Scenario A: Harvesting Primary Producers: The long-term effects (10 years after

harvesting) were the same regardless of whether a plant group was harvested for 1

year or 3 years (Fig. 6.10). Only the removal of seagrass had a long-term effect on

seagrass biomass; all other plant groups showed no significant long-term change in

biomass (Fig. 6.10). Plant removal had negative long-term effects on the top

predator groups (groups 1 to 4). The removal of Chaetomorpha linum had the most

drastic effect, with changes in biomass ranging from -0.4% for dolphins to -0.7% for

waterbirds. Plant removal negatively impacted the Blue Swimmer Crab, but some

target fish like Aldrichetta forsteri (group 15) and Mugil cephalus (group 16)

responded to harvesting with a small increase in biomass.

- 262 -

The magnitude of short-term effects was much higher than for long-term effects,

with short-term effects showing up to a 25% change in biomass whereas the

maximum long-term effect was a 1.6% change in biomass (for gastropods) (Fig. 6.10).

A 3 year plant harvesting program caused stronger effects than a 1 year program

(Fig. 6.11). Cladophora montagneana strongly responded to a removal of algae,

macrophytes and Chaetomorpha linum with a change in biomass of more than 15%

(Fig. 6.11). Marine detritivorous fish (group 8) had small negative responses to any

harvesting scenario, as did the top predator groups (groups 1 to 4, Fig. 6.11).

- 263 -

Table 6.5: Development of the different functional groups without any perturbations in primary production or fishing pressure in an Ecosim scenario run over 15 years

changes in biomass (in % ) after a time period of

Group name initial biomass in t/km²

2 years

4 years

6 years

8 years

10 years

15 years

1 dolphins 0.06 -2 +1 +12 +29 +52 +114

2 waterbirds 0.02 +32 +76 +136 +219 +331 +679

3 piscivorous waterbirds 0.18 +17 +60 +154 +305 +492 +909

4 sharks 0.001 +22 +70 +163 +297 +459 +849

5 marine omnivorous fish 0.20 +15 +13 -12 -48 -70 -84

6 marine carnivorous fish 0.29 +29 +79 +162 +255 +348 +559

7 marine herbivorous fish 0.27 +10 -15 -28 -28 -23 -21

8 marine detritivorous fish 0.26 +462 +2018 +4674 +7398 +9920 +14808

9 estuarine omnivorous fish

0.16 -11 -29 -54 -78 -90 -96

10 estuarine carnivorous fish

0.43 +81 +277 +585 +733 +567 +90

11 estuarine herbivorous fish

0.30 +87 +55 +6 -3 +9 +33

12 estuarine detritivorous fish

0.30 +2 -1 -2 -2 -2 -3

13 Whiting 0.44 +87 +265 +368 +321 +221 +68

14 Arripis georgianus 0.25 +50 +99 +101 +77 +68 +92

15 Aldrichetta forsteri 0.64 +13 +31 +30 +5 -28 -78

16 Mugil cephalus 0.65 +9 +20 +21 +10 -10 -45

17Torquigener pleurogramma

2.12 +46 +44 +35 +34 +38 +53

18 bivalves 9.96 -47 -73 -77 -75 -72 -70

19 gastropods 0.76 -34 -55 -65 -72 -80 -95

20 Western King Prawn 1.71 +54 +60 +57 +55 +57 +65

21 Blue Swimmer Crab 1.89 +41 +83 +119 +141 +164 +207

22 crustaceans 173.78 +6 +13 +16 +16 +16 +16

23 worms 52.93 +92 +154 +175 +178 +178 +177

24 zooplankton 63.87 -28 -45 -51 -52 -54 -57

25 microscopic algae 37.06 +22 +39 +45 +47 +49 +52

26 algae 5.73 -2 -4 -9 -11 -8 -2

27 macrophytes 4.52 -25 -39 -49 -52 -53 -52

28 Chaetomorpha linum 28.31 -1 -5 -10 -13 -15 -17

29 Cladophora montagneana

0.22 -21 -89 -100 -100 -100 -100

30 seagrass 10.41 -31 -46 -40 -28 -23 -31

31 detritus 20.24 +8 +14 +15 +15 +14 +12

Total 417.96 +11 +21 +27 +29 +31 +33

- 264 -

-0,4

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Biomass of functional groups


- 265 -

-0,8

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- 266 -

Figure 6.10: Long-term effect (after 10 years) of 1 year (dark grey) and 3 year (light grey) selective algal harvesting (algae, macrophytes, Chaetomorpha linum, Cladophora montagneana and seagrass) on the biomasses of all functional groups and on total biomass Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine omnivorous fish, 6: marine carnivorous fish, 7: marine herbivorous fish, 8: marine detritivorous fish, 9: estuarine omnivorous fish, 10: estuarine carnivorous fish, 11: estuarine herbivorous fish, 12: estuarine detrivorous fish, 13: whiting, 14: Arripis georgianus, 15: Aldrichetta forsteri, 16: Mugil cephalus, 17: Torquigener pleurogramma, 18: bivalves, 19: gastropods, 20: Western King Prawn, 21: Blue Swimmer Crab, 22: crustaceans, 23: worms, 24: zooplankton, 25: microscopic algae, 26: algae, 27: macrophytes, 28 Chaetomorpha linum, 29: Cladophora montagneana, 30: seagrass, 31: detritus

-1,8

-1,4

-1,0

-0,6

-0,2

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- 267 -

-5,0

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1 year harvesting 3 year harvesting

-5,0

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- 268 -

-10,0

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-1,5

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- 269 -

Figure 6.11: Short-term effect (year after harvesting ended) of 1 year (dark grey) and 3 year (light grey) selective algal harvesting (algae, macrophytes, Chaetomorpha linum, Cladophora montagneana and seagrass) on the biomasses of all functional groups and on total biomass

Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine omnivorous fish, 6: marine carnivorous fish, 7: marine herbivorous fish, 8: marine detritivorous fish, 9: estuarine omnivorous fish, 10: estuarine carnivorous fish, 11: estuarine herbivorous fish, 12: estuarine detrivorous fish, 13: whiting, 14: Arripis georgianus, 15: Aldrichetta forsteri, 16: Mugil cephalus, 17: Torquigener pleurogramma, 18: bivalves, 19: gastropods, 20: Western King Prawn, 21: Blue Swimmer Crab, 22: crustaceans, 23: worms, 24: zooplankton, 25: microscopic algae, 26: algae, 27: macrophytes, 28 Chaetomorpha linum, 29: Cladophora montagneana, 30: seagrass, 31: detritus

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Reducing the biomass of microscopic algae for a short period of time (3 years)

caused an increase in biomass of: top-predators (groups 1 to 4); some fish groups

(e.g. marine carnivorous, marine detritivorous and herbivorous fish: groups 6, 7, 8,

11); Western King Prawns (group 20); and Blue Swimmer Crabs (group 21, Fig. 6.12).

Some fish groups responded with a massive decline in biomass, including marine and

estuarine omnivorous fish (groups 5 and 9); estuarine carnivorous fish (group 10);

whiting (group 13); Aldrichetta forsteri; and Mugil cephalus (groups 15 and 16).

Invertebrate groups like gastropods (group 19) and zooplankton (group 24)

presented a decrease in biomass.

The long-term effects of a 10% and 50% reduction in biomass of microscopic algae

were similar (Fig. 6.12). A small (10%) permanent reduction of microscopic algae

affected the biomass of the functional groups less than a short-term reduction (Fig.

6.12). The permanent decrease in microscopic algae (Fig. 6.12) led to a decrease in

biomass of all upper trophic levels, with only three groups showing a slight increase

in biomass -- estuarine herbivorous fish (group 11), bivalves (group 18), gastropods

(group 19, Fig. 6.12). A permanent decrease in microscopic algae had hardly any

impact on the other groups of the first trophic level, with only macrophytes (group

27) showing a change in biomass of more than 1% (Fig. 6.12). The permanent

reduction in microscopic algae led to a reduction in total biomass, whereas the

short-term reduction (3 years) resulted in an increase in overall biomass (Fig. 6.12).

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Figure 6.12: Long-term effect (10 years after harvesting) of reducing microscopic algae by 10% or 50% biomass on all other functional groups and effect of a permanent 10% reduction in phytoplankton biomass Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine omnivorous fish, 6: marine carnivorous fish, 7: marine herbivorous fish, 8: marine detritivorous fish, 9: estuarine omnivorous fish, 10: estuarine carnivorous fish, 11: estuarine herbivorous fish, 12: estuarine detrivorous fish, 13: whiting, 14: Arripis georgianus, 15: Aldrichetta forsteri, 16: Mugil cephalus, 17: Torquigener pleurogramma, 18: bivalves, 19: gastropods, 20: Western King Prawn, 21: Blue Swimmer Crab, 22: crustaceans, 23: worms, 24: zooplankton, 25: microscopic algae, 26: algae, 27: macrophytes, 28 Chaetomorpha linum, 29: Cladophora montagneana, 30: seagrass, 31: detritus

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10% reduction over 3 years, long-term effect 10% permanent reduction over 10 years

50% reduction over 3 years, long-term effect


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Phytoplankton blooms affected all trophic levels in the model (Fig. 6.13). Top

predators (groups 1 to 4) were able to profit from phytoplankton blooms in each

scenario and presented an increase in biomass (Fig. 6.13). Short-term effects led to

drastic biomass increases of some fish groups (groups 5 to 17) as well as Cladophora

monatgneana (group 29). In contrast, long-term effects caused mainly negative

biomass changes in the fish community. Phytoplankton blooms did not change the

total biomass of the model substantially in the long-term -- only a 10 x increase in

microscopic algal biomass caused an increase in total group biomass by 19%.

Doubling the biomass of microscopic algae led to a shrinking of total group biomass

by 1% straight after blooming occurred.

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Figure 6.13: Short-term and long-term effects of phytoplankton blooms, caused by an increase in biomass of microscopic algae by factor 2 and by factor 10 Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine omnivorous fish, 6: marine carnivorous fish, 7: marine herbivorous fish, 8: marine detritivorous fish, 9: estuarine omnivorous fish, 10: estuarine carnivorous fish, 11: estuarine herbivorous fish, 12: estuarine detrivorous fish, 13: whiting, 14: Arripis georgianus, 15: Aldrichetta forsteri, 16: Mugil cephalus, 17: Torquigener pleurogramma, 18: bivalves, 19: gastropods, 20: Western King Prawn, 21: Blue Swimmer Crab, 22: crustaceans, 23: worms, 24: zooplankton, 25: microscopic algae, 26: algae, 27: macrophytes, 28 Chaetomorpha linum, 29: Cladophora montagneana, 30: seagrass, 31: detritus

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Fishing scenarios

The Ecosim catch scenarios demonstrated that fishing affects all functional groups in

the ecosystem model (Tables 6.6, 6.7). Under the current fishing effort scenario, the

total catch is expected to increase from 4 to 12 t/km2 with most catch biomass

resulting from the Blue Swimmer Crab catch (Table 6.6). In contrast, an increase in

the recreational fishing sector would lead to a small decrease in total catch and

negatively affect the Blue Swimmer Crab catch by 10% (Table 6.6). Only an increase

of the beach seine and gill net fishing fleets led to an increase in crab catch (Table

6.6). An increase in fishing effort did not lead to higher catches of target fish species,

like Aldrichetta forsteri, Mugil cephalus; however, it was possible to step up the

catches of Arripis georgianus by up to 22% (Table 6.6). A decrease in recreational

fishing effort led to drastic decreases of fish catches. In particular, the catches of no-

target fish species (groups 5 to 12) declined drastically by up to 52% (Table 6.6). A

reduction of recreational and gill net fishing sectors had the biggest impact on total

catch with a decrease in catch biomass of 19 and 11% (Table 6.6).

The fishing sectors affected all functional groups in the model except Cladophora

montagneana (Table 6.7). An increase in recreational fishing effort led to an increase

in the biomasses of top predators, like dolphins, waterbird groups, and sharks, but

estuarine fish groups and target species (Aldrichetta forsteri, Mugil cephalus,

Western King Prawns and Blue Swimmer Crab) showed a drastic decline in biomasses

(Table 6.7). A change in the recreational fishing fleet had the biggest impact on total

biomasses and the biggest impact on the biomass of the Blue Swimmer Crab,

whereas a change in beach seine fishery had the smallest impact on total biomasses,

but presented the largest impact on target fish species (Arripis georgianus,

Aldrichetta forsteri, Mugil cephalus) (Table 6.7).

The different fishing fleets affected all functional groups, but the impact on primary

producers was small with a maximum change in biomass of 3.8% (Table 6.7). Worms

and crustaceans increased in biomass with increasing fishing effort with the biggest

impact coming from the recreational fishing sector (Table 6.7). Bivalves and

gastropods responded more strongly to changes in fishing effort than other non-

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target invertebrate groups. Bivalves increased with increasing recreational and crab

trap efforts, while gastropods increased drastically with decreasing fishing effort of

the recreational, gill net and crab trap fishing efforts (Table 6.7).

A closure of one fishing fleet affected the total biomasses of the model slightly, but

only the closure of the beach seine fleet led to a small increase (1%) in total biomass

(Table 6.8). A fishing closure of the different fleets caused a stagnation or decrease

of seagrass, microscopic algae, worms, crustaceans and marine herbivorous fish.

The biomass of Aldrichetta forsteri and Mugil cephalus increased with any fleet

closure. The biomass of the estuarine fish groups showed substantial increases (of up

to 102%) with fleet closure, except for the closure of beach seine which led to a

decrease (Table 6.8). Invertebrate groups like gastropods, Western King Prawn and

Blue Swimmer Crab showed a strong biomass increase after closing the recreational

fishing sector (Table 6.8). Although the top predator groups (dolphins, waterbird

groups, sharks) showed mixed responses to fishing closure, all groups declined in

biomass after closing the crab trap fleet and all increased in biomass after closing the

beach seine fleet (Table 6.8).

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Table 6.6: Changes in biomass of total catch (in %) after a 50% increase and 50% decrease of fishing effort of the recreational (rec.), beach

seine, gill net and crab trap fleets; only the effort of one fleet is changed, while the other fleets in the Ecosim scenario operate with

current effort (F=1) and these results here present changes in catch estimates after a time period of 10 years

* catches in t/km²

Basic Ecosim scenario with current fishing

effort 50% increase in fishing effort 50% decrease in fishing effort

Group name initial catch*

catch after 10 years* rec

beach seine gill net

crab trap rec

beach seine gill net crab trap

4 sharks 0.0001 0,001 50 -1 12 4 -48 4 -11 -4

5 marine omnivorous fish 0.011 0,003 18 9 5 -4 -23 -10 -8 4

6 marine carnivorous fish 0.109 0,487 41 5 43 6 -35 -4 -37 -6

7 marine herbivorous fish 0.004 0,003 29 4 21 0 -27 -4 -21 0

8 marine detritivorous fish 0.007 0,725 64 4 43 7 -52 -3 -35 -7

9 estuarine omnivorous fish 0.006 0,001 -3 9 7 -7 -6 -11 -10 8

10 estuarine carnivorous fish 0.110 0,733 -0,5 6,5 -16,0 -0,6 9,2 -9,6 -7,1 1,1

11 estuarine herbivorous fish 0.005 0,006 24 3 20 0 -25 -3 -19 0

12 estuarine detritivorous fish 0.004 0,004 24 3 19 0 -25 -4 -19 0

13 Whiting 0.141 0,452 8 11 10 0 -4 -16 -17 0

14 Arripis georgianus 0.099 0,166 20 1 22 3 -23 -3 -25 -4

15 Aldrichetta forsteri 0.295 0,212 -6 -15 -3 -2 12 -4 -2 3

16 Mugil cephalus 0.361 0,325 -7 -21 -3 -2 9 21 2 2

17 Torquigener pleurogramma 0.003 0,004 24 1 35 0 -17 0 -34 0

20 Western King Prawn 0.052 0,082 20 0 13 0 -20 0 -13 0

21 Blue Swimmer Crab 3.512 9,281 -10 1 7 0 -20 0 -8 -1

Total 4.720 12,484 -1 1 9 1 -19 -1 -11 -2

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Table 6.7: Changes in biomass (in %) of all functional groups as response to a 50% increase and 50% decrease of fishing effort of

the recreational (rec), beach seine, gill net and crab trap fleets; only the effort of one fleet is changed, while the other fleets in the

Ecosim scenario operate with current effort (F=1). The results present changes in biomass after a time period of 10 years

50% increase in fishing effort 50% decrease in fishing effort

Group name rec beach seine gill net crab trap rec

beach seine gill net crab trap

dolphins 9.2 -3.7 -0.5 2.8 -8.8 6.7 0.3 -2.7

waterbirds 11.0 0.7 4.0 2.0 -7.1 -0.4 -4.1 -1.8

piscivorous waterbirds 20.7 -2.5 6.2 4.8 -21.1 4.6 -3.5 -4.9

sharks 0.3 -1.5 12.0 3.8 3.9 4.0 -10.8 -3.8

marine omnivorous fish -14.3 3.7 -2.6 -3.8 24.5 -5.4 -0.3 4.3

marine carnivorous fish 22.8 -0.8 10.9 5.6 -23.4 1.8 -10.8 -5.6

marine herbivorous fish 1.9 0.2 0.7 0.3 -0.7 -0.1 -0.8 -0.3

marine detritivorous fish 29.8 0.5 19.3 6.8 -34.8 0.6 -19.0 -7.1

estuarine omnivorous fish -26.7 4.6 -5.4 -7.3 40.8 -7.0 3.2 8.2

estuarine carnivorous fish -7.4 1.4 -38.9 -0.6 18.0 -4.8 48.6 1.1

estuarine herbivorous fish -1.7 -0.4 -0.6 -0.1 1.2 0.5 1.6 0.1

estuarine detritivorous fish -1.4 0.0 -0.9 -0.2 1.6 0.0 0.9 0.2

Whiting 4.1 -9.6 -10.7 0.5 0.4 8.9 8.5 -0.2

Arripis georgianus 0.6 -16.5 10.3 3.5 -4.8 21.8 -16.1 -3.5

Aldrichetta forsteri -7.8 -37.9 -13.2 -2.3 14.0 51.2 10.9 2.7

Mugil cephalus -8.2 -45.1 -7.2 -1.7 10.6 117.2 6.6 1.8

Torquigener pleurogramma 3.8 0.6 3.5 0.0 2.8 -0.1 -4.7 0.3

bivalves 19.3 -1.7 -1.9 4.3 -21.9 2.0 2.6 -4.4

gastropods -26.6 1.7 -18.8 -7.0 43.2 -3.0 23.6 7.8

Western King Prawn -0.9 0.1 2.7 -0.3 1.5 0.2 -3.6 0.4

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Blue Swimmer Crab -35.6 0.6 6.5 -7.9 34.4 0.1 -7.5 7.8

crustaceans 0.4 0.1 0.1 0.0 -0.1 -0.1 -0.2 0.0

worms 3.0 0.4 0.1 0.5 -2.0 -0.3 -0.6 -0.5

zooplankton -13.0 -0.8 -2.7 -2.5 10.8 0.5 3.4 2.4

microscopic algae 3.8 0.3 1.0 0.7 -3.0 -0.2 -1.3 -0.7

algae 1.0 0.5 0.7 0.3 -0.5 -0.5 -0.6 -0.2

macrophytes -1.2 1.3 -2.1 -0.3 3.9 -1.5 3.4 0.4

Chaetomorpha linum -1.4 0.0 -1.0 -0.4 2.7 0.0 1.3 0.4

Cladophora montagneana 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

seagrass 2.1 0.0 0.0 0.4 -2.5 -0.1 -0.6 -0.5

detritus -0.7 0.0 -0.6 -0.2 0.9 -0.1 0.5 0.2

Total 1.9 0.0 0.8 0.4 -1.6 0.1 -0.9 -0.4

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Table 6.8: Effects of closure of different fishing fleets on the biomasses of all functional groups; only one fleet is closed in these Ecosim scenarios, while the other fleets operate with current effort (F=1) results were compared to the Ecosim scenario with current fishing effort (Table 6.6) and this table presents changes in biomasses in %,; scenarios were run for 10 years

recreational beach seine gill net crab trap

dolphins -12 20 1 -5

waterbirds -9 0 -8 -3

piscivorous waterbirds -35 14 -3 -10

sharks 25 14 -19 -8

marine omnivorous fish 52 -14 -5 9

marine carnivorous fish -37 6 -19 -11

marine herbivorous fish 0 0 -2 -1

marine detritivorous fish -61 3 -35 -14

estuarine omnivorous fish 78 -18 2 17

estuarine carnivorous fish 48 -16 102 3

estuarine herbivorous fish 2 1 4 0

estuarine detritivorous fish 3 0 2 0

Whiting -1 16 13 0

Arripis georgianus -14 51 -33 -7

Aldrichetta forsteri 27 115 17 6

Mugil cephalus 20 470 12 4

Torquigener pleurogramma 10 1 -10 1

bivalves -46 5 7 -9

gastropods 91 -8 49 16

Western King Prawn 3 1 -8 1

Blue Swimmer Crab 63 1 -15 15

crustaceans 0 0 -1 0

worms -4 0 -2 -1

zooplankton 19 1 7 5

microscopic algae -5 0 -3 -1

algae 1 -1 -1 0

macrophytes 13 -3 8 1

Chaetomorpha linum 6 0 3 1

Cladophora montagneana 0 0 0 0

seagrass -5 0 -2 -1

detritus 1 0 1 0

Total -3 1 -2 -1

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6.4 General Conclusions

Vulnerability settings are critical for the fitting to time series process and for any

Ecosim scenario (Christensen et al., 2005). In this study, the analysis of vulnerability

settings found that some predator/prey interactions were crucial for a successful

curve fitting, while other interactions did not have any impact at all.

