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Graduate Research School Available Projects Statistical tools for robust climate change biology

Statistical tools for robust climate change biology

Title of Project

Statistical tools for robust climate change biology

Advisor/s

Dr Martijn van de Pol, Dr Liam Bailey (IZW Berlin), Dr Birgen Haest (Switzerland)

College or Research Centre

College of Science & Engineering

Summary of Project

Climate change biology is a relevant and rapidly developing field that studies the impacts of climate change on biodiversity and what we can best do about it. The reliability of impact assessment and mitigation strategies strongly depends on how accurately we can quantity the sensitivity of species to current and future climate change. A key step is to robustly quantify a climate response curve that describes the sensitivity of biological traits to changes in weather. This allows us to predict how much for example body size, survival or population growth of species changes if global warming reaches +2 degrees. Typically, this is done by collecting biological data across environmental (spatial) gradients and/or many years, such that we can correlate biological variation to spatiotemporal variation in weather. However, it is usually unknown over which period in the year the weather affects the biological response, as weather may have lagged effects (e.g. via food that grows after rain). Furthermore, climate change is causing many aspects of the weather to change (temperature, rain, wind) and it is often unclear which aspect drives biological responses. Finally, species move around, and particularly for migratory species a key challenge it to determine at what location the weather is driving decision to leave (e.g. do tail winds at departure, during migration or weather condition at arrival grounds drive migration decisions?). Systematic analytical tools to determine the properties of climate signals urgently need to be developed and benchmarked (e.g. R-package), and there is also the opportunity to apply them to various ecological dataset. This project may provide opportunities for overseas visits to collaborators as well.

Key Words

statistics; climate change; ecology; programming

Would suit an applicant who

has an interest in quantitative ecology, statistics and some experience in programming.

Updated: 15 Nov 2021