A Case Study from Asterias Amurensis

Quantifying search and control performance during marine invasive surveys: a case study from Asterias amurensis

Submitted by

KIMBERLEY A. MILLERS Bachelor of Science (Hons)

Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy

March 2015 School of BioSciences Faculty of Science The University of Melbourne

Produced on archival quality paper

Abstract

Marine invasive species are a global threat to marine biodiversity. Effective management of invasive species depends on accurate population information. To best inform management, surveys of abundance, occupancy and detectability must be carefully designed, and account for uncertainty. However, many marine invasive management programs currently do not record and account for the uncertainty of detectability. In this thesis, I document the limitations of searching for a marine invasive species during eradication programs and examine ways to improve detectability in future survey designs.

Specifically, I undertake a series of empirical surveys to test how effective observers are at detecting the northern Pacific seastar, Asterias amurensis; this species poses a serious threat to native and commercial species in southern Australia. I use artificial silicone replicas of A. amurensis during empirical surveys so as to eliminate the risk of spreading the marine invasive species. I use data combined with Bayesian methods to develop a population catch-effort model, which provides insights into what influences detectability and whether eradication at these sites was a viable management goal. Finally, I take a novel approach to testing optimal search theory under field conditions. Optimal search theory has been used to support resource allocation when managing invasive species. This is the first time, to my knowledge, environmental decision theory has been tested empirically by examining applications of search theory for any species in an ecological setting.

I found that animal size, target distance from the transect line and group clustering size all affect detectability. I also found that pre-survey training reduced the frequency of incorrect detections of A. amurensis for two native co-occurring species by up to 16.1%. I also demonstrate the amount of search effort required to eradicate

populations at a site is often considerably higher than the effort actually invested to completely remove a population of A. amurensis. Lastly, I found that using an optimal search strategy compared to three other routinely used strategies during surveys for A. amurensis can improve the number of seastars removed by upwards of 12% for a 20 minute search budget.

Evaluating how well previous removal efforts eliminate marine invasive species from a site, and understanding the uncertainty of survey design are critical to improving future post-border management responses. The northern Pacific seastar A. amurensis will continue to threaten the marine environment in its non-native distribution. Understanding how to improve survey design will continue to be essential for active and successful management.

ii Declaration

This is to certify that:

i) the thesis comprises only my original work towards the PhD except where indicated in the Preface, ii) due acknowledgement has been made in the text to all other material used, iii) the thesis is fewer than 100,000 words in length, exclusive of tables, maps, bibliographies and appendices.

Kimberley A. Millers March 2015

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iv Preface

The work outlined in this thesis was carried out at the School of BioSciences, University of Melbourne. The content of this thesis is the result of work undertaken during my PhD, which includes outcomes of my own and work done in collaboration with others. To produce a coherent body of work I present the first chapter of this thesis as a brief review of the current state of marine invasive species management, research aims and thesis outline. The subsequent four chapters each present analysis of data collected during my PhD. The final chapter brings together results of all previous chapters, provides management recommendations, and discusses future research directions. The contributions to jointly authored works contained in this thesis are described below.

Chapter 2 In this chapter I carried out the experimental data collection, collated removal program data (2009- 2013), did the analysis, and wrote the paper. The idea for the paper was conceived by Michael McCarthy, Jan Carey and me. Tracey Rout provided early WinBUGS code and Steffan Howe and Matt Hoskins provided seastar removal data. This chapter is to be submitted for publication as:

Millers KA, Carey J, Howe S, McCarthy M. Assessing the physical removal for control efforts of the invasive seastar, Asterias amurensis in Victoria Australia. [In prep.]

Chapter 3 In this chapter I carried out the field experiments, data analysis and wrote the manuscript. The idea for this paper was conceived by Michael McCarthy, Jan Carey and me. Michael McCarthy supervised the development of statistical coding. This chapter is to be submitted for publication as:

Millers KA, Carey J, McCarthy M. Multilevel Bayesian models to estimating imperfect detection during visual surveys for an invasive seastar. [In prep.]

Chapter 4 In this chapter I carried out the field experiments, data analysis and wrote the manuscript. The idea for this paper was conceived by Michael McCarthy, Jan Carey and me. This chapter is to be submitted for publication as:

Millers KA, Carey J, McCarthy M. How to train your observer. The role of pre-survey training for an invasive marine species. [In prep.]

Chapter 5 In this chapter I carried out the field experiments, data analysis and wrote the manuscript. The idea for this paper was conceived by Michael McCarthy, Georgia Garrard, Joslin Moore and me. R code was developed by Will Morris, John Baumgartner and me. This chapter is to be submitted as both a research and methods paper for publication as:

Millers KA, Lin W, Moore J, Garrard G, Morris W, Baumgartner J, Weiss J, Hauser C, & McCarthy M. Testing the benefit of decision theory for environmental surveys. [In prep.]

Millers KA, Morris W, Baumgartner J, Hauser C, & McCarthy M. An R package for optimizing the allocation of surveillance effort when designing surveys. [In prep.]

vi Acknowledgments

I would like to thank my supervisor, Professor Michael McCarthy, for his guidance and patience. His enthusiasm for mathematics, applied ecology and conservation is contagious. I would like to thank my supervisor, Dr Jan Carey, who introduced me to research in the temperate marine environment. Her field and practical assistance beyond the textbooks was most helpful. I feel very privileged to have supervisors who have generously invested time and advice throughout the various stages of my PhD journey.

I would also like to thank Dr Steffan Howe from Parks Victoria for guiding my research from a management perspective. I would like to thank all the volunteers who took part in this research. I am grateful to Pelican Expeditions, Rob Timmers & Seal Diving Services, Parks Victoria Friends Groups (Jawbone, Ricketts Point, Point Cook and Mud Island) and all those 174 volunteers who gave time in the pursuit of finding ‘plastic stars’ with the goal of lessening the impact of our invasive friend, the northern Pacific seastar. I would like to thank all the Parks Victoria staff that assisted with this project: Emily Verey, Chris Haywood, Matt Hoskins, Jonathon Stevenson and Mark Rodrigue. Thanks to the staff at Dalchem Pty Ltd. for specialist advice and assistance on developing the seastar mimics.

Funding for this PhD was kindly provided through an Australian Research Council (ARC) Linkage Grant between The University of Melbourne and Parks Victoria.

I would also like to thank Carlie Alexander, Els van Burm and Dr Jacqui Pocklington who assisted on many a cold day in the field. My research would not have been possible without their help. A special thank you to Dr James Camac, John Baumgartner and Will Morris for your discussions, R coding lessons and support. Thank you to Dr

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Laura Pollock and Dr Rebecca Derbyshire for all the coffee breaks, helpful guidance and warm advice. I would like to thank the entire Quantitative and Applied Ecology Lab (QAECO) and the Biosecurity Risk Analysis Lab (CEBRA) at The University of Melbourne for your support, encouragement and advice. It would have been a lonely journey without the discussion, debate and laughs. To those I shared an office space with (and there’s a few of you) thank you for sharing the experience and being instrumental in helping me through my candidacy journey. Thank you to Dr Cindy Hauser, Dr Tracey Rout, Dr Joslin Moore, Dr Georgia Garrard, John Weiss and Wan-Jou Lin for data and modeling advice at various stages throughout my PhD.

A special thanks to my family. Words cannot express how grateful I am to my parents for all of the sacrifices that you’ve made on my behalf. I would also like to thank all of my family and friends who supported me in writing and encouraged me to strive towards completing. Nikki, Bec, Laney and Mackenzie thanks for keeping me going.

Kimberley A. Millers

viii Table of Contents

Abstract ...... i

Declaration ...... iii

Preface ...... v

Acknowledgments ...... vii

Table of Contents ...... ix

List of Figures ...... xiii

List of Tables ...... xvii

Chapter 1: General Introduction – search and population inferences for marine invasive species 1

1.1 Overview ...... 2 1.2 Surveys and post-border management of marine invasive species ...... 3 1.3 Imperfect detectability in marine invasive species and ecology ...... 7 1.3.1 What is imperfect detectability? ...... 7 1.3.2 How has imperfect detectability been accounted for in ecological and marine invasive studies? ...... 8 1.4 Optimising search effort ...... 9 1.5 Refining and validating marine invasive surveys ...... 10 1.6 Asterias amurensis and its management in Australia ...... 11 1.6.1 Background to the origin and biology ...... 12 1.6.2 Recorded Asterias amurensis distribution in Australia ...... 12 1.6.3 Management options ...... 14 1.7 Aims of Thesis ...... 17 1.8 Thesis Outline ...... 18

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs 21

Abstract ...... 22 2.1 Introduction ...... 23 2.2 Materials and Methods...... 25 2.2.1 Study species ...... 25 2.2.2 Study sites and removal program effort ...... 26 2.2.3 Artificial seastar mimic detectability experiments ...... 35 2.2.4 Bayesian catch-effort removal model ...... 37 2.3 Results ...... 40 2.3.1 Detectability of A. amurensis artificial mimics...... 40 2.3.2 Population size estimates during eradication programs ...... 41 2.3.3 Predicted search effort for eradication ...... 44 2.4 Discussion ...... 46 2.4.1 Measuring the feasibility of eradication ...... 46 2.4.2 Catch-effort model limitations ...... 48 2.4.3 Management implications ...... 49

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar 51

Abstract ...... 52 3.1 Introduction ...... 53 3.2 Methods and Materials...... 55 3.2.1 Study area and targets ...... 55 3.2.2 Survey design ...... 56 3.2.3 Data analysis ...... 60 3.3 Results ...... 64 3.4 Discussion ...... 70 3.4.1 Factors influencing detectability ...... 70

x 3.4.2 Implications for seastar surveys and management ...... 72

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis 73

Abstract ...... 74 4.1 Introduction ...... 75 4.2 Methods and Materials...... 77 4.2.1 Study site and species ...... 77 4.2.2 Visual survey technique ...... 78 4.2.3 Participants of the experimental surveys ...... 80 4.2.4 Statistical analysis ...... 81 4.3 Results ...... 86 4.3.1 Incorrect detection of two native seastars as Asterias amurensis ...... 86 4.3.2 Detectability of Asterias amurensis during detection experiments .... 86 4.3.3 Bayesian logistic regression for U. granifera incorrect detections ...... 88 4.4 Discussion ...... 91

Chapter 5: An empirical test of decision theory for resource allocation for a marine invasive species 93

Abstract ...... 94 5.1 Introduction ...... 95 5.2 Materials and Methods...... 97 5.2.1 Optimal search model ...... 97 5.2.2 Field-based tests using experimental surveys ...... 99 5.2.3 Estimating detection rates ...... 101 5.2.4 Comparing predicted and observed performance ...... 102 5.3 Results ...... 106 5.4 Discussion ...... 112

Chapter 6: General Discussion 115

6.1 Overview ...... 116 6.2 Summary of significant findings ...... 117 6.3 Management implications ...... 119 6.4 Future research directions ...... 121 References ...... 125

Appendices ...... 143

A. Catch-Effort R Code ...... 143 B. Logistic Regression R Code ...... 144 C. Example of survey experience questionnaire ...... 145 D. Example of project information form ...... 146

xii List of Figures

Figure 1.1. A conceptual framework for the management phases of marine invasive species. Highlighted in the coloured boxes are the five stages of management. Black fill boxes emphasise the use of surveys to monitor and detect marine invasive species. Adapted from Office of the Auditor General of Canada (2008). ....5

Figure 1.2. The northern Pacific seastar, Asterias amurensis in the Maribyrnong River, Victoria. The photo illustrates the plasticity of the colouration found in this species, with individuals ranging from complete yellow to dominating purple hues...... 14

Figure 1.3. A simple decision framework for the eradication or control of a marine invasive species. Orange boxes highlight where the aims of this thesis address individual aspects of the framework...... 18

Figure 2.1. Removal sites for Asterias amurensis in Victoria, Australia. Black filled circles represent sites in Port Phillip Bay where removal programs where undertaken by government agency or stakeholder groups between 2010–2013. Sites in eastern Victoria of (A) San Remo (2011), (B) Anderson Inlet (2003) and (C) Tidal River (2012), represent newly-established incursions where government agencies have led active eradication actions. Dates indicate the year A. amurensis was first detected at the three sites...... 27

Figure 2.2. Maps of Asterias amurensis eradication sites in eastern Victoria at (a) San Remo (c) Anderson Inlet and (e) Tidal River. Red hashed areas represent the approximate search area at each site. Photos of the site conditions are represented at (b) San Remo, (d) Anderson Inlet and (f) Tidal River. Source Google Earth images 2014...... 32

Figure 2.3. Number of Asterias amurensis removed during eradication programs in eastern Victoria, Australia since first detected at a site. These eradication sites at (a) San Remo (n=7), (b) Anderson Inlet (n=17) and (c) Tidal River (n=13) are used to model the catch-effort...... 33

Figure 2.4. Observers preparing for a line search at Tidal River, Victoria in 2012. Insert: Asterias amurensis specimens removed during a single physical removal event of eradication surveys...... 34

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Figure 2.5. Artificial mimics of Asterias amurensis used during detection experiments at Tidal River and Anderson Inlet, Australia. Two size classes of 9.4 cm and 5.8 cm maximum arm length were used in the study. Mimic seastars were made from silicone rubber and hand painted...... 36

Figure 2.6. Estimated probability of removing a seastar per search effort (diver hours) at three sites where incursions of Asterias amurensis have occurred. Estimates (black) are generated using a Bayesian catch-effort model. Estimates from detection trials are represented from Anderson Inlet (blue) and Tidal River (red)...... 42

Figure 2.7. Estimated mean population size (shaded region is 95% CI) of Asterias amurensis at sites in eastern Victoria of San Remo, Anderson Inlet and Tidal River. Estimates were modelled using a Bayesian catch-effort removal model with an uninformative detection rate...... 43

Figure 2.8. Observed (dark filled circles) and mean predicted search effort (shaded region is the 95% CI) (diver hours) (light filled circles) needed to remove an entire population of seastars at three sites in eastern Victoria. Each point represents the amount of search effort on a removal event since first detecting Asterias amurensis at a site...... 45

Figure 3.1. Examples of mimic seastars (a) used during detection surveys for Asterias amurensis. Three size classes were used with a maximum arm length of 3.5, 5.8 and 9.4 cm. A real (b) Asterias amurensis found in coastal waters of Victoria, Australia. All artificial mimics were made from silicone moulding rubber with embedded weights and hand painted...... 56

Figure 3.2. Illustration of snorkelling (a) and walking (b) transects for the detection of Asterias amurensis mimics in Victoria, Australia. Each transect is 50 m in length (li), and was defined by a measuring tape (thick line). Distance along the transect line represented locationy in the detectability model. Each transect extended four metres from the measuring tape. Distance from the tape to each mimic represented locationx in the detectability model. Note the difference between snorkeling and walking search area where walking survey area was only to one side of centre transect line, therefore area was only half that for snorkeling...... 57

xiv of random errors are presented. Estimates are based on centered and standardized (2 SD) transformed data. ψ represents the mean posteriors for the standard deviations for random effects – observer (obs), mimic (mimic) and transect (trans)...... 65

Figure 3.4. Predicted mean detection probability of Asterias amurensis in Victoria Australia across search effort increase (0–20 mins) using visual detection methods of snorkeling (a and b) and walking (c). Predictions are based on the snorkel model M13 and walk model M2. The average observer, habitat and search time used to calculate predictions from the experimental surveys. All variables were kept at the standardised mean (zero) used in the Bayesian logistic regression except when assessing the predicted individual effect of a variable. The average search time was used to predict the probability of detection was; snorkel= 8.53 mins and walk= 8.22 mins...... 66

Figure 3.5. Mean (solid line) estimates of the probability of detecting Asterias amurensis during snorkeling surveys as a function of distance. Dashed lines represent the 95% credible interval. All predictor variables were kept at the dataset means and a mean search effort of 8.53 mins...... 67

Figure 4.1. Illustration of living Coscinasterias muricata (a), Uniophora granifera (b) and Asterias amurensis (c), and compared to artificial plastic mimics (d-f) used in incorrect detection experiments. Photo of live animals: A. Newton...... 79

Figure 4.2. Examples of educational material develop for Asterias amurensis and co-occurring native seastar species Coscinasterias muricata, Uniophora granifera in Victoria coastal waters. Photo credits Australian Department of Agriculture Fisheries and Forestry (DAFF) and Victorian Department of Environment and Primary Industry (DEPI)...... 80

Figure 4.3. Mean (95% CI) proportion of Uniophora granifera incorrectly detected as Asterias amurensis mimics found during incorrect detection test surveys. Searchers had informed (grey fill) or uniformed (light fill) prior knowledge for the presence of decoy mimics during surveys...... 88

Figure 5.1. An example using four quadrats (ni = 1, 2, 3, 4) of the optimal allocation of search effort (proportion) for any overall search budget (B) based on the Hauser and McCarthy (2009) optimal surveillance model. The relative density of targets in the quadrats (wi) within a search site increases in each quadrat from low to high (1 to 4). As the overall search budget (B) increases the allocation of effort between the quadrats (ni) approaches a uniform distribution. The grey

line represents a uniform distribution of search effort allocation across all quadrats...... 98

Figure 5.2. Mean percentage of targets detected observed (blue) and predicted (red) of the optimal (a, e), uniform (b, f), proportional (c, g) and subjective (d, h) comparative search allocation strategies. The number of detections is presented as the percentage of the total number of available targets for surveillance budgets of 10 minutes (left column) 20 minutes (right column). Error bars are presented as the 95% CI between observers...... 107

Figure 5.3. Mean observed (blue) and predicted (red) benefit of the optimal search strategy over the uniform (a, d) and proportional (b, e) and subjective (c, f) effort allocations. Mean benefit is presented as a percentage of the total number of available targets for surveillance budgets of 10 minutes (left column) 20 minutes (right column). Error bars are presented as the 95% CI between observers...... 108

Figure 5.4. Mean observed (blue) and predicted (red) benefit of the optimal search strategy over the uniform (a-c) and proportional (d-f) effort allocations, presented as a percentage of the total number of available targets for surveillance budgets 20 minutes. Target type and number of observers (n) are indicated at the top of each column. Dashed lines represent the observed value and the solid lines represent the predicted. Shading represents the 95 % confidence interval between observers. Detection rates used to calculate the optimal allocation is based on the mean of prior independent detection surveys.109

Figure 5.5. Difference in detection rate for target groups among observers at each quadrat used in the experimental optimal surveillance tests. Differences in detection rate (targets min-1) is the residual between the observed detection rate for each quadrat and the mean predicted rate from previous or calibration detection quadrat. Box plot represent the minimum, 25 percentile, median (solid line), mean (dashed line), 75 percentile and maximum values. The mean predicted detection rate is over-predicted with values <0, equal with values at 0 (solid grey line) and under-predicted with values >0...... 110

Figure 5.6. Mean (95% CI) difference (mins) between the optimal and subjective judgement search effort (minutes) allocations across densities for targets used in the experimental tests. Top row (a–c) represents 10 minutes search budget (dark fill circles) and bottom row (d-f) represents 20 minutes search budget (light fill circles). Positive values represent observers over-allocate time and negative values represent an under-allocation of time...... 111

xvi List of Tables

Table 1.1. Examples of post-border management of marine invasive species via removal methods...... 6

Table 1.2. Description of control methods available for post-border management for Asterias amurensis...... 16

Table 2.1. Summary of the three sites where agency-led Asterias amurensis removal programs have occurred in eastern Victoria, Australia. Program duration, number of seastars removal, search effort and site characteristics are presented...... 28

Table 2.2. Site level variables for detectability trials at Anderson Inlet and Tidal River using artificial mimic seastars of Asterias amurensis...... 37

Table 3.1. Variables used in the logistic regression models for the detection of Asterias amurensis artificial mimics in Victoria, Australia using two visual search methods...... 59

Table 3.2. Summary statistics for visual experimental surveys for the detection of Asterias amurensis mimics in Victoria. Two methods of visual detection were used: Snorkel and Walk. VIF = variance inflation factor...... 63

Table 3.3. Comparison of multilevel Bayesian logistic regressions of visual detection for Asterias amurensis mimics using snorkel methods...... 68

Table 3.4. Comparison of multilevel Bayesian logistic regressions of visual detection for Asterias amurensis mimics using walk methods...... 69

Table 4.1. Confusion matrix for surveys for the invasive seastar, Asterias amurensis during incorrect detection experiments...... 82

Table 4.2. Variable descriptions of the Bayesian logistic regression models for the incorrect detection of artificial native seastar mimics for Asterias amurensis in Victoria, Australia...... 84

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Table 4.3. Confusion matrix values for incorrect detection experiments of Asterias amurensis. Records of Uniophora granifera and Coscinasterias muricata are measures of false positive records...... 87

Table 4.4. Mean and standard deviation (SD) for the variables of the Bayesian logistic regression models for the incorrect detection of artificial native seastar mimics. Variance inflation factor (VIF) is presented as a measure of collinearity and SD is represents the standard deviation...... 89

Table 4.5. Model estimates for a Bayesian logistic regression...... 90

Table 4.6. Posterior distribution for binary logistic regression model, M3. Coefficient estimates are based on centralised and standardised transformed data inputs...... 90

Table 5.1. Summary of the targets, quadrats and detection information used to test the optimal search model (Hauser & McCarthy, 2009)...... 103

xviii Chapter 1: General Introduction – search and population inferences for marine invasive species

Chapter 1

Chapter 1: General Introduction – search and population inferences for marine invasive species

1.1 Overview

Invasive marine species are a global threat to marine ecosystems and biodiversity—a threat only predicted to increase as current and future invasive species become more widely distributed (Carlton 1996, Ruiz et al. 1997, Ruiz et al. 2000). When introduced to a new environment, invasive species can spread rapidly, making incursions difficult to manage if not detected early (Thresher 1999, Myers et al. 2000, Sliwa et al. 2009). The challenge for researchers and managers is to provide accurate information on the occurrence and abundance of marine invasive species using limited data. A decision to initiate a removal or eradication program for a marine invasive species often relies on information collected from observer-based surveys (Critchley et al. 1986, Miller et al. 2004, de León et al. 2013). Marine surveys have considerably increased the availability of marine invasive species data over the past 50 years (Elton 1958, Carlton 2011). However, despite the growing interest in managing marine species invasions, few studies have examined or incorporated the uncertainty of information collected during stakeholder observer-based surveys (Delaney et al. 2008, Kanary et al. 2010).

