Remote Islands Are Vulnerable to Non-Indigenous Species: Utilization of Data Analytics to Investigate

Remote islands are vulnerable to non-indigenous species: utilization of data analytics to investigate potential modes of introduction and pest interceptions Barbara Kachigunda This thesis is presented for the degree of Doctor of Philosophy College of Science, Health, Engineering and Education and Harry Butler Institute, Murdoch University Perth, Western Australia Australia DECLARATION I declare that this thesis is my own account of my research and contains as its main content work which has not been previously submitted for a degree at any tertiary educational institution. Barbara Kachigunda 10 August 2020 A note on formatting and style. This thesis comprises submitted manuscripts, each of which represents a chapter. This thesis has continuous pagination. Barbara Kachigunda i STATEMENT OF CONTRIBUTIONS The ideas, development and writing up of all the chapters in the thesis were the principal responsibility of myself, working within the Harry Butler Institute and the College of Science, Health, Engineering and Education under the supervision of Professor Simon McKirdy (Murdoch University), Dr. Grey Coupland (Murdoch University), Professor Kerrie Mengersen (University of Queensland), Dr. Devindri Perera (Murdoch University) and Mr. Johann van der Merwe (Chevron, Australia Pvt Ltd). The inclusion of co-authors acknowledges active collaboration in the final outcomes. Privacy and confidentiality clause Data is part of Chevron's on-going project. Chevron has the right on how the data and output should be used subsequently. ii ACKNOWLEDGEMENTS I am sincerely grateful to my principal supervisor Professor Simon McKirdy for accepting to mentor me and put together a team of expert supervisors who provided me with individual specific expert advice to make my research complete. Professor Kerrie Mengersen (Queensland University of Technology) and Dr. Devindri Perera (Murdoch University) for their statistical and programming expertise and Mr Johann van der Merwe (Chevron, Australia, Pty Ltd) for providing the opportunity to work with a real-life data set and contextualizing the data into scientific hypotheses content. Dr Grey Coupland walked with me both academically and as a mentor, more than her call of duty to ensure my success. She provided me with encouragement, inspiration, feedback and sometimes stretched me beyond what I thought I was capable of, all for my betterment. She always reminded me to live and enjoy the moment. Professor McKirdy and Dr. Coupland opened opportunities for me, quietly gave me wings to fly and for that I am grateful, you made me a better person. I will forever be indebted to my family and friends who have supported me through this journey culminating in this PhD thesis. They inspired me and stood by me when times were tough, and always spurred me to do my best. Thank you. Special mention and acknowledgement of Professor Shashi Sharma for opening the opportunity to do this programme and Mrs. Wynette Francis for being a good friend and colleague. You built me from a self-doubting person to a courageous and determined scholar/ researcher. I wish to acknowledge Cameron McCain for her programming expertise and Dr. Justin McDonald for sound scientific advice. I also wish to acknowledge Professor Yonglin Ren as my supervisory chair throughout my study period. I wish to acknowledge Mr. Johann van der Merwe and Ms. Jenna van Niekerk for contextualizing the data into scientific hypotheses. iii I am thankful to Chevron Australia Pty Ltd for allowing me access to this invaluable data for my thesis. I wish to thank Murdoch University and specifically the Harry Butler Institute for the support throughout my research period. I am grateful to the Australian Government for the financial support through the Australian Postgraduate Award and the Murdoch Conference Travel Award which enabled me to travel both locally and abroad for research conferences. iv ABSTRACT Biosecurity in Australia and globally is based on understanding and protection of our national health, economy, industries, and environment from the negative effects associated with invasive pests and pathogens. The biosecurity continuum includes pre-border preparedness, border protection and post-border management, eradication, and control. The biosecurity system in Australia aims to manage risks and reduce the likelihood and adverse consequences of pest and disease incursions on human, animal and plant health, the environment, and the economy. To identify biosecurity risks and solve pertinent issues in biosecurity, analysts must gather and collate information for multiple factors and from a variety of sources in areas including agriculture, the environment and public health. The amount and complexity of biosecurity data have exposed the limitations in traditional statistical methodologies in addressing issues in biosecurity management. Biosecurity surveillance data is challenging in terms of non-normality, over- dispersion and typically zero-inflated. This type of data follows a natural process rather than a pre- specified process or distribution models, and often contains a large proportion of zeros. Application of appropriate statistical models to analyse these unique data sets is essential to effective biosecurity decision-making. The data used throughout this thesis were typically characteristic of biosecurity data, containing a large proportion of zeros, non-normality, and over- dispersion. Data used were collected as part of a biosecurity program implemented on Barrow Island, a remote island off the western Australian coastline, prior to and during the development of an industrial project on the island. In the following research, the first step encompassed evaluation of a range of candidate statistical models for describing biosecurity border and post-border detection of terrestrial non-indigenous species. v The dataset was fitted with a variety of models including lognormal linear model, Poisson and negative binomial generalized linear models, zero-inflated model, a three-component mixture mode and a clustering analysis approach. A clustering analysis approach was adopted using a generalization of the popular k-means algorithm appropriate for mixed-type data. The analysis approach involved determination of the most appropriate number of clusters using just the numerical data, then subsequently including covariates to the clustering. Based on the counts alone, three clusters gave an acceptable fit and provided information about the underlying data characteristics. Incorporation of covariates into the model suggested four distinct clusters dominated by physical location and type of detection. Though the three-component log-normal mixture model provided detailed insight into the distribution of the data by dividing the data according to their distinct characteristic of numerical ordering, the clustering model was the preferred approach for this study. Availability of more relevant data would greatly improve the model. Broader use of cluster models in biosecurity data is recommended, with testing of these models on more datasets to validate the model choice and identify important explanatory variables. Investigation of the diverse routes by which non-indigenous species can be introduced is also of key importance to biosecurity. A gap in many introductory pathway studies is the limited consideration given to multiple introduction pathways occurring simultaneously. Multiple pathways of non-indigenous species introduction to Barrow Island were investigated and fifteen potential modes of introduction were identified in association with importing location and personnel required for the project. Three-way management prioritisation using boosted regression modelling to determine the most important factors influencing the detection of non-indigenous species at the biosecurity border was assessed. Factors considered in detecting non-indigenous species included potential modes of introduction, detection type, border inspection point (physical location on Barrow Island), phase of industrial development, year, and month of detection, of vi which detection type, border inspection point and potential modes of introductions were key factors. Cargo vessel and inward bound passenger numbers peaked during the construction period and were associated with an increase in the number of live non-indigenous species detections. Exposed potential modes of introductions (e.g. flat racks and vessel topsides) contained a more diverse species assemblage, while potential modes of introductions associated with human habitation and activity had the highest likelihood of introducing live non-indigenous species. The nature of these potential modes of introductions potentially allowed non-indigenous species habitation of niche areas and/or provided a suitable food supply. Invertebrates comprised 73% of the detections, with 43% live non-indigenous species. Structures such as landings and jetties were recorded as invasion hotspots, consistent with being the first point of entry for arriving vessels. Human-inhabited environments reported abundant commensal non-indigenous species. Our study indicates that biosecurity surveillance programs need to prioritise management of specific species, potential modes of introductions, and sensitive and susceptible sites to target potential invasions. Biosecurity managers should prioritise potential modes of introductions with the highest likelihood of live non-indigenous species detection based on specific potential modes of introductions characteristics, including

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