Specific aims of the conference were: to identify the specific features of sampling schemes used to obtain length-frequency data for stock assessment; to review length-based methodologies for stock assessment of fish and aquatic invertebrates, with particular reference to their precision and accuracy; to review and test computer programs implementing length-based methods.

Ocean harvests have plateaued worldwide and many important commercial stocks have been depleted. This has caused great concern among scientists, fishery managers, the fishing community, and the public. This book evaluates the major models used for estimating the size and structure of marine fish populations (stock assessments) and changes in populations over time. It demonstrates how problems that may occur in fisheries dataâ€"for example underreporting or changes in the likelihood that fish can be caught with a given type of gearâ€"can seriously degrade the quality of stock assessments. The volume makes recommendations for means to improve stock assessments and their use in fishery management.

This selection of methods is based on lecture notes used at a FAO/DANIDA training course held in Mombasa, Kenya, in May-June 1980. The methods presented are: regression and correlation, estimation of growth parameters from length-frequency data, estimation of mortalities (total, natural, fishing mortality) and analysis of catch and effort data. A brief annotated bibliography of tropical fish stock assessment is included.

This document reviews the several fish stock assessment methods based on length-frequency analysis. Emphasis is placed on sampling and collection of length-frequency data, to the estimation of population parameters such as growth, mortality and recruitment and the estimation of catch selectivity. Attention is given to the conversion of length to age using age-length keys, slicing length-frequency composition, and using modes. It reviews long-and short-term effects of changes in selection and fishing mortality, and discusses methods for long-term assessments and for short-term projections. The manual gives several examples of the most common methods used and stresses the advantages of using personal computers and the most recent software for data processing and analysis.

This publication contains guidelines for fish stock assessment and fishery management using the software tools and other outputs developed by the UK Department for International Development's Fisheries Management Science Programme (FMSP) from 1992 to 2004. It includes a CD-ROM with the installation files for each of the four FMSP software tools: LFDA (Length Frequency Data Analysis), CEDA (Catch Effort Data Analysis), YIELD and ParFish (Participatory Fisheries Stock Assessment).

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.