Generalised growth models for aquatic species with an application to blacklip abalone (Haliotis rubra)


Autoria(s): Lloyd-Jones, Luke R.; Wang, You-Gan; Nash, Warwick J.
Data(s)

2014

Resumo

This paper presents a maximum likelihood method for estimating growth parameters for an aquatic species that incorporates growth covariates, and takes into consideration multiple tag-recapture data. Individual variability in asymptotic length, age-at-tagging, and measurement error are also considered in the model structure. Using distribution theory, the log-likelihood function is derived under a generalised framework for the von Bertalanffy and Gompertz growth models. Due to the generality of the derivation, covariate effects can be included for both models with seasonality and tagging effects investigated. Method robustness is established via comparison with the Fabens, improved Fabens, James and a non-linear mixed-effects growth models, with the maximum likelihood method performing the best. The method is illustrated further with an application to blacklip abalone (Haliotis rubra) for which a strong growth-retarding tagging effect that persisted for several months was detected. (C) 2013 Elsevier B.V. All rights reserved.

Identificador

Lloyd-Jones, Luke R. and Wang, You-Gan and Nash, Warwick J. (2014) Generalised growth models for aquatic species with an application to blacklip abalone (Haliotis rubra). Ecological Modelling, 272 . pp. 311-322. ISSN 0304-3800; 1872-7026

http://era.daf.qld.gov.au/4332/

Relação

http://dx.doi.org/10.1016/j.ecolmodel.2013.10.012

http://era.daf.qld.gov.au/4332/

Palavras-Chave #Fishery research #Shellfish fisheries
Tipo

Article

PeerReviewed