Arts and Sciences, Irving K. Barber School of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Muselle, Brian Thayer
Master of Science - MSc
University of British Columbia
The apple maggot, Rhagoletis pomonella, is an economically important native pest of pome fruits in the US and Canada. BCâs Okanagan Valley is currently the only significant commercial apple-producing region in North America that remains free of its impact. This is likely to change soon, as R. pomonella is well established within certain counties in adjacent Washington State. Extensive sampling and control efforts have been undertaken since the pestâs arrival in Washington in the late 1970s. Data from American agencies provided a foundation for spatially explicit agent-based models (ABM), which I developed and used to evaluate fly movement and establishment under different management scenarios within comparable regions in the Okanagan. ABMs enable simultaneous manipulation of agent behaviour (here, insect dispersal and human management practices) and other factors within a spatially explicit landscape context, in ways that would otherwise be infeasible to explore in the field. My objective was to use ABM simulations and their outcomes to inform future management and survey efforts in advance of R. pomonella establishing in the Okanagan. I first constructed a validation model situated in Washington State landscapes, informed by historical data from state agencies, and then a predictive model for Okanagan landscapes where R. pomonella has yet to establish. Using ABMs to simulate pest invasion in a new area is, to my knowledge, novel. I calibrated my model parameters using information from the literature and data describing historical spread in Washington. The Washington-based validation model yielded a 52.8 â 82.0% agreement for the locations of apple maggot infestation and a 52.1 â 81.9% agreement for the locations of apple maggot infestation severity. The predictive simulations within Okanagan landscapes yielded maps expressing risk of infestation under various management and spread scenarios. They showed the most important management strategy for reducing apple maggot infestation was public education. Management strategies aside, the simulations suggested that the Okanagan regionsâ environment is not especially suitable for apple maggot establishment, though this finding should be considered preliminary. My research highlights key data gaps, and demonstrates the utility of ABMs for pest management scenario modeling.
Agent-based scenario models of invasion and movement of Rhagoletis pomonella (Diptera: Tephritidae) within the southern interior of British Columbia by Brian Thayer Muselle B.Sc. [Hons], The University of British Columbia Okanagan, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The College of Graduate Studies (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2019 © Brian Thayer Muselle, 2019 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Agent-based scenario models of invasion and movement of Rhagoletis pomonella (Diptera: Tephritidae) within the southern interior of British Columbia submitted by Brian Thayer Muselle in partial fulfillment of the requirements of the degree of Master of Science . Dr. Jason Pither, Biology Department, Irving K. Barber School of Arts and Sciences Co-Supervisor Dr. Howard Thistlewood, Agriculture & Agri-Food Canada Co-Supervisor Dr. Lael Parrott, Biology Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Robert Lalonde, Biology Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Sylvie Desjardins, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber School of Arts and Sciences University Examiner iii Abstract The apple maggot, Rhagoletis pomonella, is an economically important native pest of pome fruits in the US and Canada. BCâs Okanagan Valley is currently the only significant commercial apple-producing region in North America that remains free of its impact. This is likely to change soon, as R. pomonella is well established within certain counties in adjacent Washington State. Extensive sampling and control efforts have been undertaken since the pestâs arrival in Washington in the late 1970s. Data from American agencies provided a foundation for spatially explicit agent-based models (ABM), which I developed and used to evaluate fly movement and establishment under different management scenarios within comparable regions in the Okanagan. ABMs enable simultaneous manipulation of agent behaviour (here, insect dispersal and human management practices) and other factors within a spatially explicit landscape context, in ways that would otherwise be infeasible to explore in the field. My objective was to use ABM simulations and their outcomes to inform future management and survey efforts in advance of R. pomonella establishing in the Okanagan. I first constructed a validation model situated in Washington State landscapes, informed by historical data from state agencies, and then a predictive model for Okanagan landscapes where R. pomonella has yet to establish. Using ABMs to simulate pest invasion in a new area is, to my knowledge, novel. I calibrated my model parameters using information from the literature and data describing historical spread in Washington. The Washington-based validation model yielded a 52.8 â 82.0% agreement for the locations of apple maggot infestation and a 52.1 â 81.9% agreement for the locations of apple maggot infestation severity. The predictive simulations within Okanagan landscapes yielded maps expressing risk of infestation under various management and spread scenarios. They showed the most important management strategy for reducing apple maggot infestation was public education. Management strategies aside, the simulations suggested that the Okanagan regionsâ environment is not especially suitable for apple maggot establishment, though this finding should be considered preliminary. My research highlights key data gaps, and demonstrates the utility of ABMs for pest management scenario modeling. iv Key Words [apple maggot, Rhagoletis pomonella, agent-based modeling, ABM, landscape ecology, British Columbia, Okanagan, Washington State, hawthorn, Crataegus, NetLogo, spread, invasive, apple, University of British Columbia, historical, predictive, validation] v Lay Summary I present a model simulating the spread of the apple maggot fruit fly within central Washington State, and its potential for application to the Okanagan Valley, BC. The model combines apple maggot biology, dispersal, and reproduction, landscape and climate features, and management practices, to generate local and regional scale outcomes that identify areas most at risk. My results suggest that management, particularly public education, plays an important role in slowing the spread of the pest, but that the Okanagan Valley is not a well-suited environment for large population sizes. This model could be further expanded and improved and be a powerful tool for the appropriate authorities to assess management choices. The model illustrates the potential of agent-based models as an application for modeling of invasive orchard insect pest species. vi Preface This research was conducted at the University of British Columbia, Okanagan campus, in both the Biodiversity and Landscape Ecology Research Facility and the Complex Environmental Research Laboratory, and at the Summerland Research and Development Centre of Agriculture and Agri-Food Canada. It was co-supervised by Dr. Jason Pither and Dr. Howard Thistlewood, with the assistance of Dr. Lael Parrott for agent-based modeling. I was responsible for the entirety of the work presented in this thesis: development of the computer models, performance of experiments, interpretation and analysis of data, creating of figures and tables, and writing of the thesis. A version of chapter 3 and 4 is a manuscript in preparation for submission to PeerJ and the Journal of Artificial Societies and Social Simulation (JASSS). A version of Appendix C is a biology note in preparation for submission to the Entomological Society of BC. vii Table of Contents Abstract ......................................................................................................................................... iii Key Words .................................................................................................................................... iv Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ ix List of Figures ............................................................................................................................... xi List of Abbreviations ................................................................................................................. xiii Acknowledgments ...................................................................................................................... xiv Introduction ................................................................................................................................... 1 I Literature Review ....................................................................................................................... 5 I.I SYNOPSIS ............................................................................................................................. 5 I.II INVASION ........................................................................................................................... 5 I.III THE APPLE MAGGOT, RHAGOLETIS POMONELLA .................................................... 6 I.III.I Taxonomy ...................................................................................................................... 6 I.III.II Biology and Life Cycle ................................................................................................ 7 I.III.III Hosts ........................................................................................................................... 9 I.III.IV Apple Susceptibility ................................................................................................. 10 I.III.V Natural Enemies ......................................................................................................... 11 I.III.VI Dispersal ................................................................................................................... 11 I.IV MANAGEMENT AND SPREAD HISTORY OF APPLE MAGGOT ............................. 14 I.IV.I Invasion in the West .................................................................................................... 14 I.IV.II Management in Washington State ............................................................................. 16 I.IV.III Impending Invasion in the Okanagan Valley ........................................................... 18 I.IV.IV Management Strategies ............................................................................................ 21 I.V MODELING AND ANALYZING THE SPREAD OF APPLE MAGGOT ....................... 23 I.V.I Spread Pattern Analysis of Apple Maggot ................................................................... 23 I.V.II Agent-Based Modeling ............................................................................................... 26 II Methods and Materials for the Model .................................................................................. 30 II.I SYNOPSIS .......................................................................................................................... 30 II.II ASSEMBLY OF AVAILABLE DATA FOR THE MODEL ............................................ 30 II.II.I Apple Maggot Data ..................................................................................................... 30 II.II.II Host Data ................................................................................................................... 30 II.II.III Geospatial Data ......................................................................................................... 33 II.III BUILDING OF THE MODEL ......................................................................................... 35 II.III.I Statement of Importance ............................................................................................ 35 II.III.II Focal Areas ............................................................................................................... 36 II.III.III Experiment Treatments ........................................................................................... 41 II.III.IV Netlogo and BehaviorSpace .................................................................................... 41 viii II.III.V Validation and Statistical Methods ........................................................................... 43 II.IV ASSUMPTIONS AND LIMITATIONS .......................................................................... 44 III Development of the Model Using Washington Data .......................................................... 46 III.I SYNOPSIS ........................................................................................................................ 46 III.II MODELING PROCESS OF THE VALIDATION MODEL ........................................... 46 III.II.I Validation Process ..................................................................................................... 47 III.III PARAMETERIZATION OF SPREAD RATE ............................................................... 48 III.III.I Determining Spread Rate with Real-World Data ..................................................... 48 III.III.II Determining Spread Rate using a Sensitivity Analysis ........................................... 50 III.IV CALIBRATION OF SPREAD PATTERN USING POM .............................................. 51 III.V RESULTS OF THE VALIDATION MODEL ................................................................. 51 III.V.I General Model Outcomes .......................................................................................... 52 III.V.II Effects of Management Configurations on Spread .................................................. 54 III.V.III Validation Metrics ................................................................................................... 56 III.VI DISCUSSION OF THE VALIDATION MODEL .......................................................... 64 IV Implementation of the Model in the Okanagan Valley ...................................................... 67 IV.I SYNOPSIS ........................................................................................................................ 67 IV.II MODELING PROCESS OF THE PREDICTIVE MODEL ............................................. 67 IV.III RESULTS OF THE PREDICTIVE MODEL .................................................................. 67 IV.III.I General Model Outcomes ......................................................................................... 68 IV.III.II Effects of Management Configurations on Predictive Spread ................................ 76 IV.IV DISCUSSION OF THE PREDICTIVE MODEL ........................................................... 79 V General Discussion and Conclusion ...................................................................................... 84 V.I THESIS REFLECTION ...................................................................................................... 84 V.II SIGNIFICANCE ............................................................................................................... 85 V.II.I Climate Change ........................................................................................................... 86 V.II.II Importance of the Model to the Non-Okanagan Reader ............................................ 87 V.III RECOMMENDATIONS BASED ON THE MODEL ..................................................... 87 V.IV FUTURE WORK ............................................................................................................. 88 V.V CONCLUSION ................................................................................................................. 89 Bibliography ................................................................................................................................ 91 Host Plant Occurrence Records .......................................................................................... 102 Appendices ................................................................................................................................. 103 APPENDIX A: OVERVIEW, DESIGN CONCEPTS, AND DETAILS ................................ 103 APPENDIX B: SPATIALLY EXPLICIT EXAMINATION OF POPULATION SPREAD IN RHAGOLETIS POMONELLA IN CENTRAL WASHINGTON STATE ............................... 131 APPENDIX C: CRATAEGUS SPP. DISTRIBUTION ANALYSIS IN THE OKANAGAN VALLEY ................................................................................................................................. 144 APPENDIX D: R CODE AND NETLOGO DETAILS .......................................................... 152 ix List of Tables Table 2.1. Host data points collected for each validation model extent. .............................................. 31 Table 2.2. Host data points collected for each predictive model extent. .............................................. 31 Table 2.3. GIS files and other dataset files collected for each validation model extent. ..................... 34 Table 2.4. GIS files and other dataset files collected for each predictive model extent. ..................... 35 Table 3.2. Comparison of simulation attributes for both non-management configurations and top management configurations for the validation small-extent model. .................................................... 55 Table 3.3. Comparison of simulation attributes for both non-management configurations and top management configurations for the validation large-extent model. ..................................................... 55 Table 3.4. Binary confusion matrix presenting the spatial agreement between apple maggot infestation simulation and real-world apple maggot infestation observed in Wenatchee, Washington. ............................................................................................................................................... 57 Table 3.5 Binary confusion matrix presenting the spatial agreement between apple maggot infestation simulation and real-world apple maggot infestation observed in central Washington State. ........................................................................................................................................................... 57 Table 3.6. Confusion matrix presenting the spatial agreement between severity of apple maggot infestation simulation and severity of real-world apple maggot infestation observed in Wenatchee, Washington. ............................................................................................................................................... 58 Table 3.7. Confusion matrix presenting the spatial agreement between severity of apple maggot infestation simulation and severity of real-world apple maggot infestation observed in central Washington State.. ..................................................................................................................................... 59 Table 4.1. The best fit management scenarios for the small-extent model and all three spread scenarios.. ................................................................................................................................................... 68 Table 4.2. The best fit management scenarios for the large-extent model and all three spread scenarios.. ................................................................................................................................................... 68 Table 4.3. Comparison of simulation attributes for both âcontrolsâ and top management strategy combinations for the predictive small-extent model. ............................................................................. 77 Table 4.4. Comparison of simulation attributes for both âcontrolsâ and top management strategy combinations for the predictive large-extent model. .............................................................................. 77 Table 4.5. Significance of simulation attributes for top management strategy combinations for small-extent predictive model using an ANOVA and a Tukey-Kramer test. ...................................... 78 Table 4.6. Significance of simulation attributes for top management strategy combinations for large-extent predictive model using an ANOVA and a Tukey-Kramer test. ...................................... 78 Table A1. Output data and their relevance. ......................................................................................... 119 Table A2. List of patch-specific attributes and descriptions. .............................................................. 121 Table A3. List of global attributes and descriptions. ........................................................................... 124 Table A4. Model interface details. ......................................................................................................... 127 Table B1. One sample t-test comparison of the MCP areas. ............................................................... 135 Table B2. Matched paired t-tests analysis for change in isopleth area from year to year (d.f. = 8).139 x Table B3. Spread analysis table for square-root area regression. ...................................................... 140 Table B4a. Spread analysis table for 1-fly threshold distance regression. ......................................... 142 Table B4b. Spread analysis table for 2-fly threshold distance regression. ........................................ 142 Table B4c. Spread analysis table for 3-fly threshold distance regression. ......................................... 143 Table B5. Estimates of apple maggot spread from two analytical methods when based upon point data (km/year).. ....................................................................................................................................... 143 Table C1. Hawthorn characteristics collected during scouting sessions. ........................................... 145 Table C2. The HSI model statistics. ....................................................................................................... 151 Table C3. Statistics on the hawthorn occurrence records fit to the HSI model. ............................... 151 xi List of Figures Figure 2.1. Area of focus for the model. Includes both validation model focus areas (WA) and predictive model focus areas (BC). .......................................................................................................... 37 Figure 2.2. Area of focus for the small-extent validation model, Wenatchee, WA, USA. .................. 38 Figure 2.3. Study area for the small-extent predictive model, Kelowna, BC, Canada. ...................... 39 Figure 2.4. Area of focus for the large-extent validation model, central Washington state, USA. .... 40 Figure 2.5. Study area for the large-extent predictive model, Okanagan Valley, BC, Canada. ........ 41 Figure 3.1. Final maps of the simulation for Wenatchee.. ..................................................................... 53 Figure 3.2. Final maps of the simulation for central Washington State. ............................................. 54 Figure 3.3. Proportional representation of severity of infestation for the small-extent, Wenatchee. 60 Figure 3.4. Proportional representation of severity of infestation for the large-extent, central Washington State.. ..................................................................................................................................... 61 Figure 3.5. Infestation extent represented by distance in km between the initial point of infestation in Wenatchee to the infested hosts obtained (A-C) by the simulation (A = extreme spread scenario, B = moderate spread scenario, and C = casual spread scenario) and (D) in the real world. ............. 63 Figure 3.6. Infestation extent represented by distance in km between the initial point of infestation in central Washington state to the infested hosts obtained (A-C) by the simulation (A = extreme spread scenario, B = moderate spread scenario, and C = casual spread scenario) and (D) in the real world. .......................................................................................................................................................... 64 Figure 4.1. Patch infestation frequency of the small-extent simulation.. ............................................. 69 Figure 4.2. Patch infestation frequency of the large-extent simulation. ............................................... 70 Figure 4.3. Host point infestation frequency of the small-extent simulation.. ..................................... 71 Figure 4.4. Host point infestation frequency of the large-extent simulation.. ..................................... 72 Figure 4.5. Host point infestation frequency of the small-extent simulation with respect to host sites being infested in both âcontrolâ and âmanagedâ simulations.. ............................................................... 74 Figure 4.6. Host point infestation frequency of the large-extent simulation with respect to host sites being infested in both âcontrolâ and âmanagedâ simulations.. ............................................................... 75 Figure A1. Model flow diagram. ............................................................................................................ 109 Figure A2. Model interface. .................................................................................................................... 130 Figure B1. Annual spatial extent of population spread for R. pomonella in host trees in central Washington State between 2003 to 2012 as determined by MCP analysis. ....................................... 134 Figure B2. Apple maggot MCP area based on locations of infested host trees by year from 2003 to 2012 for central Washington State using 100%, 95%, and 90% MCP calculations. ........................ 135 Figure B3. Annual spatial extent of population spread for R. pomonella in host trees in central Washington State between 2003 to 2012 as determined by kernel density analysis. ........................ 138 Figure B4. Graphical representation of the apple maggot infestation area of kernel isopleths (logged) over time for central Washington State between 2003 and 2012. ........................................ 138 xii Figure B5. Graphical representation of the change in the geographical centroids of the annual isopleths results from the kernel density analysis. ............................................................................... 139 Figure B6. Spread analysis graph for square-root area regression.. .................................................. 140 Figure B7. Spread analysis graph for distance regression. ................................................................. 141 Figure C1. Hawthorn sites collected by the Thistlewood lab throughout the Okanagan Valley 2015-2017. .......................................................................................................................................................... 146 Figure C2. HSI model output using the habitat suitability index formula. ....................................... 150 Figure C3. The Maxent output model (with all variables). ................................................................. 151 xiii List of Abbreviations Agent-Based Model (ABM) Agriculture and Agri-Food Canada (AAFC) British Columbia (BC) Canadian Food Inspection Agency (CFIA) Coefficent of Variation (CV) Habitat Suitability Index (HSI) MaxENT (Maximum Entropy) Minimum Convex Polygon (MCP) Okanagan-Kootenay Sterile Insect Release (OKSIR) Okanagan-Similkaneen-Shuswap-Thompson Valleys (Okanagan Valley) Overview, Design concepts, and Details (ODD protocol) Pattern-Oriented Model (POM) Summerland Research and Development Centre (SuRDC) Species Distribution Model (SDM) Washington State Department of Agriculture (WSDA) xiv Acknowledgments This research was funded in part by the Natural Sciences and Engineering Research Council, the University of British Columbia, and by the Research Affiliate Program of Agriculture and Agri-Food Canada, under Dr. Thistlewood at Summerland Research and Development Centre. I would like to acknowledge the support of the UBC Okanagan Internal Research Grant Program for their contributions of the Graduate Student Travel Grant towards my poster presentation at the International Congress of Entomology 2016 in Orlando, Florida. Thank you to the following people: Dr. Jason Pither [co-supervisor]; Dr. Howard Thistlewood [co-supervisor]; Dr. Lael Parrott and Dr. Robert Lalonde [committee members]; Meaghan Dinney, Maliya Cassels, Marco Sanelli, Jaime Chalissery, Brigitte Rozema [Summerland Research and Development Centre lab colleagues]; Michael Klaus [Washington State Department of Agriculture help/advice]; Dr. Wee Yee [United States Department of Agriculture help/advice]; Paul Couch [A.P.P.L.E. website help]; Dr. James Phipps and Peter Zika [hawthorn ID help]; and to my Nana and my wife Sarah Alexis. Thank you to the following organizations for providing data: The Global Biodiversity Information Facility (GBIF), the Consortium of Pacific Northwest Herbaria (PNWH), and the Canadian Museum of Nature (CM) for hawthorn occurrence records data in the Okanagan Valley; the Okanagan-Kootenay Sterile Insect Release (OKSIR) program for apple and crabapple data for the Okanagan Valley; the Canadian Food Inspection Agency (CFIA) for host data for the Okanagan Valley; the City of Kelowna for providing ornamental hawthorn locations in Kelowna; and the Washington State Department of Agriculture (WSDA) for providing the apple maggot trap survey data as well as professional opinion and suggestions throughout. 1 Introduction Tree-fruit crops in North America account for billions of dollars annually and serve global markets from Europe (United Kingdom) to Asia (Taiwan and Russia) to South America (Columbia) (Krissoff et al. 1997). Chief among tree-fruit production are apples, whose crop value in the US in 1996 was $1.7 billion (Krissoff et al. 1997). Washington is the largest producer of fresh apples in the United States, accounting for nearly 65-75% of the US fresh apple supply (Zhao et al. 2007; Perez and Plattner 2015). Specifically, central Washington State produces the majority of apples in the state, with a worth of US$1.4 billion in 2009 (Yee et al. 2012). In Canada, British Columbia (BC) accounted for 39.0% of all Canadian fruit sales in 2014. In 2013, BC exported $19 million worth of apples ($17 million worth of apples to the US alone). BCâs apple production is estimated to have a farm gate value of $42 million. The main contributor to apple production in BC (and Canada) is the Okanagan-Similkaneen-Shuswap-Thompson Valleys (Okanagan Valley) (Statistics Canada 2014; AAFC 2015; BCMA 2013b). Like any agricultural crop, tree fruits are vulnerable to a variety of pests, including weeds (approximately USD$32 billion in damages annually), mammals and birds (approximately USD$1.6 billion in damages annually), plant pathogens (approximately USD$33 billion in damages annually), and insect and mites (approximately USD$35 billion in damages annually). These pests may also introduce viruses and diseases that can further damage crops and increase the costs (Pimentel et al. 2000). The majority of these costs are associated with invasive pests. Annually, invasive agricultural pests cost the industry $140 billion in damages and management in the US alone (Pimentel et al. 2000; Gutierrez and Ponti 2013). Minimizing impacts of orchard pests is a constant challenge, and a variety of methods are being used to impede spread, including removing hosts, spraying host material, and educating the public (to minimize long-distance, human-mediated dispersal of pests). In heterogeneous landscapes where the crops are interspersed with alternative host plants, management is especially challenging. For example, knowing the distribution of potential host plants is a crucial part of management, specifically to avoid mass spread. One of the most efficient ways to contain or slow spread of an insect pest species is to target isolated satellite populations established via long-range movement events (Liebhold and Tobin 2008). Kovacs et al. (2011) estimated that reducing the number of new satellite populations of the emerald ash borer between 2005 and 2010 could have delayed 2 economic costs of $1.0 to $7.4 billion USD. This suggests that investment in protocols to prevent or slow expansion of new satellite populations of pest organisms, including invasive pests, is not only cost-effective, but important in the efficient management of pest organisms. The economical and biological impact of invasive pests warrant greater effort into understanding their spread dynamics (Hastings et al. 2005). Among the costliest pests of tree fruits are the fruit flies (reviewed by Suckling et al. 2016). Fruit flies belong to the family Tephritidae, which includes about 4200 species in 500 genera, and is one of the most economically important insect groups worldwide. Over 200 species in this family are known to attack commercially produced fruit worldwide, costing countries hundreds of millions of dollars through crop losses and pest control (Foote et al. 1993, pg. 1; White and Elson-Harris 1992, pg. 1). Suckling et al. (2016) looked at a subset of global fruit fly eradication programs and found a mean cost of $12 million USD. In addition to surveillance and quarantine, the most important aspects of the eradication programs included male destruction, protein bait sprays, fruit destruction, and sterile insect techniques. Success was primarily a result of early detection of small populations, with success declining as sizes of populations, area of infestation, and delay in detections increase (Dowell 1991; Suckling et al. 2016). An important group among the fruit flies are the genus Rhagoletis (Loew), a large group of small to medium flies that are black or yellowish-brown in colour with prominently banded wings (Foote et al. 1993, pg. 323). Unlike most genera in the family Tephritidae, the primary method to distinguish morphologically similar species in the genus Rhagoletis was by using the host and distribution data (White and Elson-Harris 1992, pg. 99). Today, morphometrics (Yee et al. 2011) and genetics (Jean et al. 2013) have shown serious promise. R. pomonella (Walsh) is among the most economically important tephritids native to the United States and southern Canada due to its highly destructive nature to commercially grown fruit, especially apples (Foote et al. 1993, pg. 360). More research has been conducted on R. pomonella than almost any other fruit fly (Foote et al. 1993, pg. 324). Due to its potential to spread, many import countries either impose strict regulations for imports from Washington State or ceased importing altogether, as the concern for apple maggot spread was deemed too high a risk (Krissoff et al. 1997). In 1984, it was estimated that the impact of the apple maggot to Washington Stateâs economy, primarily apple production, would be 71 to 93 million dollars 3 annually. This would include the loss of export markets, increased fruit load in domestic markets, and the loss of early season domestic markets (Brunner 1987). In this thesis, I used agent-based simulation models to explore the potential spread of R. pomonella in the Okanagan Valley. ABMs have been used before to model historical pest spread, and pest control and management (Ceddia et al. 2009; Phan et al. 2010; Rebaudo et al. 2011; Nguyen et al. 2011; Carrasco et al. 2012; Caplat et al. 2012; Guerreiro et al. 2013; Rebaudo and Dangles 2013; Anderson and DragiÄeviÄ 2015; Wang et al. 2015; Lux et al. 2016; Lux 2018). However, I am aware of only a couple of studies that have modeled invasive spread of an orchard insect pest within new territory (Lux et al. 2016; Lux 2018). Here I describe how I used an ABM to predict the potential spread of apple maggot in the Okanagan Valley of BC under different management scenarios. A practical objective was to inform management and survey efforts, which in turn aim to minimize the threat posed by the apple maggot to the region. A secondary practical objective was to use the ABM to help educate the public and raise awareness of serious agricultural and economical important pests (Rebaudo et al. 2011). My focal research questions were: How is the apple maggot likely to spread within the Okanagan Valley if and when it arrives and how do management strategies influence apple maggot spread? Addressing these questions using field experiments would be ideal approach, but would be unfeasible at relevant scales. Agent-based models (ABM) provide one means to understand the spread of orchard pests, and their managements using three approaches: demographic and dispersal processes, spatial structure, and decision processes. Respectively, these approaches can help identify what the key drivers of spread are (population dynamics), help determine the role of the spatial structure of the landscape on the invasive species (landscape processes) and help inform management structures and control measures (human management). Although each approach has been used before, very few studies have integrated all three aspects (Caplat, Coutts, and Buckley 2012). The following specific objectives were present in my research workflow, and form the bases for the thesis chapters (based on the ABM building process in Railsback and Grimm 2011): 4 1 â To conduct a literature review on the main topics of my thesis including apple maggot biology, management, and agent-based modeling. 