pelagic stereo-bruvs: development and implementation of a ...€¦ · calibrated to scientific...

Pelagic stereo-BRUVs: Development and

implementation of a fishery-independent technique

to study pelagic fish assemblages

Julia Santana-Garcon

BSc. Marine Biology (James Cook University, Australia)

MASc. Conservation Biology (James Cook University, Australia)

This thesis is presented for the degree of

Doctor of Philosophy at the University of Western Australia

The UWA Oceans Institute and

School of Plant Biology

Faculty of Natural and Agricultural Sciences

2015

iii

Abstract

Pelagic fish are ecologically important to marine ecosystems and are a highly

valuable resource, yet little is known about the population status and ecology of

many species. Research on the biology and ecology of pelagic fish largely relies on

fishery-dependent data from commercial and recreational fisheries. However, the use

of fishery-dependent data alone can lead to sampling biases due to gear selectivity

and heterogeneous fishing effort. Alternatively, fishery-independent surveys can

adopt more robust sampling designs, but often employ the same fishing gear and as

such, catchability and size-selectivity biases remain. Emerging technologies like

remote sensing, acoustic cameras and video-techniques are providing new non-

destructive options for cost-effective ecological sampling in vast and remote

environments such as the open ocean.

Baited Remote Underwater Video systems (BRUVs) have proven to be a robust

fishery-independent method to obtain estimates of biodiversity, relative abundance,

behaviour and, when stereo-cameras are used, size and biomass of a range of marine

species. Stereo-BRUVs had not been tested in the pelagic environment, but evidence

suggested that they could overcome some of the difficulties of sampling in the water

column. In this thesis, I develop and validate the use of pelagic stereo-BRUVs as a

non-destructive fishery-independent technique to study highly mobile species in the

pelagic environment, and I examine the strengths and limitations of this method in

order to optimise their implementation in future studies.

Pelagic stereo-BRUVs proved to be an effective tool to sample fish assemblages in

the water column at depths ranging between 5 and 120 metres, in coastal waters and

at exposed offshore sites along the coast of Western Australia. Differences in the

vertical distribution of fish assemblages were detected (Chapter 2), which confirms

the potential of this method to study the vertical distribution and diel migration

patterns of both pelagic and demersal fishes. Measures of univariate and multivariate

precision were used to optimise the sampling regime of pelagic stereo-BRUVs

(Chapters 2 and 3). A sample unit size (soak time) of 120 minutes and a sample size

of at least 8 replicates per treatment are recommended to study pelagic fish

assemblages in tropical or warm-temperate waters. In order to account for the spatial

iv

and temporal variability of pelagic systems and to facilitate future comparisons

across studies using this method, we encourage maximizing replication given the

resources available while standardizing the soak time. Pelagic stereo-BRUVs were

calibrated to scientific longline surveys, a standard technique used to sample highly

mobile species like sharks (Chapter 4). The proportion of sharks sampled by both

methods was comparable across a latitudinal gradient sampled. The estimates of

relative abundance (catch per unit of effort) obtained from each camera system were

equivalent to those from 5 to 30 longline hooks. Moreover, pelagic stereo-BRUVs

were used to assess the effects of a spatial fishing closure on highly mobile fish

species (Chapter 5). The spatial area closure studied was found to have no significant

effect on the species composition or the relative abundance of pelagic fish. These

findings suggest that small spatial area closures may not be adequate for the

conservation and management of highly mobile fish, even if they have proved

effective for reef-associated species. Highly mobile pelagic fish species, such as

tunas, mackerel and some shark species proved difficult to measure, as these species

were observed furthest from the camera systems. Thus I discuss methods to increase

the attraction rate of pelagic fish to the stereo-cameras to enhance the performance of

pelagic stereo-BRUVs in future studies. Finally, I explored the potential of stereo-

video to make behavioural observations in the pelagic environment (Chapter 6). The

first in situ behavioural observations of a presettlement schooling priacanthid were

recorded using pelagic stereo-BRUVs. Additional information on the fish length,

mean swimming speed and spacing among individuals were also possible given the

use of stereo-video systems. This work added to the limited body of literature on the

schooling behaviour of the early pelagic stages of demersal fishes.

In conclusion, I demonstrate that pelagic stereo-BRUVs are an effective, non-

destructive and fishery-independent sampling approach to study pelagic ecosystems.

This method can provide data on the species composition, behaviour, relative

abundance and size distribution of highly mobile fish assemblages at broad spatial

and temporal scales. I also demonstrate that pelagic stereo-BRUVs can be calibrated

to standard techniques, are suitable for studies on rare or threatened species and can

be implemented in areas closed to fishing. This thesis critically assesses the use of

pelagic stereo-BRUVs and collates all the information needed to implement, adopt

and develop this method in future studies.

v

Publications from this Thesis

Articles published

Chapter 2

Santana-Garcon, J., Newman, S.J., Harvey, E.S., 2014. Development and

validation of a mid-water baited stereo-video technique for investigating

pelagic fish assemblages. Journal of Experimental Marine Biology and

Ecology 452, 82-90.

Author contribution: JSG designed the experiment, collected and analysed the data,

and wrote the manuscript. ESH and SJN supervised the study and reviewed the

manuscript.

Chapter 4

Santana-Garcon, J., Braccini, M., Langlois, T.J., Newman, S.J., McAuley, R.B.,

Harvey, E.S., 2014. Calibration of pelagic stereo-BRUVs and scientific

longline surveys for sampling sharks. Methods in Ecology and Evolution, 5.

824–833. DOI: 10.1111/2041-1210X.12217.

Author contribution: JSG collected data, conducted statistical analyses, and wrote

the manuscript. MB and TJL assisted in some components of data analysis. ESH,

SJN and RBM supervised the study and provided logistical support for the field

expedition to take place. All co-authors reviewed the manuscript and provided

comments.

Chapter 5

Santana-Garcon, J., Newman, S.J., Langlois, T.J., Harvey, E.S., 2014. Effects of a

spatial closure on highly mobile fish species: an assessment using pelagic

stereo-BRUVs. Journal of Experimental Marine Biology and Ecology, 460,

153-161. DOI: 10.1016/j.jembe.2014.07.003.

vi

Author contribution: JSG collected data, conducted statistical analyses, and wrote

the manuscript. ESH, SJN supervised the study and facilitated the field expedition.

All co-authors reviewed the manuscript and provided comments.

Chapter 6

Santana-Garcon, J., Leis, J.M., Newman, S.J., Harvey, E.S., 2014. Presettlement

schooling behaviour of a priacanthid, the Purplespotted Bigeye Priacanthus

tayenus (Priacanthidae: Teleostei). Environmental Biology of Fishes 97(3),

277-283.

Author contribution: JSG designed the experiment, collected and analysed the data,

and wrote the manuscript. JML provided advice on the ecology of presettlement fish

and species identification. ESH and SJN supervised the study and all co-authors

reviewed the manuscript.

Appendix 1

Anderson, M.J., Santana-Garcon, J., 2015. Measures of precision for dissimilarity-

based multivariate analysis of ecological communities. Ecology Letters 18(1), 66-73.

Author contribution: MJA developed the statistical methods presented and wrote the

final manuscript. JSG provided data, wrote early drafts and contributed in final

revisions of the manuscript. Some of the work presented in this article that is

relevant to this thesis, has been condensed and presented in chapter 3.

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Acknowledgements

First and foremost, I would like to thank my supervisors Euan Harvey, Stephen

Newman, Tim Langlois and Gary Kendrick for their advice and support. I feel I had

the best guidance possible throughout my thesis. I am also thankful to all my co-

authors of the publications that have resulted from the chapters presented here. Their

input and manuscript reviews were much appreciated and improved my work

substantially.

Funding and logistical support throughout the project were provided by the School

of Plant Biology (University of Western Australia) and the Department of Fisheries

(Government of Western Australia). I was supported by a Scholarship for

International Research Fees, a University Postgraduate Award (International) from

the University of Western Australia, and a UWA Safety-Net Top-Up Scholarship.

Work presented here was undertaken under the approval of UWA Animal Ethics

(RA/3/100/1035). And fieldwork in Ningaloo Marine Park was approved within the

Department of Environment and Conservation permit SF008486.

I would like to acknowledge the contribution of many colleagues at the University of

Western Australia and Department of Fisheries WA that helped with fieldwork,

logistics and advice. I am grateful to the experienced skippers P. and T. Pittorini for

their assistance during the design of the deployment method for pelagic stereo-

BRUVs. I am also thankful to K. Cure, J. Goetze, T. Bond and S. Bennett that

reviewed early drafts of some chapters, and to H. Twose for her assistance with the

illustration of the camera system.

Many thanks to the Fish Ecology Group (a.k.a. ‘Botany-Annex-II team’), the

working environment and friendship made us stronger and I could not have asked for

a better team (including the best officemate) to share the experience of my PhD.

Finally, special thanks to Scott Bennett. This thesis would not have started and,

definitely, could not have been completed without his constant inspiration,

encouragement, support and many reviews. Thanks to all my friends and family for

their support and for sharing this amazing journey with me.

ix

Table of Contents

Abstract .............................................................................................................. iii

Publications from this Thesis................................................................................ v

Articles published....................................................................................................... v

Acknowledgements ........................................................................................... vii

Table of Contents ............................................................................................... ix

List of Figures ................................................................................................... xiii

List of Tables ..................................................................................................... xvii

CHAPTER 1 - General introduction ...................................................................... 19

1.1 Background and rationale ................................................................................... 19

1.2 Specific aims ....................................................................................................... 22

1.3 Study area .......................................................................................................... 26

CHAPTER 2 - Development and validation of a mid-water baited stereo-video

technique for investigating pelagic fish assemblages .......................................... 29

2.1 Abstract ............................................................................................................. 29

2.2 Introduction ....................................................................................................... 29

2.3 Methods............................................................................................................. 34

2.3.1 Study area............................................................................................................. 34

2.3.2 Sampling technique .............................................................................................. 34

2.3.3 Experimental design ............................................................................................. 34

2.3.4 Image analysis ...................................................................................................... 35

2.3.5 Data analysis ......................................................................................................... 36

2.4 Results ............................................................................................................... 37

2.5 Discussion .......................................................................................................... 43

CHAPTER 3 - Multivariate pseudo standard error as a measure of precision:

optimisation for sampling pelagic fish assemblages ............................................ 49

3.1 Abstract ............................................................................................................. 49

3.2 Introduction ....................................................................................................... 50

x

3.3 Methods ............................................................................................................ 52

3.3.1 Multivariate pseudo-Standard Error .................................................................... 53

3.3.2 Optimisation of sampling effort for multivariate analyses .................................. 56

3.4 Results ............................................................................................................... 56

3.5 Discussion .......................................................................................................... 57

CHAPTER 4 - Calibration of pelagic stereo-BRUVs and scientific longline surveys for

sampling sharks ................................................................................................. 61

4.1 Abstract ............................................................................................................. 61

4.2 Introduction ....................................................................................................... 62

4.3 Methods ............................................................................................................ 64


4.3.2 Video analysis ....................................................................................................... 67

4.3.3 Statistical analysis ................................................................................................. 68

4.4 Results ............................................................................................................... 70

4.5 Discussion .......................................................................................................... 74

CHAPTER 5 - Effects of a spatial closure on highly mobile fish species: an

assessment using pelagic stereo-BRUVs ............................................................. 81

5.1 Abstract ............................................................................................................. 81

5.2 Introduction ....................................................................................................... 81

5.3 Methods ............................................................................................................ 85

5.3.1 Study area............................................................................................................. 85


5.3.3 Experimental design ............................................................................................. 86

5.3.4 Image analysis ...................................................................................................... 87

5.3.5 Data analysis ......................................................................................................... 88

5.4 Results ............................................................................................................... 89

5.5 Discussion .......................................................................................................... 95

CHAPTER 6 - Presettlement schooling behaviour of a priacanthid, the

purplespotted bigeye Priacanthus tayenus (Priacanthidae: Teleostei) .............. 101

6.1 Abstract ........................................................................................................... 101

6.2 Introduction ..................................................................................................... 101

6.3 Material and methods ...................................................................................... 103

6.4 Results ............................................................................................................. 105

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6.5 Discussion ........................................................................................................ 107

CHAPTER 7 - General discussion ....................................................................... 111

Literature cited ................................................................................................ 119

Appendix 1 - Measures of precision for dissimilarity-based multivariate analysis of

ecological communities ................................................................................... 133

xiii

List of Figures

Figure 1.1 Classification of marine environments. Pelagic habitat is defined here as

the entire ocean water column, including coastal and oceanic waters. .............. 19

Figure 1.2 Flow diagram outlining the background, rationale and general structure

of this thesis. ...................................................................................................... 23

Figure 1.3 Study area off the coast of Western Australia. Colour-coded panels and

markers indicate the specific study areas for each chapter. ............................... 27

Figure 2.1 Map of the study area in Ningaloo Marine Park, Western Australia. Sites

are in Tantabiddi (TN1, TN2) and Coral Bay (CB1, CB2). ............................... 33

Figure 2.2 Deployment method and frame details of pelagic stereo-BRUVs. .......... 35

Figure 2.3 Principal Coordinates Analysis plot showing differences in fish

assemblages sampled at two depth treatments (Depth 1, surface and Depth 2,

deep) using pelagic stereo-BRUVs. Different symbols show the sites sampled in

Tantabiddi (TN1, TN2) and Coral Bay (CB1, CB2) in Ningaloo Marine Park,

Western Australia. .............................................................................................. 40

Figure 2.4 Mean relative abundance (MaxN) of fish taxa sampled at two different

deployment depths using pelagic stereo-BRUVs in Ningaloo Marine Park:

Coral Bay and Tantabiddi. The vertical axes are shown in logarithmic scale and

error bars represent standard error. Some species are shown grouped at a higher

taxonomic level and species that had only one individual recorded are excluded.

............................................................................................................................ 40

Figure 2.5 Species accumulation curve and relative abundance accumulation curve

(B) in 15 minute time intervals for soak times up to 3 hours. Error bars

represent standard error. ..................................................................................... 41

Figure 2.6 Mean precision and 95 % confidence intervals for (A) species richness

and (B) relative abundance data using pelagic stereo-BRUVs with three

different soak times. Samples simulated using bootstrapping procedures with

replacement. Lower values of precision indicate more precise data. ................. 42

xiv

Figure 2.7 Estimate of the cost, in time, for sampling fish assemblages using

different soak times with pelagic stereo-BRUVs. Cost includes the time for

fieldwork with 5 camera systems and video analysis. ....................................... 43

Figure 3.1 (a) Study area in Ningaloo Marine Park (Western Australia) and (b)

design of pelagic stereo-BRUVs for sampling mid-water fish assemblages. .... 53

Figure 3.2 Multivariate pseudo standard error as a function of sample size on the

basis of Bray-Curtis dissimilarities calculated on square-root transformed fish

abundance data sampled using pelagic stereo-BRUVs for a soak time of 60

minutes only, showing results (means with 2.5 and 97.5 percentiles as error

bars) from 10,000 resamples obtained using either the permutation or bootstrap

approach. ............................................................................................................ 57

Figure 3.3 Multivariate pseudo standard error as a function of sample size on the

basis of Bray-Curtis dissimilarities calculated on square-root transformed fish

abundance data sampled using pelagic stereo-BRUVs for all three soak times

(60, 120 and 180 minutes), using the double-resampling method, with

permutation-based means and bias-adjusted bootstrap-based error bars (with

10,000 resamples for each). ............................................................................... 57

Figure 4.1 Location of study sites along the coast of Western Australia.................. 65

Figure 4.2 Deployment design of the (a) scientific longline shots and (b) pelagic

stereo-BRUVs used to sample requiem sharks. ................................................. 66

Figure 4.3 Mean relative abundance of fish species sampled using scientific longline

surveys and pelagic stereo-BRUVs. Catch per unit of effort (CPUE) is shown as

catch per hour for 10 hooks (CPUE10) in longline samples and as MaxN per

hour in stereo-BRUVs. a Benthic sharks were caught in longlines due to the

deployment design setting of a proportion of hooks near the bottom................ 71

Figure 4.4 Mean species proportion of target species sampled using scientific

longline surveys and pelagic stereo-BRUVs. P-values show non-significant

differences between sampling methods. ............................................................ 71

xv

Figure 4.5 Relative abundance (CPUE) of target species along a latitudinal gradient

(32° - 24° S) sampled using scientific longline surveys and pelagic stereo-

BRUVs. Trendlines illustrate the ANCOVA result. .......................................... 72

Figure 4.6 Equivalence of sampling effort required to estimate relative abundance of

requiem sharks. Plot shows mean p-values from one-way PERMANOVAs

testing the differences between pelagic stereo-BRUVs (MaxN per hour) and

scientific longline surveys (catch*h-1 *hook-1) across different levels of

sampling effort (number of hooks). Values in the shaded area (P < 0.05) are

statistically significant. Grey line is shown for reference as the general pooling

cut-off (P > 0.25). ............................................................................................... 73

Figure 4.7 Comparison of kernel density estimate (KDE) probability density

functions for the length distributions of requiem shark species caught by pelagic

stereo-BRUVs and scientific longline surveys. Grey bands represent one

standard error either side of the null model of no difference between the KDEs

for each method. ................................................................................................. 76

Figure 5.1 Location of (A) the Houtman Abrolhos Islands and (B) the study sites

inside and outside the spatial area closure at Easter Group. The area shaded in

light grey shows the Reef Observation Area closed to fishing since 1994. ....... 85

Figure 5.2 Deployment design of pelagic stereo-BRUVs to sample fish assemblages

in the water column. ........................................................................................... 86

Figure 5.3 Total abundance (sum of MaxN) of fish taxa sampled using pelagic

stereo-BRUVs in areas open and closed to fishing at the Houtman Abrolhos

Islands. ............................................................................................................... 90

Figure 5.4. The mean species richness per site (A) and the mean relative abundance

per site of the main target groups (B) in areas open and closed to fishing at the

Houtman Abrolhos Islands. Error bars show standard error. ............................. 92

Figure 5.5 The mean range, distance of the individuals to the stereo-camera system,

for fish genus recorded using pelagic stereo-BRUVs. Grey silhouettes represent

demersal species and black silhouettes are pelagic and highly mobile fish. The

xvi

shaded area shows the field of view of the cameras where fish length can be

measured. ........................................................................................................... 94

Figure 5.6 Comparison of kernel density estimate (KDE) probability density

functions for the length distributions of pink snapper Chrysophrys auratus in

areas open and closed to fishing. Grey bands represent one standard error either

side of the null model of no difference between the KDEs for each method. ... 94

Figure 6.1 Deployment method (a) and details (b) of the pelagic stereo Baited

Remote Underwater Video system (stereo-BRUVs). ...................................... 104

Figure 6.2 Still photos of presettlement Priacanthus tayenus (mean total length 24.1

mm) captured from video recorded in Coral Bay, Western Australia. ............ 104

Figure 6.3 Quantitative parameters for three groups of presettlement Priacanthus

tayenus recorded using pelagic stereo cameras in Coral Bay, Western Australia

(group A, 8 individuals; group B, 9 individuals and group AB, 17 individuals).

(a) Mean Total Length (TL; mm); (b) mean Nearest Neighbour Distance (NND;

mm) and (c) mean NND divided by the average TL. Error bars represent

standard error. .................................................................................................. 107

xvii

List of Tables

Table 2.1 Taxonomic group and stage of specimens recorded using pelagic stereo-

BRUVs in Ningaloo Marine Park. Stage of individuals was identified as either

adult (AD) or juvenile (J). .................................................................................. 38

Table 2.2 PERMANOVA testing for the effects of deployment depth on the

structure of fish assemblages sampled using pelagic stereo-BRUVs. Results are

based on Bray-Curtis similarity matrices using dispersion weighted and square-

root transformed data. All tests were undertaken using 9,999 permutations

under the reduced model; when less than 100 unique permutations were

obtained, Monte Carlo P values were used and are shown in italics. Values

highlighted in bold are significant...................................................................... 39

Table 4.1 Summary of ANCOVA tests with method as factor and latitude as

covariate. Abundance data was collected with pelagic stereo-BRUVs and

scientific longline surveys along an 8-degree latitudinal gradient. P-values in

bold are statistically significant. ......................................................................... 73

Table 4.2 Precision estimates for target species sampled using pelagic stereo-

BRUVs and scientific longline surveys. Precision (p) was estimated as a ratio of

the standard error and the mean abundance per deployment. Note that lower

values of p indicate better precision. .................................................................. 74

Table 4.3 Summary of the lengths of target species measured on pelagic stereo-

BRUVs and caught on scientific longline surveys. Maximum (Max), minimum

(Min) and mean fork length (FL) are shown in millimetres. ............................. 75

Table 5.1 PERMANOVA based on log 5 Modified Gower dissimilarities to test the

effects of spatial area closures (Status) on the composition and relative

abundance of fish assemblages sampled using pelagic stereo-BRUVs. ............ 90

Table 5.2 Frequency of occurrence of species sampled using pelagic stereo-BRUVs

inside (Closed) and outside (Open) a spatial fishing closure at the Houtman

Abrolhos Islands. ............................................................................................... 91

xviii

Table 5.3 Summary of univariate PERMANOVA tests for species richness and

relative abundance of the main target fish groups sampled using pelagic stereo-

BRUVs. Analyses were based on Euclidean distance dissimilarities of the raw

data to test for differences in Status (areas open and closed to fishing) and Sites.

Fields in bold-faced show significant P-values. ................................................ 92

Table 5.4 Summary of length and range measurements at genus level combining

areas open and closed to fishing. The mean length (cm) represents fork length

and the mean range (m) is the average distance of individuals to the stereo-

camera system. The number of individuals measured [N] is shown in brackets

and (*) shows the genus of demersal target species. ......................................... 93

19

CHAPTER 1 - General introduction

1.1 Background and rationale

Importance of the pelagic environment

The pelagic marine environment (i.e. the entire ocean water column; Figure 1.1)

constitutes 99% of the biosphere volume, accounts for half of the photosynthesis and

supports, directly or indirectly, almost all marine life (Angel, 1993; Game et al.,

2009). It also has a direct impact on the pace and extent of climate change; oceans

play a major role in the global carbon cycle and absorb most of the carbon dioxide

entering the atmosphere through human activities (Field, 2002; Hays et al., 2005;

Siegenthaler and Sarmiento, 1993). Moreover, pelagic ecosystems provide essential

resources to humans and account for about 80% of the global fisheries catch (Game

et al., 2009; Grantham et al., 2011; Halpern et al., 2008; Sumaila et al., 2007; Worm

et al., 2006).

Figure 1.1 Classification of marine environments. Pelagic habitat is defined here as the entire ocean water column, including coastal and oceanic waters.

Pelagic ecosystems are affected by multiple threats including pollution, climate

change and overexploitation (Bianchi and Morri, 2000; Halpern et al., 2008;

Robison, 2009). Global pelagic fisheries are declining (Pauly et al., 1998), and

20

climate change is altering the global ocean circulation patterns and mixing,

interfering with primary and secondary production, and disturbing the stability of

pelagic ecosystems (Cheung et al., 2013a; Cheung et al., 2013b; Pinsky et al., 2013).

These processes have resulted in a loss of biodiversity and the depletion of several

target species from selected areas of the world’s oceans (Lewison et al., 2004; Myers

and Worm, 2003; Myers et al., 2007; Pauly et al., 2002). Ocean biodiversity loss

affects the stability of the ecosystem, increases the risk of population collapse and

reduces its potential for recovery (Worm et al., 2006). The conservation of marine

biodiversity and improvement in the sustainability of fisheries are major

environmental and economic challenges (Pauly, 2009).

Lack of data in pelagic ecosystem research



many species (Bakun, 1996; Freon et al., 2005). Although our understanding of

individual species has improved; our knowledge of the species diversity, abundance

and patterns at the community level is still poor (Evans et al., 2011; Santos et al.,

2013a; Worm et al., 2005). Research on the biology and ecology of pelagic fish

largely relies on fishery-dependent data from commercial and recreational fisheries

(Myers and Worm, 2003; Ward and Myers, 2007). However, the use of fishery-

dependent data alone can lead to sampling biases due to gear selectivity and

heterogeneous fishing effort that can discriminate among species and habitats

(Bishop, 2006; Murphy and Jenkins, 2010; Thorson and Simpfendorfer, 2009).

Alternatively, fishery-independent surveys use more robust sampling designs, but

often employ the same commercial fishing gear (e.g. longlines, gillnets, trawls) and

as such, catchability and size-selectivity biases remain (Simpfendorfer et al., 2002;

Ward and Myers, 2005b). Similarly, it is often not possible to use extractive methods

to sample within areas that are closed to fishing or where protected, endangered and

threatened species might be caught (Assis et al., 2007; Murphy and Jenkins, 2010).

Thus many studies have highlighted the need to improve our capacity to monitor the

ecology and health of pelagic ecosystems using non-destructive and fishery-

independent techniques (Claudet et al., 2010; Heagney et al., 2007; Murphy and

Jenkins, 2010; Ward and Myers, 2005a).

21

Potential of emerging new technologies

The high cost of obtaining representative samples in the pelagic environment, which

comprises large areas with high spatial and temporal variability, has often inhibited

the use of fishery-independent methods (Bishop, 2006). However, emerging

technologies like remote sensing, acoustic cameras and video-techniques are

providing new options for cost-effective ecological sampling in vast and remote

environments such as the open ocean (Axenrot et al., 2004; Bertrand et al., 2002;

Murphy and Jenkins, 2010; Trenkel and Berger, 2013). Baited Remote Underwater

Video systems (BRUVs) have been demonstrated to be a robust fishery-independent

method to obtain estimates of biodiversity, relative abundance, behaviour and, when

stereo-cameras are used, size and biomass of a range of marine species (Harvey et

al., 2013; Harvey et al., 2012c; Mallet and Pelletier, 2014; White et al., 2013;

Zintzen et al., 2011).

Baited stereo-video methods

Baited video techniques are increasingly used for assessing fish community structure

in a variety of environments including deep water (Bailey et al., 2007; Zintzen et al.,

2012), estuaries (Gladstone et al., 2012) and both tropical and temperate reefs

(Harvey et al., 2012a; Harvey et al., 2012c; Langlois et al., 2010; Unsworth et al.,

2014). Stereo-BRUVs use bait to attract fish to the field of view of the cameras so

that individuals can be identified, counted and accurately measured (Cappo et al.,

2003; Dorman et al., 2012). Stereo-BRUVs have not been tested in the pelagic

environment, but evidence suggests that they could overcome some of the difficulties

of sampling in the water column. For instance, pelagic fish are often recorded during

benthic BRUV deployments (Cappo et al., 2004; Jones et al., 2003) and, single-

camera mid-water BRUVs, although unable to measure fish lengths, have been

successfully trialled to survey pelagic ecosystems (Heagney et al., 2007). However,

further research would be needed to expand the use of stereo-BRUVs to the pelagic

environment.

