Numerical Ecology with R

by Daniel Borcard, Francois Gillet, Pierre Legendre

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 clustering, ordination and canonical ordination. The last two chapters make use of these methods to explore important and contemporary issues in 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 (

Genres: Science, Programming, Biology

450 pages, Paperback

First published January 1, 2011

Reviews

[Nathan · Rating 5 out of 5]

The 2nd edition (2018) is a major update. Specifically, it includes more polished code for many of the spatial analyses that were not yet included in established R packages when the 1st edition was published. Now, in addition to the previous focus on the 'vegan' package, these sections also make extensive use of the 'adespatial' package (among others) to conduct MEM and other spatial analyses. Other updates include more detailed descriptions of clustering and constrained ordination, with better descriptions of the R functions and clearer suggestions for when to use certain approaches over others.

[Tomek · Rating 3 out of 5]

3.5 stars: This could be a seminal book for community ecologists given the need to understand specialized analyses in the increasingly ubiquitous and flexible (and free) R language. The authors do several things well. They use only a few data sets so that readers can thoroughly understand their composition as opposed to jumping around from dataset to dataset, obfuscating and complicating the actual analysis. They generally avoid overly-technical jargon. Ecologists are not statisticians and while it may be useful to have some background in the mathematics of it, most of us are really focused on the applications. Finally, they present a wide array of possible tools and analyses with clear documentation as to how to implement them.

However...

They omitted much of the output of the code they present. While this saves space, it also prevents easy interpretation of the results. I can understand wanting to encourage the reader to try it out for themselves, but it's infuriating to constantly interrupt my reading to code something on the computer and sometimes (heavens forbid) I don't have a computer available to me. Also, the interpretation of the results are sometimes wanting, or rather, the link between the numerical output and its ecological interpretation is lacking. Finally, while they presented a number of useful analyses, they don't go into as much depth or clarity as I would like regarding the selection process (i.e., when to use which analysis). There is some of this implicitly in the descriptions of the analyses, but it's too obtuse, in my opinion.

All in all, a useful primer for ecologists. I imagine this would be an excellent textbook for students, as it has review questions and assignments built into the text. However, I wish it provided more concrete detail for researchers and practitioners who have bounded and practical objectives for reading and learning from this text.

[Huong · Rating 5 out of 5]

practical examples and beginner-friendly explanation.

[Julian · Rating 3 out of 5]

This book is very decent. The writing is generally pretty good, especially for a book on statistics - combined with the R code and "cookbook" examples, it's a good introduction to the topics inside. (I used it mostly to learn about, and learn how to code, ordination techniques for biological data.)

However, the layout really throws me off: pages of text, followed by pages of "boxes" of code, followed by pages of text referring to code 4-5 pages ahead or behind...not the easiest to use. Nonetheless, especially in combination with other books on statistics geared toward ecologists (looking at you, "Analysing Ecological Data" by Zuur et al. and "Numerical Ecology" by Legendre & Legendre), this book is very useful.

[Dgg32 · Rating 4 out of 5]

Easy starts, difficult ends. The last two chapters require a lot of understanding in linear algebra and statistics. Trying the code sample along the way is highly recommended.

[Kayla · Rating 4 out of 5]

Read for BIOL 4052 Community Ecology Lab: Spring 2017, UNT. Found PDF online for free.

This book was fairly handy. My TA provided most of the script for for us, or we walked through it together in class. But I did reference the book a few times to complete homework assignments. I found a PDF of the book online for free by Googling the title and the author, as my TA suggested.