[Download] "Tracking Animal Migration with Stable Isotopes" by Keith A. Hobson & Leonard I. Wassenaar " Book PDF Kindle ePub Free

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eBook details

• Title: Tracking Animal Migration with Stable Isotopes
• Author : Keith A. Hobson & Leonard I. Wassenaar
• Release Date : January 13, 2018
• Genre: Nature,Books,Science & Nature,Agriculture,Life Sciences,Professional & Technical,Ecology,Environment,
• Pages : * pages
• Size : 20708 KB

Description

Tracking Animal Migration with Stable Isotopes, Second Edition, provides a complete introduction to new and powerful isotopic tools and applications that track animal migration, reviewing where isotope tracers fit in the modern toolbox of tracking methods. The book provides background information on a broad range of migration scenarios in terrestrial and aquatic systems and summarizes the most cutting-edge developments in the field that are revolutionizing the way migrant individuals and populations are assigned to their true origins. It allows undergraduates, graduate students and non-specialist scientists to adopt and apply isotopes to migration research, and also serves as a useful reference for scientists.

The new edition thoroughly updates the information available to the reader on current applications of this technique and provides new tools for the isotopic assignment of individuals to origins, including geostatistical multi-isotope approaches and the ways in which researchers can combine isotopes with routine data in a Bayesian framework to provide best estimates of animal origins. Four new chapters include contributions on applications to the movements of terrestrial mammals, with particular emphasis on how aspects of animal physiology can influence stable isotope values.
Includes an animal physiology component that is an in-depth overview of the cautions and caveats related to this techniqueCovers marine and aquatic isoscapes and methods to track marine organisms for researchers trying to apply isotopic tracking to animals in these environmentsFeatures state-of-the-art statistical treatments for assignment and combining diverse datasets

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