Abstracts (first author)

Talk 

Plasticity in signalling behaviour reflects genetics as well as relative size in burying beetles (Nicrophorus vespilloides)

Author(s): Carter MJ, Head ML, Moore AJ, Royle NJ

Summary:

In animals with intra-sexual competition for mates signalling strategies to attract reproductive partners often vary between individuals. How much of this variation is conditional and adopted when an individual is likely to be at a competitive disadvantage and how much is genetically based is a difficult question to address, as there can be strong pleiotropy between the conditions (e.g., social status or size) and the likelihood of adopting a given strategy. To tease apart the contributions of genetics and competitive status of males here we compare the likelihood of adopting a resource-based or satellite signalling behaviour in populations of burying beetles that have experienced 14 generations of artificial bi-directional selection for mating rate. We hypothesized that if signalling behaviour is genetically influenced, males selected for high mating frequency would exhibit more persistent signalling behaviour when on a resource required for reproduction (mouse carcass) than males selected for low mating frequency. Alternatively, if signalling tactics are conditional, we predicted that relative size would influence signalling behaviour more than which selection line males were from. We found that the extent of signalling on a carcass reflected the focal males relative size more than selection history. However, the extent of “sneaking” behaviour (signalling off the carcass) reflected both selection regime and relative size. Thus, we find that genetics and conditional influences make different contributions to alternative mating strategies.


Video


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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