It is not clear why some interactions were more important for the fitting procedure

than others. For example, the vulnerability setting that described the zooplankton/

detritus interaction (Fig. 6.8) had a drastic impact on the fitting procedure and was

classified among category 3 vulnerabilities (Table 6.4). Of course, zooplankton fed on

detritus and this interaction exhibited an important predator/prey relationship on

the bottom of the food web. The interaction marine detritivorous fish/ detritus was

critical and this vulnerability setting was also classified among category 3 (Table 6.4).

In both cases, detritus was an important prey component, but not every strong

predator/ prey relationship in the model presented category 3 vulnerabilities.

Dolphins and sharks, which were among the top predator groups in this model (see

Table 4.2, Chapter 4), did not present any category 3 vulnerabilities for any of their

prey groups. Furthermore, some of the vulnerabilities describing the relationships

between sharks and their prey groups even belonged to category 1 (Table 6.4),

meaning that these settings did not have any impact at all. Some interactions at

upper trophic levels were critical for the curve fitting procedure, e.g. certain

piscivorous waterbirds/ fish interactions (Table 6.4). However, interactions at lower

trophic levels (Predator groups 22, 23 and 24, Table 6.4) were also critical and some

even represented category 3 interactions. Thus, the main predator groups, like

dolphins and sharks, do not appear to automatically define the curve fitting process.

This study used three categories (1, 2, 3) to categories effect of different

vulnerability settings on the interactions between functional groups. Category 2

included interactions in which the lowest sums of squares were achieved at low

vulnerability settings (v=1 or 2, Fig. 6.7). Apparently, vulnerabilities that belong to

category 2 represent “bottom-up” interactions, as the vulnerability settings are

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small. The low vulnerabilities indicate that an increase in predator biomass does not

affect the biomass of the prey group because capture rates are determined by a

physiological or behavioural trait of the prey (Christensen et al., 2005, p. 149). For

example, bottom-up control can occur if prey are generally protected (e.g. by hiding

in the substrate) and become available to predators only when they leave the

feature affording protection (Christensen et al., 2005, p. 86). The mechanism(s) by

which bottom-up control might occur in the Peel-Harvey Estuary is unclear. For

example, it is not known if the prey groups of the predator/prey relationships that

belong to category 2 (Table 6.4) are actually able to hide. Further research on the

interaction of prey and predator groups is needed to investigate the hypothesis that

if prey groups are able to find protection from predation, the vulnerability settings

are always small. Behavioural studies would improve the tuning process, as the new

findings would be able to confirm or refute the vulnerability settings identified by

using drivers such as fishing effort data or fishing mortality data in a historic dataset.

Further analysis is necessary to compare the vulnerability settings with those of

other models and determine if the three categories of vulnerabilities identified in

this study are consistent with the results of other models.

The model fitting procedure had to be repeated three times to produce an

acceptable outcome. Fitting a model for many groups inevitably means some

compromises have to be made and, thus, that careful consideration should be given

to ensuring that the model is “tailorfit to specific data depending on the purpose of

the application” (Mackinson & Daskalov, 2007, p. 74). Here, the data were very poor,

as no long-term historic dataset was available that included all operating fishing

fleets in the estuary. A dataset had to be created for a small number of species,

which made significant assumptions about the recreational catch in the estuary

(Malseed & Sumner, 2001). Although a good fit was achieved, the Ecosim results

should treated with caution, as the estimated catches might be inaccurate, with the

generated historical dataset either over- or under-estimating the actual catch. A

long-term monitoring program would solve this program for future modelling

exercises.

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It is worth emphasising that the purpose of undertaking the Ecosim simulations was

to evaluate the impact of fishing on the ecosystem model and to characterise the

potential effects of different management options, not to determine sustainable

catch levels. The simulations showed that fishing affected almost all functional

groups in the model, not just the target species. The recreational fishing sector also

had a very strong impact on many functional groups, particularly Blue Swimmer

Crabs and other invertebrate groups like bivalves and gastropods. This fishing sector

affected every trophic level of the estuary food web and significantly affected

estuarine fish groups. These findings indicate that it is necessary to collect more data

on this fishing fleet, its effort and its catches in order to manage the estuary

sustainably.

The commercial fishing sector affected functional groups less than the recreational

sector, but affected a range of estuarine fish groups including non-target fish species.

The number of operating fishing vessels has declined over the last decades due to

political pressure. The results of this study suggest that it may be not advisable to

close those fleets completely as some aspects of the estuary ecosystem appear to

benefit from increasing fishing pressure. Some fish groups and some target species

responded positively to the closure of certain fleets, while others – particularly

waterbirds and other top predators – did not (Table 6.8). These Ecosim analyses

indicate that more data are needed for a sustainable management and that the

coexistence of fleets might be a better solution for sustaining catches and group

biomasses in the future. This is an area that needs further empirical research, but

this study presents several intriguing hypotheses to investigate.

The other question investigated in this study dealt with the impact and effectiveness

of reducing different primary producers. Models for other systems have found that

primary producers have a substantial impact on other functional groups within an

ecosystem and also on catches (Brown et al., 2010). This study assessed which

scenario would be the most sustainable management scenario for reducing the

biomasses of functional groups, in particular of primary producers. Similar to an

Italian estuarine ecosystem (Brando et al., 2004), the selective algal harvesting had

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drastic effects after one year of harvesting. However, the long-term effect of

removing plants was negligible. As the removal of plants accumulated on beaches

has already been undertaken before in the Peel-Harvey Estuary (Lavery et al., 1999),

the costs for such operations are straightforward to estimate.

However, although this study indicates that selective plant removal is a reasonable

management tool for this estuary, nutrient reduction and, thus, the permanent

reduction of microscopic algae might be more ecologically and economically

worthwhile (Fig. 6.12). Although blooms of Nodularia spumigena no longer occur in

the estuary because the salinities are too high for this species (Huber, 1985), the

estuary now contains several phytoplankton species (e.g. Heterosigma akashiwo)

that cause blooms in other ecosystems (Guiry & Guiry, 2010). While the effects of

phytoplankton blooms on the ecosystem depend on the size (i.e. the biomass) of the

bloom, even blooms that only double the biomass of microscopic algae can have

drastic long-term effects, as suggested by the persistence of a 20% biomass

reduction of some fish groups 10 years after the blooming occurred in the model.

This study strongly supports the conclusion that a reduction in phytoplankton

through management of nutrient input in the estuarine catchment represents the

only ecological and economical management scenario that provides long-term

sustainability for this ecosystem.

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Chapter 7

Use of Ecospace as an evaluation method to identify an effective Marine Protected Area and to assess the effects of plant habitat reduction through dredging on the

ecosystem of the Peel-Harvey Estuary, with emphasis on the impacts on waterbirds

7.1 Introduction

The Peel-Harvey Estuary is located about 80 km south of Perth, the capital of

Western Australia (Hale & Butcher, 2007). Increasing urban development in the area

is placing pressure on the Peel-Harvey Estuary. Land lying to the north and north-

west of the Peel Inlet and along the west coast of the Harvey Estuary is used for

residential development, while land to the east of the estuary is mainly used for

agriculture and beef farming (Lane et al., 2002; Ninox-Wildlife-Consulting, 1990). A

new highway built to the east of the Peel-Harvey Estuary and an increasing

population in Western Australia are likely to lead to further expansion of residential

areas in the Peel region.

These changes in land use will impact the estuarine waterbody and, thus, the

estuarine ecosystem. Along the eastern shore of Peel Inlet, the estuary is very

shallow (≤ 0.5m) and is covered by macroalgae and seagrass, making the area poorly-

suited for recreation (Hale & Butcher, 2007; Wilson et al., 1999). Nonetheless, canals

with waterfront properties were constructed at the river mouth of the Murray River

(at the town of Yunderup) along the east coast of Peel Inlet. In the past, the

accumulation of decaying plant material along the shoreline was accompanied by an

unpleasant smell, a nuisance that – if the area is further developed for housing –

might lead to further harvesting and removal of plant biomass in the future (Bradby,

1997; Lavery et al., 1999). The first objective of this chapter is to investigate the

impact the removal of plant biomass might have on the ecosystem in the Peel Inlet.

The second objective for this chapter is to assess the ecological value of a Marine

Protected Area (MPA) MPA along the northern end of Point Grey in the Peel-Harvey

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Estuary. Point Grey, a peninsula between the Peel Inlet and the Harvey Estuary, is a

potential focus for future residential development in the Peel region. Approximately

6,500 residents may move to this area once the development project is finished

(http://www.pointgrey.com.au/). The hypothetical MPA examined in this chapter

will encompass three different habitat types. The chapter compares this MPA design

with a different MPA design of the same size but located at the east coast of the Peel

Inlet, adjacent to the Austin Bay Nature Reserve and covering mainly plant habitat

with a high level of primary production.

The third objective of this chapter is to consider future environmental impacts on

waterbirds, which are a key environmental value for the estuary. The Peel-Harvey

Estuary is part of the Peel-Yalgorup Ramsar site, which also includes a number of

small lakes to south of the estuary (Hale & Butcher, 2007). The Peel-Yalgorup Ramsar

site is classified as a ‘Wetland of International Importance’ under the Ramsar

Convention on Wetlands (Hale & Butcher, 2007), making the Peel-Yalgorup wetland

system internationally important for the conservation of migratory birds (Hale &

Butcher, 2007). The site supports large numbers of waterbirds, as well as birds that

use the wetland system as summer sanctuary (Hale & Butcher, 2007). Waterbirds

use the Peel-Harvey Estuary as feeding and nesting habitat (Lane et al., 2002; Ninox-

Wildlife-Consulting, 1990). Due to the importance of the estuary as waterbird

habitat, the impact on waterbirds is investigated here in an ecosystem context by

analysing spatial interactions.

Ecospace is a useful tool for evaluating the potential efficacy of different policy

alternatives before they are subjected to the cost and logistical effort of rigorous

empirical analysis and experimental field testing (Walters et al., 1999). Ecospace,

which divides the Ecopath/Ecosim ecosystem model up into a grid of spatial cells,

has historically been used to examine spatial aspects (dynamics) of fisheries

management issues (e.g. the effects of area-based controls on fish stocks (Babcock et

al., 2005). This chapter used Ecospace to investigate the ecological effects of

reducing plant habitat through dredging using the Ecopath model presented in

earlier chapters. As this approach does not have an explicit fisheries management

http://www.pointgrey.com.au/

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focus, it can be considered an unusual application of Ecospace. To confirm the

validity of this approach, I contacted two experts (Dr. Villy Christensen and Dr. Carl

Walters, both from the University of British Columbia) to ask their opinion. Dr.

Walters observed that “it is completely appropriate to use Ecospace to examine

policies that change spatial structure of an ecosystem in ways that alter vulnerability

to predation over parts of a spatial field” (Pers. comm. Carl Walters, University of

British Columbia).

In this chapter, Ecospace is used to investigate three research questions:

1. Will a reduction in plant biomass through dredging lead to a decrease in

waterbird biomasses?

2. Will the introduction of a MPA positively affect waterbirds?

3. Will a MPA that covers different habitats is more effective than a MPA of the

same size that mainly covers one habitat?


Ecospace

The simulations in this chapter use the mass-balanced model of the Peel-Harvey

Estuary after the opening of the Dawesville Channel (‘post DC’ Ecopath model)

developed in Chapter 4. In Chapter 6, the temporal dynamics of each functional

group were explored using Ecosim (Table 6.5). The underlying Ecosim scenario for

the Ecospace simulations includes all gears and sectors under the current fishing

pressure (F = 1).

Ecospace represents biomass dynamics over two-dimensional space and time

(Walters et al., 1999). The user can develop a two-dimensional map by defining

rectangular grids of cells. Each cell is assigned to a different habitat type and within

each cell, the biomass densities are treated as homogenous for trophic interactions,

- 288 -

fishing and movement calculations (Walters et al., 1999). Emigration flows occur

from the four surrounding cells that border the cell. Emigration rates to the “outside

world” (i.e. to the space outside the boundaries of the grid) are assumed to be

compensated by immigration rates from that outside world (Walters et al., 1999).

For N active (non-land) grid cells, each fishing fleet or gear type k can cause a total

mortality of N · Fk over the whole grid and thus, for each cell c the fishing mortality is

estimated according to:

c

kckckkc GGNFF /

where N is the number of active grid cells; Fk is the fishing mortality caused by gear

type k; and Gkc represents the weighted ‘attractiveness’ of the single cell c to fleet k

(Walters et al., 1999). Ecospace also considers if cell c is open to fishing or if the

habitat is suitable for fishing, which is described by parameter Gkc. In regard to

fishing Ecospace takes the catchability of animals (i) to gear k, as well as the relative

costs of fishing by gear k in cell c into account (Walters et al., 1999).

In Ecospace, each biomass at any simulated moment t is estimated according to:

icricricricricr

icr BEZgCIdt

dB,,,,,,,,,,

,, )()(

where: Br,c,i describes the Ecopath biomass pool i, at the cell defined by r: grid map

row and c: grid map column; Ir,c,i is the immigration rate from surrounding cells; Cr,c,i

is the food consumption rate; Zr,c,i is the total mortality; and Er,c,i is the total

emigration rate of pool i in the defined cell (Walters et al., 1999).

By solving the algorithm implemented in Ecospace, the spatial biomass equilibrium

can be found quickly. However, it should be kept in mind that, in most cases, the

time behaviours predicted by Ecospace will only present general indications of the

speed and general direction(s) in which biomass responses might occur after change

in management (e.g. the implementation of an MPA) (Walters et al., 1999).

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Settings

To assess spatial dynamics in Ecospace, the habitats in the estuary need to be

defined and assigned to each functional group and to the fishing sectors. Four

habitats were chosen for the Peel-Harvey Estuary based on data from nearshore

habitat mapping (Valesini et al., 2009), bathymetry (Hale & Butcher, 2007) and the

distribution of primary producers (Wilson et al., 1999). The habitat without

vegetation was divided into shallow mud areas and deeper sandy areas. Areas with

submerged aquatic vegetations were defined as ‘plant cover’ habitat; these areas

were not specified according to the type of vegetation, as no data was available

allowing the assignment of the remaining functional groups to different vegetation

types, such as seagrass, Cladophora montagneana or other macrophytes. The

habitat ‘rock’ was added as final habitat; this substrate is common at boat ramps at

the west coast of the estuary (Valesini et al., 2009).

Each functional group was assigned to the four different habitats (Table 7.1). In

Ecospace, a fraction of the biomass of each functional group is moving, but this

movement reflects a random dispersal movement (in km/ year), rather than any

directed seasonal migration (Christensen et al., 2005). A dispersal rate was defined

for each functional group, ranging from 0 for sessile groups to 300km year-1 for

highly mobile groups (Table 7.1). The dispersal rate for ‘passive’ plankton organisms

was set to 300 km year-1 for phytoplankton and zooplankton (Walters et al., 1999)

and to 10 km year-1 for the ‘passive’ detritus group, which was assumed to be

digested before reaching a higher dispersal rate (Table 7.1). The dispersal rate from

the bad habitat was lower than from the preferred habitat (Table 7.1), which is the

logical consequence of the habitat assignment (that is, the animals spend more time

in their preferred habitat and move longer distances in and out of these grid cells,

than in the bad habitat) (Christensen et al., 2005; Walters et al., 1999).

It was assumed that the feeding rate in the bad habitat was only 50%, 10%, or 1% of

the feeding rate in the preferred habitat, depending on the amount of food found in

the bad habitat and the amount of time spent in that area (Table 7.1). For example,

the dolphin population in the Peel-Harvey Estuary is thought to favour the deeper

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areas surrounding the Dawesville Channel and thus, the feeding rate in shallow and

plant habitats will be rather low (Table 7.1). Also, herbivorous fish that mainly feed

on aquatic plants were assumed to have a low feeding rate in habitats where plants

are not abundant (Table 7.1).

The vulnerabilities in bad habitat for top predators were eliminated by setting the

vulnerability to 1, as the top predators do not face any vulnerability to predation

(Table 7.1,(Walters et al., 1999). For groups that occur in all habitats, the

vulnerability was set to 2 (Table 7.1). For the remaining groups, the vulnerability

settings in Ecosim (Table 6.2) were used as basis for estimating the vulnerability to

predation in bad habitat. This vulnerability setting was estimated by determining

how sensitive a group is to predation (summing up all vulnerabilities for each group,

Table 6.2) and dividing the sum by the number of total predator groups in the system

(Table 7.1). This estimate is critical, as the vulnerability to predation in the bad

habitat can be higher or lower than the vulnerability in the preferred habitat.

In the literature, vulnerability in bad habitat is often set to default value of 2, which

indicates that vulnerability to predation in bad habitat is twice as high as in the

preferred habitat (Lozano-Montes et al., 2012; Mackinson & Daskalov, 2007; Zeller &

Reinert, 2004). However, as this default value will be incorrect for at least some

species, a basic kind of sensitivity analysis was applied in which simulations were run

with ratios set at high values and set at low values. The sensitivity analysis was

recommended by Dr. Carl Walters (personal communication, University of British

Columbia).

The sensitivity analysis was performed in two steps. Firstly, all vulnerabilities were

set to a low value, such as the default value 2 (except for the top predators, who did

not face predation pressure). Secondly, the highest vulnerability identified during the

Ecosim fitting procedure (Table 6.2) was adopted for the plant removal Ecospace

scenario. This analysis considers different vulnerability settings; thus, it presents the

impact of the vulnerability settings on the results and allows a critical evaluation of

the outcome of the plant removal scenario.

- 291 -

Table 7.1: Ecospace settings for the different functional groups

Assigned Habitat Bad Habitat

Group name Sand, deep

Mud, shallow

Plant cover

Rock

Base dispersal

rate (km year-1)

Relative dispersal

rate

Relative vulnerability to

predation

Relative feeding

rate

dolphins X 300 30 1 0.01

waterbirds X X 30 30 1 0.1

piscivorous waterbirds X X X X 300 5 1 0.5

sharks X X X 300 5 1 0.2

marine omnivorous fish X X 30 10 212 0.5

marine carnivorous fish X X X X 30 10 2 0.5

marine herbivorous fish X 30 3 1 0.01

marine detritivorous fish X X 30 5 19 0.1

estuarine omnivorous fish X X 30 10 5 0.5

estuarine carnivorous fish X X X X 30 10 2 0.5

estuarine herbivorous fish X 30 3 2 0.01 estuarine detritivorous fish X 30 5 1 0.01

Whiting X X X X 100 10 2 0.5

Arripis georgianus X X X X 100 10 2 0.5

Aldrichetta forsteri X X 100 10 198 0.5

Mugil cephalus X X 100 10 215 0.5 Torquigener pleurogramma X 30 5 89 0.5

bivalves X X 1 1 177 0.5

gastropods X X X X 5 3 2 0.5

Western King Prawn X X X X 10 3 2 0.5

Blue Swimmer Crab X X X X 10 10 2 0.5

crustaceans X X X X 3 1 2 0.5

worms X X 3 1 23 0.5

zooplankton X X X X 300 3 2 0.5

microscopic algae X X X 300 30 193 0.5

algae X - 1 3 0.5

macrophytes X - 1 4 0.5

Chaetomorpha linum X - 1 22 0.5

Cladophora montagneana X - 1 58 0.5

seagrass X - 1 24 0.5

detritus X X X X 10 5 2 0.5

- 292 -

The habitat for the fishing fleets was defined for the different fishing gears (Table

7.2). For the commercial fishing sector, it was assumed that nets and ‘other’ fishing

gear were mainly applied in deeper areas and in areas without vegetation, whereas

crab traps were set in shallows and plant habitats (Table 7.2). The recreational

fishing sector is boat-based and shore-based and targets both finfish and

invertebrates (Malseed & Sumner, 2001), and is therefore likely to use all habitats

(Table 7.2).

Table 7.2: Assignment of the fishing sectors/ gears to the four habitats (deep sand, shallow mud, plant cover and rock) defined for the Peel-Harvey Estuary; the commercial sector operating with nets (beach seine, gill nets), crab traps and ‘other’ fishing gears Sand, deep Mud, shallow Plant cover Rock

Recreational X X X X

Beach seine X X

Gill net X X

Other X

Crab trap X X

Ecospace considers “areas of enhanced primary production to account for localized

productivity variations due to factors such as freshwater nutrient loading and

upwelling” (Walters et al., 1999, p. 542). Two levels of relative primary production

(levels 1 and 2, Fig. 7.1) were defined for the Peel-Harvey Estuary (Walters et al.,

1999). The closer a cell was to the entrance of the discharging rivers, the higher the

level of relative primary production assigned (Fig. 7.1).

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Figure 7.1: Basemap of the Peel-Harvey Estuary showing the assumed two levels of relative primary production with highest productivity closest to the discharging river entrances (the river mouths of the Serpentine and Murray Rivers in the north-east of the estuary and of the Harvey River in the south).

Ecospace is able to capture seasonal migration of each functional group and the user

can draw the monthly movement pattern in the grid map (Christensen et al., 2005).

However, detailed monthly migration patterns for the functional groups in the Peel-

Harvey Estuary are unknown and so, this migration aspect was not included for the

‘post DC’ Peel-Harvey model. The Ecospace model is based on the Ecopath model

- 294 -

(Walters et al., 1999) and the assumptions inherent to the Ecopath model also play a

“relevant role” in Ecospace (Ortiz & Wollf, 2002, p. 617). For the Peel-Harvey

estuarine model, migration was considered in Ecopath by adjusting the diets of

migrating groups, e.g. waterbirds. A fraction of the diets was assigned to imports,

which means that the food is consumed outside of the Peel-Harvey area due to

seasonal emigration/ immigration, see chapter 3 for the detailed description of each

functional group, including migration.

Ecospace scenarios

Four management scenarios, involving four Ecospace basemaps, were analysed: (1)

current spatial dynamics (Fig. 7.2); (2) a hypothetical partly dredged Peel Inlet, with

large areas being cleared from aquatic plants (Fig.7.3); and (3) & (4) two hypothetical

Marine Protected Areas, one in the eastern part of Peel Inlet, adjacent to Austin Bay

Nature Reserve (Fig. 7.4) and another adjacent to Point Grey peninsula (Fig. 7.5), an

area subject to future urban development. The potential effectiveness of the MPA

scenarios was assessed by their effect on fishing effort through area-based closures.

