Hydroacoustic Assessment of Fish Community Size Spectra &

Refinement of Hydroacoustic Estimates of Size

by

Abby Ann Daigle

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Ecology & Evolutionary Biology

University of Toronto

© Copyright by Abby Daigle 2017

ii

Hydroacoustic Monitoring of Fish Community Size Spectra

Abby Daigle

Master of Science

Ecology & Evolutionary Biology University of Toronto

2017

Abstract

A fundamental purpose of ecology is to understand the underlying processes that give

rise to community structure. For highly size structured systems, such as freshwater fish

communities, a size-based approach can be more appropriate than a species-based approach. The

size spectra framework is one such size-based approach. Size spectra represent abundance (or

biomass) as a function of organism size. Log transformation of both axes often results in a

characteristic negative linear relationship. This thesis explores the ability of size spectra to be

used as a monitoring tool. Chapter One focuses on the ability of size spectra to detect change in

fish community structure during a whole-lake manipulation. Chapter Two investigates how fish

behaviour can impact the accuracy of hydroacoustic sampling methods. This thesis highlights the

applicability of size-spectra monitoring for freshwater fish communities, as well as the

importance of verifying the assumption of horizontal swimming through the acoustic beam.

iii

Acknowledgements

I first and foremost have to thank my supervisors, Brian Shuter and Don Jackson. The

continued support you both provided for exploring and pursuing new interests has been greatly

appreciated. I further must thank the additional members of my advisory committee, Dak de

Kerckhove and Gary Spules. The comments and insight you both provided during our meetings

helped focus my thesis, and provided valuable recommendations regarding how to move

forward. Moreover, a big thanks to Dak for providing me with several opportunities where I was

able to expand my field-related skill sets, while exploring picturesque Ontario lake ecosystems.

Ken Minns, Henrique Giacomini, and Brian Kielstra are all to thank for helping me

through various aspects of the mathematical modelling universe, in addition to the more

technical help in executing such models using statistical software. For field research, I must

thank Scott Milne of Milne Technologies for conducting all hydroacoustic surveys and for

assistance with troubleshooting the hydroacoustic software. Additionally, I would like to thank

my friends at the Experimental Lakes Area who not only helped lug car batteries along small

hiking trails, but also kept Scott and me safe and well fed.

I must also thank my officemates, David and Darren, who were often able to help clarify

the scientific story I was trying to tell. My family, particularly my brother and sister-in-law, who

have continued to be curious and supportive of my academic pursuits. And finally, my friends,

Liana, Salvatore, and Darren, who have kept me smiling from start to finish.

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Table of Contents

Acknowledgements ........................................................................................................................ iii Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................. vi List of Figures .............................................................................................................................. viii

List of Appendices .......................................................................................................................... x General Introduction ....................................................................................................................... 1

0.1 Understanding Ecological Community Structure ................................................................................1 0.2 Size Spectra as a Monitoring Tool ......................................................................................................2 0.3 Hydroacoustics as a Data Collection Method .....................................................................................3

Chapter One: Assessing Impacts of Loss of Lake Connectivity on Fish Community Size Spectra

Abstract ........................................................................................................................................... 4 1.1 Introduction ............................................................................................................................... 5

1.2 Methods..................................................................................................................................... 7 1.2.1 Study Area ........................................................................................................................................7 1.2.2 Experimental Design ........................................................................................................................9 1.2.3 Fish surveys ....................................................................................................................................11 1.2.4 Size Spectra ....................................................................................................................................14 1.2.5 Statistical Analysis .........................................................................................................................14

1.3 Results ..................................................................................................................................... 17 1.4 Discussion ............................................................................................................................... 24

1.4.1 Limitations ......................................................................................................................................25 1.4.2 Conclusions ....................................................................................................................................26

Chapter Two: Refining Hydroacoustic Estimates of Size Distributions: Quantifying and

Accounting for Systematic Patterns in Swimming Behaviour

Abstract ......................................................................................................................................... 28 2.1 Introduction ............................................................................................................................. 29

2.1.1 Objectives .......................................................................................................................................31 2.2 Methods................................................................................................................................... 33

2.2.1 Study Area ......................................................................................................................................33 2.2.2 Data Collection ...............................................................................................................................35

2.2.2.1 Trawling & Traps ...............................................................................................................................35 2.2.2.2 Hydroacoustics ...................................................................................................................................35

2.2.3 Cleaning Hydroacoustic Data .........................................................................................................36 2.2.3.1 Noise Removal ..................................................................................................................................36 2.2.3.2 Fish School Removal ..........................................................................................................................36 2.2.3.3 Fish Track Detection ..........................................................................................................................36

2.2.4 Statistical Analysis .........................................................................................................................40

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2.3 Results ..................................................................................................................................... 42 2.3.1 Description of Fish Behaviour ........................................................................................................42 2.3.2 Comparing Length Estimates .........................................................................................................48 2.3.3 Comparing Length Distributions ....................................................................................................51 2.3.4 Chaoborus Abundance ...................................................................................................................54

2.4 Discussion ............................................................................................................................... 56 2.4.1 Description of Fish Behaviour ........................................................................................................56 2.4.2 Comparing Length Distributions ....................................................................................................59 2.4.3 Conclusions ....................................................................................................................................60

References ..................................................................................................................................... 62 Appendices .................................................................................................................................... 69

Appendix A – Normalizing Fish Counts .................................................................................................69 Appendix B – Standardizing Sampling Effort .........................................................................................70 Appendix C – Centring by Mean Bin Size ..............................................................................................71 Appendix D – Size Spectra by Year ........................................................................................................72

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List of Tables Table 1 – Survey data indicating the lake, year, month, date, and number of nighttime replicates

completed. ........................................................................................................................... 12 Table 2 – The parameters and settings for the surveys conducted on L626 and L373 between

2010 and 2016 using the EK60 120 kHz transducer. .......................................................... 12 Table 3 – Single-target detection settings used in Echoview®. ................................................... 13 Table 4 – Track-detection setting used in Echoview®. ................................................................ 13 Table 5 – Information and abbreviation of variables used in all LME models. ........................... 16 Table 6 – Results from the LME model analysis showing variation in fish count per size class in

L626 and L373 (n = 289 observations within 32 groups from 6 years). The table shows the variables included in the model (represented by letters), the random structure of the model (in brackets within the model), and the fixed effect interactions (indicated by *). Model selection is based on AIC. A = abundance, S = size, L = lake, T = time, Y = year, and R = replicate. df = degrees of freedom, DAICi = AICi – AICmin, w = Akaike weights explaining total variance, R2

m = marginal R2 value, R2c = conditional R2 value. Models with a DAICi <

2 are highlighted in bold. ..................................................................................................... 18 Table 7 – Parameter estimates (b) for the fixed effects from the best-fitting models (DAICi < 2)

in Table 6. ............................................................................................................................ 19 Table 8 – Variance estimates for the random effects from the most parsimonious model (M14,

DAICi = 0) in Table 6. Y = year, L = lake, R = replicate. ................................................... 19 Table 9 – The parameters and settings for the two stationary EK60, 120 kHz transducers

deployed in L626 August 2016. .......................................................................................... 38 Table 10 - Single target detection settings used in Echoview. ..................................................... 38 Table 11 – Track detection setting used in Echoview. ................................................................. 39 Table 12 – Mean target length and sample size for each sampling night. Data analysed from the

stationary hydroacoustic system (sampling night 1-7) was limited to a 2-hour period each evening (10:30pm-12:30am). The mean target length for the entire sample was calculated using two different methods: (1) max TS per FT and (2) mean TS per FT. Note that sampling night 8 corresponds to the trawl data. Trawling lasted for approximately 8-minutes and covered ~ 350 m. The mean target length was determined by taking the mean of the individuals caught. Max and mean TS per FT did not apply here. ........................... 41

Table 13 – Mean, variance, and standard deviation (SD) from each sampling night and for the

different methods of determining length (mm) estimates. .................................................. 50

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Table 14 – Output summary from Tukey HSD post-hoc test. Difference indicates the difference (in mm) between the means of two size distributions. The lower and upper bounds (in mm) for the 95% confidence interval for the mean is provided, as well as the adjusted p-value. P-adj<0.05 are considered significant. ................................................................................ 53

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List of Figures Figure 1 – Location of the ELA within Canada. The province of Ontario is highlighted in green.

The location of the ELA is represented by the yellow star. .................................................. 8 Figure 2 - Satellite image of the ELA in northwestern Ontario. L626 is shown in relation to three

lakes (in blue) located within the same watershed. L373 (reference lake, shown in red) is located just south of L626. (A) The water flow path of the watershed before manipulation (blue arrows indicate direction of flow). (B) The water flow path after manipulation (yellow line indicates where the dam was constructed). ..................................................... 10

Figure 3 – Fitted values of normalized fish counts as a function of size. The most parsimonious

model (M14, Table 6) was used to generate the fitted normalized fish counts (solid lines) for six different years (0, 2-7). Colours indicate different times. Time 0 corresponds with 2010, time 2 with 2012, etc. Points indicate the raw values used to generate the fitted lines. (A) gives the fitted values for the manipulated lake, L626. (B) gives the fitted values for the reference lake, L373. ..................................................................................................... 21

Figure 4 - Plot of slopes and intercepts from the fitted data against time. Time 0 corresponds to

2010. A vertical line is drawn at time 1 (2011), indicating when the manipulation was implemented. The manipulated lake (L626) is blue, while the reference lake (L373) is red. (A) depicts fitted slope vs. time. (B) depicts fitted intercept vs. time. ................................ 22

Figure 5 - Fitted slope (A) and intercept (B) as a function of the light extinction coefficient (m-1)

for 2010, and 2012-2016. The manipulated lake (L626) is blue, while the reference lake (L373) is red. ....................................................................................................................... 23

Figure 6 – Definition of tilt angle of a fish. The tilt angle is positive when the head is up and

negative when the head is down. From Simmonds, J., & MacLennan, D. (2005). Fisheries Acoustics Theory and Practice (Second Ed). Cornwall: Blackwell Science. ..................... 32

Figure 7 – Satellite image of L626 from the Experimental Lakes Area in northwestern Ontario.34 Figure 8 – TS echogram displaying hydroacoustic data from the transducer placed at a 10 m

depth. Different colours reference different TS values, as indicated by the multicoloured dB scale on the right of the image. Moving left to right across the echogram indicates time. Black horizontal lines occur at 2 m intervals, referenced from the face of the transducer (~lakebed). (A) TS echogram illustrating typical fish behaviour during night. This screen shot comes from data collected on August 5th, 2016 around 11:15 pm. The grey speckling, contained mostly between the 6 m and 8 m lines, is likely backscatter from Chaoborus. The zigzag swimming pattern is observed in multiple instances. (B) A magnified single fish target displaying the zigzag swimming pattern. (C) A magnified portion of the echogram between the 8 m and 10 m lines. (D) For comparison, a TS echogram from the same day, but earlier in the evening (~6:30 pm). Much of the water column is empty. Two fish schools are detected above the 6 m line. ...................................................................... 44

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Figure 9 – Target strength (TS) as a function of time for individual fish tracks (FTs). Each point represents one single-target detection (i.e. one echo). FTs group single targets that come from the same individual. Pings (transmission of an acoustic wave) occurred at intervals of 0.5 seconds. Shaded and non-shaded areas correspond to descending and ascending movement in the water column, respectively. (A-F) correspond to individual FTs (i.e. the tracked movement of one fish within the acoustic beam). .................................................. 45

Figure 10 – Density plots for fish lengths from each night between 10:30 pm and 12:30 am.

