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/).