Research into marine invasive species often involves estimates of abundance and occurrence to assist management decisions of containment, control or eradication (Bax et al. 2001, Wotton and Hewitt 2004) (Figure 1.1). However, surveys and inventories are often expensive, time consuming and cover vast areas of the marine environment (Coutts and Forrest 2007). Management tools such as species distribution models (also known as habitat suitability models) have been used to quantify marine invasive species incursions based on survey records (Inglis et al. 2006). Having a clear understanding of species-specific and site-specific uncertainty of survey methods is critical for defining the amount of search effort required for a given detectability proficiency and for making accurate estimates of population dynamics (Hayes et al. 2005).

2 Chapter 1: General Introduction – search and population inferences for marine invasive species

In this thesis I quantify the search and control performance of observers during management surveys for a marine invasive species. Detectability errors in survey observations are common when visual assessment methods are used to locate individuals or species (Kéry and Schmidt 2008). Testing the ability of observers to detect and remove populations is an important element of invasive species management. Two ways to deal with detectability errors in observation records are (i) to develop statistical tools to extrapolate from empirical data after collection and (ii) to test the performance of observers whilst conducting surveys. In recent years, statistical methods to incorporate and estimate the uncertainty of detectability and search effort have been developed (MacKenzie and Kendall 2002). Testing observers during surveys is a direct way to estimate survey uncertainty and may provide insights into improving survey designs and thus confidence in population inferences.

In the following sections I review current literature on marine invasive species post- border management, the problem of searching for hidden targets, and the need to evaluate survey design in marine environments. I also introduce the model species, Asterias amurensis, on which this thesis is developed. Finally, I outline the thesis aims and objectives in a summary of chapters in the context of addressing the thesis questions.

1.2 Surveys and post-border management of marine invasive species

The pressure on our national borders to prevent the entry of marine invasive species has increased globally as vectors of transport via our oceans also grows (Carlton and Geller 1993). Preventing marine invasive species from entering new environments through marine borders is challenging due to the lack of clear physical boundaries. Marine borders are ‘leaky’ and allow species to cross boundaries and colonise new environments. Post-border management of marine invasive species is defined here as actions taken to reduce the spread of an introduced species. The process of post-

Chapter 1: General Introduction – search and population inferences for marine invasive species

border management is often thought of as a series of successive steps (Figure 1.1), with early detection, rapid response and population management being fundamental to effective methods (Wotton and Hewitt 2004, Forrest et al. 2006). The available literature addresses many of these stages separately and collectively (McEnnulty et al. 2001, Thresher and Kuris 2004, Wotton and Hewitt 2004, Locke and Hanson 2009).

A fundamental element of post-border management is the use of surveys to monitor and detect individuals (Figure 1.1). Post-border surveys provide evidence a marine species has entered a new area and colonised (Hewitt and Martin 2001). These surveys commonly use a variety of methods to detect and monitor species (Thresher and Kuris 2004). Examples of post-border management where surveys have been used to detect and remove or minimise marine invasive incursions are outlined in Table 1.1. As the global rate of marine invasive incursions continue to increase (Cohen and Carlton 1998), managers require appropriate management protocols coupled with survey data that encapsulate uncertainty to make informed management decisions.

Environmental mangers face challenges when deciding from a range of alternative management actions and often enlist evidence-based information to support these decisions (Cook et al. 2009). Survey information for marine invasive species has the potential to inform post–border management decisions and assist in maximising the available resources. A significant body of literature exits on optimal invasive management using survey data including: prioritising sites for eradication (Hauser and McCarthy 2009), when to stop monitoring for an invasive species (Regan et al. 2006), when to declare eradication of an invasive (Rout et al. 2009a) and trapping or removal intensity (Bogich et al. 2008). However, rarely, do managers and decision-makers use these optimal invasive management tools and survey data to assist in preventing and controlling the spread of marine invasive species.

4 Chapter 1: General Introduction – search and population inferences for marine invasive species

Chapter 1: General Introduction – search and population inferences for marine invasive species

Table 1.1. Examples of post-border management of marine invasive species via removal methods.

Introduced Species Location Method of Removal Reference Eradication Status Chlorophyta Caulerpa taxifolia California, USA Chemical treatment (Anderson 2005) Local eradication Phaeophyceae Undaria pinnatifida Tasmania, Australia Physical removal (Hewitt et al. 2005) (Experiment only) Undaria pinnatifida Chatham Islands, New Heat treatment (Wotton et al. 2004) Local eradication Zealand Sargassum muticum Bembridge, Isle of Wight Physical removal, (Critchley et al. 1986) & Little evidence of UK herbicides & biological (Farnham and Gareth Jones local eradication control 1974) Ascophyllum nodosum California, USA Physical removal (Miller et al. 2004) Local eradication Tunicata Didemnum vexillum Holyhead Harbour, North Chemical treatment (wrap (Griffith et al. 2009) Little evidence of Wales treatment) (Sambrook et al. 2014) local eradication Polychaeta Terebrasabella Cayucos, California, USA Physical removal (Culver and Kuris 2000) Local eradication heterouncinata Mollusca Mytilopsis sallei Darwin Harbour, Australia Chemical treatment (Bax et al. 2002) Local eradication Dreissena polymorpha Lake George, New York Physical removal (Wimbush et al. 2009) Local eradication Perna perna near Tasman Bay, central Dredging (Hopkins et al. 2011) Local eradication New Zealand Perna viridis Careening Bay, WA Physical removal (Piola and McDonald 2012) Local eradication Echinodermata Asterias amurensis Anderson Inlet, Australia Physical removal (Holliday 2005) Local eradication Vertebrata Pterois volitans Caribbean Physical removal (de León et al. 2013) On going

6 Chapter 1: General Introduction – search and population inferences for marine invasive species

1.3 Imperfect detectability in marine invasive species and ecology

After their initial introduction, invasive species can frequently go undetected in a new environment (Crooks and Soulé 1999). The term imperfect detectability comes from sampling theory where it is used to describe a common problem of not being able to detect all individuals, populations or species within a sampling or experimental unit (Seber 1982, Seber 1986). Imperfect detectability has been addressed in the ecological literature extensively (reviewed in Kellner and Swihart 2014). Yet few studies of marine invasive species assess imperfect detectability during management surveys (Delaney and Leung 2010, Kanary et al. 2010). Hayes et al. (2005) used estimated detection rates when considering the sensitivity of port surveys and the attempt to eradicate the Asian Green Mussel, Perna viridis in Cairns, Australia. The authors discuss the lack of information collected about imperfect detectability of marine invasive species and the value of gathering this type of information during similar surveys. By assessing and incorporating imperfect detectability into management of marine invasive species it can improve imprecise estimates of populations and allow for more efficient survey design.

1.3.1 What is imperfect detectability?

Sampling is a stochastic process where imperfect detectability routinely leads to the underestimation of true population parameters at a sampling location, including abundance, density, number of populations and species richness (Nichols et al. 2000, MacKenzie et al. 2005, Royle et al. 2005). In this thesis I consider the problem of estimating parameters of an incompletely sampled population from single visits to a site. Detectability can be measured by the probability of detecting a individuals during a single survey at a site given that individuals are present at the site at the time of surveying and are available to be detected (Garrard et al. 2008, Delaney and Leung 2010, Moore et al. 2010, Alexander et al. 2012, McCarthy et al.

Chapter 1: General Introduction – search and population inferences for marine invasive species

2013). Surveys are imperfect when individuals remain undetected (false negative) and when individuals are absent but are incorrectly detected as a different species (false positive). Detection of individuals can be represented as a Bernoulli process, with 1 representing detection, and 0 representing no detection. The detection probability, Pr(d), of individuals during n independent surveys can be described as:

n Pr(dp) =−− 1 (1 ) , (1.1) where is the probability of detecting an individual (i.e. its detectability) during a single survey. Detectability is commonly less than one across many taxa and environments (Bailey et al. 2004, Wintle et al. 2005, Heard et al. 2006, Mackenzie 2006, Chen et al. 2009, Harvey et al. 2009, Christy et al. 2010, Moore et al. 2010). Few marine studies have estimated imperfect detectability for marine invasive species during management surveys (Delaney and Leung 2010, Kanary et al. 2010).

1.3.2 How has imperfect detectability been accounted for in ecological and marine invasive studies?

In the past forty years the number of statistical models developed to account for imperfect detectability when estimating populations across various taxa and environments has increased considerably (Otis et al. 1978, Seber 1982, Buckland et al. 2001, Williams et al. 2002a). However, Kellner and Swihart (2014) reviewed 537 research papers involving ecological surveys and found only 23% of these studies accounted for imperfect detection. Guillera-Arroita et al. (2014) demonstrated how uncertainty can be introduced into population estimates by not incorporating imperfect detectability and how more reliable estimates can be formulated. Katsanevakis et al. (2012) reviewed current methods available to account for imperfect detectability during studies of marine populations and communities. With a growing field of literature describing imperfect detectability, areas of marine invasive monitoring and management need to consider how best to

8 Chapter 1: General Introduction – search and population inferences for marine invasive species

measure and incorporate such information in designing and inferring population estimates.

In recent years, a number of key marine invasive studies have considered the sensitivity and effectiveness of surveys to imperfect detectability. Delaney and Leung (2010) conducted field experiments to test the sampling sensitivity to observer techniques for the invasive crab, Carcinus maenas, while Kanary et al. (2010) determined the effectiveness of SCUBA divers conducting underwater visual searches for the vase tunicate, Ciona intestinalis. In both studies, the invasive marine species was found to have a detectability less than one during surveys. These studies emphasis the need to quantify imperfect detectability during marine invasive surveys. Understanding how and what influences imperfect detectability is fundamental to improving survey design and inferring management outcomes.

1.4 Optimising search effort

The benefit of producing detectability models is that search strategies can be optimised to assist in designing cost-effective surveys. Sampling and surveying for marine invasive species is typically time-consuming and expensive (Hayes et al. 2005). Given limited resources (logistical or financial) that often constrain marine invasive surveys, resources should be allocated to maximize benefits (e.g. number of individuals removed). Motivated by this problem, recent progress towards developing targeted survey designs and monitoring programs has occurred. Optimal search effort is of particular relevance to invasive management, with a significant body of ecological literature available (Mehta et al. 2007, Hauser and McCarthy 2009, Cacho and Hester 2011). Applying such approaches to survey design allows for more cost-effective methods to be developed. Furthermore, Baxter and Possingham (2011) found when searching for an invasive species a cursory-widespread strategy is only optimal compared to a more focussed

Chapter 1: General Introduction – search and population inferences for marine invasive species

searching strategy when knowledge is deficient and lacking or the target species is considered widespread. Pacifici et al. (2012) suggested that placing more search effort at a site where species occurrence and detectability are high is more cost- effective. However, Mehta et al. (2007) suggests that for invasive species with high impacts, it is optimal to devote significant search effort to detecting a species, even if detectability is low. Understanding the relevance of imperfect detectability in the design of cost-effective surveys is clearly important for managers dealing with marine invasive species. Hauser and McCarthy (2009) developed an optimal resource allocation model that indicates how search effort can be invested optimally across multiple sites, and reveals when to stop control efforts at one site and move on to another. Optimal survey design models consider the following elements: (i) the probability the species is present at each site; (ii) the probability of detection given presence; and (iii) the benefits of surveillance at each site.

1.5 Refining and validating marine invasive surveys

Visual surveys are an important tool for post-border management of marine invasive species assisting early detection, rapid response, control and even eradication (previously discuss in Section 1.2). However, evaluation and validation of these survey methods can help identify the most effective survey design and assist observers to find targets with less effort. Campbell et al. (2007) reviewed five common survey methods used to detect and monitor marine invasive species and assessed the strength and weaknesses of each. Bishop and Hutchings (2011) compared surveys described in 46 reports about marine invasive species at Australian ports, and assessed their effectiveness using taxonomic and invasion patterns indicators. Post-border management requires consideration of sensitivity and accuracy of survey methods (Hayes et al. 2005).

10 Chapter 1: General Introduction – search and population inferences for marine invasive species

In recent years, volunteer programs to assist in managing marine invasive species have become more common, leading to an increase in information available to managers. However, the skills amongst observers in these programs may vary considerably. Delaney et al. (2008) determined that volunteers working on an invasive crabs in an intertidal environment can have species identification accuracy of at least 80% with limited scientific training. This improved to 95% accuracy with at least 2 years university education. Goffredo et al. (2010) found evidence that 76% of data recorded by volunteers involved in sampling 61 marine taxa was 50– 80% accurate. However, other ecological studies have raised questions about the reliability of data from unqualified observers (Foster-Smith and Evans 2003). Several studies have evaluated observer experience and bias in underwater surveys. Thompson and Mapstone (1997) report effects of observer and of training in underwater visual surveys, but highlight that variation in observational data can be improved through appropriate training and calibration of observer records. Edgar et al. 2004 found that with more observer experience, more species would be found. Therefore, assessing the quality of observer data is an important practice to ensuring sound inference from surveys of marine invasive species.

1.6 Asterias amurensis and its management in Australia

In this section, I describe background information about the species selected for studying search and management efficiency in this thesis, the northern Pacific seastar, Asterias amurensis. Given the research interest and public awareness of this species, there is a considerable body of literature regarding aspects of the species’ biology, distribution, invasion history and existing management options. I summarise these in the following sections.

Chapter 1: General Introduction – search and population inferences for marine invasive species

1.6.1 Background to the origin and biology

The northern Pacific seastar, Asterias amurensis (Lütkin), of family Asteriidae, is a five-armed seastar with a small central disc, native to the waters of Japan, Korea, China, Russia and Alaska. Adult A. amurensis are distinguishable from Australian native seastars by morphological characteristics. The species is distinguished by its characteristic tapering arms that have upturned tips. A. amurensis shows a great deal of colour plasticity, with individuals ranging from being entirely yellow to having purple hues on its dorsal side (Figure 1.2). Small, jagged rows of spines run down each arm on both the dorsal and ventral sides (Byrne et al. 2013). These spines also line the ventral groove where the tube feet are located. Adult A. amurensis can grow upwards of 40–50 cm in its native range(Byrne et al. 2013). This species reproduces asexually and sexually where a female can produce 10–20 million eggs annually (Hatanaka and Kosaka 1959). In its native range, larvae have been recorded to spend >50 days in the water column before settling (Paik et al. 2005).

1.6.2 Recorded Asterias amurensis distribution in Australia

A. amurensis is thought to have been introduced to Australia in the mid 1980s in the Derwent Estuary in Tasmania. The species was formally identified in 1992 (Turner 1992, Buttermore et al. 1994) and subsequently populations have established in the estuary. Surveys conducted in the Derwent Estuary in 1999 indicated that seastar densities ranged from 0.02 to greater than 2 per m2 (Ling 2000) with high A. amurensis abundance recorded around structures such as wharfs and pontoons. Other observations suggest that the density may have been greater than 9 per m2 in the early 1990s (Buttermore et al. 1994). In Tasmania, this species has been recorded outside the Derwent Estuary, as far north as Henderson Lagoon in the state’s north-east (Proctor and McManus 2001). Having been

12 Chapter 1: General Introduction – search and population inferences for marine invasive species

introduced nearly 30 years ago, A. amurensis is now considered an influential part of the Derwent Estuary benthic community.

Between August 1995 and August 1996, three adult A. amurensis were discovered approximately 600 km north of the Tasmanian populations, in western Port Phillip Bay, Victoria (Garnham 1998, Parry and Cohen 2001). Local commercial fishers working in the area were responsible for the initial discovery of these specimens. This resulted in immediate surveys of the area. However, these surveys did not discover additional specimens. In April 1997, a fourth specimen was discovered in the northern reaches of Port Phillip Bay at Victoria Dock. These four specimens were all adult seastars. In January 1998, the first juvenile seastars were found on growing ropes at a mussel farm in Dromana Bay in south eastern Port Phillip Bay. Initial eradication efforts were unsuccessful and populations of A. amurensis established across the bay within five years of the initial discovery. Population size after five years of establishment was estimated at >100 million individuals in Port Phillip Bay (Talman et al. 1999). Control efforts at local scales within the 1930 km2 Port Phillip Bay region continue to occur, with particular attention directed towards areas of key ecological and stakeholder value.

In 2003, A. amurensis was discovered for the first time in Victorian waters outside Port Phillip Bay, at Anderson Inlet. Rapid response to this incursion occurred through an agency-led stakeholder removal program. In 2006, eradication was declared a success at this site (Holliday 2005). In 2011 a small population was detected at San Remo and the following year at Tidal River. These three eastern Victorian sites are discussed further in Chapter 2.

In Australia, A. amurensis is currently restricted to the waters of Victoria and Tasmania, Australia. The risk of secondary incursions continues to challenge mangers in areas of Australia currently free of the invasive species.

Chapter 1: General Introduction – search and population inferences for marine invasive species

1.6.3 Management options

Australia’s National Control Plan (NCP) for A. amurensis is a document that directs an agreed national response to reduce impacts and minimise the spread of this species (Aquenal 2008). There are a number of methods that are available to remove and manage A. amurensis populations. Table 1.2 outlines the physical, chemical and biological control methods previously used for this species. Manual removal is a common method when the density of A. amurensis is low. Examples of where manual removal has been undertaken include the following:

14 Chapter 1: General Introduction – search and population inferences for marine invasive species

• In July 1993, a team of 22 divers removed approximately 6000 A. amurensis specimens from an approximately 6000 m2 area at a site close to wharves in the Derwent Estuary, Tasmania. Further removal efforts were carried out around the wharves in August 1993 where three tonnes (>24 000 specimens) of seastars were removed. Removal efforts where considered unsuccessful as the area was re-invaded within weeks of divers’ removal efforts (Johnson 1994, Ward and Andrew 1995).

• In June 2001, 401 seastars were physically removed from Henderson Lagoon, Tasmania. In August 2001 a further 238 seastars where removed using the same manual removal technique. At the time, the lagoon was not open to the sea, and on the 31 August it was reopened. Managers at the time of removal suggest manual removal and a rapid change to the salinity on reopening the lagoon was sufficient to remove A. amurensis from the site (Proctor and McManus 2001).

• From 13 March 2004 to 28 February 2005, a volunteer removal program took place at Anderson Inlet, Victoria. A total of 229 A. amurensis specimens were removed during 17 events, with 335 diver hours invested. In June 2005, eradication was declared, as population numbers were low and considered not at a sufficient density for successful reproduction (Holliday 2005). Further details are described in Chapter 2.

