Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence / Edition 1

Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence / Edition 1

ISBN-10:
0120887665
ISBN-13:
9780120887668
Pub. Date:
11/17/2005
Publisher:
Elsevier Science
ISBN-10:
0120887665
ISBN-13:
9780120887668
Pub. Date:
11/17/2005
Publisher:
Elsevier Science
Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence / Edition 1

Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence / Edition 1

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Overview

Occupancy Estimation and Modeling is the first book to examine the latest methods in analyzing presence/absence data surveys. Using four classes of models (single-species, single-season; single-species, multiple season; multiple-species, single-season; and multiple-species, multiple-season), the authors discuss the practical sampling situation, present a likelihood-based model enabling direct estimation of the occupancy-related parameters while allowing for imperfect detectability, and make recommendations for designing studies using these models.

* Provides authoritative insights into the latest in estimation modeling
* Discusses multiple models which lay the groundwork for future study designs
* Addresses critical issues of imperfect detectibility and its effects on estimation
* Explores the role of probability in estimating in detail


Product Details

ISBN-13: 9780120887668
Publisher: Elsevier Science
Publication date: 11/17/2005
Edition description: New Edition
Pages: 344
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Dr. MacKenzie is biometrician for Proteus Wildlife Research Consultants in New Zealand. His main area of expertise is in using occupancy models for monitoring and research. He started working in this area while on a year long stint at Patuxent Wildlife Research Center with Drs William L. Kendall and James D. Nichols during 2000/01. He has acted as a statistical consultant to the Department of Conservation, Ministry of Fisheries and the U.S. Geological Survey. In 2002 Darryl was awarded a prestigious Fast-Start Marsden Grant from the Royal Society of New Zealand for research into optimal study designs for estimating the proportion of area occupied by a target species.

James Nichols received a B.S. in Biology from Wake Forest Univ., M.S. in Wildlife Management from Louisiana State Univ., and Ph.D. in Wildlife Ecology from Michigan State Univ. He has spent his entire research career at Patuxent Wildlife Research Center working for the U.S. Fish and Wildlife Service, the National Biological Service, and now the U.S. Geological Survey. He is currently a Senior Scientist at Patuxent. His research interests focus on the dynamics and management of animal populations and on methods for estimating population parameters.

Dr Royle is a Senior Scientist and Research Statistician at the U.S. Geological Survey's Patuxent Wildlife Research Center. His research is focused on the application of probability and statistics to ecological problems, especially those related to animal sampling and demographic modeling. Much of his research over the last 10 years has been devoted to the development of methods illustrated in our new book. He has authored or coauthored more than 100 journal articles, and co-authored the books Spatial Capture Recapture, Hierarchical Modeling and Inference in Ecology and Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, all published by Academic Press.

Table of Contents

Ch. 1: Introduction
Ch. 2: Occupancy in Ecological Investigations
Ch. 3: Fundamental Principles of Statistical Inference
Ch. 4: Single-species, Single-season Occupancy Models
Ch. 5: Single-species, Single-season Models with Heterogeneous Detection Probabilities
Ch. 6: Design of Single-Season Occupancy Studies
Ch. 7: Single-species, Multiple-seasons Occupancy Models
Ch. 8: Occupancy Data for Multiple Species: Species Interactions
Ch. 9: Occupancy in Community Level Studies
Ch.10: Future Directions
References
Index

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Provides cutting-edge information on ways to improve ecological statistics and estimation using four distinct classes of models for discussion

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