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Assessing the relative importance of estuarine stressors to the benthic index, M-AMBI in U.S. waters
Citation:
Pelletier, Peg AND Mike Charpentier. Assessing the relative importance of estuarine stressors to the benthic index, M-AMBI in U.S. waters. 13th National Monitoring Conference, Virginia Beach, VA, April 24 - 28, 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 most strongly relate 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:
M-AMBI, a multivariate benthic index, has been used by European and American (U.S.) authorities to assess estuarine and coastal health and has been used in scientific studies throughout the world. It has further been shown to be related to multiple pressures and stressors, but the relative importance of individual stressors within a multiple stressor context has not generally been assessed. In this study, we assembled data collected between 1999 and 2015 by the U.S. Environmental Protection Agency using consistent methods. 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. Random forest (RF) was used to identify those variables most strongly related to M-AMBI. Because RF is a compilation of multiple, nonlinear models, we then assessed which 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. 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.