Chapter 1: General Introduction – search and population inferences for marine invasive species

Table 1.2. Description of control methods available for post-border management for Asterias amurensis.

Control Description Reference Method

Physical

Manual Manual removal of seastars using divers has proven both (Johnson 1994, Proctor Removal unsuccessful and successful. Seastar removal by divers is 2 and McManus 2001, most successful when seastar populations are >2 m . Holliday 2005)

Trapping Trapping seastars has resulted in limited success. The (Andrews et al. 1996) density of available food sources for the seastars, and trap mesh size, affect catch rates. Trap placement experiments have shown that traps are ineffective for ongoing population control after initial manual removal by divers.

Dredging Specialised dredging tows have shown effective at (reviewed in McEnnulty catching seastars. This method of control has significant et al. 2001) impacts on non-target species and surrounding environment. Evidence that seastars reinvade a cleared area has been recognised.

Chemical

Poisons Quick lime and other such poisons are available for (Goggin 1998) application. However, these are not species specific and potentially harm other species and the surrounding environment.

Salinity Laboratory experiments show A. amurensis larvae do not (Goggin 1998) survive exposure to <9.75 ppt after 2 mins. Adults have varying responses to salinity changes, where some have survived exposed to 26 ppt while others have been killed after 9 days at exposure levels of 24 ppt. Field conditions during surveys suggest that rapid changes in salinity can induce death.

Biological Several parasitic crustaceans, a gastropod and a ciliate (Kuris et al. 1996, Byrne have been suggested as potential biological controls for et al. 1997a) A. amurensis. However, many of these are not host- specific and have the potential to affect non-target species.

16 Chapter 1: General Introduction – search and population inferences for marine invasive species

1.7 Aims of Thesis

This thesis aims to quantify components of search and control strategies using field-based assessments of survey sensitivity and accuracy. Management frameworks such as NCP are commonly not supported by information at the species level concerning survey efficiency and uncertainty. It is unclear whether observer contributions in removal surveys to reduce marine invasive species population sizes have any impact on long-term management. Uncertainty and errors in data records can produce unreliable inference about an incursion, creating barriers to successful management of marine invasive species. Testing observers’ ability to find targets in a novel habitat from a fixed population distribution allows information to be measured about influences at that particular spatial and temporal scale.

The overall aim can be focused into three objectives, one of which focuses on the reliability problem:

1. Develop population catch-effort models to study the feasibility of management options for a marine invasive species;

2. Develop procedures to test the probabilities of detection and incorrect detection during visual surveys; and

3. Test a theory-based search effort allocation support tool for marine invasive species under empirical conditions in the field.

How each of the above aims of this thesis addresses different aspects of a decision management framework of a marine invasive species is represented in Figure 1.3.

Chapter 1: General Introduction – search and population inferences for marine invasive species

Figure 1.3. A simple decision framework for the eradication or control of a marine invasive species. Orange boxes highlight where the aims of this thesis address individual aspects of the framework.

1.8 Thesis Outline

This section briefly outlines the structure of the thesis. The thesis is organised into three main parts: Part I identifies the problem of search and population inference of marine invasive species, defines the thesis aims and provides appropriate reviews of the relevant literature; Part II seeks to examine the three stated objectives and forms the original principal contribution of the thesis; and, finally, Part III briefly synthesises the findings of the thesis and links the thesis outcomes to future research. Additionally there is an Appendix that provides supporting code models and other material. Outlined below is a detailed description of the chapters in Parts II and III.

18 Chapter 1: General Introduction – search and population inferences for marine invasive species

Part II In Chapter 2, I use a Bayesian catch-effort model to assess the feasibility of removal of A. amurensis during agency-led management programs. In this chapter, I present information about the search effort invested, and the number of seastars removed, since 2003 at three sites in Victoria, Australia. At these three sites incursions have occurred, and managers have responded in order to decrease the population size with the aim of eradication. I question the feasibility of reducing a population and whether eradication may not be a viable management goal given the amount of search effort required to remove the entire population, and possible observer- specific or site-specific variation in detectability.

In Chapter 3, I take an empirical assessment of the influences of detectability, and consider which factors affect the detectability of a seastar during surveys. Using experimental data and a range of parameters permits an understanding of detectability that managers may draw on to reduce uncertainty when designing new surveys. I use two similar visual searching techniques, snorkelling and walking, to examine differences in detectability amongst observers.

In Chapter 4, I measure the frequency of incorrect detection of A. amurensis observations during surveys when co-occurring species are present. The effect of pre-survey training on observers’ ability to morphologically distinguish species during surveys is measured. This experiment provides evidence for generating guidelines for training and searching for A. amurensis. In addition, this information can be used by managers to improve the observational estimates of population dynamics made during a program, while incorporating type I and II errors.

In Chapter 5, I empirically test the use of theoretical and practical surveillance strategies in order to improve the allocation of search effort during surveys. This is the first empirical study to test the use of search theory under ecological settings, and suggests the potential of much broader application in other disciplines where

Chapter 1: General Introduction – search and population inferences for marine invasive species

search allocation is used. This study has implications for changing the way managers allocate resources during surveys for marine invasive species.

Part III In Chapter 6, the implications of this thesis are summarised in a general discussion. I make recommendations about the use of detection estimates, search effort, and training of searchers during marine invasive species management. Finally, I consider future research directions and management recommendations for A. amurensis (and similar species) in the context of survey design and population management.

20 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Chapter 2

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Abstract

Globally, considerable effort is invested in containing, controlling and eradicating populations of marine invasive species each year. Once a species establishes in a new environment, eradication is often difficult and post-border management may be reduced to limiting secondary spread. Physical removal of individuals from a population is a common method used in many marine invasive management programs. Reducing the population size of an invasive species can decrease potential damage at a site. Using information from previous removal events, the feasibility of eradicating a marine invasive from a site can be assessed. In this chapter, I evaluate the extent to which removals achieved eradication of recent incursions of the invasive seastar, Asterias amurensis, in Victoria, Australia. Eradication surveys using SCUBA diving surveys at three locations at San Remo (2011), Anderson Inlet (2003) and Wilsons Promontory (2012) were assessed. Using a Bayesian catch-effort model, I estimate the number of seastars remaining at a location after individual removal surveys and predict the search effort (divers hours) required to remove the remaining population. At Anderson Inlet and Tidal River, I use artificial seastar mimics to estimate the proportion of seastar found during replicated eradication surveys. The observed search effort (diver hrs per removal event) of the locations were 1.5–14.5, 1.0–11.25 and 2–53.3 for Tidal River, San Remo and Anderson Inlet, respectively. The mean predicted amount of search effort (diver hrs per removal event) for the locations were 7–57, 5–30, 609– 995 for Tidal River, San Remo and Anderson Inlet, respectively. The overall proportion of mimics found during the experimental detection surveys were 37% and 82% from Anderson Inlet and Tidal River, respectively. The findings from this chapter provide useful measures of eradication feasibility of A. amurensis and the estimated potential search effort required to remove this species from future incursions.

22 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

2.1 Introduction

Management strategies available for containing, controlling or eradicating marine invasive species are often impeded by the lack of scientific information and limited resources (Bax et al. 2001, Thresher and Kuris 2004). Without intervention, small incursions of marine invasive species can rapidly increase and become large-scale problems (Ruiz et al. 1997). Worldwide, there has been a concerted effort to remove and eliminate populations of marine invasive species from newly established sites (Anderson 2005, Wimbush et al. 2009, de León et al. 2013). However, few examples are available of successful marine invasive eradications (Culver and Kuris 2000, Bax et al. 2001, Miller et al. 2004, Wotton et al. 2004, Anderson 2005, Hopkins et al. 2011, Piola and McDonald 2012). Eradication can be described as the elimination of individuals from a defined geographical area where the population size is reduced to zero. Early intervention when the population density is low and relatively confined is often the strategy for successful eradication (Culver and Kuris 2000, Miller et al. 2004, Anderson 2005). Therefore, effective management requires understanding whether complete eradication is a realistic goal for a newly detected incursion.

Recent studies have determined when eradication is an appropriate management strategy for several marine invasive species (Edwards and Leung 2008, Sambrook et al. 2014). Eradication is often an appealing strategy of marine invasive management because alternatives such as containing and controlling the species require long-term resource investments and ongoing management actions. In recent years, citizen volunteer management programs have become popular as they provide an inexpensive approach to eradication and ongoing control options of marine invasive species. Eradication of a marine invasive species may be more cost-effective than other forms of post-border management but should only be attempted if it is considered feasible or the impact of the invader considered high enough to attempt eradication.

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

The need to continue eradication measures after reaching very low population size has been the source of confusion among stakeholders who provide resources for such management programs. A population might recover if removal activities are terminated before true eradication is achieved. Management decisions for terrestrial invasive species often focus on monitoring and surveillance to assess whether to attempt eradication, the progress of eradication (Keith & Spring 2013) and when to declare eradication complete (Regan et al. 2006, Rout et al. 2009b, Rout et al. 2014). However, few studies address such issues for marine invasive management (Hayes et al. 2005, Edwards and Leung 2008).

Management responses for marine invasive species must account for the probability that individuals remain at a site after intervention. Understanding the amount of effort required to detect and remove a species from a site is a critical problem for management decisions (Cacho and Hester 2011). Catch per unit effort is a much-used ecological measure of the abundance of a target species. One approach has been to use catch-effort models to assess species eradication (Ramsey et al. 2009, Barron et al. 2011, Rout et al. 2014). There are few successful marine invasive eradication attempts, so gaining valuable information using catch- effort models may improve the overall efficiency and effectiveness of resource allocation during a program.

Since the introduction of the invasive seastar, Asterias amurensis into southeastern Australia during the 1980s, new populations continue to threaten native species and habitats (Goggin 1998). Monitoring and manipulative experiments have documented common problems associated with outbreaks of A. amurensis throughout its non-native range in south-eastern Australia (Cohen et al. 2000, Ross et al. 2002, Ross et al. 2003b) including declines of the endangered native handfish, Brachionichthys hirsutus (Bruce and Green 1998) and the effects on commercial species (Lockhart and Ritz 2001, Ross et al. 2002). Given the impacts of A.

24 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

amurensis, many public stakeholder programs have attempted to remove individuals from coastal waters of southeastern Australia (Johnson 1994, Proctor and McManus 2001, Holliday 2005). However, it is unclear how much effort is required for these to be effective. Having a better estimate of effort required to successfully remove a population of A. amurensis would help to ensure efficient allocation of removal effort.

This chapter aims to evaluate the extent to which physical removal surveys achieved eradication of recent incursions of the invasive seastar, Asterias amurensis, in Victoria, Australia. I assess the catch-effort and removal success for A. amurensis from agency-led stakeholder removal programs at sites newly occupied by the invasive seastar. I empirically test the detection of A. amurensis during experimental trials. I estimate the population size at each site during individual removal events. Finally, I use this information to estimate the probability of removing a seastar at each site and predict the required effort to remove the entire population under similar conditions.

2.2 Materials and Methods

2.2.1 Study species

The northern Pacific seastar, Asterias amurensis is a predatory seastar native to the regions of Japan, China, Russia and Alaska. Fully grown adults can reach upwards of 40-50 cm (Byrne et al. 2013) in diameter and have been observed to reach maturity at 3.6–5.5 cm in their native habitat (Hatanaka and Kosaka 1959). This generalist feeding species is known to prey on commercial species including scallops, mussels and clams (Lockhart and Ritz 2001, Ross et al. 2002). This species has few predators in southern Australia with the eleven armed seastar, Coscinasterias muricata and the giant spider crab Leptomithrax gaimardii recorded to feed on this five-armed

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

seastar (Parry and Cohen 2001, Ling and Johnson 2012). A. amurensis has been found in habitats ranging from muddy shallow areas to rocky subtidal habitats up to 200 m deep (Byrne et al. 2013). This species has become a very successful invader in its non-native range due to wide salinity tolerances, high fecundity where females are capable of producing >20 million eggs per year and rapid growth. Further details of the life-history of A. amurensis can be found in Chapter 1.6.

2.2.2 Study sites and removal program effort

Efforts to manage A. amurensis incursions have been carried out within the 1930 km2 Port Phillip Bay region and at three sites in coastal waters of eastern Victoria (Figure 2.1). This chapter examines the three eastern sites where A. amurensis populations have undergone agency-led removal programs to reduce and eradicate individuals at Anderson Inlet (38.6426°, 145.7152°) in 2003; San Remo (38.5201°, 145.3647°) in 2011; and Tidal River (39.0285°, 146.3182°) in 2012 (Figure 2.1). At all three sites, agency–led stakeholder removal programs were undertaken using volunteers to detect and remove individuals using underwater surveys and physical removal of A. amurensis. Below describes the removal efforts, methods, searching methods and local habitat characteristics of each of the three sites. The removal effort at the three sites are summarised in Table 2.1.

26 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

San Remo Tidal River Anderson Inlet (B) (A) (C)

Date first November 2011 November 2003 May 2012 detected

Removal program 25 September 2011 13 March 2004 to 16 May 2012 to duration to 15 March 2013 28 February 2005 27 October 2012

Number of A. amurensis 21 229 163 removed

Removal SCUBA and snorkel SCUBA diver SCUBA diver methods diver (drift) (drift) (line search)

Area (m2) 48 000 420 000 21 000

Total diver (hrs) 50 335 56

Number of 7 17 13 removal events

28 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Site A. San Remo is located at the south-eastern entrance of Western Port and consists of basalt intertidal and subtidal rocky reefs (Figure 2.2a & b). This site is unique to Victoria with a habitat assemblage of sediments, rock, algae and seagrasses. It has a highly diverse intertidal community with rare opisthobranch and bryozoan species present and has been listed under the Victorian Flora and Fauna Guarantee (FFG) Act 1988 (O'Hara 1995). There is a deep channel located adjacent to this site between San Remo and Phillip Island with fast flowing tidal currents. Coastal development in the area has seen a jetty and bridge built at the site to service the adjacent township of San Remo.

On 25 September 2011, a local dive operator first detected A. amurensis at the San Remo jetty during a recreational scuba dive. Five specimens were recovered during this first detection dive. As a response to this incursion, government agencies began a removal program within three weeks of first detecting specimens. A total of seven removal events occurred from 25 September 2011 to 15 March 2013. SCUBA and snorkel dive surveys resulted in the physical detection and removal of only 21 specimens from this site. The incursion site was approximately 48 000 m2 (100 m x 480 m), which included the jetty, bridge structures and FFG area (Figure 2.2a & b). The majority of specimens (81% of the total collection) were found in the jetty area. Facilitated by government agency staff, volunteers from various stakeholder groups including recreational dive clubs, agency citizen science groups and researchers invested approximately 50 diver hours over seven events, averaging 8.3 diver hours per removal event. Observers used both SCUBA and snorkelling methods to drift dive through the site during incoming flood tides. No A. amurensis specimens have been recorded since November 2011 at the site (Figure 2.3). San Remo is an ongoing monitoring site because of the potential for widespread invasion of A. amurensis in Western Port.

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Site B. Anderson Inlet is a narrow tidal estuary located on the eastern Victorian coastline with an area of 13.25 km2 (OzCoasts 2013) (Figure 2.2c & d). Several small streams flow into the estuary. The largest tributary is the Tarwin River originating in the east and flowing to the head of the inlet. The 1.5 km wide entrance comprises a moving sandbar extending into the waters of Bass Strait, and its morphology is influenced by tidal currents, wave action, wind direction and river flow (OzCoasts 2013). The inlet is dominated by tidal flats with smaller areas of channels, mangroves and saltmarsh (OzCoasts 2013).

On the 25 November 2003, A. amurensis was first detected at Anderson Inlet after 2 specimens washed up on a beach close to the entrance (Holliday 2005). From 25 November 2003 to 28 February 2004, 46 specimens were recovered from the Inverloch beach after washing ashore. A government agency-led management response was initiated to delimit the outbreak. The eradication program took place from 13 March 2004 to 28 February 2005. The incursion site was located 250 m inside the entrance of Anderson Inlet and estimated to cover approximately 420 000 m2 (425 m x 980 m) (Figure 2.2c & d). Observers used SCUBA methods to drift dive through the site during incoming flood tides. A total of 229 A. amurensis specimens were removed during 17 removal events over the 11 month period of the eradication intervention (Figure 2.3). Across the 17 events, 335 diver hours were invested with an average of 19.7 diver hours per event. A stand down management decision was made on the 6 June 2005 as population numbers were low and considered not at a density able to successfully reproduce (Holliday 2005). Surveys of the infested site in 2007 and 2012 failed to find further A. amurensis individuals. Full details of the Victorian Government led program are available in Holliday (2005).

30 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Site C. Tidal River is a narrow estuary approximately 2.5 km long, with a small stream draining from the surrounding catchment into Norman Bay, adjacent to the Wilsons Promontory Marine National Park. A sand bar periodically forms across the estuary mouth where a low-lying flood plain is located (Figure 2.2e & f). Tannins leach from the surrounding peat soils and decomposing vegetation to stain the water brown. The mouth of Tidal River is inundated by tidal flow. The stream has a depth range from very shallow to 4 m. A bridge was constructed across the River approximately 600 m from its mouth. There is little coastal development adjacent to Tidal River (located within the Wilsons Promontory National Park), other than the footbridge and a boat ramp.

On the 16 May 2012, A. amurensis was first seen close to the bridge crossing at Tidal River, Wilsons Promontory. Government agencies were quick to respond due to the close proximity to the marine protected area of Wilsons Promontory Marine National Park. Specimens were removed within hours of being first detected. The incursion site was approximately 21 000 m2 (approximately 40 m x 460 m) in area. A removal program consisting of SCUBA and snorkel dive methods was undertaken between 16 May 2012 and 27 October 2012. Observers conducted line searches using a rope line technique across the width of the river (Figure 2.4). A total of 163 specimens were removed during this initial removal program (Figure 2.3). A total of 13 events was undertaken with a total of 56 diver hours invested. In September 2013, a single specimen was removed from the site. At this stage, success of the eradication attempt at this site in uncertain with a natural event of freshwater input at the invasion site.

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

32 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Figure 2.4. Observers preparing for a line search at Tidal River, Victoria in 2012. Insert: Asterias amurensis specimens removed during a single physical removal event of eradication surveys.

34 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

2.2.3 Artificial seastar mimic detectability experiments

At the Anderson Inlet and Tidal River removal sites, I conducted experiments to estimate the ability of observers to detect a seastar during surveys for A. amurensis. Detectability experiments were carried out on 19 May 2012 at Anderson Inlet and 2 June 2012 at Tidal River. At both sites the experiments used similar methods to those used for the management surveys describe in section 2.2.2. A summary of the information of each trial site is provided in Table 2.1. At Anderson Inlet, 11 observers conducted drift SCUBA searches, where observers would drift with the incoming current through the entrance of the inlet. GPS devices where attached to the surface of dive pairs to calculate the approximate amount of effort invested within the search area during the extended drift dive. At Tidal River, 11 observers conducted a line search where divers progressively moved along a rope anchored at either side to the 40 m wide river. Once distributed along the rope line, divers swam in a line for the entire length of the search area.

The search area at each site was approximately the same as the search area under the actual removal events described in Section 2.2.2; 420 000 m2 at Anderson Inlet and 21 000 m2 at Tidal River. Observers spent a total of 3.2 and 2.4 diver hours searching at Anderson Inlet and Tidal River, respectively. Detectability targets for the trials were artificial mimic seastars that were placed haphazardly in the search area (Figure 2.5). Densities of seastars were similar to those observed of A. amurensis in populations considered to be low-density in Australia (Andrews et al. 1996, Ling 2000). The mimic seastars were made from Silicone Moulding Rubber (Delchem Pty Ltd) and hand painted (a detailed description of mimics is provided in Section 3.2.1).

Densities of mimic seastars within a search area were 0.0003 per m2 and 0.003 per m2 for Anderson Inlet and Tidal River, respectively. Mimic seastars were made in two size classes 5.8 cm and 9.4 cm as measured by maximum arm length. Each

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

mimic was given an individual identification number. As each mimic was placed, its identification number and size class were recorded to track which individual mimic had been recovered during searching experiments. All observers were instructed prior to the commencement of searches and told to continuously and independently search for mimic seastars. Observers were shown examples of the artificial seastars prior to searching. Seastars where removed from the site once detected by an observer.

5 cm

36 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Table 2.2. Site level variables for detectability trials at Anderson Inlet and Tidal River using artificial mimic seastars of Asterias amurensis.

