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Utility of mixed effects models to inform the stock structure of whiting in the Northeast Atlantic Ocean ArchiMer
Barrios, Alexander; Ernande, Bruno; Mahe, Kelig; Trenkel, Verena; Rochet, Marie-joelle.
Stock structure of whiting (Merlangius merlangus) in the North East Atlantic is unclear. This study uses mixed effects models to analyse growth variability as a way to investigate stock identification. Growth trajectories for 634 individuals and length-at-age data for 78,686 individuals were analysed for spatial coherence and temporal synchrony in the parameters of the von Bertalanffy growth model. Growth was found to differ among most ICES divisions, and temporal fluctuations were poorly synchronized between areas. This study illustrates how growth analyses can contribute to stock identification, in addition to other data.
Tipo: Text Palavras-chave: Growth; Whiting; North East Atlantic; Otolith; Von Bertalanffy growth model; Mixed effects models.
Ano: 2017 URL: http://archimer.ifremer.fr/doc/00373/48415/48810.pdf
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Addressing scope of inference for global genetic evaluation of livestock R. Bras. Zootec.
Tempelman,Robert John.
Genetic evaluations should become more accurate with the advent of whole genome selection (WGS) based on high density SNP panels. The use of WGS should then accelerate genetic gains for production traits given likely decreases in generation interval due to the greater intent to select more animals based just on their genotypes rather than phenotypes. However, past and current genetic evaluations may not generally connect well to the intended scope of inference. For example, estimating haplotype effects from the data of a single reference population does not bode well for the use of WGS in other diverse environments since the scope of inference is too narrow; conversely, WGS based on estimates, for example, derived from daughter yield deviations of dairy...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Bayesian inference; Genotype by environment interaction; Heterogeneous variances; Mixed effects models; Multiple traits; Random effects.
Ano: 2010 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982010001300029
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