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Identification of ecoregion-specific watershed and estuary variables affecting benthic invertebrate condition
Citation:
Pelletier, M. AND M. Charpentier. Identification of ecoregion-specific watershed and estuary variables affecting benthic invertebrate condition. Eighth Interagency Conference on Research in the Watersheds (ICRW8), Corvallis, OR, June 05 - 08, 2023.
Impact/Purpose:
Benthic invertebrates (which include clams and worms) have been traditionally used to monitor the environment because they are sedentary, live in or on the sediment, and respond predictably to pollution. These communities are often summarized into a single measure (a benthic index) that can be used by managers to assess the condition of estuaries. The EPA’s coastal monitoring program, the National Coastal Condition Assessment (NCCA) uses an index called M-AMBI. In this study, a statistical technique (random forest) was used to identify which environmental stressors were most strongly related to M-AMBI determined condition (for example, poor or good). At the national scale, better condition was correlated with clearer waters, lower amounts of agriculture in the watershed, and lower carbon and metal concentrations in estuarine sediments. Other stressors were identified as being important at the ecoregional scale, although sediment metal concentrations and watershed agriculture were identified as being important in most ecoregions. This technique allows an initial identification of important stressors that can refine more intensive local studies.
Description:
The EPA’s National Aquatic Resource Surveys (NARS) assesses the condition of the nation’s waters and identifies which stressors are responsible for adverse condition. For most of the freshwater surveys, a relative risk approach is used, but it cannot be applied to the coastal survey due to the low number of poor sites. For this study we used random forest (RF) as an alternate method to identify those variables most strongly related to M-AMBI, the benthic index used in estuarine portion of the coastal survey (NCCA). We assembled data collected by the coastal survey between 1999 and 2015. These data included sediment and water quality measures and benthic invertebrate data which were used to calculate M-AMBI. We further assembled watersheds for all US estuaries with benthic data and calculated land use metrics. Because RF is a compilation of multiple, nonlinear models, we then assessed which of those variables had a direct relationship with M-AMBI. The resulting variables were then assessed using RF to identify the subsets of variables that produced an effective and parsimonious model. This process was conducted at the national and ecoregional scale and the variables identified as being most important to predict M-AMBI were compared with literature reports of ecological pattens in a given area. At the national scale, better condition was correlated with clearer waters, lower amounts of agriculture in the watershed, and lower carbon and metal concentrations in estuarine sediments. Other stressors were identified as being important at the ecoregional scale, although sediment metal concentrations and watershed agriculture were identified as being important in most ecoregions. Our results suggest that this technique is useful to identify the most important variables impacting M-AMBI at broad spatial scales, even when the percentage of sites in Bad or Poor condition is low.
