Increasing the utility of Indicator Species Analysis.
The identification of species associated with or indicative of groups of samples is a common aspect of ecological research, including studies of environmental management. Indicator Species Analysis (ISA) permits statistically rigorous assessments of these indicator species, but its usage is currently restricted to simple designs. I describe three improvements that greatly increase the utility of ISA. First, exact permutation tests require that the correct exchangeable units be permuted; these exchangeable units may vary among factors. Secondly, the consistency of indicators can be assessed using meta-analytic techniques to combine the results from multiple sites. Thirdly, while ISAs are classically conducted using abundance data, a simplified ISA can be conducted using binary (presence/absence) data. These improvements are illustrated by identifying indicators of grazing treatment (inside or outside exclosure) at three sites in a southwestern ponderosa pine Pinus ponderosa forest. Most indicators were consistent among sites, and the number of significant indicators was reduced 37-40% by combining results from multiple sites. Species that occurred at multiple sites were more likely to be indicators than those present at a single site. Simplified ISAs produced very similar results to classical ISAs: both methods identified the same group as having the maximum Indicator Value in >93% of tests. Compared to abundance data, however, the presence/absence data used in a simplified ISA are easily collected and efficiently published in data tables or appendices. Synthesis and applications. Meta-analytic techniques and simplified Indicator Species Analyses can increase the ability to analyse new or previously published data, and permit rigorous assessments of the consistency of indicator status spatially or temporally. Other recommendations to improve the utility of ISA include identifying organisms to as fine a taxonomic resolution as possible, providing detailed descriptions of groups and typologies, ensuring adequate sample sizes within groups, reporting the sample size and frequency of all species in all groups, and publishing data for all species - whether or not they are significant indicators - to prevent publication bias in future meta-analyses.