Specifically, the effect of removing recreational and commercial fishing effort was

examined by comparing the outcomes of the two MPA scenarios, with each MPA

being closed to fishing by both sectors. Ecospace predictions were based on

simulations that were performed for a period of ten years.

- 295 -

Figure 7.2: Basemap of the Peel-Harvey Estuary showing the current distribution of the four habitats: (a) shallow mud (brown); (b) deep sand (sand); (c) rock (grey rocks); and (d) plant cover (green). Land cells are coloured in dark grey. The two central points for boating in the estuary were defined as ports (brown circles); these are located at the Dawesville Channel and at Mandurah, in the North of the Peel Inlet.

- 296 -

Figure 7.3: Basemap of the Peel-Harvey Estuary showing the hypothesized habitat distribution after the reduction of plant habitat through dredging in the north-eastern part of Peel Inlet. The habitats are: (a) shallow mud (brown); (b) deep sand (sand); (c) rock (grey rocks); and (d) plant cover (green). Land cells are coloured in dark grey. The two central points for boating in the estuary were defined as ports (brown circles); these are located at the Dawesville Channel and at Mandurah, in the North of the Peel Inlet.

- 297 -

Figure 7.4: Basemap of the Peel-Harvey Estuary showing the hypothesized Marine Protected Area (MPA) at the eastern part of Peel Inlet, which contains plant habitat with very high primary production (black grid) and is adjacent to the Austin Bay Nature Reserve. The habitats are: (a) shallow mud (brown); (b) deep sand (sand); (c) rock (grey rocks); and (d) plant cover (green). Land cells are coloured in dark grey. The two central points for boating in the estuary were defined as ports (brown circles); these are located at the Dawesville Channel and at Mandurah, in the North of the Peel Inlet.

- 298 -

Figure 7.5: Basemap of the Peel-Harvey Estuary showing the hypothesized Marine Protected Area (MPA) at the Point Grey peninsula with the MPA covering three different habitat types (black grid) and having the same dimension as the MPA at the eastern Peel Inlet (Fig. 7.4). The habitats are: (a) shallow mud (brown); (b) deep sand (sand); (c) rock (grey rocks); and (d) plant cover (green). Land cells are coloured in dark grey. The two central points for boating in the estuary were defined as ports (brown circles); these are located at the Dawesville Channel and at Mandurah, in the North of the Peel Inlet.

- 299 -

7.3 Results

A. Fishing scenarios

Current State Scenario: Ecospace predicted a high biomass concentration in the

eastern part of Peel-Inlet and southern part of the Harvey Estuary after a temporal

simulation period of ten years (Fig. 7.6). Dolphins and bivalves were the only

functional groups that did not present a high biomass in these areas; these two

groups showed an elevated biomass level in the deeper, sand habitats (Fig. 7.6). The

centre of the Peel Inlet also contained high biomass concentrations for some

functional groups, including Blue Swimmer Crabs, Arripis georgianus, Aldrichetta

forsteri, Mugil cephalus and Torquigener pleurogramma (Fig. 7.6).

Fishing Effort Scenarios: All Ecospace scenarios for different levels of fishing effort

(that is, current fishing effort or closure of commercial or recreational fishing

sectors) predicted a decrease in total biomass (Table 7.3). However, the decrease

was greater if fishing sectors were closed. For example, closing the recreational

sector led to a decrease in total biomass of 7% and banning the commercial sector

caused a decline of total biomass of 11%. Changes in fishing effort did not affect the

long-term development of primary producers, as their biomass changes were similar

across all fishing effort scenarios. While algae, macrophytes and Chaetomorpha

linum increased in biomass, all other plant groups declined. Microscopic algae

declined in biomass when the commercial fishing sector was closed (Table 7.3).

Most invertebrate groups declined in biomass; only worms increased slightly under

all scenarios with fishing effort. The fish groups presented mixed responses. Some

single species groups declined in biomass, but marine detritivorous fish and

estuarine carnivorous fish showed drastic biomass increases, in particular in the

eastern part of Peel Inlet and the southern part of the Harvey Estuary (Fig. 7.6). All

top predators except for dolphins increased in biomass. Dolphins only increased in

biomass if the commercial sector was closed (Table 7.3).

- 300 -

Only five functional groups showed divergent responses in different scenarios:

dolphins, marine omnivorous fish, Mugil cephalus, worms and microscopic algae. All

other groups presented consistent and generally to partially constant biomass

responses under differing fishing pressures. The fishing scenarios mainly affected the

biomass distribution of the second trophic level and target species (compare figures

7.6, 7.7, and 7.8). Banning the commercial fishing sector resulted in much lower

biomass levels of invertebrate groups, Arripis georgianus and Aldrichetta forsteri,

particularly in the centre of the Peel Inlet (Figs. 7.6, 7.7. and 7.8).

- 301 -

Table 7.3: % change in biomass predicted by Ecospace for a scenario (Fig. 7.2) under current fishing effort and with no commercial or recreational fishing effort in the Peel-Harvey Estuary scenarios were based on basemap Fig. 7.2 and changes were calculated comparing the biomasses at the start and end (after 10 years) for each scenario

Group name F = 1 no commercial fishery no recreational fishery

dolphins -9 10 -7

waterbirds 119 114 125

piscivorous waterbirds 94 114 73

sharks 97 180 222

marine omnivorous fish 2 -39 -10

marine carnivorous fish 85 88 62

marine herbivorous fish 17 14 18

marine detritivorous fish 3981 2916 2973

estuarine omnivorous fish -39 -47 -43

estuarine carnivorous fish 1709 2568 1832

estuarine herbivorous fish 6 6 6

estuarine detritivorous fish 48 49 50

Whiting 123 45 94

Arripis georgianus -59 -97 -75

Aldrichetta forsteri -37 -75 -45

Mugil cephalus -11 248 -11

Torquigener pleurogramma -58 -79 -59

bivalves -63 -87 -71

gastropods -62 -55 -59

Western King Prawn -33 -66 -38

Blue Swimmer Crab -43 -89 -47

crustaceans -24 -29 -25

worms 20 -22 11

zooplankton -34 -19 -30

microscopic algae 9 -8 5

algae 66 66 67

macrophytes 43 46 45

Chaetomorpha linum 36 36 36

Cladophora montagneana -72 -70 -67

seagrass -13 -13 -13

detritus -20 -25 -21

Total -5 -11 -7

- 302 -

Figure 7.6: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary (basemap Fig. 7.2, Scenario shown in Table 7.3 under current fishing scenario with F = 1), with red indicating high and blue indicating low deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

- 303 -

Figure 7.7: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario where the commercial fishing sector was closed (scenario based on basemap Fig. 7.2, scenario shown in Table 7.3 no commercial fishing), with red indicating high and blue indicating low deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

- 304 -

Figure 7.8: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario where the recreational fishing sector was closed (scenario based on basemap Fig. 7.2, scenario shown in Table 7.3 no recreational fishing), with red indicating high and blue indicating low deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

- 305 -

B. Reducing plant habitat scenario

The reduction of plant habitat caused the biomass distribution of some groups to

change compared to the basic Ecospace scenario without any changes (compare

Figs. 7.6 and 7.9). Herbivorous fish groups showed high biomass concentrations in a

smaller area. Fish groups like whiting, Arripis georgianus, Aldrichetta forsteri, Mugil

cephalus and Torquigener pleurogramma extended their habitat area and also

increased in biomass in the area that was cleared (Fig. 7.9). Sharks showed the same

trend, as well as invertebrate groups, such as gastropods, Western King Prawn, Blue

Swimmer Crab, crustaceans and worms (Fig. 7.9). The long-term effect of plant

removal led to an increase in the biomass of almost all functional groups except for

piscivorous waterbirds, marine and estuarine herbivorous fish, bivalves and

zooplankton which decreased in biomass (Fig. 7.10).

To investigate the reliability of these outcomes, the sensitivity of the vulnerability

settings was analysed and the scenario was also run with low vulnerabilities (default

value 2) and high Ecosim vulnerabilities. The results demonstrated that the settings

impacted the biomasses of the functional groups (Table 7.4). A high vulnerability

setting predicted identical biomasses for five functional groups (groups 1, 3, 4, 11

and 12), whereas low settings predicted identical biomass for only one group (group

9). The low vulnerability settings predicted higher biomasses for 24 groups and for

the total biomass; in contrast, high vulnerabilities predicted higher biomass

estimates for only five functional groups (groups 2, 7, 10, 19, and 24).

Although the impact of varying fishing effort on catches was not analysed in detail

for this Ecospace scenario, increasing biomasses (Fig. 7.10) also caused catches to

increase from 3.9 to 5.0 t/km2, with the largest fraction of catch belonging to the

recreational sector with 2.25 t/km2.

- 306 -

Figure 7.9: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario where aquatic plants in the Peel Inlet are removed through dredging (scenario based on basemap Fig. 7.3, scenario shown in Table 7.4 run with settings presented in Table 7.1), with red indicating high and blue indicating low deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

- 307 -

Figure 7.10: Effects of plant removal and, consequently, reduction of plant habitat, presented as percentage (%) change in biomass for each functional group (Fig. 7.9). Biomasses were compared to the trends in biomass under current habitat distribution and fishing effort (Fig. 7.6). The scenario was run for ten years.

-30 -20 -10 0 10 20 30 40 50

dolphins

waterbirds


sharks









Whiting

Arripis georgianus


Mugil cephalus


bivalves

gastropods

Western King Prawn

Blue Swimmer Crab

crustaceans

worms

zooplankton

microscopic algae

algae

macrophytes

Chaetomorpha linum


seagrass

detritus

Total

Effect of plant removal (% change in biomass)

- 308 -

Table 7.4: Biomass estimates (in t/km²) for each functional group in an Ecospace scenario where plant habitat is reduced in Peel Inlet (Fig. 7.9). The scenarios were run with: (i) the mean vulnerability settings shown in Table 7.1; (ii) low settings (default value 2); and (iii) high settings (maximum vulnerability identified in Ecosim, see Table 6.2). In each scenario, the vulnerability of top predators (groups 1 to 4) was set to 1, as they did not face predation pressure.

Group name Table 7.1 low v high v 1 dolphins 0.06 0.07 0.06 2 waterbirds 0.05 0.03 0.17 3 piscivorous waterbirds 0.34 0.36 0.34 4 sharks 0.0018 0.0020 0.0018 5 marine omnivorous fish 0.24 0.27 0.20 6 marine carnivorous fish 0.43 0.49 0.41 7 marine herbivorous fish 0.36 0.35 0.41 8 marine detritivorous fish 8.52 9.73 7.75 9 estuarine omnivorous fish 0.10 0.10 0.09 10 estuarine carnivorous fish 9.06 9.34 9.61 11 estuarine herbivorous fish 0.30 0.29 0.30 12 estuarine detritivorous fish 0.91 0.89 0.91 13 Whiting 1.12 1.19 1.05 14 Arripis georgianus 0.12 0.14 0.09 15 Aldrichetta forsteri 0.42 0.45 0.36 16 Mugil cephalus 0.58 0.63 0.55 17 Torquigener pleurogramma 1.06 1.10 0.98 18 bivalves 2.90 2.94 2.84 19 gastropods 0.19 0.12 0.20 20 Western King Prawn 0.88 0.90 0.84 21 Blue Swimmer Crab 1.09 1.17 0.98 22 crustaceans 115.74 116.76 114.54 23 worms 57.88 60.00 54.89 24 zooplankton 30.61 29.43 31.52 25 microscopic algae 31.89 32.45 31.39 26 algae 15.78 16.30 14.12 27 macrophytes 10.56 10.94 9.70 28 Chaetomorpha linum 49.20 50.35 46.63 29 Cladophora montagneana 0.15 0.33 0.13 30 seagrass 13.65 15.32 8.35 31 detritus 10.52 10.64 10.38

Total 364.72 373.07 349.82

- 309 -

C. Marine Protected Area (MPA) Scenarios

The response of the ecosystem to either MPA scenario was generally similar (Figs.

7.11 and 7.12). The introduction of a MPA did not lead to changes in biomass

distributions (Figs.7.11 and 7.12), but did lead to changes in biomass. Compared to

the current state (Fig. 7.6), the predicted biomasses of the target species Arripis

georgianus, Aldrichetta forsteri and Mugil cephalus increased in biomass (Fig. 7.13).

The predicted biomasses of marine fish, bivalves, worms, and microscopic algae, as

well as the total biomass of the ecosystem increased after introduction of a MPA

(Fig. 7.13). However, some groups decreased in biomass, including zooplankton and

Blue Swimmer Crabs (Fig. 7.13). In general, the functional groups showed similar

trends in predicted biomasses for the introduction of a MPA at either location (i.e.

Peel Inlet or Point Grey) (Fig. 7.13). Dolphins, sharks and estuarine carnivorous fish

showed a decline in biomass after the establishment of an MPA at Point Grey (Fig.

7.13).

- 310 -

Figure 7.11: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area in the Peel Inlet (scenario based on basemap Fig. 7.4), with red indicating high and blue indicating low deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

- 311 -

Figure 7.12: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area at Point Grey (scenario is based on basemap Fig. 7.5), with red indicating high and blue indicating low deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

- 312 -

Figure 7.13: Percentage (%) change in biomass for each functional group after introduction of a MPA at the eastern Peel Inlet (Fig. 7.4, grey bars) or at Point Grey (Fig. 7.5, black bars). Changes are compared to the trends in biomass under the current fishing scenario and current habitat distribution (Fig. 7.6).

The trends in predicted biomass of catches differed between scenarios (Fig. 7.14).

After introducing a MPA at Point Grey, Ecospace predicted an increase in catch for all

fishing gear-types except for crab traps. In contrast, the introduction of a MPA at

Peel Inlet led to a decrease in catch for all gears and the total catch (Fig. 7.14).

-10,0 -5,0 0,0 5,0 10,0 15,0 20,0 25,0 30,0

dolphins

waterbirds


sharks









Whiting

Arripis georgianus


Mugil cephalus


bivalves

gastropods

Western King Prawn

Blue Swimmer Crab

crustaceans

worms

zooplankton

microscopic algae

algae

macrophytes

Chaetomorpha linum


seagrass

detritus

Total

% change in biomass

MPA Point Grey MPA Peel Inlet

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Figure 7.14: Percentage (%) change in catch exhibited for the different commercial fishing gears and the recreational fishing sector after introduction of a MPA at the eastern Peel Inlet (Fig. 7.4, grey bars) or at Point Grey (Fig. 7.5, black bars). Catch changes are compared to the catches under the current fishing scenario and current habitat distribution (Fig. 7.9)

The introduction of the MPA at Peel Inlet led to a change in biomass distribution of

the lower trophic level functional groups; this change differed significantly after

closing the recreational (Fig. 7.15) or the commercial (Fig. 7.16) fishing sectors. The

most drastic changes were predicted for bivalves, gastropods, Western King Prawns,

Blue Swimmer Crabs. Fish groups like Arripis georgianus and Aldrichetta forsteri were

expected to decrease drastically after closing the commercial fishing sector (Fig.

7.17). Mugil cephalus increased drastically in biomass (379%) after closure of the

commercial fishing sector, but this group was predicted to increase under all fishing

scenarios (Fig.7.17). Overall, the total biomass was predicted to decrease slightly if

one fishing sector was closed, but was almost stable (+0.3%) under the current

fishing scenario (Fig. 7.17).

Under the Peel Inlet MPA scenario, closure of fishing sectors led to a decrease of

total catches and only a ban of recreational fishing resulted in increasing catches

(Fig. 7.18). Even if the commercial fishery was closed, catches for the recreational

fishery did not increase (Fig. 7.18).

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0

5

10

15

20

beach seine crab trap gill net other recreational Total

% change in catch

MPA Point Grey MPA Peel Inlet

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Figure 7.15: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area in the Peel Inlet after closing the recreational fishing sector (scenario is based on basemap Fig. 7.4, scenario results also presented in Fig. 7.17 ‘no rec’), with red indicating high and blue indicating low deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

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Figure 7.16: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area in the Peel Inlet after closing the commercial fishing sector (scenario is based on basemap Fig. 7.4, scenario results also presented in Fig. 7.17 ‘no com), with red indicating high and blue indicating low deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

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Figure 7.17: Percentage (%) change in biomass exhibited for the different functional groups after introduction of a MPA at Peel Inlet under the current fishing scenario (black bars) and under the scenarios of closure of commercial (grey bars) and recreational (light grey bars) fishing sectors. Biomass changes are compared to the trends in biomass under the current fishing scenario and current habitat distribution (Fig. 7.6). Note that, for Mugil cephalus,

-100 -80 -60 -40 -20 0 20 40 60 80 100

dolphins

waterbirds


sharks









Whiting

Arripis georgianus


*Mugil cephalus


bivalves

gastropods

Western King Prawn

Blue Swimmer Crab

crustaceans

worms

zooplankton

microscopic algae

algae

macrophytes

Chaetomorpha linum


seagrass

detritus

Total

% change - MPA Peel Inlet

F=1 no rec no com

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Ecospace predicted a 379% increase in biomass after closure of the commercial fishing sector -- this result was not included in the graph so as to maintain the current scale.

Figure 7.18: Percentage (%) change in catch exhibited for the different commercial fishing gears and the recreational fishing sector after introduction of a MPA at the eastern Peel Inlet under the current fishing scenario (black bars) and under the scenarios of closure of the commercial (grey bars) and recreational (light grey bars) fishing sectors. Catch changes are compared to the catches under the current fishing scenario and current habitat distribution (Fig. 7.6).

The introduction of the MPA at Point Grey led to a change in biomass distribution in

the lower trophic level functional groups; this change was similar to the changes

after introducing a MPA at Peel Inlet (Figs. 7.19 and 7.20). Biomass distribution of

some groups changed, particularly after closure of the commercial fishing sector (Fig.

7.20). Similar to the MPA Peel Inlet scenario, the most drastic changes were

predicted for bivalves, gastropods, Western King Prawns, Blue Swimmer Crabs; fish

groups like Arripis georgianus, Aldrichetta forsteri and Torquigener pleurogramma

were also predicted to decrease drastically after closing the commercial fishing

sector (Fig. 7.21). The predictions of biomass change for Mugil cephalus were similar

for both MPAs (Fig. 7.21). Under the current fishing scenario, the biomasses of top

predator groups like dolphins and sharks decreased slightly, whereas these groups

increased in biomass after closure of a fishing sector (Fig. 7.22). Piscivorous

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0

50

100


% changes in catch - MPA Peel Inlet

F=1 no rec no com

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waterbids decreased by 12% after introducing a MPA at Point Grey and closure of

the recreational fishing sector (Fig.7.22).

Under the current fishing scenario (F=1), the catches of all gears and fleets were

predicted to increase after establishment of an MPA at Point Grey (Fig. 2.22).

However, after closing one fishing sector, the total catches declined. After closure of

commercial fishing, recreational catches decreased by 46% (Fig. 7.22). The closure of

the recreational fishing sector led to an increase in crab trap catches of almost 110%;

other gears showed similar trends but at a smaller magnitude (Fig.7.22).

Figure 7.19: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area at Point Grey after closing the recreational fishing sector (scenario is based on basemap Fig. 7.5, scenario results also presented in Fig. 7.21 ‘no rec’), with red indicating high and blue indicating low

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deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

Figure 7.20: Biomass distributions predicted by Ecospace for the Peel-Harvey Estuary exhibiting the biomasses for a scenario introducing a Marine Protected Area at Point Grey after closing the commercial fishing sector (scenario is based on basemap Fig. 7.5, scenario results also presented in Fig. 7.21 ‘no com’), with red indicating high and blue indicating low deviations from the Ecopath baseline for each functional group. The colour scale is linear in biomass, shading from red (high) to blue (low) (Walters et al., 1999).

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Figure 7.21: Percentage (%) change in biomass exhibited for the different functional groups after introduction of a MPA at Point Grey under the current fishing scenario (black bars) and under the scenarios of closure of commercial (grey bars) and recreational (light grey bars) fishing sectors. Biomass changes are compared to the trends in biomass under the current fishing scenario and current habitat distribution (Fig. 7.6). Closing the commercial fishing sector resulted in a 376% increase in biomass for Mugil cephalus Ecospace -- this result was not included in the graph to maintain the current scale.

-100 -80 -60 -40 -20 0 20 40 60 80

dolphins

waterbirds


sharks









Whiting

Arripis georgianus


*Mugil cephalus


bivalves

gastropods

Western King Prawn

Blue Swimmer Crab

crustaceans

worms

zooplankton

microscopic algae

algae

macrophytes

Chaetomorpha linum


seagrass

detritus

Total

% change - MPA Point Grey

F=1 no rec no com

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Figure 7.22: Percentage (%) change in catch exhibited for the different commercial fishing gears and the recreational fishing sector after introduction of a MPA at Point Grey under the current fishing scenario (black bars) and under the scenarios of closure of commercial (grey bars) and recreational (light grey bars) fishing sectors. Catch changes are compared to the catches under the current fishing scenario and current habitat distribution (Fig. 7.6)

7.4 Discussion

Data availability

A lack of data meant that only four habitats could be defined for these Ecospace

scenarios (Table 7.1). Thus, interactions between floral and faunal communities have

not been explored in detail. For example, the fish community over Cladophora beds

may well differ from fish community over seagrass areas, but the data needed to

ascribe further detail to habitat ‘plant cover’ and to assign functional groups to the

different plant habitats are simply not available.

Piscivorous waterbirds

Piscivorous waterbirds are an important predator group in the Peel-Harvey Estuary,

not only for fish, but also for invertebrate prey groups (see chapter 4). Waterbirds

and piscivorous waterbirds were impacted by fishing (Table 7.3) and would benefit

slightly from an introduction of a MPA, in particular a MPA at Point Grey (Fig. 7.13).

