Abstracts (first author)


Sequentially varying reproductive strategies in a parasitic flatworm: riding a population genomic roller coaster

Author(s): Aisala H, Hansen H, Lumme J


Breeding systems have major impacts on population genetic processes. At ecological time scale, asexuality is a superior reproductive strategy, because it provides the ability to colonize new environments quickly and allows populations to grow exponentially in favorable conditions. In spite of high fitness in short term, small effective population size and lack of effective recombination reduce the adaptive potential and eventually lead to accumulation of deleterious mutations. Thus, at evolutionary time scale, sexual reproduction becomes more advantageous. Our study focuses on the genus Gyrodactylus (Platyhelminthes, Monogenea), which consists of small host specific fish parasites. In this genus, combination of clonality and sexuality has proved to be an ideal strategy to be successful at both time scales. New genetic combinations are created by occasional sexual reproduction, and the fittest genotypes can then be amplified by clonal parthenogenesis and spread to new geographical areas, assuming a suitable vector like farmed salmonids. Over time, rare sexuality in a population consisting of few genotypes and increasing selection pressure by the host cause the gene pool to become even narrower, and the declining fitness leads inevitably towards extinction. However, the genus is extremely species-rich, containing perhaps thousands of specific lineages on different hosts. The rescue seems to happen through host switching by hybridization among distant lineages. These switches provide a way out of the otherwise inescapable evolutionary dead-end, and bring the riders back to the highest peak of the population genomic roller coaster.


Chairman: Octávio S. Paulo
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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