Graduate Research School Available Projects Population dynamical responses to climate change on a continental scale
Population dynamical responses to climate change on a continental scale
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Title of Project
Population dynamical responses to climate change on a continental scale
Advisor/s
Dr Martijn van de Pol
College or Research Centre
College of Science & Engineering
Summary of Project
Species respond very different to climate change, with some species benefitting and others suffering. A major challenge is to understand what causes this diversity in responses, as this will help us to predict the winners and losers from global warming and prioritize conservation strategies. However, it has proven challenging to make progress on this, as species’ responses are typically highly idiosyncratic. At the same time, even within species, different population also exhibit widely different responses. Understanding intra-specific variation in climate responses may be more feasible and a logical first step, as populations mainly differ in their environment, while the species life-history typically varies little within species ranges. However, intra-specific comparisons are extremely rare, as it requires long-term data from many different sites of the same species. Here we propose to apply population dynamical models on one of the best studied species in the wild, for which data is available for over 30 populations across the continents of its species range. Hierarchical population models can be combined with path analysis to study the importance of different pathways by which climate affects traits (e.g. body size, or timing of reproduction), which in turn affect demographic rates (reproduction and survival) and ultimately population growth. Next a comparison can be made how the environment of the different populations affects the impact and pathways by which global warming affects population change in this species.
Key Words
climate change; population ecology; comparative study; statistics
Would suit an applicant who
has an interest in climate change and population ecology. Some affinity with mathematical modelling and experience with Bayesian statistics is desirable.
Updated: 15 Nov 2021