 Generalised growth models for aquatic species with an application to blacklip abalone (Haliotis rubra)Luke R. Lloyd-Jones, You-Gan Wang and Warwick J. Nash Ecological Modelling, 2014, vol. 272, issue C, 311-322 Abstract: 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. Keywords: Aquatic species growth; von Bertalanffy model; Gompertz model; Maximum likelihood method; Multiple tag-recapture data; Tagging effect (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:272:y:2014:i:c:p:311-322 DOI: 10.1016/j.ecolmodel.2013.10.012 Access Statistics for this article Ecological Modelling is currently edited by Brian D. Fath More articles in Ecological Modelling from Elsevier |
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