Integrating genetic data and population viability analyses for the identification of harbour seal (Phoca vitulina) populations and management units. Issue 4 (February 2014)

• Record Type: Journal Article
• Authors: Olsen, Morten T.; Andersen, Liselotte W.; Dietz, Rune; Teilmann, Jonas; Härkönen, Tero; Siegismund, Hans R.
• Is Part Of: Molecular ecology. Volume 23:Issue 4(2014)
• Page Start: 815
• Page End: 831
• Publication Date: 2014-02
• Subjects: Molecular ecology -- Periodicals; Molecular population biology -- Periodicals; 576
• DOI: 10.1111/mec.12644
• Languages: English
• ISSNs: 0962-1083

Abstract

<abstract abstract-type="main" id="mec12644-abs-0001"> <title>Abstract</title> <p>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 <sc>vortex</sc> simulation model in order to determine whether the inferred genetic units could be classified as management units according to Lowe and Allendorf's (Molecular Ecology, 19, 2010, 3038) '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 several 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 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 'nongenetic' methods and that the combined use of genetic data and PVAs constitutes a promising approach for delineating populations and management units.</p> </abstract>