Network-agent based model for simulating the dynamic spatial network structure of complex ecological systems

Taylor M. Anderson and Suzana Dragićević

Ecological Modelling, 2018, vol. 389, issue C, 19-32

Abstract: Non-spatial ecological networks provide insight into the organization and interaction between biological entities. More recently, biological dispersal is modelled using spatial networks, static sets of georeferenced habitat patches that connect based on a species’ maximum dispersal distance. However, dispersal is complex, where spatial patterns at the landscape scale emerge from interactions between ecological entities and landscape features at much finer individual scales. Agent-based modelling (ABM) is a computational representation of complex systems capable of capturing this complexity. Therefore, this study develops a network-ABM (N-ABM) that combines network and complex systems theory to simulate complex evolving spatial networks. The developed N-ABM approach is implemented on the case study of the emerald ash borer (EAB) bark beetle using geospatial datasets in Ontario, Canada. The N-ABM generates dynamic spatial network structures that emerge from interactions between the EAB and tree agents at the individual scale. The resulting networks are analyzed using graph theory measures. Analysis of the results indicates a relationship between preferential attachment in insect host selection and the emergent scale-free network structure. The N-ABM approach can be used to represent dynamic ecological networks and provides insight into how network structure emerges from EAB dispersal dynamics, useful for forest management.

Keywords: Network-agent based modeling; Spatial networks; Ecological complex systems; Geographic information systems; Forest insect infestation; Emerald ash borer (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:389:y:2018:i:c:p:19-32

DOI: 10.1016/j.ecolmodel.2018.10.008

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