Using R for Modelling and Quantitative Methods in Fisheries (Chapman & Hall/CRC The R Series)

Haddon, Malcolm

ISBN 10: 0367469898 / ISBN 13: 9780367469894

Published by Chapman and Hall/CRC, 2020

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Bibliographic Details

• Title: Using R for Modelling and Quantitative Methods in Fisheries (Chapman & Hall/CRC The R Series)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2020
• Language: English
• ISBN 10: 0367469898
• ISBN 13: 9780367469894
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Author

Dr. Malcolm Haddon has at least 35 years of experience in fisheries science, having worked in the Department of New Zealand Fisheries, the University of Sydney, the Australian Maritime College, the University of Tasmania, and, most recently, in Australia’s Commonwealth Scientific and Industrial Research Organization (CSIRO), from which he recently retired. He has worked with: Crustacea, including crabs, prawns, and rock lobster; Mollusca, including scallops and abalone; and scale-fish, many and various. Dr. Haddon’s interests are these days focussed on all aspects of resource assessment and simulation testing of resource management using management strategy evaluation. He considers himself fortunate to have become an adjunct professor in the Institute of Marine and Antarctic Sciences at the University of Tasmania and an Honorary Research Fellow at Oceans and Atmosphere, CSIRO, in Hobart, Tasmania. In both institutions he continues to collaborate with colleagues, most recently beginning to contribute to two research programs at the university on abalone population dynamics and management.