Comparison of Tributary Survival Estimates of Steelhead using Cormack–Jolly–Seber and Barker Models: Implications for Sampling Efforts and Designs

Abstract

We conducted simulations to compare the precision and bias of survival estimates from Cormack–Jolly–Seber (CJS) and Barker models to known parameter values based on empirical data for steelhead/resident Rainbow Trout Oncorhynchus mykiss from the John Day River, Oregon. We simulated seasonal differences in recapture and survival rates, and we varied the number of fish tagged, recapture and resight rates, sample site size, and fish movement (migratory or resident). Survival estimates from the Barker model had higher precision and lower or equal bias in comparison with estimates from the CJS model under almost all simulation scenarios. The precision of Barker survival estimates increased the most as the number of tagged fish increased from 50 to 200 (CV = 0.4–0.09). The Barker model's superior performance was dependent on the availability of resight data; such data are becoming more readily available, especially in places where large numbers of individuals are PIT-tagged and where an interrogation infrastructure exists (e.g., Columbia River basin). Tagging of 75–100 fish/site during high-capture periods (e.g., summer and fall) and focusing on the resighting of fish with fixed or mobile interrogators during low-capture periods (i.e., winter and spring) may be the most cost-effective strategy for improving estimates of juvenile steelhead survival.

Received April 18, 2014; accepted August 26, 2014

ACKNOWLEDGMENTS

We thank Ian Tattam for leading the field crews and participating in the data collection efforts. Nicholas Weber provided help in collecting, organizing, and summarizing the data. Robert Al-Chokhachy aided in earlier analyses highlighting differences between mark–recapture model outputs. Chris Jordan was instrumental in guiding the larger context of this research. The work was funded by the Bonneville Power Administration and the National Oceanic and Atmospheric Administration as part of the Integrated Status and Effectiveness Monitoring Program (project number 2003-017).