Postdoctoral Research Fellow in Ecological Modelling for Tree Health

The Post

Applications are invited for a Postdoctoral Research Fellow to work within the interdisciplinary research project newLeaf (Learning to adapt to an uncertain future: linking genes, trees, people and processes for more resilient treescapes) , funded by UKRI under the Future of UK Treescapes program.

The overall aim of newLeaf is to evaluate options for using the natural genetic variation within tree species to keep pace with expected changes in climate and the biotic environment. The project is structured into several interrelated work packages and of these the successful candidate will work within a team investigating future risks to UK forests from pests and diseases – a key part of the biotic environment to which tree species must adapt.

The emphasis of the position will be on developing statistical models to map and predict spatial patterns of tree pest and disease occurrence or emergence across the UK in relation to abiotic, biotic and human risk factors. The successful candidate will use existing databases of pest and disease detection, with information on their species, location, timing, setting and host tree. They will lead the production of predictive models for the likelihood or burden of pest and disease occurrence, using gridded data on potential drivers of risk such as land use, human activities, climate, forest connectivity etc. From these predictive models, new risk maps will be produced to understand the spatial variation in threats to different forests and explore covariance and trade-offs between resilience to P&Ds and climate across the UK, working closely with forest pathologists and stakeholders to frame and interpret the models. The fellow will lead the publication of the findings in scientific papers and present the work at scientific conferences and to meetings of the wider project team and other stakeholders with interest in forestry, conservation and plant health.

The post will be based at the University of Stirling in central Scotland, UK.

Description of Duties

• Bring together existing spatial data on pest and disease burdens and risk factors in a form suitable for the statistical modelling
• Use machine learning or other statistical modelling approaches to investigate how relationships among risk factors drive pest and disease burdens across UK forests
• To use the statistical models to predict gradients in pest and disease burdens across the UK
• Become part of the wider newLeaf project team of forest ecologists, geneticists, statisticians, social scientists and creative artists, for example by attending project meetings and events
• Write and contribute to publications and disseminate research findings using other appropriate media

Essential Criteria

• A PhD in Ecology, Plant Pathology, Statistics or a related field
• Expertise in use of R or other statistical/machine learning software, including for spatial predictive modelling (e.g. species distribution modelling, spatial point processes)
• Expertise in use of GIS (QGIS, ArcGIS or equivalent)
• Experience in data acquisition and processing, analysis, and interpretation of complex spatial and temporal data

For further information, including a full description of duties, essential criteria and details on how to apply, please see Vacancy details | About | University of Stirling