Tackling biased selection estimates using individual-level environmental conditions: A case study in a wild population of Soay sheep

• Applicant: Dr. Rebecca Nagel
• Subject Area: Evolution, Anthropology ; Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 515410943

Accurate predictions of phenotypic change over time inform not only the outcome of natural selection and the rate at which quantitative traits evolve, but also have implications for our understanding of population dynamics and the potential adaptive response of populations to climate change. Unmeasured variables, however, likely account for a large portion of the covariation predicted between traits of interest and fitness in wild populations. In particular, a number of modelling and empirical studies suggest that neglecting to account for environmental heterogeneity significantly biases the strength and direction of estimated selection gradients. To understand the nature and spatial structure of covariance between environmental variables, traits of interest and fitness, micro-scale meteorological data must be paired with individual land use data and detailed phenotypic and genetic data. For this Fellowship project, I plan to pair newly collected micro-meteorological data with data from the long-term research project of wild Soay sheep to test the hypothesis that incorporating fine-scale, individual-based environmental conditions into multivariate trait-fitness regression models will produce more robust predictions of selection. Overall, this empirical work will pioneer the application of statistical procedures, the formal justification for which has only very recently been provided in evolutionary theory, and which are likely to become an important and widely-used tool across a variety of study systems.

• DFG Programme: WBP Fellowship
• International Connection: United Kingdom
• Host: Dr. Michael Blair Morrissey