Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model

Wadkin, Laura E.; Golightly, Andrew; Branson, Julia; Hoppit, Andrew; Parker, Nick G.; Baggaley, Andrew W.

Abstract

Invasive woodland pests have substantial ecological, economic, and social impacts, harming biodiversity and ecosystem services. Mathematical modelling informed by Bayesian inference can deepen our understanding of the fundamental behaviours of invasive pests and provide predictive tools for forecasting future spread. A key invasive pest of concern in the UK is the oak processionary moth (OPM). OPM was established in the UK in 2006; it is harmful to both oak trees and humans, and its infestation area is continually expanding. Here, we use a computational inference scheme to estimate the parameters for a two-node network epidemic model to describe the temporal dynamics of OPM in two geographically neighbouring parks (Bushy Park and Richmond Park, London). We show the applicability of such a network model to describing invasive pest dynamics and our results suggest that the infestation within Richmond Park has largely driven the infestation within Bushy Park.

Citation

Wadkin, L. E., Golightly, A., Branson, J., Hoppit, A., Parker, N. G., & Baggaley, A. W. (2023). Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model. Diversity, 15(4), Article 496. https://doi.org/10.3390/d15040496

• Journal Article Type: Article
• Acceptance Date: Mar 21, 2023
• Online Publication Date: Mar 28, 2023
• Publication Date: 2023-04
• Deposit Date: Nov 8, 2023
• Publicly Available Date: Nov 8, 2023
• Journal: Diversity
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 4
• Article Number: 496
• DOI: https://doi.org/10.3390/d15040496
• Keywords: Nature and Landscape Conservation; Agricultural and Biological Sciences (miscellaneous); Ecological Modeling; Ecology
• Public URL: https://durham-repository.worktribe.com/output/1901954