Rm. Peterman et al., Comparison of parameter estimation methods for detecting climate-induced changes in productivity of Pacific salmon (Oncorhynchus spp.), CAN J FISH, 57(1), 2000, pp. 181-191
Citations number
36
Categorie Soggetti
Aquatic Sciences
Journal title
CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES
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.