New polymorphic microsatellite markers of the endangered meadow viper ( Vipera ursinii ) identified by 454 high-throughput sequencing: when innovation meets conservation

The Next Generation Sequencing (pyrosequencing) technique allows rapid, low-cost development of microsatellite markers. We have used this technology to develop 14 polymorphic loci for the endangered meadow viper (Vipera ursinii). Based on 37,000 reads, we developed primers for 66 microsatellite loci and found that 14 were polymorphic. The number of alleles per locus varies from 1 to 12 (for 30 individuals tested). At a cost of about 1/3 that of a normal microsatellite development, we were able to define enough microsatellite markers to conduct population genetic studies on a non-model specie

Unravelling landscape variables with multiple approaches to overcome scarce species knowledge: a landscape genetic study of the slow worm

Landscape genetics was developed to detect landscape elements shaping genetic population structure, including the effects of fragmentation. Multifarious environmental variables can influence gene flow in different ways and expert knowledge is frequently used to construct friction maps. However, the extent of the migration and the movement of single individuals are frequently unknown, especially for non-model species, and friction maps only based on expert knowledge can be misleading. In this study, we used three different methods: isolation by distance (IBD), least-cost modelling and a strip-based approach to disentangle the human implication in the fragmentation process in the slow worm (Anguis fragilis), as well as the specific landscape elements shaping the genetic structure in a highly anthropized 16km2 area in Switzerland. Friction maps were constructed using expert opinion, but also based on the combination of all possible weightings for all landscape elements. The IBD indicated a significant effect of geographic distance on genetic differentiation. Further approaches demonstrated that highways and railways were the most important elements impeding the gene flow in this area. Surprisingly, we also found that agricultural areas and dense forests seemed to be used as dispersal corridors. These results confirmed that the slow worm has relatively unspecific habitat requirements. Finally, we showed that our models based on expert knowledge performed poorly compared to cautious analysis of each variable. This study demonstrated that landscape genetic analyses should take expert knowledge with caution and exhaustive analyses of each landscape element without a priori knowledge and different methods can be recommende

Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata

Plant species are known to adapt locally to their environment, particularly in mountainous areas where conditions can vary drastically over short distances. The climate of such landscapes being largely influenced by topography, using fine-scale models to evaluate environmental heterogeneity may help detecting adaptation to micro-habitats. Here, we applied a multiscale landscape genomic approach to detect evidence of local adaptation in the alpine plant Biscutella laevigata. The two gene pools identified, experiencing limited gene flow along a 1-km ridge, were different in regard to several habitat features derived from a very high resolution (VHR) digital elevation model (DEM). A correlative approach detected signatures of selection along environmental gradients such as altitude, wind exposure, and solar radiation, indicating adaptive pressures likely driven by fine-scale topography. Using a large panel of DEM-derived variables as ecologically relevant proxies, our results highlighted the critical role of spatial resolution. These high-resolution multiscale variables indeed indicate that the robustness of associations between genetic loci and environmental features depends on spatial parameters that are poorly documented. We argue that the scale issue is critical in landscape genomics and that multiscale ecological variables are key to improve our understanding of local adaptation in highly heterogeneous landscapes

Unravelling landscape variables with multiple approaches to overcome scarce species knowledge : a landscape genetic study of the slow worm

Landscape genetics was developed to detect landscape elements shaping genetic population structure, including the effects of fragmentation. Multifarious environmental variables can influence gene flow in different ways and expert knowledge is frequently used to construct friction maps. However, the extent of the migration and the movement of single individuals are frequently unknown, especially for non-model species, and friction maps only based on expert knowledge can be misleading. In this study, we used three different methods: isolation by distance (IBD), least-cost modelling and a strip-based approach to disentangle the human implication in the fragmentation process in the slow worm (Anguis fragilis), as well as the specific landscape elements shaping the genetic structure in a highly anthropized 16 km2 area in Switzerland. Friction maps were constructed using expert opinion, but also based on the combination of all possible weightings for all landscape elements. The IBD indicated a significant effect of geographic distance on genetic differentiation. Further approaches demonstrated that highways and railways were the most important elements impeding the gene flow in this area. Surprisingly, we also found that agricultural areas and dense forests seemed to be used as dispersal corridors. These results confirmed that the slow worm has relatively unspecific habitat requirements. Finally, we showed that our models based on expert knowledge performed poorly compared to cautious analysis of each variable. This study demonstrated that landscape genetic analyses should take expert knowledge with caution and exhaustive analyses of each landscape element without a priori knowledge and different methods can be recommended

Data from: Very high resolution digital elevation models: are multi-scale derived variables ecologically relevant?

Digital Elevation Models (DEMs) are often used in landscape ecology to retrieve elevation or first derivative terrain attributes such as slope or aspect in the context of species distribution modelling. However, DEM-derived variables are scale-dependent and, given the increasing availability of very high resolution (VHR) DEMs, their ecological relevance must be assessed for different spatial resolutions. In a study area located in the Swiss Western Alps, we computed VHR DEMs-derived variables related to morphometry, hydrology and solar radiation. Based on an original spatial resolution of 0.5 meters, we generated DEM-derived variables at 1m, 2m and 4m spatial resolutions, applying a Gaussian Pyramid. Their associations with local climatic factors, measured by sensors (direct and ambient air temperature, air humidity and soil moisture) as well as ecological indicators derived from species composition, were assessed with multivariate Generalized Linear Models (GLM) and Mixed Models (GLMM). Specific VHR DEM-derived variables showed significant associations with climatic factors. In addition to slope, aspect and curvature, the underused wetness and ruggedness indices modeled measured ambient humidity and soil moisture, respectively. Remarkably, spatial resolution of VHR DEM-derived variables had a significant influence on models’ strength, with coefficients of determination decreasing with coarser resolutions or showing a local optimum with a 2m resolution, depending on the variable considered. These results support the relevance of using multi-scale DEM variables to provide surrogates for important climatic variables such as humidity, moisture and temperature, offering suitable alternatives to direct measurements for evolutionary ecology studies at a local scale

Whole genome duplications and recruitment of ecologically relevant genes in alpine Mustards

Polyploid taxa represent excellent models to address the underpinnings of genome evolution and the building up of new species in heterogeneous environments. Here, we present an overview of recent works in the alpine Biscutella laevigata autopolyploid complex (Brassicaceae). Transcriptomics inferred recurrent whole genome duplication (WGD) events specific to clade of species and that were used to infer processes fostering genome evolution across different timescales: (i) After a 7-8 million years old WGD event, intense chromosomal repatterning selected for clusters of retained duplicates enriched in functions associated with responses to abiotic stresses. Low coverage genome sequencing unraveled the dynamics of several retrotransposons, supporting interplay between genome reorganization and environmental opportunities in shaping the evolution of paleopolyploids. (ii) Retrotransposons in autotetraploids having recolonized the Alps after the ice ages showed considerable dynamics going along with ecological radiation following this recent WGD. Ecological genomics involving transplant experiment indeed supported distinct autopolyploid gene pools firmly associated with contrasted habitats despite gene flow. These ecotypes demonstrated adaptive differentiation at loci whose functions match habitat requirements. WGDs thus recurrently fostered genome reorganization and adaptive recruitment of genes responding to environmental factors, indicating that similar proximate and ultimate factors of genome dynamics may consistently act through time