Forecasting how forest communities will respond to climate change and invasive species

Theme: Biodiversity, Ecology & Conservation

Primary Supervisor:

Daniel Maynard

Genetics, Evolution and Environment, UCL

Daniel Maynard's Profile Picture

Secondary Supervisor:

Tim Newbold

Genetics, Evolution and Environment, UCL

Tim Newbold's Profile Picture

Project Description:

Forests encompass enormous biodiversity and provide a wealth of ecosystem services responsible for the health, well-being, and livelihoods of humans worldwide. As the climate warms, trees are expected to experience unique climate conditions far exceeding their native ranges. This situation is further exacerbated by the rapid spread of non-native species worldwide, fundamentally restructuring forest ecosystems by causing species to go locally extinct. Understanding how tree species and forest communities will respond to the double threats of climate change and invasive species are critical questions, and ones that remain exceedingly difficult to answer.

The goal of this project is to overcome the limitations of existing approaches by expanding on a new modelling framework that focuses on understanding how intact communities, not just individual species, will respond to disturbances. This PhD will combine functional trait information, climate variables, and human disturbance history to develop a series of statistical tools to identify the dominant features determining how communities respond and adapt to changing environmental conditions. By applying these models to high-resolution American and European forest inventory data, this project will help make real-world projections of how species, communities, and ecosystem functions will change over the coming century

Policy Impact of Research:

This work will help identify which forested areas and ecosystem types are particularly susceptible to climate change and the introduction of non-native species. These results will directly inform management and conservation efforts to ensure resilient forests by helping to identify which species and community types are at high risk of local or regional extinction, thereby aiding in the development of climate-smart forests that can thrive under future climate conditions.

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