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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 i 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 iii 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: v 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 vii 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 ...................................................................................................................
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