Stock Identification Methods - 9780123970039
Un libro in lingua di Cadrin Steven X. (EDT) Kerr Lisa A. (EDT) Mariani Stefano (EDT) edito da Academic Pr, 2013
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Stock Identification Methods is a comprehensive review of the various disciplines used to study the population structure of fishery resources. It represents the experience and perspectives of worldwide 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 multidisciplinary analyses and interdisciplinary 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 basic tenets of population biology and management needs, Stock Identification Methods offers a unified framework for understanding stock structure using various methods, by promoting an understanding of the relative merits and sensitivities of each approach.
* 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
Informazioni bibliografiche
- Titolo del Libro in lingua: Stock Identification Methods
- Sottotitolo: Applications in Fishery Science
- Lingua: English
- Autori : Cadrin Steven X. (EDT) Kerr Lisa A. (EDT) Mariani Stefano (EDT)
- Editore: Academic Pr
- Collana: Academic Pr (Hardcover)
- Data di Pubblicazione: 29 Ottobre '13
- Genere: SCIENCE
- Pagine: 566
- Dimensioni mm: 228 x 152 x 0
- ISBN-10: 0123970032
- EAN-13: 9780123970039