of several excellent textbooks, and the increasing awareness of the responsibility of
ecologists with regard to the proper design and analysis of their research. This
awareness makes the task easier for teachers as well.
Until the first years of this millennium, however, a critical ingredient was still
missing for the teaching to be efficient and for the practice of statistics to become
generalized among ecologists: a set of standard packages available to everyone,
everywhere. A biostatistics or numerical ecology course means nothing without
practical exercises. A course linked to commercial software is much better, but it
is bound to restrict future applications if the researcher moves and loses access to the
software that he or she knows. Furthermore, commercial packages are in most cases
written for larger audiences than the community of ecologists and they may not
include all the functions required for analysing ecological data. The R language
resolved that issue, thanks to the dedication of the many researchers who created and
freely contributed extensive, well-designed, and well-documented packages. Now
the teacher no longer has to say: “this is the way PCA works... on paper;” she or he
can say instead: “this is the way PCA works, now I will show you on-screen how to
run one, and in a few minutes you will be able to run your own, and do it anywhere in
the world on your own data!”
Another fundamental property of the R language is that it is meant as a selflearning environment. A book on R is therefore bound to follow that philosophy, and
must provide the support necessary for anyone wishing to explore the subject by
himself or herself. This book has been written to provide a bridge between the theory
and practice of numerical ecology, that anyone can cross. Our dearest hope is that it
will make many happy teachers and happy ecologists.
Since they are living entities, both the field of numerical ecology and the
R language evolve. As a result, much has happened in both fields since the
publication of the first edition of Numerical Ecology with R in 2011. Therefore, it
was time not only to update the code provided in the first edition, but also to present
new methods, provide more insight into existing ones, offer more examples and a
wider array of applications of the major methods. We also took the opportunity to
present the code in a more attractive way, generated by R Markdown in RStudio
® ,
with different colours for functions, objects, arguments and comments.
Our dearest hope is that all this will make many more happy teachers and happy
ecologists.
Montréal, QC, Canada
Daniel Borcard
Besançon, France
François Gillet
Montréal, QC, Canada
Pierre Legendre
vi
Preface
ecologists with regard to the proper design and analysis of their research. This
awareness makes the task easier for teachers as well.
Until the first years of this millennium, however, a critical ingredient was still
missing for the teaching to be efficient and for the practice of statistics to become
generalized among ecologists: a set of standard packages available to everyone,
everywhere. A biostatistics or numerical ecology course means nothing without
practical exercises. A course linked to commercial software is much better, but it
is bound to restrict future applications if the researcher moves and loses access to the
software that he or she knows. Furthermore, commercial packages are in most cases
written for larger audiences than the community of ecologists and they may not
include all the functions required for analysing ecological data. The R language
resolved that issue, thanks to the dedication of the many researchers who created and
freely contributed extensive, well-designed, and well-documented packages. Now
the teacher no longer has to say: “this is the way PCA works... on paper;” she or he
can say instead: “this is the way PCA works, now I will show you on-screen how to
run one, and in a few minutes you will be able to run your own, and do it anywhere in
the world on your own data!”
Another fundamental property of the R language is that it is meant as a selflearning environment. A book on R is therefore bound to follow that philosophy, and
must provide the support necessary for anyone wishing to explore the subject by
himself or herself. This book has been written to provide a bridge between the theory
and practice of numerical ecology, that anyone can cross. Our dearest hope is that it
will make many happy teachers and happy ecologists.
Since they are living entities, both the field of numerical ecology and the
R language evolve. As a result, much has happened in both fields since the
publication of the first edition of Numerical Ecology with R in 2011. Therefore, it
was time not only to update the code provided in the first edition, but also to present
new methods, provide more insight into existing ones, offer more examples and a
wider array of applications of the major methods. We also took the opportunity to
present the code in a more attractive way, generated by R Markdown in RStudio
® ,
with different colours for functions, objects, arguments and comments.
Our dearest hope is that all this will make many more happy teachers and happy
ecologists.
Montréal, QC, Canada
Daniel Borcard
Besançon, France
François Gillet
Montréal, QC, Canada
Pierre Legendre
vi
Preface