investigations into the light requirements of seagrasses in northeast australia

Investigations into the light requirements of

seagrasses in northeast Australia

Benjamin J. Longstaff

INVESTIGATIONS INTO THE LIGHT REQUIREMENTS OF SEAGRASSES IN NORTHEAST AUSTRALIA

A THESIS

SUBMITTED BY

BENJAMIN J. LONGSTAFF BSC. (HONS.)

TO THE

DEPARTMENT OF BOTANY

THE UNIVERSITY OF QUEENSLAND

AUSTRALIA

IN FULFILMENT OF THE REQUIREMENTS FOR

THE DEGREE OF DOCTOR OF PHILOSOPHY WITHIN

THE UNIVERSITY OF QUEENSLAND

APRIL 2003

STATEMENT

The work presented in this thesis is, to the best of my knowledge and belief,

original except as acknowledged in the text. I hereby declare that I have not

submitted this material either whole or in part, for a degree at this or any

other Institution.

Signed............……....................................

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Acknowledgements

I thank my wife Andrea for her constant support, encouragement and abiding belief in my

ability to finish this thesis and become a fully-fledged “seagrass farmer”. Nothing will

make me happier than turning off the computer at the weekends again, throwing the

camping gear in the car and heading to our favourite camping spots (well OK, perhaps

that weekend at Kingfisher Resort IOU would be better!!). Dear Matthew, thank you for

helping me understand what life is all about…...March 21st 2001, a night I will never

forget.

I would like to thank my supervisor Bill Dennison for the opportunities he provided, the

skills he shared, and the enthusiasm for the marine environment that he radiates. Thank

you Bill, for making marbot what it truly is, a happy, supportive, sharing, encouraging

and stimulating working environment – this would never have been the case without your

philosophy to work, staff and students. I give thanks to my co-supervisors Neil

Loneragan and surrogate supervisor James Udy for reviewing sections of this thesis and

for providing guidance. I am extremely grateful to Carol Booth for her meticulous and

valuable reviews of draft chapters. I really appreciated the time and effort Francis Pantus

put into to the spatial prediction maps in chapter 5. Ed Drew, thankyou for introducing

me to the world of spectroradiometry – I would still love a day of sailing ‘Cymodocea’

around the bay when the opportunity arises. Simon Costanzo - like hell I still owe you for

seagrass tagging!!! What about the endless seagrass depth ranges I conducted for you in

the Tweed River (don’t forget, while I was up to my neck in freezing cold water, you

were cruising by in some flash boat deploying your algae) and if that doesn’t square us

up, then risking my life for you in Cardwell certainly does. In all seriousness, I could not

have wished for a better friend and colleague to share the journey with. We have some

great stories to keep retelling and I hope plenty of opportunities to generate more. There

are many more past and present marbots I would like to thank for their friendship, help

and support, Mark O’Donohue, Tim Carruthers, Chris Roelfsema, Andrew Watkinson,

Joelle Prange, Catherine Collier, Norm Duke, Katherine Chaston, Paul Bird, Alan

Goldizen, Dianna Kliene, Eva Abal, and Cindy Heil.

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Thanks mum for being a great mum, for opening my eyes to the wonders of nature and

for seeding my interest in marine science. Thank you dad for your encouragement,

support and being able to bounce ideas off you over a bottle of fine red wine. Tom, I’m

not sure how many time you said, “hang in there you are so close”, -- your

encouragement meant a lot to me. I would like to use this opportunity to defend the

accusations printed on my bruver Roberts debut CD cover: I did not discourage you from

buying a car – just from buying an expensive car. Joking apart, I am very proud of your

achievements, and remember we’re all relying on your talents to make us rich (because

the seagrass farmer certainly can’t!!). To Auntie Norma –, thank you for keeping me fed

and all of us sane in these last few months. Not forgetting Gary ‘Anchorman’ Boyd,

thank you for your friendship, the odd cup of tea and the non-scientific thought-

provoking conversations.

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Investigations into the light requirements

of seagrasses in northeast Australia

Abstract

Northeast Australia has extensive and diverse seagrass meadows that have been, or have

the potential to be affected by long-term and acute light reduction. Over the past century,

sediment input into coastal waters has increased, resulting in long-term reduction in light

penetration to the region’s seagrasses. As northeast Australia has a tropical monsoonal

climate, coastal waters are also periodically inundated with large plumes of sediment

laden freshwater, resulting in acute reduction of light.

The aims of this thesis were to:

1) Review appropriate techniques of measuring light penetration to seagrasses;

2) Review the processes leading to long-term reduction in light penetration’;

3) Determine the minimum light requirements (MLR) of two northeast Australian

seagrasses (Zostera capricorni and Halodule pinifolia) and their capacity to persist

when deprived of light;

4) Assess the effects of a flood event on the deep-water seagrasses (Halophila ovalis and

Halophila spinulosa) of Hervey Bay.

The five main measurements used to assess light penetration to seagrasses are: Secchi

disk, seagrass maximum depth limit, instantaneous photosynthetic photon flux density

(PPFD), continuous PPFD and spectral distribution. These approaches to estimating the

light environment for seagrasses were reviewed, the type of measurements possible

summarised and their advantages and disadvantages discussed. Continuous long-term

logging of light is the recommended approach for determining the MLR of seagrasses as

it ensures accurate assessment of longer time periods and takes into account the temporal

variability. However, in turbid environments light sensors foul rapidly and hence require

constant cleaning. In response to this problem in monitoring light, a device that wipes the

sensors clean at regular intervals was designed and constructed during my PhD. The

design, construction, and trial of this cleaning device is described.

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Light reduction processes were investigated by collating, synthesising and analysing

several important data sets (e.g. seagrass distribution, resuspension processes and Secchi

depth) collected in Moreton Bay, southeast Queensland. Sediment resuspension and flood

events are presented as the most important processes leading to increased turbidity and

hence having the greatest influence on seagrass distribution. Seagrass was either absent or

had a shallow maximum depth limit (MDL ≈ 1m) in the western Bay, where turbidity

(Secchi depth <1m) is maintained by wind-driven resuspension of muddy river deposits.

The light requirement (i.e. MLR and persistence below MLR) of two northeastern

Australian seagrasses (H. pinifolia and Z. capricorni) was investigated. MLR was

assessed using continuous long-term light logging, and persistence below MLR was

investigated by depriving seagrasses of light using shade screens. H. pinifolia received an

average of 9 mol photons m-2 d-1 at its MDL of 1m. Similarly, Z. capricorni, required 10

mol photons m-2 d-1 to survive, and penetrated to 1-3 m in depth, depending on water

clarity. However, H. pinifolia persisted for longer (> 78 days) periods of time in the dark

compared to Z. capricorni (≈55 days). H. pinifolia’s resilience to long periods in the dark

may facilitate its survival in an environment of frequent light deprivation. The potential

application of this research to management includes:

a. Modelling potential seagrass habitat under improved water clarity conditions. A

significant improvement in water clarity in Moreton Bay (i.e. kd=0.3 throughout the

bay) would increase the potential area where Z. capricorni could grow from

approximately 10% to 50% of the bay’s total area (assuming no other limiting factor);

b. Development of biological indicators of impending seagrass die-off due to light

deprivation.

The effect of a major flood event on the survival and light penetration to deepwater

seagrasses was investigated in Hervey Bay during February 1999. The flood event was

similar in magnitude to the 1992 flood event within the bay, which caused the temporary

loss of 1,000km2 of seagrass. The flood had a sizeable but temporary impact: at the inner

plume region (approx. 8m deep and 20km from the river mouth), the biomass of H. ovalis

was greatly reduced within 30 days, while H. spinulosa was not affected after 30 days but

had a significant loss by day 73. After the flood event, Halophila ovalis recovered with

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average light availability of only 2.8 mol photons m-2 d-1 light. The loss of seagrass was

not as widespread as that in 1992. The reduced impact in 1999 could be attributed to the

following reasons:

a. In 1999, there was only one major event whereas in 1999, there were 2 major inputs

of fresh water during a 3-week period.

b. Sediment settled out more rapidly in 1999 due to relatively calm post flood

conditions.

c. A single catchment flood in 1999 and a double catchment flood in 1992.

In summary, this thesis demonstrates that long-term and acute light reduction processes

affect the distribution and biomass of seagrasses in NE Australia. Species such as H.

pinifolia and Z. capricorni persisted in shallow turbid waters where the temporally

variable, but higher quantity of available light favours their survival. The ubiquitous H.

ovalis persists in both shallow-turbid and deep-clear environments. This broad niche is

due to a low MLR (facilitated by its morphology and physiology) and rapid recovery

after periods below its MLR.

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Significant contributions

1. Comprehensive review of the methods used to measure light penetration to

seagrass. The review is based upon available literature and experience gained

while conducting this thesis.

2. A minor contribution is the research and design of an ‘automatic cleaning device

that eliminates sensor fouling during continuous underwater light logging. The

device can increase measurement accuracy, save cost, time and effort.

3. Established the link between seagrass distribution, increased suspended solid

concentrations and resuspension processes in Moreton Bay, Australia.

4. Established the light requirements of the dominant seagrass in Moreton Bay

(Zostera capricorni). Including: minimum light requirements (MLR) for long-

term survival, capacity to survive below its MLR and the spectral quality of

available light.

5. Confirmed that light availability is the primary environmental factor controlling

Zostera capricorni distribution within Moreton Bay.

6. Predicted the area of Moreton Bay that Z. capricorni could colonise if effective

management could achieve maximum coastal water clarity (assuming no other

limiting factors).

7. Determined that Halodule pinifolia has a high degree of tolerance to light

deprivation, thus clarifying why this species can persist in an environment that

frequently experiences floods.

8. Provided resource managers with a new approach of monitoring seagrass ‘health’

during light limiting scenarios (e.g. dredge plumes). Seagrass physiology and

morphology are used as an early warning indicator of impending seagrass die-off.

9. Investigated the link between light availability and deepwater seagrass survival

during a flood in Hervey Bay. Established that a single large flood alone will not

lead to the widespread seagrass die-off that was reported in 1992.

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Publication status of thesis chapters

Chapter 2:

Carruthers, T. J. B., B. J. Longstaff, W. C. Dennison, E. G. Abal, and K. Aioi.

2001. Measurement of light penetration in relation to seagrass. P. 369-392. in F.

T. Short and R. G. Coles, [eds.]. Global seagrass research methods. Elsevier,

Amsterdam.

Chapter 3:

Longstaff, B.J., K. Bell, G. Andrews and W.C. Dennison. Continuous light

monitoring in the aquatic environment: an automatic cleaning device that

eliminates sensor fouling. Submitted to Limnology and Oceanography.

Chapter 6:

Longstaff, B. J., and W. C. Dennison. 1999. Seagrass survival during pulsed

turbidity events: The effects of light deprivation on the seagrasses Halodule

pinifolia and Halophila ovalis. Aquatic Botany 65: 105-121.

Contents

Chapter 1 1 Introduction 1

1.1 Seagrass light requirements 1 1.2 Seagrass persistence below their minimum light requirement 2 1.3 Light reduction processes 3 1.4 Seagrass survival mechanisms 4 1.5 Northeast Australia’s deteriorating water clarity 4 1.6 Seagrasses of northeast Australia 6 1.7 Aims of the thesis 9 1.8 Thesis overview 9

Chapter 2 13 Measurement of light penetration to seagrass 13

Abstract 13 2.1 Introduction 15 2.2 Atmospheric light 15 2.3 Light in the water column 16 2.4 Measurement of light in relation to seagrasses 18

2.4.1 Measuring Secchi depth 18 2.4.2 Measuring instantaneous light quantity (PPFD) 19 2.4.3 Seagrass maximum depth limit as an indicator of mean annual light 21 2.4.4 Continuous light quantity (PPFD) monitoring 23 2.4.5 Measuring light quality (spectral distribution) 25

2.5 Conclusion 25 Chapter 3 27

Continuous light monitoring in the aquatic environment: an automatic cleaning device that eliminates sensor fouling 27

Abstract 27 3.1 Introduction 28 3.2 Design and construction 29 3.3 Deployment 32 3.4 Trial and application 32

3.4.1 Methods 32 3.4.2 Results 32

3.5 Conclusions 34 Chapter 4 35

The influence of sediment resuspension on seagrass distribution in Moreton Bay, Australia 35

Abstract 35 4.1 Introduction 36 4.2 Methods 38

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4.2.1 Study region 38 4.2.2 Sediment processes 38 4.2.3 Water clarity 40 4.2.4 Seagrass distribution and maximum depth limit 41

4.3 Results 42 4.3.1 Sediment processes 42 4.3.2 Water Clarity 44 4.3.3 Seagrass distribution and depth penetration 48

4.4 Discussion 52 4.4.1 Sediment resuspension and long-term light reduction in Moreton Bay 52 4.4.1 long-term light reduction and seagrass distribution in Moreton Bay 54

4.5 Conclusion 56 Chapter 5 57

Light requirements of the seagrass Zostera capricorni in Moreton bay, Australia 57


5.2.1 Study Sites 61 5.2.2 Determining Z. capricorni minimum light requirements 61 5.2.3 Assessing Z. capricorni distribution in relation to minimum light requirements 62 5.2.4 Assessing the spectral quality of available light 63 5.2.5 Seagrass characteristics 64 5.2.6 Simulating flood events: Seagrass responses to light deprivation 65 5.2.7 Analysis of data 66

5.3 Results 67 5.3.1 Determining Z. capricorni minimum light requirements 67 5.3.2 Assessing Z. capricorni distribution in relation to minimum light requirements 72 5.3.3 Assessing the spectral quality of available light 75 5.3.4 Seagrass characteristics 79 5.3.5 Simulating flood events: Seagrass responses to light deprivation 80

5.4 Discussion 83 5.4.1 Long-term light requirements of Z. capricorni 83 5.4.2 Assessing the spectral quality of available light 87 5.4.3 Assessing Z. capricorni distribution in relation to minimum light requirements 89 5.4.4 Seagrass characteristics 90 5.5.5 Simulating flood events: Seagrass responses to light deprivation 91 5.5.6 Conceptualising seagrass-light interactions in Moreton Bay 92

Chapter 6 95 Seagrass survival during pulsed turbidity events: The effects of light deprivation on the seagrasses Halodule pinifolia and Halophila ovalis 95


6.2.1 Site Selection 99 6.2.2 Natural light gradient investigation 100 6.2.3 Light deprivation experiment 100

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6.2.4 Sample analysis 101 6.2.5 Statistical analysis 103

6.3 Results 104 6.3.1 Natural light gradient 104 6.3.2 Responses to light deprivation 106

6.4 Discussion 111 Chapter 7 115

Impact of a flood plume on the deepwater seagrass of Hervey Bay, Australia 115


7.2.1 Study sites and schedule 117 7.2.2 Plume formation 118 7.2.3 Inner plume characterisation 118 7.2.4 Seagrass collection and analysis 119 7.2.5 Statistical Analysis 121

7.3 Results 122 7.3.1 Plume formation 122 7.3.2 Water Quality 124 7.3.3 Light availability 125 7.3.4 Seagrass analysis 127

7.4 Discussion 131 Chapter 8 137

Conclusions, management implications, and future research 137 8.1 Conclusions 137

8.1.1 Measuring light penetration to seagrasses 137 8.1.2 Light reduction processes 138 8.1.3 The influence of light availability on northeast Australian seagrasses 139

8.2 Management implications 140 8.3 Future research 142

References 143

Chapter 1 Introduction

Throughout the world, the unique assemblage of marine angiosperms known as seagrasses are

critical components of coastal ecosystems. Seagrasses increase primary productivity, support

complex food webs and provide habitat to numerous species of fauna and flora (Larkum et al.,

1989). Regrettably, global seagrass distribution is declining due to both natural causes (e.g.

wasting disease (Giesen et al., 1990a) and exposure to desiccation (Seddon et al., 2000)) and

anthropogenic influences (Short and Wyllie-Echeverria, 1996; Walker and McComb, 1992).

The most common cause of seagrass loss is a reduction in light penetration to the seagrass

(Walker and McComb, 1992). While light reduction processes are complex, they can occur as

short intense events such as a flood, and/or as gradual long-term occurrences. Seagrass

minimum light requirements (Dennison et al., 1993), capacity to survive periods below the

minimum light requirement and survival strategies under reduced light conditions are species

specific (Walker et al., 1999). With 60 species of seagrass currently recognised throughout

the world (Kuo and den Hartog, 2001), substantial research effort is required to understand

the interaction between seagrasses and their light environment. The research reported in this

thesis is underpinned by the goal to improve this understanding. Only with such

understanding can coastal management strategies be effective at preserving these unique and

intriguing marine plants.

1.1 Seagrass light requirements

Light is often a limiting resource to seagrass because they have high minimum requirements

(Dennison et al., 1993) and live in the coastal zone where light penetration is variable, often

low and spectrally altered. Seagrasses have high light requirements because (i) they lack

accessory pigments, limiting the spectral range of light utilisation (Frost-Christensen and

Sand-Jensen, 1992), (ii) they have high respiratory demand due to large quantities of non-

photosynthetic material (i.e. roots, rhizomes) (Fourqurean and Zieman, 1991), and (iii) they

need to regularly oxygenate the roots and rhizomes to mitigate anoxic sediment conditions

(Terrados et al., 1999; Goodman et al., 1995; Smith et al., 1984; Pregnall et al., 1984).

Minimum light requirements (MLR) for seagrasses range from 4.4 - 29% of surface irradiance

(Dennison et al., 1993). Although it is accepted that seagrasses have high MLR in relation to

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terrestrial and other marine plants (Dennison et al., 1993), few studies have accurately

quantified MLR for specific species. Most estimates of MLR are based on the relationship

between the maximum depth limit and the light attenuation coefficient (kd) (Dennison et al.,

1993; Duarte, 1991). This approach has yielded a wide range of inter- and intra-specific

estimates of MLR. While these differences may in part be attributed to specific physiological

characteristics, the variability may also be due to methodological limitations. Methods which

provide average values for light penetration fail to provide information about the variable and

complex subsurface light environment (Moore et al., 1997). For example, periodic

instantaneous measures of light with either a Secchi disk or quantum sensor may not detect

pulsed events that can significantly affect seagrass survival (Longstaff et al., 1999; Moore et

al., 1997). The most accurate and ecologically meaningful method of determining MLR is to

conduct continuous long-term light logging (Carruthers et al., 2001). To date, only four

studies (Moore et al., 1997; Dunton, 1994; Onuf, 1996; Lee and Dunton, 1997) have used this

approach for investigating seagrass systems. Two of these studies (Dunton, 1994; Onuf, 1996)

were closely related and defined the MLR of Halodule wrightii in Southern Texas. One study

used the approach to quantify light during shading (Lee and Dunton, 1997) and the other to

investigate requirements during transplantation experiments (Moore et al., 1997). As most

MLR estimates are based on infrequent light attenuation measures (Dennison et al., 1993;

Duarte 1991) and few temporally intensive studies have been conducted, the MLR for the vast

majority of species remains unknown.

While the MLR of most seagrass species is yet to be defined, broad inter-specific differences

are evident when considering the relationship between MLR and depth distribution. These

differences are most likely linked to the growth strategy and architecture of the seagrass. For

example, the deepest growing seagrasses are dominated by species bearing small rhizomes

(e.g. Halophila spp.) whereas shallower species tend to have large, more robust rhizomes (e.g.

Posidonia spp.) (Duarte, 1991).

1.2 Seagrass persistence below their minimum light requirement

Seagrasses are not only dependent upon receiving sufficient total quantities of light over the

long-term, but also upon their capacity to persist through periods when light is reduced

temporarily below their minimum requirements (Walker et al., 1999). Consequently,

measures of total annual quanta to define MLR cannot always be used to predict seagrass

presence and survival (Moore et al., 1997). Temporary reduction in light below minimum

requirements can arise from processes such as wind-driven resuspension of sediments and

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flooding (Zimmerman et al., 1995; Preen et al., 1992). The easiest and most commonly used

method of assessing seagrass persistence under such conditions of episodic light reduction is

by simulating conditions using shade screens. Shading experiments have demonstrated a large

inter-specific variation in the capacity of seagrasses to persist below their MLR, ranging from

1-13 months (Longstaff et al., 1999; Gordon et al., 1994). In addition, MLR does not appear

to be directly correlated to a species’ capacity to persist in conditions below MLR. For

example, Halophila spp. have low MLR, but have limited ability to persist under light

deprivation (Longstaff et al., 1999; Walker et al., 1999). Whereas, Zostera marina has higher

MLR (20% of surface: Dennison et al., 1993) and can survive for long periods (over 8

months) below its MLR (Backman and Barilotti, 1976).

Thus, the long-term survival of seagrass species may depend not only on the average daily

light quantity, but the number of ‘critical days’ of light deprivation a particular species can

survive in a given period (Zimmerman et al., 1991).

1.3 Light reduction processes

Processes and factors reducing light penetration to seagrass are diverse and include nutrient

enrichment, settling of sediments on their leaves, pulsed turbidity events and increased

suspended sediment loads (Walker and McComb, 1992). Nutrient enrichment can stimulate

algal (epiphytic, free floating and phytoplankton) growth that competes with the seagrasses

for light (Hauxwell et al, 2001; Lapointe et al., 1994; Cambridge et al., 1986). Seagrass

growing in low energy environments with high sediment inputs may be deprived of light by

fine mud settling upon the leaf blades (Bulthuis, 1983a). Pulsed turbidity events may arise

from “seasonal inputs of river-borne suspended sediments” (Moore et al., 1997) or flood

events (Preen et al., 1995). The most common factor leading to seagrass loss is an increase in

suspended sediments from terrestrial inputs and sediment resuspension leading to a long-term

reduction in light (Hall et al., 1999; Moore et al., 1997; Giesen et al., 1990b).

The light environments that result from these processes can be extremely diverse in terms of

the spatio-temporal variability in quantity and spectral quality. Extremes in the spatio-

temporal variability of light reduction are long-term, with low attenuation processes (such as

minor sediment resuspension) at one end of the spectrum and acute light deprivation events

(such as pulsed turbidity events from flood plumes) at the other. In general, however,

variability and attenuation of light decreases as distance from the source of the pollution (i.e.

river mouth, nutrient discharge outlet) increases.

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1.4 Seagrass survival mechanisms

Seagrasses use two main mechanisms to survive light reduction events. Firstly, seagrasses

may adapt physiologically and morphologically to lower light levels by reducing carbon

demand (i.e. maintain a carbon balance) and increasing light harvesting capacity (Alcoverro et

al., 2001; Lee and Dunton, 1997; Abal et al., 1994). Carbon balance adjustments include

reducing growth rates, decreasing the ratio of photosynthetic to non-photosynthetic tissue and

utilising stored carbohydrates (Lee and Dunton, 1997; Czerny and Dunton, 1995). Light

harvesting capacity is enhanced by processes such as increasing chlorophyll content and leaf

surface area, and by reducing self-shading through canopy thinning (Abal et al., 1994).

The second mechanism used to survive light reduction depends on the growth form and life

history strategy. Small seagrasses have high turnover rates, rapid growth responses and tend

to be very responsive to environmental conditions (Walker et al., 1999). They also tend to

produce large quantities of seeds (Kuo and Kirkman, 1995; Kuo et al., 1993) that can remain

viable within coastal sediments for years (McMillan, 1991). Long-term survival of these

smaller seagrasses is therefore dependent on their ability to recover rapidly from seeds once

favourable conditions prevail rather than their capacity to persist through an event

(Kenworthy, 2000; Walker et al., 1999). In contrast, larger seagrasses have a greater capacity

to persist below their MLR, having slower turnover rates, large robust rhizomes and

significant carbohydrate reserves. However, if loss does occur, recovery is slow because seed

production is limited and growth responses are slow (Walker et al., 1999).

1.5 Northeast Australia’s deteriorating water clarity

Despite the relatively low population of Australia, extensive modifications to the terrestrial

environment have occurred over the 200 years of European settlement. For example, more

than 85% of the area of northeast Australia’s coastal (Queensland) catchments has been

developed for agriculture, with only 11% considered pristine (Moss et al., 1992) (Fig. 1.1,

1.2). The majority of agriculture in this region is cattle grazing and sugar cane production.

Agricultural activities increase erosion by exposing soil and increasing surface flow through

tree removal. Cattle grazing also increases soil erosion significantly because the land used has

a fragile soil crust that is readily destroyed by cattle hooves. Over the past 140 years,

increased soil erosion has been estimated to result in a 3-4-fold increase in the export of

sediment from northeast Australian catchments to the coast (Moss et al., 1992).

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Northeast Australia’s climate ranges from subtropical in the south (28oS) to tropical in the

north (10oS). As northeast Australia has a subtropical/tropical climate, the region experiences

occasional cyclones in the summer months that can result in very large rainfall events. While

both regions are typified with hot wet summers (wet season) and mild dry winters (dry

season), the tropics tend to have more rainfall in the summer and a greater frequency of

cyclones. As rainfall is very variable from season to season (wet-dry) and from year to year

(El-Nino-La-Nina; cyclone-no cyclone), catchment runoff and the resulting sediment

transport predominantly occurs in pulses of very high loads (Pailles and Moody, 1996;

Mitchell, 1988) (Fig. 1.2). While historical evidence is sparse, it is self-evident that suspended

solid concentrations in northeast Australian coastal waters have increased over the past

century. Available historical data for the Brisbane River (southern Queensland) has shown,

for example, a four-fold increase in suspended solid concentrations since 1917 (Dennison and

Abal, 1999).

Two processes most likely control current spatio-temporal variability in suspended solid

concentrations along northeast Australia’s coastline. Firstly, long-term increases in suspended

sediments have been associated with sediment resuspension (Hamilton, 1994). Resuspension

readily occurs in shallow regions because of fine muddy deposits and wind-wave generated

by the predominantly southeast winds (Wolanski and Spagnol, 2000). As a result, water

clarity increases with distance to the shore because water depth increases and, hence, near-bed

wind-wave energy decreases (Hamilton, 1994) (Fig. 1.1). Secondly, acute increases in

suspended sediment occur from pulsed riverine inputs due to wet period rainfall and cyclones

(Mitchell, 1988) (Fig. 1.2).

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Figure 1.1. Conceptual model showing long-term light reduction processes and the interaction with seagrass

distribution and survival. Research conducted in this thesis is presented below the conceptual model.

1.6 Seagrasses of northeast Australia

Australia’s vast and varied coastline contains the largest and most diverse seagrass

assemblages in the world (Walker et al., 1999). Over half the known species occur in

Australian waters. The high diversity is in part due to the overlap of tropical and temperate

seagrass floras and the considerable endemism present in certain bioregions (Carruthers et al.,

in press; Walker and Prince, 1987).

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Figure 1.2. Conceptual model showing acute light reduction processes and the interaction with seagrass

distribution and survival. Research conducted in this thesis is presented below the conceptual model.

In northeast Australia, over 18,400km2 of seagrass have been recorded and another 4,000km2

are expected to be recorded in areas still under study (Lee Long et al., 2000). To date, fifteen

species have been documented, with at least two species endemic to northeast Australia (Lee

Long et al., 2000). Northeast Australia seagrasses are found in four broad habitats: river

estuaries, coastal waters, deep water and coral reefs, with each habitat corresponding

respectively to an increase in distance from terrestrial inputs (Carruthers et al., in press; Lee

Long et al., 1993). River estuaries tend to support seagrass meadows that are shallow and/or

intertidal, have high shoot densities, but low species diversity. Coastal seagrasses are most

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often found in areas that are sheltered from prevailing southeasterly trade winds, for instance,

in bays and behind islands. However, they are prone to physical disturbance due to tropical

storms and cyclones. Deepwater seagrasses have been recorded in mainland bays (Hervey

Bay, Prince Charlotte Bay) and within the protected inter-reef waters of the Great Barrier

Reef. Deepwater seagrasses are found at between 15 and 58 m depth and are all species of

Halophila. Seagrasses can colonise reef flats and the deeper water between reefs. Because of

the low nutrient status of these systems, the seagrasses that colonise these areas are often

nutrient limited (Carruthers et al., in press; Lee Long et al., 1993).

Long-term and acute light reduction events have led to seagrass loss along the northeast

Australian coastline (Kirkman, 1997) (Fig. 1.1, 1.2). The most significant recorded losses

have been attributed to acute light reduction (Kirkman, 1997; Preen et al., 1995). A classic

example of acute light reduction impact is the loss of 1000km2 of seagrasses from Hervey Bay

in 1992 after 2 floods and a cyclone in short succession (Preen et al., 1995). The loss of

seagrass from the bay was only temporary (Deepwater seagrass meadows completely

recovered by 1998; McKenzie et al., 2000), but it had a catastrophic impact on the ecology of

the bay, including the starvation and mass migration of the bays 2000 dugong (Preen and

March, 1995). While significant areas of seagrass loss have been recorded, it is also likely that

historical losses have occurred unknowingly. Extensive seagrass surveys in northeast

Australia commenced in the mid 80’s (various references in Lee Long et al., 1993), and it is

estimated that 15% of the seagrasses within this region still remain unmapped (Lee Long et

al., 2000) 17 years later (this is not because of the unrelentless effort of the survey team, but a

reflection of the vast and remote coastline of northeast Australia). As water clarity has been

deteriorating over past century (as the catchments have been progressively cleared for

agriculture), significant seagrass loss is likely to have occurred before these surveys.

Furthermore, as surveying the States seagrasses is such as huge and time-consuming task it

will be many years between surveys, during which significant seagrass losses can occur

unknowingly and therefore without the opportunity for management actions to mitigate loss.

Because northeast Australia has vast areas of seagrass that are largely left unmonitored and

there is high potential for loss due to both long-term and acute light reduction, it is essential

that effective management practices be implemented to protect the remaining seagrasses.

Effective long-term management and protection requires a sound understanding of the major

processes effecting the distribution and survival of seagrasses. However, to date there has

9

been no detailed investigations as to the light requirements of northeast Australian seagrasses.

The aims of this thesis were formulated to address this deficiency in knowledge.

1.7 Aims of the thesis

This thesis investigates the impact of long-term and acute light reduction on the distribution

and survival of seagrasses in northeast Australia.

Broad aims of each chapter are:

Investigate and review techniques of measuring light penetration to seagrass (Chapter

2).

Develop technology to save cost, time and effort while improving the accuracy of

continuous long-term underwater light logging (Chapter 3).

Investigate the influence of sediment resuspension processes on the distribution of

seagrasses in Moreton Bay (Chapter 4).

Determine the minimum light requirements of the seagrass Zostera capricorni in

Moreton Bay in terms of the quantity and quality of available light at maximum depth

limit, and capacity to persist below the minimum light requirements (Chapter 5).

Investigate the effects of light deprivation on the survival of Halodule pinifolia and

Halophila ovalis growing in the shallow turbid waters of the Gulf of Carpentaria

(Chapter 6).

Assess the effects of reduced light penetration, from a flood event, on the survival of

deep-water seagrasses (Halophila ovalis and Halophila spinulosa) growing in Hervey

Bay (Chapter 7).

Summarise the key findings from each chapter and make recommendation for

effective management of northeast Australian seagrasses in relation to ensuring

sufficient light for their long-term survival (Chapter 8).

1.8 Thesis overview

There are many methods of measuring light penetration to seagrasses, and these different

methods provide very different information and cover a range of expenses and effort.

Therefore it is essential that the appropriate technique be adopted for the specific question

being addressed. Chapter 2 details five general approaches that are widely used for measuring

light, including appropriate application, the relative benefits and problems of each method. It

10

is becoming increasingly evident that small-scale temporal and spatial variability in light are

significant to seagrass, therefore long-term continuous logging of light is the recommended

approach for most of the aims addressed in this thesis.

However, in turbid environments light sensors foul rapidly and therefore require constant

cleaning. The most cost effective and accurate approach to avoiding sensor fouling is to use a

device that automatically cleans the sensor at regular intervals. No such device has been

described in the literature (or is commercially produced), thus necessitating the design and

construction of a novel device. Chapter 3 describes the design, construction, and trial of this

cleaning device.

Three different study locations, incorporating a range of habitat types and species, were

chosen to investigate the role of long-term and acute light reduction on northeast Australian

seagrasses (Fig. 1.3).

