Mathematical models of Local Adaptation – MOLA

Understanding the conditions for the emergence and the maintenance of diverse life forms is a major goal of evolutionary biology and ecology. Adaptation to different environmental conditions or different niches is one of the prominent explanations to account for the diversity of life. However, the presence of heterogeneous environmental conditions does not guarantee a greater genetic or phenotypic diversity: dispersal of individuals across different habitats and the resulting flow of genes, stochastic events such as genetic drift in small populations, or the presence of genetic correlations between traits are examples of constraints that may hamper adaptation to local conditions and thereby limit the emergence and maintenance of diversity.

The project MOLA, “Mathematical Models of Local Adaptation” will explore different types of models of adaptation to heterogeneous environments, to gain a better understanding of the ecological conditions favouring the emergence and maintenance of diversity. The overarching theme of the project is the exploration, with mathematical models, of the interplay between neutral and selective processes in spatially structured populations and their impact on evolutionary dynamics. The project articulates around two main objectives and will be carried out over 48 months.
The first objective, divided into four separate tasks, will provide a comprehensive and multi-faceted exploration of the effects of explicit spatial structuring on local adaptation. Most models and metrics of local adaptation so far typically ignore the effect of distance and only focus on population subdivision; isolation by distance however may play an important role in shaping the patters of local adaptation and needs to be taken into account. The objective entails the analysis of mathematical models of adaptation to heterogeneous environmental conditions, either abiotic (Task 1) or biotic (Task 2), both in a spatially explicit context; but also the derivation of statistical measures of local adaptation taking isolation by distance into account (Task 3), as well as a meta-analysis of the published studies investigating local adaptation in a spatially explicit context (Task 4). This first objective forms the basis of a PhD project; all of these tasks will therefore be primarily performed by a PhD student (to be named), closely supervised by the project coordinator (F.D.) and co-supervised by the head of the joined research group (A.L.).
The second objective, divided into three separate tasks, will explore various aspects of the effects of environmental heterogeneity and population subdivision on local adaptation and diversification, going beyond the usual limiting assumptions of models (such as the assumption equal sizes of subpopulations, the consideration of a limited number of traits under selection, or the constancy of selection regimes). The aims of this second objective are the formulation of predictions for the coevolution of dispersal and local adaptation in a range of population structures (Task 5); but also the exploration of how the number of traits under selection affects patterns of local adaptation (Task 6) and finally the derivation of predictions for the dynamics of the variance of a quantitative trait under a balance of mutation, drift and diversifying selection (Task 7).

A better theoretical understanding of the conditions and mechanisms favouring the emergence and maintenance of diversity, already interesting in its own right and a central aim of evolutionary biology, can also be used to inform management decisions for the conservation of endangered species, and can also help us improve our predictions of the effects of climate change on species diversity.

The project will be carried out in the SMILE team (Stochastic Models for the Inference of Life Evolution), led by Prof A. Lambert, located in the Centre for Interdisciplinary Research in Biology (CIRB, CNRS/UMR 7241 INSERM U1050) at the Collège de France in Paris