Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
Conference on Applied Statistics in Agriculture
Modeling Fish Length Distribution Using a Mixture TechniqueMany methods have been developed in the last 70 years to predict the natural mortality rate, M, of a stock based on empirical evidence from comparative life history studies. These indirect or empirical methods are used in most stock assessments to (i) obtain estimates of M in the absence of direct information, (ii) check on the reasonableness of a direct estimate of M, (iii) examine the range of plausible M estimates for the stock under consideration, and (iv) define prior distributions for Bayesian analyses. The two most cited empirical methods have appeared in the literature over 2500 times to date. Despite the importance of these methods, there is no consensus in the literature on how well these methods work in terms of prediction error or how their performance may be ranked. We evaluate estimators based on various combinations of maximum age (tmax), growth parameters, and water temperature by seeing how well they reproduce over 200 independent, direct estimates of M. We use tenfold cross-validation to estimate the prediction error of the estimators and to rank their performance. With updated and carefully reviewed data, we conclude that a tmax-based estimator performs the best among all estimators evaluated. The tmax-based estimators in turn perform better than the Alverson-Carney method based on tmax and the von Bertalanffy K coefficient, Pauly's method based on growth parameters and water temperature and methods based just on K. It is possible to combine two independent methods by computing a weighted mean but the improvement over the tmax-based methods is slight. Based on cross-validation prediction error, model residual patterns, model parsimony, and biological considerations, we recommend the use of a tmax-based estimator (M = 4.899tmax^(−0.916), prediction error = 0.32) when possible and a growth-based method (M = 4.118K^(0.73)Linf^(−0.33), prediction error = 0.6) otherwise.
Canadian Journal of Fisheries and Aquatic Sciences
Linking fishing mortality reference points to life history traits: an empirical study2012 •
Genetics Selection Evolution
Survival, growth and sexual maturation in Atlantic salmon exposed to infectious pancreatic necrosis: a multi-variate mixture model approach2013 •
Genetics Selection Evolution
Comparison of methods for analysis of selective genotyping survival data2006 •
Genetics Selection Evolution
A bivariate quantitative genetic model for a threshold trait and a survival trait2006 •
Arquivo Brasileiro de Medicina Veterinária e Zootecnia
Estimativas de parâmetros genéticos para características de desempenho de suínos em fase de crescimento e terminação2005 •
2015 •
Plant breeders and educators working with the International Potato Center (CIP) needed freely available statistical tools. In response, we created first a set of scripts for specific tasks using the open source statistical software R. Based on this we eventually compiled the R package agricolae as it covered a niche. Here we describe for the first time its main functions in the form of an article. We also review its reception using download statistics, citation data, and feedback from a user survey. We highlight usage in our extended network of collaborators. The package has found applications beyond agriculture in fields like aquaculture, ecology, biodiversity, conservation biology and cancer research. In summary, the package agricolae is a well established statistical toolbox based on R with a broad range of applications in design and analyses of experiments also in the wider biological community .
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.
2005 •
Journal of dairy science
Using conformation traits to improve reliability of genetic evaluation for herd life based on survival analysis2002 •
The Quarterly Review of Biology
Population Genetics: Principles and Applications for Fisheries Scientists. Edited by Eric M Hallerman. Population Genetics: Principles and Applications for …2004 •
2005 •
2007 •
1999 •
Canadian Journal of Fisheries and Aquatic Sciences
Effect of individual variability on estimation of population parameters from length-frequency data1998 •
Frontiers in Genetics
Quantitative Genetics of Growth Rate and Filet Quality Traits in Atlantic Salmon Inferred From a Longitudinal Bayesian Model for the Left-Censored Gaussian Trait Growth Rate2020 •
2003 •
Canadian Journal of Fisheries and Aquatic Sciences
Comparison of virtual population analysis and statistical kill-at-age analysis for a recreational, kill-dominated fishery2005 •
Transactions of the American Fisheries Society
A Simple Method for Estimating Survival Rate from Catch Rates from Multiple Years2007 •
2006 •
Transactions of the American Fisheries Society
Estimating Mortality from Mean Length Data in Nonequilibrium Situations, with Application to the Assessment of Goosefish2006 •
Journal of Animal Science
Application of multivariate heavy-tailed distributions to residuals in the estimation of genetic parameters of growth traits in beef cattle2013 •
2008 •
Canadian Journal of Fisheries and Aquatic Sciences
Determination of relative survival of two stocked walleye populations and resident natural-origin fish by microsatellite DNA parentage assignment2002 •
African Journal of Marine Science
A Statistical Model for Stock Assessment of Southern Bluefin Tuna with Temporal Changes in Selectivity2003 •
Canadian Journal of Fisheries and Aquatic Sciences
A model for categorical length data from groundfish surveys2004 •