GULF OF CARPENTARIAHabitat: Exposed intertidal/subtidalSpecies: Halodule pinifolia, Halophila ovalisResearch: Minimum light requirements & survival below MLR

HERVEY BAYHabitat: Deepwater BaySpecies: Halophila ovalis, Halophila spinulosaResearch: Impact of flood event

MORETON BAYHabitat: Shallow BaySpecies: Zostera capricorniResearch: Sediment resuspension,minimum light requirements & survival below MLR

GULF OF CARPENTARIAHabitat: Exposed intertidal/subtidalSpecies: Halodule pinifolia, Halophila ovalisResearch: Minimum light requirements & survival below MLR

HERVEY BAYHabitat: Deepwater BaySpecies: Halophila ovalis, Halophila spinulosaResearch: Impact of flood event

MORETON BAYHabitat: Shallow BaySpecies: Zostera capricorniResearch: Sediment resuspension,minimum light requirements & survival below MLR

Figure 1.3. Location of study regions and principal research conducted at these regions.

Chapter 4 synthesises several important data sets to present the case that sediment

resuspension is the most important physical process affecting seagrass distribution and depth

range in Moreton Bay (Fig. 1.1). The following chapter (5) focuses on the most abundant

seagrass in Moreton Bay (Zostera capricorni), describing the minimum light requirements of

the species (quantity and quality), the effect of light availability on its growth and

morphology, and the effects of acute light reduction on its survival (Fig. 1.1, 1.2). A similar

approach is taken in Chapter 6, which focuses on Halodule pinifolia growing on shallow sub-

tidal and intertidal flats in the Gulf of Carpentaria. The minimum light requirements of

Halodule pinifolia are ascertained, the effect of light availability on its physiology and

11

morphology and the effects of acute light reduction on its survival are described (Fig. 1.1,

1.2). In February 1999, a large flood event within the catchment of Hervey Bay provided the

opportunity to monitor the effects of turbidity plumes on deepwater seagrasses (Fig. 1.2).

Chapter 7 describes the light environment for 70 days following the flood event and the

impact this had on the survival of Halophila ovalis and Halophila spinulosa. Finally, Chapter

8 summarises the most significant results of the thesis.

Chapter 2 Measurement of light penetration to seagrass

Publication status

Carruthers, T. J. B., B. J. Longstaff, W. C. Dennison, E. G. Abal, and K. Aioi. 2001.

Measurement of light penetration in relation to seagrass. P. 369-392. in F. T. Short and R. G.

Coles, [eds.]. Global seagrass research methods. Elsevier, Amsterdam.

(Aspects of the original book chapter have been changed to increase relevance to this thesis)

Abstract

Atmospheric and water column conditions significantly affect light available to seagrass.

Because light is the most important factor controlling the distribution and biomass of

seagrasses, it is important to be able to measure and characterise light in aquatic habitats,

either to establish the suitability of a habitat for seagrass growth, or to better understand the

ecophysiology of seagrass communities. There are five main approaches to characterising

light penetration in relation to seagrasses: Secchi disk, instantaneous photosynthetic photon

flux density (PPFD), seagrass maximum depth limit, continuous PPFD and spectral

distribution. This chapter reviews each approach to measuring light penetration to seagrass

with emphasis on appropriate application, the type of measurements possible and the

advantages and disadvantages of each approach. Secchi disk provides a cheap rapid and

robust method of measuring light attenuation. Instantaneous PPFD measurements can provide

the most accurate measure of attenuation, light profile and the total quantity of light. Both the

Secchi disk and instantaneous PPFD measures are suited for surveys and monitoring when

many sites are required over time. Seagrass maximum depth limit can be used effectively as

an integrative measure of the annual mean attenuation coefficient, although it is not

recommended as a sole monitoring tool as the method can only detect reductions in water

quality that have already resulted in seagrass decline. The optimum quantification of light

available to seagrasses is continuous light monitoring, however this approach has a relatively

high hardware and installation cost. Sensors that measure PPFD do not differentiate for the

visible spectrum (400-700 nm). Utilising a spectroradiometer that measures light intensity for

all wavelengths across the spectrum overcomes this issue. However spectroradiometry is

14

more costly and complex than techniques to measure PPFD alone. The choice and application

of these methods should be informed by clear questions, otherwise effort and expense is likely

to be wasted.

15

2.1 Introduction

Seagrasses require light for photosynthesis; however, they live in a generally low and variable

light environment. Seagrasses have higher light requirements than phytoplankton, macroalgae

and terrestrial plants as they have to support a larger proportion of non-photosynthetic tissue

and are often rooted in anoxic sediments (Dennison et al., 1993; Duarte, 1991). Consequently,

light has long been considered the most important factor controlling the distribution and

biomass of seagrasses (Ostenfield, 1908). For this reason, it is important to be able to measure

and characterise light in aquatic habitats, either to establish the suitability of a habitat for

seagrass growth, or to better understand the ecophysiology of seagrass communities.

There are five main approaches to characterising light penetration to seagrasses: Secchi disc,

seagrass maximum depth limit, instantaneous photosynthetic photon flux density (PPFD),

continuous PPFD and spectral distribution. Each of these approaches is suited to specific

applications and provides specific information. Furthermore, each approach has certain

advantages and disadvantages when compared to the other techniques. This chapter reviews

each approach to measuring light penetration to seagrass, with emphasis on appropriate

application, the type of measurements possible and the advantages and disadvantages of each

method.

Atmospheric and water column condition significantly affect light available to seagrass and

can influence the manner in which light measurements should be conducted, it is important to

understand the processes involved. The first two sections of this chapter will review the

influence of the atmospheric and water column on seagrass light availability.

2.2 Atmospheric light

Solar radiation is comprised of both diffuse (scattered) and direct radiation, and on a clear day

when the sun is high in the sky, light hitting the water surface is approximately 20% diffuse

and 80% direct (Kirk, 1994). The diffuse portion is ‘skylight’ and is responsible for the blue

colour of the sky. When the atmosphere is clean and dry, and the sun directly overhead,

approximately 14% of total solar irradiance is absorbed and reflected. However, when there is

either dust or moisture in the atmosphere this can be as high as 40% (Kirk, 1994). The

spectral nature of light also changes as light passes through the atmosphere. The short

wavelength ultra-violet light (200-400 nm) is scattered and also absorbed by ozone, while

long wavelength infra-red light is absorbed by water vapour and carbon dioxide. The result is

16

that the relative proportion of photosynthetically active radiation (PAR) (400-700 nm) is

higher at the surface of the earth than it is in the upper atmosphere.

Type and thickness of cloud can greatly influence light measurements. Small amounts of

cloud not obscuring the sun will actually increase total irradiance hitting the surface of the

earth by increasing the amount of scattered light directed towards the earth. However, dense

uniform cloud can reduce incident light by 70% or more (Monteith, 1973). In patchy cloud

conditions, incident light may decrease by 20-50% as a cloud passes over the sun; care needs

to be taken to account for this variation when measuring light penetration.

Overall, as a mean over the entire year, approximately 47% of the light entering the earth’s

atmosphere reaches the surface of the earth, while the rest is scattered or absorbed by clouds

or other gaseous or particulate matter in the atmosphere (Fig. 2.1).

Figure 2.1. Fate of light entering the earth’s atmosphere

2.3 Light in the water column

After reaching the water surface, the light is further reflected and refracted before it arrives at

the submersed seagrass beds. The proportion of solar radiation reflected from the water

surface depends upon the angle of the sun and surface conditions. In calm conditions, 2% of

surface light is reflected when the sun is directly overhead. The proportion of light reflected

increases in an exponential manner as solar elevation decreases. Surface conditions have little

effect on the reflectance of sunlight from high solar elevations, but the relationship between

reflection and surface conditions becomes complex as the solar elevation decreases (Kirk,

1994). Generally, the light climate is more stable when the sun is high in the sky. Light is also

attenuated at the water surface due to refraction as it changes direction and slows moving

from the air to the denser water.

17

Although reflection and refraction significantly alter light as it passes into water, a much

greater influence on how much light finally reaches seagrass is the absorption that occurs

within the water column (Kirk, 1994). Water itself absorbs light, particularly in the red

wavelengths and, therefore, at depths great than 30 m light consists of only blue and green

wavelengths (Fig. 2.2a). Phytoplankton absorbs mainly in the blue and red wavelengths, so

when there are large quantities of phytoplankton in water, the transmitted light is mainly of

green wavelengths (Fig. 2.2b). Absorption of light by suspended particles such as sediment

and organic matter as well as dissolved substances, including humic acids or tannins from

wetlands, contribute to this light reduction and result in blue-green and yellow-orange light,

respectively (Fig. 2.2c, d).

Figure 2.2. Light attenuation properties of water and suspended matter - reduction in light and change in spectral

distribution.

Light absorption and light scattering by water and its contents results in reduction of light

with increasing depth. The relationship between these factors can be defined by the Beer-

Lambert exponential decay function (Equation 2.1):

Iz = Ioe-Kd z (2.1)

Where Iz is light measured at depth z, I0 is light measured just under the surface and Kd is the

light attenuation coefficient in units of m-1.

18

2.4 Measurement of light in relation to seagrasses

The five approaches widely used for measuring light penetration to seagrasses are

summarised in Table 2.1., with an indication of the applicability of each method and their

relative advantages and disadvantages.

2.4.1 Measuring Secchi depth

The Secchi disc is a round, black and white disc that is lowered through the water until the

distinction between white and black quadrants is no longer visible to the human eye (Fig. 2.3).

Secchi measurements are robust and, if taken carefully, can be successfully compared across

most atmospheric and sea surface conditions.

Figure 2.3. Method of measuring the Secchi depth

The only legitimate, and also the most informative, parameter to assess using the Secchi disk

is ‘Secchi depth’, often denoted as ZSD (Kirk, 1994; Preisendorfer, 1986). However, it is still

common to convert ZSD to an approximation of attenuation coefficient Kd. The difficulty with

this conversion is that it depends upon the optical properties of the water such as the colour,

turbidity and concentration of chlorophyll a (Koenings and Edmundson, 1991; Geisen et al.,

1990b),). Because water bodies vary substantially in their optical properties Kd*ZSD has

repeatedly been found to vary in accordance with the optical properties causing the light

attenuation (Table 2.2 (Koenings and Edmundson, 1991). For this reason it is important to

define the relationship between ZSD and Kd for the water body being measured before the

conversion is conducted.

Table 2.2: Relationship between light attenuation (Kd - as measured with a photoelectric light meter) and Secchi Disk depth (ZSD) for water bodies with three distinct optical properties (adapted from (Koenings and Edmundson, 1991). Optical property Kd* ZSD ISD:Io (%) Site description

Turbid 1.44 22.6 Turbid coastal waters; North Pacific Clear 1.7 15.8 Seawater in the English Channel

Coloured 2.7 3.6 Humic Stained lake in Alaska

19

There are many benefits to using a Secchi disk to measure light, the main ones being that it is

easy, requires little equipment or expertise, and is inexpensive. For these reasons, the Secchi

disk is used widely throughout the world. The most appropriate use of Secchi Depth is for

surveys or monitoring programs comparing many sites over time. The disadvantages of

Secchi disks are that it only provides an estimation of Kd and light availability, and accurate

measurement is not possible under certain conditions (where Secchi depth > actual depth, and

in very deep clear water where the disk becomes too small to distinguish between the black

and white quarters). Even so, the ease and reliability of this technique and the multitude of

studies that have been successful in comparing Secchi depth across a diversity of spatial and

temporal scales will ensure that this method justifiably continues to be commonly used in the

future.

2.4.2 Measuring instantaneous light quantity (PPFD)

Photoelectric light meters are commonly used to measure underwater light fields. These light

meters generally measure light as moles of quanta (photons) between 400-700 nm in units of

µmol quanta m-2 s-1, that is, photosynthetic photon flux density (PPFD) (Kirk, 1994). There

are two types of sensors: 2π sensors that measure direct light (e.g. downwelling irradiance)

and 4π sensors that measure direct as well as scattered light. In turbid waters, high amounts of

particulate matter may result in the scattered light being as much as 70% of the total light

available in the water column and therefore using a 2π sensor will underestimate the light

available for seagrass growth (Dunton, 1994). For measuring attenuation coefficients (Kd), 2π

or 4π sensors can be used, recognising the slight difference in the type of measure. However,

Stumpf et al. (1999) found negligible difference between diffuse attenuation coefficient (Ko:

using 4π sensor) and direct attenuation coefficient (Kd: using 2π sensor), in Chesapeake Bay,

Moore et al. (1997) report a relationship of Kd=0.97*Ko (r2=0.96).

Instantaneous light measures provide three categories of information. Firstly, they provide a

total quantity of light at the time and place of measure. Secondly, they can be used to generate

light profiles, i.e. the quantity of light at selected depth within the water column (Fig. 2.4).

This measure is particularly relevant to understanding light in stratified water. Finally, by

measuring light at two known depths simultaneously, the attenuation coefficient can be

calculated (Fig. 2.4). Instantaneous measure of light is preferable to Secchi depth alone when

accurate measurements of the attenuation coefficient are required, or the water is highly

coloured, stratified or very clear.

Table 2.1 Summary of methods used for measuring light in relation to seagrass habitat.

Secchi Seagrass Maximum Depth limit

Instantaneous PPFD Continuous PPFD Spectral distribution

Application

- Routine monitoring; when rapid, simple, cheap assessment required

- Assessing spatial trends

- Long-term trends in water quality/Kd

- Seagrass distribution in relation to light availability

- Rapid light monitoring; assessing spatial or temporal trends

- Light profiles

- Accurate temporal resolution

- Accurate minimum light requirements

- Assess spectral quality of available PAR

- Instantaneous or continuous mode

Measurement

- Secchi depth (ZSD) - Approximation of Kd

- Mean annual Kd - Seagrass depth distribution

- Instantaneous PPFD - Instantaneous Kd

- Light profiles

- Short and long-term PPFD - Short and long-term Kd

- Spectral composition of PAR

- Spectral attenuation of PAR

Advantages

- Cheap, rapid and simple to use

- Easy interpretation of data - Widely used method - Easy comparison to other

studies

- Moderately easy to conduct

- Infrequent monitoring - Easy interpretation of

results

- Relatively easy - Used when Secchi depth>

actual depth - Conduct profiles

- Detects temporal trends eg. pulsed turbidity, seasons

- Most relevant to seagrass ecophysiology

- Reduces site visits

- Quality of PAR - Influence of water colour,

suspended sediments and depth on spectral distribution

Disadvantages

- Only approximation of Kd and light availability

- Cannot be used if Secchi depth> actual depth

- Only used if light is limiting factor and well defined deep edge of meadow

- Moderate hardware costs - Sun angle affects Kd - Time consuming to obtain

accurate temporal resolution

- Quality of light unknown

- High hardware cost - Sun angle effect Kd - Sensor fouling - Quality of light unknown

- Very high hardware cost - Interpretation difficult

Figure 2.4. Method of calculating the attenuation coefficient (Kd) from data collected inside and outside a

sediment plume in Hervey Bay, Australia (Chapter 7)

As instantaneous light measurements are relatively quick and easy to conduct they are best

applied when rapid monitoring of spatial or temporal trends is required. Measuring

instantaneous light with a PPFD sensor has a moderate hardware cost, provides excellent

measures of either direct or diffuse attenuation coefficient. The only limitation of this method

is that at low solar angles (high latitude, early and late in the day) attenuation coefficients are

overestimated as the light passes on an oblique angle to the sensor – so for accurate results

this method should be used within 2 hrs of solar noon. Attenuation coefficient will continue to

be a useful and informative parameter of water quality as it affects seagrass occurrence and

survival. Therefore, there will continue to be an important role for instantaneous light meters

in monitoring, surveys, as well as in research to address broad scale ecological questions

about seagrass meadows.

2.4.3 Seagrass maximum depth limit as an indicator of mean annual light

Light availability is frequently the primary environmental factor controlling the depth

penetration of seagrass (Abal and Dennison, 1996; Dennison et al., 1993). The relationship

between maximum depth limit (MDL) of seagrass and light availability has been recognised

since the early 1900s (Ostenfeld, 1908). The relationship has been generalised across all

species and depths (Duarte, 1991).

Maximum depth limit is defined as seagrass depth in relation to mean sea level (Fig. 2.5). The

MDL of seagrass can be used effectively as an integrative measure of the annual mean

attenuation coefficient. Calculating Kd (the attenuation coefficient) relies on some further

information, as there are species differences in physiological light requirements (minimum

light requirement for seagrasses varies from 4-29 % between species) (Dennison et al., 1993;

22

Duarte, 1991). For many species, studies have shown that the minimum light requirement is

consistent across the species. With this species-specific value for MLR and the value for the

MDL, it is possible to calculate Kd. using the Beer-Lambert equation.

Figure 2.5. Schematic Profile showing seagrass depth range and seagrass maximum depth limit.

To solve this equation for Kd, it is necessary to calculate the MDL of the seagrass as well as

the MLR of the species as a percentage of surface light – either measured or derived from the

literature. The equation to calculate Kd from these parameters is (Equation 2.3):

Kd = -ln (MLR/100)/MDL (2.3)

MDL can be an effective integrator of water quality, although it is not recommended as a sole

monitoring tool as the method can only detect reductions in water quality which have already

resulted in seagrass decline (Dennison and Abal, 1996). Thus, it does not provide an early

warning of water quality decline, but is a very appropriate method to document improvements

in water quality after remediation efforts. With this method, it is important to select sites in

which light is the main factor limiting seagrass depth– that is, sites with no excessive currents

or evidence of frequent disturbance. The method is most effective where the seagrass meadow

is growing on a gradual depth gradient so that the maximum depth limit can be accurately

measured. In addition, a continuous meadow is preferable to a highly patchy seagrass bed

because the deep edge is easier to define. In some cases, the relationship of light to maximum

depth distribution breaks down and the method is ineffective. This occurs, for example, when

the macrophyte canopy structure alleviates light stress by growing up to the surface to source

sufficient light even in highly turbid waters (Middelboe and Markager, 1997; Hootsman et al.,

1995).

23

2.4.4 Continuous light quantity (PPFD) monitoring

Continuous light monitoring provides the optimum quantification of the light available to

seagrasses (Moore et al., 1997; Dunton, 1994), but has a relatively high hardware and

installation cost, and data interpretation is moderately complex.

Continuous light monitoring provides accurate information about temporal variability that is

not easily gained using instantaneous methods. The importance of temporal changes in

seagrass light availability is becoming increasing evident. For example, the impact on

seagrass survival of short-term changes in light availability driven by a large tidal range was

highlighted by Koch and Beer (1996). While long-term declines in light availability are well

known to have caused seagrass loss throughout the world, the important role of pulsed

turbidity events in also causing decline has recently been identified (Longstaff and Dennison,

1999; Moore et al. 1997; Zimmerman et al., 1991). Only with continuous long-term light

monitoring can the effects of pulsed light reduction events be accurately assessed. Most

studies to date attempting to provide information about minimum light requirements, for the

purposes of seagrass management, have used instantaneous techniques (i.e. Secchi or

quantum sensor) at a range of sampling frequencies and durations. Some species have shown

very variable minimum light requirements across different studies. For example, the reported

minimum light requirement for Heterzostera tasmanica has varied between 4 and 20% of

surface light (Dennison et al., 1993). This may be partly due to the inaccuracy of infrequent

instantaneous light measurements. Continuous measurement may aid in clarifying the reason

for this high variation. Another benefit of this method is that data can be collected during

severe or unfavourable weather conditions when it is unsafe for boating.

The most commonly used units for expressing continuously logged light are µmol quanta m-2

s-1, mol quanta m-2 d-1, the light saturation period (Hsat) and % of surface light. The unit µmol

quanta m-2 s-1 is generally used for short-term continuous logging, for example to discern dial

changes in light availability due to factors such as tidal cycles. Long-term continuous data is

usually expressed as µmol quanta m-2 d-1 plotted for each day over the monitoring period

and/or average value over the monitoring period. The portion of the day (in hours) in which

light exceeds the saturating irradiance is defined as Hsat (Fig. 2.6). The light saturation period

has been proposed to be the most relevant method of assessing the light requirements of

seagrasses (Dennison and Alberte, 1982; 1985).

24

Figure 2.6. Method for calculating Hsat, where Ic=compensation irradiance, Ik=saturating irradiance, Hcomp=hours

above compensation irradiance and Hsat=hours above saturating irradiance.

Deployment time for light meters depends upon the specific reason for conducting the

monitoring. They may be deployed for relatively short periods (e.g. a few days) to assess

short-term changes in light due to factors such as tidal cycles. Continuous monitoring over

several months allows monitoring of changes in light during a flood event or light availability

during manipulative experiments. Long-term monitoring may be conducted to elucidate

seasonality or inter-annual variability.

A commonly used format of expressing the quantity of light required by, or available to,

seagrass is ‘percentage surface light’. However, this unit has two major shortcomings.

Firstly, the quantity of surface light around the world varies considerably due to latitude,

season and cloud cover. This can lead to similar calculations of ‘percent surface light’ despite

very different quantities of light reaching the seagrass. Secondly, there are no set protocols as

whether ‘surface light’ measures are conducted either immediately below the water surface

(Kenworthy and Fonseca, 1996) or above water (Dunton, 1994). As highlighted in section

2.3, substantial quantities of light can be reflected by the water surface (10% on average;

Kirk, 1994), hence a light sensor placed immediately below the water surface would measure

less light than one above. Calculations of percent surface light using a reference point

immediately under the water surface would result in values higher than those calculated with

a reference point above the water. Clearly a set protocol for ‘surface light’ measures is

required.

Comparisons of light required/received would be more appropriately conducted using total

quanta (i.e. mol photons m-2 d-1) as the unit of measure.

A potential source of error when conducting continuous light measurements is sensor fouling.

This may be caused by fine particulate matter, formation of a bio-film or large suspended

items (e.g. loose algae). Loggers can be cleaned manually; however, in some cases cleaning

25

would need to be carried out daily, which is generally impractical and costly. An effective

alternative is an automated cleaning device that attaches to the sensor and brushes it clean at

regular intervals (e.g. every 30 mins) (see Chapter 3).

2.4.5 Measuring light quality (spectral distribution)

In the same way that measuring PPFD was a large advance on simply measuring total light

energy, measuring spectral distribution provides much more detailed information than PPFD

alone.

Seagrass capacity to harvest light varies across the light spectrum, with greater absorption in

the blue and red regions of the spectrum than in the green regions. As light passes through a

body of water, the light spectrum is altered according to the depth and the inherent optical

properties of the water (Fig. 2.2). It is likely that not only the quantity of available light but

also the spectral quality is critical to seagrass survival.

Sensors that measure PPFD do not differentiate for the visible spectrum (400-700 nm), and

hence may overestimate light available to seagrass (Moore et al., 1997). As a result, (i) MLR

established using PPFD sensors alone may vary depending upon the spectral quality of the

light, and (ii) identical PPFD values recorded at different times and/or locations may not

equate to identical photosynthetically usable radiation (PUR) (see Morel (1978) for discussion

of PUR).

Assessing water quality parameters such as colour and suspended solid concentrations can be

used to investigate the role of light quality (Kenworthy and Fonseca, 1996; Gallegos and

Kenworthy, 1996; Gallegos, 1994). However, the most effective technique utilises a

spectroradiometer that measures light intensity for all wavelengths across the spectrum.

Techniques to measure light quality have only recently been applied to seagrass biology

(Gallegos, 1994). The past lack of research may in part be attributed to the high cost and

complexity of the instruments. Although high quality instruments are becoming less

expensive and easier to use, spectroradiometry is still more costly and complex than

techniques to measure PPFD.

2.5 Conclusion

Understanding and documenting the light climate of seagrass meadows is essential to

understanding many aspects of survival, ecology and physiology of the species being

considered. Therefore, accurate measurement is important if reliable and informative

26

conclusions are to be drawn. In this chapter, I have presented information about and assessed

the advantages and disadvantages of five techniques for assessing light availability, ranging

from cheap and simple options (Secchi depth) to more expensive and more technically

complex options (spectroradiometry). It is recommended that continuous light monitoring be

used if long-term light requirements are required, and instantaneous light measurements

applied when spatial surveys are required. Finally, it is very important that choice and

application of the method of light monitoring in the field be informed by clear questions,

otherwise effort and expense is likely to be wasted.

Chapter 3 Continuous light monitoring in the aquatic

environment: an automatic cleaning device that eliminates sensor fouling

Publication status

Longstaff, B.J., K. Bell, G. Andrews and W.C. Dennison. Continuous light monitoring in the

aquatic environment: an automatic cleaning device that eliminates sensor fouling. Submitted

to Limnology and Oceanography

Abstract

Continuous light monitoring is the optimum method for quantification the long-term light

requirements of submersed aquatic vegetation. However, sensor fouling is common due to

sediment accretion and/or algal growth and therefore regular cleaning is required. Manual

cleaning is expensive and often impractical. This chapter presents a solution to the problem of

fouling with the design of an automated sensor cleaner and presents the results of a trial

application. The cleaning device consists of a rotating brush driven by a small electric motor

located in a watertight housing. The brush rotates at pre-set time intervals and the rest position

is 180o from the light sensor. The trial was conducted on the western shore of Moreton Bay.

Two light meters (Dataflow) were secured to the benthos with the sensors positioned at equal

heights, and a cleaning device was attached to one of these sensors. Within 7 days of

deployment the non-cleaned light sensor was covered in a film of sediment, while the cleaned

light sensor had no visible sign of fouling. The rate of sensor fouling was consistent over the

deployment period, with approximately 7% reduction in recorded light per day. This led to

only 50% of the light present at day 7 being recorded by the non-cleaned sensor. Using a

sensor-cleaning device as described can increase accuracy of continuous light data and save

cost, time and effort.

28

3.1 Introduction

As discussed in Chapter 2, the most accurate and ecologically meaningful method of

assessing the long-term light requirements of submerged aquatic vegetation is by continuous

monitoring (Carruthers et al, 2001; Moore et al., 1997; Dunton, 1994). A major source of

potential error when conducting continuous light monitoring is sensor fouling. The type and

rate of fouling depends upon a range of environmental and biological factors associated with

the particular water body such as water temperature and suspended sediment concentration.

Sensors placed in cold coastal waters of Alaska were only covered in a slight film of silt after

1 year of deployment (Dunton, 1990), while sensors fouled within days after being deployed

in a turbid bay in sub-tropical Australia (personal observation). One or more of the following

processes may cause sensor fouling: (i) settling of fine particulate matter, (ii) formation of a

bio-film or (iii) large items suspended in the water column (such as detached macroalgae)

becoming caught on the sensor/logger.

A number of approaches have been used (with varying success) to address the problem of

sensor fouling during continuous light monitoring. One approach is to manually clean the

sensor at prescribed periods (e.g. Lee et al., 1997; Dunton, 1994). Not only is this method

time consuming and potentially expensive, but it may also lead to errors in the data if the

sensors are not cleaned frequently enough. In an effort to overcome this problem, some

researchers correct the data for the fouling effect. This is achieved by using identical light

sensors to measure the light before and after cleaning of the fouled sensor (Moore et al.,

1997). However, this approach assumes that the rate of fouling is constant. Another approach

to solving the problem is to reduce the rate of fouling. Dring and Luning (1994) found that

placing an air-filled glass dome over light sensors reduced silt deposition and algae

colonisation, while Onuf (1996) tried to addressed the problem by wrapping transparent

plastic wrap around the sensor. While each of these methods have worked to some degree, a

device that automatically cleans the sensors at regular intervals could have improved the

reliability of the data while saving time, effort and expense.

This chapter describes the design of a developed cleaning device, provides basic information

required for construction, presents the results of a short-term trial and briefly discusses the

effectiveness of the device during the yearlong light-monitoring program.

29

3.2 Design and construction

The cleaning device was designed to meet the following criteria. The device was required to:

I. Be self contained, small, compact and easily deployed,

II. Be submersible to at least 10m depth and be left unattended for 4-6 weeks between

servicing,

III. Have the capacity to regulate the interval between wipes in order to accommodate a

range of fouling rates,

IV. Have a cleaning brush which did not interfere with ambient light (i.e. the brush must

be positioned at 180o away from the sensor between rotations),

V. Have a clutch between the rotating brush and driver motor - the clutch would facilitate

deploying the device at the correct height for an effective clean and save the motor

from “burn-out” if the rotating blade became entangled with floating debris,

VI. Be relatively cheap and easy to construct.

The resulting cleaning device consisted of a wiper blade driven by a small electric motor (Fig.

3.1). The electric motor was coupled directly to a planetary gearbox with a 400-1 gear down

providing 15kg of torque. The shaft from the gearbox was coupled to the inner sleeve of the

clutch system. A spring-loaded ball bearing was mounted into the inner clutch sleeve, with the

ball bearing set to push into a small groove of the outer clutch sleeve. Adjusting the length of

the spring set the tension between the inner and outer clutch sleeves. The outer clutch sleeve

was connected to a stainless steal shaft that passes through the nylon (oil impregnated) end-

cap and finally attaches to a small brush.

A - Cleaning brush B - Stainless steel shaft C - O-ring: seals between shaft and nylon top plug D - Nylon (oil impregnated) end plug E - O-ring: seals between nylon end plug and polypropylene tube F - Spacer/mounts - mounts the gearbox, motor and circuit board to the end plug G -Clutch system: – outer barrel with grove for spring loaded ball bearing H - Clutch system – inner barrel with hole for spring loaded ball bearing and magnet I - Spring and ball bearing – inserted into inner barrel of clutch J - Magnet – used in conjunction with reed switch to stop motor after one rotation K - Gearbox – clutch connector L - Casing – clear polypropylene M - Gearbox - planetary gearbox N - Gearbox – mounting screws O - Reed switch – located on underside of circuit board P - Circuit board Q - Electric motor (3v) – RC260 motor R - Gearbox to electric motor mount S - O-ring – seals between nylon base plug and polypropylene tube T - 4 x AA battery pack – power electric motor U - 1 x 9v battery – powers timing mechanism V - Nylon (oil impregnated) end plug

Figure 3.1. Photograph and diagram of cleaning device depicting the layout and major components.

2cm

ABCDEFGHIJKL

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Q

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KM

ML

PQ

ABC

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2cm

2cm2cm

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2cm

The frequency of brush rotations was controlled by a simple circuit board mounted to the side

of the clutch and gearbox. A 9v battery provided an electrical current to a clock mechanism

within the circuit board. The clock mechanism consisted of a Schmitt Trigger configured as a

free running oscillator generating pulses at approximately 0.05 second intervals (Fig. 3.2a).

The pulse generated by the clock mechanism fed into a 12-stage Ripple Carry Binary Counter

(Fig. 3.2b). The binary counter reduced the number of pulses over a given time frame. Pulse

frequency was reduced by counting a set number of pulses according to a binary scale (i.e. 10,

100, 1000), and a single pulse was released once the set numbers of pulses was received. The

frequency of wiper rotations was controlled at this stage by selecting the level of the pulse

reduction with a Delay Select Switch (Fig. 3.2c). For example, a 0.05 second input interval

generated by the Schmitt Trigger, selected to a 100,000 delay, resulted in an output pulse of

approximately 1.5 hour intervals. The output pulse from the Binary Counter trigged a

Monostable (Fig. 3.2d) to produce a single stable current to a transistor (Fig. 3.2e). This in

turn switched power to the electric motor driving the attached wiper blades. After one

rotation, a magnet set into the drive shaft closed a reed switch (Fig. 3.2f), allowing a pulse to

travel to the Monostable. The Monostable produced a single stable current to the transistor

that turns off the electrical current to the motor. The cycle then repeated at the intervals set

using the Delay Select Switch.

Clock(a)

Binary Counter

(b)+9v

+6v

Delayselect

Switch(c)

+9v

Reed Switch

(f)

Magnet mountedon shaft

Motor

Monostable (d)Transistor (e)

Clock(a)

Binary Counter

(b)+9v

+6v

Delayselect

Switch(c)

+9v

Reed Switch

(f)

Magnet mountedon shaft

Motor

Monostable (d)Transistor (e)

Figure 3.2. Stylised diagram of electronic circuit board used to control timing between rotations.

The underwater housing for the components consisted of a clear Perspex tube fitted with

nylon end caps. To keep the housing watertight, o-rings were set between the cap and the

Perspex tube. Stainless steal screws were used to secure the caps to the tube. A small o-ring

was also used to seal the gap between the end-cap and the stainless steal drive shaft.

32

3.3 Deployment

To correctly deploy a sensor and cleaning device, they needed to be securely attached to each

other at the correct distance apart. The height of the wiper blade needed to be positioned so

that it could rotate freely while maintaining good contact with the sensor. In addition, the

wiper blade needed to be positioned at 180o to the light sensor when at rest to avoid affecting

the down welling light (Fig. 3.3).

