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Elsevier

Stock Identification Methods

Applications in Fishery Science

Stock Identification Methods provides a comprehensive review of the various disciplines used to study the population structure of fishery resources. It represents the worldw… Leer más

Descripción

Stock Identification Methods provides 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, Stock Identification Methods offers a unified framework for understanding stock structure by promoting an understanding of the relative merits and sensitivities of each approach.

Puntos claves

* Describes eighteen 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

De interès para

Fishery scientists and managers; students studying fish biology and related aquatic sciences.

Índice

I. INTRODUCTION
Overview
Definition of Management Units, Stock Units, and Populations
Migration and the Stock Concept
Environmental versus Genetic Influence on Identification Characters

II. LIFE HISTORY TRAITS
Distribution of Life Stages
Life History Parameters

III. NATURAL MARKS-MORPHOLOGICAL ANALYSES
Morphometric Outlines
Morphometric Landmarks
Texture Methods
Meristics

IV. NATURAL MARKS-ENVIRONMENTAL SIGNALS
Parasites as Biological Tags
Fatty Acid Profiles

V. NATURAL MARKS-GENETIC ANALYSES
Chromosome Morphology
Allozymes
Mitochondrial DNA
Microsatellites
Random Amplified Polymorphic DNA (RAPD)
Amplified Length Polymorphic DNA (AFLP)

VI. APPLIED MARKS
Internal and External Tags
Electronic Tags
Otolith Thermal Marking

VII. STOCK IDENTIFICATION DATA ANALYSIS
Stock Identification Data Requirements in Quantitative Assessments
Statistical Algorithms for Stock Composition Analysis
Discriminant Function Analysis
Neural Networks in Classifying Biological Populations
Maximum Likelihood Estimators of Stock Composition
Non-parametric Methods of Estimating Classification Variability
Analysis of Tagging Data

VIII. APPLICATION OF STOCK IDENTIFICATION DATA IN RESOURCE MANAGEMENT
Application of Stock Identification Data in Resource Management
The Role of Stock Identification Data in Formulating Fishery Management Advice
Identifying Fish Farm Escapees
Real Time Application of Stock Identification Information

Detalles del producto

Sobre los editores

LK

Lisa A. Kerr

Lisa Kerr is a fisheries ecologist at the Gulf of Maine Research Institute (Portland, ME). Lisa is broadly interested in understanding the structure and dynamics of fish populations, with the goal of enhancing our ability to sustainably manage fisheries and ecosystems as a whole. She is particularly motivated to identify complex stock structure and understand the role it plays in the stability and resilience of local and regional populations. Lisa employs a diverse skill set to address critical ecological questions related to population structure that are also directly applicable to fisheries management. Her expertise includes structural analysis of fish hard parts (e.g. otoliths, vertebrae) and the application of the chemical methods (stable isotope, radioisotope, and trace element analysis) to these structures. She also uses mathematical modeling as a tool to understand how biocomplexity within fish stocks (e.g., spatial structure, connectivity, life cycle diversity) impacts their response to natural climatic oscillations, climate change, fishing, and management measures.
Afiliaciones y experiencia
Gulf of Maine Research Institute, Portland, ME, USA

SC

Steven X. Cadrin

Afiliaciones y experiencia
Northeast Fisheries Science Center, Woods Hole, MA, USA

KF

Kevin D. Friedland

Afiliaciones y experiencia
University of Massachusetts, Amherst, MA, U.S.A.

SM

Stefano Mariani

Afiliaciones y experiencia
School of Environment & Life Sciences, University of Salford, UK

JW

John R. Waldman

Afiliaciones y experiencia
Hudson River Foundation, New York, NY, U.S.A.

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