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multiple stressor risk assessment, Chinook Salmon, Yakima River


Data files available below

This data set is in support of Landis et al (in press 2024). A key question in understanding the implications of climate change is 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 (BN-RRM) integrated the effects of organophosphate pesticides concentrations, water temperature, and dissolved oxygen levels on the Chinook salmon population in the Yakima River Basin, Washington, USA, with the endpoint being no net loss to the population described by a three patch metapopulation age structured model. Climate change-induced changes in water quality parameters (temperature and dissolved oxygen levels) were incorporated into the model based on projected climatic conditions in the 2050s and 2080s. Pesticide concentrations in the original model were modified assuming different bounding scenarios of pest control strategies in the future, as climate change may alter pest numbers and species and thus the required emission of pesticides. Our results suggest that future direct and indirect changes to the Yakima River Basin result in a high probability (62%) that the salmon population will drop below the management goal of no net loss. The key driver in salmon population risk was found to be increases in temperature levels, with pesticide concentrations playing little to no role, as indicated by the sensitivity analysis. However, indirect effects to community structure and dynamics, such as changes in the food web, were not considered. Our study demonstrates the feasibility of incorporating the direct effects of climate change and its indirect effects on chemical emissions into an integrated Bayesian network relative risk framework. It also highlights the value of using Bayesian networks for identifying key drivers of ecological risk and elucidating possible mitigation measures to avoid unacceptable changes in risk. Future research needs are also described for incorporating climate change projections into exposure-driven ecological risk assessments.

The Netica file can be opened and read with the free download version of Netica available at The structure of the model and the notes for each node and the conditional probability tables can then be accessed. A licensed version of Netica can run and modify the file.


CDDNet230311.neta (27 kB)
Netica file




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