Risk Evaluation of Invasive Species Transport Across the U.S.-Canada Border in Washington State

Non-indigenous invasive species (NIS) create a multitude of undesired economic, social, and ecological effects. Financial costs include reduced revenue and property value, and prevention and control expenditures (Pimentel et al., 2000). Social impacts include reduction in preferred uses including cultural and recreational activities, as well as loss of valued aesthetic qualities and civic pride in the surrounding ecological landscape (Bureau of Land Management, 2006). Ecological impacts include changes in soil and water quality, alteration of habitats, and displacement of native species (Elton, 1958)

Evaluating Non-Indigenous Species Eradication Options in a Bayesian Network Derived Adaptive Management Framework

Many coastal regions are facing problems with the introduction and spread of non-indigenous species (NIS). Common efforts addressing this issue include eradicating these species, which can occur at different stages of the NIS invasions, such as elimination of these species before being introduced to the habitat, or removal of the species after settlement. Eradication methods can either target multiple species (such as with ballast water treatments) or single species eradication, with chemical and/or mechanical treatment options. Little information is available as to the consequences of some of these eradication practices in terms of ecological and toxicological impacts to the surrounding habitat. A regional risk assessment using a Bayesian Network Model, is being conducted in Padilla Bay, Washington, a National Estuarine Research Reserve. The objectives of this study are to 1) determine the vectors of introduction that are associated with higher risks of NIS invasions and 2) analyze various management options that will reduce the risk of NIS introductions, while being least disruptive to the marine community. The Bayesian Network Model is advantageous because it allows us to analyze various adaptive management options for controlling NIS, comparing and contrasting methods such as chemical and mechanical eradication, as well as various treatments of ballast water before it is released into coastal waters. The results from this study will allow us to evaluate the likelihood of NIS risk reduction outcomes from each management option in respect to the endpoints in the surrounding habitats. The framework from the risk assessment and adaptive management will be adaptable for other regions interested in the eradication of NIS organisms

Design and Analysis of Multispecies Toxicity Tests for Pesticide Registration

The community conditioning hypothesis describes ecological structures as historical, nonequilibrial, and by definition complex. Indeed, the historical nature of ecological structures is seen as the primary difference between single-species toxicity tests and multispecies test systems. Given the complex properties of ecological structures, multispecies toxicity tests need to be designed accordingly with appropriate data analysis tools. Care must be taken to ensure that each replicate shares an identical history, or divergence will rapidly occur. Attempting to realize homogeneity by linear cross inoculation or waiting for an equilibrium state to occur assumes properties that ecological structures do not have. Data analysis must also incorporate the dynamic and hyperdimensional nature of ecological structures. Univariate analysis of individual variables denies the fundamental character of ecological structures as complex systems. A variety of methods, such as correspondence analysis, nonmetric multidimensional scaling, and nonmetric clustering and association analysis, are available to search for patterns and to test their relationships to experimental treatments. Visualization techniques including Space–Time Worms and redundancy analysis are also critical in attempting to understand the dynamic nature of these structures. Reliance upon the traditional analysis methods, such as ANOVA and the estimation of LOECs (lowest observable effects concentrations) or NOECs (no observable effects concentrations), comparable to those of single-species toxicity tests, is to be blind to the unique and complex nature of multispecies toxicity tests. Fundamental design criteria for multispecies toxicity tests, data analysis, and interpretation are presented

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 (BNRRM) 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 South East Queensland (SEQ), Australia. The model predicts Dissolved 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 percent 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 percent 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

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

Technical Memo: Incorporating Mixture Toxicity into Bayesian Networks to calculate risk to pesticides in the Upper San Francisco Estuary.

This memo presents the methods we have developed to calculate risk of mixtures of pesticides for the Upper San Francisco Estuary (USFE). We used curve fitting to estimate the exposure-response curves for each individual chemical and then the mixture. For the mixture the models were normalized for specific ECx values. In that way the curve fitting was optimized for effects that are similar to most threshold values. A Bayesian network was then built that incorporated four different pesticides and a specific mode of action. The input distributions of the pesticides were measured amounts from each of the six risk regions. Sensitivity analysis identified the components of the Bayesian network most important in determining the toxicity. We did demonstrate that curve fitting using additive models for mixtures can be used to estimate fish toxicity in this proof-of-concept model. Bifenthrin and the specific risk region were the two variables that were most important to the risk calculation. These techniques appear applicable to estimating risk due to the variety of chemicals and other stressors in the USFE and to the multiple endpoints under managemen

Development of a hybrid Bayesian network model for predicting acute fish toxicity using multiple lines of evidence

A hybrid Bayesian network (BN) was developed for predicting the acute toxicity of chemicals to fish, using data from fish embryo toxicity (FET) testing in combination with other information. This model can support the use of FET data in a Weight-of-Evidence (WOE) approach for replacing the use of ju-venile fish. The BN predicted correct toxicity intervals for 69%–80% of the tested substances. The model was most sensitive to components quantified by toxicity data, and least sensitive to compo-nents quantified by expert knowledge. The model is publicly available through a web interface. Fur-ther development of this model should include additional lines of evidence, refinement of the discre-tisation, and training with a larger dataset for weighting of the lines of evidence. A refined version of this model can be a useful tool for predicting acute fish toxicity, and a contribution to more quantitative WOE approaches for ecotoxicology and environmental assessment more generally.publishedVersio

San Francisco Delta Risk Assessment Year 1 Report Appendices

The Relative Contributions of Contaminants to Environmental Risk in the Upper San Francisco Estuary: Progress Report Year 1: Appendices Prepared for: The Metropolitan Water District of Southern California Prepared by: Wayne G. Landis, Steven R. Eikenbary, Ethan A. Brown, Colter P. Lemons, Emma E. Sharpe, and April J. Markiewicz Institute of Environmental Toxicology, Huxley College of the Environment Western Washington University Bellingham, WA 98225 June 30, 202

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

San Francisco Delta Risk Assessment Year 1 Report

The Relative Contributions of Contaminants to Environmental Risk in the Upper San Francisco Estuary: Progress Report Year 1 Prepared for: The Metropolitan Water District of Southern California Prepared by: Wayne G. Landis, Steven R. Eikenbary, Ethan A. Brown, Colter P. Lemons, Emma E. Sharpe, and April J. Markiewicz Institute of Environmental Toxicology, Huxley College of the Environment Western Washington University Bellingham, WA 98225 June 30, 202