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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Academic Press

9780120887668

9780080455044

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229 X 152

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

Description

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.

Readership

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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