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The population level is often the biological endpoint addressed in ecological risk assessments (ERAs). However, ERAs tend to ignore the metapopulation structure, which precludes an understanding of how population viability is affected by multiple stressors (e.g., toxicants and environmental conditions) at large spatial scales. Here we integrate metapopulation model simulations into a regional‐scale, multiple stressors risk assessment (Bayesian network relative risk model [BN‐RRM]) of organophosphate (OP) exposure, water temperature, and DO impacts on Chinook salmon (Oncorhynchus tshawytscha). A matrix metapopulation model was developed for spring Chinook salmon in the Yakima River Basin (YRB), Washington, USA, including 3 locally adapted subpopulations and hatchery fish that interact with those subpopulations. Three metapopulation models (an exponential model, a ceiling density‐dependent model, and an exponential model without dispersal) were integrated into the BN‐RRM to evaluate the effects of population model assumptions on risk calculations. Risk was defined as the percent probability that the abundance of a subpopulation would decline from their initial abundance (500 000). This definition of risk reflects the Puget Sound Partnership's management goal of achieving "no net loss" of Chinook abundance. The BN‐RRM model results for projection year 20 showed that risk (in % probability) from OPs and environmental stressors was higher for the wild subpopulations—the American River (50.9%–97.7%) and Naches (39.8%–84.4%) spring Chinook—than for the hatchery population (CESRF 18.5%–46.5%) and the Upper Yakima subpopulation (21.5%–68.7%). Metapopulation risk was higher in summer (58.1%–68.7%) than in winter (33.6%–53.2%), and this seasonal risk pattern was conserved at the subpopulation level. To reach the management goal in the American River spring Chinook subpopulation, the water temperature conditions in the Lower Yakima River would need to decrease. We demonstrate that 1) relative risk can vary across a metapopulation's spatial range, 2) dispersal among patches impacts subpopulation abundance and risk, and 3) local adaptation within a salmon metapopulation can profoundly impact subpopulation responses to equivalent stressors. Integr Environ Assess Manag 2021;17:95–109. © 2020 SETAC KEY POINTS: Fine‐scale risk estimates can be produced by integrating metapopulation model simulations into a Bayesian network relative risk model (BN‐RMM).Risk differed across spring Chinook salmon subpopulations in the Yakima River Basin, Washington, USA, highlighting the importance of considering life history differences when making risk predictions for environmental stressors.Water temperature is the risk factor that requires management to prevent loss of Chinook population abundance, given our modeling efforts.Small sample sizes for organophosphate in winter introduced sufficient uncertainty to increase the influence of toxicological effects in winter. [ABSTRACT FROM AUTHOR]

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 KEY POINTS: The incorporation of acetylcholinesterase (AChE) inhibition (biomarker) and the output from a population model can be combined with environmental factors in a quantitative fashion.Environmental conditions contributed to a larger proportion of the risk compared to the toxicological pathways, but toxicology contributed up to 20% of the risk.The sensitivity analysis describing the influence of the variables on the predicted risk varied, depending on seasonal conditions.In order to reach the management goal, we calculated that an increase in juvenile and adult survival and a reduction in toxicological effects are required. [ABSTRACT FROM AUTHOR]

The rapid evolution of toxin resistance in animals has important consequences for the ecology of species and our economy. Pesticide resistance in insects has been a subject of intensive study; however, very little is known about how Drosophila species became resistant to natural toxins with ecological relevance, such as α-amanitin that is produced in deadly poisonous mushrooms. Here we performed a microarray study to elucidate the genes, chromosomal loci, molecular functions, biological processes, and cellular components that contribute to the α-amanitin resistance phenotype in Drosophila melanogaster. We suggest that toxin entry blockage through the cuticle, phase I and II detoxification, sequestration in lipid particles, and proteolytic cleavage of α-amanitin contribute in concert to this quantitative trait. We speculate that the resistance to mushroom toxins in D. melanogaster and perhaps in mycophagous Drosophila species has evolved as cross-resistance to pesticides, other xenobiotic substances, or environmental stress factors. [ABSTRACT FROM AUTHOR]

B Learning Objectives: b Methicillin-resistant Staphylococcus aureus (MRSA) is a relevant pathogen for patients with hospital-acquired (HAP) and ventilator-associated pneumonia (VAP), with an incidence of 20-40% in these populations. Secondary outcomes included anti-MRSA antibiotic-free days from days 0-7 and mean cost of anti-MRSA therapy in MRSA-negative patients. B Conclusions: b Incorporating MRSA PCR into a pneumonia treatment protocol in a SICU reduces unnecessary empiric anti-MRSA therapy and does not decrease appropriate prescribing for MRSA-positive patients. [Extracted from the article]

One outcome of the 2022 Society of Environmental Toxicology and Chemistry Pellston Workshop on incorporating climate change predictions into ecological risk assessments was the key question of how to integrate ecological risk assessments that focus on contaminants with the environmental alterations from climate projections. This article summarizes the results of integrating selected direct and indirect effects of climate change into an existing Bayesian network previously used for ecological risk assessment. The existing Bayesian Network Relative Risk Model integrated the effects of two organophosphate pesticides (malathion and diazinon), water temperature, and dissolved oxygen levels on the Chinook salmon population in the Yakima River Basin (YRB), Washington, USA. The endpoint was defined as the entity, Yakima River metapopulation, and the attribute was defined as no decline to a subpopulation or the overall metapopulation. In this manner, we addressed the management objective of no net loss of Chinook salmon, an iconic and protected species. Climate change-induced changes in water quality parameters (temperature and dissolved oxygen levels) used models based on projected climatic conditions in the 2050s and 2080s by the use of a probabilistic model. Pesticide concentrations in the original model were modified assuming different scenarios of pest control strategies in the future, because climate change may alter pest numbers and species. Our results predict that future direct and indirect changes to the YRB will result in a greater probability that the salmon population will continue to fail to meet the management objective of no net loss. As indicated by the sensitivity analysis, the key driver in salmon population risk was found to be current and future changes in temperature and dissolved oxygen, with pesticide concentrations being not as important. Integr Environ Assess Manag 2024;20:419-432. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)., (© 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).)