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A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos
Citation:
Angradi, T., W. Bartsch, A. Trebitz, V. Brady, AND J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, 43(1):108-120, (2017).
Impact/Purpose:
Determining reference conditions for benthic invertebrates in Great Lakes Estuaries using traditional methods is difficult because confounding natural variation in conditions is high even at small spatial scales, and undisturbed areas are very few. The objective of this paper was to use existing data to derive empirical models that can be used to define reference conditions for Great Lakes Areas of Concern. Depth and other factors were used to determine condition class thresholds for use in benthic assessments. The results of this paper will support removal of the “degraded benthos” beneficial use impairment for the St. Louis River Area of Concern and has potential applications across the Great Lakes and elsewhere.
Description:
We compiled and modelled macroinvertebrate assemblage data from samples collected in 1995-2014 from the estuarine portion of the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective to create depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) that can be used to plan remediation and restoration actions, and to assess progress toward achieving removal targets for the degraded benthos beneficial use impairment. The relationship between depth and benthos metrics was wedge-shaped. We therefore used 90th percentile quantile regression to define the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly larvae (e.g., Hexagenia). We also created a scaled trimetric index from the first three metrics. We examined gear type (standard vs. petite Ponar sampler), exposure class (derived from fetch), geographic zone of the AOC, and substrate type for confounding effects on the limiting depth. The effect of gear type was minimal. Metric values were generally higher at more exposed locations, but we judged the exposure effect less important for model application than variation among three geographic zones, so we combined data across exposure classes and created separate models for each geographic zone of the AOC. Based on qualitative substrate data for most samples, we judged the effect of substrate type on models to be small except for rare substrates types. We compared our results for ephemerid larval density in the St. Louis River AOC to data from elsewhere in the Laurentian Great Lakes. We found that the depth limited abundance pattern we observed in the AOC was general for the data we examined. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data.