Figure 3.3. Diagram depicting the attachment of the cleaning device to a light sensor and logger, and an easy

system of securing the combined set-up to the sediment.

3.4 Trial and application

3.4.1 Methods

A trial was conducted on the western shore of Moreton Bay. Two light meters (Dataflow)

were secured to the benthos with the sensors positioned at equal water depth, approximately

10cm below the water surface at lowest astronomical tide. A cleaning device was attached to

one of the sensors and set to wipe the sensor clean at hourly intervals. The light loggers were

programmed to provide the average quantity of light over a 15min period. The light meters

and cleaning unit were collected 7 days after deployment.

3.4.2 Results

Within 7 days of being deployed, the non-cleaned sensor was covered in a film of sediment,

while the cleaned sensor had no visible sign of fouling. Fouling of the non-cleaned sensor

started to occur almost immediately, with the sensor recording only 92% of the light that was

33

recorded by the cleaned sensor during the first day of deployment (Fig. 3.4). The rate of

sensor fouling was consistent over the deployment period, with approximately 7% reduction

in recorded light per day. This led to only 50% of light present at day 7 being recorded by the

non-cleaned sensor. Over the 7 trial days, both sensors recorded a total decline in light (Fig.

3.4). This was in response to the tidal cycle. Highest light penetration occurred on day 0 as

low tide occurred at midday. As the trial progressed, high tide moved towards midday,

resulting in more water above the sensors during the brightest period of the day.

0

100

200

300

400

500

600

Period after deployment (d)

PPFD

( µm

ol p

hoto

ns m

-2s-1

)

0 1 2 3 4 5 6 7

Sensor cleaning devise usedNo sensor cleaning devise used

0102030405060708090

100

0 1 2 3 4 5 6 7


Ligh

t at n

on-c

lean

ed s

enso

r as a

%of

cle

aned

sens

or

Non-cleaned sensor

Cleaned sensor

High tide time (hh:mm)08:03 08:40 09:15 09:50 10:26 11:04 11:46 12:35

0

100

200

300

400

500

600


PPFD

( µm

ol p

hoto

ns m

-2s-1

)

0 1 2 3 4 5 6 7

Sensor cleaning devise usedNo sensor cleaning devise usedSensor cleaning devise usedNo sensor cleaning devise used

0102030405060708090

100

0 1 2 3 4 5 6 7


Ligh

t at n

on-c

lean

ed s

enso

r as a

%of

cle

aned

sens

or

Non-cleaned sensor

Cleaned sensor

0102030405060708090

100

0 1 2 3 4 5 6 70 1 2 3 4 5 6 7


Ligh

t at n

on-c

lean

ed s

enso

r as a

%of

cle

aned

sens

or

Non-cleaned sensor

Cleaned sensor

High tide time (hh:mm)08:03 08:40 09:15 09:50 10:26 11:04 11:46 12:35

Figure 3.4. Photosynthetic photon flux density (PPFD) recorded by a sensor that was cleaned automatically at hourly intervals in comparison to light recorded by a non-cleaned sensor during the 7 day trial.

The cleaning device was successfully used during the subsequent light monitoring studies in

Moreton Bay (Chapter 5) and Hervey Bay (Chapter 7). In Moreton Bay, the cleaning device

was deployed continuously at 4 sites (1 to 3m deep) throughout the bay for 10 months. The

unit was serviced at 3 to 4 week intervals to download light data, and exchange batteries

within the cleaning device. While the device successfully cleaned the sensors for the vast

majority of the monitoring period the approach is not infallible. Proliferous macroalgae

growth at the side of the light sensor/cleaning units can shade the sensor (despite the unit

34

cleaning the sensor itself). Furthermore, macroalgae floating in the water column can become

attached to the sensor, jamming the wiper blade.

3.5 Conclusions

The described submersible cleaning device provided an effective means of avoiding light

sensor fouling during continuous light monitoring. As the rate at which light sensors become

fouled can be very rapid (in the present trial, 50% of light was blocked by fouling after only 7

days), such a device may be required for both short and long-term continuous light

monitoring. Using a sensor-cleaning device as described can increase accuracy of continuous

light data and save time effort and expenses (after development costs, a single unit could be

constructed for less than AUD$500).

Chapter 4 The influence of sediment resuspension on

seagrass distribution in Moreton Bay, Australia

Abstract

Seagrass loss and reduced maximum depth limit in Moreton Bay has been associated with

elevated suspended solids concentrations. While terrestrial runoff is the major sediment

source, the pulsed nature of river flow in the region makes it unlikely that runoff is the cause

of the long-term increases in turbidity. This chapter synthesises and links several important

data sets to present the case that sediment resuspension is the primary cause of the long-term

increases in turbidity and, hence, seagrass distribution in Moreton Bay. Key parameters and

processes reviewed are sediment transport and deposition, water clarity, and seagrass

distribution and depth range. The majority of sediments are transported into Moreton Bay

during infrequent high flow events. Sediment during these events can settle relatively rapidly

(<30 days) within 20km of the river mouth. Sediment deposition is concentrated in the

western region of the Bay, resulting in a large pool of muddy sediments in that region. During

summer the predominant east to south winds generate waves that readily resuspend the

sediment, resulting in turbid water. During winter water clarity increases as the predominant

wind direction changes offshore (to southwest) resulting in only small wind-waves in the

western bay and, therefore, minimal sediment resuspension. Seagrass distribution and depth

limit in Moreton Bay is largely influenced by water clarity and bathymetry. The deepest and

most extensive seagrass meadows exist in the clear eastern bay waters. In contrast, seagrass is

either absent or shallow in the western bay due to light limiting conditions. Between 1987 and

1998 an estimated 5 000ha of seagrass (or 18% of the total) were lost from Moreton Bay.

Seagrass loss during this period is attributed to both long-term and acute light reduction

processes and also water movement/unstable sediment.

36

4.1 Introduction

Seagrasses require high levels of light for survival and are therefore susceptible to processes

that reduce light penetration. Nutrient enrichment, smothering of leaves by sediment, pulsed

turbidity events and increased suspended sediment loads can all lead to decreased light

penetration (Walker and McComb, 1992). Nutrient enrichment can stimulate algal (epiphytic,

free floating and phytoplankton) growth that competes with seagrasses for light. Over 3 000ha

of Posidonia meadows were lost from Cockburn Sound (Western Australia) during the 1980s

as a direct effect of nutrient stimulated epiphytic growth (Cambridge et al., 1986). Seagrass

growing in low energy environments with high sediment inputs may be deprived of light by

fine mud settling upon the leaf blades. The loss of 17 000ha of intertidal Heterozostera

tasmanica meadows in Western Port Bay (Victoria, Australia) was attributed to this process

(Bulthuis, 1983a). In recent years the impact of pulsed turbidity events on seagrass loss has

become more apparent (Longstaff et al., 1999; Moore et al., 1997; Zimmerman et al., 1991).

Although pulsed events deprive seagrasses of light for relatively short periods of time, light

reduction is substantial, so the impact on seagrass can be significant. This was the case in

Hervey Bay (Queensland, Australia), where 1 000km2 of seagrasses were lost in 1992 after

two flood events and a cyclone over a 3-week period (Preen et al., 1995).

Perhaps the most important, but least understood, process leading to seagrass loss is a long-

term increase in suspended sediment. The source of suspended sediments is invariably

terrigenous. Sediments accumulate in coastal systems because of increasing catchment

erosion. Seagrass survival in these systems is dependent upon sediment input rate,

concentration and fate, and the minimum light requirements of the seagrass. Seagrasses are at

greatest risk in regions of continual sediment input and slow output, and in areas where

suspended sediments are maintained in suspension or frequently resuspended.

Increased suspended sediment concentrations have resulted in seagrass loss in Moreton Bay,

Australia (Abal and Dennison, 1996). Abal and Dennison attributed high turbidity within the

bay to “terrestrial run-off from the bay’s extensive catchment”. While terrestrial runoff is the

source of sediment within Moreton Bay, the pulsed nature of river flow in the region makes it

unlikely that runoff is the cause of long-term turbidity. This chapter synthesises and links

several important data sets to present the case that (1) sediment resuspension is the most

important physical process long-term water clarity in Moreton Bay; (2) long-term reduction in

water clarity significantly effects seagrass distribution in the bay.

37

Most data sets presented in this chapter were collected during the Moreton Bay and Brisbane

River study, an initiative of the South East Queensland Water Quality Management Strategy

(SEQRWQMS). The study was initiated to address water quality issues that link sewage and

diffuse loadings with environmental degradation (Abal et al., 2001). As the study was

conducted as 17 separate tasks, there was little scope within the study for a detailed overview

and comparison of the data in relation to the bays seagrass. This chapter draws upon data from

5 of the 17 tasks, to address this deficiency and improve the current understanding of the

processes affecting seagrass distribution and survival in Moreton Bay. Data was obtained

from the following tasks: Benthic Floral Nutrient Dynamics (Udy et al., 1999); Sediment

Nutrient Toxicant Dynamics (Heggie et al., 1999); Resuspension Dynamics (You et al.,

1999); Seagrass-Light Relationships (Longstaff et al., 1999a; Chapter 5) and Design and

Implementation of Baseline Monitoring (Dennison et al., 1999).

38

4.2 Methods

4.2.1 Study region

Moreton Bay is located in subtropical eastern Australian (27oS). The bay is formed by two

large islands to the east (Moreton Island and Stradbroke Island) and the mainland to the west

(Fig. 4.1). The bay’s catchment area is 21 220 km2, equating to 14 times the area of the bay

itself (Dennison and Abal, 1999). Moreton Bay catchment and surroundings has a population

of over 2 million and is considered to be the fourth fastest growing region in the world

(Skinner et al., 1998). As the population has increased significant changes to land use have

occurred. Agricultural cropping now accounts for 5% of the land area, grazing and forestry

66%, public land 17%, and urban use 11% (Capelin et al., 1998). Because of the change in

land use, 10% of rural land in the catchment has now been identified as subject to severe

erosion, and 70% to moderate erosion.

4.2.2 Sediment processes

4.2.2.1 Sediment transport and deposition

Four rivers flow into the western side of Moreton bay. Brisbane River has the largest

catchment (76%) followed by Logan River (18%), Pine River (4%) and Caboolture River

(2%) (Fig. 4.1). The nature of sediment transport within these rivers was reviewed in terms of

river discharge patterns. The total monthly discharge of the 4 largest rivers flowing into

Moreton Bay is used to illustrate sediment transport patterns. Discharge data was obtained

from the Queensland Department of Natural Resources.

Analysis of sediment composition throughout Moreton Bay was used to assess the location of

river sediment deposition. Sediment analysis was conducted as part of the ‘Sediment

Nutrients and Toxicant Dynamics’ task (Heggie et al., 1999) commissioned by SEQRWQMS.

Sediment composition (% mud) was measured at 56 sites (evenly spaced across the bay)

within the bay during September 1997. Sediment samples were collected either remotely

using a box core or by SCUBA diver. Sediment was sieved into three size classes, dried, and

then weighed. The sediment fractions were contoured over Moreton Bay to generate a map of

mud distribution (Heggie et al., 1999).

39

Figure 4.1. Location and major features of the study region. Including water depth, major rivers and bays.

4.2.2.2 Sediment resuspension

Resuspension processes were assessed in Deception Bay between 11 March and 1 April 1998

as part of the ‘Resuspension Dynamics’ task commissioned by SEQRWQMS (You et al.,

1999). A tripod mounted with a range of instruments was deployed at approximately 6m

water depth during this period. The instruments deployed included a current meter, a

nephelometer for measuring turbidity, and a light sensor. The current meter (InterOcean

Systems) accurately measures tides, currents and wind-waves, and was mounted 1m above the

sediment surface. The instantaneous water velocity measured by the current meter was used to

calculate the near-bed wave orbital velocity (You et al., 1999). The nephelometer (Optical

Back Scatterance (OBS) sensors), detected scattered radiation from suspended matter. The

OBS sensors were mounted at 0.2, 0.5 and 1.0m above the sediment surface. The OBS

sensors were calibrated to a range of suspended solid concentrations. The light sensor

40

(Dataflow) was mounted 0.5 m above the sediment surface and programmed to integrate light

over 6 min periods. A further light sensor was deployed at the deep edge of the Zostera

capricorni meadow within Deception Bay as a component of the ‘Seagrass-Light

Relationships task commissioned by SEQRWQMS (Longstaff et al., 1999b; See chapter 5 for

full details of deployment). This second light sensor facilitates the comparison of

resuspension processes between areas with and without seagrass cover. Wind direction and

speed data was measures at a weather station near the Brisbane River mouth. Data was

supplied by the Bureau of Meteorology.

4.2.3 Water clarity

Spatial and temporal variability in water clarity were quantified using a Secchi disk as part of

the ‘Design and Implementation of Baseline Monitoring‘ task (Dennison et al., 1999)

commissioned by SEQRWQMS. Spatial variability was determined by conducting Secchi

depth measures at 150 sites throughout the Bay on 10 and 11 March 1998. Sampling sites

were chosen to maximise precision in the western Bay, where the greatest impacts were

known to occur. Secchi depths were conducted according to the standard techniques (Chapter

2). Spatial analysis techniques were used to generate a map of Secchi depths within the bay

(see Costanzo et al., 2000 for details).

Temporal variability in water clarity was quantified by measuring Secchi depth at five sites at

monthly intervals between October 1997 and July 1998. The five sites were positioned to

incorporate major regions of Moreton Bay (Southern Islands region, Waterloo Bay, Eastern

Bay, Bramble Bay and Deception Bay) (Fig. 4.1). Although the location of the Secchi depth

measures did not coincide exactly with the location of the seagrass MDL monitoring sites,

both data sets were used to assess for a relationship between Secchi Depth and MDL (see

below). Confidence that the Secchi depth measured is the same as that at the MDL was based

upon the Secchi Depth spatial prediction models (using 150 sites) conducted in that October

1997 and the March 1998.

At the same time that Secchi depth was measured in both the spatial and temporal surveys,

water samples were collected for chlorophyll a analysis. Chlorophyll a analysis was

conducted according to the standard acetone extraction-spectrophotometer technique

described in Parson et al., (1984).

41

4.2.4 Seagrass distribution and maximum depth limit

Seagrass distribution in Moreton Bay was determined as part of the ‘Benthic Flora and

Nutrient Dynamics’ task (Udy et al., 1999) commissioned by SEQRWQMS. Seagrass

distribution was assessed using visual estimates at over 2000 sites within the bay. At each site

the percent cover of each species was recorded. Seagrass distribution maps were generated

using GIS software. These maps were used to calculate the current area of seagrass in

Moreton Bay and to identify areas and quantities of seagrass loss since the 1987 survey

conducted by Hyland et al., (1989).

Maximum depth limit (MDL) of seagrass is defined as the depth limit of seagrass in relation

to mean sea level (Carruthers et al., 2001; Chapter 2). MDL of the seagrass Zostera

capricorni was measured at 10 sites within Moreton Bay during July and August 1998. The

method used was based on the Abal and Dennison (1996) technique, in which the depth limit

of seagrass is measured using a survey staff and level.

Correlation between MDL and light attenuation was generated using the temporal Secchi

depth data (October 1997 – July 1998). Average Secchi depth at each site was calculated, then

converted to a light attenuation coefficient (Kd) according to the Beer-Lambert exponential

decay function: Iz = Io e-kdz using Secchi depth as a measure of light attenuation (i.e.

kd=1.7/Zsd; See Chapter 2 for full details).

42

4.3 Results

4.3.1 Sediment processes

4.3.1.1 Sediment transport and deposition

Frequency and volume of river discharge, hence sediment transport processes, of the 4 major

rivers flowing into Moreton Bay are presented (Fig. 4.2). All rivers displayed very sporadic

discharge rates over the 10-year period assessed, although the absolute volumes discharged

differed markedly between each of the rivers. Discharge volumes were generally low/non-

existent for a number of years in succession (e.g. 1993/94/95), followed by short periods (1-2

months) of very high discharge (e.g. 1996).

Sediment composition analysis revealed a large river delta extending from the Brisbane River

mouth into the central and northern regions of Moreton Bay (Fig. 4.3). Sediment in the central

region of the delta was 80-100% mud, however, the greatest area of the delta was comprised

of 30-80% mud. A smaller, separate region of muddy sediment (20–80% mud) occurred in the

southern region of the bay (Fig. 4.3)(Heggie et al., 1999).

Tota

l mon

thly

flow

(ML)

Logan River

0

100,000

200,000

300,000

400,000

500,000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

Brisbane River (Lockyer Creek+Bremer River)

0

100,000

200,000

300,000

400,000

500,000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

Caboolture River

0

100,000

200,000

300,000

400,000

500,000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

Pine River

0

100,000

200,000

300,000

400,000

500,000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

Tota

l mon

thly

flow

(ML)

Logan River

0

100,000

200,000

300,000

400,000

500,000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

Brisbane River (Lockyer Creek+Bremer River)

0

100,000

200,000

300,000

400,000

500,000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

Caboolture River

0

100,000

200,000

300,000

400,000

500,000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

Pine River

0

100,000

200,000

300,000

400,000

500,000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

Figure 4.2. Total monthly flow of the major rivers flowing into Moreton Bay (upper Brisbane River).

43

N

Sediment composition% mud

NN

Sediment composition% mud

Figure 4.3. Sediment composition (% mud) in Moreton Bay. (Adapted from: Heggie et al., 1999; Task ‘Sediment Nutrient and Toxicant dynamics).

4.3.1.2 Sediment resuspension

During the 20-day study period, wind speed varied from 0 to 33 km h-1 and wind direction

rotated through all degrees of the compass (Fig. 4.4a). Near-bed water velocity fluctuated

between 5 to 15 cm s-1 for 75% of the monitoring period, with the small fluctuations

attributable to tidal flow (Fig. 4.4b). Near-bed velocity increased significantly when the wind

direction was between 90o and 180o (east to south). During these periods (April 11-12: 19-20

and 24-27) waves generated by the wind increased near-bed velocity considerably (exceeding

50 cm s-1 at times). When the wind direction was between east and south, near-bed velocity

was proportional to the wind speed. Suspended solid concentrations above the sediment

surface were generally less than 80 mg l-1, when near-bed velocities were slow (5-15 cm s-1)

(Fig. 4.5c). However, when near-bed velocity increased during east to south winds, suspended

solids at 0.2m above the sediment surface increased to concentration in excess of 300 mg –1.

Light penetration to the sensor was inversely related to suspended sediment concentrations

and hence near-bed velocity (Fig. 4.4d). During the strong east to south winds, light

penetration decreased significantly, with no light penetrating to the sensors on April 19, 25

44

and 26, and almost no light on April 11, 12, 20 and 27. Light was also measures at a nearby

seagrass meadow during the same period. Light penetration at the seagrass meadow followed

an identical pattern to that measured at the resuspension monitoring site (Fig. 4.4e). However,

the quantity of light at the seagrass monitoring sites was significantly less because of the

shallower depth (Water depth ≈ 3m at the seagrass site in comparison to ≈ 8m at the

resuspension monitoring site).

In summary, at the study sites in Deception Bay a wind speeds in excess of 10km h-1 from 90o

to 180o direction (east to south) increased near-bed velocity, resulting in high rates of

sediment resuspension. Wind speeds less than 10km h-1 or from a direction other than 100o to

180o had negligible effect on near-bed velocity and hence resuspension rates. The majority of

sediments settled rapidly after a resuspension event (within a day); however, finer particles

took up to 5 days to settle.

4.3.2 Water Clarity

4.3.2.1 Spatial variability

Secchi depth measured throughout Moreton Bay revealed a strong east to west gradient in

water clarity. Water clarity on the western side of Moreton Bay (Bramble Bay and Southern

Deception Bay) was poor, with Secchi depths between 0.2 and 1m recorded. Immediately east

of this region (including Northern Deception Bay, Waterloo Bay and the southern Islands

region) Secchi depths between 1.0 and 1.7m were recorded. Clearest waters were recorded in

the eastern bay, with Secchi depths over 4m (Fig. 4.5).

Chlorophyll a concentrations were low (less than 2 µg L-1) for all regions of Moreton Bay,

except Bramble Bay (Fig. 4.6). In Bramble Bay, chlorophyll a concentrations ranged between

2-10 µg L-1, with the higher concentrations (5-10 µg L-1) occurring in the southern part of the

bay.

4.3.2.2 Seasonal changes

Monthly Secchi depths and chlorophyll a concentrations from 5 regions of Moreton Bay are

presented with river flow (Lockyer River; total monthly) and wind data (average speed and

median direction) (Fig 4.7). These data sets are combined to show seasonal changes in water

clarity and the potential causes of these changes.

45

The monthly Secchi data demonstrates that water clarity in the western and southern bay is

poor during the summer months (October to March) with Secchi depths between 0.5 and 2

meters recorded (Fig. 4.7a). Water clarity improves significantly in these regions during late

autumn to winter (April to July), with Secchi depths of 3 to 5m recorded in July 1998. The

onset of increased water clarity in each of the study regions did not occur at similar times.

Water clarity started to improve in Waterloo Bay and Southern Islands in March to April,

followed by Deception Bay and Bramble Bay in May to June. Secchi depth at the eastern bay

sites was consistently deeper than the other sites, (6m) except during June and July when

similar Secchi depths occurred.

Chlorophyll a concentrations were low (< 2µg L-1) at all sites and times with the exception of

Bramble Bay, which had high concentrations (6 to 8 µg L-1) from October to December, and

during April and May (11 and 12 µg L-1 respectively). As chlorophyll a concentrations are

low for most regions of Moreton Bay, the effect on water clarity would be small. However,

the high values recorded in Bramble Bay would have a greater effect on water clarity. During

the winter months (June and July) concentrations within Bramble Bay also decreased to less

than 2µg L-1, and this would contribute to the increase water clarity in this region during

winter.

River discharge into Moreton Bay was very low during the study (Fig. 4.7). The small flow

that did occur during the summer months decreased to nothing in the winter. As river flow

ceased during the winter, the very small quantities of sediment discharged into Moreton Bay

would also stop during the winter, and this small reduction may contribute to the increased

winter water clarity.

Average monthly wind speed during the study period was between 10 and 15km h-1 for all

months. In October 1997 the median monthly wind direction was 60o (north easterly). Over

the following 9 months, the predominant wind direction gradually changed from northeast to

southwest (220o). This gradual change in predominant wind direction is most likely having

the overriding effect on water clarity in Moreton Bay. During the summer, the east to

southeast winds generate large wind-waves in the western bay that would readily resuspend

the shallow muddy sediment in that region. As the predominant wind changes to the

southwest, the wind becomes offshore, resulting in small wind-waves in the west and hence

minimal sediment resuspension.

46

0100

200300400500

600

Ligh

t (µ

mol

phot

ons

m-2

s-2)

20

10

Susp

ende

d se

dim

ents

(mg

l-1)

400

320

240

160

80

0.2m0.5m1.0m*

Sensor height

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

1020304050

Nea

r bed

vel

ocity

(cm

s-1 )

March 1998

B.

C.

D.

E.

win

d di

rect

ion(

O) w

ind speed (kmh

-1)A.

90

180

270

360

05101520253035

= Wind direction for maximum resuspensionx = Wind direction

0100

200300400500

600

Ligh

t (µ

mol

phot

ons

m-2

s-2)

20

10

Susp

ende

d se

dim

ents

(mg

l-1)

400

320

240

160

80

0.2m0.5m1.0m*

Sensor height0.2m0.5m1.0m*

Sensor height

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

1020304050

Nea

r bed

vel

ocity

(cm

s-1 )

1020304050

Nea

r bed

vel

ocity

(cm

s-1 )

March 1998

B.

C.

D.

E.

win

d di

rect

ion(

O) w

ind speed (kmh

-1)A.

90

180

270

360

05101520253035

= Wind direction for maximum resuspensionx = Wind direction

Figure 4.4. Interaction between wind speed and direction, near-bed velocity, sediment resuspension and light penetration in Deception Bay during March 1998. (Adapted from: A. Bureau of Meteorology; B., C., D., Task ‘ Resuspension Dynamics’ (You et al., 1999); E, Task ‘Seagrass-Light Relationships’ (Longstaff et al., 1999a)). [Note: data for figures E was collected at a different location than that for figures A, B,C,D; See text for more

detail]

47

N

Secchi Depth (m)

NN

Secchi Depth (m)

Figure 4.5. Secchi depth within Moreton Bay during 10 - 11 March, 1998. (Adapted from: Task ‘Design and Implementation of Baseline Monitoring’, Dennison et al., 1999)

N

Chlorophyll a concentration(µg L-1)

NN

Chlorophyll a concentration(µg L-1)

Figure 4.6. Chlorophyll a concentration within Moreton Bay during 10 – 11 March, 1998. (Adapted from: Task ‘Design and Implementation of Baseline Monitoring’, Dennison et al., 1998).

48

0123456789

Oct-97

Nov-97

Dec-97

Jan-98

Feb-98

Mar-98

Apr-98

May-98

Jun-98

Jul-98

Date

Sec

chi D

epth

(m)

Eastern Bay Bramble Bay

Waterloo Bay

Southern Islands

Deception Bay

2468

1012

Chl

orop

hyll a

(µg

l-1)

100

200

300

Tota

l mon

thly

flow

(ML)

90

180

270

360

0

5

10

15

Med

ian

mon

thly

win

ddi

rect

ion

(o )

Ave

rage

mon

thly

win

dSp

eed

(km

h-1

)

= Wind direction for maximum resuspension

A.

B.

C.

D.

Speed

Direction

0123456789

Oct-97

Nov-97

Dec-97

Jan-98

Feb-98

Mar-98

Apr-98

May-98

Jun-98

Jul-98

Date

Sec

chi D

epth

(m)

Eastern Bay Bramble Bay

Waterloo Bay

Southern Islands

Deception Bay

Eastern Bay Bramble BayBramble Bay

Waterloo Bay

Southern Islands

Deception Bay

Southern Islands

Deception Bay

2468

1012

2468

1012

Chl

orop

hyll a

(µg

l-1)

100

200

300

Tota

l mon

thly

flow

(ML)

100

200

300

Tota

l mon

thly

flow

(ML)

90

180

270

360

0

5

10

15

90

180

270

360

0

5

10

15

Med

ian

mon

thly

win

ddi

rect

ion

(o )

Ave

rage

mon

thly

win

dSp

eed

(km

h-1

)

= Wind direction for maximum resuspension

A.

B.

C.

D.

Speed

Direction

Figure 4.7. Changes in Secchi depth, wind direction and speed, river flow (Lockyer River) and chlorophyll a

concentration between October 1997 and July 1998. (Adapted from: Bureau of Meteorology; Task ‘Design and Implementation of Baseline Monitoring’, (Dennison et al., 1999)).

4.3.3 Seagrass distribution and depth penetration

The 1998 seagrass survey (Udy et al., 1999) determined that there was 20 500 hectares of

seagrass in Moreton Bay. The most expansive seagrass meadows occurred on the shallow

eastern banks and in Waterloo Bay. In most other regions of the bay where seagrass was

49

found, it occurred as a narrow strip along the coastline. No seagrass was found in Bramble

Bay or Southern Deception Bay.

A comprehensive survey of seagrass distribution in Moreton Bay was first conducted in 1987

(Hyland et al., 1989), providing the opportunity to assess changes in seagrass distribution

over this 11 year period. A comparison of the two studies reveals areas of substantial loss and

a few small areas of gain. The total area of seagrass in 1987 was 25 000 ha; by 1998 this had

decreased to 20 500 ha, a loss of 5 000 ha (or 18%) over the 11 year period (Fig. 4.8). The

most extensive areas of loss were southern Deception Bay (2 000 ha), Moreton Island region

(1 700 ha) and the Southern Islands region (800ha).

Zostera capricorni maximum depth limit (MDL) in Moreton Bay ranged from 0.7 to 2.9m.

Shallowest MDL occurred in western (0.7m) and southern (1.2) bay, while in the eastern and

northern regions, MDL was close to 3m. In southern Deception Bay, MDL surveys between

1993 and 1995 showed a large but shallow (MDL = 0.75m) Z. capricorni meadow. However,

no seagrass was found in this region during the 1998 MDL monitoring.

A significant linear relationship occurred between the average light attenuation coefficient

(calculated from Secchi depth) at each site and the seagrass depth range (excluding the

Deception Bay site). A comparison of the Deception Bay data to the linear relationship shows

that Zostera capricorni is growing at least 1 meter deeper than expected when considering the

water clarity.

50

2.8

0

0.7

1.3

1.5

2.9

2.42.0

1.7

1.2

N

Seagrass Change1987 - 1998

UnchangedLossGain = seagrass depth range0.0

2.8

0

0.7

1.3

1.5

2.9

2.42.0

1.7

1.2

NN


UnchangedLossGain


UnchangedLossGain = seagrass depth range0.0

Figure 4.8. Seagrass maximum depth limit and distribution. Seagrass distribution shown as areas of loss, gain

and no change since the 1987 survey by Hyland et al., (1989)

51

0

1

2

3Zost

era

capr

icor

nim

axim

um d

epth

lim

it(m

)

Light attenuation (m-1)

0 0.5 1.0 1.5

Deception Bay(not included in correlation)

r2=0.90

1

2

3Zost

era

capr

icor

nim

axim

um d

epth

lim

it(m

)

Light attenuation (m-1)

0 0.5 1.0 1.5

Deception Bay(not included in correlation)

r2=0.9

Figure 4.9. Correlation between Zostera capricorni maximum depth limit and the average annual light

attenuation coefficient calculated from the monthly Secchi depth measures conducted in each region. Deception Bay not included in correlation.

52

4.4 Discussion

4.4.1 Sediment resuspension and long-term light reduction in Moreton Bay

The majority of sediment entering Moreton Bay arrives during short periods of high river

flow. River flow rates (hence sediment input) into Moreton Bay, for the most part, are

influenced by rainfall, catchment land use and water management systems (weirs and dams)

(Arthington and Mosisch, 1998). Rainfall in the Moreton Bay region is generally low in the

winter (May-August) due to predominant cool dry westerly winds, and wet in the summer due

to monsoonal systems. The introduction of dams and weirs, and changes in catchment land

use, have altered the natural intermittent flow regime, increasing peak flood discharge and

decreasing volume between floods (Arthington and Mosisch, 1998). Flow during periods of

low rainfall is decreased by water abstraction for agriculture and retention by weirs and dams.

For example, the Brisbane River catchment (which is 73% of the bay’s catchment area) is

over 13 100km2 in area, but 8 300km2 of the catchment is dammed with no net flow from the

dam entering the bay during low flow periods (Pers. Comm. SEQWC). Increases in peak

flood discharge and flood levels have been attributed to catchment clearing. Seventy five

percent of the Moreton Bay catchment has been cleared over the past 200 years for

agricultural and urban activities (Capelin et al., 1998).

As a consequence of the rainfall patterns, changes in catchment land use and water

management, river flow rates are generally low/non-existent for a number of years in

succession, followed by a short period of high flow. As flow rates are low/non-existent for

most months of the year, the residence time during these periods is very long (e.g. up to 188

days in the middle reaches of the Brisbane River) (McEwan, 1998). Any sediment inputs into

a river during these periods will remain within that section of the river until a high flow event

(Caitcheon et al., 2001).

The consequence of pulsed river flows on sediment transport has been well documented in

northeast Australian rivers (Cooper and Riley, 1996; Taylor and Devlin 1997); these studies

have found that the majority of sediment transported to the coast occurs during the first major

rainfall of the wet season. It has been estimated that 80% of sediment entering rivers can

remains in the river until it is flushed to the coast during the first high flow event (Arakel et

al., 1989).

In 1996 a 1 in 20 year flood event provided the opportunity to assess sediment input and

deposition processes in Moreton Bay (Moss, 1998). During peak flow, sediment

53

concentrations within the river were as high as 500-1000 mg L-1 (low flow concentrations

range between 10-100 mg L-1), resulting in 150,000 t of sediment being delivered to the bay

during the 24hour period of flooding (Davies and Eyre, 1998). The majority of the suspended

solids settled out of the water column in the west of the bay within one month of the flood

(Moss, 1998). Repeated sediment deposition in this region of the bay has resulted in a large

muddy river delta extending north from the Brisbane River mouth. The shape and sediment

composition of the river delta is determined by; (a) proximity to the river mouth, (b) waves

and tides influencing a northwest accumulation, and (c) high residence times (50+ days) in the

western side of the bay, minimising transport of sediment out of the bay (Lang et al., 1998).

