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ANALYSIS OF LOTIC MACROINVERTEBRATE ASSEMBLAGES IN CALIFORNIA'S CENTRAL VALLEY
Citation:
Griffith, M. B., P. Husby, R. K. Hall, P R. Kaufmann, AND B H. Hill. ANALYSIS OF LOTIC MACROINVERTEBRATE ASSEMBLAGES IN CALIFORNIA'S CENTRAL VALLEY. Presented at North American Benthological Society, Keystone, CO, May 29-June 2, 2000.
Impact/Purpose:
The goal of this research is to develop methods and indicators that are useful for evaluating the condition of aquatic communities, for assessing the restoration of aquatic communities in response to mitigation and best management practices, and for determining the exposure of aquatic communities to different classes of stressors (i.e., pesticides, sedimentation, habitat alteration).
Description:
Using multivariate and cluster analyses, we examined the relaitonships between chemical and physical characteristics and macroinvertebrate assemblages at sites sampled by R-EMAP in California's Central Valley. By contrasting results where community structure was summarized as metrics or genera abundances, we identified metrics or taxa diagnostic of lotic stressors and compared the sensitivities of these approaches to the stressors. Redundancy analysis (RDA) of metrics extracted three significant axes, accounting for 88% of the relation. RDA axes were correlated with channel morphology and substrates. Canonical correspondence analysis (CCA) of genera abudances extracted two significant axes, accounting for 23% of the relation. CCA axes were correlated with salts associated with irrigation and with channel morphology and substrates, but did not separate these gradients. Cluster analysis identified eight RDA groups and ten CCA groups. The differences among groups with respect to selected environmental variables were similar for metrics or genera abundances, but the agreement in group assignments based on the two approaches was not greater than expected if sites were assigned randomly. CCA measures variation primarily as the changes in genera abundances relative to each other. Metrics measure other characteristics, such as richness and taxa relative abudances. These approaches can be used together to design diagnostic metrics.