Abstracts (first author)


Evolution of senescence in heterogeneous landscapes

Author(s): Cotto O, Ronce O


The current theory of senescence is developed in a very simple ecological and demographic context, with a unique population at equilibrium in a homogeneous habitat. In the wild, species live in a variable environment in space and time, where the assumption of equilibrium is often transgressed. In this study, we use models of quantitative genetics in structured populations in order to investigate the evolution of senescence in a variable environment. Adaptation to local environment depends on phenotypic traits which expression varies with age. We study different scenarios where the environment changes abruptly, gradually or cyclically with time and where the environment is heterogeneous in space with different populations connected by migration. The strength of selection decreases with age, which predicts slower adaptation of traits expressed late in the life cycle, potentially generating stronger senescence in habitats where selection changes in space or in time. This prediction is however complicated by the fact that the genetic variance also increases with age. With numerical calculations, we found that in most cases the rate of senescence is enhanced when the environment varies. Especially, migration between different habitats is a durable source of senescence in heterogeneous landscapes. We also show that the rate of senescence can sometime decrease transiently, when the population is not at equilibrium, with possible implications in experimental evolution and in the study of invasive species. Our results highlight the need to study age-specific adaptation, as a changing environment can impact differently each age-class with different consequences on demography.



Chairman: Octávio S. Paulo
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XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon


Computational Biology & Population Genomics Group