The resuspension study demonstrated that the shallow muddy river delta deposits is readily

resuspended by the wind-waves generated during east to south winds (You et al., 1999). As

the predominant wind direction is from the east to south for most months of the year,

sediments are continually resuspended, resulting in a long-term reduction in water clarity

(notwithstanding the change in wind direction during winter). Although the data presented is

for one area and depth of Moreton Bay, the processes observed can be used to infer

resuspension dynamics in other regions of the bay. For example, Bramble Bay would have

similar sediment resuspension characteristics to Deception Bay, as the sediment type, water

depth and wind-wave height and frequency (Bureau of Meteorology; significant wave height

and period prediction) are similar to Deception Bay. Whereas, resuspension processes in the

Eastern Bay would be significantly less as the sediment is comprised mainly of sand (i.e. less

than 20% mud) and is protected from the predominant summer southeast winds.

Phytoplankton can also reduce water clarity, with the reduction in clarity proportional to

biomass present (Kirk, 1994). For most regions of Moreton Bay (Except Bramble Bay),

phytoplankton biomass is low and hence will have negligible effect on light penetration. Abal

and Dennison (1996) calculated that these low chlorophyll a concentrations account for as

little as 3% of light attenuation, compared to 44% for suspended solids. However, light

attenuation by phytoplankton would have been significantly higher in Bramble Bay for most

months of the year because of the large biomass present. This large phytoplankton biomass in

Bramble Bay was due to elevated nutrient availability in this region caused by local sewage

discharge (O’Donohue et al., 2000).

Resuspension processes in seagrass meadows are significantly different to those on bare

sediment (Ward et al., 1984; Koch, 1999; Terrados and Duarte, 2000; Gacia and Duarte,

2001). Seagrasses bind sediment (Fonseca, 1989) and reduce near-bed water velocity

54

(Fonseca and Cahalan, 1992), which, in turn, tends to reduce resuspension and increases

deposition rates (Ward et al., 1984; Koch, 1999). Although Moreton Bay seagrass meadows

would be reducing near-bed water velocity, clarity of water overlying water is still reduced by

sediment resuspension processes, as demonstrated by light monitoring at the deep edge of Z.

capricorni in Deception Bay (Fig. 4.44e). Decreased light availability to seagrasses during

wind events can be attributed to the combination of two processes: 1) Resuspension with the

seagrass meadow. Seagrasses beds tend to trap finer particles than adjacent non-vegetated

areas (Almasi et al., 1987; Wanless, 1981), these finer particles resuspend at slow near bed

velocities (Koch, 1999): 2) Most seagrass meadows in Moreton Bay have a relatively narrow

distribution and are therefore inclose proximity to regions of higher resuspension. Turbid

waters from bare sediment regions will be readily transported over the seagrass meadows by

tides.

4.4.1 long-term light reduction and seagrass distribution in Moreton Bay

Rapid light attenuation due to sediment resuspension is almost certainly the primary

environmental factor controlling seagrass distribution and maximum depth limit in western

and southern Moreton Bay. Seagrass is absent in the most turbid regions of the bay (e.g.

Bramble Bay, Southern Deception Bay), has shallow MDL in moderately turbid regions (e.g.

Waterloo Bay), while the deepest and most expansive meadows occur in the least turbid

regions (eastern Moreton Bay). Light as a limiting factor for seagrasses in Moreton Bay was

first identified by Abal and Dennison (1996), demonstrating a significant correlation between

seagrass depth range and light attenuation in the southern bay. In accordance with their

research, the present study identified suspended solids as the primary cause of light

attenuation, however it is contended that the long-term increase in turbidity is the result of

sediment resuspension rather than ‘terrestrial inputs’ as suggested by Abal and Dennison

(1996).

Sediment resuspension has been identified as an important process affecting seagrass depth,

distribution and survival in other regions of the world. Giesen et al., (1990b) suggest that

sediment resuspension rates increased after Zostera marina die-off during the 1930’s wasting

disease epidemic in the Dutch Wadden Sea. Sediment became unstable after the die-off,

leading to increased resuspension, which in turn, inhibited seagrass recovery processes

(Giesen et al., 1990b). Sediment resuspension has also been implicated for seagrass loss and

reduced distribution in two shallow bays in Florida (Tampa Bay, (Schoellhamer, 1995);

Florida Bay, Hall et al., 1999).

55

While light availability is the limiting factor of seagrasses for most of the bay, it does not

apply to the North and South Passage regions. These regions of the bay are characterised by

clear water, shallow sandbanks, strong tidal currents and dynamic moving sandy sediment.

The strong currents and unstable sediments in these regions most likely inhibit seagrass

colonisation (See Koch, 2001).

In agreement with Abal and Dennison (1996), it was found that the maximum depth limit

(MDL) of Zostera capricorni correlates with light attenuation when considering three of the

four monitoring sites. However, Z. capricorni in Deception Bay was growing approximately

1.0m deeper than predicted by light attenuation, suggesting that there may be intra-specific

differences in the minimum light requirements for Z. capricorni (Chapter 5 discusses this

aspect of Z. capricorni’s light requirements in more detail).

Between 1987 and 1998 approximately 5 000ha of seagrass were lost from Moreton Bay (Udy

et al., 1999) and substantial loss before this period is likely (e.g. loss from Bramble Bay)

(Dennison and Abal, 1999). The largest area of loss in the 11 year period between surveys

occurred in southern Deception Bay (2 000 ha). The initial loss of seagrass in this region may

be due to the 1996 flood event (Abal et al., 1998). However, that seagrass did not recover

after the transient light reduction event may be due to increased sediment resuspension in

response to increased near-bed wave velocity and decrease sediment cohesion. Even partial

seagrass loss from an acute light deprivation event may lead to complete long-term seagrass

loss through a feedback mechanism. That is, as seagrasses reduce resuspension, a partial loss

of seagrass may result in increased sediment resuspension that in turn causes further seagrass

loss (Walker and McComb, 1992; Olsen, 1996). The second largest area of seagrass loss (1

700 ha) was in the clear waters of northeast Moreton Bay. As discussed, this region of the bay

is characterised by strong currents causing significant sand movement, that inturn, may lead to

seagrass loss. Natural loss-recovery cycles of seagrass due to sediment movement in this

region of Moreton Bay have been identified by Kirkman (1978). Seagrass loss in the southern

Moreton Bay region was first recognized by Abal and Dennison (1996) during their 1992-93

survey. While their surveys did not quantify the area of loss, it is evident from the map

produced that seagrass distribution in 1992-93 was similar to 1998. This comparison

highlights that the majority of seagrass loss in this region occurred between 1987 (the Hyland

et al., 1989 survey) and 1992-93, and little seagrass loss occurred after this period.

56

4.5 Conclusion

Seagrass distribution in Moreton Bay has decreased by approximately 18% in the past 11

years, and substantial loss preceding this period has been contended. Seagrass loss in western

and southern Moreton Bay is almost certainly due to both acute light reduction events (e.g.

floods) and resuspension of flood deposits. Lack of seagrass recovery after a flood event is

most likely due to increased sediment resuspension because of denuded substrate. As

sediment resuspension may be responsible for initial seagrass loss and inhibits recovery after

loss, it can be concluded that sediment resuspension is the most important physical process

affecting seagrass distribution in western Moreton Bay. However, in eastern Moreton Bay

processes such as water movement and burial by migrating sand is the primary cause of

seagrass loss.

While the present chapter identified the physical processes causing light reduction to seagrass,

it could only speculate on the interaction of these processes on seagrass distribution and

survival. To accurately assess how seagrasses distribution is affected by both long-term and

acute light reduction processes, it is necessary to determine the long-term light requirements

of seagrass species and persistence under light limiting conditions. The following chapter

address these issues by focussing on the dominant species in Moreton Bay, Zostera

capricorni.

Chapter 5 Light requirements of the seagrass Zostera

capricorni in Moreton bay, Australia

Abstract

Loss of seagrass in Moreton Bay has been attributed to long-term and acute light reduction

processes. Long-term light reduction processes were investigated by determining the

minimum light requirements (quantity and spectral quality) of the dominant seagrass in

Moreton Bay (Zostera capricorni). Acute light reduction processes were investigated by

conducting light deprivation experiments. During 1997/1998, light quantity and various

seagrass characteristics were assessed at Z. capricorni maximum depth limit (MDL) at four

locations within Moreton Bay. In addition, light was monitored in a region now devoid of

seagrass. Light deprivation experiments were conducted at each of the four monitoring

locations during May 1998. Light quantity was measured using PAR sensors (400-700nm)

and underwater spectra using a spectroradiometer. The quality of underwater light for

photosynthesis was calculated from the underwater spectra and the light absorption

characteristics of Z. capricorni leaves. The quantity of light at MDL was 30% of surface light

(annual mean of 10 mol photons m-2d-1) at three sites and 15% at the fourth site. Light quality

was similar across the bay (despite large site differences in underwater spectra), with

approximately 68% of available light being absorbed for photochemistry. Light deprivation

experiments demonstrated that Z. capricorni could persist through deprivation events of less

than 55 days duration. The minimum light requirement of Z. capricorni was used to generate

spatial prediction maps of potential seagrass habitat and to review processes influencing the

current distribution within the bay. The present study demonstrated the effectiveness of using

minimum light requirements and light deprivation experiments for providing insight into

seagrass distribution and survival.

58

5.1 Introduction

Seagrass distribution is controlled by multiple interacting environmental factors. Water

motion (current velocity, wave energy and turbulence), sediment characteristics (grain size,

compaction, nutrient content, pore-water sulphide concentrations) and biological interactions

(grazing) have all been shown to affect seagrass distribution (Koch, 2001; Koch, 1999; Udy

and Dennison, 1997a; Fonseca and Kenworthy, 1987; Barko and Smart, 1986). Although each

of the factors can affect seagrass distribution, the availability of light is consistently found to

be the most important limiting factor (Dennison et al., 1993, Abal and Dennison, 1996).

The distribution and maximum depth limit of seagrasses in Moreton Bay has been largely

attributed to long-term and acute light reduction processes, with long-term light reduction due

to sediment resuspension processes (Chapter 4). While the relationship between seagrass

distribution and light penetration has been explored in the southern bay (in terms of water

quality requirements: Abal and Dennison, 1996), there is little understanding of the light

requirements of Morton Bay’s seagrass. Basic questions, such as how much light is required

to maintain a seagrass meadow, and what the effects of flood events are on seagrass survival,

have yet to be resolved. Moreton Bay seagrasses cannot be effectively managed until basic

principals affecting their distribution and survival are understood. Once seagrass minimum

light requirements are known it will be possible to model potential seagrass loss/gain under

deteriorating/improving water clarity scenarios, and to review current seagrass distribution in

relation to areas where sufficient/insufficient light penetrates for survival.

Continuous long-term light monitoring with a PPFD (Photosynthetic Photon Flux Density)

sensor and data logger is the most appropriate method of assessing the minimum light

requirements (MLR) of seagrass (Chapter 2). However, sensors that measure PPFD are

weighted equally across the visible spectrum (400-700nm wavelength) and, hence, do not

indicate the quality or usefulness of the light for photosynthesis (Gallegos, 1994; Kirk, 1994).

As PAR (Photosynthetically Active Radiation) passes through a body of water, the quality of

the light is altered by the presence of suspended and dissolved matter and with increasing

water depth. The spectral composition of light at seagrass depth can be significantly different

from that of light at the surface. It is important to assess the spectral composition of PAR as

the efficiency of light capture and utilisation by seagrass is wavelength specific.

Late last century, Engelmann (1883, in Enriquez et al., 1994) hypothesised that the shape of

the absorption spectrum of aquatic macrophytes affected their depth distribution. While

59

substantial research conducted on macroalgae has failed to support the hypothesis (Enriquez

et al., 1994; Dring, 1986), there has been minimal research on the same issue in relation to

seagrasses. Buesa (1975) first demonstrated that light spectra may be important in influencing

the depth distribution of seagrasses, by showing that red light was more useful for Thalassia

testudinum photosynthesis and blue light for Syringodium filiforme. Since this work of Buesa,

spectral research has predominantly focused on measuring the underwater light spectra of

coastal waters (Moore et al., 1997; Carter and Rybicki, 1990, McPherson and Miller, 1987)

without defining the usefulness of the light to the seagrass. As underwater light spectra in

coastal regions such as Moreton Bay can be very diverse (Kirk, 1994) it is important to assess

the effects of different spectra on seagrass distribution and depth.

Moreton Bay periodically receives large plumes of sediment and nutrient-laden fresh water

due to floods (Heil et al., 1998). These events can deprive seagrass of light for their duration,

potentially leading to temporary or permanent seagrass loss. In the absence of a flood event,

the light deprivation impact of floods can be simulated in shading experiments that provide

‘worst case’ scenario, i.e. no direct light penetration. Seagrass die-off during light deprivation

results from depletion of stored carbohydrates and/or increased sediment toxicity due to

anaerobic root processes (Lee and Dunton, 1997). As seagrass capacity to store carbohydrates

and sediment sulphide conditions may be site specific, the effect of light deprivation on

survival may also be site specific. An assessment of flood impact on seagrass survival should

therefore address regional differences in seagrass tolerance to light deprivation.

The aim of the present study was to determine the relationships between light availability and

distribution and survival of the most abundant seagrass within Moreton Bay, Zostera

capricorni. In specific, the following aspects were investigated:

The long-term MLR of Z. capricorni in Moreton Bay.

Spatial and temporal variability of Z. capricorni growth and morphology at maximum

depth limit.

Quality of light at maximum depth limit.

The current distribution of Z. capricorni in relation to the available light and MLR.

The potential for using MLR to predict the effects of changing water clarities on

distribution of Z. capricorni within Moreton Bay

60

The capacity of Z. capricorni to persist through periods of light deprivation in

different regions of the bay.

61

5.2 Methods

5.2.1 Study Sites

Five study sites, each representative of a major region within Moreton Bay, were established

in September 1997. The sites were selected to incorporate a range of water clarities (Fig. 5.1).

Seagrass light availability, growth and morphology were assessed at four of the sites (South

Moreton, Pelican Banks, Waterloo Bay and Deception Bay) and light availability only was

monitored at Bramble Bay due to the absence of seagrass. Light deprivation experiments were

successfully conducted at each of the seagrass sites except South Moreton.

N

Pelican Banks

South Moreton

Moreton Is.

Bramble Bay

Waterloo Bay

Moreton Bay

North Stradbroke Is.

Deception Bay

Brisbane City

N

Pelican Banks

South Moreton

Moreton Is.

Bramble Bay

Waterloo Bay

Moreton Bay

North Stradbroke Is.

Deception Bay

Brisbane City

Figure 5.1. Location of study sites in Moreton Bay: = Surface light monitoring: = light monitoring in region of historical seagrass loss: ★ = seagrass, light monitoring and light deprivation experiment sites.

5.2.2 Determining Z. capricorni minimum light requirements

Light monitoring was conducted from February to December 1998 using 2π light sensors

(Dataflow, Australia). Light sensors were positioned at the maximum depth limit (MDL) of

Zostera capricorni at each site. Each sensor was positioned at the top of the seagrass canopy.

To assess the quantity of light in a region where seagrass loss has been recorded, an additional

sensor was placed in Bramble Bay at approximately 3m below lowest astronomical tide (Fig.

5.1). Surface light was measured at the University of Queensland, which was within 20 to 55

km of all the submerged sensors (Fig. 5.1). All light loggers were programmed to record the

average quantity of PAR (400 to 700nm wavelength) over a 15min period. For the first six

62

months of the experiments, light attenuation within the water column was measured by

placing a second light sensor vertically above (and slightly to the side of) the lower unit

(vertical separation was between 0.5 and 0.7m depending upon the site). Light attenuation

(Kd) was calculated every 15 min period using the Beer-Lambert equation (Chapter 2;

Equation. 2.1). The number of hours per day seagrasses were receiving photosynthesis

saturating light (Hsat) was calculated for each day, then averaged over the year. Light

saturation was considered to occur at 150 µmol photons m-2 s-1 (Longstaff, unpubl. data;

Flanigan and Critchley, 1996). Light sensors were calibrated against a Li-Cor 2π quantum

sensor three times over the monitoring period. The submerged light sensors were cleaned at

approximately hourly intervals during deployment by an automatic submersible cleaning

device, designed and constructed for this project (Chapter 3).

5.2.3 Assessing Z. capricorni distribution in relation to minimum light

requirements

Using information about the MLR of a species and the subsurface light conditions (either

actual or predicted) it is possible to (a) review current distribution of that species in relation to

regions with sufficient/insufficient light for survival, and (b) model potential seagrass

loss/gain under deteriorating/improving water clarity scenarios.

Firstly, the quantity of light at the sediment surface was calculated from March 1998 bay-

wide Secchi depth survey (Fig. 4.6), bathymetry and surface light. Using these parameters,

light at sediment surface was calculated according to the Beer-Lambert exponential decay

function: Iz = Io e-kdz using Secchi depth as a measure of light attenuation (i.e. kd=1.7/Zsd; See

Chapter 2 for full details). Light quantity between Secchi depth monitoring sites was

statistically predicted, then mapped on GIS software using the MLR of Z. capricorni as

contour intervals.

Seagrass distribution was determined using visual cover estimates at over 2000 sites within

the bay (Chapter 4). At each site the percent cover of seagrass and bare substrate was

recorded. Zostera capricorni presence/absence at each survey site obtained and mapped using

GIS software.

Modelling the impacts of different water clarity was done in the same way except that instead

of calculating the actual subsurface light conditions (i.e. from Secchi depth), light at the

sediment surface was calculated using the light attenuation coefficient of clear

coastal/estuarine waters (i.e. a kd of 0.3 m-1) and the surface light data for March 1998.

63

Potential Z. capricorni habitat was established by mapping the modelled benthic light

conditions of clear coastal waters and by using the MLR determined from the light sensors as

contour intervals on the map (This habitat assessment is purely based on light requirements

and assumes no other limiting factors such as sediment conditions or water motion).

5.2.4 Assessing the spectral quality of available light

Underwater spectral irradiance was classified at each of the light-monitoring sites (excluding

Deception Bay due to hazardous boating conditions) in May 1998. Spectral irradiance was

measured for a period of 5 min at two depths, between 10am and 2pm to minimise variation

of the spectrum due to the changing sun angle. Measurements were made with an MER 1000

Underwater Spectroradiometer (Biospherical Instruments). The underwater spectroradiometer

recorded spectral irradiance at 11 discrete wavelengths (410, 441, 488, 520, 540, 570, 589,

625, 656, 671, and 694 nm), spanning the PAR spectrum. Thirty of these sets of values were

measured every minute and the mean logged as a discrete data set. Wavelength-specific

attenuation was calculated using the shallow and deep data sets and the Beer-Lambert

equation (Chapter 2, Equation. 2.1). These attenuation values were used to calculate the

spectral quality at the maximum depth of seagrass survival. To aid comparison of spectral

quality between sites all data was normalised to wavelength of maximum irradiance (589nm).

Light absorption properties of Z. capricorni leaves were assessed on three random samples

collected from each site. Reflectance of each wavelength of light between 400 and 700nm was

determined using the integrating sphere technique (Moss and Loomis, 1951) and light

transmittance was determined using the opal glass technique (Shibata, 1959). A stable

halogen light source was directed into the integrating sphere (Biospherical instruments) via a

condensing lens. Light within the sphere was transmitted to a spectroradiometer (ASD) via a

fiberoptic cable placed at 90o to the light source within a sample port. The spectroradiometer

measured light energy at 0.7nm intervals between 400 and 700nm wavelengths. Leaf samples

were placed in front of a second sample port located at 90o to the light source and 180o to the

spectroradiometer port. A standard curve was generated immediately before sample analysis

by placing reflectance standards in front of the reflectance port and recording the change in

light energy for each 0.7nm wave interval. Leaf samples were then placed in front of the

reflectance port. The light quantity was recorded and reflectance was determined by reference

to the standard curve. As the spheres’ port diameter was greater than the width of the

seagrass, several leaves had to be carefully positioned across the port, ensuring no gap or

overlap between the leaves (this was facilitated by using a plastic clip that held the leaves

64

together). Despite using a stable light source, the quantity of blue light (400-500 nm) reflected

was extremely variable because of the very low quantities supplied by the halogen light

source. For this reason all data between 400 and 500 nm wavelengths was not reported and a

standard reflectance of 5% was used for all absorptance (i.e. the percentage of light absorbed)

calculations for this range of light. Transmittance of light through Z. capricorni leaves was

assessed using the opal glass technique. Cleaned leaf samples were mounted in a scanning

spectrophotometer (Beckman) fitted with an opal glass unit. Light transmittance through each

sample was measured at 1nm intervals between 400 and 700nm. Absorptance (A) was

calculated from the proportion of light transmitted through the leaf (T) and the leaf reflectance

(R) as: A = 100 – T – R.

PUR (Photosynthetically Usable Radiation) as a proportion of PAR was estimated from the

underwater light spectrum and the leaf absorptance at each site. In order to compute PAR and

PUR across the entire spectrum from the 11 separate wavelength bands measured by the

underwater spectroradiometer, the relevant spectra (absorptance and underwater spectra) were

considered as a series of blocks. The width of each block in nm was taken to extend from

halfway between a nominated wavelength and the next consecutive nominated wavelength to

halfway between the next consecutive nominated wavelengths. The PAR for a particular

wavelength block was calculated by multiplying the PAR by the number of wavelengths in

that block. The proportion of usable light in each block was calculated as the average

absorptance for each block range (block absorptance). The PUR for each block was calculated

by multiplying block PAR by block absorptance. PUR (as a percentage of PAR) across the

entire spectrum was calculated as the sum of block PURs divided by the sum of block PARs

(Equation 5.1):

PUR = (Σ(block PUR)/(Σ(block PAR)) x100 5.1

where block PAR is the total PAR in each block divided by the number of wavelengths in

each block, and block PUR is the block PAR multiplied by the block absorptance.

5.2.5 Seagrass characteristics

The biomass, canopy height and shoot density of seagrass was measured at each site in spring

(September 1997), summer (January 1998) and winter (June 1998). A stratified random

sampling design was used for seagrass collection. Three cores (15cm diameter) were

randomly collected at the maximum depth of seagrass survival at each sample time. The

sediment was rinsed from the cores in situ and the seagrass frozen prior to sorting in the

65

laboratory. Seagrass biomass was sorted into the above ground (leaf and sheath) and below

ground (roots and rhizome) components. Sorted material was rinsed with fresh water to

remove salt, dried at 60oC and weighed to determine the dry weight biomass of each

component. Prior to drying, the shoot density of each sample was determined by inspection,

and the 10 longest shoots were measured to estimate the canopy height.

Leaf growth was determined in September 1997, January 1998 and June 1998 using the “leaf

hole punch” technique (Dennison, 1990a). At the deep edge of each site, three areas were

randomly selected then marked with flagging tape. Five to ten shoots within the immediate

vicinity of the flagging tape were pierced with a needle immediately above the basal

meristem. After a 5-9 day period the shoots were collected and the growth calculated by

measuring the vertical separation of the needle holes in relation to the oldest leaf. Total

seagrass growth was determined using the “rhizome tagging technique” (Dennison, 1990b).

Three quadrats were defined in an area adjacent to the leaf hole punch sites. Within these

quadrats, 10-20 terminal shoots were tagged by securing a small wire loop around the

rhizome. After a 2-month period, the shoots were collected for analysis. Growth of each shoot

was determined by adding (i) the biomass of shoot tissue after the wire tag to (ii) an estimate

of the senesced leaf biomass, calculated from the number of leaf scars and the biomass of the

3rd oldest leaf of each shoot.

5.2.6 Simulating flood events: Seagrass responses to light deprivation

Light deprivation experiments were initiated at each site in June 1998. Z. capricorni plants

growing at the deep edge of each site were deprived of light for 55 days using a circular

screen (2m diameter) suspended 10 – 15 cm above the canopy of the seagrass (see Chapter 6,

Fig. 6.2). To block out all light, the screens were covered in black polythene. A circular fence

was erected around the edge to exclude larger animals (despite the use of exclusion fencing,

the South Moreton experiment had to be abandoned due to very persistent animals seeking

protection under the screens). Control sites were randomly placed in close proximity to the

screens. The centre of each control area was marked with the same steel support used to

secure the shade screens. Leaf growth was assessed during the first week of shading using the

method described above. Samples for biomass and morphology assessment were collected at

the start of shading, then 21 and 55 days later. At each sample time, one seagrass core (15cm

diameter) was collected from each control site (n=3, randomly located near the experimental

site) and shade replicate (n=3). Samples were washed, dried and sorted as described above.

66

5.2.7 Analysis of data

5.2.7.1 Seagrass characteristics

Significant differences in seagrass parameters between the sampling times and between sites

were assessed with 2-way ANOVAs using “Statistica” software. All data was tested for

homogeneity of variance (Cochran’s test) prior to the ANOVA. Parameters (biomass and

growth) that had significant differences in the variance were log transformed before further

analysis. Due to an unbalanced data set, data from three sites was used in the ANOVAs (no

data for Waterloo Bay in September). The ANOVA design was treated as random for all

analyses. Parameters with significant differences (p<0.05) between sites or between times

were further analysed with a Tukey’s post-hoc test (including the Waterloo Bay site) with

significance set at p<0.01 (due to multiple comparisons).

5.2.7.2 Simulating flood events: seagrass responses to light deprivation

Significant differences in seagrass parameters during light deprivation were tested using

regression analysis. Light treatment affect was assessed at each site by comparing the slopes

of the regression equation (days versus seagrass parameter) using Student’s t test in a fashion

analogous to that of testing for differences between two population means (Zar, 1999).

Differences in treatment effect between sites was assessed with an analysis of covariance

procedure using the simple linear regression slopes of each seagrass parameter. Parameters

with significant differences between sites were further analysed with a Tukey’s multiple

comparison test to determine which slopes were not equal. As leaf growth was only assessed

at one time period, significant differences between light treatments (control vs light-deprived)

were assessed using a two-way ANOVA. Significant differences were tested with a post-hoc

Tukey’s test to assess differences in leaf growth at each site.

67

5.3 Results

5.3.1 Determining Z. capricorni minimum light requirements

Annual surface PPFD followed a distinct seasonal pattern, with average weekly PPFD more

than 50 mol photon m-2 d-1 in the summer declining to less than 20 mol photon m-2 d-1 in the

winter (Fig. 5.2). The mean annual surface irradiance during the study period was 34 (±14)

mol photon m-2 d-1 (Table 5.1). At a shorter time scale, light availability varied daily in

response to changes in cloud cover, with dense cloud cover reducing surface light by up to

80%.

Table 5.1. Maximum depth limit and mean (± 1 std dev) quantity of light at the maximum limit of Zostera capricorni within Moreton Bay (* no seagrass present - depth of light meter presented)

Surface South

Moreton

Pelican

Banks

Waterloo

Bay

Deception

Bay

Bramble

Bay

Maximum Depth Limit (m) - 2.9 1.3 1.4 2.8 3*

Light attenuation (Kd; m-1) (Calculated from6 month period only)

- 0.5 (0.3) 1.1 (0.4) 1.4 (0.8) 1.07 (0.61) 2.1 (0.8)

Mean daily light

(mol photons m-2 d-1)

33.7(14.2) 10.6 (5.3) 9.25 (5.1) 10.9 (6.3) 4.59 (3.4) 1.6(2.2)

% of surface light 100 35 (12) 31(15) 36 (19) 16 (12) 6 (10)

% surface light Coefficient of

Variation

0.39 0.49 0.51 0.76 1.7

Light saturation period

(hours day-1 PAR >150 µmol

photons m-2 s-1)

9.24 (1.8) 6.61 (2.32) 6.03 (2.57) 6.45 (2.72) 3.2 (2.8) 0.71 (1.84)

During 1998 the mean daily PPFD at Z. capricorni MDL was 10 mol photons m-2 s-1 for the

South Moreton, Pelican Banks and Waterloo Bay sites (Table 5.1). However, PPFD at the

Deception Bay site was substantially less, with only 4.6 mol photons m-2 d-1 recorded. In

Bramble Bay, a region of historical seagrass loss, the mean daily PPFD at 3m depth was only

1.6 mol photons m-2 d-1. The results for the light saturation period (Hsat) followed a similar

pattern to those for the mean daily light: ≈6 hours per day that PPFD>150µmol photons m-2s-1

for each the three southern seagrass sites, but only about half that for Deception Bay and one

tenth that for Bramble Bay.

68

1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-June 1-Ju ly 1-Aug 1-Sept 1-Oct 1-Nov 1-Dec 1-Jan 1998 Date 1999

0102030405060 a) Surface

0

5

10

15

20

25b) South Moreton (MDL=2.9m )

0

5

10

15

20

25 c) Pelican Banks (MDL = 1.3)

0

5

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15

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25 d) Waterloo Bay (MDL=1.4)

0

5

10

15

20

25e) Deception Bay (MDL=2.8)

0

5

10

15

20

25 f) Bramble Bay (light meter at 3m depth)

PPFD

(mol

pho

tons

m-2

d-1)

1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-June 1-Ju ly 1-Aug 1-Sept 1-Oct 1-Nov 1-Dec 1-Jan 1998 Date 1999

0102030405060 a) Surface

0

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25 c) Pelican Banks (MDL = 1.3)

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25 d) Waterloo Bay (MDL=1.4)

0

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25e) Deception Bay (MDL=2.8)

0

5

10

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20

25

0

5

10

15

20

25 f) Bramble Bay (light meter at 3m depth)

PPFD

(mol

pho

tons

m-2

d-1)

Figure 5.2. (a) Average weekly light (PAR) at a surface monitoring station, (b-e) the maximum depth limit (MDL) of Zostera capricorni at the four seagrass study regions, and (f) in an area of historical seagrass loss

(Bramble Bay).

Seasonal patterns of PPFD were notably different between sites during the monitoring period.

Due to consistently clear water (Kd ≈ 0.5 m-1), the South Moreton site followed a seasonal

pattern similar to that of the surface light; exhibiting a summer high PPFD and a winter low

(Fig. 5.2b). Seasonal trends in light availability were less pronounced at the Waterloo Bay,

Pelican Banks and Deception Bay sites (Fig. 5.2c-e), due to the combined effect of surface

69

light decreasing and water clarity increasing from summer to winter (summer Kd ≈ 1 m-1;

winter Kd ≈ 0.5 m-1). For most months of the year, almost no light penetrated to the benthos in

Bramble Bay; however, during the winter months water clarity increased considerably

(summer Kd ≈ 2.7 m-1: winter Kd ≈ 0.5 m-1), resulting in the mean weekly PPFD increasing to

8 mol photons m-2 d-1 during this period (Fig 5.2f).

The large difference in water clarity between sites was reflected in the different surface-to-

subsurface relationships (Fig. 5.3). The amount of subsurface light correlated strongly with

surface light at the south Moreton site, with an r2 value showing that 57% of the variance is

explained by a linear model. The amount of subsurface light at the second eastern bay site

(Pelican Banks) displayed a weaker correlation with surface light, with only 28% of the

variance explained by the linear model. The correlation between subsurface and surface light

was progressively weaker at the Waterloo Bay, Deception Bay and Bramble Bay sites (Fig.

5.3).

Assessing subsurface light as a percentage of surface light (percent surface light) revealed

patterns in light availability that is due to factors other than atmospheric conditions (i.e. water

column and tidal processes). Variability in percent surface light was site and season specific

(Table 5.1, Fig. 5.4 & 5.5). South Moreton had the least variable percent surface light, with a

coefficient of variation over the year of 39%. Pelican Banks and Waterloo Bay displayed

similar annual coefficient of variation of 50% and Deception Bay and Bramble bay recorded

the highest variability with annual coefficient of variation of 76% and 140% respectively

(Table 5.1). During the winter months (May-July) coefficient of variation at the western bay

sites (Waterloo, Deception and Bramble Bay’s) decreased slightly, while the eastern bay sites

recorded increases (South Moreton and Pelican Banks) (Fig. 5.5).

Although percent surface light at MDL displayed a high degree of temporal and spatial

variability, annual trends were evident. All sites with the exception of South Moreton

recorded a higher percent surface light during the winter months (Fig. 5.4). However, the

timing (i.e. month of the year) and scale of change between seasons was site specific.

