Analyzing Large-Scale Conservation Interventions with Bayesian Hierarchical Models: A Case Study of Supplementing Threatened Pacific Salmon

A variety of conservation interventions such as habitat restoration and captive breeding have been used to prevent species extinctions. We evaluated the effects of a large-scale supplementation program on the density of adult Chinook salmon Oncorhynchus tshawytscha currently from the Snake River basin listed under the U.S. Endangered Species Act. We analyzed 43 years of data from 22 populations, accounting for random effects across time and space using a Bayesian hierarchical model. We found that varying degrees of supplementation over a period of 25 years increased the density of natural-origin adults, on average, by 0-8% relative to non-supplementation years. Thirty-nine of the 43 year effects were at least two times larger in magnitude than the mean supplementation effect, suggesting common environmental variables play a more important role in driving inter-annual variability in adult density. Residual variation in density varied considerably across the region, but there was no systematic difference between supplemented and reference populations. Our results demonstrate the power of hierarchical Bayesian models to detect diffuse effects of management interventions and to quantify the variability of intervention success. Nevertheless, our study could not address whether ecological factors were more important than genetic considerations in determining the response to supplementation.