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| 001 | EBC5124550 | ||
| 003 | MiAaPQ | ||
| 005 | 20240729131555.0 | ||
| 006 | m o d | | ||
| 007 | cr cnu|||||||| | ||
| 008 | 240724s2017 xx o ||||0 eng d | ||
| 020 |
_a9780124072459 _q(electronic bk.) |
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| 020 | _z9780124071971 | ||
| 035 | _a(MiAaPQ)EBC5124550 | ||
| 035 | _a(Au-PeEL)EBL5124550 | ||
| 035 | _a(CaPaEBR)ebr11466376 | ||
| 035 | _a(OCoLC)1012345211 | ||
| 040 |
_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ |
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| 050 | 4 | _aQH352 .O238 2018 | |
| 082 | 0 | _a591.7/88 | |
| 100 | 1 | _aMacKenzie, Darryl I. | |
| 245 | 1 | 0 |
_aOccupancy Estimation and Modeling : _bInferring Patterns and Dynamics of Species Occurrence. |
| 250 | _a2nd ed. | ||
| 264 | 1 |
_aSan Diego : _bElsevier Science & Technology, _c2017. |
|
| 264 | 4 | _c©2018. | |
| 300 | _a1 online resource (668 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 505 | 0 | _aFront Cover -- Occupancy Estimation and Modeling -- Copyright -- Contents -- Preface -- Acknowledgments -- Part I Background and Concepts -- 1 Introduction -- 1.1 Operational De nitions -- 1.2 Sampling Animal Populations and Communities: General Principles -- 1.2.1 Why? -- 1.2.2 What? -- 1.2.3 How? -- 1.3 Inference About Dynamics and Causation -- 1.3.1 Generation of System Dynamics -- 1.3.2 Statics and Process vs. Pattern -- 1.4 Discussion -- 2 Occupancy Applications -- 2.1 Geographic Range -- 2.2 Habitat Relationships and Resource Selection -- 2.3 Metapopulation Dynamics -- 2.3.1 Inference Based on Single-Season Data -- 2.3.2 Inference Based on Multiple-Season Data -- 2.4 Large-Scale Monitoring -- 2.5 Multi-Species Occupancy Data -- 2.5.1 Inference Based on Static Occupancy Patterns -- 2.5.2 Inference Based on Occupancy Dynamics -- 2.6 Paleobiology -- 2.7 Disease Dynamics -- 2.8 Non-Ecological Applications -- 2.9 Discussion -- 3 Fundamental Principals of Statistical Inference -- 3.1 De nitions and Key Concepts -- 3.1.1 Random Variables, Probability Distributions, and the Likelihood Function -- 3.1.2 Expected Values and Variance -- 3.1.3 Introduction to Methods of Estimation -- 3.1.4 Properties of Point Estimators -- Bias -- Precision (Variance and Standard Error) -- Accuracy (Mean Squared Error) -- 3.1.5 Computer Intensive Methods -- 3.2 Maximum Likelihood Estimation Methods -- 3.2.1 Maximum Likelihood Estimators -- 3.2.2 Properties of Maximum Likelihood Estimators -- 3.2.3 Variance, Covariance (and Standard Error) Estimation -- 3.2.4 Con dence Interval Estimators -- 3.2.5 Multiple Maxima -- 3.2.6 Observed and Complete Data Likelihood -- 3.3 Bayesian Estimation -- 3.3.1 Theory -- 3.3.2 Computing Methods -- 3.4 Modeling Predictor Variables -- 3.4.1 The Logit Link Function -- 3.4.2 Interpretation -- 3.4.3 Estimation -- 3.5 Hypothesis Testing. | |
| 505 | 8 | _a3.5.1 Background and De nitions -- 3.5.2 Likelihood Ratio Tests -- 3.5.3 Goodness of Fit Tests -- 3.6 Model Selection -- 3.6.1 Akaike's Information Criterion (AIC) -- 3.6.2 Goodness of Fit and Overdispersion -- 3.6.3 Quasi-AIC -- 3.6.4 Model Averaging and Model Selection Uncertainty -- 3.6.5 Bayesian Assessment of Model Fit -- 3.6.6 Bayesian Model Selection -- 3.7 Discussion -- Part II Single-Species, Single-Season Occupancy Models -- 4 Basic Presence/Absence Situation -- 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 -- 4.3.1 Geissler-Fuller Method -- 4.3.2 Azuma-Baldwin-Noon Method -- 4.3.3 Nichols-Karanth Method -- 4.4 Model-Based Approach -- 4.4.1 Building a Model -- Observed Data Likelihood -- Complete Data Likelihood -- 4.4.2 Estimation -- 4.4.3 