Colours correspond to the sampling night. Density plots with the dashed outline correspond to fish lengths calculated using the max TS value, while the density plots lacking a dashed outline correspond to those calculated using the mean TS value. An ANOVA indicated that the means of the two groups are significantly different (df=1, p<<0.05). The distribution range was limited to 10 mm-65 mm target lengths. ............................................................ 49

Figure 11 – Density plots for fish lengths. Colours correspond to the sampling night. Density

plots with the dashed outline correspond to fish lengths calculated using the max TS value, while the density plots lacking a dashed outline correspond to those calculated using the mean TS value. The solo pink density plot corresponds to the trawling data (night 8). An ANOVA indicated that the means of the three groups are significantly different (df=1, p<<0.05). The distribution range was limited to encompass 10 mm – 65 mm target lengths. ............................................................................................................................................. 52

Figure 12 – Chaoborus abundance as a function of depth and time of day. Abundance is

represented as the number of individuals per 31.25 L. Depth (m) indicates the water column depth at which the sample was collected, relative to lakebed. Daytime samples (yellow) decrease in abundance while moving closer to lake surface (located at 13 m), while nighttime samples (grey) were highest at 7 m. .......................................................... 55

Figure 13 – Illustration of the hypothesis of how fish are producing the observed hydroacoustic

zigzag pattern. The broken grey lines indicate the spread of the acoustic beam as it is directed from the bottom of the lake upwards, to the surface. The solid grey arrows indicate the direction of fish movement. The blue fish is used to indicate fish orientation within the beam. The curved blue lines indicate active swimming. (A) Fish are positioned close to horizontal (in regard to the direction of the incident wave) before ascending. (B) Active swimming during the ascending path positively increases the tilt angle as the fish becomes more vertical in the water column. (C) When the peak of the ascending path is reached, active swimming ceases and the fish drifts passively to a lower position in the water column. During the drift, the tilt angle decreases toward zero as the fish becomes more horizontal. (D) When the trough of the descending path is reached, the fish resumes active swimming, continuing the cycle of the zigzag swimming pattern. ..................................... 58

Figure 14 – Fitted values of normalized fish counts as a function of size. The most parsimonious

model (M14, Table 6) was used to generate the fitted normalized fish counts for six different years (0, 2-7, A-F respectively). Red corresponds to the manipulated lake, and blue corresponds to the reference lake. ............................................................................... 72

x

List of Appendices Appendix A – Normalizing Fish Counts.......................................................................................69 Appendix B – Standardizing Sampling Effort...............................................................................70 Appendix C – Centring by Mean Bin Size....................................................................................71 Appendix D – Size Spectra by Year..............................................................................................72

1

General Introduction

0.1 Understanding Ecological Community Structure

A fundamental goal of ecology is to understand the underlying processes that give rise to

community structure. That is, ecologists aim to describe how underlying interactions between

species, as well as between species and the environment result in the species diversity and

overall patterns of species abundances observed in ecological systems (Elton, 1927). Two

common approaches taken to aid in the understanding of these ideas are the niche concept

(species-centric) and the size spectra framework (size-centric) (Elton, 1927; Gilljam et al., 2011).

In terrestrial ecology, application of the niche concept is often favoured when trying to

determine the underlying processes of community structure. The aim of this concept is to explain

the observed community structure by determining the functional role of the component species

within the ecosystem. Historically, this approach typically views animal species as a collection

of identical individuals with invariant traits, where the complexities of ontogenetic diet shifts

(often associated with size) are ignored (Gilljam et al., 2011; Nakazawa, 2015; Woodward &

Hildrew, 2002). As such, the niche concept may not be appropriate for communities comprised

of species that occupy different functional roles throughout their lifetime.

An alternative approach to understanding community structure is through the use of

community size spectra, a concept designed to represent energy flow through a community

regardless of species identity. This concept is particularly applicable for aquatic communities,

where many species will assume different functional roles (dictated by size) throughout their

lifetime (Trebilco et al., 2013). Because aquatic communities are highly size-structured, body

size is often a reliable indicator of who is eating whom. This general observation was first noted

by Elton (1927) through his idea of a ‘pyramid of numbers’, which describes how animal

abundance is negatively related to body size. Elton went on to state that two factors drive this

pattern: (1) large animals eat smaller animals; and (2) small animals can increase their abundance

faster than large animals. Elton’s ‘pyramid of numbers’ was later employed by Lindeman (1942)

in developing his framework that viewed ecosystems in terms of the dynamics of their

component trophic levels, giving rise to the idea of trophic pyramids. Although ecologists do not

view ecosystems strictly through the trophic framework suggested by Lindeman (1942), the

study of energy flow through an ecosystem remains a central focus of community ecology.

2

Size-spectra are constructed by plotting community abundance as a function of organism

size. The log transformation of both variables often produces a characteristic negative linear

relationship (Bianchi et al., 2000; Shin et al., 2005). Size-spectra analysis emphasizes two

important spectral parameters, the y-intercept and the slope. The y-intercept provides

information regarding the total biomass (or abundance) of the system, while the slope provides

an indication of trophic structure (relative abundance of small versus large organisms) (Murry &

Farrell, 2014; Sprules & Barth, 2016). Monitoring of these spectral parameters has the potential

to indicate when ecosystems are experiencing some form of external pressure (e.g. intensive

fishing, eutrophication, species invasion, climate change, etc.) (Blanchard et al., 2005; Emmrich

et al., 2011; Rochet & Benoit, 2012; Yvon-Durocher et al., 2011). Although trophic pyramids are

an intuitive way to view community structure, construction of community size spectra can serve

as a robust and informative representation of energy flow within an ecosystem.

0.2 Size Spectra as a Monitoring Tool

Size-spectra analyses have only recently begun being used to monitor marine (Bianchi et

al., 2000; M. Rochet & Trenkel, 2003), and freshwater communities (Emmrich et al., 2011;

Murry & Farrell, 2014). Specifically, in Canada there has been a push by aquatic scientists to test

the robustness of size spectra as an ecological indicator of ecosystem health in freshwater

communities. Here, ecosystem health refers to the ability of a system to maintain its baseline

state. An effective ecological indicator is an aspect of a system that can be efficiently monitored

and that reflects the status of the overall ecosystem being managed (Jennings, 2005; Rice &

Rochet, 2005). Lake fish community size spectra may meet the requirements of a robust

ecological indicator, as the spectra are sensitive to changes in system biomass and community

trophic structure (Pollom & Rose, 2015). Moreover, detectable shifts in size spectrum parameters

have been shown to occur within ecologically meaningful timescales (De Kerckhove et al.,

2016). On a practical level, community size spectra have the potential to become an effective

tool for managers of aquatic ecosystems. Size spectra monitoring programs can reduce the

amount of ecosystem sampling required, and they can indicate what aspect of the system is under

pressure (total biomass, specific size-classes, or both) (Sprules & Barth, 2016).

More generally, continuing to research the strengths and weaknesses of community size-

spectra as an ecological indicator furthers the literature available on community structure viewed

through a size-based lens. As previously stated, the niche concept has limited applicability for

3

communities comprised of individuals that occupy different functional roles throughout their

lifetime due to ontogenetic changes (Gilljam et al., 2011; Nakazawa, 2015; Trebilco et al., 2013;

Woodward & Hildrew, 2002). Thus, viewing communities as a function of their comprising size

classes may help in understanding the underlying structuring processes.

0.3 Hydroacoustics as a Data Collection Method

The popularity of utilizing hydroacoustics for sampling freshwater ecosystems continues

to grow. Hydroacoustic surveys in small freshwater lakes require minimal staff, reduce on-site

processing time, and collect samples from a greater volume of water, in comparison to traditional

netting programs. With the exclusion of the near surface and just above lakebed regions,

hydroacoustic technologies can detect targets at all depths ensonified by the acoustic beam. The

size of targets that can be detected range from microscopic plankton to large fish schools, and

depends largely on the frequency being used (Simmonds & MacLennan, 2005). One current

drawback of hydroacoustics is their inability to discriminate between species. Thus, for situations

where abundance and species identity must be known, a combined field program, involving

parallel netting and hydroacoustic sampling, is essential. However, for studies where the relative

abundance of small versus large organisms is the focus, such as with the use of community size

spectra, hydroacoustic echo sounders are an ideal method for collecting the required data.

4

Chapter 1

Assessing Impacts of Loss of Lake Connectivity on Fish

Community Size Spectra

Abstract

Ecological communities are threatened by the pervasiveness of human-ecosystem

interactions occurring at multiple scales. Often, large-scale regional changes in abiotic factors

can trigger small-scale local biological responses. However, given the complexity of local-scale

interactions, it is difficult to predict how responses will manifest, and their magnitude. We

sought to determine if the loss of lake connectivity, a large-scale change, could trigger a

detectable local-scale response in fish community size structure. Structure was monitored using

the size spectra framework, which represents energy flow through a system as a function of size,

rather than species identity. A whole-lake manipulation was conducted whereby a 4th-order lake

was converted into a 1st-order lake. Annual hydroacoustic surveys were completed in 2010 (pre-

manipulation), and from 2012-2016 (post-manipulation) to determine estimates of fish size

(length). A nearby reference lake was monitored to account for natural background variation.

The data were analyzed using a linear mixed effects model. Overall, we were unable to detect a

change in the fish community size spectra of the manipulated lake. However, it is possible that

enough time has not yet passed for the full effect of the experiment to emerge.

5

1.1 Introduction

Human-environment interactions are widespread and occur at multiple scales (Galvani et

al., 2016). For example, climate-change science predicts how temperature and precipitation will

change at global scales and within large geographic regions. However, it is less clear how

changes in these global-scale factors will impact local ecosystems. Given the complexity of

local-scale interactions, a detailed understanding of the interacting factors is necessary. For

example, in the 1960s and 1970s, the large-scale problem of lake eutrophication was negatively

impacting the water quality of the Great Lakes. Whole-lake nutrient addition experiments

conducted at the Experimental Lakes Area identified phosphorous addition as the key factor in

driving lake eutrophication (Schindler, 1974). Similarly, in the 1970s and 1980s, the large-scale

problem of acid rain was hypothesized to be responsible for the deterioration of fish populations

in lakes of eastern North America and northern Europe. Investigations at the Experimental Lakes

Area supported the hypothesis, showing that the addition of sulfuric acid to lakes impacted lake

food webs, leading to collapsed fish populations (Schindler & Turner, 1982). Therefore, whole

ecosystem-based studies provide a proven method for elucidating the link between a large-scale

change and local-scale response.