Waterloo Bay and Pelican Banks recorded a gradual increase in percent surface light from

summer to winter, followed by a gradual decrease in spring. At Deception Bay and Bramble

Bay the winter increase was more rapid and short lived (July to August only). The average

surface light received by Z. capricorni during 1998 was between 31 and 35% for the South

Moreton, Pelican Banks and Waterloo Bay sites and only 16% at the Deception Bay site

(Table 5.1).

70

A) South MoretonR2 = 0.5726

0

510

15

2025

30

0 20 40 60

D) Deception Bay

R2

= 0.0841

0

5

10

15

20

25

30

0 20 40 60

Surface PPFD (mol photons m-1 d-1)E) Bramble Bay

R2= 0.0973

0

5

10

15

20

25

30

0 20 40 60Surface PPFD (mol photons m-1 d-1)

Sub

surf

ace

PPFD

(mol

pho

tons

m-1

d-1)

B) Pelican BanksR2 = 0.2787

0

5

10

15

20

25

30

0 20 40 60

C) Waterloo BayR2 = 0.1581

0

5

10

15

20

25

30

0 20 40 60

A) South MoretonR2 = 0.5726

0

510

15

2025

30

0 20 40 60

D) Deception Bay

R2

= 0.0841

0

5

10

15

20

25

30

0 20 40 60

Surface PPFD (mol photons m-1 d-1)E) Bramble Bay

R2= 0.0973

0

5

10

15

20

25

30

0 20 40 60Surface PPFD (mol photons m-1 d-1)

Sub

surf

ace

PPFD

(mol

pho

tons

m-1

d-1)

B) Pelican BanksR2 = 0.2787

0

5

10

15

20

25

30

0 20 40 60

C) Waterloo BayR2 = 0.1581

0

5

10

15

20

25

30

0 20 40 60

Figure 5.3. Relationship between sub-surface and surface light at the 5 monitoring sites within Moreton Bay.

71

0

20

40

60

80

0

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40

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80

0

20

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80

1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-June 1-July 1-Aug 1-Sept 1-Oct 1-Nov 1-Dec 1-Jan 1998 Date 1999

a) South Moreton (MDL=2.9m )

b) Pelican Banks (MDL = 1.3)

c) Waterloo Bay (MDL=1.4)

d) Deception Bay (MDL=2.8)

e) Bramble Bay (light meter at 3m depth)

Ligh

t ava

ilabi

lity

(% o

f sur

face

ligh

t)

0

20

40

60

80

0

20

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0

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0

20

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60

80







Ligh

t ava

ilabi

lity

(% o

f sur

face

ligh

t)

Figure 5.4. Light availability expressed as percent of surface light at the 5 subsurface light monitoring sites. MDL = maximum depth limit of Zostera capricorni.

72

020406080

100120140

SouthMoreton

PelicanBanks

WaterlooBay

DeceptionBay

BrambleBay

Site

Coe

ffici

ent o

f Var

iatio

n February - AprilMay - JulyAugust - October

020406080

100120140

SouthMoreton

PelicanBanks

WaterlooBay

DeceptionBay

BrambleBay

Site

Coe

ffici

ent o

f Var

iatio

n February - AprilMay - JulyAugust - October

Figure 5.5. Variability (Coefficient of Variation) in the percentage of surface light penetration during three seasons of 1998.


requirements

Light at the sediment surface during the March 1998 Secchi depth survey was calculated and

mapped using the MLR determined for Z. capricorni (5 and 10 mol photons m-2 d-1). During

March 1998, the benthos in most regions of Moreton Bay received less than the 10 mol

photons m-2 d-1 required to support Z. capricorni (Fig. 5.6). Those areas that received

sufficient light were the shallow northern and eastern banks and a shallow strip along the edge

of the mainland and the islands. For most regions of the bay, the map of benthic light

availability (Fig. 5.6) closely matched the distribution of Z. capricorni within Moreton Bay

(Fig. 5.7), with Z. capricorni restricted to narrow coastal strip and the shallow clear water of

the Eastern Banks. Notable exceptions were the Northern Banks and shallow regions of

Bramble Bay where seagrass does not occur but the light during the survey was sufficient to

support Z. capricorni.

73

N

> 5 mol photons m-2 d-1

5 - 10 mol photons m-2 d-1

< 10 mol photons m-2 d-1

Quantity of light at sediment surface

NNN




Quantity of light at sediment surface> 5 mol photons m-2 d-1










NN

Figure 5.6. Spatial distribution map depicting regions of Moreton Bay where light penetration to substrate is greater than the minimum light requirements of Zostera capricorni. Underwater light calculated from bay-wide

survey of Secchi depth, surface light and bathymetry.

74

N

= Present= Absent

Zostera capricorni distribution

NN

= Present= Absent

Zostera capricorni distribution= Present= Absent

Zostera capricorni distribution

Figure 5.7. Distribution of Zostera capricorni in Moreton Bay.

Benthic light availability was also mapped using light attenuation of clear coastal water (kd

=0.3) to assess potential Z. capricorni habitat if water clarity improved to that of clear coastal

waters (kd = 0.3) (Fig. 5.8). As the same attenuation coefficient was used throughout the bay,

predicted benthic light availability was purely a function of depth. When 10 mol photons m-2

d-1 is used as the MLR, the spatial prediction map shows that areas shallower than 5m have

the potential to support Z. capricorni if water clarity improves (Fig. 5.8), this being

approximately half of the bay. These areas include each of the western bays and the southern

islands region. Using 5 mol photons m-2 d-1 as the MLR increases the total seagrass potential

within the bay by a small extent, namely in the Deception Bay and Bramble Bay regions.

75














NN

Figure 5.8. Spatial distribution map of predicted light penetration to the substrate using light attenuation coefficient of 0.3m-1 throughout the bay. Prediction used to establish potential Zostera capricorni habitat in a

scenario of optimum improvement of water clarity.


Wavelength attenuation assessment was conducted at the Bramble Bay, Waterloo Bay and

South Moreton sites only (Fig. 5.9a). Bramble Bay exhibited the greatest attenuation of light

at all wavelengths. Blue light was attenuated at a much greater rate than green and red light;

for example, Kd at 410 nm wavelength was 3.3 m-1, but only 1.28 m-1 at 589 nm and 1.7 m-1 at

694nm wavelength. Waterloo Bay followed a fairly similar trend but with a maximum Kd of

1.5 m-1 at 410nm and a minimum of 0.6 m-1 at 589nm wavelength. Attenuation at the South

Moreton site was low and similar at all wavelengths (0.18 to 0.7 m-1).

76

0.0

1.0

2.0

3.0

4.0

400 500 600 700

Ligh

t atte

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A) Light attenuation (kd)

0.0

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B) Relative irradiance at 1m depth

South Moreton

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Pelica

n Ban

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Bramble Bay

Surface (x1.2)

South Moreton

Waterloo Bay

Bramble Bay

0.0

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C) Relative irradiance at seagrass depth

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Pelica

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Figure 5.9. (a) Spectral attenuation at 3 monitoring sites; (b) relative spectral irradiance (normalised at 589nm) of surface light and at depth of measurement (1 meter) and, (c) relative spectral irradiance at the maximum depth

of seagrass survival at that site calculated from seagrass depth range and (a) and (b). All measurements conducted in May 1998.

Most of the spectral curves obtained showed maximum irradiance underwater at 589 nm and

were therefore most readily compared when normalised at that wavelength (Fig. 5.9b). The

quantity of photons reaching the earth’s surface was relatively consistent over the green to red

region of the light spectrum (500-700 nm). However, between 400 and 500 nm the relative

quantity of photons steadily declined, with irradiance at 400 nm only 50% of peak irradiance

77

at wavelength 589 nm. The greatest penetration of light into the water column consistently

occurred in the 500-600 nm range of the spectrum, with all sites exhibiting similar relative

irradiance in this region of the spectrum. Relative irradiance at the MDL was calculated from

the light quantities at 1m depth and the Kd of each wavelength of light. There was little

deviation from the 1 m baseline for the Waterloo Bay and Pelican Banks sites because the

MDL of Z. capricorni at these sites is about 1m (Fig. 5.9c). However, as the South Moreton

and Bramble Bay sites are considerably deeper, significant deviation from the 1m baseline

occurred. Deeper water at South Moreton resulted in a reduction in red light (absorbed by the

water), but blue light enrichment (due to lack of water colour), relative to the attenuation at

589 nm wavelength. Pelican Banks and Waterloo Bay sites displayed similar light spectra at

MDL, with reduced quantities of both blue and red light. Bramble Bay recorded the greatest

attenuation of blue and red light, with almost no blue light and relatively little red light

penetration.

Leaf reflectance at each site had similar shaped spectral curves and percent surface values

(Fig. 5.10a). A maximum reflectance of 10% occurred at 550 nm wavelength and a minimum

reflectance of 3% at 670 nm. The shape of the absorptance spectra was also similar between

sites; however, the quantity of light absorbed at some sites differed. At 400 nm over 90% of

the light presented to the leaf was absorbed. Absorptance decreased gradually between 400

and 500 nm to 85-90% and then declined rapidly to the minimum absorption of 40-60% at

550 nm. A second peak in absorptance occurred at 675 nm where 90% of supplied light was

absorbed. Absorptance at the South Moreton and Deception Bay sites was consistently (i.e. at

all wavelengths) greater than that at the Pelican Banks and Waterloo Bay sites. Waterloo Bay

had the lowest absorptance at 550 nm and Pelican Banks had the lowest between 400 and 500

nm.

78

10

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(B)

Figure 5.10. (A) Absorption and reflection spectra of Zostera capricorni leaves collected from the 4 study sites within Moreton Bay. (B) Proportion of available light spectra absorbed for photosynthesis in each spectral block

measured by the underwater spectroradiometer. Data normalised at 589nm to facilitate comparison between sites.

The proportion of light usable for photosynthesis (PUR) was calculated from the underwater

light spectrum and the absorptance of Zostera capricorni leaves (as described above). Z.

capricorni at Waterloo Bay, Pelican Banks and South Moreton sites used approximately 68%

of available PAR for photosynthesis (Table 5.2). So that light utilisation processes could be

compared between sites, the block PUR for each site was normalised to 589nm and plotted

(Fig. 5.10b). While the total PUR for each site was similar, utilisation differed across the PAR

spectrum. Z. capricorni at South Moreton used more light between 400 and 550nm and less

light between 600 and 700 nm when compared to Pelican Banks and Waterloo Bay.

Table 5.2. Average light absorbed by Zostera capricorni leaves and the proportion of available light at maximum depth limited used by the seagrass (Photosynthetically Usable Radiation: PUR).

South Moreton Pelican Banks Waterloo Bay Deception Bay

Average leaf absorption (%) 74.8 70.6 69.7 76.3

PUR (% of available PAR) 68 65 62 nd

79


All seagrass parameters differed significantly between sites when assessed with a 2-way

ANOVA (Table 5.3). Similarly, productivity and shoot density of seagrass differed

significantly between sample times, and below ground biomass and leaf growth had a

significant site combined with time interaction (Table 5.3).

Above ground biomass at Deception Bay and South Moreton sites was significantly (P<0.01)

lower than at the Pelican Banks and Waterloo Bay sites during September and February

(Tukey’s post hoc; Table 5.4). However, by June the above ground biomass at Pelican Banks

and Waterloo Bay had declined, so that all sites had similar values (24-45 gdrym-2). Below

ground biomass followed a similar pattern to above ground biomass (i.e. lower at Deception

Bay and South Moreton sites); however, for most sample times this was not significant at the

p<0.01 level that was required for the multiple comparisons. Similarly, mean shoot densities

were often quite different between sites, but were not significantly different at the p<0.01

level of significance. However, the ANOVA time effect followed by post hoc analysis

indicated that during February shoot densities were significantly lower than during the other

sample times. Post hoc analysis also revealed that canopy height was significantly greater at

Pelican Banks than the other three sites, with maximum canopy heights of 262mm and

168mm respectively.

Table 5.3. Mean squares (MS), F-statistic (F) and significance levels of selected terms in a two-way ANOVA used to test for differences in seagrass biomass, shoot characteristic and productivity between sites (excluding Waterloo Bay site) and sample times. ** 0.01< P<0.001: ***P<0.001 Site Time Site*Time

MS F MS F MS F

df 2 2 4

Biomass

Above ground 0.79 24.4*** 0.077 2.3ns 0.11 3.4ns

Below ground 0.58 27.6*** 0.057 2.7ns 0.17 0.02***

Shoots

Density 1503044 9.7** 988043 6.4** 50989 0.3ns

Canopy height 39771 60.5*** 167 0.25ns 195 0.29ns

Productivity

Whole plant 3.83 48.9*** 0.405 5.17** 0.37 4.7ns

Leaf growth 0.55 45***

0.45 36***

0.046 3.64**

80

Table 5.4. Mean values of seagrass biomass, growth and morphology at the four monitoring sites during three sampling periods.

Sampling period

South Moreton

Pelican Banks

Waterloo Bay

Deception Bay

Biomass Above ground September 34a 171b nd 33a (g dry wt m-2) February 41ab 107c 99bc 22a June 41a 45a 40a 24a Below ground September 144a 305a nd 46b (g dry wt m-2) February 135a 100a 205a 80a June 194a 77a 183a 43a Shoots Density September 728a 765a nd 1474a (shoots m-2) February 429a 392a 1306b 1148ab June 1325a 896a 1194a 1736a Height September 156a 262b nd 126a (mm) February 168a 246b 165a 126a June 163a 246b 161a 112a Productivity Leaf growth September 1.4a 2.6b nd 0.6 a mg shoot-1 d-1 February 1.8b 2.3b 1.2ab 0.6a June 0.4a 0.8 a 0.3a 0.4a Whole plant September 2.9a 7.6b nd 1.12a mg shoot-1 d-1 February 2.52ab 3.3a 2.0b 0.4c June 2.1ab 3.1b 1.2a 1.5a

Rows with values the same letter are not significantly different (p<0.05). nd = no data

Leaf growth rates were significantly greater at Pelican Bank than at the other sites, with a

maximum rate of 2.6 mg shoot–1 d-1 in September compared to 0.6 mg shoot–1 d-1 at Deception

Bay and 1.4 mg shoot–1 d-1 at South Moreton. All sites had a significant reduction in leaf

growth in winter (June sampling). Whole plant productivity was generally lower at the

Deception Bay and Waterloo Bay sites, while Pelican Banks tended to record the highest

productivity at all sample times.


Depriving seagrass of light for 55 days had a significant effect on the above ground biomass,

shoot density and canopy height of Z. capricorni at all the sites (Fig. 5.11, Table 5.5 & 5.6).

81

0

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Bay

Figure 5.11. Biomass of Zostera capricorni over a 55 d period of light deprivation in 3 regions of Moreton Bay

After 55 days in the dark, above ground biomass declined to 5%, 1%, and 10% of control

levels at the Deception Bay, Pelican Banks and Waterloo Bay sites respectively (Table 5.5 &

5.6). Pelican Banks and Deception Bay both had significant declines in shoot density and total

biomass over the experimental period; however, the decline in total biomass and below

ground biomass at Waterloo Bay was not significant (Table 5.6). When the rate of seagrass

decline at each site (during shading) was compared, no significant difference for any of the

parameters assessed, except above ground biomass, was found. The rate at which the above

ground biomass declined during light deprivation was significantly greater (p < 0.05) at the

Pelican Banks site than at the Waterloo Bay site.

Table 5.5. Mean biomass, shoot density and productivity of Zostera capricorni during 55 days of light deprivation at 3 locations within Moreton Bay, Queensland Australia. Significant changes during light deprivation were assessed using regression analysis (Full regression results in Table 5.6) (n=3: ns = not significant* 0.01< P<0.05: ** 0.01< P<0.001)

Pelican Banks Waterloo Bay Deception Bay t=0 t=40d t=55d sign t=0 t=40d t=55d sign t=0 t=40d t=55d sign Biomass Total Cont. 233 125 239 176 59 71 (g dry wt m-2) Shade

122 85 70

**

223 146 87

ns 67 34 14

**

Above ground Cont. 69 53 59 43 15 21 (g dry wt m-2) Shade

45 7 0.4

**

40 21 4

** 24 1.8 1

*

Below ground Cont. 164 131 179 133 44 50 (g dry wt m-2) Shade

77 78 70

ns 183 125 82

ns 43 32 13

*

Shoots Density Cont. 858 1101 1157 1512 1512 1232 (shoot m-2) Shade

896 261 130

**

1474 504 205

ns 1194 1120 541

*

Canopy height Cont. 206 193 157 nd 105 110 (mm) Shade

246 154 41

**

161 148 54

* 112 58 23.5

**

Productivity t = 0 - 7d t = 0 - 7d t = 0 - 7d mg shoot-1 d-1 Cont. 0.83 0.3 0.4 Shade 0.36** 0.25 ns 0.24 *

g m-2 d-1 Cont. 0.745 0.36 0.7 Shade 0.33** 0.3ns 0.4 *

82

Table 5.6. Results of regression analyses to assess the significance of light deprivation on Zostera capricorni biomass and shoot characteristics.

Pelican Banks Waterloo Bay Deception Bay α β r2 p α β r2 p α β r2 p

Cont. 131 1.5 0.4 232 -0.59 0.024 66 0.003 Biomass: total (g dry wt m-2)

Shade 124 -0.96 0.4 ** 229 -2.4 0.35 ns 69 -0.94 0.8 **

Cont. 48 0.242 0.1 42 0.152 0.1 23 -0.094 0.08 Biomass:above (g dry wt m-2)

Shade 45 -0.87 0.9 ** 42 -0.61 0.78 ** 23 -0.45 0.79 *

Cont. 83 1.26 0.4 189 -0.74 0.04 43 0.092 0.03 Biomass: below (g dry wt m-2)

Shade 78 -0.09 0.0 ns 187 -1.77 0.23 ns 46 -0.492 0.55 *

Cont. 862 2.7 0.0 1238 2.3 0.06 142 -1.21 0.003 Shoot Density (shoots m-2)

Shade 899 -14.5 0.76 ** 1270 -9.8 0.37 * 149 -23.6 0.84 *

Cont. 246 -0.9 0.4 168 -0.77 0.27 111 -0.05 0.01 Canopy height (mm)

Shade 262 -3.8 0.7 ** 174 -1.6 0.44 * 115 -1.5 0.9 **

83

5.4 Discussion

5.4.1 Long-term light requirements of Z. capricorni

Continuous long-term light logging proved to be an effective and informative method for

measuring light penetration to seagrass in Moreton Bay. Specifically, this approach enabled

elucidation of a number of interesting features about the subsurface light environment and

implications for seagrass distribution and MDL: (i) short-term variability and seasonal

changes in light penetration was site specific; (ii) light at Z. capricorni MDL was not

consistent across the bay, with quantities recorded at Deception Bay only half that recorded at

the southern sites; (iii) sub-surface light penetration appears to be the primary limiting factor

of Z. capricorni’s depth limit and distribution in Moreton Bay. However, as illustrated by the

lower light requirements at Deception Bay, other environmental factors such as sediment

characteristic can play a significant role; (iv) in a region of reported seagrass loss the absence

of seagrass is due to insufficient light penetration.

The quantity of light penetrating to Z. capricorni in Moreton Bay was spatially and

temporally variable. As sediment resuspension has been identified as the primary cause of

light reduction (Chapter 4), the variability is due to the multiple processes affecting

resuspension rates (i.e. sediment type, water depth, exposure to wind-waves etc) (Koch, 1999;

You et al., 1998; Jing and Ridd, 1996). Consistent with these findings, the lowest and most

variable light penetration in Moreton Bay occurred in the western and southern bays where

dynamic resuspension processes occur (Chapter 4). As discussed in Chapter 4, during winter

water clarity improved in the western bay and deteriorated in the eastern bay, in response to

the change in predominant wind direction. Effectively, this resulted in an increase in the

percent of surface light penetrating, and a decrease in the day-to-day variability (except at

South Moreton where water clarity was more constant over the year). The combination of

reduced surface light in the winter months and increased clarity during the same period

resulted in minimal seasonal changes in light availability.

In turbid coastal waters such as Moreton Bay, tides can also have a pronounced effect on the

variability and quantity of light available to seagrass (Koch and Beer, 1996; Dring and

Luning, 1994; Carter and Rybicki, 1990). For example, high tide during the middle of the day

will result in less light penetration to seagrass than a mid-day low tide. The influence of the

tidal cycle on light penetration is related to the clarity of the water and the tidal range (Koch

and Beer, 1996). In Moreton Bay, with an average tide of 1.5m, tides would have the greatest

84

influence on light during the summer months in the western bay when water clarity is poorest

and a lesser effect during the winter when clarity is improved.

The average quantity of light at MDL during the study period was 10 mol photons m-2 d-1 for

the three southern sites, but was only 5 mol photons m-2 d-1 at the Deception Bay site.

Similarly, as discussed in Chapter 4, MDL at Deception Bay is deeper than expected when

considering the relationship between MDL and light attenuation at the southern sites. While it

was beyond the scope of this thesis to comprehensively investigate the reasons for the

different MLR, possible explanations for the two different MLR are proffered here: 1) Z.

capricorni at Deception Bay was persisting below its MLR; 2) Z. capricorni at all sites were

receiving similar quantities of usable light, with the monitoring technique rather than

physiological requirements explaining the discrepancy; and 3) seagrasses have different light

requirements under differing environmental conditions. The following few sections of this

chapter review each of these possibilities in more detail.

Was Z. capricorni persisting below its minimum requirements in Deception Bay?

Seagrasses can use stored carbohydrates and reduce carbon demand to persist below their

MLR (Longstaff et al., 1999; Burk et al., 1996; Lee and Dunton, 1995). The duration of

persistence is dependent upon the intensity of light reduction (Lee and Dunton 1997; Bulthuis

1983) and the species (Czerny and Dunton, 1995). Some species can persist for long periods

below their minimum requirements; for example, Halodule wrightii survived 3 years of light

limitation before die-off (Onuf, 1996) and Heterozostera tasmanica persisted ten months at

half its minimum requirements (4.7% of surface light) (Bulthuis, 1983). However, light

deprivation experiments in the present study and shading studies by Abal (1996) indicate that

Z. capricorni cannot survive below its MLR for long periods (i.e. near complete die-off

occurs after 55 days in the dark). Therefore, it is unlikely that Z. capricorni was persisting

below MLR during the ten-month monitoring period when light availability at Deception Bay

was constantly less than the southern sites.

Was Z. capricorni receiving similar quantities of usable light at all sites despite the measured

differences

Sensors that measure photosynthetically active radiation are weighted equally across the

visible spectrum (400-700nm wavelength), and hence do not indicate the quality or usefulness

of the light for photosynthesis (Gallegos, 1994; Kirk, 1994). As a result, MLR established

using PPFD sensors alone might vary depending upon the spectral quality of the light.

85

Kenworthy and Fonseca (1996) used this methodological shortcoming to explain the different

MLR (24 and 37% of surface light) for Halodule Wrightii growing at two sites in close

proximity. As the site with the highest MLR had more water colour, Kenworthy and Fonseca

(1996) contended that the available light was of poorer spectral quality and therefore a greater

quantity was required for survival. While spectral analysis was not conducted in Deception

Bay during the study due to adverse boating conditions, subsequent studies have shown light

quality to be poorer in Deception Bay than in the South Moreton region (Longstaff et al.,

2001). Therefore, both the quality and quantity of light at the MDL of the Deception Bay Z.

capricorni meadow is less than that at the southern sites. As such, a shallower MDL would be

expected.

Monitoring light at canopy height does not account for ‘within canopy’ processes such as

epiphyte loading (Cebrian et al., 1999), light absorption by the leaves (Enriquez et al., 1994)

and self-shading (Carruthers and Walker, 1997), which can all influence the quantity of light

available for photochemistry. While the low epiphyte cover on seagrass at all sites (pers.

observ.) excludes the possibility of differential epiphyte shading between sites, the greater

leaf absorption capacity at Deception Bay (76%) compared to the southern bay sites (69-74%)

means more of the available light will be used. Differences in canopy structure (i.e. shoot

density, height and width) can significantly affect plant Radiation Use Efficiency (RUE)

(Bedahl et al., 1972). Theoretical modelling of barley plants predicts that RUE is higher in

canopies composed of many small leaves than in canopies with fewer larger leaves (Bedahl et

al., 1972). If this were applicable to seagrasses, RUE at Deception Bay would be greater than

at the other sites because of the smaller (thinner and shorter) and denser leaves. However, this

would need to be experimentally confirmed, particularly as (Carruthers, 1999) found no

difference in RUE in two Amphibolis meadows (A. griffithii and A. antartica) with different

leaf size and density.

Does the MLR of Z. capricorni depend upon other environmental conditions?

Seagrass physiology and morphology are affected by multiple interacting environmental

factors and not light availability alone (Koch, 2001). Environmental factors shown to have a

direct effect on seagrasses include sediment characteristics (compaction, nutrient availability,

sulphide concentrations) and water motion (waves, currents and turbulence). Z. capricorni in

Deception Bay had a smaller ratio of below ground biomass to above ground biomass the

southern sites. The below-to-above ground ratio of aquatic plants can be affected by factors

86

such as sediment nutrient concentrations (Lee and Dunton, 2000; Udy and Dennison, 1997a)

and sediment type (Idestam-Almquist and Kautsky, 1995; Barko and Smart, 1986). Sediment

nutrient addition experiments conducted next to, but at slightly shallower sites than, the study

sites demonstrated that Z. capricorni in Deception Bay is not nutrient limited whereas

seagrass in the southern sites exhibited nutrient limiting responses such as increased above-

ground and reduced below-ground biomass in response to nutrient addition (Udy et al., 1999).

With less non-photosynthetic tissue to support, Z. capricorni growing in Deception Bay may

require less light than at the southern sites.

In summary, long-term light monitoring and expected poor spectral quality of light would

dictate Z. capricorni in Deception Bay to have much shallower MDL than was observed. The

lower MLR of the Deception Bay Z. capricorni population may be due to increased radiation

use efficiency, greater light absorption and lower respiratory demand.

Dennison et al., (1993) reported intra-specific differences in seagrass MLR (e.g. Heterzostera

tasmanica ranged from 4.4 to 20.2% of surface light). While these differences may be related

to the method used to calculate MLR (i.e. from maximum depth limits and light attenuation

coefficient) the present study demonstrates that intra-specific variation in MLR may not

always be due to deficiencies in the measuring technique. If MLR is affected by physiology

and morphology, then species such as Z. capricorni with a high degree of morphological

plasticity are likely to have greater intra-specific MLR than species with limited plasticity.

Further research is clearly required to investigate the role of seagrass morphology on MLR.

Estimates of seagrass light requirements range between 4.4 and 29% of surface light

(Dennison et al., 1993). Based on these estimates it can be concluded that the MLR of Z.

capricorni in the southern bay (31-36% of surface light) are high in relation to other species.

However, as accurate assessments of MLR (i.e. those using similar techniques as the present

study) are rare, a precise comparison between species at this stage is limited. The most

detailed assessments of seagrass MLR are those conducted on Halodule wrightii (Kenworthy

and Fonseca, 1996; Dunton, 1994). Dunton (1994) provided the first and the longest continual

data set of light available to seagrass, calculating that Halodule wrightii requires 18% of

surface light to survive. By monitoring light over five consecutive years, Dunton (1994)

demonstrated the long-term (4 year) effect of a brown tide on light penetration in one region,

and large inter-annual variability (due to sporadic brown tides) in a second region. The

present study was only conducted over a ten-month period and therefore provides no insight

into inter-annual variability. The most likely cause of inter-annual variability would be

87

changes in weather patterns caused by Southern Oscillation episodes, i.e. El Nino and La

Nina. When the Southern Oscillation Index is negative and favouring an El Nino event, trade

winds are generally weaker, rainfall and cloud cover is less and fewer tropical cyclones occur.

El Nino events are, therefore, likely to result in increased light penetration. In contrast, La

Nina events (positive SOI) are likely to result in less light penetration as trade winds are

stronger, rainfall and cloud cover increases and cyclones are more common (Partridge, 1991).

The present study was conducted during a change from a negative SOI (El Nino: 1997 to

April 1998) to a positive SOI (April 1998 onwards). Consequently, weather patterns during

the present study could be considered ‘typical’ as there was neither floods nor drought.

Surveys in 1987 (Hyland et al., 1989) and 1998 (Udy et al., 1999) failed to find seagrasses in

Bramble Bay, an area where anecdotal evidence suggests they once survived (Abal et al.,

1998). The present study clearly demonstrated light penetration in Bramble Bay was

insufficient to support Z. capricorni, with almost zero light penetration for most months of the

year at 3m water depth. However, the quantity of light increased to levels that could support

seagrass in the winter, sufficient, for example, for Halophila spp. (Erftemeijer and Stapel,

1999; Dennison et al., 1993). Halophila spp utilise a life history strategy to persist in variable

light environments such as Bramble Bay, by producing seeds that persist through

unfavourable conditions (such as low light) and then rapidly germinate and grow during

favourable conditions (Inglis, 2000; Kenworthy, 2000). The absence of rapid growing

opportunistic species such as Halophila spp. from Bramble Bay (Inglis, 2000; Kenworthy,

2000) may be due to a lack of viable seeds and/or other unfavourable environmental

conditions (e.g. anoxic sediments) inhibiting seed germination.


To determine the MLR for seagrasses, values for both underwater spectral irradiance and

absorptance should be combined to provide the biological effectiveness of a given value of

PAR under different conditions of water quality. Gallegos (1994) partially addressed this

challenge by developing a simulation model to calculate spectral irradiance underwater and

then weighting the values obtained by a relative absorption spectrum for Zostera marina. He

called the resulting irradiance value PUR (photosynthetically usable radiation). Using this

approach, Gallegos (1994) calculated that while Z. marina requires 22% of surface light to

survive (Dennison et al., 1993), it only requires 13% penetration of usable surface light. In

other words, 60% of light that penetrated to the seagrass was usable for photosynthesis. The

present study is the first to define PUR for a seagrass using measured underwater light spectra

88

and the absorption spectrum. Despite using a very different approach to Gallegos (1994), the

PUR values determined in the present study (62-68%) were very similar to his modelled

values. Of note is the very small range of PUR values obtained (62-68%) in a bay of markedly

different underwater spectra. The similarity can be attributed to the two major factors

influencing the spectra – water depth and turbidity. In turbid regions, light penetrating to the

seagrass was depleted in the blue wavelength by suspended matter, but because the seagrasses

had shallow depth limit there was more red light penetration (Kirk, 1994). In contrast, in clear

regions more blue light penetrated, while red light was depleted because of the greater MDL

of the seagrasses (Kirk, 1994). Consequently, the seagrasses were receiving a very similar

total PUR although the light used was obtained from different regions of the light spectrum.

In western Moreton Bay spectral irradiance will most likely vary between seasons as the

optical properties change in response to processes such as sediment resuspension rates and

phytoplankton biomass. Changes in spectral irradiance will result in differing PUR values

between seasons. For example, during the winter months when sediment resuspension rates

decline, more blue light will penetrate, resulting in a higher PUR (as a percentage of available

PAR).

As seagrasses have few accessory pigments, light-harvesting capacity can only be increased

through processes such as increasing the chlorophyll concentration and decreasing the

chlorophyll a-to-b ratio (in addition to morphological changes) (Abal et al., 1994). Although

chlorophyll levels were not assessed in the present study, some conclusions can nonetheless

be drawn on the basis that chlorophyll concentrations in aquatic macrophytes are proportional

to the total light absorption (Enriquez et al., 1994; Frost-Christensen and Sand-Jensen, 1992).

The effect of chlorophyll concentration on Z. capricorni leaf absorption was evident from the

range of total absorbance values recorded (69% - 76%). The greatest influence of chlorophyll

concentration on light absorption occurred in the green region of the spectra, where large

inter-site variability occurred. In contrast, there was relatively little difference in absorption in

the red and blue regions of the spectra. As more green light penetrates than light from any

other region of the spectrum, increased green light absorption with increased chlorophyll

concentration would increase light absorption considerably.