Variable Anderson Inlet Tidal River

Search Area (m2) 420 000 21 000 Number of divers 11 11 Search effort (hrs) 3.2 2.4 Number of mimics seastars 121 (71, 52) 71 (42,31) (5.8 cm and 9.4 cm max. arm length) Total number of seastars recovered 45 58

Density of mimics (seastar m2) 0.000288 0.00286

The estimated rate of detection (λi) of a seastar at each site is:

di −−ln 1 mi (2.1) λi = , ti

where di is the number of individual mimics detected at site i, mi is the total number of mimics at site i and ti is the total time (mins) spent searching by observers at site i.

2.2.4 Bayesian catch-effort removal model

I used a Bayesian catch-effort model described by Rout et al. (2014) to assess the population size and removal effort of A. amurensis during management events. I assume that all seastars detected were (1) removed during population removal events and (2) populations had no growth (closed population to reproduce) and (3 that all animals have the same probability of detection per unit time, and hence

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

that the waiting time between captures is exponential. Search effort and seastar removal per event (Figure 2.2) were used to parameterize the catch-effort model. Each site had different search methods, so was treated as a separate population. As individual divers and their search effort varied considerably, effort was measured as the total number of diver hours during each removal event. The number of seastars removed during each event was recorded. The model requires that all animals have the same probability of detection per unit time, and hence that the waiting time between captures is exponential. The rate of a seastar being detected was assumed to be constant, and detections occurred randomly as a Poisson process, so an exponential model of time to detection of each individual was used (McCarthy et al. 2013). However, this may not always be true as the last few organisms may be more difficult to detect than the first ones.

The modelled probability of removing (pi) each seastar at site i given a search effort

(diver hours) in search j of tj is:

−λ ijt (2.2) peij, =1 − ,

where λi is the detection rate of each seastar at site i. By using a constant detection rate for a site, I did not accommodate spatial or temporal heterogeneity in the rate of detection. This might be particularly significant for those individuals or locations for which detection is lowest – with a tendency to be the last detected. Additionally, independence can be compromised when heterogeneity in detectability affecting observers is not modelled and can bias population estimators (Mackenzie 2006, McClintock et al. 2010). With many ecological surveys, l factors that influence the detection probability are often poorly understood, and more information is required to by collected be inform the detection probability used in the study.

I modelled the number of removals in each event j at each of the three sites as:

38 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

(2.3) nij,~, Binomial( pij, N ij ,1− ) ,

where Ni,j is the number of sestars remaining at site i after removal event j. The number of seastars at each site prior to the next removal event is then:

NNij,= ij ,1− − n ij , . (2.4)

Thus, Ni,0 is the initial number of seastars prior to any removals at site i. I generated a posterior distribution for the population size initially and after each removal event. I also generated a posterior distribution for the effort (δi,j) required in a single removal event to reduce the expected population to one individual, which I define as eradication, assuming individuals must mate to reproduce. This effort is defined as:

1 − log Nij, (2.5) δij, = , λij,

where Ni,j is the number of seastars removed at each event. Note, when Ni,j is one or zero, required eradication effort is zero by definition.

I specified a prior uniform distribution for N0, the number of seastars at a site before any removal has taken place with a lower limit of 1 and an upper limit of an estimated maximum seastar population size. The upper limit (estimated maximum seastar population size) was derived from considering the search area (m2) and a low to moderate infestation of A. amurensis (<0.5 m2) in Australia (Ling 2000). This equates to a population size of 210000, 24000 and 10500 for Anderson Inlet, San Remo and Tidal River, respectively. I specified a prior uniform distribution for the detection rate parameter of λt with a lower limit of 0 and an upper limit of 10. This is an uninformative prior, although it could be replaced by an informative prior based on the artificial experimental detectability trials using the artificial mimics

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

outlined in Section 2.2.3. Using an uninformative prior here is designed to determine population estimates of removal efforts when detectability was not measured at the time of surveying.

Each site is modelled separately and the model posterior distributions were based on 4 chains of 50 000 Markov chain Monte Carlo (MCMC) methods with every 5th sample retained to reduce autocorrelation, following a 10 000 iteration burn-in. Initial values of the models parameters were generated randomly. Convergence was assessed by looking at the Brook–Gelman–Rubin (BGR) diagnostic plots that demonstrate that convergence was achieved when the value of the numeric diagnostic was close to 1. All analyses were done using OpenBUGS (Lunn et al. 2000).

2.3 Results

2.3.1 Detectability of A. amurensis artificial mimics

Experimental detectability data were pooled across searchers at each location at Tidal River and Anderson Inlet sites. Artificial mimic detectability experiments were not conducted at San Remo. The overall proportion of mimics found from the Anderson Inlet site was 37% (45 out of 121) and 82% (58 out of 71) from Tidal River. The difference in the proportion of seastars detected between the two artificial mimics sizes of 5.8 cm and 9.4 cm was 27% and 67% for Anderson Inlet and 50% and 97% for Tidal River, respectively. A total of 112 minutes was invested in searching at Anderson Inlet and 465 minutes at Tidal River. The detection rate for seastar mimics was 0.145 seastars hr-1 for the Anderson Inlet and 0.777 seastars hr-1 for Tidal River. The probability of detecting an artificial seastar at a site is over predicted using these experimental detection rate estimates from both Anderson Inlet and Tidal River (Figure 2.6).

40 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

2.3.2 Population size estimates during eradication programs

The estimated initial mean (95% CI) population size of 22 (21–24), 263 (246–289) and 165 (163–169), at the three sites before eradication activities began was slightly larger than the cumulative number of individuals removed of; 21, 232 and 163 at the three sites of San Remo, Anderson Inlet and Tidal River, respectively (Figure 2.3 and 2.7). Both the Tidal River (after 134 days since first detection) and San Remo (after 478 days since first detection) populations were estimated to decline to low population sizes at the time when eradication program was stopped. Tidal River was estimated to have only have 2 (1–6 95% CI) and San Remo to have 1 (1–3 95% CI). Estimates at the Anderson Inlet site after eradication attempts where closed were 30 (14–57 95% CI) after 461 days since first detection. Therefore the catch-effort model is estimating that some A. amurensis individual(s) remain undetected at each site.

The estimated detection rates generated by the catch-effort model varied considerably between the three sites (Figure 2.6). The Anderson Inlet site had a lower mean (95% CI) rate of 0.006 (0.004–0.007) seastars hr-1 compared to San Remo at 0.01 seastar hr-1 (0.05–0.15) or Tidal River at 0.08 (0.07- 0.01) seastar hr-1 sites (Figure 2.6). In comparison the probability of removing a seastar during the experimental mimic trials was much higher than the removal rates estimated during the agency-led eradication programs using the Bayesian catch-effort model (Figure 2.6). Uninformative prior distributions were chosen for the detection rate parameter at each site of the catch-effort model due to the high variability of the estimates during the experimental trials.

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

42 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

2.3.3 Predicted search effort for eradication

During each removal event, the predicted mean (95% CI) search effort required to remove the population of seastars at each site declined as more individuals were removed over the duration of the program (Figure 2.8). At the Tidal River and San Remo sites, the predicted search effort estimates overlapped with the observed amount of effort spent searching during each removal event (Figure 2.8). Observed search effort invested at Tidal River ranged between 1.5–14.5 diver hrs per removal event while the mean (95 % CI) predicted amount varied between 7.1 (0–23.4) – 57.3 (48.04–68.82) diver hrs. At San Remo, the observers ranged between 1.0– 11.25 diver hrs per removal event while the mean required search effort (95% CI) varied between 5.0 (0–15.6) and 30.4 (18.21–52.6) diver hrs. Lastly, at Anderson Inlet, the observed effort ranged between 2–53.3 diver hrs while the mean (95 % CI) predicted effort required to achieve eradication varied substantially between 609.5 (385.9–909.6) and 994.8 (787.352.6–1287) diver hrs.

44 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

2.4 Discussion

2.4.1 Measuring the feasibility of eradication

Managing a marine invasive species is challenging and eradication is often attempted with a succession of removal events that aim to reduce the population to zero (or 1 for sexually reproducing species) (Culver and Kuris 2000, Hewitt et al. 2005, Holliday 2005, Hopkins et al. 2011). The results of the present study demonstrate that physical removal used in combination with a relatively small search area and low density can be effective in reducing seastar population sizes of Asterias amurensis and in eradication. The Tidal River and San Remo population sizes were estimated and observed to diminish to near zero. However, at Anderson Inlet 30 seastars (14–57 95% CI) were estimated to remain at the site after the program ended in 2005 even though no seastars have been recorded at the site in 2007 and 2012 surveys. Previous programs have physically removed Asterias amurensis with some success (Proctor and McManus 2001) and failure (Johnson 1994).

Complete removal of seastars can be challenging, with local eradication attempts being more feasible due to their isolation, delimitation and often low density (Bax et al. 2002, Hopkins et al. 2011, Piola and McDonald 2012). Such populations may not be entirely established at a site and therefore the incursion may not be widely spread compared to a much larger, well-established population. Populations at all three sites in this study were considered low density (<400 seastars per site) and spread across a small area (range between 21 000 m2—480 000 m2). In high density populations of A. amurensis in the Derwent Estuary, Tasmania, removal events have seen >24 000 individuals removed from an area with no effect on population control (Johnson 1994). A smaller population of <500 individuals has successfully been removed at Henderson Lagoon, Tasmania (Proctor and McManus 2001). Culver and Kuris (2000) indicated that the successful eradication of an invasive

46 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

sabellid population of Terebrasabella heterouncinata in California was in part associated with the incursion remaining spatially restricted. Using the methods described in the current study, physical removal of A. amurensis using volunteer divers appears to permit successive harvesting and operate more effectively in contained and low density populations.

The timing and duration of management actions is an element that should be considered alongside the feasibility of eradication. At Anderson Inlet the credible intervals where larger than the other sites modelled in the study. This can be attributed to the larger area of the site and variability of effort-number of seastars removed. At all three sites of this study the time since introduction is unknown. However, time since first detection and removal commencement varies. Bax et al. (2002) demonstrated that rapid response to the incursion of the black striped mussel, Mytlopsis sallei in Darwin Harbour, Australia was a contributing factor to eliminating this species from the area. Anderson (2007) suggest that when growth rates of the invasive seaweed Caulerpa taxifolia are used, the projected criteria for successful eradication was three full growing seasons since a presence observation is made.

The challenge facing most eradication programs for invasive species is deciding when to stop the program under the assumption that all individuals have been removed (Rout et al. 2009a). At each of the three sites in this study, the observed number of individuals removed during an event was eventually reduced to close to zero and subsequent surveys found no individuals. However, the lack of detections during surveys may give misleading evidence to stop an eradication program. Declaring eradication too soon may result in the re-establishment of a population. Recent studies have developed methods to successfully estimate when eradication should be declared. Rout et al. (2009a) use sighting records to find the optimal number of absent surveys after which eradication should be declared for an invasive species. Regan et al. (2006) use the number of absent consecutive surveys

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

to estimate the optimal number of surveys before eradication is declared. Further investigation using similar methods may guide future eradication surveys for A. amurensis considering consecutive absent surveys occurred at sites.

2.4.2 Catch-effort model limitations

Estimating the detection probability of a target is a key component of catch-effort models (Barron et al. 2011, Rout et al. 2014). While detectability has been demonstrated to vary between observers (Thompson and Mapstone 1997, Williams et al. 2006, Dickens et al. 2011) and sites (Edgar et al. 2004, MacNeil et al. 2008) during underwater surveys. This study used simple methods to estimate detectability from calibration experiments and from the generated catch-effort model. However, more complex interactions of species, site and observers are expected to have changed the detection probability in this study. For example, using different searchers at a site and at different events in this study could affect individual target detectability. Therefore, the assumption that an individual seastar or an observer’s detectability was constant and waiting time is exponential during surveys is limited without more formal investigation. Using mimics allows us to draw inferences about imperfect detection by having a known population size. However, differences between the mimic and wild seastar detection probabilities could have pronounced effects on inferring the catch-effort model. Further investigation of the differences between mimics and wild seastars are required before the full extent of the variation is understood. Many ecological studies have examined the effects of detection, and I examine this problem for A. amurensis surveys more closely in Chapters 3 and 4.

As with closed population models, the catch-effort model used in this study assumes no additions (no reproduction, immigration or emigration) to the population are made during the duration of eradication attempts at each site. It is unknown how A. amurensis was introduced at each site. Therefore, further re-

48 Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

introductions and population growth are a possibility. Failing to include possible population growth estimates in the catch-effort model decreases the accuracy of the population and search effort estimates. Bayesian catch-effort models have been successful in including population growth parameters into estimates (Rout et al. 2014). For example, Rout et al. (2014) demonstrated the use of population growth parameters in a Bayesian catch-effort model to inform population estimates during eradication attempts of an invasive species. There is limited information on population growth rates, movement patterns and re-introduction processes for A. amurensis that makes it difficult to include such measures in this current study.

2.4.3 Management implications

In this study, I estimate the required search needed to remove populations of a marine invasive seastar. Information on population size and search effort estimates can be used for future A. amurensis incursions where volunteer physical removal methods are used. Managers considering future eradication programs can use the methods from this study to design surveys with a number of questions in mind: (1) the amount of search effort needed for a successful eradication program (2) the minimum number of volunteers required based on search effort estimates, (3) the need to record parameters of search effort, detectability and number of individuals removed at the time of management actions and (4) under different scenarios of detectability and population growth - how much of the population would be left if eradication was declared at a particular point during the eradication program.

This study intended to use seastar removal data for a given search effort to validate local eradication effort. At one out of the three sites more effort was needed to ensure complete eradication. The small number of seastars removed at San Remo is impractical to estimate a population size and I would suggest that this site be updated when more information is available. The Bayesian framework of the catch-

Chapter 2: Evaluating the eradication of Asterias amurensis during removal programs

effort model also means that the model parameters can be updated as new monitoring data comes to hand with the posteriors from this analysis forming the priors for a new analysis. Finally, methods for estimating population size for eradication feasibility assessments are most useful when integrated with costs and rigorous accounts of detectability and population dynamics.

50 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Chapter 3

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Abstract

Global efforts to control and eradicate marine invasive species routinely use observer- based surveillance surveys to detect and remove individuals. Failing to accurately estimate detectability makes population assessments less reliable. This in turn directly reduces the success of management efforts for a marine invasive species. Using artificial mimics of the invasive seastar, Asterias amurensis (Asteroidea), detection surveys were conducted to assess the influence of selected variables on detectability. Observers conducted a total of 40 intertidal walking surveys and 73 shallow subtidal snorkeling surveys in Victoria, Australia. Using multilevel Bayesian logistic regressions, I determined which variables at transect, observer and species-levels have the greatest influence on detection. The proportions of seastars found during surveys were similar between detection methods of snorkeling (59%) and walking (58.4%). The size of a seastar, the size of a group of seastars and the distance away from the transect influenced detection the most. Understanding imperfect detectability for a marine invasive species provides information for managers to improve survey designs, understand true abundance and occupancy estimates from sample counts, and implement management programs.

52 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

3.1 Introduction

Marine invasive species are considered key drivers of worldwide biodiversity loss and ecological change (Bax et al. 2003, Molnar et al. 2008). Global efforts to control and eradicate marine invasive species have prompted surveys at a range of locations to estimate their abundance and distribution (Smith et al. 2002, Hewitt et al. 2004, Arenas et al. 2006, Whitfield et al. 2007, Delaney et al. 2008). Rarely, are all individuals or species detected during ecological surveys. Failing to explicitly account for imperfect detectability results in imprecise population estimates and eventually reduces the success of management (MacKenzie et al. 2005, Kéry and Schmidt 2008, Monk 2014).

Imperfect detectability is a known problem during ecological surveys, with many methods available to account for and estimate imperfect detection probabilities of individuals (MacKenzie et al. 2002, Williams et al. 2002b). Effective survey methods that properly account for uncertainty can maximize the effort spent during surveys (Field et al. 2005, Mackenzie and Royle 2005, Hauser and McCarthy 2009). An increasing number of tools available for management decisions account for imperfect detectability. For example, the level of monitoring needed to detect new incursions (Regan et al. 2006), allocation of effort over space to maximize detections (Hauser and McCarthy 2009) and when to declare successful eradication (Rout et al. 2009b) all account for imperfect detectability. However, few current marine invasive species management decisions measure and account for imperfect detectability.

Visual surveys are methods routinely used to record the presence and absence of a marine invasive species during population control surveys (Hewitt and Martin 2001, Whitfield et al. 2007). Assessing the effectiveness of visual surveys requires an estimate of imperfect detectability. Estimating what influences detection during surveillance surveys for marine invasive species is a problem well recognized (Hayes et al. 2005, Delaney and Leung 2010). Yet, the detectability of marine invasive species is

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

not well studied and many management programs are routinely designed with limited information about species detectability.

Finding a species or individuals of species during marine surveys can be challenging with observer skill (Kanary et al. 2010, Dickens et al. 2011) and the environment (Bozec et al. 2011) known to influence detectability. Additionally, different species during the same survey can have different detection probabilities (MacNeil et al. 2008). The resources used to monitor and remove a species during surveys often varies considerably between surveys and sites. Differences in observers’ skill and experience have been demonstrated to influence imperfect detectability (Collier et al. 2007, Chen et al. 2009, Fitzpatrick et al. 2009, Moore et al. 2010, Alexander et al. 2012). The environment in which the species is located can also influence the probability of detection, by providing places to hide or opportunities for camouflage (Dearden et al. 2010). Characteristics of a species such as size, clustering and mobility are also known to influence detectability (Cappo et al. 2006). Understanding the degree to which different factors influence detection can assist survey design and can be incorporated into targeted management decisions for marine invasive species.

The absolute population size of a species is rarely known during ecological surveys. However, experimental studies to assess detectability have overcome this limitation by undertaking surveys in areas where a population is known and abundance can be empirically changed (Christy et al. 2010, McCarthy et al. 2013). Studies have investigated detection of species using observer-based experiments (Hayes et al. 2005, Fitzpatrick et al. 2009, Christy et al. 2010, Moore et al. 2010, Alexander et al. 2012). These studies provide controlled environments where variables that influence detection can be monitored. However, very few experimental observer-based studies have been conducted to investigate detectability for marine invasive species (Delaney and Leung 2010, Kanary et al. 2010).

In this study, I examine the potential influences of selected transect, observer and species-level characteristics on the detectability of the Northern Pacific seastar,

54 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Asterias amurensis. Removal programs designed to eradicate, control and reduce outbreak populations have been designed for A. amurensis in coastal waters of Australia (Johnson 1994, Proctor and McManus 2001, Holliday 2005). A. amurensis poses ecological and economic threats to southern Australian marine environments (Ross et al. 2003a, Ross et al. 2004). Little is known about what influences detection during visual surveillance and removal surveys. Reducing the spread of this species is a key management objective in its non-native area (Bax and Dunstan 2004, Dommisse and Hough 2004, Bax et al. 2006). Populations continue to spread and the risk of new incursions are high (Byrne et al. 1997b). This study estimates imperfect detectability of an invasive seastar to help guide surveys for A. amurensis populations and improve post border management decisions.

3.2 Methods and Materials

3.2.1 Study area and targets

Visual detection surveys were conducted in intertidal and shallow subtidal habitat during April to May 2010 and November 2010 to March 2011 in Port Phillip Bay (38.004298°S, 145.030813°E) and Corner Inlet (38.833587°S, 146.389854°E), Victoria, Australia. Sites were selected by having representative habitat where Asterias amurensis have previously been observed (Cohen 2001, Hewitt et al. 2004). Targets to be detected during the surveys were artificial mimics of A. amurensis made from RTV2 Silicone Moulding Rubber (Dalchem Pty Ltd) (Figure 3.1). Each seastar was hand painted to replicate the coloration (yellow–purple) of the species. Seastar mimics were made in three sizes with a maximum arm length of 3.5, 5.8 and 9.4 cm. Weights were imbedded during mimic construction to minimize target movement by surrounding water currents during the experimental surveys. In addition to providing a known population size, the use of artificial mimics during this study instead of living animals eliminated possible movement of targets and potential spread of this highly invasive species.

