Context

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 elements listed above 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 (AOD) 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.

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

Aims

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 (Hymenoscyphus fraxineus), Dothistroma needle blight (Dothistroma septosporum) 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.

Objectives

• 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.

Expected outcomes

• 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.