Research question

A fundamental question that needs to be addressed is ‘are pelagic stereo-BRUVs an

effective fishery-independent technique to study pelagic fish assemblages?’ And if

22

so, ‘what is the optimal sampling procedure in order to characterise and analyse

changes in these ecological communities?’ In this thesis, I develop and validate the

use of pelagic stereo-BRUVs as a non-destructive fishery-independent technique to

study highly mobile species in the pelagic environment, and I examine the strengths

and limitations of this method in order to optimise their implementation in future

studies (Figure 1.2).

The purpose of this research project is to develop and assess an alternative sampling

approach to overcome some of the difficulties associated with studying pelagic

ecosystems. The overarching goal of the thesis is to contribute to the limited body of

knowledge on the demographics, ecology and behaviour of fish assemblages that

inhabit the water column. This first section provides a broad overview of the

background and rationale of the project and, I present the specific aims that are

addressed in subsequent chapters. Each chapter is written as a stand-alone

manuscript to facilitate publication, thus chapters have their own specific

introduction sections and consequently may include some elements of the

background information presented here.

1.2 Specific aims

Design, development and validation of pelagic stereo-BRUVs

In chapter 2, I develop and describe a deployment method to use stereo-BRUVs in

the water column and test their ability to sample pelagic fish assemblages.

Characterising ecological communities requires key decisions regarding the method

and sampling effort needed to analyse the assemblage of interest (i.e. size and

number of sampling units; Andrew and Mapstone, 1987). There is considerable

variation in the soak time (time that cameras remain underwater) used across studies

using baited video techniques in different environments (Gladstone et al., 2012).

Studies on reef fish assemblages use soak times between 20 and 60 minutes (Stobart

et al., 2007; Watson et al., 2010; Willis and Babcock, 2000), and deep-water studies

deploy cameras from 1 to several hours (Bailey et al., 2007; Jones et al., 2003;

Zintzen et al., 2012). However, standardising the sample unit size to be used in the

pelagic environment will facilitate comparison across studies. In order to optimise

and standardise the sampling regime for future studies using pelagic stereo-BRUVs,

23

Figure 1.2 Flow diagram outlining the background, rationale and general structure of this thesis.

BA

CK

GR

OU

ND

AN

D R

AT

ION

ALE

Importance of marine

pelagic environment

Pelagic ecosystem

research is data poor

Potential of emerging new

technology for ecological

sampling

Baited video techniques

ingreasingly used in various

marine environments

Food sustainability

and other resources

Ecologically importantto marine ecosystems

Threads:

- climate change

- pollution

- overexploitation

Loss of biodiversity,

depletion of target

species

Need for monitoring and

ecosystem-based management

Research on pelagic species presents

difficulties for collection of accurate

survey data

Lack of data on:

- ecology and demographics

- population status

- vertical distribution

- behaviour

Knowledge relies mainly on

Fishery-dependent

techniques

Extractivefishery-independent

techniques

Need for implementation of

non-destructive fishery-independent

methods

Not suitable to sample

threatened species or in

areas closed to fishing

Sampling biases:

- gear selectivity

- targeted sampling effort

A combination of

methods would improve

data quality

Progress and cost-reduction

of video technology and

data storage

- standardised

- fishery-independent

- permanent record

Provide data on

species richness,

relative abundance

and behaviour

if stereo-video Also provides data

on fish length,

biomass and

swimming speed

Stereo-BRUVs have

not been tested in the

pelagic environment

Potential of pelagic

stereo-BRUVs to study

pelagic fish assemblages

e.g. deep water, temperate and

tropical reefs, estuaries

RE

SE

AR

CH

QU

ES

TIO

N

Are pelagic stereo-BRUVs an effective fishery-independent technique

to study pelagic fish assemblages?

SP

EC

IFIC

AIM

S

Design, development and

validation of pelagic stereo-BRUVs

Optimisation of sampling effort

for multivariate analyses

Calibration of pelagic stereo-BRUVs

to standard sampling techniques

Monitoring the effects of spatial

fishing closures on pelagic fishStudying in situ behaviour of fish

in the pelagic environment

CHAPTER 2 CHAPTER 3 CHAPTER 4 CHAPTER 5 CHAPTER 6

- Optimisation of sampling based

on cost and precision (univariate):

- soak time

- replication

- Sampling of vertical distribution

of pelagic fish in the water column

- Optimisation of sampling based

on a measure of multivariate

precision

- Description of pseudo-multivariate

Standard Error (MultSE) as a

measure of precision

- Comparison to scientific longline

surveys

- Effort equivalence

- Gear selectivity

- Monitoring of highly-mobile

species at broad spatial scales

- Explore effects of protection on

highly mobile species

- Assess pelagic stereo-BRUVs to

monitor spatial area closures

- Contribute to ongoing debate on

the use of spatial closures for the

management of pelagic species

- Report in situ behavioural

observations of presettlement

juvenile fish

- Provide quantitative data on

behaviour using stereo-video

(fish length, swimming speed and

schooling parameters)

- Pelagic fish are often

recorded on benthic BRUVs

- Single-camera BRUVs

have been tested in the

mid-water (Heagney et al. 2007)

Poor data quality:

- unreported catch

- poor species

identification for

low-value catch

24

I explored the effects of different soak times and replication on the precision and cost

of sampling.

This chapter also investigates whether differences in the vertical composition of fish

assemblages can be detected by deploying pelagic stereo-BRUVs at different depths

in mid-water pelagic environments. The physical boundaries of fish communities in

pelagic ecosystems are not well defined and further research is needed to understand

the vertical distribution and diel migrations of pelagic and demersal species in the

water column (Gray, 1997). Fish are distributed throughout the mid-water at

different depths and there is evidence that differences in the deployment depth of

sampling systems can affect the estimates of abundance and species richness

(Heagney et al., 2007; Ward and Myers, 2006). The work encapsulated in Chapter 2

has been peer-reviewed and published in the Journal of Experimental Marine

Biology and Ecology (Santana-Garcon et al., 2014a).

Optimisation of sampling effort for multivariate analyses

Chapter 3 expands the work presented on the optimisation of sampling based on the

precision of the sampling technique. Precision refers to the repeatability of

measurements; the degree of concordance among multiple estimates of a given

parameter (such as the mean) for the same population (Andrew and Mapstone, 1987;

Cochran and Cox, 1957). To date, there is no measure of multivariate precision that

can be used to assist researchers in decision-making processes regarding adequate

sampling requirements, this is especially relevant for studies using multi-species

count data that are gathered to characterise ecological communities. I describe the

use of a pseudo-multivariate standard error (MultSE) as a measure of multivariate

precision and I use estimates of MultSE to determine the appropriate sample unit

size (soak time) and replication for studies utilising pelagic stereo-BRUVs and using

dissimilarity-based multivariate analyses. This chapter includes concepts developed

as part of a larger study undertaken in collaboration with Prof. Marti J. Anderson

from Massey University (New Zealand) that has resulted in a manuscript currently in

review for publication in the journal Ecology Letters (Anderson and Santana-Garcon,

In review; Appendix 1).

25

Calibration of pelagic stereo-BRUVs to standard sampling

techniques

In chapter 4, I calibrate the sampling ability of pelagic stereo-BRUVs to a standard

sampling technique used to study highly mobile species in the pelagic environment.

In particular, I compare the catch composition, relative abundance and length

distribution of fish assemblages sampled using pelagic stereo-BRUVs and scientific

longline surveys. The study is focused on sampling sharks of the family

Carcharhinidae (requiem sharks) and calibrates the sampling effort required for each

technique to obtain equivalent samples of the target species. Research on shark

populations is largely based on fishery-dependent data (Myers and Worm, 2003) and

fishery-independent studies that use fishing-based techniques, like the commonly

used scientific longline surveys (Simpfendorfer et al., 2002). Technical advances are

opening new opportunities for non-destructive research evaluations of these highly

mobile species (White et al., 2013). However, in order to understand the potential of

novel techniques it is necessary to compare and calibrate these against traditional

methods. The work presented in Chapter 4 has been published in the journal

Methods in Ecology and Evolution (Santana-Garcon et al., 2014d).

Monitoring the effects of spatial closures on pelagic fish

In chapter 5, I explore the effects of protection on pelagic species and assess the

potential of pelagic stereo-BRUVs as a monitoring technique for spatial area

closures. There has been extensive research examining the response of demersal

species to spatial fishing closures in coastal waters (Babcock et al., 2010; Ballantine,

2014; Claudet et al., 2008). However, the methods used have often overlooked or not

specifically targeted the mid-water pelagic environment and, consequently, the

response of mobile pelagic species to spatial closures is not clear and is the subject

of ongoing debate (Claudet et al., 2010; Davies et al., 2012; Game et al., 2009;

Kaplan et al., 2010). Moreover, the implementation of large offshore spatial closures

is increasing around the world (Davies et al., 2012; Kaplan et al., 2014;

Notarbartolo-Di-Sciara et al., 2008; Sheppard, 2010), thus there is a need to improve

our capacity to monitor changes in these marine communities using non-destructive

fishery-independent methods is critical (Claudet et al., 2010). This chapter aims to

contribute to the development of monitoring techniques and also to the ongoing

26

debate on the use of spatial area closures for the conservation and management of

highly mobile species both in coastal waters and the high seas. Chapter 5 has been

peer-reviewed and published in the Journal of Experimental Marine Biology and

Ecology (Santana-Garcon et al., 2014b).

Studying in situ behaviour of fish in the pelagic environment

Chapter 6 presents the potential of utilising pelagic stereo-BRUVs to study the in

situ behaviour of fish in the pelagic environment. I report behavioural observations

recorded on video of presettlement juvenile fish and, provide quantitative data using

stereo-video on their length, swimming speed and schooling parameters. The biology

of the pelagic phase of demersal fishes has been extensively studied, but direct

observations and quantitative data on their behaviour in the pelagic environment is

limited and only available for a small number of species (Leis, 2010; Leis and

Carson-Ewart, 1998; Masuda, 2009). Stereo-video techniques offer unique

opportunities and the ability to study the ontogeny of fish behaviour in their natural

environment as they provide a permanent record, can be used at depths beyond the

limits of diver-based studies and can sample both during the day or at night using

lights (Harvey et al., 2012a). This chapter is the result of collaboration with Dr Jeff

Leis from the Australian Museum, he supervised and reviewed my work on juvenile

fish behaviour, and this has been published in the journal Environmental Biology of

Fishes (Santana-Garcon et al., 2014c).

1.3 Study area

The work presented in this thesis was undertaken in Australia, off the west coast of

Western Australia (WA; Figure 1.3). Initial trials of the deployment method for

pelagic stereo-BRUVs were carried out in Perth metropolitan waters allowing the

prototypes to be refined before the main field campaigns. Fieldwork for chapters 2, 3

and 6 took place in the Ningaloo Marine Park (23° 48’ - 21° 48’ S). Ningaloo Reef is

a fringing coral reef that stretches for approximately 270 km adjacent to the semi-

arid North-West Cape of WA. The sites were ~35 m deep and between 1 and 2 km

offshore from the reef slope. Data for chapter 4 was collected across ~950 km along

the coastline of WA (32° - 24° S) at sites 15 - 80 km offshore and at depths ranging

from 35 to 106 m. Finally, fieldwork for chapter 5 was undertaken at the Houtman

27

Abrolhos Islands, 60 km offshore from the mid-west coast of WA (28° 15’ - 29° 00’

S). The sites were located off the reef edge in the Easter Group and were between 30

and 35 m deep.

Figure 1.3 Study area off the coast of Western Australia. Colour-coded panels and markers indicate the specific study areas for each chapter.

29

CHAPTER 2 - Development and validation of a mid-water baited stereo-video technique for investigating pelagic fish assemblages

2.1 Abstract

Understanding the abundance, demographics and composition of pelagic fish communities

has historically relied on fisheries catch data or destructive fishery-independent methods.

Here, we test and validate the use of a pelagic stereo-Baited Remote Underwater Video

system (BRUVs) as a non-destructive, fishery-independent approach to study pelagic fish

assemblages. We investigated whether differences in the vertical composition of fish

assemblages could be detected with pelagic stereo-BRUVs by sampling at different depths

in the water column. The effects of soak time and replication on the precision and cost of

sampling were explored to allow for the optimization and standardization of future pelagic

stereo-BRUVs studies. Pelagic stereo-BRUVs effectively identified 43 fish taxa from 18

different families in the mid-water, 5 and 20 m below the surface, in the Ningaloo Marine

Park (Western Australia). The fish assemblages sampled at the two mid-water depths were

significantly different demonstrating that this method could be used to investigate the

vertical distribution and diel migration patterns of both pelagic and demersal fishes.

Precision estimates under different sampling regimes showed that a soak time of 120 min

and a sample size of at least 8 replicates per treatment would be optimal for sampling using

pelagic stereo-BRUVs in tropical or warm-temperate areas. In order to account for the

spatial and temporal variability of the system and to facilitate future comparisons across

studies using this method, we encourage maximizing replication given the resources

available while standardizing the soak time. Pelagic stereo-BRUVs may provide a useful,

non-destructive method to improve our understanding on the ecology and behavior of fishes

in pelagic ecosystems.

2.2 Introduction



many species (Bakun, 1996; Freon et al., 2005). Although our understanding of

individual species has improved, our knowledge of the species diversity, abundance

30

and patterns at the community level is still poor (Angel, 1993; Evans et al., 2011;

Santos et al., 2013b; Worm et al., 2005). Research on pelagic fish presents

difficulties for the collection of accurate survey data (Heagney et al., 2007) and often

relies exclusively on fisheries catch data (Myers and Worm, 2003; Ward and Myers,

2007).

The use of fishery-dependent data alone has many shortcomings, as it can lead to

sampling biases in the size and type of fish it targets. Similarly, it is often not

possible to sample destructively within areas that are closed to fishing, thus

impeding the assessment of the effects of protection on pelagic fish assemblages

(Murphy and Jenkins, 2010). For many pelagic species, the implementation of

fishery-independent surveys is inhibited by the high cost of obtaining representative

samples from a vast habitat which has high spatial and temporal variability (Bishop,

2006). However, many studies have highlighted the need to further develop fishery-

independent methods to assess the ecology and health of pelagic ecosystems

(Claudet et al., 2010; Heagney et al., 2007; Ward and Myers, 2005b). Murphy and

Jenkins (2010) reviewed current and emerging fishery-dependent and independent

observational methods used in marine spatial monitoring to obtain population and/or

habitat data in order to assess marine biodiversity and population trends. The review

highlights the potential of emerging technologies like remote sensing, acoustic

cameras and Baited Remote Underwater Video systems (BRUVs), and concludes

that a combination of methods would be the most effective way to reduce biases and

increase the quality of data.

The use of BRUVs has increased in recent years as it provides a standardized, non-

destructive and fishery-independent approach for estimating biodiversity indices and

relative abundance measures of a range of marine species (Harvey et al., 2007;

Langlois et al., 2010; Stobart et al., 2007; Stoner et al., 2008; Watson et al., 2010;

White et al., 2013; Willis and Babcock, 2000). This technique uses bait to attract

individuals into the field of view of a camera so that species can be identified and

individuals counted (Dorman et al., 2012). When stereo-camera pairs are used,

precise length and biomass estimates can be obtained (Cappo et al., 2006; Harvey

and Shortis, 1995; Harvey et al., 2010). The use of stereo-BRUVs to estimate

diversity, relative abundance and size structure of fish communities has been tested

and compared to other sampling techniques (Cappo et al., 2004; Ellis and Demartini,

31

1995; Harvey et al., 2012c; Langlois et al., 2010; Watson et al., 2010). Baited video

techniques have proven to be a robust method for assessing fish community structure

in deep water (Bailey et al., 2007; Zintzen et al., 2012), estuaries (Gladstone et al.,

2012) and tropical or temperate reefs (Langlois et al., 2010; White et al., 2013).

Despite the increase in fishery-independent techniques used to assess demersal fish

assemblages, the development and trial of such methodologies to survey pelagic

species remain limited. Recent evidence supports the use of stereo-video as a

possible tool for pelagic fish monitoring, with the potential to overcome some of the

difficulties associated with surveying pelagic ecosystems, if the technique can be

suitably adapted, developed and validated. Pelagic fish are often observed during

benthic BRUVs deployments (Cappo et al., 2004; Jones et al., 2003), and single-

camera mid-water BRUVs have been successfully trialed to survey pelagic

ecosystems (Heagney et al., 2007). Moreover, the use of pelagic (or mid-water)

stereo-camera pairs would provide accurate length and biomass measurements of

pelagic fish (Harvey et al., 2003; Santana-Garcon et al., 2013), as well as, the

opportunity to obtain behavioral data to better understand pelagic ecosystems

(Santana-Garcon et al., 2013).

One of the biggest challenges when implementing a sampling program for pelagic

fish assemblages is the definition of the pelagic community itself. Benthic

ecosystems are relatively easy to define by their horizontal distribution and very

abrupt boundaries in habitat (i.e. reef to sand) which provide structure to the fish

community (Habeeb et al., 2005). In pelagic ecosystems, these physical boundaries

are not well defined and there is also the vertical dimension to consider as pelagic

fish are distributed throughout the water column at different depths (Gray, 1997;

Holling, 1992). Differences in the deployment depth of sampling systems may

therefore affect the estimates of abundance and species richness in the pelagic

environment (Heagney et al., 2007; Ward and Myers, 2006). This parameter, which

potentially affects the sampling ability of BRUVs, is not well understood. Therefore,

pelagic stereo-BRUVs should allow for camera systems to be deployed and remain

at a predetermined depth using anchored or drifting systems. The deployment of

these systems at different mid-water depths could provide a powerful fishery-

independent technique to better understand pelagic fish assemblage composition, as

well as, the vertical distribution and diel migration patterns of both pelagic and

32

demersal fishes. A fundamental question that needs to be addressed for pelagic

stereo-BRUVs to be an effective monitoring and assessment methodology is ‘can

they detect differences in the vertical composition of pelagic fish assemblages?’

Another challenge with defining the structure of pelagic fish communities is

determining the spatial and temporal scales at which they need to be sampled in

order to capture the diversity and relative abundance of fishes living within them

(Habeeb et al., 2005). For BRUVs that remain stationary, this can be determined by

the time that cameras are left recording (soak time) in order to capture the majority

of species present in the area. Soak time is known to have a strong influence in the

estimates of abundance obtained from fishery-dependent techniques such as longline

operations (Ward et al., 2004). For sampling using baited video techniques, there is

considerable variation in the soak times used across studies in different environments

(Gladstone et al., 2012). Deep water BRUVs use soak times between one and several

hours (Bailey et al., 2007; Jones et al., 2003; Zintzen et al., 2011) and studies on reef

fish assemblages tend to deploy cameras for 20 to 60 min (Stobart et al., 2007;

Watson et al., 2010; Willis and Babcock, 2000). In order to optimize resources and

standardize the use of mid-water BRUVs in the pelagic environment, we explore the

performance of the method under various sampling regimes (i.e. soak times and

number of replicates) and evaluate the associated sampling costs.

Precision has been commonly used to optimize sampling effort in studies involving

univariate data (Bartsch et al., 1998; Downing and Downing, 1992; Pringle, 1984).

For example, Gladstone et al., (2012) assessed the precision of the species richness

and abundance estimates under different soak time regimes to optimize the sampling

effort of benthic BRUVs in estuarine environments. The precision of a sampling

technique refers to the repeatability of its measurements, the degree to which

repeated observations under unchanged conditions lead to the same result (Cochran

and Cox, 1957). Precision is an attribute of the sampling procedure and can be

assessed relatively easily from characteristics of the sample data (Andrew and

Mapstone, 1987). It is usually expressed numerically by measures of imprecision

like standard deviation, variance, coefficients of variation and most commonly, as a

ratio of the standard error (SE) and the mean (Andrew and Underwood, 1989;

Downing and Downing, 1992; Hellmann and Fowler, 1999). Given the resources

33

available, any method should use the sampling effort that yields the greatest

precision (Pringle, 1984).

The aims of this study are therefore to understand and validate the use of pelagic

stereo-BRUVs as an effective fishery-independent approach to study pelagic fish

assemblages. In particular, we describe the design and use of pelagic stereo-BRUVs

and present the results of a pilot study in the Ningaloo Marine Park (Western

Australia) to test the effects of deployment depth on the ability of this technique to

survey pelagic fish in the water column. Furthermore, the effects of soak time and

replication on the precision and cost of sampling are explored to allow for the

optimization and standardization of future studies. The advantages, limitations and

requirements for future development of this emerging sampling technique are also

discussed.

Figure 2.1 Map of the study area in Ningaloo Marine Park, Western Australia. Sites are in Tantabiddi Passage (TN1, TN2) and Coral Bay (CB1, CB2).

34

2.3 Methods

2.3.1 Study area

This study was conducted in March 2012 at two locations, Coral Bay and

Tantabiddi, in Ningaloo Marine Park (23° 48’ S - 21° 48’ S; Figure 2.1). Ningaloo

Reef is a fringing coral reef which stretches for approximately 270 km adjacent to

the semi-arid north-west cape of Western Australia. The sites sampled at both

locations were 35 m deep and between 1 and 2 km offshore from the reef slope. Site

depth was recorded to the nearest 0.5 m using the depth sounder onboard.

2.3.2 Sampling technique

The pelagic stereo-BRUVs used in this study were designed to be deployed,

anchored and to remain at a predetermined depth in the water column. Two Sony

CX12 high definition digital cameras were mounted 0.7 m apart on a galvanized

steel frame designed for mid-water deployment (Figure 2.2). The cameras were

converged inwards at 8° to gain a maximum field of view and to allow for fish

length measurements to be made (Harvey et al., 2010), although these length

measurements are not assessed here. The bait consisted of 800 g of crushed pilchards

(Sardinops sagax) in a wire mesh basket suspended 1.2 m in front of the cameras.

The bait arm acts as a rudder and keeps the camera system facing downstream of the

current. The use of ballast and sub-surface floats effectively reduces movement from

surface waves and allows for control over deployment depth. The deployment

system presented here was developed in collaboration with commercial fishermen

and allows for the effective deployment of several camera systems from a wide

range of boat types and sizes. These pelagic stereo-BRUVs have been used in depths

ranging from 5 to 200 m (Santana-Garcon et al., 2014d), but the deployment method

presented here can also be adapted to greater depths

2.3.3 Experimental design

The experimental design consisted of 3 factors: Location (2 levels, random: Coral

Bay and Tantabiddi), Site (2 levels, nested in Location, random) and Depth (2 levels,

fixed: Depth 1 – surface and Depth 2 – deep). Systems deployed at Depth 1

(surface) were designed to remain in the mid-water at ca 5 m depth (6.88 ± 0.26 m,

35

Mean ± SE), 30 m above the bottom. Systems at Depth 2 (deep) were deployed to ca

20 m (22.01 ± 0.23 m), at about 15 m above the bottom. For each depth, six replicate

deployments were conducted at each site. Overall, 48 mid-water stereo camera

systems were deployed and each recorded for 3 h. Samples were collected during

daylight hours, allowing at least 1 h between sampling and sunrise or sunset, to

avoid possible crepuscular variation in fish assemblages (Myers, 2013). A minimum

distance of 500 m was allowed between replicates at any time to avoid or minimize

potential overlap of bait plumes and to reduce the likelihood of fish moving between

replicates. Deployment depth was interspersed at each site so that different replicates

sampled the two depths simultaneously.

Figure 2.2 Deployment method and frame details of pelagic stereo-BRUVs.

2.3.4 Image analysis

The video images were analyzed using the software ‘EventMeasure (Stereo)’

(SeaGIS Pty Ltd). All fish recorded were quantified and identified to the lowest

taxonomic level possible. To avoid repeat counts of individual fish re-entering the

field of view, a conservative measure of relative abundance (MaxN) was recorded as

the maximum number of individuals of the same species appearing at the same time

(Priede et al., 1994). For the analysis of the influence of soak time on the relative

abundance and species composition, Cumulative MaxN was recorded at 15 minute

time intervals in order to record the number of new species and individuals observed

at each time period.

36

2.3.5 Data analysis

Deployment depth

Multivariate assemblage data were analyzed using non-parametric permutational

analysis of variance (PERMANOVA) in the PRIMER-E statistical package

(Anderson et al., 2008). The species abundance data was pre-treated with ‘dispersion

weighting’ to reduce the weight given to species that presented high spatial

clustering (Clarke and Gorley, 2006). Down-weighting was only applied to species

that exhibited significant evidence of clumping (Clarke et al., 2006b). A Bray-Curtis

coefficient on square root transformed data was chosen to allow mid-range and low

abundance species to exert an influence on the calculation of similarity (Clarke and

Warwick, 2001). A total of 9,999 permutations were computed to obtain P

significance values. Factor interactions with high P values (> 0.25) were pooled

(Underwood, 1997) and when the number of unique permutations was less than 100,

a Monte Carlo P value was used to interpret the result (Anderson et al., 2008). In

order to provide a suitable visual complement to the output given in PERMANOVA,

distance among centroids was calculated and plotted using Principal Coordinates

Analysis (PCO).

Optimization of sampling effort

The relationship between soak time and the number of species or individuals

observed by a sampling technique can be represented by species accumulation

curves and cumulative abundance curves. Species richness and relative abundance

curves were plotted for 15 minute time intervals up to 180 min of soak time.

The univariate species richness and total abundance (MaxN) raw data were used to

calculate the mean precision of the sampling technique for different sample sizes

(number of deployments) and for 3 soak times: 60, 120 and 180 min. For the

calculation of precision, data from only one of the depth treatments (surface) was

used to avoid confounding effects among treatments. However, all other data

combinations were explored and produced equivalent results to those presented here.

Precision (P) was calculated as:

P = SE / Mean

37

where Mean is the sample mean and SE the standard error . Bootstrapping

procedures were used to simulate samples from 2 to 12 replicates using the 24

replicates available. Replicates were selected randomly with replacement and the

procedure repeated 1000 times for each sample size. Mean precision and the 2.5 and

97.5 percentiles were calculated and plotted using the packages ‘reshape2’

(Wickham, 2007) and ‘ggplot2’ (Wickham, 2009) in R software version 2.15.2 (R

Core Team, 2012).

The cost differences among the three soak times and number of replicates were

estimated as a combination of the time required in the field and in the lab for video

analysis (see Gladstone et al., 2012). Field time was estimated for the deployment of

5 camera systems from a small boat (i.e. 5.5 m in length) allowing 30 min for the

deployment and retrieval of 5 systems including the travel among deployment sites.

