
| Title | Multi-Metric Index Development for Biological Monitoring in Washington State Streams | |
| Month-Year Published | July 2003 | |
| Online Availability |
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| Short Description |
The Freshwater Monitoring Unit at the Washington State Department of Ecology assesses the water and biological quality of surface waters in Washington State. This document describes an approach for biological classification and multi-metric index development in Washington State using benthic macroinvertebrates. The document also presents biological classification results for Western Washington and calibrated multi-metric indices for the Puget Lowland and Cascades regions. Level III ecoregions were the most useful for stream classification, although other landscape and reach-scale variables also may be useful after more data are accrued. Calibrated multi-metric indices of the Puget Lowland and Cascades ecoregions yielded discrimination efficiencies of 80.0% and 87.5%, respectively. This report was revised and reposted on December 1, 2003. (Also see abstract below) | |
| Publication Number | 03-03-035 | |
| Author(s) | Wiseman, C.D. | |
| Print Availability | ||
| Number of pages | 48 pp. | |
| Keywords | monitoring, water | |
| Abstract | Long Description |
This document describes classification and multi-metric index calibration approaches for biological criteria development in Washington State using benthic macroinvertebrates. This document also presents biological classification results for western Washington and proposed multi-metric indices for the Puget Lowland and Cascades regions. In the context of an a priori classification scheme, level III ecoregion was the single most important variable accounting for biological variability. Other landscape and reach-scale variables could clearly account for more variability. However, further stratification is not practical at this time, due to the paucity of regional reference sites surveyed. Calibrated multi-metric indices of the Puget Lowland and Cascades (combined) ecoregions yielded discrimination efficiencies of 80.0% and 87.5%, respectively. |
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