In Canada, climate change threatens to alter the abiotic factors of small (<500 ha),

oligotrophic lakes (Stasko et al., 2012). For example, climate trends indicate that much of

southern Canada is experiencing an altered hydrological cycle (Zhang et al., 2000), transitioning

to a warmer and wetter environment with increased rates evapotranspiration and more extreme

drought and wet events (Hengeveld et al., 2005; Huntington, 2006; Magnuson et al., 1997;

Zhang et al., 2000). Such changes can lead to an increase in drier conditions experienced by a

watershed, impacting the duration and frequency of lake connectivity.

The lake landscape position is a framework used to describe how lakes within a

watershed are connected, either through surface or groundwater flow (Martin & Soranno, 2006).

Since a loss of lake connectivity affects a lake’s landscape position, the framework can be used

to inform expectations for how lake abiotic factors will be altered by drier conditions. Two

notable lake characteristics that are impacted by lake position are the quantity and quality of

dissolved organic carbon (DOC) and the water residence time (WRT). DOC can be used as a

proxy for water clarity (Jones, 1992; Martin & Soranno, 2006). In general, DOC accumulation is

positively related to lake landscape position (Martin & Soranno, 2006; Soranno et al., 1999); the

6

greater the number of lakes upstream, the greater the in-lake DOC concentration and the darker

the lake water colour becomes. Conversely, WRT is negatively related to the number of

upstream lakes (Müller et al., 2013). As such, a change in lake connectivity that alters the WRT

can then be expected to alter time-dependent lake processes (e.g. photodegredation,

mineralization, and sedimentation) affecting the overall nutrient budget (Algesten et al., 2003;

Battin et al., 2009; Pace & Cole, 2002; Schindler et al., 1996; Verburg et al., 2013;

Weyhenmeyer et al., 2012) and physical structure (e.g. temperature profile, thermocline depth

and strength, oxygen profile, light penetration depth) of the lake (Butcher et al., 2015; Pace &

Cole, 2002; Schindler et al., 1996). Overall, the lake landscape position is an effective tool for

generating expectations regarding how lake abiotic factors will be impacted by connectivity loss.

The expected change in abiotic factors that would occur as a result of the loss of lake

connectivity can directly and indirectly impact lake fish communities. For example, changes in

the lake temperature profile affects fish thermoregulation and foraging behaviors (Biro et al.,

2007; Goyer et al., 2014; Snucins & Gunn, 2000). Similarly, changes in water clarity can alter

foraging success rates and prey selectivity for visual predators (Estlander et al., 2010), while

changes to dissolved oxygen concentration can affect overall fish condition (Magnuson et al.,

1997). These examples illustrate how changes resulting from a loss of lake connectivity are not

restricted to abiotic variables. Instead, the changes interact among the biotic components with the

potential to impact the resident fish community.

Given the complexity of abiotic and biotic interactions structuring boreal lake fish

communities, there is a clear need to further investigate how these communities will respond to

climate-induced changes such as altered lake connectivity (Keller, 2007; Stasko et al., 2012).

One approach to do this is to analyze fish community size spectra, a concept that represents

energy flow through a community, regardless of species identity. Size spectra are constructed by

plotting community abundance as a function of size class. The log transformation of both

variables often produces a characteristic negative linear relationship (Bianchi et al., 2000; Shin et

al., 2005). Size spectra analysis provides two important spectral parameters, the y-intercept

(elevation) and the slope. The spectral intercept provides information regarding total abundance

(or biomass) of the system, while the slope provides an indication of the relative abundance of

small and large individuals (Daan et al., 2005). Monitoring of these spectral parameters has the

potential to indicate when ecosystems are experiencing some form of external pressure such as

7

intensive fishing (Blanchard et al., 2005; Lewin et al., 2006; McDonald & Hershey, 1989;

Rochet & Benoit, 2012) and climate warming (Yvon-Durocher et al., 2011).

This paper describes the outcome of a whole-lake manipulation experiment designed to

examine how freshwater lake ecosystems might be impacted by drier conditions. Specifically, we

examined how the size structure of the resident fish community is altered by a loss of lake

connectivity. We analyzed fish community change by monitoring size spectra through time to

determine if the whole-lake manipulation had a detectable effect on the spectral parameters of

the resident fish community. We further monitored a similar, nearby lake to serve as our

reference. We hypothesized that a loss of lake connectivity would elicit a change in spectral

intercept. We chose to leave the direction of the change unstated because the loss of lake

connectivity is expected to initiate a change in multiple factors that have opposing impacts on the

total abundance of the system. We further hypothesized that the spectral slope would become

steeper, reflecting an increased ratio of small to large fish.

1.2 Methods

1.2.1 Study Area

This study was conducted from 2010-2016 at the Experimental Lakes Area (ELA) in

northwestern Ontario (Figure 1). Lake 626 (L626) and Lake 373 (L373) were chosen to be the

focal lakes for this study in part because of their relative proximity and comparable area. L626 is

an oligotrophic, 4th-order lake that has an area of 26.2 ha and a maximum depth of 13 m. L373 is

an oligotrophic, 1st-order lake that has an area of 23.7 ha and a maximum depth of 21 m. L373 is

a long-standing reference lake for the ELA and was used as a reference for this study. Notable

differences exist between the two lakes regarding the zooplankton and fish communities. In

terms of the zooplankton, Mysis (Mysis diluvania) are dominant in L373 (Wall & Blanchfield,

2012), while Chaoborus (Chaoborus sp.) dominate L626. Although Lake Trout (Salvelinus

namaycush) are the dominant predator in both lakes, the prey fish communities differ. L626 is

primarily comprised of Yellow Perch (Perca flavescens, ~97% of total catch; Wall &

Blanchfield, 2012), while L373 is comprised of Pearl Dace (Margariscus margarita), Finescale

Dace (Chrosomus neogaeus), and Northern Redbelly Dace (Chrosomus eos).

8

Figure 1 – Location of the ELA within Canada. The province of Ontario is highlighted in green. The location of the ELA is represented by the yellow star.

9

1.2.2 Experimental Design

Baseline surveys were conducted on L626 and L373 in 2010, before manipulation

occurred. In 2011, the inflow of L626 was blocked and diverted, effectively making L626 a 1st-

order lake (Figure 2). This manipulation mimics a loss of lake connectivity that could occur

under drier conditions induced by climate change. These conditions would normally result from

either a lack of adequate precipitation, or by the rate of evapotranspiration becoming greater than

the rate of precipitation (Murdoch et al., 2000; Schindler et al., 1985). Post-manipulation surveys

were conducted from 2012-2016.

10

Figure 2 - Satellite image of the ELA in northwestern Ontario. L626 is shown in relation to three lakes (in blue) located within the same watershed. L373 (reference lake, shown in red) is located just south of L626. (A) The water flow path of the watershed before manipulation (blue arrows indicate direction of flow). (B) The water flow path after manipulation (yellow line indicates where the dam was constructed).

(A) (B)

11

1.2.3 Fish surveys Nighttime surveys were conducted each year on both lakes and began a minimum of one

hour after sunset. Surveys were completed within the pelagic zone by conducting parallel

transects perpendicular to the long axis of the lake. Sampling occurred in August all years, with

the exception of 2012 (September) and 2013 (July). Achieving balanced replication within and

among years proved challenging (Table 1), due to a combination of weather and timing.

Hydroacoustic data were collected with an EK60 echo sounder (Kongsberg Maritime,

Kongsberg, Norway) transmitting to four split-beam transducers (120, 333, and 710 kHz), all

mounted on an adjustable metal arm attached to the port side of the research vessel. When the

arm was deployed, three transducers (120, 333, and 710 kHz) faced downward and one faced

horizontally (120 kHz) away from the boat. The transducers were submerged to a depth of 0.5

m. In this study, the 120 kHz downward facing transducer was used to survey fish. Surveys were

conducted at a constant speed of 11.1 km/hr (6 knots). Raw hydroacoustic data were processed

using Echoview® (Myriax Software Pty. Ltd. Version 5.2.70) having adjusted the relevant

parameters and settings (Table 2). Single targets were detected using the Single Target Detection

module with specified settings (Table 3).

A target strength (TS) threshold (-61.0 dB) was applied to the data in Echoview® to

eliminate Chaoborus (a zooplankton) echoes. This was necessary because the anatomy of

Chaoborus incorporates two sets of paired air sacs. These air sacs interact with the transmitted

sound waves, creating weak echoes (in comparison to fish echoes) that are detected by the

hydroacoustic receiver. By setting a TS threshold, it prevents Chaoborus data from being

incorporated as fish data.

12

Table 1 – Survey data indicating the lake, year, month, date, and number of nighttime replicates completed.

Lake Year Month Dates # of Nighttime Replicates

626 2010 August 21 – 23 3 373 2010 August 18 – 21 3 626 2012 September 5 – 8 2 373 2012 September 8 – 10 4 626 2013 July 16 – 18 3 373 2013 July 18 – 21 2 626 2014 August 5 – 8 3 373 2014 August 8 – 11 3 626 2015 August 14 – 16 3 373 2015 August 17 – 19 2 626 2016 August 5 – 6 2 373 2016 August 4 2

Table 2 – The parameters and settings for the surveys conducted on L626 and L373 between 2010 and 2016 using the EK60 120 kHz transducer.

Variable Setting or Value Absorption coefficient 0.140136 dB/m EK60 Sa correction -0.2600 dB Transducer gain 25.1800 dB Major axis 3dB beam angle 8.41° Major axis angle offset -0.04° Major axis angle sensitivity 23.000000 Minor axis 3dB beam angle 4.07° Minor axis angle offset 0.02° Minor axis angle sensitivity 23.000000 Sound Speed 1458.75 m/s Transmitted power 150.0 W Transmitted pulse length 0.128 ms TVG range correction Simrad Ex60 Two-way beam angle -22.2800 dB re 1sr

13

Table 3 – Single-target detection settings used in Echoview®.

Variable Setting or Value Compensated TS threshold (dB) -61.00 dB Pulse length determination level (dB) 6.00 dB Minimum normalized pulse length 0.30 Maximum normalized pulse length 1.50 Beam compensation model Simrad Lobe Maximum beam compensation (dB) 12.00 dB Maximum standard deviation of Minor-axis angles (degrees) 0.537° Major-axis angles (degrees) 0.537°

Table 4 – Track-detection setting used in Echoview®.

