Occupancy Estimation and Modeling

Occupancy Estimation and Modeling

Inferring Patterns and Dynamics of Species Occurrence

1st Edition - November 17, 2005

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  • Authors: Darryl MacKenzie, James Nichols, J. Royle, Kenneth Pollock, Larissa Bailey, James Hines
  • eBook ISBN: 9780080455044
  • Hardcover ISBN: 9780120887668

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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

  • Preface

    1. Introduction

    1.1. Operational Definitions

    1.2. Sampling Animal Populations and Communities General Principles




    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


    Computing Methods

    3.4. Modeling Auxiliary Variables

    The Logit Link Function


    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


    Model Averaging and Model Selection Uncertainty

    3.7. Discussion

    4. Single-species, Single-season Occupancy Models

    4.1. The Sampling Situation

    4.2. Estimation of Occupancy If Probability of Detection Is 1 or Known Without Error

    4.3. Two-step Ad Hoc Approaches

    Geissler-Fuller Method

    Azuma-Baldwin-Noon Method

    Nichols-Karanth Method

    4.4. Model-based Approach

    Building a Model


    Example: Blue-ridge Salamanders

    Missing Observations

    Covariate Modeling

    Violations of Model Assumptions

    Assessing Model Fit


    4.5. Estimating Occupancy for a Finite Population or Small Area

    Prediction of Unobserved Occupancy State

    A Bayesian Formulation of the Model

    Blue-ridge Two-lined Salamanders Revisited

    4.6. Discussion

    5. Single-species, Single-season Models with Heterogeneous Detection Probabilities

    5.1. Site Occupancy Models with Heterogeneous Detection

    General Formulation

    Finite Mixtures

    Continuous Mixtures

    Abundance Models

    Model Fit

    5.2. Example: Breeding Bird Point Count Data

    5.3. Generalizations: Covariate Effects

    5.4. Example: Anuran Calling Survey Data

    5.5. On the Identifiability of Ψ

    5.6. Discussion

    6. Design of Single-season Occupancy Studies

    6.1. Defining a “Site”

    6.2. Site Selection

    6.3. Defining a “Season”

    6.4. Conducting Repeat Surveys

    6.5. Allocation of Effort: Number of Sites vs. Number of Surveys

    Standard Design

    Double Sampling Design

    Removal Sampling Design

    More Sites vs. More Repeat Surveys

    6.6. Discussion

    7. Single-species, Multiple-season Occupancy Models

    7.1. Basic Sampling Scheme

    7.2. An Implicit Dynamics Model

    7.3. Modeling Dynamic Changes Explicitly

    Modeling Dynamic Processes When Detection Probability Is 1

    Conditional Modeling of Dynamic Processes

    Unconditional Modeling of Dynamic Processes

    Missing Observations

    Including Covariate Information

    Alternative Parameterizations

    Example: House Finch Expansion in North America

    7.4. Investigating Occupancy Dynamics

    Markovian, Random, and No Changes in Occupancy


    Example: Northern Spotted Owl

    7.5. Violations of Model Assumptions

    7.6. Modeling Heterogeneous Detection Probabilities

    7.7. Study Design

    Time Interval Between Seasons

    Same vs. Different Sites Each Season

    More Sites vs. More Seasons

    More on Site Selection

    7.8. Discussion

    8. Occupancy Data for Multiple Species: Species Interactions

    8.1. Detection Probability and Inference about Species Co-occurrence

    8.2. A Single-season Model

    General Sampling Situation

    Statistical Model

    Reparameterizing the Model

    Incorporating Covariate Information

    Missing Observations

    8.3. Addressing Biological Hypotheses

    8.4. Example: Terrestrial Salamanders in Great Smoky Mountain National Park

    8.5. Study Design Issues

    8.6. Extension to Multiple Seasons

    8.7. Discussion

    9. Occupancy in Community-level Studies

    9.1. Investigating the Community at a Single Site

    Fraction of Species Present in a Single Season

    Changes in the Fraction of Species Present over Time

    9.2. Investigating the Community at Multiple Sites

    Single-season Studies: Modeling Occupancy and Detection

    Single-season Studies: Species Richness Estimation

    Example: Avian Point Count Data

    Multiple-season Studies

    9.3. Discussion

    10. Future Directions

    10.1. Multiple Occupancy States

    10.2. Integrated Modeling of Habitat and Occupancy

    10.3. Incorporating Information on Marked Animals

    10.4. Incorporating Count and Other Data

    10.5. Relationship Between Occupancy and Abundance

    10.6. Discussion

    Appendix: Some Important Mathematical Concepts



Product details

  • No. of pages: 344
  • Language: English
  • Copyright: © Academic Press 2005
  • Published: November 17, 2005
  • Imprint: Academic Press
  • eBook ISBN: 9780080455044
  • Hardcover ISBN: 9780120887668

About the Authors

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
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, MD, USA

J. Royle

J. Royle
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.

Affiliations and Expertise

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

Kenneth Pollock

Kenneth Pollock

Affiliations and Expertise

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

Larissa Bailey

Larissa Bailey

Affiliations and Expertise

Colorado State University, USA

James Hines

Affiliations and Expertise

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

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