Analysis of regional scale risk of whirling disease in populations of Colorado and Rio Grande cutthroat trout using a Bayesian belief network model

Bayesian Networks in Environmental Risk Assessment: A Review

Human activities both depend upon and have consequences on the environment. Environmental risk assessment (ERA) is a process of estimating the probability and consequences of the adverse effects of human activities and other stressors on the environment. Bayesian networks (BNs) can synthesize different types of knowledge and explicitly account for the probabilities of different scenarios, therefore offering a useful tool for ERA. Their use in formal ERA practice has not been evaluated, however, despite their increasing popularity in environmental modeling. This paper reviews the use of BNs in ERA based on peer-reviewed publications. Following a systematic mapping protocol, we identified studies in which BNs have been used in an environmental risk context and evaluated the scope, technical aspects, and use of the models and their results. The review shows that BNs have been applied in ERA, particularly in recent years, and that there is room to develop both the model implementation and participatory modeling practices. Based on this review and the authors' experience, we outline general guidelines and development ideas for using BNs in ERA. Integr Environ Assess Manag 2021;17:62-78. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

The Origin, Development, Application, Lessons Learned, and Future Regarding the Bayesian Network Relative Risk Model for Ecological Risk Assessment

In 2012, a regional risk assessment was published that applied Bayesian networks (BN) to the structure of the relative risk model. The original structure of the relative risk model (RRM) was published in the late 1990s and developed during the next decade. The RRM coupled with a Monte Carlo analysis was applied to calculating risk to a number of sites and a variety of questions. The sites included watersheds, terrestrial systems, and marine environments and included stressors such as nonindigenous species, effluents, pesticides, nutrients, and management options. However, it became apparent that there were limits to the original approach. In 2009, the relative risk model was transitioned into the structure of a BN. Bayesian networks had several clear advantages. First, BNs innately incorporated categories and, as in the case of the relative risk model, ranks to describe systems. Second, interactions between multiple stressors can be combined using several pathways and the conditional probability tables (CPT) to calculate outcomes. Entropy analysis was the method used to document model sensitivity. As with the RRM, the method has now been applied to a wide series of sites and questions, from forestry management, to invasive species, to disease, the interaction of ecological and human health endpoints, the flows of large rivers, and now the efficacy and risks of synthetic biology. The application of both methods have pointed to the incompleteness of the fields of environmental chemistry, toxicology, and risk assessment. The low frequency of exposure-response experiments and proper analysis have limited the available outputs for building appropriate CPTs. Interactions between multiple chemicals, landscape characteristics, population dynamics and community structure have been poorly characterized even for critical environments. A better strategy might have been to first look at the requirements of modern risk assessment approaches and then set research priorities. Integr Environ Assess Manag 2021;17:79-94. © 2020 SETAC.

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.

Representing causal knowledge in environmental policy interventions: Advantages and opportunities for qualitative influence diagram applications

This article develops and explores a methodology for using qualitative influence diagrams in environmental policy and management to support decision-making efforts that minimize risk and increase resiliency. Influence diagrams are representations of the conditional aspects of a problem domain. Their graphical properties are useful for structuring causal knowledge relevant to policy interventions and can be used to enhance inference and inclusivity of multiple viewpoints. Qualitative components of influence diagrams are beneficial tools for identifying and examining the interactions among the critical variables in complex policy development and implementation. Policy interventions on social-environmental systems can be intuitively diagrammed for representing knowledge of critical relationships among economic, environmental, and social attributes. Examples relevant to coastal resiliency issues in the US Gulf Coast region are developed to illustrate model structures for developing qualitative influence diagrams useful for clarifying important policy intervention issues and enhancing transparency in decision making. Integr Environ Assess Manag 2018;14:381-394. Published 2018. This article is a US Government work and is in the public domain in the USA.

Comparison of Sampling and Detection Methods for Chinook Salmon and Steelhead Naturally Infected with Myxobolus cerebralis

