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Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

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Lloyd-Jones, L. R., Nguyen, H. D., McLachlan, G. J., Sumpton, W. and Wang, Y.-G. (2016) Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics, 72 (4). pp. 1255-1265. ISSN 0006341X

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Article Link: http://dx.doi.org/10.1111/biom.12531


Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

Item Type:Article
Business groups:Animal Science
Keywords:Blue swimmer crab Growth model estimation Length-frequency data Minorization–maximization algorithm Mixture modeling
Subjects:Science > Statistics > Statistical data analysis
Aquaculture and Fisheries > Fisheries > Fishery conservation
Aquaculture and Fisheries > Fisheries > Fishery research
Live Archive:23 Jun 2016 03:50
Last Modified:03 Sep 2021 16:50

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