Reducing the dimensionality of soil microinvertebrate community datasets using Indicator Species Analysis: Implications for ecosystem monitoring and soil management

Soil microinvertebrates are closely associated with soil decomposition and nutrient cycles and may be particularly responsive indicators for soil management practices. However, identification of appropriate bioindicator species for many systems has been severely limited by a lack of information on species taxonomy, distribution, and functional role. We evaluated Indicator Species Analysis (ISA) as an objective method for assessing the indicator potential of different taxa without regard to their ecological role or expected management response. Restricting ordination and site classification to significant indicator morphotaxa reduced the dimensionality of the community data matrix by 69% while only slightly decreasing the efficiency of unsupervised classification (from 87.2 to 84.4%); the percentage of total variability explained by first two PCA axes increased following ISA. When these same indicator morphotaxa were used to classify an independent set of samples, the percentage of total variability explained by the first two PCA axes increased from 55.3 to 65.2%; cluster analysis of the test dataset correctly classified 47 out of 50 plots by cover type (94% accuracy). However, restriction of analysis to indicator morphotaxa alone reduced detection of differences between sampling dates relative to the complete dataset. Although care needs to be taken to ensure that the dataset used for indicator selection is fully representative of underlying temporal and spatial variability, ISA appears to overcome many of the limitations associated with parametric and multivariate approaches for identifying indicator morphotaxa and has the potential to greatly reduce the taxonomic expertise and labor costs associated with sorting and identification of soil microarthropods. Published by Elsevier Ltd.