Abstracts (first author)

Talk 

Host-parasite coevolution: local interactions can limit the severity of fitness costs associated with range expansion

Author(s): Ashby B

Summary:

Many host-parasite systems are known to undergo coevolutionary arms races, where reciprocal increases in attack and defence mechanisms can lead to the emergence of broader resistance and infectivity ranges through time. Recent studies have identified that the extent of population mixing can play a crucial role in host-parasite interactions and can shape the coevolution of many traits, including range expansion. Much of this existing theory is based on the analysis of metapopulations, which incorporate a certain degree of spatial structure, but do not capture local interactions between individuals within subpopulations. These local interactions are known to be critical in many epidemiological scenarios, but their role in the coevolution of resistance and infectivity ranges is unclear. Here, we explore how the impact of fitness costs associated with range expansion is affected by the degree of population mixing at these very fine scales. We present results from an individual based model of microbial communities that incorporates a well-established framework of genetic specificity matching empirical observations of range expansion among bacteria and phage. We show that global competition in well-mixed populations leads to rapid selective sweeps, preventing range expansion at high fitness costs. In spatially-structured environments however, we find that local competition and spatial clustering can maintain coevolutionary arms races even when fitness costs are high. These findings highlight the importance of local interactions between individuals in shaping coevolutionary dynamics.



Contacts

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

Address

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
Portugal

Website

Computational Biology & Population Genomics Group 
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