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

3.2.2 Survey design

Two methods of visual search were used to detect mimic seastars; (1) intertidal walking and (2) snorkeling in shallow subtidal habitats. Transects formed with measuring tapes were used during detection surveys to define a search area where the target locations and the observer’s paths were controlled. Each transect was 50 m x 8 m (snorkel) and 50 m x 4 m (walk) in size (Figure 3.2). Mimic seastars were placed at previously-determined random locations on a 0.1 m grid with a ± 0.1 m accuracy within the transects. Individual seastar mimics were placed in clusters of 1, 3 or 7 at their defined location. The minimum closest-neighbour distance between clusters was calculated using the Euclidean metric from the center of each group of clustered

56 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

mimics (Greenacre and Primicerio 2014). Densities of seastars were similar to those observed of A. amurensis in populations considered to be low-density in Australia (Andrews et al. 1996, Ling 2000). Density of mimic seastars within a search area ranged between 0.047 – 0.08 per m2 and 0.08 – 0.17 per m2 for snorkeling and walking methods, respectively. Differences are due to search area differences (400 m2 and 200 m2 respectively). Nine separate snorkeling transects and seven walking transects were surveyed. A total of 170 and 121 mimics where placed out during the snorkeling and walking surveys, respectively.

Figure 3.2. Illustration of snorkelling (a) and walking (b) transects for the detection of

Asterias amurensis mimics in Victoria, Australia. Each transect is 50 m in length (li), and was defined by a measuring tape (thick line). Distance along the transect line represented locationy in the detectability model. Each transect extended four metres from the measuring tape. Distance from the tape to each mimic represented locationx in the detectability model. Note the difference between snorkeling and walking search area where walking survey area was only to one side of centre transect line, therefore area was only half that for snorkeling.

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

A total of 39 observers participated in the snorkeling and 25 in the walking surveys with 14 completing surveys using both methods (with 1—4 transects per observer). A total of 73 snorkeling and 40 walking transects were completed. Searchers were instructed to search continuously and to follow the centerline of the transect during surveys. The tape measure for snorkeling surveys was the middle of the search area and the boundary of the walking transects (Figure 3.2). There was no fixed time budget for search effort and each searcher determined the time taken to complete a survey. Searchers recorded the total search time (mins/sec) to complete a survey, the mimic seastar location along the transect line (0-50 m) and the direction (left, center or right) to the mimic seastar relative to the transect.

During the surveys, a total of 741 walking and 1336 snorkeling seastar detections were possible. Searchers were given instructions on the survey methods and shown a mimic seastar to ensure all searchers had a visual anchor of the target prior to commencing a survey. Searcher experience variables were measured by having each searcher complete a questionnaire assessing their last snorkel experience (weeks), number of years they had been snorkeling and if they had any previous experience in undertaking marine scientific surveys (summary see Table 3.1).

58 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Table 3.1. Variables used in the logistic regression models for the detection of Asterias amurensis artificial mimics in Victoria, Australia using two visual search methods.

Model Variable range by survey method Variable Description (units) level Snorkel Walk

Transect depth Water depth (m) 0.9 – 2.2 0.2–0.5 cover Vegetation coverage group (%) 0–20, 40–60 or 80–100 0–20, 40–60 or 80–100 density Density of seastars (seastar m2) 0.047 – 0.08 0.08 – 0.17 effort Search effort per transect (minutes) 2:19–19:22 1:18–19:16

Observer lsnorkel Time since last snorkel (weeks) 0–58 – expsnorkel Time since first snorkel (years) 0–57 – expsurvey Survey experience (no or yes) 0 or 1 0 or 1

Target size Length of arm of seastar (cm) 3.5, 5.8 or 9.4 3.5, 5.8 or 9.4 cluster Number of individuals in of group 1, 3 or 7 1, 3 or 7 locationx Distance away from transect (m) 0–4 0–4 locationy Distance along transect (m) 0–50 0–50 near Nearest Neighbour (m) 0.8–6.8 0.8–6.8 side Side of transect (centre, right or left) 0, 1 or -1 –

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Environmental variables were measured for each transect to estimate their influence on detection of A. amurenisis. Transects were selected with vegetation (seagrass and algal) cover in three categories (0-20%, 40-60% and 80-100%). Water depth varied across transects with a mean height between 0.9 m – 2.2 m (± 0.3 m) and 0.2 m – 0.5 m (± 0.3 m) for snorkeling and walking transects, respectively. Surveys were conducted 1.5 hour before and after a tide to minimize changes in depth during surveys for both methods. Visibility at the time of surveys replicated conditions when surveys are conducted for agency-led removal programs (Holliday 2005). Visibility was measured using a secchi disk and was greater than 3.5 m (± 0.5 m) during all snorkeling surveys. Wind conditions (speed and direction) at the time of surveys were relatively calm and not thought to disrupt visibility. A summary of measured variables from the detection surveys is presented in Table 3.1.

3.2.3 Data analysis

Bayesian detection model based on logistic regression

Data for the two search methods of snorkeling and walking were analysed separately. Individual seastar identifications were matched using the closest location grid coordinates recorded by an observer to the measured coordinates recorded during mimic placement. A false positive record was documented if no match could be made within a 2 m radius of the observer’s seastar location. The 8 records for snorkeling and 4 for walking (approximately 1% of detections in each case) were removed from the data prior to analysis due to records not matching the listed pre-defined location of mimics. These records were considered false positive detections by observers.

I used a Bayesian multi-level (hierarchical) logistic regression to model transect, observer and target-level influences on seastar detectability (Gelman 2004). The success and failure to detect a seastar during surveys was:

60 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

1 if detection is true yijk =  0 if otherwise

where yijk is the detection status of seastar i, by searcher j in transect k. Detection status is modelled as a Bernoulli distribution given by:

(3.1) yijk ~Bernoulli ( pijk ) ,

where pijk is the probability of detecting seastar i by searcher j at transect k, which in turn was modeled as:

()−λijkt jk (3.2) pijk =1 − exp ,

where λijk represents the rate at which searcher j detects seastar i on transect k, and tjk is the total search effort on transect k for searcher j. Rate of detection is a measure that captures differences in detectability due to covariates. I chose a set of covariates at transect, observer and target-level that are known to influence detectability (see Table 1) (Collier et al. 2007, Chen et al. 2009, Moore et al. 2010, Alexander et al. 2012).

I used a standard logit link function for a logistic regression model:

pi (3.3) logit(λijk )= log =αβ +1 Χ 1,ijk + β 2 Χ 2, ijk +… β n Χ n, ijk + ν k + ο jk + ω ijk , 1 − pi

where α is the intercept constant and the values of βi are the regression coefficients of covariates Χi, νk is a random effect of transect k, οj is a random effect of observer j and ωi is a random effect of an individual seastar mimic i. Random effects νk, οj and ωi were included to account for variation in detectability not explained by the covariates. All covariates were centered (by subtracting the mean) and standardised (by dividing by two standard deviations) prior to analysis (Gelman and Hill 2007). I specified a normal prior distribution for the intercept α and all βi with a mean of 0 and a standard

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

deviation of 1000. Random effects were assumed to be drawn from normal distributions with means of zero and standard deviations that were estimated. Prior distributions for the standard deviations for random effects of transect, observer and mimic were uniform distribution between 0 and 1000. Convergence in the Bayesian MCMC samples were checked by examining trace plots of the posterior distributions.

To avoid excessive collinearity amongst predictor variables (Harrell 2001, Fox and Weisberg 2011), I used the Variation Inflation Factor (VIF) as a diagnostic statistic produced by linear regression using the car Package (Fox and Weisberg 2014) of R version 3.0.0 (R Development Core Team 2008) to assess collinearity. A VIF value > 10 is considered to indicate multicollinearity (Neter et al. 1990), in which case highly correlated variables should be excluded from analysis. In this study no variables indicated multicollinarity (Table 3.2).

The posterior distributions for both the snorkeling and walking models were based on 4 chains of 100 000 Markov chain Monte Carlo (MCMC) methods with every 5th sample retained to reduce autocorrelation, following a 50 000 iteration burn-in. Initial values of the models parameters were generated randomly. All analyses were done using R2jags version (Su and Yajima 2012) from the interface of JAGS 3.4.0 (Plummer 2013a, b) in R 3.0.0 (R Development Core Team 2008). A backwards stepwise logistic regression was carried out to incorporate only those variables that clearly influenced the detection of mimic seastars during the experimental surveys. I used the deviance information criterion (DIC), to compare the set of candidate models (Spiegelhalter et al. 2002). Models <2 DIC units from the best model (model with the lowest DIC) where considered to have good relative fit and models with DIC 3—7 have less support (Spiegelhalter et al. 2002).

62 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Snorkel Walk Model Variable Std. Std. level Mean VIF Mean VIF Deviation Deviation Transect depth 1.85 0.41 1.71 0.34 0.11 2.49 cover 1.03 0.59 1.03 0.86 0.71 2.31 density 0.06 0.01 3.43 0.13 0.04 2.38 effort 8.88 4.15 2.07 8.37 3.98 1.45 Observer lsnorkel 5.31 9.95 1.39 – – – expsnorkel 20.10 16.16 1.26 – – – expsurvey 0.52 0.50 1.47 0.50 0.50 1.05 Target size 5.95 1.44 1.03 5.97 1.55 1.02 cluster 1.75 1.79 1.51 1.87 1.90 1.46 locationx 1.33 1.05 1.43 1.34 1.08 1.31 locationy 25.28 15.12 1.01 24.0 14.64 1.13 near 2.90 1.18 1.15 2.53 1.83 1.43 side -0.02 0.96 1.09 – – –

Predicted detection probabilities

The number of successful and failed seastar mimic detections can be estimated using the coefficients from the best fitting model of the backwards stepwise Bayesian logistic regression models. The posterior means of the regression coefficients of the best-fit model were used to estimate the rate of detection, λi derived from the Bayesian logistic regression. The mean searching time for snorkeling and walking invested by searchers was used to estimate detection probabilities (Table 3.2) based on eqn 3.2.

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

3.3 Results

The proportion of seastar mimics detected during surveys were similar between the two visual search methods with 780 of 1336 (58.4%) seastar mimics detected using snorkeling and 437 of 741 (59.0%) detected using walking surveys. Overall the observed proportion of seastars detected during each survey varied considerably among the variables at target, observer or transect levels (Figure 3.3). The walking and snorkeling survey data supported different models ( Table 3.3 and Table 3.4).

Transect, observer and target variables contributed to explaining the detection patterns of Asterias amurensis mimics (Figure 3.3). However, target/species level coefficients more strongly influenced detectability than observer or transect coefficients. Bayesian 95% credible intervals for the regression parameters, however, overlapped zero for the majority of the variables, indicating the direction of the effect of the covariates on the 0.95 quantile in this study is ambiguous (Figure 3.3). Detectability increased with target size and cluster grouping size, but declined with distance from the centerline of the transects using snorkeling methods (Figure 3.3). While target size increased detectability using walking methods and included in the final model (Figure 3.3). The predicted probability of detecting individuals increased with arm length and the number of individuals in the cluster (Figure 3.4). By using search effort between 0 to 20 minutes the detection probabilities increased for both search methods and for all sizes of individuals and clusters (Figure 3.4). Detection probabilities declined with distance from the transect centerline for the snorkeling methods (Figure 3.5).

64 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Figure 3.3. Posterior mean estimates (and 95% credible intervals) (logit scale) for the best fitting Bayesian logistic regression detection models for snorkel – M13 (a) and walking – M2 (b) surveys. The intercept, variables and standard deviation of random errors are presented. Estimates are based on centered and standardized (2 SD) transformed data. ψ represents the mean posteriors for the standard deviations for random effects – observer (obs), mimic (mimic) and transect (trans).

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

66 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Table 3.3. Comparison of multilevel Bayesian logistic regressions of visual detection for Asterias amurensis mimics using snorkel methods.

6 3 Ψ Model Variables1 DIC2 pD4 ΔDIC5 TRANS OBS MIMIC

M10 SIZE, CLUSTER, NEAR, LOCATIONX, LOCATIONY, 1417.8 1191.6 226.2 0 0.536 0.777 1.140 SNORKELEXP, SURVEYEXP, COVER, DEPTH, EFFORT M13 SIZE, CLUSTER, MIMICSIDE, NEAR, LOCATIONX, 1417.9 1191.1 226.8 0.1 0.623 0.680 1.132 LOCATIONY, DENSITY, LSNORKEL, SNORKELEXP, SURVEYEXP, COVER, DEPTH, EFFORT M12 SIZE, CLUSTER, MIMICSIDE, NEAR, LOCATIONX, 1418.6 1191.5 227.1 0.8 0.744 0.691 1.130 LOCATIONY, LSNORKEL, SNORKELEXP, SURVEYEXP, COVER, DEPTH, EFFORT M2 SIZE, LOCATIONX 1419.6 1193.1 226.5 1.8 0.726 0.695 1.161

M3 SIZE, CLUSTER, LOCATIONX 1420.1 1192.4 227.7 2.3 0.601 0.706 1.168

M9 SIZE, CLUSTER, LOCATIONX, LOCATIONY, 1420.9 1192.7 228.2 3.1 0.754 0.672 1.108 SNORKELEXP, SURVEYEXP, COVER, DEPTH, EFFORT M11 SIZE, CLUSTER, NEAR, LOCATIONX, LOCATIONY, 1421.7 1191.9 229.8 3.9 0.753 0.689 1.121 LSNORKEL, SNORKELEXP, SURVEYEXP, COVER, DEPTH, EFFORT M6 SIZE, CLUSTER, LOCATIONX, SURVEYEXP, 1423.1 1193.6 229.5 5.3 0.583 0.712 1.096 COVER, EFFORT M1 SIZE 1423.1 1193.4 229.7 5.3 0.932 0.693 1.238 M4 SIZE, CLUSTER, LOCATIONX, COVER 1423.7 1193.5 230.2 5.9 0.804 0.700 1.113 M7 SIZE, CLUSTER, LOCATIONX, SNORKELEXP, 1424.8 1193.7 231.1 7 0.628 0.676 1.100 SURVEYEXP, COVER, EFFORT M8 SIZE, CLUSTER, LOCATIONX, SNORKELEXP, 1425.5 1193.5 232.0 7.7 0.733 0.672 1.103 SURVEYEXP, COVER, DEPTH, EFFORT M0 – 1426.1 1193.2 232.8 8.3 0.880 0.696 1.343 M5 SIZE, CLUSTER, LOCATIONX, COVER, EFFORT 1426.2 1194.9 231.3 8.4 0.532 0.707 1.090

1Variables included in a selected model (DENSITY= number of stars per unit area (seastars m2); DEPTH = water depth (m); COVER= vegetation coverage (%); EFFORT = search effort per transect (minutes); LSNORKEL = time since snorkel activity (weeks); EXPSNORKEL = time since first snorkel (years); EXPSURVEY= survey experience (no or yes); SIZE= length of arm of seastar (cm); CLUSTER = number of individuals in of group; LOCATIONX= distance away from transect (m); LOCATIONY= distance along transect (m); NEAR= nearest neighbour (m); SIDE= side of transect (centre, right or left)) 2Deviance information criterion 3Deviance sampled 4 , and is an approximation of model complexity 5 C, change in DIC from the best DIC model 6Mean Posterior standard deviation of the random effects (trans=transect; obs=observer; mimic=mimic)

68 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

Table 3.4. Comparison of multilevel Bayesian logistic regressions of visual detection for Asterias amurensis mimics using walk methods.

6 3 Ψ Model Variables1 DIC2 pD4 ΔDIC 5 TRANS OBS MIMIC

M0 – 696.5 559.7 136.7 0.0 1.866 0.489 1.651 M1 COVER 697.1 559.7 137.5 0.6 1.847 0.483 1.666 M2 SIZE, COVER 700.1 561.2 138.9 3.6 1.846 0.486 1.559 M10 SIZE, CLUSTER, NEAR, LOCATIONX, 701.7 560.8 140.9 5.2 3.435 0.406 1.559 LOCATIONY, SURVEYEXP, COVER, DEPTH, DENSITY, EFFORT M9 SIZE, CLUSTER, NEAR, LOCATIONX, 702.7 561.4 141.3 6.2 2.085 0.403 1.537 LOCATIONY, SURVEYEXP, COVER, DENSITY, EFFORT M8 SIZE, CLUSTER, LOCATIONX, LOCATIONY, 703.4 561.5 141.9 6.9 2.166 0.398 1.519 SURVEYEXP, COVER, DENSITY, EFFORT M3 SIZE, CLUSTER, COVER 704.9 562.2 142.7 8.4 2.084 0.477 1.492

M7 SIZE, CLUSTER, LOCATIONX, LOCATIONY, 705.2 562.1 143.1 8.7 1.779 0.397 1.507 SURVEYEXP, COVER, EFFORT M6 SIZE, CLUSTER, LOCATIONX, LOCATIONY, 706.7 560.8 145.9 10.2 1.946 0.486 1.505 COVER, EFFORT M5 SIZE, CLUSTER, LOCATIONX, LCOVER, 709.2 561.5 147.8 12.7 1.814 0.476 1.489 EFFORT M4 SIZE, CLUSTER, COVER, EFFORT 710.5 562.6 147.9 14.0 1.941 0.474 1.463

1Variables included in a selected model (DENSITY= number of stars per unit area (seastars m2); DEPTH = water depth (m); COVER= vegetation coverage (%); EFFORT = search effort per transect (minutes); EXPSURVEY= survey experience (no or yes); SIZE= length of arm of seastar (cm); CLUSTER = number of individuals in of group; LOCATIONX= distance away from transect (m); LOCATIONY= distance along transect (m); NEAR= nearest neighbour (m)) 2Deviance information criterion 3Deviance sampled 4 , and is an approximation of model complexity 5 , change in DIC from the best DIC model 6Mean Posterior standard deviation of the random effects (trans=transect; obs=observer; mimic=mimic)

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

3.4 Discussion

This study establishes imperfect detection for Asterias amurensis during management surveys is influenced by environmental, survey design and observer-level factors. Visual surveys have generally underestimated the abundance of cryptic species in marine environments (Willis et al. 2006). With the commonly used belt transect methods for subtidal and intertidal marine sampling, observers detected less than 60% of seastars using snorkeling and walking methods within the search area. This is considerably higher than other marine invasive species where records of less than 30% have been recently observed with the Indo-Pacific lionfish in the Caribbean (Green et al. 2013). The relationship between sampling method, detectability and species population estimates are well researched in the literature with many examples from marine (Ebeling and Laur 1985, Cappo and Brown 1996, Edgar and Barrett 1999, MacNeil et al. 2008, Green et al. 2013) and terrestrial (Chen et al. 2009, Christy et al. 2010, Moore et al. 2010, Pacifici et al. 2012, McCarthy et al. 2013) studies available. However, few marine invasive studies examine detectability and its associated uncertainty (Delaney and Leung 2010, Kanary et al. 2010).

3.4.1 Factors influencing detectability

The probability of detecting a seastar using visual search methods varies considerably between and within factors in this study. The factors that had influence on detectability were group cluster size, maximum arm length and the seastar’s distance from the centerline transect. Bozec et al. (2011) recognised the effect of species size in the detection patterns of coral reef fish observations. The size of A. amurensis varies considerably across its non-native range in southeastern Australia (Ling 2000) and making it difficult to correct detection error when sampling. In this study, the probability of observers detecting a seastar declined with decreasing size between 9.4–3.5 cm maximum arm lengths. Distance from the transect was another important factor, with probabilities of detection being less than 50% when seastars were less

70 Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

than 3 m from the searcher. Despite the frequent use of distance sampling in ecological studies, few marine invertebrate studies use this technique to estimate population parameters (Katsanevakis 2009). One of the most critical assumptions in distance sampling is that the detectability of the target on a transect line is known (Buckland et al. 2001). This study provides an opportunity to explicitly test this assumption where seastar detection is estimated to be <0.8 on the transect line (0m). Such estimates can be incorporated when using this technique to make more accurate population estimates (Buckland et al. 2001).

This study suggests that detectability declines with increasing macroalgae cover. Complex habitat structure and topographic complexity tend to reduce detectability of marine species (Bozec et al. 2011). Surveys have been undertaken for A. amurensis in efforts to reduce their impact on the surrounding biota in varying habitats including ports (Ling 2000), mussel farms (Andrews et al. 1996), seagrass beds and sand-flats (Proctor and McManus 2001, Holliday 2005). Therefore, then need to consider the effect of habitat structure on detectability for this species is essential. A number of other factors measured had limited influence on the detection of seastars during this experimental study. Distance between closest neighbouring specimens had little effect on the detection of seastars in this study. In addition, abundance is known to influence detectability during searching studies (McCarthy et al. 2013). However, in this study, the abundance of seastars was selected to represent a low-density infestation at the beginning of an introduction or after undertaking significant removal efforts and therefore any true effect of density may be obscured by having a limited range.

