Chapter 1
Introduction
1.1 Why Numerical Ecology?
Although multivariate analysis of ecological data already existed and was being
actively developed in the 1960’s, it really flourished in the years 1970 and later.
Many textbooks were published during these years, among them the seminal
Écologie numérique (Legendre and Legendre 1979), and its English translation
Numerical Ecology (Legendre and Legendre 1983). The authors of these books
unified under one single roof a very wide array of statistical and other numerical
techniques and presented them in a comprehensive way, not only to help researchers
understand the available methods of statistical analysis, but also to explain how to
choose and apply them in an ordered, logical way to reach their research goals.
Mathematical explanations were not absent from these books, and provided a
precious insider look into the various techniques, which was appealing to readers
wishing to go beyond the simple user level.
Since then, numerical ecology has become ubiquitous. Every serious researcher
or practitioner has become aware of the tremendous interest of exploiting painfully
acquired data as efficiently as possible. Other manuals have been published
(e.g. Orlóci and Kenkel 1985; Jongman et al. 1995; McCune and Grace 2002;
McGarigal et al. 2000; Zuur et al. 2007; Greenacre and Primicerio 2013; Wildi
2013). A second English edition of Numerical Ecology was published in 1998,
followed by a third in 2012, broadening the perspective and introducing numerous
methods that were unavailable at the times of the previous editions. The progress
continues. In this book we present some of the developments that we consider most
important, albeit in a more user-oriented way than in the abovementioned manuals,
using the R language. For the most recent methods, we provide explanations at a
more fundamental level when we consider it appropriate and helpful.
Not all existing methods of data analysis are addressed in this book, of course.
Apart from the most widely used and fruitful methods, our choices are based on
our own experience as quantitative community ecologists. However, small sections
© Springer International Publishing AG, part of Springer Nature 2018
D. Borcard et al., Numerical Ecology with R, Use R!,
https://doi.org/10.1007/978-3-319-71404-2_1
1
Introduction
1.1 Why Numerical Ecology?
Although multivariate analysis of ecological data already existed and was being
actively developed in the 1960’s, it really flourished in the years 1970 and later.
Many textbooks were published during these years, among them the seminal
Écologie numérique (Legendre and Legendre 1979), and its English translation
Numerical Ecology (Legendre and Legendre 1983). The authors of these books
unified under one single roof a very wide array of statistical and other numerical
techniques and presented them in a comprehensive way, not only to help researchers
understand the available methods of statistical analysis, but also to explain how to
choose and apply them in an ordered, logical way to reach their research goals.
Mathematical explanations were not absent from these books, and provided a
precious insider look into the various techniques, which was appealing to readers
wishing to go beyond the simple user level.
Since then, numerical ecology has become ubiquitous. Every serious researcher
or practitioner has become aware of the tremendous interest of exploiting painfully
acquired data as efficiently as possible. Other manuals have been published
(e.g. Orlóci and Kenkel 1985; Jongman et al. 1995; McCune and Grace 2002;
McGarigal et al. 2000; Zuur et al. 2007; Greenacre and Primicerio 2013; Wildi
2013). A second English edition of Numerical Ecology was published in 1998,
followed by a third in 2012, broadening the perspective and introducing numerous
methods that were unavailable at the times of the previous editions. The progress
continues. In this book we present some of the developments that we consider most
important, albeit in a more user-oriented way than in the abovementioned manuals,
using the R language. For the most recent methods, we provide explanations at a
more fundamental level when we consider it appropriate and helpful.
Not all existing methods of data analysis are addressed in this book, of course.
Apart from the most widely used and fruitful methods, our choices are based on
our own experience as quantitative community ecologists. However, small sections
© Springer International Publishing AG, part of Springer Nature 2018
D. Borcard et al., Numerical Ecology with R, Use R!,
https://doi.org/10.1007/978-3-319-71404-2_1
1