Fluctuating selection behind phenotypic polymorphism: an empirical approach using subterranean and surface crustaceans as a model

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Fluctuating selection behind phenotypic polymorphism: an empirical approach using subterranean and surface crustaceans as a model
Duration:

1. 11. 2017 – 31.10. 2020

PI / SubBioLab Coordinator:

Cene Fišer, Hungarian co-PI: Gábor Herczeg

About:

Understanding phenotypic variation in the wild is the central topic of evolutionary biology. Phenotypic traits vary around mean values, selected to maximize individual’s fitness. This variation is maintained by various incompletely understood proximate and ultimate mechanisms. In this research project we aim for a better understanding of the mechanisms maintaining within-population phenotypic variation. We will focus on the ultimate explanation stating that phenotypic variation is maintained by temporally and spatially fluctuating selection stemming from environmental variation. Previous studies testing this hypothesis mostly focused on populations living in variable, and to some extent unpredictable environments to show how selection fluctuates within and between years. Yet, few studies tested the complementary part of this theory, predicting a decline of phenotypic and genetic variation in environments with minimal spatio-temporal environmental variation within and between generations. We utilize subterranean animals as a model and test whether phenotypic variation of organisms declines in stable and predictable environments. To gain an in-depth understanding of evolution of phenotypic variation, we are studying variation from the within-individual to the between-species level of biological organization and compare inhabitants of relatively stable and predictable (cave) to unstable and stochastic (surface) habitats. Study organisms are isopod (Asellus) and amphipod (Niphargus) crustaceans, each with surface and subterranean representatives.

Funded by:

Slovenian Research Agency, project number N1-0069

Hungarian National Research, Development and Innovation Fund: #SNN-125627

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