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 research, 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.
Our related projects
GENESCALE
[2014-2017] SNSF interdisciplinary project in collaboration with the WSL group of Ecological Genetics, the Laboratory of Evolutionary Botany at the University of Neuchâtel and the G2C Institute at the University of Applied Sciences Western Switzerland in Yverdon-les-Bains (HEIG-VD). The goal is to investigate the contribution of Very High Resolution (VHR) Digital Elevation Models (DEMs) acquired by means of Unmanned Aerial Vehicle (SenseFly drones) for multiscale analysis in landscape genomics.
An emerging objective in molecular ecology is to identify the appropriate spatial scale at which to study adaptation in plants. An answer to this may come from landscape genomics, which amalgamates population genetics and evolutionary theory with landscape ecology, i.e. the conformation of landscape elements and local environmental conditions. To date, the establishment of links between genetic polymorphisms and local environmental conditions largely relies on limited numbers of molecular markers and on data from coarse interpolation of long-term climatic conditions. While deep genotyping of entire genomes has become feasible in recent years owing to frantic technological progress, the use of remote sensing for describing landscape and microsite conditions has remained underexploited. DEMs have great potential to produce environmental variables (primary and secondary topographic attributes) that may help to identify genomic regions possibly involved in adaptive processes. It has been recently shown that VHR DEMs can be ideally generalized using wavelet transform to produce environmental variables at different nested scales, resulting in a more continuous representation of the landscape, as it exists in nature. The processing of association models between environmental variables extracted from these DEMs and genome-wide polymorphisms are likely to provide important insights on the impact of scale on the significance of these associations.
Here, we propose to deduce environmental conditions across four regions in the north-western Swiss Alps using DEMs acquired by unmanned aerial vehicle (UAV, eBee, SenseFly technology), and by light detection and ranging (LIDAR) data. We will confront these high-resolution environmental descriptions with whole-genome polymorphisms, including variation in the distribution of transposable elements (TEs) obtained from next generation re-quencing-based genome characterization of individual plants. The goal of GENESCALE is to answer the following questions: (i) at what spatial scale, and in response to which environmental factors, can is it possible to identify signals of local adaptation, and (ii) to what degree do genic vs. non-genic fractions of the genome contribute to the genome-wide signatures of adaptation?
As a target species, we will use Arabis alpina, a widespread Brassicaceae with divergent ecological requirements and likely to become a model plant for ecological genomics.