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
  • Occupancy Estimation and Modeling

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    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.

    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

    Readership

    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 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

    Details

    No. of pages:
    344
    Language:
    English
    Copyright:
    © 2006
    Published:
    Imprint:
    Academic Press
    Print ISBN:
    9780120887668
    Electronic ISBN:
    9780080455044

    About the authors

    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

    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

    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