Occupancy Estimation and Modeling - 1st Edition - ISBN: 9780120887668, 9780080455044

Occupancy Estimation and Modeling

1st Edition

Inferring Patterns and Dynamics of Species Occurrence

Authors: Darryl MacKenzie James Nichols J. Royle Kenneth Pollock Leslie Bailey James Hines
Hardcover ISBN: 9780120887668
eBook ISBN: 9780080455044
Imprint: Academic Press
Published Date: 17th November 2005
Page Count: 344
84.95 + applicable tax
90.95 + applicable tax
54.99 + applicable tax
68.95 + applicable tax
Compatible Not compatible
VitalSource PC, Mac, iPhone & iPad Amazon Kindle eReader
ePub & PDF Apple & PC desktop. Mobile devices (Apple & Android) Amazon Kindle eReader
Mobi Amazon Kindle eReader Anything else

Institutional Access


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.

Key Features

  • 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


Ecologists, population animal researchers, biologists, and graduate students in related areas

Table of Contents


  1. Introduction 1.1. Operational Definitions 1.2. Sampling Animal Populations and Communities General Principles Why? What? How? 1.3. Inference about Dynamics and Causation Generation of System Dynamics Statics and Process vs. Pattern 1.4. Discussion
  2. Occupancy in Ecological Investigations 2.1. Geographic Range 2.2. Habitat Relationships and Resource Selection 2.3. Metapopulation Dynamics Inference Based on Single-season Data Inference Based on Multiple-season Data 2.4. Large-scale Monitoring 2.5. Multispecies Occupancy Data Inference Based on Static Occupancy Patterns Inference Based on Occupancy Dynamics 2.6. Discussion
  3. Fundamental Principles of Statistical Inference 3.1. Definitions and Key Concepts Random Variables, Probability Distributions, and the Likelihood Function Expected Values Introduction to Methods of Estimation Properties of Point Estimators Computer-Intensive Methods 3.2. Maximum Likelihood Estimation Methods Maximum Likelihood Estimators Properties of Maximum Likelihood Estimators Variance, Covariance (and Standard Error) Estimation Confidence Interval Estimators 3.3. Bayesian Methods of Estimation Theory Computing Methods 3.4. Modeling Auxiliary Variables The Logit Link Function Estimation 3.5. Hypothesis Testing Background and Definitions Likelihood Ratio Tests Goodness of Fit Tests 3.6. Model Selection The Akiake Information Criteria (AIC) Goodness of Fit and Overdispersion Quasi-AIC Model Averaging and Model Selection Uncertainty 3.7. Discussion
  4. Single-species, Single-season Occupancy Models 4.1. The Sam


No. of pages:
© Academic Press 2006
Academic Press
eBook ISBN:
Hardcover ISBN:

About the Author

Darryl MacKenzie

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.

Affiliations and Expertise

Proteus Research and Consulting, Dunedin, New Zealand

James Nichols

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.

Affiliations and Expertise

U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland, USA

J. Royle

Dr Royle is currently a 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.

Affiliations and Expertise

Research Statistician, U.S. Geological Survey, Patuxent Wildlife Research Center, USA

Kenneth Pollock

Affiliations and Expertise

North Carolina State University, Department of Zoology, Raleigh, NC USA

Leslie Bailey

Affiliations and Expertise

Rothamsted Research in Harpenden, Hertfordshire, UK

James Hines

Affiliations and Expertise

U.S.Geological Survey, Patuxent Wildlife Research Center, Laurel, MD USA


"MacKenzie et al. write clearly and make sensible points that are illustrated with excellent case studies and figures..." - Erica Fleishman, Stanford University, Department of Biological Sciences, for ECOLOGY