### Abstract

Three basic methods for estimating year-class strength given several research surveys or commercial catch indices of recruitment are described. Two are regression methods - calibration regression and predictive regression. The third method is factor analysis, in which the covariance between the indices is modelled as a function of the relationship to the underlying true, but unobservable, recruitment. All three of the methods estimate recruitment as an inverse variance weighted average of the estimates from each of the index series. Tests indicate that factor analysis and calibration with shrinkage perform best overall. Calibration can be quite sensitive to missing data, however, and may break down if the most recent year's recruitment is far from the mean of the absolute abundance series. Under these conditions, factor analysis performs better in simulation trials. -from Authors

Language | English |
---|---|

Pages | 379-387 |

Number of pages | 9 |

Journal | ICES Journal of Marine Science |

Volume | 49 |

Issue number | 4 |

DOIs | |

Publication status | Published - Nov 1992 |

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### Keywords

- commercial catches
- year-class strength
- research surveys
- simulation tests

### Cite this

*ICES Journal of Marine Science*,

*49*(4), 379-387. https://doi.org/10.1093/icesjms/49.4.379

}

*ICES Journal of Marine Science*, vol. 49, no. 4, pp. 379-387. https://doi.org/10.1093/icesjms/49.4.379

**Combining information from commercial catches and research surveys to estimate recruitment : a comparison of methods.** / Rosenberg, A. A.; Kirkwood, G. P.; Cook, R. M.; Myers, R. A.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Combining information from commercial catches and research surveys to estimate recruitment

T2 - ICES Journal of Marine Science

AU - Rosenberg, A. A.

AU - Kirkwood, G. P.

AU - Cook, R. M.

AU - Myers, R. A.

PY - 1992/11

Y1 - 1992/11

N2 - Three basic methods for estimating year-class strength given several research surveys or commercial catch indices of recruitment are described. Two are regression methods - calibration regression and predictive regression. The third method is factor analysis, in which the covariance between the indices is modelled as a function of the relationship to the underlying true, but unobservable, recruitment. All three of the methods estimate recruitment as an inverse variance weighted average of the estimates from each of the index series. Tests indicate that factor analysis and calibration with shrinkage perform best overall. Calibration can be quite sensitive to missing data, however, and may break down if the most recent year's recruitment is far from the mean of the absolute abundance series. Under these conditions, factor analysis performs better in simulation trials. -from Authors

AB - Three basic methods for estimating year-class strength given several research surveys or commercial catch indices of recruitment are described. Two are regression methods - calibration regression and predictive regression. The third method is factor analysis, in which the covariance between the indices is modelled as a function of the relationship to the underlying true, but unobservable, recruitment. All three of the methods estimate recruitment as an inverse variance weighted average of the estimates from each of the index series. Tests indicate that factor analysis and calibration with shrinkage perform best overall. Calibration can be quite sensitive to missing data, however, and may break down if the most recent year's recruitment is far from the mean of the absolute abundance series. Under these conditions, factor analysis performs better in simulation trials. -from Authors

KW - commercial catches

KW - year-class strength

KW - research surveys

KW - simulation tests

UR - http://www.scopus.com/inward/record.url?scp=0027020656&partnerID=8YFLogxK

U2 - 10.1093/icesjms/49.4.379

DO - 10.1093/icesjms/49.4.379

M3 - Article

VL - 49

SP - 379

EP - 387

JO - ICES Journal of Marine Science

JF - ICES Journal of Marine Science

SN - 1054-3139

IS - 4

ER -