Eco-evolutionary feedback as an engine for maintaining biological diversity in heterogeneous landscapes: toward landscape-level theory of eco-evolutionary dynamics

In the last two decades, studies have revealed that evolutionary changes can occur rapidly on ecological timescales.

This indicates that ecological and evolutionary processes can interact and influence one another; this phenomenon is called eco-evolutionary dynamics. For eco-evolutionary dynamics to occur, populations need to harbour sufficient standing genetic variation. Many factors influence genetic variation, one of which is spatial population structure.

Many populations living in heterogeneous landscapes occur as spatially subdivided populations connected by dispersal, known as metapopulations.

Habitat network structure (spatial configuration of habitat patches) influences not only the demography of metapopulations but also the distribution of genetic materials by mediating population size and gene flow.

This implies that eco-evolutionarily dynamic responses may be spatially unevenly distributed in heterogeneous landscapes.

The mosaic of eco-evolutionary coupling could be partly predictable from habitat network structure and the movement of organisms.

The goal of this project is to investigate how habitat network structure can shape the spatial structure of standing genetic variation in metapopulations and, consequently, how it can influence spatial eco-evolutionary dynamics.

This project will build a theoretical understanding of the effects of major dimensions of habitat network structure on genetic variation and local adaptation in metapopulations.

The model will be applied to ecologically and genetically well-characterised empirical metapopulations studied by the supervisor.

This project will combine eco-evolutionary metapopulation modelling and genomic and field population data to mechanistically understand the role of space in eco-evolutionary dynamics.

I will conduct research in France for the first time, gain new skills in genomics and population genetics, and sharpen and transfer my existing modelling and remote sensing skills at the host organisation.