Furthermore, for each day of fieldwork, 2 h were added to account for the

preparation of gear and travel time to the field locations. A maximum of 9 h was

allowed for fieldwork days, including preparation and time in the field. Times

required for video analysis in the lab were highly variable depending on the number

of fish and species present in the video but were most commonly about 1.5, 2 and 2.5

h for soak times of 60, 120 and 180 min respectively. The values presented here are

only used as a guide and were used to compare among sampling efforts. This time

calculation will change significantly across studies depending on the field location

and the resources available.

2.4 Results

From the 48 pelagic stereo-BRUVs deployed, 144 h of video were analyzed and a

total of 6,988 individuals of 43 taxa were identified representing 18 families. Of the

43 taxa defined, 31 were confidently identified to species, 6 were grouped to genus

and 6 to either family or a higher taxonomic level (Table 1). Species were grouped to

higher taxonomic levels when they could not be identified confidently across all

videos. Some of the specimens observed were juvenile fish in their pelagic phase

(17.3 %) and a large proportion of the individuals were pooled under the category of

‘various small pelagics’ (70.4%). This category included fish schooling in high

numbers from the family Clupeidae and small carangids from the genus Decapterus,

38

Selar and Selaroides among others; their identification to species level was not

possible for the present study. Juvenile fish can be difficult to identify from video

alone given their small size and their similar morphology at this size so some species

were grouped as ‘unidentified juveniles’ for the analysis.

Table 2.1 Taxonomic group and stage of specimens recorded using pelagic stereo-BRUVs in Ningaloo Marine Park. Stage of individuals was identified as either adult (AD) or juvenile (J).

Family Stage Family (cont.) Stage

Species (AD, J) Species (AD, J)

Acanthuridae Monacanthidae

Acanthurus mata AD Aluterus monoceros J

Belonidae Aluterus scriptus AD, J

Tylosurus crocodilus AD Cantherhines pardalis J

Caesionidae Eubalichthys caeruleoguttatus J

Caesio teres AD Paramonacanthus choirocephalus J

Carangidae Other J

Alectis sp. J Myliobatidae

Alepes sp. AD Myliobatidae sp. AD

Atule mate AD Nomeidae

Carangoides fulvoguttatus AD Nomeidae spp. J

Elagatis bipinnulata AD Priacanthidae

Gnathanodon speciosus J Priacanthus tayenus J

Seriolina nigrofasciata J Rhincodontidae

Other J Rhincodon typus AD

Carcharhinidae Scombridae

Carcharhinus albimarginatus AD Acanthocybium solandri AD

Carcharhinus amblyrhynchos AD Grammatorcynus bicarinatus AD

Carcharhinus plumbeus AD Scomberomorus sp. AD

Carcharhinus obscurus/brachyurus AD Thunnus albacares AD

Galeocerdo cuvier AD Thunnus tonggol AD

Other AD Other Thunnus spp. AD

Echeneidae Sphyraenidae

Echeneis naucrates AD Sphyraena barracuda AD

Remora remora AD Sphyrnidae

Fistulariidae Sphyrna mokarran AD

Fistularia sp. J Tetraodontidae

Lutjanidae Lagocephalus sceleratus AD

Lutjanus bohar AD Unknown

Lutjanus fulviflamma AD Unidentified juveniles J

Various small pelagic (mainly clupeids and small carangids)

AD, J

39

Deployment depth

The three-way PERMANOVA determined that species composition differed

significantly between locations and depths (both P < 0.05), among sites (P < 0.001)

and there was a significant interaction (P < 0.001) between each of these factors

(Table 2.2). Although the PCO plot shows a greater distance between the assemblage

centroids at the different locations (Coral Bay and Tantabiddi; PCO1 axis) than

between the two depths (PCO2 axis), there is a clear separation between depths at

each site (Figure 2.3). Most species were recorded at both depths but the assemblage

differences reported between treatments were mainly driven by species that only

occurred at one of the two depths sampled (Figure 2.4). The species that were

associated with surface deployments alone (Depth 1) include Tylosurus crocodilus,

Elagatis bipinnulata and the scombrid Acanthocybium solandri. Sharks of the

family Carcharhinidae were mostly sampled in the deeper deployments (Depth 2)

except for Carcharhinus plumbeus and Galeocerdo cuvier which, in this study, were

exclusive to the deployments near the surface. Also unique to the shallower depth

treatment was a whale shark Rhincodon typus and the specimens of Remora remora

associated with it. Exclusively sampled on the deeper deployments, 15 m above the

bottom were Alepes sp., Acanthurus mata, Caesio teres, Grammatorcynus

bicarinatus and Lagocephalus sceleratus. Species more commonly associated with

the reef such as Lutjanus fulviflamma and Lutjanus bohar, were also reported in the

deep treatment only. Tuna species in the genus Thunnus were identified at both

depths but the species Thunnus tonggol and Thunnus albacares were only recorded

at the deeper deployments.

Table 2.2 PERMANOVA testing for the effects of deployment depth on the structure of fish assemblages sampled using pelagic stereo-BRUVs. Results are based on Bray-Curtis similarity matrices using dispersion weighted and square-root transformed data. All tests were undertaken using 9,999 permutations under the reduced model; when less than 100 unique permutations were obtained, Monte Carlo P values were used and are shown in italics. Values highlighted in bold are significant.

Source of variation df MS Pseudo-F P (perm) Location 1 30262 4.757 0.011

Depth 1 7916.6 2.473 0.035

Site (Location) 2 6361.6 4.948 <0.001

Pooleda 3 3201.6 2.490 <0.001

Residual 40 1285.6 Total 47 a Pooled: (Site(Location) x Depth Treatment) + (Location x Depth Treatment)

40

Figure 2.3 Principal Coordinates Analysis plot showing differences in fish assemblages sampled at two depth treatments (Depth 1, surface and Depth 2, deep) using pelagic stereo-BRUVs. Different symbols show the sites sampled in Tantabiddi (TN1, TN2) and Coral Bay (CB1, CB2) in Ningaloo Marine Park, Western Australia.

Figure 2.4 Mean relative abundance (MaxN) of fish taxa sampled at two different deployment depths using pelagic stereo-BRUVs in Ningaloo Marine Park: Coral Bay and Tantabiddi. The vertical axes are shown in logarithmic scale and error bars represent standard error. Some species are shown grouped at a higher taxonomic level and species that had only one individual recorded are excluded.

41

Optimization of sampling effort

The mean species richness and mean relative abundance (MaxN) was estimated

cumulatively in 15 min time intervals (Figure 2.5). The slope of the species

accumulation curve is steep as soak time increases, the slope of the curve is reduced

after ca 90 min but the trend is still increasing at a reduced rate at 180 min. The

abundance curve follows a similar pattern, but shows a steeper increase with the

mean number of individuals rising steadily in 15 minute intervals throughout the

time sampled.

Figure 2.5 Species accumulation curve and relative abundance accumulation curve (B) in 15 minute time intervals for soak times up to 3 hours. Error bars represent standard error.

The mean precision and 95 % confidence intervals were calculated for the species

richness and abundance data for the three different soak times (60, 120 and 180 min)

and for sample sizes ranging from 2 to 12 replicates (Figure 2.6). Note that lower

values of precision indicate more precise data. The effect of soak time on the mean

precision remains consistent across the different sample sizes with precision

improving with the increase of soak time. The improvement in precision is greater

42

when soak time is extended from 60 to 120 min than in the period from 120 to 180

min. Precision also improves with an increase in replication, reaching an optimum at

8 replicates with only minimal marginal improvement beyond that number of

replicates. The increase in sample unit size and replication not only reduces mean

precision values, but also reduces the range of their 95% confidence intervals to a

great extent. The values of precision calculated from the species richness data are

lower (more precise) than those from the abundance data across all soak times and

sample sizes.

Figure 2.6 Mean precision and 95 % confidence intervals for (A) species richness and (B) relative abundance data using pelagic stereo-BRUVs with three different soak times. Samples simulated using bootstrapping procedures with replacement. Lower values of precision indicate more precise data.

The cost (working time) of sampling increases as the sample size increases for the

three different soak times (Figure 2.7). As expected, cost also escalates with soak

time. Since the time-cost was calculated for fieldwork using 5 camera systems, the

field cost increases every 5 replicates. For example, in order to increase from 10 to

12 replicates, with 180 min soak time, one extra day of fieldwork is required. The

43

time allocated to video analysis increases linearly with sample size and also slowly

increases for longer soak times.

Figure 2.7 Estimate of the cost, in time, for sampling fish assemblages using different soak times with pelagic stereo-BRUVs. Cost includes the time for fieldwork with 5 camera systems and video analysis.

2.5 Discussion

Pelagic stereo-BRUVs were effectively used to sample fish in the mid-water

environment identifying 43 taxa from 18 different families. The technique allowed

us to count and identify not only fish attracted to the bait, but also other fish in the

area and those swimming within the field of view by chance (e.g. the whale shark

Rhincodon typus). Moreover, small schooling fish and juveniles in their pelagic

phase were attracted to the physical structure and aggregated around the camera

system which, in some instances, attracted bigger pelagic fish to the field of view.

This effect of acting as a fish aggregation device enhances the sampling ability of

BRUVs in such an open environment as it concentrates individuals from a broader

area within a distance that allows for their identification and measurement (Bailey et

al., 2007).

Video techniques also provide a permanent record of the fish species occurring in an

area. The imagery recorded reduces the need for skilled observers in the field as

species can be identified in the laboratory by various observers and, video clips or

captions sent to experts for confirmation (Cappo et al., 2003). Despite having a

44

permanent record, the identification of some pelagic fish to the species level from

video alone have proved challenging and some had to be grouped at higher

taxonomic levels. This may be due to the adaption of pelagic fish to be almost

invisible in the mid-water as their coloration is countershaded (i.e. dark on the dorsal

side and pale on ventral side) and reflective which redirects light to make them

appear somewhat transparent. Furthermore, a wide range of taxa share similar traits

such as a streamlined fusiform body, pointy snouts and deeply forked and lunate

caudal fins. The difficulty in identifying pelagic species is also reported in other non-

extractive sampling methods like hydroacoustics (Lawson et al., 2001) and

underwater visual census, which are often not well suited to survey the pelagic

environment (Claudet et al., 2010).

In this study we classified a large proportion of the individuals observed under the

category ‘various small pelagics’ (70.4 %) which included mainly small carangids

and clupeids. Classification to lower taxonomic levels was not consistently possible

across all replicates due to the similarity between species, their small size and rapid

motion. This group fits within the definition of ‘small pelagic fish’ by Freon et al.

(2005), which defined them as shoaling epipelagic species preying on plankton,

characterized by high horizontal and vertical mobility in coastal areas and that, as

adults, are usually 5 to 30 cm in length. Small pelagic fish are gregarious and, as

observed in our study, often several species of similar size and body shape gather in

single schools (Freon and Misund, 1999). Pooling these species in our analysis was

considered suitable as they share specific traits in relation to their habitat, biology,

behavior and ecology (Freon et al., 2005). However, small pelagic fish often

constitute the bulk of the biomass in marine ecosystems; hence studies that require

more detail on these species should consider this limitation of pelagic stereo-BRUVs

and could use them in combination with a range of other sampling techniques.

The fish assemblages sampled at the two mid-water depths were significantly

different. These differences were mainly driven by species that during our study

were recorded at only one of the deployment depths. Benthic BRUVs have been used

to describe the changes in community structure at different depths (Bailey et al.,

2007; Goetze et al., 2011; Zintzen et al., 2012). The present study only tested the

effects of deployment depth in shallow water (35 m) by comparing mid-water

deployments at 5 and 20 m. However, it validates the use of this technique in the

45

future to investigate the distribution patterns along broader depth ranges. Vertical

migrations of fish are known to be common and driven by changes in the distribution

of prey and predator species and by levels of light that modify species detection

(Axenrot et al., 2004; Freon et al., 2005). In order to gain a better understanding on

how these processes affect pelagic and demersal fish communities, depth structure

and diel migrations could be explored using pelagic stereo-BRUVs at different

depths during the day and night.

An increase in soak time will affect the estimates of diversity and relative abundance

obtained from pelagic stereo-BRUVs as longer sampling times record greater

numbers of species and individuals. This has also been reported for estimates from

pelagic longline surveys (Ward et al., 2004) and in other studies using baited video

techniques (Willis and Babcock, 2000). The increase in species richness and

abundance in relation to longer soak times could be the result of the bait plume

dispersal attracting fish from a greater distance (Sainte-Marie and Hargrave, 1987).

However, standardizing soak time across deployments would provide comparable

estimates of richness and abundance (Cappo et al., 2006; Cappo et al., 2003; Willis

and Babcock, 2000). Soak time can also affect the behavior of fish, as longer

deployments allowed cautious individuals that first remain in the distance to get

closer to the bait, which in turn improves their identification and measurement. For

example, some sharks in the genus Carcharhinus that could not be confidently

identified to species level during the first hour of deployment, subsequently

approached the bait later in the video allowing identification to species level. Soak

time in the past was mainly limited by the capacity and cost of the recording media

(Harvey et al., 2010). Currently, while storage capacity has increased and costs have

decreased, soak time mainly depends on the resources available as increasing soak

time incurs greater costs both in the field and the succeeding laboratory analysis. In

addition, if detecting biodiversity (i.e. rare fish) was an objective then cost would

increase as longer soak times would be required.

In order to optimize the use of pelagic stereo-BRUVs in the Ningaloo Marine Park,

we estimated the precision of the data under different sampling regimes. In these

waters and at the depths sampled, a soak time of 120 min and a sample size of at

least 8 replicates per treatment are optimal for sampling the pelagic assemblage. We

believe that similar regimes will be suitable for sampling other warm-temperate and

46

tropical coastal environments. Different indices of assemblage composition showed

distinct levels of precision under the same sampling regime. The species richness

data produced more precise estimates than the abundance data so studies might adapt

their sampling effort depending on their objectives. In general, precision improved

when soak time was extended from 60 to 120 min, but longer deployments did not

significantly improve the quality of the data any further. Moreover, as expected,

precision improved rapidly when increasing the number of replicates (Andrew and

Mapstone, 1987; Gladstone et al., 2012). It is important to increase spatial and

temporal replication in order to account for the high variability of the processes that

determine the distribution of fish in the pelagic environment (Bakun, 1996). We

encourage maximizing replication given the resources available while standardizing

the soak time to facilitate future comparisons across studies using pelagic stereo-

BRUVs.

The cost of sampling increased for bigger sample sizes and longer deployment times.

Since the cost of sampling for 180 min of soak time was greater than that for 120

min, but the quality of the data was not significantly enhanced, we would

recommend using the resources to increase sample size rather than soak time. In

addition, increasing the number of camera systems available in future studies will

increase replication at no greater field time-cost.

The main advantage of using pelagic stereo-BRUVs to investigate the ecology of

pelagic fish is that they are a non-destructive and fishery-independent technique that

can be standardized and used to compare fish assemblages across treatments, spatial

areas and over time. Moreover, stereo-video can obtain accurate length

measurements of the pelagic fish observed (Santana-Garcon et al., 2013). Pelagic

stereo-BRUVs may have the potential to monitor the effects of fishing closures on

pelagic fish in the same way as benthic BRUVs have been used to study such effects

on demersal target species (Goetze et al., 2011; Harvey et al., 2012b; McLean et al.,

2010; Watson et al., 2009). In addition, remote video techniques can be used in areas

and at depths beyond the limits of diver based studies (Cappo et al., 2006), as well

as, for monitoring fish assemblages both during the day and at night using lights

(Harvey et al., 2012a). Pelagic stereo-BRUVs provide a window to the mid-water

that allows for in situ behavioral studies that would otherwise take place in the lab

(Santana-Garcon et al., 2013). The behavior of pelagic fish in their natural

47

environment is largely unknown and could be revealed using remote stereo-video as

has occurred in deep water studies (Zintzen et al., 2011).

All sampling techniques have some inherent biases and limitations (Murphy and

Jenkins, 2010), pelagic stereo-BRUVs are no exception. As discussed, the

identification to species level can be difficult, especially for juvenile fish and small,

highly mobile species. Another shortcoming of stationary camera systems is that

only a relatively restricted volume of water is sampled, but this is improved with the

use of bait and by deploying several systems simultaneously to increase spatial

replication (Harvey et al., 2007). Despite attracting fish to the field of view, this

technique produces zero inflated data due to the high spatial and temporal variability

of the pelagic environment. Generally, zeros play a special role in the analysis of

assemblage data and analyzing assemblage matrices with many zeros may have

limited statistical power or require more complex analysis to identify patterns and

changes in the community (Clarke et al., 2006a). Consequently, in order to reduce

the patchiness and variability of the data, greater sampling effort might be needed

when sampling the mid-water in comparison to other environments such as reef

habitats.

Biodiversity assessments need to be made at the community, habitat and landscape

levels in order to predict changes over time (Gray, 1997). Most methods used in the

pelagic environment are looking at very specific questions or at very broad scales

(i.e. ocean wide); yet pelagic stereo-BRUVs could help explore patterns and

processes occurring at the assemblage or community level. Fish diversity in the

pelagic environment is generally low in comparison to that of the benthic habitats

and the boundaries of ecosystems are more loosely defined and difficult to determine

(Angel, 1993; Gray, 1997). During this study, we observed extreme temporal

variability in both the assemblage composition and abundance. For example, the

same site that recorded no fish or very low abundance across replicates one day had

high diversity and abundance the next day. Consequently, it is very important to

include temporal replication and sample simultaneously across different treatments

as we did in this study. Also, when possible, replicates should be deployed at the

same time across sites and locations. Due to logistical and resourcing constraints, we

sampled the two locations on different weeks so some of the differences observed

across locations might be driven by the temporal variability of the system.

48

In comparison to the common benthic BRUVs (Cappo et al., 2001), the logistics and

use of pelagic stereo-BRUVs in the field are very similar. The fact that the main

structure is suspended in mid-water and that only a ballast is deployed to the bottom

facilitates their use on complex habitats (Merritt et al., 2011). A singular trait of the

pelagic camera systems is that the bait arm acts as a rudder and makes the cameras

face downstream of the current so that fish following the bait plume are observed

approaching the cameras. Another unique feature of pelagic stereo-BRUVs is the use

of sub-surface floats that stabilize the image by reducing the effect of wave motion.

The use of bait in benthic BRUVs has been well investigated (Dorman et al., 2012;

Harvey et al., 2007), but further research is needed to explore the role of bait in

pelagic deployments, as well as the effect of other visual or acoustic cues to attract

pelagic fish to the field of view.

In conclusion, pelagic stereo-BRUVs are a novel technique that can be used to

effectively investigate pelagic fish assemblages. Future research should further

explore the potential and limitations of this technique in comparison to other

sampling methods. The results from this study can provide guidance to standardize

and optimize the resources available in future sampling programs. Pelagic stereo-

BRUVs used in combination with other techniques will improve our understanding

of the ecology and behavior of pelagic ecosystems.

49

CHAPTER 3 - Multivariate pseudo standard error as a measure of precision: optimisation for sampling pelagic fish assemblages

Prelude

The research presented in this chapter includes concepts developed as part of a larger

study undertaken in collaboration with Prof. Marti J. Anderson (MJA) from Massey

University (New Zealand). In this chapter, I have condensed the results from

Anderson and Santana-Garcon (In review; Appendix 1) that are directly applicable to

this thesis. As a result of this collaboration, MJA developed the statistical method

and the R script that I have used for the analysis of my data. The chapter has kept the

language as in the submitted manuscript (e.g. using we instead of I) to acknowledge

the work undertaken by both authors.

3.1 Abstract

The accurate and precise description of biological patterns is essential to most

sampling techniques. However, unlike in univariate statistics, there is no measure of

multivariate precision described to assess sampling effort in multivariate ecological

studies. We propose the use of a pseudo multivariate dissimilarity-based standard

error (MultSE) as a direct analogue to the univariate standard error, and as a useful

approach to assess the adequacy of sampling effort for multivariate studies. We use

this measure of multivariate precision to optimise the sampling unit size and

replication for studies using pelagic stereo-BRUVs to sample fish assemblages. The

results suggest that a soak time of 120 minutes and a sample size of at least 8

replicates per treatment is an appropriate sampling effort to obtain precise data using

pelagic stereo-BRUVs in tropical or warm-temperate waters. These findings are in

line with previous recommendations based on univariate precision estimates. We

suggest that multivariate ecological studies should use measures of multivariate

precision, such as MultSE, in order to optimise their sampling regime.

50

3.2 Introduction

The use of multivariate analysis in ecological studies is becoming increasingly

important. Ecologists often need to sample whole assemblages of species at once to

test hypotheses concerning the effects of experimental factors or environmental

impacts (Anderson, 2001). The accurate and precise description of biological

patterns is essential to most sampling techniques used in ecology (Andrew and

Mapstone, 1987). However, unlike in univariate statistics, for multivariate data there

is no obvious measure of multivariate precision that can be used on pre-existing data

to assist researchers in the decision-making processes regarding an adequate

intensity of sampling (Anderson and Santana-Garcon, In review; Appendix 1).

The precision of a sampling technique refers to the repeatability of its measurements;

the degree of concordance among multiple estimates of a given parameter (such as

the mean) for the same population (Cochran and Cox, 1957). Precision is most

affected by sampling effort, which depends on the sampling unit size and the number

of replicates (Andrew and Mapstone, 1987). An increase in the sample unit size (e.g.

quadrat size, transect length, soak time) often involves an increase in the cost of

sampling (Bartsch et al., 1998; Gladstone et al., 2012; Langlois et al., 2010; Pringle,

1984). For instance, for video techniques, longer soak times limit the number of

replicates that can be undertaken per day in the field, and the associated time

required for subsequent video analysis increases. Thus, given the resources available,

any method should use the sampling effort that yields the most adequate precision

(Pringle, 1984).

Precision can be used to optimise sampling effort in studies involving univariate data

(Bartsch et al., 1998; Downing and Downing, 1992; Gladstone et al., 2012; Pringle,

1984). To measure species richness or the relative abundance of a single species

(univariate), methods exist to allow researchers to assess (e.g. from a series of

replicates obtained in pilot investigations) the adequacy of any given choice of

sampling-unit size and/or number of replicates (see review in Andrew and Mapstone,

1987). In particular, for univariate data one may calculate a value of precision for a

given sample of n units of a given size. Univariate precision can be calculated from a

sample as the standard error divided by the mean; namely, , where ySEp /=

51

, s is the sample standard deviation, is the sample mean and n is the

number of sampling units. Note that, as values of p decrease, precision improves.

Measures of precision can be calculated from pilot data, a plot of p vs. n can be

drawn, and we expect a gradual decrease and levelling-off in the value of p with

increasing n in such a plot. One may then consider that a value of n around this

apparent asymptote in p (i.e., where the slope of the curve becomes very small)

would be reasonable to use for future investigations of the population, as further

increases in n would not result in substantial improvements in precision.

For instance, the precision of univariate data obtained under different sampling

regimes has been assessed in order to optimise the sampling effort of a novel video

technique used in pelagic environments (Santana-Garcon et al., 2014a). Pelagic

Baited Remote Underwater stereo-Video systems (pelagic stereo-BRUVs) are a non-

destructive fishery-independent method that provide estimates of species

composition, relative abundance, length measurements, and behavioural observations

of pelagic fishes (Cappo et al., 2003; Harvey et al., 2003; Heagney et al., 2007;

Santana-Garcon et al., 2014a; Santana-Garcon et al., 2014c). The parameters

potentially affecting the sampling ability of pelagic stereo-BRUVs include soak time

(time that cameras are left recording underwater) and the number of replicate

deployments. The replication required to use this sampling technique in the pelagic

environment has been optimised under different soak time regimes using estimates

of univariate precision for species richness and abundance data (see Santana-Garcon

et al., 2014a). However, the precision and optimisation of the sampling of these fish

assemblages using multivariate data was yet to be explored.

We propose the use of a pseudo multivariate dissimilarity-based standard error

(MultSE) – a direct analogue to the univariate standard error – as a useful quantity

for assessing sample-size adequacy for multivariate ecological studies. This can be

calculated easily from a single set of pilot data or on residuals after conditioning on

some more complex sampling design or model. We further propose a double-

resampling technique (permutation followed by bootstrapping) in order to quantify

uncertainty in MultSE values with increasing numbers (or sizes) of sampling units. In

this study, we calculate the MultSE for data on pelagic fish assemblages sampled

nsSE /= y

52

using pelagic stereo-BRUVs and compare it to particular univariate precision results

obtained from the same dataset and presented in Santana-Garcon et al. (2014a).

The aim of this study is to demonstrate that MultSE can be used as a way of

optimising the sampling regime of multivariate studies in much the same way as

precision has been used in a univariate context. Moreover, we aim to optimise

sampling of pelagic fish assemblages using pelagic stereo-BRUVs by identifying an

appropriate soak time and replication beyond which there is no substantial

improvement in MultSE. Finally, we compare the resulting optimal sampling effort

to that obtained from univariate precision estimates.

3.3 Methods

The data presented was collected near Tantabiddi Passage, in the Ningaloo Marine

Park (21°53’ S, 113°56’ E). Ningaloo Reef is a fringing coral reef which stretches

for approximately 270 km adjacent to the semi-arid north-west cape of Western

Australia. The two sites sampled at this location were ~35 m deep and between 1 and

2 km offshore from the reef slope (Figure 3.1a). The pelagic stereo-BRUV system

used in this study (Figure 3.1b) is described in detail in Santana-Garcon et al.

(2014a). The systems were designed to remain in the mid-water at ca. 5 m depth

(7.17 ± 0.54 m, Mean ± SE), 30 m above the bottom and were deployed for a soak

time of 3 hours. The data presented herein was obtained from 12 pelagic stereo-

BRUVs deployments; 6 replicates conducted at each site. All samples were collected

during daylight hours, allowing at least one hour between sampling and sunrise or

sunset to avoid possible crepuscular variation in fish assemblages (Myers, 2013).

The video images were analysed using the software ‘EventMeasure (Stereo)’

(SeaGIS Pty Ltd). All fish recorded were quantified and identified to the lowest

taxonomic level possible. To avoid repeat counts of individual fish re-entering the

field of view, a conservative measure of relative abundance (MaxN) was recorded as

the maximum number of individuals of the same species appearing at the same time

(Priede et al., 1994). MaxN was recorded at 60, 120 and 180-minute soak time

intervals.

53

Figure 3.1 (a) Study area in Ningaloo Marine Park (Western Australia; site 1 is TN1 and site 2 is TN2 as per defined in Chapter 2) and (b) design of pelagic stereo-BRUVs for sampling mid-water fish assemblages.

