Using spatial modelling to understand environmental predisposition to tree diseases

Project lead(s)   Dr. Nathan Brown I Environmental modeller ; Dr. Elena Vanguelova I Senior biogeochemist, soil research lead ; Dr. Sandra Denman I Senior pathologist | Forest Research
Organisation lead   Forest Research

Contributors/partners   Forest Research: Samantha Broadmeadow, Tree Health Research Group, Tree Health Diagnostic and Advisory Services (THDAS), ObservaTree, Paul Taylor (Climate spatial datasets); National Soil Resources Institute, James Hutton Institute, UK Centre for Ecology and Hydrology

Project status   Active (2022–2025)
Project funding  £155,000
Research outcome   Pests and diseases spatial risk modelling and mapping
Trees and woodland across the UK are facing unprecedented environmental challenges through climate change, disturbance (fire, wind or human action) and pest and disease threats. Understanding and mitigating the impacts of these threats is a complex challenge because the individual interact with each other. For example, environmental conditions such as drought may weaken trees and predispose them to the actions of pest insects or pathogens. We need to improve our understanding of these relationships so that we can make informed decisions about new woodland planting or restocking.

Recent research on Acute Oak Decline has demonstrated significant correlations with warm, dry parts of the country, high levels of atmospheric nitrogen deposition and low sulphur pollutant levels. Knowledge of trends between disease occurrence and environmental conditions has enabled predictions to be made describing which areas are prone to AOD infection and could be a powerful tool to identify areas in Britain where trees are and will be under stress in the future.

Identifying significant environmental factors for other tree pests and diseases could be a powerful tool to identify areas in Britain where trees are under stress now, and will be under stress in the future. This information can inform species choice for planting and support management decisions for existing woodlands, leading to better survival and resilience of existing trees, forests and woodlands.

Research aims and objectives
This project aims to analyse current pest and disease distributions to reveal correlations with environmental conditions and management history. It will develop GB wide predictions for current and future risk from three case studies: ash dieback, Dothistroma needle blight and aerial Phytophthora species. Model predictions will support guidance on the best places for trees to be planted to minimise risks for pest and disease incursions.
Project description
We will build statistical models using environmental datasets and known disease distributions to identify trends that affect disease occurrence and/ or severity for the three case studies. In addition, we will extend and refine existing AOD models to include new observation data. The selected examples represent serious tree diseases in the UK, affecting a diverse range of woodland types from coniferous plantations to native broadleaves. Each case study presents unique challenges for analysis with different data types available. Collectively they offer the opportunity to apply a range of analytical techniques, that could be adapted rapidly to explore further the risk of spread of novel pest or disease arrivals in the future.
Outputs and audiences
The project will produce a series of models and spatial risk maps that will be used to guide forest management advice These outputs will be of direct use to scientists and policy makers as well as practitioners involved in woodland planning and management.
Image of a dark crack in the bark of an oak tree, which is a symptom of acute oak decline.
A deep lesion in the bark of an oak tree – a symptom of acute oak decline

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