Abstracts (first author)


Can we use molecular techniques to measure inbreeding in wild populations of highly inbred, bottlenecked species?


Author(s): Taylor H, Ramstad K, Kardos M


Understanding the effects of inbreeding depression is important for viability assessment and effective management of rare and endangered species, but accurately measuring inbreeding in such species remains challenging. Highly inbred species that have experienced severe bottleneck events typically lack the behavioural pedigree information, high levels of genetic diversity and large population sizes (and thus sample sizes) required to accurately estimate individual inbreeding. This raises the question of whether investing resources into research in this area is worthwhile for conservation managers and, if so, what the best approach might be. We modeled the accuracy of pedigrees constructed using varying numbers of simulated microsatellite markers for the little spotted kiwi (Apteryx owenii), a species that has experienced a recent, severe bottleneck and that exhibits extremely low genetic variation. We found that the number of microsatellite markers required to reconstruct precise pedigrees and provide accurate inbreeding coefficients will be cost prohibitive for the majority of conservation studies. Alternative approaches, such as heterozygosity fitness correlations, would also require a prohibitively large number of neutral markers. Pairwise relatedness measures show promise for estimating inbreeding in this species and those with similar histories, but genomic measures such as runs of homozygosity (ROH) currently provide the most effective alternative for measuring inbreeding. We acknowledge that genomic methods may also prove too costly currently for many conservation programmes and emphasise that conservation managers should weigh the costs and benefits of alternative strategies prior to investing in genetic measures as part of their rescue strategies.


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