Écologie profonde | EPFL Graph Search

L’écologie profonde (deep ecology en anglais) ou radicale est une philosophie écologiste contemporaine qui se caractérise par la défense de la valeur intrinsèque des êtres vivants et de la nature, c’est-à-dire une valeur indépendante de leur utilité pour les êtres humains. Elle attribue plus de valeur aux espèces et aux différents écosystèmes que ne le font les mouvements écologiques classiques, ce qui entraîne le développement d’une éthique environnementale. Tandis que l’écologisme classique, bien que développant de nouvelles alternatives, pose toujours la satisfaction des besoins humains comme finalité (anthropocentrisme) et attribue au reste du vivant le statut de « ressource », l’écologie profonde réinscrit les finalités humaines dans une perspective plus large, celle du vivant (biocentrisme) afin de prendre en compte les besoins de l’ensemble de la biosphère, notamment des espèces avec lesquelles la lignée humaine coévolue depuis des milliers d’années. Historique Le

Numerical Ecology with R

Daniel Borcard, François Gillet

Numerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the authors accompany the users through the exploration of the methods by means of applied and extensively commented examples. Users are invited to use this book as a teaching companion at the computer. The travel starts with exploratory approaches, proceeds with the construction of association matrices, then addresses three families of methods: clustering, unconstrained and canonical ordination, and spatial analysis. All the necessary data files, the scripts used in the chapters, as well as the extra R functions and packages written by the authors, can be downloaded from a web page accessible through the Springer web site(http://adn.biol.umontreal.ca/~numericalecology/numecolR/). This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book. The three authors teach numerical ecology, both theoretical and practical, to a wide array of audiences, in regular courses in their Universities and in short courses given around the world. Daniel Borcard is lecturer of Biostatistics and Ecology and researcher in Numerical Ecology at Université de Montréal, Québec, Canada. François Gillet is professor of Community Ecology and Ecological Modelling at Université de Franche-Comté, Besançon, France. Pierre Legendre is professor of Quantitative Biology and Ecology at Université de Montréal, Fellow of the Royal Society of Canada, and ISI Highly Cited Researcher in Ecology/Environment.

Springer2011

Soil protistology rebooted: 30 fundamental questions to start with

Bertrand Fournier, Thierry Jean Heger, Vincent Eric Jules Jassey, Alexandre Jousset, Santosh Kumar, Mariusz Lamentowicz, Enrique Lara, Edward Mitchell

Protists are the most diverse eukaryotes. These microbes are keystone organisms of soil ecosystems and regulate essential processes of soil fertility such as nutrient cycling and plant growth. Despite this, protists have received little scientific attention, especially compared to bacteria, fungi and nematodes in soil studies. Recent methodological advances, particularly in molecular biology techniques, have made the study of soil protists more accessible, and have created a resurgence of interest in soil protistology. This ongoing revolution now enables comprehensive investigations of the structure and functioning of soil protist communities, paving the way to a new era in soil biology. Instead of providing an exhaustive review, we provide a synthesis of research gaps that should be prioritized in future studies of soil protistology to guide this rapidly developing research area. Based on a synthesis of expert opinion we propose 30 key questions covering a broad range of topics including evolution, phylogenetics, functional ecology, macroecology, paleoecology, and methodologies. These questions highlight a diversity of topics that will establish soil protistology as a hub discipline connecting different fundamental and applied fields such as ecology, biogeography, evolution, plant-microbe interactions, agronomy, and conservation biology. We are convinced that soil protistology has the potential to be one of the most exciting frontiers in biology. (C) 2017 Elsevier Ltd. All rights reserved.

Pergamon-Elsevier Science Ltd2017

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