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Using R for Modelling and Quantitative Methods in Fisheries has
evolved and been adapted from an earlier book by the same author
and provides a detailed introduction to analytical methods commonly
used by fishery scientists, ecologists, and advanced students using
the open-source software R as a programming tool. Some knowledge of
R is assumed, as this is a book about using R, but an introduction
to the development and working of functions, and how one can
explore the contents of R functions and packages, is provided. The
example analyses proceed step-by-step using code listed in the book
and from the book's companion R package, MQMF, available from
GitHub and the standard archive, CRAN. The examples are designed to
be simple to modify so the reader can quickly adapt the methods
described to use with their own data. A primary aim of the book is
to be a useful resource to natural resource practitioners and
students. Featured Chapters: Model Parameter Estimation provides a
detailed explanation of the requirements and steps involved in
fitting models to data, using R and, mainly, maximum likelihood
methods. On Uncertainty uses R to implement bootstrapping,
likelihood profiles, asymptotic errors, and Bayesian posteriors to
characterize any uncertainty in an analysis. The use of the Monte
Carlo Markov Chain methodology is examined in some detail. Surplus
Production Models applies all the methods examined in the earlier
parts of the book to conducting a stock assessment. This included
fitting alternative models to the available data, characterizing
the uncertainty in different ways, and projecting the optimum
models forward in time as the basis for providing useful management
advice.
Using R for Modelling and Quantitative Methods in Fisheries has
evolved and been adapted from an earlier book by the same author
and provides a detailed introduction to analytical methods commonly
used by fishery scientists, ecologists, and advanced students using
the open-source software R as a programming tool. Some knowledge of
R is assumed, as this is a book about using R, but an introduction
to the development and working of functions, and how one can
explore the contents of R functions and packages, is provided. The
example analyses proceed step-by-step using code listed in the book
and from the book's companion R package, MQMF, available from
GitHub and the standard archive, CRAN. The examples are designed to
be simple to modify so the reader can quickly adapt the methods
described to use with their own data. A primary aim of the book is
to be a useful resource to natural resource practitioners and
students. Featured Chapters: Model Parameter Estimation provides a
detailed explanation of the requirements and steps involved in
fitting models to data, using R and, mainly, maximum likelihood
methods. On Uncertainty uses R to implement bootstrapping,
likelihood profiles, asymptotic errors, and Bayesian posteriors to
characterize any uncertainty in an analysis. The use of the Monte
Carlo Markov Chain methodology is examined in some detail. Surplus
Production Models applies all the methods examined in the earlier
parts of the book to conducting a stock assessment. This included
fitting alternative models to the available data, characterizing
the uncertainty in different ways, and projecting the optimum
models forward in time as the basis for providing useful management
advice.
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 Online. 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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