Choosing And Interpreting Diversity Measures
Published 1988 · Mathematics
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.