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R. Ferrière

Eco-Evolutionary Mathematics (EEM)

  • Goal Understanding how ecological and evolutionary processes interact and influence population dynamics, community diversity, and ecosystem function.
  • Background The goal of our team is to contribute to the mathematical synthesis of ecology and evolution. The ecology of a biological system determines the dynamics of resources and consumers in the system ; hence the selective pressures acting upon heritable variation among individuals. As selection operates, the distribution and expression of genotypes change, which feeds back on the system’s ecology. The EEM team designs new mathematical tools to investigate each element of this ‘eco-evolutionary feedback loop’, and to build and analyze integrative models of the ecology/evolution interface. We apply our mathematical theory to core problems of evolutionary biology.
  • Research highligts Stochastic nonlinear models of populations are essential for understanding how ecological systems persist in time and space and how evolutionary innovations spread. Our work shows how the stochastic expression of individual traits and the nonlinearity of ecological interactions can affect the dynamics of spatially distributed populations (in terms of synchronization, extinction, invasion by competitors, predators or pathogens) and the spread of heritable variation. We have established a rigorous mathematical framework to derive macroscopic models of adaptive evolution from microscopic processes operating at the level of individuals. Applications to behavior have yielded new explanations for cooperation and social networking (volunteering, chromo-dynamics). Applications to coevolution have shed new light on the stability of mutualism, and on the evolution of polymorphism in pathogen virulence. Applications to life-history evolution and the evolution of aging are under way. A new class of eco-evolutionary models describing rapid evolution has been discovered. We apply these models to microbial systems, in which rapid evolution is amenable to experiments in the lab. We developed the softwares ULM and ZEN for efficient numerical simulations, and we design coalescence- based statistical methods to detect « ecological footprints» in molecular data.

Champagne C., Salthouse D., Paul R., Cao-Lormeau V-M., Roche B., Cazelles B. “Structure in the variability of the basic reproductive number (R0) for Zika epidemics in the Pacific islands.” eLife 5, e19874 (2016).

Garcia T, Doulcier G, De Monte S. “The evolution of adhesiveness as a social adaptation.” eLife 4, e08595 (2015).

Lee W, van Baalen M, Jansen VAA. “An evolutionary mechanism for diversity in siderophore-producing bacteria.” Ecology Letters 15, 119–125 (2012).

Navascues M, Legrand D, Campagne C, Cariou ML, Depaulis F. “Distinguishing migration from isolation using genes with intragenic recombination: detecting introgression in the Drosophila simulans species complex.” BMC Evolutionary Biology 14, doi:10.1186/1471 (2014).

Rainey PB, De Monte S. “Resolving conflicts during the evolutionary transition from cells to multicellular life.” Ann. Rev. Ecol. and Syst. 45, 599-620 (2014).

Sauterey B, B Ward, J Rault, C Bowler, D Claessen. “The implications of eco-evolutionary processes for the emergence of marine plankton community biogeography.” The American Naturalist 190, 116-130 (2017).

Travis J, Reznick DN, Bassar RD, Lopez-Sepulcre A, Ferriere R, Coulson T. “Do eco-evo feedbacks help us understand nature? Answers from studies of the Trinidadian guppy.” Advances in Ecological Research 50, 1-40 (2014).