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

Gene drive in species complexes: defining target organisms

Engineered gene drives, which bias their own inheritance to increase in frequency in target populations, are being developed to control mosquito malaria vectors. Such mosquitoes can belong to complexes of both vector and nonvector species that can produce fertile interspecific hybrids, making vertical gene drive transfer (VGDT) to sibling species biologically plausible. While VGDT to other vectors could positively impact human health protection goals, VGDT to nonvectors might challenge biodiversity ones. Therefore, environmental risk assessment of gene drive use in species complexes invites more nuanced considerations of target organisms and nontarget organisms than for transgenes not intended to increase in frequency in target populations. Incorporating the concept of target species complexes offers more flexibility when assessing potential impacts from VGDT.

Critical gaps in nanoplastics research and their connection to risk assessment

Reports of plastics, at higher levels than previously thought, in the water that we drink and the air that we breathe, are generating considerable interest and concern. Plastics have been recorded in almost every environment in the world with estimates on the order of trillions of microplastic pieces. Yet, this may very well be an underestimate of plastic pollution as a whole. Once microplastics (<5 mm) break down in the environment, they nominally enter the nanoscale (<1,000 nm), where they cannot be seen by the naked eye or even with the use of a typical laboratory microscope. Thus far, research has focused on plastics in the macro- (>25 mm) and micro-size ranges, which are easier to detect and identify, leaving large knowledge gaps in our understanding of nanoplastic debris. Our ability to ask and answer questions relating to the transport, fate, and potential toxicity of these particles is disadvantaged by the detection and identification limits of current technology. Furthermore, laboratory exposures have been substantially constrained to the study of commercially available nanoplastics; i.e., polystyrene spheres, which do not adequately reflect the composition of environmental plastic debris. While a great deal of plastic-focused research has been published in recent years, the pattern of the work does not answer a number of key factors vital to calculating risk that takes into account the smallest plastic particles; namely, sources, fate and transport, exposure measures, toxicity and effects. These data are critical to inform regulatory decision making and to implement adaptive management strategies that mitigate risk to human health and the environment. This paper reviews the current state-of-the-science on nanoplastic research, highlighting areas where data are needed to establish robust risk assessments that take into account plastics pollution. Where nanoplastic-specific data are not available, suggested substitutions are indicated.

Bayesian network-based risk assessment of synthetic biology: Simulating CRISPR-Cas9 gene drive dynamics in invasive rodent management

Gene drive technology has been proposed to control invasive rodent populations as an alternative to rodenticides. However, this approach has not undergone risk assessment that meets criteria established by Gene Drives on the Horizon, a 2016 report by the National Academies of Sciences, Engineering, and Medicine. To conduct a risk assessment of gene drives, we employed the Bayesian network-relative risk model to calculate the risk of mouse eradication on Southeast Farallon Island using a CRISPR-Cas9 homing gene drive construct. We modified and implemented the R-based model "MGDrivE" to simulate and compare 60 management strategies for gene drive rodent management. These scenarios spanned four gene drive mouse release schemes, three gene drive homing rates, three levels of supplemental rodenticide dose, and two timings of rodenticide application relative to gene drive release. Simulation results showed that applying a supplemental rodenticide simultaneously with gene drive mouse deployment resulted in faster eradication of the island mouse population. Gene drive homing rate had the highest influence on the overall probability of successful eradication, as increased gene drive accuracy reduces the likelihood of mice developing resistance to the CRISPR-Cas9 homing mechanism.

Recommendations for environmental risk assessment of gene drive applications for malaria vector control

Building on an exercise that identified potential harms from simulated investigational releases of a population suppression gene drive for malaria vector control, a series of online workshops identified nine recommendations to advance future environmental risk assessment of gene drive applications.

Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks

Technical, economic, regulatory, environmental, and social and political interests make the process of selecting an appropriate wastewater treatment technology complex. Although this problem has already been addressed from the dimensioning approach, our proposal in this research, a model of decision making for conventional secondary treatment of municipal wastewater through continuous-discrete, non-parametric Bayesian networks was developed. The most suitable network was structured in unit processes, independent of each other. Validation, with data in a mostly Mexican context, provided a positive predictive power of 83.5%, an excellent kappa (0.77 > 0.75), and the criterion line was surpassed with the location of the model in a receiver operating characteristic (ROC) graph, so the model can be implemented in this region. The final configuration of the Bayesian network allows the methodology to be easily extended to other types of treatments, wastewater, and to other regions.