2 â To compile the available data from Washington State for meaningful rates of spread and pattern of spread, and to collect, and add to, the data describing the distribution and abundance of potential host plants in the Okanagan Valley, specifically Crataegus spp., or hawthorn. These data are crucial to the success of the predictive model. 3 â To construct an ABM that aims to reproduce the pattern of spread observed between 2003 and 2012 within central Washington State. This serves as the validation model. 4 â To build a model that may predict potential spread within a representative landscape within the Okanagan Valley. This serves as the predictive model. Spread patterns are compared among alternative management strategies to evaluate and explore the influences that landscape structure, host plant distribution, and management may have on invasion patterns. 5 I Literature Review I.I SYNOPSIS The objective of this chapter is to conduct a literature review on the main topics of my thesis including apple maggot biology, management, and agent-based modeling. I.II INVASION Pest invasions can impose significant economic and environmental impacts by affecting agricultural practices and natural resources within managed landscapes (Rebaudo and Dangles 2013). Like other invasive species, fruit fly invasion can be divided into four phases: (i) introduction, where the fly departs from its native habitat and arrives in a new location as an alien; (ii) colonization, the newly arrived fly must undergo reproduction and complete at least one generation; (iii) naturalization, where a population slowly builds and with each generation comes better adapted to the local conditions; and (iv) spread, where the fly population has appropriate age structure and sex ratio that allows for rapid growth and spread to surrounding habitats (Liebhold and Tobin 2008; Aluja and Liedo 2013, pg.116). While natural, short-range movement events, such as simple diffusion, create a spread front, it is more likely that long-range movement events accelerate spread. These long-range movement events are often anthropogenic, are unpredictable relative to short-range movement events, and are the dominant mode of spread for many invasive species. This combination of short-range and long-range movements, or âstratified dispersalâ, is a major driver in the invasive species spread process (Liebhold and Tobin 2008). To limit the damage of an invasive pest species, immediate development of a management plan is required. This can be accomplished by using the current and potential distributions of the population, as well as potential dispersal vectors (Muirhead et al. 2006), see examples of experiences with the Queensland fruit fly, Bactrocera tryoni (Froggatt) (Meats and Edgerton 2008), the European cherry fruit fly, Rhagoletis cerasi (Linnaeus), and eastern American cherry fruit fly, Rhagoletis cingulate (Loew) (Johannesen et al. 2013), and the Western cherry fruit fly, Rhagoletis indifferens (Curran) (Kumar et al. 2014). Management strategies that 6 are common among each invasion phase include international quarantines and inspection (introduction phase), detection and eradication (colonization and naturalization phases), and domestic quarantines and barrier zones (spread phase) (Liebhold and Tobin 2008). I.III THE APPLE MAGGOT, RHAGOLETIS POMONELLA Native to eastern North America, the apple maggot was first described by B.D. Walsh in 1867 as Trypeta pomonella Walsh, but was noticeably destructive before this time (Dean and Chapman 1973; Brunner 1987; White and Elson-Harris 1992, pg. 375). By 1873, the apple maggot was already a well-established pest of New England orchards; over 100 years later, this pest is still a threat to orchards in the New England area, including south along the Atlantic coast to Florida, west to Texas, and south to Mexico (Dean and Chapman 1973). I.III.I Taxonomy The apple maggot belongs to the insect group known as the âfruit fliesâ due to their infestation of fruit during the larval âmaggotâ stage; or sometimes referred to as peacock flies due to their colouration and habit of waving their wings (Porter 1928; Foote et al.1993, pg.1). The apple maggot informally belongs in the Pomonella complex group, compring four species of Rhagoletis with very similar wing patterns: R. pomonella (Walsh) (apple maggot), R. mendax (Curran) (blueberry maggot), R. zephyria (Snow) (snowberry maggot), and R. cornivora (Bush) (dogwood berry maggot) (Foote et al. 1993, pg. 326). The Pomonella complex is recognized by its characteristic âF-shapedâ band on its wing (Foote, Blanc, and Norrbom 1993, pg. 359). However, unlike the apple maggot, the remaining member of the Pomonella complex infest only one or two plant genera with no overlap of host species (Brunner 1987). R. zephyria is present in both the Okanagan Valley and Washington State (Brunner 1987; Foote et al. 1993, pg.344). Confusion between the members of this group has occurred for nearly a century. For many decades, a taxonomic distinction between Rhagoletis pomonella and R. mendax was controversial, mainly due to their morphological and geographical similarities and the fact that they can produce viable and fertile offspring in the laboratory crosses. However, Feder et al. (1989), in a genetic study of R. pomonella and R. mendax, found that 11 of 29 allozymes surveyed were species-specific alleles; evidence that these flies are genetically distinct sibling 7 species. They also found that the flies rarely, if at all, hybridize in nature, and that differences in host plants acted as a pre-mating barrier to gene flow between the two (Feder et al. 1989). Another crucial example of the complexity within the Pomonella complex is the distinction between R. zephyria and R. pomonella; both inhabit similar environments but have significantly different economic effects. Yee et al. (2011) found that comparing the genitalia of apple maggot and snowberry maggot was the most accurate way to discriminate between the two very similar species. The male surstylus shape classified 99.8% of males correctly to species, and the female aculeus shape classified 85.3% of females to species. Combining the female aculeus shape and the its length increased the accuracy of the classification to 94.5% (Yee et al. 2011). I.III.II Biology and Life Cycle The apple maggot produces one generation per year. Females oviposit their eggs under the skin of the fruit, causing small dimples and depressions. The eggs are white in colour with slightly yellow ends and are about 0.90 mm long and about 0.25 mm wide (Porter 1928; Dean and Chapman 1973). Eggs are laid singly under the skin of the fruit and hatch within 3 - 7 days. The hatched larvae (maggots) burrow through and eat the fruit before maturing in 13 - 50 + days (dependent on temperature and hardiness of fruit). The larvae are cream-coloured, legless, and maggot-like with no distinct head with two visible, black rasping mouth hooks at the narrower anterior end. They are about 6.5 to 8 mm long and about 1.5 to 2 mm wide when mature (Porter 1928; Dean and Chapman 1973; Brunner 1987; Bush et al. 2005). Averill and Prokopy (1987) showed that there is a significant negative relationship between percentage larvae survival and initial number of eggs per fruit. Survivorship of larvae decreases significantly from about 70% in fruits containing a single egg to about 45% in fruits containing two eggs, and continues to decline with added eggs. Increased density of eggs also decreases the size of pupae, which in turn prolongs maturation and daily fecundity. Also, larvae that hatch first in multi-infested fruit have an advantage over larvae that hatch later, often resulting in later larvae failing to complete development (Averill and Prokopy 1987). Mature larvae emerge from the fruit, usually after the fruit drops to the ground, and burrow into the soil to overwinter as pupae under host trees (Brunner 1987; Bush et al. 2005). Larvae that enter the soil to pupate penetrate no further down than four inches (Porter 1928). The pupa is oval in shape, about 4.5 mm long and about 2.1 mm wide, and when first formed, is cream to white in colour before becoming golden brown and 8 darker as it ages (Porter 1928; Dean and Chapman 1973). Adults emerge chiefly in late June to early July, and continue to emerge until early October (Brunner 1987; Bush et al. 2005). The adult is black-bodied with white stripes and head and legs rust-red to yellowish in colour. They are slightly smaller than an average house fly, at about 0.15 to 0.20 inches with a wingspan of 0.30 to 0.43 inches (Dean and Chapman 1973; Brunner 1987; Bush et al. 2005). The wing pattern has distinct black bands in the form of an âFâ, which is the key to identify this species among its relatives (Brunner 1987; Bush et al. 2005). The timing of emergence from overwintering sites depends on the soilâs temperature and type, rainfall, geographical location, and topography (Bush et al. 2005). Activity levels of adult apple maggot usually exhibit three peaks, corresponding to the maturation of different hostsâ fruit: the first peak is when early apple varieties mature in mid-summer; then as native hawthorn and apple varieties mature in late summer/ early fall; and finally, when ornamental hawthorn and late apple varieties mature in late fall (Brunner 1987). Some adults may not emerge for two to three years, perhaps as a form of bet-hedging (Brunner 1987; Bush et al. 2005); those that pupate for more than one winter insure the survival of individuals should there be a crop failure during the first summer (Porter 1928). Adults require 7 - 10 days to feed (aphid honeydew, bird droppings, among other exudates) and become sexually mature (Dean and Chapman 1973; Brunner 1987; Bush et al. 2005). To obtain sufficient carbohydrate to satisfy the basic energy requirements for survival and maintenance, apple maggot adults feed on bird droppings, aphid honeydew, and host fruit leachate. When these sources are unavailable, apple maggot flies are able to obtain carbohydrates from leaf surfaces, including leaf surface bacteria, pollen, insect frass, and uric acids; but such sources do not provide sufficient energy to support significant egg development. Host foliage leachate supported fly longevity, but not egg development (Hendrichs et al. 1993). Mating, as well as courtship rituals such as wing movements, certain body postures, and pheromone releasing of the male, occurs on the leaves of host plants (Aluja et al. 2001). Mature females are attracted to ripening fruit for egg-laying and will move between host trees as the fruit ripens (Dean and Chapman 1973; Brunner 1987; Bush et al. 2005). When a female R. pomonella oviposites on a fruit, she releases a contact pheromone that deters most other females from laying their eggs on the same fruit. Larger fruits require a longer time (about 7 seconds longer) and distance (about 5 cm further) to deposit the pheromone relative to smaller fruit (Averill and Prokopy 1987). Over 9 its mature adult lifespan of 2 - 4 weeks, females can lay a total of up to 500 eggs, with each egg on a separate fruit (Brunner 1987; Bush et al. 2005). In a study looking at the sexual behaviour of Rhagoletis species, R. pomonella females on average mated 4.17 +/- 0.6 times in one observational day, peaking during the late morning. The duration of each mating was on average nearly 50 minutes (Aluja et al. 2001). I.III.III Hosts Before European colonists introduced the domestic apple to North America, the apple maggotâs original host was hawthorn (Bush et al. 2005). A switch from hawthorn to apples appear to have happened within the last 150 years (Feder et al. 2003; Porter 1928). R. pomonella is now known to use at least 27 host species from 9 genera, all belonging to the Rosaceae family including Amelanchier (Saskatoon berry), Aronia (chokeberry), Cotoneaster, Crateagus (hawthorn), Malus (apple and crabapple), Prunus (cherry), Pyracantha (firethorn), Pyrus (pear), and Sorbus (rowan) (Foote et al. 1993, pg. 359-60). Within Washington, apple maggot is known to infest apple (Malus domestica Borkh), crab apple (Malus spp.), Suksdorfâs hawthorn (Crataegus suksdorfii Sarg.), English hawthorn (Crataegus monogyna Jacq.), apricot (Prunus armeniaca L.), cherry plum (Prunus cerasifera Ehrh.), smooth hawthorn (Crataegus laevigata Poir.), Japanese plum (Prunus salicina Lindl.), European mountain ash (Sorbus aucuparia L.), western mountain ash (Sorbus scopulina Greene), parney cotoneaster (Cotoneaster lacteus W.W.Sm.), European cotoneaster (Cotoneaster integerrimus Medik.), Asian pear (Pyrus pyrifolia Burm.), cranberry cotoneaster (Cotoneaster apiculatus Rehder), flowering crab apple, black hawthorn (Crataegus douglasii Lindl.), Washington hawthorn, cockspur hawthorn (Crataegus crus-galli L.), Chinese haw (Crataegus pinnatifida Bunge), European pear, and garden plum (Yee 2008; Yee and Goughnour 2008). Crataegus It is well known that Malus and Crategus are major hosts of R. pomonella, but the distribution and biology of Crataegus spp. is not well described in Western Canada, especially in the study areas chosen for my thesis. Therefore, it is critical to understand the genus Crataegus as well as its distribution within the study areas. 10 Most hawthorn species belong to the genus Crataegus in the family Rosaceae. Crataegus (from the Greek kratos) means âstrengthâ due to the hardiness of the wood. The common name âhawthornâ comes from the Anglo-Saxon haguthorn, meaning âa fence with thornsâ, from its early use as a hedge (Parish et al. 1996, pg. 57). There are about 140 species of hawthorn worldwide (Phipps et al. 2003, pg. 62). The original hosts of apple maggot were native hawthorn, but the flies eventually expanded their host range to cultivated apples that were introduced to North America during colonial times (Dean and Chapman 1973). There has been successful rearing of the apple maggot in New York State from large fruited Crataegus punctate (Jacq.), C. albicans (Ashe), C. pruinose (Wendl.), C. brainerdi (Sarg.), and C. macrosperma (Ashe), but failed rearing from C. neofluvialis (Ashe) and C. oxyacantha (L.); both smaller fruited species (Porter 1928). The distribution of the apple maggot in western United States largely coincides with the range of the most common native hawthorn, the black hawthorn, C. douglasii. As of 1988, 60 of the 66 AM-infested counties in Washington State lie within the recorded botanical range of the latter plant (Dowell 1991; Yee et al. 2012; Yee et al. 2014). Within the Okanagan Valley, there is little data on Crataegus distribution; principally on ornamental Crataegus distribution, that is, residential and city-planted Crataegus species. It is known that the most common wild Crataegus in the Okanagan Valley is also C. douglasii. The black hawthorn is a low to mid elevation hawthorn species widely scattered and locally abundant along streambanks and lakes, on open, warm slopes, in open deciduous forests, as well as along roadsides and fields (Lyons and Merilees 1995, pg. 90; Parish et al. 1996, pg. 57). They are typically not found in dry, cold climates and are present in the lower 2/3 of BC. They have a similar distribution in Washington State, in mostly coastal forest and ponderosa pine ecosystems (Lyons and Merilees 1995, pg. 57, 90). I.III.IV Apple Susceptibility Apple susceptibility to apple maggot attack is influenced by two main factors: 1) the attractiveness of the fruit to the adults; and 2) the suitability of the fruit for the larvae, which, for the most part, depends upon the ease with which the flesh of the fruit is broken down. Minor factors include the presence or absence in the immediate vicinity of a more susceptible fruit, the amount of fruit present, soil texture, and weather conditions (Porter 1928; Dean and Chapman 1973). 11 Two main forms of injury occur: 1) females depositing eggs in the flesh of the fruit, cause sunken, dimple-like spots where the surrounding tissues fail to grow; and 2) feeding larvae tunnel through the fruit, causing brown trails of broken-down tissue. The latter injury can lead to a worthless, rotten interior in an otherwise healthy exterior of the fruit (Porter 1928; Dean and Chapman 1973). Early season infestation of fruit will cause bumpy, mature fruit. Larval damage appears as distinct brown tunnels through the flesh of the fruit. Heavy infestation can lead to mushy, rotted, and premature drop of fruit (Brunner 1987; Bush et al. 2005). The pest poses no threat to human health (CFIA 2016). I.III.V Natural Enemies Natural predators of the apple maggot include various species of spiders including Dendryphantes militaris (Hentz), which actively captures the flies in apple trees, hemipterous insects including reduviids, egg parasites including Anaphoidea conotracheli (Girault), and larval parasites including Opius melleus (Gahan) and O. ferrugineus (Gahan) (Porter 1928; Dean and Chapman 1973). Other known parasitoids of R. pomonella include: two braconids, Biosteres melleus (Gahan), which exclusively parasitizes the larvae that infest the fruits of the native hawthorn, C. douglasii, and Opius downesi (Gahan), which exclusively parasitizes pupae from fruits of the ornamental hawthorn, C. monogyna (Jacq); a pteromalid, Pteromalus spp., and two eulophids, Tetrastichus spp., parasitize the larvae that infests fruits from both hawthorn species. These parasitoids, however, have different rates of parasitism for R. pomonella infesting apples and those infesting hawthorns. Parasitism occurs significantly more frequently in ornamental hawthorn than native hawthorn and in hawthorns than in apples (Gut and Brunner 1994; Feder 1995). It should be noted that these predators and parasites do little in the way of controlling the apple maggot (Porter 1928; Dean and Chapman 1973). I.III.VI Dispersal Most Tephritids are estimated to have short dispersal distances: the Mediterranean fruit fly ranges from 156 m to 700 m; Rhagoletis cerasi (L.) within 400 m; Anastrepha fraterculus (Wied.) within 200 m; Bactrocera oleae (Rossi) within 400 m; Bactrocera dorsalis (Hendel) ranges from 0.02 to 1.90 km (Dominiak, 2012); Bactrocera tryoni (Froggatt) within 800 m (Meats and Edgerton 2008). Patterns of dispersal distance in fruit flies may have evolved in 12 response to its hosts plants, particularly the phenology and distribution within the landscape. Some of the primary factors that influence the dispersal distance include availability of food and its suitability, climate, biological life histories (specifically life span), and escaping predation. For example, long-distance dispersal in the Queensland fruit fly, Bactrocera tryoni, has been linked to isolated favorable habitats that are separated by unfavoruable landscapes, forcing a longer flight. They generally avoided areas lacking food, water, or shelter and aggregate in areas with these available (Dominiak, 2012). Substantial dispersal in fruit flies is most likely when the fruit crop fails at adult emergence, and/or when local fruit is scarce or heavily infested. The stimuli that induced flies to disperse under these two scenarios are the lack of foraging success and the frequent contact with the oviposition deterring pheromone left by previous females (Fletcher 1989). Only a small proportion of the immature fruit flies disperse long-distances, and very little of them succeed as flies disperse in random directions and not in pairs; thus, finding a mate whilst traveling is unlikely (Dominiak, 2012). For apple maggot, after emerging as adults in the summer, host fruit is in the immediate vicinity, minimizing the need for extensive dispersal. If hosts are present, very few flies will move more than 100 m from the release site. However, if released outside an orchard, some flies may travel at least 1.5 km in search of hosts. In a dispersal study conducted by Bourne et al. (1934), approximately 1000 marked apple maggot flies were released on a small apple tree and daily observations were taken for the next few weeks. In the first few days following the release, specimens were collected within a 54.9 m vicinity of the point of release on ornamental shrubs. About two weeks later, a single specimen was collected approximately 665.7 m from the point of release. In total, 143 flies were recaptured, most of them within immediate vicinity of the release site (Bourne et al. 1934). Similarly, of 3152 marked and released flies, 130 were recaptured at varying distances from 59 to 213 m away from the release site and 53.8% were captured between 112 and 135 m. Of those recaptured, 38.4% were 16 to 20 days old; 13.1% were 31 to 37 days old (Phipps and Dirks 1933). Maxwell (1968) found that of the approximately 2800 marked adults released, 607 were recaptured evenly in all traps situated in all bearing trees in the orchard. Another interesting aspect to this study was that it examined how high flies dispersed. A cotton barrier lay 14 m beyond the outside apple row in the orchard; it was 61 m long by 4 m high, but the bottom 0.6 m was open. Traps were placed vertically on this barrier and of the 377 flies captured (45 marked, the rest were orchard-emerged flies), 77% were captured at 2.5 - 4 m 13 above ground, suggesting that the flies leaving the orchard fly above such barriers more so than below them (Maxwell 1968). Natal hosts can also play a role in apple maggot dispersal. In a flight-tunnel study, apple maggots reared from domesticated apple (Malus pumila Mill.), hawthorn (Crataegus mollis Scheele), and flowering dogwood1 (Cornus florida L.) (Cornaceae) flew to their respective natal blend 68-83% of the time when given the option of all three (Linn et al. 2005). In another study, female apple maggots previously exposed to apples accepted apple more frequently than hawthorn, and vice versa. However, flies exposed to hawthorns attempted to oviposit in apples less frequently than flies exposed to apples attempting to oviposit in hawthorn; relative to naive, or untrained, flies. Both size and surface chemistry played a role in the fliesâ learning ability (Papaj and Prokopy 1986). These results show that apple maggots infesting different hosts possess a degree of behavioural discrimination towards non-natal blends and that female apple maggots learn to reject both novel physical and novel chemical stimuli (Papaj and Prokopy 1986; Linn et al. 2005). Based on such studies, apple maggot appears to stay within ~ 150 meters of their emergence site, assuming hosts are readily available bearing non-infested (lack of other apple maggot eggs/larvae) fruit. In extreme cases, apple maggot will disperse 1.5 km or more to find fruit if in their emergence site they are in frequent contact with marking pheromone or are unsuccessful to forage. Mountains and treeless landscapes are considered barriers to the apple maggot, but dispersal can occur along rivers or creeks, where hawthorns are generally found (Yee 2008). Tephritid Trap Efficacy Tephritid fruit fly detection has been at the centre of a debate dating back to the 1950s, regarding poor trapping response and efficacy (see Papadopoulos et al. 2013; Gutierrez et al. 2014; Carey et al. 2014). The core of the debate surrounds the question of whether intermittent positive trap observations reflect low-level populations occasionally rising above detection levels, or whether these reflect repeated introductions (see Barinaga 1990; Dowell 1991; Chen 1 Apple maggot, Rhagoletis pomonella, is only known to attack hosts of Rosaceae. Therefore, the flies that were reared out of the dogwood plant (Cornaceae) may have been the dogwood maggot, Rhagoletis cornivora, which is extremely morphologically similar to R. pomonella. 14 2010; Carey et al. 2017). With respect to apple maggot, this has also been as issue, as the red sphere and yellow sticky traps and bait used to monitor the fly are ineffective, and require essentially every host tree to be equipped with traps (Jones and Davis 1989; Agnello et al. 1990; Klaus 1997-2015; Klaus et al. 2007; Yee et al. 2017; Thistlewood H, AAFC, personal communication, 2018). It is possible, therefore, that the positive apple maggot trap data collected by the WSDA (see next chapter) underestimates of the extent of the apple maggot infestation. I.IV MANAGEMENT AND SPREAD HISTORY OF APPLE MAGGOT I.IV.I Invasion in the West It is suggested that domestic apples were introduced to Mexico in 1522, and to New England, USA, around 1622 (White and Elson-Harris 1992, pg. 375). The first published report of apple maggot as an apple pest in the US was in 1866, and by 1896, it was first detected in Aldolphustown, Ontario, Canada, spreading within a dozen years to Como and St. Hilaire, Quebec, and Digby, Nova Scotia (Brunner 1987; Yee et al. 2014; CFIA 2016b). Over 100 years later, apple maggot was still a key commercial pest in North America (Brunner 1987). Specifically, it is native to southeast Saskatchewan and east to Nova Scotia, Canada and northeastern United States down to Florida and as far west as Minnesota, Texas, and Kansas and everywhere in between (Porter 1928; Brunner 1987). For decades, the northeastern US has been a hot spot for apple maggot research on its life history (Chapman 1933; Chapman and Hess 1941; Glass 1960; Dean and Chapman 1973) and on controlling measures (Herrick 1920; Chapman and Hammer 1934; Dean and Chapman 1973; Reissig 1988). In the United States today, the apple maggot is now an invasive pest along the west coast states as well as Colorado, Utah, and Idaho; with 38 US states under apple maggot quarantine (Brunner 1987; Yee et al. 2014). In Canada today, it is widespread throughout eastern provinces (Prince Edward Island, New Brunswick, Nova Scotia, Quebec, and Ontario), with the exception of Newfoundland (Yee et al. 2014; CFIA 2016b). In western Canada, it was found in noncommercial host trees in Manitoba, Saskatchewan, and Alberta in 2005. In British Columbia, it was detected in the Fraser Valley, Greater Vancouver, and Vancouver Island in 2006, and Prince George in 2013 (Yee et al. 2014). The entire province of BC was recorded as apple maggot-free until 2006, when new lures were introduced (CFIA 2016b). The detections in BC in 2006 occurred simultaneously with a 15 major change in lure types and the distribution pattern observed that year is clearly not from a one-year infestation (Thistlewood H, AAFC, personal communication, 2019). Populations of apple maggot in western United States were most likely introduced from the eastern United States (Dowell 1991; Hood et al. 2013). Genetic surveys of microsatellite loci in Washington State suggest that the introduction was recent, within the last 40 years (Sim et al. 2017). Apple maggot may have been present in a few locations in the West as early as the late 1940s, but took several years to spread and build a detectable population before dying out. It has been suggested that earlier populations were augmented later by multiple introductions (Dowell 1991). The apple maggot was first detected in western North America at Portland, Oregon, in 1979 and by 1986, it had expanded its range to six states: Oregon, Washington, California, Utah, Idaho, and Colorado (Brunner 1987). In Washington alone, the apple maggot has established itself in 17 western counties and in 4 counties in central and eastern Washington, including as far north as King County (Brunner 1987; Bush et al 2005). As of 2014, the number of counties free of apple maggot in California is 26 (44.8%); Idaho 4 (9.1%); Oregon 13 (36.1%); and Washington 16 (41.0%) (CFIA 2016). The habitat where the apple maggot has been detected varies from the mild, cool, coastal areas and the hot, dry areas of Washington, Oregon, and California, to the mountains of Utah (Brunner 1987). The first detection in Washington State of the apple maggot was in 1980 in Vancouver (Clark County) at two different sites. In 1981, it was detected in six sites in Vancouver and in 11 sites in the Columbia River Gorge extending east into Stevenson (Skamania County). By 1982, it was detected in two sites in Cowlitz County, just north of Vancouver, in 51 sites in the Columbia River Gorge extending east into White Salmon (Klickitat County), and in two sites in Spokane. In 1983, the apple maggot expanded its existing infestations, but was also found in new sites in Spokane and in Klickitat County, and was also detected in Lewis County and Pacific counties north and west of Vancouver. By 1984, apple maggot was detected as far east as Goldendale near the Columbia River Gorge, and for the first time in Washington, apple maggot was detected in commercial apple orchards in Klickitat County. Further expansion of existing infestation areas occurred in Washington, until a new detection was discovered in King County, near Seattle, in 1985. In 1986, several new sites were detected in King County, and first detected in Grays Harbor, Pierce, and Wahkiakum counties. From 1980 to 1986, the apple maggot established a continuous distribution with high populations in the Clark and parts of Lewis and Skamania 16 counties, and a discontinuous distribution in areas north of Clark County (Brunner 1987). Nearly 30 years later, it was trapped near the US/Canadian border. There appear to be two distinct spread patterns of apple maggot in Washington State: a quick spread along the west coast and a slow spread in the central area. This may have something to do with management and environmental conditions. The coastal forest ecosystem of western Washington receives 1,000 to 1,800 mm/year of precipitation, compared with the drier, sagebrush and ponderosa pine ecosystem of central Washington nearly 200 km east, which only receives about 200 to 450 mm/year. The climate in western Washington is much more wet with milder temperatures from spring to fall relative to central Washington. The wet conditions and higher plant abundances in western Washington may partially explain why the apple maggot survival, longevity, density, and host range are higher relative to that of apple maggots in drier conditions. There are also more fruit trees outside commercial orchards and in residential areas in western Washington compared to central Washington (Yee and Goughnour 2008). Unlike in western Washington, the apple maggot seems to target only apple and black hawthorn in central Washington. The narrower host breadth of the apple maggot in south central Washington, relative to western Washington, may be due to low population densities, strong host preference due to lack of host abundance outside of orchards, and/or the hot and dry conditions of the area (Yee 2008). Kumar et al. (2016) modeled the potential distribution of apple maggot using CLIMEX and MaxEnt, as well as an apple distribution map, and found that most habitats within central Washington State have low to moderate suitability. They suggest that areas in the model where apple maggot is predicted to be present but is absent or at least not yet detected in the real world, is evidence for slow spread or spread disruption caused by human management. I.IV.II Management in Washington State Technical barriers influence the flow of apples across borders and can have an impact on the domestic market by reducing or eliminating imports. These barriers include health-related sanitary and phytosanitary measures, animal and plant health, food standards, and environmental resource conservation measures. Most of the time, if a country or state is free of a damaging pest there are heavy import regulations with the country or state with that pest, which could prevent exporting from that area. Many import countries either constructed strict regulations for imports or ceased importing altogether with Washington as the concern for apple maggot spread was 17 deemed to be too high a risk (Krissoff et al. 1997). The USDA considers an orchard âthreatenedâ if an apple maggot is detected within 0.5 miles from the orchardâs border during the season or if no control measures were carried out since the previous seasonsâ detection (Klaus 2000). Eradication programs began almost immediately upon detection of apple maggot in western states. State containment/eradication programs in the 1980s in California, Oregon, Utah, and Washington all showed promise, but ultimately failed to eradicate or stop the spread of apple maggot (Dowell 1991; Suckling et al. 2016). The Washington State Department of Agriculture (WSDA), in 1982, initiated an apple maggot program to determine its distribution and to prevent its establishment in major apple-producing areas (Brunner 1987). The total estimated economic cost for Washington State to manage apple maggot was in the tens of millions in 1985 (Schotzko 1982). From 1981 to 1986, the state set up over 27,000 traps, catching over 3,600 apple maggots, costing the state over $1.5 million and spraying over 300 kg of pesticide. All hosts within an 800-mile radius of positive traps were treated at 10- to 14-day intervals or removed. Removal of hosts became more prevalent, especially in 1984 where over 100,000 host trees were removed (Dowell 1991). The WSDA focused on setting up traps on non-commercial, residential host trees in populated areas, abandoned apple orchards, and feral, roadside host trees. These host trees included apple, crabapple, and both native and ornamental hawthorn (Sandoe 2006). With regard to Kittitas and Yakima counties, only 0 - 4.7% of all traps on apple, crabapple, and hawthorn trees were positive for apple maggot over a 30+ year period. This implies that management efforts and/or natural factors/barriers have prevented populations to reach levels seen in western Washington. Natural factors, including a hotter, drier climate relative to other areas where population sizes are significantly greater including New York and western Washington, are thought to potentially play a much larger role in suppressing population growth than management. Overall, results suggest that development of large populations of apple maggot around commercial apple orchards in central Washington is unlikely, even in black-fruited hawthorn (Yee et al. 2012). According to the WSDA trap records, every year between 2003-2012 saw the first caught apple maggot around early to mid-July, except in 2003, when the first was caught in late June. Traps were set up in mid-June and taken down late September. The protocols for deploying additional traps following an apple maggot positive catch are three-fold: (i) the core area, within a 0.5 mile radius of the positive catch site, receives high density trapping 18 (about 500 to 1000 traps), where as much as 100% of available hosts are trapped; (ii) the 1-mile buffer area, within a 1 mile radius of the positive catch site, receives moderate density trapping (about 500 traps per square mile), where as much as 50% of available hosts are trapped; and (iii) the 2 mile buffer area, within a 2 mile radius of the positive catch site, receives low density trapping (about 250 traps per square mile), where as much as 25% of available hosts are trapped. Approximately 25 to 50 % of the traps in non-infested or non-quarantined areas will rotate locations into new trap site areas annually (Klaus et al. 2007). The discovery of multiple life stages within a county indicates a reproducing population, which means the apple maggot is established in the area, thus designating the area as âquarantinedâ. To be considered established, all life stages must be present the current population is expected to multiple and continue to spread. Because pupae can survive up to four years in soil, if no further detections of apple maggot after four years since the last detection occurs, then the area may have the âquarantineâ status lifted (Klaus et al. 2007). Overall, most countries accept Washingtonâs pest management as adequate protection against apple maggot spread (Krissoff et al. 1997). By using insecticides and removing host plant species, apple maggot numbers dropped, but were not eliminated (Brunner 1987). I.IV.III Impending Invasion in the Okanagan Valley BC hosts approximately 8,804 acres of cultivated apple production, accounting for 75% of all BC orchard land (BCMA 2013b; AAFC 2015). The Okanagan Valley is located in the BC interior, sitting in the rainshadow of the Cascade and Coast Mountains. The Okanagan Valley is divided into three regional districts: the North Okanagan, the Central Okanagan, and the South Okanagan. The valley itself extends roughly 200 km north of the Canada/US border and roughly 20 km wide throughout. Its landscape consists of low hills and oblong lakes that drain south through the Okanagan River into the Columbia River (Holland 1976; Marsh, 2006). The Okanagan Valley is a semi-arid basin that has dry, hot summers with low humidity, relatively mild winters and springs, and low precipitation, ranging from 290 to 450 mm annually (Marsh 2006; Crawford 2012). Most of the valley receives about 2,000 hours of sunlight annually (Marsh 2006). Glaciers that covered the valley some 10,000 years ago left behind cultivatable soils, especially in the bottomlands, benches, and terraces; this left ideal conditions for agriculture (Holland 1976; Crawford 2012). 