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

Author
item O Neill, Katherine
item Godwin, Harry
item Jimenez Esquilin, Aida

Submitted to: Soil Biology and Biochemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/6/2010
Publication Date: 6/1/2010
Citation: O Neill, K.P., Godwin, H.W., Jimenez Esquilin, A.E. 2010. Reducing the dimensionality of soil microinvertebrate community datasets using Indicator Species Analysis: Implications for ecosystem monitoring and soil management. Soil Biology and Biochemistry. 42:145-154.

Interpretive Summary: Sustainable soil management requires simple, intuitive, and repeatable indicators for assessing the effects of agricultural management practices. Soil microinvertebrates are closely associated with decomposition and nutrient cycles and may be particularly responsive indicators of soil function. However, identification of appropriate bioindicator species 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 soil microinvertebrates without the requirement for detailed information about taxonomy, ecological role, or expected management response. Restricting ordination and site classification to significant indicator species allowed us to significantly reduce the amount of data required for analysis with only a slight reduction in classification efficiency. Although care needs to be taken to ensure that the dataset used for indicator selection is fully representative of underlying variability, ISA has the potential to greatly reduce the analytical complexity and expense of collecting soil microfaunal assemblage data and may open the door to a greater inclusion of these assessments in agricultural studies.

Technical Abstract: 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 species without regard to their ecological role or expected management response. Restricting ordination and site classification to significant indicator morphospecies 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 three PCA axes increased following ISA. When these same indicator species were used to classify an independent set of samples, the percentage of total variability explained by the first three PCA axes increased from 64.2% to 77.1%; cluster analysis of the test dataset correctly classified 47 out of 50 plots by cover type (94% accuracy). However, restriction of analysis to indicator species 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 species and has the potential to greatly reduce the taxonomic expertise and labor costs associated with sorting and identification of soil microinvertebrates.