Introductory Fisheries Analyses With R

A How-To Guide for Conducting Common Fisheries-Related Analyses in R Introductory Fisheries Analyses with R provides detailed instructions on performing basic fisheries stock assessment analyses in the R environment. Accessible to practicing fisheries scientists as well as advanced undergraduate and graduate students, the book demonstrates the flexibility and power of R, offers insight into the reproducibility of script-based analyses, and shows how the use of R leads to more efficient and productive work in fisheries science. The first three chapters present a minimal introduction to the R environment that builds a foundation for the fisheries-specific analyses in the remainder of the book. These chapters help you become familiar with R for basic fisheries analyses and graphics. Subsequent chapters focus on methods to analyze age comparisons, age-length keys, size structure, weight-length relationships, condition, abundance (from capture-recapture and depletion data), mortality rates, individual growth, and the stock-recruit relationship. The fundamental statistical methods of linear regression, analysis of variance (ANOVA), and nonlinear regression are demonstrated within the contexts of these common fisheries analyses. For each analysis, the author completely explains the R functions and provides sufficient background information so that you can confidently implement each method. Web Resource The author’s website at http://derekogle.com/IFAR/ includes the data files and R code for each chapter, enabling you to reproduce the results in the book as well as create your own scripts. The site also offers supplemental code for more advanced analyses and practice exercises for every chapter.

A How-To Guide for Conducting Common Fisheries-Related Analyses in R Introductory Fisheries Analyses with R provides detailed instructions on performing basic fisheries stock assessment analyses in the R environment. Accessible to practicing fisheries scientists as well as advanced undergraduate and graduate students, the book demonstrates the flexibility and power of R, offers insight into the reproducibility of script-based analyses, and shows how the use of R leads to more efficient and productive work in fisheries science. The first three chapters present a minimal introduction to the R environment that builds a foundation for the fisheries-specific analyses in the remainder of the book. These chapters help you become familiar with R for basic fisheries analyses and graphics. Subsequent chapters focus on methods to analyze age comparisons, age-length keys, size structure, weight-length relationships, condition, abundance (from capture-recapture and depletion data), mortality rates, individual growth, and the stock-recruit relationship. The fundamental statistical methods of linear regression, analysis of variance (ANOVA), and nonlinear regression are demonstrated within the contexts of these common fisheries analyses. For each analysis, the author completely explains the R functions and provides sufficient background information so that you can confidently implement each method. Web Resource The author’s website at http://derekogle.com/IFAR/ includes the data files and R code for each chapter, enabling you to reproduce the results in the book as well as create your own scripts. The site also offers supplemental code for more advanced analyses and practice exercises for every chapter.

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.

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.

This book is aimed at conservation and development practitioners who need to learn and use R in a part-time professional context. It gives people with a non-technical background a set of skills to graph, map, and model in R. It also provides background on data integration in project management and covers fundamental statistical concepts. The book aims to demystify R and give practitioners the confidence to use it. Key Features: • Viewing data science as part of a greater knowledge and decision making system • Foundation sections on inference, evidence, and data integration • Plain English explanations of R functions • Relatable examples which are typical of activities undertaken by conservation and development organisations in the developing world • Worked examples showing how data analysis can be incorporated into project reports

This was the first effort to standardize methods for inland fish sampling across North America and, as such, was a joint effort contributed to by a large group of talented and dedicated individuals, in addition to the authors and coeditors. While these sampling methods were primarily developed for North America, most could be used with little or no modification in other areas of the world. Those involved with standard sampling efforts in North America would welcome the opportunity to collaborate with scientists from other regions to develop methods to share data worldwide.