(1998), implemented in the software MolKin 2.0 (Gutiérrez et al. 2005). Briefly, for each sample we estimated (1) within-sample diversity measured as allelic richness of the sample relative to the allelic richness of the other samples of the same species, and (2) genetic differentiation of the sample in relation to the other samples of the same species using a measure related to Nei’s D ST and G ST (Gutiérrez Selleck MM-102 et al. 2005). Positive values of relative diversity and/or differentiation for a particular sampled region indicate that the sample of that region contributes positively to total genetic diversity of the global
Baltic population. Negative values correspondingly indicate that the relative diversity or divergence of the sample in question is low
and does not contribute to total genetic diversity (Petit et al. 1998). The values for relative diversity and differentiation were used to categorize each sample into one of four categories, as identified by Swatdipong et al. (2009) including (i) higher diversity-higher divergence, (ii) higher diversity-lower divergence, (iii) lower diversity-higher divergence, and (iv) lower diversity-lower divergence. Samples in each category can be expected to be characterized by the differing roles of migration Cilengitide cost and genetic drift affecting the genetics of populations. Categories i and ii are considered to have the largest potential of containing unique genetic material and should potentially be prioritized in conservation (Swatdipong
et al. 2009). The observed strong divergence of Baltic populations from Atlantic conspecifics (Johannesson and André 2006) prompted the exclusion of Atlantic samples from these analyses to amplify the diversity-divergence classification within the Baltic Sea. The difference Org 27569 in the distribution of observed frequencies of the four diversity-divergence categories in different geographic regions relative to the expected frequencies under the null hypothesis of random distribution of diversity-divergence was tested with a χ 2 test for independence. Areas of genetic discontinuities We used the software Barrier 2.2 (Manni et al. 2004) to locate areas of major genetic discontinuities. Barrier applies Monmonier’s algorithm to detect the areas of highest genetic change on a map (genetic barriers) where the samples are represented by their geographic coordinates and connected by Delauney triangulation. The software produces as many barriers as the user defines, regardless of how strong these barriers are, i.e. if they are supported by significant F ST values or not. For example in the case of the Atlantic herring in this study, there is no significant differentiation among populations within the Baltic Sea, but Barrier still identifies genetic breaks if asked to do so.