19 In its infancy, agriculture in the Okanagan Valley was focused on cattle ranching, for which the landscape was well suited. By the late 1800s and early 1900s, the commercial orchard industry was developed by land speculators who bought parcels of ranch land and sub-divided them into orchard plots (Crawford 2012). Apple trees were first recorded in agricultural crops in Canada in 1610. Over the years, settlers began planting apples on their homesteads and farms before apple production became commercialized in Ontario in 1796. It was not until the late 1800s when commercial apple production began in the Okanagan (AAFC 2015). Today, between 2-5% of the Okanagan population is involved in agricultural production (Crawford 2012). The Okanagan is the main apple producing area in BC, with roughly 12,600 acres dedicated to apple production (BCMA 2013b). In 2006, there was 3,865 farms in the Okanagan, consisting of about 190,000 hectares (Crawford 2012). Roughly 98% of all apple production in BC comes from the Okanagan Valley, which accounts for about 30% of all apples grown in Canada. Over a billion apples, weighing 136,000 to 195,000 tonnes, are produced in the Okanagan Valley annually, making the value of BC apples about 80% of all BC tree fruit production (BCMA 2013b). Currently, the only remaining commercial apple-producing regions in North America free of this pest are the Okanagan Valley of BC (BCMA 2013; CFIA 2016b). Because of this, any potential detrimental effect on apple production in the Okanagan Valley is taken seriously, including the management of agricultural pests. The success and rate of post-entry spread for an invasive pest depends not only on its ecology, but also host distributions, potential dispersal vectors, including human-induced or environmental factors, and our ability to assess and manage the pest (Parry et al. 2013). The BC interior is geographically isolated from other known apple maggot populations due to the surrounding steep mountain ranges. Therefore, the most likely routes the apple maggot can arrive in the Okanagan Valley include natural spread from Washington State or by human-induced transportation, by bringing in host materials (soil, fruit) from quarantined areas in Washington State or lower mainland. Because of the potential pathways, the most likely to be infested are hosts in unsprayed orchards, homeownersâ yards, transportation routes, and riparian corridors, which are very prevalent in the Okanagan (Bush et al. 2005; CFIA 2016b; CFIA 2017). Public education, highway signage, surveying (including trapping), and federal movement requirement certificates are some of the ways to delay or slow the spread of the apple maggot into the Okanagan Valley; some of which are currently in place. However, the simplest way to prevent 20 the spread of apple maggot is to not grow or plant apple trees (Bush et al. 2005; CFIA 2016b; CFIA 2017). Despite this, apple maggot spread is still an issue as it continues moving north in Washington State to the Okanagan Valley. The Canadian Food Inspection Agency (CFIA) is responsible for the regulation of apple maggot host materials, including fruit, trees, and soil in order to prevent the introduction and spread of the apple maggot in BC. Presently, apple maggot is not considered established in the Okanagan Valley, which remains the only major apple-producing regions in North America free of apple maggot (McAllister 2015; CFIA 2016b). The CFIA has monitored the BC interior for apple maggot using some 430 traps since its detection in coastal BC in 2006. By 2015, a novel plan for placement and rotation of locations was in place from the laboratory of Dr. Howard Thistlewood of Agriculture and Agri-Food Canada in Summerland, developed using data on hosts from many agencies, and a GIS. Using the novel plan, a single female apple maggot was captured on a trap on a backyard apple in a residential area in West Kelowna. The fruits, tree hosts, and soil around the positive site are under regulatory control (i.e. restriction of host material movement) and a delimitation survey is underway. A second positive site in Kelowna was located on an ornamental hawthorn in 2016. No commercial orchards were immediately nearby the single occurrences. As of fall 2018, no new occurrences have been recorded (CFIA 2016b; CFIA 2017). The protocol for deploying traps around a positive site include placing traps on all hosts within 200 m of the positive site, 50% of all hosts within 800 m, and all nurseries and apple production sites within 2.5 km of the positive site were trapped (CFIA 2016b). The CFIA considers the status of apple maggot as either absent (where no adult female or larvae have been detected), transient (where one or more female are detected, but no larvae in fruit within 800 m of an adult detection), or present (where both adult and larvae in fruit are detected within 800 m of each other during the same year). Areas may return to the status of absent if an area has no apple maggot detections for at least 3 consecutive years (CFIA 2017). The number of traps used in the Okanagan Valley in the year after detection has doubled (CFIA 2016b). However, the immediate detection of apple maggot in the first year of a novel host-based deployment plan suggests that more await detection and reinforces conclusions from US agencies that the comparably limited trapping in BC is inadequate. Moreover, experiences 21 from California and Washington suggests that once the apple maggot is established in an area it cannot be eradicated from it (Yee 2008). The establishment of an apple maggot population in the BC interior will add significant, economical, and ecological costs, including: local quarantine (control and management), immediate direct costs (decreased fruit quality and yield), increased production costs (chemical usage, pest control), and economic cost (restrictions on domestic and international markets) as experienced in some areas of Washington State (Zhao et al. 2007), potential disruption in the Okanagan-Kootenay Sterile Insect Release program (OKSIR) for codling moth, and impacts on native species including hawthorn. I.IV.IV Management Strategies There are numerous methods of control for agricultural pests including sterile insect techniques, pheromone mating disruption, mass trapping, soft pesticides (bait sprays or insect growth regulators), pest evasion (having earlier maturing varieties), biological control, and integrated control (combination of different methods). However, dependency on any one method for season long harvest is not feasible, and either integrated control or chemical control is still the most effective method for controlling agricultural pests (AliNiazee 1995). Apple maggot is a quarantine pest due to having numerous import restrictions and when given enough time, can cause up to 100% yield loss (AAFC 2015). In the absence of resistant apple cultivars, the economic threshold of apple maggot is very low (AAFC 2015; CFIA 2017). For these reasons, management of apple maggot is crucial, and several methods have been adopted over the decades and continue to be used across North America to manage apple maggot: avoidance practice by trap crops or perimeter spraying; prevention practice by crop residue removal or management at the end of season, and removal of other host species; and monitoring practice by scouting and trapping, recording pest movement, and collecting weather data for degree day modeling. Suppression of apple maggot is accomplished by either pesticide rotation for resistance management, biological pesticides, ground cover or physical barriers, trapping, targeted pesticide applications, or a combination of some or all practices. Mass trapping, by hanging multiple traps on every single host tree, can be economically feasible and an alternative to using insecticides to controlling apple maggot in smaller orchards, as spraying is not needed; however, this is not practical for larger commercial orchards as it is extremely 22 expensive (Reissig 1988; Prokopy 1995; AAFC 2015; CFIA 2017). Removing dropped fruit prevents the maggots from pupating and removing host trees disallows the adults to mate and lay eggs (Porter 1928). Various state universities dealing with apple maggot recommend diverse integrated pest management strategies, including public education, removing infested fruit or hosts, and insecticide usage: University of Minnesota extension, PennState extension, Washington State university extension, Michigan State university extension, University of California, and University of Florida, among others (as of June 16, 2017). The potential costs of apple maggot management are significant. The total estimated cost per acre per year to control apple maggot was USD$47.47 (in 1981 dollars), which included equipment, wages/labour, and materials. Growers are estimated to lose millions of dollars due to increase production costs as well as potential loss of domestic and export markets. It is estimated that an additional one or two sprays annually would be needed to control apple maggot in infested sites. Such potential costs in Washington State are between $26,740,000 to $117,343,000 annually from additional sprays, quarantine compliance costs, crop losses, and reduced consumer purchases (Schotzko 1982). On top of this, an additional 111,531 to 517,462 kg of active ingredient of pesticide will need to be applied annually for control. The prior scenario assumes that apple maggot will be as great of a problem as it is in the Eastern USA. Or, at minimum, 90% of apple acreage may not be affected, in which case costs associated with apple maggot may reduce to be $2.7 million to $11.2 million annually to growers. The latter scenario assumes that the range of the black hawthorn represents the final distribution of the apple maggot [see âCrataegusâ under Chapter I.III.III for assumption reasoning] (Dowell 1991). Public Education Public education is an important management strategy when dealing with invasive pests that are prone to long-distance human-mediated dispersal events (Barlow et al. 2014), as is the case with the apple maggot. Public awareness about apple maggot in Washington State has grown through WSDA and County Pest Board outreach activities, including: brochures, open houses, 35+ years of surveying activities, WSU extension, website, highway quarantine signs, etc (Klaus 1997-2015). 23 Insecticide Use In northeastern orchards, apple maggot accounts for more insecticide usage annually than any other insect pest in apple orchards. This is in part because most northeastern orchards are adjacent to wooded or semi-wooded areas with available hosts, mainly wild apple and hawthorn. Therefore, there is always an influx of flies from these sources. Also, chemical insecticides are the only viable control tactic of apple maggot, especially for large-scale commercial orchards throughout the northeast (Reissig 1988). Similar outcomes are seen for the western cherry fruit fly, Rhagoletis indifferens, a close relative of the apple maggot in the Pacific Northwest region. Chemical insecticide control of cherry fruit fly is the most important method of control, as the chemicals used are effective, convenient, and easily available for growers (AliNiazee 1995; AAFC 2015b). Host Removal The WSDA apple maggot survey has played a significant role in informing homeowners of the risks of owning backyard fruit trees, often resulting in homeowners removing their fruit trees. This reduces the risk of apple maggot infestation, and also the risk of infestation by codling moth and other fruit pests (Klaus 2015). Likewise, in the Okanagan Valley, the Okanagan-Kootenay Sterile Insect Release program (OKSIR) had a host tree removal incentive program for landowners with codling moth host trees on their properties. I.V MODELING AND ANALYZING THE SPREAD OF APPLE MAGGOT I.V.I Spread Pattern Analysis of Apple Maggot Spread is the process of range expansion from a geographical area a species currently occupies to a new area. Understanding and estimating the spread rates of invasive species is crucial to pest management and developing pest risk maps (Tobin et al. 2015). In most cases, the key process by which invasive pests spread is by long-distance dispersal, usually by transporting infested plants into new areas that have ideal conditions for said pest (Rebaudo et al. 2011). The most likely scenario under which fruit flies travel long distances involves artificial transportation via exported infested fruit. They mature in the fruit and find satisfactory conditions in its new habitat to pupate and develop into adults later (Porter 1928). Estimates on the frequency and 24 distance of long-distance dispersal events are critical for spread rate models. However, due to their stochastic nature, they are difficult to determine and therefore difficult to implement. Species that are closely associated with humans tend to experience these events more often, which can drive the overall patter of invasion (Suarez et al. 2001). Many invasive organisms exhibit a spread behaviour known as stratified dispersal, which involves both short-distance movement, primarily natural dispersal of the organism, and long-distance movement, primarily anthropogenic-assisted dispersal. Anthropogenic movement of host material allows for long-distance dispersal of invasive organisms into new establishment areas often tens or hundreds of kilometers away from the originating infestation. From here, the isolated satellite population spreads and eventually joins with the originating spreading population; allowing for a much greater spread rate of the invasive organism (Siegert et al. 2015). Species that exhibit more than one mode of dispersal (i.e. diffusion and long-distance dispersal) can greatly affect confidence in modeling the spread rate or invasive pattern of the species, especially when detailed chronological spread histories of the species are lacking (Suarez et al. 2001). An invasive forest pest, the emerald ash borer, exhibits stratified dispersal; short-distance movement is influenced by local environmental conditions and long-distance movement is influenced by anthropogenic movement of infested host material, including nursery stock, firewood, and branches from infested trees. The result of anthropogenic movement of host material has led to increased local and regional rate of spread via satellite populations (Siegert et al. 2015). A dynamic simulation model was developed by Zhao et al. (2007) to simulate the economic impacts, including apple production and distribution costs, of apple maggot spread in Washington State. The results from their model indicate that as the speed of the population spread decreases, so too does the management costs. For example, a 10 % reduction in the speed would result in a marginal annual benefit of approximately $1.52 million. Therefore, under the assumptions of the model, the authors suggest that if the current apple maggot management policy reduces the spread speed by 10 % and costs under $1.52 million, it is economically optimal; alternatively, reducing the amount of resources invested in the management of apple maggot is economical if exceeding $1.52 million. Based on their model, costs of exporting to Canada increases by 30 % if the exported products come from apple maggot-infested areas. Additionally, should apple maggot retain its historical spread speed, all apple production in 25 Washington will be infested in 34 years, under the scenarios of the model; the apple industry as a whole would suffer an annual loss of $4 - $8 million (Zhao et al. 2007). Therefore, it is critical to not only understand the spread pattern of apple maggot, but also to understand how to impede the spread. We cannot proceed to the latter unless there is understanding of the former. There are several methods to analyze and estimate patterns of spread including using Minimum Convex Polygon (MCP), kernel density analysis techniques, and the square-root area regression and distance regression methods used by Tobin et al. (2015). There are two types of data used in these estimations: point data, such as trap data, and polygonal data, such as infested geographical areas (Tobin et al. 2015). MCPs are one of the oldest and most widely used estimators of home ranges and are primarily used for comparative analyses and single population studies. However, they are sensitive to sample sizes and outliers and can create biases. Kernels are a nonparametric, probabilistic estimator of home ranges that are based on the complete utilization distribution, and they are primarily implemented with time-series data. Kernels are becoming the method of choice over MCPs as they can account for multiple centres of activity and are robust to spatial resolution changes in the data (Borger et al. 2006). MCP and kernel density analysis are both applied commonly for wildlife movement, especially mammals, but rarely considered for insect movement. Tobin et al. (2015) illustrated two useful estimators: the square-root area regression, which involves square-rooting the total infested area divided by pi; and the distance regression, which involves regressing the distance of an infested location from an origin point. The square-root area regression is based on the analysis of distance-to-time, which uses successive measurements of the invaded area. The distance regression is based on regressing the distance of an infested point from the origin point (Tobin et al. 2015). In my study, I used all four methods. Plant Host Distribution An important aspect to predicting and analyzing pest spread is to understand the distribution of hosts. Species Distribution Models (SDM) can be used to predict the potential distribution of hosts, including MaxEnt and Habitat Suitability Index (HSI) models (Miller et al. 2007). MaxEnt, or maximum-entropy, is an algorithm that is widely used for modeling speciesâ distributions. The MaxEnt algorithm uses georeferenced environmental data layers (such as elevation, precipitation, etc.) in combination with georeferenced occurrence records, and 26 produces a model of the predicted distribution of the given species, with the output being a geospatial data layer in which cell values represent a measure of likelihood of occurrence (Phillips et al. 2004). The performance of the MaxEnt output is assessed using the MaxEnt-generated area under the receiver operating characteristic (ROC) curve (AUC). AUC values typically range from zero to one; values above 0.9 indicate high performance, values between 0.5 and 0.9 indicate poor to moderate performance, and values below 0.5 indicate performance worse than random (Swets, 1988; Kumar et al. 2016). Another approach that can be used to predict the potential distribution of host species is the HSI model, or âexpert opinionâ method, to predict the locations of species using variables from the literature and applying prioritizing HSI values (0 = not suitable, 1 = highly suitable) to them to produce a model of the range, then using the georeferenced occurrence locations to determine how well these points are representative to the distribution. As described below, I used both the MaxEnt and HSI methods to model the potential distribution of hawthorn in the Okanagan Valley. I.V.II Agent-Based Modeling Eradication is impossible in most cases of invasive species, especially orchard pests, and because of this, an alternative approach to management must be applied: slowing the spread process. Although this approach may not fully solve the issue, it can delay the impacts or reduce the extent of the orchard pest species. Modeling can be used to explore this alternative approach and has been recently used to conceptualize the spread process of orchard pest species (Caplat et al. 2012). There are two types of modeling category for post-entry spread of a pest: analytical methods, which are defined as deterministic mathematical expressions, and mechanistic, or process-based methods, such as ABMs. The latter have become relatively important recently for ecological modeling as they tend to be increasingly flexible across spatial scales, encompassing both short and long-distance dispersal events. This has been made easier as computing power to support large, complex data has been increasing over the years. Mechanistic ABMs can better represent the complexity of dispersal mechanisms compared to an analytical approach when there are fewer constraints to consider, such as limited data or uncertainty factors. Despite their power, there are relatively few examples of ABMs being used to model dispersal for post-entry 27 spread (Parry et al. 2013). Advantages to ABMs include: they can account for environmental heterogeneity, they can reproduce emergent patterns from individual dynamics, they are flexible, and they are scale free. The disadvantages are that they can be very complex, they can be data intensive, and they are generally species specific. Generally, the data required for this type of model include life history traits, behavioural ecological traits, source of invasion, and landscape data (Parry et al. 2013). ABMs offer a direct and flexible approach to simulating interactions between population dynamics and multiple agents, and for discovering patterns of behavior and organization that emerge from these interactions through prescribed sets of rules in a spatial domain (Grimm et al. 2006; Rebaudo et al. 2011; Caplat et al. 2012). ABMs offer a comparatively new way of conceptualizing how complex systems work and have recently become popular as a means to understanding agent and system behaviour, especially in economics or pest management (Rebaudo et al. 2011). ABMs allows us to simulate and analyze the complexity of real-world dynamics of biological, social, and other complex systems in a controlled, virtual world, where the landscape is described in the form of a grid or matrix of cells. ABMs are concerned with at least two levels and their interactions: how the system changes due to the agents, and how the agents change due to the system (Railsback and Grimm 2011). ABMs are useful tools to simulate interactions between orchard pests and agricultural landscapes. ABMs can help us understand and predict orchard pest dispersal, by combining pest life histories and landscape GIS files. Despite their promise, ABMs have been underused in integrated pest management theory and practice. Traditional models of orchard pest spread focus on the spatial dynamics and lack any activities one would see as part of pest management strategies. Thus, these models consider only the pest and landscape characteristics and rarely consider the social environment (Rebaudo and Dangles 2013). ABMs have the power to accommodate multiple agents (environmental features such as climate and landscape structure and human behaviour such as pest management), non-linearity, unpredictability, and time lags (Rebaudo et al. 2011). This has allowed managers to not only gain a better understanding of the ecological drivers of spread rates, but to be provided with the tools necessary to test a diverse array of management strategies (Caplat et al. 2012). A typical ABM process involves formulating a question, gathering of necessary data, parameterizing and calibrating the data, implementing the model, and analyzing the implications 28 of the model (Railsback and Grimm 2011). To ensure the model is structurally realistic, the use of pattern-oriented modeling (POM) is often applied to the ABM process. POM is a way to explain observed patterns by creating a more rigorous and comprehensive agent-based modeling system. The key is to use multiple patterns observed in the real-world systems and apply them to the model as guides. In this way, the modelâs complexity is optimized, and its uncertainty is reduced (Grimm et al. 2005). Validating the Model Achieving complete model validation requires that the model be both conceptually and operationally valid. Conceptual validity describes how well the model represents the real world. It relies on known system theories and drives model development. Operational validity involves validating the simulation results against real world system data. (Heath et al. 2009). Three main methods of validation are required to make a model both conceptually and operationally valid: descriptive output validation (which involves matching model output results with available actual data), predictive output validation (which involves matching model output results with yet-to-be-acquired actual data), and input validation (which involves applying actual data to the model before running it to ensure that the fundamental structure and behaviour of the model is sound) (Bianchi et al. 2007). With respect to the predictive model, I used input validation, by applying real-world data of the system into the model, as well as predictive output validation. However, because we do not yet have apple maggot spread data within the Okanagan Valley to compare with the predictive spread model, this validation method must be delayed and validated and compared at a later time. With respect to the validation model, I used both the input validation as well as descriptive output validation methods. Specifically, I used a hierarchically structured validation process for qualitative model applicability, which includes assessing the different model levels separately; this involves obtaining the optimal parameter values, called model calibration or parameter identification, as well as the process of analyzing effects of parameters, called a sensitivity analysis). This is followed by visual inspection, statistical comparison, and expert opinion of model output (Railsback and Grimm 2011; Kubicek et al. 2015). Visual inspection is a quick way to check accordance of model results with empirical data, but is subjective. Statistical comparisons between model results and data derived from empirical studies are more 29 objective but may nonetheless be limited to evaluating correlative associations rather than causal relationships. Finally, expert opinion provides inclusion of user knowledge and testing of qualitative model correctness (processes and results), but the evaluation may be subjective and biased by stakeholder interests (Railsback and Grimm 2011; Kubicek et al. 2015). My original intent was to provide all three techniques to validate my model. However, logistical and time constraints precluded the consideration of expert opinion. 30 II Methods and Materials for the Model II.I SYNOPSIS Initially, I compiled the available data from the literature on apple maggot biology and dynamics, from online geospatial databases for both the Okanagan Valley and central Washington State, and from various agencies on apple maggot trap records. I also collected and compiled data describing the distribution and abundance of potential host plants in the Okanagan Valley, specifically Crataegus spp., or hawthorn. These data are crucial to the success of the predictive model. II.II ASSEMBLY OF AVAILABLE DATA FOR THE MODEL II.II.I Apple Maggot Data I gathered data pertaining to apple maggot biology (including reproduction and lifespan), dynamics (including spread and host selection), and life histories (including emergence and mortality rate) from the literature. I also obtained apple maggot trap survey data from the Washington State Department of Agriculture (WSDA), which provided positive trap occurrence records throughout central Washington State from 2003-2012 (Table 2.1). Details on how these data were implemented in the model are given in the ODD protocol (Appendix A). II.II.II Host Data I focused on four main plant hosts of apple maggot in the focal areas of the study (see literature review chapter and Chapter II.III.II): apples, crabapples, and native and ornamental hawthorns. The host data include georeferenced occurrence records of apple maggot hosts including apple, crabapple, and native and ornamental hawthorns, and these were taken from the WSDA apple maggot trap survey data (Klaus 1997-2015, Table 2.1) and other sources (Table 2.2). Quantity of host occurrence records was dependent on availability for each extent. 31 Table 2.1. Host data points collected for each validation model extent. Central Washington State Wenatchee Source Apple Crabapple N Hawthorn O Hawthorn Positive Sites*** Apple Crabapple N Hawthorn O Hawthorn Positive Sites*** WSDA* 43195 25528 8972 11848 864 1600 1052 153 475 8 Patches** 653 604 323 712 246 281 204 18 109 8 WSDA = Collection data from the Washington State Department of Agriculture (apple maggot trap data) for 2003-2012 * Some data points are duplicates as they are the combination of traps from 2003-2012. ** Some data points were recorded near one another, and therefore the patch numbers will be lower than total data points as the patches can incorporate more than one data point. However, the patch host type will be the majority host points, see ODD protocol (Appendix A) for more details. *** Positive sites are included in the total sites for the 4 host types (i.e. of the 612 host sites in Wenatchee, 8 sites are positive sites as well). Table 2.2. Host data points collected for each predictive model extent. Okanagan Valley Kelowna Source Apple Crabapple N Hawthorn** O Hawthorn** ? Hawthorn** Apple Crabapple N Hawthorn** O Hawthorn** ? Hawthorn** GBIF 511 7 PNWH 1 66 1 KEL 130 130 TL 39 2 562 62 2 124 25 CFIA 1262 271 26 1 822 289 150 14 289 OKSIR 6547 2264 1134 458 CM 8 Total 7849 2537 1173 193 822 1425 608 146 155 289 Patches* 960 756 283 534 1122 529 73 294 GBIF = Collection data from the Global Biodiversity Information PNWH = Collection data from the Consortium of Pacific Northwest Herbaria KEL = Collection data from the city of Kelowna Parks Services TL = Collection data from the Thistlewood lab at SuRDC (personal) CFIA = Collection data from the CFIA internal survey report on apple maggot 1999-2016 OKSIR = Collection data from the Sterile Insect Release program CM = Collection data from the Canadian Museum of Nature * Some data points were recorded near one another, and therefore the patch numbers will be lower than total data points as the patches can incorporate more than one data point. However, the patch host type will be the majority host points. Host points subject to removal or new development. 1 positive trap site is included in the total sites for the 4 host types (i.e. of the 2018 host sites in Kelowna, 1 site is a positive site as well). ** For hawthorn, the âNâ, âOâ, and â?â indicate native, ornamental, and unknown, respectively. CFIA only notes whether the hawthorn is wild, and assume all occurrences not designated as âwildâ are ornamental; thus, â?â hawthorn is used as ornamental hawthorn for the model. 