Abstract
The identification of populations and spatial genetic patterns is important for ecological and conservation research, and spatially explicit individual-based methods have been recognised as powerful tools in this context. Mammalian carnivores are intrinsically vulnerable to habitat fragmentation but not much is known about the genetic consequences of fragmentation in common species. Stone martens (Martes foina) and red foxes (Vulpes vulpes) share a widespread Palearctic distribution and are considered habitat generalists, but in the Iberian Peninsula stone martens tend to occur in higher quality habitats. We compared their genetic structure in Portugal to see if they are consistent with their differences in ecological plasticity, and also to illustrate an approach to explicitly delineate the spatial boundaries of consistently identified genetic units. We analysed microsatellite data using spatial Bayesian clustering methods (implemented in the software BAPS, GENELAND and TESS), a progressive partitioning approach and a multivariate technique (Spatial Principal Components Analysis-sPCA). Three consensus Bayesian clusters were identified for the stone marten. No consensus was achieved for the red fox, but one cluster was the most probable clustering solution. Progressive partitioning and sPCA suggested additional clusters in the stone marten but they were not consistent among methods and were geographically incoherent. The contrasting results between the two species are consistent with the literature reporting stricter ecological requirements of the stone marten in the Iberian Peninsula. The observed genetic structure in the stone marten may have been influenced by landscape features, particularly rivers, and fragmentation. We suggest that an approach based on a consensus clustering solution of multiple different algorithms may provide an objective and effective means to delineate potential boundaries of inferred subpopulations. sPCA and progressive partitioning offer further verification of possible population structure and may be useful for revealing cryptic spatial genetic patterns worth further investigation.
Highlights
In conservation biology, interest is growing on the population genetic status and viability of species traditionally classed as ‘common’ [1,2,3] since many abundant species are under the impact of human population growth and actions [4]
We used a combination of Bayesian and multivariate techniques to characterise population structure and reveal spatial genetic patterns for further investigation in two common mesocarnivores, the stone marten and the red fox, across Portugal
The identification of hidden genetic structure is important for the management of species, even those that are currently abundant and widespread so as to guide management actions that can prevent them from becoming threatened in the future
Summary
Interest is growing on the population genetic status and viability of species traditionally classed as ‘common’ [1,2,3] since many abundant species are under the impact of human population growth and actions [4]. In the face of the current biodiversity crisis, sound management and conservation requires genetic information to preserve species as dynamic entities capable of coping with environmental change [5]. Much remains unknown about the genetic consequences of habitat fragmentation and heterogeneity on common, vagile and apparently continuously distributed species These species are generally assumed to have absent or negligible population structure or an isolation-by-distance pattern (IBD). An increasing number of studies are showing that unexpected patterns of genetic differentiation can be observed in mobile and widespread habitat generalists [9,10,11,12] These patterns may be complex and difficult to interpret, among other reasons because they may have been influenced by historical events, topographic features, recent landscape fragmentation, and/or habitat preferences [9,13,14,15]. Higher migration rates may erode signatures of previous genetic differentiation [19] and prevent structuring and divergence across semi permeable barriers [20]
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