How we count counts: Examining influences on detection during shoreline surveys of marine debris.

Shoreline surveys are a common approach for documenting loads of marine macrodebris (≥ 2.5 cm). When surveys are conducted repeatedly over time and space, patterns in source, abundance, geographic distribution, and composition can be detected. Yet to realize their full potential, monitoring programs that rely on surveys must grapple with high variability in debris abundance, and appropriately manage uncertainty when reporting estimates of debris quantity. A potentially important source of bias in estimating debris loads from shoreline monitoring datasets is variability in debris detection rates. With this in mind, we conducted field experiments using common strip-transect marine debris survey protocols, designed to test detection of macrodebris. We quantified how protocol, shoreline, and debris characteristics influence the detectability of marine macrodebris. Detection rates varied according to debris distance from observer (0-5 m), number of observers, debris characteristics (size, color), and shoreline substrate. Our results highlight considerations for monitoring program design. Comparisons across datasets should be approached cautiously given differences in survey protocols and sources of bias that may affect debris density estimates should be quantified and addressed. We hope these results will inform marine debris monitoring efforts that are optimized for intended data use and impact.

Walking the talk: The responsibility of the scientific community for mitigating conference-generated waste.

An estimated 19-23 million metric tons of global plastic waste reportedly entered aquatic environments in 2016 with mounting evidence that plastic marine debris causes ecological effects across all levels of biological organization in aquatic systems. Scientific conferences generate opportunities for waste through food and beverage services, giveaways, marketing and registration materials, poster and trade exhibits, attendee travel, lodging services, and local transportation. Zero waste measures instituted at the Sixth International Marine Debris Conference resulted in the avoidance of 76,300 single-use items. Zero waste is a process defined by a spectrum of actions ranging from no reduction whatsoever to generation of absolutely no waste. Achieving 100% zero waste is very difficult. Deciding where on the spectrum you wish to land and being comfortable with that target is paramount for event planning. Planning for reduced waste takes time, funding, and determination, but environmentally-themed organizations have a responsibility to lead by example.

The multiple stressor ecological risk assessment for the mercury-contaminated South River and upper Shenandoah River using the Bayesian network-relative risk model.

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.

Evaluating nonindigenous species management in a Bayesian networks derived relative risk framework for Padilla Bay, WA, USA.

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.