Comparison of parameter estimation methods for detecting climate-induced changes in productivity of Pacific salmon (Oncorhynchus spp.)

Pacific salmon (Oncorhynchus spp.) populations can experience persistent ch anges in productivity, possibly due to climatic shifts. Management agencies need to rapidly and reliably detect such changes to avoid costly suboptima l harvests or depletion of stocks. However, given the inherent variability of salmon populations, it is difficult to detect changes quickly, let alone forecast them. We therefore compared three methods of annually updating es timates of stock-recruitment parameters: standard linear regression, Walter s' bias-corrected regression, and a Kalman filter. We used Monte Carlo simu lations that hypothesized a wide range of future climate-induced changes in the Ricker a parameter of a salmon stock. We then used each parameter esti mation method on the simulated stock and recruitment data and set escapemen t targets and harvest goals accordingly. In these situations with a time-va rying true Ricker a parameter, Kalman filter estimation resulted in greater mean cumulative catch than was produced by the standard linear regression approach, Walters' bias correction method, or a fixed harvest rate policy. This benefit of the Kalman filter resulted from its better ability to track changing parameter values, thereby producing escapements closer to the opt imal escapement each year. However, errors in implementing desired manageme nt actions can significantly reduce benefits from all parameter estimation techniques.