3.3.1 Multivariate pseudo-Standard Error

As a direct analogue to the univariate measure of precision, we consider a

multivariate measure of pseudo standard error, as follows (MultSE, first described by

Anderson, 2001; full description of the method in Anderson and Santana-Garcon, In

review). Let D be a ( ) matrix of dissimilarities among all pairs of

sampling units, i = 1, ..., n and j = 1, ..., n. Using Huygens’ theorem (e.g. Anderson,

2001; Legendre and Anderson, 1999), the sum of squared inter-point dissimilarities

divided by the number of sampling points (n) is directly interpretable as a

multivariate measure of pseudo sums of squares in the space of the chosen

dissimilarity measure:

(1)

The quantity in equation (1) is also equivalent to the sum-of squared distances from

individual sampling points to their centroid in the space of the chosen resemblance

nn × }{ ijd

ndsn

i

n

ij

ij /)1(

1 1

2∑ ∑

−

= +=

=

54

measure (Anderson, 2001). Furthermore, dividing this by (n – 1) yields a quantity

that is directly interpretable as a multivariate measure of pseudo variance in the

space of the chosen dissimilarity measure:

(2)

Indeed, in the case of a single variable and Euclidean distance, s is equal to the usual

classical univariate sum-of-squares and v is equal to the usual classical unbiased

measure of the sample variance.

Note however that the word “pseudo” has been used throughout here to distinguish s

in (1) above from the classical multivariate sums-of-squares-and-cross-products

(SSCP) matrix and v in (2) above from the classical multivariate sample variance-

covariance matrix. The classical matrices will clearly take into account correlation

structures in the multivariate (Euclidean) space of the original variables, which these

pseudo measures do not. To see this, note that in the case of multivariate data and

Euclidean distance, s in (1) is equal to the trace of the classical SSCP matrix and v in

(2) is equal to the trace of the classical sample variance-covariance matrix.

Finally, based on the above, a multivariate pseudo standard error therefore can be

calculated as:

(3)

Although the univariate measure of precision takes the standard error divided by the

sample mean, this is not possible in the present case because the sample mean is

clearly a vector here (being multivariate). However, we consider that m in (3) is

perfectly suitable as a measure of precision within a given study, because the

estimate of the mean (generally obtained as the sample average for univariate

analysis) is unbiased for any sample size. The only consequence of failing to divide

by the mean is to produce a value which is not standardized in any way, hence can

only be used within the context of a given study, and not compared among studies.

Thus, although some chosen value of precision for univariate analysis might be

suggested as a rule of thumb to be applied to studies across a given field (e.g. p = 0.2

or p = 0.5, etc.), the value of m in (3) is dependent on the scale of the dissimilarity

ndvn

i

n

ij

ijn/

)1(

1 )1(

2)1(

1 ∑ ∑−

= +=−

=

nvm /=

55

measure chosen, so no such generalization for its value across multiple studies is

apparent. However, m will still nevertheless be quite useful for examining relative

precision for different sample sizes within a given study.

We propose that, given a set of pilot multivariate data having a total of N sampling

units, one may draw a random subset (without replacement) of size n = 2, 3, 4, ..., N

and for each of these random subsets, a value of m can be calculated, which may be

denoted in each case by , , ... . Repeat this process for a very large

number of random draws (say 10,000), then calculate the mean values obtained

under permutation for each sample size as , , ... . A plot of vs. k

for k = 2, 3, ..., N will then provide the graphic we require to assess precision with

increasing sample size. We propose that these means be calculated using sampling

without replacement (i.e., using a permutation method) specifically to ensure that the

values obtained will be unbiased.

Next, we propose that a bootstrapping method be used to draw error bars on the plot.

It is clear that the number of possible permutations for n = N is 1 (i.e., this is simply

the full set of data) and further that the number of random subsets without

replacement that would yield unique values for (equivalent to simply

leaving out one observation at a time) is also limited to just N possible values.

Hence, we propose obtaining a further random subset as a random draw with

replacement (i.e., a bootstrap sample), of size n = 2, 3, 4, ..., N and for each of these

to calculate a value of m, denoted in each case by , , ... . Consider the

0.025 and 0.975 quantiles of the bootstrap distribution for each sample size n = k for

k = 2, 3, ..., N as and , respectively. Unlike the permutation

approach, the bootstrap distribution is known to be biased (e.g. Davison and

Hinkley, 1997). An empirical estimate of the bias, for each sample size k, is given

by:

(4)

Hence, bias-adjusted 0.025 and 0.975 quantiles are provided by

and , (5)

π2=nm

π3=nm

πNnm =

π2=nm

π3=nm

πNnm =

πknm =

π)1( −= Nnm

β2=nm

β3=nm

βNnm =

)025.0(βknq = )975.0(β

knq =

)( βπknknkn mmb === −=

})025.0({ knkn bq == +β })975.0({ knkn bq == +β

56

respectively. As our proposed method uses permutations to obtain mean values for

the plot, and a separate bias-adjusted bootstrapping approach to obtain appropriate

error bars, we shall refer to this overall process generally as a “double resampling”

method. We note also that clearly other choices of quantiles may easily be calculated

here in a similar fashion (e.g. 0.25, 0.75, etc.), depending on the context.

The R code (R Core Team, 2014) developed for the methods proposed is provided as

a supplement in Anderson and Santana-Garcon (In review; Appendix 1).

3.3.2 Optimisation of sampling effort for multivariate analyses

In order to understand the performance of the permutation and bootstrap method

described above, we assess the means and quantiles of MultSE with increasing

sample size using each method separately. This analysis is conducted only for the 60

min soak time data, and is based on the square-root transformed abundance estimates

and Bray-Curtis dissimilarities. Next, we used the double resampling approach to

estimate the mean and quantiles of MultSE for the three soak times (60, 120 and 180

min) for 1 to 12 replicate samples.

3.4 Results

First, we examined the means and quantiles of MultSE with increasing sample size

using either the permutation or the bootstrap method alone for the 60 minute soak

time. Both approaches show precision improving with increasing sample size.

However, the permutation method alone yields obvious constraints on the error bars

for larger sample sizes, while the bootstrap method alone yields means that are

biased downwards (Figure 3.2). Thus, this motivates the use of the double

resampling approach described.

Using the double resampling method shows that MultSE as a measure of multivariate

precision improves when soak time is extended from 60 to 120 minutes, but

extending soak time to 180 minutes does not improve the precision of the data

further. Moreover, MultSE also improves with replication and it stabilises when

replication reaches 7 or 8 deployments (Figure 3.3). The range of the 95 %

confidence intervals is reduced with the increase of replication more than with the

extension of the soak time.

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Figure 3.2 Multivariate pseudo standard error as a function of sample size on the basis of Bray-Curtis dissimilarities calculated on square-root transformed fish abundance data sampled using pelagic stereo-BRUVs for a soak time of 60 minutes only, showing results (means with 2.5 and 97.5 percentiles as error bars) from 10,000 resamples obtained using either the permutation or bootstrap approach.

Figure 3.3 Multivariate pseudo standard error as a function of sample size on the basis of Bray-Curtis dissimilarities calculated on square-root transformed fish abundance data sampled using pelagic stereo-BRUVs for all three soak times (60, 120 and 180 minutes), using the double-resampling method, with permutation-based means and bias-adjusted bootstrap-based error bars (with 10,000 resamples for each).

3.5 Discussion

We have described a measure of precision for multivariate data (MultSE) based on

inter-point dissimilarities that is a direct analogue of the well-known univariate

standard error. A double resampling method allows unbiased measures of variation

in MultSE to be obtained. This estimate can be calculated from pilot data alone and

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will allow for the optimisation of sampling in a broad range of ecological studies

using multivariate data.

The use of MultSE as a measure of precision to optimise the sampling of pelagic

stereo-BRUVs shows that precision is improved by extending camera deployments

from 60 to 120 minutes, but no appreciable precision is gained by deploying the

cameras longer than 120 minutes. Moreover, the levelling-off in MultSE occurs for

sample sizes around n = 7 or 8 replicates, indicating that these or larger sample sizes

would be appropriate when using this technique to study mid-water fish assemblages

in Ningaloo Marine Park.

The results obtained are in line with the findings from Santana-Garcon et al., (2014a)

that used the same dataset to calculate univariate precision and determined that

reasonably precise estimates of total abundance and species richness of pelagic fish

assemblages could be achieved using ca. 8 replicate deployments of pelagic stereo-

BRUVs each of 120 minutes duration. Both for the univariate and multivariate

precision, the idea of “levelling-off” of precision values must rest in the eyes of the

beholder. This approach is intended to be a heuristic diagnostic tool only, and not a

prescription for defining appropriate sampling designs. Nevertheless, the analyses of

MultSE in this case coincide well with the results obtained for the precision of either

of the univariate measures of richness and total abundance (Santana-Garcon et al.,

2014a). This coincidence might not always occur, however, and will depend on the

nature of the data and the characteristics that are emphasised by the chosen

dissimilarity measure (Anderson and Santana-Garcon, In review; Appendix 1).

This approach to calculate MultSE is further expanded and tested on more complex

designs in Anderson and Santana-Garcon (In review; Appendix 1). We recognise

some limitations to the method described; for instance, the measure takes no account

of the shape of the multivariate data cloud, only its overall dispersion (size). Future

developments should include better characterization of the actual shape of the data

cloud, which might be achieved, for example, through the use of multivariate kernel

density estimation (KDE) in the principal coordinate (PCO) space of the

resemblance measure. This will require enhanced computer power and programming

to cope with analyses of high-dimensional datasets like those typically found in

ecology. Future work should focus also on how such approaches might compare with

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a more classical approach to measure precision, such as the determinant of a

normalized variance-covariance matrix in the PCO space.

A further limitation of the approach outlined here is its scale-dependency. Values of

MultSE will necessarily depend on the dissimilarity measure used, so cannot be

easily compared across studies. On the other hand, estimates of univariate precision

are standardised as they take the standard error divided by the mean, which allow for

precision values to be defined as acceptable and applied as a rule of thumb across

studies (e.g. precision values of 0.2 or 20% have been described as a ‘tolerable error’

in ecological studies (Bartsch et al., 1998). More work is needed to assess what

levels of multivariate precision (as measured by MultSE) might be acceptable for

different resemblance measures that are bounded and have a common scale, such as

Bray-Curtis. However, values of MultSE will still be useful for sampling

optimisation as it allows for the comparison of relative precision across sample sizes

within a study.

In conclusion, we suggest that multivariate ecological studies should use measures of

multivariate precision, such as the MultSE described here, in order to optimise their

sampling regime. As suggested by the univariate precision measures, estimates of

multivariate precision indicate that studies sampling with pelagic stereo-BRUVs

should use soak times of 120 minutes and sample sizes of at least 8 replicates.

Further calculations should be carried out in other areas but we believe that similar

sampling regimes will be suitable for sampling other tropical and warm-temperate

coastal environments.

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CHAPTER 4 - Calibration of pelagic stereo-BRUVs and scientific longline surveys for sampling sharks

4.1 Abstract

1. Our understanding of the ecology of sharks and other highly mobile marine

species often relies on fishery-dependent data or extractive fishery-independent

techniques that can result in catchability and size-selectivity biases. Pelagic Baited

Remote Underwater stereo-Video systems (pelagic stereo-BRUVs) provide a

standardised, non-destructive and fishery-independent approach to estimate

biodiversity measures of fish assemblages in the water column. However, the

performance of this novel method has not yet been assessed relative to other standard

sampling techniques.

2. We compared the catch composition, relative abundance and length distribution of

fish assemblages sampled using pelagic stereo-BRUVs and conventional scientific

longline surveys. In particular, we focused on sharks of the family Carcharhinidae

(requiem sharks) to assess the sampling effectiveness of this novel technique along a

latitudinal gradient off the coast of Western Australia. We calibrated the sampling

effort required for each technique to obtain equivalent samples of the target species

and discuss the advantages, limitations and potential use of these methods to study

highly mobile species.

3. The proportion of sharks sampled by pelagic stereo-BRUVs and scientific

longline surveys was comparable across the latitudinal gradient. Carcharhinus

plumbeus was the most abundant species sampled by both techniques. Longline

surveys selected larger individuals of the family Carcharhinidae in comparison to the

length distribution data obtained from pelagic stereo-BRUVs. However, the relative

abundance estimates (catch per unit of effort) from the pelagic stereo-BRUVs were

comparable to those from 5 to 30 longline hooks.

4. Pelagic stereo-BRUVs can be calibrated to standard techniques in order to study

the species composition, behaviour, relative abundance and size distribution of

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highly mobile fish assemblages at broad spatial and temporal scales. This technique

offers a non-destructive fishery-independent approach that can be implemented in

areas that may be closed to fishing, and is suitable for studies on rare or threatened

species.

4.2 Introduction

Emerging technologies are providing new options for cost-effective ecological

sampling. These technical advances dramatically increase the opportunities for in

situ ecological and behavioural research in vast and remote environments such as the

open ocean (Murphy and Jenkins, 2010). However, in order to understand the

potential of novel techniques it is necessary to compare and calibrate them against

traditional methods.

Studying the ecology and assessing the status of sharks is challenging due to their

generally high mobility, ontogenetic shifts in habitat preference and broad

geographic range (Dulvy et al., 2008). Our understanding on the biology and ecology

of sharks and other highly mobile marine species largely relies on fishery-dependent

data from commercial and recreational fisheries (Myers and Worm, 2003). The use

of fishery-dependent data alone can lead to sampling biases due to gear selectivity

and heterogeneous fishing effort that discriminate among species and habitats

(Murphy and Jenkins, 2010; Simpfendorfer et al., 2002). Alternatively, fishery-

independent surveys use more robust sampling designs, but often employ the same

commercial fishing gear (e.g. longlines, gillnets, trawls) and as such, catchability and

size-selectivity biases remain (McAuley et al., 2007a).

Scientific longline surveys are among the most commonly used fishery-independent

methods for studying the demography and ecology of shark populations

(Simpfendorfer et al., 2002). These surveys provide measures of relative abundance,

sex ratio and length distribution of a range of shark species (McAuley et al., 2007b).

Additionally, longlines allow the collection of samples for population biology

studies (e.g. genetics, age, growth, reproduction, diet) and the deployment of

conventional and electronic tags (Meyer et al., 2010). However, in order to obtain

length or biomass data from longline surveys, sharks must be caught, retrieved and

handled out of the water. The handling of sharks aboard research vessels aims to

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maximise survival of individuals, but all captured fish are exposed to varying

degrees of physiological stress and physical trauma that can induce pre- or post-

release mortality (Skomal, 2007). Consequently, these extractive techniques may not

be suitable for sampling rare or threatened species, or used in areas that are closed to

fishing (Murphy and Jenkins, 2010).

Baited Remote Underwater Video systems (BRUVs) provide an alternative

standardised, non-extractive and fishery-independent approach that is widely used to

estimate biodiversity indices and relative abundance measures of a range of marine

species (Cappo et al., 2003; Langlois et al., 2012a; Santana-Garcon et al., 2014c),

including sharks (Brooks et al., 2011; Goetze and Fullwood, 2013; White et al.,

2013). This technique uses bait to attract individuals into the field of view of a

camera so that species can be identified and individuals counted (Dorman et al.,

2012). When stereo-camera pairs are used, precise length measurements can be made

and biomass estimated (Harvey et al., 2010). Pelagic stereo-BRUVs are a novel

method that sets camera systems at a predetermined depth in the water column as

opposed to the commonly used benthic deployment where stereo-BRUVs are set on

the seafloor. This deployment design allows pelagic stereo-BRUVs to estimate the

composition, relative abundance and length distribution of fish assemblages that

inhabit the water column (Heagney et al., 2007; Santana-Garcon et al., 2014a).

Methodological comparisons assist in validating the utility of innovative sampling

methods and to understand the advantages and limitations of different techniques.

The use of benthic BRUVs to survey demersal fish assemblages has been compared

to scientific fishing surveys including trawl (Cappo et al., 2004), trap (Harvey et al.,

2012c), hook and line (Langlois et al., 2012b), and longline (Ellis and Demartini,

1995). Additionally, benthic BRUVs and scientific longline surveys were compared

to estimate the diversity and relative abundance of sharks in the Bahamas (Brooks et

al., 2011). These studies found that the species composition determined with baited

video techniques differed to the catch of trawls (Cappo et al., 2004) but it was

comparable to some extent to the catch of traps and longlines (Brooks et al., 2011;

Harvey et al., 2012c). Estimates of relative abundance differed among techniques,

especially for rare species (Brooks et al., 2011; Harvey et al., 2012c). However,

differences in length distribution of species taken in traps and hooks or recorded on

stereo-BRUVs were not biologically significant (Langlois et al., 2012b). Pelagic

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stereo-BRUVs have been used to assess pelagic fish assemblages (Santana-Garcon et

al., 2014a) but their performance has not been assessed relative to other sampling

techniques.

The present study aims to compare the catch composition, relative abundance and

length distribution of fish sampled by pelagic stereo-BRUVs and conventional

scientific longlines. The sampling effort required for each technique to obtain

equivalent relative abundance samples is determined for each of the target species. In

particular, surveys were conducted along a latitudinal gradient off the coast of

Western Australia to target sharks of the family Carcharhinidae, commonly known

as requiem sharks. Given that longlines used large hooks to target sharks, we

hypothesised that the methods would differ in total catch composition with pelagic

stereo-BRUVs providing data on a broader range of species. However, we expect

that both methods would generate comparable estimates of relative abundance and

length distribution for the targeted shark species. Finally, the advantages and

limitations of pelagic stereo-BRUVs and scientific longline surveys to study highly

mobile species are discussed.

4.3 Methods


We conducted a longline and pelagic stereo-BRUVs survey in August 2012 at 10

sites over 950 km along the coastline of Western Australia (Figure 4.1). Sites were

15 to 80 km offshore at depths ranging between 35 and 106 metres, although most

sites were 40 to 60 metres deep. Data were recorded from 31 pelagic stereo-BRUVs

and 31 scientific longline deployments targeting requiem sharks. Three replicate

deployments of each method were conducted simultaneously at each site, with the

exception of one site in the Houtman Abrolhos Islands where four replicates of each

technique were undertaken. The number of replicates for each method was limited

by logistical constraints of this research expedition. During deployment, both

methods were interspersed following a straight line with a separation of at least one

kilometre between deployments of either method to avoid or minimize potential

overlap of bait plumes and, to reduce the likelihood of fish moving between

replicates.

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Figure 4.1 Location of study sites along the coast of Western Australia.

Scientific longline surveys

Scientific longline surveys were conducted as part of the annual shark monitoring

and tagging program of the Department of Fisheries (Western Australia). Surveys

were designed to target requiem sharks. The longlines were 500 m in length and

comprised ~50 J-shaped hooks of size 12/0 baited with sea mullet (Mugil cephalus;

half a fish per hook) and attached to the main line via 2 m metal snoods (Figure

4.2a). Lines were designed to hold hooks approximately 8 metres above the seafloor.

However, hooks near the ballast remained closer to the bottom; this was confirmed

during retrieval as fragments of benthos such as sponges were occasionally caught

(Santana-Garcon pers. obs.). Longlines were set before dawn at ~5 am and soak time

ranged between 2.5 and 6 hours, depending on the time required for retrieval and

processing of the catch. Upon retrieval, all individuals caught were identified to the

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species level and their fork length (FL) was measured. Catch per unit of effort

(CPUE), a measure of abundance where catch is standardised across deployments of

different sampling effort, was calculated as the catch of each longline divided by the

soak time (hours) and the number of hooks used. In the present study, we defined

CPUE10 as the catch per hour per 10 hooks as a measure to facilitate comparison

between methods (the number of hooks was chosen on the basis of results presented

herein).

Figure 4.2 Deployment design of the (a) scientific longline shots and (b) pelagic stereo-BRUVs used to sample requiem sharks.

Pelagic stereo-BRUVs

Pelagic stereo-BRUVs were adapted to match the deployment characteristics of

longlines so that both methods sampled at similar depths, for equal periods of time

and using the same bait. During the deployment of pelagic stereo-BRUVs, cameras

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were placed in the mid-water, approximately 8 to 10 metres above the bottom

(Figure 4.2b). This technique uses ballast and sub-surface floats in order to anchor

the systems, enabling control over the deployment depth and reducing movement

from surface waves (Santana-Garcon et al., 2014a). The camera systems consisted of

two Sony CX12 high definition digital cameras mounted 0.7 m apart on a steel frame

and converged inwards at 8 degrees to allow the measurement of fish length (Harvey

et al., 2010). The bait consisted of 1 kg of mullet (Mugil cephalus; fish cut in halves)

in a wire mesh basket suspended 1.2 m in front of the cameras. As for longlines,

camera deployments were set before dawn, at 5 am in the morning, and soak time

ranged between 2.5 and 6 hours depending on the time required for longline

retrieval. Videos were analysed for the full length of the deployment. A blue light

(wavelength 450-465 nm) was fitted on the frame, between the cameras, in order to

illuminate the field of view during the sampling hours before dawn. Blue light

wavelength is thought to be below the spectral sensitivity range for many fish

species (Von Der Emde et al., 2004), and therefore, it is expected to have minimal

impact on fish behaviour (Harvey et al., 2012b).

4.3.2 Video analysis

Stereo-camera pairs were calibrated before and after the field campaign using CAL

software (SeaGIS Pty Ltd) following Harvey & Shortis (1998). The video images

obtained from pelagic stereo-BRUVs were analysed using the software

‘EventMeasure (Stereo)’ (SeaGIS Pty Ltd). All fish observed were quantified,

identified to the lowest taxonomic level possible and measured. However, for this

study, small pelagic fish species in the family Clupeidae and small carangids from

the genus Decapterus, Selar and Selaroides among others were excluded from the

analysis. A conservative measure of relative abundance, MaxN, was recorded as the

maximum number of individuals of the same species appearing in a frame at the

same time. MaxN avoids repeat counts of individual fish re-entering the field of view

(Cappo et al., 2003; Priede et al., 1994). MaxN per hour was used in order to

standardise sampling effort across all deployments due to variable soak times.

Length measurements (FL) were made from the stereo-video imagery for each

individual within 7 metres of the camera system recorded at the time of MaxN.

Individuals must be measured when their body is straight which can be difficult for

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sharks given their swimming behaviour, as such, in order to improve the accuracy of

shark measurements, the length of each individual was determined from an average

of five measurements obtained in different video frames (Harvey et al., 2001a).

4.3.3 Statistical analysis

Comparison of catch composition

Differences in species composition between scientific longlines and pelagic stereo-

BRUVs were tested using one-way univariate permutational analysis of variance

(PERMANOVA; Anderson et al., 2008). Proportional data facilitates the

comparison of composition patterns sampled by each method as it standardises all

samples to the same scale (Jackson, 1997). Hence, for each of the five species of

requiem sharks recorded, we used proportional data to emphasise the contribution of

each species to the total number of individuals caught per deployment and method.

Proportional data were calculated from CPUE data across all replicates and were

arcsine transformed to normalise possible binomial distributions (Zar, 1999).

Euclidean distance was used to generate the dissimilarity matrices (Anderson et al.,

2011b), P-values were obtained using permutation tests (9,999 permutations) for

each individual term in the model and, Monte Carlo p-values were used to interpret

the result when the number of unique permutations was less than 100 (Anderson,

2001). Data manipulation and graphs across the study were undertaken using the

packages ‘reshape2’(Wickham, 2007), ‘plyr’ (Wickham, 2011) and

‘ggplot2’(Wickham, 2009) in R (R Core Team, 2013).

Catch comparison along a latitudinal gradient

The ability of the two methods to describe spatial patterns along a latitudinal

gradient (32° to 24° S) was compared. For each of the target species, a one-way

analysis of covariance (ANCOVA) was conducted with method as a factor and

latitude as a covariate. A significant interaction between latitude and method would

indicate that the methods were not comparable across the latitudinal range. Analyses

were based on Euclidean distance resemblance matrices calculated from arc sine

transformed proportional data. Statistical significance was tested using 9,999

permutations of residuals under a reduced model.

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Equivalence of sampling effort

For the target species, the equivalent longline and pelagic stereo-BRUVs sampling

effort was determined by performing a series of statistical tests on the abundance

estimates obtained from BRUVs (MaxN per hour) and from a range of longline

effort data sets (1-50 hooks). Random samples of our data were taken with

replacement and the differences between methods were tested using univariate

PERMANOVAs based on Euclidean distance resemblance matrices of the raw

CPUE data, with method as a fixed factor (Anderson et al., 2011b). P-values were

obtained from 9,999 permutations using the ‘adonis’ function from the ‘vegan’

package (Oksanen et al., 2013) in R. This process was bootstrapped (1000 times) to

generate a distribution of p-values across sampling efforts for the target species.

Additionally, we compared the sampling precision of both techniques at the family

level and for each target species. The precision of a sampling method refers to the

repeatability of its measurements under unchanged conditions, it can be expressed

numerically by measures of imprecision like standard deviation, variance and most

commonly, as a ratio of the standard error (SE) and the mean (Andrew and

Mapstone, 1987). Here, we estimated precision (p) as p = SE / Mean, where the

mean and standard error were obtained from the abundance per deployment for each

sampling technique.

Comparison of length distributions

For the target species, length distributions obtained from pelagic stereo-BRUVs and

longline surveys were compared using kernel density estimates (KDE). The KDE

method is sensitive to differences in both the shape and location of length

distributions (Sheather and Jones, 1991). KDE analyses were conducted using the R

packages ‘KernSmooth’ (Wand, 2013) and ‘sm’ (Bowman and Azzalini, 2013)

following the method described by Langlois et al. (2012b). For each species, the

statistical analysis between the pairs of length distributions collected by each method

was based on the null model of no difference and a resulting permutation test. The

statistical test compared the area between the KDEs for each method to that resulting

from permutations of the data into random pairs. To construct the test, the geometric

mean between the bandwidths for stereo-BRUVs and longline data were calculated

70

(Bowman and Azzalini, 1997). If the data from both methods have the same

distribution, the KDEs should only differ in minor ways due to within population

variance and sampling effects (Langlois et al., 2012b). The ‘sm.density.compare’

function in the ‘sm’ package was used to plot the length distributions where the

resulting grey band shows the null model of no difference between the pair of KDEs.

4.4 Results

Comparison of catch composition

Scientific longline surveys used 1,671 baited hooks (125 hours) and caught 236

individuals of 18 different species. Pelagic stereo-BRUVs recorded 123 hours of

video in 31 deployments with a total of 124 individuals of 20 species identified. The

numerous small pelagic fish (TL < 250 mm) observed in the video were not included

in the species count or in the analyses. Teleost species were almost exclusively

sampled by pelagic stereo-BRUVs, while the semi-pelagic sharks were sampled by

both methods (Figure 4.3). Due to the deployment design of longlines, a proportion

of the hooks adjacent to the ballast were set close to the bottom and, consequently,

benthic sharks were almost exclusively sampled by this method.

