Handbook of Capture-Recapture Analysis

Every day, biologists in parkas, raincoats, and rubber boots go into the field to capture and mark a variety of animal species. Back in the office, statisticians create analytical models for the field biologists' data. But many times, representatives of the two professions do not fully understand one another's roles. This book bridges this gap by helping biologists understand state-of-the-art statistical methods for analyzing capture-recapture data. In so doing, statisticians will also become more familiar with the design of field studies and with the real-life issues facing biologists.

Reliable outcomes of capture-recapture studies are vital to answering key ecological questions. Is the population increasing or decreasing? Do more or fewer animals have a particular characteristic? In answering these questions, biologists cannot hope to capture and mark entire populations. And frequently, the populations change unpredictably during a study. Thus, increasingly sophisticated models have been employed to convert data into answers to ecological questions. This book, by experts in capture-recapture analysis, introduces the most up-to-date methods for data analysis while explaining the theory behind those methods. Thorough, concise, and portable, it will be immensely useful to biologists, biometricians, and statisticians, students in both fields, and anyone else engaged in the capture-recapture process.

Author: Steven C. Amstrup
Publisher: Princeton University Press
Published: 11/20/2005
Pages: 336
Binding Type: Paperback
Weight: 1.01lbs
Size: 8.20h x 5.42w x 0.88d
ISBN: 9780691089683

About the Author
Steven C. Amstrup researches bears and their ecosystems. His interests include distribution and movement patterns as well as wildlife population dynamics. Trent L. McDonald is a statistician and project manager with Western EcoSystems Technology, Inc. and Adjunct Professor of Statistics at the University of Wyoming. Bryan F. J. Manly is the author of several books on the statistics of natural selection, multivariate analysis, resource selection by animals, research study designs, computer-intensive statistics, and environmental statistics.