Modelling the evolution of adaptive responses to climate change across spatial landscapes

Rebecca Hoyle, Orly Razgour, Mark Chapman

Through increasing periods of drought, higher temperatures and extreme events, future climate change will introduce new selection pressures on species. As many species will be unable to shift their ranges fast enough to track suitable climatic conditions[1], their ability to thrive or even to survive will depend on how well and how quickly they are able to adapt to new conditions. Climate change vulnerability assessments are often based on forecasts from species distribution models, that project forward where current suitable climatic conditions for a given species will be found in the future. However this fails to account for the possibility that populations can evolve adaptations to new conditions and so survive in (or near) their current range[2]. Consequently these predictions can be inaccurate, potentially leading to misplaced conservation efforts. This PhD project will develop a forecasting framework for species ranges that includes evolutionary responses to environmental change. We will test our approach using data from European bat species showing adaptation along climatic gradients. Bats provide an excellent case study of adaptation in long-lived wild species because of their sensitivity to environmental change, and therefore can demonstrate the potential of our approach for forecasting future distributions for species of conservation concern.


This interdisciplinary project will use a mathematical quantitative genetics model of trait evolution in response to environmental change to investigate the future distribution of the threatened barbastelle bat, Barbastella barbastellus. The model will describe migration and adaptation of populations across a spatial domain, and will extend existing approaches [e.g. 3] by considering realistic spatial landscapes and by comparing model outputs to data on genetic adaptation of this bat to climatic gradients, with the ultimate aim of improving predictive capability in real-world scenarios. The project will have two complementary strands: i) developing a spatial quantitative genetics model and solving it numerically to simulate the bats’ evolutionary response; and ii) extracting suitable measures of genetic adaptation from existing genomic data on barbastelle bats and comparing them with model outputs. Supervisors will provide guidance on initial approaches, for example using partial differential equations and principal component analysis; however there is wide scope for innovation in both strands, and the possibility of extending the model to include adaptation through phenotypic plasticity.


University Of Southampton

The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted in the School of Mathematical Sciences. Specific training will include: mathematical modelling and numerical simulation in evolutionary biology; analysis of RNAseq data and population genomics analysis. This is a highly interdisciplinary project that provides an opportunity for a maths or physics graduate to move into theoretical evolutionary biology and learn about global change ecology, while gaining skills in bioinformatics and cross-disciplinary communication. The successful candidate will be exposed to a broad disciplinary spectrum of current research through regular seminar series in both applied mathematics and biological sciences.


Essential skills required:

First degree level familiarity with ordinary differential equations and either partial differential equations or patch/island-based discrete spatial models; experience of numerical solution of mathematical problems using a programming language such as Python or Matlab. An interest in evolutionary biology is essential, but you do not need to have studied it previously.

Eligibility & Funding Details: 

Please see for details.


Background Reading: 

[1] Loarie, S.R. et al (2009) The velocity of climate change, Nature, 462, 1052-1057, doi: 10.1038/nature08649

[2] Razgour, O. et al (2019) Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections, PNAS, 116, 10418-10423,

[3] Chevin, L.-M. and Lande, R. (2011) Adaptation to marginal habitats by evolution of increased phenotypic plasticity, Journal of Evolutionary Biology, 24, 1462-1476, doi: 10.1111/j.1420-9101.2011.02279.x