Modelling the growth of Northern Cod

Mohammed, Zakiyah (2015) Modelling the growth of Northern Cod. Masters thesis, Memorial University of Newfoundland.

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Abstract

We develop a body size growth model of Northern cod (Gadus morhua) in Northwest Atlantic Fisheries Organization (NAFO) Divisions 2J3KL during 2009-2013. We use individual length-at-age data from the bottom trawl survey in these divisions during 2009–2013. We use the Von Bertalanffy (VonB) model extended to account for between-individual variations in growth, and variations that may be caused by the methods which fish are caught and sampled for length and age measurements. We assume between-individual variation in growth appears because individuals grow at a different rate (k), and they achieve different maximum sizes (l∞). We also included measurement error in length and age in our model since ignoring these errors can lead to biased estimates of the growth parameters. We use the structural errors-invariables (SEV) approach to estimate individual variation in growth, ageing error variation, and the true age distribution of the fish. Our results shows the existence of individual variation in growth and ME in age. According to the negative log likelihood ratio (NLLR) test, the best model indicated: 1) different growth patterns across divisions and years. 2) Between individual variation in growth is the same for the same division across years. 3) The ME in age and true age distribution are different for each year and division.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/12182
Item ID: 12182
Additional Information: Includes bibliographical references (pages 130-133).
Keywords: Structural error in variable model, stratified sampling, Functional error in variable model, Growth of Northern cod, Variation in fish growth
Department(s): Science, Faculty of > Mathematics and Statistics
Date: December 2015
Date Type: Submission
Library of Congress Subject Heading: Atlantic cod--Growth--Mathematical models; Atlantic cod--Age determination; Errors-in-variables models; Parameter estimation

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