Abstract: "As an aspiring student in forest ecology, I was never more distressed about my career choice than when a senior graduate student predicted that I would spend more time learning statistics than any other subject. Twenty years later, the contents of my bookshelf and hard drive provide compelling evidence of the truth of this prediction. Community ecology has collected an eclectic mix of multivariate techniques to quantify the relationships among species and their environment. The challenge has always been to sort through the lot to find the most appropriate approach given the question and the data. While I still sometimes pine for the simplicity of a two-way analysis of variance (ANOVA), Bruce McCune and James Grace's book, Analysis of Ecological Communities satisfies my craving for statistical clarity. They provide an exemplary guide to the convoluted terrain of multivariate data analysis".

Austral Ecology, (2003) 28/4 p.469, reproduced with permission Analysis of Ecological Communities From John A Ludwig Tropical Savannas CRC CSIRO Sustainable Ecosystems Atherton, Queensland, Australia

As a user of PC-ORD since version dot, I was delighted to see Bruce McCune provide another excellent product through MJM Software; this time a reference book on community analysis. Like my own, now dated, Statistical Ecology: A Primer on Methods and Computing (J. A. Ludwig & J. F. Reynolds, 1988, Wiley & Sons, New York), Analysis of Ecological Communities is aimed at the practical user of community analysis techniques. I feel that the authors have achieved their aim. I found this book easy to read and understand - the style of presentation is excellent. For each method covered by a chapter, the authors provide a background to the technique - what it does and when to use it - followed by how it works and, importantly, what the key results to report are. The authors also provide simple, easy-to-follow examples that apply the method. They finish each methods chapter with a description of some of the major variants known for the method; these are well referenced so that readers can pursue detail, if interested.

However, before getting into these methods chapters, the authors provide a number of overview chapters that cover what is typically in multivariate ecological data sets and how these data are collected by sampling and measuring. Some of the basic properties of these data, such as species diversity, are also covered in these early chapters. Why these data need to be screened for outliers and may need to be transformed for statistical and ecological reasons are also discussed. I particularly enjoyed chapter 5 on species responses to environmental gradients and the 'dust bunny' distribution - how ecological community data is like fluff and lint that accumulates in the corner of a room - you will enjoy reading and thinking about 'dust bunny' data distributions. In these early chapters, the authors also nicely cover the ways to estimate the similarity or distance (dissimilarity) between items in ecological data sets - the first step in most multivariate analyses. They do this without trying to be comprehensive and highly technical, as this has been done in the second edition of Numerical Ecology (P. Legendre & L. Legendre, 1998, Elsevier, Amsterdam). Rather, McCune and Grace use simple examples and illustrations to provide readers with an intuitive feel for the advantages and disadvantages of various distance measures. I really appreciated their introduction to the problem, and newer solutions, to the fact that the meaningfulness of distance measures between items declines as data become increasingly heterogeneous.

Chapters 10-22 cover classification and ordination topics and methods. I could briefly describe the contents of each chapter; however, the authors have already done this in their overview chapters on classification and ordination, chapters 10 and 13, respectively. Rather, I will note a couple of things that I found most interesting in these chapters from my own personal experiences with these methods. I liked the authors' arguments for scaling clustering dendrograms by a function that provides the amount of information lost at each step in a hierarchical cluster analysis. If this function is rescaled from 0 to 100% information, one obtains a good feeling for where along the clustering path most information is being lost. This provides a useful guide as to where to best 'cut the stems' in the dendrogram to form groups. Like many others using non-metric multidimensional scaling as an ordination technique, I have found results intuitively pleasing, that is, the positioning of samples in ordination space provided useful ecological interpretations. However, I also felt frustrated by the fact that in some cases quite different ordinations were obtained by different starting configurations. Which one to choose? In version 4 of PC-ORD, McCune provides an 'autopilot' to assist the user make many runs with different starting configurations. What I appreciate in chapter 16 is that McCune fully describes his autopilot procedure and his rationale for selecting the best starting configuration, and hence, final ordination.

Chapters 23-28 cover methods for comparing multivariate groups, including the standard parametric multivariate analysis of variance and discriminant analysis techniques and newer non-parametric Mantel tests and multiresponse permutation procedures. Of course, application of these methods requires that groups have been defined, either by classification and ordination techniques, or by some other ecological criteria such as soil type. In these multivariate group comparison chapters, I found the authors' discussion of 'when to use it' most useful.

Dean Urban and James Grace contribute the final two chapters on classification-regression trees and structural equation modelling. I have always wanted to learn more about how classification and regression trees apply to community ecology problems, and Urban provides this in chapter 29. Structural equation modelling is a relatively new box of tools that was developed in the 1970s in the fields of econometrics, sociology and psychology. Grace and others have only recently applied these tools to community ecology. In chapter 30, Grace provides the terminology and background needed to appreciate that structural equation modelling is a suite of multivariate procedures for addressing questions and testing hypotheses about multiple causal relationships in ecological data - a most enlightening chapter.

