Using R for Modelling and Quantitative Methods in Fisheries

By: Malcolm Haddon

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Hardcover | 15 August 2020 | Edition Number 1

At a Glance

Hardcover
352 Pages

Dimensions(cm)
23.39 x 15.6 x 2.06

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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.

Industry Reviews

"This is an excellent update to the earlier versions of this book by Dr. Haddon and is a "go-to" resource for fisheries students and professionals alike. The development of a comprehensive R package to accompany the well-written text is a substantial update to the earlier text. One of the strengths of this book is the integration between the text and the examples provided which I have found really helps to reduce the time required to implement these methods into ones on research. Highly recommended text."
- Bill Pine, University of Florida

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