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Inferring Population Genetic Structure in Widely and Continuously Distributed Carnivores: The Stone Marten (Martes foina) as a Case Study.

Vergara M, Basto MP, Madeira MJ, Gómez-Moliner BJ, Santos-Reis M, Fernandes C, Ruiz-González A - PLoS ONE (2015)

Bottom Line: However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations.To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale.We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology and Animal Cell Biology, Zoology Laboratory, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain; Systematics, Biogeography and Population Dynamics Research Group, Lascaray Research Center, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain.

ABSTRACT
The stone marten is a widely distributed mustelid in the Palaearctic region that exhibits variable habitat preferences in different parts of its range. The species is a Holocene immigrant from southwest Asia which, according to fossil remains, followed the expansion of the Neolithic farming cultures into Europe and possibly colonized the Iberian Peninsula during the Early Neolithic (ca. 7,000 years BP). However, the population genetic structure and historical biogeography of this generalist carnivore remains essentially unknown. In this study we have combined mitochondrial DNA (mtDNA) sequencing (621 bp) and microsatellite genotyping (23 polymorphic markers) to infer the population genetic structure of the stone marten within the Iberian Peninsula. The mtDNA data revealed low haplotype and nucleotide diversities and a lack of phylogeographic structure, most likely due to a recent colonization of the Iberian Peninsula by a few mtDNA lineages during the Early Neolithic. The microsatellite data set was analysed with a) spatial and non-spatial Bayesian individual-based clustering (IBC) approaches (STRUCTURE, TESS, BAPS and GENELAND), and b) multivariate methods [discriminant analysis of principal components (DAPC) and spatial principal component analysis (sPCA)]. Additionally, because isolation by distance (IBD) is a common spatial genetic pattern in mobile and continuously distributed species and it may represent a challenge to the performance of the above methods, the microsatellite data set was tested for its presence. Overall, the genetic structure of the stone marten in the Iberian Peninsula was characterized by a NE-SW spatial pattern of IBD, and this may explain the observed disagreement between clustering solutions obtained by the different IBC methods. However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations. The detected subdivision could be attributed to the influence of the rivers Ebro, Tagus and Guadiana, suggesting that main watercourses in the Iberian Peninsula may act as semi-permeable barriers to gene flow in stone martens. To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale. We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species.

No MeSH data available.


Map of the study area showing the results of the Bayesian clustering algorithms and the DAPC analysis.Individuals are represented by dots and coloured to reflect the cluster they were assigned to with the highest membership coefficient in each analysis. The Tagus, Ebro and Guadiana rivers are represented in blue. a) Plot from Structure Harvester for STRUCTURE results showing the modal value of K = 2 for the ΔK method (left axis; blue dots connected by line) and K = 4 for the maximum likelihood method (Mean L(K)± SD, right axis; open circles); in the latter case the chosen K is that after which L(K) plateaus or increases slightly and the variance between runs increases; b) BAPS results (K = 4); c) TESS results (K = 4); d) GENELAND results (K = 3);e) DAPC identified K = 5 as the optimal number of clusters, each indicated by different colours and inertia ellipses, the latter shown on the first two axes.
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pone.0134257.g003: Map of the study area showing the results of the Bayesian clustering algorithms and the DAPC analysis.Individuals are represented by dots and coloured to reflect the cluster they were assigned to with the highest membership coefficient in each analysis. The Tagus, Ebro and Guadiana rivers are represented in blue. a) Plot from Structure Harvester for STRUCTURE results showing the modal value of K = 2 for the ΔK method (left axis; blue dots connected by line) and K = 4 for the maximum likelihood method (Mean L(K)± SD, right axis; open circles); in the latter case the chosen K is that after which L(K) plateaus or increases slightly and the variance between runs increases; b) BAPS results (K = 4); c) TESS results (K = 4); d) GENELAND results (K = 3);e) DAPC identified K = 5 as the optimal number of clusters, each indicated by different colours and inertia ellipses, the latter shown on the first two axes.

Mentions: Among the Bayesian clustering approaches, the non-spatial algorithm implemented in STRUCTURE identified two clusters at the uppermost level (K = 2, represented by dots and squares in Fig 3a) following the criterion of Evanno et al. [71]. Individuals located at the NE and SW extremes had the highest membership coefficients (>0.9) while the other samples elsewhere in the study area showed admixed ancestry (data not shown), a pattern consistent with IBD structure (see below and also Fig 4a). Progressive partitioning suggested subdivision of the two clusters above, a clustering solution also hinted by the ΔK criterion. Of the four clusters, only one was relatively well delimited (in south Portugal, represented in yellow), but it was possible to roughly associate the others with NE Spain (Basque Country and Catalonia, in green), with an area from east to northwest Spain (in red), and with an area from north Portugal to south Spain (in purple) (Fig 3a). BAPS and TESS also suggested K = 4 but with different cluster compositions among each other and from progressive partitioning in STRUCTURE, especially for the two middle clusters between NE Spain and SW Portugal (Fig 3b and 3c).


