2002
DOI: 10.1139/f02-016
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Estimating salmon stock–recruitment relationships from catch and escapement data

Abstract: This paper presents an analysis of stock–recruitment data that takes account of natural variation in stock productivity (process error) and inaccurate escapement counts (measurement error). We formulate the model using dynamic state variables and take advantage of related techniques for parameter estimation, such as an extended Kalman filter. Our recruitment function depends explicitly on parameters relevant to management and includes various cases of historical interest. We adopt Bayesian methods for assessin… Show more

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Cited by 33 publications
(16 citation statements)
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“…Consistently with Kimura et al (1996), Punt (2003) and Schnute and Kronlund (2002), the results of the SSM were not markedly sensitive to violation of the assumption of equal process and observation error variance ( = 1). Thus, if no information is available on the relative importance of process versus observation errors, our results continue to support the use of the default choice = 1.…”
Section: Choice Among the Estimation Methodssupporting
confidence: 88%
“…Consistently with Kimura et al (1996), Punt (2003) and Schnute and Kronlund (2002), the results of the SSM were not markedly sensitive to violation of the assumption of equal process and observation error variance ( = 1). Thus, if no information is available on the relative importance of process versus observation errors, our results continue to support the use of the default choice = 1.…”
Section: Choice Among the Estimation Methodssupporting
confidence: 88%
“…It would also be possible to jointly estimate the total error and partition the total error into process and observation error components using variance partitioning where an informative prior would be required for the fraction of the total error that is associated with say observation errors (e.g. see variance transformations in table 4 in Schnute and Kronlund 2002). In this application, we opted to fix the standard deviation for the observation errors; this is equivalent to using a very informative prior and relaxing this prior would likely result in increased uncertainty.…”
Section: Biomass Dynamic Modelmentioning
confidence: 99%
“…For the former, longerterm strategic questions, managers place higher priority on information about systematic temporal trends in productivity than on the high frequency noise of year-to-year variability. The properties of the Kalman filter noted above make this method well suited to such strategic management needs (Walters 1986;Peterman et al 2000;Schnute and Kronlund 2002).…”
Section: Introductionmentioning
confidence: 99%