Estimating stock parameters from trawl cpue-at-age series using year-class curves

Type Article
Date 2007-03
Language English
Author(s) Cotter A1, Mesnil Benoit2, Piet G3
Affiliation(s) 1 : Cefas Lab, Lowestoft NR33 0HT, Suffolk, England.
2 : IFREMER, Dept EMH, F-44311 Nantes, France.
3 : Inst Marine Resources & Ecosyst Studies, NL-1970 AB Ijmuiden, Netherlands.
Source ICES Journal of Marine Science (1054-3139) (Oxford university press), 2007-03 , Vol. 64 , N. 2 , P. 234-247
DOI 10.1093/icesjms/fsl025
WOS© Times Cited 13
Keyword(s) Year class curve, Plaice, North Sea, Iteratively weighted least squares, Forward validation, Fish stock assessment, Cpue, Analysis of relative residual variance
Abstract A year-class curve is a plot of log cpue (catch per unit effort) over age for a single year class of a species (in contrast to the better known catch curve, fitted to multiple year classes at one time). When linear, the intercept and slope estimate the log cpue at age 0 and the average rate of total mortality, Z, respectively. Here, we suggest methodological refinements within a linear least squares framework. Candidate models may include a selectivity term, fleet-specific parameters, and polynomials in year to allow for gradual variations of Z. An iterative weighting method allows for differing precisions among the different fleets, and a forward (one-step ahead) validation procedure tests predicted cpue against observed values. Choice of the best approximating model(s) is made by ranking the biological credibility of each candidate model, then by comparing graphic plots, precision of prediction, and the Akaike Information Criterion. Two example analyses are (i) a comparison of estimated and true results for five stock simulations carried out by the US National Research Council, and (ii) modelling three beam trawl surveys for plaice (Pleuronectes platessa) in the North Sea. Results were consistent with known, age-related, offshore migrations by plaice. Year-class curves are commended as a widely applicable, statistically based, visual, and robust method.
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