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0

50

100

150


% changes in catch - MPA Point Grey

F=1 no rec no com

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MPA scenarios

The MPA at Point Grey was more effective than the MPA in the Peel Inlet, as it

resulted in slightly higher total biomasses (0.8%, Fig. 7.13) than the MPA Peel Inlet

(0.3%) as well as higher biomasses of some functional groups, such as bivalves,

worms and marine fish groups (Fig. 7.18). The Point Grey MPA resulted in an

increase in catch biomasses for each fishing gear-type (Fig.7.14) and only the closure

of one fishing fleet led to a decrease in catches (Fig. 7.22). The analysis of biomasses

of each functional group (Fig. 7.21) showed that the functional groups generally did

not increase in biomass after the introduction of MPA Point Grey. The total biomass

decreased as well.

These results suggest that the increase in catches does not result from an increase in

production because of the introduction of MPA Point Grey (i.e. is, the MPA does not

appear to produce higher biomasses leading to higher catches). Rather, the Ecospace

predictions indicated that fishing pressure appeared to concentrate at the boundary

of where the Point Grey MPA would be situated. Fishing pressure often concentrates

at the boundaries of MPAs in “response to local increases in fish availability near that

boundary” (Walters et al., 1999, p.549). This concentration of effort can cause the

emigration and immigration rates of target species to become strongly imbalanced,

leading to depressed densities in the MPA (Walters et al., 1999). Walters et al.,

(1999, p.549) describe this as a “worrisome scenario”.

Local increases in fish availability near the boundary of the MPA could not be

detected by comparing Figures 7.19 and 7.20 after introducing a MPA at Point Grey

in the Peel-Harvey Estuary. However, the analysis of biomasses indicated that the

MPA Point Grey affected the biomasses of target species (Fig. 7.13 and 7.21).

Furthermore, the MPA is surrounded by deeper and shallow areas, making the

boundary of MPA Point Grey accessible for every fishing gear-type used in the

estuary. Thus, the results suggest that fishing pressure outside of the MPA Point

Grey was high (Fig. 7.14) and, therefore, this MPA can only be effective if the fishing

pressure is reduced or managed sustainably for the different target fish species

throughout the estuary.

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In contrast, the MPA Peel led to a decrease in total catch of about 3% (Fig.7.14). The

boundary of the MPA at Peel Inlet is only accessible by shore-based fishing; however,

the MPA is adjacent to Austin Bay Nature Reserve, where vehicle access is restricted

by fringing wetlands (Bamford & Rutherford, 2007) (Fig. 3.1). Thus, the MPA would

be sealed off, as the small number of roads in this area would make it difficult to

access. The MPA at Peel Inlet led to lower catches under the current fishing scenario

and catches declined further under lower fishing effort (Fig. 7.18). At the Point Grey

MPA, the total catch only declined when the fishing effort was alleviated (Fig. 7.22).

Varying the fishing pressure by reducing the commercial or recreational fishing effort

showed the marked impact of the recreational sector as, in each scenario, the

biomass distributions presented higher biomass levels after the closure of the

recreational fishing sector, with and without MPAs (Figs. 7.8, 7.15 and 7.19).

These findings suggest that an MPA at Point Grey would have a positive effect on the

biomasses of functional groups and target species and also increase the total

biomass of the system (Fig. 7.13). However, these effects depend strongly on

fisheries management (Figs. 7.21 and 7.22). While under the current fishing effort

scenario catches slightly increased, closure of the recreational fishing sector led to a

decrease in total catch from about 4.1 to 2.4 t/km2 by the commercial sector (-46%,

Fig. 7.22). Closure of the commercial fishing sector led to the system changing

drastically in the long-term (Fig. 7.20), leading to a decrease in total catch after 10

years to about 1 t/km2 by the recreational sector (-75%, Fig. 7.22). Thus, for a MPA to

be a successful management tool in the Peel-Harvey Estuary, managers and

stakeholders would need to find a solution regarding sustainable fisheries

management and waterbird conservation.

Ecospace has been applied to improve fisheries management in many countries. For

example, Ecospace simulations were applied to: improve the management of

demersal fish stocks in the Faroe Islands marine ecosystem (Zeller & Reinert, 2004);

evaluate a management strategy of La Rinconada Marine Reserve in Chile (Ortiz et

al., 2009); manage the harvest of a benthic ecosystem in Tongoy Bay in Chile (Ortiz &

Wollf, 2002); and manage the lobster fishery in Western Australia (Lozano-Montes et

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al., 2012). To improve fisheries management in the Peel-Harvey Estuary based on

Ecospace, it is essential to collect more data, in particular recreational catch and

effort data to produce robust model predictions.

Removal of plant biomass

The removal of plant biomass through dredging at the northern Peel Inlet (Fig.7.3)

had drastic effects on the ecosystem as it boosted the biomasses of most functional

groups, as well as the total biomass of this ecosystem model (Figs. 7.9 and 7.10). This

dredging scenario was spread equally across all primary producers located within

this ‘plant cover’ habitat (Table 7.1). It is important to note that removal of seagrass

areas had different effects on some functional groups than did removal of

Cladophora beds (see Ecosim results in chapter 6). If more data are provided on the

habitat-food web relationships in the Peel-Harvey Estuary, these predictions could

be run at a finer spatial scale. This would deliver more accurate predictions when

investigating the effects of plant removal on the ecosystem.

Waterbirds benefited from the reduction of plant habitat, whereas piscivorous

waterbirds declined 3% in biomass after ten years (Fig. 7.10). Under the current

fishing effort scenario, the total biomass of the system and of the fish community

increased (Fig. 7.10). Thus, while the major prey groups of piscivorous waterbirds

increased in biomass, this bird group was not able to profit from increased prey

biomasses in the model. The Ecospace simulations indicate that the reason for this

decline in bird biomass might be competition for fish. As catches also increased

drastically in scenarios, piscivorous waterbirds were in direct competition with the

fishing sectors as well with other piscivorous predators (e.g. dolphins and in

particular sharks) that: (a) showed drastic increases in biomass (Table 7.3) and (b)

benefited greatly from easing fishing pressure (Figs. 7.17 and 7.21). Thus, the

Ecospace simulations clearly indicate that the sustainable management of the fishing

sectors is essential for waterbird conservation (Fig. 7.13). It should be emphasised

that only the feeding habitat in the water body has been analysed here. This study

did not investigate if a terrestrial habitat change due to the construction of

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residential areas around the estuary would impact on the nesting sites of birds or if

the birds might be disturbed by the increased presence of humans.

Vulnerability

The sensitivity of the vulnerability settings in the plant removal scenario were

analysed to assess the reliability of these Ecospace predictions. Interestingly, low

vulnerabilities (default value 2) led to higher biomasses than high vulnerabilities,

which presented much lower biomass estimates (Table 7.4). This finding is consistent

with another Ecospace-based study, which found that the magnitude of changes

was markedly greater from the bottom-up control (represented by low

vulnerabilities: Christensen et al., 2005) than to vulnerabilities estimated by Ecosim

(Ortiz et al., 2009, p. 3421). However, Ortiz et al. (2009) noted that the difficulty in

assessing the study’s validity, particularly because few Ecospace models are available

for comparison. This suggests that more research is necessary to investigate the

impact of vulnerability settings in Ecospace and define guidelines for appropriate

values.

The settings used here (Table 7.1) presented biomass results that were in between

the two extreme settings. However, matches between predicted and high/low values

did occur and occurred far more frequently with high rather than low vulnerabilities.

Thus, it is unlikely that the default value of 2 actually represents the predator/prey

dynamics of the system in the preferred or bad habitat (see also analysis in Chapter

6, Tables 6.2 and 6.4, Figs. 6.7 and 6.8). Setting the vulnerability settings to default

would lead to higher biomasses and, thus, the effect of plant removal might be

overestimated. It is possible that some predator/prey interactions represent Lotka-

Volterra dynamics, suggesting that it would be legitimate to use high vulnerability

settings (Christensen et al., 2005). High vulnerabilities represent top-down control

processes, which are described as Lotka-Volterra dynamics (Christensen et al., 2005,

p. 86). In these cases, the predator biomass impacts how much of the prey biomass

is consumed and those interactions are characterised by rapid oscillations of prey

and predator biomasses, as well as unpredictable behaviour (Christensen et al.,

2005). Some predator/prey interactions in this model might follow Lotka-Volterra

dynamics, as oscillations occurred in the vulnerability analyses presented in the

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earlier chapters (see chapter 5, Fig. 5.3 B) and, for these cases, high vulnerability

settings would be appropriate for any Ecospace scenario.

These findings indicate that predator/prey interactions need to be studied in detail

and that vulnerability settings should be treated with caution. As the settings used

here were developed with caution and were obtained from the Ecosim fitting

procedure, they are likely to represent the dynamics of this ecosystem as accurately

as the constraints of the available dietary data allows. The sensitivity analysis

indicated that applying the default value did not enhance the results and, thus, that

use of the settings developed for this study (Table 7.1) was appropriate. The finding

that the plant removal led to an increase in biomass of some functional groups is

consistent with the findings in Ecosim. However, Ecospace showed how the spatial

distribution of some functional groups is impacted, a finding that could enhance the

conservation efforts and sustainable management of the estuarine ecosystem.

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Chapter 8

General Discussion

This chapter reviews the thesis as a whole, discusses key findings, and assesses the

implications. Part I reviews aspects of the modelling techniques used in this thesis.

Part II discusses the implications of this study for the management of the Shark Bay

and Peel Harvey ecosystems.

I. Modelling techniques Qualitative vs. quantitative modelling

This study applied two different types of modelling tools: (1) qualitative modelling

(or loop analysis) and (2) quantitative approaches using the

Ecopath/Ecosim/Ecospace package. Both modelling tools provided insights into the

dynamics of ecosystems, e. the Peel-Harvey ecosystem pre- and post-Dawesville

Channel, and improved our understanding of how these two ecosystem states

function.

Both modelling techniques produced robust and reliable results. However, if precise

predictions are required to evaluate a management scenario, Ecopath with Ecosim is

the more appropriate methodology, as this approach can deliver quantitative

information on changes in biomass and catches. In contrast, qualitative modelling

can only indicate the direction of change, which might not be sufficient for

evaluating management options. Nonetheless, qualitative models are the preferred

method when management decisions must be made quickly and when detailed

datasets for the ecosystem are not available.

Shark Bay ecosystem

The ecosystem in Shark Bay is mainly affected by fishing. Loop analysis was applied

to model the behavioural dynamics and trophic interactions of this seagrass

ecosystem (Chapter 2). One advantage of loop analysis is that the modeller is free to

design the model according to the requirements of the particular ecosystem context.

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For example, in the Shark Bay model the condition of megafauna had a strong

influence on the behaviour of these animals and, thus, the model had to encompass

this factor.

Finding the optimal structure of the qualitative model is the most difficult part of

qualitative modelling. All interactions in the qualitative model have the same

strength, which overestimates interactions that are weak in the real ecosystem and

underestimates strong ones. While grouping weak variables (Dambacher et al., 2003)

may minimize this effect, this procedure requires substantial knowledge about the

strength of interactions that are to be modelled. If the strengths of the interactions

are not well-known, this grouping is unlikely to be valid.

Developing different model alternatives might help identify the best model

structure. In Chapter 2, the structure of the behavioural model (Fig. 2.2) was based

on differential equations (Appendix 1). The underlying differential equations

determined the interaction coefficients for the community matrix and, therefore,

also the structure of the signed digraph. However, the structure of the behavioural

model led to difficulties in the model analysis. For example, a press perturbation to

the tiger shark variable led to a zero response sign for some variables, indicating that

positive and negative feedback loops outweigh each other. It was difficult to

determine which of those feedback loops was strong enough to define the overall

response sign. Simplifying the model structure by developing smaller submodels did

not solve this problem, as the critical structure had to be maintained (Appendix 1).

However, by applying data and magnifying the interaction coefficients, the

magnitude of the feedback loops could be estimated which helped in determining

the response sign of a variable.

The application of data in this analysis therefore showed that the magnification of

feedback loops overcame the problem of negative and positive loops outweighing

each other. Furthermore, this analysis showed that the interactions strengths differ,

as some response signs were determined by feedback loops with power six and

some by loops with power four. Introducing a magnitude of interaction strength to

this modelling technique would improve its suitability for management, as managers

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often require more detailed information than just the sign of the response, e.g. an

increase or decrease in target fish due to an increase in fishing pressure.

Nonetheless, this modelling technique is useful for improving our understanding of

ecosystem dynamics and showing that the results of this qualitative modelling

approach were robust and reliable.

Ecopath as a tool for ecosystem-based management

The application of the Ecopath modelling package has known limitations, which I will

not discuss in detail here except to examine them within the context of this case

study (Christensen et al., 2004). The Ecopath models of the Peel-Harvey Estuary

indicated gaps in the data that need to be addressed in the future to optimise the

utility of this modelling approach for ecosystem-based management. In addition,

data on diets, consumption, mortality and detritus would further improve the data

pedigree of these models (Chapter 3). Finally, adequate monitoring programs would

improve the biomass estimates that are available and deliver the long-term data set

that is essential for model calibration (Chapter 6).

Ecosim for investigating fishing scenarios

Chapter 6 demonstrated how useful the Ecosim modelling technique is for exploring

the effects of different fishing scenarios, particularly because it allows investigators

to extend their focus beyond that of a single species and to consider other functional

groups in the model. This allows an ecosystem-wide perspective that would not

otherwise be possible. However, while Ecosim can be a great tool for ecosystem-

based fisheries management, an effective calibration process is essential for the

outcome to be meaningful. For this reason, Ecosim was applied only to demonstrate

the dynamics of the functional groups under different fishing pressures, and not to

assess maximum sustainable yields and catch rates. The modelling results in Chapter

6 indicate that the second and other upper trophic levels in the Peel Harvey

ecosystem will probably decrease further in biomass. The scenarios presented in

Chapters 4 and 6 also suggest that the ecosystem has shifted toward a new

equilibrium state. More research is necessary to test this hypothesis and to deliver a

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long-term data set that allows optimised model calibration as well as the

investigation of potential fishing effort scenarios.

Combining Ecopath, Ecosim, and Ecospace

Combining Ecopath with Ecosim allows investigators to study habitat change or

habitat destruction scenarios (Chapter 7). Further geographic shifts in human

population density are highly likely for the Peel-Harvey region, as the population

increases steadily and infrastructure is built to accommodate settlement around

previously under-developed sections of the Peel-Harvey Estuary (e.g. around the

new Perth-Bunbury highway). Canal development has already altered the nature of

the estuary foreshore. The canals and rivers are important habitats and, ideally,

should have been included in the Ecospace scenario developed here. However, these

areas had to be left out because of a lack of data.

If appropriate data are applied, Ecospace is a useful approach for investigating

different management scenarios and their impact on the ecosystem. The Ecospace

scenario presented in Chapter 7 was quite simple – only four habitats were assigned

and the functional groups were treated as resident. The lack of data describing

migration within the estuary on a monthly basis precluded a more complex design.

Nevertheless, the modelling presented in Chapter 7 indicated that the value of

Ecospace for assessing the effects of human activities (e.g. fishing and dredging) on

the ecosystem at broad spatial scale.

Ecospace can be applied specifically to evaluate marine protected areas. Chapter 7

examined potential marine protected areas (MPAs) along the south-eastern coast of

the Peel Inlet and at the peninsula at Point Grey. As seagrass seems to have great

impact on the biomass of the main target species, the Blue Swimmer Crab (see

Chapter 6), the MPAs were simulated in these two different areas with plant cover.

More research is necessary to fill data gaps and reduce the uncertainty in Ecospace

parameters and its underlying Ecopath model. Nevertheless, the findings from the

simulations in Chapter 7 indicate that Ecospace is an appropriate tool to investigate

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and evaluate the efficacy of MPAs and assess the effect of habitat changes on the

ecosystem.

Vulnerability settings

Vulnerability settings are critical parameters in Ecosim and Ecospace. This study

investigated vulnerability settings in detail and the results clearly indicate that more

research needs to be done. In Ecospace, vulnerability settings are often just set to

default values (Lozano-Montes et al., 2012; Mackinson & Daskalov, 2007; Zeller &

Reinert, 2004) or, if vulnerabilities are quantified, settings of 1 and 100 are applied

(Ortiz & Wollf, 2002). The results of this study suggest that either of these

approaches may be problematic.

The analysis of vulnerability settings in Ecosim clearly showed that some

vulnerability settings needed to be treated with caution, as they drastically affected

the fitting procedure (category 3, Table 6.4). In contrast, other settings did not show

any impact at all (category 1, Table 6.4). In the literature, the vulnerability settings

usually range from 1 to 10, with low settings (value = 1) implying that the interaction

is resource-controlled and high values indicating ‘top-down’ control (Lozano-Montez

et al., 2011; Mackinson & Daskalov, 2007). In this study, the vulnerability setting with

the lowest sums of squares was applied in the manually performed fitting procedure

(Chapter 6, Table 6.2). For category 2 settings (Fig. 6.7), the sums of squares were

smallest when vulnerability was set to 1 or 2, suggesting that category 2 interactions

could represent ‘bottom-up’ control. Similarly, it is possible that high vulnerability

settings could represent ‘top-down’ control. However, the strongest predator/prey

interactions identified in this study (category 3) occurred at both high and low

vulnerability settings (Tables 6.2 and 6.4).

Other issues also arose with the vulnerability settings. For example, it was not

possible to establish a correlation between the vulnerability findings and the food

web. In addition, the Ecosim vulnerability settings were correlated with the settings

applied in Ecospace. Thus, when assessing the vulnerability to predation in a bad

habitat, it was important to consider the settings in Ecosim. The vulnerability settings

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applied in this study (Table 7.1) were based the premise that vulnerability to

predation could validly derived by: (1) ignoring any spatial aspect of predation and

(2) dividing the sums of the vulnerabilities by the number of predators in the system.

While this approach is not ideal, the basic sensitivity analysis presented in Chapter 7

indicated that the approach nonetheless calculated biomass estimates that were

more reliable than if the analysis had set the vulnerability setting in bad habitat to

the default value.

Given how critical vulnerability settings appeared to be for this ecosystem, it would

be useful to perform a category analysis in other Ecosim models, in order to find out

if the three categories identified here also occur in other ecosystems. Furthermore,

we do not know enough about predator/prey relationships and the vulnerability to

predation in different habitats – more research in this area may drastically affect

model predictions and thus the reliability of the predictions in Ecosim and Ecospace.

II. Considerations for Ecosystem Management Managing eutrophication in the Peel Harvey

If a eutrophic waterbody is to be managed sustainably, several factors argue that

constructing an artificial entrance channel is not a wise approach. Firstly, the costs of

building (and maintaining, if dredging is required) an artificial entrance channel are

high. Secondly, the channel might breach international agreements and national

laws relating to the environment in some countries (e.g. Germany). Thirdly, the

channel may have undesirable environmental impacts and can even exacerbate

existing problems. Finally, a channel may be ineffective in the long-term if the

underlying causes (e.g. excessive nutrient inputs) are not dealt with.

In terms of cost, the Dawesville Chanel was designed and constructed to decrease

nutrient concentrations and, consequently, primary production in the Peel-Harvey

Estuary (Peel Inlet Management Authority, 1990). The construction costs were

estimated at $37 million (http://en.wikipedia.org/Dawesville_Channel). However,

the costs of the Dawesville Channel may actually be much higher, as the services

http://en.wikipedia.org/Dawesville_Channel

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provided by the estuary ecosystem have declined after the channel opening (Chapter

4). Indeed, provisioning, regulating and supporting services all appear to have

declined (Table 4.4).

Finally, nutrient concentrations in the estuary are still high (Hale & Butcher, 2007)

and marine phytoplankton species have entered the estuary which are known to

cause algal blooms in other ecosystems (see Chapter 6). If these species cause

blooms in the future, the effects on the Peel-Harvey Estuary may be severe (Fig.

6.13). In that event, the only effective management intervention would be to effect a

substantial reduction of nutrients in the waterbody.

In fact, the results from this study indicate that a permanent reduction of

microscopic algae is the only sustainable management scenario that leads to a long-

term reduction in primary production (Fig. 6.12). The reduction of plant biomass

resulted in drastic biomass changes in Orbetello Lagoon, Italy (Brando et al., 2004).

The Ecosim scenarios in this study suggest however, that the harvest of primary

producers had mainly short-term effects and had no effect in the long run (see

Chapter 6, Figs. 6.10 and 6.11). Also, the results indicated that selective harvesting

must be treated with caution, as the reduction in one primary producer might

enhance the biomass of others. For example, Cladophora montagneana increased in

biomass after other primary producer groups were reduced. This and similar side-

effects need to be considered for sustainable management scenarios. Ecospace

modelling indicated that reducing the plant habitat through dredging led to a boost

in biomasses and even increased all primary producers in the model (Chapter 7; Fig.

7.10). Thus, neither the reduction of plant biomass nor the reduction in plant habitat

led to desired long-term effects, such as the reduction in primary production.

These findings indicate that the most promising management scenario for dealing

with eutrophication is the reduction in nutrients and thus, the decrease in

phytoplankton biomass. This management strategy was also part of the Peel-Harvey

management plan in 1990s (Peel Inlet Management Authority, 1990, 1994).

However, its aims have not been turned into reality (Hale & Butcher, 2007). In the

Peel-Harvey Estuary, the nutrients that are flushed into the water body come from

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the fertilizers that are applied in the catchment (Bradby, 1997; McComb, 1992).

While the application of fertilizers has been optimised and natural vegetation was

planted to reduce the nutrient input (Bradby, 1997), these measures have not been

enough to solve the nutrient problem (Hale & Butcher, 2007). Planting trees will also

not aid in the reduction of nutrient inputs, as the Dawesville Channel increased

salinities within the estuary, harming salt-sensitive natural vegetation fringing the

estuary (Gibson, 2001).

