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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 this book 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 the book, are available online
(URL: http://adn.biol.umontreal.ca/~numericalecology/numecolR/).
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