Occupancy Estimation and Modeling, 1st Edition

Inferring Patterns and Dynamics of Species Occurrence

Occupancy Estimation and Modeling, 1st Edition,Darryl MacKenzie,James Nichols,J. Royle,Kenneth Pollock,Larissa Bailey,James Hines,ISBN9780120887668

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Cutting-edge information on improving ecological statistics and estimation

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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


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.


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

Darryl MacKenzie

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

Affiliations and Expertise

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

View additional works by J. Andrew Royle

Kenneth Pollock

Affiliations and Expertise

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

Larissa Bailey

Affiliations and Expertise

US Geological Survey Patuxent Wildlife Research Center Laurel, Maryland USA

James Hines

Affiliations and Expertise

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

Occupancy Estimation and Modeling, 1st Edition

Preface 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 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 Estimation Example: Blue-ridge Salamanders Missing Observations Covariate Modeling Violations of Model Assumptions Assessing Model Fit Examples 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 ? 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 Equilibrium 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 References Index

Quotes and reviews

"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

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