Algorithm Data - 4D (range, angles, & time) Track Detection Major Axis Minor Axis Range Alpha 0.5 0.5 0.25 Beta 0.2 0.2 0.3 Target Gates Major Axis Minor Axis Range Exclusion distance (m) 12.0 12.0 0.35 Missed ping expansion (%) 10.0 10.0 0.45

Weights Major axis 30.0 Minor axis 30.0 Range 40.0 TS 0 Ping gap 0

Track Acceptance Minimum number of single targets in a track 1 Minimum number of pings in track (pings) 1 Maximum gap between single targets (pings) 1

14

1.2.4 Size Spectra Single target detections were converted into fish tracks using the fish track detection

module in Echoview® with non-default settings (Table 4). This is done to ensure that multiple

pings coming from one fish are documented as one fish rather than as several individual fish.

Fish tracks were exported from Echoview® and converted to fish length estimates using Love’s

(1971) general relationship (Hartman et al., 2000):

!" = 19.1 ∗ ()* +, + 0.9 ∗ log23

1000 − 23.9

where Lm is the total length (m), c is the speed of sound in water (m/s) and f is the transmitted

frequency (kHz). Once all sizes were determined, an upper size threshold was set at 800 mm.

This threshold was incorporated due to the presence of extreme outliers that did not make

biological sense for our given lakes. The outliers were investigated and determined to be a result

of two closely positioned fish being detected as one large fish.

Logarithmic binning was used to establish fish community size spectra, with the log10 of

fish count plotted against the log2 of each bin size. Fish count per bin was normalized using the

corresponding linear bin width (Appendix A). Sampling effort was standardized using a volume

adjustment factor (Wheeland & Rose, 2016) (Appendix B). This standardization method does not

extrapolate abundance estimates to encompass the entire lake volume. Instead, it standardizes

sampling effort based on the volume sampled during each survey. This method is acceptable

since we collected data only from the pelagic zone. We cannot assume that the shallow, littoral

areas display similar fish abundance patterns. Data were centred to reduce the correlation

between slopes and intercepts (Daan et al., 2005; Sprules & Barth, 2016) (Appendix C).

1.2.5 Statistical Analysis Linear mixed effects (LME) modelling with random intercepts and slopes was chosen for

analysing the processed data. The random structure (between replicate variation in spectrum

slope and intercept + residual random variation of individual abundance estimates around each

fitted linear spectrum) of the model was specified prior to model selection based on the known

structure of the experimental design (Zuur et al., 2013). The full model was then created by

adding relevant fixed effects and their interactions (Burnham & Anderson, 2002). Reduced

models were created by systematically removing interactions and main effects. We note the two

15

interactions of primary interest, (1) the two-way interaction between time and lake (T*L)

representing evaluation of the spectral intercepts and (2) the three-way interaction between time,

lake, and size (T*L*S) evaluating the spectral slopes. If these interactions are not incorporated

into the best model(s), it would indicate that neither spectral intercept or slope has significantly

changed over the course of the experiment. Model selection was completed using the Akaike

Information Criterion (AIC) for model comparison, with all models fitted using maximum

likelihood (ML). Best models were considered as those with a DAIC < 2 (Burnham & Anderson,

2002). Once best models were chosen, they were refitted using restricted maximum likelihood

(REML; Zuur et al., 2013).

All modelling and statistical analyses were completed using R3.3.2. Models were created

using the lmer function in the lme4 package (Bates et al., 2015). Marginal and conditional R2

values were determined using the r.squaredGLMM function in the MuMIn package (Barton,

2016). P-values for the parameter estimates (β) from the best models were calculated using the

lmerTest function from the lmerTest package (Kuznetsova et al., 2016). For modelling, the

variables (Table 5) used were set as either fixed or random, with the exception of size. Since the

variable size was incorporated into both the random intercept and the random slope, its effect

(fixed or random) can differ depending on its location in the model.

16

Table 5 – Information and abbreviation of variables used in all LME models.

Variable Name

Abbrev. Description Category Type Effect

Count C Normalized fish count per size bin on log10 scale Response Continuous -

Time T Years since initiation of experiment Predictor Continuous Fixed

Size S Midpoint of centered size bin on log2 scale Predictor Continuous Fixed/Random

Year Y Year data was collected Predictor Categorical Fixed

Lake L Name of lake sampled Predictor Categorical Fixed

Replicate R Replicate survey Predictor Categorical Random

17

1.3 Results From 2010-2016, 32 hydroacoustic surveys were conducted in our manipulated and

reference lakes altogether. This produced 289 observations of normalized fish counts from

approximately 7,500 individual fish detections.

The best-fitting models (M14, M10, and M12, Table 6) have a high combined Akaike

weight (w~0.65) indicating a high relative likelihood for their selection. Notably, absent from the

best-fitting models are the two interaction terms that were of prime importance in determining if

the experimental manipulation had a detectable impact on the spectral intercept or slope. This

indicates that the experimental manipulation did not produce a detectable change in the

parameters of the L626 size spectrum over the post-manipulation observed period. All best-

fitting models included size as a fixed effect (Table 6). M10 and M12 each included an

additional fixed effect, time and lake, respectively. However, examination of their parameter

estimates (b) indicate that the time and lake effects are not significant (p=0.209 and p=0.795

respectively, Table 7). The effect of size is significant in all top models (p<0.05, Table 7). This

means that the main predictor of fish count per size class is size.

18

Table 6 – Results from the LME model analysis showing variation in fish count per size class in L626 and L373 (n = 289 observations within 32 groups from 6 years). The table shows the variables included in the model (represented by letters), the random structure of the model (in brackets within the model), and the fixed effect interactions (indicated by *). Model selection is based on AIC. A = abundance, S = size, L = lake, T = time, Y = year, and R = replicate. df = degrees of freedom, DAICi = AICi – AICmin, w = Akaike weights explaining total variance, R2

m = marginal R2 value, R2c = conditional R2 value. Models with a DAICi < 2 are highlighted in

bold.

Code Model df AIC DAICi w R2m R2

c

14 C~S+(1+S|Y:L:R) 6 399.747 0 0.307 0.787 0.836 10 C~T+S+(1+S|Y:L:R) 7 400.331 0.584 0.230 0.789 0.836 12 C~S+L+(1+S|Y:L:R) 7 401.681 1.933 0.117 0.788 0.836 9 C~T+S+L+(1+S|Y:L:R) 8 402.219 2.472 0.089 0.789 0.836 6 C~T+S+L+T*S+(1+S|Y:L:R) 9 402.809 3.062 0.066 0.79 0.835 7 C~T+S+L+T*L+(1+S|Y:L:R) 9 403.659 3.912 0.043 0.791 0.835 8 C~T+S+L+S*L+(1+S|Y:L:R) 9 404.085 4.337 0.035 0.789 0.836 3 C~T+S+L+T*S+T*L+(1+S|Y:L:R) 10 404.303 4.556 0.031 0.792 0.834 1 C~T+S+L+T*S+T*L+S*L+T*S*L+(1+S|Y:L:R) 12 404.727 4.98 0.025 0.798 0.834 4 C~T+S+L+T*S+S*L+(1+S|Y:L:R) 10 404.731 4.984 0.025 0.79 0.835 5 C~T+S+L+T*L+S*L+(1+S|Y:L:R) 10 405.494 5.747 0.017 0.791 0.835 2 C~T+S+L+T*S+T*L+S*L+(1+S|Y:L:R) 11 406.202 6.455 0.012 0.792 0.834 13 C~T+(1+S|Y:L:R) 6 490.477 90.73 0 0.002 0.865 16 C~(1+S|Y:L:R) 5 491.449 91.701 0 0 0.873 11 C~T+L+(1+S|Y:L:R) 7 492.473 92.726 0 0.002 0.836 15 C~L+(1+S|Y:L:R) 6 493.410 93.663 0 0 0.873

19

Table 7 – Parameter estimates (b) for the fixed effects from the best-fitting models (DAICi < 2) in Table 6.

Model Parameter b SE t-value Pr(b>|z|)

M14 Intercept -0.793 0.0351 -22.59 <2e-16 Size -0.560 0.0235 -23.86 <2e-16

Intercept -0.853 0.0579 -14.72 <2e-16 M10 Size -0.559 0.0235 -23.77 <2e-16

Time 0.020 0.0160 1.27 0.209 Intercept -0.785 0.0466 -16.85 <2e-16

M12 Size -0.560 0.0231 -24.28 <2e-16 Lake -0.017 0.0658 -0.26 0.795

Table 8 – Variance estimates for the random effects from the most parsimonious model (M14, DAICi = 0) in Table 6. Y = year, L = lake, R = replicate.

Groups Name Variance Std. Dev. Y:L:R Intercept 0.0158 0.126

Size 0.0101 0.101 Residual 0.1948 0.441

20

Examining the variation explained by the random effect of survey replicate on intercept

and slope (i.e. size term) indicates that this source of variation is small relative to the remaining

residual variance (variance associated with the intercept is ~8% of the residual variance, while

the slope variance is ~5% of the residual variance) (Table 8). We note here that the random

portion of our model does not address the possible influence of random year-to-year variation in

spectrum slope and intercept. Additional analyses would be needed to assess the influence of this

effect.

Figure 3 displays how the fitted fish counts depend on size class regardless of year and

lake. There is no striking difference in the pattern of change among years in the manipulated lake

(L626) when compared with the reference lake (L373) (see Appendix D for a more detailed

yearly paired comparison of lake size spectra). Such a result is expected given that the top model

revealed that no significant effect on the structure of the size spectrum in L626 could be

detected. Note that the model was used to predict count values only for years where data were

collected.

Figure 4 separates each size spectrum into the two parameters of interest, slope and

intercept. For both parameters, there is no clear trend regarding how they changed over time.

Interestingly, the two figures (A & B) appear to have a negative relationship. That is, when slope

is decreasing, intercept is increasing, and vice versa. It is known that the intercept and slope of

size spectra are correlated when unadjusted (Gómez-Canchong et al., 2013). However, given that

the data were centred, which is a common method for reducing the correlation between spectral

slopes and intercepts (Sprules & Barth, 2016), it is surprising to observe such a strong pattern.

Figure 5 illustrates the relationship between the spectral parameters and the light

extinction coefficient (m-1), Kd. The Kd gives an indication of the amount of light absorbed in a

vertical column of water one metre in length. A lower Kd value indicates greater light

penetration, and thus a clearer lake. The Kd values present in Figure 5 come from water

chemistry data collected at the ELA. Mean values taken from August of each year are shown.

Spence et al. (2017) analyzed a more robust Kd dataset spanning 2011-2014 and found that a

significant change in Kd was detectable. This change was associated with L626 becoming clearer

and thus more similar to L373.

21

Figure 3 – Fitted values of normalized fish counts as a function of size. The most parsimonious model (M14, Table 6) was used to generate the fitted normalized fish counts (solid lines) for six different years (0, 2-7). Colours indicate different times. Time 0 corresponds with 2010, time 2 with 2012, etc. Points indicate the raw values used to generate the fitted lines. (A) gives the fitted values for the manipulated lake, L626. (B) gives the fitted values for the reference lake, L373.