Inferring Population Genetic Structure in Widely and Continuously Distributed Carnivores: The Stone Marten (Martes foina) as a Case Study.

Vergara M, Basto MP, Madeira MJ, Gómez-Moliner BJ, Santos-Reis M, Fernandes C, Ruiz-González A - PLoS ONE (2015)

Map of the study area showing the results of the Bayesian clustering algorithms and the DAPC analysis.Individuals are represented by dots and coloured to reflect the cluster they were assigned to with the highest membership coefficient in each analysis. The Tagus, Ebro and Guadiana rivers are represented in blue. a) Plot from Structure Harvester for STRUCTURE results showing the modal value of K = 2 for the ΔK method (left axis; blue dots connected by line) and K = 4 for the maximum likelihood method (Mean L(K)± SD, right axis; open circles); in the latter case the chosen K is that after which L(K) plateaus or increases slightly and the variance between runs increases; b) BAPS results (K = 4); c) TESS results (K = 4); d) GENELAND results (K = 3);e) DAPC identified K = 5 as the optimal number of clusters, each indicated by different colours and inertia ellipses, the latter shown on the first two axes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4519273&req=5

pone.0134257.g003: Map of the study area showing the results of the Bayesian clustering algorithms and the DAPC analysis.Individuals are represented by dots and coloured to reflect the cluster they were assigned to with the highest membership coefficient in each analysis. The Tagus, Ebro and Guadiana rivers are represented in blue. a) Plot from Structure Harvester for STRUCTURE results showing the modal value of K = 2 for the ΔK method (left axis; blue dots connected by line) and K = 4 for the maximum likelihood method (Mean L(K)± SD, right axis; open circles); in the latter case the chosen K is that after which L(K) plateaus or increases slightly and the variance between runs increases; b) BAPS results (K = 4); c) TESS results (K = 4); d) GENELAND results (K = 3);e) DAPC identified K = 5 as the optimal number of clusters, each indicated by different colours and inertia ellipses, the latter shown on the first two axes.
Mentions: Among the Bayesian clustering approaches, the non-spatial algorithm implemented in STRUCTURE identified two clusters at the uppermost level (K = 2, represented by dots and squares in Fig 3a) following the criterion of Evanno et al. [71]. Individuals located at the NE and SW extremes had the highest membership coefficients (>0.9) while the other samples elsewhere in the study area showed admixed ancestry (data not shown), a pattern consistent with IBD structure (see below and also Fig 4a). Progressive partitioning suggested subdivision of the two clusters above, a clustering solution also hinted by the ΔK criterion. Of the four clusters, only one was relatively well delimited (in south Portugal, represented in yellow), but it was possible to roughly associate the others with NE Spain (Basque Country and Catalonia, in green), with an area from east to northwest Spain (in red), and with an area from north Portugal to south Spain (in purple) (Fig 3a). BAPS and TESS also suggested K = 4 but with different cluster compositions among each other and from progressive partitioning in STRUCTURE, especially for the two middle clusters between NE Spain and SW Portugal (Fig 3b and 3c).

Bottom Line: However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations.To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale.We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology and Animal Cell Biology, Zoology Laboratory, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain; Systematics, Biogeography and Population Dynamics Research Group, Lascaray Research Center, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain.

ABSTRACT
The stone marten is a widely distributed mustelid in the Palaearctic region that exhibits variable habitat preferences in different parts of its range. The species is a Holocene immigrant from southwest Asia which, according to fossil remains, followed the expansion of the Neolithic farming cultures into Europe and possibly colonized the Iberian Peninsula during the Early Neolithic (ca. 7,000 years BP). However, the population genetic structure and historical biogeography of this generalist carnivore remains essentially unknown. In this study we have combined mitochondrial DNA (mtDNA) sequencing (621 bp) and microsatellite genotyping (23 polymorphic markers) to infer the population genetic structure of the stone marten within the Iberian Peninsula. The mtDNA data revealed low haplotype and nucleotide diversities and a lack of phylogeographic structure, most likely due to a recent colonization of the Iberian Peninsula by a few mtDNA lineages during the Early Neolithic. The microsatellite data set was analysed with a) spatial and non-spatial Bayesian individual-based clustering (IBC) approaches (STRUCTURE, TESS, BAPS and GENELAND), and b) multivariate methods [discriminant analysis of principal components (DAPC) and spatial principal component analysis (sPCA)]. Additionally, because isolation by distance (IBD) is a common spatial genetic pattern in mobile and continuously distributed species and it may represent a challenge to the performance of the above methods, the microsatellite data set was tested for its presence. Overall, the genetic structure of the stone marten in the Iberian Peninsula was characterized by a NE-SW spatial pattern of IBD, and this may explain the observed disagreement between clustering solutions obtained by the different IBC methods. However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations. The detected subdivision could be attributed to the influence of the rivers Ebro, Tagus and Guadiana, suggesting that main watercourses in the Iberian Peninsula may act as semi-permeable barriers to gene flow in stone martens. To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale. We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species.

No MeSH data available.