TREE vol. 3, no. 5, May 7988

Data Handling

Multivariate Analysis of Ecological Communities

by P.G.N. Digby and R.A. Kempton, Chapman & Hall, 1987. f25 hbk, fl2 pbk (vi + 206 pages) ISBN 0 4 12 24640 6 hbk/‘O 4 12 24650 3 pbk

Twenty years ago, the ability to per- form an analysis of variance was probably the qualifying mark for a would-be quantitative ecologist. To- day that doubtful honor has passed to multivariate analysis, boosted by the ready availability of computer packages. Yet it is clear that too many users of multivariate tech- niques simply do not understand the real nature of what they are attemp- ting.

My first perusal of Digby and Kempton’s book, therefore, excited me, for it attempts to address important fundamentals. Opening chapters on ‘Ecological data’, de- fining the basis of data types and what one can and cannot do with each, and on ‘Preliminary inspection of data’, emphasizing basic inspec- tion of data, precede the chapters on specific methods. These first chap- ters cover issues in multivariate work that are too often ignored by novices, and it is encouraging to find nearly a quarter of the book devoted to their discussion. Nevertheless, I have some reservations about the presentation adopted here. One !s that the treatment of missing data IS barely considered, despite so many ecological data sets being affected by this problem. A second relates to the issue of data inspection: it is all very well to urge visual display of data sets and to present illustrative examples of how to pack much irl- formation into visual displays, but without pointing out the techniques of generating these displays, the computer-package-borne MVA addicts will not change their ways. Thus, whilst I applaud Digby and Kempton’s support for Tukey’s Ex- ploratory Data Analysis’, I regret they did not also direct attention to the availability of computer pro- grams for EDA e.g. in Velleman and Hoaglin’s book* or in packages such as SYSTAT3.

The next four chapters of the book cover ordination, comparison of ordination, classification and analy- sis of symmetry. The ordination chapter covers direct gradient analy- sis, principal components analysis, correspondence analysis, grouped data methods (canonical variates, canonical correlation), principal coordinate analysis and non-metric ordination. In addition. the

‘horseshoe effect’ in ordination and two case studies of MVA are discus- sed.

Digby and Kempton leave one in no doubt as to where their prefer- ences as to technique or presen- tation lie, but the flip side of this approach, at least in this book, is that alternatives are not adequately treat- ed. Chapter 7, on the topic of com- puting, suffers badly from this de- fect. The chapter opens nicely, laying out the four options available for ecological multivariate analysis: the ‘do-it-yourself’ programming approach, the use of ecology- oriented packages such as the Cor- nell Ecology Programs, specialized multivariate programs and general- purpose packages (BMD, SPSS, SAS and Genstat). However, the sub- sequent discussion of these options is superficial, to the point of irri- tation, and selective, to the point of bias. Thus, most of p. 179 is given over to a list of directives in Genstat, the author’s preferred package, and the five pages following are devoted to examples of Genstat programs. I grant that Genstat matrix procedures are powerful and natural for multi- variate analysis calculations, but I would not extend this argument to advocating its use by novices (or even by those with modest experi- ence). The language structure forces greater understanding of his or her undertaking on the user but, by the same token, is more intimidating for the novice. Nor is the syntax one that I feel comfortable with: it possesses much of the elegance and power of, say, the ALGOL language, but most ecologists seem more at home with the FORTRAN-like SAS or SPSS commands. None of these compari- sons are made in Digby and Kemp- ton’s text, who prefer statements such as ‘Genstat provides a flex- ibility of use that is absent from the other three [BMDP, SAS, SPSS], with the possible exception of SAS.’ This cavalier neglect of alternatives is a major weakness in a text that sets out from basics. Moreover, it is argu- able that the pro-Genstat arguments advanced in this text are actually wrong in respect of power, flexibility and ease of use if compared to the deployment of SAS or SPSS by ecol- ogists with experience equivalent to Digby and Kempton’s (Genstat) ex- perience.

The remaining three chapters cover ordination, classification pro- cedures and analysis of asymmetry models. Of these, the first two are straightforward in their treatments. I particularly liked the inclusion of a section addressing the issue of visual displays of classification out-

put. Chapter 6 addresses the difficult issue of matrices of asymmetric similarity indices such as prevail in successional communities or in social hierarchies. Here are clearly discussed the relative merits of approximating the data by averaging the two similarities (i.e. averaging S,, and S,; where i and j are row and column indices respectively) as an approximation, of comparing row- based analysis versus a column- based analysis of the same dataset, and of skew-symmetry analysis (par- titioning the coefficients into their average association (Si,+S,,) /2 and their difference from that average (S,,-S,,) /2 and comparing analysis based on separate matrices of aver- age and difference coefficients). The points made are well brought out in three case studies discussed in de- tail. The book concludes with an in- troduction to matrix algebra.

I find it difficult to assess the over- all value of this book. The authors’ Preface clearly points to an expected audience of practising multivariate analysts but some of the sections, as indicated above, seem geared more to the novice than to the profession- al. As a source for review of those topics addressed it is good, but its selectivity of treatment makes it im- possible to use as a standard refer- ence. I am left feeling that Digby and Kempton should have taken twice the space and brought their experi- ence and insights to bear in a fully comprehensive review of the field.

Raymond J. O’Connor

Department of Wildlife, 240 Nutting Hall, University of Maine. Orono, ME 04469. USA

References 1 Tukey, J. (1977) Exploratory Data Analysis, Addison-Wesley 2 Velleman, P.F. and Hoaglin, D.C. (1981) Application, Basics, and Computing of Exploratory Data Analysis, Dunbury Press 3 Wilkinson, L. (1986) SYSTAT- The System for Statistics, SYSTAT

Author’s Correction In the article by William Rice in TREE (January 1988, pp. Z-3), the fifth to last line on page 2, column 1, should read ‘is therefore reduced to 3/2 N,’ not 4/3 N as written. We apologize for this error.

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