89


requirements

Seagrass distribution was assessed in relation to light penetration by comparing the MLR map

(Fig. 5.6) and the Z. capricorni presence/absence map (Fig. 5.7). As predicted from the MLR

results, Z. capricorni only occurred in areas of Moreton Bay receiving at least 10 mol photons

m-2 d-1, substantiating the hypothesis that light was the major factor affecting distribution

within the bay. The absence of Z. capricorni from regions receiving more than the MLR

indicates that processes other than light are also limiting. The largest area of seagrass absence

with sufficient light penetration for survival is the Northern Banks. This region of Moreton

Bay has shallow dynamic sand banks that are exposed to strong currents. It is unlikely that

seagrass could colonise this region because high current velocity inhibits colonisation by

restricting seed and ramet establishment (Koch, 2001), with the further possibility of sediment

burial if seagrass does manage to colonise. The MLR map also shows that the shallow areas

of Bramble Bay receive sufficient light to support Z. capricorni. While the lower depth limit

of seagrasses is controlled by the light availability, the upper limit is controlled by processes

such as desiccation and wave action (Koch, 2001). As Bramble Bay is relatively exposed to

southeasterly wind waves (Bureau of Meteorology, wave height prediction) it is likely that the

theoretical upper distribution limit, controlled by wave action, is deeper than the maximum

depth limit controlled by light availability. However, the light distribution map is based on a

single measurement in time (i.e. bay-wide Secchi depth measures conducted in March 1998),

so an accurate comparison of MLR and seagrass distribution cannot be made until both

temporally and spatially extensive surveys of light attenuation have been conducted.

The MLR of a species can be used as a tool to predict the effects of long-term light

attenuation changes on mature seagrass meadows. Predictive tools are crucial for resource

managers who need to assess the likely effects of proposed management actions (such as

reduced sediment input) and to provide water clarity guidelines for seagrass habitats survival.

In the present study, predictive modelling was conducted to provide a ‘best case’ scenario,

whereby potential seagrass habitat (based on light availability) was calculated using the light

attenuation coefficient for clear coastal water (i.e. kd=0.3) (Fig. 5.7). This predictive

modelling clearly shows that Moreton Bay would support significantly larger seagrass

meadows (covering approximately half of the bay) if maximum water clarity were obtained.

However, it must be noted that this prediction assumes that long-term light is limiting

90

distribution and that other processes sediment conditions and water motions are suitable for

seagrass survival and pulsed turbidity events do not lead to permanent seagrass loss.

It must also be noted that the predicted distribution is based on light requirements derived for

mature plants. It is quite feasible that Z. capricorni seedlings have a higher light requirement

than mature plants, as they have less carbohydrate reserved and no colonially integrating

rhizomes (Pers. Comm. Jud Kenworthy). If Z. capricorni seedlings have higher light

requirements than mature plants, then the predicted distribution (Fig 5.8) would be smaller or

seedlings would need to take advantage of a long period of higher light availability (e.g. a still

summer when ambient light is high and attenuation is low) in order to mature and in doing so,

reduce its MLR.


While the MDL of Z. capricorni in Moreton Bay was undoubtedly controlled by light

penetration, factors other than light were having the overriding influence on morphology and

growth. Previous studies have shown that seagrasses morphology (shoot density, leaf area,

biomass), physiology (photosynthetic rate, chlorophyll and carbohydrate concentrations) and

growth rates correlate with increasing water depth and, hence, with decreasing light

penetration (Tomasko and Dawes, 1990; West, 1990; Dennison and Alberte, 1986; Pirc,

1985). The general responses to decreased light with depth include; reduced biomass, shoot

density and growth. Similarly, these parameters also tend to decrease in response to light

reduction during shading (Longstaff et al., 1999; Czerny and Dunton, 1995; Abal et al.,

1994). Based on these results it was expected that Z. capricorni morphology and growth

throughout Moreton Bay would be similar at MDL. However, significant site differences were

found (e.g. canopy height and growth rates were significantly greater at Pelican Banks)

indicating that other environmental factors were controlling growth and morphology. In the

present study, the monitoring sites were not only located in regions with considerably

different water clarities, but also with different sediment characteristics (Heggie et al., 1999)

and water motion regimes (Bureau of Meteorology, significant wave height predictions). For

example, Z. capricorni at Pelican Banks experienced slight currents and was rooted in fine

soft sediments, whereas Z. capricorni at Deception Bay was exposed to high wave energy and

was rooted in sandier sediment. Each of these physical processes (and light availability)

affects seagrass morphology, physiology and growth characteristics in a manner specific to

the physical conditions of the site.

91


The importance of acute light reduction events for compromising long-term seagrass

distribution and survival is becoming increasingly evident (Longstaff et al., 1999; Moore et

al., 1997). Examples that highlight the significance of these events include the loss of

1000km2 of seagrass in Hervey Bay, Australia after two floods and a cyclone within a 3-week

period in 1992 and the lesser-known loss of 100km2 of seagrass in Torres Strait (Kirkman,

1997). While it has been demonstrated that Z. capricorni distribution and MDL is dependent

upon long-term light availability in Moreton Bay, acute light reduction event caused by

flooding rivers may also affect Z. capricorni distribution and survival. In the absence of a

flood event during the present study (Moreton Bay experiences a sizable flood event every 20

years on average), shade screens were used to simulate light deprivation. The results of the

shading experiment were similar to those of other light deprivation studies: growth rates

decreased to limit carbon utilisation, leaves rapidly senesced (in the present study the leaves

died or partially died before breaking off at the junction between the leaf and the sheath) and

finally the below ground tissue broke down (Longstaff and Dennison, 1999; van Katwijk et

al., 1998; Gordon et al., 1994). However, the rate at which seagrasses die off in this manner

differs markedly between species and this can have important implications for seagrass long-

term survival. Species such as Posidonia sinuosa have a high degree of tolerance to light

deprivation, surviving for over 140 days before complete die-off. At the other extreme,

Halophila ovalis will die off within 40 days (Longstaff et al., 1999). The rate of Z. capricorni

decline in the present study was between these two extremes, with die-off occurring shortly

after 55days in the dark. This rate of die-off was consistent across the experimental sites (with

the exception of Pelican Banks which recorded slightly faster leaf loss) indicating different

site conditions (e.g. water clarities, sediment types) and seagrass morphologies do not

influence seagrass persistence under light deprivation. These results can be used to predict the

impact of large flood events on Z. capricorni in Moreton Bay, although they do not account

for other potential co-occurring impacts such as sediment smothering, increased herbicide

concentrations and decreased salinity. As the 1996 flood in Moreton Bay reduced light for

less than 50 days (Moss, 1998), it is unlikely that the flood alone caused complete Z.

capricorni die-off in the southern Deception Bay region. Sediment resuspension would have

increased with significantly reduced canopy height, shoot density and biomass. As the

seagrass were already persisting in low light conditions (MDL = 0.75 before the flood event)

(Abal and Dennison, 1999) further reduction in light caused by increased sediment

92

resuspension may have reduced light below Z. capricorni minimum long-term requirements,

leading to loss of the remaining seagrass and inhibiting recovery. Thus, an acute light

deprivation event can directly cause seagrass loss, while the indirect effect (increased

sediment resuspension) may contribute to further seagrass loss and prevent recovery.

5.5.6 Conceptualising seagrass-light interactions in Moreton Bay

Effective management of Moreton Bay’s remaining seagrasses requires a clear understanding

of the processes effecting their distribution and survival. These complex processes are

simplified with the aid of conceptual models (Fig. 5.12). Two closely associated processes are

likely to have caused present day seagrass distribution in Moreton Bay, these being long-term

and acute light reduction.

Seagrass loss in Moreton Bay has undoubtedly occurred due to long-term light reduction

processes. Long-term light reduction is initiated by increased sediment input, but is intensified

through a series of positive feedback mechanisms that hinge upon the role of seagrass in

reducing sediment resuspension (Walker and McComb, 1992; Olsen, 1996). That is, as

seagrasses reduce resuspension, a partial loss of seagrass through long-term light reduction

may result in increased sediment resuspension that in turn causes further seagrass loss. In

some regions of Moreton Bay (e.g. Bramble Bay), this feedback loop may have caused

complete seagrass loss. In other regions (e.g. western and southern regions of the bay), long-

term light reduction has restricted the seagrass to a thin shallow strip along the edge of the

mainland and the islands.

Acute light reduction events may also significantly affect the distribution and survival of

seagrasses in Moreton Bay. During floods, large volumes of sediment- and nutrient-laden

fresh water flow into the bay. Light is rapidly attenuated by the sediment and high

phytoplankton biomass stimulated by increased water column nutrient levels (Moss, 1998;

Heil et al., 1998). Flood events that deprive seagrass of light for more than 30 days lead to the

loss of Halophila ovalis (Longstaff et al., 1999) and events longer than 55 days lead to the

die-off of Zostera capricorni. Partial seagrass loss as a result of shorter light deprivation

events may lead to long-term loss due to increase resuspension rates.

93

Figure 5.12. Conceptual model of processes effecting seagrass distribution in Moreton Bay.

Chapter 6 Seagrass survival during pulsed turbidity events:

The effects of light deprivation on the seagrasses Halodule pinifolia and Halophila

ovalis Publication status

Longstaff, B. J. and W. C. Dennison (1999) Seagrass survival during pulsed turbidity events: The effects of light deprivation on the seagrasses Halodule pinifolia and Halophila ovalis. Aquatic Botany 65: 105-121

(The introduction and parts of the discussion have been modified for the thesis)

Abstract

Pulsed turbidity events caused by factors such as flooding rivers have the potential to

seriously impact seagrass communities by depriving the plants of all available light. The

effects of light deprivation was investigated on the survival, morphology and physiology of

the tropical seagrasses Halodule pinifolia and Halophila ovalis growing in the South-East

Gulf of Carpentaria, Australia, a region where pulsed flood events are common. Additionally,

physiological and morphological responses to light availability along natural gradients were

examined. Responses to both experimental and natural light gradients were investigated for

their potential use as indicators of impending seagrass loss during pulsed turbidity events.

Halodule pinifolia was deprived of light for 80 days using in situ shade screens and the

following parameters measured at 3 depths and under the shade screens: biomass, shoot

density, canopy height, amino acid content, chlorophyll content, δ13C signature, %C and sugar

concentration. The quantity of light was extremely variable, with mean daily irradiances

between 9-12 mol photons m-2 d-1, and a range of 0.05 and 42 mol photons m-2 d-1. Halodule

pinifolia leaf amino acid content increased with increased water depth (from 8 to 18 µmol g

fresh wt), chlorophyll a to b ratio decreased (from 2.4 to 2.1) and δ13C values became more

negative (from -9 to -12). Halophila ovalis displayed little tolerance to light deprivation, with

plant death occurring after 38 d in the dark. Halodule pinifolia showed a high degree of

tolerance to light deprivation with no biomass loss before day 38 and complete die-off

predicted after 100 days. Shoot density, biomass and canopy height all declined after 38 days.

96

Physiological parameters that responded significantly to the light deprivation were the amino

acids which increased (from 20 to 80 µmol g fresh wt), the chlorophyll a to b ratio which

decreased (from 2.5 to 2.1) and the δ13C values which became more negative (from –9 to -10).

Changes in leaf physiology (eg. amino acid content, chlorophyll content and δ13C) occurred

before morphological changes (eg. biomass, shoot density, canopy height) or die-off, and

were thus considered to be potential indicators of impending seagrass die-off during light

deprivation. In conclusion, only long duration (>38 d) pulsed turbidity events would have a

detrimental impact on H. pinifolia growing in the Gulf of Carpentaria and that by assessing

specific physiological responses, seagrass loss during pulsed turbidity events can predicted.

97

6.1 Introduction

Seagrass resilience under light limiting conditions has been associated with the form and

function of the species (Walker et al., 1999). With the smaller, colonising species such as

Halophila and Halodule hypothesised to have little capacity to survive under light limitation,

while the larger, climax species such as Posidonia and Enhalus having a significantly greater

capacity (Walker et al., 1999). Previous research has shown that this theory holds true for

some species (e.g. Halophila ovalis at one end of the spectrum and Posidonia australis at the

other) (Longstaff et al., 1999; Fitzpatrick and Kirkman, 1995) but for most species the

relationship between form and function, and persistence under light limiting conditions

remains undefined.

Halodule pinifolia is a tropical seagrass that only occurs in northern Australia and south-East

Asia (Short et al., 2001). In the Gulf of Carpentaria (Northern Queensland), H. pinifolia tends

to occur in shallow subtidal and intertidal regions near river mouths (Poiner et al., 1987). The

climate in Northern Queensland is characterised by pronounced wet season and an increased

frequency of cyclones compared to Southern Queensland. Consequently, rivers in this region

tend to flood more frequently than in the south. Based on the function-form model it could be

assumed that H. pinifolia survival in these flood-affected environments is based on life history

strategy (i.e. rapid germination, growth and sexual reproduction during favourable conditions,

and seeds persisting in the sediment during unfavourable conditions (Inglis, 2000)) rather than

persistence through the events. However, surveys have shown that Halodule pinifolia

meadows in Northern Queensland are perennial (Rasheed, 2001), indicating greater resilience

to low light stress than is predicted from the function-form model.

Pulsed turbidity events can also result from marine engineering programs such as dredging,

pylon driving and wall constructions. Like naturally occurring turbidity events, these plumes

have the potential to lead to seagrass loss if not managed correctly. Monitoring programs

during these activities often rely on assessing the impact after it has occurred, for example,

measuring decline in biomass and shoot density at plume impacted sites in comparison to

control sites (see Onuf, 1994). Seagrasses loss could be prevented in these circumstances if

sub-lethal indicator of seagrass stress could be used as an indication of impending die-off. As

seagrass physiology responds to environmental stresses such as light limitation before

morphological responses, it is feasible that physiological changes could be used as biological

indicators of impending loss (Lee and Dunton, 1997).

98

The purpose of this study was to determine the impacts of pulsed turbidity events on the

survival seagrass meadow containing Halodule pinifolia and Halophila ovalis. In addressing

this aim, the effects of natural light gradient (occurring with increased water depth) and the

effects of light deprivation (achieved through shading) were studied. In addition, a suite of

morphological and physiological responses of H. pinifolia were assessed to determine if any

of these responses to light deprivation could be used as biological indicators of impending

seagrass loss during pulsed turbidity events.

99

6.2 Methods

6.2.1 Site Selection

The study was conducted in a remote 15 km2 area of seagrass in the South Eastern corner of

the Gulf of Carpentaria, Australia, near the Port of Karumba (S17o 27.4’ E140o45.6’) (Fig.

6.1). Weather patterns in this tropical location are characterised by summer monsoonal rains

resulting in pulsed river flows. Frequency and duration of the pulsed river flows was assessed

through the interpretation of long-term (10 years) rainfall data for the region (obtained from

the Bureau of Meteorology).

Port ofKarumbaNorman

River

Bynoe River

Gulf of Carpentaria

NSite 4Site 4(Deep)(Deep)

Site 3Site 3(Mid)(Mid)

Site 5Site 5(Deep)(Deep)

Site 2Site 2(Shallow)(Shallow)

Site 1Site 1Experimental siteExperimental site

(Shallow)(Shallow)

Alligator BankAlligator Bank

Norman river

Norman riverAustralia

Intertidal mud flatsSeagrass meadows

Saltpans/Scrub Boundary of seagrass meadow

2 km

NN500 m

Port ofKarumbaNorman

River

Bynoe River

Gulf of Carpentaria

NSite 4Site 4(Deep)(Deep)

Site 3Site 3(Mid)(Mid)

Site 5Site 5(Deep)(Deep)

Site 2Site 2(Shallow)(Shallow)

Site 1Site 1Experimental siteExperimental site

(Shallow)(Shallow)

Alligator BankAlligator Bank

Norman river

Norman riverAustralia

Intertidal mud flatsSeagrass meadows

Saltpans/Scrub Boundary of seagrass meadow

2 km

NN500 m

Figure 6.1. Location of seagrass, experimental site and natural light gradient monitoring sites on Alligator Banks in the South-East Gulf of Carpentaria.

Seagrass was present predominantly within the intertidal region, with an approximate vertical

distribution ranging between 0.1 m below lowest astronomical tide (LAT) and 1.5 m above

LAT in a region that experiences a tidal range of 3 m. The dominant seagrass in the area was

Halodule pinifolia, a thin-leaved seagrass with a relatively homogenous distribution.

Halophila ovalis, a small broad-leafed seagrass occurred as an understorey species with a low

biomass and a patchy distribution.

Five sampling sites were established in the seagrass meadow between July and November

1996 (Fig. 6.1). Two sites were located towards the shallow edge of the meadow (Sites 1 and

2), one site was located within the middle of the meadow (Site 3) and two sites were located

at the deep edge of the meadow (Sites 4 and 5). Shade screens were deployed at the shallow

edge (Site 1). Continuous light measurements were conducted at one shallow site (Site 1) and

the two deep sites (Sites 4 and 5). All sites were used for measurements of physiological and

morphological responses to natural light gradients.

100

6.2.2 Natural light gradient investigation

Light availability was measured using 2 π light sensors (Dataflow systems, Australia)

programmed to determine the average quantity of light over a 5 min. interval. Two of these

light sensors were placed at the deep edge of the seagrass meadow (Sites 4 and 5) and one

near the shallow edge of the meadow (Site 1) (Fig. 6.1). At these sites the light sensor was

arranged so that light at the top of the canopy was being monitored. The sensors were cleaned

at 7-10 day periods during which time the data from the loggers was downloaded.

To determine the variability of seagrass morphology and physiology along the natural light

gradient, samples of seagrass were collected from the 5 monitoring sites in October, 1996. At

each monitoring site 3 seagrass cores were randomly collected using a sediment corer (15 cm

dia., 25 cm deep). Cores were washed in salt water to remove sediments and then stored at -

18oC until analyses.

6.2.3 Light deprivation experiment

Seagrasses were deprived of light using 2.2 m diameter screens covered in black polythene.

Three of these screens were randomly erected about the experimental site by suspending them

0.25 m above the substrate surface using steel rods (Fig. 6.2). Three non-shaded control

treatments (2.2m dia.) were also randomly established about the site. Severing connecting

rhizomes with a knife prevented translocation of material between shaded and adjacent non-

shaded seagrasses.

The quantity of light available to the seagrass at the shaded and non-shaded area was

determined using a 2π light sensor and a logger which was programmed to average quanta

over a 5 min. interval. The light loggers were secured into the sediment with the sensor

protruding to the top of the seagrass canopy. The sensors were cleaned at 7-10 day periods

during which time the data from the loggers was downloaded. The gradient of light under the

shade screens, from the edge to the centre, was determined at 0.2 m intervals using a Li-Cor

4π quantum sensor.

101

0 0.2 0.4 0.6 0.8 1 00.20.40.60.811.2

2

50

46

Distance into centre of shade screen (m)

% of am

bient light% o

f am

bien

t lig

htQuantity of light penetrating beneath shade screen

Shade screen (2.2m diameter) Steel supports

Mean high water

Mean low water

2

50

46

0 0.2 0.4 0.6 0.8 10 0.2 0.4 0.6 0.8 1 00.20.40.60.81 00.20.40.60.811.2

2

50

46

Distance into centre of shade screen (m)

% of am

bient light% o

f am

bien

t lig

htQuantity of light penetrating beneath shade screen

Shade screen (2.2m diameter) Steel supports

Mean high water

Mean low water

2

50

46

Figure 6.2. Shading apparatus used to deprive seagrasses of light. The 2.2m screen is covered in black polythene and then suspended above the seagrass with steel supports. Graph demonstrates the percentage of ambient light

that can penetrate beneath the shade screens.

Seagrass samples were removed from the shaded and control sites at 7-14 days intervals for a

78 d period. At each sample time, 3 cores of seagrass were removed from each replicate using

a sediment corer (15 cm dia., 25 cm deep). Cores were washed in salt water to expose intact

root, rhizome and shoot material, the washed seagrass was immediately frozen until required

for analysis. To avoid destruction of the shaded area through the continued removal of

sediment cores, each core that was removed from an experimental unit was replaced by a core

collected at least 10 m distance from the experimental site. To avoid coring a previously cored

area, a plastic tag was placed in the centre of transplanted cores to mark its location. At the

termination of the experiment, a number of transplanted cores were carefully retrieved to

determine the extent of rhizome exchange between transplanted areas and surrounding area.

This investigation established that minimal exchange of rhizome between transplanted and

non-transplanted seagrass occurred.

6.2.4 Sample analysis

6.2.4.1 Biomass, shoot density and canopy Height

Seagrass samples collected from the shading experiment and natural light gradient survey,

were sorted into above-ground (leaves) and below-ground (rhizome + roots) material. Below-

ground tissue was rinsed in freshwater then oven-dried (60oC; 48 h). Above-ground material

was retained for determination of shoot density (m-2) and canopy height. Canopy height was

defined as the mean length of the 10 longest shoots per sample. After morphometric analysis,

above-ground material was dried (60oC; 48h) for biomass determination.

102

6.2.4.2 Amino acids

Fresh tissue from the central section of approximately ten Halodule pinifolia leaves was used

for all amino acid analyses. The central section of the leaf was used to avoid new basal

growth and the older decaying leaf tip. The fresh leaf material was weighed (~ 0.1g), diced

into 1 mm2 sections and placed in 2 ml of methanol. Samples were then stored at <4oC until

quantitatively analysed for amino acids with a Beckman 6300 High Performance Amino Acid

Analyser.

6.2.4.3 Chlorophyll content

Chlorophyll analysis was conducted on the central 40 mm of three leaf blades per site. The

width of the 40 mm sections was measured using Vernier callipers in order to determine the

area of leaf material analysed. Leaf blades were macerated using a razor blade then ground

using a mortar and pestle with 10 ml of 80% acetone. The acetone extracts were stored at 4oC

for 12 h to settle suspended material. Absorbance of the extracts was then measured in a

spectrophotometer at 725, 663 and 645 nm (Dennison, 1990c). Chlorophyll a and b

concentrations were calculated according to Arnon (1949).

6.2.4.4 Stable carbon isotope ratio (δ13C) and percent carbon (%C)

Samples of fresh leaf tissue were collected from each survey site, oven dried (60oC; 48 h),

then ground to a powder in a rotary vane mill. Ground samples were oxidised in a Roboprep

CN Biological Sample Converter and the resulting gases analysed for percentage carbon

(%C), 13C and 12C isotopes using a continuous flow-isotope ratio mass spectrometer (Europa

Tracemass). Carbon stable isotope ratios (δ13C) are expressed as the relative per mil (%o)

difference between the sample and the standard of Pee Dee Belemnite carbonate (Peterson

and Fry, 1987).

6.2.4.5 Storage carbohydrate analysis

Oven-dried rhizome material was ground to a fine powder using a rotary vane mill. Sugars

were removed from the ground tissue with 3 sequential 5 min. extractions in 80% (v/v)

ethanol at 80oC. Sugar content of the extract was determined by the phenol-sulphuric acid

colorimetric method (Dubois et al., 1956) using sucrose as a standard.

103

6.2.4.6 Leaf growth

The effect of light reduction on leaf growth was assessed after 60 d of light deprivation. At

each shade site a 25 cm2 area of seagrass was defined using plastic pegs. All seagrass leaves

within the defined area were trimmed to the height of the sediment surface with scissors.

After 7 d, a core of seagrass was carefully removed from the centre of the marked area. Leaf

growth was calculated as the quantity of leaf material produced (g dry wt) during the 7 d

period. Growth per shoot was calculated by dividing the new leaf biomass by the number of

shoots. Growth per m2 was calculated by multiplying the growth per shoot by the shoot

density.

6.2.5 Statistical analysis

6.2.5.1 Natural light gradient investigation

Means and standard errors of all variables were calculated for each survey site. The

relationship between measured variables and sample depth was statistically tested using one-

way analyses of variance, followed by the Fisher’s pair-wise comparison of means. A

probability value of p<0.05 was considered significant.

6.2.5.2 Responses to light deprivation

Means and standard errors of each variable were calculated for each light treatment and

sample time. Split unit analysis of variance (ANOVA) was used to test for significant

difference between light treatments. Significant results were followed with a one-way

ANOVA to test for differences between the treatment and control at each sample time.

Seagrass parameters only measured after 78 d of shading were tested using Students’ t-test. A

probability value of p<0.05 was considered significant for all tests. All statistical analyses

were conducted using the MINITAB software package.

104

6.3 Results

6.3.1 Natural light gradient

Rainfall in the South-East Gulf of Carpentaria is generally restricted to 3-4 months of the year

with a dry period separating the wet seasons (Fig. 6.3). The present study was conducted

during the dry season, with very little rainfall occurring. Rainfall during the rainy season is

also variable, with days of extreme falls (200mm) being followed by days of little rainfall

(<20mm). This rainfall data not only demonstrates the extremes of rainfall, but also indicates

the pulsed nature of the river flows and resultant turbidity plumes in the region.

0

20

40

60

80

100

120

140

160

180

200

Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan88 89 90 91 92 93 94 95 96 97 98

Jul

Time (mmm-yy)

Rain

fall

(mm

)

0

20

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80

100

120

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160

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200

0

20

40

60

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Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan88 89 90 91 92 93 94 95 96 97 9888 89 90 91 92 93 94 95 96 97 98

Jul

Time (mmm-yy)

Rain

fall

(mm

)

Figure 6.3. Daily rainfall for the Port of Karumba region between January 1988 and July 1998. Data highlight the pulsed nature of the rainfall in the study region.

Light monitoring over the 4 month monitoring period demonstrated that the ambient light

environment in the South-East Gulf of Carpentaria was highly variable even within the dry

season. Periods occurred when the seagrass was receiving almost no light and days when the

seagrass was receiving very high light (Table 6.1). The variability of the light climate was due

to the intertidal location of the seagrass in an environment of naturally turbid water. Surface

irradiance values during the experimental period were high and consistent due to long periods

of cloudless days. Quantity of light reaching the seagrass at site 1 was, on average, half the

surface irradiance, but as with all the site, the light was temporally extremely variable. Periods

of very low or zero light would occur on days when high tides occurred at midday, and high

light conditions occurred when low tides occurred during midday. At the deep edge of the

seagrass meadow, mean daily irradiance was only 15-20% of surface irradiance, and for the

same reasons as the shallow site was temporally extremely variable (Table 6.1).

105

Table 6.1. Quantity of light received by seagrass at monitoring sites and experimental area. LAT = Lowest astronomical tide

Approx. depth (m above/below LAT)

Mean Daily Irradiance


Max.–Min. Daily Irradiance


Light availability (% surface Irradiance)

Surface 63 78 - 7 100

Site 1 (Control) 1.5 m above LAT 30 58 - 0.1 47

Site 1 (Shaded) 1.5 m above LAT 0.106 0.175 - 0 0.16

Site 4 (Deep edge) 0 m at LAT 12 42 - 0.1 19

Site 5 (Deep edge) 0.1 m below LAT 9 39 - 0.05 14

H. pinifolia was the dominant seagrass in the study region, with a biomass of 113 g dry wt m-2

being recorded at site 4. In comparison, Halophila ovalis biomass is significantly lower with

no H. ovalis growing at the deeper sites and very little (less than 3 g dry wt m-2) at the shallow

site. The biomass of H. pinifolia did not decline with depth (as was predicted by known

responses to reduced light with depth) with the deepest sites recording both the greatest (113

g dry wt m-2) and least (17 g dry wt m-2) biomass values (Table 6.2). The narrow width of

Halodule pinifolia leaves meant that very high shoot densities could occur with 3528 shoots

m-2 recorded at the shallow site (Table 6.2). The mean canopy height of H. pinifolia shoots

was not significantly different between sites (15 to 17 cm) except for site 4 which had

significantly shorter shoots (12 cm).

Amino acid concentrations of H. pinifolia leaves at the 5 survey sites ranged from 8 to 18

µmol g fresh wt -1 (Table 6.2). Unlike the biomass and shoot density, the amino acid

concentrations showed an increasing trend with water depth. Leaves from the shallow sites

had significantly lower (p<0.05) amino acid concentrations than the deep sites. However,

Amino acid concentration at the mid-depth site was not significantly different from those at

the shallow site or deep site 4.

The ratio of chlorophyll a to b within H. pinifolia leaves ranged from 2.0 to 2.4 (Table 6.2). A

decrease in chl a to b ratio was observed with increasing water depth; ratios at the deep edge

sites being significantly lower than the ratios at the mid and shallow sites. H. pinifolia leaf

chlorophyll a + b concentrations at the 5 sites ranged from 22 to 37 mg chl a to b cm-2 (Table

6.2). There was no observable relationship between chlorophyll content and depth.

106

Table 6.2. Mean values for H. pinifolia morphology and physiology at the 5 monitoring sites. Values in each row with the same letter are not significantly different at p<0.05.

Shallow Mid Deep

Site 1 Site 2 Site 3 Site 4 Site 5

Biomass (g dry wt m-2) 96a 31a 35a 113a 17a

Shoot density (shoots m-2) 3528a 2371abc 1736bd 3715a 1363cd

Canopy height (cm) 15a 17a 16a 12 16a

Amino acid conc.(µmol g freshwt-1) 8a 8.6a 10.6ac 13.7bc 17.6b

Chlorophyll content (mg chl a+b cm-2) 21a 36 28b 23a 30b

Chlorophyll ratio (Chl a/b) 2.4a 2.3a 2.4a 2.2b 2.0b

Carbon isotope ratio (δ13C) -9.9a -9.3a -10.7b -11.9c -12.3c

Sugar conc. (mg g dry wt-1) 21a 21a 40a 35a 26a

Leaf carbon content (%) 32.5ab 31.7a 33.5acd 34.9c 34.1bd

The ratio of the light 12C isotope in relation to the heavier 13C isotope (δ13C ) also changed

down the depth gradient. The mean δ13C of H. pinifolia leaves at the two deep edge sites was

–12 %o (Table 6.2). This value was significantly different (p < 0.05) than the mean value –10

%o, recorded at the two shallow sites. Mean % carbon concentration in H. pinifolia leaves

ranged between 32 and 35%. The two deep edge sites had slightly higher mean values (34-

35%) than the shallow sites (32-33%), however, these differences were not significant

(p>0.05). Sugar concentrations in the rhizome of H. pinifolia from the 5 survey sites ranged

between 20 and 40 mg g-1. There was no relationship between sugar content and seagrass

depth (Table 6.2).

6.3.2 Responses to light deprivation

The quantity of light across the radius of the shade screens was assessed using a 4 π light

sensor that monitors incoming light from all angles. Using this sensor we demonstrated that

light at the edge of the screen was reduced by half and that light was rapidly reduced over the

first 0.2 m to less than 2% of ambient light (Fig. 6.2). Long-term monitoring of light was

conducted using 2 π sensors that only measure down-welling light and as a consequence

values would be lower than those measured using a 4 π sensor. Long-term monitoring showed

that the seagrass received an average down welling irradiance 0.106 mol photons m-2 d-1,

corresponding to 0.35% of ambient light (Table 6.1).

107

Tota

l bio

mas

s (g

dry

wt m

-2)

0

20

40

60

80

100

120

Tota

l bio

mas

s (g

dry

wt m

-2)

Experimental period (d)

0

0.5

1

1.5

2

2.5

3

3.5

4

0 10 20 30 40 50 60 70 80

Control

0% ambient

b) Halophila ovalis

Control

0% ambient

a) Halodule pinifolia

Tota

l bio

mas

s (g

dry

wt m

-2)

0

20

40

60

80

100

120

0

20

40

60

80

100

120

Tota

l bio

mas

s (g

dry

wt m

-2)


0

0.5

1

1.5

2

2.5

3

3.5

4

0

0.5

1

1.5

2

2.5

3

3.5

4

0 10 20 30 40 50 60 70 80

Control

0% ambient

b) Halophila ovalis

Control

0% ambient

a) Halodule pinifolia

Figure 6.4. Responses of Halodule pinifolia and Halophila ovalis total biomass to 78 days of light deprivation.

No significant decline in the biomass of H. pinifolia was observed after 38 days of shading to

0% of ambient light (Fig. 6.4a). Time course analysis demonstrated that biomass declined

rapidly after day 38, with the biomass at day 78 being 70% less than at day 38. Extrapolation

of the biomass decline beyond the duration of the experiment indicates that complete plant

die-off would occur after 90 to 100 days in the dark. Biomass of H. ovalis receiving 0% of

ambient light, declined rapidly during the first 38 days of light deprivation, with nearly all the

H. ovalis having died by day 38 (Fig. 6.4b).

The density of H. pinifolia shoots during shading followed a similar pattern as the biomass

(Fig. 6.5a). No significantly decline in shoot density (P>0.05) occurred over the first 38 days

of light deprivation but between day 38 and 78 the shoot densities declined significantly

(P<0.05) to less than 1,200 shoots m-2. Mean canopy height of H. pinifolia prior to shading

was 13 cm (Fig. 6.5b). Canopy height of shaded plants increased significantly (P<0.05) over

the first 38 days of light deprivation to 15 cm. Between day 38 and 78 canopy height declined

significantly (p<0.05) to 10 cm.