The target shark species caught included sandbar Carcharhinus plumbeus, tiger

Galeocerdo cuvier, blacktip C. limbatus/tilstoni and milk Rhizoprionodon acutus

sharks, and Carcharhinus spp*. The latter combines four requiem species that could

not be confidently distinguished across all videos (bronze whaler C.brachyurus,

dusky C.obscurus, spinner C.brevipinna and spot-tail C.sorrah sharks). The common

blacktip C. limbatus and the Australian blacktip C. tilstoni sharks are also combined

here as there are no external morphological features that distinguish these species

(Harry et al., 2012).

For each of the target species, there was no significant difference in the proportion

sampled by either method (P > 0.05; Fig. 4). The sandbar shark C. plumbeus was the

most abundant species for both methods followed by Carcharhinus spp* (Figures

4.3 and 4.4). Using pelagic stereo-BRUVs, the third and fourth most abundant

species were G. cuvier and C. limbatus/tilstoni, whereas with longlines these species

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were the fourth and third most abundant respectively. Rhizoprionodon acutus was

the least abundant and it was rarely recorded by either method.

Figure 4.3 Mean relative abundance of fish species sampled using scientific longline surveys and pelagic stereo-BRUVs. Catch per unit of effort (CPUE) is shown as catch per hour for 10 hooks (CPUE10) in longline samples and as MaxN per hour in stereo-BRUVs. a Benthic sharks were caught in longlines due to the deployment design setting of a proportion of hooks near the bottom.

Figure 4.4 Mean species proportion of target species sampled using scientific longline surveys and pelagic stereo-BRUVs. P-values show non-significant differences between sampling methods.

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Catch comparison along a latitudinal gradient

Pelagic stereo-BRUVs and scientific longline surveys showed similar patterns of

abundance for all target species along the 950 km latitudinal gradient (Figure 4.5).

The ANCOVA revealed no significant interaction between method and latitude for

any of the target species (P > 0.05). The species proportional abundance did not

differ significantly between methods whereas latitude had a significant effect on the

distribution of C. plumbeus with a greater abundance present in the northern sites

(Table 4.1). Rhizoprionodon acutus and C. limbatus/tilstoni also showed a

significant effect of latitude in their distribution as they were only recorded north of

Shark Bay, the most northern sampling sites (~24° S). For G. cuvier and the species

complex (Carcharhinus spp*), there was no significant effect of latitude or method.

Figure 4.5 Relative abundance (CPUE) of target species along a latitudinal gradient (32° - 24° S) sampled using scientific longline surveys and pelagic stereo-BRUVs. Trendlines illustrate the ANCOVA result.

Equivalence of sampling effort

PERMANOVA tests on bootstrapped CPUE data and a range of sampling efforts

indicated that the relative abundance of requiem sharks obtained from each camera

system (MaxN per hour) is statistically comparable (P > 0.05) to a sample obtained

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from 5 to 30 hooks with similarities peaking at 12 hooks (Figure 4.6). This range of

effort equivalence for requiem sharks is largely driven by C. plumbeus, the most

abundant species in this study. The range of equivalence varied among species, for

C. plumbeus effort equivalence ranged between 3 and 30 hooks, with similarities

peaking at 10 hooks. Carcharhinus spp* and G. cuvier showed no significant

difference between methods when MaxN per hour was compared to the catch of 1 to

50 hooks but similarities peaked at 24 and 21 hooks, respectively. Results for C.

limbatus/tilstoni and R. acutus were inconclusive due to the low abundance recorded

with both techniques.

Table 4.1 Summary of ANCOVA tests with method as factor and latitude as covariate. Abundance data was collected with pelagic stereo-BRUVs and scientific longline surveys along an 8-degree latitudinal gradient. P-values in bold are statistically significant.

Latitude Method La x Me Carcharhinus plumbeus <0.001 0.878 0.108 Carcharhinus spp* 0.486 0.291 0.571 Galeocerdo cuvier 0.350 0.226 0.208 Carcharhinus limbatus/tilstoni 0.022 0.682 0.303 Rhizoprionodon acutus 0.003 0.834 0.410

Figure 4.6 Equivalence of sampling effort required to estimate relative abundance of requiem sharks. Plot shows mean p-values from one-way PERMANOVAs testing the differences between pelagic stereo-BRUVs (MaxN per hour) and scientific longline surveys (catch*h-1 *hook-1) across different levels of sampling effort (number of hooks). Values in the shaded area (P < 0.05) are statistically significant. Grey line is shown for reference as the general pooling cut-off (P > 0.25).

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Precision estimates of pelagic stereo-BRUVs and scientific longline surveys were

similar for the Carcharhinidae family, C. plumbeus, G. cuvier and C.

limbatus/tilstoni (Table 4.2). For Carcharhinus spp* and R. acutus, estimates

obtained from longline surveys were more precise. Note that, as the values of p

decrease, the precision of the sampling technique improves. We found that both

techniques were considerably less precise at sampling uncommon species compared

to the more abundant species. Precision values of 1 indicate that individuals of that

species were only recorded in one deployment.

Table 4.2 Precision estimates for target species sampled using pelagic stereo-BRUVs and scientific longline surveys. Precision (p) was estimated as a ratio of the standard error and the mean abundance per deployment. Note that lower values of p indicate better precision.

p BRUVs p Longlines

Family Carcharhinidae 0.236 0.206 Carcharhinus plumbeus 0.273 0.238 Carcharhinus spp* 0.427 0.270 Galeocerdo cuvier 0.376 0.331 Carcharhinus limbatus/tilstoni 1 0.964 Rhizoprionodon acutus 1 0.736

Comparison of length distributions

There were no significant differences in the shape of the length distributions sampled

with both methods for the family Carcharhinidae and, at the species level, for C.

plumbeus, Carcharhinus spp* and G. cuvier. However, there were significant

differences in the location (i.e. mean length) of the length distributions for the

Carcharhinidae and, at the species level, for C. plumbeus. For these taxa, longline

surveys were more selective of larger individuals (Table 4.3, Figure 4.7). Standard

error bands are wide for those species with small sample sizes; therefore the

interpretation of the results should be undertaken with caution.

4.5 Discussion

We demonstrated that pelagic stereo-BRUVs provide an alternative non-lethal

method of sampling sharks that can be calibrated with standard methods such as

scientific longline surveys. The proportion of Carcharhinidae species sampled by

pelagic stereo-BRUVs and scientific longline surveys was comparable across the

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study. Pelagic stereo-BRUVs provided a comparable estimate of Carcharhinidae

species that is proportional to longline surveys, whilst also providing abundance

information on other teleost species that were not targeted or captured by longlines

due to the selectivity of the hooks. Longlines sampled a greater proportion of benthic

shark species due to the deployment design that set hooks adjacent to the ballast in

close proximity to the benthos.

Table 4.3 Summary of the lengths of target species measured on pelagic stereo-BRUVs and caught on scientific longline surveys. Maximum (Max), minimum (Min) and mean fork length (FL) are shown in millimetres.

Species Max FL (mm) Min FL (mm) Mean FL (mm)

BRUVs Longline BRUVs Longline BRUVs Longline

Family Carcharhinidae 2937 2870 587 702 1348 1367 Carcharhinus plumbeus 1386 1600 587 730 1089 1280 Carcharhinus spp* 1855 2110 1157 1534 1534 1627 Galeocerdo cuvier 2937 2870 1285 930 2028 1823

The species composition of the Carcharhinidae between the two methods was also

consistent across a broad latitudinal gradient. These findings support previous

studies that define baited video techniques as a suitable, standardised and non-

extractive approach to study the distribution of mobile species across broad spatial

scales (Langlois et al., 2012a; White et al., 2013). In the current study, C. plumbeus

was the most abundant requiem shark species captured by both sampling methods. It

was recorded throughout the study area, but in greater numbers at the northern sites.

Galeocerdo cuvier and the Carcharhinus spp* complex also occurred throughout the

study area, and showed no significant pattern along the latitudinal gradient for either

method. Carcharhinus limbatus/tilstoni and R.acutus were only recorded in the most

northern sites.

Each pelagic stereo-BRUV system yielded equivalent relative abundance estimates

for requiem sharks to that of 5 to 30 hooks in scientific longlines. Effort equivalence

between techniques peaked at 12 hooks for requiem sharks and, at the species level,

effort equivalence peaked at 10, 21 and 24 hooks for C. plumbeus, G. cuvier and C.

spp*, respectively. Due to logistic constraints we could only deploy one camera

system for every longline (50 hooks). Although the target species composition and

relative abundance derived from these techniques were comparable, in absolute

terms longlines caught a greater number of individuals of the target species (159)

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than those recorded at MaxN on BRUVs (36). In addition, it should be noted that the

methods differed in the area covered and in the amount of bait used. Longline shots,

with a length of 500 m each and more than 10 times the amount of bait in the water,

have a greater ability to attract or encounter fish than a single baited camera system

(Brooks et al., 2011). Thus, increasing sampling effort of the non-extractive pelagic

stereo-BRUVs to approximately one camera deployment for every 10 to 24 hooks is

recommended to exert a sampling effort equivalent to the commonly used scientific

longline surveys.

Precision estimates for both techniques were similar at family level and for the most

abundant target species. Precision is most affected by sampling effort; thus

increasing replication would rapidly enhance sampling precision (Andrew and

Mapstone, 1987). In this study, the number of deployments of pelagic stereo-BRUVs

per site was limited by the complexity of using two methods at once. However,

future studies using this technique could deploy more camera systems

simultaneously, which would rapidly boost replication without added field-time cost

(Santana-Garcon et al., 2014a) and, in turn, it would rapidly enhance their sampling

precision.

Figure 4.7 Comparison of kernel density estimate (KDE) probability density functions for the length distributions of requiem shark species caught by pelagic stereo-BRUVs and scientific longline surveys. Grey bands represent one standard error either side of the null model of no difference between the KDEs for each method.

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Stereo-BRUVs remove biases due to gear selectivity, such as hook size, that are an

undesired by-product of conventional fishing methods (Cappo et al., 2003). In the

present study, longline surveys were selective towards larger individuals of the

family Carcharhinidae in comparison to pelagic stereo-BRUVs. At the species level,

size selectivity was only significant for C. plumbeus, but KDE tests for other shark

species might lack power to detect differences between methods due to the small

sample sizes available (N < 50) (Bowman and Azzalini, 1997). Mean fork length of

C. plumbeus (1280 mm) and C. spp* (1627 mm) recorded from longline surveys

were larger than those recorded from stereo-BRUVs (1089 and 1534 mm).

Conversely, tiger sharks were on average larger on stereo-BRUVs (2028 mm) in

comparison to longlines (1823 mm). Previous studies have shown species-specific

differences in the length distributions of fish sampled with stereo-BRUVs and line

fishing (Langlois et al., 2012b), or traps (Harvey et al., 2012c) but the differences

reported were not biologically significant. In the present study, low replication was a

major limitation in the analysis of length distributions; hence, further research is

needed to continue exploring the differences between size selectivity of longlines

and pelagic stereo-BRUVs.

Video techniques have proven to be non-intrusive, causing no physical trauma or

physiological stress to the individuals recorded (Brooks et al., 2011). Despite

attempts to minimise the impact on sharks caught in longline surveys, mortality does

occur and the level of post-release mortality is not known (Skomal, 2007). The non-

destructive nature of stereo-BRUVs allows for deployment in fragile and protected

areas and reduces the negative effects of extractive gears when targeting rare and

threatened species (White et al., 2013). Additionally, remote video techniques

provide a permanent record of species behaviour in their natural environment

(Santana-Garcon et al., 2014c; Zintzen et al., 2011). A recurrent behaviour across

shark species observed during video analysis was that individuals were first observed

far from the camera system but remain in the area patrolling the bait source. They

approach the bait in a cautious manner over time. This behaviour suggests that

longer soak times facilitate the recognition of individual features including species,

sex or external markings. Nonetheless, this territorial behaviour could also prevent

other individuals of the same or other species from approaching the cameras, which

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could affect estimates of species composition and relative abundance (Klages et al.,

2014).

Many species of the family Carcharhinidae are externally similar and visual

identification can be difficult. Identification of individuals to species level from

video alone is the main limitation of pelagic stereo-BRUVs to study requiem sharks

(Santana-Garcon et al., 2014a). Species identification can also be restricted in

fishery-dependent methods, species may be misidentified or pooled under general

categories (Walker, 1998). Identifying features of requiem sharks are often subtle

and the most important of these are tooth shape and numbers, position of the dorsal

fins, colour, and the presence or absence of an interdorsal ridge. These features can

be difficult to assess during the rapid processing of sharks caught on longlines and

this is exacerbated when using remote video techniques. Although most species

could be distinguished on video when individuals come close to the cameras,

identification of some species across all replicate videos may not be possible.

Another constraint of video techniques, although not assessed in this study, are

limitations to identifying the sex of individuals. Claspers of mature males were often

visible, but identification of females and young males with uncalcified claspers was

more challenging. The lack of this information could limit the use of video

techniques in studies of intra-species demographics (Brooks et al., 2011). However,

advances in high-definition digital video and automation of the identification of key

morphological characteristics could improve the rates of identification of species,

sex and even discrimination between individuals (Harvey et al., 2010; Shortis et al.,

2013).

This assessment of the novel pelagic stereo-BRUVs and its comparison to the

commonly used scientific longline surveys provides a better understanding of the

strengths and limitations of each technique. The two methods produce comparable

estimates of relative abundance and species composition for requiem sharks, and the

choice of sampling technique in future should depend on the specific aims of the

study. Scientific longline surveys continue to be a more appropriate approach for

research targeting species that could not be confidently identified on video, or

studies on population biology that require finer intra-specific information such as sex

ratio or reproduction information, the collection of tissue samples (e.g. genetic and

isotopic analyses), or the implantation of conventional or electronic tags (McAuley

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et al., 2007b). Stereo-BRUVs, however, provide a suitable sampling method that can

be calibrated to standard techniques for studies with broad spatial and temporal

scales, directed at questions of species composition, behaviour, relative abundance

and size distribution of fish assemblages (Langlois et al., 2012a; Santana-Garcon et

al., 2014c; Watson and Harvey, 2009), including highly mobile species (Brooks et

al., 2011; Santana-Garcon et al., 2014a; White et al., 2013). Furthermore, studies

conducted on rare or threatened species, and in areas that are closed to fishing might

require a non-intrusive approach like baited video techniques (White et al., 2013).

Our study demonstrated that pelagic stereo-BRUVs can provide comparable

information to longline surveys on the relative abundance and size composition of

requiem sharks, and determined the required sampling effort to calibrate both

methods.

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CHAPTER 5 - Effects of a spatial closure on highly mobile fish species: an assessment using pelagic stereo-BRUVs

5.1 Abstract

The effects of a spatial area closure on pelagic fish assemblages within the Houtman

Abrolhos Islands were assessed using mid-water pelagic stereo-BRUVs. The spatial

area closure within the Easter Group of the Houtman Abrolhos Islands was found to

have no significant effect on the species composition and relative abundance of

pelagic fish assemblages. The most abundant demersal target species recorded was

pink snapper (Chrysophrys auratus) and, individuals measured within the spatial

closure were significantly larger than those sampled in the area open to fishing. This

spatial area closure is of moderate size (22.3 km2), but the spatial management of

highly mobile species may require larger area closures than those for reef-associated

species. The monitoring of pelagic species both in large and small spatial area

closures is required in order to better understand how mobile species respond to this

management strategy. Some species were only recorded in relatively low numbers

using pelagic stereo-BRUVs. Moreover, some of the highly mobile pelagic fish

species, such as tunas, mackerel and some shark species proved difficult to measure,

as these species were observed furthest from the camera systems. While pelagic

stereo-BRUVs are an effective fishery-independent approach to monitor spatial

fishing closures, improving the power of replicates by pooling individual

deployments, and increasing the attraction rate of pelagic fish to the stereo-cameras,

will enhance their performance in future studies.

5.2 Introduction

Spatial area closures such as marine reserves, fish habitat protection areas and other

forms of spatial fishing closures have been widely implemented around the world in

coastal areas to protect a broad range of species from exploitation and for

biodiversity conservation (Dichmont et al., 2013; Edgar et al., 2014; Russ and

Alcala, 2011). However, species with different life histories and ecological traits are

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likely to respond differently to protection (Claudet et al., 2010). There has been

extensive work undertaken on assessing the effects of spatial area closures on

demersal or sedentary species (Babcock et al., 2010; Ballantine, 2014; Claudet et al.,

2008). However, the methods used have often missed or not targeted the mid-water

and, consequently, the response of mobile pelagic species to spatial closures is the

subject of ongoing debate (Claudet et al., 2010; Davies et al., 2012; Game et al.,

2009; Gruess et al., 2011; Kaplan et al., 2010). Highly mobile species are not

expected to respond positively to discrete spatial fishing closures because these are

often of limited size and theoretical evidence suggests that effective protection

requires the areas closed to fishing to be larger than the home range of the

individuals targeted (Gruess et al., 2011; Palumbi, 2004; Walters et al., 2007).

However, recent studies provide evidence suggesting that mobile species may

benefit from spatial area closures (Bond et al., 2012; Claudet et al., 2010; Goetze and

Fullwood, 2013; Jensen et al., 2010; Knip et al., 2012; Pichegru et al., 2010).

Spatial management of pelagic environments in the open ocean, such as closing

areas of the high seas to fishing, is being increasingly discussed (de Juan and

Lleonart, 2010; Game et al., 2009; 2010; Grantham et al., 2011; Hyrenbach et al.,

2000; Kaplan et al., 2010; Mills and Carlton, 1998; Norse, 2005; Sumaila et al.,

2007; White and Costello, 2014). Simultaneously, large offshore marine spatial area

closures are also increasingly being established around the world (Davies et al.,

2012; Kaplan et al., 2014; Notarbartolo-Di-Sciara et al., 2008; Sheppard, 2010). The

main challenges for the implementation of pelagic spatial area closures include: (1)

the need for large closures in order to cover the broad home ranges of pelagic species

(Kaplan et al., 2010); and (2) difficulties associated with compliance because the

majority of pelagic environments (64%) occur outside national jurisdictions making

enforcement difficult and expensive (Sumaila et al., 2007; White and Costello,

2014). Moreover, pelagic fish hotspots can be difficult to identify and are likely to

vary in space and time (Hyrenbach et al., 2000; Norse, 2005). Research on pelagic

ecosystems is often data poor and there is a lack of demographic and baseline data

for many pelagic species (Claudet et al., 2010; Freon and Misund, 1999; Worm et

al., 2005). Research on pelagic fish presents difficulties for the collection and

interpretation of survey data, and often relies exclusively on fisheries catch data that

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can lead to sampling biases and is not suitable for sampling in areas that are closed to

fishing (Heagney et al., 2007; Ward and Myers, 2007).

Spatial monitoring, both inside and outside spatial area closures, is needed to

quantify the effect of protection on fish assemblages (Murphy and Jenkins, 2010). It

is essential to improve our capacity to monitor changes in marine communities, and

particularly in pelagic ecosystems, using non-destructive and fishery-independent

techniques where applicable (Claudet et al., 2010). Combining traditional sampling

methods and emerging technologies like remote sensing, echo-sounder transects and

Baited Remote Underwater stereo-Video systems (stereo-BRUVs) has been

discussed as being the most effective way to improve the quality of data for spatial

management (Murphy and Jenkins, 2010). For instance, pelagic stereo-BRUVs may

have the potential to monitor the effects of spatial area closures on pelagic fish

(Claudet et al., 2010; Heagney et al., 2007; Santana-Garcon et al., 2014a; Santana-

Garcon et al., 2014d), in the same way that stereo-BRUVs have been widely used to

study such effects on demersal fishes (Goetze et al., 2011; Harvey et al., 2012b;

McLean et al., 2010; Watson et al., 2009).

Baited video techniques are increasingly used to obtain estimates of biodiversity,

relative abundance, behaviour and, when stereo-cameras are used, size and biomass

of a range of marine species (Bailey et al., 2007; Harvey et al., 2013; Harvey et al.,

2012c; Langlois et al., In press; Mallet and Pelletier, 2014; Santana-Garcon et al.,

2014c; White et al., 2013). This method uses bait to attract individuals to the field of

view of the cameras so that individuals can be counted, identified and accurately

measured (Dorman et al., 2012; Hardinge et al., 2013). BRUVs have proven to be a

robust method for assessing fish community structure in deep water (Bailey et al.,

2007; Zintzen et al., 2012), estuaries (Gladstone et al., 2012), tropical or temperate

reefs (Harvey et al., 2012b; Harvey et al., 2012c; Langlois et al., 2010; Unsworth et

al., 2014) and the pelagic environment (Heagney et al., 2007; Santana-Garcon et al.,

2014a; Santana-Garcon et al., 2014d). As opposed to the commonly used benthic

deployments set on the seafloor, pelagic stereo-BRUVs set the camera systems at a

predetermined mid-water depth, which allows the study of pelagic and mobile fish

assemblages that inhabit the water column (Santana-Garcon et al., 2014a).

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Monitoring the response of mobile species to spatial management in coastal areas

can contribute to understanding the effects of protection in larger offshore spatial

area closures and improve the management of pelagic species in the future. Studying

these effects in nearshore spatial area closures is logistically and economically more

feasible than monitoring large offshore fishing closures (Edgar et al., 2014; Sumaila

et al., 2007). For instance, the Houtman Abrolhos Islands, located off the mid-west

coast of Western Australia, comprise a series of well studied spatial area closures

established to protect valuable and vulnerable reef fish species (Dorman et al., 2012;

Fitzpatrick et al., 2013; Harvey et al., 2012b; McLean et al., 2010; Nardi et al., 2004;

Sumner, 2008; Watson et al., 2007; Watson et al., 2009; Watson et al., 2010). The

Abrolhos are comprised of 4 island groups (North Island, Wallabi group, Easter

group, and Pelsaert group) and each group has one area closed to fishing (these

spatial area closures are termed Reef Observation Areas; ROAs). These areas have

been closed to scalefish fishing since 1994, thus catching fish by line, spear or any

other method is prohibited, but lobster pots are permitted. Periodic monitoring of the

effects of protection on the fish assemblages and, particularly, on the demersal target

species has shown a variety of responses over the years including an increase in

abundance (Harvey et al., 2012b; Nardi et al., 2004; Shedrawi et al., 2014; Watson et

al., 2007), increase in average fish size or biomass due to protection (McLean et al.,

2010; Watson et al., 2009), or no significant difference between the assemblages

inside and outside the fishing closures (Dorman et al., 2012). However, none of the

studies in the area have targeted their sampling to the pelagic environment, thus the

effect of these spatial area closures on highly mobile species remains largely

unknown.

We sampled fish assemblages in the water column in areas open and closed to

fishing using pelagic stereo-BRUVs. The aims of this study were to (1) explore the

effects of protection on pelagic species in the Houtman Abrolhos Islands; (2) assess

the potential of pelagic stereo-BRUVs as a monitoring technique for spatial area

closures; and (3) contribute to the ongoing debate on the use of spatial area closures

for the conservation and management of highly mobile species both in coastal waters

and the high seas.

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5.3 Methods

5.3.1 Study area

The Houtman Abrolhos Islands are located 60 km offshore from the mid-west coast

of Western Australia between 28° 15’ S and 29° 00’ S. In this study, pelagic stereo-

BRUVs were used to survey fish assemblages in the mid-water at 6 sites in the

Easter group (Figure 5.1). Sites were selected haphazardly, off the reef edge (in the

pelagic environment above the base of the reef slope) between 30 and 35 m deep.

Three sites were within the area closed to fishing and three in areas where fishing is

permitted. The Easter group ROA covers 22.29 km2 and is the second largest spatial

area closure at the Houtman Abrolhos Islands.

Figure 5.1 Location of (A) the Houtman Abrolhos Islands and (B) the study sites inside and outside the spatial area closure at Easter Group. The area shaded in light grey shows the Reef Observation Area closed to fishing since 1994.


The pelagic stereo-BRUVs used in this study were designed to be deployed,

anchored and to remain at ~10 m depth in the water column (Figure 5.2). The bait

arm acts as a rudder and keeps the camera system facing downstream of the current.

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The use of ballast and sub-surface floats effectively reduces movement from surface

waves and allows for control over the deployment depth. A full description of the

deployment method is provided in Santana-Garcon et al. (2014a). The camera

systems consisted of two Sony CX12 high definition digital cameras mounted 0.7 m

apart on a steel frame and converged inwards at 8 degrees to allow the measurement

of fish length (Harvey et al., 2010). The bait consisted of 800 g of crushed pilchards

(Sardinops sagax) in a wire mesh basket suspended 1.2 m in front of the cameras.

Figure 5.2 Deployment design of pelagic stereo-BRUVs to sample fish assemblages in the water column.

5.3.3 Experimental design

The experimental design consisted of 2 factors: Status (2 levels, fixed: Open and

Closed to fishing) and Site (3 levels, nested in Status, random). Twelve replicate 2-

hour deployments were conducted at each site. This sampling effort was considered

suitable following the results obtained from Santana-Garcon et al. (2014a) that

estimated the optimal soak time and replication required for sampling using pelagic

stereo-BRUVs. Over 4 days of sampling, 72 mid-water stereo-camera systems were

deployed allowing a minimum distance of 500 m between replicates sampled

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simultaneously, thus minimizing the potential for overlap of bait plumes and to

reduce the likelihood of fish moving between replicates. Further research on bait

plume dispersion in the mid-water is needed in order to confirm the minimum

distance that should be allowed between deployments and to estimate the sampling

area (Heagney et al., 2007; Rizzari et al., 2014). Samples were collected during

daylight hours, allowing at least 1 h between sampling and sunrise or sunset, to

avoid possible crepuscular variation in fish assemblages (Birt et al., 2012). Sampling

was interspersed so that all 6 sites were being sampled at the same time in order to

avoid differences between sites being confounded by the temporal variability of

pelagic fish assemblages.

5.3.4 Image analysis

Stereo-camera pairs were calibrated before and after fieldwork using the software

CAL (SeaGIS Pty Ltd). Calibration followed the procedure outlined in Harvey and

Shortis (1998) to allow for accurate length and distance measurements of the fish

sampled. The video images were analysed using the software ‘EventMeasure

(Stereo)’ (SeaGIS Pty Ltd). All fish observed were quantified, identified to the

lowest taxonomic level possible and measured. A conservative measure of relative

abundance, MaxN, was recorded as the maximum number of individuals of the same

species appearing in a frame at the same time. MaxN avoids repeat counts of

individual fish re-entering the field of view (Cappo et al., 2003; Priede et al., 1994).

