Effects of length-at-age data on growth and management benchmark F-0.1 estimates in the face of size-selective mortality

Authored by J Martinez-Garmendia

Date Published: 1997

DOI: 10.1016/s0165-7836(97)00057-x

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

The effects of fishery-dependent and fishery-independent length-at-age sampling in the face of size-selective exploitation on growth parameter and management benchmark F-0.1 estimation are explored. An individual-based population numerical model was used to simulate growth, natural and fishing mortalities and two sampling schemes for a short-lived and a long-lived population under various exploitation conditions. For the proposed sampling schemes, biases introduced in von Bertalanffy growth parameters and F-0.1 estimates indicated that fishery-dependent sampling was a much poorer source of length-at-age data than fishery-independent data from populations affected by size-selective mortality. Based on the assumed gear selectivity patterns, catches of simulated trawls obtained under high levels of exploitation provided poorer length-at-age data than when the catches were obtained under low levels of exploitation. The opposite happened for the length-at-age data from catches of fisheries operating with the simulated gillnet selectivities. Also, selection of larger individuals from the population by the gears affected both fishery-dependent and independent length-at-age samples more than when small mesh-sizes were used. For most of the cases studied, biases of F-0.1 showed similar trends to biases of the parameter K, but with smaller absolute magnitudes. It should be noted that these results are constrained by the assumptions of the scenarios investigated and, therefore, they could not be generalized. However, the approach proposed here offers interesting possibilities for further exploration. (C) 1997 Elsevier Science B.V.
Tags
Dynamics Variability Parameters