Detecting footprints of selection in Ovis aries by a spatial analysis approach

Detecting adaptive loci in the genome is essential as it gives the possibility to understand what proportion of a genome or which genes are under the pressure of natural selection. In this paper, we used a Spatial Analysis Method (SAM) recently developed to detect signatures of natural selection in sheep breeds.With the contribution of Geographical Information Systems, environmental variables, and AFLP data, multiple univariate logistic regressions are run to test for association between allelic frequencies at marker loci and environmental variables. The results of the application of this method to sheep breeds are compared with those obtained with a standard population genetics approach.

Amphibian conservation in human shaped environments

Flavio Zanini

Global biodiversity is experiencing a worrying decline. Habitats destruction, associated to their degradation and fragmentation are among the greatest causes. Amphibians are particularly interesting because they are more threatened and decline more rapidly than either birds or mammals. In this context, the objective of our research is to improve some methodological approaches and offer practical scientific bases for decision making in landscape management and amphibian conservation. Our study focuses on fragmented Swiss landscapes. We developed a method that uses land-cover data and expert knowledge to enable a spatially explicit assessment of 1) the temporal changes in the nature conservation value of the landscape and 2) the rehabilitation potential of the landscape. We applied this Geographical Information Systems (GIS) based approach in the Swiss Rhone plain and we used the years 1900 as the reference state. The method constitutes a helpful tool for communication, decision-making and biological conservation management in landscape planning. Effective and optimal species management strategies can only be formulated after relationships between species distribution and environmental factors have been identified. Concerning amphibians, several approaches exist but they generally suffer from two limitations: 1) the spatial autocorrelation (i.e. the dependency between two observations) in data is rarely explicitly analyzed, even if it may affect the accuracy of species-habitat relationships models. We showed how this spatial autocorrelation can be measured and included in logistic models with the example of the agile frog (Rana dalmatina) in north-eastern Switzerland. We used the Moran's I and the autologistic model (i.e. a logistic model including a measure of the spatial arrangement of the response variables). We found that if spatial autocorrelation is not considered, then conclusions on species-habitat relationships can be incorrect. 2) The effect of landscape on amphibian occurrence in ponds is often assumed to be equal in every direction (isotropic). However, barriers and inhospitable surfaces may reduce movement patterns and the area around ponds accessible to species. This implies that the ideal circular area has in reality a shape depending on the surrounding landscape. We developed a method to determine the effect of habitat variables on amphibian species distribution, considering physical barriers in their movement around ponds. We studied two amphibian species: the common toad (Bufo bufo) and the common frog (Rana temporaria) in the Rhone plain. We demonstrated that reducing the boundaries of circular area following barriers, allowed to compute landscape predictors which better explained species distribution. These results suggested that the proposed approach is more pertinent than the traditional circular buffers analysis. Our results stress the necessity to consider barriers and ecological corridors in species distribution models in order to avoid incorrect inferences. Species distribution models are usually established for a single region. It is generally unknown whether the identified relationships between species distribution and environmental variables can be directly transferred to another geographical area. We examined landscape-level habitat relationships for six amphibian species by measuring correlations with their presence in 655 ponds of five different regions. We analyzed several models by using the information-theoretic approach and the Akaike Information criterion (AIC). For five out of six species, the best models predict that site occupation probability depends on region. Our results suggest that caution is needed when predictions of species occurrence and species management strategies are done using models built in other geographic regions. We also observed that connectivity was generally more explicative than landscape variables. In addition, we found that the spatial scale at which habitat affected species occurrence varied from pond to several km around ponds. Management strategies for amphibian conservation should be conducted taking into account the geographic context, connectivity of ponds and habitat characteristics at multiple spatial scales. Finally, we demonstrated that the landscape, separating patches in metapopulation models, has to be considered in order to avoid incorrect conclusions on population viability analyses. We explored how patch occupancy is sensitive to Euclidean (shortest) versus a landscape-based distance (least-cost). We found: 1) from a theoretical standpoint, that inter-patch landscape affects patch occupancy; 2) from a practical and conservation standpoints, which patches should be considered in priority for landscape management. The approach was illustrated in the case of two metapopulations of the Yellow-bellied Toad in the Rhone plain. We applied successfully the developed practical approaches to the case of several amphibian species, but they can doubtlessly be extended to any species functioning on a spatially defined patch basis (e.g. pond, nesting place, den …), structured as a metapopulation and affected by landscape structure during movement. By improving and combining spatially explicit approaches, we are more likely to provide wildlife managers with tools for valuable decision making.

EPFL2006

Un rôle pour la science de l'information géographique en écologie moléculaire La détection de régions du génome soumises à la sélection naturelle

Régis Caloz, Stéphane Joost, Pierre Taberlet

We introduce a new method to detect signatures of natural selection in the genome based on the application of spatial analysis, with the contribution of Geographical Information Systems, environmental variables, molecular data, and multiple univariate logistic regressions. Its use allows the identification of the same genomic regions as revealed by a theoretical approach in population genomics, but also the uncovering of environmental parameters responsible for selection which was so far difficult. This research also opens up avenues for GISciences research in molecular ecology.

2008

Multi-scale landscape genomic models

Kevin Leempoel

Environmental heterogeneity is one of the main actors of biodiversity and species adaptation as it exerts a selective pressure on observable characteristics of living organisms. Consequently, local adaptation favours certain genetic variants and, by doing so, leaves a footprint in the genetic heritage across populations. The identification of these adaptive genetic variations is the main objective of landscape genomics and allows, among other things, to study the role of specific regions of the genome in evolutionary processes. Landscape genomics studies also provide essential information for species conservation and for the prediction of migrations due to environmental changes. To identify these adaptations to the environment, it is necessary to define a study area where the populations are sampled. However, defining the scale of the study area is not a trivial task. In fact, whether the work is carried out at a local or at a broad scale determines the relevance of environmental factors and the type of signature of selection that will be observed. In addition, the concept of scale in ecology takes into account not only the extent of the study area but also the pattern and density of the geographic distribution of observations, and the spatial resolution of predictors (environmental variables), which is intrinsically linked to the extent. However, a priori indications about the relevance of any resolution over another are rare in the literature and it is therefore essential to question this issue. In this thesis, we propose a multi-scale landscape genomic framework to identify signatures of adaptation to the environment. This multidisciplinary framework lies at the interface between geographic information systems, spatial analysis, environmental modelling, population genetics and computer science. Specifically, we focus on the relevance of variables derived from Digital Elevation Models (DEMs) and on the application of multi-scale analysis aiming to detect signatures of selection. We applied this analytical framework to three case studies, comprising four species: Biscutella laevigata sampled at a local scale, Plantago major at a regional scale, sheep and goats at a large scale. In particular, the case of B. laevigata allowed us to evaluate the role of topographic features based on Very High Resolution DEMs and to include DEM-derived variables as predictors in association models to study the adaptation of species to their local environment. On the other hand, the case of Moroccan sheep and goats permitted to include for the first time whole genome sequence data within landscape genomic models. The results revealed several important findings. We showed that micro-climate variability is highly dependent on topographic factors at a local scale and that therefore, DEMs are relevant for understanding species adaptation to a mountainous environment. We also demonstrated that it is essential to consider the scale of spatial representativeness by assessing DEM-derived variables at various spatial resolutions. Indeed, two out of three case studies showed that the models involving topographic variables were sensitive to changes in resolution. In summary, we used several landscape genetics approaches to understand the role of environmental factors in the local adaptation of various species. Our findings mainly provide an important contribution to the understanding and use of scale in landscape genomics, also useful in landscape ecology.