32 Apples Apples are economically important to the Okanagan Valley, and their distribution (both commercial and wild) is mapped by the Okanagan-Kootenay Sterile Insect Release (OKSIR) program for purposes of annual taxation and program delivery. Commercial apples and some wild apple data were collected from the OKSIR program and the Canadian Food Inspection Agency (CFIA), but additional Agriculture and Agri-Food Canada (AAFC) surveys revealed many undocumented wild apples. Hawthorns In contrast with apples, the distribution of hawthorn within the Okanagan Valley is poorly known. I therefore undertook surveys and a citizen science project for the purpose of collecting host occurrence records and constructing Species Distribution Models (SDM) for hawthorn in the region (see Appendix C for more details). Surveys: Initial field excursions during the summer of 2015 yielded baseline information about habitat preferences of native hawthorns. Specifically, these forays spanning north, central, and south Okanagan revealed that suitable habitat was characterized by little to no shade, proximity to water (whether it be a creek, river, or lake), and some slope facilitating water runoff. Using regional maps, these criteria were subsequently used to locate additional potential hawthorn locations, which were then visited later in the summer when hawthorns began to fruit. I used a standard reference guide (Ministry of Forests and Range 2010) to characterize sites. Sites were defined by hawthorn plant occurrences over approximately 100 meters apart with no target species between them. I acquired data about ornamental hawthorns from tree technicians in several towns across the Okanagan Valley who maintain tree inventory and location data, and these were added to the novel sites documented for the present study. Citizen science: I developed a citizen science website to augment survey efforts. I designed this website for two reasons: (i) to provide information and awareness of the apple maggot to the residents of the Okanagan Valley, and (ii) to obtain additional georeferenced occurrence records of hawthorns in the Okanagan Valley. This particular citizen science project aids in the locations of hawthorn species, both ornamental and wild, as well as wild apple and crabapple. This aspect of my study served to support my secondary practical objective: using the 33 ABM to help educate the public and raise awareness of serious agricultural and economical important pests, particularly apple maggot (see Appendix C for more details). SDMs: I also used the hawthorn occurrence records obtained from the literature as well as my own findings to build models of Habitat Suitability Index (HIS) and MaxEnt (version 3.3.3k Phillips et al. 2017) for hawthorn distribution in the Okanagan Valley so as to identify likely hawthorn sites (Muselle et al. manuscript in prep). Details of host distribution collection, specifically those data collected by me, are available in Appendix C. I provided a more realistic hawthorn distribution within the Okanagan Valley, specifically a 92% increase in wild hawthorn occurrence records for the large-extent and a 564% increase for the small-extent (Table 2.2 and indicated as âTL). Importantly, a new trap deployment strategy (2017-2019) was developed by Dr. Thistlewood (AAFC) for apple maggot in the Okanagan Valley, based in part on the survey data I collected, and is now being used by the CFIA. II.II.III Geospatial Data The geospatial (landscape) data describe elevation, roads, waterbodies, and orchards. The climate data include temperature and precipitation. For the small-scale extent climate, local climate data were used (Tables 2.3 and 2.4 for validation model and predictive model, respectively). For the large-scale extent climate, I divided the extent into three sections: north, central, and south. I chose three climate stations per section based on availability of data for years 2003 to 2012 and distribution (validation model) or for the most recent year and distribution (predictive model); the mean values of temperature and precipitation were calculated per section. These climate stations illustrate that temperature increases and precipitation decreases in central Washington State as latitude decreases, as expected (not shown). 34 Table 2.3. GIS files and other dataset files collected for each validation model extent. Dataset file Type Wenatchee Central Washington State Road Shapefile SuRDC SuRDC River/stream Shapefile SuRDC SuRDC Lake Shapefile SuRDC SuRDC Boundary Shapefile SuRDC SuRDC Pome orchard Shapefile **** **** Elevation ASCii file SuRDC *** Temperature Textfile http://www.usclimatedata.com/climate/wenatchee/washington/united-states/uswa0487/2016/2 * http://www.wrcc.dri.edu/summary/Climsmwa.html** Precipitation Textfile http://www.usclimatedata.com/climate/wenatchee/washington/united-states/uswa0487/2016/2 * http://www.wrcc.dri.edu/summary/Climsmwa.html** Apple location Shapefile WSDA AM report WSDA AM report Crabapple location Shapefile WSDA AM report WSDA AM report Native hawthorn location Shapefile WSDA AM report WSDA AM report Ornamental hawthorn location Shapefile WSDA AM report WSDA AM report Positive trap location Shapefile WSDA AM report WSDA AM report WSDA AM report = personal communication with Mike Klaus of the Washington State Department of Agriculture apple maggot annual report from 2003-2012. SuRDC = data retrieved via personal communication with Howard Thistlewood of the Summerland Research and Development Centre. * US Climate data version 2.2. Obtained April 26, 2017. Data averaged from 2003-2012. Trace amounts were assumed 0.254 mm ** Obtained June 11, 2016. Years of data = 2003-2012. Data averaged from 2003-2012. Trace amounts were assumed 0.254 mm *** 30 arc-second DEM of North America (Data Basin Dataset) - for WA elevation (http://www.arcgis.com/home/item.html?id=5771199a57cc4c29ad9791022acd7f74) cite: National Aeronautics and Space Administration (NASA), the United Nations Environment Programme/Global Resource Information Database (UNEP/GRID), the U.S. Agency for International Development (USAID), the Instituto Nacional de Estadistica Geografica e Informatica (INEGI) of Mexico, the Geographical Survey Institute (GSI) of Japan, Manaaki Whenua Landcare Research of New Zealand, and the Scientific Committee on Antarctic Research (SCAR). **** WSDAâs Natural Resource Assessment Section (sent by Perry Beale) 2012 pome orchard data, not all orchards represented here are commercially active, and some features and/or attribute info may be out of date. 35 Table 2.4. GIS files and other dataset files collected for each predictive model extent. Dataset file Type Kelowna Okanagan Valley Road Shapefile SuRDC SuRDC River/stream Shapefile SuRDC SuRDC Lake Shapefile SuRDC SuRDC Boundary Shapefile SuRDC SuRDC Pome orchard Shapefile SuRDC SuRDC Elevation ASCii file SuRDC SuRDC Temperature Textfile https://kelowna.weatherstats.ca for 2016* http://weather.gc.ca/forecast/canada/index_e.html?id=BC** Precipitation Textfile https://kelowna.weatherstats.ca for 2016* http://weather.gc.ca/forecast/canada/index_e.html?id=BC** Apple location Shapefile CFIA, OKSIR, TL CFIA, OKSIR, PNWH, TL Crabapple location Shapefile CFIA, OKSIR CFIA, OKSIR, TL Native hawthorn location Shapefile CFIA, TL, GBIF, PNWH, CFIA, GBIF, PNWH, TL, CM Ornamental hawthorn location Shapefile CFIA, TL, KEL TL, KEL, CFIA Positive trap location Shapefile CFIA AM report CFIA AM report GBIF = Collection data from the Global Biodiversity Information PNWH = Collection data from the Consortium of Pacific Northwest Herbaria KEL = Collection data from the city of Kelowna Parks Services TL = Collection data from the Thistlewood lab at SuRDC (personal) CFIA = Collection data from the CFIA internal report on apple maggot OKSIR = Collection data from the Sterile Insect Release program CM = Collection data from the Canadian Museum of Nature SuRDC = data retrieved via personal communication with Howard Thistlewood of the Summerland Research and Development Centre. * Obtained June 12, 2017. Trace amounts were assumed 0.254 mm ** Data year = 2016. Trace amounts were assumed 0.254 mm II.III BUILDING OF THE MODEL II.III.I Statement of Importance I strongly recommend reading the Overview, Design concepts, and Details (ODD) protocol (see Appendix A, which followed Grimm et al. (2006)) before continuing, as it not only details how the data from the previous section were used in the model, but it also pertains to important aspects in the following section and subsequent chapters that may be needed to fully understand and appreciate the processes involved hereafter. A standard protocol that aims to help modelers describe in detail their agent-based models 36 is presented as the ODD protocol, which consists of Overview, Design concepts, and Details, which are further subdivided into compartments: Purpose, State variables and scales, Process overview and scheduling, Initialization, Input, and Sub-models. This standardized protocol helps modellers communicate their methods more effectively and facilitates computational reproducibility (Grimm et al. 2006; Railsback and Grimm 2011). Heath et al. (2009) conducted a critical review of the modeling literature, including 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an ABM. Underscoring the importance of the ODD protocol, they found that 37.3% of articles did not provide any details on the software used for the construction and simulation of their model, only 15.8% of articles gave any reference to their completed model, 65% of models were not fully validated, and the primary validation technique used was expert opinion and qualitative comparisons of behaviour (Heath et al. 2009). II.III.II Focal Areas The main study areas were central Washington State, for the validation model, and the Okanagan Valley, for the predictive model (Figure 2.1). I implemented the models at two spatial extents: a small or local, city-wide extent, and a large, region-wide extent. For the small-scale extent, I used the city of Wenatchee within central Washington State as the focus and used the city of Kelowna (BC), for the validation and predictive models, respectively. For the large-scale extent, I used central Washington State and used the entire Okanagan Valley, plus some minor areas surrounding the valley, for the validation and predictive models, respectively. I chose these representatives as they feature similar geography and climate to that of their cross-border counterparts. The model divided the focal area into patches, or cells, of sizes dependent on the extent, as described in the ODD (Appendix A). Two sites represent the origin point of infestation for the Wenatchee and central Washington State extents. First catch during the 2003-2012 apple maggot survey was the Wenatchee Cemetery in Wenatchee (Sept 21, 2005; WSDA apple maggot survey reports) for the small-scale extent and 212 E. âSâ street in Yakima (June 20, 2003; WSDA apple maggot survey reports) for the large-scale extent. Only two positive apple maggot trap occurrence records have been officially identified in the Okanagan Valley: one in West Kelowna in 2015, which serves as the large-scale extent 37 origin location, and one in Kelowna in 2016, which serves as the small-scale extent origin location. For the models, I assumed these are actual origin points of a reproducing population and spread would start at these locations, however, in reality these positive trap locations may represent isolated occurrences without a reproducing population (WSDA apple maggot survey reports, CFIA 2017). Figure 2.1. Area of focus for the model. Includes both validation model focus areas (WA) and predictive model focus areas (BC). Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. Small-Scale Extent: Wenatchee and Kelowna Wenatchee is located in central Washington State, in both Chelan and Douglas counties. The boundary used for the model (Figure 2.2), is a rectangle encompassing the entire city. No positive traps were located beyond the boundary in the Wenatchee municipality. The polygon area is 143.73 km2, which resulted in 139,971 patches, where each patch is 1,026.86 m2. Kelowna is located in central Okanagan Valley. The boundary used for the model (Figure 2.3), is the municipal boundary of the city. The polygon area is 261.28 km2, which resulted in 50,799 patches, with each patch size at 5,143.41 m2. I chose Kelowna as a comparison for 38 Wenatchee as they share similar climate (temperature and precipitation), and also size and geography. Kelowna is 417 metres above sea level and Wenatchee is 195 metres above sea level. Figure 2.2. Area of focus for the small-extent validation model, Wenatchee, WA, USA. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 39 Figure 2.3. Study area for the small-extent predictive model, Kelowna, BC, Canada. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. Large-Scale Extent: Central Washington State and Okanagan Valley Central Washington State incorporates the following counties: Okanogan, Chelan, Douglas, Grant, Adams, Franklin, Walla Walla, Benton, Yakima, Kittitas, and Klickitat. These counties served as the boundary for the model (Figure 2.3). The polygon area is 71,906.72 km2, which resulted in 91,257 patches, where each patch is 787,958.40 m2. The boundary of the Okanagan Valley used in the model incorporates the regional boundary of the Okanagan Valley (north, central, and south, as well as watersheds) with the west and north portion partially cut off as they were beyond the valley bounds (Figure 2.5). The polygon area is 17,821.26 km2, which resulted in 71,818 patches, with each patch size at 248,144.75 m2. 40 Figure 2.4. Area of focus for the large-extent validation model, central Washington state, USA. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 41 Figure 2.5. Study area for the large-extent predictive model, Okanagan Valley, BC, Canada. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. II.III.III Experiment Treatments I explored three of the most important strategies for controlling apple maggot: public education (Porter 1928; Dowell 1991; Klaus 2015), insecticide use (Porter 1928; Dean and Chapman 1973; Schotzko 1982; Brunner 1987; Reissig 1988; Dowell 1991; Agriculture and Agri-Food Canada (AAFC) 2015; Klaus 2015), and host removal (Porter 1928; Dean and Chapman 1973; Brunner 1987; Dowell 1991; Agriculture and Agri-Food Canada (AAFC) 2015; Klaus 2015). These three strategies serve as the experimental treatments of the model. See the ODD protocol, in Appendix A, for more details on how I implemented the management strategies in the model. II.III.IV Netlogo and BehaviorSpace I built the ABM using NetLogo (version 5.3.1 (February 29, 2014); Wilensky 1999), which is an advanced, free, and open source ABM and simulation program. The reason for choosing NetLogo over other ABM programs (e.g. Repast) is that: (i) I am familiar with the unique coding language of NetLogo; (ii) NetLogo has extensive documentation and tutorials; (iii) building 42 models in NetLogo is comparatively straightforward; (iv) NetLogo is not as RAM-intensive as other ABM programs; and (v) NetLogo is the most widely used agent-based modeling system because of its basic programming requirement and ease of use (Wilensky 1999). Several studies have developed ABMs using Netlogo (Rebaudo et al. 2011; Rebaudo and Dangles 2013; Guerreiro et al. 2013; Thiele et al. 2014; Fachada et al. 2015; Shook et al. 2015; Wang et al. 2015). Model building resources were based on Railsback and Grimm (2011). Due to the usually stochastic nature of ABMs, between 10 to 100 runs are required for each combination of parameters. Increasing model complexity increases the number of unique parameter combinations, which presents logistical challenges (Shook et al. 2015). Netlogo partially addresses these challenges through a tool called BehaviorSpace, which allows for multiple simulations of different parameter configurations to run simultaneously. Nevertheless, each execution requires a single processor core; a computer with 4 processor cores can run 4 different runs simultaneously (Wilensky 1999; Railsback and Grimm 2011; Shook et al. 2015). For reference, my most complex models (described below) included 195 unique parameter combinations and 5,850 individual simulation runs. Repetitions Simulations for each parameter configuration in a model are referred to as repetitions (Wilensky 1999). Due to the stochastic nature of the model, I ran multiple repetitions for each parameter configuration. I defined the required number of repetitions as the number that yielded no significant change (among subsequent repetitions) in the coefficient of variation of % patches infested (Thiele et al. 2014). For this procedure I used one parameter configuration: Public Education at 50%, Insecticide Use at 0%, Host Removal at 50 %, and I assumed that other combinations would yield similar results. I found 20 repetitions to be optimal, but due to logistical constraints (particularly computation time), I used 15 repetitions per parameter configuration. This decision may have resulted in increased noise and thus decreased power in my simulation experiment. 43 II.III.V Validation and Statistical Methods Statistical Analysis Statistical analyses were only performed for the spread rate calculations and experimental treatment output comparisons. Matched paired t-tests were used to compare the change in spread isopleth areas from one year to the next year. T-tests were used to compare management strategy configurations and their corresponding model output attributes within same spread scenarios. An ANOVA and Tukey-Kramer tests were also performed to compare management strategy configurations and their corresponding model output attributes between different spread scenarios. The following programs were used to analyze the data: ArcGIS 10.1 (ESRI 2011), the Home Range Tools extension version 2.0.20 (Rodgers et al. 2015), and R version 0.98.1085 (R Development Core Team 2008) for the analysis. Isopleths were produced using the kernel density tool in ArcGIS 10.1. Metric Tests I used metric tests to validate the validation model, and I used the four metrics based on Anderson and Dragicevic (2015). These four measures compare the simulated results of the spread and infestation of apple maggot with real-world observations via apple maggot trap records from 2003-2012: (i) spatial agreement in location of infestation, which includes whether hosts were infested or not infested; (ii) spatial agreement on severity of infestation, which includes the severity of infestation (high, medium, low, none) of each host; (iii) non-spatial, proportional agreement on severity of infestation; and (iv) agreement of the extent and rate of infestation, which includes distance of propagation from the epicenter. These metric tests represent the most important validation process for my model, and acts as a proxy for statistical comparison. Visual Inspection This represents the least rigorous validation process and involves visually assessing the similarity between model output and the historical maps (empirical data). 44 II.IV ASSUMPTIONS AND LIMITATIONS II.IV.I Data Limitations Variables whose uncertainty potentially affects model accuracy include: (i) long-distance dispersal frequency; (ii) insecticide use effectiveness; (iii) actual costs of implementing different management strategies; (iv) natural and managed spread rate; and (v) management strategy levels/intensities used to manage apple maggot. Only limited data are available for each of these variables, hence their associated uncertainty. Additional information that may have been useful for the model but was either insufficient to use or not collected at all during the management effort in Washington State include: (i) a breakdown of budget; (ii) staff needed; (iii) time spent implementing management strategies; and (iv) management strategy effort (i.e. proportional representation of resources allocated to specific strategies). Knowledge of hawthorn distribution and abundance in the Okanagan Valley is incomplete and challenging to estimate, though my SDMs sought to address these challenges (see Appendix C). Most importantly, like all models, my ABM simplifies otherwise complex dynamics in the real-world, and thus model outputs should be interpreted accordingly. II.IV.II Assumptions of the Model The data describing apple maggot spread in central Washington State reflects the combined influences of climate, host availability, natural spread, and also management activities. Unfortunately, the specific impacts of the management activities are unknown. Nevertheless, I assumed that the Washington data are suitable for the purpose of modeling potential spread patterns within the Okanagan Valley. For my model, I made the following assumptions: â¢ human intervention/management did play a significant role in mitigating spread in central Washington State; â¢ the three main methods used to manage apple maggot were public education, host removal, and insecticide use, and no other method; â¢ the origin location for each extent in the validation model is when the first apple maggot was caught during the time period between 2003-2012 and for each extent in the predictive model is the two known apple maggot positive sites, where all origin sites contain a reproducing population to start the spread; 45 â¢ all apple maggots are of the same ecotype (thus no variation in host preference), i.e. no apple or hawthorn races; â¢ each fly in the population has the same base attributes (reproduction rate, mortality rate, spread rate), but with some stochasticity added; â¢ certain factors remain constant through time (e.g. climate, host fruit abundance), when in reality they change in quality; â¢ it is assumed that the parameter values derived from the Washington data are appropriate and translate appropriately to the predictive model; â¢ exposure to any management treatment does not affect apple maggot biology (ie: fecundity does not change in response to management); â¢ all biological variables are the best representation of apple maggot; â¢ comparisons between Kelowna and Wenatchee, and between the Okanagan Valley and central Washington State, are appropriate; â¢ scaling, or translating code and parameters from the large-scale extent to the small-scale extent and vice versa, is challenging. I wrote the code to accommodate this issue. The model calculates patches infested in its output, not apple maggot count per patch. Consequently, the scaling errors are assumed to have much reduced importance. 46 III Development of the Model Using Washington Data III.I SYNOPSIS I constructed an ABM to reproduce the apple maggot pattern of spread observed between 2003 and 2012 within central Washington State, and this serves as the validation model. III.II MODELING PROCESS OF THE VALIDATION MODEL An ideal ABM methodology to answer my thesis question would involve: (i) collecting the necessary data relating to each main component of the model including the apple maggot, the landscape, and management. I would have data illustrating what management strategies were used and how intense each were to affect the spread rate of apple maggot as seen in Washington State. This is crucial for comparing against the natural spread rate of apple maggot; (ii) I would then validate the validation modelâs simulated natural spread against real-world natural spread data. This is important to predict natural spread within a new area. I would also validate the validation modelâs simulated managed spread against real-world managed spread. This is important to make sure management strategies used in the model accurately match real-world effects of management. Validating these aspects of the model would increase our confidence in the model for prediction purposes; (iii) with the validation model validated against real-world data, I could then simulate natural spread of apple maggot into a new environment, the Okanagan Valley, to predict likely spread patterns. After, I would apply a full factorial exploration of management strategy configurations to determine which combination best mitigates spread. This would allow our confidence in recommendation to management and survey efforts to increase; and (iv) after analyzing the model, I could recommend which management strategy combinations work best, which do not, and which areas within the new environment we should focus our management efforts on. Unfortunately, I did not have access to data that describe spread rate, specifically data describing ânatural spread rateâ and âmanaged spread rateâ which were conflated in the historical data from Washington State. It was not clear how much of the spread we saw was due to natural spread process of AM or due to management efforts. Also, like spread rate, there are no real management values I can pull from previous studies. We also do not have data 47 illustrating how intense each management strategy was to affect the spread rate of apple maggot and the spread pattern seen in Washington State; we only know that the main management strategies used were host removal, public education, and insecticide spray. Overall, there were too many confounding and unknown factors within the data; the Washington data (2003-2012) include the potential effects of intensive pest management (see previous chapters) (Klaus M, WSDA, personal communication, 2017), but in the end, with the available data, I could not distinguish a spread front for apple maggot in central Washington State. Therefore, my ABM methodology was altered: (i) I calculated spread rate based on different scenarios of likely natural spread, and thus should be considered tentative, but the best estimates available (see Chapter III.III); (ii), once I calculated an appropriate spread rate, I conducted a Pattern-Oriented Modelling (POM) analysis in attempts to reproduce the spread pattern of apple maggot as seen in central Washington State from the years 2003-2012 (see Chapter III.IV); (iii), I validated the POM analysis using various metrics comparing the model to real-world data (see Chapter III.V); and (iv), I applied the validated model to a novel landscape to predict the spread pattern of apple maggot (see Chapter IV). The three key components to the model were the fly population, the landscape, and the management, which double as the three main interactions within the model. The three main processes included the flyâs survival and reproduction (which depends on climate and host availability), fly dispersal through the landscape via diffusion processes (density dependent), and host distribution (climate and density dependent). Therefore, the model experimental variable is management strategies and the model response variable is infestation of patches. In a similar study, Yonow et al. (2004) built a population dynamics model for the Queensland fruit fly. Some model inputs that they used were taken as a template for this model (see ODD protocol, Appendix A). III.II.I Validation Process I used four equally useful metrics (based from the Anderson and Dragicevic (2015)) to validate the validation model: (i) spatial agreement in location of infestation; (ii) spatial agreement on severity of infestation; (iii) non-spatial, proportional agreement on severity of infestation; and (iv) agreement of the extent and rate of infestation. The analysis of the results using these metrics are as follows: (i) binary confusion metric comparing infested and non- 48 infested hosts with simulation results and real-world observations and determining the accuracy; (ii) confusion matrix comparing the severity of infestation (high, moderate, low, none) with simulation results and real-world observations and determining the precision of each; (iii) determining the proportion of infestation (high, moderate, low, none) with simulation results and real-world observations; and (iv) a histogram representing the amount of infested trees over the distance from the epicenter (Anderson and DragiÄeviÄ 2015). Spatial severity of infestation levels are determined by the calculated standard deviation of apple maggot density per host tree, based on real-world trap catch data: patches with values that are > 1.5 of the standard deviation are defined as high apple maggot infestation; from 0.5 - 1.5 of the standard deviation are defined as moderate apple maggot infestation; and patches with values that are < 0.5 of the standard deviation are defined as low apple maggot infestation. This classification is applied to both real-world data and the simulation data. For metric 1 and 2, the accuracy and precision of the model were discussed. Model accuracy determines the overall agreement between the model and the validation data with respect to all classes (ie for metric 1, infested hosts and non-infested hosts combined), while model precision determines the agreement between the model and the validation data with respect to one class specifically (ie for metric 1, just infested hosts) (Anderson and DragiÄeviÄ 2015). III.III PARAMETERIZATION OF SPREAD RATE III.III.I Determining Spread Rate with Real-World Data To explore the historical apple maggot spread in central Washington State, I examined the invasion history using MCP, kernel density analysis (Borger et al. 2006), and square-root area regression and distance regression methods (Tobin et al. 2015). I performed the MCP analysis at 100%, 95%, and 90% inclusion rates. A 100% MCP creates a polygon that encompasses all positive trap site locations, a 95% MCP creates a polygon that excludes 5% of the outlying data points, and a 90% MCP creates a polygon that excludes 10% of the outlying data points. I used an isopleth, or probability contour, of 0.9, which also contains the annual infestation sites, with lower isopleths, from 0.8 to 0.1, identifying clusters. I chose the 2003 June 20th Yakima site as the origin point for the distance regression as it was the first catch site of the 49 dataset used. Both methods were performed using thresholds of one, two, and three or more flies per trap. The area of focus for this analysis included the entire central Washington State and Wenatchee. Apple maggot positive trap data from 2003-2012 (Klaus 1997-2015) provided the best means for analysis (complete datasets, total male and female per trap, dates and locations, relatively recent to the start of this analysis in 2015). Consistent year-to-year climate values and landscape structures were assumed. The figures and tables produced for this section are given in Appendix B. Attempting to calculate spread rate of an invasive insect is crucial for management but is also challenging. At first glance, the amount of trap data available in central Washington State (see Chapter II) had potential to produce useful spread rates. The challenge with the apple maggot dataset is that we are dealing with mostly single-catches with no cohesive expansion; mostly sporadic sites from year to year. The majority of traps (approximately 83%) only caught 1 specimen, and rarely did any catch more than a few specimens throughout the 2003-2012 datasets. Tobin et al. (2015) used thresholds of 1, 10, and 100 or more moths per trap to estimate the spread rate of gypsy moth using a square-root area regression and a distance regression. The data used to conduct apple maggot spread using these methods were insufficient compared to what the Tobin et al. (2015) had, as moving from thresholds of 1 to 2 to 3 apple maggot per trap resulted in significantly less data points to work with. Another challenge with this data is that trap density, location, and numbers varied from year to year. The positive trap locations were so spatially sparse and the traps themselves rarely captured more than one specimen, that it was difficult or impossible to produce reliable spread rates. Tobin et al. (2015) found that the methods produced similar spread rates, but stress that point data from poor trapping systems could produce misleading rates of spread. However, there are many factors that influence our inability to accurately predict spread rate, including episodic temporal variation, environmental heterogeneity, diversity or interactions within the invaded community, long range dispersal events, and evolutionary dynamics (local adaptation or development of phenotypic plasticity) (Hastings et al. 2005). Also, if an area was infested one year and not the next year, it does not mean they disappeared or was just an outlier occurrence; it could mean we are not detecting them, or management suppressed them. Point data from trap surveys are greatly influenced by the sensitivity of the method used for making the measurements: if the trap is insufficient to 50 begin with, then the data may be misleading and may provide an underestimate of population density or an underestimate of the timing of the first catch (Tobin et al. 2015). The analyses provided details on clustering of populations and illustrated a lack of intense overall spreading of apple maggot. Finally, the results were not convincing of any apparent expansive front, which may have something to do with the extensive management that went on during the years the data were collected. III.III.II Determining Spread Rate using a Sensitivity Analysis Due to the constraints of real apple maggot data for estimation of spread rate, a calibration of spread values was required before management parameters were estimated. To obtain likely natural spread rates, I conducted a sensitivity analysis using three types of spread, which included: (i) simple diffusion from an origin point using the âdiffusionâ variable; (ii) long-distance non-human-mediated jump events; and (iii) and long-distance human-mediated dispersal, see ODD protocol (Appendix A). Sensitivity analyses are used to explore how strong the influences of varying inputs, especially model parameters, are on the outputs of the model (Thiele et al. 2014). In the model, the diffusion value represents the proportion of apple maggot that spread from one patch to an adjacent patch. The long-distant, non-human-mediated value represents the frequency, out of 100, that a long-distance natural event does not occur (ie a value of 90 means that these events occur 10% of the time). And the long-distant, human-mediated value represents the frequency, out of 100, that a long-distance event via human transportation does not occur. Diffusion percentage values range from 0.1 to 0.35, steps of 0.05 â 0.1; long-distance, non-human-mediated dispersal frequency values range from 50 to 95, steps of 5 â 10; and long-distance, human-mediated dispersal frequency values range from 60 to 95, steps of 5 â 10. This produced 120 different spread configurations per extent. I repeated each configuration 5 times, and the raster maps for each configuration were averaged. The simulations were done in NetLogo (Program version 5.3.1.) using model version 1.18. Of the 120 unique configurations, only three were chosen based on most infested patches (extreme), least infested patches (casual), and in the middle (moderate) [not shown]. Three different spread scenarios were used due to uncertainty with actual spread rates, and therefore using scenarios involving both extremes and a middle-ground is thought to be ideal to obtain a variety of patterns that could potentially match the historical spread. 51 III.IV CALIBRATION OF SPREAD PATTERN USING POM I used POM to adjust the values for three management strategies (public education, insecticide use, and host removal) so as to calibrate the correct combination that reproduced the spread pattern of apple maggot in central Washington State. The values obtained from the sensitivity analysis were used as calibrated values. I included a management strategy configuration of 0% and assume no management intervention; this was expected to be worse than any management parameter configuration as I attempted to simulate a ânaturalâ spread and provide a baseline scenario of no management intervention. Management strategies have intensity levels (0% = no intensity, 25% = minimal intensity, 50% = moderate intensity, 75% = high intensity, and 100% = maximal intensity) at steps of 25%. Because all three main management strategies are used in the real-world to suppress apple maggot, the baseline configuration was 25% for each, in addition to 0% or no management. With this, 195 parameter combinations (65 management combinations based on the three management strategies (including a no-management combination) x 3 different spread scenarios as per the sensitivity analysis) for each of the validation model extents (Wenatchee and central Washington State) were simulated for 15 repetitions, totaling 5,850 simulations. The purpose of running the 195 parameter configurations is to calibrate and validate the model so that I can have some confidence in the modelâs ability to do predictive work. The simulations were done in NetLogo (Program version 5.3.1.) using model version 1.19. III.V RESULTS OF THE VALIDATION MODEL Overall, the simulations took 100 days, 8 hours, and 54 minutes for all extents and spread scenarios to complete (including simulations for the predictive model). Table 3.1 provides the parameter configurations for each extent and spread scenario used for the validation model based on the POM analysis. These six management configurations (three per extent) were determined to be the best configurations for each extentsâ spread scenarios based on overall percentage of real-world infested patches simulated as infested and overall percentage of real-world non-infested patches simulated as non-infested. These specific management configurations are thus carried over for use in the prediction model. 