−3

−2

−1

0

1

2

−2 0 2Log2(Length (mm))

Log 1

0(Normalized

Count

)

Time023456

A

−3

−2

−1

0

1

2

−2 0 2Log2(Length (mm))

Log 1

0(Normalized

Count

)

Time023456

B

22

Figure 4 - Plot of slopes and intercepts from the fitted data against time. Time 0 corresponds to 2010. A vertical line is drawn at time 1 (2011), indicating when the manipulation was implemented. The manipulated lake (L626) is blue, while the reference lake (L373) is red. (A) depicts fitted slope vs. time. (B) depicts fitted intercept vs. time.

−1.0

−0.8

−0.6

−0.4

0 2 4 6Time (Years)

Slope Lake

373626

A

−1.0

−0.8

−0.6

−0.4

0 2 4 6Time (Years)

Intercept

Lake373626

B

23

Figure 5 - Fitted slope (A) and intercept (B) as a function of the light extinction coefficient (m-1) for 2010, and 2012-2016. The manipulated lake (L626) is blue, while the reference lake (L373) is red.

2010

2010

−1.0

−0.8

−0.6

−0.4

0.3 0.4 0.5 0.6Light Extinction Coefficient (m−1)

Slope Lake

aa373626

A

2010

2010

−1.0

−0.8

−0.6

−0.4

0.3 0.4 0.5 0.6Light Extinction Coefficient (m−1)

Intercept

Lakeaa373626

B

24

1.4 Discussion This study sought to determine if the loss of lake connectivity would alter the size

structure of a resident fish community. We monitored fish community size spectra with

particular interest in accounting for changes in the spectral intercept and slope. Although we had

logical reasons to expect alteration in these parameters given our experimental design, our results

did not detect a significant change. This is highlighted by the fact that, of the three top models

(M14, M10, and M12; Table 6), none incorporate either of the explanatory interaction terms.

That is, if a change in spectral intercept were to be detected, the interaction between time and

lake (i.e. T*L) would be part of the top models. Similarly, if a significant change in spectral

slope were to be detected, then the top models would include the three-way interaction between

time, lake, and size (i.e. T*L*S). Instead, the top models chosen all specified only size as the

significant fixed explanatory variable (Table 7). The identification of size as being a significant

parameter is unsurprising since we know that aquatic communities are highly size structured

(Jennings et al., 2001; Kerr & Dickie, 2001).

These results could be indicating that the manipulation did not alter the L626 fish

community. However, it is also possible that other factors have affected our ability to detect

change. One possibility is that we have not yet recorded a sufficiently long data series. The

length of the experiment is important for two reasons. First, examination of the among-year

variation in size spectra for the reference lake (Figure 3B) indicates that there is natural variation

in the normalized fish counts among years, particularly at the smaller end of the size spectra. As

such, a longer dataset would provide us with the ability to account for this natural variation and

elucidate the overall trend within the fluctuations, assuming there is one. Second, given that Lake

Trout are a long-lived species (20+ years; Carlander, 1969), the effects of the lake manipulation

will take a greater number of years before changes in the larger size classes become detectable.

This is because individuals that have achieved a particular size threshold will likely be able to

tolerate sub-optimal conditions for many years. Such tolerance has previously been recorded at

ELA during a lake acidification experiment (Schindler et al., 1985). During the course of the lake

acidification, the condition of adult Lake Trout deteriorated, but population numbers remained

high. Conversely, recruitment of juvenile Lake Trout eventually collapsed, resulting in no

recruitment over a four-year period. Given this information, it is possible that the slow response

in larger individuals, as noted by the lower variation observed at the larger end of the size spectra

25

(Figure 3), is a result of adult Lake Trout being insensitive indicators of change over a time

frame of a few years.

Changes in water clarity, driven by changes in DOC, were expected to be the major force

behind any alterations to fish community size spectra. As such, we decided to plot the spectral

intercept and slope against mean August values for the light extinction coefficient (Kd), an

indicator of water clarity. These values come from a large dataset being collected for all

collaborative members of this experiment at the ELA. Recently, Spence et al. (2017) published a

paper giving first effects of the manipulation on water chemistry. Their dataset incorporated

information from 2011-2014 and found a significant change in Kd, indicating L626 was

becoming clearer and more similar to L373. This increase in water clarity is observed in Figure

5, but the increase does not display a clear relationship with either of the spectral parameters.

A secondary force that was expected to alter normalized fish counts, and thus fish

community size spectra, was the loss of a migration route from upstream lakes. Indeed, Fathead

Minnow and Finescale Dace have not been observed since the installation of the dam in 2011

(personal communication with Dr. Mike Rennie, Lakehead University). However, the ability to

conclude that their loss is a result of the experimental manipulation is weak because these

species, as well as other cyprinids, had already shown a strong population decline in 2008 (Wall

& Blanchfield, 2012). The decline was triggered by the introduction of Yellow Perch sometime

between 2001 and 2008. Regardless, there is little evidence to support the idea that the loss of a

migration route has impacted fish community size spectra. That is, although we do see variation

in the smaller size classes among years, this variation is similar in magnitude to the amount

observed in the reference lake (Figure 3). The same is true for the limited variation observed in

the larger size classes (Figure 3).

1.4.1 Limitations Although the reference lake (L373) compared well to the manipulated lake (L626) in

terms of location, water chemistry, and physical structure (Spence et al., 2017), it differed

considerably in terms of the species composition. Regarding zooplankton, the dominant species

in L373 is M. diluvania, while Chaoborus sp. are dominant in L626. This is an important

distinguishing feature when using hydroacoustics for data collection, as Chaoborus have two sets

of paired air sacs that are strong reflectors of acoustic waves. This results in a noisier dataset

coming from the manipulated lake and could potentially impact the measurements extracted for

26

analysis. In terms of fish, the most notable difference is in regard to Yellow Perch, a species that

dominates the manipulated lake (L626), but is completely absent from the reference lake (L373).

Having different species could lead to a difference in behaviours exhibited between the

communities, which could impact the accuracy of measurements (individual size and total

abundance – See Chapter 2).

The presence of White Sucker in both the reference and manipulated lake prevented us

from constructing size spectra that are fully representative of the resident fish communities. This

is because White Sucker is a bottom feeding species. Their proximity to the lakebed makes them

difficult to detect when conducting hydroacoustic surveys because of the acoustic near bottom

dead zone. This ‘dead’ zone refers to the fact that targets cannot be detected when they are

located near the lakebed (Simmonds & MacLennan, 2005). This is because the echo from the

target is engulfed by the much stronger echo from the lakebed. Although the absence of White

Sucker from our data limits our ability to make inferences about the entire fish community, their

absence is consistent with the model of ecosystem structure that underlies the size spectrum

framework: namely, a pelagic food chain where gape-limited predation ensures that, as energy

goes up the food chain, most of this flow is from small to large organisms. White Sucker do not

fit cleanly into this scheme because they do not participate directly in the pelagic food web: they

have specialized mouth structures for bottom feeding, and depend largely on energy lost from the

pelagic web through excretion and mortality.

Regarding replication, this experiment was limited in various ways. Greater among year

replication would be useful in gaining the potential to elucidate significant trends from

background variation, as previously mentioned. We would further advise that future studies try

to increase the number of before-manipulation years so that the natural variation in each

experimental lake can be better estimated. Additionally, an increase in before-manipulation years

would allow researchers to test for ecosystem equilibrium prior to alteration, rather then simply

assuming equilibrium. In an ideal world, greater replication at the lake level would occur.

However, for various reasons (practical and ethical), lake-level replication for a manipulative

experiment is often difficult to achieve.

1.4.2 Conclusions The loss of lake connectivity due to climate change is expected to have serious

consequences for small (<500 ha), oligotrophic lakes in southern Canada. We took advantage of

27

a planned whole-lake experiment that induced a loss of lake connectivity to investigate how such

an alteration would impact the size structure of the resident fish community. Analysis of data

collected over 6 years (5 post-manipulation years, 1 pre-manipulation year) has indicated that no

significant change in the fish community size structure is currently detectable using our size

spectra approach. It is possible that enough time has not yet passed in order for change to be

detected. De Kerckhove et al. (2016) illustrated that the time required to detect change in spectral

parameters impacted by a press perturbation depended on variation in size spectra estimates

(within year), as well as the magnitude of the among-year change (with greater magnitude of

change being detected earlier). As such, if the experimental manipulation has produced a

relatively small change, a greater number of years will be required until that change can be

detected.

28

Chapter 2

Refining Hydroacoustic Estimates of Size Distributions: Quantifying and Accounting for Systematic Patterns in Swimming Behaviour

Abstract Hydroacoustic sampling is gaining recognition as an effective and efficient methodology

for in-situ studying of freshwater ecosystems. A key assumption when utilizing hydroacoustic

technology for estimates of fish size is that fish are swimming horizontally through the acoustic

beam. Preliminary observations at the Experimental Lakes Area in 2016 indicated that small prey

fish in Lake 626 were breaking this assumption during nighttime hydroacoustic surveys. We

sought to describe and quantify this behaviour, and investigate the impact of the non-horizontal

swimming on hydroacoustic based estimates of fish length. An upward facing 120 kHz stationary

hydroacoustic transducer was placed at a depth of 10 m at the bottom of Lake 626. The

transducer recorded data for 24-hours over a 7-day period. Nighttime data were extracted to

check for indications of non-horizontal swimming, as well as to calculate estimates of fish

length. Given the suspicion of non-horizontal swimming, two sets of length estimates were

generated, each from a different statistic (i.e. mean or max) of the same hydroacoustic parameter

(target strength). This was done to determine if statistic choice impacted size estimates, given the

novel behaviour. To determine the accuracy of hydroacoustic length estimates, a single nighttime

trawling event occurred, which provided a direct measure of the fish length distribution. Clear

examples of systematic, non-horizontal swimming were detected from the hydroacoustic data.

Moreover, neither statistic used to calculate estimated lengths proved accurate, although the

magnitude of inaccuracy was larger when using the mean target strength statistic rather than the

maximum target strength. This study highlights the importance of verifying the assumption of

horizontal swimming when using hydroacoustic sampling methods to estimate fish length.

29

2.1 Introduction The use of hydroacoustic technology in freshwater habitats continues to grow. Hydroacoustic

methods of data collection are providing more rigorous ways to investigate and understand these

ecosystems by circumventing some of the biases inherent in traditional netting methods (Pope et

al., 2005; Rudstam et al., 1984). Moreover, given that the operation of hydroacoustic technology

is independent of light availability, these methods are expanding our ability to document in-situ

fish behaviour in light-limited systems. Hydroacoustic data collection involves sending sound

waves (signal) into a water column, and recording the reflected sound waves that return (echo).

The strength of the signal is known, while the strength of the echo is recorded by the

hydroacoustic receiver. After accounting for the practicalities of working with sound in water

(removing background noise, adjusting for sound spreading, etc.), the echo represents the

reflective strength of the object that encountered the signal (Simmonds & MacLennan, 2005).

Commonly, an object’s reflective strength is discussed in terms of its target strength (TS). TS is

measured in decibels (dB), thus representing a logarithmic measure of the signal to echo ratio

(Simmonds & MacLennan, 2005).

