We have conducted a regional scale risk assessment using the Bayesian Network Relative Risk Model (BN-RRM) to calculate the ecological risks to the South River and upper Shenandoah River study area. Four biological endpoints (smallmouth bass, white sucker, Belted Kingfisher, and Carolina Wren) and 4 abiotic endpoints (Fishing River Use, Swimming River Use, Boating River Use, and Water Quality Standards) were included in this risk assessment, based on stakeholder input. Although mercury (Hg) contamination was the original impetus for the site being remediated, other chemical and physical stressors were evaluated. There were 3 primary conclusions from the BN-RRM results. First, risk varies according to location, type and quality of habitat, and exposure to stressors within the landscape. The patterns of risk can be evaluated with reasonable certitude. Second, overall risk to abiotic endpoints was greater than overall risk to biotic endpoints. By including both biotic and abiotic endpoints, we are able to compare risk to endpoints that represent a wide range of stakeholder values. Third, whereas Hg reduction is the regulatory priority for the South River, Hg is not the only stressor driving risk to the endpoints. Ecological and habitat stressors contribute risk to the endpoints and should be considered when managing this site. This research provides the foundation for evaluating the risks of multiple stressors of the South River to a variety of endpoints. From this foundation, tools for the evaluation of management options and an adaptive management tools have been forged. Integr Environ Assess Manag 2017;13:85-99. © 2016 SETAC.

Many coastal regions are encountering issues with the spread of nonindigenous species (NIS). In this study, we conducted a regional risk assessment using a Bayesian network relative risk model (BN-RRM) to analyze multiple vectors of NIS introductions to Padilla Bay, Washington, a National Estuarine Research Reserve. We had 3 objectives in this study. The 1st objective was to determine whether the BN-RRM could be used to calculate risk from NIS introductions for Padilla Bay. Our 2nd objective was to determine which regions and endpoints were at greatest risk from NIS introductions. Our 3rd objective was to incorporate a management option into the model and predict endpoint risk if it were to be implemented. Eradication can occur at different stages of NIS invasions, such as the elimination of these species before being introduced to the habitat or removal of the species after settlement. We incorporated the ballast water treatment management scenario into the model, observed the risk to the endpoints, and compared this risk with the initial risk estimates. The model results indicated that the southern portion of the bay was at greatest risk because of NIS. Changes in community composition, Dungeness crab, and eelgrass were the endpoints most at risk from NIS introductions. The currents node, which controls the exposure of NIS to the bay from the surrounding marine environment, was the parameter that had the greatest influence on risk. The ballast water management scenario displayed an approximate 1% reduction in risk in this Padilla Bay case study. The models we developed provide an adaptable template for decision makers interested in managing NIS in other coastal regions and large bodies of water.

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Invasive zebra and quagga mussels (Dreissena spp.) in the Great Lakes of North America are biomonitors for chemical contaminants, but are also exposed to microplastics (<5 mm). Little research has examined in situ microplastic ingestion by dreissenid mussels, or the relationship between microplastics and chemical contaminants. We measured microplastics and chemical contaminants in mussel tissue from Milwaukee Harbor (Lake Michigan, United States) harvested from reference locations and sites influenced by wastewater effluent and urban river discharge. Mussels were deployed in cages in the summer of 2018, retrieved after 30 and 60 days, sorted by size class, and analyzed for microplastics and body burdens of three classes of contaminants: alkylphenols, polyaromatic hydrocarbons, and petroleum biomarkers. Microplastics in mussels were higher in the largest mussels at the wastewater-adjacent site after 30 days deployment. However, there was no distinction among sites for microplastics in smaller mussels, and no differences among sites after 60 days of deployment. Microplastics and chemical contaminants in mussels were not correlated. Microplastics have a diversity of intrinsic and extrinsic factors which influence their ingestion, retention, and egestion by mussels, and which vary relative to chemicals. While dreissenid mussels may not serve as plastic pollution biomonitors like they can for chemical contaminants, microplastics in dreissenid mussels are widespread, variable, and have unknown effects on physiology, mussel-mediated ecosystem processes, and lake food webs. These data will inform our understanding of the spatial distribution of microplastics in urban freshwaters, the role of dreissenid mussels in plastic budgets, and models for the fate of plastic pollution.

Marine debris is a threat to our ocean that can be more effectively addressed through monitoring and assessment of items stranded on shorelines. This study engaged citizen scientists to conduct shoreline marine debris surveys according to a published NOAA protocol within the Greater Farallones and Olympic Coast National Marine Sanctuaries on the west coast of the United States. Here, we use the results of these multi-year monitoring data to estimate marine debris abundance and temporal trends, and identify drivers of debris loads. Changes in debris counts and composition are shown to reflect seasonal patterns of coastal upwelling and downwelling, but longer temporal trends in overall debris loads depend on the sampling window. Identifying drivers of stranded debris is challenging given the observational nature of the data. A linear increase in total expected debris counts was observed when up to five participants are conducting a survey, suggesting a need to standardize the number of participants and their search pattern for debris in shoreline monitoring efforts. Lastly, we discuss the application of shoreline marine debris data to evaluate the impact of management decisions and identify new targets for mitigation.

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