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 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 of these 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 patterns 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. This technique also provides an initial identification of important stressors that can be used to target more intensive local studies.
•Random forest (RF) used to identify stressors most strong related to M-AMBI.•Parsimonious models developed nationally and ecoregionally.•Nationally: better condition = clear water, ↓Ag, ↓sediment C and metals.•Results coincided with local expectations and literature reports.•Technique useful at broad scales even when percentage of sites in poor condition is low.
•Random forest model provides variable importance for predicting algal biomass.•Bayesian regression model was used to predict trophic state classes in US estuaries.•Bayesian updating provides ...framework to build on prior ecological knowledge.•Model places changes at one site into national-scale comparative context.
One of the goals of coastal ecological research is to describe, quantify and predict human effects on coastal ecosystems. Broad cross-systems assessments to classify ecosystem status or condition have been developed, but are not updated frequently, likely because a lot of information and effort is needed to implement them. Such assessments could be more useful if the probability of being in a class indicating status or condition could be predicted using widely available data and information, providing a useful way to interpret changes in underlying predictors by considering their expected impact on ecosystem condition. To illustrate a possible approach, we used chlorophyll-a as an indicator of condition, in place of the intended comprehensive condition assessment. We demonstrated a predictive approach starting with a random forest model to inform variable selection, then used a Bayesian multilevel ordered categorical regression to quantify a coastal trophic state index and predict system status. We initially fit the model using non-informative priors to water quality data (total nitrogen and phosphorus, dissolved inorganic nitrogen and phosphorus, secchi depth) from 2010 and a regional factor. We then updated the model using prior distributions based on posterior parameter distributions from the initial fit and data from 2015. The Bayesian model demonstrates an intuitive way to update a model or analysis with new data while retaining the benefit of prior knowledge and maintaining flexibility to consider new kinds of information. To illustrate how the model could be used, we applied our developed trophic state index and classification to a time series of water quality data from Boston Harbor, a coastal ecosystem that has undergone significant changes in nutrient inputs. The analysis shows how water quality status and trends in Boston Harbor can be understood in the comparative ecological context provided by data from estuaries around the continental US and illustrates how the analytical approach could be used as an interpretive tool by non-practitioners of Bayesian statistics as well as a framework for further model development and analysis.
Our understanding of how ecosystems function has changed from an equilibria-based view to one that recognizes the dynamic, fluctuating, nonlinear nature of aquatic systems. This current understanding ...requires that we manage systems for resilience. In this review, we examine how resilience has been defined, measured and applied in aquatic systems, and more broadly, in the socioecological systems in which they are embedded. Our review reveals the importance of managing stressors adversely impacting aquatic system resilience, as well as understanding the environmental and climatic cycles and changes impacting aquatic resources. Aquatic resilience may be enhanced by maintaining and enhancing habitat connectivity as well as functional redundancy and physical and biological diversity. Resilience in aquatic socioecological system may be enhanced by understanding and fostering linkages between the social and ecological subsystems, promoting equity among stakeholders, and understanding how the system is impacted by factors within and outside the area of immediate interest. Management for resilience requires implementation of adaptive and preferably collaborative management. Implementation of adaptive management for resilience will require an effective monitoring framework to detect key changes in the coupled socioecological system. Research is needed to (1) develop sensitive indicators and monitoring designs, (2) disentangle complex multi-scalar interactions and feedbacks, and (3) generalize lessons learned across aquatic ecosystems and apply them in new contexts.
•The benthic index, M-AMBI, has been adapted for use in US coastal waters.•US M-AMBI accurately classified a priori good and bad sites.•US M-AMBI was well correlated with local indices.•US M-AMBI ...removed the salinity bias seen in US AMBI.
The multivariate AMBI (M-AMBI) is an extension of the AZTI Marine Biotic Index (AMBI) that has been used extensively in Europe, but not in the United States. In a previous study, we adapted AMBI for use in US coastal waters (US AMBI), but saw biases in salinity and score distribution when compared to locally calibrated indices. In this study 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 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 (83–100% accuracy vs. 84–95%, 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 may be applicable to areas of the US coast that do not have a locally derived benthic index.
