Integration of Chlorpyrifos Acetylcholinesterase Inhibition, Water Temperature, and Dissolved Oxygen Concentration into a Regional Scale Multiple Stressor Risk Assessment Estimating Risk to Chinook Salmon.

We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT), and dissolved oxygen concentration (DO) in 4 watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle-Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN-RRM) was used to conduct this ecological risk assessment and was modified to contain an acetylcholinesterase (AChE) inhibition pathway parameterized using data from CH toxicity data sets. The completed BN-RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling runs up to 50-y timeframes. There were 3 primary conclusions drawn from the model-building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65% to 85%. Environmental conditions contributed to a larger proportion of the risk compared to CH. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential than CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management goal, we calculated the conditions that would increase juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. Integr Environ Assess Manag 2019;00:1-15. © 2019 SETAC.

Using Bayesian networks to predict risk to estuary water quality and patterns of benthic environmental DNA in Queensland.

Predictive modeling can inform natural resource management by representing stressor-response pathways in a logical way and quantifying the effects on selected endpoints. This study demonstrates a risk assessment model using the Bayesian network relative risk model (BN-RRM) approach to predict water quality and, for the first time, eukaryote environmental DNA (eDNA) data as a measure of benthic community structure. Environmental DNA sampling is a technique for biodiversity measurements that involves extracting DNA from environmental samples, amplicon sequencing a targeted gene, in this case the 18s rDNA gene (which targets eukaryotes), and matching the sequences to organisms. Using a network of probability distributions, the BN-RRM model predicts risk to water quality objectives and the relative richness of benthic taxa groups in the Noosa, Pine, and Logan estuaries in Southeast Queensland (SEQ), Australia. The model predicts Dissloved Oxygen more accurately than the chlorophyll a water quality endpoint and photosynthesizing benthos more accurately than heterotrophs. Results of BN-RRM modeling given current inputs indicate that the water quality and benthic assemblages of the Noosa are relatively homogenous across all sub risk regions, and that the Noosa has a 73%-92% probability of achieving water quality objectives, indicating a low relative risk. Conversely, the Middle Logan, Middle Pine, and Lower Pine regions are much less likely to meet objectives (15%-55% probability), indicating a relatively higher risk to water quality in those regions. The benthic community richness patterns associated with risk in the Noosa are high Diatom relative richness and low Green Algae relative richness. The only benthic pattern consistently associated with the relatively higher risk to water quality is high richness of fungi species. The BN-RRM model provides a basis for future predictions and adaptive management at the direction of resource managers. Integr Environ Assess Manag 2019;15:93-111. © 2018 SETAC.

Using the Bayesian network relative risk model risk assessment process to evaluate management alternatives for the South River and upper Shenandoah River, Virginia.

We have conducted a series of regional scale risk assessments using the Bayesian Network Relative Risk Model (BN-RRM) to evaluate the efficacy of 2 remediation options in the reduction of risks to the South River and upper Shenandoah River study area. The 2 remediation options were 1) bank stabilization (BST) and 2) the implementation of best management practices for agriculture (AgBMPs) to reduce Hg input in to the river. Eight endpoints were chosen to be part of the risk assessment, based on stakeholder input. Although Hg contamination was the original impetus for the site being remediated, multiple chemical and physical stressors were evaluated in this analysis. Specific models were built that incorporated the changes expected from AgBMP and BST and were based on our previous research. Changes in risk were calculated, and sensitivity and influence analyses were conducted on the models. The assessments indicated that AgBMP would only slightly change risk in the study area but that negative impacts were also unlikely. Bank stabilization would reduce risk to Hg for the smallmouth bass and belted kingfisher and increase risk to abiotic water quality endpoints. However, if care were not taken to prevent loss of nesting habitat to belted kingfisher, an increase in risk to that species would occur. Because Hg was only one of several stressors contributing to risk, the change in risk depended on the specific endpoint. Sensitivity analysis provided a list of variables to be measured as part of a monitoring program. Influence analysis provided the range of maximum and minimum risk values for each endpoint and remediation option. This research demonstrates the applicability of ecological risk assessment and specifically the BN-RRM as part of a long-term adaptive management scheme for managing contaminated sites. Integr Environ Assess Manag 2017;13:100-114. © 2016 SETAC.