The evidence in the literature varies considerably on the effect of observer experience and their ability to find targets in an ecological context (Edgar et al. 2004, Fitzpatrick et al. 2009). I found no evidence that observer experience was correlated to the detectability of the seastars. This is supported by studies that found observer variation in visual surveys to be irregular and often less influential than other factors (Thompson and Mapstone 1997, Edgar et al. 2004, Williams et al. 2006, Chen et al. 2009). Williams et al. (2006) suggested that observer experience differences is unlikely to be a major

Chapter 3: An empirical evaluation of factors affecting detectability of an invasive seastar

problem for survey programs that pool data from multiple observers with similar experience levels. However, this study may not have captured the true measure of observer experience by using snorkel experience (time since last snorkel, the number of years since first snorkel) and previous marine invasive survey involvement. Alternatively, many ecological studies have shown observer experience to significantly influence detectability (Fitzpatrick et al. 2009, Moore et al. 2010). Furthermore, observer experience variation could potentially be a serious factor for survey and management programs where observers gain experience by participating in surveys causing detectability changes amongst single observers over multiple surveys. Even though observer experience has the potential to cause variation in survey records it can be recognised to allow incorporation into detectability inferences.

3.4.2 Implications for seastar surveys and management

This study has provided useful insights into how effectiveness of visual detection methods could deviate during a removal or surveillance survey for an invasive marine species. To increase the accuracy and reliability of population information for A. amurensis, imperfect detection and association factors should be incorporated in management decisions: (1) the level of monitoring needed to detect new incursions (Regan et al. 2006), (2) allocation of effort over space to maximize detections (Hauser and McCarthy 2009) and (3) when to declare successful eradication (Rout et al. 2009b). Managers need to balance the quality of data and the resources devoted to seastar post-border management. The multilevel effects of site, observer and species have potential to regulate survey design for this species. This and similar ecological knowledge can be used to design ‘best practice’ surveys for determining the ideal conditions under which to undertake monitoring surveys. Given the variability of detectability for this species, managers need to be cautions and make informed decisions about future management options for this species.

72 Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

Chapter 4

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

Abstract

The ability to accurately distinguish a species from a co-occurring species during surveys is important to a broad range of ecological applications including when estimating abundance and occupancy. Incorrect identification of specimens during a survey may generate erroneous records and lead to the true status of species or populations inaccurately being perceived at a site. In this chapter, I estimate the frequency of incorrect detection of a marine invasive seastar, Asterias amurensis in the presence of two native co-occurring seastars, Coscinasterias muricata and Uniophora granifera in Port Phillip Bay, Australia. I use artificial mimics of each species to replicate the presence of wild individual specimens with a known population size. A total of 49 observers participated in the snorkeling surveys of one of four 50 m transects. Observers recorded the location of A. amurensis on a transect during the survey. I test the effect of pre-survey training by dividing the participants into two groups, informed and uninformed. Only the informed group was made aware of the presence of co- occurring species in the search area prior to undertaking the survey. All participants were given details of the presence of A. amurensis, clear instructions of its appearance and the survey methods at the site. Of the detections of Uniophora granifera, 16.1% were recorded as A. amurensis, and 1.5% of Coscinasterias muricata were recorded as A. amurensis. Detectability of A. amurensis did not show an effect of seastar size or pre-survey training, with 53% of A. amurensis detected during surveys. This study provides information on the frequency of incorrect detection during management surveys of A. amurensis and suggests false detections can occur during management surveys. By identifying and measuring the extent of false detection during surveys, more efficient surveys can be designed and count errors can be incorporated into population estimates.

74 Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

4.1 Introduction

Occupancy and abundance measures of a species are widely used to inform marine invasive management (Campbell et al. 2007, Ojaveer et al. 2014). However, this information relies on the ability to correctly identify and assess the true status of a species at a site. Observers of a species need to ensure records during surveys are accurate to provide quality data and meaningful inferences (MacKenzie et al. 2005). Recent marine invasive studies have focused on the need to deal with imperfect detectability (Delaney and Leung 2010, Kanary et al. 2010) (and Chapter 3) when estimating population parameters, with few studies explicitly quantifying incorrect detections during surveys. Understanding the accuracy of information collected by different observers during searching surveys can provide knowledge for developing standard ecological survey designs (Garrard et al. 2008, Chen et al. 2009, Fitzpatrick et al. 2009, Moore et al. 2010).

Marine surveys commonly use visual methods to recognise and detect the presence of a species (Edgar et al. 2004, MacNeil et al. 2008, Dearden et al. 2010, Kanary et al. 2010, Dickens et al. 2011). During these surveys, observers distinguish between co- occurring species using morphological characteristics. Detection errors occur when an individual is incorrectly identified and can lead to false positives when an individual is truly absent from a site. Alternatively, a false negative may occur when an individual is present but recorded as absent. Therefore, survey records may not necessarily represent the true occurrence status of a species at a site (Lobo et al. 2010). Even low rates of false detectability can lead to a substantial difference in population estimates (Tyre et al. 2003, Sutherland et al. 2013). Recently, quantitative methods have been developed to estimate false positive and false negative errors during ecological surveys (Royle and Link 2006, Clement et al. 2014). However, despite the importance of incorrect detectability to population estimates, few marine invasive surveys have incorporated these error types.

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

Sampling design can have a significant impact on the overall accuracy of a population estimate. Simply increasing the number of observers without also improving their identification skills can expand the search effort but may not change the relative frequency of incorrect detections. When co-occurring species are difficult to distinguish during a survey, the variability in an observer’s identification skill may influence the accuracy of a specimen’s detectability and identification (Thompson and Mapstone 1997). Considerable effort is routinely invested to train and educate observers when undertaking marine invasive surveys to reduce such errors as incorrect detection (Conservation Council of South Australia 2011) . Training observers appears to be an effective way to reduce observer variation when field data are collected by volunteers and professional scientists (Williams et al. 2006, Gollan et al. 2012).

In this study, I estimated the frequency of two native Asteriidae species commonly found in the region, Coscinasterias muricata and Uniophora granifera that could be mistaken for the invasive seastar, Asterias amurensis during monitoring surveys. I used artificial mimics of each species in replicate surveys under controlled conditions to test the performance of observers to correctly identify the target species. I tested the effect of pre-survey training by dividing the participants into two groups, (1) informed and (2) uninformed. Both groups were instructed on survey methods for detecting A. amurensis. Only the informed group was provided with additional pre-survey information on the appearance of non-target species similar in appearance to the target species, A. amurensis. This study helps identify circumstances where incorrect detections of a selected marine invasive species, A. amurensis may occur. This information can assist in designing more efficient surveillance surveys and be incorporated into estimates of population abundance or occupancy.

76 Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

4.2 Methods and Materials

4.2.1 Study site and species

Visual surveys were conducted using underwater transect snorkel methods at four shallow subtidal sites in Port Phillip Bay, Australia. Surveys were conducted on 3 April 2011 at Clifton Springs (38.1532°S, 144.5497°E), 18 February 2012 at Swan Bay I (38.262883°S, 144.659746°E), 7 March 2012 at Swan Bay II (38.266165°S, 144.648156°E) and 11 March 2012 at Williamstown (37.866319°S, 144.881519°E). Each of the four sites shared similar bottom substrata and depth. Habitats at all sites were dominated by a variety of Ulva and Zostera species. Each site had a silt sandy substrata composition with few crevices and boulders to obscure field of view. Sites were selected with approximately 40–60 % of the bottom substrata covered with algal and seagrass species. Water depth varied across transects with a mean height between 1.2 –1.8 m (± 0.3 m). Underwater visibility ranged between 3–7 m at all sites at the time of surveys. Surveys were conducted within 1.5 hours of a tide change to minimise changes in depth during surveys. Environmental conditions during the survey replicated those of previous stakeholder management programs for A. amurensis in southern Australia and previously described in Chapter 3 (Johnson 1994, Proctor and McManus 2001, Holliday 2005).

The primary target for the surveys consisted of artificial mimics of the invasive seastar Asterias amurensis in two sizes of 5.8 cm and 9.4 cm maximum arm length (used in Chapters 2 and 3) (Figure 4.1). Artificial mimics of two other native Asteriidae species commonly found in the region, Coscinasterias muricata and Uniophora granifera, were used as ‘decoy’ mimics (Figure 4.1). The maximum arm length of decoy species was 13.1 cm for C. muricata and 5.2 cm for U. granifera. These mimics were used to test the influence of their presence on the accuracy of observers recording A. amurensis during visual searching surveys. Both C. muricata and U. granifera have previously been incorrectly detected as A. amurensis during management actions and public

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

stakeholder activities (Dommisse and Hough 2004). Government agencies have led a public awareness campaign including signage and factsheets to educate various stakeholders in Victoria since the early 2000s (Figure 4.2). Seastar mimics were hand painted to replicate the colouration of actual living specimens (Figure 4.1). Each mimic was made from RTV2 Silicone Moulding Rubber (Dalchem Pty Ltd) with weights embedded during construction to reduce movement by water currents. The visual features of the mimics added realism to the test, since observers needed to recognise the targets amongst other organisms on the substrata under survey conditions.

4.2.2 Visual survey technique

Visual detection surveys were undertaken using snorkelling protocols described previously for seastar monitoring in Chapter 3. Each survey consisted of a 50 m transect line laid subtidally to define the centre the search area within which the targets are randomly positioned (Figure 3.2a). The transect line on the seabed was marked using a tape measure and acted as a pathway for observers to follow during surveying. Mimic seastars were placed at previously-determined random locations on a 0.1 m grid with a ± 0.1 m accuracy. Thirty-two U. granifera, 32 C. muricata and 66 A. amurensis mimics were placed out during the visual detection experiments across all four transects. The densities were designed not to overwhelm an observer with sightings but to allow individual recognition of the search target. A single observer snorkeled over the transect line looking either side of the centre markings for approximately 4 m. Each transect covered a search area of approximately 200 m2 (50 m x 8 m). Once an observer detected what s/he thought was an A. amurensis, the observer recorded: the position on the transect line (0–50 m) and the distance away from the transect (0–4 m). There was no fixed budget for search effort defined for a single transect search and each observer determined the time taken to complete a survey. The total search time (mins/sec) an observer took to complete a survey was recorded.

78 Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

Figure 4.2. Examples of educational material develop for Asterias amurensis and co- occurring native seastar species Coscinasterias muricata, Uniophora granifera in Victoria coastal waters. Photo credits Australian Department of Agriculture Fisheries and Forestry (DAFF) and Victorian Department of Environment and Primary Industry (DEPI).

4.2.3 Participants of the experimental surveys

A total of 49 observers participated in the snorkeling surveys with all participants surveying only one transect. A possible total of 807 A. amurensis, 397 U. granifera and 397 C. muricata s were available to be recorded by observers across the four transects. Observer experience variables were measured by having each observer complete a questionnaire (Appendix C) assessing how recently they had snorkelled (weeks), the number of years they had been snorkeling and if they had any previous experience in undertaking marine scientific surveys (yes or no). Plain and clear instructions were provided to all observers prior to undertaking a survey. These included search procedures, how to record observations and A. amurensis morphological

80 Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

characteristics. Prior to searching at a site the participants were divided into two groups after being instructed in the search methods. Observers were randomly assigned to either group, with approximately half the number of observers in each of the two groups at a site. Group 1 was informed only of A. amurensis presence in the area and shown A. amurensis mimics before undertaking the search task. Group 2 was informed that all three species were present within the search area and shown the mimics to visually familiarise themselves with each. Each observer of Group 2 was reminded that when surveying it is important to recognise the differences between species and to accurately make identification before recording a detection. Members of Group 1 were unaware that further information had been given to group 2. In total 20 of the 49 observers (Group 2) were given extra information regarding what to look out for. The remaining 29 participants (Group 1) remained uninformed during surveys of the additional native decoy specimens.

4.2.4 Statistical analysis

I consider three types of records from the visual surveys: true positive, false positive and false negative (Table 4.1). I focus on (1) false positive records where another species (U. granifera and C. muricata) are mistaken for A. amurensis. I measure overall (2) true positives that represent records when A. amurensis is present and (3) false negatives are represented when A. amurensis is present but recorded as absent (non- detection). An outcome not considered in this study was true negatives - when A. amurensis is not recorded and not present. Observers were instructed to only record A. amurensis and not U. granifera or C. muricata as recording information about these co-occurring species would influence the measured pre-training treatment effect.

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

Table 4.1. Confusion matrix for surveys for the invasive seastar, Asterias amurensis during incorrect detection experiments.

Reality

Asterias Present Asterias Absent

False Positive True Positive (Type I error)

Asterias When another species Present When Asterias is is mistaken for present and recorded Asterias

False Negative True Negative* (Type II error)

asured/ Record Perceived Asterias Absent When Asterias is When Asterias is not Me present but recorded recorded and not as absent present

*True negative not measured in this study

Incorrect detectability model based on Bayesian logistic regression

I developed an explanatory model for the probability of false positive records from data on the misidentified detection of U. granifera or C. muricata as A. amurensis. Data from detection surveys were pooled across the four transects. Individual records of A. amurensis made by observers were compared with the pre-defined mimic locations of A. amurensis, U. granifera and C. muricata. A false positive (mistaken detection or type I error) observation occurred when A. amurensis was recorded by an observer at the coordinates for a U. granifera or C. muricata mimic (Table 4.1).

82 Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

To model observer and species-level influences on incorrect detection of A. amurensis, I used a Bayesian multi-level logistic regression model (Gelman 2004) (previously used in Chapter 3). I referred to the success and failure of incorrect (misidentified) detection of A. amurensis as:

where yij is the incorrect detection status of seastar i (U. granifera and C. muricata), by observer j. I modelled incorrect detection status as drawn from a Bernoulli distribution given by:

, (4.1)

where pij is the probability of incorrect detection of seastar i (U. granifera and C. muricata) by observer j, which was modeled as:

, (4.2)

where λij represents the rate of observer j misidentifying a detected seastar i and tj is the total search effort invested at a transect by searcher j. The rate of incorrect detection (λij) is depends on two factors: the rate at which the non-target mimics (U. granifera and C. muricata) are seen by the observer, and incorrect detection of those as A. amurensis.

I modeled the rate of incorrect detection using selected covariates that were used during previous detection experiments (Chapter 3). These included three covariates at the observer–level: last snorkel experience (weeks) (lsnorkel), number of years they had been snorkeling (expsnorkel) and if they had any previous experience in undertaking marine scientific surveys (expsurvey). I also used a target-level variable of the distance from the transect line (locationx). Finally, I also used pre-survey training

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

status and survey effort as factors that influence incorrect detection. A summary of covariates used in the Bayesian logistic model is given in Table 4.2.

Table 4.2. Variable descriptions of the Bayesian logistic regression models for the incorrect detection of artificial native seastar mimics for Asterias amurensis in Victoria, Australia.

Variable Description (units) Range

Observer effort Search effort per transect (minutes) 3.42–20.39 lsnorkel Time since last snorkel (weeks) 0–400 expsnorkel Time since first snorkel (years) 0–50 expsurvey Survey experience (no or yes) 0–1 Pre-survey training (uninformed or trained 0–1 informed) Target locationx Distance away from transect (m) 0.4–3.9

I used a standard logit link function for a logistic regression model:

, (4.3)

where α is the intercept constant and βi is the coefficient of covariate Χi , οj is a random effect of observer j and ωij is a random effect of an individual seastar mimic i (U. granifera and C. muricata). For both random effect terms uncertainty is represented by the estimates’ standard deviation. All variables were transformed by centering (subtracting the mean) and standardising (dividing by 2 standard deviations) prior to analysis (Gelman and Hill 2007).

I used a normal distribution with mean of zero and standard deviation of 1000 for the prior for each of the intercept α and all βi. I used a uniform distribution between zero and 1000 for the as the priors for the standard deviation of the random effects of

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observer (οj) and mimic (ωij). The posterior distributions of the model parameters were based on 4 chains of 50 000 Markov chain Monte Carlo (MCMC) methods after discarding the initial 20 000 iterations as a burn-in. Thinning was performed with every 5th sample retained to reduce autocorrelation. The initial values for the model parameters were randomly generated within the model specification function. Convergence assessments were conducted for each model by examining posterior density and trace plots. All analyses were done using R2jags version (Su and Yajima 2012) from the interface of JAGS 3.4.0 (Plummer 2013a, b) in R 3.0.0 (R Development Core Team 2008).

A backwards-stepwise elimination of target and observer variables was used in the logistic regression models. The first model fitted was a full model of all six variables. Variables that had the lowest contribution to the model (lowest coefficient mean estimates) were removed iteratively, with the six resulting models compared using the deviance information criterion (DIC). The model with the lowest DIC was considered the best fit model (Spiegelhalter et al. 2002). Odds ratios for the covariates in the best fit model where generated from the coefficient estimates of the Bayesian logistic model.

To assess collinearity amongst predictor variables (Harrell 2001, Fox and Weisberg 2011), I used the Variation Inflation Factor (VIF) as a diagnostic statistic produced by linear regression using the car Package (Fox and Weisberg 2014) of R version 3.0.0 (R Development Core Team 2008) as described in Chapter 3. A VIF value > 10 is considered to indicate multicollinearity (Neter et al. 1990), in which case one or more highly correlated variables should be excluded from analysis.

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

4.3 Results

4.3.1 Incorrect detection of two native seastars as Asterias amurensis

Overall, 16.1% (64 out of 397 observations) of Uniophora granifera mimics were recorded as Asterias amurensis and 1.5% (6 out of 397 observations) of Coscinasterias muricata mimics were recorded as A. amurensis (Table 4.3). Incorrect detection of at least one U. granifera as A. amurensis was recorded by 40% of informed observers during surveys and 69% of uninformed observers. C. muricata was incorrectly detected as A. amurensis at least once by 5% of informed and 10% of uninformed observers. Mean (95% CI) incorrect detection of U. granifera for A. amurensis was higher at 25.1% (15.2–35.0%) in the absence of pre-survey training compared to the lower rates of 9.7% (1.8–17.6%) incorrect detection where observers had pre-survey training (Figure 4.3). Because of the very low incorrect detection rate of C. muricata, no further analysis was conducted on that species.

4.3.2 Detectability of Asterias amurensis during detection experiments

Overall a mean of 53% of A. amunrensis targets were detected and correctly identified (true positives) across sites and observers (Table 4.3). Therefore, 47% of targets failed to be detected and identified (false negative records) as A. amurensis. The effect of pre-survey training had only no effect on correctly detection with a mean (95% CI) of 50.5% (42.5–58.5%) for uninformed observers and 53.8% (48.2–59.5%) across all observers and treatment groups.

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Table 4.3. Confusion matrix values for incorrect detection experiments of Asterias amurensis. Records of Uniophora granifera and Coscinasterias muricata are measures of false positive records.

REALITY

Asterias Present Asterias Absent

False Positive True Positive (Type I error)

Uniophora

When 64 out of 397 427 out of Asterias When Asterias another (16.1%) 807 Present is present and species is (53%) recorded mistaken for Coscinasterias

Asterias 6 out of 397 (1.5 %)

False Negative True Negative* (Type II error)

Asterias When Asterias When Asterias Any other MEASURED/ PERCEIVED RECORD PERCEIVED MEASURED/ Absent is present but 80 out of 807 is not recorded species present recorded as (47%) and not present correctly absent** identified as not Asterias

*True negative not measured in this study ** Type II error implies any other species present correctly identified as not Asterias.

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

4.3.3 Bayesian logistic regression for U. granifera incorrect detections

In this study no variables showed multicollinearity (Table 4.4). The best-fit model (smallest DIC) using survey records of U. granifera incorrectly detected as A. amurensis from the backwards elimination logistic regressions was model M3. The M3 model incorporated observer-level covariates of: (i) time since last snorkel (lsnorkel) and (ii) pre-survey training (trained) together with a target-level covariate (iii) distance away from the centerline of the transect (locationx) (Table 4.5). The Bayesian regression model indicated that all three covariates of M3 had mean coefficients that decreased incorrect detection as each covariate increased (Table 4.6). As the 95% credible interval contains 0, indicating that the direction of the effect of the covariate lsnorkel on the 0.95 quantile in this study is ambiguous (Odds Ratio =-3.0 (-8.8–0.3 95% CI))

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(Table 4.6). Giving observers simple pre-survey training decreased the incorrect detection of U. granifera as A. amurensis with a mean (95% CI) coefficient of -1.89 (- 3.71– -0.32) (Odds Ratio = 0.15 (0.02–0.72 95% CI)) (Table 4.6). While the distance from the centerline (locationx) also decreased incorrect detection of U. granifera as A. amurensis with a mean (95% CI) coefficient of -1.659(-3.188– -0.355) (Odds ratio of 0.19 (0.041–0.70) (Table 4.6). The direction of the effect of the covariates lsnorkel on the 0.95 quantile in this study is ambiguous as the CIs for this covariate encompass 0. Other covariates of target and observer-level not measured in this study are important as the mean standard deviation (95 % CI) values of the random effects for target (sd_mim) and observer (sd_obs) were greater than 0.5 (Table 4.6).