52 Table 3.1. The best fit management configurations for both validation model extents and all three spread scenarios for each extent. Management strategies were shortened for simplicity: citizen% = public education intensity, spray% = insecticide spray intensity, and removal% = host removal intensity. citizen% spray% removal% Extent* Spread Scenario Infested Positive Patches % Overall** Non-Infested Non-Positive Patches % Overall*** 100 75 25 WA Extreme 63 25.6 1697 82.9 100 100 100 WA Moderate 58 23.6 1787 87.3 50 100 25 WA Casual 59 24.0 1797 87.8 100 75 75 WEN Extreme 7 87.5 316 52.3 100 25 50 WEN Moderate 7 87.5 360 59.6 100 25 25 WEN Casual 4 50.0 498 82.5 * WA = central Washington State, the large-extent model. WEN = Wenatchee, the small-extent model. ** represents the % of real-world infested (positive trap) patches simulated in the model as infested; see Table 2.1 for details on positive trap count per extent. *** represents the % of real-world non-infested (non-positive trap) patches simulated in the model as non-infested; see Table 2.1 for details on non-positive trap count per extent. III.V.I General Model Outcomes When the spread scenario decreased from extreme, to moderate, to casual, the infestation intensity decreased, with the higher density infestation patches closer to the origin point. This is shown in Figure 3.1 (small-extent) and Figure 3.2 (large-extent), patch infestation frequency, where infestation density is shown in shades of red, and host point data illustrate infested or non-infested status. Conversely, as spread scenario decreased, the infested host points decrease, and relatively more infested host points occurred closer to the origin point. 53 Figure 3.1. Final maps of the simulation for Wenatchee. No management scenarios (experimental treatments at 0%) are on the top, while managed configurations are on the bottom. Spread scenarios are in order from left to right: extreme, moderate, and casual. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 54 Figure 3.2. Final maps of the simulation for central Washington State. No management scenarios (experimental treatments at 0%) are on the top, while managed configurations are on the bottom. Spread scenarios are in order from left to right: extreme, moderate, and casual. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. III.V.II Effects of Management Configurations on Spread The measured attributes of the simulation (Table 3.2 for small-extent and Table 3.3 for large-extent) revealed that all three of the management configuration spread scenarios yielded significant differences within same spread scenario (non-managed vs managed configurations, t-test, p < 0.001). My implementation of the management configuration had a desirable effect, in that it yielded differences in the effects of management strategies. 55 Table 3.2. Comparison of simulation attributes for both non-management configurations and top management configurations for the validation small-extent model. Management strategies were shortened for simplicity: citizen% = public education intensity, spray% = insecticide spray intensity, and removal% = host removal intensity. *LDD_H = human-mediated, long-distance dispersal event *Scenario refers to spread scenario Table 3.3. Comparison of simulation attributes for both non-management configurations and top management configurations for the validation large-extent model. Management strategies were shortened for simplicity: citizen% = public education intensity, spray% = insecticide spray intensity, and removal% = host removal intensity. *LDD_H = human-mediated, long-distance dispersal event *Scenario refers to spread scenario Citizen% Spray% Removal% Extent ScenarioMean % overall patches infestedMean % orchard patches infestedMean % host patches infestedMean % undetected patches infestedMean # positive patches infestedMean # non-positive patches infestedMean # host patches removedMean # orchard patches sprayedMean # LDD_H events avoidedMean % apple patches remainingMean % crabapple patches remainingMean % N hawthorn patches remainingMean % O hawthorn patches remainingMean total costMean cost for overall infested patchesMean cost for infested orchard patchesMean cost for host patch removalMean cost for LDD_H avoidanceMean cost for orchard patch spraying0 0 0 WEN Extreme 6.06 6.72 14.22 6.00 2.20 85.53 0.00 0.00 0.00 100.00 100.00 100.00 100.00 187,045.53 147,310.87 39,734.67 0.00 0.00 0.000 0 0 WEN Moderate 3.40 4.43 7.48 3.36 1.60 44.60 0.00 0.00 0.00 100.00 100.00 100.00 100.00 127,745.47 98,680.67 29,064.80 0.00 0.00 0.000 0 0 WEN Casual 1.02 1.19 2.77 1.01 1.20 15.93 0.00 0.00 0.00 100.00 100.00 100.00 100.00 40,454.53 36,307.07 4,147.47 0.00 0.00 0.00100 75 75 WEN Extreme 4.05 2.88 11.90 4.04 2.60 70.27 72.40 767.13 379.80 85.69 89.87 97.41 89.85 213,351.77 41,534.78 1,108.53 24,389.05 145,778.00 541.40100 25 50 WEN Moderate 2.32 1.24 6.42 2.33 1.73 37.53 39.20 358.67 233.07 92.72 94.35 98.15 93.70 127,348.78 28,052.42 453.87 8,880.40 89,877.67 84.43100 25 25 WEN Casual 0.65 0.37 1.93 0.65 1.27 10.53 11.53 102.00 81.07 97.58 98.89 100.00 97.74 41,359.00 9,459.00 169.60 1,270.70 30,435.73 23.97Citizen% Spray% Removal% Extent ScenarioMean % overall patches infestedMean % orchard patches infestedMean % host patches infestedMean % undetected patches infestedMean # positive patches infestedMean # non-positive patches infestedMean # host patches removedMean # orchard patches sprayedMean # LDD_H events avoidedMean % apple patches remainingMean % crabapple patches remainingMean % N hawthorn patches remainingMean % O hawthorn patches remainingMean total costMean cost for overall infested patchesMean cost for infested orchard patchesMean cost for host patch removalMean cost for LDD_H avoidanceMean cost for orchard patch spraying0 0 0 WA Extreme 11.87 29.54 38.38 9.88 91.47 792.73 0.00 0.00 0.00 100.00 100.00 100.00 100.00 1,885,327.12 1,267,793.78 617,533.33 0.00 0.00 0.000 0 0 WA Moderate 6.78 17.32 27.23 5.46 77.53 550.07 0.00 0.00 0.00 100.00 100.00 100.00 100.00 969,734.45 652,004.98 317,729.47 0.00 0.00 0.000 0 0 WA Casual 2.16 6.19 12.23 1.59 50.47 231.73 0.00 0.00 0.00 100.00 100.00 100.00 100.00 321,624.10 210,280.23 111,343.87 0.00 0.00 0.00100 75 25 WA Extreme 0.90 4.71 10.54 0.41 46.40 195.13 241.53 12365.60 359.67 94.84 89.76 93.99 82.22 403,184.37 137,582.50 92,088.67 23,905.95 140,934.60 8,672.65100 100 100 WA Moderate 0.53 2.22 6.74 0.25 39.27 115.20 154.40 5686.40 225.47 97.67 93.84 97.61 86.76 259,368.23 63,382.70 39,084.67 64,854.40 86,730.13 5,316.3350 100 25 WA Casual 0.82 0.99 2.10 0.76 18.00 32.13 47.67 1889.53 41.53 98.90 98.76 99.17 95.74 48,525.93 23,696.52 9,316.67 6,008.22 7,828.60 1,675.93 56 III.V.III Validation Metrics Refer to Chapter I.V.II for background on validating the model and refer to Chapter III.II.I for validation processes. Metric 1 I created a binary confusion matrix to present the accuracy of the model with respect to spatial agreement of patch locations between real-world infestation and simulated infestation (Table 3.4 for small-extent and Table 3.5 for large-extent). For the small-extent, the accuracy of the model under the three spread scenarios of extreme, moderate, and casual spread was 52.8%, 60.0%, and 82.0%, respectively. Similarly, the precision of simulated infestations of host patches was 87.5% for extreme, 87.5% for moderate, and 50.0% for casual, compared with the precision of simulated non-infestation of host patches, which was 52.3% for extreme, 59.6% for moderate, and 82.5% for casual. For the large-extent, the accuracy of the model under the three spread scenarios of extreme, moderate, and casual spread was 76.8%, 80.5%, and 81.0%, respectively. Similarly, the precision of simulated infestations of host patches was 25.6% for extreme, 23.6% for moderate, and 24.0% for casual, compared with the precision of simulated non-infestation of host patches, which was 82.9% for extreme, 87.3% for moderate, and 87.8% for casual. The modelâs accuracy in simulating the location of apple maggot infestation for both extents improves as the spread scenario changes from extreme, to moderate, to casual. The modelâs precision of simulated infestations of host patches, however, declines as spread scenarios change from extreme, to moderate, to casual for both extents. Conversely, the modelâs precision of simulated non-infestations of host patches improves for both extents with the same changes in spread scenario. 57 Table 3.4. Binary confusion matrix presenting the spatial agreement between apple maggot infestation simulation and real-world apple maggot infestation observed in Wenatchee, Washington. The bold values quantify a match between the state of the simulated patches and the state of the patches in reality. Simulation (top), real world data (left). A = extreme scenario, B = moderate scenario, and C = casual scenario. A) Infested Not Infested Precision (%) Infested 7 1 87.5 Not Infested 288 316 52.3 B) Infested Not Infested Precision (%) Infested 7 1 87.5 Not Infested 244 360 59.6 C) Infested Not Infested Precision (%) Infested 4 4 50.0 Not Infested 106 498 82.5 Table 3.5 Binary confusion matrix presenting the spatial agreement between apple maggot infestation simulation and real-world apple maggot infestation observed in central Washington State. The bold values quantify a match between the state of the simulated patches and the state of the patches in reality. Simulation (top), real world data (left). A = extreme scenario, B = moderate scenario, and C = casual scenario. A) Infested Not Infested Precision (%) Infested 63 183 25.6 Not Infested 349 1697 82.9 B) Infested Not Infested Precision (%) Infested 58 188 23.6 Not Infested 259 1787 87.3 C) Infested Not Infested Precision (%) Infested 59 187 24.0 Not Infested 249 1797 87.8 58 Metric 2 I created a confusion matrix to present the accuracy of the model with respect to spatial agreement between real-world infestation severity and simulation infestation severity (Table 3.6 for small-extent and Table 3.7 for large-extent). For the small-extent, the model had an accuracy of 52.1% for extreme, 59.5% for moderate, and 81.9% for casual in simulating the location of severity of apple maggot patch infestation. For the large-extent, the model had an accuracy of 75.7% for extreme, 79.2% for moderate, and 79.8% for casual. Table 3.6. Confusion matrix presenting the spatial agreement between severity of apple maggot infestation simulation and severity of real-world apple maggot infestation observed in Wenatchee, Washington. The bold values quantify a match between the state of the simulated patches and the state of the patches in reality. N/A indicates no patch was classified as moderate infestation. Simulated (top), real world data (left). A = extreme scenario, B = moderate scenario, and C = casual scenario. A) Not Infested Low Moderate High Precision (%) Not Infested 316 228 40 20 52.3 Low 1 3 1 2 42.9 Moderate 0 0 0 0 N/A High 0 1 0 0 0.0 B) Not Infested Low Moderate High Precision (%) Not Infested 360 187 47 10 59.6 Low 1 4 1 1 57.1 Moderate 0 0 0 0 N/A High 0 1 0 0 0.0 C) Not Infested Low Moderate High Precision (%) Not Infested 498 91 15 0 82.5 Low 3 3 0 1 42.9 Moderate 0 0 0 0 N/A High 1 0 0 0 0.0 59 Table 3.7. Confusion matrix presenting the spatial agreement between severity of apple maggot infestation simulation and severity of real-world apple maggot infestation observed in central Washington State. The bold values quantify a match between the state of the simulated patches and the state of the patches in reality. Simulated (top), real world data (left). A = extreme scenario, B = moderate scenario, and C = casual scenario. A) Not Infested Low Moderate High Precision (%) Not Infested 1697 266 54 29 82.9 Low 183 28 9 0 12.7 Moderate 0 0 0 15 0.0 High 0 0 0 11 100.0 B) Not Infested Low Moderate High Precision (%) Not Infested 1787 209 30 20 87.3 Low 166 27 6 21 12.3 Moderate 11 1 1 2 7.1 High 11 0 0 0 0.0 C) Not Infested Low Moderate High Precision (%) Not Infested 1797 235 8 6 87.8 Low 168 31 11 10 14.1 Moderate 10 4 0 1 0.0 High 9 2 0 0 0.0 Metric 3 The model simulates the different levels of infestation severity relatively closer to real-world levels as the spread scenario changes from extreme to moderate to casual (Figure 3.3 for small-extent and Figure 3.4 for large-extent). This is more evident in the large-extent casual spread scenario (Figure 3.4C). 60 A) B) C) Figure 3.3. Proportional representation of severity of infestation for the small-extent, Wenatchee. A = extreme scenario, B = moderate scenario, and C = casual scenario. Figure created by Brian Muselle. 98.751.81.137.90.0 6.70.2 3.60102030405060708090100Real SimulatedProportion %Not Infested Low Infestation Moderate Infestation High Infestation98.759.01.131.40.07.80.2 1.80102030405060708090100Real SimulatedProportion %Not Infested Low Infestation Moderate Infestation High Infestation98.782.01.115.40.02.50.20.20102030405060708090100Real SimulatedProportion %Not Infested Low Infestation Moderate Infestation High Infestation 61 A) B) C) Figure 3.4. Proportional representation of severity of infestation for the large-extent, central Washington State. A = extreme scenario, B = moderate scenario, and C = casual scenario. Figure created by Brian Muselle. 89.382.09.612.80.72.70.5 2.40102030405060708090100Real SimulatedProportion %Not Infested Low Infestation Moderate Infestation High Infestation89.3 86.29.610.30.7 1.60.5 1.90102030405060708090100Real SimulatedProportion %Not Infested Low Infestation Moderate Infestation High Infestation89.3 86.69.6 11.90.7 0.80.5 0.70102030405060708090100Real SimulatedProportion %Not Infested Low Infestation Moderate Infestation High Infestation 62 Metric 4 The spatial extent of the infestation in Wenatchee and central Washington State are represented using a histogram where the distances between the origin patch and infested host patches are illustrated (Figure 3.5 for small-extent and Figure 3.6 for large-extent). For the small-extent, the mean distance from the origin point to infested points in the real-world data was 2.67 km, and for the simulation: extreme = 1.84 km, moderate = 1.61 km, and casual = 1.25 km. The maximum distance from the origin point to infested points in the real-world data was 5.66 km, and for the simulation: extreme = 6.84 km, moderate = 5.06 km, and casual = 6.08 km. For the large-extent, the mean distance from the origin point to infested points in the real-world data was 44.90 km, and for the simulation: extreme = 34.21 km, moderate = 21.93 km, and casual = 70.27 km. The maximum distance from the origin point to infested points in the real-world data was 220.27 km, and for the simulation: extreme = 220.94 km, moderate = 229.61 km, and casual = 243.32 km. Although the frequency distributions of distances within simulated and real-world data differ overall, some similarities emerged: (i) a second peak begins to emerge around the 3 km distance mark in the small-extent histograms and around the 100 km distance mark in the large-extent histograms; (ii) the majority of positive sites were within the 1 km distance mark in small-extent histograms and within the 50 km in large-extent; and (iii) for both the small-extent and large-extent histograms, there is a moderate right-skew in the data and results. 63 A) B) C) D) Figure 3.5. Infestation extent represented by distance in km between the initial point of infestation in Wenatchee to the infested hosts obtained (A-C) by the simulation (A = extreme spread scenario, B = moderate spread scenario, and C = casual spread scenario) and (D) in the real world. Figure created by Brian Muselle. 64 A) B) C) D) Figure 3.6. Infestation extent represented by distance in km between the initial point of infestation in central Washington state to the infested hosts obtained (A-C) by the simulation (A = extreme spread scenario, B = moderate spread scenario, and C = casual spread scenario) and (D) in the real world. Figure created by Brian Muselle. III.VI DISCUSSION OF THE VALIDATION MODEL When comparing simulated and real-world data, there was a 52.8 â 82.0 % agreement for the locations of the infestation in Wenatchee, and a 76.8 â 81.0 % agreement in central Washington State. In terms of the spatial agreement on severity of infestation, when comparing simulated and real-world data, there was a 52.1 â 81.9 % agreement for the locations of the infestation severity in Wenatchee, and a 75.7 â 79.8 % agreement in central Washington State. In terms of the proportional agreement (non-spatial) on severity of infestation, the model simulated the different levels of infestation severity relatively closer to real-world levels as the spread scenario changes from extreme, to moderate, to casual for both Wenatchee and central 65 Washington State. With respect to the agreement of the extent and rate of infestation from 2003 to 2012, the mean distance and maximum distance from the origin point to infested points in the real-world data was 2.67 km and 5.66 km, respectively, for Wenatchee, and 44.90 km and 220.27 km, respectively, for central Washington State. The simulation results provided the mean distance and maximum distance from the origin point to infested points in Wenatchee of between 1.25 â 1.84 km and 5.06 â 6.84 km, respectively, and of between 21.93 â 70.27 km and 220.94 â 243.32 km, respectively, for central Washington State. The variation between the small-extent and large-extent can be explained by the difference in the size of each extent, the difference in the quantity of infested and non-infested real-world sites in each extent, and the difference in the management strategy configurations needed to match the historical spread patterns within each extent. Given the number of sources of variability in the real world and the difficulty of capturing it all with the model, I considered these comparisons acceptable in terms of validating the model against empirical data. These validation results suggest that the present ABM did a reasonable job at matching the spread pattern of apple maggot where both the spatial extent and severity of infestation are simulated relatively realistically. Though, because the validation of this model was based on the validation process of Anderson and DragiÄeviÄ (2015), a comparison should be made. Their simulation provided an overall accuracy of 72 % with respect to the overall agreement between the model and historical data with respect to infested and non-infested hosts (compared to my model overall accuracy of between 52.8 â 82.0 %, depending on the specific extent and spread scenario observed), and an accuracy of 64 % with respect of the severity of infestation per host (compared to my model overall accuracy of between 52.1 â 81.9 %, depending on the specific extent and spread scenario observed) (Anderson and DragiÄeviÄ 2015). These accuracies are similar to the rates observed in my apple maggot validation model, and as such were determined as acceptable. The error rate, with respect to metric 1 (see Tables 3.4 and 3.5), in predicting invaded patches as non-invaded ranged from 12.5 â 50.0 % (for small-extent) and from 74.4 â 76.4 % (for large-extent), while in predicting non-invaded patches as invaded yielded values ranging from 17.5 - 47.7 % (for small-extent) and from 12.2 - 17.1 % (for large-extent). In Muirhead et al. (2006) emerald ash borer ABM, error rate in predicting invaded sites as non-invaded was twice as great relative to predicting non-invaded sites as invaded, 35 % and 16 %, respectively 66 (Muirhead et al. 2006). Clearly, and similarly to Muirhead et al. (2006) study, the error rate in my model in predicting non-invaded patches as invaded was lower relative to predicting invaded patches as non-invaded. I implemented the validation model on real datasets from central Washington State and Wenatchee, WA, from 2003 to 2012. Host inventory, climate, and other GIS data were used to represent the spatial environment. I performed the model verification based on the literature documenting the life cycle processes of the apple maggot to represent its behavior. I calibrated the model using estimates of various spread methods and I validated the model using datasets delimiting the spatial extent and severity of apple maggot infestation from 2003 to 2012. It is difficult to determine whether management actually played a role in apple maggot suppression; it is plausible that environmental conditions in central Washington State were such that management played little in mitigating the spread, and that natural spread would have been slow regardless of management intensity. However, for the purposes of this thesis, I assumed management actually played a significant role. Based on the data implemented into the model and the model results, the model is considered fully validated and the application to a new environment, the Okanagan Valley, was with confidence. 67 IV Implementation of the Model in the Okanagan Valley IV.I SYNOPSIS In this chapter, I built a model that may predict potential spread within a representative landscape within the Okanagan Valley. This serves as the predictive model and is based on and sensitive to the validation model (Chapter III). Spread patterns are compared among alternative management strategies to evaluate and explore the influences that landscape structure, host plant distribution, and management may have on invasion patterns. IV.II MODELING PROCESS OF THE PREDICTIVE MODEL For the predictive model, I used the best parameter configurations as per the validation model, which produced the closest pattern as seen in the real-world data in central Washington State. These were then used to explore when spread is mitigated to the greatest degree, measured by the least amount of area infested costing the least amount of resources and whether areas were infested regardless of management, within Kelowna (small-extent) and the Okanagan Valley (large-extent). The predictive model is implemented on real datasets based on the validation model. Details and information pertaining to the modeling process can be found in the ODD protocol (Appendix A). Confidence in the predictive model is further backed by the validation process of the validation model. Overall, this totals 6 parameter configurations (2 predictive model extents x 3 different spread scenarios as per the sensitivity analysis in Chapter III.III.II). In addition, no-management scenarios for the 6 parameter configurations were simulated. Each configuration ran with 15 repetitions = totaling 180 simulations. The simulations were done in NetLogo (Program version 5.3.1.) using model version 1.19. In total, the simulations took 100 days, 8 hours, and 54 minutes for all extents and spread scenarios to complete (including simulations for the validation model). IV.III RESULTS OF THE PREDICTIVE MODEL For the purpose of ease, all configurations with 0 for citizen%, spray%, and removal% are referred to as âcontrolâ, and all configurations that are not are referred to as âmanagedâ. 68 The best fit management configurations, as per the validation model, for the predictive model are shown in Table 4.1 (small-extent) and Table 4.2 (large-extent). Table 4.1. The best fit management scenarios for the small-extent model and all three spread scenarios. Management strategies were shortened for simplicity: citizen% = public education intensity, spray% = insecticide spray intensity, and removal% = host removal intensity. citizen% spray% removal% Extent* Spread Scenario Infested Positive Patches % Overall** Non-Infested Non-Positive Patches % Overall*** 0 0 0 KEL Extreme 1 100 102 5.1 0 0 0 KEL Moderate 1 100 236 11.7 0 0 0 KEL Casual 1 100 851 42.2 100 75 75 KEL Extreme 1 100 1108 54.9 100 25 50 KEL Moderate 1 100 1358 67.3 100 25 25 KEL Casual 1 100 1651 81.9 * KEL = Kelowna, the small-extent model. ** represents the % of real-world infested (positive trap) patches simulated in the model as infested; see Table 2.2 for details on positive trap count for this extent. *** represents the % of real-world non-infested (non-positive trap) patches simulated in the model as non-infested; see Table 2.2 for details on non-positive trap count for this extent. Table 4.2. The best fit management scenarios for the large-extent model and all three spread scenarios. Management strategies were shortened for simplicity: citizen% = public education intensity, spray% = insecticide spray intensity, and removal% = host removal intensity. citizen% spray% removal% Extent* Spread Scenario Infested Positive Patches % Overall** Non-Infested Non-Positive Patches % Overall*** 0 0 0 OK Extreme 1 100 659 26.0 0 0 0 OK Moderate 1 100 677 26.7 0 0 0 OK Casual 1 100 807 31.9 100 75 25 OK Extreme 1 100 2049 80.9 100 100 100 OK Moderate 1 100 2218 87.6 50 100 25 OK Casual 1 100 1671 66.0 * OK = Okanagan Valley, the large-extent model. ** represents the % of real-world infested (positive trap) patches simulated in the model as infested; see Table 2.2 for details on positive trap count for this extent. *** represents the % of real-world non-infested (non-positive trap) patches simulated in the model as non-infested; see Table 2.2 for details on non-positive trap count for this extent. IV.III.I General Model Outcomes When the spread scenario decreased from extreme, to moderate, to casual, the infestation intensity decreased, with the higher density infestation patches closer to the origin point. This is 69 shown in Figure 4.1 (small-extent) and Figure 4.2 (large-extent), patch infestation frequency, where infestation density is shown in shades of red, and host point data illustrate infested or non-infested status. Conversely, as spread scenario decreased, the infested host points decrease, and relatively more infested host points occurred closer to the origin point. Figure 4.1. Patch infestation frequency of the small-extent simulation. No management scenarios (experimental treatments at 0%) are on the top, while managed scenarios are on the bottom. Spread scenarios are in order from left to right: extreme, moderate, and casual. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 70 Figure 4.2. Patch infestation frequency of the large-extent simulation. No management scenarios (experimental treatments at 0%) are on the top, while managed scenarios are on the bottom. Spread scenarios are in order from left to right: extreme, moderate, and casual. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. The same type of pattern can be seen when using host point data to illustrate infested or non-infested patches (Figure 4.3 for small-extent and Figure 4.4 for large-extent). When the spread scenario decreased from extreme, to moderate, to casual, the number of infested host points decreased, with the higher density of infested host points closer to the origin point. 71 Figure 4.3. Host point infestation frequency of the small-extent simulation. Spread scenarios are in order from top to bottom: extreme (left), moderate (right), and casual (bottom). Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 72 Figure 4.4. Host point infestation frequency of the large-extent simulation. Spread scenarios are in order from top to bottom: extreme (left), moderate (right), and casual (bottom). Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 73 In determining which host locations are infested regardless of management, Figure 4.5 (small-extent) and Figure 4.6 (large-extent) illustrate host points infested both during âcontrolâ and âmanagedâ simulations. For the small-extent, the percentage of host points infested regardless of management for each spread scenario were 44.8% (or 905 points) for extreme, 31.3% (or 632 points) for moderate, and 13.3% (or 268 points) for casual. For the large-extent, 19.1% (or 484 points) for extreme, 12.4% (or 315 points) for moderate, and 34.0% (or 862 points) for casual. 74 Figure 4.5. Host point infestation frequency of the small-extent simulation with respect to host sites being infested in both âcontrolâ and âmanagedâ simulations. Red dots indicate the host point was infested both during the âcontrolâ simulation and âmanagedâ simulation. Spread scenarios are in order from top to bottom: extreme (left), moderate (right), and casual (bottom). Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 75 Figure 4.6. Host point infestation frequency of the large-extent simulation with respect to host sites being infested in both âcontrolâ and âmanagedâ simulations. Red dots indicate the host point was infested both during the âcontrolâ simulation and âmanagedâ simulation. Spread scenarios are in order from top to bottom: extreme (left), moderate (right), and casual (bottom). Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 76 IV.III.II Effects of Management Configurations on Predictive Spread The measured attributes of the simulation (Table 4.3 for small-extent and Table 4.4 for large-extent) revealed that all three of the management configuration spread scenarios yielded significant differences within same spread scenario (âcontrolâ vs âmanagedâ configurations, t-test, p < 0.0001). This shows that management configurations were significantly different from non-management configurations. Comparisons between management configuration spread scenarios (âmanagedâ vs âmanagedâ) were determined using an ANOVA test, followed by a Tukey-Kramer post-hoc test (Table 4.5 for small-extent and Table 4.6 for large-extent). 77 Table 4.3. Comparison of simulation attributes for both âcontrolsâ and top management strategy combinations for the predictive small-extent model. Management strategies were shortened for simplicity: citizen% = public education intensity, spray% = insecticide spray intensity, and removal% = host removal intensity. *LDD_H = human-mediated, long-distance dispersal event *Scenario refers to spread scenario Table 4.4. Comparison of simulation attributes for both âcontrolsâ and top management strategy combinations for the predictive large-extent model. Management strategies were shortened for simplicity: citizen% = public education intensity, spray% = insecticide spray intensity, and removal% = host removal intensity. *LDD_H = human-mediated, long-distance dispersal event *Scenario refers to spread scenario Citizen% Spray% Removal% Extent ScenarioMean % overall patches infestedMean % orchard patches infestedMean % host patches infestedMean % undetected patches infestedMean # positive patches infestedMean # non-positive patches infestedMean # host patches removedMean # orchard patches sprayedMean # LDD_H events avoidedMean % apple patches remainingMean % crabapple patches remainingMean % N hawthorn patches remainingMean % O hawthorn patches remainingMean total costMean cost for overall infested patchesMean cost for infested orchard patchesMean cost for host patch removalMean cost for LDD_H avoidanceMean cost for orchard patch spraying0 0 0 KEL Extreme 20.13 31.80 28.90 18.43 1.00 589.00 0.00 0.00 0.00 100.00 100.00 100.00 100.00 781,039.10 430,812.83 350,226.27 0.00 0.00 0.000 0 0 KEL Moderate 12.32 18.42 17.71 11.39 1.00 361.67 0.00 0.00 0.00 100.00 100.00 100.00 100.00 439,150.78 257,115.32 182,035.47 0.00 0.00 0.000 0 0 KEL Casual 4.03 6.79 6.30 3.62 1.00 128.00 0.00 0.00 0.00 100.00 100.00 100.00 100.00 159,626.52 91,993.58 67,632.93 0.00 0.00 0.00100 75 75 KEL Extreme 9.34 6.62 11.54 9.55 1.00 232.00 232.27 2989.27 375.07 89.92 88.47 86.58 83.56 273,699.12 45,336.80 6,119.33 74,496.15 145,639.53 2,107.30100 25 50 KEL Moderate 6.23 3.44 7.22 6.50 1.00 144.67 145.60 1294.07 227.07 93.81 92.85 94.34 88.37 149,155.53 29,967.28 1,854.00 30,220.30 86,809.47 304.48100 25 25 KEL Casual 2.15 1.11 2.60 2.25 1.00 51.60 51.80 448.73 84.33 98.01 97.35 97.63 95.33 48,938.47 11,084.95 935.47 4,844.17 31,968.53 105.35Citizen% Spray% Removal% Extent ScenarioMean % overall patches infestedMean % orchard patches infestedMean % host patches infestedMean % undetected patches infestedMean # positive patches infestedMean # non-positive patches infestedMean # host patches removedMean # orchard patches sprayedMean # LDD_H events avoidedMean % apple patches remainingMean % crabapple patches remainingMean % N hawthorn patches remainingMean % O hawthorn patches remainingMean total costMean cost for overall infested patchesMean cost for infested orchard patchesMean cost for host patch removalMean cost for LDD_H avoidanceMean cost for orchard patch spraying0 0 0 OK Extreme 29.57 61.95 56.81 26.54 1.00 1443.00 0.00 0.00 0.00 100.00 100.00 100.00 100.00 1,806,507.97 1,409,473.17 397,034.80 0.00 0.00 0.000 0 0 OK Moderate 19.39 48.61 44.62 16.25 1.00 1133.93 0.00 0.00 0.00 100.00 100.00 100.00 100.00 1,180,317.42 893,651.95 286,665.47 0.00 0.00 0.000 0 0 OK Casual 7.46 19.86 20.49 5.78 1.00 521.13 0.00 0.00 0.00 100.00 100.00 100.00 100.00 430,653.15 330,563.82 100,089.33 0.00 0.00 0.00100 75 25 OK Extreme 1.93 2.36 6.46 1.38 1.00 162.80 163.40 1240.73 371.80 95.19 93.41 98.68 88.06 256,014.97 78,144.98 11,983.87 22,723.20 142,292.27 870.65100 100 100 OK Moderate 1.42 1.91 5.13 0.96 1.00 129.13 130.00 1033.40 227.00 95.92 95.14 99.08 90.36 217,340.02 51,004.48 9,498.27 69,045.27 86,825.27 966.7350 100 25 OK Casual 2.39 2.55 4.13 2.24 1.00 105.67 104.07 1070.20 38.67 96.28 95.80 97.01 94.73 41,192.42 18,653.83 3,135.33 11,176.25 7,224.53 1,002.47 78 Table 4.5. Significance of simulation attributes for top management strategy combinations for small-extent predictive model using an ANOVA and a Tukey-Kramer test. Management strategies were shortened for simplicity: citizen% = public education intensity, spray% = insecticide spray intensity, and removal% = host removal intensity. *LDD_H = human-mediated, long-distance dispersal event *Scenario refers to spread scenario Table 4.6. Significance of simulation attributes for top management strategy combinations for large-extent predictive model using an ANOVA and a Tukey-Kramer test. Management strategies were shortened for simplicity: citizen% = public education intensity, spray% = insecticide spray intensity, and removal% = host removal intensity. *LDD_H = human-mediated, long-distance dispersal event *Scenario refers to spread scenario ScenarioMean % overall patches infestedMean % orchard patches infestedMean % host patches infestedMean % undetected patches infestedMean # non-positive patches infestedMean # host patches removedMean # orchard patches sprayedMean # LDD_H events avoidedMean % apple patches remainingMean % crabapple patches remainingMean % N hawthorn patches remainingMean % O hawthorn patches remainingMean total costMean cost for overall infested patchesMean cost for infested orchard patchesMean cost for host patch removalMean cost for LDD_H avoidanceMean cost for orchard patch sprayingCasual-Moderate p < 0.0001 p < 0.01 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001not significant p < 0.0001 p < 0.0001 p < 0.0001 p < 0.05 p < 0.0001 p < 0.0001 p < 0.0001not significant p < 0.0001 p < 0.0001not significantModerate-Extreme p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.05 p < 0.0001 p < 0.0001 p < 0.0001Extreme-Casual p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.05 p < 0.0001 p < 0.0001 p < 0.0001r-square 0.972 0.538 0.917 0.958 0.917 0.916 0.476 0.991 0.807 0.862 0.613 0.779 0.98 0.911 0.196 0.946 0.99 0.605ScenarioMean % overall patches infestedMean % orchard patches infestedMean % host patches infestedMean % undetected patches infestedMean # non-positive patches infestedMean # host