Overall, the results of this thesis strongly suggest that the only sustainable

management strategy that has no severe side-effects is a permanent and effective

limitation of nutrients that leading to a permanent small (10%) reduction in

phytoplankton biomass (Fig. 6.12). This management strategy will only affect one

group of stakeholders, the agricultural industry. It needs to be kept in mind that

agriculture is responsible for the eutrophication of the Peel-Harvey Estuary. The

costs of all management actions, e.g. the channel construction, the long-term costs

of the decline in ecosystem-services and the future costs that might evolve if

phytoplankton blooms will occur again – these costs were, and continue to be,

covered by the community. While the results of this study suggest the effective

management scenario for this estuary, it is not clear if the political parties will ever

achieve a reduction of nutrients, given resistance from farmers in the past who were

not willing to make changes in their production or who feared high costs (Bradby,

1997). Thus, it remains to be seen how or if this management strategy will be

implemented in the future.

Bird conservation

The Peel-Harvey Estuary is part of a RAMSAR site and therefore represents an

internationally important estuary for migrating birds (Bradby, 1997; Hale & Butcher,

2007). Waterbirds, in particular piscivorous waterbirds are important predators in

the estuary (Chapter 4, Fig. 4.15). Bird numbers appear to be decreasing, which is

consistent with the findings of this study, as Ecopath modelling showed a decline in

biomass for both bird groups in the model (Chapter 4). Ecospace predicted an

increase in bird biomass after establishing a MPA at Point Grey, but this was only

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effective under sustainable fisheries management (Chapter 7). Ecosim demonstrated

that these bird groups were affected by fishing, but a decrease in fishing effort did

not directly lead to an increase in bird biomass (Figs. 6.7 and 6.8). Thus, these effects

need to be analysed in detail and need to be monitored for the purpose of bird

conservation. Also, it is necessary to consider terrestrial habitat change, e.g. impacts

on nesting sites, as this study only investigated the waterbody, the birds’ feeding

habitat.

Fishing in the Peel-Harvey Estuary

The Peel-Harvey Estuary is fished extensively, particularly for the main target

species, the Blue Swimmer Crab (Chapter 6). Ecopath with Ecosim can be applied for

fisheries management in this estuary. However, the data available for the

recreational fishing sector are very poor (Malseed & Sumner, 2001). The extent to

which the recreational fishing sector operates and its impacts on the ecosystem can

only be estimated, as this recreational fishing is not routinely monitored and fishing

licences are not required. The results of this study indicate that the recreational

fishing sector impacts heavily on the estuarine ecosystem and needs to be managed

sustainably (Chapters 4, 6 and 7). Consequently, more data on the fishing pressure in

the Peel-Harvey Estuary are necessary in order to be able to use the modelling

described in this study for ecosystem-based fisheries management.

Fishing in Shark Bay

The seagrass ecosystem of Shark Bay is characterised by top-down control from tiger

sharks (Chapter 2). Despite the fact that only some megafauna species are major

components of the adult tiger shark’s diet, these species are impacted strongly by

the presence of tiger sharks and leave the feeding grounds in the seagrass beds in

summer. This control system has the potential to become imbalanced if, in common

with other populations of this species worldwide, there is a decrease in the

abundance of adult tiger sharks in the Shark Bay population. Targeting of tiger sharks

by fishers in the waters of Northern Australia and Indonesia has increased steadily

during the last years, which might impact the tiger shark stock in Shark Bay. The

ecological importance of this species and the potential impact that decrease of the

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population is likely to have on the ecosystem highlight the importance of sustaining

the tiger shark population in Shark Bay. This case study indicates that not only

trophic, but also behavioural interactions can be important for the dynamics and

stability of ecosystems. Consequently, behavioural interactions should be considered

for ecosystem-based fisheries management.

Conclusion

This PhD study identified key data gaps and used several modelling tools to identify

sustainable management strategies. Even though new questions and scientific issues

emerged, this study clearly demonstrated that an artificial entrance channel is not a

suitable management tool. As eutrophication is a problem for aquatic ecosystems in

many countries (Brando et al., 2004; Lin et al., 2007; Patricio & Marques, 2006), this

study should encourage managers to focus on nutrient reduction, as this is a

sustainable management strategy that will conserve biodiversity and maintain the

structure and functionality of the affected ecosystem.

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References

Agusti, S., Duarte, C.M.& Kalff, J. (1987) Algal cell size and the maximum density and

biomass of phytoplankton. Limnology and Oceanography, 32, 983-986. Allaby, M. (1999) A Dictionary of Zoology, Second edition edn. Oxford University

Press. Allen, D.G. (1986) Organic phosphorus in soils of the Peel-Harvey catchment area.

Government Chemical Laboratories, Perth, Western Australia, 30 pp. Althauser, L.L. (2003) An Ecopath/Ecosim analysis of an estuarine food web: seasonal

energy flow and response to river-flow related perturbations. Louisiana State University, USA, Master of Science thesis.

Amir, O.A., Berggren, P., Ndaro, S.G.M.& Jiddawi, N.S. (2005) Feeding ecology of the Indo-Pacific bottlenose dolphin (Tursiops aduncus) incidentally caught in the gillnet fisheries off Zanzibar, Tanzania. Estuarine, Coastal and Shelf Science, 63, 429-437.

Anderson, P.K. (1981) Studies of Dugongs at Shark Bay, Western Australia. II. Surface and Subsurface Observations. Australian Wildlife Research, 9, 85-99.

Anonymous (2001) Shark Bay World Heritage Property - Environmental values, cultural issues and potential petroleum industry impacts. Department of Environmental Protection, Western Australia, 1-219.

Anonymous (2005) Final application to the Australian Government Department of the Environment and Heritage on the developmental octopus fishery and octopus fishing in the Cockburn Sound (Line and Pot) managed fishery. Departmen of Fisheries, Perth, Western Australia, 24-28.

Aragones, L.V., Lawler, I.R., Foley, W.J.& Marsh, H. (2006) Dugong grazing and turtle cropping: grazing optimization in tropical seagrass systems? Oecologia, 149, 635-647.

Arreguin-Sanchez, F., del Monte-Luna, P., Diaz-Uribe, J.G., Gorostieta, M., Chavez, E.A.& Ronzon-Rodriguez, R. (2007). Trophic model for the ecosystem of La Paz Bay, Southern Baja California Peninsula, Mexico. In INCOFISH ecosystem models: transiting from Ecopath to Ecospace. (eds W.J.F. Le Quesne, F. Arreguin-Sanchez & S.J.J. Heymanns), Vol. Fisheries Centre Research Reports 15(6). Fisheries Centre, University of British Columbia.

Arshad, A., Kamarudin, E.M.S.& Saad, C.R. (2006) Study on fecundity, embryology and larval development of Blue Swimming Crab Portunus Pelagicus (Linnaeus, 1758) under laboratory conditions. Research Journal of Fisheries and Hydrobiology, 1, 35-44.

Babcock, E.A., Pikitch, E.K., McAllister, M.K., Apostolaki, P.& Santora, C. (2005) A perspective on the use of spatialized indicators for ecosystem-based fishery management through spatial zoning. ICES Journal of Marine Science, 62, 469-476.

Baird, D., McGlade, J.M.& Ulanowicz, R.E. (1991) Comparative ecology of six marine ecosystems. Philosophical Transactions of the Royal Society of London, 333, 15-29.

- 338 -

Bamford, M.R.& Rutherford, B. (2007) Waterbird monitoring in Austin Bay, Peel Inlet, for the proposed Austin Cove Development, Yunderup. Report prepared for RPS Environment Pty Ltd., Subiaco, Western Australia, 15 pp.

Barausse, A., Duci, A., Mazzoldi, C., Artioli, Y.& Palmeri, L. (2007). Trophic model of the Northern Adriatic Sea, an eutrophic and highly exploited ecosystem. In INCOFISH ecosystem models: transiting from Ecopath to Ecospace. (eds W.J.F. Le Quesne, F. Arreguin-Sanchez & S.J.J. Heymanns), Vol. Fisheries Research Centre Reports 15(6), Fisheries Centre, University of British Columbia.

Barquete, V., Bugoni, L.& Vooren, C.M. (2008) Diet of Neotropic cormorant (Phalacrocorax brasilianus) in an estuarine environment. Marine Biology, 153, 431-443.

Barros, N.B.& Wells, R.S. (1998) Prey and feeding patterns of resident bottlenose dolphins (Tursiops truncatus) in Sarasota Bay, Florida. Journal of Mammalogy, 79, 1045-1059.

Baum, J.K., Myers, R.A., Kehler, D.G., Worm, B., Harley, S.J.& Doherty, P.A. (2003) Collapse and conservation of shark populations in the Northwest Atlantik. Science, 299, 389-392.

Bellchambers, L.M.& de Lestang, S. (2005) Selectivity of different gear types for sampling the blue swimmer crab, Portunus pelagicus L. Fisheries Research, 73, 21-27.

Bender, E.A., Case, T.J.& Gilpin, M.E. (1984) Perturbation experiments in community ecology: theory and practice. Ecology, 65, 1-13.

Bicknell, C. (2006) Review of sand bypassing at Dawesville and Mandurah - Coastal Engineering Investigation. Department for Planning and Infrastructure, Fremantle, Western Australia, Technical Report 446, 1-40.

Birch, P.B. (1980) Phosphorus export from coastal plain catchments. The Peel - Harvey estuarine system study (1976-1980), Department of Conservation & Environment, Perth, WA, Bulletin No. 99.

Birch, P.B. (1982) Phosphorus export from coastal plain drainage into the Peel-Harvey estuarine system of Western Australia. Australian Journal of Marine and Freshwater Research, 33, 23-32.

Bishop, J., Bertrand, N., Evison, W., Gilbert, S., Grigg, A., Hwang, L., Kallesoe, M., Vakrou, A., van der Lugt, C.& Vorhies, F. (2010) TEEB - The Economics of Ecosystems and Biodiversity. Report for Business, Executive Summary 2010.

Bjorndal, K.A. (1997) Foraging ecology and the nutrition of sea turtles. In: Peter L.Lutz, John A. Musick (ed) "Biology of sea turtles", CRC Press, Boca Raton, 199-231.

Blaber, S.J.M.& Wassenberg, T.J. (1989) Feeding ecology of the piscivorous birds Phalacrocorax varius, P. melanoleucos and Sterna bergii in Moreton Bay, Australia: diets and dependence on trawler discards. Marine Biology, 101, 1-10.

Black, R., Robertson, A.I., Peterson, C.H.& Peterson, N.M. (1990) Fishes and benthos of near-shore seagrass and sandflat habitats at Monkey Mia Shark Bay, Western Australia. In "Research in Shark Bay", Report of the France-Australe Bicentenary Expedition Committee, pp 245-261.

Black, R.E., Lukatelich, R.J., McComb, A.J.& Rosher, J.E. (1981) Exchange of water, salt, nutrients and phytoplankton between Peel Inlet, Western Australia, and

- 339 -

the Indian Ocean. Australian Journal of Marine & Freshwater Research, 32, 709-720.

Black, R.E.& Rosher, J.E. (1980) Hydrology and meterology. The Peel - Harvey estuarine system study (1976-1980), Department of Conservation & Environment, Perth, WA, Bulletin No. 89.

Bond, C.E. (1979) Biology of Fishes W.B. Saunders Company, Philadelphia, USA, 514 pages.

Borgatti, S.P., Luczkovich, J.J., Johnson, J.C.& Everett, M.G. (2003) Defining and measuring trophic role similarity in food webs using regular equivalence. Journal of Theoretical Biology, 220, 303-321.

Bowman, B.& Bamford, M.R. (2008) Waterbird monitoring in the conservation zone of the creery. Environmental Protection Authority, Perth, Western Australia.

Bradby, K. (1997) Peel-Harvey: the decline and rescue of an ecosystem "Greening the catchment taskforce" Mandurah, Western Australia.

Bradshaw, M. (2004) A combination of state and market through ITQs in the Tasmanian commercial rock lobster fishery: the tail wagging the dog? Fisheries Research, 67, 99-109.

Brando, V.E., Ceccarelli, R., Libralato, S.& Ravagnan, G. (2004) Assessment of environmental management effects in a shallow water basin using mass-balance models. Ecological Modelling, 172, 213-232.

Bridge, N.F., Mackay, D.& Newton, G. (1998) Biology of the ornate angel shark (Squatina tergocellata) from the Great Australian Bight. Marine Freshwater Research, 49, 679-686.

Brown, C.J., Fulton, E.A., Hobday, A.J., Matear, R.J., Possingham, H.P., Bulman, C., Christensen, V., Forrest, R.E., Gehrke, P.C., Gribble, N.A., Griffiths, S.P., Lozano-Montes, H., Martin, J.M., Metcalf, S., Okey, T.A., Watson, R.& Richardson, A.J. (2010) Effects of climate-driven primary production change on marine food webs: implications for fisheries and conservation. Global Change Biology, 16, 1194-1212.

Bruce, B.D. (1992) Preliminary Observations on the Biology of the White Shark, Carcharodon carcharias, in South Australian Waters. Australian Journal of Marine & Freshwater Research, 43, 1-11.

Bundy, A., Lilly, G.R.& Shelton, P.A. (2000) A mass balance model of the Newfoundland-Labrador Shelf. Canadian Technical Report of Fisheries and Aquatic Sciences, 2310, 172 pp.

Bunting, J. (2002) Draft bycatch action plan for the Shark Bay Prawn Managed Fishery. Department of Fisheries, Perth, Western Australia, Fisheries Management Paper No. 147.

Campbell, D., Battaglene, T.& Shafron, W. (1992) Economics of resource conservation in a commercial shark fishery. Australian Journal of Marine & Freshwater Research, 43, 251-262.

Carrier, J.C.& Pratt, H.L. (1998) Habitat management and closure of a nurse shark breeding and nursery ground. Fisheries Research, 39, 209-213.

Chapinn III, F.S., Walker, B.H., Hobbs, R.J., Hooper, D.U., Lawton, J.H., Sala, O.E.& Tilman, D. (1997) Biotic control over the functioning of ecosystems. Science, 277, 500-504.

- 340 -

Cheal, A.J.& Gales, N.J. (1992) Growth, sexual maturity and food intake of Australian Indian Ocean Bottlenose Dolphins, Tursiops truncatus, in captivity. Australian Journal of Zoology, 40, 215-223.

Chong, V.C.& Sasekumar, A. (1981) Food and Feeding Habits of the White Prawn Penaeus merguiensis. Marine Ecology Progress Series, 5, 185-191.

Christensen, V. (1995a) Ecosystem maturity - towards quantification. Ecological Modelling, 77, 3-32.

Christensen, V. (1995b) A model of trophic interactions in the North Sea in 1981, the year of the stomach. DANA, 11, 1-28.

Christensen, V.& Pauly, D. (1993) Flow characteristics of aquatic ecosystems. In 'Trophic models of aquatic ecosystems' (eds V. Christensen & D. Pauly), Vol. 260, pp. 390, ICLARM Conf. Proc.

Christensen, V.& Walters, C.J. (2000). Ecopath with Ecosim: methods, capabilities and limitations. In Methods for assessing the impact of fisheries on marine ecosystems of the North Atlantic (eds D. Pauly & T.J. Pitcher), Vol. 8 (2), pp. 79-105. UBC, Fisheries Centre Research Reports.

Christensen, V., Walters, C.J.& Pauly, D. (2004) Ecopath with Ecosim: methods, capabilities and limitations. Ecological Modelling, 172, 109-139.

Christensen, V., Walters, C.J.& Pauly, D. (2005) Ecopath with Ecosim: a User's Guide. Fisheries Centre, University of British Columbia, Vancouver., November 2005 edition, 154 p. (available online at www.ecopath.org).

Compagno, L.J.V. (1984) Sharks of the World: An annotated and illustrated catalogue of shark species known to date. FAO Fisheries Synopsis, Food and Agriculture Organization of the United Nations. Rome, Italy, No. 125, vol. 4, pt. 2. .

Cruz-Escalona, V.H., Arreguin-Sanchez, F.& Zetina-Rejon, M. (2007) Analysis of the ecosystem structure of Laguna Alvarado, western Gulf of Mexico, by means of a mass balance model. Estuarine, Coastal and Shelf Science, 72, 155-167.

Dambacher, J.M. (2001) Qualitative analysis of the community matrix. PhD Dissertation, University of Oregon, USA, 117pp.

Dambacher, J.M., Brewer, D.T., Dennis, D.M., Macintyre, M.& Foale, S. (2007) Qualitative Modelling of Gold Mine Impacts on Lihir Island’s Socioeconomic System and Reef-Edge Fish Community. Environ. Sci. Technol., 41, 555-562.

Dambacher, J.M., Li, H.W.& Rossignol, P.A. (2002) Relevance of community structure in assessing indeterminacy of ecological predictions. Ecology, 85, 1372-1385.

Dambacher, J.M., Li, H.W.& Rossignol, P.A. (2003a) Qualitative predictions in model ecosystems. Ecological Modelling, 161, 79-93.

Dambacher, J.M., Li, H.W., Wolff, J.O.& Rossignol, P.A. (1999) Parsimonious interpretation of the impact of vegetation, food, and predation on snowshoe hare. Oikos, 84(3), 530-532.

Dambacher, J.M., Luh, H.-K., Li, H.W.& Rossignol, P.A. (2003b) Qualitative stability and ambiguity in model ecosystems. The American Naturalist, 161, 876-888.

Dambacher, J.M.& Ramos-Jiliberto, R. (2007) Understanding and predicting effects of modified interactions through qualitative analysis of community structure. The Quarterly Review of Biology, 82, 227-250.

Dambacher, J.M.& Rossignol, P.A. (2001) The golden rule of complementary feedback. ACM SIGSAM Bulletin, 35(4), 1-9.

http://www.ecopath.org)/

- 341 -

Dann, P. (1999) Foraging behaviour and diets of red-necked stints and curlew sandpipers in south-eastern Australia. Wildlife Research, 27, 61-68.

de Jonge, V.N.& Colijn, F. (1994) Dynamics of microphytobenthos biomass in the Ems estuary. Marine Ecology Progress Series, 104, 185-196.

de Lestang, S., Hall, N.& Potter, I.C. (2003) Influence of a deep artificial entrance channel on the biological characteristics of the blue swimmer crab Portunus pelagicus in a large microtidal estuary. Journal of Experimental Marine Biology and Ecology, 295, 41-61.

de Lestang, S., Platell, M.E.& Potter, I.C. (2000) Dietary composition of the blue swimmer crab Portunus pelagicus L. Does it vary with body size and shell state and between estuaries? Journal of Experimental Marine Biology and Ecology, 246, 241-257.

de Ruiter, P.C., Neutel, A.-M.& Moore, J.C. (1995) Energetics, patterns of interaction strengths, and stability in real ecosystems. Science, 269, 1257-1260.

del Monte-Luna, P., Arreguin-Sanchez, F.& Lluch-Belda, D. (2007) Marine Ecosystem Analyses in the Gulf of Ulloa, Mexico: BAC meets Ecopath

INCOFISH Ecosystem models: Transiting from Ecopath to Ecospace, UBC, Fisheries Centre Research Reports, 15, 114-133.

Department of Fisheries, W.A. (2002) Review of recreational take of coral in Western Australia. Fisheries Management Paper, No. 163.

Department of Fisheries, W.A. (2004) Fish stock and fishery enhancement in Western Australia. Fisheries Management Paper, No. 175.

Dill, L.M., Heithaus, M.R.& Walters, C.J. (2003) Behaviorally mediated indirect interactions in marine communities and their conservation implications. Ecology, 84, 1151-1157.

Dodrill, J.W.& Gilmore, R.G. (1977) Land birds in the stomachs of Tiger Sharks, Galeocerdo cuvieri (Peron and Lesueur). Auk, 95, 585-586.

Duan, L.J., Li, S.Y., Liu, Y., Moreau, J.& Christensen, V. (2009) Modeling changes in the coastal ecosystem of the Pearl River Estuary from 1981 to 1998. Ecological Modelling, 220, 2802-2818.

Ducker, S.C., Foord, N.J.& Knox, R.B. (1977) Biology of Australian Seagrasses: the Genus Amphibolis C. Agardh (Cymodoceaceae). Australian Journal of Botany, 25, 67-95.

Dudley, S.F.J., Anderson-Reade, M.D., Thompson, G.S.& McMullen, P.B. (2000) Concurrent scavenging off a whale carcass by great white sharks, Carcharodon carcharias, and tiger sharks, Galeocerdo cuvier. Fishery Bulletin, 98, 646-649.

Dunne, J.A., Martinez, N.D.& Williams, R.J. (2002) Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecology Letters, 5, 558-567.

Ebert, D.A. (2002) Ontogenetic changes in the diet of the sevengill shark (Notorynchus cepedianus). Marine Freshwater Research, 53, 517-523.

Eisenack, K.& Kropp, J. (2001) Assessment of management options in marine fisheries by qualitative modelling techniques. Marine Pollution Bulletin, 43, 215-224.

Fasham, M.J.R., Evans, G.T., Kiefer, D.A., Creasey, M.& Leach, H. (1995) The use of optimization techniques to model marine ecosystem dynamics at the JGOFS

- 342 -

Station at 47 degrees N 20 degrees W [and Discussion]. Philosophical Transactions: Biological Sciences, 348, 203-209.

Fennel, K., Losch, M., Schroeter, J.& Wenzel, M. (2001) Testing a marine ecosystem model: sensitivity analysis and parameter optimization. Journal of Marine Systems, 28, 45-63.

Feuillade, J.B., Feuillade, M. (1987) Modelling steady-state growth and photosynthesis rates of Oscillatoria rubescens continuous cultures in relation to temperature and irradiance. Journal of Plankton Research, 9, 445-457.