The AZTI Marine Biotic Index (AMBI) requires less geographically-specific calibration than other benthic indices, but has not performed as well in US coastal waters as it has in the European waters ...for which it was originally developed. Here we examine the extent of improvement in index performance when the Ecological Group (EG) classifications on which AMBI is based are derived using local expertise. Twenty-three US benthic experts developed EG scores for each of three regions in the United States, as well as for the US as a whole. Index performance was then compared using: (1) EG scores specific to a region, (2) national EG scores, (3) national EG scores supplemented with standard international EG scores for taxa that the US experts were not able to make assignments, and (4) standard international EG scores. Performance of each scheme was evaluated by diagnosis of condition at pre-defined good/bad sites, concordance with existing local benthic indices, and independence from natural environmental gradients. The AMBI performed best when using the national EG assignments augmented with standard international EG values. The AMBI using this hybrid EG scheme performed well in differentiating apriori good and bad sites (>80% correct classification rate) and AMBI scores were both concordant and correlated (rs=0.4–0.7) with those of existing local indices. Nearly all of the results suggest that assigning the EG values in the framework of local biogeographic conditions produced a better-performing version of AMBI. The improved index performance, however, was tempered with apparent biases in score distribution. The AMBI, regardless of EG scheme, tended to compress ratings away from the extremes and toward the moderate condition and there was a bias with salinity, where high quality sites received increasingly poorer condition scores with decreasing salinity.
Estuaries are dynamic transition zones linking freshwater and oceanic habitats. These productive ecosystems are threatened by a variety of stressors including human modification of coastal ...watersheds. In this study, we examined potential linkages between estuarine condition and the watershed using multimodel inference. We examined attributes at the watershed scale as well as those associated with riparian areas but found that they were highly correlated. We also examined whether attributes closer to the estuary were more strongly related to benthic invertebrate condition and found that this was not generally true. In contrast, variability within the estuary strongly impacted model results and suggests that future modeling should incorporate estuarine variability or focus on the individual stations within the estuary. Modeling estuarine condition indicated that inherent landscape structure (e.g., estuarine area, watershed area, watershed:estuary ratio) is important to predicting benthic invertebrate condition and needs to be considered in the context of watershed/ estuary planning and restoration.
Water resource managers seeking to optimize stream ecosystem services and ions of water from watersheds need an understanding of the importance of land use, physical and climatic characteristics, and ...hydrography on different low flow components of stream hydrographs. Within 33 USGS gaged watersheds of southern New England, we assessed relationships between watershed variables and a set of low flow parameters by using an information‐theoretical approach. The key variables identified by the Akaike Information Criteria (AIC) weighting factors as generating positive relationships with low flow events included percent stratified drift, mean elevation, drainage area, and mean August precipitation. The extent of wetlands in the watershed was negatively related to low flow magnitudes. Of the various land use variables, the percentage of developed land was found to have the highest importance and a negative relationship on low flow magnitudes, but was less important than wetlands and physical and climatic features. Our results suggest that management practices aimed to sustain low flows in fluvial systems can benefit from attention to specific watershed features. We draw attention to the finding that streams located in watersheds with high proportions of wetlands may require more stringent approaches to withdrawals to sustain fluvial ecosystems during drought periods, particularly in watersheds with extensive development and limited deposits of stratified drift.
Core Ideas
Watershed features affect the resilience of streams to sustain low flows.
Stratified glacial drift deposits help sustain low flows of streams.
Watersheds with high % wetlands warrant careful management of withdrawals.
Urban development is not among the strongest predictors of low flows.
Aquatic organisms are exposed to many toxic chemicals and interpreting the cause and effect relationships between occurrence and impairment is difficult. Toxicity Identification Evaluation (TIE) ...provides a systematic approach for identifying responsible toxicants. TIE relies on relatively uninformative and potentially insensitive toxicological end points. Gene expression analysis may provide needed sensitivity and specificity aiding in the identification of primary toxicants. The current work aims to determine the added benefit of integrating gene expression end points into the TIE process. A cDNA library and a custom microarray were constructed for the marine amphipod Ampelisca abdita. Phase 1 TIEs were conducted using 10% and 40% dilutions of acutely toxic sediment. Gene expression was monitored in survivors and controls. An expression-based classifier was developed and evaluated against control organisms, organisms exposed to low or medium toxicity diluted sediment, and chemically selective manipulations of highly toxic sediment. The expression-based classifier correctly identified organisms exposed to toxic sediment even when little mortality was observed, suggesting enhanced sensitivity of the TIE process. The ability of the expression-based end point to correctly identify toxic sediment was lost concomitantly with acute toxicity when organic contaminants were removed. Taken together, this suggests that gene expression enhances the performance of the TIE process.
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