Écologie du paysage | EPFL Graph Search

L'écologie du paysage est une sous-discipline de l'écologie qui consiste en l'étude des processus écologiques à l'échelle des paysages. Le paysage est alors défini comme une portion de territoire hétérogène, composée d’ensembles d’écosystèmes en interaction et est considéré comme un niveau d’organisation des systèmes écologiques, supérieur à l’écosystème. Plus largement, l'écologie du paysage s'intéresse à la dynamique spatiale et temporelle des composantes biologiques, physiques et sociales des paysages anthropisés et naturels. Elle associe pour cela des disciplines telles que l'écologie, la géographie, les sciences sociales ou l'urbanisme. L'écologie du paysage est ainsi devenue une branche interdisciplinaire des sciences. L'IALE (Association internationale d'écologie du paysage) définit l'écologie du paysage comme "l'étude de la variation [des conditions environnementales] dans et entre les paysages à différentes échelles spatiales et temporelles. Elle cherche à comprendre les

Beyond the patch: on landscape-explicit metapopulation dynamics

Jonathan Giezendanner

Climate change threatens biodiversity and species distribution all over the world at unprecedented rates. Human induced changes to landscape structure and habitat are redefining the relation between species and their environment. Understanding, characterizing and modeling the relation between complex landscape features and geographical species presence, the subject of this thesis, have thus gained paramount importance in order to make informed decisions on near- and far-term management strategies for species and landscape protection. The present thesis pays particular attention to the understanding of how mountain landscapes, described as heterogeneous habitat matrices, govern the presence of a species in space and time. To this end, throughout this thesis a metapopulation model is considered, a spatially-explicit model based on the balance between colonization and extinction events in areas of different suitability. The classical patch-based modelling approach is extended here in various ways in order to incorporate landscape-explicit information about the habitat matrix. A first theoretical study is performed on geometric, realistic, and real landscapes which highlights how different levels of connectivity, the degree to which different areas of similar habitat are actually linked together in the landscape (say, derived from geomorphic structures, such as valleys and peaks or emerging structures shaped by fluvial erosion and tectonic uplift) impact the dynamic of mountain species under climate change. A second, applied, study investigates how different landscape descriptors derived from Earth Observation data can be used in order to dynamically model the spatial, landscape-explicit, presence of two carabid species (Pterostichus flavofemoratus and Carabus depressus) in the Gran Paradiso National Park. In a final study, the consistency of the metapopulation model is investigated when considering different resolutions of the landscape matrix, i.e. different levels of coarse-graining. This last part is fundamental to the understanding of how conclusions drawn from local studies can be made general and extrapolated to larger regions. Overall, this thesis bridges the fields of population and landscape ecology, focusing on modelling metapopulation dynamics in complex landscapes seen as the substrate for ecological interactions. The result from the three studies show that metapopulation ecology can profit from the insights of landscape ecology. The combination of the two fields can be a useful tool in furthering the understanding and monitoring of mountain species.

EPFL2020

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.

EPFL2015

Integrating models across temporal and spatial scales to simulate landscape patterns and dynamics in mountain pasture-woodlands

Alexander Ludwig Johannes Peringer

Context Pasture-woodlands are semi-natural landscapes that result from the combined influences of climate, management, and intrinsic vegetation dynamics. These landscapes are sensitive to future changes in land use and climate, but our ability to predict the impact on ecosystem service provisioning is limited due to the disparate scales in time and space that govern their dynamics. Objectives To develop a process-based model to simulate pasture-woodland landscapes and the provisioning of ecosystem services (i.e., livestock forage, woody biomass and landscape heterogeneity). Methods We modified a dynamic forest landscape model to simulate pasture-woodland landscapes in Switzerland. This involved including an annual herbaceous layer, selective grazing from cattle, and interactions between grazing and tree recruitment. Results were evaluated within a particular pasture, and then the model was used to simulate regional vegetation patterns and livestock suitability for a similar to 98,000 ha landscape in the Jura Vaudois region. Results The proportion of vegetation cover types at the pasture level (i.e., open, semi-open and closed forests) was well represented, but the spatial distribution of trees was only broadly similar. The entire Jura Vaudois region was simulated to be highly suitable for livestock, with only a small proportion being unsuitable due to steep slopes and high tree cover. High and low elevation pastures were equally suitable for livestock, as lower forage production at higher elevations was compensated by reduced tree cover. Conclusions The modified model is valuable for assessing landscape to regional patterns in vegetation and livestock, and offers a platform to evaluate how climate and management impact ecosystem services.