Schools of small pelagic fish (total length < 200 mm), commonly referred to as

baitfish, were excluded from the analysis.

Range (distance of the fish to the system) and fork length (FL) were measured from

the stereo-video imagery for each individual recorded at the time of MaxN. In order

to obtain quantitative measurements of fish using stereo-video, individuals must be

observed simultaneously in the field of view of the two stereo-cameras. Moreover, to

ensure precision and accuracy of length measurements, fish must be within a

predefined distance and orientation to the camera system (Harvey et al., 2010). In

this study, fish were measured within a range of 8 m and when oriented at more than

45° to the stereo-video system.

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5.3.5 Data analysis

Multivariate assemblage data were analysed using non-parametric permutational

analysis of variance (PERMANOVA), in the PRIMER-E statistical package (version

6.1.15, Anderson et al., 2008). A two-way PERMANOVA was conducted to test for

differences in fish assemblages sampled in the mid-water at sites open and closed to

fishing. A log5 Modified Gower dissimilarity matrix was chosen for the multivariate

analysis to exclude joint absences and to place emphasis on detecting changes in the

species composition and relative abundance (Anderson, 2006; Anderson et al.,

2011a). Log 5 was considered suitable given that the largest MaxN value in the

dataset was just 65. A total of 9,999 permutations of the data were computed to

obtain P significance values. If the number of unique permutations was less than

100, a Monte Carlo P value was used to interpret the result (Anderson et al., 2008).

Univariate PERMANOVAs were used to test for differences in species richness and

relative abundance of some target species groups between areas open and closed to

fishing. All univariate analyses were conducted using the Euclidean distance

dissimilarity measure on the raw data (Anderson et al., 2011a). The target species

groups chosen for analysis were: mackerels (Grammatorcynus bicarinatus and

species of the genus Scomberomorus), jacks (species of the family Carangidae),

tunas (species of the genus Thunnus), sharks (Galeocerdo cuvier and species of the

genus Carcharhinus) and demersal fishes targeted by commercial and recreational

fishing (Chrysophrys auratus, Glaucosoma hebraicum, Lethrinus nebulosus,

Choerodon rubescens and Plectropomus leopardus). Mackerels, jacks and tunas are

considered to be the pelagic fish most highly targeted by fishers at the Houtman

Abrolhos Islands. The demersal target species group includes reef-associated species

that were observed in the mid-water and are those highly targeted by fishers (Watson

et al., 2009).

The length distributions of species in the areas open and closed to fishing were

compared using kernel density estimates (KDEs). Only species that had length

measurements of at least 10 fish both inside and outside the spatial area closure were

included in this analysis (N>20). The KDE method is sensitive to differences in both

the shape and location of length distributions (Sheather and Jones, 1991). KDE

analyses were conducted using the packages ‘KernSmooth’ (Wand, 2013) and ‘sm’

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(Bowman and Azzalini, 2013) in R software version 3.0.2 (R Core Team, 2014)

following the method described by Langlois et al. (In press; 2012b). For each

species, the statistical analysis between the pairs of length distributions was based on

the null model of no difference and a resulting permutation test. The statistical test

compared the area between the KDEs for each method to that resulting from

permutations of the data into random pairs. To construct the test, the geometric mean

between the bandwidths for measurements inside and outside the spatial area closure

was calculated (Bowman and Azzalini, 1997). If the data from both status have the

same distribution, the KDEs should only differ in minor ways due to within

population variance (Langlois et al., 2012b). The ‘sm.density.compare’ function in

the ‘sm’ package was used to plot the length distributions where the resulting band

shows the null model of no difference between the pair of KDEs. This shaded band

is centred on the mean KDE and extends one standard error above and below,

thereby indicating which regions of the length-frequency distribution are likely to be

causing any significant differences (Bowman and Azzalini, 2013).

5.4 Results

There was no significant difference in the species composition and relative

abundance of pelagic fish assemblages recorded in areas open and closed to fishing

at the Houtman Abrolhos Islands. The two-way PERMANOVA indicated that

neither status nor site had a significant effect on community structure (P < 0.05;

Table 5.1). From the 72 pelagic stereo-BRUVs deployed, a total of 350 individuals

of 24 taxa were identified representing 10 families. Twenty-eight deployments

(39%) did not record any fish (11 inside and 17 outside the spatial area closure). In

total, there were more individuals recorded within the area ‘closed’ to fishing (212

individuals of 20 taxa), than at the ‘open’ sites where fishing is permitted (138 of 14

taxa). All taxa were confidently identified to the species level except for two taxa

that were grouped at the genus level (Scomberomorus spp and Pseudocaranx spp)

and one taxon that was defined as a species complex (Carcharhinus spp). The latter

combines four shark species of the genus Carcharhinus (bronze whaler

Carcharhinus brachyurus, dusky C. obscurus, spinner C. brevipinna and spot-tail C.

sorrah sharks) that cannot be confidently distinguished from video imagery alone

(Last and Stevens, 2009).

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Table 5.1 PERMANOVA based on log 5 Modified Gower dissimilarities to test the effects of spatial area closures (Status) on the composition and relative abundance of fish assemblages sampled using pelagic stereo-BRUVs.

Source df MS Pseudo-F P (perm)

Status 1 0.847 0.828 0.528 Site (Status) 4 1.023 1. 079 0.372 Residual 66 0.947 Total 71

Figure 5.3 Total abundance (sum of MaxN) of fish taxa sampled using pelagic stereo-BRUVs in areas open and closed to fishing at the Houtman Abrolhos Islands.

The data recorded using pelagic stereo-BRUVs was sparse and many species were

only recorded in low numbers (between 1 and 88 individuals per species; Figure

5.3). The frequency of occurrence of most species was also low with species

recorded in 1 to 14 deployments throughout the study (Table 5.2). Amberjack

Seriola dumerili was the species with the highest number of individuals sighted and

was recorded in 3 deployments, only within the area closed to fishing. Pink snapper

Chrysophrys auratus (previously known as Pagrus auratus; Gomon et al., 2008) was

the most abundant of the demersal species observed in the mid-water; they occurred

in higher numbers inside the spatial area closure but were also recorded in the area

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open to fishing (observed in 10 and 4 deployments respectively). All other demersal

species observed (spangled emperor Lethrinus nebulosus, baldchin groper

Choerodon rubescens, dhufish Glaucosoma hebraicum and coral trout Plectropomus

leopardus) were only recorded within the area closed to fishing. Other species that

were only detected in the ‘closed’ sites included yellowtail kingfish Seriola lalandi,

gold-spotted trevally Carangoides fulvoguttatus, skipjack trevally Pseudocaranx spp

and blacktip shark Carcharhinus limbatus/tisltoni. On the other hand, there were a

few species like wahoo Acanthocybium solandri and yellowfin tuna Thunnus

albacares that were only recorded in the areas open to fishing.

Table 5.2 Number of deployments where species were sampled using pelagic stereo-BRUVs inside (Closed) and outside (Open) a spatial fishing closure at the Houtman Abrolhos Islands.

Number of

deployments sighted

Species Open Closed

Acanthocybium solandri 1 0 Carangoides fulvoguttatus 0 2 Carcharhinus limbatus 0 1 Carcharhinus plumbeus 1 1 Carcharhinus spp 5 5 Choerodon rubescens 0 1 Chrysophrys auratus 4 10 Echeneis naucrates 0 2 Eubalichthys caeruleoguttatus 1 3 Galeocerdo cuvier 1 1 Glaucosoma hebraicum 0 1 Gnathanodon speciosus 1 0 Grammatorcynus bicarinatus 2 4 Lethrinus nebulosus 0 5 Plectropomus leopardus 0 1 Pseudocaranx spp 0 1 Remora remora 1 1 Scomberomorus spp 7 7 Selaroides leptolepis 1 0 Seriola dumerili 0 3 Seriola lalandi 0 4 Thunnus alalunga 2 2 Thunnus albacares 1 0 Thunnus tonggol 3 2

Total species richness was higher in the sites closed to fishing than in the fished area

(Figure 5.4A). The univariate statistical analysis found significant differences in

species richness between sites, but differences for status were not significant (Table

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5.3). The mean relative abundance of mackerels, tunas and sharks was greater in the

fished area while jacks and the demersal target species were more abundant within

the ROA (Figure 5.4B). However, the univariate PERMANOVAs for these species

groups showed that these differences were not statistically significant for status

given the variability between sites.

Figure 5.4. The mean species richness per site (A) and the mean relative abundance per site of the main target groups (B) in areas open and closed to fishing at the Houtman Abrolhos Islands. Error bars show standard error.

Table 5.3 Summary of univariate PERMANOVA tests for species richness and relative abundance of the main target fish groups sampled using pelagic stereo-BRUVs. Analyses were based on Euclidean distance dissimilarities of the raw data to test for differences in Status (areas open and closed to fishing) and Sites. Fields in bold-faced show significant P-values.

Source Species

Richness Mackerels Jacks Tunas Sharks

Demersal target species

Status 0.263 0.581 0.157 0.407 0.409 0.439 Site (Status) P < 0.05 0.446 0.849 P < 0.05 0.804 P < 0.05

Highly mobile pelagic fish like tunas, mackerel and some shark species proved

difficult to measure lengths, as they were the species observed furthest from the

pelagic stereo-BRUVs (Figure 5.5). In contrast, demersal target species and some

reef associated pelagic fish, like the genera Pseudocaranx and Seriola, were

recorded much closer to the camera systems. The mean fork length and mean ranges

were estimated at the genus level (Table 5.4). Fork length was measured for only 131

individuals (37% of the total), 49 of these individuals were measured in the area

open to fishing and 82 within the fishing closure. Additionally, measures of range

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were estimated for 200 fish (57 %), 65 individuals within the ROA and 135 outside

the spatial area closure. Only pink snapper C. auratus fitted in the criteria to be

included in the KDE analysis (i.e. length measurements available for at least 10 fish

inside and outside the spatial closure). Although the shape of the length distribution

of C. auratus sampled in the areas open and closed to fishing did not differ

significantly (P = 0.99); the location of the length distributions (i.e. the mean length)

were significantly different (P < 0.01). On average, larger pink snapper were

recorded within the spatial area closure (Figure 5.6). However, the low number of

individuals measured both inside and outside the spatial fishing closure for all other

species prevented us from doing further statistical analysis on their length structure

or estimated biomass. Distance of individuals to the camera system was one of the

main factors preventing accurate length estimates.

Table 5.4 Summary of length and range measurements at genus level combining areas open and closed to fishing. The mean length (cm) represents fork length and the mean range (m) is the average distance of individuals to the stereo-camera system. The number of individuals measured [N] is shown in brackets and (*) shows the genus of demersal target species.

Genus Mean Length

(cm) [N] Mean Range

(m) [N] Acanthocybium - 11.10 [1] Carangoides 122.2 [3] 5.05 [4] Carcharhinus 180.4 [5] 9.95 [15] Choerodon* 69.9 [1] 2.21 [1] Chrysophrys* 64.5 [48] 2.35 [56] Galeocerdo 324.5 [1] 6.17 [1] Glaucosoma* - 1.96 [1] Grammatorcynus 72.8 [20] 7.49 [32] Lethrinus* 68.7 [13] 2.27 [14] Plectropomus* - 2.29 [1] Pseudocaranx 35.2 [1] 1.36 [1] Scomberomorus 98.2 [6] 9.80 [25] Seriola 101.4 [26] 3.17 [30] Thunnus 92.7 [4] 10.48 [12]

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Figure 5.5 The mean range, distance of the individuals to the stereo-camera system, for fish genus recorded using pelagic stereo-BRUVs. Grey silhouettes represent demersal species and black silhouettes are pelagic and highly mobile fish. The shaded area shows the field of view of the cameras where fish length can be measured.

Figure 5.6 Comparison of kernel density estimate (KDE) probability density functions for the length distributions of pink snapper Chrysophrys auratus in areas open and closed to fishing. Grey bands represent one standard error either side of the null model of no difference between the KDEs for each method.

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5.5 Discussion

We found no significant difference between the species composition and relative

abundance of fish assemblages sampled in the water column inside and outside the

Easter group spatial area closure at the Houtman Abrolhos Islands. Sharks and

targeted pelagic species like mackerels and tuna were recorded in higher numbers

outside the spatial area closure but the effect of the fishing closure was not

statistically significant due to variation between sites. Additionally, jacks mostly of

the genus Seriola, were almost exclusively recorded within the spatial area closure.

Jacks are less mobile than other pelagic fish such as tunas and are known to have

responded positively to spatial protection (Edgar et al., 2014; Russ et al., 2004).

However, we found that the small proportion of deployments where schools of jacks

were recorded prevented this difference from being statistically significant and thus

conclusive.

The lack of significant effect on pelagic fish assemblages could be an indication that

the spatial area closure we studied, which is not representative of all habitat types in

the region and was originally designed to protect reef associated species, might not

be adequate, or large enough, to have an effect on highly mobile species like

mackerels, tunas and sharks. Our results contrast with other studies that have found

that mobile species could benefit from protection, whatever their home-range and

irrespective of the size of the spatial area closure (Claudet et al., 2010). However, it

has been suggested that small area closures could in fact benefit only a fraction of

individuals of the population that may be ‘less mobile’ as a result of intra-specific

differences in movement behaviour (Davies et al., 2012), but such an effect would

not be detected in this study. Moreover, similarities in species composition and

abundance of pelagic fish assemblages inside and outside this spatial area closure

may be attributable to the relatively low fishing pressure targeting pelagic fish

species in the study area in comparison to demersal species. Monitoring pelagic

species in other spatial area closures of a range of sizes and exploitation levels would

improve the interpretation of our results. Furthermore, recording physical parameters

such as current speed and bathymetry, throughout the study area would allow testing

for confounding factors that could be driving the differences in fish assemblages

between sites.

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Although the differences were again not statistically significant, demersal target

species were, in total, recorded in higher numbers within the spatial area closure.

These results are in line with recent studies in this area that found no significant

difference in the abundance of demersal target species inside and outside the fishing

closure using benthic BRUVs (Dorman et al., 2012). However, pelagic stereo-

BRUVs are not designed to target demersal species and only sampled a fraction of

the demersal species population, mainly large individuals that venture off the reef

into the mid-water attracted to the bait plume. This behaviour might differ between

fish in the areas open and closed to fishing as target species within spatial area

closures can become bolder and respond stronger to bait or diver surveys (Davidson,

2001; Willis et al., 2000). Moreover, pink snapper (C. auratus) measured within the

spatial area closure were significantly larger than those sampled in the area open to

fishing.

From our results, we can infer that pelagic species might not show a positive

response to spatial area closures even if these have proved to be effective for

demersal target species. Hence, future monitoring and management plans should

incorporate sampling techniques that target the pelagic environment when assessing

any change in fish community structure. Most methods used in the pelagic

environment are destructive, target specific species as a result of gear selectivity (e.g.

hook size, bait type) and look at very specific questions or at very broad scales (i.e.

ocean wide). Pelagic stereo-BRUVs can help explore patterns occurring at the

assemblage or community level (Santana-Garcon et al., 2014d). This study provides

baseline data on the pelagic fish diversity and relative abundance at the Houtman

Abrolhos Islands that can be compared to future surveys in the area. Pelagic stereo-

BRUVs have proven to be a robust, non-destructive and fishery-independent method

that can be standardised to study fish assemblages across spatial areas and over time

(Santana-Garcon et al., 2014a; Santana-Garcon et al., 2014d). This method provides

a permanent record of the species in the area and allows for the observation of fish

behaviour in their natural environment (Santana-Garcon et al., 2014c). Moreover,

remote video techniques can be used in areas and at depths beyond the limits of

diver-based studies (Cappo et al., 2006), and sampling can take place both during the

day or at night using lights (Fitzpatrick et al., 2013; Santana-Garcon et al., 2014d).

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However, the identification of taxa to the species level from video alone has proven

challenging and some species can only be grouped at higher taxonomic levels. This

is a common limitation across video techniques (Harvey et al., 2014; Mallet and

Pelletier, 2014), but it is exacerbated for pelagic species because many taxa share

similar morphological traits (i.e. coloration, streamlined fusiform body, pointy snout

and deeply forked tails), and they have adapted to appear almost invisible in the

pelagic environment (Santana-Garcon et al., 2014a). We also observed that larger

pelagic species often remain further from the camera systems than demersal species,

which complicates both their identification and measurement. We were able to

measure only a small fraction of the individuals recorded, 37 % were measured for

fork length and 57 % for range. The percentage of fish measured in this study using

pelagic stereo-BRUVs is lower than that of other studies using similar stereo-

BRUVs deployed on the seafloor (Harvey et al., 2012c; Langlois et al., 2012b;

Watson et al., 2009). Given the low measurement rate for pelagic species, length

distributions of species inside and outside the spatial area closure could only be

compared for one demersal species, pink snapper (C. auratus). The larger pelagic

species remained further from the stereo-cameras than demersal species, which often

approached the systems and fed on the bait bag (Figure 5.5). This difference in

behaviour between demersal target species and some pelagic species can be in part

attributed to the fact that large pelagic fish are largely visual predators that need to

visually locate their prey. Some shark species (e.g. genus Carcharhinus) are known

to remain far from the camera system initially, but as they “patrol” the area they

come closer to the bait source if the cameras are left in the water for longer soak

times (e.g. soak times of 2 to 6 h, Santana-Garcon et al., 2014d). Therefore,

increasing the attraction rate of pelagic fish to the cameras and adapting the method

for calibration of stereo-video to allow for accurate measurements at a greater

distance from the stereo-cameras, would improve the data in future studies using

pelagic stereo-BRUVs.

Pelagic ecosystems are highly variable both temporary and spatially, so high

replication is required to detect community structure. Moreover, sampling should be

conducted at different times of the year to account for seasonal variability and, when

possible, monitoring should include a temporal component to detect changes of the

fish assemblages over time (e.g. annually or every 5 years). In this study we used 12

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replicates per site following the recommendations from Santana-Garcon et al.

(2014a). However, the data obtained was sparse and was dominated by zeros, which

could lead to difficulties detecting patterns in the community structure (Clarke et al.,

2006a). We defined a replicate as each single 2-hour deployment of the camera

systems, yet future studies could pool various deployments to obtain more powerful

replicates (e.g. reducing the number of samples with no species recorded). For

instance, if the number of sites was increased, the deployments within sites could be

treated as sub-samples in order to improve the distribution of dissimilarities for

multivariate analyses (this is further developed in a subsequent publication).

Moreover, increasing the attraction of pelagic fish to the cameras would not only

improve species identification and length measurements as suggested, but also

increase the power of each camera deployment. Research on visual, sound and odour

attractants using mid-water baited video techniques has shown that a combination of

attractants increases the number of fish in the field of view and brings individuals

closer to the camera (Rees et al., 2014). Moreover, pelagic species often school

around floating objects and other structures in the mid-water, these are commonly

named fish aggregation devices (FADs) (Dempster, 2005). Small pelagic fish have

been reported to aggregate around pelagic stereo-BRUVs as if attracted to their

physical structure (Santana-Garcon et al., 2014a), thus camera systems could be

further adapted to act as FADs in order to increase the number of fish present in the

area that enter the field of view of the stereo-cameras.

Following the increase in spatial area closures in the open ocean (Davies et al., 2012;

Kaplan et al., 2014) and the push for spatial management of the high seas (Game et

al., 2009; White and Costello, 2014), it is essential that sampling methods adapt to

monitoring in this highly dynamic environment in order to understand the effects of

spatial management on pelagic species. Pelagic stereo-BRUVs have not been tested

in open ocean conditions and further development of the technique would be

required to effectively deploy baited cameras in the high seas. Recent technology

developments including inexpensive camera systems, small and robust designs that

can be attached to commercial fishing gear, automatic data recording when systems

are deployed in the water and a capacity for long deployment times (e.g. months)

(Pember et al., 2014) could all be incorporated into pelagic stereo-BRUV systems.

Adaptation of video techniques to open ocean monitoring could include the

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integration of such systems to satellite-tracked structures, like the FADs used by

commercial fishermen to target tuna (Davies et al., 2014). There is also progress in

the automation of image analysis including software to detect when fish are in the

field of view, and automatic species identification and measurement of individuals

(Shortis et al., 2013). These developments will facilitate the use of pelagic stereo-

BRUVs in broad temporal and spatial scale studies in order to assess the response of

pelagic species to large oceanic spatial area closures. Moreover, a combination of

fishery-independent sampling methods, including emerging technologies like remote

sensing and echo-sounder transects, would improve the quality of data on pelagic

species (Murphy and Jenkins, 2010).

In conclusion, we found no significant effect of a small spatial area closure on the

mid-water fish assemblages at the Houtman Abrolhos Islands, which supports the

theory that spatial management of highly mobile species may require larger area

closures than those targeting reef-associated species (Kaplan et al., 2010). Our study

provides baseline data for future studies and highlights the need to include

monitoring of pelagic species both in large and small spatial area closures in order to

understand how mobile species respond to these management strategies. We found

that pelagic stereo-BRUVs are an effective fishery-independent approach to monitor

spatial area closures. However, improving the power of replicates by pooling

individual deployments, and increasing attraction of pelagic fish to the stereo-

cameras, will enhance their performance in future studies. Further development and

trial of pelagic stereo-BRUVs will be required to implement this technique in

oceanic environments, which will be critical for monitoring the effects of the

increasing spatial area closures in the high seas. The use of fishery-independent

methods to understand the natural variability of pelagic fish assemblages, which has

been poorly studied in past monitoring programmes (Claudet et al., 2010), will allow

for a better management and conservation of these highly mobile species both in

coastal and open ocean waters.

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CHAPTER 6 - Presettlement schooling behaviour of a priacanthid, the purplespotted bigeye Priacanthus tayenus (Priacanthidae: Teleostei)

6.1 Abstract

We report in situ behavioural observations of presettlement schooling in Priacanthus

tayenus off Coral Bay, Western Australia collected using pelagic Baited Remote

Underwater Stereo-Video systems. Two groups of fish (8 and 9 individuals) were

observed that aggregated into a single school. Mean total length was 24.1 mm (12.5

– 30.2 mm). The fish swam at a mean speed of 8.5 cm s-1 in a group spacing

themselves more or less evenly at a distance of around one body length from the

nearest neighbour within the school. P. tayenus appeared to be sometimes associated

with juveniles of other species. The results presented here add to the limited, but

growing body of literature on the schooling behaviour of the early pelagic stages of

demersal fishes.

6.2 Introduction

Most marine teleost fish species have a pelagic larval phase during their early life-

history. The processes that occur during that period determine their dispersal and

recruitment, and may influence the distribution of adult populations (Mora and Sale,

2002). Extensive research has focused on the biology of the pelagic phase of

demersal fishes, but direct observations and quantitative data on their behaviour in

the pelagic environment is limited and only available for a small number of species

(Leis, 2010; Leis and Carson-Ewart, 1998; Masuda, 2009; Masuda et al., 2003). Leis

(2010) reviewed the limited information available on the ontogeny of schooling in

the pelagic larvae of marine demersal fishes. Schooling may provide protection from

predators, accelerate learning and enhance swimming and orientation abilities

(Masuda, 2009; Shaw, 1978).

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Presettlement schooling has been described in a few reef fish families including

Gobiidae (Breitburg, 1991), Mugilidae (Kingsford and Tricklebank, 1991), Mullidae

(Leis and Carson-Ewart, 1998; McCormick and Milicich, 1993), Lutjanidae,

Microdesmidae and Pomacentridae (Leis and Carson-Ewart, 1998). However, the

difficulties associated with studying these small and inconspicuous fish in situ has

resulted in most behavioural studies relying on estimates from fish collected in

plankton nets, purse seines or light traps (McCormick and Milicich, 1993; Ohman et

al., 1998) and on observations of reared larvae or laboratory experiments (Leis and

Carson-Ewart, 1998; Leis et al., 1996; Masuda, 2009).

We report in situ observations of presettlement schooling behaviour in priacanthids

off Coral Bay, Western Australia. The behaviour of juvenile Priacanthus tayenus

(Richardson, 1846) was recorded using pelagic Baited Remote Underwater Stereo-

Video systems (stereo-BRUVs). This technique was being trialled to investigate the

distribution of pelagic fish assemblages as it potentially provides a standardised,

non-destructive and fishery independent approach to estimate abundance, diversity

and length of fish in the pelagic environment (Harvey et al., 2004; Heagney et al.,

2007). Presettlement reef fishes were observed in all 48 video deployments; these

observations present a unique opportunity to describe their behaviour in situ.

Presettlement priacanthids were observed in 5 of the 48 videos, and schools of two

or more fish were recorded on 3 of those 5 deployments. This observation provides

the first quantitative data on their presettlement schooling behaviour.

Priacanthids, commonly called bigeyes, comprise a small circum-tropical family of

marine fishes that are characterised generally by extremely large and bright eyes, a

deep body, rough scales and red coloration (Starnes, 1988). Some species are

commercially valuable in South East Asian trawl fisheries and in reef-based fisheries

throughout the tropics (Lester and Watson, 1985; Seah et al., 2011; Senta, 1978).

The purplespotted bigeye, Priacanthus tayenus, is a small species (maximum total

length 29 cm) characterised by deep purple to inky black spots in the pelvic fins

(Starnes, 1988). Adults are epibenthic, can occur in large schools and inhabit rocky

reefs and open bottom areas at depths between 20 and 200 m. Larvae and the early

juvenile stages are pelagic (Starnes, 1988).

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Most aspects of the biology of the early life-history stages of priacanthids are largely

unknown, in particular, behaviour during the pelagic dispersal phase. Schooling

behaviour has not been reported for juvenile priacanthids; nonetheless, large

aggregations have been reported around surface lights at night in the West Indies

(Caldwell and Bullis, 1971), the western North Atlantic (Caldwell, 1962a) and from

trawl net catches in the Philippines (Senta, 1978).

This study aims to contribute to the knowledge of presettlement schooling behaviour

in the early stages of Priacanthus tayenus. Accurate length measurements,

swimming speed and schooling parameters are described. Stereo-video techniques

offer unique abilities to study the ontogeny of fish behaviour, and this study presents

the first in situ quantitative data on schooling behaviour of the pelagic early-life

history stage of a demersal fish species.

