There is growing evidence that populations can undergo changes on so rapid timescales that it is hard to disentangle evolutionary processes from ecological dynamics resulting from the organisms' interactions. Such rapid evolutionary changes are of major co ncern in many fields of biology, spanning from epidemiology to conservation ecology, especially in relation with human-induced modifications of the environment. Being able to predict the effect of the introduction of new species, such as genetically modifi ed organisms, or of the modification of the relationship among species in an ecosystem, such as habitat fragmentation or increased exchange of parasites, is of fundamental importance for human welfare and the future of life on Earth.In spite of the great r elevance of such processes, most evolutionary modeling has relied on a clear-cut timescale separation between fast ecology and slow genetic innovation. This project aims at develop a systematic mathematical approach to fast evolution in a changing environm ent, and to identify biological quantifiers for tackling its effect.To this end, the expertise of the host institution in methods for mathematical modeling of biological phenomena will be complemented by the applicant's experience in physics of complex sys tems.As a starting point, the project will address two specific settings in which the traditional assumptions of separation of timescales is inadequate: spatially structured populations and host-pathogen interactions.These studies are meant to open the per spective of evolving timesales: the relationship between the pace of innovation and the ecosystem dynamics, determining the ultimate fate of a population, must itself be subject to mutation and selection. The incorporation of this phenomenon in the models is expected to provide new predictive tools for the evaluation of the capability of Earth's biota to bear anthropogenic damage.
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