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

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 a...

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Veröffentlicht in:Biometrics Jg. 72; H. 4; S. 1255 - 1265
Hauptverfasser: Lloyd-Jones, Luke R., Nguyen, Hien D., McLachlan, Geoffrey J., Sumpton, Wayne, Wang, You-Gan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Blackwell Publishing Ltd 01.12.2016
Wiley-Blackwell
Schlagworte:
Algorithms
Animals
aquatic organisms
BIOMETRIC METHODOLOGY
biometry
Blue swimmer crab
Brachyura - growth & development
Coastal environments
coasts
Computer simulation
Crabs
Crustaceans
Data processing
Decapoda
Fisheries
Fisheries - statistics & numerical data
Growth model estimation
Growth models
Length-frequency data
Marine
Mathematical models
Minorization-maximization algorithm
Mixture modeling
Models, Biological
Models, Statistical
Normal Distribution
Optimization
Parameter estimation
Portunus
Queensland
Simulation
Statistical analysis
Statistical methods
statistical models
Stock assessment
Test procedures
Time dependence
Time Factors
variance
ISEW, Australia, Queensland
Queensland
Minorization-maximization algorithm
Blue swimmer crab
Length-frequency data
Mixture modeling
Growth model estimation
ISSN:0006-341X, 1541-0420, 1541-0420
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Zusammenfassung: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.
Bibliographie:ark:/67375/WNG-DLM1L8HK-L
ArticleID:BIOM12531
istex:43B83455D4C0D90F7E1257BE20B87166979A7E3C
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.12531