6.3 Material and methods

The data presented here are based on observations of Priacanthus tayenus in March

2012 at Coral Bay (23° 1’ S; 113° 44’ E), Ningaloo Marine Park. Ningaloo Reef is a

fringing coral reef which stretches for approximately 270 km adjacent to the semi-

arid north-west cape of Western Australia. The pelagic stereo-BRUVs consisted of

two SONY HDR CX12 video cameras mounted 0.7 m apart on a base bar inwardly

converged at 8 degrees to gain an optimised field of view of 7 m (Harvey and

Shortis, 1995). The cameras recorded video imagery at 25 frames per second in

MPEG Transport Stream format (MTS), which was converted to high-definition

MPEG format (Harvey et al., 2010). The system was deployed offshore of the reef

slope in a depth of 35 m, and left moored 3 hours in mid-water, 15 m above the

bottom (Figure 6.1). The bait consisted of ~800 g of pilchards (Sardinops sagax) in a

wire mesh basket suspended 1.2 m in front of the two cameras. The video images

were analysed using the software ‘EventMeasure (Stereo)’ (SeaGIS Pty Ltd, see

http://www.seagis.com.au/event.html). EventMeasure Stereo is a purpose built event

logger which an operator can use to manually record the identification and number

fish and their behaviour at a particularly time on a video sequence. By using a

mouse, an operator can determine the location of a point in three-dimensional space

(x, y and z) relative to the centre of the two cameras. As the time sequence is

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recorded it is possible to calculate the swimming speeds of a fish. It is also possible

to measure the distance to a fish (Harvey and Shortis, 1995; Harvey et al., 2004) and

make accurate and precise measurements of fish length (Harvey et al., 2001a; b;

2002). Species identification was confirmed by Wayne Starnes (personal

communication) from still images and video clips (Figure 6.2).

Figure 6.1 Deployment method (a) and details (b) of the pelagic stereo Baited Remote Underwater Video system (stereo-BRUVs).

In order to obtain quantitative measurements of fish using stereo-video, individuals

must be observed simultaneously in the field of view of the two stereo cameras.

Furthermore, to ensure precision and accuracy of measurements, fish must be within

a predefined distance and orientation to the camera system (Harvey et al., 2010).

Given the small size of the fish targeted in the present study, individuals were only

measured within 2 metres of the cameras and, whenever possible, when the head and

tail of the fish aligned at more than 50° perpendicular to the stereo-video system.

Only individuals from 3 of the 5 videos that recorded priacanthids met the above

criteria. Consequently, the quantitative data presented is from one video that

recorded presettlement schooling behaviour and two videos that showed solitary fish.

Figure 6.2 Still photos of presettlement Priacanthus tayenus (mean total length 24.1 mm) captured from video recorded in Coral Bay, Western Australia.

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From the image analysis, the behaviour of presettlement priacanthids was described

and their total length (TL) measured from the stereo-video imagery. Total length for

each individual was determined as the average of the measurements at 5 different

frames and the mean TL for each group was calculated from the replicate

measurements of all the individuals in the school. The present study defines

schooling to be fish of the same species swimming in the same direction and in close

proximity, approximately 1 to 2 body lengths apart (Shaw, 1978). The term ‘group’

is used here to facilitate the description of schools that enter the field of view at

different times. Two groups of presettlement fish (A and B) aggregated into a single

school, group (AB). Nearest Neighbour Distance (NND), a quantitative measurement

of schooling behaviour, is the average distance between each fish in the group and its

nearest neighbour (Masuda, 2009; Masuda et al., 2003). From the stereo-video

imagery, NND was sampled from 10 different frames for group A and B and from 5

different frames for group AB. The difference in replicates is due to the short time

that group AB was visible from both cameras. Replicate measurements were at least

25 frames apart (1 second). The mean NND was divided by the average TL of fish in

the group (NND/TL) to facilitate comparison among aggregations of individuals of

different sizes.

Swimming speed was estimated by measuring the distance travelled by an individual

over 25 video frames (equivalent to 1 second) averaged across 3 consecutive

seconds. Mean swimming speed of presettlement schooling priacanthids was

obtained from the speed estimated for 10 individuals at various times of the video.

The swimming speed of solitary fish could only be measured for a single individual

in one video; therefore it was discarded from the analysis.

6.4 Results

In the deployment that recorded schooling, the first priacanthid individuals were

observed after 40.5 minutes. At that time, a school of four P. tayenus entered the

field of view for only one second, and no measurements were made because the

group was only visible from one of the cameras of the stereo system. Presettlement

priacanthids entered the field of view a second time at 60.8 minutes for a period of 2

minutes and the school at that time had 8 individuals (group A). The mean total

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length of the priacanthids in this group was 24.4 ± 0.3 mm (± Standard Error) and

the individuals ranged from 23.0 ± 0.9 to 27.0 ± 0.4 mm. The NND of group A was

40.0 ± 3.6 mm and the NND/TL averaged 1.6.

A second school of 9 P. tayenus, group B, entered the field of view nearly two

minutes after the first group (at 62 min) and was visible for 30 seconds until both

schools merged into a single group (AB). Individuals in group B were estimated to

average a total length of 23.9 ± 2.2 mm (mean ± S.E.) and ranged from 12.5 ± 0.6 to

30.2 ± 0.4 mm. The mean NND for group B was 22.3 ± 2.4 mm and the NND/TL

was 0.9.

The combined group AB consisted of 17 P. tayenus and was visible for only 18

seconds. The mean TL of individuals in the school was 24.1 ± 0.5 mm and the NND

was 32.3 ± 1.8 mm. The NND/TL was 1.3. There was no clear relationship between

the size of the groups and their NND. Figure 6.3 provides a summary of the

quantitative measurements estimated for the three groups of presettlement P.

tayenus.

Further observations included a school of 7 priacanthids recorded 20 minutes later

(at 81 min), but no measurements were possible because the group was visible from

only one of the cameras of the stereo video system. Later in the video (110.2 min) a

school of 25 fish was recorded. One juvenile carangid, 4 nomeids and 20

presettlement priacanthids could be distinguished in that school. However, the small

size of the individuals and their distance from the camera prevented identification of

all individuals, estimation of their TL or confirmation of that group as AB. Mean

swimming speed of the observed presettlement P. tayenus was 8.5 ± 1.5 cm s-1 and

the measurements for 10 individuals ranged between 4.8 and 18.2 cm s-1. These

estimates are equivalent to a mean swimming speed of 3.6 ± 0.7 TL s-1 and ranged

between 2.0 and 7.5 TL s-1.

In the two videos analysed with solitary presettlement priacanthids, TL of the

individual was also determined as the average of the measurements in 5 different

frames. One solitary individual was 17.3 ± 0.2 mm and the other was 24 ± 0.8 mm.

Priacanthids and monacanthids were the only identifiable demersal fishes in their

pelagic stage observed during this study. Monacanthid fishes, observed in most

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video deployments, were very structure-orientated, attracted to the structure of the

camera system, and did not join the schools of priacanthids.

Figure 6.3 Quantitative parameters for three groups of presettlement Priacanthus tayenus recorded using pelagic stereo cameras in Coral Bay, Western Australia (group A, 8 individuals; group B, 9 individuals and group AB, 17 individuals). (a) Mean Total Length (TL; mm); (b) mean Nearest Neighbour Distance (NND; mm) and (c) mean NND divided by the average TL. Error bars represent standard error.

6.5 Discussion

These results provide the first observations and quantitative data on the presettlement

schooling behaviour of any priacanthid during its pelagic phase. The presettlement

P. tayenus observed schooling were on average 24.1 mm TL (12.5 – 30.2 mm), and

swam in a group spacing themselves more or less evenly at around one body length

from the nearest neighbour within the school (Figure 6.3). P. tayenus appeared to be

sometimes associated with juvenile carangids or nomeids. The individuals observed

alone were within the same size range as those observed schooling. Due to the small

sample size, the results presented here may not reflect all the natural behaviours of

this species. However, this study provides the first look at in situ presettlement

behaviour using a video technique and may stimulate further work in this field.

Many fish species school during their pelagic phase regardless of their behaviour

after settlement or as adults (Shaw, 1978). NND is the parameter most commonly

used in studies of fish schooling behaviour and NND/TL can be used to compare

schooling across different size classes and species (Masuda, 2009; Masuda et al.,

2003; Soria et al., 2007; Torisawa et al., 2011). The values of NND calculated for

the observed presettlement P. tayenus (0.9 – 1.6 NND/TL) are comparable to the

estimates for other species. Schools of the carangid Pseudocaranx dentex at 25 mm

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TL have a NND/TL of 0.5 to 1.2 (Masuda, 2009) and the scombrid Scomberomorus

niphonius, which is considered to form loose schools in comparison to most pelagic

fishes, has a NND/TL of 1.2 to 1.5 at a standard length of 19 to 23 mm (Masuda et

al., 2003). However, these studies on the ontogeny of schooling behaviour focus on

pelagic fish species and are limited to laboratory-based experiments using hatchery

reared larvae.

Generally, marine fish in their pelagic phase swim at speeds between 3 – 15 body

lengths per second (Leis, 2010). The in situ swimming speed estimated for the

observed priacanthids, on average 3.6 TL s-1 (8.5 cm s-1), fits in the slower end of

this general estimate. An average swimming speed greater than 3 cm s-1, is

considered to be influential for the dispersal of marine fishes (Leis, 2006; Leis,

2010). However, the measurements of swimming speed presented here should be

treated with caution as speeds are expected to vary and estimates may be influenced

by physical processes such as current and surge.

The limited information available on the behaviour of priacanthids at early stages

makes difficult the comparison and interpretation of the data presented here. Senta

(1978) noted that maturing juveniles of P. tayenus are recruited into schools at about

90-100 mm standard length (SL) and reach 200 mm SL one year after recruiting. It is

not known if the individuals observed in this study, 12.5 – 30.2 mm, were near

settlement. However, Pristigenys alta, another priacanthid species that has similar

maximum size to P. tayenus, is reported to transform from pelagic to demersal phase

at 35 to 55 mm SL (Caldwell, 1962b; Starnes, 1988).

Pelagic stereo-video systems may have the potential to overcome some of the

difficulties associated with studying the behaviour of juvenile fish in the pelagic

environment. Juveniles of various pelagic and demersal species were attracted to

these mid-water structures. Video techniques could be used for in situ behavioural

research at depths beyond the limits of diver-based studies and also for night

observation using a range of light wavelengths or image intensification technology

(Harvey et al., 2012a), and thus could contribute to overcoming a major gap in

knowledge about diel effects on the behaviour of fish larvae (Leis, 2010). Despite

the difficulties of identifying juvenile fish to the species level from video alone, this

technique could be used in combination with other methods, such as purse seine or

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light traps, to facilitate identification and allow for further morphological and genetic

studies. Further work is needed to understand the strengths and limitations of pelagic

stereo video techniques for studying juvenile fish in their pelagic phase.

In conclusion, the observations on priacanthids presented here add to the limited, but

growing body of literature on the schooling behaviour of larval and juvenile

demersal fishes. P. tayenus swam in small groups and merged with other

presettlement conspecific schools to form larger groups. Sometimes these

presettlement schools swam in association with other species. Presettlement

schooling behaviour has also been observed in at least six other families of demersal

fish and seems to represent an important behaviour in the early life stages of many

demersal fish species (Leis and Carson-Ewart, 1998).

The observations and measurements of the behaviour of presettlement fishes

presented demonstrate the potential of non destructive stereo-video techniques for

collecting additional information which is complementary to the existing techniques.

Rather than using bait, artificial or natural materials could be placed in front of the

camera system to form a fish aggregation device for juvenile fishes.

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CHAPTER 7 - General discussion

My research shows that pelagic stereo-BRUVs are an effective, non-destructive and

fishery-independent sampling approach to study pelagic ecosystems. This method

can be calibrated to commonly utilised standard techniques (chapter 4), is suitable

for studies over broad spatial and temporal scales, and can address questions on

species composition, behaviour, relative abundance and size distribution of fish

assemblages in the pelagic environment. In this thesis, I show that pelagic fish

assemblages are stratified by depth (chapter 2), which suggests that pelagic stereo-

BRUVs have the potential to explore the vertical distribution and diel migrations of

fish assemblages in the pelagic environment. My results also indicate that small

spatial area closures may not be adequate for the conservation and management of

highly mobile fish, even if they have proved effective for reef-associated species

(chapter 5). One of the common themes throughout my study was the high temporal

and spatial variability of fish assemblages in the pelagic environment. I investigated

the soak time and the level of replication required to maximise the precision of

sampling with pelagic stereo-BRUVs (chapters 2 and 3). In chapter 6, I demonstrate

how stereo-video can be used to make behavioural observations of juvenile fish in

the pelagic environment.

This thesis critically assesses the use of pelagic stereo-BRUVs and collates all the

information needed to implement, adopt and develop this method in future studies.

The aim of this discussion is to summarise the main findings of the project and

evaluate the strengths and limitations of pelagic stereo-BRUVs as a sampling

method for pelagic ecosystems.

Deployment method

The deployment method presented in chapter 2 and used throughout this project was

developed following the advice of experienced commercial fishermen. This method

allows for pelagic stereo-BRUVs to be deployed from both large research vessels or

from small boats. It also allows multiple systems to be deployed simultaneously in

order to maximise replication without adding field-time costs. The deployment

method presented here is moored to the seafloor and was used at depths from 5 to

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120 metres, in coastal waters and at exposed offshore sites. The design of the system

makes the bait arm act as a rudder so that the cameras face downstream of the

current, improving the observation of individuals that approach the camera following

the bait plume. The ballast and subsurface floats minimise the vertical movement of

the cameras that may be driven by wave motion. This has proven to be a major

advantage over other systems tested in the mid-water that failed to stabilise the

cameras (e.g. Letessier et al., 2013). The associated costs of building pelagic stereo-

BRUVs and the on-board logistics of using them are similar to those for the

commonly used benthic BRUVs.

Further development and trials are needed to expand the use of pelagic stereo-

BRUVs on drifting systems, and to adapt this technique to open ocean sampling. The

main concerns to take into account would be to: (1) establish the means to track the

position of the systems, to enable retrieval at some pre-determined time (boats

should not remain in the area as this would confound effects of sampling); (2)

explore the differences in sampling area and bait dispersal between drifting and

moored systems (this is important as drifting systems have the potential to cover

large areas based on current speeds); and (3) minimise the effects of both the drifting

action and wave motion to ensure stability of the camera systems underwater.

Attaching stereo-video systems to drifting Fish Attraction Devices (FADs) could

potentially enhance the use of video techniques on the high seas.

Optimisation of sampling effort

I assessed the performance of pelagic stereo-BRUVs under different sampling

regimes in order to optimise sampling (chapter 2 and 3). I recommend standardising

the sample unit size (soak time) to facilitate data comparison across studies and

maximising replication given the resources available. In this thesis, I establish that a

soak time of 120 minutes is optimal when sampling in tropical or warm-temperate

waters, because it provides suitable precision at the assemblage level for univariate

(chapter 2) and multivariate analyses (chapter 3). Soak time has been shown to have

an effect on the number of species and the relative abundance observed (e.g. species

accumulation curve; chapter 2), and differences in behaviour over longer soak times

were also reported (e.g. sharks getting closer to the bait after patrolling the area;

chapter 4). Importantly, longer soak times increase the sampling costs because

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longer deployments limit the number of replicates that can be undertaken per day in

the field, and the associated time required for video analysis increases (Gladstone et

al., 2012; Santana-Garcon et al., 2014a). However, different soak times may be

required in specific cases (e.g. longer soak times were used to compare pelagic

stereo-BRUVs to scientific longline surveys; chapter 4), thus soak time may be

determined by the objectives of the research program, the specific research question

being addressed and the species that are being targeted.

The sample size required to provide precise data on the assemblage composition and

abundance was determined to be at least 8 replicates per treatment when using

pelagic stereo-BRUVs (chapters 2 and 3). However, the data obtained from the

recommended sampling regime was often sparse and contained many zeros, which

can lead to difficulties in detecting patterns in the community structure (Clarke et al.,

2006a). In dissimilarity-based multivariate analysis, replicates that contain no

species lead to undefined dissimilarity values and, when many pairs of samples have

no (or very few) species in common, it yields uninformative dissimilarity values

(Anderson and Santana-Garcon, In review; Clarke et al., 2006a). Throughout this

project, I defined a ‘sample’ or ‘replicate’ as the data obtained from each BRUV

system deployment. However, given the nature of the data obtained with pelagic

stereo-BRUVs, I would recommend considering each deployment as a sub-sample,

and pooling deployments to obtain more powerful replicates. This is further explored

in recent research by Anderson and Santana-Garcon (In review; Appendix 1), where

data collected with pelagic stereo-BRUVs at the Houtman Abrolhos Islands (data

presented in chapter 5) is analysed to show that the distribution of dissimilarity

measures improves substantially when data from different deployments is pooled

(e.g. no undefined dissimilarities remained once the number of sub-samples

exceeded 2). Further research will allow the establishment of a general

recommendation of the number of deployments that should be pooled as replicates in

studies using pelagic stereo-BRUVs.

Fish attraction to pelagic stereo-BRUVs

Large pelagic fish species remained, on average, further from the camera systems

than demersal species (chapter 5). Greater range (distance from the cameras to the

fish recorded) complicates the detection, identification and accurate measurement of

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fish lengths (Harvey et al., 2010). I observed that pelagic fish often approached the

cameras and, some species, showed interest in the bait. However, overall, the

proportion of individuals in the area approaching the cameras was lower than that

observed during video analysis of BRUVs deployed on the seafloor. These

observations raise the questions: What is attracting fish into the field of view of

cameras in the mid-water? What cues are attracting individuals to approach the bait

or cameras? Understanding these drivers will allow researchers to improve attraction

and enhance the sampling ability of pelagic stereo-BRUVs.

In this study I recorded fish from a broad range of sizes and trophic levels (i.e. from

small juveniles and planktivorous fish, to large apex predators like tunas and sharks).

Some species were attracted to the physical structure of the cameras and floats in the

mid-water (e.g. juveniles and small pelagics aggregated around the camera system),

while other species responded specifically to the bait source (e.g. biting the bait

basket). Species such as tuna were observed swimming within the field of view, but

did not show specific interest for either the camera system or the bait. These species

may have been observed swimming within the field of view by chance or, the bait

and structure associated with the cameras attracted them to the area, but not

specifically to the bait source. Thus, I believe there is room for improvement in the

attraction cues used in pelagic stereo-BRUVs. Reflective materials, such as flashes

and lures, are commonly used for fishing and these were tested attached to the

camera frame in early trials, but no specifically evaluated. Moreover, recent research

has suggested that a combination of visual, odour and acoustic cues is the most

effective attractant for pelagic fish (Rees et al., 2014). Nonetheless, any bait method

added to the camera systems should be standardised across all deployments (Dorman

et al., 2012; Hardinge et al., 2013).

Strengths and limitations of pelagic stereo-BRUVs

In this thesis I show that pelagic stereo-BRUVs can overcome some of the

difficulties associated with studying pelagic fish assemblages. The main advantage

of pelagic stereo-BRUVs over extractive fishery-independent techniques is their

non-destructive nature, which causes no physical trauma or stress to the individuals

studied. Therefore, this method is suitable to target threatened or protected species

and to be used in areas closed to fishing (Murphy and Jenkins, 2010). Baited video

115

methods have also proven to be effective in removing the gear selectivity bias of

fishing-based methods such as hook-size selectivity in longline surveys (chapter 4;

Harvey et al., 2012c; Langlois et al., In press; Langlois et al., 2012b; Willis et al.,

2000).

Pelagic stereo-BRUVs can be used in areas and at depths beyond the limits of diver-

based studies, and sampling can take place both during the day or using lights at

night (chapter 4; Bailey et al., 2007; Fitzpatrick et al., 2013; Harvey et al., 2012b;

Zintzen et al., 2012). Video techniques provide a permanent record of fish in the

study area, allowing for video analysis to be revisited and for clips to be shared

among researchers of different expertise (Cappo et al., 2003). Stereo-video

recordings also provide the opportunity to record fish behaviour in their natural

environment, including observations of presettlement fish that were previously

limited to catch-release and lab experiments (chapter 6; Leis and Carson-Ewart,

1998; Masuda, 2009; Santana-Garcon et al., 2014c).

All sampling techniques have some inherent biases and limitations (Murphy and

Jenkins, 2010) and, as assessed and discussed throughout this thesis, pelagic stereo-

BRUVs are no exception. The main limitation of pelagic stereo-BRUVs is the

identification of all individuals to species level. This is a common shortcoming for

all video techniques (Mallet and Pelletier, 2014), but it is exacerbated for pelagic

species because many taxa share similar morphological traits (i.e. coloration,

streamlined fusiform body, pointy snout and deeply forked tails), and they have

adapted to appear almost invisible in the pelagic environment (Pepperell, 2010).

Difficulties associated with species identification are not unique to video techniques,

but are also reported for other commonly used fishery-independent methods like

hydroacoustic techniques (Lawson et al., 2001). Fishery-dependent data is not

immune to species identification issues as fishers may also group species at higher

taxonomic levels or under-report the catch of low-value species (Lewison et al.,

2004). The time required for video analysis can be a major cost and limitation of

video techniques, but advances in video processing and the automation of some

components of the analysis appear promising in reducing analysis-time in the near

future (Harvey et al., 2014; Shortis et al., 2013). Understanding the limitations and

biases of a sampling technique allows for a better use of the method in forthcoming

116

studies and provides direction to future research so that such challenges can be

overcome.

Future research direction: opportunities and challenges of using

pelagic stereo-BRUVs

I believe there is potential for pelagic stereo-video to be attached to FADs, or other

commercial fishing gear to facilitate the collection of data in the open ocean.

Similarly, pelagic stereo-BRUVs will need further development to be deployed as

drifting systems. Implementation of stereo-video systems in the vast oceanic systems

will require high replication and involve processing extensive hours of video.

Fortunately, underwater camera equipment, image storage and processing

technology are advancing rapidly (Churnside et al., 2012; Mallet and Pelletier,

2014). Automation of photogrammetric techniques to detect, identify and measure

fish are also developing rapidly (Harvey et al., 2014; Shortis et al., 2009; Shortis et

al., 2013), following the increase in the use of video techniques for fishery

applications (Churnside et al., 2012; Graham et al., 2004; Harvey et al., 2003;

Pember et al., 2014; Rosen et al., 2013; Ruff et al., 1995).

Rapid industry developments in the resolution of video imagery and further research

on alternative calibration methods will assist with reducing the challenge of

identifying and measuring fish that remain far from the camera systems (Harvey et

al., 2014). Currently, the strategy most commonly used to calibrate underwater

stereo-video systems uses a two-layered cube that fills the entire field of view of the

camera (see Harvey and Shortis, 1998; Shortis and Harvey, 1998). Depending on the

size of the cube and the configuration of the camera system, most calibrations occur

at a distance between 2 and 4 metres. This is the optimal distance for measurement.

However, the use of a multi-distance calibration (i.e. calibrations at distances of

perhaps 3, 6 and 10 metres) and incorporating all the data into one bundle adjustment

will, theoretically, produce a more robust set of camera calibration parameters across

a greater range of distances. Multi-distance calibration strategies may make it

possible to accurately measure fish at distances greater than 8 to 10 metres.

Further research is also needed to understand the role of bait in mid-water

deployments and the dynamics of bait plume dispersal. This will help, among other

117

things, to understand the area of attraction and to determine the minimum distance

that should be allowed between simultaneous deployments (Heagney et al., 2007;

Rizzari et al., 2014). Monitoring physical and oceanographic parameters such as tide,

current, wave motion and temperature were not included in my study, but I recognise

that they are critical to model bait dispersal and to characterise pelagic habitats

(Grantham et al., 2011; Gray, 1997). I hope the body of research presented in this

thesis will encourage and facilitate other researchers to implement pelagic stereo-

BRUVs for monitoring and assessing fish assemblages in pelagic ecosystems.

119

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Appendix 1 - Measures of precision for dissimilarity-based multivariate analysis of ecological communities

Final version published as: Anderson, M.J., Santana-Garcon, J., 2015. Measures of

precision for dissimilarity-based multivariate analysis of ecological communities.

Ecology Letters 18(1), 66-73.

Authors: Marti J. Anderson, New Zealand Institute for Advanced Study

(NZIAS), Massey University, Albany Campus,

[email protected].

Julia Santana-Garcon, The UWA Oceans Institute (M470) and School

of Plant Biology, The University of Western Australia, Australia,

[email protected].

Running title: Measures of precision for community ecology

Keywords: assemblage data; community ecology; dissimilarities; multivariate

analysis; PERMANOVA; precision; replicates; sampling methods; standard error;

variability

134

Abstract

Ecological studies require key decisions regarding the appropriate scale and

number of sampling units. No methods currently exist to measure precision for

multivariate assemblage data when dissimilarity-based analyses are intended to

follow. We describe tools to assess the appropriate size of a “replicate” and to

measure precision for communities. We propose a pseudo multivariate dissimilarity-

based standard error (MultSE) be used to assess sample-size adequacy and describe a

novel double-resampling method to quantify its uncertainty. These ideas are

extended to complex experimental designs by using the pseudo residual mean square

from a PERMANOVA analysis as a measure of variability, including within-cell

double-resampling methods, for rigorous inference.

Introduction

The measurement of ecological communities requires key decisions

regarding the size and number of individual sampling units that should be used in

order to characterise (and analyse) communities of interest. Two essential questions

are: (i) How big should a sampling unit be in order to adequately capture what we

mean (in a given study) by the word “replicate” and (ii) How many such replicates

are needed in order to get reasonably precise measures of multivariate variability for

purposes of testing relevant null hypotheses regarding community structure?

To measure the density or relative abundance of a single species (univariate),

methods exist to allow researchers to assess (e.g., from a series of replicates obtained

in preliminary pilot investigations) the adequacy of any given choice of sampling-

unit size and/or number (e.g., Andrew & Mapstone 1987; Downing & Downing

1992). In particular, for univariate data one may calculate a value of precision for a

given sample of n units of a given size. Precision measures the degree of

135

concordance among multiple estimates of a given parameter (such as the mean) for

the same population (Cochran & Cox 1957). This is reflected by the variability of an

estimate; precision increases as variance decreases. Although variance is

independent of sample size, precision is measurable from the sampling programme

and increases with the number and size of sampling units.

An appropriate measure of univariate precision can be calculated from a

given sample as the standard error divided by the mean; namely, ySEp /= , where

, s is the sample standard deviation, is the sample mean and n is the

number of sampling units. Note that, so defined, as values of p decrease, precision

improves. Measures of precision can be calculated from pilot data, a plot of p vs. n

can be drawn, and we expect a gradual decrease and levelling-off in the value of p

with increasing n in such a plot. One may then consider that a value of n around this

apparent asymptote in p (i.e., where the slope of the curve becomes very small)

would be reasonable to use for future investigations of the population, as further

increases in n would not result in substantially greater increases in precision.

Alternatively, given a set of pilot data, one may calculate a value of n required to

achieve a desired level of precision set a priori (such as p = 0.2, etc.) as

, rounded up to the nearest integer.

