1 Computational and RNA Biology, Department of Biology, Faculty of Science, Københavns Universitet2 Natural History Museum of Denmark, Natural History Museum of Denmark, Faculty of Science, Københavns Universitet3 Institut for Bioscience - Biodiversitet4 Institut for Bioscience - Havpattedyrforskning5 Swedish Museum of Natural History6 Natural History Museum of Denmark, Natural History Museum of Denmark, Faculty of Science, Københavns Universitet7 Swedish Museum of Natural History8 Computational and RNA Biology, Department of Biology, Faculty of Science, Københavns Universitet
Identification of populations and management units is an essential step in the study of natural systems. Still, there is limited consensus regarding how to define populations and management units, and whether genetic methods allow for inference at the relevant spatial and temporal scale. Here, we present a novel approach, integrating genetic, life-history and demographic data to identify populations and management units in southern Scandinavian harbour seals. First, 15 microsatellite markers and model- and distance-based genetic clustering methods were used to determine the population genetic structure in harbour seals. Second, we used harbour seal demographic and life-history data to conduct population viability analyses (PVAs) in the VORTEX simulation model in order to determine whether the inferred genetic units could be classified as management units according to Lowe and Allendorf's (2010) "population viability criterion" for demographic independence. The genetic analyses revealed fine-scale population structuring in southern Scandinavian harbour seals and pointed to the existence of six genetic units. The PVAs indicated that the census population size of each of these genetic units was sufficiently large for long-term population viability, and hence that the six units could be classified as demographically independent management units. Our study suggests that population genetic inference can offer the same degree of temporal and spatial resolution as "non-genetic" methods, and that the combined use of genetic data and PVAs constitute a promising approach for delineating populations and management units. This article is protected by copyright. All rights reserved.
Molecular Ecology, 2014, Vol 23, Issue 4, p. 815-831