Variable Mean SD VIF

Observer effort 9.11 4.00 1.16 lsnorkel 9.13 45.20 1.04 expsnorkel 9.15 11.88 1.23 expsurvey 0.11 0.31 1.14 trained 0.41 0.49 1.06 Target locationx 2.00 1.07 1.00

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

Table 4.5. Model estimates for a Bayesian logistic regression.

6 3 Ψ Model Variables1 DIC2 pD4 ΔDIC 5 OBS MIMIC

M3 lsnorkel, trained, locationx 252.5 175.6 76.9 0.0 0.5 1.4

M4 lsnorkel, expsnorkel, trained, 254.7 176.2 78.5 2.2 2.1 1.3 locationx

M5 lsnorkel, expsnorkel, 255.8 175.8 80.0 3.3 2.1 1.3 expsurvey, trained, locationx

M6 lsnorkel, expsnorkel, 258.0 175.0 83.0 5.5 2.3 1.3 expsurvey, trained, locationx, effort M1 lsnorkel 259.2 177.9 81.2 6.7 2.0 1.6

M0 – 262.3 179.7 82.6 9.8 1.9 1.6

M2 lsnorkel, locationx 265.6 178.0 87.6 13.1 2.1 1.3

1Variables included in a selected model (EFFORT = search effort per transect (minutes); LSNORKEL = time since snorkel activity (weeks); EXPSNORKEL = time since first snorkel (years); EXPSURVEY= survey experience (no or yes); TRAINED= pre-survey training treatment (no or yes); LOCATIONX= distance away from transect (m) 2Deviance information criterion 3Deviance sampled 4 , and is an approximation of model complexity 5 C, change in DIC from the best DIC model 6Mean Posterior standard deviation of the random effects (obs=observer; mimic=mimic)

Table 4.6. Posterior distribution for binary logistic regression model, M3. Coefficient estimates are based on centralised and standardised transformed data inputs.

Parameter Mean St. Dev. 2.5% CI Median 97.50%4)

α -5.715 0.652 -7.139 -5.665 -4.586 lsnorkel1 -3.002 2.426 -8.849 -2.535 0.302 trained2 -1.89 0.854 -3.715 -1.847 -0.328 locationx3 -1.659 0.716 -3.188 -1.615 -0.355 sd_mim4 1.36 0.509 0.552 1.294 2.539 sd_obs5 2.014 0.499 1.217 1.956 3.159

Note: The dependent variable is incorrect detection of target by an observer. Incorrect detection is coded as 1, 0 otherwise. CI is the credible interval. The variance is equal to the standard deviation (St. Dev.) 1lsnorkel, β parameter of time since last snorkel activity (weeks) 2trained, β parameter of pre-survey training group (informed and uninformed) 3locationx, β parameter of adjacent distance between mimic location and transect line (meters) 4sd_min, is the standard deviation of the random effect for mimic (target) 5sd_obs, is the standard deviation of the random effect for mimic (target

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4.4 Discussion

Sampling accurately and precisely is a constraint that challenges field ecologists and resource managers. In ecological surveys, visual recognition and detectability methods present a number of challenges including incorrect detection of specimens (Royle and Link 2006, Miller et al. 2011). In this chapter, I showed evidence that providing observers with pre-survey instructions and training prior to undertaking a survey can reduce incorrect detection of A. amurensis specimens as much as 16.1%. This supports past studies that have shown that training and observer experience can improve species identification in underwater surveys including volunteer marine invasive surveys (Edgar et al. 2004, Williams et al. 2006, Delaney et al. 2008). For example, Delaney et al. (2008) determined that volunteers working on the invasive crabs (Carcinus maenas and Hemigrapsus sanguineus) can have species identification accuracy of at least 80% with limited scientific training and have this improve to 95% accuracy with at least 2 years university education. In the present study, giving simple and clear instructions for co-occurring species at a site an observer can reduce the likelihood of incorrectly detecting A. amurensis. These findings have implications on how the accuracy of future volunteer marine invasive species programs can potentially be improved by additional on-site training such as introducing co-occurring awareness.

The field of volunteer-based conservation and monitoring continues to increase as it becomes more popular for a wide range of stakeholders. The advantages of using volunteers during marine invasive species surveys include: (i) a larger pool of volunteers to provide search effort efficient to conduct extensive surveys and (ii) large financial savings for survey programs. In this study, the evidence suggests that by providing brief pre-survey training, the effect of an observer’s inexperience can be diminished to a level where incorrect detection is comparable with those observers more experienced. Thompson and Mapstone (1997) reported the effects of training and differences in observer ability during underwater visual fish surveys and provides suggestions that observer variation can be improved by training. An option to

Chapter 4: Effects of observer training on incorrectly detecting Asterias amurensis

reducing the overall errors caused by imperfect detection may be by providing pre- survey training to all observers prior to conducting field surveys.

Distance is an important parameter in sampling the benthic habitat where approaches including distance sampling can assist in making population estimates. In Chapter 3, I showed that increasing distance away from the transect centerline reduced the detectability of A. amurensis. In this present study, increasing distance away from the centre transect line also supported a similar concept where distance is a contributing factor of correctly identified detection records during U. granifera–A. amurensis experimental surveys. This study strengthens the use of distance sampling applications for populations estimates in benthic marine habitats. For example, Katsanevakis (2009) estimate the abundance of an endangered marine benthic species Pinna nobilis using distance sampling through SCUBA diving methods.

This study has implications for management programs looking to undertake removal events and surveillance programs for A. amurensis and similar species. During removal programs observers maybe more cautious and be more questioning of a specimen’s identity. However routine surveys may not be questionable of specimen identification and therefore allow greater errors to occur during stakeholder marine invasive species management programs.

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Chapter 5

Chapter 5: An empirical test of decision theory for resource allocation for a marine invasive species

Abstract

Environmental decision theory provides quantitative tools and methodologies to evaluate the overall benefit of management actions. Optimal search theory for species is a form of environmental decision theory. Optimal search theory provides quantitative tools to optimise resource allocation when designing environmental surveys. Despite the prolific use of environmental decision theory, to my knowledge, it has not been formally evaluated when applied to field-related ecological searching problems. In this chapter I evaluate the use of environmental decision theory by examining applications of search theory for a marine invasive species search problem. Using detection surveys from two experimental studies, I compare the extent of the benefit of using optimal search effort allocation when searching for objects compared to uniform, proportional and subjective search strategies. The two experiments were carried out in a grassy lawn and a seagrass-algal bed with targets of golf tees and artificial seastars of Asterias amurensis (used in previous chapters), respectively. At each of the experimental locations, multiple search quadrats were established and the targets of varying densities were positioned randomly within quadrats. These scenarios were designed to represent environmental monitoring surveys where observers would be given the task to find as many individuals within a spatial or temporal constraint. Twelve different observers participated in each of the two experiments. I observed an increase in the mean number of targets found during mimic A. amurensis searching experiments as high as 6.1% as a result of switching from the uniform to the optimal distribution of 20 minutes search effort. Similarly, during the golf tee experiment, a mean increase of 11.3% (yellow tees) and 6.7% (green tees) was observed between the uniform and optimal strategy for 20 minutes search effort. The predicted number of targets found was more than that observed, demonstrating imprecise detection estimates was a confounding factor of the comparisons. The results showed that optimal decision theory is a robust and resilient strategy in finding the best possible returns compared to other approaches. This study helps confirm that using the best search strategy could improve environmental management especially of marine invasive species.

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5.1 Introduction

Decision theory is a set of methods and tools designed to improve the way decisions are made (Klein et al. 1993). The philosophy of using quantitative reasoning to assist in decision-making has universal application with many disciplines adopting a mathematical optimisation approach to solve complex problems (Freund and Schapire 1997, Dee and Gerber 2012). Implementing decision theoretic approaches are not without costs (both financially and logistically) and their use is often sought when a decision is needed to be made between competing alternative actions (Field et al. 2004).

In environmental management settings, decision theory uses quantitative approaches to form and optimise decisions for the management of species, ecosystems, and other aspects of the environment (Possingham et al. 2001). The practical application of decision theory for environmental problems has grown rapidly in the past decade (McDonald-Madden et al. 2008, Joseph et al. 2009, Regan et al. 2011). Globally, environmental decision theory is providing novel and robust management solutions (White et al. 2010, Levin et al. 2013). For instance, spatial prioritization, which forms a major component of environmental decision theory (Moilanen et al. 2009), is helping rezone coastal habitats when developing marine protected areas (Osmond et al. 2010). Despite the global application of decision theory, the advantage of using this approach instead of alternative methods are yet to be formally investigated under field conditions. Critical evaluation of decision theory by field-testing optimisation models are required to make recommendations that improve the efficacy of ecological surveys.

Optimal surveillance is a form of environmental decision theory that has been applied to ecological survey design (Regan et al. 2006, Mehta et al. 2007, Bogich et al. 2008, Baxter and Possingham 2011). Information gathered from ecological surveys provides valuable knowledge for the basis for many conservation, natural resource management and policy decisions. Techniques and tools developed to improve survey

Chapter 5: An empirical test of decision theory for resource allocation for a marine invasive species

design such as optimal surveillance can help environmental management programs over many years (Vos et al. 2000). Investment in sampling and surveying is often expensive and time consuming for environmental managers. Given limited budgets (logistical or financial) that often constrain ecological surveys, resources should be allocated to maximize benefits.

Recent developments in environmental decision theory have led to optimal survey design through various quantitative approaches that incorporate imperfect detectability and probability of occurrence from existing information (Hauser and McCarthy 2009, Giljohann et al. 2011, Regan et al. 2011, Pacifici et al. 2012). These new optimal surveillance tools use models to determine survey design questions including the optimal search effort invested at different sites, and/or when to stop control efforts at one site and move on to another. Optimal survey design models consider the following elements: (i) the probability the species is present at each site: (ii) the probability of detection given presence: and (iii) the different benefits of surveillance at each site.

Search theory is an optimal surveillance framework that prioritizes limited resources over a search area to maximize the detection of hidden targets. Originally developed for search and destroy problems in the military (Koopman 1946, Stone 1975), search theory has increasingly been applied to a variety of search problems including invasive species management (Hauser and McCarthy 2009, Giljohann et al. 2011), mineral resource exploration (Mangel 1983), job search (Lentz 2009), economic investment markets (Elder et al. 1999) and psychology (Shaw and Shaw 1977). The general form of search theory uses existing information or predicted estimates of the probability of the object(s) being present, the probability of detection given presence and the total search budget available.

In this chapter, I use an optimal search model parameterised by time-to-detection and presence/absence data for targets from marine and terrestrial habitats to evaluate the advantage of using search theory during searching tasks in two different habitats:

96 Chapter 5: An empirical test of decision theory for resource allocation for a marine invasive species

marine algal bed and a grassy lawn. This is the first study, to my knowledge, to test environmental decision theory with empirical data and the first to test search theory in an ecological setting. Finally, this study highlights the value of quantitative decision– making processes to aid search effort allocation during environmental survey design.

5.2 Materials and Methods

5.2.1 Optimal search model

I tested the optimal search model developed by Hauser and McCarthy (2009) ( ). This model allocates search time over a series of locations and can account for trade- offs between the probability of detection, probability of presence and the costs and benefits of searching. In the tests I undertook, the probability of presence was known and I focus on the trade-off between detection probability and the benefit of searching each location. The abundance of targets at a location represents the benefit of searching. Hence, the expected number of individuals that remained undetected in searches across a search area with n locations is:

n (5.1) −λiix Lw= ∑ i exp , i=1

where λi is the instantaneous detection rate of individuals in location i, wi is the number of individuals at each location and xi is time spent searching location i. The optimal solution is the search time for each location xi that minimised the expected number of undetected individuals L subject to a budget constraint on the total time spent searching (B = xi), assuming non-negative search times (xi ≥ 0). The optimal

allocation depends on the budget B, the estimated rate of detection of individuals λi

Chapter 5: An empirical test of decision theory for resource allocation for a marine invasive species

and on the relative number of individuals in each location (wi / wi) (Hauser and

McCarthy 2009).

98 Chapter 5: An empirical test of decision theory for resource allocation for a marine invasive species

The key model assumptions are that encounters between observers and targets are random and so the detection rate remains constant over the course of the search (i.e. the probability of non-detection declines exponentially with increased effort) and that the rate of detection is known. These assumptions are unlikely to be true in reality, because the assumption of random encounters embodied by the exponential function is only an approximation and the estimated rate of detection will have error. There is an assumption that it takes no time to move between quadrats, with only time spent searching an area included in the algorithm. Here, I evaluate the benefit of the optimal search model for real searches under field conditions. Tests are experiments using controlled densities of targets, in which observers aim to detect as many targets as possible and the optimal search time across a set of locations was determined for fixed budgets.

5.2.2 Field-based tests using experimental surveys

The two experiments were carried out in a grassy lawn and a seagrass-algal bed. At each of the experimental locations, search sites were quadrats that were distributed across relatively uniform environments. Targets were positioned randomly within quadrats; with the number of targets varied among quadrats. Prior to conducting surveys, observers were shown the targets, and instructed to search each quadrat continuously for a prescribed time with the aim of finding as many targets as possible. Observers were randomly assigned an order of quadrats and a starting point along the boundary of each quadrat to ensure some independence in encounters of targets among observers. Each observer chose his/her own search direction and path after being instructed to search continuously and find as many targets within each quadrat for a prescribed time. The detection rate λi was the key parameter to be estimated in each case. A calibration quadrat in each of the grassy lawn and seagrass-algal bed experiments was used to estimate detection rate λi for the experiment (see section 5.2.3 for more information on how detection rates were estimated). In total, observers searched the calibration quadrat and all subsequent test quadrats, where the number

Chapter 5: An empirical test of decision theory for resource allocation for a marine invasive species

of test quadrats varied between the two experiments. In all test experiments it was assumed that the detection rate of targets was the same for all observers. It was assumed that differences in relative abundances among quadrats were known, so that the tests evaluated how model performance depended on the estimate of detection rate and the assumptions underpinning the model (eqn. 1). Time to detection was recorded for each target found within a quadrat, and the target marked temporarily to avoid double counting by the same observer. The methods specific to each test experiment are described below and summarized in Table 5.1.

(1) Grassy lawn with green and yellow golf tees. Searching surveys by 10 observers were conducted for green and yellow golf tees in a grassy lawn area at The University of Melbourne (South Lawn) over two days in early April 2011. Four 10 m x 10 m (100 m2) quadrats were randomly assigned within the experimental site. Within four quadrats, 5, 20, 60 and 100 green and yellow tees (5.4 cm length) were inserted in the ground at random locations (on a 0.1 m grid). The height of tees matched the height of the surrounding grass, which was a close-cut lawn. A fifth quadrat (calibration quadrat) with 20 yellow and 20 green golf tees was used to estimate each observer’s detection rate λi separately (see Section 5.2.3). After searching the calibration quadrat, each 10 observers independently surveyed the four quadrats and recorded the time-to-detection for individual tees. The experience of observers varied with most having completed ecological surveys prior to the experiment. The amount of prescribed time each observer spent searching a quadrat was determined before the commencement of the experiment. A minimum of 10 minutes searching time for each quadrat was undertaken by all searchers. Time spent searching by each observer in each quadrat ranged between 10-30 minutes.

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(2) Seagrass-algal bed with an invasive seastar. Searching surveys by 12 people were conducted on three days of December 2011 and a single day in January 2012. Three sites 10 m x 10 m (100 m2) were randomly assigned within the Ricketts Point Marine Sanctuary, Port Phillip Bay (37°59'25.12"S, 145°01'35.20"E). The targets were painted artificial mimics of the invasive seastar, Asterias amurensis made from silicone with an arm diameter of 8.5 cm. A total of 8, 24 or 40 seastars were located randomly within each quadrat (on a 0.1 m grid). A fourth quadrat (calibration quadrat) with 20 seastars was used to estimate the detection rate (see Section 5.2.3). All quadrats were patchy seagrass/algal beds dominated by Zostera spp. with 40-60 % vegetation cover. Quadrats were moved within the study area after the first two searching dates, as a small number of targets were not recovered after the second session in December. Each of the 12 observers surveyed each quadrat once by snorkelling for 15 minutes in each. Visibility at the time of searching ranged from 3-5 m that is typical for this location. Water depth varied between quadrats and with tidal phase, and ranged from 0.85 m to 1.8 m.

5.2.3 Estimating detection rates

In order to estimate the detection rates, I used calibration surveys at the time of the experimental searching surveys. This was undertaken for the grassy maintained lawn (with yellow and green tees) and the seagrass-algal bed (with mimic seastars). These methods allow for individual detection rate estimates of species (i.e. target group) for each observer to be calculated. All calibration surveys were conducted prior to undertaking the experimental surveys of this study. Design and environmental conditions of the calibration quadrat was a replicate of that used in the experimental surveys. For both the golf tees and seastar surveys, a quadrat 10 m x 10 m (100 m2) was used with 20 individuals of each target type randomly located within the area. Observers individually searched the calibration quadrat for a total of 10 mins and 15 mins for the golf tees and seastars, respectively, Observers were not made aware of

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the density of targets located in the search area prior to or during the survey. Time to detection for each target found during the calibration survey was recorded. The detection rate λi, for an individual observer per target type during the calibration survey is:

di (5.2) λi = n , twii()−+ d i∑ s i j=1

where di is the number of targets detected in the calibration quadrat i, wi is the number of targets in the calibration quadrat i, ti is the total time (secs) spent searching quadrat i, si is the individual time-to-detection (secs) for each target found. When searching for more than one target group (e.g. multiple golf tee colours) within a quadrat, detection rates are considered independent.

5.2.4 Comparing predicted and observed performance

Time-to-detection under four different search strategies

Using the observer time-to-detection data for each target (yellow tee, green tee and mimic seastar) from the field surveys we compared how many individual targets would be found using a selected search strategy. The performance of the optimal search model was evaluated by comparing the number of detections achieved using the optimal allocation to those that would be achieved using three plausible alternative allocation strategies.

The three comparative allocation strategies were: (1) a uniform allocation of effort across all quadrats (xi = B/n); (2) effort proportional to the number of targets in each quadrat (xi = Bwi / wi); and (3) a subjective judgement made by the observers when they were informed of the relative densities in each quadrat and the estimated detection rate.

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Table 5.1. Summary of the targets, quadrats and detection information used to test the optimal search model (Hauser & McCarthy, 2009).

Search Habitat Target No. of No. of Target abundance Search time Mean detection rate Ka observer s quadrats (individual quadrat) per quadrat (min-1) (Std. Error) (mins) (95% CI) Grassy lawn Green golf tees 9 4 185 10–30b 0.06882 1.045 (0.025) (5, 20, 60, 100) (0.15864, 0.24843) Yellow golf tees 10 4 185 10–30b 0.20354 0.98 (0.018) (5, 20, 60, 100) (0.04831, 0.08932) Seagrass–algal Seastar mimic 12 3 72 15 0.16266 1.235 (0.025) bed (8, 24, 40) (0.13682, 0.18851) a k, shape parameter when fitting a Weibull distribution, the exponential distribution (k=1)(k<1 detection decreases with time) (k>1 detection rate increase with time) b Note time spent searching in each quadrat varied among observers

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During the subjective judgement allocation process, observers were asked to allocate effort to each quadrat when the search budget, B was set at 10 minutes and 20 minutes, respectively. The predicted benefit of the optimal search strategy was calculated as the difference between eqn (5.1) when using the optimal values of xi, and those determined using each of the three comparative allocations. The observed benefit of the optimization was calculated by the difference between the number of targets detected when using the optimal search allocation solution and the number detected when using a comparative allocation.

