Analysis Of Otolith Microchemistry Using Bayesian Hierarchical Mixture Models
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Analysis of Otolith Microchemistry Using Bayesian Hierarchical Mixture Models
Abstract: The field of fisheries research commonly uses classical statistical classification methods to estimate the proportion of fish that return to natal spawning grounds to spawn. With the advent of otolith microchemical analysis, researchers are able to extract information from fish ear stones (otoliths) about the chemical composition of water in which fish have spent distinct life history stages. Here we present a method of analysis set in the Bayesian statistical paradigm, which enables explicit incorporation of habitat information into the analysis. The ecological system is seen as arising from a mixture of disparate fish populations and information from the biological relationships inherent in otolith formation is exploited through the hierarchical model structure. We present the model and motivation, demonstrate the validity of the model through simulation studies, and conclude with an analysis of a data set from Lake Erie.
An Introduction to Fish Migration
Since the publication of The Migrations of Fish by Prof. Alexander Meek in 1916, a number of books have been published on this subject. However, most of these books only cover one type of migratory mechanisms. This book aims to overcome this drawback by presenting a comprehensive coverage of all life history strategies-potadromy, anadromy, catadrom
Stock Identification Methods
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