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Project Code [2024-NE-1263]
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Project title
Ecological Forecasting of Tree Resistance to Pathogens: Ash Dieback and Beyond
Primary Funding Agency
Environmental Protection Agency (EPA)
Co-Funding Organisation(s)
Department of Agriculture, Food and Marine
Lead Organisation
University of Dublin, Trinity College (TCD)
Lead Applicant
Silvia Caldararu
Project Abstract
Outbreaks of plant diseases, such as the recent widespread effects of ash dieback in Ireland and across Europe, and historically, Dutch Elm disease and other outbreaks pose a major threat to both natural and managed habitats. With the inevitable advancement of climate change, both the risk of new pathogens and plant susceptibility are likely to increase. As species in general migrate northward, ecosystems in Ireland are likely to be exposed to new species of pathogens, to which local plants may not be adapted. This will be compounded by abiotic stressors such as drought, heat, and extreme rainfall, making plants more vulnerable to new infections. Therefore, it is critical that we have the tools needed to forecast such outbreaks and take short- and medium-term decisions about landscape management. In this project, we will build an ecological forecasting tool based on plant eco-physiological models, with an aim to provide localised, short-term information on the probability and degree of infection and to what extent a tree can survive. The tool will be built upon an existing model (EDPest) and further developed and validated to represent the climate, species, and management of Irish landscapes. To ensure the model is grounded in data E-PATH will, in parallel with model development, include an on the ground measurement campaign which will provide insights into the biotic and abiotic factors behind variation in tree resistance to infection as well as validation data for the model. This will include both direct observations of tree infection and plant physiological properties relevant to the theory of plant defence. Point measurements will be scaled up using remote sensing data, which will allow infection detection and assessment at a larger scale with the potential for automation. The project will primarily focus on ash dieback due to it being widespread right now but since the model to be built is based on theoretical principles, it will be general enough to be applied to other diseases. The outcome of E-PATH will be a deployable forecasting tool that can uniquely take in local meteorological and biological information to produce a probabilistic forecast of tree response to infection. The tool can be used for decision making once an outbreak has begun, allowing effective landscape management and disease mitigation.
Grant Approved
€591,060.99
Research Hub
Protecting and Restoring Our Natural Environment
Research Theme
Implementing effective protect and restore solutions
Initial Projected Completion Date
28/02/2029