Modelling Establishment Risk for Non-Indigenous Species Using
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Modelling establishment risk for non-indigenous species using aquarium fish as a case study Lidia Della Venezia Department of Biology McGill University Montreal, Quebec, Canada June 2019 A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Doctor of Philosophy in Biology © Lidia Della Venezia, 2019 Table of contents Dedication . v Acknowledgements . vi Thesis abstract . vii Résumé . ix List of tables . xii List of figures . xv Preface . xviii Thesis format and style . xviii Contributions of co-authors . xix Original contributions to knowledge . xx General introduction . 1 Introduction . 1 Risk assessment and risk management . 2 Non-indigenous species establishment . 4 Missing data in ecological datasets . 7 Methodological approach . 9 Thesis outline . 10 References . 12 Chapter 1: The rich get richer: invasion risk across North America from the aquarium pathway under climate change . 22 1.1 Abstract . 23 1.2 Introduction . 25 1.3 Methods . 28 1.3.1 Model formulation . 28 1.3.2 Data and variable choice . 29 ii 1.3.3 Model fitting . 33 1.4 Results . 34 1.5 Discussion . 38 1.6 Acknowledgements . 43 References . 44 Connecting statement . 57 Chapter 2: Guiding rapid response to non-indigenous aquarium fish: identifying risk factors for persistent versus casual establishment . 58 2.1 Abstract . 59 2.2 Introduction . 61 2.3 Methods . 64 2.3.1 Variable choice . 66 2.3.2 Model fitting . 67 2.3.3 Multiplicative risk factors . 68 2.4 Results . 70 2.4.1 Multiplicative risk factors . 70 2.4.2 Re-evaluating "risky" species in terms of persistence . 72 2.4.3 Comparing establishment sub-stages: casual versus persistent . 73 2.5 Discussion . 74 2.6 Acknowledgements . 78 References . 79 Connecting statement . 92 Chapter 3: Filling in FishBase: a more powerful approach to the imputation of missing trait data . 93 3.1 Abstract . 94 3.2 Introduction . 96 3.3 Methods . 100 3.3.1 Trait data . 100 3.3.2 Comparison of existing imputation methods . 101 3.3.3 Novel imputation protocol: CRU . 102 iii 3.3.4 Model averaging and gap-filling . 107 3.3.5 Validation procedure . 107 3.4 Results . 109 3.4.1 Performance of the imputation models . 109 3.4.2 Validation of the uncertainty estimates . 110 3.4.3 Filling in FishBase . 111 3.5 Discussion . 112 3.5.1 Model comparison and ensemble imputation . 112 3.5.2 Uncertainty estimation . 113 3.5.3 Caveats . 115 3.6 Conclusions . 116 3.7 Acknowledgements . 117 References . 118 General conclusion . 132 References . 135 Appendices . 136 Appendix A: Supplementary material for Chapter 1 . 137 Appendix B: Supplementary material for Chapter 2 . 141 Appendix B.1 . 142 Appendix B.2 . 147 Appendix B.3 . 150 Appendix B.4 . 153 Appendix B.5 . 154 Appendix C: Supplementary material for Chapter 3 . 156 iv Dedication I dedicate this thesis to my entire family, but especially to my parents, Ketty e Livio, you are everything I could ever ask for. To my sister Claudia, my brother Giulio, my nephews Chiara e Lorenzo, and my dear nonna Noemi, you constantly fill my heart with love and joy. To my best friends Elisabetta, Silvia, Mery and Marina, you always support me and are pure happiness. And to Paolo, your smile brings the best out of me. v Acknowledgements I would like to thank my supervisor Prof. Brian Leung, without whom my work would have not been possible and who has allowed me to grow both academically and personally. I also thank Prof. Gregor Fussmann and Prof. Frédéric Guichard for serving on my supervisory committee and for their guidance, particularly during major direction changes in my research. I am very grateful to my co-author Jason Samson, for providing insightful discussions and strong encouragement. Many thanks are due to my lab mates, Johanna Bradie, Corey Chivers, Kristina Enciso, Alyssa Gervais, Emma Hudgins, Dat Nguyen, Victoria Reed, Natalie Richards, Anthony Sardain, Dylan Schneider, and Shriram Varadarajan, for their consistent support and help. Further, I acknowledge the Canadian Aquatic Invasive Species Network, the Fonds vert of the Quebec government, McGill University, the National Sciences and Engineering Research Council, and the Quebec Centre for Biodiversity Science for providing the necessary funding to pursue my PhD research. I am especially grateful to my closest friends in Montreal, who have seen me through some of the happiest and toughest times of my life. In chronological order, I thank Chiara, Jay, Enrico, Veronica, Franco, Luca, Estelle, Gianni, Fabrizio, Nunzio, Alessandra, Simone, Salvatore, Raul, Andrea and Anna. Merci à mes francophones préférés, Céline, Andréa, Mathilde et Mattia. Finally, my appreciation goes to Mundo Lingo for having been a second home and having allowed me to meet some of