Finn, J.T. (1980) Flow analysis of models of the Hubbard Brook ecosystem. Ecology, 61, 562-571.

FishBase (2006) 'FishBase: A global System Information on Fishes'. Available at www.fishbase.org, accessed February, March 2009

Fletcher, W.J., Chesson, J., Sainsbury, K.J., Hundloe, T.J.& Fisher, M. (2005) A flexible and practical framework for reporting on ecologically sustainable development for wild capture fisheries. Fisheries Research, 71, 175-183.

Francesconi, K.& Clayton, D. (1996) Shark Bay World Heritage Property Management Paper for Fish Resources. Department of Fisheries, Perth, Western Australia, Fisheries Management Paper No. 91, 1-118.

Gabrielson, J.O.& Lukatelich, R.J. (1985) Wind-related resuspension of sediments in the Peel-Harvey estuarine system. Estuarine, Coastal and Shelf Science, 20, 135-145.

Gales, R., Brothers, N.& Reid, T. (1998) Seabird mortality in the Japanese tuna longline fishery around Australia, 1988-1995. Biological Conservation, 86, 37-56.

Garrigue, C. (1998) Distribution and biomass of microphytes measured by benthic chlorophyll a in a tropical lagoon (New Caledonia, South Pacific). Hydrobiologia, 385, 1-10.

Gartner, A. (2010) Trophic implications of light reductions for Amphibolis griffithii seagrass fauna, PhD thesis, Edith Cowan University, Western Australia.

Geijsel, P. (1983) A comparison between the feeding biology of mullets in the Swan-Avon and Peel-Harvey systems. Murdoch University, Honours thesis.

Gerold, V.& Hughes, R.G. (1994) Feeding behaviour and diet of Corophium volutator in an estuary in southeastern England. Marine Ecology Progress Series, 114, 103-108.

Gerritse, R.G., Wallbrink, P.J.& Murray, A.S. (1998) Accumulation of phosphorus and heavy metals in the Peel-Harvey Estuary in Western Australia: results of a preliminary study. Estuarine, Coastal and Shelf Science, 47, 679-693.

Gevaert, F., Davoult, D., Creach, A., Kling, R., Janquin, M.-A., Seuront, L.& Lemoine, Y. (2001) Carbon and nitrogen content of Laminaria saccharina in the eastern English Channel: biometrics and seasonal variations. Journal of the Royal Society of Western Australia, 81, 727-734.

Gibson, N. (2001) Decline of the riverine trees of the Harvey River delta following the opening of the Dawesville Channel. Journal of the Royal Society of Western Australia, 84, 116-117.

Gladstone, W. (1987) The eggs and larvae of the sharpnose pufferfish Canthigaster valentini (pisces: Tetraodontidae) are unpalatable to other reef fishes. Copeia, 1, 227-230.


- 343 -

Goh, J. (1992) The biology of the sandy sprat Hyperlophus vittatus in coastal waters along the West Coast of Australia. Murdoch University, Honours thesis.

Gordon, D.M., Birch, P.B.& McComb, A.J. (1981) The ecology of Cladophora in the Peel-Harvey estuarine system. The Peel - Harvey estuarine system study (1976-1980), Department of Conservation & Environment, Perth, WA, Bulletin No. 91.

Gordon, D.M.& McComb, A.J. (1989) Growth and production of the green alga Cladophora montagneana in a eutrophic Australian estuary and its interpretation using a computer program. Water Research, 23, 633-645.

Goy, J. (1990) Preliminary observations on the zooplankton of Shark Bay, Western Australia. In "Research in Shark Bay", Report of the France-Australe Bicentenary Expedition Committee, pp 107-113.

Groom, C.J.& Coughran, D.K. (2012) Three decades of cetacean strandings in Western Australia: 1981 to 2010. Journal of the Royal Society of Western Australia, 95, 63-76.

Guiry, M.D.& Guiry, G.M. (2010) AlgaeBase. World-wide electronic publication, National University of Ireland, Galway. http://www.algaebase.org.

Hale, J.& Butcher, R. (2007) Ecological character description of the Peel-Yalgorup Ramsar site. Report to the Department of Environment and Conservation and the Peel-Harvey Catchment Council, Perth, Western Australia, 186 pp.

Hamel, K.S.& Huber, A.L. (1985) Relationship of cellular phosphorus in the cyanobacterium Nodularia to phosphorus availability in the Peel-Harvey estuarine system. Hydrobiologia, 124, 57-63.

Harvey, C.J., Cox, S.P., Essington, T.E., Hansson, S.& Kitchell, J.F. (2003) An ecosystem model of food web and fisheries interactions in the Baltic Sea. ICES Journal of Marine Science, 60, 939-950.

Hatase, H., Sato, K., Yamaguchi, M., Takahashi, K.& Tsukamoto, K. (2006) Individual variation in feeding habitat use by adult female green sea turtles (Chelonia mydas): are they obligately neritic herbivores? Oecologia, 149, 52-64.

Hays, G.C., Broderick, A.C., Glen, F.& Godley, B.J. (2002) Change in body mass associated with long-term fasting in a marine reptile: the case of green turtles (Chelonia mydas) at Ascension Islands. Canadian Journal of Zoology, 80, 1299-1302.

Heithaus, M.R. (2001a) The biology of tiger sharks, Galeocerdo cuvier, in Shark Bay, Western Australia: sex ratio, size distribution, diet, and seasonal changes in catch rates. Environmental Biology of Fishes, 61, 25-36.

Heithaus, M.R. (2001b) Predator-prey and competitive interactions between sharks (order Selachii) and dolphins (suborder Odontoceti): a review. Journal of Zoology, London, 253, 53-68.

Heithaus, M.R. (2004) Predator-prey interactions. in "Biology of sharks and their relatives", CRC Press, Boca Raton, 487-521.

Heithaus, M.R. (2005) Habitat use and group size of pied cormorants (Phalacrocorax varius) in a seagrass ecosystem: possible effects of food abundance and predation risk. Marine Biology, 147, 27-35.

Heithaus, M.R., Dill, L.M., Marshall, G.J.& Buhleier, B. (2002a) Habitat use and foraging behavior of tiger sharks (Galeocerdo cuvier) in a seagrass ecosystem. Marine Biology, 140, 237-248.

http://www.algaebase.org/

- 344 -

Heithaus, M.R., Dill, L.M. (2002) Food availability and Tiger Shark predation risk influence Bottlenose Dolphin habitat use. Ecology, 83, 480-491.

Heithaus, M.R., Frid, A.& Dill, L.M. (2002b) Shark-inflicted injury frequencies, escape ability, and habitat use of green and loggerhead turtles. Marine Biology, 140, 229-236.

Heithaus, M.R., Frid, A., Wirsing, A.J., Bejder, L.& Dill, L.M. (2005) Biology of sea turtles under risk from tiger sharks at a foraging ground. Marine Ecology Progress Series, 288, 285-294.

Heithaus, M.R., Frid, A., Wirsing, A.J., Dill, L.M., Fourqurean, J.W., Burkholder, D., Thompson, J.& Bejder, L. (2007a) State-dependent risk-taking by green sea turtles mediates top-down effects of tiger shark intimidation in a marine ecosystem. Journal of Animal Ecology, 76, 837-844.

Heithaus, M.R., Frid, A., Wirsing, A.J.& Worm, B. (2008) Predicting ecological consequences of marine top predator declines. Trends in Ecology & Evolution, 23, 202-210.

Heithaus, M.R., Hamilton, I.M., Wirsing, A.J.& Dill, L.M. (2006) Validation of a randomization procedure to assess animal habitat preferences: microhabitat use of tiger sharks in a seagrass ecosystem. Journal of Animal Ecology, 75, 666-676.

Heithaus, M.R., Marshall, G.J., Buhleier, B.M.& Dill, L.M. (2001) Employing Crittercam to study habitat use and behavior of large sharks. Marine Ecology Progress Series, 209, 307-310.

Heithaus, M.R., Wirsing, A.J., Dill, L.M.& Heithaus, L.I. (2007b) Long-term movements of tiger sharks satellite-tagged in Shark Bay, Western Australia. Marine Biology, 151, 1455-1461.

Heritage, A.G.D.o.t.E.a. (2006) Coastal water quality, Western Australia’s Peel-Harvey estuarine system. paper prepared for the 2006 Australian State of the Environment Committee, Department of the Environment and Heritage, Canberra, 1-5.

Heupel, M.R., Carlson, J.K., Simpfendorfer, C.A. (2007) Shark nursery areas: concepts, definition, characterization and assumptions. Marine Ecology Progress Series, 337, 287-297.

Heupel, M.R.& Hueter, R.E. (2002) Importance of prey density in relation to the movement patterns of juvenile blacktip sharks (Carcharhinus limbatus) within a coastal nursery area. Marine Freshwater Research, 53, 543-550.

Hillman, K., McComb, A.J.& Walker, D.I. (1995) The distribution, biomass and primary production of the seagrass Halophila ovalis in the Swan/Canning Estuary, Western Australia. Aquatic Botany, 51, 1-54.

Holland, K.N., Wetherbee, B.M., Lowe, C.G.& Meyer, C.G. (1999) Movements of tiger sharks (Galeocerdo cuvier) in coastal Hawaiian waters. Marine Biology, 134, 665-673.

Holts, D.B., Julian, A., Sosa-Nishizaki, O.& Bartoo, N.W. (1998) Pelagic shark fisheries along the west coast of the United States and Baja California, Mexico. Fisheries Research, 39, 115-125.

Hornberger, G.M.& Spear, R.C. (1980) Eutrophication in the Peel-Inlet І. The problem-defining behavior and a mathematical model for the phosphorus scenario. Water Research, 14, 29-42.

- 345 -

Hosack, G.R., Hayes, K.R.& Dambacher, J.M. (2008) Assessing model structure uncertainty through an analysis of system feedback and Bayesian Networks. Ecological Applications, 18, 1070-1082.

Hourston, M., Platell, M.E., Valesini, F.J.& Potter, I.C. (2004) Factors influencing the diets of four morphologically divergent fish species in nearshore marine waters. Journal of the Marine Biological Association of the United Kingdom, 84, 805-817.

Hubas, C.& Davoult, D. (2006) Does seasonal proliferation of Enteromorpha sp. affect the annual benthic metabolism of a small macrotidal estuary? (Roscoff Aber Bay, France). Estuarine, Coastal and Shelf Science, 70, 287-296.

Huber, A.L. (1980) Cyanobacteria and nitrogen fixation in the Peel-Harvey estuarine system. The Peel - Harvey estuarine system study (1976-1980), Department of Conservation & Environment, Perth, WA, Bulletin No. 94.

Huber, A.L. (1985) Factors affecting the germination of akinetes of Nodularia spumigena (Cyanobacteriaceae). Applied and Environmental Microbiology, 49, 73-78.

Huber, A.L. (1986) Nitrogen fixation by Nodularia spumigena Mertens (Cyanobacteriaceae). 1: Field studies and the contribution of blooms to the nitrogen budget of the Peel-Harvey Estuary, Western Australia. Hydrobiologia, 131, 193-203.

Huisman, J.M., Kendrick, G.A., Walker, D.I.& Couté, A. (1990) The marine algae of Shark Bay, Western Australia. In "Research in Shark Bay", Report of the France-Australe Bicentenary Expedition Committee, pp 89-100.

Humphreys, W.F. (1979) Production and respireation in animal populations. Journal of Animal Ecology, 48, 427-453.

Humphries, R.B., Hornberger, G.M., Spear, R.C.& McComb, A.J. (1984) Eutrophication in Peel Inlet - III, a model for the nitrogen scenario and a retrospective look at the preliminary analysis. Water Research, 18, 389-395.

Hurley, P.C.F. (1998) A review of the fishery for pelagic sharks in Atlantic Canada. Fisheries Research, 39, 107-113.

Hurwitz, A. (1895) Ueber die Bedingungen, unter welchen eine Gleichung nur Wurzeln mit negativen reellen Theilen besitzt. Mathematische Annalen, 46, 273-285.

Hyndes, G.A., Kendrick, A.J., MacArthur, L.D.& Stewart, E. (2003) Differences in the species- and size-composition of fish assemblages in three distinct seagrass habitats with differing plant and meadow structure. Marine Biology, 142, 1195-1206.

Hyndes, G.A., Platell, M.E.& Potter, I.C. (1997) Relationships between diet and body size, mouth morphology, habitat and movements of six sillaginid species in coastal waters: implications for resource partitioning. Marine Biology, 128, 585-598.

Jakowyna, B.N. (2000) Nutrients in tributary inflows to the Peel-Harvey estuarine system: status and trend (1983-1998). Water and Rivers Commission, Water Resource Technical Series, No. WRT 23.

Jeffries, C. (1974) Qualitative stability and digraphs in model ecosystems. Ecology, 55, 1415-1419.

- 346 -

Jernakoff, P.& Nielsen, J. (1998) Plant–animal associations in two species of seagrasses in Western Australia. Aquatic Botany, 60, 359-376.

Justus, J. (2004) Qualitative scientific modeling and loop analysis. Southwest Colloquium in the History and Philosophy of the Life Sciences, 22pp.

Kangas, M., McCrea, J., Fletcher, W., Sporer, E.& Weir, V. (2006) Shark Bay Prawn Fishery. Department of Fisheries, Perth, Western Australia, ESD Report Series No. 3, 1-128.

Kangas, M., Weir, V., Fletcher, W. and Sporer, E. (2006) Shark Bay Scallop Fishery. Department of Fisheries, Perth, Western Australia, ESD Report Series No. 2.

Kangas, M.I. (2000) Synopsis of the biology and exploitation of the blue swimmer crab, Portunus pelagicus Linnaeus, in Western Australia. Fisheries Research Division, Western Australia, Fisheries Research Report, 1-22.

Kavanagh, P., Newlands, N., Christensen, V.& Pauly, D. (2004) Automated parameter optimization for Ecopath ecosystem models. Ecological Modelling, 172, 141-149.

Kearney, R.E. (2002) Co-management: the resolution of conflict between commercial and recreational fishers in Victoria, Australia. Ocean & Coastal Management, 45, 201-214.

Kemper, C.M.& Gibbs, S.E. (2001) Dolphin interactions with tuna feedlots at Port Lincoln, South Australia and recommendations for minimising entanglements. Journal of Cetacean Research and Management, 3, 283-292.

Kendrick, A.J.& Hyndes, G.A. (2005) Variations in the dietary compositions of morphologically diverse syngnathid fishes. Environmental Biology of Fishes, 72, 415-427.

Kerford, M. (2005) The ecology of the bar bellied sea snake (Hydrophis elegans) in Shark Bay, Western Australia. Master of Science Thesis, Simon Fraser University, pp.98.

Kibirige, I., Perissinotto, R.& Nozias, C. (2003) Grazing rates and feeding preferences of the mysid shrimp Gastrosaccus brevifissura in a temporarily open estuary in South Africa. Marine Ecology Progress Series, 251, 201-210.

Kimmerer, W.J., McKinnon, A.D., Atkinson, M.J.& Kessel, J.A. (1985) Spatial Distributions of Plankton in Shark Bay, Western Australia. Australian Journal of Marine & Freshwater Research, 36, 421-432.

Krebs, C.J., Boutin, S., Boonstra, R., Sinclair, A.R.E., Smith, J.N.M., Dale, M.R.T., Martin, K.& Turkington, R. (1995) Impact of Food and Predation on the Snowshoe Hare Cycle. Science, 269, 1112-1115.

Lane, J.A., Clarke, A.G.& Pearson, G.B. (2002) Waterbirds of Peel-Harvey Estuary in the mid 1970s. Department of Conservation and Land Management, Perth, Western Australia.

Langdon, C. (1988) On the causes of interspecific differences in the growth-irradiance relationship for phytoplankton. II. A general review. Journal of Plankton Research, 10, 1291-1312.

LaPointe, B.E., Littler, M.M.& Littler, D.S. (1992) Nutrient availability to marine macroalgae in siliciclastic versus carbonate-rich coastal waters. Estuaries, 15, 75-82.

Lasiak, T.& McLachlan, A. (1987) Opportunistic utilization of mysid shoals by surf-zone teleosts. Marine Ecology Progress Series, 37, 1-7.

- 347 -

Latchford, J. (1996) Samphire flats of the Peel-Harvey estuary, WA. published by 'Peel Preservation Group Inc'., Mandurah, Western Australia, 1-12.

Lavery, P.S., Bootle, S.& Vanderklift, M. (1999) Ecological effects of macroalgal harvesting on beaches in the Peel-Harvey Estuary, Western Australia. Estuarine, Coastal and Shelf Science, 49, 295-309.

Lavery, P.S., Lukatelich, R.J.& McComb, A.J. (1991) Changes in the biomass and species composition of macroalgae in a eutrophic estuary. Estuarine, Coastal and Shelf Science, 33, 1-22.

Lavery, P.S.& McComb, A.J. (1991a) Macroalgal-sediment nutrient interactions and their importance to macroalgal nutrition in a eutrophic estuary. Estuarine, Coastal and Shelf Science, 32, 281-295.

Lavery, P.S.& McComb, A.J. (1991b) The nutritional eco-physiology of Chaetomorpha linum and Ulva rigida in Peel Inlet, Western Australia. Botanica MArina, 34, 251-260.

Lees, K.& Mackinson, S. (2007) An Ecopath model of the Irish Sea: ecosystems properties and sensitivity analysis. Science Series Technical Report, Cefas Lowestoft, 138, 49 pp.

Lenanton, R.C.J.& Potter, I.C. (1987) Contribution of estuaries to commercial fisheries in temperate Western Australia and the concept of estuarine dependence. Estuaries, 10, 28-35.

Lepesteur, M., McComb, A.J.& Moore, S.A. (2006) Do we all face the same risk when bathing in the estuary? Water Research, 40, 2787-2795.

Levins, R. (1974) The qualitative analysis of partially specified systems. Annals of the New York Academy of Science, 231, 123-138.

Levins, R. (1975) The search for macroscopic in ecosystems. Simulation Councils Proceedings Series, 5, 213-222.

Levins, R. (1998) Qualitative Mathematics for understanding, prediction, and intervention in complex ecosystems D. Rapport, R. Constanza, P.R. Epstein, C.Gaudet and R. Levins, in "Ecosystem Health", Blackwell Science.

Libralato, S., Christensen, V.& Pauly, D. (2006) A method for identifying keystone species in food web models. Ecological Modelling, 195, 153-171.

Lin, H.-J., Shao, K.-T., Jan, R.-Q., Hsieh H-L, Chen, C.-P., Hsieh, L.-Y.& Hsiao, Y.-T. (2007) A trophic model for the Danshuei River Estuary, a hypoxic estuary in northern Taiwan. Marine Pollution Bulletin, 54, 1789-1800.

Link, J.S. (2002) What does ecosystem-based fisheries management mean? Fisheries, 27, 18-21.

Linke, T.E., Platell, M.E.& Potter, I.C. (2001) Factors influencing the partitioning of food resources among six fish species in a large embayment with juxtaposing bare sand and seagrass habitats. J.Exp.Mar.Biol.Ecol., 266, 193-217.

Lo, P.J. (1991) Diet of the White-faced Heron on Manawatu pastures. Notornis, 38, 63-71.

Loneragan, N.R., Potter, I.C., Lenanton, R.C.J.& Caputi, N. (1986) Spatial and seasonal differences in the fish fauna in the shallows of a large Australian estuary. Marine Biology, 92, 575-586.

Loneragan, N.R., Potter, I.C., Lenanton, R.C.J.& Caputi, N. (1987) Influence of environmental variables on the fish fauna of the deeper waters of a large Australian estuary. Marine Biology, 94, 631-641.

- 348 -

Lowe, C.G., Wetherbee, B.M., Crow, G.L.& Tester, A.L. (1996) Ontogenetic dietary shifts and feeding behaviour of the tiger shark, Galeocerdo cuvier, in Hawaiian waters. Environmental Biology of Fishes, 47, 203-211.

Lozano-Montes, H., Babcock, R.& Loneragan, N.R. (2012) Exploring the effects of spatial closures in a temperate marine ecosystem in Western Australia: A case study of the western rock lobster (Panulirus cygnus) fishery. Ecological Modelling, 245, 31-40.

Lozano-Montez, H.M., Loneragan, N.R., Babcock, R.C.& Jackson, K. (2011) Using trophic flows and ecosystem structure to model the effects of fishing in the Jurien Bay Marine Park, temperate Western Australia. Marine & Freshwater Research, 65, 421-431.

Lukatelich, R.J.& McComb, A.J. (1986a) Distribution and abundance of benthic microalgae in a shallow southwestern Australian estuarine system. Marine Ecology Progress Series, 27, 287-297.

Lukatelich, R.J.& McComb, A.J. (1986b) Nutrient levels and the development of diatom and bluegreen algal blooms in a shallow Australian estuary. Journal of Plankton Research, 8, 597-618.

Lukatelich, R.J., McComb, A.J. (1986) Nutrient levels and the development of diatom and bluegreen algal blooms in a shallow Australian estuary. Journal of Plankton Research, 8, 597-618.

Lukatelich, R.J., McComb, A.J. (1989) Seasonal changes in macrophyte abundance and composition in the Peel-Harvey estuarine system. Waterways Commission Report, N0. 16.

MacArthur, L.D.& Hyndes, G.A. (2007) Varying foraging strategies of Labridae in seagrass habitats: Herbivory in temperate seagrass meadows? Journal of Experimental Marine Biology and Ecology, 340, 247-258.

Mackinson, S.& Daskalov, G. (2007) An ecosystem model of the North Sea to support an ecosystem approach to fisheries management: description and parameterisation. Science Series Technical Report, Cefas Lowestoft, 142, 196 pp.

Mackinson, S., Daskalov, G., Heymanns, J.J., Neira, S., Arancibia, H., Zetina-Rejon, M., Jiang, H., Cheng, H.Q., Coll, M., Arreguin-Sanchez, F., Keeble, K.& Shannon, L. (2009) Which forcing factors fit? Using ecosystem models to investigate the relative influence of fishing and changes in primary productivity on the dynamics of marine ecosystems. Ecological Modelling, 220, 2972-2987.