patches removedMean # orchard patches sprayedMean # LDD_H events avoidedMean % apple patches remainingMean % crabapple patches remainingMean % N hawthorn patches remainingMean % O hawthorn patches remainingMean total costMean cost for overall infested patchesMean cost for infested orchard patchesMean cost for host patch removalMean cost for LDD_H avoidanceMean cost for orchard patch sprayingCasual-Moderate p < 0.0001not significantnot significant p < 0.0001not significant p < 0.05not significant p < 0.0001not significantnot significant p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.001 p < 0.0001 p < 0.0001not significantModerate-Extreme p < 0.0001not significant p < 0.01 p < 0.01 p < 0.01 p < 0.01not significant p < 0.0001not significant p < 0.01not significant p < 0.01 p < 0.0001 p < 0.0001not significant p < 0.0001 p < 0.0001not significantExtreme-Casual p < 0.001not significant p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001not significant p < 0.0001 p < 0.05 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001 p < 0.0001not significantr-square 0.592 0.04 0.431 0.676 0.418 0.436 0.015 0.992 0.158 0.31 0.541 0.71 0.963 0.85 0.41 0.945 0.991 0.007 79 Some key attribute comparisons to consider include percent change in host patch infestation and in undetected patches, as well as percent change in management costs. When analyzing the reduction of mean infested patches when switching from âcontrolâ to âmanagedâ configurations in the small-extent, there was a 60.07 % (for extreme), a 59.23 % (for moderate), and a 58.73 % (for casual) reduction in infested host patches. In terms of the large-extent, there was an 88.63 % (for extreme), an 88.50 % (for moderate), and a 79.84 % (for casual) reduction in infested host patches. In addition, when analyzing the reduction of mean undetectable infested patches (patches that are neither host nor orchard patches) when switching from âcontrolâ to âmanagedâ configurations in the small-extent, there was a 48.18 % (for extreme), a 42.93 % (for moderate), and a 37.85 % (for casual) reduction in infested undetectable patches. In terms of the large-extent, there was a 94.80 % (for extreme), a 94.09 % (for moderate), and a 61.25 % (for casual) reduction in infested host patches. Lastly, when analyzing the reduction of mean total costs associated with the management of and the infestations of apple maggot when switching from âcontrolâ to âmanagedâ configurations in the small-extent, there was a 64.96 % (for extreme), a 66.04 % (for moderate), and a 69.34 % (for casual) reduction in the total costs associated with apple maggot. In terms of the large-extent, there was an 85.83 % (for extreme), an 81.59 % (for moderate), and a 90.43 % (for casual) reduction in total costs associated with apple maggot. These results illustrated consistency in the proportion of reduction of infested patches, infested undetectable patches, and total costs associated with apple maggot when spread scenarios decreased from extreme, to moderate, to casual; increasing the confidence in the model. IV.IV DISCUSSION OF THE PREDICTIVE MODEL These predictions of apple maggot spread in a new environment are generated by a model that I calibrated and validated to historical data, and therefore is the best I can do in the absence of future data. In terms of apple maggot spread with no human intervention (no management, i.e. âcontrolâ), one could assume a more rapid spread and higher infestation rates in urban areas. This is in part because urban trees are irrigated and produce fruit on a more consistent basis, relative to wild specimens, and, in addition, apple maggot has the added benefit of a wide variety of hosts 80 and fruiting periods to exploit. On the other hand, one could assume a much slower spread in the rural areas where there are commercial orchards that may apply sprays for other pests such as codling moth and leafrollers; many of these sprays could likely provide some control of apple maggot (Klaus M, WSDA, personal communication, 2018). We can see some patterning in the final âcontrolâ maps alluding to these assumptions, where apple maggot densities appear to be denser in urban areas relative to rural areas. The three spread scenarios used in the model were extreme, where apple maggot spread involves higher levels of diffusion and more frequent long-distance dispersal events, casual, where spread involves lower levels of diffusion and less frequent long-distance dispersal events, and moderate, where spread levels are intermediate between of extreme and casual, see Appendix A for more details. In terms of the small-extent, Kelowna, public education was the most important and the most consistent management strategy; the high levels of this strategy illustrated the removal of many of the satellite populations seen in the non-managed scenarios. The host removal strategy became consistently less important as the spread scenario changed from extreme to moderate to casual, perhaps due to the spreading of the fly proportionally becoming less intense and therefore more rigorous management was not needed to control the fly. The insecticide spray strategy had less of an effect for moderate and casual scenarios, but was more important in the extreme scenario, perhaps due to the more intense spreading of the fly, especially in areas with orchards. In terms of the large-extent, Okanagan Valley, there was less consistency of management strategies relative to the small-extent, perhaps due to the sheer size and complexity of the large-extent. However, the insecticide spray strategy was more important in the large-extent relative to the small-extent, perhaps due to the larger orchard area available. Public education was again very important, however, the casual scenario ideal for the central Washington State validation model did not translate well as the public education level was lower and therefore more satellite populations appeared. This could be a result of differences in landscape and host distribution between the Okanagan Valley and central Washington State; an important aspect to consider when predicting apple maggot spread in the Okanagan Valley based on central Washington State survey data. Managed configurations illustrated an interesting outcome in that there are higher rates of infestation near the origin point relative to non-managed configurations (see Figures 4.1 and 4.2). This may be due to the fact that these managed configurations have the public education strategy 81 at very high intensity, resulting in lower rates of successful long-distance, human-mediated dispersal events, which in turn decreases the frequency of introducing new population points away from the origin point. Because of the stochasticity of the long-distance, non-human-mediated dispersal events and the lack of distant population points, by chance spread became more intense near the origin point. This is also evident in Figures 3.1 and 3.2 of the validation model. Another interesting outcome of the model was that there was one exception in the trend of decreasing infestation levels as spread scenarios decreased from extreme, to moderate, to casual. The large-extent simulation predicted the casual spread scenario under management to produce more sites with low level infestation and more point sources of infestation relative to the moderate and extreme spread scenarios (see Figures 4.2, 4.4, and 4.6). A possible reason as to why this happened may be due to management configurations; what appeared to be the best management configuration during the validation process for the large-extent casual spread scenario did not seem to translate well. The management configuration for the large-extent casual spread scenario of the validation model did not need 100 % intensity for the public education strategy, unlike the other spread scenarios. This was because fly diffusion and long-distance dispersal events occur less frequently during a casual spread scenario, and thus 100 % intensity for the public education strategy would not have covered the distant positive traps in validation modelâs large-extent, therefore a lower intensity was needed. In the predictive model, the management configurations were fixed, and because public education was not at 100 % intensity, long-distance dispersal events occurred more frequently, thus illustrating a more infested landscape relative to moderate and extreme spread scenarios in the predictive model. As well, the predictive model landscape was smaller relative to the validation model landscape, and as such the host patches and available patch space were denser; thereby creating more opportunity for flies to reach host patches and reproduce. Agent-based models have usefully predicted the spread of invasive pests, such as brown plant hoppers in Vietnam (Phan et al. 2010; Nguyen et al. 2011), drywood termite in the Azores (Guerreiro et al. 2013), European cherry fruit fly and Mediterranean fruit fly in Europe (Lux et al. 2016, Lux 2018, respectively), rusty crayfish in Oregon, USA (Messager and Olden 2018). Their goals were to use the simulations results to support decision-makers in appropriate measures to manage each, generally in urban areas. Similarly, the present model could support 82 decision-makers when apple maggot invades the Okanagan Valley. It provides a useful and visual representation of scenarios in which the fly could spread with and without the aid of management strategies, as seen in the validation model (Chapter III). My results illustrate that management of apple maggot has the potential to provide a 59.3 â 85.7 % reduction of mean infested locations, a 43.0 â 83.4 % reduction of mean undetectable infested locations, and a 66.8 â 86.0 % reduction of mean total costs associated with the management of apple maggot. These estimates also further support that management of apple maggot has significant potential in slowing or impeding their spread. Rebaudo et al. (2011) developed a model using NetLogo to simulate the spread of the invasive insect pest, the potato moth, in an agricultural landscape in Ecuador, and divided their model into an ecological sub-model and a social sub-model. They showed that farmer movement and knowledge play a significant role and that farmers can learn from this model to better manage their crops against pest risks (Rebaudo et al. 2011). Similarly, Ceddia et al. (2009) found, using an ABM for an insect pest invasion, that coordination between agents (farmers) is crucial to controlling invasive species. When non-commercial farmers considered the impacts of the pest, then commercial farmers could spend less on pest control, which increased the farming income on the landscape. However, if non-commercial farmers did not consider the impacts, then commercial farmers spent more on pest control, which decreased the income (Ceddia et al. 2009). Although the present model does not incorporate farmer movement or knowledge, it does incorporate long-distance, human-mediated jump events, as shown in the predictive modelâs tables and figures to be the more important management strategy (public education to prevent these events). Specifically, this is evident in both the final management strategy configurations used (5 of 6 had 100% public education as the best representation via the validation model) and the POM analysis during the validation process (where the majority of the top management strategy configurations were reliant on public education (not shown)). My model also incorporated insecticide use and showed that when a higher proportion of orchards sprayed in the presence of apple maggot, a lower degree of infestation of orchard patches occurred. Muirhead et al. (2006) found that human population size and activity played a significant role in the spread of the emerald ash borer beetle. Also, that the probability of infestation was inversely related to the distance from the beetle epicentre; diffusive dispersal from the epicentre has a much greater predictability relative to long distance dispersal to other areas. This can also 83 be seen in the figures for the present model; as distance from the origin point increases, the infestation levels decrease. However, in their model, it could not account for 17 of the 48 new populations in the region, many of which could not be explained by diffusive spread. Similarly, in the present model, many of the new infested areas of the extents may be explained by long-distance, human-mediated spread events, with later diffusive spread accounting for the expansion. It is shown in these results that a focus on public education lowered rates of long-distance, human-mediated dispersal events, and lowered rates of overall spread of apple maggot. Owing to the low levels found of apple maggot to date in the Okanagan Valley, the model cannot be directly assessed. However, Kumar et al. (2016) combined a niche model MaxEnt, a mechanistic model CLIMEX, and an apple distribution map to model potential risk of establishment of apple maggot around the world. They estimated the probability of establishment in the Okanagan Valley as unsuitable (<0.2) to low (0.2-0.3). The simulation runs of my model produced a frequency of occurrence of apple maggot similar to those of Kumar et al. (2016), in that frequency of occurrence was mainly less than 0.2, suggesting that the Okanagan Valley is not an overly suitable environment for large apple maggot populations. 84 V General Discussion and Conclusion V.I THESIS REFLECTION My focal research questions for the model were to determine how the apple maggot would likely spread within the Okanagan Valley if and when it arrives and how would management strategies influence the spread. These questions were answered by following a typical ABM process, in the form of chapters in my thesis. In chapter II, I collected and assembled relevant data for the model. Host data were provided by the WSDA for the validation model, and various organizations provided the host data for the predictive model. Hawthorn host data were scarce, thus personal scouting sessions throughout the Okanagan Valley had to occur to obtain coverage. Crataegus is not a well-studied genus, and there is still much to learn about its biology and distribution. In chapter III, using the software NetLogo, I developed an agent-based model (ABM) that aimed to reproduce the historical spread pattern of apple maggot observed within central Washington State. I constructed both a regional, large-scale spread model (central Washington State) and a local, small-scale spread model (Wenatchee) in attempts to recreate the historical spread. These models formed the validation model. I validated the model by visual inspection of model output and actual historical maps, as well as statistical metric tests as per Anderson and DragiÄeviÄ (2015), including spatial agreement of infestation, severity of infestation, and spread distance between the simulation outputs and real-world data. These models provided adequate validation and were used as the basis for the predictive Okanagan Valley spread model in chapter IV. In chapter IV, I applied the validation model to a novel landscape. I constructed both a regional, large-scale spread model (Okanagan Valley) and a local, small-scale spread model (Kelowna) in attempts to predict the apple maggot spread in the Okanagan Valley. Focus was on the total area affected and areas at which infestation occurred both with and without management to evaluate the influences that landscape structure, host plant distribution, and management may have on invasion patterns. The model illustrated the importance of satellite populations and establishment point populations. 85 The limitations of this model pertained to time and certain data being unavailable. The assumptions of this model pertained to the data and values being accurately representative of apple maggot dynamics, the model being validated using the Washington data, and the apple maggot spread rate values produced through a sensitivity analysis and POM were appropriate. V.II SIGNIFICANCE Following an inevitable introduction of apple maggot into the Okanagan and surrounding valleys, a failure to act could result in quarantined orchards, reduced marketability of fruit, losses in local and international markets, and increased costs related to control, survey, storage, and inspection of apples and other host material. This has happened and continues to happen in certain areas in Washington State. Measures should be put into place to prevent or decrease the spread of the apple maggot into BCâs main apple-growing areas, and such efforts are in happening via regulation and citizen information. My aim was to build an agent-based model of apple maggot spread in order to inform management strategies for that impending invasion. Agent-based modeling of invasive orchard insect pest spread has generally involved monitoring spread of the pest in a new area, simulating this spread as best as possible, and using the model to predict spread beyond initial infestation. To my knowledge, this may be the first study on validating invasive spread of an insect via ABM for the purpose of applying the model to an area where the invasive pest is not established. This is perhaps due to the fact that each landscape is unique, and conditions are such that these pests may or may not establish or survive and applying a spread model in one environment to another is most likely non-feasible. Fortunately, the validation model in central Washington State, the âestablishedâ area, is relatively similar in both landscape and climate to the Okanagan Valley, the ânon-establishedâ area. The size and complexity of the landscape simulation is another novel feature of my work. Many ABMs of invasive pest spread focus only on a city or an area surrounding a city and rarely on a landscape larger than a few hundred km2 (Phan et al. 2010; Nguyen et al. 2011; Rebaudo et al. 2011; Carrasco et al. 2012; Guerreiro et al. 2013; Anderson and DragiÄeviÄ 2015; Lux et al. 2016; Lux 2018). Some incorporate GIS and climate data, while others do not. My model incorporates GIS data, climate data, and simulating for large landscapes: the small-scale extents of Kelowna and Wenatchee are 261 and 144 km2, respectively, and large-scale extents of the 86 Okanagan Valley and central Washington State are 17,800 and 71,900 km2, respectively. The uniqueness of this approach in the absence of measured parameters (spread rate of apple maggot, for example) and of similar models (invasive orchard pest spread within a new environment) illustrates the power of a promising, if complex, tool for management. Plus, itâs a working model that can be adjusted or updated as new data becomes available. V.II.I Climate Change There is very high confidence that temperature increase is strongly affecting terrestrial biological systems. Such effects include earlier timing of spring events, poleward and upward shifts in ranges of plants and animal species, earlier greening of vegetation, longer growing seasons, and alterations in disturbances of forests and agriculture due to pests (IPCC 2007; IPCC 2014a). Warming in western Canada will likely lead to decreased snowpack and coldness, altering overwintering and cold-intolerant pestsâ life histories, which may lead to increased insect outbreaks (IPCC 2007; IPCC 2014b). Earlier flowering and maturity in apples have been observed worldwide, including Canada, which could mean earlier emergence of apple maggot (IPCC 2014a). Anecdotally, hawthorn in the Okanagan Valley has been observed to flower and mature earlier as well. As exacerbated by climate change, insect outbreaks will put more agricultural crops at risk. Insect predators and herbivores respond differently, which may reduce insect predation (IPCC 2014a). Protection against insect pests in agriculture will have to be intensified due to the change in species composition in these areas due to northward migrations of insects and plants (IPCC 2007). If warming continues and growing seasons of fruits are extended, there may be an increase in the number of fruit fly generations or outbreaks, thus increasing the damage inflicted. Migratory insects, with increasing temperatures, could colonize larger areas of crops, further reducing crop yield. On average, 18% of worldwide crop yield is lost due to pests. It is to be expected that insect damage to plants will increase as temperatures increase (IPCC 2014a). A warmer climate in the Okanagan Valley may lead to increase winter survival of pest insects, introduction of new pests, and increase in pest management costs (Crawford 2012). Therefore, it is critical to be prepared for the imminent arrival of new pests in the Okanagan Valley, including the apple maggot. 87 V.II.II Importance of the Model to the Non-Okanagan Reader As the last commercial apple-producing region in North America free of apple maggot, the Okanagan Valley was therefore an important region to consider using ABM to predict the potential spread of apple maggot. With validation based on real-world apple maggot trap data from Washington State, my model produced heat maps of likely areas of infestation within the Okanagan Valley which may be of interest for management efforts to focus on. The significance of this is that my model illustrates how ABM can be used to influence efficient use of management resources on agriculturally important pests, pests that affect the food we rely on. In extension, this method can be used for other important pests in regions that are of concern. Specifically, by using direct and established data for a particular pest, one could follow my ABM to predict said pestâs potential spread within a new environment. With climate change, insect pest outbreaks may worsen, and life histories may alter, see previous section. Thus, it is important to fully understand the pest in question and how it may establish in an environment of concern in order to minimize the economic damage it may cause. V.III RECOMMENDATIONS BASED ON THE MODEL The limitations of my model meant, first and foremost, that I could not conduct the ideal ABM study that would provide unambiguous advice about best management strategies moving forward; the existing data simply do not yet allow for this. Recommendations based on my model are therefore hindered due to these limitations. However, I can nonetheless learn from this approach and identify key data gaps and provide some estimation of likely spread within the Okanagan Valley. The two main practical objectives I set out for this model was: (i) to inform management and survey efforts, which in turn aim to minimize the threat posed by the apple maggot to the Okanagan Valley; and (ii) to use the ABM to help educate the public and raise awareness of serious agricultural and economical important pests (Rebaudo et al. 2011). In terms of the first practical objective, I learned that, in the model, management was required to match the central Washington State spread and that public education was most represented in the top management configurations, suggesting this strategy is important. The first recommendation I have based on the results of the model is to increase public education on important agricultural pests so as to decrease the risk of spreading the pests via moving infested host plant material. A second 88 recommendation is to focus management efforts in urban areas where wild and backyard host plants are more likely to be untreated and unmanaged. This could potentially decrease the chances of âhiddenâ population growth. While I did not explore this in the model, I did consider it (see next section). However, this effort has been already implemented in the real world with the apple maggot trap survey conducted by the Canadian Food Inspection Agency (CFIA); I provided many of the wild hawthorn occurrence records across the Okanagan Valley for the survey (as of 2015). In terms of the secondary practical objective, public outreach was achieved by constructing a citizen science website called A.P.P.L.E. (Apple maggot Prevention Project using Landscape Ecology) (see Appendix C for more details). This interactive, web-based project (http://applemaggotproject.weebly.com) served as: (i) a citizen science portal where residents of the Okanagan Valley could submit locations of host plants of the invasive apple maggot, Rhagoletis pomonella, specifically apple and hawthorn; and (ii) a public resource for information on apple maggot and its host plants. Several organizations, including newspaper outlets, non-profits, garden clubs, and governmental agencies, were contacted and agreed to help communicate and spread the website through their organizations and clients. My only recommendation from this objective relates to my first recommendation of the first objective in that public outreach and education is an important aspect to managing agricultural pests of concern. Anecdotally, many citizens and orchardists I interacted with knew little to nothing about apple maggot or its host plants, including hawthorn. V.IV FUTURE WORK The model considers two spatial extents: region-wide, or large-scale, spread (central WA and Okanagan Valley) and city-wide, or small-scale, spread (Kelowna and Wenatchee). I examined in field work a third spatial extent for rural spread, comparing Leavenworth, in central Washington State, with Armstrong or Enderby, in the Okanagan Valley, but suitable data were severely lacking or unavailable in BC. It was to serve as a comparison between urban (Wenatchee and Kelowna) and rural (Leavenworth and Armstrong/Enderby) environments and may have provided additional information on spread. Real data for some variables were not available, such as frequency of human-mediated dispersal events, or proportion of flies immigrating and emigrating from an area. Consequently, 89 reasonable values from the literature were used. Similarly, a comparison of the distance the model population spreads over time with real-world spread rates may be possible from output raster files, but time prevented such an analysis. Two as yet unexplored aspects of the model were: (i) choosing a starting point other than the origin for a simulation. This model allows for the potential to explore different scenarios, including introducing the origin population within the predictive model in new locations. Of interest would be natural spread from the Washington-BC border, which is a likely scenario; and (ii) comparing the contribution of each of four main host plant types to the spread. If any host distribution contributes more to the spread than another, one could confirm this by simulating with each host type removed and comparing spread patterns. These two aspects would have served as another research on question how landscape structure and the distribution of hosts influence apple maggot spread. An attempt at running the model on a computer cluster failed. It would have saved time, but some factors worked, and some did not, so the approach was not viable. Three different computers were used, saving time but not as much as if a cluster had been possible. Lastly, and importantly, receiving expert opinion from Washington State staff, especially WSDA, for the validation process would have been ideal, and can still occur in the future. Expert opinion on model functions and results would have improved the validation process. However, due to time and travel constraints, this validation process also was not possible. V.V CONCLUSION The purpose of this thesis was to explore the use of ABM to simulate the potential spread of an orchard pest, Rhagoletis pomonella in the Okanagan Valley, to which it is currently not found, by validating the model against real-world apple maggot data from Washington State. ABM is a relatively new concept and idea of conceptualizing how complex systems work but has recently increased in its use as an alternative method to understanding behaviour, especially in economics or pest management (Rebaudo et al. 2011). These powerful tools enable simultaneous manipulation of agent behaviour (e.g. insect dispersal, human management action) and other variables (e.g. host density and distribution) within a spatially explicit landscape context (Railsback and Grimm 2011). These advantages have allowed modelers to answer questions unfeasible in the field, such as predicting the spread of agriculturally important pests within a 90 new landscape. Using an ABM to predict, using established spread patterns, pest attribute variables, and climatic and landscape data, the potential spread of a pest in a given area, whilst interacting with the local agricultural agents, can prove economically and environmentally efficient. In summation, the model provides decent estimates of spread distribution, through qualitative maps, of the fly that may have the potential to provide industry with insight into likely population establishment areas. The aim of these predictions was to improve management and survey efforts, which has the potential to decrease the apple maggotâs spread and threat to commercial agriculture and home gardens, as well as be used to educate the public and raise awareness of serious agricultural and economical important pests. This project may also aid with trap deployment strategies for the Okanagan Valley based on the sensitive areas suggested by the model. Lastly, this project has the potential to influence the use of agent-based modeling in agricultural entomology. In conclusion, Rhagoletis pomonella, or apple maggot, is a significant orchard pest, especially to apples. The Okanagan Valley is the last commercial apple-producing region in North America free of apple maggot. However, the region has had two positive traps since 2015. Because of the significant impact the apple maggot could potentially cause in the Okanagan Valley, including decrease in fruit quality and quantity, increases in pesticide usage, and local quarantine, it is crucial to understand how it will likely spread and establish here. 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Environmental entomology 46(6):1351-1358. Yonow T, Zalucki MP, Sutherst RW, Dominiak BC, Maywald GF, Maelzer DA, Kriticos DJ. 2004. Modelling the population dynamics of the Queensland fruit fly, Bactrocera (Dacus) tryoni: a cohort-based approach incorporating the effects of weather. Ecological Modelling 173(1):9-30. Zhao Z, Wahl T, Marsh T. 2007. Economic effects of mitigating apple maggot spread. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 55(4):499-514. 102 Host Plant Occurrence Records Canadian Museum of Nature. Ottawa, Canada. http://nature.ca/collections-online CFIA-ACIA internal report on apple maggot 1999-2016 Consortium of Pacific Northwest Herbaria. http://www.pnwherbaria.org/data/search.php Dickinson TA, Lo EY, Talent N, Love RM. 2008. Black-fruited hawthorns of western North Americaâone or more agamic complexes? Botany, 86(8):846-865. Global Biodiversity Information Facility. Copenhagen, Denmark. http://www.gbif.org/occurrence/search Thistlewood lab. 2014 â 2016. Summerland Research and Development Centre (SuRDC) 103 Appendices APPENDIX A: OVERVIEW, DESIGN CONCEPTS, AND DETAILS ODD (Overview, Design concepts, Details) protocol for the predictive and validation models (Grimm et al, 2006; Railsback and Grimm 2011). AI Overview AI.I Purpose The purpose of this agent-based model is to simulate the spread of apple maggot, Rhagoletis pomonella, through a landscape so as to provide information on the effect of different management strategies on the pattern of spread. The model is split into two main categories: the predictive model and the validation model. Both models follow the same rules, except for: (i) the purposes; and (ii) the GIS data, which are represented by their unique extent. The model simulates the historical spread of apple maggot in Washington State and, once validated, is applied to the Okanagan Valley, wherein it is not yet established. Two spatial extents are simulated: city-wide or small-extent (Wenatchee, WA, and Kelowna, BC) and region-wide or large-extent (central Washington State and Okanagan Valley). The validation (historical) model describes and simulates the spread of the apple maggot in central Washington State. It should have a reasonable spatial congruence with the measured spread from 2003 to 2012, for confidence in the predictive model, because it is used as a template for the predictive model. Similarly, its focus is on two extents: small-extent (Wenatchee) and large-extent (central Washington). The predictive model demonstrates the potential spread of the apple maggot in the Okanagan Valley. It permits testing of different scenarios and a cost/benefit analysis of management strategies, at two extents: small-extent (Kelowna) and large-extent (Okanagan Valley). Confidence in this model is backed by the validation model. The simulation uses parameters which are static or configurable and variable, such as apple maggot biology (static parameters with stochastic variables), landscape (static), climate (static 104 with stochastic variables), and management (parameters to configure). The focus is on which combination of management strategies provides the optimal spread, based on historical data. The fixed parameters in the model include life history traits, primarily reproduction and spread, which can be assumed to be similar between Washington and Okanagan Valley populations. Variable parameters permit analysis of any effect on fly spread, such as host removal, public education, and insecticide spray. These were proven to be the most important aspects of mitigating invasive spread. The model was developed using Netlogo version 5.3.1. (Wilensky 1999). AI.II Entities, State Variables, and Scales AI.II.I Entities Landscape: each patch (or cell) has its own variable of elevation (in m.a.s.l.), climate (maximum, minimum, and mean temperature in oC and average precipitation amount in mm), fly densities (immature, gravid females, and mature), and habitat quality (type of host and the quantity of food resources available). Host plants (apple, crabapple, and native and ornamental hawthorn) are located in predetermined patches, based on real-world locations. The landscape is static and shows no seasonal or diurnal variation aside from temperature and precipitation. Agents: patches. Each patch contains a population of flies, stored as a patch variable. Populations of flies in patches disperse throughout the landscape following a simple rule-based approach and interact with the landscape. Fly dispersal is not biased by landscape variables because the spatial resolution is too coarse for flies to perceive their environment. AI.II.II Scales Spatial scale: the spatial scales of the model are as follows: Predictive model extents 1) Kelowna (small-extent): 261.28 km2 and 50,799 patches = 5,143.41 m2 per patch 2) Okanagan Valley (large-extent): 17,821.26 km2 and 71,818 patches = 248,144.75 m2 per patch Validation model extents 3) Wenatchee (small-extent): 143.73 km2 and 139,971 patches = 1,026.86 m2 per patch 105 4) Central Washington State (large-extent): 71,906.72 km2 and 91,257 patches = 787,958.40 m2 per patch Each extent is loaded into a 451 x 451 patch world; an environment consisting of 203,401 patches. Extent sizes (see above) varied as they were governed by a combination of climate and elevation data limitations and availability, as well as the NetLogo patch size implementation capability. Because of size differences between the patch world and the extents loaded into it, null patches (patches with no values and are otherwise non-existing in the specific extent) are present. Scale dependent variables are scaled accordingly depending on simulation extent. All models are interchangeable, and the variables and attributes do not change (except for the landscape, temperature, precipitation, or elevation) between the validation model and the predictive model, or the small-extent and large-extent models. The mean scale of the large-extent models is 518,051.58 m2 per patch, or 167.92 times larger than the 3,085.14 m2 per patch at the small-extent models. The scale does affect certain variables such as host density. However, it is assumed that only 50% of the proposed scaling is usable for apple maggot in large-extent because within the large-extent, not all area within a host patch would actually be host plants. Thus, the scale-dependent variables are 83.96 times larger in the large-extents than in small-extents. Temporal scale: time is in time-steps, where each time step represents 1 day. Day 1 represents June 1, and day 153 represents October 31, the end of one season. The model ends on the last day of the fifth season, i.e. time-step 766. AI.III Process Overview At each time step, fly populations, host availability, climate and, if applicable, management strategies are adjusted. More specifically, population of flies per patch disperse through the landscape searching for hosts via diffusion, long-distance dispersal events plus diffusion, and/or human-mediated long-distance dispersal events plus diffusion. When moving to new cells, flies cannot move onto waterbody cells or too high an elevation. If applicable, host patches infested may be removed, populations within orchard patches may be subject to sprays, and long-distance human-mediated dispersal events may be prevented. 