the most interesting and charming people I know, the Jarry pool for being my aquatic oasis during the summer, YouTube for always providing the perfect work soundtrack, and my kickboxing instructors and partners for having prevented several breakdowns during the most challenging months of my PhD. vi Thesis abstract Invasive species cause substantial ecological and economic damages. While major modelling improvements have been made in the last decades, predictions are primarily based on single species analyses, look at a single factor at a time (i.e. environmental conditions, species traits, and propagule pressure, individually), and consider quite broad stages (e.g., establishment), which may be usefully resolved into smaller sub-stages. Further, although models arguably provide the most coherent, sophisticated predictions, most quantitative models remain unused in policy-oriented invasive species risk assessments, which largely rely on expert- opinion and simple summation across individual factors believed to influence invasions (i.e. scoring-based approaches). Of course, for quantitative analyses, data limitations typically exist. To allow quantitative methods to be even more powerful and broadly usable, approaches are needed to alleviate those limitations and optimally use the available information. In this thesis, I advance the field of invasion biology, contributing to each of the three issues identified above. In Chapter 1, I consider the three main predictors of biological invasions: environment, propagule pressure and species traits, and I integrate these into a coherent multispecies, geographically explicit model. I show the importance of their combination, and forecast that, for the aquarium fish invasion pathway, "the rich get richer" in that the most vulnerable current locations are likely to suffer the greatest increase in new invasions in the future. By employing an integrative approach and a multispecies perspective, this work provides support to decision making for resource managers and policy makers, and a better understanding of non-indigenous species establishment. In Chapter 2, I recognize that while prevention may be ideal, it is not always achievable, vii and prioritizing rapid responses is necessary for the effective management of potentially harmful non-indigenous species. To address issues of rapid response, we need to more finely resolve the establishment phase of biological invasions, and determine what happens after a new species has been detected (i.e. whether long-term persistence occurs). In this chapter, I have three objectives: 1) I separate casual (i.e. temporary) establishment and persistence (i.e. lack of subsequent extirpation), using the framework developed in Chapter 1 to generate a multispecies, geographically explicit model to prioritize rapid response efforts; 2) recognizing that mathematical models often remain unused in policy, I convert the model parameters into simple multiplicative risk factors, to facilitate broader adoption outside the modelling field; 3) from a fundamental perspective, I show that propagule pressure is important for casual establishment, but does not help predict subsequent persistence. Species traits are the most important group of predictors for casual establishment, while the environment is most relevant for persistence. In Chapter 3, I develop an approach to more powerfully use available data (focusing on species traits). From Chapters 1 and 2, it became apparent that about 80% of trait values were missing in the trait database used (FishBase). Thus, I develop a novel, fast, simple method for imputation, based on trait Correlation, taxonomic Relatedness and Uncertainty minimization (CRU). I compare it against existing cutting edge approaches, and show that CRU was the most accurate. Additionally, I consider and demonstrate that including CRU into an ensemble model combining existing techniques (Phylopars and missForests) yields even greater accuracy. This work fills in a substantial subset of FishBase, but also provides an approach for the imputation of other trait databases. Overall, this thesis advances understanding of ecological processes and informs environmental management while using the best available information. viii Résumé Les espèces envahissantes causent des dommages écologiques et économiques importants. Bien que des améliorations majeures aient été apportées à la modélisation au cours des dernières décennies, les prédictions reposent principalement sur des analyses par espèce, examinent un facteur à la fois (conditions environnementales, caractéristiques des espèces et pression des propagules, individuellement) et prennent en compte des étapes assez larges (par exemple, l'établissement), qui peuvent être utilement décomposées en sous-étapes plus petites. En outre,