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Adaptation of multivariate AMBI (M-AMBI) for use in US coastal waters
Citation:
Pelletier, M., D. Gillett, A. Hamilton, T. Grayson, V. Hansen, E. Leppo, S. Weisberg, AND A. Borja. Adaptation of multivariate AMBI (M-AMBI) for use in US coastal waters. Coastal and Estuarine Research Foundation (CERF) 25th Biennial Conference, Mobile, Alabama, November 03 - 07, 2019.
Impact/Purpose:
The National Coastal Condition Assessment (NCCA) monitors estuaries every 5 years to evaluate condition. Measures include water quality, ecological fish condition (fish contaminant load), sediment condition, and benthic condition. Benthic condition has been evaluated using individual regional indices, where available, and salinity adjusted diversity on the West Coast. This approach has been criticized because of the concern that the indices were not comparable with each other. Because the indices were locally calibrated, ‘bad’ condition in the northeast might not be comparable to ‘bad’ condition in the west. For this reason, a development of a nationwide index was desired. In a previous study, the AZTI marine biotic index (AMBI), a European weighted tolerance index was adapted for use in US coastal waters using locally calibrated tolerance values. This index was promising, but biases in salinity and score distribution were seen when compared to three locally calibrated indices. In this study, we expanded on our original study to explore use of multivariate AMBI (M-AMBI), an extension of the AMBI index. We modified M-AMBI for US waters and compared its performance to that of US AMBI. Index performance was evaluated in three ways: 1) concordance with local indices presently being used as management tools in three geographic regions of US coastal waters, 2) classification accuracy for sites defined a priori as good or bad, and 3) insensitivity to natural environmental gradients. US M-AMBI performed well. It was highly correlated with all three local indices and removed the compression in response seen in moderately disturbed sites with US AMBI. US M-AMBI and US AMBI did a similar job correctly classifying sites as good or bad in local validation datasets (83 to 100% accuracy vs. 84 to 95%, respectively). US M-AMBI also removed the salinity bias of US AMBI so that lower salinity sites were not more likely to be incorrectly classified as impaired. The US M-AMBI appears to be an acceptable index for comparing condition across broad-scales such as estuarine and coastal waters surveyed by the US EPA’s National Coastal Condition Assessment, and for application to the many parts of the US coast that do not already have a locally derived benthic index.
Description:
The National Coastal Condition Assessment (NCCA) is an EPA program that assesses the condition of estuaries every 5 years. Benthic condition has historically been evaluated using benthic indices which were developed using different methods in different areas of the country. This led to concern that the indices might not be comparable, so a multi-year effort to develop a nationwide benthic index was initiated. The AMBI/M-AMBI approach, a tolerance-based index first developed in European waters for use under the Water Framework Directive, was selected. After developing an integrated list of tolerance values for use in US coastal waters, the AMBI algorithm was applied. Although US AMBI was able to differentiate between a priori good and bad sites from three different areas of the country and was correlated with the local indices from these areas, there were biases in salinity and score distribution when compared to locally calibrated indices. Because of these biases, we modified M-AMBI for US waters and compared its performance to that of US AMBI. Index performance was evaluated in three ways: 1) concordance with local indices presently being used as management tools in the same three areas, 2) classification accuracy for sites defined a priori as good or bad, and 3) insensitivity to natural environmental gradients. US M-AMBI was highly correlated with all three local indices and removed the compression in response seen in moderately disturbed sites with US AMBI. US M-AMBI and US AMBI did a similar job correctly classifying sites as good or bad in local validation datasets (83 to 100% accuracy vs. 84 to 95%, respectively). US M-AMBI also removed the salinity bias of US AMBI so that lower salinity sites were not more likely to be incorrectly classified as impaired. US M-AMBI allows a consistent way to compare condition on a national scale and is being applied to the 2015 and 2020 National Coastal Condition Assessment survey.