Malseed, B.E.& Sumner, N.R. (2001) A 12-month survey of recreational fishing in the Peel-Harvey Estuary of Western Australia during 1998-1999. Fisheries Research Report, No. 127, 1-52.

Mandurah (2011) Tourism Strategy 2011-2016. City of Mandurah, Western Australia. Marsh, L.M. (1990) Hermatypic corals of Shark Bay, Western Australia. In "Research

in Shark Bay", Report of the France-Australe Bicentenary Expedition Committee, pp 115-128.

Martin, G.R., Jarrett, N.& Williams, N. (2007) Visual fields in Blue Ducks Hymenolaimus malacorhynchos and Pink-eared Ducks Malacorhynchus membranaceus : visual and tactile foraging. Ibis, 149, 112-120.

May, R.M. (1972) Will a large system be stable? Nature, 238, 413-414. May, R.M. (1973) Qualitative stability in model ecosystems. Ecology, 54, 638-641.

- 349 -

McAuley, R. (2006) Northern Shark Fisheries Status Report. State of the Fisheries Report 2005/06, eds W.J. Fletcher and F. Head, Department of Fisheries, Western Australia, 165-169.

McAuley, R.B., Simpfendorfer, C.A., Hyndes, G.A.& Lenanton, R.C.J. (2007) Distribution and reproductive biology of the sandbar shark, Carcharhinus plumbeus (Nardo), in Western Australian waters. Marine Freshwater Research, 58, 116-126.

McAuliffe, T.F., Lukatelich, R.J., McComb, A.J.& Qiu, S. (1998) Nitrate applications to control phosphorus release from sediments of a shallow eutrophic estuary: an experimental evaluation. Australian Journal of Marine & Freshwater Research, 49, 463 - 473.

McComb, A.J., Humphries, R. (1992) Loss of nutrients from catchments and their ecological impacts in the Peel-Harvey estuarine system, Western Australia. Estuaries, 15, 529-537.

McComb, A.J.& Lukatelich, R.J. (1990) Inter-relationships between biological and physicochemical factors in a database for a shallow estuarine system. Environmental Monitoring and Assessment, 14, 223 - 238.

McComb, A.J., Qiu, S., Lukatelich, R.J., McAuliffe, T.F. (1998) Spatial and temporal heterogeneity of sediment phosphorus in the Peel-Harvey estuarine system. Estuarine, Coastal and Shelf Science, 47, 561-577.

Metcalf, S.J., Dambacher, J.M., Hobday, A.J.& Lyle, J.M. (2008) Importance of trophic information, simplification and aggregation error in ecosystem models. Marine Ecology Progress Series, 360, 25-36.

Millenium Ecosystem Assessment, U.N., Washington, USA (2005). Ecosystems and their services. In www.maweb.org/documents/document.300.aspx.pdf.

Miller, B. (1979) Ecology of the Little Black Cormorant, Phalacrocorax sulcirostris, and Little Pied Cormorant, P. melanoleucos, in Inland New South Wales I. Food and Feeding Habits. Wildlife Research, 6, 79-95.

Mitchell, R.W., Blight, S.J., , Gaughan, D.J.& Wright, I.W. (2002) Does the mortality of released Sardinops sagax increase if rolled over the headline of a purse seine net? Fisheries Research, 57, 279-285.

Moisander, P.H., McClinton, E.& Paerl, H.W. (2002) Salinity effects on growth, photosynthetic parameters, and nitrogenase activity in estuarine planktonic cyanobacteria. Microbial Ecology, 43, 432-442.

Monaco, M.& Ulanowicz, R.E. (1997) Comparative ecosystem trophic structure of three U.S. mid-Atlantic estuaries. Marine Ecology Progress Series, 161, 239-254.

Moulton, P.L., Walker, T.I.& Saddlier, S.R. (1992) Age and Growth Studies of Gummy Shark, Mustelus antarcticus Guenther, and School Shark, Galeorhinus galeus (Linnaeus), from Southern Australian Waters. Australian Journal of Marine & Freshwater Research, 43, 1241-1267.

Muraoka, D. (2004) Seaweed resources as a source of carbon fixation. Bulletin of Fisheries Research Agency, Supplement No. 1, 59-63.

Murdoch, T.J.T., Glasspool, A.F., Outerbridge, M., Ward, J., Manuel, S., Gray, J., Nash, A., Coates, K.A., Pitt, J., Fourqurean, J.W., Barnes, P.A., Vierros, M., Holzer, K.& Smith, S.R. (2007) Large-scale decline in offshore seagrass meadows in Bermuda. Marine Ecology Progress Series, 339, 123-130.

http://www.maweb.org/documents/document.300.aspx.pdf

- 350 -

Murray-Jones, S.& Steffe, A.S. (2000) A comparison between the commercial and recreational fisheries of the surf clam, Donax deltoides. Fisheries Research, 44, 219-233.

Myers, R.A., Baum, J.K., Shepherd, T.D., Powers, S.P.& Peterson, C.H. (2007) Cascading effects of the loss of apex predatory sharks from a coastal ocean. Science, 315, 1846-1850.

Navarro-Angulo, L.& Robledo, D. (1999) Effects of nitrogen source, N:P ratio and N-pulse concentration and frequency on the growth of Gracilaria cornea (Gracilariales, Rhodophyta) in culture. Hydrobiologia, 398/399, 315-320.

Neira, S.& Arancibia, H. (2007) Modelling the food web in the upwelling ecosystem off central Chile in the year 2000. INCOFISH Ecosystem models: Transiting from Ecopath to Ecospace, UBC, Fisheries Centre Research Reports, 15, 71-86.

Nicklisch, A., Shatwell, T.& Koehler, J. (2008) Analysis and modelling of the interactive effects of temperature and light on phytoplankton growth and relevance for the spring bloom. Journal of Plankton Research, 30, 75-91.

Nielsen, M.V.& Tonseth, C.P. (1991) Temperature and salinity effect on growth and chemical composition of Gyrodinium aureolum Hulburt in culture. Journal of Plankton Research, 13, 389-398.

Nilson, S.G.& Nilson, I.N. (1976) Number, food and consumption, and fish predation by birds in lake Mockeln, southern Sweden. Ornis Scandinavica, 7, 61-70.

Ninox-Wildlife-Consulting (1990) The significance of mosquito breeding areas to the waterbirds of Peel Inlet, Western Australia. Waterways Commission Report, No. 20.

Okey, T.A., Banks, S., Born, A.F., Bustamante, R.H., Calvopina, M., Edgar, G.J., Espinoza, E., Farina, J.M., Garske, L.E., Reck, G.K., Salazar, S., Shepherd, S., Toral-Granda, V.& Wallem, P. (2004) A trophic model of Galapagos subtidal rocky reef for evaluating fisheries and conservation strategies. Ecological Modelling, 172, 383-401.

Okey, T.A.& Mahmoudi, B. (2002) An ecosystem model of the West Florida Shelf for use in fisheries management and ecological research. Florida Marine Research Institute, St. Petersburg, 163pp.

Okey, T.A.& Pauly, D. (1999) A mass-balanced model of trophic flows in Prince William Sound: decompartmentalizing ecosystem knowledge. Ecosystem Approaches for Fisheries Management, AK-SG-99-01, 621-635.

Olsen, A.M. (1959) The status of the School Shark Fishery in South-eastern Australian waters. Australian Journal of Marine & Freshwater Research, 10, 150-176.

Olson, R.J.& Watters, G.M. (2003) A model of the pelagic ecosystem in the eastern tropical pacific ocean. Inter-American Tropical Tuna Commission Bulletin, 22, 135-219.

Ortiz, M., Avendano, M., Campos, L.& Berrios, F. (2009) Spatial and mass balanced trophic models of La Rinconada Marine Reserve (SE Pacific coast), a protected benthic ecosystem: Management strategy assessment. Ecological Modelling, 220, 3413-3423.

Ortiz, M.& Wolff, M. (2002) Application of loop analysis to benthic systems in northern Chile for the elaboration of sustainable management strategies. Mar Ecol Prog Ser, 242, 15-27.

- 351 -

Ortiz, M., Wolff, M. (2004) Qualitative modelling for the Caete Mangrove Estuary (North Brazil): a preliminary approach to an integrated eco-social analysis. Estuarine, Coastal and Shelf Science, 61, 243-250.

Ortiz, M.& Wollf, M. (2002) Spatially explicit trophic modelling of a harvested benthic ecosystem in Tongoy Bay (central northern Chile). Aquatic Conservation: Marine and Freshwater Ecosystems, 12, 601-618.

Otway, N.M., Bradshaw, C.J.A.& Harcourt, R.G. (2004) Estimating the rate of quasi-extinction of the Australian grey nurse shark (Carcharias taurus) population using deterministic age- and stage-classified models. Biological Conservation, 119, 341-350.

Paine, R.T. (1992) Food-web analysis through field measurement of per capita interaction strength. Nature, 355, 73-75.

Palomares, M.L.D.& Pauly, D. (1998) Predicting food consumption of fish populations as functiona of mortality, food type, morphometrics, temperature and salinity. Marine & Freshwater Research, 49, 447-453.

Patricio, J.& Marques, J.C. (2006) Mass balanced models of the food web in three areas along a gradient of eutrophication symptoms in the south arm of the Mondego estuary (Portugal). Ecological Modelling, 197, 21-34.

Pauly, D. (1984) Fish population dynamics in tropical waters: a manual for use with programmable calculators The WorldFish Center.

Pauly, D.& Christensen, V. (1995) Primary production required to sustain global fisheries. Nature, 374, 254-256.

Pauly, D., Christensen, V.& eds. (1996) Mass-balance models of Northeastern Pacific ecosystems. Fisheries Centre Research Reports, 4, 133pp.

Pauly, D., Christensen, V.& Walters, C. (2000) Ecopath, Ecosim and Ecospace as tools for evaluating ecosystem impact of fisheries. ICES Journal of Marine Science, 57, 697-706.

Pauly, D., Christensen, V., Dalsgaard,J., Froese, R., Torres jr., F. (1998) Fishing down marine food webs. Science, 279, 860-863.

Pauly, D., Soriano-Bartz, M.& Palomares, M.L.D. (1993) Improved construction, parametrization and interpretation of steady-state ecosystem models. in: V. Christensen and D. Pauly (eds.), Trophic models of aquatic ecosystems, ICLARM Conf. Proc., 26, 1-13.

Pauly, D., Tristes, A., Capuli, E.& Christensen, V. (1998) Diet composition and trophic levels of marine mammals. ICES Journal of Marine Science, 55, 467-481.

Pedersen, M.F.& Borum, J. (1996) Nutrient control of algal growth in estuarine waters. Nutrient limitation and the importance of nitrogen requirements and nitrogen storage among phytoplankton and species of macroalgae. Marine Ecology Progress Series, 142, 261-272.

Peel Inlet Management Authority (1990) Draft Peel Inlet Management Programme Review. Waterways Commission Report, No. 18, 134 pp.

Peel Inlet Management Authority (1994) Dawesville Channel: environmental impacts and their management. Waterways Commission Report, Report No. 50, 1-14.

Pen, L.J. (1990) The biology of four species of native and two species of introduced fish in south-western Australia. Murdoch University, PhD thesis.

- 352 -

Pepperell, J.G. (1992) Trends in the distribution, species composition and size of sharks caught by gamefish anglers off South-eastern Australia, 1961-90. Australian Journal of Marine & Freshwater Research, 43, 213-225.

Perrin, W.F.& Gilpatrick, J.W.J. (1994) Spinner dolphin Stenella longirostris. In S.H. Ridgway and S.R. Harrison, editors. Handbook of Marine Mammals, Vol. 5. The first book of dolphins. Academic press, London.

Pikitch, E.K., Santora, C., Babcock, E.A., Bakun, A., Bonfil, R., Conover, D.O., Dayton, P., Doukakis, P., Fluharty, D., Heneman, B., Houde, E.D., Link, J., Livingston, P.A., Mangel, M., McAllister, M.K., Pope, J.& Sainsbury, K.J. (2004) Ecosystem-based fishery management. Science, 305, 346-347.

Pitcher, T.J., Buchery, E.& Trujillo, P. (2002) Spatial simulations of Hong Kong's Marine Ecosystem: Forecasting with MPAs and human-made Reefs. Fisheries Centre Research Reports, 10, 162 pp.

Platell, M.& Hall, N. (2006) Synthesis and gap assessment of fish dietary data required for modelling ecosystems in south-western Australia. Fisheries Research and Development Corporation, Australia, FRDC project 2002/016, Final report, 126 pp.

Platell, M.E., Potter, I.C.& Clarke, K.R. (1998) Resource partitioning by four species of elasmobranchs (Batoidea: Urolophidae) in coastal waters of temperate Australia. Marine Biology, 131, 719-734.

Platell, M.E., Potter, I.C., Clarke, K.R. (2001) Partitioning of food resources amongst 18 abundant benthic carnivorous fish species in marine waters on the lower west coast of Australia. Journal of Experimental Marine Biology and Ecology, 261, 31-54.

Pond, B. (2005) Stock exclusion from waterways in the Peel-Harvey coastal plain catchment Coastal Catchment Initiative Project, Final Progress Report, 1-10.

Potter, I.C., Baronie, F.M., Manning, R.J.G., Loneragan, N.R. (1989) Reproductive biology and growth of the Western School Prawn, Metapenaeus dalli, in a large Western Australian estuary. Australian Journal of Marine & Freshwater Research, 40, 327-340.

Potter, I.C., Cheal, A.J., Loneragan, N.R. (1988) Protracted estuarine phase in the life cycle of the marine pufferfish Torquigener pleurogramma. Marine Biology, 98, 317-329.

Potter, I.C., Loneragan, N.R., Lenanton, R.C.J.& Chrystal, P.J. (1983a) Blue-green algae and fish population changes in a eutrophic estuary. Marine Pollution Bulletin, 14, 228-233.

Potter, I.C., Loneragan, N.R., Lenanton, R.C.J., Chrystal, P.J.& Grant, C.J. (1983b) Abundance, distribution and age structure of fish populations in a Western Australian estuary. Journal of Zoology, London, 200, 21-50.

Potter, I.C., Manning, R.J.G.& Loneragan, N.R. (1991) Size, movements, distribution and gonadal stage of the western king prawn (Penaeus latisulcatus) in a temperate estuary and local marine waters. Journal of Zoology, London, 223, 419-445.

Preen, A.R., Marsh, H., Lawler, I.R., Prince, R.I.T.& Shepherd, R. (1997) Distribution and abundance of Dugongs, Turtles, Dolphins and other Megafauna in Shark Bay, Ningaloo Reef and Exmouth Gulf, Western Australia. Wildlife Research, 24, 185-208.

- 353 -

Prince, J.D., Potter, I.C., Lenanton, R.C.J.& Loneragan, N.R. (1982) Segregation and feeding of atherinid species (Teleostei) in south-western Australian estuaries. Marine & Freshwater Research, 33, 865-880.

Puccia, C.J.& Levins, R. (1985) Qualitative modeling of complex systems Harvard University Press, Cambridge, Massachusetts.

Punt, A.E., Walker, T.I.& Prince, J.D. (2002) Assessing the management-related benefits of fixed-station fishery-independent surveys in Australia's Southern Shark Fishery. Fisheries Research, 55, 281-295.

Punt, A.E., Walker, T.I., Taylor, B.L.& Pribac, F. (2000) Standardization of catch and effort data in a spatially-structured shark fishery. Fisheries Research, 45.

Quirk, J.& Ruppert, R. (1965) Qualitative economics and the stability of equilibrium. Review of Economic Studies, 32, 311-326.

Randall, J.E. (1992) Review of the Biology of the Tiger Shark (Galeocerdo cuvier). Australian Journal of Marine & Freshwater Research, 43, 21-31.

Rice, J.C. (2000) Evaluating fishery impacts using metrics of community structure. ICES Journal of Marine Science, 57, 682-688.

Riegmann, R., de Boer, M.& de Senerpont Domis, L. (1996) Growth of harmful marine algae in multispecies cultures. Journal of Plankton Research, 18, 1851-1866.

Robertson, A.I.& Klumpp, D.W. (1983) Feeding habits of the southern Australian garfish Hyporhamphus melanochir: a diurnal herbivore and nocturnal carnivore. Marine Ecology Progress Series, 10, 197-201.

Rogers, P., Hall, N.& Valesini, F. (2010) Science Strategy for the Peel-Harvey Estuary, prepared for the Peel-Harvey Catchment Council. Murdoch University, Western Australia, 114 pp.

Romano, N.& Zeng, C. (2006) The effects of salinity on the survival, growth and haemolymph osmolality of early juvenile blue swimmer crabs, Portunus pelagicus. Aquaculture, 260, 151-162.

Romano, N., Zeng, C. (2007) Acute toxicity of sodium nitrate, potassium nitrate, and potassium chloride and their effects on the hemolymph composition and gill structure of early juvenile blue swimmer crabs (Portunus pelagicus Linnaeus, 1758) (Decapoda, Brachyura, Portunidae). Environmental Toxicology and Chemistry, 26, 1955-1962.

Rose, T. (1994) Comparisons of the benthic and zooplankton communities in the eutrophic Peel-Harvey and nearby Swan Estuaries in south-western Australia. PhD thesis, Murdoch University, Perth, Western Australia

Rose, T. (2003) Water quality monitoring programme for the Peel-Harvey coastal catchment. Water quality monitoring program and infrastructure project for the Peel-Harvey; Department of Environment, Perth, Western Australia, 1-35.

Rybarczyk, H.& Elkaim, B. (2003) An analysis of the trophic network of a macrotidal estuary: the Seine Estuary (Eastern Channel, Normandy, France). Estuarine, Coastal and Shelf Science, 58, 775-791.

Rybarczyk, H., Elkaim, B., Ochs, L.& Loquet, N. (2003) Analysis of the trophic network of a macrotidal ecosystem: the Bay of Somme (Eastern Channel). Estuarine, Coastal and Shelf Science, 58, 405-421.

Rykiel, E.J.J. (1996) Testing ecological models: the meaning of validation. Ecological Modelling, 90, 229-244.

- 354 -

Salini, J.P., Blaber, S.J.M.& Brewer, D.T. (1992) Diets of Sharks from Estuaries and Adjacent Waters of the North-eastern Gulf of Carpentaria, Australia. Australian Journal of Marine & Freshwater Research, 43, 87-96.

Sarre, G.A. (1999) Age composition, growth rates, reproductive biology and diets of the black bream Acanthopagrus butcheri in four estuaries and a saline lake in south-western Australia. Murdoch University, PhD thesis.

Schmitz, O.J. (1997) Press perturbations and the predictability of ecological interactions in a food web. Ecology, 78, 55-69.

Sewell, P.L. (1982) Urban groundwater as a possible nutrient source for an estuarine benthic algal bloom. Estuarine, Coastal and Shelf Science, 15, 569-576.

Shaw, J. (1986) The fish and invertebrate fauna of adjacent weed and sand areas in the Swan Estuary. Murdoch University, Honours thesis.

Sicko-Goad, L.M., Schelske, C.L.& Stoermer, E.F. (1984) Estimation of intracellular carbon and silica content of diatoms from natural assemblages using morphometric techniques. Limnology and Oceanography, 29, 1170-1178.

Siemon, N., Davis, G., Hubbard, P.& Duckworth, A. (1993) Fringing vegetation of the Serpentine River in the Shire of Serpentine- Jarrahdale and City of Rockingham, Western Australia. Waterways Commission Report, No. 38.

Simpfendorfer, C. (2009) Galeocerdo cuvier. In: IUCN 2010. IUCN Red List of Threatened Species, Version 2010, www.iucnredlist.org, Downloaded January 2010.

Simpfendorfer, C.A. (1992) Biology of Tiger Sharks (Galeocerdo cuvier) caught by the Queensland Shark Meshing Program off Townsville, Australia. Australian Journal of Marine & Freshwater Research, 43, 33-43.

Simpfendorfer, C.A. (1998a) Diet of the Australian sharpnose shark, Rhizoprionodon taylori, from northern Queensland. Marine Freshwater Research, 49, 757-761.

Simpfendorfer, C.A., Donohue, K. (1998b) Keeping the fish in ‘fish and chips’: research and management of the Western Australian shark fishery. Marine Freshwater Research, 49, 593-600.

Simpfendorfer, C.A., Donohue, K., Hall, N. (2000) Stock assessment and risk analysis for the Whiskery Shark (Furgaleus macki (Whitley)) in south-western Australia. Fisheries Research, 47, 1-17.

Simpfendorfer, C.A., Goodreid, A., McAuley, R.B. (2001a) Diet of three commercially important shark species from Western Australian waters. Marine Freshwater Research, 52, 975-985.

Simpfendorfer, C.A., Goodreid, A., McAuley, R.B. (2001b) Size, sex and geographic variation in the diet of the tiger shark, Galeocerdo cuvier, from Western Australian waters. Environmental Biology of Fishes, 61, 37-46.

Simpfendorfer, C.A., Kitchingman, A.M., McAuley, R.B. (2002) Distribution, biology and fisheries importance of the pencil shark Hypogaleus hyugaensis, in the waters off south-western Australia. Marine Freshwater Research, 53, 781-789.

Simpfendorfer, C.A.& Unsworth, P. (1998) Reproductive biology of the whiskery shark, Furgaleus macki, off south-western Australia. Marine Freshwater Research, 49, 687-693.

http://www.iucnredlist.org/

- 355 -

Simpfendorfer, C.A., Unsworth, P. (1998c) Gill-net mesh selectivity of dusky sharks (Carcharhinus obscurus) and whiskery sharks (Furgaleus macki) from south-western Australia. Marine Freshwater Research, 49, 713-718.