108

0

1000

2000

3000

4000

Shoo

t den

sity

(Sho

ots

m-2)

Control

0% ambient


8

12

16

Can

opy

heig

ht (c

m)

0 10 20 30 40 50 60 70 80

Control

0% ambient10

14

(a)

(b)

0

1000

2000

3000

4000

0

1000

2000

3000

4000

Shoo

t den

sity

(Sho

ots

m-2)

Control

0% ambient


8

12

16

Can

opy

heig

ht (c

m)

0 10 20 30 40 50 60 70 800 10 20 30 40 50 60 70 80

Control

0% ambient10

14

(a)

(b)

Figure 6.5. Effects of light deprivation on the shoot density and canopy height of the seagrass Halodule

pinifolia.

The amino acid concentration of H. pinifolia increased significantly (p<0.05) during shading,

with concentrations at day 67 being over 7 times the control concentrations (Fig. 6.6a). The

amino acids proline, glutamine and asparagine comprised over 80% of the total amino acid

content of these plants.

Chlorophyll concentration of non-shaded H. pinifolia was 21 mg cm-2 however, after 78 days

of shading chlorophyll concentrations increased significantly (p<0.05) to 32 mg chl a to b m-2

(Table 6.3). Prior to shading, the chl a to b ratio in H. pinifolia leaves was 2.5 (Fig. 6.6b).

During shading, the chl a to b ratio declined significantly (p<0.05) to 2.15. At day 38

however, the chlorophyll a to b ratio of control plants also declined, so that on day 38 there

was no significant difference between plant from the 0% ambient light treatment and the

control treatment.

109

Table 6.3. Means for Halodule pinifolia leaf physiology and growth after 80 days light deprivation. Values in each row with the same letter are not significantly different at p<0.05.

Control

78 days light

deprivation Chlorophyll conc. (mg chl a+b cm-2) 21 ±0.9a 28 ±0.9b

Rhizome sugar conc. (mg g dry wt-1) 21 ±1.7a 23 ±2.5a

Leaf % C 32 ±0.6a 26 ±0.5b

Shoot growth rate (mg dry wt shoot-1 d-1) 0.22 ±0.03a 0.1 ±00a

Areal growth rate (mg dry wt m-2 d-1) 736 ±73a 202 ±10a

During light deprivation the lighter 12C isotope was preferentially taken up over the heavier 13C isotope and as a consequence the δ13C value declined during shading (Fig. 6.6c). Initial

δ13C values were approximately –9 %o, and declined significantly to –10.5 %o when shaded

(Fig. 6.6c). Both the concentration of sugar in the rhizomes, and % of carbon in the leaves did

not change during shading (Table 6.3). Growth rates of Halodule pinifolia were significantly

reduced during light deprivation with growth rates per shoot being reduced by 50% in

response to light deprivation (Table 6.3). Due to low shoot densities in the light deprived

treatment, growth rates per m-2 were reduced by over 70 % when compared to control values.

110

Chl

orop

hyll

ratio

(Chl

a/b

)

1.7

1.9

2.1

2.3

2.5

2.7

Control

0% ambient

20

40

60

80

100

120

Leaf

am

ino

acid

con

cent

ratio

n ( µ

mol

g fr

esh.

wt -

1 )Control

0% ambient

(a)

(b)

0 20 40 60 8010 30 50 70

δ13 C

(%o)

-12

-10

-8

-6

0% ambient

Control

(c)


Chl

orop

hyll

ratio

(Chl

a/b

)

1.7

1.9

2.1

2.3

2.5

2.7

Control

0% ambient

20

40

60

80

100

120

20

40

60

80

100

120

Leaf

am

ino

acid

con

cent

ratio

n ( µ

mol

g fr

esh.

wt -

1 )Control

0% ambient

(a)

(b)

0 20 40 60 8010 30 50 700 20 40 60 8010 30 50 70

δ13 C

(%o)

-12

-10

-8

-6

-12

-10

-8

-6

0% ambient

Control

(c)

Experimental period (d)Experimental period (d)

Figure 6.6. Physiological response of Halodule pinifolia to 78 days of light deprivation. (a) Changes in leaf amino acids concentrations, (b) changes in chl a to b ratio, (c) changes in the leaf carbon isotope ratio (13C).

111

6.4 Discussion

Light monitoring at the maximum depth limit has established that on average, H. pinifolia

requires an average of 9 mol photons m-2 d-1 (14% of surface irradiance (SI)). This study was

conducted in the “dry” season, a period when surface light and water clarity is likely to be

greatest (Hillman and Raaymakers, 1996) (Fig. 5.3). Consequently the average minimum light

requirements of H. pinifolia may be considerably less than that measured in the present study

as the increased cloud cover and rainfall during the “wet” season would decrease light

significantly. Nevertheless, the minimum light requirement of 14% SI (MLR) during the

monitoring period is similar to the MLR (18% of SI) of Halodule wrightii (Dunton, 1994) and

the average MLR of all seagrasses combined (11% of SI) (Duarte, 1991).

Results from the light deprivation experiment indicate that Halodule pinifolia is not only able

to survive in an environment with a low average light availability, but is also able to survive

for an extended period of time in near darkness. The capacity of H. pinifolia to persist below

its minimum light requirement is considerably better than predicted from the Walker et al.,

(1999) function-form model. Walker et al., (1999) contended that H. pinifolia should display

limited tolerance to environmental perturbations such as light deprivation, as it is a small,

rapid growing, colonising species. Halodule pinifolia’s resilience to light deprivation

(approximately 100 days) is similar to that recorded for Heterzostera tasmanica (Bulthuis,

1983a) which survived for 3 months at 2% surface light, but considerably less than Posidonia

sinuosa that had a 45% reduction in shoot density after 148 days at 1% surface light (Gordon

et al., 1994). It is also interesting to note that H. pinifolia recorded no biomass or shoot

density decline for the first 38 days of shading, whereas most other time-course shading

studies (Longstaff et al., 1999; Gordon et al., 1994; Bulthuis, 1983a) recorded an immediate

response to reduced light availability. The resilience of H. pinifolia to long periods of light

deprivation is likely to be of paramount importance to the long-term survival of this species in

the Gulf of Carpentaria, as the seagrass experiences long periods of light deprivation during

the tropical cyclone season. The distribution and abundance of Halodule pinifolia at the Port

of Karumba has been assessed biannually (wet and dry season) between 1994 and 2000

(Rasheed et al., 2001). These surveys showed only minor changes to seagrass distribution and

abundance after two consecutive years of flooding caused by tropical cyclones.

In contrast to the long survival time of Halodule pinifolia, Halophila ovalis displayed a low

tolerance to light deprivation, with complete plant death occurring after 38 days in the dark. A

112

similar intolerance to light deprivation has also been demonstrated for monospecific H. ovalis

plants growing in sub-tropical waters (Longstaff et al., 1999). In this study complete plant

death was estimated to occur after 30 days of light deprivation. As H. ovalis displays limited

tolerance to light deprivation, the long-term survival strategy of this species in this region

may be based on it ability to rapidly regrow from seed and/or vegetative fragments after light

deprivation. This long-term survival strategy of Halophila species to perturbations has also

been suggested to by Kenworthy (1992).

Most previous research has concentrated on assessing the impact of more moderate light

reductions on seagrasses (Backman and Barilotti, 1976; Bulthuis, 1983a; Dennison and

Alberte, 1982). Some recent shading studies indicate that the period of time a seagrass can

survive below the minimum light requirement may be increased by photoadaptive responses

such as increased chlorophyll content, changes in the chlorophyll a to b ratio, increased

canopy height and shoot thinning (Lee and Dunton, 1997; Czerny and Dunton, 1995; Van

Lent et al., 1995; Abal et al., 1994). In the present study, Halodule pinifolia was also noted to

photoadapt, with increased chlorophyll content, decreased chlorophyll a to b ratio and an

increased canopy height being recorded. However, because the species received virtually no

light, these responses would have had little positive effect on photosynthetic rates. In fact, the

energetic cost of producing and maintaining more pigments and longer shoots, with little net

return, may have had a negative impact on the long-term survival.

Several parameters were responsive to both the natural light gradient and the light

deprivation; chlorophyll a to b ratio, leaf amino acid concentration and leaf δ13C value.

Decreasing chl a / b with depth has been observed in a number of seagrass species including

Zostera marina (Dennison and Alberte, 1985), Halophila ovalis (Longstaff et al., 1999)

Halophila spp., Halodule spp., Syringodium spp. Thalassia spp. (Wigington and McMillan,

1979) and Thalassia testudinum (Lee and Dunton, 1997). A decrease in the chlorophyll a / b

has been considered an adaptive response that increases the light absorption efficiency of the

seagrass (Lee and Dunton, 1997; Abal, 1996).

Amino acid concentrations in seagrasses are responsive to a number of environmental

variables: Salinity stress increased proline concentrations in four subtropical seagrass species

(Pulrich, 1986) and water depth was shown to affect amino acid concentrations in Posidonia

oceanica (Pirc, 1984) but not Thalassodendron ciliatum (Parnik et al., 1992). Ambient

sediment nutrient concentration and sediment nutrient addition can also have a significant

effect on amino acid concentrations (Udy and Dennison, 1997a; Udy and Dennison, 1997b).

113

The elevated amino acid content at depth and in response to light deprivation in the present

study could be related to the balance of nutrient vs light limitation of seagrass growth, with

the pronounced light limitation leading to high amino acid concentrations.

The carbon isotope ratio (δ13C) of Halodule pinifolia leaves became more negative during

shading and in response to increased water depth. The δ13C signature of seagrasses is affected

by a broad range of environmental conditions including light availability (Longstaff et al.,

1999; Abal, 1996; Grice et al., 1996), water motion (France and Holmquist, 1997), water

temperature and the source of the dissolved inorganic carbon pool (DIC) (Hemminga et al.,

1994; Cooper and Deniro, 1989). In the present study it is unlikely that water temperature and

DIC source caused the decline in δ13C with depth as the sites were situated relatively close

together. Similarly, the poor correlation between shoot density and δ13C suggests that water

motion is not causing the change in δ13C with depth (Abal, 1996). Decreasing δ13C with depth

and in response to shading may be due to a reduction in the uptake of 13C in relation to 12C.

The preferential uptake of 12C over 13C occurs because less energy has to be expended in the

uptake of 12C in comparison to 13C (Longstaff et al., 1999; Abal, 1996; Grice et al., 1996).

In the present study some seagrass parameters such as shoot density and biomass did not

show a relationship with depth. For example, highest (site 4) and lowest (site 5) biomass and

shoot densities were recorded at the two deep sites. These differences indicate that other

environmental factors (apart from light) are influencing the physiology and the morphology of

the seagrass. Other environmental influences known to affect seagrasses that may also explain

the difference in site 4 and 5 seagrass include nutrient availability (Short, 1987; Udy and

Dennison, 1997a) and sediment characteristics (composition and compaction) (Koch, 2001). It

interesting to note that the sediment characteristics at site 4 (coarse sand) were considerably

different to site 5 (mud). Thus the a) high root and rhizome biomass and b) low amino acid

and chlorophyll content at site 4 in comparison to site 5 may be the result of nutrient

limitation within the sediment at that site.

By assessing a range of seagrass physiological and morphological responses to light reduction

it may be possible to predict impending seagrass loss during light reduction events. Such

predictive capacity would be invaluable in situations where the duration and extent of

turbidity plumes can be controlled (eg., during dredging operations). By investigating the

responses of Halodule pinifolia to natural light gradients and light deprivation, a suite of

suitable parameters can be selected to indicate seagrass health. Changes in shoot density,

114

blade width and chlorophyll content during shading have been identified as indicators of

Thalassia testudinum stress during light reduction (Dunton, 1994).

Morphological responses have traditionally been used to indicate a detrimental impact on the

seagrass community has started (e.g. Posidonia sinuosa, shoot density and leaf length)

(Gordon et al., 1994). In the present study, the morphological responses observed were

decreases in biomass, shoot density and canopy height. Physiological responses, however, can

detect declining seagrass health and impending seagrass die-off before substantial

morphological changes occur. Physiological responses of impending Halodule pinifolia loss

during light deprivation were identified these being; 1) increases in amino acid content, 2)

decreases in chlorophyll a / b ratios 3) decreases in δ13C values (Fig. 6.7).

Physiological Responses•Increased amino acids•Decreased chl a / b•Decreased δ13C

Morphological Responses•Decreased biomass•Decreased canopy height•Decreased shoot density

Total seagrassdie-off

Halodule pinifolia

Period of light deprivation

≈100 d

Physiological Responses•Increased amino acids•Decreased chl a / b•Decreased δ13C

Morphological Responses•Decreased biomass•Decreased canopy height•Decreased shoot density

Total seagrassdie-off

Halodule pinifolia

Period of light deprivation

≈100 d

Figure 6.7. Summary of the physiological and morphological changes in Halodule pinifolia during light

deprivation.

Although it is well recognised that seagrasses will exhibit morphological and physiological

responses to light reduction, the type of response, the intensity of the response and the time

required for a response to occur depends upon: a) the species of seagrass (e.g. Grice et al.,

1996), b) the intensity and duration of the light reduction (Bulthuis, 1983a; Gordon et al.,

1994) and c) the interaction of other environmental conditions such as water temperature

(Bulthuis, 1983b) and nutrient availability (Abal, 1996; Van Lent et al., 1995). Because of the

complexity of biological and environmental factors that can influence seagrasses responses to

light reduction, it is necessary that early warning indicators be developed for the specific

seagrass community that may be affected.

Chapter 7 Impact of a flood plume on the deepwater

seagrass of Hervey Bay, Australia

Abstract

In February 1999, flood levels within the Mary River (Queensland, Australia), were

comparable to those of the 1992 flood that led to the temporary loss of 1000km2 of seagrass in

Hervey Bay. Because of the similarity, a research program was developed to study the impact

of the flood plume on the bay’s deepwater seagrass. Three inner plume sites were established

within the southern region of the bay. At each site, seagrass biomass was collected,

submersible light loggers deployed and water quality sampled at 5, 30 and 73 days after the

peak plume. Data obtained from this survey was supplemented with data from a pre-existing

monitoring program within the central region of the bay. The combined data provided

information on the inner plume, the plume edge and an area outside the influence of the

plume. Light availability at the inner sites was reduced to less than 2 µmol photon m-2 d-1 for

17 days post flood, then increased to 2 - 4 µmol photon m-2 d-1 for the remaining 56 days of

monitoring. The flood plume had a rapid impact on Halophila ovalis at the inner sites with

die-off within 30 days. However, H. ovalis at the edge-plume and non-plume sites had a

strong seasonal pattern with rapid biomass decline between December 1998 and April 1999,

and this masked any plume effect. In comparison, Halophila spinulosa was more resilient

with no loss recorded by day 30, but a significant loss by day 73 at the inner site. H. spinulosa

biomass also declined significantly at the edge-plume sites when compared to the non-plume

site. The 1999 flood had a sizeable but temporary impact upon the bay’s deepwater

seagrasses. A repeat of the 1992 widespread seagrass loss did not occur despite floods of

similar scale. The reduced impact in 1999 can be attributed to the fact that floodwaters were

delivered to the bay in a single localised plume that settled out rapidly due to relatively calm

post-flood conditions.

116

7.1 Introduction

Extensive deepwater seagrass meadows have been discovered along the northeast coast of

Australia within the past 15 years (Lee Long et al., 1993; Lee Long et al., 1996). These

deepwater seagrasses exist within the protected waters of the Great Barrier Reef lagoon and

Hervey Bay where water clarity allows sufficient light penetration for their survival (Lee

Long, 1993). As in other deepwater regions of the world with seagrass, such as the Caribbean

(Williams, 1988; Josselyn et al., 1986; Buesa, 1975) Red Sea (Jacobs & Dicks, 1985) and SE

Asia (Erftemeijer & Stapel, 1999), the seagrasses recorded along the NE Australian coastline

are all Halophila spp. Six species of Halophila have been recorded in the deep waters of

northeast Australia, but mixed H. ovalis and H. spinulosa meadows dominate. Deepwater

seagrasses have been recorded to a depth of 58m although the majority are located between 23

and 33 meters (Lee Long et al., 1996; Lee Long et al., 1993).

Due to the large body of water above deepwater seagrass, available light is low (Erftemeijer

& Stapel, 1999; Josselyn et al., 1986; Williams & Dennison, 1990), of poor spectral quality

(with red light absorbed by the overlying water) and significantly affected by small changes in

water clarity. While deepwater seagrasses have a low minimum light threshold, their capacity

to survive once light diminishes below this threshold is limited (Longstaff et al., 1999;

Williams, 1988). The limited capacity of deepwater seagrasses to survive below their

minimum light requirements makes them vulnerable to processes, such as floods that lead to

further reduction in light availability.

The effect of flood plumes on Hervey Bay deepwater seagrasses was first observed in 1992

when the Mary and Burrum Rivers flooded twice within a three-week period (Preen et al.,

1995). These floods resulted in the loss of 1000km2 of seagrass within the bay (Preen et al.,

1995). This rapid and extensive loss of seagrass had a devastating effect on the dugong

population (Dugong dugon), which decreased from 1752 to 71 individuals (Preen & Marsh,

1995). The wide-scale loss of deepwater seagrasses was attributed to a reduction in light

availability due to the flood plumes that formed within the bay. Within two years of the flood

a clear pattern of seagrass recovery was observed (Preen et al., 1995).

In February 1999, the Mary River flooded once again after heavy and prolonged rainfall. As it

was considered that the Hervey Bay seagrasses could once again be under threat, a research

program was rapidly developed (within 5 days of the peak flood) to assess the affects of large

flood plumes on seagrass survival and to determine the role of light availability.

117

7.2 Methods

7.2.1 Study sites and schedule

Hervey Bay is located along the subtropical eastern seaboard of Australia. This large

(3,940km2), U-shaped bay is formed by a sand island to the east (Fraser Island) and mainland

Australia to the west (Fig. 7.1). Maximum water depth within the bay is over 30 m, although

half of the bay is 10-20m deep, a quarter is less than 10m and a quarter is over 20m. In

December 1998 there was an estimated 2,307 km2 of seagrass cover in Hervey Bay, with 43%

of this cover comprising continuous deep water meadows of Halophila spinulosa and

Halophila ovalis (McKenzie et al., 2000).

Two river systems discharge into the southern region of the Bay: the Mary River and the

Burrum River. The Mary River is considerably larger than the Burrum River with a catchment

area of 9,600km2, while the Burrum River has a catchment of only 2,300km2. Natural

vegetation within the Mary River catchment has been systematically removed since European

settlement. Progressive clearing has reduced the catchment from 89% forest to only 36%, with

61% of the catchment now used for grazing (Preen et al., 1995).

Mary River

Hervey Bay

BurrumRiver

10m de

pth

cont

our

(~18 m)

(~8 m)

Australia

Inner- plume

Edge-plume

Non-plume

(~14 m)

1

2

3

Floodplume

1

1

2

2

Study region

Fras

er Is

land

Grea

t San

dyst

raits

Mary River

Hervey Bay

BurrumRiver

10m de

pth

cont

our

(~18 m)

(~8 m)

AustraliaAustralia

Inner- plume

Edge-plume

Non-plume

(~14 m)

1

2

3

Floodplume

1

1

2

2

Study region

Fras

er Is

land

Grea

t San

dyst

raits

Figure 7.1. Location and water depth of study sites within Hervey Bay.

Three study regions were established within the Bay (Fig. 7.1). The study regions were

selected according to dispersion patterns of the flood plume and the location of pre-existing

118

monitoring sites. The regions chosen incorporated an area outside the influence of the distinct

flood plume (non-plume), an area at the outer edge of the plume (edge-plume) and an area

within the inner part of the plume (inner-plume). Water depth in the non-plume, edge-plume

and inner-plume regions was 18, 14 and 8m respectively. Two sampling sites 5-10km apart

were set up in each region, with the exception of the inner plume where the third site was

established for light and water quality monitoring (no seagrass was present at this site at any

stage of the study). The edge and non-plume sites were established 2 months before the flood

(December, 1998: 60 days prior to the flood) as part of a long-term monitoring program (Fig.

7.2) (McKenzie et al., 2000). Post-flood monitoring at these regions was conducted in April,

August and November of 1999 (50, 170 and 260 days post flood). The three inner-plume sites

were established 5 days after the peak flood plume and were re-visited for sample collection

and data transfer at days 30 and 72.

Non-plume&

Outer Plume

Inner Plume W. Q. W. Q. W. Q.Light

Flood Plume

Dec-98 Jan-99 Feb-99 Mar-99 Apr-99 May-99 Jun-99 Jul-99 Aug-99 Sep-99 Oct-99 Nov-99 Dec-99

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

(5) (30) (72)

(-60) (50) (170) (260)

Non-plume&

Outer Plume

Inner Plume W. Q. W. Q. W. Q.Light

Flood Plume

Dec-98 Jan-99 Feb-99 Mar-99 Apr-99 May-99 Jun-99 Jul-99 Aug-99 Sep-99 Oct-99 Nov-99 Dec-99Dec-98 Jan-99 Feb-99 Mar-99 Apr-99 May-99 Jun-99 Jul-99 Aug-99 Sep-99 Oct-99 Nov-99 Dec-99Dec-98 Jan-99 Feb-99 Mar-99 Apr-99 May-99 Jun-99 Jul-99 Aug-99 Sep-99 Oct-99 Nov-99 Dec-99

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

Seagrassbiomass

(5) (30) (72)

(-60) (50) (170) (260)

Figure 7.2. Timing and location of sample collection (seagrass biomass) and monitoring (light and water quality) in relation to the flood plume. Sample days pre and post peak flood plume are indicated in brackets.

7.2.2 Plume formation

Expansion and retraction of the flood plume was mapped by the Queensland Parks and

Wildlife Service using aerial observation. During each flight, the distinct highly turbid edge of

the plume was mapped using a global positioning system and landmarks. The less turbid

diffuse plume remaining after the initial expansion and contraction of the distinct plume was

not mapped. River flow data for the Mary and Burrum River was obtained from the

Department of Natural Resources.

7.2.3 Inner plume characterisation

7.2.3.1 Water quality

Salinity and total suspended solids were assessed at the inner plume sites at 5, 30 and 72 days

after the peak flood plume. Salinity of surface water was measured with a water quality probe

(Horiba U-10). Total suspended solids of surface water was measured using the Gravimetric

119

Technique (Parson et al. 1996), in which a known volume of water is passed through a pre-

weighed GFF filter. The filter was oven dried, then re-weighed to determine the weight of

suspended material in the water sample.

7.2.3.2 Light monitoring

Five days after peak plume, light profile assessments were conducted at each of the three

inner plume sites. A Li Cor 2π cosine corrected light sensor was attached to a metal frame and

lowered through the water column. Light was recorded at 2m intervals on a logging unit

attached to the sensor via a submersible cable.

At each inner plume site a single 2π cosine corrected light meter (Dataflow) was secured at

seagrass canopy height. The light meters continuously monitored light and were programmed

to give the mean quantity over a 15min period. Automatic cleaning devices were attached to

the side of the light meters to wipe sediment off the light sensor at approximately 60min

intervals. The light meters and cleaning units were serviced at 30 and 73 days after peak flood

plume (to download data and replace batteries). An additional light meter was placed at the

town of Hervey Bay (approximately 15km away from the furthest light monitoring site) to

monitor surface light.

The portion of the day in which light exceeds the saturating irradiance is defined as Hsat

(Dennison & Alberte, 1985). In the present study, a saturating irradiance of 150 µmol quanta

m-2 s-1 was used to determined Hsat. This intensity was used because the saturating irradiances

of photosynthesis of Halophila spp is between 100 – 200 µmol quanta m-2 s-1 (Hillman et al.,

1995; Dennison, 1987).

7.2.4 Seagrass collection and analysis

(Note: There are no generally accepted criteria for using the terminology ‘deepwater’

seagrass. For the purpose of clarity, all seagrass in this chapter will be referred to as

‘deepwater’, recognising some of the study regions were as shallow as 8m).

7.2.4.1 Inner plume

Mixed Halophila ovalis and Halophila spinulosa meadows were present at two of the three

inner-plume sites (sites 1 and 2). Randomly selected, triplicate biomass samples were

removed with a sediment corer (15cm diameter, 25cm deep) ensuring all leaf, root and

rhizome material was collected. Additional H. spinulosa leaf material (triplicate random

samples) was collected for chlorophyll analysis. Biomass cores were rinsed in fresh water to

120

remove sediment and salt residue then separated into the above- (leaves) and below-ground

(rhizomes and roots) components. Above-ground material was further separated into the two

species present while below-ground material was kept as a whole as it was not possible to

accurately distinguish between H. ovalis and H. spinulosa roots and rhizome tissue. Once

separated, the tissue was oven dried (60°) and then weighed.

Chlorophyll was extracted from the leaves by acetone extraction. The surface area of small

fragments was calculated and then the fragments were finely chopped in 10 ml 90% acetone.

The samples were stored at 4°C for 24 hours to allow for chlorophyll extraction. Absorbance

of the extract was then measured in a spectrophotometer at 750, 664 and 647 nm and pigment

concentrations were calculated (Dennison, 1990c).

7.2.4.2 Edge- and Non-plume

Seagrass biomass in the edge- and non-plume regions was surveyed by Queensland

Department of Primary Industries (McKenzie et al., 2000). The survey was conducted using

real-time towed video and a sled net in a replicated asymmetrical BARI (Before, After,

Reference, Impact) design. Above-ground biomass estimates were based upon 10 random

frames at a 1 second accuracy, allocated within the 5 minutes of footage obtained at each site.

Above-ground biomass was determined by a “visual estimates of biomass” technique

modified from Mellors (1991). The video was paused at each of the 10 random time frames

selected. If the bottom was not visible the tape was advanced to the nearest point on the tape

where the bottom was visible. From this frame an observer recorded an estimated rank of

seagrass biomass and species composition. To standardise biomass estimates a 0.25m2

quadrat, scaled to the video camera lens used in the field, was superimposed on the screen. On

completion of the videotape analysis, the video observer ranked five to ten additional quadrats

that had been previously videoed for calibration. These quadrats were videoed in front of a

stationary camera, and then seagrass samples harvested, dried and weighed. A regression

curve was calculated for the relationship between the observer ranks and the actual harvested

value. This curve was used to calculate above-ground biomass for all estimated ranks made of

the survey sites. All observers had significant linear regressions (r2=0.98) for calibrations of

above-ground biomass estimates against the harvested quadrats. A second set of video

images of quadrats where seagrass samples had been harvested, dried and weighed was used

by the observers as a quick reference to minimise any drift in estimation over time during a

series of video estimations. Sites that were used for biomass estimation were selected at

121

random from the entire data set to limit the potential for bias through time. Taxonomic

specimens were collected from the towed dredge samples and by divers where ground

truthing was undertaken. Seagrass species were identified according to taxonomic keys of

Kuo and McComb (1989) and Lanyon (1986) (McKenzie et al., 2000).

7.2.5 Statistical Analysis

Means and standard errors of each seagrass variable were calculated. To assess the impact of

the flood plume on seagrass biomass, 2-way ANOVAs were used to test for significant site,

time and site x time interactions for each species in each region (inner-, edge- and non-plume

regions). Significant interactions were followed with a Tukey’s honest significant difference

test. Two-way ANOVAs were also used to test the significance of total suspended solids

(TSS) and seagrass chlorophyll content at each inner-site and time.

122

7.3 Results

7.3.1 Plume formation

Mary River discharge volumes during the 1999 flood were the highest recorded since

February 1992 (Fig. 7.3). In February 1999, total monthly discharge of the Mary River was

1,785,000 ML, corresponding to 86% of that recorded during the April 1992 flood (Table 7.1).

In 1992 a second flood occurred within 30 days of the first, leading to a further 1.2 million

ML of turbid water discharging into the Great Sandy Straits and Hervey Bay. A second flood

did not occur in 1999. In contrast to the Mary River, the Burrum River did not flood in 1999

with only 1727 ML of water discharged during February compared to 124,895 ML in

February 1992.

Table 7.1. Mary River discharge volume during the 1992 and 1999 floods.

Year First flood Second flood

Month

Discharge (ML/month)

Discharge (% of 1992)

Month

Discharge (ML/month)

Discharge (% of 1992)

1992 February 2,062,000 March 1,274,000

1999 February 1,785,000 86% March 477,000 37% (No March flood -data presented for comparison)

123

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1992 1993 1994 1995 1996 1997 1998 1999

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

1992 1993 1994 1995 1996 1997 1998 1999

Tota

l mon

thly

riv

er d

ischa

rge

(ML)

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1992 1993 1994 1995 1996 1997 1998 1999

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

1992 1993 1994 1995 1996 1997 1998 1999

Tota

l mon

thly

riv

er d

ischa

rge

(ML)

Figure 7.3. Total monthly discharge (ML) from the Mary and Burrum rivers between 1992

and 1999.

On 11 February 1999 a well-defined turbidity plume extended into the southern region of

Hervey Bay (Fig. 7.4). The plume continued to expand into the bay for the following 24 hours

to a distance of over 60km from the river mouth (peak plume). The well-defined turbidity

plume retreated as rapidly as it advanced, with expansion and retraction of the plume

occurring in little over 5 days. The aerial mapping exercise was not able to define the extent

and duration of the diffuse plume that remained after the retraction of the distinct plume. The

duration of the diffuse plume at the inner sites was assessed using water quality and benthic

light monitoring data.

124

Burrum River mouth

Mary River Mouth

Day 1 (12/2/99)Peak flood plumeHervey Bay

Burrum River mouth

Mary River Mouth

Hervey Bay Day 2 (13/2/99)

Burrum River mouth

Mary River Mouth


Burrum River mouth

Mary River Mouth

Hervey BayDay 0 (11/2/99)

FraserIsland

Grea

t San

dyst

raits

FraserIsland

FraserIsland

FraserIsland

Grea

t San

dyst

raits

Gre

at S

andy

stra

its

Grea

t San

dyst

raits

Burrum River mouth

Mary River Mouth

Day 1 (12/2/99)Peak flood plumeHervey Bay

Burrum River mouth

Mary River Mouth


Burrum River mouth

Mary River Mouth


Burrum River mouth

Mary River Mouth

Hervey BayDay 0 (11/2/99)

FraserIsland

Grea

t San

dyst

raits

FraserIsland

FraserIsland

FraserIsland

Grea

t San

dyst

raits

Gre

at S

andy

stra

its

Grea

t San

dyst

raits

Figure 7.4. Expansion and contraction of the distinct turbidity plume within Hervey Bay (diffuse plume not

mapped).

7.3.2 Water Quality

Five days after the peak flood plume, there was still a gradient in surface water salinity and

suspended solids from the Mary River mouth north into Hervey Bay (Fig. 7.5). Salinity at the

site nearest to the Mary River mouth (site 3) was 26%o and increased to 35%o at the furthest

site (site 1). By the second sampling period (35 days after peak flood plume) salinity had

increased to 35%o at the two furthest sites (sites 1 and 2) and to 32%o at the nearest site (site

3). During the final sampling period salinity had recovered to 36%o at all sites.

125

Days after peak flood

5 31 720

10

20

30

40

Sal

inity

%o

site 1

site 2site 3

TSS

(mg

L-1 )

Site 3

Site 1

Site 2

0

5

10

15

20

25

30

5 30 72Days after peak flood

5 31 725 31 720

10

20

30

40

Sal

inity

%o

site 1

site 2site 3

TSS

(mg

L-1 )

Site 3

Site 1

Site 2

0

5

10

15

20

25

30

5 30 72

Figure 7.5. Salinity and total suspended solids of surface water at each of the inner plume monitoring sites 5, 31 and 72 days after the peak flood

Over the monitoring period, TSS concentrations were significantly different (P<0.05) at

inner-plume sites 1 and 3 only (Fig. 7.5). The site closest to the river mouth (site 3) had the

highest concentrations with >20 mg L-1 5 days after peak plume, decreasing to 5 mg L-1 at day

72. At site 2, TSS concentrations were 11 mg L-1 at day 5, decreasing to 4 mg L-1 by day 72.

TSS concentrations at site 1 were low at each sample time, with only 6 mg L-1 measured at

day 5, decreasing to 2 mg L-1 by day 72.

