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Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty


by R. Hilborn, C.J. Walters

List Price: $89.95
Price: $68.22
You Save: $21.73 (24%)
Available: Usually ships in 4 to 7 weeks
Sales Rank: 828417
Studio: Springer
Binding: Paperback
Number Of Pages: 592
Publication Date: November 30, 2003
Publisher: Springer


ACCESSORIES

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EDITORIAL REVIEWS

Product Description
Quantitative Fish Stocks Assessment is the first textbook to capture the spirit of the "new fisheries management", in that it is driven by a recognition of the dynamic nature of fish populations and the application of computers and systems analysis to fisheries problems. It contains detailed numerical methods for parameter estimation, confidence bounds, and the formulation of harvesting strategies. The book builds upon the three themes in the subtitle. Choice refers to alternative management actions and alternative models. Dynamics reviews basic concepts of ecology and the interactions between fish and fishermen, as the latter respond to biological variability and to economic opportunity and regulation. Uncertainty describes the all-pervasive feature of fish stocks assessment. This text, written by two highly respected authorities in the field, should also become a standard reference in the area for professionals. It is clearly written at the level accessible to senior undergraduates and is easily understandable without advanced calculus and linear algebra.


CUSTOMER REVIEWS (Average Customer Rating: 4.0 based on 1 review)

The book to have if you work with fish  
If you ask anyone who deals with fisheries management, they recommend this book. It's thorough, readable even by those of us who don't have have advanced math degrees, and focuses on the practical consequences of guessing how many fish there really are in the sea. If you want to know what's behind the curtain when managers declare how many fish you can catch, this is the book to read.
April 19, 2000


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Modelling and Quantitative Methods in Fisheries
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The Ecological Detective: Confronting Models with Data
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