For multivariate data, especially multi-species count data that are gathered

with the intention of characterising ecological communities, there is no obvious

measure of multivariate precision that can be used to assist researchers in decision-

making processes regarding adequate sampling. First, what do we mean by “the

community”? We presume here that the researcher has already defined the

community conceptually initially by reference to a particular spatial and temporal

inferential domain (e.g., trees on Great Barrier Island, invertebrates within a given

depth-range on the Norwegian continental shelf within a particular pre-defined area

specified by latitude and longitude, etc.). This domain is also likely to include

specificity caused by logistic constraints in sampling organisms which further limits

nsSE /= y

2)]/([ ypsn =

136

inference (e.g., only macro-invertebrates large enough to be retained on a 0.5 mm

sieve, only trees greater than a certain diameter at breast height (dbh), etc.).

Although these decisions might be somewhat arbitrary, often based on convenience

or historical legacies (hence, the word “assemblage” is preferable to the word

“community” for multi-species datasets, see in particular Underwood 1986), we

presume the researcher is sampling from the same “universe” with respect to these

definitions within a given study (as in Colwell et al. 2004), and that what is required

to characterise the community, so defined, from a sampling perspective, will follow

directly from this.

Multivariate analyses of ecological communities are very often based on

measures of dissimilarity, such as Bray-Curtis, Hellinger or Jaccard, that

appropriately emphasise changes in the composition of the identities or relative

abundances of individual species (Legendre & Gallagher 2001; Clarke et al. 2006;

De Càceras et al. 2013). Thus, although a more classical Euclidean-based

multivariate analogue of the above univariate precision index might be considered

(e.g., such as the determinant of a variance-covariance matrix on normalized data,

see also Kotz & Lovelace 1998; Pearn & Kotz 2006), these implicitly or explicitly

rely on assumptions of multivariate normality, and are unlikely to shed any light on

ecologically relevant compositional information.

Non-parametric measures of precision for multivariate data have been

developed recently by Šiman (2014). These are based on the calculation of

multivariate quantiles using halfspace depth regions (Rousseeuw & Ruts 1999;

Serfling 2002). Although these ideas might well be applied fruitfully to sample

points in a principal coordinate (PCO) space that preserves a chosen dissimilarity

measure (e.g., Gower 1966; Legendre & Anderson 1999; McArdle & Anderson

2001), there remain important problems. The regions must be convex, the

calculations require a large number of data points, and the computations are so

demanding as to make them unfeasible for any more than 4 or 5 variables (Šiman

137

2014). However, datasets in community ecology typically are high-dimensional with

many species.

One aspect of community structure is richness – the number of species –

which is known simply to increase with sample size (or sample area). A plot of the

cumulative number of species with increases in the number of sampling units (a

species-accumulation curve, Gotelli & Colwell 2001; Magurran 2003) can be drawn

from a set of samples taken from a given area. The point at which this curve begins

to level-off can indicate the number of samples beyond which not many new species

will be encountered. The usual purpose of such plots, however, is generally to

estimate the total number of species in a given (usually large) area (e.g., Ugland et

al. 2003, Colwell et al. 2004; Gray et al. 2004), rather than to infer the number of

sampling units required to characterize variation in community structure within that

area. Furthermore, the size of individual sampling units is usually driven heavily by

logistic constraints (e.g., only 1m2 plots can be examined within a reasonable length

of time in the field while diving under SCUBA, etc.). Yet, if the sizes of individual

sampling units are too small, then many units may contain no species at all (leading

to undefined dissimilarity values) and there also may be many pairs of units that

have no (or very few) species in common, yielding dissimilarity values that are

uninformative (Clarke et al. 2006).

Our purpose here is to describe some simple approaches to aid in the

assessment of appropriate sampling of ecological communities for subsequent

analysis using dissimilarity-based multivariate methods (e.g., Clarke & Gorley 2006;

Anderson et al. 2008). First, we consider the goal of obtaining enough information

about the community at the level of an individual replicate that will yield reasonable

distributions of dissimilarity values – i.e., we address the question: “How many

sampling units, when pooled together (i.e., sub-samples), are needed (or, what size of

individual sampling unit is needed) to provide a reasonable measure of community

structure for comparative analysis?” In other words, what is reasonable to use as a

138

“replicate” sampling unit? We propose the use of simple graphics that show key

features of the distributions of dissimilarity measures with increasing sizes/numbers

of sub-samples.

Second, presuming an appropriate replicate sampling unit (potentially

comprised of a number of pooled sub-samples) has been defined, we then ask: “How

many replicates are needed to sufficiently characterize the community being sampled

with reasonable precision for subsequent dissimilarity-based multivariate analysis?”

We propose the use of a pseudo multivariate dissimilarity-based standard error

(MultSE) – a direct analogue to the univariate standard error – as a useful quantity

for assessing sample-size adequacy for ecological studies. This can be calculated

easily from a single set of pilot data or on residuals after conditioning on some more

complex sampling design or model. We further propose a double-resampling

technique (permutation followed by bootstrapping) in order to quantify uncertainty

in MultSE values with increasing numbers (or sizes) of sampling units.

Methods & Results

The methods proposed are illustrated through ecological examples. R code (R

Core Team 2014) and associated datasets (as *.csv files for input into R) are

provided in Supporting Information (see Appendix S1).

Choosing the number of sub-samples to pool in order to obtain “replicates”

In some cases, multivariate community data may be very sparse; individual

sampling units may contain very few species and arguably do not represent the

larger-scale community of interest very well. Hence, one may combine (pool) data

from several replicates (effectively treating them as sub-samples) before proceeding

with multivariate analyses. Several important dissimilarity measures commonly used

in ecology – namely, Bray-Curtis, Sørenson or Jaccard – lose resolution and can

139

become increasingly sporadic near their boundaries (Clarke et al. 2006). This tends

to occur when individual sampling units contain few or no species, hence many pairs

of units have no species in common. Two sampling units with no species in them are

also simply “undefined” by any of these three measures. While Clarke et al. (2006)

suggested a “feathering-in” of a dummy species (present everywhere) to help combat

this problem, another approach would be to pool information across multiple small-

scale sampling units to obtain a larger-scale replicate. But how many should be

pooled together to achieve an appropriately informative “replicate”?

Santana-Garcon et al. (2014) studied the vertical distribution of pelagic fish

assemblages off Western Australia using pelagic Baited Remote Underwater stereo-

Video systems (BRUVs) and determined that reasonably precise estimates of total

abundance and richness of fish assemblages could be achieved using ca. 8 replicate

deployments of video cameras each of 120 minutes’ duration. Similar gear was used

to study fish assemblages at the Houtman-Abrolhos Islands (28°40' S, 113°50' E).

Here, the sampling design consisted of n = 12 pelagic stereo-BRUV deployments at

a depth of 10 m in the water column from each of 6 sites: 3 were inside and 3 were

outside of an area closed to fishing (see Santana-Garcon et al. in review, for details).

A total of 24 fish species were recorded in this study. Preliminary analyses based on

a variety of resemblance measures (not shown) found no significant effects for any

of the factors, so the study effectively offers a total of N = 72 sampling units that

may be considered as genuine replicate measures of the same “universe” – in this

case, the pelagic fish community at the Abrolhos Islands.

These data are extremely sparse and are dominated by zeros. A species-

accumulation curve does not appear to show any clear “levelling-off” (Fig. 1a). The

distribution of Bray-Curtis dissimilarities among the 72 original replicates after

square-root transformation (Fig. 1b) shows a highly skewed distribution, with 76.4%

of the pair-wise dissimilarities being equal to 1.0 (no species in common) and 14.8%

being undefined, corresponding to pairs of sampling units that had no species in

140

them. When groups of individual sampling units (which we should properly consider

now as “sub-samples”) are pooled together, the distribution of dissimilarities

improves substantially (Fig. 2).

Although semi-parametric (e.g., PERMANOVA, Anderson et al. 2008) and

non-parametric (e.g., ANOSIM, Clarke & Gorley 2006) multivariate analyses based

on dissimilarity measures or their ranks do not assume anything specific regarding

the distribution of dissimilarities used as input, it is nevertheless desirable that the

chosen measure being used has some genuine information content, some reasonable

resolution (allowing distinction between large and small dissimilarities across a

given dataset), as well as having an appropriate contextual meaning for tests of

hypotheses regarding relevant ecological properties (such as composition) for the

communities of interest. For example, if sparseness has arisen because of some

ecological phenomenon under study (such as predation), then perhaps a different

dissimilarity measure which treats joint absences as a sign of similarity should be

considered (such as the Gower measure, e.g., Langlois et al. 2005). Similarly, if

dissimilarities of 100% (no species in common) have arisen because the data

genuinely span very large biogeographic distances, then a taxonomic resemblance

measure (e.g., Clarke et al. 2006) may be appropriate. However, if the reason for the

sparseness is indeed simply that the scale of the sampling units is simply too small to

capture much, then treating these as sub-samples and pooling them together is in

order before proceeding. In particular, methods that fit linear models in the space of

the original resemblance measure (i.e., PERMANOVA, DISLTM, dbRDA or CAP –

see Anderson et al. 2008) rely critically on the information content of the measure

itself.

We propose randomizing the order of the sampling units and plotting the mean

(along with 0.025 and 0.975 quantiles) for (at least) two quantities: (i) the proportion

of dissimilarities that are equal to 1.0 (100% dissimilar), and (ii) the proportion of

dissimilarities that are undefined (i.e., “NaN”), with increasing numbers of pooled

141

sub-samples being used to form the replicate sampling units. Doing this for the

Abrolhos dataset shows no undefined dissimilarities remain once the number of sub-

samples exceeds 2, and no replicates that are 100% different remain after pooling

more than 10 sub-samples (Fig. 1b). This accords with what is shown in Fig. 2 for

the original ordering of sub-samples, where a much more reasonable distribution of

dissimilarities is observed when the number of sub-samples per replicate is greater

than or equal to 8.

A multivariate measure of pseudo standard error

As a direct analogue to the univariate measure of precision, we consider a

multivariate measure of pseudo standard error (MultSE, first described by Anderson

et al. 2001), as follows. Let D be a ( ) matrix of dissimilarities among all

pairs of sampling units, i = 1, ..., n and j = 1, ..., n. Using Huygens’ theorem (e.g.,

Legendre & Anderson 1999; Anderson 2001), the sum of squared inter-point

dissimilarities divided by the number of sampling points (n) is directly interpretable

as a multivariate measure of pseudo sums of squares in the space of the chosen

dissimilarity measure:

(1)

The quantity in equation (1) is also equivalent to the sum-of squared distances from

individual sampling points to their centroid in the space of the chosen resemblance

measure (Anderson 2001). Furthermore, dividing this by (n – 1) yields a quantity

that is directly interpretable as a multivariate measure of pseudo variance in the

space of the chosen dissimilarity measure:

(2)

nn× }{ ijd

ndsn

i

n

ij

ij /)1(

1 1

2∑ ∑

−

= +=

=

ndvn

i

n

ij

ijn/

)1(

1 )1(

2)1(

1 ∑ ∑−

= +=−

=

142

Indeed, in the case of a single variable and Euclidean distance, s is equal to the usual

classical univariate sum-of-squares and v is equal to the usual classical unbiased

measure of the sample variance.

Note that the word “pseudo” has been used throughout here to distinguish s in

(1) above from the classical multivariate sums-of-squares-and-cross-products

(SSCP) matrix and v in (2) above from the classical multivariate sample variance-

covariance matrix. The classical matrices will clearly take into account correlation

structures in the multivariate (Euclidean) space of the original variables, whereas

these pseudo measures do not. To see this, note that in the case of multivariate data

and Euclidean distance, s in (1) is equal to the trace of the classical SSCP matrix and

v in (2) is equal to the trace of the classical sample variance-covariance matrix.

Finally, based on the above, a multivariate pseudo standard error therefore can

be calculated as:

(3)

Although the univariate measure of precision takes the standard error divided by the

sample mean, this is not possible in the present case because the sample mean is

clearly a vector here (being multivariate). However, we consider that m in (3) is

perfectly suitable as a measure of precision within a given study, because the

estimate of the mean (generally obtained as the sample average in univariate

analysis) is unbiased for any sample size. The only consequence of failing to divide

by the mean is to produce a value which is not standardized in any way, hence can

only be used within the context of a given study, and not compared among studies.

Thus, although some chosen value of precision for univariate analysis might be

suggested as a rule of thumb to be applied to studies across a given field (e.g., p =

0.2 or p = 0.5, etc.), the value of m in (3) is dependent on the scale of the

dissimilarity measure chosen, so no such generalization for its value across multiple

studies is apparent. However, as illustrated by the examples below, m will still

nvm /=

143

nevertheless be quite useful for examining relative precision for different sample

sizes within a given study.

We propose that, given a set of pilot multivariate data having a total of N

sampling units, one may draw a random subset (without replacement) of size n = 2,

3, 4, ..., N and for each of these random subsets, a value of m can be calculated,

which may be denoted in each case by , , ... . Repeat this process for

a very large number of random draws (say 10,000), then calculate the mean values

obtained under permutation for each sample size as , , ... . A plot of

vs. k for k = 2, 3, ..., N will then provide the graphic we require to assess

precision with increasing sample size. We propose that these means be calculated

using sampling without replacement (i.e., using a permutation method) specifically

to ensure that the values obtained will be unbiased.

Next, we propose that a bootstrapping method be used to draw error bars on

the plot. It is clear that the number of possible permutations for n = N is 1 (i.e., this is

simply the full set of data) and further that the number of random subsets without

replacement that would yield unique values for (equivalent to simply

leaving out one observation at a time) is also limited to just N possible values.

Hence, we propose obtaining a further random subset as a random draw with

replacement (i.e., a bootstrap sample), of size n = 2, 3, 4, ..., N and for each of these

to calculate a value of m, denoted in each case by , , ... . Consider the

0.025 and 0.975 quantiles of the bootstrap distribution for each sample size n = k for

k = 2, 3, ..., N as and , respectively. Unlike the permutation

approach, the bootstrap distribution is known to be biased (e.g., Davison & Hinkley

1997). An empirical estimate of the bias, for each sample size k, is given by:

(4)

Hence, bias-adjusted 0.025 and 0.975 quantiles are provided by

π2=nm π

3=nm πNnm =

π2=nm π

3=nm πNnm =

πknm =

π)1( −= Nnm

β2=nm β

3=nm βNnm =

)025.0(βknq = )975.0(β

knq =

)( βπknknkn mmb === −=

144

and , (5)

respectively. As our proposed method uses permutations to obtain mean values for

the plot, and a separate bias-adjusted bootstrapping approach to obtain appropriate

error bars, we shall refer to this overall process generally as a “double resampling”

method. We note also that clearly other choices of quantiles may easily be calculated

here in a similar fashion (e.g., 0.25, 0.75, etc.), if desired.

Choosing an appropriate size or number of replicates using MultSE

The precision of univariate data has been assessed in order to optimise the

sampling effort of BRUV techniques in estuarine (Gladstone et al. 2012) and pelagic

environments (Santana-Garcon et al. 2014). The parameters potentially affecting the

sampling ability of pelagic stereo-BRUVs include soak time (time that cameras are

left recording underwater) and the number of replicate deployments.

Data were collected in March 2012 near Tantabiddi Passage in Ningaloo

Marine Park (21°53’ S, 113°56’ E), Western Australia. The pelagic stereo-BRUVs

used in this study are described in Santana-Garcon et al. (2014). The systems were

designed to remain in the mid-water at ca. 5 m depth (7.17 ± 0.54 m, Mean ± SE), 30

m above the bottom and were deployed for a soak time of up to 3 hours. There were

12 replicate deployments (6 at each of 2 sites) obtained during day light hours. Video

images were analysed using the software ‘EventMeasure (Stereo)’ (SeaGIS Pty Ltd).

All fish recorded were quantified and identified to the lowest taxonomic level

possible. To avoid repeating counts of individual fish re-entering the field of view, a

conservative measure of relative abundance (MaxN) was recorded as the maximum

number of individuals of the same species appearing at the same time (Priede et al.

1994). MaxN was recorded for each of three different soak times: 60, 120 and 180

minutes.

})025.0({ knkn bq == +β })975.0({ knkn bq == +β

145

No previous assessment has been done to optimize multivariate precision for

dissimilarity-based community analysis of these fish assemblages. We wished to

identify: (i) an appropriate length of time that cameras should be deployed; and (ii)

an appropriate number of replicate deployments beyond which no substantial

increases in MultSE would accrue.

First, we examined the means and quantiles of MultSE with increasing sample

size using either the permutation or the bootstrap method alone for the 60 minute

soak time, with analyses based on square-root transformed abundances and Bray-

Curtis dissimilarities. The permutation method alone yields obvious constraints on

the error bars for larger sample sizes, while the bootstrap method alone yields means

that are biased downwards (Fig. 3a). This motivates the use of our double resampling

approach, which shows that no appreciable precision is gained by deploying the

cameras longer than 120 minutes, and that a levelling-off in MultSE occurs for

sample sizes around n = 7 or 8 (Fig. 3b).

We readily concede that the idea of “levelling-off” must rest in the eyes of the

beholder; the approach we suggest is intended to be a heuristic diagnostic tool only,

and not a prescription. Nevertheless, the analyses of MultSE in this case coincide

well with the results obtained for the precision of either of the univariate measures of

richness or total abundance (Santana-Garcon et al. 2014). This coincidence might

not always occur, however, and will depend on the nature of the data and the

characteristics that are emphasised by the chosen dissimilarity measure.

A further example of the proposed method yields additional insight. We

consider next an analysis of soft-sediment macrofauna described by Ellingsen &

Gray (2002), who studied beta diversity and its relationship with environmental

heterogeneity in benthic marine systems over large spatial scales in the North Sea.

Samples of soft-sediment macrobenthic organisms were obtained from 101 sites

occurring in five large areas along a transect of 15 degrees of latitude. A total of 809

146

taxa were recorded overall, and replicate sampling units here consisted of

abundances pooled across five benthic grabs (sub-samples) obtained at each site. For

more details, see Ellingsen & Gray (2002), Ugland et al. (2003) and Anderson et al.

(2006). Of interest here is to identify the number of replicate sampling units required

to adequately sample the benthic communities for comparative analysis on the basis

of the Jaccard resemblance measure. MultSE was calculated with the double

resampling method for increasing numbers of replicates separately for each of the

five areas. Results show clear heterogeneity in the MultSE values across the 5

different areas, with the greatest variability occurring in area 3, followed by areas 4

and 5, then area 2 and finally area 1 (Fig. 4a). This agrees naturally with the

differences in multivariate dispersions found previously (using PERMDISP;

Anderson 2006) using the full set of data (Anderson et al. 2006; 2008). What is

added to our understanding here by considering values of MultSE with increasing

sample size (including the double-resampling method) is that these differences in

dispersion are already apparent once sample sizes within each area reach about n =

10 to 12 sampling units. Knowledge of what level of sampling may be required to

identify significant heterogeneity can be quite useful. When dealing with large areas,

for example, better estimates of overall richness can be obtained by generating

species accumulation curves for each of several smaller sub-sampled areas, due to

heterogeneity in community structure (turnover) across the larger sampling extent

(Ugland et al. 2003; 2005).

Calculating MultSE for more complex designs

The idea of using MultSE as a measure of precision can be readily extended for

use with more complex sampling designs by noting that the residual mean square

provided from a PERMANOVA analysis (Anderson 2001) is itself a measure of

multivariate pseudo error variance in the space of the chosen dissimilarity measure.

Thus, as described in McArdle & Anderson (2001), let D = { } be an ( )

dissimilarity matrix, let A = { } = { }, and let G = be

ijd nn ×

ija 22

1ijd− )()( 11 11IA11I ′−′−

nn

147

Gower’s (1966) centered matrix, where 1 is a column of 1’s of length n and I is an (

) identity matrix. Next, let matrix X be an ( ) design matrix of full rank

that codes for the full PERMANOVA model, including intercept (for example, in a

one-way ANOVA design, g would be equal the number of groups). Finally, let

matrix H be the usual projection matrix for the design, namely .

The residual mean square for the PERMANOVA model (Anderson et al. 2008)

estimates error variability in the space of the resemblance measure and is calculated

as:

(6)

where “tr” denotes the trace of a matrix. After obtaining v from equation (6), one can

then apply equation (2) to calculate precision and perform the double resampling

method for increasing numbers of replicates, as described above. Note however that

the replicates here occur within the structure of a model framework – a specific

ANOVA design. This means the double resampling method must be applied

separately for increasing numbers of samples within each individual cell in the

design. For example, in the case of a one-way ANOVA model, the double

resampling is done separately within each group.

By way of example, consider data on fish assemblages from the Poor Knights

Islands in New Zealand, described by Willis & Denny (2000) and Anderson &

Willis (2003). Divers counted temperate reef fishes belonging to each of 62 species

in each of nine 25 m × 5 m transects at each site. Data from the transects were

pooled at the site level and a number of sites around the Poor Knights Islands were

sampled at each of three different times: September 1998 (15 sites), March 1999 (21

sites) and September 1999 (20 sites). These span the point in time when the Poor

Knights Islands were classified as a no-take marine reserve (October 1998).

Analyses of MultSE with the double resampling method vs. increasing sample

sizes for each of the three times of sampling (based on log(x+1)-transformed data

nn× gn ×

XXXXH ′′= −1][

}){()(1 GHI −=−

trvgN

148

and Bray-Curtis dissimilarities), was initially done separately for each time and

showed no sign of heterogeneity of dispersions, unlike the Norwegian macrofauna

(cf. Fig. 4b and Fig. 4a). The residual mean square (quantity v from equation (6)

above) can therefore legitimately be calculated as a single measure of error

variability from the residuals of a one-way ANOVA model for the three groups

corresponding to the three times of sampling (Fig. 5). Precision in the residual mean

square levels off and does not improve substantially for these assemblages once the

number of sites sampled within each time reaches around 7 or 8 (Fig. 5).

Discussion

We have provided some tools for assessing an appropriate number and scale of

sampling units for dissimilarity-based multivariate analysis of ecological data. First,

we consider it is wise, before embarking on dissimilarity-based modelling, to

examine the distributions of dissimilarities directly to check if large numbers of

values approach the limits of what the chosen dissimilarity measure is able to

measure. If so, then perhaps the choice of dissimilarity measure should be revisited.

Another possibility is that large numbers of sparse sampling units are present which

should be pooled together to provide enough information regarding the community

of interest prior to subsequent analysis. We have provided a general tool for

assessing the numbers of sub-samples that might be chosen to define a reasonable

“replicate” in this context.

Second, we have proposed a measure of precision for multivariate data

(MultSE) based on inter-point dissimilarities that is a direct analogue of the well-

known univariate standard error. A double resampling method allows unbiased

measures of variation in MultSE to be obtained. We recognize the clear limitations of

this approach. The measure takes no account of the shape of the multivariate data

cloud, only its overall dispersion (size). Future developments should include better

characterization of the actual shape of the data cloud, which might be achieved, for

149

example, through the use of multivariate kernel density estimation (KDE) in the

principal coordinate (PCO) space of the resemblance measure. This will require

enhanced computer power and programming to cope with analyses of high-

dimensional datasets like those typically found in ecology. Future work should focus

also on how such approaches might compare with, say, a more classical approach to

measure precision, such as the determinant of a normalized variance-covariance

matrix in the PCO space.

A further limitation of the approach outlined here is its scale-dependency.

Values of MultSE will necessarily depend on the dissimilarity measure used, so

cannot be easily compared across studies. More work is needed to assess what levels

of precision (as measured by MultSE) might be acceptable for different resemblance

measures that are bounded and have a common scale, such as Bray-Curtis.

Finally, although we have described the analysis of precision using the residual

mean square from a PERMANOVA model only for a one-way case, extensions to

higher-way models are clearly very easy to implement using the formulation in

equation (6) for any model specified fully within matrix X. Some care may be

needed in such cases, however, to ensure that the double resampling is performed

within appropriate combinations of factor levels, given the particular higher-way

design of interest.

Acknowledgements

MJA was supported by a Royal Society of New Zealand Marsden Grant

MAU1005. Logistic support for acquiring the data analysed here from Western

Australia was provided by the University of Western Australia and the Department

of Fisheries, Government of Western Australia.

150

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SUPPORTING INFORMATION

Additional Supporting Information may be downloaded via the online version

of this article at Wiley Online Library (www.ecologyletters.com).

155

FIGURES

Fig. 1. (a) Species accumulation curve vs number of sampling units and (b)

proportion of Bray-Curtis dissimilarities equal to 1.0 (no species in common) or

undefined (i.e. NaN) for the Abrolhos dataset (N = 72, p = 24) with increasing

numbers of sampling units being pooled together to define a replicate. Error bars

indicate the 2.5 and 97.5 percentiles of the distribution of values obtained under

1000 permutations of the order of sampling units.

156

Fig. 2. Frequency distributions for Bray-Curtis dissimilarities for the Abrolhos

dataset (also showing the frequency of undefined (i.e. NaN) dissimilarities) with

increasing numbers of sampling units being pooled together to define a replicate,

from n = 2 (top) to n = 10 (bottom). Pooling of units was done simply in order of

their occurrence in the data file.

157

Fig. 3. Multivariate pseudo standard error as a function of sample size on the basis of

Bray-Curtis dissimilarities calculated on square-root transformed fish abundance

data from Ningaloo (a) for a soak time of 60 minutes only, showing results (means

with 2.5 and 97.5 percentiles as error bars) from 10,000 resamples obtained using

either the permutation or bootstrap approach and (b) for all three soak times, using

the double-resampling method, with permutation-based means and bias-adjusted

bootstrap-based error bars (with 10,000 resamples for each).

158

Fig. 4. Multivariate pseudo standard error as a function of sample size (a) for each of

5 different areas on the basis of Jaccard dissimilarities for macrofaunal communities

from the Norwegian continental shelf and (b) for each of 3 different times on the

basis of Bray-Curtis dissimilarities calculated on log(x+1)-transformed counts of

fishes from the Poor Knights Islands, New Zealand. A double-resampling scheme

was used to generate means for each sample size using 10,000 permutations and

error bars as bias-adjusted 2.5 and 97.5 percentiles from 10,000 bootstrap resamples.

159

Fig. 5. Multivariate pseudo residual mean square from a one-way ANOVA model as

a function of sample size on the basis of Bray-Curtis dissimilarities calculated on

log(x+1)-transformed counts of fishes from the Poor Knights Islands, New Zealand.

A double-resampling scheme was used to generate means for each sample size using

10,000 permutations and error bars as bias-adjusted 2.5 and 97.5 percentiles from

10,000 bootstrap resamples. Resampling was done separately within each of the

three sampling times (i.e., within each of the 3 groups in the one-way ANOVA

model design).

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