COS 62-2 - Detecting the effects of management regime shifts in dynamic environments using multi-population state-space models

Tuesday, August 8, 2017: 1:50 PM
D129-130, Oregon Convention Center
Matt Falcy, Fish Research, Oregon Dept. Fish and Wildlife, Corvallis, OR and Erik Suring, Oregon Dept. Fish and Wildlife, Corvallis, OR

Detecting the effectiveness of management actions intended to increase the abundance of threatened or exploited species can help resolve uncertainties about cost-effective management tactics intended to meet societal values. However, the complexity of ecological systems can make it difficult to identify important factors causing change in population abundance. This difficulty extends from detecting natural regime shifts to management-induced regime shifts. The adult abundance of naturally-produced coho salmon (Oncorhynchus kisutch) on the Oregon Coast generally declined until these fish were listed as threatened under the Endangered Species Act in 1998. The subsequent rebuilding of Oregon coastal coho adult abundance is coincident with increased habitat restoration, reduced hatchery production, and reduced harvest. Importantly, ocean survival also improved, thereby complicating the assessment of management effectiveness. We combined 46 years of data associated with 18 populations of Oregon coastal coho. Spawner-to-smolt relationships were modeled with Bayesian hierarchical state-space implementations of the logistic hockey stick recruitment function. Our models estimated the relative reproductive success of hatchery spawners, and permitted change in the parameters of the smolt recruitment functions at a time when significant change in management occurred.


We found more evidence for decline than increase in parameters of smolt recruitment, suggesting that changes in physical oceanographic conditions are responsible for recent increases in adult abundance. The reproductive success of hatchery-origin fish relative to natural-origin fish was 0.51 with a 95% credible interval from 0.19 to 0.89. While some management effects may unfold on longer time-scales than we observed, the analysis nonetheless provides a framework for evaluating the effects of management in a manner that is analogous to detecting natural regime shifts.