106 Simulations are each run for 153 âdaysâ, or time steps, for 5 âyearsâ, totaling 766 âdaysâ (an extra day is added for data export purposes). The fly population starts at the first known positive trap site of the particular extent and diffuses each day until they die or find a host patch to reproduce. When applicable, management may affect the spread. Adult fly populations all die off at the end of each year, and any immature flies will emerge throughout the following year, when the cycle starts again (Figure A1). Fly population is updated each day according to mortality (crude, temperature-dependent, and precipitation-dependent) and reproduction (mating rate, fecundity). Patches with hosts allow for populations to reproduce. Some flies will move to adjacent cells regardless of whether or not fly density is too high or host resources is too low. If applicable, three management strategies affect fly populations and fly dispersal, namely public education, host removal, and insecticide use. They may affect the fly populations via human-mediated dispersal, or events may be prevented if infested host patches are removed or if orchards receive insecticide spray to control flies. Each management strategy has an optional intensity value from 0% to 100%, in steps of 25%. The cost of managing the flies is calculated for every infested patch, when a host patch is removed, when insecticide spray is used, or when a human-mediated dispersal event is prevented. Costs have no real-world meaning, rather they are used as a comparative tool to determine which management configurations mitigate the apple maggot spread the most, yet âcostâ the least to implement. The spatial coordinates of every infested patch on the landscape, as well as other simulation details, are recorded at each time step. AII Design Concepts AII.I Basic principles Potentially vulnerable and invulnerable areas in the Okanagan Valley, and costs of managing the fly, are identified. The ABMâs drivers include natural diffusion of apple maggot, host distribution, and human interaction (management). The independent variables (factors) are time, landscape structure, and management strategies, while the dependent variables are patch infestation, pest spread, and cost. The independent variable âmanagement strategiesâ include 107 public education, insecticide use, and host removal. Management strategies have intensity levels (0% = none, 25% = minimum, 50% = moderate, 75% = high, and 100% = maximum). They can be combined, e.g. 25% public education, 50% insecticide, and 75% host removal. In addition, one combination exists of 0% for each (to simulate a spread without management). AII.II Emergence Apple maggot biology and dynamics are not coded to change in response to management, rather fly dispersal patterns may emerge from both the landscape structure and the specific management parameter configurations applied. AII.III Adaptation There is no adaptation. AII.IV Objectives The agents in the model have no objects. AII.V Learning There is no learning. AII.VI Prediction Extensive spread and establishment will most likely be achieved by long-distance human-mediated dispersal. Removing hosts around orchards will reduce likelihood of orchard invasion, coupled with insecticide. AII.VII Sensing There is no sensing. The fly population is assumed to not be able to perceive the landscape beyond their current patch. AII.VIII Interaction Flies interact with the landscape (reproduce on host patches) and, indirectly, with citizens (human-induced dispersal, removal of hosts, spraying of orchards). 108 AII.IX Stochasticity Citizens may induce long-distance spread by providing a new origin patch at a random location with a random number of flies. Similarly, hosts can be removed randomly within the vicinity of an orchard, or anywhere else. Climate, temperature and precipitation, changes each âdayâ based on real-world daily values. However, some stochasticity exists, so day 30 of one year may not have the same values as day 30 of another year. Dispersal of the fly, via diffusion, is constant with a small stochastic component. Reproduction productivity is also stochastic via an exponentially distributed random floating-point number. Finally, the observer may choose where to start the infestation. AII.X Collectives There are no collectives. AII.XI Observation At each time step, the x- and y-coordinates of the infestation are exported to a text file and generated into a heat map. Output graphs illustrating cost over time, available hosts over time, and infested patches over time are exported as Excel files. AIII Details AIII.I Initialization Geospatial data (shapefiles, rasters) including roads, waterbodies, orchards, host locations, and elevation, as well as climate data (text files) with maximum, minimum, and mean temperature and precipitation, are loaded into the specific extent for each patch/cell. A starting location for the fly population is determined by the observer, or the location of the first real-world catch site for the extent may be chosen. The basic model flow is presented in Figure A1. In essence, apple maggot spreads throughout the landscape, reproduce on host patches, and are subjected to management strategies. Figure A1 represents one time-step, or one âdayâ in the 109 model, where at the end of each âdayâ, fly population, climate, and hosts are updated. The model runs for 153 time-steps during the apple maggot season. Any reproduction events that occurred will carry over to the next season as overwintering pupae. This occurs for five seasons, totaling 766 time-steps, or âdaysâ. After which the simulation run ends and information on each patch is extracted, including how many seasons each patch was infested. Figure A1. Model flow diagram. Figure created by Brian Muselle. AIII.II Input data Landscape GIS data including shapefiles: lakes, rivers, roads, pome orchards, and host locations, and raster files: elevation; manipulated in Arcgis and imported into Netlogo. Weather and precipitation text files are imported into Netlogo. See chapter II for more details. 110 AIII.III Sub-models Following Rebaudo et al. (2011) approach, each model-complex (the validation and predictive models) is split into two sub-models: an ecological sub-model, which governs the pest population dynamics and environmental factors; and a social model, which governs the human interaction factors including removing infested hosts. AIII.III.I Ecological sub-model Yonow et al. (2004) built a population dynamics model for the Queensland fruit fly. In the same way, some of their model inputs are employed in apple maggot simulations. These include activity for flight and reproduction, mortality from temperature and precipitation, and egg production. All biological attributes are fixed, and fractional values are rounded to nearest whole number when applicable. AIII.III.I.I Fly spread scenario Three spread scenarios are used for the apple maggot model: extreme, moderate, and casual, with assumptions employed for each as follows: Extreme: management played a huge role in mitigating spread, where spread involves higher levels of diffusion and more frequent long-distance dispersal events. Casual: possible that management did little in the ways of mitigating spread, spread would be slow or sparse in central Washington State, and spread involves lower levels of diffusion and less frequent long-distance dispersal events. Moderate: levels are intermediate between of extreme and casual. The spread values for each spread scenario, see section 3.3.1.2, were used in lieu of actual spread rates of apple maggot that were unavailable. AIII.III.I.II Fly dispersal Initial spread origin point is fixed on the patch where the first real-world apple maggot positive trap location occurred and specific to each extent. Flies disperse in three ways: simple diffusion, long-distance non-human-mediated dispersal, and long-distance human-mediated dispersal. The latter is discussed in section 3.3.2.2. The diffusion primitive follows a simple procedure: diffusion value, from 0 to 1, equates to each patch provides equal shares of the fly population to the surrounding 8 neighbouring patches per time step. For example, a patch with 111 fly population value of 100, and diffusion value of 0.2, equates to 20% of 100 flies to the surrounding 8 patches, or 2.5 flies, per time step. For small-extents, the diffuse value is âvalueâ + random-float 0.1, while for large-extents, the diffuse value is âvalueâ + random-float 0.05. The patches are larger in the region-wide extents, and therefore less diffusion is assumed to happen. âValueâ depends on the spread scenario: 0.3 (extreme), 0.2 (moderate), and 0.1 (casual). Long-distance non-human-mediated dispersal, this represents wind dispersal and small jump dispersal events of up to 1.5 km (the maximum distance a single fly may travel). A random number generator returns a number from 1 to 100 each day. If the value is higher than 50 (extreme), 70 (moderate), or 90 (casual), then a random patch within 1.5 km of an origin patch that is not a waterbody and not higher than 1000 m will spawn a population of random 100 * the activity index value. The activity index affects the reproduction and dispersal of the populations and is represented as (Yonow et al. 2004): 1 - 0.025 * precipitation_stochastic where activity for flight (LDD) and reproduction ranges from 0 (days with 40 mm or more rain [activity index = 1 - 0.025 * rain]) to 1 (days with no rain). Fly populations cannot be in a patch that is a waterbody, beyond the boundary of the extent, or a patch with an elevation above 1000 m. AIII.III.I.III Fly reproduction When a population of flies reaches a host patch, reproduction may occur. Flies can lay up to 500 eggs in their two-week life span, which is simulated by having a random-exponential variable, fecundity, have a value of 4, which gives a daily egg producing value of up to 36 (apple maggot can lay up to 500 eggs in life time and reproducing life is 14 days (500 / 14 = 35.71 eggs per day). Then, a production value is added (Yonow et al. 2004): ((((-0.11) * (maxtemperature_stochastic ^ 2)) + (5.61 * maxtemperature_stochastic) - 67.86) / 5.94) This produces a value from 0 to 1 and limits egg production to between 18.5 and 34oC and peak production at 26oC. Immature flies produce as: am_immature + (am_gravid_females * fecundity * production * activity_index) 112 Once they lay, the gravid females die during that specific time-step. Host depletion is as follows: host_density - (host_density * production) When a host patch is infested, it is assigned a true value for whether the patch was infested the current year; up to a value of 5, meaning the patch has been infested all 5 years of the simulation. Populations are assumed to have a sex ratio of males = 0.414 and females = 0.586 (Klaus 2003-2012). Gravid females are the female population * activity index. AIII.III.I.IV Fly Survival Immature flies overwinter, and when the next year starts, a proportion of them die, due to predation, parasitism, disease, and other ways (Carey 1989), before converting the surviving immatures into the carryover population. The carryover population may emerge throughout the year depending on season and climate, where more flies emerge during the peak seasons of hosts and when it rains > 2 mm. The adult population increases as carryover populations emerge, and the carryover populations decrease as they emerge. AIII.III.I.IV.I Environmental mortality Adult flies are affected by temperature, precipitation, and dispersal mortality (Yonow et al 2004) and are illustrated by these formulas: if maxtemperature_stochastic > 36 [set temperature_dependent_mortality temperature_dependent_mortality + ((0.125 * maxtemperature_stochastic - 4.5) / 10)] where mortality due to temperature increases above 36oC. if mintemperature_stochastic < -2 [set temperature_dependent_mortality temperature_dependent_mortality + (-0.07 * mintemperature_stochastic - 0.13)] where mortality due to temperature increases below -2oC. Otherwise the value is 0 when temperatures are between -2 and 36 oC. 113 if precipitation_stochastic > 40 [set precipitation_dependent_mortality precipitation_dependent_mortality + (0.003 * precipitation_stochastic - 0.13)] where mortality due to precipitation increases above 40 mm. Otherwise the value is 0 when precipitation is below 40 mm. AIII.III.I.IV.II Crude mortality This is a hypothetical dataset derived using the average stage-by-cause mortality from 25 life tables provided by Cameron and Morrison (1977) for Key Factor analysis of the apple maggot. Causes of death were due to 11 different factors in the original paper, but Carey (1989) combined them into four categories: predation, parasitism, disease, and âotherâ. âOtherâ causes of death include all other sources of mortality that are not predation, parasitism, or disease, such as nutritionally-based mortality (Carey 1989). let am_egg_pred random-float 0.0000 let am_egg_para random-float 0.0000 let am_egg_dis random-float 0.0000 let am_egg_other random-float 0.0143 let am_early_larvae_pred random-float 0.0000 let am_early_larvae_para random-float 0.2293 let am_early_larvae_dis random-float 0.0000 let am_early_larvae_other random-float 0.5998 let am_late_larvae_pred random-float 0.1024 let am_late_larvae_para random-float 0.0122 let am_late_larvae_dis random-float 0.0000 let am_late_larvae_other random-float 0.0000 let am_early_pupae_pred random-float 0.0085 let am_early_pupae_para random-float 0.0000 let am_early_pupae_dis random-float 0.0010 let am_early_pupae_other random-float 0.0000 let am_late_pupae_pred random-float 0.0123 let am_late_pupae_para random-float 0.0000 let am_late_pupae_dis random-float 0.0018 let am_late_pupae_other random-float 0.0050 where pred = predation, para = parasitism, dis = disease, and other = other. Each mortality value is added: set crude_mortality (crude_mortality_predation + crude_mortality_parasitism + crude_mortality_disease + crude_mortality_other) 114 AIII.III.I.IV.III Emergence Smith and Jones (1991) studied the effects of cold period duration and rain on adult apple maggot emergence under laboratory conditions. They found that degree-day requirements were affected by both the length of cold period and the occurrence and timing of rainfall experienced by the pupae. As the number of cold period days increased, the degree-day requirement for adult emergence decreased. Pupae exposed to rain emerged earlier than those that were not exposed to rain. Smith and Jones (1991) suggest expecting a delay or acceleration of emergence after long or short cold periods, as well as to expect an acceleration of emergence after a rainfall. Pupae are the most sensitive to rain before the 900 degree-day (DD) mark of their development. Smith and Jones (1991) showed that exposure to rain between 677 and 900 DD emerged between approximately 222 and 363 DD earlier relative to pupae not exposed to rain. This is illustrated in the emerge variable formula: [ifelse precipitation >= 20 [set emerge_rate random-float 0.1] [set emerge_rate random-float 0.05]] during peak season, and: [ifelse precipitation >= 20 [set emerge_rate random-float 0.05] [set emerge_rate random-float 0.01]] during regular season. These formulas insured that when precipitation > 20mm, apple maggot emerged faster relative to 0 cm precipitation. It also insures that the first emergence mean day is July 9, and the 50% emergence mean day August 11. Jones et al. (1990) complied data on emergence of apple maggot in several locations across North America. Using a base of 6.7oC and accumulated degree-day data starting on March 1, their results show that the first apple maggot emergence time in the east is between May 26 and July 17 (mean day June 20), and in the west it is between June 6 and August 16 (mean day July 9). The mean day for 50% emergence in the east is July 17 and for the west it is August 11 (Jones et al. 1990). Emergence affects the population by adding to the current population: set population population + (infest_carryover * emerge_rate) and subtracting from the carryover population (pupae): 115 set infest_carryover infest_carryover - (infest_carryover * emerge_rate) These formulas insure that not all apple maggot emerge in a given year. The apple maggot can be in their pupal stage for several years; for this reason, it is essential to continue trapping and monitoring in areas that were once positive, to ensure any late emergers can be detected (Brunner 1987; Bush et al. 2005). AIII.III.I.V Climate Simulations run from June 1 to October 31, where June = day 1-30, July = day 31-61, August = day 62-92, September = day 93-122, and October = day 123-153. Temperature and precipitation daily mean values are read in from a historical data file, plus a small stochastic variable to allow for annual variation. AIII.III.I.VI Hosts Apple maggot season begins at the start of June (day 1) and ends at the end of September (day 122). During this time, activity including dispersal and reproduction can happen. Apple and crabapple season begin at the end of July (day 61) and ends at the end of October (day 152), with peak season in mid-August (days 70-85). During the season, apple host density increases, especially during peak season. Native and ornamental hawthorn season begins at the beginning of July (day 31) and ends at the end of September (day 122), with peak season in mid-July (days 46-61) and in early to mid-September (days 93-108), respectively. During the season, hawthorn host density increases, especially during peak season. Host density only increases during the respective seasons and only if the host patch was not removed. Orchard patches (apple) follow the same season as apple and crabapple patches. Apple and crabapple patches gain a random 5 hosts per day, native and ornamental hawthorn patches gain a random 10 hosts per day, and orchard patches (apple) gain a random 50 hosts per day. Apple maggot peak activity depends on which host fruit is in season. Generally, there are three main peaks every year: mid to late July when black hawthorn (C. douglasii) mature, mid-August when apple matures, and early to mid-September when ornamental hawthorn matures (specifically C. monogyna) (Tracewski et al. 1987). 116 In the model, hosts are treated the same; i.e. apple maggot populations do not act differently according to the host patch. AIII.III.II Social sub-model Three main management strategies are used in the model: host-removal, which involves removing infested hosts therefore rendering that patch host-less, public education, which incorporates human-induced spread and educating the public not to move infested material, and insecticide spray, which incorporates orchards spraying insecticide when apple maggot detection is close. AIII.III.II.I Host removal Host removal involves removal of infested host plants. In the model, this involves removing a host patch should it become infested, rendering that patch host-less. Hosts closer to orchards take priority. Thus, a host-removal-intensity of 0% = no infested hosts are removed, and of 100% = all infested hosts are removed. A random number generator returns a number from 1 to 100. If the value is less than the host-removal-intensity%, a host patch will be removed. This is to simulate the percentage of hosts to be removed during the simulation. First, for any host patches near an orchard (within 1.5 km (the maximum distance a single fly may travel) - patch distance dependent on extent, see section 1.2.2), one of the host patches that are infested is removed from the simulation. If no infested host patches are near an orchard patch, then any infested host patch will be removed. Removal means no fly reproduction may occur on the patch anymore and no carryover of immatures (5% live to simulate any pupae that survived). Effectively, the higher the host-removal-intensity%, the more frequent a host patch is removed. AIII.III.II.II Public Education Public education involves educating the public to prevent the spread of apple maggot via movement of infested host material, or long-distance human-mediated dispersal events. In the model, this involves preventing a random patch from being infested with immature apple maggot (the likely stage to which long-distance dispersal events would occur at). A citizen-awareness- 117 intensity of 0% equates to no prevention of human-mediated spread, and of 100% equates to all human-mediated spread is prevented. A random number generator returns a number from 1 to 100. If the value is more than 50 (extreme), 70 (moderate), or 90 (casual), the act of human-mediated dispersal begins. Another random number generator returns a number from 1 to 100. If the value is more than the citizen-awareness-intensity%, a long-distance dispersal event occurs, but if less, a long-distance dispersal event does not occur. This simulates that although awareness was at a certain percentage, the remaining citizens are not aware. A long-distance dispersal event means that any patch that is a road (likely spread point) will have a population of random 100 immatures appear at random Effectively, the higher the citizen-awareness-intensity%, the less frequent a long distance, human-mediated dispersal event occurs. In an earlier ABM model, calibration determined that there is a 30% chance of successful establishment of a satellite population, whether it be long-distance dispersal via adult hitchhiking, or from an immature stage in infested host material (Anderson and DragiÄeviÄ 2015). AIII.III.II.III Insecticide Use Insecticide use involves insecticide treatment of orchard patches when apple maggot detections are within a certain distance. In the model, this involves orchard patches spraying when a host patch within 800 m of the orchard patch is infested. Orchard patches surrounding the main spraying orchard patch may also start treatment. 800 m (1/2 mile in Washington State) from orchard patches was used because this is considered to be the distance at which the WSDA considers orchards threatened by infested host plants (Klaus 2000). For large-extents, the distance is doubled to 1600 m to accommodate the larger patch sizes within those extents. A spray-intensity of 0% equates to no orchard patches spraying when a host patch within 800 m is infested, of 50% equates to 50% of orchard patches spraying when a host patch within 800 m is infested and 50% of orchard patches surrounding the main orchard patch may start treatment, and of 100% equates to all of the orchard patches spraying when a host patch within 800 m is infested and 100% of orchard patches surrounding the main orchard patch may start treatment. Neighbouring orchard patches may spray if a random spray variable value is less than the spray-intensity%. 118 Only orchard patches that are not spraying or have not been harvested or have at least 1 spray round left may spray. Harvest occurs when an orchard patch reaches its host carrying capacity. In northeastern orchards, typically one to three insecticide spray applications per orchard are applied annually for apple maggot control (Reissig 1988; Thistlewood H, AAFC, personal communication, 2018). Spraying typically occurs every 10 to 21 days in the emergence period, depending on pesticide (BCMA 2013). Therefore, each orchard patch receives 3 spray rounds, each lasting 14 to 21 time-steps. Spray duration lasts the whole duration, regardless if there are no infested host patches within 800 m. Spraying also only occurs during the apple season. When an orchard patch is spraying, it cannot harvest, even if it has reached its host carrying capacity. Spraying stops when the duration is done or if precipitation exceeds 25 mm. The variable num_spray counts every time step the orchard patch is spraying. Spraying will kill 95% of the population (arbitrary, not 100% effective), which includes adults and immatures. Effectively, the higher the spray-intensity%, the more frequently orchard patches spray. AIII.III.II.IV Management Costs As a comparative tool, the âcostâ of managing apple maggot is calculated during a simulation run so as to compare with other management configurations. One âcostâ value is added per time-step for every infested patch, when a host patch is removed (to simulate the costs of removing the host and monitoring the site afterwards), when insecticide spray is used (to simulate the costs of applying insecticide spray to the orchard), or when a human-mediated dispersal event is prevented (to simulate the costs of having highway signage or spreading awareness to educate the public). Because of these simplifications, direct comparisons between management costs (i.e. comparing the values of infested patch costs and host patch removal costs within the same management configuration simulation run) cannot be made; only comparisons of management costs between different management configurations can be made (i.e. comparing the values of infested patch costs between two different management configuration simulation runs). 119 AIII.III.III System AIII.III.III.I Output At the end of the simulation, after 5 years or 766 days, the simulation ends and exports 2 main files: an excel spreadsheet of output data and an ASCII file of the infested patches. The table data includes (i) % of overall patches, % of orchard patches, and % of undetected patches infested, (ii) # available of specific hosts left, (iii) costs, (iv) contribution % for each management strategy, and (v) management stats (# host patches removed, # of patches that avoided long-distance dispersal events, and # times orchard patches sprayed) (Table A1). These data are used for the following: (i) to determine which parameter configuration produces the least amount of infested patches and infested orchard patches, (ii) to determine how many hosts were removed, (iii) how much it cost to manage the population for that particular parameter configuration, (iv) to keep track of the parameter configurations, and (v) for further data analysis. The ASCII format output file includes a raster of all patches that were infested during the 5-year simulation and the number of years each of the infested patches were infested. This is used to visually compare the infestation extent for each parameter configuration and for model pattern-based validation purposes. Table A1. Output data and their relevance. Output Data Parameter Measurement Relevance Infested patches % total infested patches during simulation Infested orchard patches % total infested orchard patches during simulation Infested undetectable patches % total infested non-host, non-orchard patches during simulation Positive-Positive # total patches with positive sites that were infested during the simulation (should be close to total real world infested sites for more accurate model) 120 Output Data Parameter Measurement Relevance Negative-Positive # total patches with non-positive sites that were infested during the simulation (should be close to 0 real world infested sites for more accurate model) host-removed? # how many hosts were removed during the simulation num-spray? # how many orchard patches sprayed during the simulation LDD_H_avoided? # how many long-distance spread events were avoided during the simulation Apple patches left % how many apple host patches are left after the simulation Crabapple patches left % how many crabapple host patches are left after the simulation N Hawthorn patches left % how many native hawthorn host patches are left after the simulation O Hawthorn patches left % how many ornamental hawthorn host patches are left after the simulation Total costs # total cost of simulation Patch costs # total cost of infested patches of simulation Orchard costs # total cost of infested orchard patches of simulation Host costs # total cost of removing hosts of simulation Citizen costs # total cost of avoiding long distance dispersal events of simulation Spray costs # total cost of orchard sprays of simulation 121 AIV Model details Tables A2-A4 provide attributes and details pertaining to the model. Table A2 provides descriptions on patch-specific variables, table A3 provides descriptions on global variables (considers all patches), and table A4 provides descriptions of the model interface. Figure A2 illustrates the model interface. Table A2. List of patch-specific attributes and descriptions. Variable Description am_immature density of immature flies in patches population density of flies in patch am_gravid_females density of gravid females in patches available to reproduce infest_carryover for next yearâs emergence emerge_rate rate at which adults emerge spray orchard patch random variable spray_left each orchard patch gets 3 spray durations activity_index index for population activity, reproduction and LDD origin_point? start of a spread, especially at start of simulation and LDD events host_patch? does this patch have a host? 122 Variable Description host? what type of host is this patch? positive? is this patch a positive trap site? host_density density of hosts in patch host_infested? is the host infested? patches_infested_ever? if the patch has ever been infested, even if it currently isnt hosts_infested_ever? if the host patch has ever been infested, even if it currently isnt orchard_infested_ever? if the orchard has ever been infested, even if it currently isnt host_removed? is this host patched removed? LDD_H_avoided? was a long-distance human-mediated dispersal event prevented on this patch? patch_infested_year1? patch_infested_year2? patch_infested_year3? patch_infested_year4? patch_infested_year5? if the patch was infested in a particular year - for raster export patch_infested_allyears? combination of all 5 years - for raster export road? is this patch a road? waterbody? is this patch a waterbody? large_waterbody? waterbodies with at least 50% of neighbours also waterbodies 123 Variable Description boundary? is this patch within the world boundary? orchard? is this patch an orchard? spray? is the orchard spraying? num_spray number of days orchard has sprayed countdown spray duration harvested? has the orchard been harvested? elev elevation of patch maxtemperature maximum temperature of patch max_temperature_stdev standard deviation of maximum temperature maxtemperature_stochastic to induce temperature randomness on all patches meantemperature mean temperature of patch mean_temperature_stdev standard deviation of mean temperature meantemperature_stochastic to induce temperature randomness on all patches mintemperature minimum temperature of patch min_temperature_stdev standard deviation of minimum temperature 124 Variable Description mintemperature_stochastic to induce temperature randomness on all patches precipitation precipitation of patch precipitation_stdev standard deviation of precipitation precipitation_stochastic to induce precipitation randomness on all patches Table A3. List of global attributes and descriptions. Variable Description building-dataset lakes-dataset roads-dataset rivers-dataset orchard-dataset apple-dataset crabapple-dataset nhawthorn-dataset ohawthorn-dataset boundary-dataset stream-dataset minorroads-dataset states-dataset traps-dataset positives-dataset crop-dataset landuse-dataset shapefile datasets; variables to set GIS and host data in for specific model extents precipitation-data mintemperature-data maxtemperature-data meantemperature-data max-temperature-stdev-data (mean, min) maxtemperature-data-north (central, south) textfile datasets; variables to set climate data and standard deviations in elevation textfile dataset: variable to set elevation data in host-site whether the patch is a host patch or not 125 Variable Description awareness enforcement removal LDD ldd_% LDD_H ldd_h_% for random variables time month day to know what day and month it is am-season? apples-season? apples-season-peak? hawthorns-season? Nhawthorns-season-peak? Ohawthorns-season-peak? to know if it is any of the host-specific season or season peaks monthendlist set to determine when the next month starts raster_file set up export files as a raster file stochastic_factor_temperature stochastic_factor_precipitation the stochastic factor added to climate data to have variation infested-patch-cost host-removal-cost spray-cost orchard-cost education-cost costs for specific management efforts initial-host-patches to count how many host patches are in the extent carrying_capacity apple_carrying_capacity hawthorn_carrying_capacity orchard_carrying_capacity set to limit patch infestation overload crude_mortality crude_mortality_dispersal crude_mortality_predation crude_mortality_other crude_mortality_disease crude_mortality_parasitism crude-related mortality variables temperature_dependent_mortality precipitation_dependent_mortality climate-related mortality variables 126 Variable Description am_males% am_females% % of female and male flies on the patch year to know what year it is year_export for raster export purposes total_patches_available total patches available for current extent total_orchards_available total orchard patches available for this extent total_undetectables_available total patches that are not hosts or orchards available for this extent total_apples_available total_crabapples_available total_nhawthorns_available total_ohawthorns_available available host-specific patches left apple_left crabapple_left nhawthorn_left ohawthorn_left % hosts specific patches left patches_infested total number of patches infested - for plot patches_infested_ever total number of patches infested - cumulative - for plot orchard_patches_infested total number of orchard patches infested - for plot orchard_patches_infested_ever total number of orchard patches infested - cumulative - for plot hosts_infested total number of host patches infested - for plot hosts_infested_ever total number of host patches infested - cumulative - for plot 127 Variable Description undetected_infested total number of non-host patches infested - for plot ;; we can only detect presence of AM on host traps undetected_infested_ever total number of non-host patches infested - cumulative - for plot ;; we can only detect presence of AM on host traps patch_price cost for infested patch orchard_price cost for infested orchard patch host_price cost for host patch removed citizen_price cost for preventing ldd_h spray_price cost for spraying an orchard patch total-costs total for all costs Table A4. Model interface details. Interaction Name Type Description Values Initialize Button Set up the model NA Step Button Simulate model for 1 time-step NA Continuous Button Continuously simulate model until complete NA first-catch-site? Switch If on, spread will start at location of first catch ever recorded On or off 128 Interaction Name Type Description Values origin-of-spread Button Click on map where to start the spread NA Reset/change extent Button Reset the simulation or press after changing extent NA x-coordinates Monitor Displays the x coordinates of the mouse on the map NA y-coordinates Monitor Displays the y coordinates of the mouse on the map NA Year Monitor Displays the current year 1 - 5 Day Monitor Displays the current day of the year (June 1 to October 31) 1 - 153 Month Monitor Displays the current month of the year (June to October) 1 - 5 shpfile-visible? Switch If on, displays the landscape on the map. Whether on or off, the values of the landscape are within each patch On or off show-elevation? Switch If on, displays the elevation on the map. Whether on or off, the values of the elevation are within each patch On or off show_positives? Switch If on, displays patches with real world positive traps. On or off show-apple? Switch If on, displays the patches with apple on the map. Whether on or off, the values of the apple patch are within each apple patch On or off 129 Interaction Name Type Description Values show-crabapple? Switch If on, displays the patches with crabapple on the map. Whether on or off, the values of the crabapple patch are within each crabapple patch On or off show-Nhawthorn? Switch If on, displays the patches with native hawthorn on the map. Whether on or off, the values of the native hawthorn patch are within each native hawthorn patch On or off show-Ohawthorn? Switch If on, displays the patches with ornamental hawthorn on the map. Whether on or off, the values of the ornamental hawthorn patch are within each ornamental hawthorn patch On or off GIS_Extent Pull down Choose the map extent to simulate Kelowna, Wenatchee, Okanagan Valley, and Central Washington State Spread_scenario Pull down Choose the spread rate scenario to simulate Extreme, moderate, casual social-influence? Switch If on, social aspects of the model may be used On or off Spray-intensity% Slider Contribution effect on orchard spraying 0 â 100, steps of 25 Host-removal-intensity % Slider Contribution effect on removing hosts 0 â 100, steps of 25 130 Interaction Name Type Description Values Citizen-awareness-intensity% Slider Contribution effect on preventing human-mediated dispersal events 0 â 100, steps of 25 Dem_load? Switch For purpose of changing extent properly whilst conducting BehaviourSpace experiments On or off Figure A2. Model interface. Figure created by Brian Muselle. 