Revised and restructured, Modeling and Quantitative Methods in Fisheries, Second Edition provides an accessible introduction to quantitative methods in fisheries. This book features new material on tests and comparisons as well as new chapters on length-based models and estimating uncertainty using Bayesian methods. It presents a structured, step-by-step approach that introduces the material in a logical sequence. This text covers a range of topics such as simple linear regression, complex nonlinear modeling, methodology, and specific fields in fisheries. It also includes numerous real world examples implemented in Excel, with workbooks for all examples available for download on the web.

Quantitative methods and mathematical modelling are of critical importance to fishery science and management but, until now, there has been no book that offers the sharp focus, methodological detail, and practical examples needed by non-specialist fishery scientists and managers, and ecologists. Modelling and Quantitative Methods in Fisheries fills that void. To date, methodology books in fisheries science have been limited to cookbook approach to problems; simple compilations; or expositions in which either too much theory or insufficient methodological detail is given. The text is organized into three sections: an introduction to modelling in fisheries and ecology, a straight methodology section covering a range of methods, and a section focusing on specific fields in fisheries science. This book is timely as it addresses a topic of recent debate in fisheries and ecology, describing and comparing the uses of Least Squares, Maximum Likelihood, and Bayesian quantitative methods. Designed as stand-alone units, each chapter provides examples from both classic and recent literature and comes with dedicated Excel spreadsheets that permit you to delve into every detail of the analysis. All of these spreadsheets serve as active examples, which can easily be modified and customized and can be used as templates for analyzing your own data. The spreadsheets permit you to learn at your own speed and cover the simplest linear regression to the more complex non-linear modelling using maximum likelihood. Data analysis and modelling are best learned by doing and not just by reading. This book illustrates, step by step, the analyses it covers. More detailed in terms of introductory quantitative methods and modelling as applied to fisheries than any other book available, Modelling and Quantitative Methods in Fisheries gives you the advantage by supplying the full details of the analysis so that understanding the material is a matter of following the book.

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.

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.

This book really began in 1980 with our first microcomputer, an Apple II +. The great value of the Apple II + was that we could take the computer programs we had been building on mainframe and mini-computers, and make them available to the many fisheries biologists who also had Apple II + 's. About 6 months after we got our first Apple, John Glaister came through Vancouver and saw what we were doing and realized that his agency (New South Wales State Fisheries) had the same equipment and could run the same programs. John organized a training course in Australia where we showed about 25 Australian fisheries biologists how to use microcomputers to do many standard fisheries analyses. In the process of organizing this and sub sequent courses we developed a series of lecture notes. Over the last 10 years these notes have evolved into the chapters of this book.

Stock Identification Methods, 2e, continues to provide a comprehensive review of the various disciplines used to study the population structure of fishery resources. It represents the worldwide experience and perspectives of experts on each method, assembled through a working group of the International Council for the Exploration of the Sea. The book is organized to foster interdisciplinary analyses and conclusions about stock structure, a crucial topic for fishery science and management. Technological advances have promoted the development of stock identification methods in many directions, resulting in a confusing variety of approaches. Based on central tenets of population biology and management needs, this valuable resource offers a unified framework for understanding stock structure by promoting an understanding of the relative merits and sensitivities of each approach. - Describes 18 distinct approaches to stock identification grouped into sections on life history traits, environmental signals, genetic analyses, and applied marks - Features experts' reviews of benchmark case studies, general protocols, and the strengths and weaknesses of each identification method - Reviews statistical techniques for exploring stock patterns, testing for differences among putative stocks, stock discrimination, and stock composition analysis - Focuses on the challenges of interpreting data and managing mixed-stock 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 workb