When utilizing hydroacoustic sampling methods for the detection of fish, size estimates can

be made based on Love’s (1971) relationship between mean dorsal aspect TS and total fish

length. Specifically, Love’s equation relates dorsal TS values detected from gas-filled swim

bladders to total length. This is because the water-air interface encountered by the signal creates

a strong echo that is easily detectable when using appropriate frequencies (Horne & Clay, 1998;

Love, 1971), making the swimbladder the dominant reflective organ. Importantly, for Love’s

equation to produce accurate size estimates, it is imperative that the swimming behaviour of the

fish is consistent with the assumption that it swim horizontally through the acoustic beam. When

fish are positioned horizontally within the acoustic beam (i.e. perpendicular to the incident wave

direction), their tilt angle is said to be zero (Simmonds & MacLennan, 2005) (Figure 6). Even

small positive or negative deviations in tilt angle away from 0 can produce large deviations in

detected TS values (Foote, 1980; Ona, 2017; Simmonds & MacLennan, 2005). Although other

variables such as species identity, total body length, and swimbladder volume are known to

affect the measured TS of a fish, its sensitivity to tilt angle is much greater (Frouzova et al.,

2005).

30

Inaccuracies in the estimation of fish sizes from hydroacoustic data can cascade to

produce further inaccuracies in indices of fish community structure. For example, school

densities are difficult to determine directly from counting individual acoustic targets. This is

because of limitations in the ability of hydroacoustic technology to resolve individual targets

situated in dense aggregations (Dickie et al., 1983; Simmonds & MacLennan, 2005). Instead, the

aggregation itself is ensonified and the backscattered acoustic energy produced is integrated.

Density can then be determined by scaling the integrated energy with a value representative of

the backscatter produced by a typical single target, or by targets derived from a known length-

frequency distribution (for aggregations where individual members vary widely in size )

(Ehrenberg, 1980). Backscatter from an individual fish can be determined by ensonifying

individual targets detected on the outskirts of the aggregation. If inaccurate estimates of fish size

are used to scale the integrated energy, potentially serious errors will be introduced into

estimates of school densities. Data from hydroacoustic surveys can also be used to create fish

community size spectra, a tool for monitoring community structure through the relative

abundance of different sized fish. Size spectra represent the log-log relationship between

organism abundance (or biomass) and organism size (Sprules & Barth, 2016). The result is often

a negative linear relationship, characteristic of a system that has many small individuals and

relatively few large individuals (Sprules & Barth, 2016). Given that estimated sizes form the

foundation of each size spectrum (when using hydroacoustic data), it is imperative that these

estimates be accurate.

In this study, we use a stationary hydroacoustic system to investigate how the accuracy of

hydroacoustic size estimates is affected by the systematic patterns of movement displayed by

small prey fish at night. We begin by describing the observed movement patterns. Then, using

two different TS statistics (max and mean TS per fish detected), we compare the length

distributions produced to quantify their similarity or difference. Finally, the paired sets of

estimated length distributions are compared to a direct estimate of the fish community length

distribution obtained from a sample of fish taken by a midwater trawl.

31

2.1.1 Objectives 1. Describe and quantify the observed fish behaviour detected from a stationary

hydroacoustic transducer.

2. Determine if there is a significant difference between estimated target length when using

different TS statistics (i.e. mean or max TS).

3. Determine if there is a difference between estimated fish lengths (from acoustic data) and

measured fish lengths (from trawling data).

32

Figure 6 – Definition of tilt angle of a fish. The tilt angle is positive when the head is up and negative when the head is down. From Simmonds, J., & MacLennan, D. (2005). Fisheries Acoustics Theory and Practice (Second Ed). Cornwall: Blackwell Science.

33

2.2 Methods 2.2.1 Study Area

This study was conducted from August 3rd to 11th, 2016 at the Experimental Lakes Area

(ELA) in northwestern Ontario. Stationary hydroacoustic transducers were used to collect fish

and invertebrate data from Lake 626 (L626) (Figure 7). L626 is an oligotrophic lake that was

historically a 4th-order lake. However, in 2011 the upstream inflow was artificially diverted for a

manipulative whole-lake experiment (see Chapter 1), effectively converting L626 into a 1st-

order lake. It has an area of 26.2 ha and a maximum depth of 13 m. Lake Trout (Salvelinus

namaycush ) are the dominant predator, with Yellow Perch (Perca flavescens) comprising much

of the remaining fish community (~97% of total catch; Wall & Blanchfield 2012). Chaoborus sp.

are the dominant zooplankton.

34

Figure 7 – Satellite image of L626 from the Experimental Lakes Area in northwestern Ontario.

L626

35

2.2.2 Data Collection 2.2.2.1 Trawling & Traps

Nighttime trawling and invertebrate sampling were conducted on the night of August

10th, 2016. Sampling began after 10:30pm (sunset at 8:33pm) to allow time for fish schools to

disaggregate and for Chaoborus to rise into the water column. To sample fish, a CanTrawl Nets

Ltd.© mid-water trawl was used. This pelagic trawl had a headline of 7.2 m and length of 13.6

m. Design modifications followed those by Emmrich et al. (2012), providing a trawl constructed

from 38 mm and 19 mm netting with a 9.5 mm knotless nylon liner in the cod-end. A single tow,

lasting for approximately 8 minutes at a speed of 2.5 kph (~350 m), captured 457 individuals.

This sample size was sufficient for the purposes of our study. Once the trawl was pulled up, fish

were removed and placed into tanks of oxygenated water. Size and species information were

recorded from each fish before releasing it back into the lake.

To sample the invertebrates, a Schindler-Patalas plankton trap with dimensions 25 cm x

25 cm x 50 cm was used. Invertebrate samples were collected from the main basin of the lake

(13 m depth) at three different depths; 1m, 6m, and 12.5m (relative to surface). Samples were

filtered and preserved with sugar formalin solution (Paterson et al., 1997). The nighttime

plankton sampling occurred after the trawling, at approximately midnight. For comparison,

daytime invertebrate samples using the same methods were collect on August 11th at noon.

2.2.2.2 Hydroacoustics

Two stationary, upward facing, 120 kHz elliptical transducers were deployed at the

southwest end of the lake and positioned on the bottom, at depths of 10 m and 13 m. Transducers

were mounted to weighted stands to ensure correct positioning for the duration of data collection.

The transducer settings and parameters used are outlined in Table 9. Data were recorded

continuously from the night of August 3rd to the morning of August 10th, 2016. Due to our

interest in calculating lengths from single-target fish detections, the data series was filtered to

include only those targets detected in the evening (approximately 10 pm to 6:30 am). Due to

processing time restrictions, from herein only the data coming from the transducer located at the

10 m depth will be discussed.

36

2.2.3 Cleaning Hydroacoustic Data 2.2.3.1 Noise Removal

Hydroacoustic noise refers to sound that is detected, but not generated, by the

hydroacoustic transducer (e.g. sound from boat motors, turbines, oil or mineral extraction,

animals, water turbulence, wind) (Simmonds & MacLennan, 2005). Using raw data, a ping

subset was quantified for each day of data collection, from areas of each echogram that did not

have biological sound-scattering targets present. We assumed the noise captured in these

‘snapshots’ is representative of the noise present throughout each day. This assumption allowed

us to remove the quantified daily noise from the corresponding hydroacoustic samples.

2.2.3.2 Fish School Removal

The standard method for detecting fish schools in Echoview is to use the Fish School

Detection algorithm. However, this is only possible when using mobile data. Given that our data

were collected using stationary transducers, fish schools were detected and removed manually by

inspection of the echogram. This involved visually identifying groups of tightly aggregated

targets, followed by use of the polygon tool to highlight and remove the school. Since we

analysed nighttime data, most schools had already disaggregated, limiting the need for school

removal.

2.2.3.3 Fish Track Detection

A Single Target Detection (STD) echogram with the settings outlined in Table 10 was

created from the TS echogram. The TS threshold was conservatively set at -70 dB to retain all

echoes coming from biological targets (i.e. zooplankton and fish). The Fish Track Detection

algorithm was then executed using the settings and parameters outlined in Table 11. It is

common practice to export fish tracks (FTs) for analysis once detected by the algorithm,

however our data required further manual processing. That is, the presence of (1) high densities

of Chaoborus at night, and (2) unexpected swimming behaviour exhibited by fish, resulted in the

need to manually edit the FTs detected by the algorithm. This was completed using a

synchronized split-screen, showing the STD echogram and the TS echogram, to allow for a

visual comparison (fish behaviour was easier to observe on the STD echogram, but the FT

algorithm only functions on the TS echogram). There were two main edits that occurred. First,

when Chaoborus densities were high, all FTs were excluded from analysis. This was done

because of the difficulty in determining which echoes came from fish and which echoes came

37

from Chaoborus. Second, when fish displayed what we termed a zigzag swimming behaviour

(Figure 3), the algorithm often failed to capture that this collection of echoes was from one

target. Instead, the algorithm misidentified one FT as two (or more) separate FTs. To correct for

this error, FTs were manually merged when the behaviour was observed. Finally, if there was

any ambiguity in the correctness of an algorithm-identified FT while completing manual edits,

the standard practice was to remove the FT. Given that we manually excluded high density

scattering areas, we chose not to calculate Sawada’s index (Sawada et al., 1993). This index

determines when the density of reflective organisms is so high that multiple scattering (echoes

that are received after scattering off multiple individuals) and shadowing (when an organism

cannot be detected because it is located in the ‘acoustic shadow’ of another organism) will affect

the accuracy of abundance estimates based on echo counting (Parker-Stetter et al., 2009).

Assuming fish swim horizontally through the acoustic beam, fish length is estimated

from Love’s (1971) equation using the mean TS per FT. However, systematic deviations from

horizontal swimming will cause mean TS values to differ from the value expected from Love’s

equation given the true length of the fish. In such a scenario, mean TS will typically

underestimate ‘true’ TS leading to a negative bias in estimates of fish length derived from Love’s

equation (Foote, 1980; Ona, 2017; Simmonds & MacLennan, 2005). We attempted to evaluate

the magnitude of this bias as follows:

(i) we analyzed the temporal sequence of TS values in representative FTs for evidence of

the presence/absence of systematic deviations from horizontal swimming;

(ii) we recorded both the mean TS and max TS for each FT and used each of these

statistics in Love’s equation to derive pairs of length estimates for each FT;

(iii) we used the two length estimates from each FT to derive two estimates of the length

distribution of pelagic fish in our study lake and compared these estimates with the observed

length distribution obtained from our midwater trawl sample.

38

Table 9 – The parameters and settings for the two stationary EK60, 120 kHz transducers deployed in L626 August 2016.

