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Modelling and Quantitative Methods in Fisheries

By: (Author) Malcolm Haddon

Manufacture on Demand

Ksh 17,100.00

Format: Hardback or Cased Book

ISBN-10: 1584885610

ISBN-13: 9781584885610

Edition Number: 2

Publisher: Taylor & Francis Inc

Imprint: Chapman & Hall/CRC

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Mar 11th, 2011

Print length: 465 Pages

Weight: 864 grams

Dimensions (height x width x thickness): 16.40 x 24.10 x 3.00 cms

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Offers an introduction to quantitative methods in fisheries. This book features material on tests and comparisons as well as chapters on length-based models and estimating uncertainty using Bayesian methods. It covers a range of topics such as simple linear regression, complex nonlinear modeling, methodology, and specific fields in fisheries.

With numerous real-world examples, Modelling and Quantitative Methods in Fisheries, Second Edition provides an introduction to the analytical methods used by fisheries’ scientists and ecologists. By following the examples using Excel, readers see the nuts and bolts of how the methods work and better understand the underlying principles. Excel workbooks are available for download from CRC Press website.

In this second edition, the author has revised all chapters and improved a number of the examples. This edition also includes two entirely new chapters:

  • Characterization of Uncertainty covers asymptotic errors and likelihood profiles and develops a generalized Gibbs sampler to run a Markov chain Monte Carlo analysis that can be used to generate Bayesian posteriors
  • Sized-Based Models implements a fully functional size-based stock assessment model using abalone as an example

This book continues to cover a broad range of topics related to quantitative methods and modelling. It offers a solid foundation in the skills required for the quantitative study of marine populations. Explaining important and relatively complex ideas and methods in a clear manner, the author presents full, step-by-step derivations of equations as much as possible to enable a thorough understanding of the models and methods.


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