I finish by recommending this book to all those interested in learning about how to better analyse ecological community data. It would also make a good textbook. At US$35, this 300-page, large-format book is good value.

Ecology: Vol. 84, No. 5, pp. 1344-1345. Analyzing Communities by Stephen B. Cox Texas Tech University Department of Environmental Toxicology Lubbock, Texas

Community ecologists are interested in the way species co-vary in space and time, or, as McCune and Grace describe, “who lives with whom and why?” As a result, they deal with data that are notoriously difficult to approach from a statistical point of view. The data are multivariate in nature, i.e., the inter-relationships between species, between environmental measurements, and between species and environmental measurements, must be taken into account. To make matters worse, these inter-relationships frequently are non-linear. In addition, data for each species consist of presence/absence, counts, or semi-quantitative (e.g., percent cover, cover class) measurements that do not adhere to the distributional assumptions required for in many statistical tests. Unfortunately, most biostatistics courses do not adequately train ecologists to deal with such data.

McCune and Grace provide an excellent resource for ecologists struggling with the difficult attributes of ecological data. Although there are a number of books that address the topic of multivariate analysis of ecological communities, this book is unique in that it successfully combines an extensive breadth of topics with descriptions that should be generally accessible to any ecologist with some basic training in statistics. It is also an excellent resource for finding ecological literature that describes and/or utilizes the techniques being presented. The authors' stated goal is to provide a book with practical utility for ecologists, and, in my opinion, they definitely have succeeded.

Overall, the book has the feel of an encyclopedia, and is arranged into six parts. Part one includes a unique treatment of what the authors call the “complete community data set.” This is a discussion that expands the notion of a site × species presence/absence or abundance matrix to include information on the environment and individual species' traits, all within the context of matrix algebra (the basics of which are covered in a short appendix). It also covers community sampling strategies, a surprisingly broad treatment of species diversity, environmental gradients, and measures of distance.

Part two contains information on good data practice. Many ecologists will be tempted to completely over-look this relatively small section of the book (this topic is mostly ignored in other books on ecological data analysis); however, do not do so! In terms of avoiding frustration and wasted time, the insight provided in this section of the book is more important than any information on new and exciting analysis techniques! I was especially pleased to see the authors note the fact that almost all analyses have to be repeated (because of some mistake or another). The book provides practical advice on how to make this repetitive process more efficient.

Parts three through six delve into the statistical techniques. Part three covers methods for finding groups, part four covers ordination, part five covers multivariate comparisons of groups, and part six covers structural models. Each technique is covered in a chapter broken into several sections: background, when to use it, how it works, what to report (which gives advice on the information that should be reported when describing results), examples, and variations (which provides information on useful variants of particular tests). Particular attention is paid to describing the relationships between tests.

The book also emphasizes nonparametric techniques that, because of the increasing availability of inexpensive computing power, are becoming practical for all ecologists. These techniques are based on permutation or optimization routines and are particularly well suited to the data that community ecologists frequently collect. Examples include non-metric multidimensional scaling, multi-response permutation procedures, and classification and regression trees.

Because of the nature of the questions they ask, community ecologists would be wise to take the time to become proficient analysts as well as insightful ecologists. This book would be a useful addition to graduate courses in community ecology, and would also provide an excellent starting place for practicing ecologists when confronted with a new analytical problem. The prose does tend to be a bit terse at times, and you may notice some minor lack of attention to detail. However, the emphasis is on providing practical advice that is useful to any ecologist. Think of it as the multivariate statistics textbook that is “by ecologists, for ecologists.”

Journal of Experimental Marine Biology and Ecology 289 (2003) 303-305 by Marti J. Anderson Department of Statistics Tamaki Campus University of Auckland Auckland, New Zealand

This book provides a general overview of some traditional and many more recently developed multivariate (and some univariate) methods for the analysis of ecological data. The book is large in size and soft-bound, resembling a manual for a computer program. Although published by MjM Software Design, the producers of the now widely-used computer program PC-ORD (McCune and Mefford, 1999), the authors are emphatic that the book is not intended to be a manual. Instead, they state in their preface that the book “expands the logic behind choosing one technique over the other” and “explains the assumptions implicit in that decision.”

The contents of the book are organized into six sections or parts: (1) an overview of community matrices, sampling issues, diversity, species distributions and distance measures; (2) data screening, adjustments, documentation and transformations; (3) methods for finding groups, including cluster analysis and TWINSPAN; (4) ordination methods, including principal component analysis (PCA), non-metric multi-dimensional scaling (NMS), correspondence analysis (CA), detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA); (5) methods for comparing groups, including Mantel tests, discriminant analysis, NPMANOVA, ANOSIM, indicator species analysis (ISA) and multi-response permutation procedures and, finally, (6) structural equation modeling (SEM) and classification and regression trees (CART). There is also a succinct appendix on elementary matrix algebra for reference.