Variable Setting or Value Absorption coefficient 0.003766 dB/m EK60 Sa correction -0.25 dB Transducer gain 21.45 dB Major axis 3dB beam angle 8.1° Major axis angle offset -0.06° Major axis angle sensitivity 18.000000 Minor axis 3dB beam angle 4.04° Minor axis angle offset -0.03° Minor axis angle sensitivity 36.000000 Sound Speed 1462.62 m/s Transmitted power 200.0 W Transmitted pulse length 0.128 ms TVG range correction Simrad Ex60 Two-way beam angle -21.500000 dB re 1 sr

Table 10 - Single target detection settings used in Echoview.

Variable Setting or Value Compensated TS threshold (dB): -70.00 Pulse length determination level (dB): 6.00 Minimum normalized pulse length: 0.70 Maximum normalized pulse length: 1.50 Beam compensation model: Simrad Lobe Maximum beam compensation (dB): 15.00 Maximum standard deviation of: Minor-axis angles (degrees): 0.537 Major-axis angles (degrees): 0.537

39

Table 11 – Track detection setting used in Echoview.

Algorithm Data - 4D (range, angles, & time) Track Detection Major Axis Minor Axis Range Alpha 0.5 0.5 0.25 Beta 0.2 0.2 0.3 Target Gates Major Axis Minor Axis Range Exclusion distance (m) 0.50 0.50 0.15 Missed ping expansion (%) 10.0 10.0 0.45

Weights Major axis 30.0 Minor axis 30.0 Range 40.0 TS 0 Ping gap 0

Track Acceptance Minimum number of single targets in a track 3 Minimum number of pings in track (pings) 3 Maximum gap between single targets (pings) 3

40

2.2.4 Statistical Analysis All statistical analyses were completed using R statistical software. We restricted our data

analysis to data collected from 10:30pm to 12:30am (2 hours total) each night. Given that our

ultimate goal was to compare the hydroacoustic length estimates to the trawling data, it was

important to compare samples coming from similar time periods. Although the 2-hour

hydroacoustic sampling period is clearly longer than the approximately 8-minutes of trawl

sampling, our rationale was that this increase in temporal sampling would counteract the lack of

spatial sampling inherent in using a stationary hydroacoustic system. ANOVA and Tukey HSD

post-hoc tests were used to compare estimates of fish community length distributions derived

from different TS summary statistics with the observed community length distribution derived

from trawl sampling.

41

Table 12 – Mean target length and sample size for each sampling night. Data analysed from the stationary hydroacoustic system (sampling night 1-7) was limited to a 2-hour period each evening (10:30pm-12:30am). The mean target length for the entire sample was calculated using two different methods: (1) max TS per FT and (2) mean TS per FT. Note that sampling night 8 corresponds to the trawl data. Trawling lasted for approximately 8-minutes and covered ~ 350 m. The mean target length was determined by taking the mean of the individuals caught. Max and mean TS per FT did not apply here.

Data Collected From Mean Target Length (mm) per sampling night

Sampling Night

Sample Size

Start Stop Max TS per FT Mean TS per FT Aug. 3rd 10:30pm Aug.4th 12:30am 35.19 26.22 1 526 Aug. 4th 10:30pm Aug.5th 12:30am 36.97 27.91 2 391 Aug. 5th 10:30pm Aug.6th 12:30am 39.3 29.35 3 449 Aug. 6th 10:30pm Aug.7th 12:30am 37.23 27.98 4 309 Aug. 7th 10:30pm Aug.8th 12:30am 36.43 27.89 5 578 Aug. 8th 10:30pm Aug.9th 12:30am 36.09 27.74 6 820 Aug. 9th 10:30pm Aug.10th 12:30am 36.35 28.09 7 261

Aug.10th @ ~10:30pm 44.35 8 (TRAWL) 457 (TRAWL)

42

2.3 Results 2.3.1 Description of Fish Behaviour Hydroacoustic fish targets were observed to be breaking the assumption of horizontal

swimming through the acoustic beam (Figure 8). Rather, fish targets appear to be swimming in a

systematic and identifiable pattern. This pattern can be described as a cycle of descending and

ascending movements within the water column. Herein this movement will be referred to as the

zigzag pattern. Strikingly, the ascending movements produce weaker TS values on average, than

the descending movements (Figure 9). In some cases (e.g. Figure 9 C,D), the TS values detected

range from -51 dB to -69 dB in the same FT. Converting these extremes into fish length

estimates (Love, 1971) produces a range of sizes from approximately 5 mm to 47 mm.

43

44

Figure 8 – TS echogram displaying hydroacoustic data from the transducer placed at a 10 m depth. Different colours reference different TS values, as indicated by the multicoloured dB scale on the right of the image. Moving left to right across the echogram indicates time. Black horizontal lines occur at 2 m intervals, referenced from the face of the transducer (~lakebed). (A) TS echogram illustrating typical fish behaviour during night. This screen shot comes from data collected on August 5th, 2016 around 11:15 pm. The grey speckling, contained mostly between the 6 m and 8 m lines, is likely backscatter from Chaoborus. The zigzag swimming pattern is observed in multiple instances. (B) A magnified single fish target displaying the zigzag swimming pattern. (C) A magnified portion of the echogram between the 8 m and 10 m lines. (D) For comparison, a TS echogram from the same day, but earlier in the evening (~6:30 pm). Much of the water column is empty. Two fish schools are detected above the 6 m line.

45

Figure 9 – Target strength (TS) as a function of time for individual fish tracks (FTs). Each point represents one single-target detection (i.e. one echo). FTs group single targets that come from the same individual. Pings (transmission of an acoustic wave) occurred at intervals of 0.5 seconds. Shaded and non-shaded areas correspond to descending and ascending movement in the water column, respectively. (A-F) correspond to individual FTs (i.e. the tracked movement of one fish within the acoustic beam).

(A)

(B)

46

(C)

(D)

47

(E)

(F)

48

2.3.2 Comparing Length Estimates We compared the lengths produced from the paired set of length estimates that were

generated by using different TS statistics (i.e. mean or max TS per FT). There were 7 replicates

per set of estimates, coinciding with the 7 full nights of data that were collected. Each replicate

varied in sample size with the lowest being n = 261 individuals, and the highest being n = 820

individuals (Table 12). Comparison of the fish length distributions using a two-way ANOVA

without interaction indicated that the means of the two sets of distributions are significantly

different (df=1, p<<0.05) (Figure 10). The mean length estimated using max TS per FT is 8.85

mm greater than the mean length estimated using mean TS per FT (Table 13).

Length distributions were further investigated to determine if estimates from the different

nights were similar. The two-way ANOVA indicated that significant differences among

sampling nights existed (df=6, p<<0.05). However, the overall difference between means was

small, ranging from 35.1 mm – 39.2 mm using max TS, and 26.2 mm – 29.4 mm using mean TS

(Table 13).

49

Figure 10 – Density plots for fish lengths from each night between 10:30 pm and 12:30 am. Colours correspond to the sampling night. Density plots with the dashed outline correspond to fish lengths calculated using the max TS value, while the density plots lacking a dashed outline correspond to those calculated using the mean TS value. An ANOVA indicated that the means of the two groups are significantly different (df=1, p<<0.05). The distribution range was limited to 10 mm-65 mm target lengths.

50

Table 13 – Mean, variance, and standard deviation (SD) from each sampling night and for the different methods of determining length (mm) estimates.

Sampling Night

Lengths (mm) Max TS per FT Mean TS per FT Trawl

Mean Variance SD Mean Variance SD Mean Variance SD 1 35.05 54.86 7.41 26.22 35.53 5.96 - - - 2 36.65 64.35 8.02 27.98 45.78 6.77 - - - 3 39.21 55.59 7.46 29.43 39.65 6.30 - - - 4 36.91 61.50 7.84 28.09 46.95 6.85 - - - 5 36.26 59.88 7.74 28.10 43.18 6.57 - - - 6 35.97 53.64 7.32 27.78 38.49 6.20 - - - 7 36.07 57.74 7.60 28.43 53.64 7.32 - - - 8 - - - - - - 44.69 31.31 5.60

Mean 36.59 58.22 7.63 28.00 43.31 6.57 44.69 31.31 5.60

51

2.3.3 Comparing Length Distributions The length distribution determined from the trawling data was incorporated into the data

frame containing the hydroacoustic length estimates. Mean lengths between the three sets of

distributions were compared using a two-way ANOVA without interactions and with incomplete

blocks. A significant difference was determined to exist between the lengths of the three sets of

distributions (df=2, p<<0.05) (Figure 11). Post-hoc analysis indicated that significant differences

existed between each combination of all three means (Tukey HSD, p-adj<0.05). The mean length

from the trawling data is 8.03 mm and 16.88 mm greater than the mean lengths estimated from

max and mean TS per FT, respectively (Table 14). The trawling data was determined to have the

smallest variance in fish lengths, followed by mean TS per FT, and finishing with max TS per

FT (Table 13).

52

Figure 11 – Density plots for fish lengths. Colours correspond to the sampling night. Density plots with the dashed outline correspond to fish lengths calculated using the max TS value, while the density plots lacking a dashed outline correspond to those calculated using the mean TS value. The solo pink density plot corresponds to the trawling data (night 8). An ANOVA indicated that the means of the three groups are significantly different (df=1, p<<0.05). The distribution range was limited to encompass 10 mm – 65 mm target lengths.

8

53

Table 14 – Output summary from Tukey HSD post-hoc test. Difference indicates the difference (in mm) between the means of two size distributions. The lower and upper bounds (in mm) for the 95% confidence interval for the mean is provided, as well as the adjusted p-value. P-adj<0.05 are considered significant.

95% Confidence Interval for Mean

Difference Lower Bound Upper Bound P-adj MEAN-MAX -8.85 -9.25 -8.46 <0.0001

Trawl-Max 8.03 7.23 8.82 <0.0001 Trawl-Mean 16.88 16.08 17.68 <0.0001

54

2.3.4 Chaoborus Abundance Chaoborus abundance from three different depths (0.5 m, 7 m, and 12 m, relative to

lakebed) and two different time periods (day and night) were compared to determine when the

zooplankton were most abundant. Daytime Chaoborus abundance decreased with increased

distance from the lakebed, while the nighttime abundance was highest at 7 m above the lakebed

(Figure 12). Overall, there were more Chaoborus detected during the night (n=108) than during

the day (n=45).

55

Figure 12 – Chaoborus abundance as a function of depth and time of day. Abundance is represented as the number of individuals per 31.25 L. Depth (m) indicates the water column depth at which the sample was collected, relative to lakebed. Daytime samples (yellow) decrease in abundance while moving closer to lake surface (located at 13 m), while nighttime samples (grey) were highest at 7 m.

