This new edition of Numerical Ecology with R guides readers through an applied exploration of the major methods of multivariate data analysis, as seen through the eyes of three ecologists. It provides a bridge between a textbook of numerical ecology and the implementation of this discipline in the R language. The book begins by examining some exploratory approaches. It proceeds logically with the construction of the key building blocks of most methods, i.e. association measures and matrices, and then submits example data to three families of approaches: clustering, ordination and canonical ordination. The last two chapters make use of these methods to explore important and contemporary issues in ecology: the analysis of spatial structures and of community diversity. The aims of methods thus range from descriptive to explanatory and predictive and encompass a wide variety of approaches that should provide readers with an extensive toolbox that can address a wide palette of questions arising in contemporary multivariate ecological analysis. The second edition of Numerical Ecology with R features a complete revision to the R code and offers improved procedures and more diverse applications of the major methods. It also highlights important changes in the methods and expands upon topics such as multiple correspondence analysis, principal response curves and co-correspondence analysis. New features include the study of relationships between species traits and the environment, and community diversity analysis.
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. Users are invited to use this book as a teaching companion at the computer. All the necessary data files, the scripts used in the chapters, as well as extra R functions and packages written by the authors of Numerical Ecology with R, are available online
- Exploratory data analysis
- Association measures and matrices
- Cluster analysis
- Unconstrained ordination
- Canonical ordination
- Spatial analysis of ecological data
Daniel Borcard is lecturer of Biostatistics and Ecology and researcher in Numerical Ecology at Universite de Montreal, Quebec, Canada. His research interests include Digital Ecology, Ecology of communities, and Soil Ecology / Zoology.
François Gillet is professor of Community Ecology and Ecological Modelling at Universite Bourgogne Franche-Comte, Besancon, France, and visiting professor at École Polytechnique Federale de Lausanne, Switzerland. His research deals with the structure, diversity, ecology and dynamics of plant communities, specifically in sylvopastoral landcapes and grassland ecosystems. He is teaching numerical ecology, community ecology, ecological modelling, hierarchy theory and adaptive management of social-ecological systems.
Pierre Legendre is a professor of ecology at Universite de Montreal. He is the founder of numerical ecology, which is a quantitative subdiscipline of community ecology. Legendre obtained an MSc in zoology from McGill University in 1969, and at age 25, he earned a PhD in biology from the University of Colorado in 1971. From 1971 to 1972, he worked as a postdoctoral fellow at Lund University. From 1972 to 1980, he was employed at Universite du Quebec a Montreal. Since 1980, he has worked at Universite de Montreal. Legendre has published 10 books and almost 300 scientific papers. He has been listed as an ISI Highly Cited Researcher in Ecology/Environment in 2001, 2014, and 2015. He is a community ecologist specialized in the study of statistical analysis methods of data from the field and the lab. His research allows scientists to use the most adapted statistical analysis for their collected data. He collaborates as a numerical ecology expert with researchers around the world.