Smith, A.D.M., Brown, C.J., Bulman, C.M., Fulton, E.A., Johnson, P., Kaplan, I.C., Lozano-Montes, H., Mackinson, S., Marzloff, M., Shannon, L.J., Shin, Y.-J.& Tam, J. (2011) Impacts of fishing low-trophic level species on marine ecosystems. Science, 333, 1147-1150.

Smith, A.D.M., Fulton, E.J., Hobday, A.J., Smith, D.C.& Shoulder, P. (2007) Scientific tools to support the practical implementation of ecosystem-based fisheries management. ICES Journal of Marine Science, 64, 633-639.

Smith, K.A. (2003) A simple multivariate technique to improve the design of a sampling strategy for age-based fishery monitoring. Fisheries Research, 64, 79-85.

Smith, N. (2000) First steps in Formalising Qualitative Systems Dynamics. Working Notes of the Fourteenth International Workshop on Qualitative Reasoning (QR-00), 138-144.

Smith, S.V.& Atkinson, M.J. (1983) Mass balance of carbon and phosphorus in Shark Bay, Western Australia. Limnology and Oceanography, 28, 625-639.

Spear, R.C.& Hornberger, G.M. (1980) Eutrophication in the Peel-Inlet ІІ. Identification of critical uncertainties via generalized sensitivity analysis. Water Research, 14, 43-49.

Stenseth, N.C. (1995) Snowshoe Hare Populations: Squeezed from Below and Above Science, 269, 1061-1062.

Stevens, J.D., Bonfil, R., Dulvy, N.K. and P.A. Walker (2000) The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES Journal of Marine Science, 57, 476-494.

Stevens, J.D., Lyle, J.M. (1989) Biology of three Hammerhead Sharks (Eusphyra blochii, Sphyrna mokarran and S. lewini) from Northern Australia. Australian Journal of Marine & Freshwater Research, 40, 129-146.

Stevens, J.D., McLoughlin, K.J. (1991) Distribution, size and sex composition, reproductive biology and diet of sharks from Northern Australia. Australian Journal of Marine Freshwater Research, 42, 151-199.

Stevens, J.D.& Wiley, P.D. (1986) Biology of two commercially important Carcharhinid sharks from Northern Australia. Australian Journal of Marine & Freshwater Research, 37, 671-688.

Stewart, E. (1998) Partitioning of habitat and dietary resources of Monacanthid species in seagrass meadows of south-western Australia. Murdoch University, Honours thesis.

Stolen, M.K.& Barlow, J. (2006) A model life table for bottlenose dolphins (Tursiops truncatus) from the Indian River Lagoon System, Florida. Marine Mammal Science, 19, 630-649.

Storr, G.M. (1990) Birds of Shark Bay area, Western Australia. In "Research in Shark Bay", Report of the France-Australe Bicentenary Expedition Committee, pp 299-311.

Strong, D.R. (1992) Are trophic cascades all wet? Differentiation and Donor-Control in speciose ecosystems. Ecology, 73, 747-754.

- 356 -

Summers, R.N.& Pech, J.D. (1997) Nutrient and metal content of water, sediment and soils amended with bauxite residue in the catchment of the Peel Inlet and Harvey Estuary, Western Australia. Agriculture, Ecosystems and Environment, 64, 219-232.

Summers, R.N., Van Gool, D., Guise, N.R., Heady, G.J.& Allen, T. (1999) The phosphorus content in the run-off from the coastal catchment of the Peel Inlet and Harvey Estuary and its associations with land characteristics. Agriculture, Ecosystems and Environment, 73, 271-279.

Sumner, N.R., Williamson, P.C.& Malseed, B.E. (2002) A 12-month survey of recreational fishing in the Gascoyne bioregion of Western Australia during 1998-1999. Department of Fisheries, Perth, Western Australia, Fisheries Research Report No. 139.

Tanner, J.E. (2003) Stability of optimal harvest regime under stochastic variation in prawn demographic rates and fishing strategies. Marine & Freshwater Research, 54, 635-642.

Tibbetts, I.R.& Carseldine, L. (2005) Trophic shifts in thre subtropical Australian halfbeaks (Teleostei: Hemiramphidae). Marine & Freshwater Research, 56, 925-932.

Trautmann, D.A.& Borowitzka, M.A. (1999) Distribution of the epiphytic organisms on Posidonia australis and P. sinuosa, two seagrasses with differing leaf morphology. Marine Ecology Progress Series, 179, 215-229.

Travers, M.J.& Potter, I.C. (2002) Factors influencing the characteristics of fish assemblages in a large subtropical marine embayment. Journal of Fish Biology, 61, 764-784.

Tristes, A.& Pauly, D. (1998) Estimating mean body masses of marine mammlas from maximum body lengths. Canadian Journal of Zoology, 76, 886-896.

Ulanowicz, R.E.& Norden, J.S. (1990) Symmetrical overhead in flow networks. International Journal of Systems Science, 21, 429-437.

UN (1992) United Nations, Convention on Biological Diversity. Valesini, F.J., Coen, N.J., Wildsmith, M.D., Hourston, M., Tweedley, J.R., Hallett, C.S.,

Linke, T.E.& Potter, I.C. (2009) Relationships between fish faunas and habitat type in south-western Australian estuaries. Fisheries Research and Development Corporation, Australia, Final Report, Project 2004/045, 486 pp.

Vidal-Hernandez, L. (2000) Exploring the Gulf of Mexico as a large marine ecosystem through a stratified spatial model. Ms Thesis, University of British Columbia, 172 pp.

Walker, D.I. (1989) Seagrass in Shark Bay, the foundations of an ecosystem. in "Biology of seagrasses: a treatise on the biology of seagrasses with special reference to the Australian region", A.W.D. Larkum, A.J. McComb, S.A. Shepherd, Elsevier: New York, 182-210.

Walker, D.I. (1990) Seagrass in Shark Bay, Western Australia. In "Research in Shark Bay", Report of the France-Australe Bicentenary Expedition Committee, pp 101-106.

Walker, T.I. (1976) Effects of Species, Sex, Length and Locality on the Mercury Content of School Shark Galeorhinus australis (Macleay) and Gummy Shark Mustelus antarcticus Guenther from South-eastern Australian Waters. Australian Journal of Marine & Freshwater Research, 27, 603-616.

- 357 -

Walker, T.I. (1998) Can shark resources be harvested sustainably? A question revisited with a review of shark fisheries. Marine Freshwater Research, 49, 553-572.

Walker, T.I., Taylor, B.L., , Hudson, R.J.& Cottier, J.P. (1998) The phenomenon of apparent change of growth rate in gummy shark (Mustelus antarcticus) harvested off southern Australia. Fisheries Research, 39, 139-163.

Wallace, J. (1979) The biology of the yellow tailed trumpeter Amniataba caudavittatus Richardson (Pisces: Teraponidae) in the Swan River Estuary, Western Australia. Murdoch University, Honours thesis.

Walters, C., Christensen, V.& Pauly, D. (1997a) Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments. Reviews in Fish Biology and Fisheries, 7, 139-172.

Walters, C., Pauly, D.& Christensen, V. (1999) Ecospace: prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of Marine Protected Areas. Ecosystems, 2, 539-554.

Walters, C.J., Christensen, V.& Pauly, D. (1997b) Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments. Reviews in Fish Biology and Fisheries, 7, 139-172.

Waltham, N.J.& Connolly, R.M. (2006) Trophic strategies of garfish, Arrhamphus sclerolepis, in natural coastal wetlands and artificial urban waterways. Marine Biology, 148, 1135-1141.

Wells, F.E., Rose, R.A. (1985) An analysis of benthic marine invertebrate communities in subtidal seagrass and sand habitats in Shark Bay, Western Australia. Records of Western Australian Museum, 12, 47-56.

Wells, F.E.& Threlfall, T.J. (1982) Density fluctuations, growth and dry tissue production of Hydrococcus brazieri (Tenison Woods, 1876) and Arthritica semen (Menke, 1843) in Peel Inlet, Western Australia. Journal of Molluscan Studies, 48, 310-320.

Wells, F.E., Threlfall, T.J.& Wilson, B.R. (1980) Aspects of the biology of molluscs in the Peel-Harvey estuarine system, Western Australia. The Peel - Harvey estuarine system study (1976-1980), Department of Conservation & Environment, Perth, WA, Bulletin No. 97.

White, W., Platell, M.E., Potter, I.C. (2004) Comparisons between the diets of four abundant species of elasmobranchs in a subtropical embayment: implications for resource partitioning. Marine Biology, 144, 439-448.

White, W.& Potter, I.C. (2004) Habitat partitioning among four elasmobranch species in nearshore, shallow waters of a subtropical embayment in Western Australia. Marine Biology, 145, 1023-1032.

White, W.T. (2007) Catch composition and reproductive biology of whaler sharks (Carcharhiniformes: Carcharhinidae) caught by fisheries in Indonesia. Journal of Fish Biology, 71, 1512-1540.

Whitney, N.M.& Crow, G.L. (2007) Reproductive biology of the tiger shark (Galeocerdo cuvier) in Hawaii. Marine Biology, 151, 63-70.

Wildsmith, M.D. (2007) Relationships between benthic macroinvertebrate assemblages and habitat types in nearshore marine and estuarine waters along the lower west coast of Australia, Murdoch University, PhD thesis

- 358 -

Williams, H.& Schaap, A.H. (1992) Preliminary results of a study into the incidental mortality of sharks in gill-nets in two Tasmanian shark nursery areas. Australian Journal of Marine & Freshwater Research, 43, 237-250.

Williams, N. (1998) Overfishing Disrupts Entire Ecosystems. Science, 279, 809. Wilson, C., Hale, J.& Paling, E.I. (1999) Macrophyte abundance and composition in

the Peel-Harvey Estuary: Comparisons before and after the opening of the Dawesville Channel (July 1985 to June 1999). Institute for Environmental Science, Murdoch University, Western Australia, Report No. MAFRL 99/5.

Wirsing, A.J., Charles, D., Crane, K., Heithaus, M.R.& Dill, L.M. (2004) Pilot study of Loggerhead Turtles in the Shark Bay World Heritage Area: movements and community based conservation. Final Report to the Department of the Environment and Heritage, Western Australia, 20 pp

Wirsing, A.J., Heithaus, M.R.& Dill, L.M. (2007) Fear factor: do dugongs (Dugong dugon) trade food for safety from tiger sharks (Galeocerdo cuvier)? Oecologia, 153, 1031-1040.

Wirsing, A.J., Heithaus, M.R., Dill, L.M. (2006) Tiger shark (Galeocerdo cuvier) abundance and growth in a subtropical embayment: evidence from 7 years of standardized fishing effort. Marine Biology, 149, 961-968.

Wirsing, A.J., Heithaus, M.R., Dill, L.M. (2007a) Can measures of prey availability improve our ability to predict the abundance of large marine predators? Oecologia, 153, 563-568.

Wirsing, A.J., Heithaus, M.R., Dill, L.M. (2007b) Living on the edge: dugongs prefer to forage in microhabitats that allow escape from rather than avoidance of predators. Animal Behaviour, 74, 93-101.

Wolff, M. (2002) Concepts and approaches for marine ecosystem research with references to the tropics. Revista de Biologia Tropical, 50, 395-414.

Wolff, M., Koch, V., Isaac, V. (2000) A trophic flow model of the Caeté Mangrove Estuary (North Brazil) with considerations for the sustainable use of its resources. Estuarine, Coastal and Shelf Science, 50, 789-803.

Wolff, M.& Soto, M. (1992) Population dynamics of Cancer polyodon in La Herradura Nay, northern Chile. Marine Ecology Progress Series, 85, 69-81.

Wolstenholme, E.F. (1999) Qualitative vs Quantitative Modelling: The Evolving Balance. The Journal of the Operational Research Society, 50, 422-428.

Wootton, J.T. (1994) Predicting Direct and Indirect Effects: an Integrated Approach Using Experiments and Path Analysis. Ecology, 75, 151-165.

Wootton, J.T.& Laska, M.S. (1998) Theoretical Concepts and Empirical approaches to measuring interaction strength. Ecology, 79, 461-476.

Worm, B., Barbier, E.B., Beaumont, N., Duffy, J.E., Folke, C., Halpern, B.S., Jackson, J.B.C., Lotze, H.K., Micheli, F., Palumbi, S.R., Sala, E., Selkoe, K.A., Stachowicz, J.J.& Watson, R. (2006) Impacts of biodiversity loss on ocean ecosystem services. Science, 314, 787-790.

Wrigley, T.J., Chambers, J.M.& McComb, A.J. (1988) Nutrient and gilvin levels in waters of coastal-plain wetlands in an agricultural area of Western Australia. Australian Journal of Marine and Freshwater Research, 39, 685-694.

Yodzis, P. (1988) The indeterminacy of ecological interactions as perceived through perturbation experiments. Ecology, 69, 508-515.

- 359 -

Young, G.C.& Potter, I.C. (2003a) Do the characteristics of the ichthyoplankton in an artificial and a natural entrance channel of a large estuary differ? Estuarine, Coastal and Shelf Science, 56, 765-779.

Young, G.C.& Potter, I.C. (2003b) Influence of an artificial entrance channel on the ichthyofauna of a large estuary. Marine Biology, 142, 1181-1194.

Young, G.C., Potter, I.C., Hyndes, G.A.& de Lestang, S. (1997) The Ichthyofauna of an intermittently open estuary: implications of Bar Breaching and low salinities on faunal composition. Estuarine, Coastal and Shelf Science, 45, 53-68.

Young, G.C., Potter, I.C. (2003) Induction of annual cyclical changes in the ichthyofauna of a large microtidal estuary following an artificial and permanent increase in tidal flow. Journal of Fish Biology, 63, 1306-1330.

Zabel, R.W., Harvey, C.J., Katz, S.L., Good, T.P.& Levin, P.S. (2003) Ecologically sustainable yield. American Scientist, 91, 150-157.

Zeller, D.& Reinert, J. (2004) Modelling spatial closures and fishing effort restrictions in the Faroe Islands marine ecosystem. Ecological Modelling, 172, 403-420.

Zhang, Y., Fu, F.-X., Whereat, E., Coyne, K.J.& Hutchins, D.A. (2006) Bottom-up controls on a mixed-species HAB assemblage: A comparison of sympatric Chattonella subsalsa and Heterosigma akashiwo (Raphidophyceae) isolates from the Delaware Inland Bays, USA. Harmful Algae, 5.

Zhang, Y., Fu, F.-X., Whereat, E., Coyne, K.J., Hutchins, D.A. (2006) Bottom-up controls on a mixed-species HAB assemblage: A comparison of sympatric Chattonella subsalsa and Heterosigma akashiwo (Raphidophyceae) isolates from the Delaware Inland Bays, USA. Harmful Algae, 5, 310-320.

- 360 -

Appendix For the sake of completeness, the dietary matrices of the balanced Ecopath models

are presented in this appendix.

Functional groups: 1: dolphins, 2: waterbirds, 3: piscivorous waterbirds, 4: sharks, 5: marine omnivorous fish, 6: marine carnivorous fish, 7: marine herbivorous fish, 8: marine detritivorous fish, 9: estuarine omnivorous fish, 10: estuarine carnivorous fish, 11: estuarine herbivorous fish, 12: estuarine detrivorous fish, 13: whiting, 14: Arripis georgianus, 15: Aldrichetta forsteri, 16: Mugil cephalus, 17: Torquigener pleurogramma, 18: bivalves, 19: gastropods, 20: Western King Prawn, 21: Blue Swimmer Crab, 22: crustaceans, 23: worms, 24: zooplankton, 25: microscopic algae, 26: algae, 27: macrophytes, 28 Chaetomorpha linum, 29: Cladophora montagneana, 30: seagrass, 31: detritus

Table 1: Dietary matrix of the balanced ‘pre DC’ Ecopath model, columns represent predator groups and rows show their prey

Prey \ predator 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

5 0,075 0,001 0,048 0,047 0,001 0,013 0,001 0,005 0,0003

6 0,413 0,001 0,048 0,047 0,013 0,002 0,001 0,005 0,0003

7 0,001 0,048 0,047 0,013 0,001 0,005 0,0003

8 0,001 0,048 0,047 0,013 0,001 0,001 0,005 0,0003

9 0,075 0,001 0,048 0,047 0,013 0,0002 0,001 0,001 0,005 0,0003

10 0,001 0,048 0,047 0,013 0,003 0,001 0,005 0,0003

11 0,001 0,048 0,047 0,013 0,001 0,005 0,0003

12 0,038 0,001 0,048 0,047 0,013 0,001 0,005 0,0003

13 0,001 0,048 0,047 0,013 0,001 0,005 0,0003

14 0,001 0,048 0,047 0,013 0,005 0,001 0,005 0,0003

15 0,075 0,001 0,048 0,047 0,013 0,001 0,005 0,0003

16 0,075 0,001 0,048 0,047 0,013 0,001 0,005 0,0003

17 0,001 0,001 0,048 0,001 0,013 0,001 0,005 0,0003

18 0,051 0,002 0,126 0,052 0,025 0,003 0,253 0,068 0,069 0,001 0,044 0,479 0,257

19 0,120 0,002 0,001 0,026 0,032 0,025 0,003 0,013 0,005 0,111 0,011 0,044 0,033 0,037

20 0,019 0,021 0,081 0,002 0,026 0,003 0,02 0,211

21 0,019 0,132 0,002 0,011 0,003 0,02 0,026 0,043

22 0,114 0,176 0,047 0,162 0,171 0,063 0,003 0,242 0,281 0,32 0,461 0,116 0,304

23 0,131 0,003 0,092 0,133 0,014 0,003 0,07 0,118 0,366 0,095 0,179 0,163 0,263

24 0,043 0,098 0,287 0,071 0,241 0,303 0,111 0,15 0,087 0,002 0,2 0,064 0,213 0,33 0,065 0,25 0,333 0,25

25 0,008 0,013 0,043 0,013 0,09 0,035 0,049 0,291 0,33 0,1 0,065 0,5 0,333 0,5

26 0,035 0,026 0,004 0,086 0,077 0,03 0,045 0,002 0,114 0,31 0,0003

27 0,029 0,012 0,086 0,077 0,045 0,007 0,007 0,139 0,11 0,033 0,0003

28 0,124 0,012 0,086 0,077 0,045 0,002 0,021 0,11 0,0003

29 0,050 0,006 0,038 0,034 0,02 0,001 0,045 0,04 0,0001

30 0,087 0,288 0,003 0,43 0,077 0,001 0,001 0,778 0,045 0,007 0,06 0,0003

31 0,006 0,625 0,046 0,072 0,5 0,001 0,025 0,227 0,34 0,25 0,291 0,028 0,25 0,334 0,25

362

Table 2: Dietary matrix of the balanced ‘post DC’ Ecopath model, columns represent predator groups and rows show their prey Prey \

predator 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

5 0,075 0,001 0,05 0,048 0,0001 0,005 0,002 0,001 0,0001 0,0003

6 0,413 0,001 0,05 0,048 0,005 0,004 0,001 0,0001 0,0003

7 0,001 0,04 0,04 0,004 0,002 0,0005 0,0001 0,0001

8 0,001 0,05 0,034 0,009 0,003 0,001 0,0001 0,0003

9 0,075 0,001 0,05 0,048 0,015 0,005 0,001 0,0001 0,0003

10 0,001 0,05 0,048 0,005 0,0003 0,002 0,001 0,0001 0,0003

11 0,001 0,04 0,040 0,004 0,002 0,009 0,0001 0,0001

12 0,03 0,001 0,04 0,040 0,004 0,002 0,0009 0,0001 0,0001

13 0,001 0,05 0,048 0,005 0,002 0,001 0,0001 0,0003

14 0,001 0,05 0,048 0,005 0,006 0,001 0,0001 0,0003

15 0,075 0,001 0,05 0,048 0,005 0,002 0,001 0,0001 0,0003

16 0,075 0,001 0,05 0,048 0,005 0,002 0,001 0,0001 0,0003

17 0,001 0,001 0,05 0,001 0,005 0,001 0,001 0,0003

18 0,051 0,002 0,14 0,038 0,009 0,003 0,316 0,069 0,066 0,001 0,044 0,479 0,2565

19 0,120 0,002 0,001 0,047 0,044 0,027 0,003 0,016 0,006 0,111 0,009 0,044 0,033 0,037

20 0,019 0,021 0,081 0,009 0,025 0,003 0,015 0,211

21 0,019 0,132 0,009 0,040 0,003 0,015 0,009 0,001 0,026 0,004

22 0,114 0,176 0,047 0,167 0,229 0,033 0,003 0,277 0,276 0,292 0,461 0,116 0,303

23 0,131 0,003 0,105 0,085 0,025 0,003 0,075 0,134 0,397 0,095 0,179 0,163 0,263

24 0,022 0,049 0,129 0,023 0,082 0,116 0,056 0,075 0,054 0,001 0,1 0,032 0,107 0,165 0,033 0,125 0,167 0,125

25 0,004 0,006 0,012 0,006 0,028 0,014 0,025 0,146 0,165 0,050 0,033 0,25 0,167 0,25

26 0,041 0,033 0,002 0,088 0,077 0,039 0,0001 0,045 0,003 0,112 0,313 0,0002

27 0,035 0,012 0,0008 0,083 0,077 0,0001 0,045 0,007 0,007 0,137 0,113 0,036 0,0002

28 0,130 0,012 0,0008 0,083 0,077 0,045 0,003 0,019 0,113 0,0002

29 0,063 0,006 0,0004 0,037 0,034 0,02 0,002 0,012 0,030 0,0001

30 0,093 0,132 0,001 0,427 0,077 0,001 0,003 0,778 0,045 0,007 0,063 0,0002

363

31 0,005 0,563 0,029 0,050 0,45 0,0009 0,023 0,204 0,306 0,225 0,262 0,0252 0,25 0,301 0,225

analysing the effects of anthropogenic activities on …...- 2 - analysing the effects of...

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