131 APPENDIX B: SPATIALLY EXPLICIT EXAMINATION OF POPULATION SPREAD IN RHAGOLETIS POMONELLA IN CENTRAL WASHINGTON STATE The purposes of this appendix were to describe the historical invasive spread of R. pomonella both at a small, local (Wenatchee) and large, regional (central Washington State) scale and examine the extent of the population spread on an annual basis using spatially explicit home range analysis tools. Understanding spread patterns of an invasive pest is a crucial part of management. I analyzed the spread of apple maggot in central Washington State using the years 2003-2012 provided by the WSDA. Due to the limitations of the data, the purpose of this analysis was to simply examine whether there is any potential to estimate spread, or at least examine the extent of the spread annually. I used three methods for analyzing spread rate: (i) Minimum Convex Polygon (MCP); (ii) kernel density analysis; and (iii) using spread methods as per Tobin et al. (2015) (see Chapter I.V.I regarding background on each of these methods). In general, the available data lacks the potential to produce any meaningful rates of spread or any patterns of spread but are still useful in visualizing the spatial distribution of apple maggot within the central Washington State. Thus, these analyses were not used in the model, and only serve as a background for the Washington State validation model. BI MCP As shown in Figure B1, the 100% MCP (which illustrates the entirety of the spatial area of infested host trees) can exaggerate the spatial extent of the spread. Applying the 95% and 90% MCP (removing 5 and 10 % of the data points, respectively) reduces this, but because it excludes some data, it also reduces the analysis strength. Figure B1 also illustrates that 90% of the data points (represented in red) fall around the Yakima-Ellensburg region, and that a large portion of the outlying infestation area (95%, and 100%) for many of the years are widely spread out. Figure B2 illustrates the significant differences between the 90%, 95%, and 100% MCP areas (one-sample t-test, p < 0.005, see Table B1), with peak infestation years in 2005 and 2010. 132 133 134 Figure B1. Annual spatial extent of population spread for R. pomonella in host trees in central Washington State between 2003 to 2012 as determined by MCP analysis. The Minimum Convex Polygon (MCP) represents the area where positive traps were found. The Red polygon represents 90% of all positive traps, yellow is 95%, and blue is 100% of all positive traps. Maps generated in ArcGis 10 using Home Range Tools 2.0. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 135 Figure B2. Apple maggot MCP area based on locations of infested host trees by year from 2003 to 2012 for central Washington State using 100%, 95%, and 90% MCP calculations. Figure created by Brian Muselle. Table B1. One sample t-test comparison of the MCP areas. MCP point inclusion rate n df t-value p-value 90% 10 9 3.0384 0.007 95% 10 9 3.5674 0.003 100% 10 9 4.2071 0.0011 BII Kernel Density Analysis Similar to that of the MCP, the kernel density analysis illustrates clustering around the Yakima-Ellensburg region, and beyond that are only isolated instances (Figure B3). Trap data from year to year is only considered and therefore not accumulative. I implemented this to illustrate the significant changes in annual clustering. Figure B4 illustrates the changes in isopleth area (logged), which are relatively constant throughout the years, except for peaks in 2005 and 2010. There was very little significance between years, however, as the variance was high, see Table B2. Figure B5 illustrates the movement of the center of the annual isopleths. In earlier years, there was some change in the center of infestation, but in later years the center of infestation was relatively stable. 05000100001500020000250002003 2004 2005 2006 2007 2008 2009 2010 2011 2012MCP Area (sqkm)Year90 95 100 136 137 138 Figure B3. Annual spatial extent of population spread for R. pomonella in host trees in central Washington State between 2003 to 2012 as determined by kernel density analysis. The 0.90 isopleth contains the maximum area identified in the apple maggot infestation, and the lower isopleths from 0.80 to 0.10, represented by the contour lines within the 0.90 isopleth, illustrate where the densest areas of the infestation. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. Figure B4. Graphical representation of the apple maggot infestation area of kernel isopleths (logged) over time for central Washington State between 2003 and 2012. Figure created by Brian Muselle. 139 Table B2. Matched paired t-tests analysis for change in isopleth area from year to year (d.f. = 8). Year Isopleth area (km2) mean diff std err t-ratio p-value 2003-2004 0.6656 0.6061 -1.0982 0.3041 2004-2005 10.1000 4.8888 -2.0660 0.0727 2005-2006 -12.6547 5.8658 2.1574 0.0315 2006-2007 0.1219 0.0259 -4.7005 0.0015 2007-2008 1.3868 0.8021 -1.7290 0.1221 2008-2009 -0.1662 0.6369 0.2610 0.8007 2009-2010 25.2519 15.7899 -1.5992 0.1484 2010-2011 -18.7078 10.9973 1.7011 0.1273 2011-2012 -4.6009 2.2973 2.0028 0.0802 Figure B5. Graphical representation of the change in the geographical centroids of the annual isopleths results from the kernel density analysis. The centroid of isopleths shows how the center of the infestation change from year to year. Figure created by Brian Muselle. BIII Tobin et al. (2015) Methods BIII.I Square-Root Area Regression Figure B6 illustrates the respective slope estimates for each population threshold in terms of the square-root area regression analysis. The annual rate of spread (+/- SE), which I ascertained from the slope estimate, is 4.8 (8.0), 1.1 (6.8), and -1.4 (8.9) km/year for the 1-, 2-, and 3-fly or more thresholds, respectively. Table B3 provides more details. 200320042005200620072008 200920102011201246.500046.600046.700046.800046.900047.000047.1000-120.8000 -120.7500 -120.7000 -120.6500 -120.6000 -120.5500 -120.5000 -120.4500 -120.4000 -120.3500LatitudeLongitude 140 Figure B6. Spread analysis graph for square-root area regression. Green = threshold 1 fly per trap, red = threshold 2 flies per trap, blue = threshold 3 or more flies per trap. See Table B3 for details. Formula = SQRT((area/pi)). Method source from Tobin et al. 2015. Figure created by Brian Muselle. Table B3. Spread analysis table for square-root area regression. Complements Figure B6. Method source from Tobin et al. 2015. Fly Threshold Spread km/year (ascertained from the slope estimate Sample size, n â number of years mean SD median min max skew SE 1 4.815 10 54.618 25.453 59.145 6.70 84.60 -0.521 8.049 2 1.078 9 18.019 20.337 8.770 1.76 56.38 -0.861 6.779 > 3 -1.408 6 15.058 21.722 5.600 0.23 55.94 -0.979 8.868 BIII.II Distance Regression Figure B7 illustrates the respective slope estimates for each population threshold in terms of the distance regression analysis. The annual rate of spread, which I ascertained form the slope estimate, is 0.76, 0.47, and -0.19 km/year for the 1-, 2-, and > 3-fly thresholds, respectively. Tables B4a-c provide more details. 141 A) B) C) Figure B7. Spread analysis graph for distance regression. Origin point used = Yakima site 2003. A = threshold 1-fly per trap, b = threshold 2-flies per trap, c = threshold 3-flies or more per trap. See Table B4a-c for details. Method source from Tobin et al. 2015. Figure created by Brian Muselle. 142 Table B4a. Spread analysis table for 1-fly threshold distance regression. Complements Figure B7A. Method source from Tobin et al. 2015. For 1-fly threshold: spread km/year (ascertaine d from the slope estimate) = 0.7603353. Year n mean SD median min max skew SE 2003 33 33.3254 16.1929 41.1123 0.6553 44.2468 -1.3203 2.8188 2004 99 39.6270 7.2928 41.6643 3.2824 53.6935 -3.8228 0.7330 2005 291 38.7467 23.2491 41.1245 1.6228 125.6419 1.2190 1.3629 2006 82 41.7294 27.7520 42.0465 3.0402 194.0575 2.7502 3.0647 2007 98 47.8159 26.8285 41.8240 2.9872 109.8498 1.1529 2.7101 2008 89 40.6881 14.5761 41.3863 3.6011 122.5285 2.5178 1.5451 2009 100 39.6973 18.3778 41.0401 4.2220 116.8894 1.9260 1.8378 2010 54 36.4883 45.2413 24.6125 1.6711 220.8033 2.2973 6.1566 2011 72 54.5492 43.7104 38.8010 3.4881 116.6766 0.2029 5.1513 2012 70 39.3375 43.5749 36.4478 1.3964 180.2555 1.6788 5.2082 Table B4b. Spread analysis table for 2-fly threshold distance regression. Complements Figure B7B. Method source from Tobin et al. 2015. For 2-fly threshold: spread km/year (ascertaine d from the slope estimate) = 0.4694723. Year n mean SD median min max skew SE 2003 0 - - - - - - - 2004 12 41.7454 0.1235 41.7389 41.6258 42.0880 1.5634 0.0357 2005 55 46.2680 30.7526 40.4139 5.5510 125.6419 1.0920 4.1467 2006 9 30.1708 19.1482 42.4901 4.6237 43.5438 -0.5915 6.3827 2007 11 54.0496 25.2133 42.5764 40.5544 108.7450 1.4243 7.6021 2008 9 40.5826 1.0741 40.5017 38.8304 42.2528 0.0682 0.3580 2009 9 46.1643 22.5801 37.5334 36.8811 106.1306 2.0418 7.5267 2010 4 24.2014 1.7007 24.6125 21.7928 25.7880 -0.4988 0.8504 2011 10 85.3037 28.9238 98.5362 44.4507 108.9246 -0.6171 9.1465 2012 18 34.2968 20.7751 37.0086 1.3964 100.6296 1.3578 4.8967 143 Table B4c. Spread analysis table for 3-fly threshold distance regression. Complements Figure B7C. Method source from Tobin et al. 2015. For 3-fly or more threshold: spread km/year (ascertaine d from the slope estimate) = -0.186729. Year n mean SD median min max skew SE 2003 0 - - - - - - - 2004 5 41.7250 0.05664 41.7417 41.6258 41.7658 -0.9608 0.0253 2005 32 51.0377 37.4843 40.2059 8.4505 125.6419 0.7396 6.6263 2006 1 4.6563 - - - - - - 2007 4 56.4957 29.5006 42.3505 40.5544 100.7276 0.7480 14.7503 2008 3 40.1521 1.4411 39.9373 38.8304 41.6885 0.1457 0.8320 2009 1 36.8930 - - - - - - 2010 1 21.7928 - - - - - - 2011 3 87.4302 37.2213 108.9199 44.4507 108.9199 -0.3849 21.4897 2012 11 40.9003 21.8096 37.0264 8.3782 100.6296 1.5623 6.5758 BIII.III Overall Table B5 summarizes the two methods of spread rate calculation used by Tobin et al. (2015) and is clear that the data does not provide any significant spread rates. It is curious, though, that the > 3 threshold decreases in annual spread for both methods. This may reflect the impact of intensive management at sites with higher trap captures; initiating strong management action to these sites may reduce the rate of spread. Table B5. Estimates of apple maggot spread from two analytical methods when based upon point data (km/year). Method source from Tobin et al. 2015. AM Threshold Square-root area regression +/- SE Distance Regression +/- SE 1 4.815 8.049 0.760 0.341 2 1.078 6.779 0.470 0.859 > 3 -1.408 8.868 -0.187 1.438 144 APPENDIX C: CRATAEGUS SPP. DISTRIBUTION ANALYSIS IN THE OKANAGAN VALLEY [Manuscript-in-Progress] The two main purposes of this appendix were: (i) from a botanical perspective, to get a better understanding of the distribution of hawthorn in general, and distribution of species of hawthorn; and (ii), from a biologist/entomologist/pest management perspective, as a primary host of the agricultural pest, R pomonella, it is crucial to know where the hosts are so that this type of research can continue for many years. Thus, I attempted to expand this knowledge by: (i) conducting field surveys; (ii) constructing a citizen science website; and (iii) building an SDMs for hawthorn in the Okanagan Valley. Anecdotally, hawthorn is not a very well-known plant; many people I encountered in the Okanagan Valley during my hawthorn scouting did not know what a hawthorn was, even if they had hawthorn on their property. Furthermore, facilities that sell hawthorn, including Bylands, frequently mis-identify the hawthorn they sell. A key component to my apple maggot spread model is the host distribution, which include hawthorn (amongst others). Therefore, a better understanding of hawthorn distribution in the Okanagan Valley is crucial. CI Surveys I surveyed sites throughout the Okanagan Valley in the summers of 2015-17 and collected data on hawthorn characteristics and distribution, see Table C1. I gained valuable knowledge and guidance in visits to Washington State by examining positive trap sites and speaking with WSDA experts. Specifically, I learned to examine sites in the Okanagan Valley with certain types of terrain (i.e. bottom of slopes, non-rocky terrain, roadside) and water (i.e. canals, sides of irrigation channels) To date, 635 hawthorn sites have been recorded: 90.2% native and 9.8% ornamental, see Figure C1. Of the native hawthorn, 37.2% of the sites had single specimens; 45.4% had 2-9 trees; and 17.5% had 10 or more hawthorn trees. The maximum number of individual trees recorded in one site was approximately 385. Of the ornamental hawthorn, 56.5% of the sites had single specimens; 32.2% had 2-9 trees; and 11.3% had 10 or more hawthorn trees. These 635 hawthorn 145 sites represent 635 potentially unmanaged apple maggot hosts; wild hawthorn and wild apple trees are a major key component to apple maggot spread. Of the hawthorn sites that had data on associated plants, 69.0% of the sites had snowberry bushes in the immediate vicinity, which is relevant to apple maggotâs sister species the snowberry maggot, Rhagoletis zephryia (morphologically similar to apple maggot, see literature review). Table C1. Hawthorn characteristics collected during scouting sessions. Data collected using a Trimble GPS system. Characteristics Specifics Location GPS location at centre of infestation or specimen, jurisdiction, slope, aspect, elevation, % soil coarse fragment, nearest city, address (if available), habitat type, shading, distance from water source, soil moisture regime Tree host density, stem type (multi or single), size, length of thorns (average), leaf characteristics (shape, vein orientation, form, colour), twig colour Flowers (during season - late spring / early summer) flower colour, number of stamen (average), anther colour, % full flower Fruits (during season - late summer / early fall) % ripe fruit, fruit colour, fruit shape, fruit diameter Technical total time spent inventorying area, survey date, plant name (native or ornamental), indicated samples collected, photographs taken, and any other comments needed 146 Figure C1. Hawthorn sites collected by the Thistlewood lab throughout the Okanagan Valley 2015-2017. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. CII Citizen Science The A.P.P.L.E. (Apple maggot Prevention Project using Landscape Ecology) website is an interactive, web-based project (http://applemaggotproject.weebly.com) that serves as: (1) a citizen science portal where residents of the Okanagan Valley can submit locations of host plants of the invasive apple maggot, Rhagoletis pomonella, specifically apple and hawthorn; and (2) a public resource for information on apple maggot and its host plants. The website became public on November 3, 2016. Distributors, organizations, and companies involved with spreading the website, can be found on the website (http://applemaggotproject.weebly.com/contact.html). 147 Below are the âLetter to Distributorâ, the âOne-Pagerâ, and the âPress Releaseâ documents used to promote the website to various organizations including newspaper outlets, non-profits, garden clubs, and governmental agencies. âLetter to Distributorâ Unit 2 - Biology Irving K. Barber School of Arts and Sciences ASC367-3187 University Way Kelowna, BC V1V 1V7 New citizen-science project: spread the word on A.P.P.L.E. and prevent the spread of apple maggots Dear Colleagues, My name is Brian Muselle, and I would sincerely appreciate your help in notifying your members or clients about A.P.P.L.E. (Apple maggot Prevention Project using Landscape Ecology). This interactive, web-based project (http://applemaggotproject.weebly.com) serves as: (1) a citizen science portal where residents of the Okanagan region (Okanagan, Similkameen, Thompson, and Shuswap valleys) can submit locations of host plants of the invasive apple maggot, Rhagoletis pomonella, specifically apple and hawthorn; and (2) a public resource for information on apple maggot and its host plants. If your organization can join in spreading the word about A.P.P.L.E., I will gladly add your contact information (email or website address) to the website. Why participate? By preventing the spread of the apple maggot, we will help to protect our local orchards from an invasive pest. We need to know more about the actual locations of host plants, primarily wild apple and native and introduced hawthorns. Adding data about host locations in our region is therefore essential, and the public can help via the A.P.P.L.E. website. We believe this research to be very relevant to the Okanagan, and that it helps to involve the public by raising awareness, and potentially slowing the spread of an invasive insect pest. About me I am a Masters student at UBCâs Okanagan campus in Kelowna, under the supervision of Dr. Jason Pither (Department of Biology) and Dr. Howard Thistlewood of Agriculture and Agri-Food Canada, Summerland, B.C. My research employs agent-based modeling to simulate the likely spread of the apple maggot in the Okanagan region. I am using data collected in Washington State to help predict patterns of spread here, to improve management and survey efforts, and hopefully to slow itâs spread. About apple maggots The invasive apple maggot, Rhagoletis pomonella, is a Tephritid fruit fly and sister species to cherry fruit flies, also Rhagoletis spp., that infest different tree fruits but in the same way. These small creatures cause a lot of damage to fruits, making them unmarketable. Apple maggot has slowly spread North across Washington State to locations near our border. So far, it has been captured at a small number of sites in the interior of B.C. If you have any questions at all, please contact me at brian.muselle@ubc.ca Thank you very much for any assistance provided! Sincerely, Brian Muselle 148 âOne-Pagerâ About A.P.P.L.E. A.P.P.L.E. (Apple maggot Prevention Project using Landscape Ecology) is an interactive, web-based project, which serves as: (1) a citizen science portal where residents of the Okanagan region (Okanagan, Similkameen, Thompson, and Shuswap valleys) can submit locations of host plants of the invasive apple maggot, Rhagoletis pomonella, specifically apple and hawthorn; and (2) a public resource for information on apple maggot and its host plants. The apple maggot is native to eastern North America, but has spread and established itself in certain counties of 6 different western states: Washington, Oregon, Idaho, California, Colorado, and Utah. This pest has cost millions of dollars annually in damaged fruit in the apple industry and has been difficult to manage. Currently, the only remaining commercial apple-producing regions in North America where the apple maggot is not established are B.C.âs Okanagan, Similkameen, Shuswap and Thompson Valleys. Why participate? By preventing the spread of the apple maggot, we will help to protect our local orchards from the damage these pests cause by burrowing and tunneling into apples. Knowing where the maggot is most likely to be found (host locations) in our region is essential, and the public can help via the A.P.P.L.E. website. We need to know more about the actual locations of host plants, primarily wild apple and native and introduced hawthorns. How you can help: â¢ Locate a host plant site. (Primary hosts are apple and hawthorn. Other hosts include, but are not limited to: sweet cherry, sour cherry, crabapple, Asian and common pear, European plum, bitter cherry, and cotoneaster.) â¢ Collect as many details about the site and plant as possible: number of plants, address, etc. â¢ Go to the A.P.P.L.E. submissions page and send us your data. Weâll add it to the map. For more information, please contact Brian Muselle at: brian.muselle@ubc.ca Tel. 250-550-4588 or visit the website: http://applemaggotproject.weebly.com. If you think you have found apple maggots, please contact the Canadian Food Inspection Agency at: 1-800-442-2342 or www.inspection.gc.ca/eng/1297964599443/1297965645317 âPress Releaseâ Brian Muselle FOR IMMEDIATE RELEASE: 03/11/16 UBC, Okanagan campus brian.muselle@ubc.ca Local student launches citizen science portal to help prevent the spread of apple maggots Website will benefit fruit-growers, home gardeners, biologists, and educators November 3, 2016 - KELOWNA, BC - A new website launched today aimed at informing the residents of the Okanagan Valley about Rhagoletis pomonella, an invasive Tephritid fruit fly also known as apple maggot. The website, called the Apple maggot Prevention Project using Landscape Ecology, or A.P.P.L.E. (http://applemaggotproject.weebly.com), also aims to engage the public in helping to prevent the spread of the apple maggot. 149 Brian Muselle, a graduate student at the University of B.C., Okanagan campus, designed the website. A.P.P.L.E. will serve to: (1) act as a citizen science portal for residents of the Okanagan Valley to participate in locating common plants that are hosts of the apple maggot, specifically hawthorn and apple; and (2) provide some information on apple maggot, and its plant hosts, to residents. Although presently not established in the Okanagan Valley, apple maggot has been detected here in the last two years, is well-established in the rest of British Columbia and Canada, as well as in certain areas of Washington State where it has moved north over the last 25 years to our border. It is only a matter of time before this destructive pest tries to establish itself in the Okanagan Valley. Brianâs research uses agent-based models to simulate the potential spread of the apple maggot in the Okanagan Valley. These models are initially constructed and tested using data from Washington State, where extensive surveys of apple maggot have occurred for over 25 years. As we prepare for the apple maggotâs impending arrival, Brianâs models have the potential to help growers and researchers optimize management and survey efforts in the Okanagan Valley. To accomplish this, he needs more data about the locations of the apple maggot's host plants, including apple and hawthorn, within the Okanagan Valley. The A.P.P.L.E. website provides the means for residents of the Okanagan Valley to easily submit information about host trees. About Brian: Brian is a Masters student at UBC-Okanagan in Kelowna under the supervision of Dr. Jason Pither, Biology Department, and Dr. Howard Thistlewood of Agriculture and Agri-Food Canada, Summerland, B.C. For more information, please contact: Brian at brian.muselle@ubc.ca or telephone: 250-550-4588 Visit the website URL: http://applemaggotproject.weebly.com. CIII Species Distribution Models For the HSI model, I sifted through the literature looking for important attributes that contribute to hawthorn distribution and analyzed how well the hawthorn occurrence records that I have obtained fit within the model. The 12 hawthorn attributes used included: elevation, slope, aspect, shade, soil moisture, soil nutrients, BEC zones, mean annual temperature, mean annual precipitation, distance from roads, distance from water, and soil pH. The HSI analysis produces values ranging from 0 to 1, where values close to 0 indicates the habitat is not suitable, while values close to 1 indicates habitat is optimal. I used the program ArcMap 10.4 to produce the HSI map, see Figure C2. The lowest HSI value for the Okanagan Valley was 0.1767 and the highest value was 0.8342, see Table C2. The mean HSI value was 0.4753 +/- 0.0840 standard deviation, suggesting a moderate suitability for hawthorn. When applying the hawthorn occurrence records of 1185 points, the lowest HSI hawthorn point value was 0.2644 and the highest value was 0.7397, see Table C3. The mean HSI value for the hawthorn points was 0.4853 +/- 0.0664 standard deviation, similar to the HSI values for the Okanagan Valley. For the MaxEnt model, I used the hawthorn occurrence records, environmental and climatic data, and the hawthorn attributes used in the HSI model to produce a probability range map, which is heavily based on the records being an accurate representation of the true distribution of hawthorn. I used the program MaxEnt (version 3.3.3k Phillips et al. 2017) to produce the MaxEnt map, see Figure C3. 150 These models assume static landscapes; however, such is not the case within the Okanagan Valley. The impacts of supplementary irrigation, rivers, canals, and other water sources to the model may depend on different species, but for hawthorn, a species that requires a water source, may benefit from these supplementary sources; thus, altering the suitability index. Therefore, it can be expected that the models presented may illustrate an underestimate in suitability for hawthorn within the Okanagan Valley. In summary, the HSI model and MaxEnt model produced useful distribution maps for hawthorn and suggest that the Okanagan Valley provides moderately suitable habitat. Figure C2. HSI model output using the habitat suitability index formula. A value close to 0 indicates habitat that is not suitable for hawthorn, while a value close to 1 indicates habitat that is optimal for hawthorn. Figure created by Brian Muselle. Service layer credits and sources: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community. 151 Table C2. The HSI model statistics. Statistics HSI Value Min 0.1767 Max 0.8342 Mean 0.4753 SD 0.0840 Table C3. Statistics on the hawthorn occurrence records fit to the HSI model. Statistics HSI Value n 1185 Min 0.2644 Max 0.7397 Mean 0.4853 SD 0.0664 Figure C3. The Maxent output model (with all variables). The model illustrates the probability of finding hawthorn, from 0%, or blue, to 100%, or red. The white squares are the hawthorn occurrences records. 152 APPENDIX D: R CODE AND NETLOGO DETAILS Statistical coding in R and administrative NetLogo details for the model. DI Chapter III Validation Model For distance measures (metric 4), first I used ArcGIS to gather point distance data from infested patches simulated in the model: #To define projections of asci file from model (averaged from Rstudio) Data management < projections and transformations < define projections < same as wa_dem (NAD_1983_utm_zone_11n) #To convert raster hosts and raster positives to point data Conversion tools < from raster < raster to point #To create a buffer around each positive raster cell Analysis tool > proximity > buffer > linear unit = 450 m Data management tools > features > minimum bounding geometry > input features = buffer, geometry type = envelope #To combine positive point data within each buffer into buffer Analysis > overlay > spatial join > target = buffer, field map = am_num; merge rule = sum #To extract buffer values to positive points Analysis > overlay > spatial join > target = positive points, field map = am_num; merge rule = first #To extract values of raster file to point data Spatial analyst tools < extraction < extract values to points < input point (hosts and positives), input raster (asci file from model), no check mark for any options #To select only points from host and positive that were simulated as positive Select raster_values > 0 from attribute table > selection > create layer from selected #To get distances between origin and host and positives Analysis tools > promiximty > near > input = host and positive, near = origin Then, Rstudio: 153 > hist(wen_con$dist_km,ylab="Positive Trap Count",xlab="Distance in km", main="") For host/positive patch infestation information: #import required libraries library(raster) library(rgdal) #load raster host patches of wenatchee/washington wen_hos <- raster("./wen_hosts.asc") plot(wen_hos) #load raster positive patches of wenatchee/washington wen_pos <- raster("./wen_pos.asc") plot(wen_pos) #name combined raster file for export later final <- "wen_casual_100_25_25" #combining raster files ###version 1 - short### #make list of ascii files spreadrate <- list.files(pattern = "*1002525.asc$") #create rasterbrick rast_spreadrate <- brick(lapply(spreadrate, raster)) #add the raster files rast_spreadrate_sum <- sum(rast_spreadrate) ###export new raster### #divide the raster by the amount of replicates * 5 (ie 15 replicates at 5 simulation years per replicate = 75) rd <- rast_spreadrate_sum / 75 #plot the raster plot(rd) #write the output raster to file r <- writeRaster(rd, filename = final, format="ascii", overwrite=TRUE) ###analyze new raster### #see frequency of raster patch values (must be integers - multiply raster by 100 to get % out of 100%) x <- rd * 100 plot(x) freq(x) 154 #see if model simulates infestation of host sites (including positive sites) wen_hos_rc <- calc(wen_hos, fun=function(x){ x[x < 1] <- NA; return(x)} ) blue <- mask(x, wen_hos_rc, maskvalue=NA, updatevalue=NA) plot(blue) freq(blue) #see if model simulates infestation of positive sites (positive sites only) wen_pos_rc <- calc(wen_pos, fun=function(x){ x[x < 1] <- NA; return(x)} ) red <- mask(x, wen_pos_rc, maskvalue=NA, updatevalue=NA) plot(red) freq(red) DII Chapter IV Predictive Model To determine significance between model simulation: #separate the two treatments and select only the data we need control = subset(ok_mod, type=="control") managed = subset(ok_mod, type=="managed") View(control) ###comparison### #patches #orchards #hosts #undetected #pos_true #pos_false #remove #num_spray #ldd_h #apple #crabapple #nhawthorn #ohawthorn #total #patch_price #orchard_price #host_price #citizen_price #spray_price ##is data normally distributed? #condition 1 - normality 155 boxplot(orchard_price~type, ok_mod) #condition 2 - equal variance var(control$orchard_price) var(managed$orchard_price) #data is normal t.test(orchard_price ~ type, ok_mod, var.equal= F) #data is not normal wilcox.test(orchards ~ type, kel_cas) #ANOVA library(multcomp) ok_all$scenario <- factor(ok_all$scenario, levels = c("cas", "mod", "ext")) boxplot(spray_price~scenario, ok_all) okAnova <- lm(spray_price ~ scenario, data = ok_all) anova(okAnova) #r-square measure okAnovaSummary <- summary(okAnova) okAnovaSummary$r.squared #tukey test okTukey <- glht(okAnova, linfct = mcp(scenario = "Tukey")) summary(okTukey) DIII NetLogo Details DIII.I Computer Hardware The simulations were run on three different devices for the purpose of accelerating completion: (i) an iMac Apple OS X version 10.9.5, 3.7 GHz Quad-Core Intel Xeon E5, 8 cores; (ii) a MacBook Pro 2016 macOS Sierra version 10.12.6, 2.9 GHz Intel Core i5, 4 cores; and (iii) a Windows 7 Enterprise 2009, 3.47 GHz Intel (R) Xeon (R) CPU, 6 cores. 156 DIII.II NetLogo Processes (used for both validation and predictive models) NetLogo uses NAD_1983 for coordinate system and UTM Transverse_Mercator for projection. Each polygon must be accompanied with a projection file (.prj) and a shapefile (.shp). Each raster file must be imported as an ASCII (.asc) file and accompanied with a projection file (.prj). In order to perform the simulations in NetLogo, the java heap space limit had to be increased from 1 GB [Xmx1024m -jar NetLogo.jar] to 15 GB [Xmx15360m -jar NetLogo.jar]. DIII.III NetLogo BehaviorSpace Process (used for both validation and predictive models) Variables (separate into 3 experiments - each with only unique management parameters): [EXTENT] [SCENARIO] ["host-removal-contribution%" 0 25 50 75 100] ["citizen-awareness-contribution%" 0 25 50 75 100] ["spray-contribution%" 0 25 50 75 100] Repetitions: 15 Measures run: patches_infested_ever ;; measure # infested patches orchard_patches_infested_ever ;; measure # infested orchards hosts_infested_ever ;; measure # infested hosts undetected_infested_ever ;; measure # infested patches that are neither hosts nor orchards (count patches with [positive? = true and patches_infested_ever? = true and host_patch? = true]) (count patches with [positive? != true and patches_infested_ever? = true and host_patch? = true]) (count patches with [host_removed? = true]) ;; count # of hosts removed (sum [num_spray] of patches) ;; count # times orchards sprayed (count patches with [LDD_H_avoided? = true]) ;; count # patches that avoided a long-distance human-mediated dispersal event apple_left ;; measure # apples left crabapple_left ;; measure # crabapples left nhawthorn_left ;; measure # nhawthorns left ohawthorn_left ;; measure # ohawthorns left total-costs ;; measure total cost patch_price ;; measure infested patch cost orchard_price ;; measure infested orchard cost host_price ;; measure host-removal cost citizen_price ;; measure citizen awareness cost spray_price ;; measure orchard spray cost Measure runs at each step? No 157 Setup: change-Extent startup Go: go Stop: N/A Final: N/A Time limit: 0 ;; ends when the simulation ends
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Agent-based scenario models of invasion and movement of rhagoletis pomonella (diptera: tephritidae) within the southern interior of British Columbia
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