Constant Detection Probability Model -- 4.4.4 Survey-Speci c Detection Probability Model -- 4.4.5 Probability of Occupancy Given Species Not Detected at a Unit -- 4.4.6 Example: Blue-Ridge Two-Lined Salamanders -- Maximum Likelihood Estimation -- Bayesian Estimation -- 4.4.7 Missing Observations -- 4.4.8 Covariate Modeling -- 4.4.9 Violations of Model Assumptions -- Violation of Closure -- Heterogeneity in Occupancy Probability -- Heterogeneity in Detection Probability -- Lack of Independence -- Species Misidenti cation -- 4.4.10 Assessing Model Fit -- 4.4.11 Diagnostic Plots -- 4.4.12 Examples -- Pronghorn Antelope -- Mahoenui Giant Weta -- Mahoenui Giant Weta: A Bayesian Analysis -- Swiss Willow Tit -- 4.5 Case Study: Troll Distribution in Middle Earth -- 4.6 Discussion -- 5 Beyond Two Occupancy States -- 5.1 The Sampling Situation -- 5.2 Model Based Approach -- 5.2.1 Observed Data Likelihood -- 5.2.2 Matrix Formulation -- 5.3 Alternative Parameterizations -- 5.4 Missing Observations. | |
| 505 | 8 | _a5.5 Covariates and Predictor Variables -- 5.6 Model Assumptions -- 5.7 Examples -- 5.7.1 California Spotted Owl Reproduction -- 5.7.2 Breeding Success of Grizzly Bears -- 5.8 Discussion -- 6 Extensions to Basic Approaches -- 6.1 Estimating Occupancy for a Finite Population or Small Area -- 6.1.1 Prediction of Unobserved Occupancy State -- A Non-Bayesian Approach -- A Bayesian Approach -- 6.1.2 Example: Blue Ridge Two-Lined Salamanders Revisited -- 6.1.3 Consequences of a Finite Population -- 6.1.4 A Related Issue -- 6.2 Accounting for False Positive Detections -- 6.2.1 Modeling Misclassi cation for a Single Season -- A General Approach -- Terminology -- Unit Con rmation Design -- Calibration Design -- Observation Con rmation Design -- Selecting an Approach -- 6.2.2 Discussion -- 6.3 Multi-Scale Occupancy -- 6.3.1 Model De nition -- 6.3.2 Example: Striped Skunks -- 6.3.3 Discussion -- 6.4 Autocorrelated Surveys -- 6.4.1 Model Description -- 6.4.2 Example: Tigers on Trails -- 6.4.3 Discussion -- 6.5 Staggered Entry-Departure Model -- 6.5.1 Model Description -- 6.5.2 Example: Maryland Amphibians -- 6.5.3 Discussion -- 6.6 Spatial Autocorrelation in Occurrence -- 6.6.1 Covariates -- 6.6.2 Conditional Auto-Regressive Model -- 6.6.3 Autologistic Model -- 6.6.4 Kriging -- 6.6.5 Restricted Spatial Regression -- 6.7 Discussion -- 7 Modeling Heterogeneous Detection Probabilities -- 7.1 Occupancy Models with Heterogeneous Detection -- 7.1.1 General Formulation -- 7.1.2 Finite Mixtures -- 7.1.3 Continuous Mixtures -- 7.1.4 Abundance-Induced Heterogeneity Models -- 7.1.5 Evaluation of Model Fit -- 7.2 Example: Breeding Bird Point Count Data -- 7.3 Modeling Covariate Effects on Detection -- 7.4 Example: Anuran Calling Survey Data -- 7.5 On the Identi ability of ψ -- 7.6 Discussion -- Part III Single-Species, Multiple-Season Occupancy Models. | |
| 505 | 8 | _a8 Basic Presence/Absence Situation -- 8.1 Basic Sampling Scheme -- 8.2 An Implicit Dynamics Model -- 8.3 Modeling Dynamic Changes Explicitly -- 8.3.1 Modeling Dynamic Processes when Detection Probability is 1 -- 8.3.2 Conditional Modeling of Dynamic Processes -- 8.3.3 Unconditional Modeling of Dynamic Processes -- 8.3.4 Missing Observations -- 8.3.5 Including Covariate Information -- 8.3.6 Alternative Parameterizations -- 8.3.7 Example: House Finch Expansion in North America -- 8.4 Violations of Model Assumptions -- 8.5 Discussion -- 9 More than Two Occupancy States -- 9.1 Basic Sampling Scheme -- 9.2 De ning an Explicit Dynamics Model -- 9.3 Modeling Data and Parameter Estimation -- 9.4 Covariates and `Missing' Observations -- 9.5 Model Assumptions -- 9.6 Examples -- 9.6.1 Maryland Green