Ecological Diversity and Its Measurement

There are three reasons why ecologists are interested in ecological diversity and its measurement. First, despite changing fashions and preoccupations, diversity has remained a central theme in ecology. The well documented patterns of spatial and temporal variation in diversity which intrigued the early investigators of the natural world (for example Clements, 1916; Thoreau, 1860) continue to stimulate the minds of ecologists today (Currie and Paquin, 1987; May, 1986). Second, measures of diversity are frequently seen as indicators of the wellbeing of ecological systems. Thirdly, considerable debate surrounds the measurement of diversity. Diversity may appear to be a straightforward concept which can be quickly and painlessly measured. This is because most people have a ready intuitive grasp of what is meant by diversity and have little difficulty in accepting, say, that tropical rain forests are more diverse than temperate woodlands or that there is a high diversity of organisms in coral reefs. Yet diversity is rather like an optical illusion. The more it is looked at, the less clearly defined it appears to be and viewing it from different angles can lead to different perceptions of what is involved. The problem has been exacerbated by the fact that ecologists have devised a huge range of indices and models for measuring diversity. Despite, or perhaps as a result of these, diversity has a knack of eluding definition and in one instance Hurlbert (1971) even went so far as to decry it as a ‘non-concept’.

A quick dip into the literature on diversity reveals a bewildering range of indices. Each of these indices seeks to characterize the diversity of a sample or community by a single number. To add yet more confusion an index may be known by more than one name and written in a variety of notations using a range of log bases. This diversity of diversity indices has arisen because, for a number of years, it was standard practice for an author to review existing indices, denounce them as useless, and promptly invent a new index. Southwood (1978) notes an interesting parallel in the proliferation of new designs of light traps and new permutations of diversity measures.

It is rarely feasible, or desirable, to census every individual in a community. Such a strategy would be prohibitively time-consuming and expensive; it would also damage or possibly even destroy the community in question. Ecologists therefore rely on sampling to provide an accurate picture of community composition. A great deal of effort over past decades has been devoted to making sampling techniques as efficient as possible. Southwood (1978) for instance describes the various approaches to sampling insect populations while Kershaw and Looney (1985) and Moore and Chapman (1985) discuss the methods available for the sampling of plant communities. Diversity studies raise a number of special problems where sampling is concerned. For example can individuals be sampled randomly? What size should samples be? What happens if individuals are not easily recognizable? How should a community be defined ? This chapter discusses these problems and provides some suggestions for solving them.

Given the large number of indices and models it is often difficult to decide which is the best method of measuring diversity. One good way to get a‘feel’ for diversity measures is to test their performance on a range of data sets. There are two approaches to this. First, by looking at contrived data it is possible to observe how the different measures react to changes in the two major components of diversity, species richness and evenness. However, in the real world it is rare for richness and evenness to vary independently in the way they so often do in artificial data sets. The second, and more realistic, approach therefore is to test the response of diversity measures to species abundances from genuine ecological communities. This chapter begins by comparing the behaviour of a range of diversity measures and models when used to estimate the diversity of two data sets, one contrived and one real. The difficulties of deciding the appropriateness of one species abundance distribution over another have already been mentioned (see Chapter 2) and quickly become apparent when models are fitted to data. Often the problems arise when a goodness of fit test fails to discriminate between different distributions. The value of goodness of fit tests in conjunction with, or instead of, graphical methods is considered in the context of the analysis of data sets.

So far this book has concentrated on the measurement of species diversity. Yet there are many studies concerned with other varieties of diversity. Attempts by ecologists to explain why some areas are species rich and others are species poor or why a species is abundant in one location but rare in another often prompts an investigation of habitat diversity. In undertaking a study of habitat diversity ecologists are asking similar questions to the ones they pose when describing species diversity. The methods devised for measuring species diversity are also employed when niche width is being investigated. Niche width is, after all, a measure of the diversity of resources utilized. The first section of this chapter therefore looks at other contexts in which measures of species diversity can be utilized.

The preceding chapters of this book have dealt primarily with the mechanical questions of calculating diversity indices, measuring abundance and determining sample size. Although the search for methods of measuring diversity is intellectually rewarding it is not a goal in itself. The true value of diversity measures will be determined by whether or not they are empirically useful.