7.3.3 Light availability

Profile assessments of light availability (expressed as % of light immediately below the

surface) were conducted at each of the inner-sites on day 5 (Fig. 7.6). Light attenuation was

greatest at the non-seagrass site closest to the river mouth, with 1% of surface light recorded

at 4m.

126

.

Wat

er d

epth

(m)

0 20 40 60 80 1000

2

4

6

8

10

% of irradiance at I0

Wat

er d

epth

(m)

0 20 40 60 80 1000

2

4

6

8

10

0

2

4

6

8

10

% of irradiance at I0

Site 1Site 2

Site 3

Figure 7.6. Light profiles (% of light immediately below surface) at each inner plume site 5 days after peak

flood.

Light attenuation at sites 2 and 3 was not as rapid. At the top of the seagrass canopy, only 1%

of surface light was present at site 2 (8m depth) and 5% at site 1 (9m depth).

Quantity of light available to seagrasses was depressed for 17 days after the peak plume with

a mean daily photosynthetically active radiation (PAR) of 2 and 0.8 µmol photons m-2 d-1

recorded at sites 1 and 2 respectively (Fig. 7.7: Table 7.2).

Table 7.2. Quantity of PAR (± 1 Std Dev) at the 4 light monitoring after the peak flood plume.

Period after peak flood (d)

Surface Site 1 Site 2 Site 3

Distance from river mouth (km) 30 24 20

Depth of site (m) 0 9 8 8

6 – 17 46 (15) 2.0 (0.7) 0.8 (0.3) 0.1 (0.1) Mean daily PAR (mol photons m-2 d-1) 17 – 70 43 (11) 4.5 (2.0) 2.8 (1.5) 0.5 (0.4)

6 – 17 100 4.49(0.9) 2.29 (1.9) 0.32 (0.35) % of surface light

17 – 70 100 10.92 (4.7) 6.7 (3.7) 1.14 (1.0)

6 – 17 10.9 (0.8) 0.6 (0.9) 0 0 mean Hsat (hours PAR >150 µmol photons m-2 s1) 17 – 70 10.4 (0.7) 3.0 (2.3) 1.4 (1.8) 0 Σ Hsat (hours PAR >150 µmol photons m-2 s1) 6 – 17 120 7.25 0 0

17 – 70 570 164 74.5 0

127

The decreased light availability during this period cannot only be attributed to the diffuse

turbidity remaining after the well-defined plume, but also to dense cloud cover. Between days

13 and 17, surface light decreased from 60 to 20 µmol photons m-2 d-1 having a

correspondingly large effect on benthic light. Mean light saturation period (Hsat: hours per day

that light saturates photosynthesis) during the first 17 days of monitoring was only 0.6 hours

at site 1, while light never increased above 150 µmol photons m-2 s-1 at site 2 during this

period (i.e. Hsat = 0). During the remaining 56 days, a mean Hsat of 2.65 hours day-1 was

recorded at site 1 but only 1.15 hour’s day-1 was recorded at site 2. Between day 30 and 40,

light availability decreased again at both sites, most likely due to wind-driven resuspension of

fine sediment deposited during the defined turbidity plume.

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

PAR

(mol

pho

tons

m-2

d-1 )

A) Surface light

B) Light at substrate depth

0123456789

10

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75Days after peak flood

PAR

(mol

pho

tons

m-2

d-1 )

Site 1Site 2

Site 3

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

PAR

(mol

pho

tons

m-2

d-1 )

A) Surface light

B) Light at substrate depth

0123456789

10

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75Days after peak flood

PAR

(mol

pho

tons

m-2

d-1 )

Site 1Site 2

Site 3

Figure 7.7. Quantity of photosynthetically active radiation (400-700 nm wavelength) on the surface and at the 3 subsurface monitoring sites. Note: no seagrass was ever present at site 3.

7.3.4 Seagrass analysis

A sparse and patchy cover of H. ovalis was recorded at the inner-site on day 5, with above-

ground biomass of 1.7 ± 0.7 g dry wt m-2 recorded. Due to highly variable data, there was no

significant difference (P>0.05) between the biomass of H. ovalis at the two sites or over the

128

three sample times (Fig.7.8: Table 7.3). However, it must be noted that at day 30 there was no

H. ovalis present, but by day 72 there was once again a sparse patchy cover. Of the samples

collected at day 72, half had no biomass present, two samples had 4-5 g dry wt m-2 and one

sample had over 56 g dry wt m-2. The range of H. ovalis above-ground biomass at the edge- and

non-plume sites (0-13 g dry wt m-2) was similar to that recorded at the inner site (0-18 g dry wt m-

2), with no significant difference between sites (Fig. 7.8: Table 7.3). A rapid and significant

(P<0.001) decline in biomass at the edge- and non-plume sites occurred between December

1998 (60 days prior to the flood) and 50 days after the flood. The apparent recovery of H.

ovalis between day 170 and 260 was only significant at the P<0.1 level of significance at the

plume edge sites and was not significant at the non-plume sites.

Table 7.3. Mean squares (MS), F-statistic (F) and significance levels of each species in a two-way ANOVA used to test for differences in above-ground biomass between sites and sample time at each region. * 0.01< P<0.05: ** 0.01< P<0.001: ***P<0.001.

Region Effect df MS t F Halophila ovalis

Inner-plume Site 12 107 0.6ns Time 12 211 1.2 ns Site x Time 12 121 0.7 ns Edge-plume Site 72 206 3.5 ns Time 72 458 7.8*** Site x Time 72 52 0.9 ns Non-plume Site 72 84 3.3 ns Time 72 345 13.5*** Site x Time 72 16 0.6 ns Halophila spinulosa Inner-plume Site 12 73 0.2 ns Time 12 1961 6.5* Site x Time 12 29 1.0 ns Edge-plume Site 72 830 8.3*** Time 72 2129 21*** Site x Time 72 553 5.5*** Non-plume Site 72 309 1.9 ns Time 72 433 2.7* Site x Time 72 212 1.3 ns

129

a) Halophila ovalis

0

5

10

15

20

25

30

35

-60 50 170 260 -60 50 170 260 5 30 73

Abo

ve g

roun

d bi

omas

s (g

dry

wtm

-2)

0

10

20

30

40

50

60

70

80

-60 50 170 260 -60 50 170 260 5 30 72

Days pre & post peak flood plumeNon-plume edge-plume Inner-plume

b) Halophila spinulosa

Site 1Site 2

Floo

d

Floo

d

Floo

d

Floo

d

Floo

d

Floo

d

a

a

a

aa

a

b

b

b bb

b

b

b

a

aa

a

aa

b

b

a

aa

bab

b

a) Halophila ovalis

0

5

10

15

20

25

30

35

-60 50 170 260 -60 50 170 260 5 30 73-60 50 170 260 -60 50 170 260 5 30 73

Abo

ve g

roun

d bi

omas

s (g

dry

wtm

-2)

0

10

20

30

40

50

60

70

80

-60 50 170 260 -60 50 170 260 5 30 72

Days pre & post peak flood plumeNon-plume edge-plume Inner-plume

b) Halophila spinulosa

Site 1Site 2Site 1Site 2

Floo

dFl

ood

Floo

dFl

ood

Floo

dFl

ood

Floo

dFl

ood

Floo

dFl

ood

Floo

dFl

ood

a

a

a

aa

a

b

b

b bb

b

b

b

a

aa

a

aa

b

b

a

aa

bab

b

Figure 7.8. Above-ground biomass of Halophila ovalis and Halophila spinulosa at the inner-plume, edge-plume and non-plume regions over a 12 month period. Columns with the same letter are not significantly different

(P<0.05).

Above-ground biomass of H. spinulosa in all regions was higher and more consistent than

that of H. ovalis (Fig. 7.8). In the inner-plume region, H. spinulosa biomass was not

significantly different (P>0.05) between the sites. Biomass remained constant for the first 30

days after the plume, but between day 30 and day 72 declined significantly (P<0.05) from 30-

36 g dry wt m-2 to 0-7 g dry wt m-2. Biomass at the edge-plume site also declined significantly

(P<0.05) after the flood plume; however, the rate of decline was not consistent between the

two sites. The rate of biomass loss was more rapid at site 2 than at site 1. Significant declines

in biomass from pre-flood values occurred before day 50 at site 2 and between day 50 and 170

at site 1. Biomass at the non-plume sites did not change significantly during the monitoring

period. H. spinulosa appeared to recover between day 170 and 260 at the edge-plume sites.

This recovery, however, was only significant at the P<0.1 level of significance.

H. spinulosa chlorophyll a and b content at the two inner-plume sites was not significantly

different (P>0.05). At day 5 a mean of concentration of 8 and 10 mg chl a and b cm-2 was

130

recorded at sites 1 and 2 respectively. By day 30, both chlorophyll a and b had decreased

significantly (P<0.05) at both sites to 2- 3 mg chl a and b cm-2. Chlorophyll was not assessed

during the final sample period, as suitable replicates could not be located.

131

7.4 Discussion

The 1999 Mary River flood event had a substantial (but most likely temporary) impact upon

the deepwater seagrasses of Hervey Bay. Loss of Halophila spinulosa was recorded in regions

within the distinct turbidity plume while loss of Halophila ovalis was recorded at all sites,

including those areas outside the distinct turbidity plume. H. spinulosa displayed the most

resilience to the flood, persisting for over 73 days at the sites closest to the river mouth, while

mortality of H. ovalis was recorded within 30 days at the inner site. Recovery (i.e. an increase

in biomass after the initial loss) was evident for each species, but not at each site. H. ovalis

recovered rapidly at the inner site (within 70 days post flood) but was slower to recover in the

outer regions. Monitoring ceased before recovery of H. spinulosa was detected at the inner

site (73 days post flood); however, the long-term monitoring program at the outer sites

detected some recovery 10 months after the flood.

The most likely explanation for the rapid seagrass die-off is a reduction in light availability

during the flood plume. Light levels were appreciably reduced for 17 days after the flood peak

and were also likely to have been reduced for a period of time before the flood peak (due to

low surface light and a diffuse plume preceding the distinct plume). Preen et al. (1995)

speculated that the complete loss of deepwater seagrass from Hervey Bay in 1992 was a result

of insufficient light available to the seagrass. They based this assumption on the

understanding that during non-flood periods the seagrasses survive at light levels close to

their minimum requirement; therefore, a slight reduction in water clarity would reduce light

below their minimum requirement threshold (Preen et al., 1995). In the present study we

confirmed that the deepwater seagrasses were receiving very low quantities of light (3 mol

photons m-2 d-1) based on those values recorded once the plume had dispersed). The values

recorded are similar to those noted for H. decipiens growing between 16-25m depth in the

Caribbean (Williams & Dennison, 1990; Williams, 1988; Josslyn et al., 1986) and H. ovalis at

20m in Indonesian waters (Erftemeijer & Stapel, 1999). In addition to low light quantity, light

quality reaching deepwater seagrass leaves is low in red wavelengths due to selective

absorption by water (see Kirk, 1994). As a result, the spectral composition of light available

to deepwater seagrass has less photosynthetically usable wavelength than the light available to

shallow regions of similar water quality.

The combination of low light quantity and poor spectral quality constitute extreme conditions

for seagrasses, highlighting the adaptability of this group of seagrasses. The morphological

and physiological characteristics of deepwater seagrasses that facilitate survival in poor light

132

conditions include: (a) low proportion of non-photosynthetic biomass to minimise demands

on carbon resources; (b) a thin cell structure that maximises light absorption; (c) low leaf area

to minimise canopy shading and (d) high turnover rate to minimise the accumulation of

epiphytes (e) a capacity to flower, fruit and set seed in very low light environments (Williams,

1988; Josselyn et al., 1986). These adaptations serve to increase light harvesting capacity

while reducing carbon demand. Ironically, some of the same adaptations that enable

deepwater seagrasses to survive in low light conditions limit their capacity to survive when

light drops below their survival threshold; specifically, a small fleshy rhizome with minimal

carbohydrate reserve and a high turnover rate are limiting (Kenworthy, 2000; Longstaff et al.,

1999).

Prior to the present study, little was known about the resilience of H. spinulosa to light

reduction (with the exception of scant information gleaned from the 1992 flood). After the

1999 flood, declines in H. spinulosa biomass were observed at both the inner- and edge-

plume sites, with a degree of recovery occurring 9 months later. H. spinulosa was less

affected by the flood than H. ovalis, with a slower rate of decline and less overall mortality.

H. spinulosa has thicker, more fibrous rhizomes than H. ovalis and may therefore have a

greater capacity for storage carbohydrates (Longstaff et al,. 1999; Walker et al., 1999). These

carbohydrates may be utilised in periods of light limitation, decreasing the rate of die-off and

thus increasing the chance of survival when conditions improve before complete die-off

occurs.

H. ovalis loss was observed at both the non- and edge-plume sites between December 1998

and April l999. The seagrass loss in these regions cannot categorically be attributed to the

flood event, as the decline may have been a seasonal change in response to decreasing surface

light. Seasonal trends in Halophila spp. biomass have been observed in a number of shallow

(Hillman et al., 1995; Lanyon & Marsh, 1995) and deepwater (Williams, 1988; Buesa, 1975)

systems, but seasonal trends have not yet been described in Australian deepwater seagrasses.

Until both seasonality and normal inter-annual variation of Australia’s deepwater seagrasses

has been investigated, it will not be possible to differentiate the impacts of flooding and

seasonal die-off. Similarly, Williams (1988) could not separate the effect of low winter light

and flood plumes on Caribbean deepwater seagrass.

At the inner sites, the low sample size in combination with the intrinsic variability of H. ovalis

distribution rendered changes in biomass over the three sample times non-significant (the

same problems obscured significant results in Buesa’s (1975) study of seasonal and depth

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trends in deepwater H. decipiens). Although not statistically significant, the loss of H. ovalis

at the inner site was rapid and complete and most likely due to the flood plume rather than

seasonality. This statement is partly based on the rapid recovery of H. ovalis after the loss. If

the rapid loss had been caused by seasonality, it is unlikely that rapid recovery would have

occurred (especially in the winter months).

The mortality of seagrass during the flood plume was to some degree expected. Research

conducted in response to the 1992 flood events (Longstaff et al., 1999) demonstrated the

sensitivity of Halophila spp. to light deprivation. Longstaff et al., (1999) simulated dark

conditions during flood events by placing black plastic screens over H. ovalis meadows.

Rapid loss of biomass, carbohydrate reserves and chlorophyll was observed followed by

mortality within 30 days. Similar responses to light reduction have been observed in H.

decipiens growing at 20m depth (Williams, 1988). Williams (1988) observed a rapid decline

in H. decipiens biomass after light availability decreased due to low winter light conditions in

conjunction with flooding. As the tissue of Halophila spp. is generally soft and fleshy,

breakdown and decay of the tissue after mortality is very rapid (50% loss of weight over a 3-

day period) (Kenworthy et al., 1989; Josselyn et al., 1986).

Although the apparent recovery between day 160 and 270 was not statistically significant (for

the reasons outlined above), the consistent increase in biomass at each of the edge- and non-

plume sites during this period suggests there was a delayed recovery. The delay in recovery

may be attributed to low winter light availability at the greater depth of these regions. The

onset of recovery was in spring, coinciding with increased surface light. However, if recovery

did occur at the edge- and non-plume sites, it took place at a slightly faster rate than was

observed after the 1992 flood, when only one site showed signs of recovery at 10 months and

a clear pattern of recovery was evident only at 20 months (Preen et al., 1995). It is not

surprising that recovery was more rapid in the present study as recovery by means of both

seed germination and vegetative propagation was possible, whereas in 1992 recovery was

restricted to seed germination only (no seagrass remained in 1992 for vegetative propagation).

As all H. ovalis plants died off at the inner sites by day 30, the apparent recovery by day 72 in

this region would have occurred through germination of seeds rather than through vegetative

propagation. Halophila spp. produce large numbers of seeds (up to 70,000 m-2) (Inglis, 2000)

that can remain viable for up to 2 years (McMillan, 1991). Seed germination would have been

triggered by the increased light availability after the plume dispersed (McMillan, 1988a,b).

Once the seeds germinated, rapid re-colonisation of the area would have been promoted by

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fast shoot growth rates in combination with frequent rhizome branching and minimal

competition for space (H. spinulosa was no longer present) (Williams, 1988). There have

been several other studies demonstrating the rapid recovery of Halophila spp after

disturbances. For example, H. ovalis recovered within 2 months of being completely buried

by sediment in shallow waters of the Philippines (Duarte et al., 1997) and deepwater H.

decipiens recovered within 3 months of a die-off event caused by a winter flood in the

Caribbean (Williams, 1988).

The concentration of chlorophyll in H. spinulosa decreased significantly (P<0.05) at the inner

sites before mortality was observed. Chlorophyll concentration in seagrasses has been shown

to relate to availability of light (Longstaff et al., 1999; Lee and Dunton, 1997; Abal et al.,

1994; Wigington & McMillan, 1979). Typically, chlorophyll content increases with water

depth, therefore increasing the efficiency of capture of the decreasing available light

(Wigington & McMillan, 1979). Chlorophyll content also responds to temporal changes in

light availability. The particular response to temporal changes depends upon the pre-light

history (Longstaff et al., 1999). Seagrass persisting in low light conditions maximise their

chlorophyll content, hence limiting their capacity for additional increases if light decreases

further. This response was observed in H. ovalis deprived of light at 0.5m and 2.5m depths.

The plants growing at 2.5m did not change their pigment content during shading, while those

at 0.5m recorded a near doubling (Longstaff et al., 1999). The response of chlorophyll to

temporal changes in light availability also depends upon the intensity of the light change.

Light deprivation experiments have demonstrated that in complete darkness chlorophyll

content will decline, but if a very small quantity of light is available the chlorophyll content

may increase (Longstaff et al., 1999). The decline in chlorophyll observed in the present study

indicates that content prior to the flood was likely to have been at a maximum, hence

precluding increases during the flood plume. Considering the very low light conditions under

which the seagrasses persist at 8m depth this is very likely. A decline in chlorophyll (rather

than just maintaining maximum concentrations) is a typical etiolation response observed in

marine and terrestrial plants alike. Chlorophyll loss may be due to the metabolism of

chlorophyll for energy and/or cessation of chlorophyll production in any new leaf tissue.

The present study reveals that the long-term survival of Halophila spp. in Hervey Bay is due

to a strategy of rapid recovery from a period of light deprivation rather than expenditure of

resources to persist through light deprivation. This accords with the life history strategy

proposed by Kenworthy (2000) and the seagrass functional form model proposed by Walker

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et al. (1999). Kenworthy (2000) proposed that Halophila spp. survive through unfavourable

light conditions by producing abundant seeds that can remain dormant in the sediment until

the light environment is suitable for vegetative growth. The functional form model proposed

by Walker et al. (1999) categorises seagrasses on the basis of their growth forms (leaf

thickness, rhizome turnover rates etc.), then relates this to ecological function (e.g. response

rate to disturbance). In essence, smaller seagrasses tend to be more responsive to

environmental conditions with faster and more significant responses (such as mortality and

recovery) than larger species.

Both the 1992 and 1999 flood events occurred during the summer when seagrass biomass is

most likely at its highest and seed set is established. These two factors may have increased the

seagrasses capacity to persist through, and then recover after the flood. However, a flood

event can occur at any time of the year (e.g. the 1996 flood event in Moreton Bay was during

the winter – normally the regions driest season), possibly leading to a different level of impact

than was recorded in the present study. For example, a spring flood event (after germination)

may have a longer-lasting impact because the capacity to recover from seed is diminished by

the reduced number of seeds with in the seed bank (i.e. most seeds may have germinated and

then died due to the flood).

The 1999 flood event did not result in the complete loss of seagrass from Hervey Bay as was

observed after the 1992 flood (despite the comparable flow rate of the Mary River). In 1992 a

complete loss of deep-water seagrass was recorded 10 month after the flood (Preen et al.,

1995). In the present study we recorded regions with no significant seagrass loss, areas of

partial loss and areas with loss followed by recovery. The difference between 1999 and 1992

seagrass survival can be attributed to the size and longevity of the flood plumes. A larger and

more persistent flood plume is likely to have occurred in 1992 than in 1999. Reasons for the

differing plume sizes include, a) both the Mary and the Burrum River flooding in 1992 when

only the Mary River flooded in 1999; b) in 1992, two flood events occurred within a 3-week

period, whereas there was a single event in 1999; and c) strong wind associated with the 1992

flood event would have kept solids suspended within the water column for a longer period

than in 1999. Clearly, a single large flood event is unlikely to cause widespread seagrass loss

in Hervey Bay unless accompanied by other conditions such as strong winds and/or a series of

floods that lead to prolonged light deprivation.

Chapter 8 Conclusions, management implications, and future

research

8.1 Conclusions

Northeast Australia has extensive and diverse seagrass meadows that have been or have the

potential to be affected by reduced light availability. As it is an almost impossible task to

assess the light requirements of each species throughout northeast Australia, this thesis

focused on the dominant species in three distinct habitats: monospecific Zostera capricorni

meadows in a subtropical bay (Moreton Bay); a mixed Halodule pinifolia, Halophila ovalis

meadow on a river-mouth mudflat within the tropics (Gulf of Carpentaria); and deepwater

mixed Halophila ovalis, Halophila spinulosa meadows in a subtropical bay (Hervey Bay).

The primary aim of the thesis was to assess the effect of long-term and acute light reduction

processes on the distribution and survival of these seagrasses. This was mainly achieved by

determining the seagrasses’ minimum light requirements (MLR) and capacity to persist below

MLR.

8.1.1 Measuring light penetration to seagrasses

An important component of evaluating the responses of seagrasses to different light regimes

is the ability to measure light penetration to seagrasses correctly and effectively. The most

commonly used methods were described in Chapter 2, including the specific applications,

advantages and disadvantages. However, without doubt, the most informative approach for

measuring light penetration to seagrass is to use a combination of methods, and the

combination may vary depending on the objectives of the study. This approach was

effectively used in Moreton Bay to assess the light environment in relation to Z. capricorni.

The temporal variability of light and Z. capricorni’s MLR were assessed using continuous

long-term logging; spatial variability of light was obtained using Secchi depth; and light

quality was assessed through spectral analysis.

Continuous long-term light logging was made significantly easier and more reliable with the

development of an automatic sensor cleaner (Chapter 3). The increased awareness and

availability of automatic cleaning devices will facilitate the use of long-term light logging to

assessing the light requirements of seagrasses. Indeed, the device has already been purchased

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and replicated to help other marine science fields. For example, a research scientist at James

Cook University has been supplied with a number of units to investigate light penetration to

near-shore corals

8.1.2 Light reduction processes

The wealth of data collected during the Moreton Bay and Brisbane River Study provided a

unique opportunity to review processes influencing water clarity and hence seagrass

distribution within Moreton Bay (Chapter 4). Sediment resuspension was identified as a

primary cause of long-term reduced water clarity in western Moreton Bay. Winds over 10 km

h-1 from the east to southeast, the predominant wind direction (except in winter) generate

waves that readily resuspend the shallow, muddy sediments of western Moreton Bay.

Sediment on the western side of the bay is continually resuspended and/or maintained in

suspension during most months of the year, except winter. This long-term reduction in water

clarity is likely to have had a significant effect on seagrass distribution within the bay.

Seagrasses were either absent or restricted in distribution in those regions of the bay that

experience the greatest rates and occurrence of sediment resuspension.

While Moreton Bay was used as a case study for investigating long-term light reduction

processes and the influence on seagrass distribution, it is without doubt that the same

sediment resuspension processes are affecting seagrass distribution along the entire northeast

Australian coastline. Continual deposition of sediments along northeast Australia has resulted

in muddy deposits up to 20 m thick and 15 km wide (Johnson and Carter, 1988), that are

readily resuspended by wind-waves (Wolanski and Spagnol, 2000). However, it must be

noted that because wet seasons are more pronounced and regular in the tropics, long-term

turbidity will also be greatly influenced by sediment input.

In addition to the long-term reduction in light penetration to seagrass, the light environment of

seagrasses is affected by acute light reduction events caused by floods. The effect of flood

events on seagrasses depends upon factors such as the size and longevity of the flood plume,

duration between flood events, location of the seagrasses within the flood plume, and the

capacity of the seagrass to persist below their MLR. This thesis demonstrates that floods can

in some cases have negligible long-term impacts on seagrass distribution while in other cases

lead to permanent seagrass loss. In an example of the former, deepwater seagrass loss during a

flood in Hervey Bay (Chapter 7) was both minimal and temporary because the flood plume

was relatively small and short-lived, and the species present could recover rapidly. In contrast,

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in Moreton Bay, a 1-in-20 year flood event during 1996 resulted in the permanent loss of

2,000 ha of Z. capricorni. This large-scale loss of seagrass highlighted the vulnerability of

Moreton Bay seagrasses to flood events, and prompted further research on the causes of die-

off (i.e. the light deprivation experiments conducted in Chapter 5).

8.1.3 The influence of light availability on northeast Australian seagrasses

Long-term availability of light was identified as the primary environmental factor affecting

the maximum depth limit of Z. capricorni and H. pinifolia. Z. capricorni penetrated to a depth

where it received an average of 10 mol photons m-2 d-1. In turbid regions of Moreton Bay this

depth was approximately 1.1 m, whereas in clearer waters, the seagrass penetrated to 3 m

(Chapter 5). In one region of Moreton Bay (Deception Bay), Z. capricorni required half the

amount of light required by seagrass in other regions of the bay. The lower MLR for

Deception Bay seagrass was attributed to increased radiation use efficiency, greater light

absorption and lower respiratory demand. H. pinifolia was limited to depths where it received

approximately 10 mol photons m-2d–1 and this limited its maximum depth to 1 m in the study

region because of high turbidity (however, as light monitoring for this species was conducted

only during the dry season, the MLR was most likely less than 10 mol photons m-2d–1)

(Chapter 6). Halophila ovalis had a much smaller MLR than the other two species at

approximately 3 mol photons m-2 d-1 and survived at depths greater than 15 m in the clear

waters of Hervey Bay (Chapter 7).

The impact of acute light reduction on seagrass survival was diverse, with each species

displaying a different degree of tolerance. H. pinifolia was the most resilient of the species

studied, persisting for over 78 days when placed into darkness using shade screens (Chapter

6). Although Z. capricorni persisted for over 55 days in the dark (due to shading), high loss of

shoots was found during the first 40 days (Chapter 5). H. ovalis displayed limited tolerance to

light deprivation with die-off after 40 days during shading and 30 days during the Hervey Bay

flood (Chapter 7).

While this thesis focused on three different regions (Gulf of Carpentaria, Hervey Bay and

Moreton Bay), the results can be used to help explain current seagrass distribution along the

entire northeast Australian coastline. Z. capricorni has high light requirements and limited

tolerance below its MLR; therefore this species will tend to inhabit regions with infrequent

floods and high quantities of light. In fact, surveys have shown that Z. capricorni only occurs

in shallow areas (<5 m deep; the 4th shallowest species along the coastline) and in the

140

southern half of the region (with the exception of Cairns Harbour), where flood events are less

frequent (Lee Long et al., 1993). As H. pinifolia has a lower MLR and a greater capacity to

persist below its MLR, it has a broader ecological niche than Z. capricorni. Seagrass surveys

have shown that H. pinifolia grows deeper (up to 7m deep) than Z. capricorni and is found

along the entire northeast Australian coastline (Lee Long et al., 1993). Finally, H. ovalis has

the broadest ecological niche of the species studied. The low MLR of this species facilitates

its survival in low light environments such as deep water. However, rather than persisting

through acute light deprivation events, this species relies on a life history strategy (rapid

germination, growth and seed production, germination and growth of seeds in low light) to

facilitate rapid recovery once the event has passed (this rapid recovery is relative to other

species, recognising that full recovery of a meadow may take a few years). These attributes

explain its distribution throughout the northeast Australian coastline to depths of 28 meters.

In conclusion, this thesis clearly indicates that both acute and long-term light reduction

processes have a profound impact upon seagrass meadows in northeast Australia, causing die-

off, altering species composition and reducing distribution. To preserve the remaining

seagrass meadows in northeast Australia it is critical that water clarity does not deteriorate

further and that flood impacts are mitigated. This can only be achieved through effective long-

term management.

8.2 Management implications

Effective long-term management and protection of northeast Australia’s remaining seagrass

meadows requires a sound understanding of the major processes effecting the distribution and

survival of the seagrasses. This thesis demonstrated that both chronic and acute light

reduction processes are primary controllers of seagrass distribution. If seagrass meadows are

to be protected and restored, management actions that reduce the impact of chronic and acute

light reduction events are required.

As sediment resuspension has been identified as the primary cause of chronic light reduction,

management actions that reduce this process are required. The area of terrestrial-derived fine-

grained sediments in potential seagrass habitats must not continue to expand. This can only be

achieved by reducing catchment erosion. Because these sediment facies can be very thick, due

to 200 years of increased catchment erosion, it is very unlikely that reducing chronic sediment

input will decrease the areas of sediment resuspension. Reducing the area of sediment

resuspension may only occur by processes that bind the sediment, therefore decreasing the

141

ease to which it resuspends. This could occur if favourable conditions for seagrass growth (i.e.

low wind speed or wind from an off-shore direction) persisted long enough for the seagrass to

germinate, then grow to an extent that the roots and rhizomes would stabilise the sediment.

Halophila spp. that produced lots of seeds (a large source of seeds would be required), only

need low light for seedling growth and have high growth rates are the most likely species to

establish. Once established, it is critical that an acute light reduction event does not lead to

die-off.

Acute light reduction caused by flooding rivers has also been identified as a critical factor

effecting the distribution of seagrass. To alleviate the real threat of floods to seagrass,

management actions that reduce sediment flushing during floods is required. In tackling this

issue, two processes need to be addressed. Firstly, gradual catchment erosion between floods

needs to be minimised because a large proportion of the sediment eroding during this period

is deposited within the rivers, only to be washed out during the first flood event. Secondly,

catchment erosion during a flood needs to be minimised by ensuring soil erosion does not

occur, and/or by ensuring any mobilised sediments do not reach the waterways (e.g. having

healthy riparian vegetation, installing stormwater quality improvement devices).

Northeastern Australia has numerous ports that require regular maintenance dredging. As

seagrass meadows occur near these dredging operations, environmental monitoring programs

are undertaken to ensure that turbidity plumes generated by dredging do not lead to seagrass

loss. However, the guidelines used to protect the seagrasses are often arbitrary, with little

relevance to seagrass. For example, during the 1996 Port of Karumba dredging program

(conducted simultaneously to the study in Chapter 6), dredging had to cease if the “turbidity

increased more than 25% over background levels for 90% of any ten day period”. The

Department of Environment permit for the operation clearly states that this ‘trigger level’ is

“arbitrarily set”. The research in this thesis provides much better estimates of environmentally

relevant ‘trigger levels’ these findings will help ensure seagrasses are protected and dredging

is not unnecessarily delayed. These trigger levels would be based on the parameter most

relevant to seagrasses, i.e. ‘light availability’, rather than turbidity values. Furthermore, an

extra safeguard to prevent seagrass loss during dredging would be to use sub-lethal indicators

of stress (e.g. the concentration of amino acids and chlorophyll in the seagrass leaves) as an

indication of impending seagrass die-off (Chapter 6).

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8.3 Future research

While this thesis has provided valuable insight on the light requirements of select northeast

Australian seagrasses, it has also identified important areas where further research is required.

Some of the most pertinent areas needing further research include:

Continued investigations into the effects of long-term and acute light reduction on

northeast Australia’s seagrass to cover a different set of species (e.g. Cymodocea spp.,

Syringodium and Enhalus) and habitats (e.g. rivers, tropical bays).

Elucidation of the causes of intra-specific variations in seagrass minimum light

requirements. This research should focus on the effects of environmental conditions

(e.g. sediment geochemistry and water motion) and morphological plasticity on

minimum light requirements.

Light requirements of deepwater seagrasses, as this group of seagrasses is a

particularly poorly understood component of the coastal environment. Research

should include detailed studies of the minimum light requirements of all deepwater

species; seasonal changes in biomass/productivity in relation to light availability; and

the spectral quality of available light.

The capacity of seagrass to recover after flood events, elucidating basic principals

such as (i) rates of recovery after flood events of various durations, and (ii) recovery

strategies, i.e. whether recovery is via vegetative propagation or seed germination.

The effects of multiple interacting processes during flood events (e.g. light reduction,

sediment smothering and herbicides) that could be affecting seagrass survival.

Shading experiments only elucidated the effects of one impact (i.e. reduced light

availability).

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