Analyzing catch-effort data by means of the Kalman filter




Reed, W.J.
Simons, C.M.

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A method of analyzing catch-effort data which uses linear statistical theory, but which, at the same time, maintains a degree of biological realism, is described and applied to catch-effort data for two fisheries. The method is based on a linear state-space model for the evolution of an unobservable state variable, the natural logarithm of the stock biomass. An observable proxy for the state is the logarithm of catch per unit effort. Randomness can be present both in the stock dynamics and in the process of catching fish. In addition both uncertainty and the possibility of convexity or concavity (hyperdepletion or hyperstability) in the relationship between stock abundance and cpue are included. The stock dynamics are modelled by a power law (log-linear) relationship between escapement and returning stock, with multiplicative lognormal noise. Maximum likelihood is used to estimate model parameters, with the Kalman filter being employed to generate the likelihood function. The method can easily be extended to include other assessments (besides cpue) of stock abundance. When only catch-effort data are available it is recommended that the primary use of the methodology be for predictions of cpue and catch.


The research described in this article formed part of Clement M. Simon's M.Sc. thesis at the University of Victoria.


catch-effort data, Kalman filter, prediction, observation error