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Abstract. Ecologists are often restricted to using or choose to use proportional- or percentage-type data with the view that it helps standardize for differences in variable totals among sampling units or individuals. This standardization to compositional data leads to constraints in the covariance and correlation structure that profoundly affect subsequent analysis and interpretation. This is another form of the problem related to the use of ratios in statistical analyses. Using simulated and zooplankton data I demonstrate the effect of using compositional data vs. the original data in correlation, ordination, and cluster analysis, which are common analytical methods in community ecology. Interpretations about the relatedness of various taxa or sites may reverse when using compositions relative to the unstandardized data. In addition, the selection of subcompositions (i.e., one or more variables are excluded when calculating the composition) may have profound and unpredictable consequences for the results. I examine some approaches proposed to deal with such data, e.g., centered log-ratio analysis, and recommend the use of correspondence analysis in multivariate studies to avoid the problems associated with differing solutions.
Key words: community ecology, statistics; compositional data, analysis; ipsative data; multivariate statistics; normative data; percentage, statistical analysis; proportion, statistical analysis; proportion, statistical analysis; statistics, proportional data.
INTRODUCTION
Ecologists must often analyze data sets comprising samples varying greatly in total species abundance. In this instance species with the greatest abundance in an observation may overwhelm the analysis and subsequent relationships may simply reflect differences in absolute abundance rather than relative abundance. To compensate for this problem, ecologists often choose to convert such data to proportions, percentages, or frequencies by dividing each variable by the total for each observation prior to more detailed analysis. The rationale for this standardization is the desire to compare all samples on a similar scale, thereby "correcting" or removing the influence of overall species abundance. Conceptually, this approach is appealing; however, it is rarely recognized that this standardization will limit the possible range of interspecific relationships as well as the patterns among the samples. Occasionally data are converted to proportions for other reasons. For example, Gates et al. (1983) found that ordinations were easier to interpret and that greater amounts of the total variance could be explained by using proportional data. In general, the...