Emerald Ash Borer Land manager Behaviour

Title: Modelling the invasion and spread of the emerald ash borer in the UK including land manager surveillance.

The emerald ash borer (EAB; Agrilus planipennis Fairmaire) is a highly destructive invasive pest of ash (Fraxinus spp.), responsible for the mortality of millions of trees in regions where it is non-native. Although EAB is not currently established in Great Britain (GB), its potential arrival poses a significant threat to native ash (Fraxinus excelsior L.), which is already under pressure from ash dieback (ADB; Hymenoscyphus fraxineus). Consequently, the development of effective surveillance and early-detection strategies for EAB is essential.

This repository contains a spatially explicit, stochastic model of EAB introduction, spread, and detection in Great Britain. The model integrates three key components:

(i) The estimated spatial prevalence of ash dieback,

(ii) The population dynamics and dispersal of EAB following arrival, and

(iii) A socio-dynamics module that simulates land-manager behaviour with respect to surveillance and tree management, based on a values-driven decision-making framework.

If EAB is detected within the model, a contingency response is triggered that includes felling of infested trees and intensified visual surveillance, with the potential to eradicate local outbreaks or slow pest spread. The model is used to evaluate the effectiveness of alternative surveillance strategies, including targeted trapping at high-risk sites, routine inspections by land managers, and volunteer-based surveillance, with or without subsidised trapping.

The code was written by Alice E. Milne, Vasthi Alonso Chavez, and Nathan Brown,
Rothamsted Research, Harpenden, UK

Release date January 2026

Version 3.0.0

Emerald Ash Borer Land manager Behaviour

Title: Modelling the invasion and spread of the emerald ash borer in the UK including land manager surveillance.

The emerald ash borer (EAB; Agrilus planipennis Fairmaire) is a highly destructive invasive pest of ash (Fraxinus spp.), responsible for the mortality of millions of trees in regions where it is non-native. Although EAB is not currently established in Great Britain (GB), its potential arrival poses a significant threat to native ash (Fraxinus excelsior L.), which is already under pressure from ash dieback (ADB; Hymenoscyphus fraxineus). Consequently, the development of effective surveillance and early-detection strategies for EAB is essential.

This repository contains a spatially explicit, stochastic model of EAB introduction, spread, and detection in Great Britain. The model integrates three key components:

(i) The estimated spatial prevalence of ash dieback,

(ii) The population dynamics and dispersal of EAB following arrival, and

(iii) A socio-dynamics module that simulates land-manager behaviour with respect to surveillance and tree management, based on a values-driven decision-making framework.

If EAB is detected within the model, a contingency response is triggered that includes felling of infested trees and intensified visual surveillance, with the potential to eradicate local outbreaks or slow pest spread. The model is used to evaluate the effectiveness of alternative surveillance strategies, including targeted trapping at high-risk sites, routine inspections by land managers, and volunteer-based surveillance, with or without subsidised trapping.

The code was written by Alice E. Milne, Vasthi Alonso Chavez, and Nathan Brown,
Rothamsted Research, Harpenden, UK

Release date January 2026

Version 1.0.0