Assessing the effects of chemical mixtures using a Bayesian network-relative risk model (BN-RRM) integrating adverse outcome pathways (AOPs) in three Puget Sound watersheds

Chemical mixtures are difficult to assess at the individual level, but more challenging at the population level. There is still little insight of the molecular pathway for numerous chemical mixtures. We have conducted a regional-scale ecological risk assessment by evaluating the effects chemical mixtures to populations with a Bayesian Network- Relative Risk Model (BN-RRM) incorporating a molecular pathway. We used this BN-RRM framework in a case study with organophosphate pesticide (OP) mixtures (diazinon, chlorpyrifos, and malathion) in three watersheds (Lower Skagit, Nooksack, Cedar) in the state of Washington (USA). Puget Sound Chinook salmon (Oncorhynchus tshawytscha) Evolutionary Significant Units (ESU) were chosen as population endpoints. These populations are a valuable ecosystem service in the Pacific Northwest because they benefit the region as a species that provide protection of biodiversity and are spiritually and culturally treasured by the local tribes. Laetz et al. (2009, 2013) indicated that organophosphate pesticide mixtures act synergistically to salmon and impair neurological molecular activity which leads to a change in swimming behavior and mortality, which then leads to changes in population productivity. Exposure response curves were generated for OP mixtures to connect the molecular pathway. Ecological stressors from dissolved oxygen and temperature were also included in our risk analysis. Synergism within the mixtures as well as increasing temperature and decreasing dissolve oxygen content lead to increasing risk to Puget Sound Chinook salmon populations. This research demonstrates a probabilistic approach with a multiple stressor framework to estimate the effects of mixtures through a molecular pathway and predict impacts to these valuable ecosystem services

Using metapopulation models to estimate the effects of pesticides and environmental stressors to Spring Chinook salmon in the Yakima River Basin, WA

Population-level endpoints provide ecological relevance to Ecological Risk Assessments (ERAs), because this is the level at which environmental management decisions are made. However, many population-level risk assessments do not reflect the spatial and temporal heterogeneity of the populations they represent, and thus preclude an understanding of how population dynamics and viability are affected by toxicants on a regional scale. We have developed a probabilistic ERA (specifically, a Bayesian Network-Relative Risk Model (BN-RRM)) that integrates an Adverse Outcome Pathway (AOP) framework, to quantify the sub-lethal and lethal effects of toxicants and environmental stressors on the metapopulation dynamics of salmonids. As a case study for developing this model, we have examined the impacts of organophosphate (OP) insecticides, water temperature, and dissolved oxygen on the Spring Chinook (Oncorhynchus tshawytscha) salmon metapopulation in the Yakima River Basin (YRB), Washington. A stochastic Matrix Metapopulation Model was developed using demographic data for three Spring Chinook salmon populations and one supplemental hatchery population in the YRB. Site specific data on OP contaminated habitats utilized by various salmonid life stages were incorporated into the metapopulation model by incrementally reducing survival parameters based on levels of exposure. Exposure scenarios were simulated for 200 replications of 50-year population projections using RAMAS Metapop©, and the results were incorporated into the BN-RRM. The results of this modeling effort indicated that small, wild Spring Chinook populations in the YRB have a greater probability of altered population dynamics when exposed to stressors than larger, supplemented populations. Additionally, the results indicated a seasonal effect of the stressors, with summer conditions posing a greater risk to salmon populations than winter conditions. This probabilistic ERA framework shows promise for estimating the spatiotemporal impacts of stressors on ESA-listed species (i.e., Pacific salmon) at the metapopulation level, where population dynamics and spatial structure create complex risk dynamics

Dataset for the Environmental Risk Assessment of Chlorpyrifos to Chinook Salmon in four Rivers of Washington State, United States

Data files available below. This data set is in support of Landis et al (in press) The integration of chlorpyrifos acetylcholinesterase inhibition, water temperature and dissolved oxygen concentration into a regional scale multiple stressor risk assessment estimating risk to Chinook salmon in four rivers in Washington State, USA. DOI: 10.1002/ieam.4199. In this research We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT) and dissolved oxygen concentrations (DO) in four 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 AChE inhibition pathway parameterized using data from chlorpyrifos toxicity datasets. The completed BN-RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling run up to 50 year timeframes. There were 4 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 percent. Environmental conditions contributed to a larger proportion of the risk compared to chlorpyrifos. 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 that 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 in 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. This research was supported by USEPA STAR Grant RD-83579501. Excel spreadsheet, model in Netica

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 2020;16:28–42. © 2019 SETA

From the molecular to population risk to ecosystem services: a risk assessment adaptive management approach for the Salish Sea

An ongoing issue in the management of the Salish Sea is the dilemma of how to integrate different scales of information into an adaptive management program to ensure restoration and protection of the resource. Often, we have data on chemical contamination in the environment and exposure to the individual, information on habitat quality, and some general information on population dynamics of a species serving as a vital sign. However, this information is not tied together with a clear and quantitative model describing causality, probability and uncertainty. Furthermore, the goal is not just to preserve that species but to protect human well-being. Human well-being has become part of the lexicon to included ecosystem services endpoints such as a sense of place, education, recreational opportunities, employment, public safety and Tribal treaty rights. In a recent publication (Harris et al. 2017) it was demonstrated that it is possible to estimate risk in a contaminated site to ecological endpoints, human health and ecosystem services using a clearly defined causal pathways and Bayesian networks. Now we are applying the integration of ecological endpoints, ecosystem services and human well-being to the scale of the Salish Sea using specific watershed and coastal regions as models. In this study we are using Skagit, the Nooksack and the Cedar watersheds as case studies. Valerie Chu and Chelsea Mitchell (this session) have described the assessment methodologies and our modeling results for these watersheds. The vital sign is also tied to specific economic ecosystem services, is important to a sense of place, and are valued by the diverse constituencies in the region. We will demonstrate how the Chinook salmon population risk model can be translated as risk to a variety of ecosystem services contributing to human well-being. These outputs then become part of a larger adaptive management program for the system