Abstracts (first author)


Does increased stress resistance reduce the ability to deal with biotic challenges? A test through selection experiments on Drosophila melanogaster


Author(s): Hangartner SB, Hoffmann AA


Humans impact ecosystems in a multitude of ways, increasingly exposing contemporary organisms to abiotic and biotic stressors. Environmental stress has strong negative impacts on biological diversity, as species can go locally extinct, if they are unable to migrate to a more benign habitat or to overcome the stressor via plastic or evolutionary adaptation. Several factors are thought to constrain adaptive evolution, such as gene flow, lack of genetic variation and genetic or functional trade-offs. Despite trade-offs being postulated as playing a central role in evolutionary theory, interactions between abiotic and biotic stress resistances have rarely been investigated in stress adaptation. This project aims to test for trade-offs between abiotic and biotic stress resistances in the well-established Drosophila melanogaster study system. Selection experiments on stress resistance provides an opportunity to study evolutionary constraints resulting from genetic trade-offs between traits. Lines that have been selected for different abiotic stressors (heat, cold and desiccation) will be tested for costs and benefits, whereas performance will be tested in outdoor cages under different biotic stressors (competition, predation, parasites) and under different climatic conditions (hot, moderate and cold days). These experiments provide a test of whether trade-offs between abiotic and biotic stress resistances are potential constraints to stress adaptation, which is crucial to better understand the evolutionary potential of contemporary populations.


Chairman: Octávio S. Paulo
Tel: 00 351 217500614 direct
Tel: 00 351 217500000 ext22359
Fax: 00 351 217500028
email: mail@eseb2013.com


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