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

Author:

Wadkin Laura E.1ORCID,Golightly Andrew2ORCID,Branson Julia3ORCID,Hoppit Andrew4,Parker Nick G.1ORCID,Baggaley Andrew W.1ORCID

Affiliation:

1. School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK

2. Department of Mathematical Sciences, Durham University, Durham DH1 3LE, UK

3. GeoData, Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK

4. Forestry Commission England, Nobel House, London SW1P 3JR, UK

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.

Funder

the EPSRC New Horizons

NERC Knowledge Exchange Fellows

Publisher

MDPI AG

Subject

Nature and Landscape Conservation,Agricultural and Biological Sciences (miscellaneous),Ecological Modeling,Ecology

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