Frogs -- 9.6.2 California Spotted Owls -- 9.7 Discussion -- 10 Further Topics -- 10.1 False Positive Detections -- 10.1.1 Con rmation Designs -- Two Detection Types -- Two Detection Methods -- Example Analysis -- 10.1.2 More Observation and Occupancy States: General Approach -- 10.1.3 Discussion -- 10.2 Autocorrelated Within-Season Detections -- 10.3 Spatial Correlation in Dynamics -- 10.4 Investigating Occupancy Dynamics -- 10.4.1 Markovian, Random, and No Changes in Occupancy -- 10.4.2 Equilibrium -- 10.4.3 Example: Northern Spotted Owl -- 10.4.4 Further Insights -- Inferring Population Trajectory from Dynamic Parameters -- Time-Invariant Dynamic Parameters Can Induce an Apparent Trend -- 10.4.5 Chronological Order of Surveys -- 10.4.6 Discussion -- 10.5 Sensitivity of Occupancy to Dynamic Processes -- 10.5.1 Two-State Situation -- 10.5.2 General Situation -- 10.6 Modeling Heterogeneous Detection Probabilities -- 10.7 Discussion -- Part IV Study Design -- 11 Design of Single-Season Occupancy Studies -- 11.1 De ning the Population of Interest. | |
| 505 | 8 | _a11.2 De ning a Sampling Unit -- 11.3 Unit Selection -- 11.4 De ning a `Season' -- 11.5 Conducting Repeat Surveys -- 11.5.1 General Considerations -- 11.5.2 Special Note on Using Spatial Replication -- 11.6 Allocation of Effort, Number of Sites vs. Number of Surveys -- 11.6.1 Standard Design -- No Consideration of Cost -- Including Survey Cost -- 11.6.2 Double Sampling Design -- 11.6.3 Removal Sampling Design -- 11.6.4 More Units vs. More Surveys -- 11.6.5 Finite Population -- 11.7 Discussion -- 12 Multiple-Season Study Design -- 12.1 Time Interval Between Seasons -- 12.2 Same vs. Different Units Each Season -- 12.3 More Units vs. More Seasons -- 12.4 More on Unit Selection -- 12.5 Discussion -- Part V Advanced Topics -- 13 Integrated Modeling of Habitat and Occupancy Dynamics -- 13.1 Introduction -- 13.2 Basic Sampling Situation -- 13.3 Model Development and Estimation -- 13.3.1 Missing Observations -- 13.3.2 Covariates -- 13.4 Biological Questions of Interest -- 13.4.1 Effect of Habitat Change on Occupancy Dynamics -- 13.4.2 Effect of Species Presence on Habitat Dynamics -- 13.5 System Summaries -- 13.6 Model Extensions -- 13.7 Example -- 13.7.1 Patuxent Spotted Salamanders -- 13.8 Discussion -- 14 Species Co-Occurrence -- 14.1 Detection Probability and Inferences About Species Co-Occurrence -- 14.2 A Single-Season Model -- 14.2.1 General Sampling Situation -- 14.2.2 Statistical Model -- 14.2.3 Derived Parameters and Alternative Parameterizations -- 14.2.4 Covariates -- 14.2.5 Missing Observations -- 14.3 Addressing Biological Hypotheses -- 14.4 Example: Terrestrial Salamanders in Great Smoky Mountains National Park -- 14.5 Extension to Multiple Seasons -- 14.6 Example: Barred and Northern Spotted Owls -- 14.7 Study Design Issues -- 14.8 Generalizing to More than Two Species -- 14.9 Discussion -- 15 Occupancy in Community-Level Studies. | |
| 505 | 8 | _a15.1 Investigating the Community at a Single Unit. | |
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 650 | 0 | _aAnimal populations-Estimates. | |
| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aNichols, James D. | |
| 700 | 1 | _aRoyle, J. Andrew. | |
| 700 | 1 | _aPollock, Kenneth H. | |
| 700 | 1 | _aBailey, Larissa. | |
| 700 | 1 | _aHines, James E. | |
| 776 | 0 | 8 |
_iPrint version: _aMacKenzie, Darryl I. _tOccupancy Estimation and Modeling _dSan Diego : Elsevier Science & Technology,c2017 _z9780124071971 |
| 797 | 2 | _aProQuest (Firm) | |
| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=5124550 _zClick to View |
| 999 |
_c132423 _d132423 |
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