Modeling forest insect infestation: GIS and agentbased

Transcription

1 Modeling forest insect infestation: GIS and agentbased approaches Taylor Anderson and Suzana Dragićević 2014 Esri Canada s Annual User Conference October 7 th, Toronto, Canada

2 Presentation Overview Emerald Ash Borer Modeling the EAB as a Complex System Cellular Automata Approach Agent Based Model Approach Conclusions and Future Work

3 Emerald Ash Borer (Agrilus planipennis; EAB)

4 EAB is a Complex System Interactions: Elements interact at a local scale to produce global patterns that are difficult to predict Very difficult to model using traditional methods Parrott & Meyer, 2002

5 How can we model EAB as a complex system?

6 Modeling Complex Systems Cellular Automata (CA) Cellular automata is a modeling method, capable of representing complex systems over geographical space, through time Elements of CA Discrete cell grid Discrete cell states Discrete dynamics (temporal iterations) Local interactions (transition rules operating on a predefined neighborhood) Spatial Analysis Spatial Analysis and Modeling and Modeling (SAM) (SAM) Research Research Lab Lab Department Department of Geography, o Geography, Simon Simon Fraser Fraser University, Canada

7 Environment Agents Modeling Complex Systems Agent-Based Model (ABM) Bottom-up, complex systems modeling approach capable of representing real-world, interacting entities referred to as agents as they interact with each other and their environment Elements of ABM Similar to CA Mobile, agents can move freely and autonomously exchanging information over geographical space Spatial Analysis Spatial Analysis and Modeling and Modeling (SAM) (SAM) Research Research Lab Lab Department Department of Geography, o Geography, Simon Simon Fraser Fraser University, Canada

8 Current EAB Infestation Modeling Statistical modeling methods to represent EAB propagation and understand the climatic and economic relationships associated with EAB spread at: Local scales (BenDor & Metcalf, 2006; BenDor et al., 2006; Mercader et al., 2011) Regional scales (Prasad et al., 2009; Muirhead et al., 2006) Within a US context Does not capture the EAB as a complex spatiotemporal system

9 Objectives (1) Design and development of a model which can capture EAB behavior and forecast complex emergent patterns of EAB spread over space and time. (2) Integrate complex systems approaches including geographic information systems (GIS), CA, and ABM in model design and development. (3) Provide a modeling tool for better understanding, analysis and potential forecast of EAB insect propagation to aid in decision making, management, and eradication.

10 Study Area

11 Determining Ash Tree Susceptibility Fuzzy Logic Used to manage the uncertainty associated with determining ash tree susceptibility Spatial Analysis Spatial Analysis and Modeling and Modeling (SAM) (SAM) Research Research Lab Lab Department Department of Geography, o Geography, Simon Simon Fraser Fraser University, Canada

12 Ash Tree Susceptibility Generation Susceptibility of each ash tree is determined by combining fuzzy criteria using MCE optimization Criteria include (a) distance from infested ash (b) distance from roads (c) distance from highways (d) density of ash in the stand (e) age of ash (f) size of ash (g) wind direction and (h) air temperature

13 Ash Tree Susceptibility Generation

14 Dynamic Simulation using CA

15 Model Calibration Accomplished using dataset containing positive locations of EAB infested ash trees provided by the Canadian Food Inspection Agency (CFIA)

16 CA Model Results and Conclusions CA model reveals complex global collective behavior resulting from Active Inactive Active local dynamics between EAB and ash tree host Results demonstrate that the urban environment is most susceptible to EAB infestation Urban Landscape Scenario Rural-Urban Landscape Scenario Rural Landscape Scenario

17 CA Model Disadvantages Limited to the representation of short distance dispersal Use of neighborhood inherent to CA methodology cannot capture long distance dispersal Does not account for population data Does not take the life cycle of the EAB into account

18 EAB Agent-Based Model Represent very small scale local dynamics and interactions between the agent beetle and ash tree landscape object Offers flexibility and mobility Removes neighborhood limitation inherent to CA methodology and captures long distance dispersal

19 Integrating GIS and ABM ABM Recursive Porous Agent Simulation Toolkit is a free open source toolkit used for agent based modeling in application to a variety of ecological and social systems Middleware middleware platform which seamlessly facilitates the integration of ArcGIS (GIS) and Repast Simphony (ABM) GIS spatial analysis, modeling, automating workflows, and visualization of data with the help of a variety of built in tools

20 EAB ABM: Prototype EAB Healthy Tree Infested Tree Programming agents in Repast Simphony Prototype has three major actions or methods Move towards Point with Highest Susceptibility Infest

21 Conclusions and Future Work Complex systems approaches provide a suitable method for modeling spatial dynamics of infestation Major limitation in both approaches with respect to data availability leading to difficulty in model calibration and validation Future work ABM development for capturing refined behaviour and interactions of EAB Integration of CA and ABM for modeling EAB at larger, regional scale

22 Acknowledgements Natural Sciences and Engineering Research Council (NSERC) of Canada: Discovery Grant awarded to Dr. Suzana Dragićević and Canada Graduate Scholarship Masters (CGS-M) awarded to Taylor Anderson The datasets were provided by the Canadian Food and Inspection Agency (CFIA)

23 References BenDor, T. K., Metcalf, S. S., Fontenot, L. E., Sangunett, B., & Hannon, B. (2006). Modeling the spread of the Emerald Ash Borer. Ecological Modelling, 197(1-2), Bone, C., Dragicevic, S., & Roberts, A. (2006). A fuzzy-constrained cellular automata model of forest insect infestations. Ecological Modelling, 192(1-2), Canadian Food Inspection Agency (CFIA). (2013). Emerald Ash Borer-Latest Information. Retrieved from Johnston, K. (2013). ArcAnalyst: Agent Based Modeling in ArcGIS. ESRI Press: Redlands, California. Muirhead, J. R., Leung, B., Overdijk, C., Kelly, D. W., Nandakumar, K., Marchant, K. R., & MacIsaac, H. J. (2006). Modelling local and long-distance dispersal of invasive emerald ash borer Agrilus planipennis (Coleoptera) in North America. Diversity and Distributions, 12(1), Mercader, R. J., Siegert, N. W., Liebhold, A. M., & McCullough, D. G. (2011). Simulating the effectiveness of three potential management options to slow the spread of emerald ash borer ( Agrilus planipennis ) populations in localized outlier sites. Canadian Journal of Forest Research, 41(2), Parrott, L., Meyer, W. (2012). Future landscapes: Managing within Complexity. Frontiers in Ecology and the Environment, 10(7), Prasad, A. M., Iverson, L. R., Peters, M. P., Bossenbroek, J. M., Matthews, S. N., Davis Sydnor, T., & Schwartz, M. W. (2009). Modeling the invasive emerald ash borer risk of spread using a spatially explicit cellular model. Landscape Ecology, 25(3), Russell, S. J., Norvig, P., Canny, J. F., Malik, J. M., & Edwards, D. D. (1995). Artificial intelligence: a modern approach (Vol. 2). Englewood Cliffs: Prentice hall.

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