Myxobolus cerebralis (Mc) is a myxozoan parasite causing whirling disease in hatchery- and natural-origin salmonids. To minimize spread of this parasite and the incidence of its associated disease, fish health professionals routinely screen fish for Mc before stocking or moving the fish to Mc-free waters. Sample collection for Mc traditionally entails lethal sampling of cranial tissue followed by pepsin-trypsin digest (PTD) and screening of the sample for mature myxobolid myxospores (PTD method); however, nonlethal sampling methods would be advantageous in some circumstances, such as when dealing with rare or otherwise valuable fish. Accordingly, we compared Mc detections in cranial cartilage by using the PTD method with PCR assays of fin biopsies collected from juvenile Chinook Salmon Oncorhynchus tshawytscha and adult steelhead O. mykiss. Cranial samples were also analyzed using PCR methods for comparative purposes. Results indicated that Mc could be detected by PCR in fin clips, but the results generated by this approach differed significantly from those associated with PTD- and/or PCR-based analysis of cranial cartilage samples. Polymerase chain reaction-based analysis-of individual head samples and head digest pools in both species as well as fins in steelhead-yielded more positive detections than PTD analysis alone. The PCR-based analysis of head and fin tissues yielded different Mc detection rates in both species, but the nature of the detection disparity varied depending on the species and/or life stage of the fish. We conclude that for lethal cranial samples, neither PTD nor PCR should be used alone, but using these techniques in concert may provide the most complete and accurate estimation of Mc presence in a group of salmonids. If imperiled or highly valuable fish are in question, nonlethal fin samples may be used to generate some information regarding Mc status, with the understanding that parasite DNA detections do not necessarily signify mature infections or disease.

A Bayesian Approach to Integrated Ecological and Human Health Risk Assessment for the South River, Virginia Mercury-Contaminated Site

We conducted a regional-scale integrated ecological and human health risk assessment by applying the relative risk model with Bayesian networks (BN-RRM) to a case study of the South River, Virginia mercury-contaminated site. Risk to four ecological services of the South River (human health, water quality, recreation, and the recreational fishery) was evaluated using a multiple stressor-multiple endpoint approach. These four ecological services were selected as endpoints based on stakeholder feedback and prioritized management goals for the river. The BN-RRM approach allowed for the calculation of relative risk to 14 biotic, human health, recreation, and water quality endpoints from chemical and ecological stressors in five risk regions of the South River. Results indicated that water quality and the recreational fishery were the ecological services at highest risk in the South River. Human health risk for users of the South River was low relative to the risk to other endpoints. Risk to recreation in the South River was moderate with little spatial variability among the five risk regions. Sensitivity and uncertainty analysis identified stressors and other parameters that influence risk for each endpoint in each risk region. This research demonstrates a probabilistic approach to integrated ecological and human health risk assessment that considers the effects of chemical and ecological stressors across the landscape.

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.

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.

A general risk-based adaptive management scheme incorporating the Bayesian Network Relative Risk Model with the South River, Virginia, as case study

Adaptive management has been presented as a method for the remediation, restoration, and protection of ecological systems. Recent reviews have found that the implementation of adaptive management has been unsuccessful in many instances. We present a modification of the model first formulated by Wyant and colleagues that puts ecological risk assessment into a central role in the adaptive management process. This construction has 3 overarching segments. Public engagement and governance determine the goals of society by identifying endpoints and specifying constraints such as costs. The research, engineering, risk assessment, and management section contains the decision loop estimating risk, evaluating options, specifying the monitoring program, and incorporating the data to re-evaluate risk. The 3rd component is the recognition that risk and public engagement can be altered by various externalities such as climate change, economics, technological developments, and population growth. We use the South River, Virginia, USA, study area and our previous research to illustrate each of these components. In our example, we use the Bayesian Network Relative Risk Model to estimate risks, evaluate remediation options, and provide lists of monitoring priorities. The research, engineering, risk assessment, and management loop also provides a structure in which data and the records of what worked and what did not, the learning process, can be stored. The learning process is a central part of adaptive management. We conclude that risk assessment can and should become an integral part of the adaptive management process. Integr Environ Assess Manag 2017;13:115-126. © 2016 SETAC.

Predicting arboviral disease emergence using Bayesian networks: a case study of dengue virus in Western Australia

A Bayesian Belief Network (BBN) for assessing the potential risk of dengue virus emergence and distribution in Western Australia (WA) is presented and used to identify possible hotspots of dengue outbreaks in summer and winter. The model assesses the probabilities of two kinds of events which must take place before an outbreak can occur: (1) introduction of the virus and mosquito vectors to places where human population densities are high; and (2) vector population growth rates as influenced by climatic factors. The results showed that if either Aedes aegypti or Ae. albopictus were to become established in WA, three centres in the northern part of the State (Kununurra, Fitzroy Crossing, Broome) would be at particular risk of experiencing an outbreak. The model can also be readily extended to predict the risk of introduction of other viruses carried by Aedes mosquitoes, such as yellow fever, chikungunya and Zika viruses.

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.