Abstracts (first author)

Talk 

Correlated evolution of learning rate studied by artificial selection in a parasitic wasp

Author(s): Liefting M

Summary:

Learning is considered a form of behavioral plasticity through which an organism can adjust its behavior to changes in the environment. Because of both the apparent costs involved in learning and the fact that the rate of learning is highly context-dependent (depending on reliability of certain cues, the value of the reward, the expected lifespan of the organism) we expect and observe many species-specific learning rates. Even closely related species can demonstrate quite different learning rates and differences in consolidation rates of memory for similar experiences. These rates are therefore most likely adapted to the needs of the organism in its natural environment. Because many aspects of learning and memory formation share rather conservative regulatory pathways, rates of learning related to different types of behavior can be correlated (e.g. overall high learning rates for different stimuli). We explored this concept in the parasitic wasp Nasonia vitripennis by creating an artificial selection line favoring a high learning rate for associating a color with finding a host (after a single associative training trial). This selection regime selects for a higher rate of forming a type of middle-term memory. We then explored whether the established ‘high-learning’ lines also exhibit a correlated higher learning rate for a novelty stimulus like odor and whether formation of other memory types (short term or long term memory) was also affected. Also, changes in a selection of life history traits like fecundity and developmental rate were considered, as well as genetic differences. Here I will present the first results of this study.



Contacts

Chairman: Octávio S. Paulo
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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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