Can Bayesian Networks be Used to Help Guide Restoration in Data Poor Northern California Watersheds?

California’s native salmonid populations are struggling, as evident by the 2008 fishing closures on one historically abundant species, Chinook Salmon (Oncorhynchus tshawytscha). One major impact on the spring-run of Chinook Salmon within the Central Valley has been the modification of natal rivers. Bayesian Networks are one modeling method that could help to understand these systems and direct restoration efforts. I constructed a Bayesian Network for Deer Creek, in Tehama County, to provide a tool for guiding restoration of spring-run Chinook Salmon spawning habitat. I applied this network on a riffle-pool reach scale to determine the suitability of each reach for spawning. To validate the network I conducted an ANOVA comparing redd densities from reaches predicted to be good against those predicted to be poor. I also conducted a sensitivity analysis on the network, to determine the influence of each independent variable. Of the four scenarios I modeled with the network, three exhibited significantly higher redd densities in reaches designated as good according to probability of redd occurrence. My results indicate that Bayesian Networks can be used to predict habitat use and prioritize restoration in a data-poor northern California watershed.