Number of target found under each search strategy

I use the search effort allocation to predict the total expected number of detections using the four different strategies (optimal, uniform, proportional and subjective). The total predicted number of targets detected by an observer across the n quadrats searched, Npred, for each search strategy was:

n  (5.3) ()−λiix Nwpred =∑ i (1 − exp ) , j=1

where is the predicted detection rate for an observer for a selected target type, xi is the search effort allocation (minutes) for an individual quadrat under the search allocation strategy and n is the number of quadrats in the study area. The observed number Ni,obs of the targets present wi in quadrat i that were detected when spending xi minutes searching was calculated as the number of targets detected within the search time. Summing across the n quadrats searched provided the observed number of targets detected as:

n wi (5.4) Ni,obs =∑∑ It()i, j < x i , ij=11 =

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where ti,j is the time taken to detect target j in quadrat i, and I() is the indicator function, equalling 1 when the argument is true and zero otherwise.

Benefit of using an optimal search strategy

The predicted and observed benefits of using the optimization were calculated as:

∆N (5.5) = strategy Benefit n , ∑ wi j=1

where ∆Nstrategy is the difference in the number of detections of comparative strategies (i.e. optimal and uniform) and wi is the number of targets present in location i. The relationship between the mean predicted (eqn 5) and observed (eqn 4) for both the optimal and uniform search effort allocation strategy was examined. When data on individual observers were available, the standard error of the benefits of the search strategy was calculated from the 95% CI in benefit among observers.

A consequence of constant detection during searching is that times of detection follow an exponential distribution. The shape parameter k of the Weibull probability distribution was used to assess whether the experimental times to detection differed from an exponential distribution. The exponential distribution is a special case of the Weibull where k=1 indicates that detection rates of individual targets are constant over time.

Alternatively if k>1, the detection rate increases over time, and when k<1 the rate decreases. Thus, k<1 indicates non-exponential times to detection (Lee 1979). The Kolmogorov-Smirnov goodness-of-fit test was used to test whether the distribution of the times to detection differs significantly from the exponential expectations (α=0.05) (Massey 1951).

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5.3 Results

The mean number of targets detected by observers across all four search strategies of (a) optimal, (b) uniform, (c) proportional and (d) subjective varied between target types and search budget (Figure 5.2). Across all habitats and target types, the optimal search strategy found a mean 7.0% –11.5% and 4.9%–12.6% more targets than a uniform allocation for surveillance budgets of 10 minutes and 20 minutes, respectively (Figure 5.3 a an d). The optimal strategy found more targets across all habitats and target types than the proportional allocation strategy, but the mean benefits were small 0.36%–2.1% and 0.18%–1.1% for search budgets of 10 and 20 minutes, respectively (Figure 5.3 b and e). The observed and predicted benefits were similar for all targets in the experimental tests. The benefit of using the optimal allocation tended to be over-predicted more frequently than under-predicted. The observed and predicted mean benefit for all targets follows similar patterns with increasing search budgets for all targets except for green golf tees (Figure 5.4).

The errors in prediction occurred primarily because detection rates were estimated imperfectly (Figure 5.5), but also because the rates of detection tended to increase with time searching, so the assumption of exponentially distributed detection times did not hold (k > 1 in most cases; Table 5.1). The detection rates of targets that were used to predict the optimal search allocation were under-predicted for the green golf tees, yellow golf tees and seastars (Figure 5.5). However, despite the imperfect detectability rate estimates, the benefit of using the optimal search strategy was well predicted compared to the alternative allocation strategies. The optimal search strategy found more targets on average than strategies based on subjective judgment (Figure 5.2). Compared to the optimal search strategy, observers’ subjective judgements tended to over allocate effort at the lower target densities and an under allocate at higher densities (Figure 5.6).

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10 minutes 20 minutes

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10 minutes 20 minutes

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Figure 5.5. Difference in detection rate for target groups among observers at each quadrat used in the experimental optimal surveillance tests. Differences in detection rate (targets min-1) is the residual between the observed detection rate for each quadrat and the mean predicted rate from previous or calibration detection quadrat. Box plot represent the minimum, 25 percentile, median (solid line), mean (dashed line), 75 percentile and maximum values. The mean predicted detection rate is over- predicted with values <0, equal with values at 0 (solid grey line) and under-predicted with values >0.

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10 minutes

20 minutes

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5.4 Discussion

This is the first study to evaluate the benefit of using environmental decision theory or search theory. I have shown that using search theory to optimize detections (Hauser and McCarthy 2009) measurably increases the number of targets detected compared to three plausible alternative approaches. This was observed despite imperfect estimates of detection rates and observed times-to-detection not matching the assumed exponential distribution. The allocation of resources is fundamental to survey design, and search theory can reduce redundant effort and improve overall survey efficiency. When applied to a wider search area or density of targets the potential gain in return is worthwhile compared to the additional effort invested. This study covered a relatively small search area to test and capture the potential application gains provided by optimal search theory. The benefits and impetus of investing in optimal search theory for a monitoring program has the potential to provide increased returns across scales and regions much larger than in this study. Compared to alternative approaches, the practical benefits of optimizing search effort demonstrate the value of this type of decision-making process in survey design and surveillance problems.

A considerable part of search theory relies on using estimates of detectability and the probability of occupancy to allocate search effort where prioritization of sites is undertaken. Methods to estimate detectability and occupancy are well known and studied (MacKenzie et al. 2005, Garrard et al. 2008, Moore et al. 2010, McCarthy et al. 2013). These estimates can be improved as monitoring programs often have information available from previous sampling visits to provide information on detection and occurrence. This study aimed to test how well a search theory approach to search effort allocation works under a controlled design of detection and occurrence. However, this study’s estimates have been derived using a calibrated quadrat prior to testing observers and are imperfect, which highlights that even with

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uncertain estimates of detectability, search theory is robust and resilient enough to achieve benefits.

Ecologists tend to overestimate the information content of survey data; this tendency toward over-confidence is common in many fields (Burgman 2005). This inability to judge the reliability of surveys might explain subjective judgements of the survey allocation task of this study which usually led to suboptimal survey designs. Few observers in this study were able to allocate their search budget optimally, underlining the usefulness of optimisation methods in the design of surveys. Methods such as environmental decision theory and search theory allow inefficiencies in survey design and budget allocation to be addressed, which in turn frees up resources for additional activities to be undertaken. Therefore, this study identifies the advantage of using the mathematical optimisation approach of search theory to improve the yields of a survey compared to alternative search allocations including a searcher’s subjective decision.

While this study focuses on testing search theory, it is also the first empirical test of any form of environmental decision theory (doubt by the examiner). Many areas of environmental management including conservation reserve design (Possingham et al. 2000, Stewart et al. 2003), invasive species management (Maguire 2004, Cacho et al. 2010) and species conservation (Regan et al. 2005) stand to benefit from the advantages of environmental decision theory methods. Given how widely these methods are used it is crucial that methods are empirically evaluated. While the first results are promising, further evaluation of different methods will help build a broad evidence base for the value of environmental decision theory.

Several research areas remained unaddressed when evaluating optimal surveillance for environmental survey design. In cases presented here, I consider multiple target groups during a search. I treat each target group as a separate search with the effect of having multiple species (targets) in a search unknown. I assume that an observer

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consistently detects hidden targets at a constant rate, which leads to detection times being distributed exponentially. An exponential model of detection time has been previously applied to search problems (Garrard et al. 2008, Hauser and McCarthy 2009, Moore et al. 2010). Alternative distributions, such as the Weibull, which allows for detection rate to change over time, may improve the performance of search theory. There are prospects for further on-ground application of search theory and environmental decision theory in environmental management decisions. Making search theory and environmental decision theory tools readily accessible to managers (Hauser 2009) will increase their uptake.

In this study, I present the first evaluation, to my knowledge, of environmental decision theory and optimal surveillance methods for ecological surveys. Now that the benefit predicted by these methods is demonstrated in practical applications, they should be used more confidently as a useful tool for improving environmental survey design.

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Chapter 6

Chapter 6: General Discussion

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6.1 Overview

In this final chapter, I draw together the main research themes addressed in this body of work, as previously identified in Chapter 1. I summarise and discuss the contributions to the practical application of marine invasive management and to the broader field of search theory. I make a number of recommendations on the role of imperfect detectability and optimal resource allocation as an important part of marine invasive management and suggest some directions for future research.

This thesis has involved using a damaging marine invasive species, Asterias amurensis as a model organism to empirically investigate the application of search theory and quantify many of its components. The primary research objectives underpinning this thesis were to: (1) develop population catch-effort models to study the feasibility of management options for a marine invasive species (see Chapter 2), (2) develop procedures to test the probabilities of detectability and incorrect detection during visual surveys (see Chapters 3 & 4) and (3) test a theoretical-based search effort allocation support tool for marine invasive species under empirical conditions in the field (see Chapter 5). To achieve these research objectives, I use data from removal programs attempting eradication for A. amurensis and conduct a series of empirical surveys under controlled field conditions. Using this information, I use and develop several statistical tools to quantify search to better understand the broader post- border management of A. amurensis incursions. The thesis takes an original and novel approach to testing environmental decision theory with empirical data, where it is the first study to address such a test of theoretical knowledge (Chapter 5).

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6.2 Summary of significant findings

Detection and survey sensitivity of marine invasive species is central to post-border management (Hayes et al. 2005). However, in regard to quantifying search and control, understanding the uncertainty from observer, species and sites is essential but often overlooked by marine invasive species management. Using mimics to make inferences about the detectability process is a valuable tool, however this approach can be limiting when considering wild specimens. I use mimics in Chapters 2, 3, 4 and 5 to ask questions about detectability where many of the underlying characteristics of the species such as the morphology, habitat preference and group clustering are controlled by survey design. However, use of mimics can limit understanding the entire detectability process using wild animals. The extent of that the mimics are relevant to wild populations requires further investigation. A comparison between my approach here and that of real specimens would allow for greater understanding of the differences between the two approaches (i.e mimics vs. wild specimens). In the following section I outline the significant findings addressed under each objective of this thesis.

Objective 1: develop population catch-effort models to study the feasibility of management options for a marine invasive species

In Chapter 2, I demonstrate the development and application of modelling the catch- effort of eradication attempts in agency-led management campaigns for the marine invasive, A. amurensis. This chapter provides a detailed account of observed effort and number of individuals removed at each event across three local incursion sites. I found that the required amounts of effort needed to completely eliminate individuals from the incursion sites where similar at two of the three sites. Similarly, at the same sites eradication was considered feasible. However, eradication seemed unlikely at the third site where the observed search effort did not match the search effort that was predicted to be required.

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Objective 2: develop procedures to test the probabilities of detection and incorrect detection during visual surveys

In Chapter 3, I empirically evaluate imperfect detectability and explore the effect of species, site and survey-specific factors for A. amurensis. Imperfect detectability is often challenging to estimate and account for due to the many factors influencing this search parameter during surveys. A hierarchical Bayesian model provides a sound statistical framework to measure such factors simultaneously. I found evidence that species size (maximum arm length), group-cluster size and the distance away from the transect line had the strongest effects on detection in the experiments. Both visual detection methods of walking and snorkelling showed similar influences on detection.

In Chapter 4, I measure the failure to accurately record an observation of A. amurensis compared to co-occurring asteroid species Uniophora granifera and Coscinasterias muricata. Observers were randomly-selected into two groups, trained and untrained, where the trained groups were given pre-survey training. There is evidence that pre- survey training reduced the level of incorrect detection across all levels of observer experience. Observers on average were more likely to misidentify Uniophora granifera for Asterias amurensis than Coscinasterias muricata for A. amurensis. However, there was no difference of true A. amurensis detection between the two observer groups.

Objective 3: test a theoretical-based search effort allocation support tool for marine invasive species under empirical conditions in the field

In Chapter 5, I use a novel application to examine the performance of search strategies and optimal search theory as a basis for allocating search effort when designing ecological surveys. Designing cost-effective surveys under time constraints is critical to the success of most monitoring programs. Optimal search theory provides an informed solution to the distribution of search effort during surveys. Detection time data obtained from experimental searching surveys were used to compare the

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number of targets found using four different search strategies. Observed detection rates used in this study were underestimated compared to the predicted detection rate. Even with this difference of detection rate, applying the optimal model of search strategy consistently yielded more targets for the same time budget than searches based on the alternative methods. The predicted benefits of using the optimisation were similar to those obtained in the experiment, indicating the robustness and practical applicability of this theoretical model. Given the advantages of applying the best time allocation strategy in this study, this provides evidence of an improved methodology for designing surveys for cost-effective invasive management.

6.3 Management implications

Common sampling errors when conducting ecological surveys include the failure to detect a species, given it is present at a site. Alternatively, a species might be recorded as being present at a site when it is in fact absent (incorrect detection). Therefore, with thousands of marine species estimated to be moving around our oceans everyday via international transport and trade (Carlton and Geller 1993, Carlton 1996, Pimentel et al. 2005) it is critical to understand how effective management actions are for these species. The research in this thesis assesses the feasibility of eradication, the uncertainty of imperfect detectability and the use of optimising tools to improve invasive-searching surveys.

In Chapter 2, catch-effort models are most useful when integrated with costs and when rigorously accounting for detectability and population dynamics. The need for managers to consider cost and catch-effort together is paramount when developing efficient and successful management programs. The inclusion of a more variable detection rate allows these effects to be modelled where heterogeneity in detection rates can be accounted for. Alternatively, Bayesian methods used in this Chapter allow updating of the catch-effort model parameters as more surveys take place. The

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uncertainty about a parameter similar to population size can be specified by a prior distribution and updated when more data becomes available until eventually converging on the truth after many updates. As this species spreads and similar eradication efforts are attempted, this approach may assist mangers to prioritise eradication sites.

Non-detection and even an under-estimation of a species occurrence could be detrimental to a system especially in the case of marine invasive species. The probability of detecting a species in a particular area can be influenced by a number of factors including search effort and the efficiency of sampling methods. However, most surveys focus on ‘how many’ and ‘what’ species are present than on how sensitive the survey might be to detecting a species. Findings from this study will contribute to models of the overall sensitivity of a chosen surveillance method when estimating the detection probability of a target species. Key management recommendations from this thesis are outlined below.

A. Develop best practice guidelines. Chapters 3 & 4 provide information on the design elements that influence detection and incorrect detection. Using information on survey design elements that influence detection and incorrect detection to guide removal and detection surveys provides an informative framework on which to base on-going management. Chapter 5 shows evidence that optimization can help design surveys that increase the overall number of targets found.

B. Monitor and adapt to changes for the effectiveness of management programs. This thesis highlights the need to monitor all aspects of control and eradication including search effort, species characteristics (e.g. size) and observer characteristics that can be used to understand future incursion events. This information could then be used to develop tools and make decisions in a more objective and informed manner. Incorporating uncertainty into informed decisions is a fundamental aspect of sound management practice. The Bayesian methods used in this thesis allows of

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updating of models as future data become available. In Chapter 2, catch-effort models are most useful when integrated with costs and when rigorously accounting for detectability and population dynamics. The need to managers to consider cost and catch-effort together is paramount when developing efficient and successful management programs.

C. Support agency-led programs with an integrated management framework between stakeholders. When resources are limited, volunteer observers can assist agencies to rapidly respond to A. amurensis incursions. This thesis examined the abilities of observers with various backgrounds and skills to undertake marine invasive species. When resources are limited, these observers have the ability to rapidly respond to A. amurensis incursions. Developing resources and opportunities where stakeholders can be utilised to monitor and removal marine invasive species is critical to long-term management.

6.4 Future research directions

As this thesis research investigates a spreading marine invasive in the broad context of empirically quantifying the post-border management phases of search and population control, it provides some insights and directions for future research in this field. More specifically, as this research attempted to develop and validate the measures of quantifying search and control data for a marine invasive, this provides the grounds to instigate many research avenues. Below, I make several suggestions are made and outlined for further research in this area of marine invasive management.

First, the study of catch–effort quantification and eradication feasibility (Chapter 2) could be improved by including costs and benefits. Using a framework that incorporates costs into the feasibility of eradication and control allows for a more practical evaluation of management campaigns to maximise the often-limited

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resources available (Regan et al. 2006, Cacho and Hester 2011, Rout et al. 2014). Therefore, an appealing future research direction could compare and contrast the findings using cost constraints on eradication.

Second, from the imperfect detectability perspective (Chapter 3 & 4), observer experience had little effect on improving the probability of detection. This research used field data to validate the model performance and predictions. However, only a limited suite of observer characteristics was used. An observer’s ability might not be limited to physically carrying out a survey in the field. Future surveys should consider using video training as a tool to educate observers and test skill level of observers. Simulated surveys using real video transects may aid in training the observer. Further, as this study has the potential for an extended approach to observer performance testing, using other species with different visual characteristics is possible.

Third, as research into testing optimal search theory and environmental decision theory (Chapter 5) is novel, there is scope for further innovation. This could include introducing more information on the detection rate function assessment. For example, using a Weibull distribution for time to detection compared to an exponential distribution that was used in this study.

Fourth, this research focused on one species in terms of eradication, detectability and search effort allocation, but multi-species surveys are commonly used in assessing marine invasive species abundance and occupancy, while this research focused on eradication, detectability and search effort allocation for a single species. Therefore, future studies could consider the effect of multi-species effects to similar research questions. This might be appealing to compare whether there is any intrinsic differences between single species and multi-species survey design. For the purpose of managing marine invasive species, this is likely to be more realistic when conducting field surveys and monitoring for multiple species at a single site.

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Lastly, habitat suitability and distribution models are common tools developed to assist when managing populations (Elith and Leathwick 2009) and have been used in optimising resource allocation of invasive species (Hauser and McCarthy 2009). Using information on detectability and search uncertainty from this study to examine an indicative and prioritisation approach to resource allocation of a marine invasive may provide useful management information. Therefore, a separate examination of current the distribution and range of this and other marine invasive species could be a more robust approach for future research directions.

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142 Appendices

Appendices

A. Catch-Effort R Code

OpenBUGS code: Catch-Effort Model for assessment management decisions for Asterias amurensis

model { N[1] <- N0 - removed[1] p[1] <- 1 - exp(-lambda*effort[1]) # prob. of removing a seastar removed[1] ~ dbin(p[1], N0)

for (j in 2:n) { removed[j] ~ dbin(p[j], N[j-1]) #removed – binomial distrib. N[j] <- N[j-1] - removed[j] #population of seastars at event j p[j] <- 1 - exp(-lambda*effort[j])#prob. of removing 1 seastar fit[j] <- p[j] * N[j-1] }

u1 <- sum(removed[1:n]) N0 ~ dunif(u1, 100000) # initial population upper limit lambda ~ dunif(0, 10)

erad <- step(1 - N[n]) #prob. of all seastars removed

for (j in 1:n) { eradeffort[j] <- (-log(1/N[j]))/ lambda eradeffort_trunc[j] <- max(eradeffort[j], 0) #trunc. eradeffort } }

143

Appendices

B. Logistic Regression R Code

JAGS code: Multilevel Bayesian Logit Model for the detection bias of Asterias amurensis during management surveys

model <- function()

{ for (i in 1:1336) { logit(lambda[i]) <- min(100, max(-100, alpha + b[1]*size[i] + b[2]*cluster[i] + b[3]*mimicside[i] + b[4]*lsnorkel[i] + b[5]*snorkelexp[i] + b[6]*surveyexp[i] + b[7]*cover[i] + b[8]*density[i] + b[9]*depth[i] + b[10]*near[i] + b[11]*locationx[i] + b[12]*locationy[i] + b[13]*effort[i] + re_obs[observer[i]] + re_mim[mimic[i]] + re_trans[transect[i]]))

p[i] <- max(0.0000001, min(0.9999999999, 1 - exp(-lambda[i] * effort[i]))) detect[i] ~ dbern(p[i]) }

alpha ~ dnorm(0.0,0.001) for (i in 1:13) { b[i] ~ dnorm(0, .0001)}

sd_obs ~ dunif(0, 100) sd_mim ~ dunif(0, 100) sd_trans ~ dunif(0, 100)

for (o in 1:39) {re_obs[o] ~ dnorm(0, sd_obs^-2)} for (m in 1:171) {re_mim[m] ~ dnorm(0, sd_mim^-2)} for (t in 1:9) {re_trans[t] ~ dnorm(0, sd_trans^-2)}

}

144 Appendices

C. Example of survey experience questionnaire

145

Appendices

D. Example of project information form

146 Appendices

147

Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: Millers, Kimberley

Title: Quantifying search and control performance during marine invasive surveys: a case study from Asterias amurensis

Date: 2015

Persistent Link: http://hdl.handle.net/11343/91707

File Description: Quantifying search and control performance during marine invasive surveys: a case study from Asterias amurensis