Regional Scale Risk to the Ecological Sustainability and Ecosystem Services of an African Floodplain System

The Phongolo floodplain is one of southern Africa's most important systems. In this study, we carried out a regional scale ecological risk assessment to evaluate the risk of multiple stressors associated with the use of the aquatic resources in the floodplain to selected social and ecological endpoints representing its sustainability. The floodplain has undergone significant changes as a result of the impacts of multiple stressors. This includes high risk of impact and threatened sustainability between the Pongolapoort Dam and the Ndumo Game Reserve (NGR). This compares to relatively low risk to the maintenance of the endpoints within the NGR. The reserve provides a protection and refuge function for regional biodiversity maintenance and ecosystem sustainability processes. In the study a range of scenarios were considered and demonstrate that the system will respond to protection measures and or increased resource use options. Should flood reductions or water quality pollution drivers continue on increasing trajectories, the condition of the Phongolo River and floodplain will probably deteriorate into an unacceptable, unsustainable state. Removal of the protection services of the NGR would result in an unsustainable ecosystem and loss of ecosystem services for regional vulnerable African communities. Additional evidence should be obtained from monitoring and research to refine, validate, and update the assessment in an adaptive management context. The risk assessment framework approach implemented in the Phongolo floodplain can contribute to the management of other floodplains ecosystems and the sustainability management of social and ecological attributes and processes of these important ecosystems.

Conceptual Bayesian networks for contaminated site ecological risk assessment and remediation support

The causal pathways of stressors that lead to impacts on individuals, populations, and communities of organisms are useful to know for designing alternatives that manage or remediate ecological risks. The ecological risk assessment (ERA) framework (USEPA, 1998b) can help to identify and prioritize management of risks. One key product of the problem formulation step in an ERA, that captures and represents causal knowledge, is the conceptual site model (CSM). The CSM is a graphical depiction of the risk environment that traces the fate and transport pathways of contaminants from sources of contamination (e.g., a leaking storage tank) to receptors (i.e., the ecological endpoints of concern in the risk assessment). The CSM guides the development of methods for assessing ecological risk scenarios and for remediation design alternatives. The qualitative and quantitative aspects of Bayesian networks may support CSM development and risk characterization. Bayesian networks provide a graphical platform geared toward probabilistic modeling making them important candidates for calculating risks in environmental assessments. The diagrammatic representation of causal Bayesian networks (i.e., the directed acyclic graphs) also adds explanatory depth for developing the evidence-base for risk characterization and remediation interventions. We call these qualitative graphs conceptual Bayesian networks (CBNs). The components of CBNs can be used to represent the variables and relationships between sources of contamination, media transfer, bioaccumulation, and risk. The connections help to compose, piece together, and explore hypothesized relationships that bring about high-risk scenarios. Causal pathway analysis of the CBNs provides visualizations of exposure pathways from initial and intermediate sources to receptors. Remediation options that would interrupt or stop the transport of contaminants to ecological receptors can then be identified. Even if the CBN is not quantified, the structures can support mechanistic and statistical designs for exposure and effects analysis and risk characterization and evaluate information needs for resolving uncertainties. This paper will examine these and other unexplored benefits of CBNs to assessment and management of contaminated sites.

Risk Assessment of Water Quantity and Quality Stressors to Balance the Use and Protection of Vulnerable Water Resources

In developing regions of the world, valuable and vulnerable water resources are being used excessively. Through water resource development, multiple water quality, flow, and other stressors threaten the sustainable use and protection of these resources. Few attempts have been made to evaluate the synergistic effects of multiple water quality and flow stressors to socioecological attributes of systems that we care about in integrated water resource management. Regional scale ecological risk assessments evaluate the probable negative effects of multiple stressors, affecting dynamic ecosystems on multiple spatial scales. The present study demonstrates how multiple water quality, flow, and other stressors that cumulatively affect the sustainability of the lower Thukela River, South Africa, can be evaluated using the relative risk model, Bayesian network (RRM-BN) approach. This risk assessment facilitated the establishment of minimum water quality and flow requirements to maintain the sustainability of this system and make water resource use and protection trade-off decisions. In this case study, the risk of 10 water resources use and protection scenarios were evaluated in a regional scale ecological risk assessment of the socioecological attributes of the lower Thukela River. In addition we evaluated the consequences associated with these scenarios based on risk pathways of multiple sources, stressors, and receptors to endpoints that represent the sustainable vision of multiple stakeholders of the system. The outcomes of the present study have contributed to new evidence to improve the water resource use efficiency and protect important resources of the lower Thukela River, to ensure sustainability. Integr Environ Assess Manag 2021;17:110-130. © 2020 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.

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 adaptive processes and adverse outcome pathways to develop meaningful, robust, and actionable environmental monitoring programs

The primary goals of environmental monitoring are to indicate whether unexpected changes related to development are occurring in the physical, chemical, and biological attributes of ecosystems and to inform meaningful management intervention. Although achieving these objectives is conceptually simple, varying scientific and social challenges often result in their breakdown. Conceptualizing, designing, and operating programs that better delineate monitoring, management, and risk assessment processes supported by hypothesis-driven approaches, strong inference, and adverse outcome pathways can overcome many of the challenges. Generally, a robust monitoring program is characterized by hypothesis-driven questions associated with potential adverse outcomes and feedback loops informed by data. Specifically, key and basic features are predictions of future observations (triggers) and mechanisms to respond to success or failure of those predictions (tiers). The adaptive processes accelerate or decelerate the effort to highlight and overcome ignorance while preventing the potentially unnecessary escalation of unguided monitoring and management. The deployment of the mutually reinforcing components can allow for more meaningful and actionable monitoring programs that better associate activities with consequences. Integr Environ Assess Manag 2017;13:877-891. © 2017 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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.