56

2.4 Discussion We believe that the majority of fish targets detected by the stationary, upward facing

transducer are Yellow Perch. This claim is supported by the dominance of Yellow Perch caught

in the trawl (456 Yellow Perch, 1 Pearl Dace), as well as by previous knowledge regarding the

abundance of Yellow Perch in L626 (~97% of total catch) (Wall & Blanchfield, 2012). We also

believe that the dense clouds of low (compared to fish echoes) TS echoes detected during the

night (Figure 8) are coming from Chaoborus. This claim is supported by basic biology of the

zooplankton. That is, Chaoborus are well known to conduct diurnal vertical migration in

environments inhabited by fish (Luecke, 1986; Teraguchi & Northcote, 1966), and the presence

of two sets of paired air sacs make them detectable by hydroacoustics operating at 120 kHz

(Malinen et al., 2005). Further, data from the plankton traps illustrate that the highest abundance

of Chaoborus was located at night at the 7 m above lakebed (Figure 12). This depth coincides

with the depth of the dense cloud of low TS echoes detected by the hydroacoustic transducers

(Figure 8).

2.4.1 Description of Fish Behaviour We have illustrated that fish are breaking the assumption of horizontal swimming though

the acoustic beam. Instead, fish appear to be swimming in a zigzag pattern that is comprised of a

series of ascending and descending movements (Figure 13). Each portion of the swimming

pattern is associated with a change in tilt angle, which alters the strength of the acoustic echo

produced, thus altering the estimate of length. Although our study did not seek to explain the

function of this behaviour, given the abundance of Chaoborus at night (Figure 12), it is possible

that the zigzag swimming pattern observed is a foraging strategy. For example, common bream

and roach (>1+ years of age) were shown to display sinusoidal swimming in a European

reservoir as a planktivorous foraging behaviour (Jarolím et al., 2010). However, that sinusoidal

swimming occurred during conditions that were optimal for visual foraging (e.g. daytime, calm

weather, cloudless skies). Certainly, optimal visual foraging conditions were not present during

the time that the zigzag swimming pattern was observed in our system. However, it is possible

that enough light was present to provide sufficient backlighting for this previously undescribed

strategy to be successful. That is, fish could be utilizing the expanded twilight period associated

with the northern latitudes during summer months, as well as the lighting from the moon, to

consume Chaoborus. This hypothesis justifies the positive tilt angle displayed by fish during

57

ascending movements (Figure 13 B & D), as such an angle would maximize the contrast (from

the visual perspective of the fish) of a suspended Chaoborus against the surface. Indeed, in an

article by Hansen et al. (2015), the relationship between sun and moon illuminance, latitude,

cloud cover, and water turbidity was shown to impact the magnitude, duration, and timing of the

‘antipredator window’. The antipredator window refers to the model developed by Clark and

Levy (1988) that represents how fish at intermediate trophic levels will face habitat use

constraints imposed by foraging gain versus predation risk. Here, we do not claim that our

system is displaying the use of this antipredator window. Instead, we focus on the knowledge

that changes in sun and moon illuminance at higher latitudes can interact in ecologically

meaningful ways to produce novel fish behaviour. We suggest future studies quantify the

relationship between nighttime light levels (e.g. moon phase, cloud cover, twilight length, etc.)

and the frequency of the zigzag behaviour.

58

Figure 13 – Illustration of the hypothesis of how fish are producing the observed hydroacoustic zigzag pattern. The broken grey lines indicate the spread of the acoustic beam as it is directed from the bottom of the lake upwards, to the surface. The solid grey arrows indicate the direction of fish movement. The blue fish is used to indicate fish orientation within the beam. The curved blue lines indicate active swimming. (A) Fish are positioned close to horizontal (in regard to the direction of the incident wave) before ascending. (B) Active swimming during the ascending path positively increases the tilt angle as the fish becomes more vertical in the water column. (C) When the peak of the ascending path is reached, active swimming ceases and the fish drifts passively to a lower position in the water column. During the drift, the tilt angle decreases toward zero as the fish becomes more horizontal. (D) When the trough of the descending path is reached, the fish resumes active swimming, continuing the cycle of the zigzag swimming pattern.

(A) (B)

(C) (D)

59

2.4.2 Comparing Length Distributions Our results indicate that the choice of TS statistic (i.e. mean or max TS per FT) used in

Love’s (1971) equation significantly alters the fish length estimates produced (Figure 10). When

compared to the trawling data, both acoustically derived length distributions significantly

underestimated the true size of the resident prey fish (Figure 11). However, the length

distribution generated using the max TS statistic produced estimates that more closely resembled

the sizes sampled during trawling (Table 14). This indicates that in our study system there is

strong biological support to estimate fish length using the max TS statistic (per FT) rather than

the commonly used mean TS statistic. The lack of agreement between the trawl samples and the

estimates produced when using the max TS could be caused by fish never being fully

perpendicular to the acoustic beam (i.e. full dorsal aspect not ensonified). Even the presence of a

small positive or negative tilt angle can impact fish length estimates calculated using Love’s

(1971) relationship. Alternatively, it is possible that Love’s (1971) relationship is inaccurate for

small yellow perch. A cage experiment by Frouzova and Kubecka (2004) showed that Love’s

(1971) relationship underestimated the length of juvenile perch. Accounting for this

underestimation in our data would mean that both of our generated length estimates would be

more similar to our trawl data than currently stated. This is particularly interesting when

considering the length estimates generated using max TS per FT, as the adjustment might

indicate that max TS estimates are not significantly different from the trawl data.

Moreover, here we would like to make an important distinction between our data

collected from a stationary hydroacoustic system, and the data collected from mobile

hydroacoustic surveys. Using a stationary system, we were able to detect individual fish tracks

that displayed the full range of zigzag behaviour. Such a detection is improbable when using

mobile surveys, as the hydroacoustic beam is continuously moving through the water column

along the length of the transect. This sampling method would likely capture only a fraction of the

zigzag behaviour observed from the stationary system, which could amplify the negative length

bias already present in the data. That is, Figure 9 shows that fish spend >>50% of their time in

the ascending movement, which is associated with lower TS values and smaller length estimates.

It is this increase in the probability of detecting the behaviour that produces the weaker echoes

that would further negatively bias the estimated length distribution.

60

Inaccurate estimates of fish length can have serious consequence for fisheries

management. In aquatic systems comprised of dense fish schools, managers utilizing

hydroacoustic technologies will often determine school biomass estimates using echo

integration. This methodology does not provide information on the number of individuals

comprising the school, but instead gives an indication of total biomass present based on the

integrated value of returned echoes. School abundance can then be determined by scaling the

integrated energy with a value representative of the echo produced by a typical individual, or by

a length-frequency distribution representative of the individuals within the school (for

aggregations where individual members vary widely in size) (Ehrenberg, 1980). In these

scenarios, use of underestimated fish lengths for scaling the integrated energy would result in an

overestimation of population size.

Similar inaccuracies may be introduced into abundance estimates derived from echo-

counting (counting individual acoustic targets). The systematic variation in TS values that we

observed from an individual fish caused the FT detection algorithm to fail to accurately group

echoes coming from a single individual: multiple FT’s were generated for single individuals.

This could produce a strong positive bias in population estimates derived from echo counting and

could further bias size distribution estimates.

2.4.3 Conclusions Our experiment highlights the importance of verifying the assumption of horizontal

swimming before determining estimates of fish length using Love’s (1971) TS-length

relationship. We illustrated that fish behaviour can inform the choice of TS statistic that should

be used when completing such estimations. However, in our case, neither TS statistic provided

adequate length estimates. As such, for aquatic systems where fish exhibit non-horizontal

swimming, we recommend that future studies attempt to develop a more appropriate

mathematical relationship between individual fish echoes and length. However, it would be

beneficial to first determine if this behaviour applies more broadly to other fish species before

developing such a relationship. Alternatively, echo integration can be used if fish are surveyed

during the daytime, when schooling. This method can be used to calculate abundance estimates

and length distributions, provided that the scaling factor for the integrated energy is obtained

through a more accurate method (e.g. netting). Given that fish abundance often dictates the catch

that can safely be removed from an exploited population (Hilborn & Walters, 1992), obtaining

61

accurate abundance information, regardless of methodology, represents a vital feature for proper

fisheries management.

62

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Appendices Appendix A – Normalizing Fish Counts Normalized count (CL) was determined by taking the log2 count data and dividing it by the corresponding linear bin width:

!" =!$%&')*+*,-.)/01)23+ℎ15.672899896

5891*-:8)+ℎ.;+ℎ10.--1<,.9)896289

Example1:

!" =2089)8?8)/*5<;-.@2893.75.9+ℎ15.67ℎ8<+.6-*@

2E −2G.H

=20

16 − 11.31

=20

4.69

= 4.26

Example2:

!" =2089)8?8)/*5<;-.@2898.25.9+ℎ15.67ℎ8<+.6-*@

2N.H −2N

=20

362.04 − 256

=20

106.04

= 0.1886

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Appendix B – Standardizing Sampling Effort Normalized fish counts per size bin were scaled using the volume adjustment factor (V). &" = !" ∗ P Where NL is the volume-adjusted count within bin L and CL is the normalized count of fish within bin L. The volume adjustment factor (V) was calculated using the volume of water sampled for a given survey (vs) and the volume of water from a reference survey (vR). P =

?Q?R

Our vR was chosen to be the first survey complete in L626, in 2010 (vR = 16,350.1 m3).

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Appendix C – Centring by Mean Bin Size Normalized counts were centred by shifting the y-intercept. To centre, the overall mean bin size had to be determined. The mean bin size is the average of the upper and lower bin midpoints. It represents the centre of the data (length) range. For our data, the log2 length range spanned from 3.5 to 10 (~11mm to 1024mm on the linear scale), with intervals of 0.5. Remember, 3.5 and 10 represent the lower and upper limits of the range, respectively. However, for plotting size spectra, it is necessary to work with the bin midpoints. Hence, the overall mean is calculated using the lower and upper midpoints rather than the limits.

S1*9289<8T1 =/,,1- + 5.:1-289@8),.89+

2

S1*9289<8T1 =9.75 + 3.75

2

S1*9289<8T1 =13.5

2

S1*9289<8T1 = 6.75 Centring can also be explained visually. Below, the mean bin size (red) is seen as the middle value between the lower and upper (blue) bin midpoints. By shifting all length values by 6.75, the entire data (length) range will be centred on the y-intercept. Centering the data removes problems arising from the correlation between slopes and their intercepts (Daan et al., 2005)

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Appendix D – Size Spectra by Year

Figure 14 – Fitted values of normalized fish counts as a function of size. The most parsimonious model (M14, Table 6) was used to generate the fitted normalized fish counts for six different years (0, 2-7, A-F respectively). Red corresponds to the manipulated lake, and blue corresponds to the reference lake.

−3

−2

−1

0

1

2

−2 0 2Size

Abundance

Lake373626

A

−3

−2

−1

0

1

2

−2 0 2Size

Abundance

Lake373626

B

−3

−2

−1

0

1

2

−2 0 2Size

Abundance

Lake373626

C

−3

−2

−1

0

1

2

−2 0 2Size

Abundance

Lake373626

D

−3

−2

−1

0

1

2

−2 0 2Size

Abundance

Lake373626

E

−3

−2

−1

0

1

2

−2 0 2Size

Abundance

Lake373626

F