Abstract
Insect infestation behaves as a complex system, characterized by non-linear spatial dynamics and emergent patterns that evolve from smaller to larger spatial scales. The emerald ash borer (EAB) is an invasive species that has infested and killed millions of ash trees across North America. Existing EAB models use traditional statistical approaches that often cannot address the spatiotemporal complexity emerging from EAB infestation processes. Moreover, these studies of insect infestation are limited by a lack of sufficient time series data. The objective of this study is to develop a geosimulation approach to overcome the challenge of data scarcity and represent EAB infestation at a regional scale. Geographic information systems (GIS), multi-criteria evaluation (MCE), and cellular automata (CA) are used to model EAB spread across different hypothetical landscape types. Simulation results represent EAB propagation and indicate different dynamics of spread for each landscape. Urban environments are identified as being at the greatest risk to the infestation. The proposed approach offers a theoretical framework and a modeling tool to represent the propagation of EAB infestation that can be applied with real geospatial datasets and potentially used in forest management strategies.
Highlights
Geosimulation modeling approaches can be used to develop tools to better understand complex spatiotemporal phenomena such as forest insect infestation dynamics
Using geographic information systems (GIS), multi-criteria evaluation (MCE) and the Cellular automata (CA) approach, namely its cellular structure and discrete cell states, this study aims to explore the use of a complex approach to represent the complex dynamics between the emerald ash borer pest and its ash tree host, and enables the representation of patterns of EAB infestation over space and time
The developed CA-EAB geosimulation model of EAB insect infestation has been applied to a hypothetical case study of the EAB infestation in the Essex County in Windsor, Ontario, Canada (42 ̋171N; 83 ̋001W) (Figure 1) where the first infestation was first identified in Canada
Summary
Geosimulation modeling approaches can be used to develop tools to better understand complex spatiotemporal phenomena such as forest insect infestation dynamics. Traditional statistical modeling methods which use top-down approaches for representing dispersal rely on large-scale empirical datasets [2,3,4] These approaches do not necessarily address the nonlinear complexity of the insect behavior. Geosimulation modeling approaches can be very useful in representing spatiotemporal phenomena such as insect infestation due to their capability of incorporating complexity inherent to these types of systems. These developed approaches can provide useful tools that can be used in forecasting how insect infestation outbreaks respond in application to various scenarios. Exploring these approaches capable of overcoming these challenges would be useful [6]
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