The style of the book is fairly relaxed, generally targeting an audience of graduate students, scientists and practitioners in ecology. This makes the material quite accessible, on the one hand, but also means that some ideas are presented as if they are simple “truths” when in reality our current understanding is far from conclusive. Unfortunately, the notation used to describe the various methods is equally fairly lax – symbols used for matrices and variables, (e.g. X, Y, A, D, p, S, etc.) have different meanings and definitions in different equations within chapters and between chapters. Also, because the book serves as a general overview and is not intended to be comprehensive, it suffers a little in places by being (perhaps by necessity) quite vague in its descriptions of certain methods. One also finds the odd minor mistake (e.g., in their short description of principal coordinate analysis, they state: “At least one of the resulting eigenvalues is always negative” (p. 121), which is not true – it depends on the distance measure chosen). It is also surprising that some published techniques obviously having a similar philosophical approach to that espoused by the authors (i.e., the use of Bray-Curtis or Sørensen dissimilarity measure and non-parametric or rank-based methods) are not even mentioned, such as SIMPER (Clarke, 1993), BIOENV (Clarke and Ainsworth, 1983) and, in the chapter on diversity, no mention of the notion of taxonomic distinctness (Warwick and Clarke, 1998).

All of this is, however, fairly richly compensated for by the authors providing many excellent and relevant references as entry points into the literature on virtually all topics that they do choose to discuss. It is also compensated for by the book having particularly excellent chapters and added information on some of the more salient methods available today, such as NMS, CCA, NPMANOVA and ISA. In fact, the authors’ chapter on NMS, which is perhaps the most important and useful ordination method for ecology, is, in my opinion, one of the most complete and accessible treatments of the topic currently available. The authors also refreshingly provide some more complete information on certain not-so-recent methods which are, perhaps through accidents of history, not currently used very widely in ecology, but perhaps should be (e.g., CART, polar ordination, SEM). These chapters really stretch the imagination and raise all sorts of interesting possibilities for future research and investigations into the potential uses of these interesting methods in ecology.

There are many gold nuggets of creativity in the book as well. It was especially fascinating to read about the authors’ so-called “dust-bunny” distribution, which is also featured on the cover of the book. This suggested shape for multivariate distributions of counts of species’ abundances is something that has not appeared in the literature to date, to my knowledge. Although no statistical basis for it is given, it does have intuitive appeal and deserves special mention and further study.

In addition, the authors do, in general, provide good sound methodological advice all the way through, thereby substantially fulfilling their promise offered in the preface. Individual chapters for each method are very usefully broken down into: Background, When to use it, How it works, What to report and Examples. The majority of examples given are from northern hemisphere terrestrial plant and forest communities, but this does not in any way detract from the book’s generality.

In short, this is an exciting new book on a rich variety of topics, old and new, that provides an important update to current literature on multivariate analysis for ecology. Whether used independently or as a companion to PC-ORD, students and practitioners alike can learn a lot from this useful and accessible text.

References

Clarke, K.R. 1993. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117-43.

Clarke, K.R., Ainsworth, M. 1983. A method of linking multivariate community structure to environmental variables. Mar. Ecol. Prog. Ser. 92, 205-219.

McCune, B., Mefford, M.J. 1999. PC-ORD. Multivariate analysis of ecological data. Version 4.0. MjM Software, Gleneden Beach, Oregon, USA.

Warwick, R.M., Clarke, K.R. 1998. Taxonomic distinctness and environmental assessment. J. Appl. Ecol. 35: 532-543.

Folia Geobotanica 40/4 2005, p. 448 by C. Neal Stewart Jr.(ed.)

This book was written by authors as an output of a community analysis course. According to the authors, it is a textbook and reference book. Multivariate techniques are nowadays the main methods used in the community studies. The present volume provides a good overview of the methods and gives following specifics fore each method: background, when to use this method, how it words, what should be reported and the most important part, examples. This guide to multivariate tools covers methods of community ecology from the beginning. Sampling designs, description of basic characteristics, grouping and ordination methods are followed by chapters introducing methods based on comparing groups of datasets, classification trees and path analysis. The structure used in this book makes it very useful and practical for a wide group of users. The book is not merely a cookbook of methods or list of equations and definitions, which makes it user friendly. The book can be recommended to many ecologists’, teachers’ and students’ bookshelves, and is not going to be covered by dust there.

Analysis of Ecological Communities Testimonials

Michael Jennings, Ecologist
U.S. Geological Survey and
Bren School of Environmental Science and Management
University of California Santa Barbara

Many thanks for “Analysis of Ecological Communities.” It rocks!

Henning Adsersen, Asociate Professor
Ecological Department of the Botanical Institute
University of Copenhagen.

Please don't be confused that I order a second copy of Analysis of Ecological Communities. It is a very good book and I can't have my own specimen in peace because my students want to consult it all the time.