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Lookup NU author(s): Dr Laura Wadkin, Andrew Golightly, Professor Nick ParkerORCiD, Dr Andrew BaggaleyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Wadkin LE, Golightly A, Branson J, Hoppit A, Parker NG, Baggaley AW
Publication type: Article
Publication status: Published
Journal: Diversity
Year: 2023
Volume: 15
Issue: 4
Print publication date: 01/04/2023
Online publication date: 28/03/2023
Acceptance date: 21/03/2023
Date deposited: 24/04/2023
ISSN (electronic): 1424-2818
Publisher: MDPI AG
URL: https://doi.org/10.3390/d15040496
DOI: 10.3390/d15040496
Data Access Statement: All statistical modelling software with the Bushy and Richmond Park time series are available on the Newcastle University data repository: https://doi.org/10.25405/data.ncl.22341289 The full OPM data used in this paper were collected, processed, and shared by the Royal Parks charity. These data can be provided from the Royal Parks upon reasonable request.
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