Spatial capture-recapture
Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementat...
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| Jazyk: | angličtina |
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Amsterdam
Elsevier
2014
Elsevier Science & Technology Academic Press |
| Vydání: | 1 |
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| ISBN: | 0124059392, 9780124059399 |
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| Abstract | Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors' approach is practical - it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package.
Comprehensive reference on revolutionary new methods in ecology makes this the first and only book on the topicEvery methodological element has a detailed worked example with a code template, allowing you to learn by exampleIncludes an R package that contains all computer code and data sets on companion website |
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| AbstractList | Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors' approach is practical - it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package.
Comprehensive reference on revolutionary new methods in ecology makes this the first and only book on the topicEvery methodological element has a detailed worked example with a code template, allowing you to learn by exampleIncludes an R package that contains all computer code and data sets on companion website |
| Author | Gardner, Beth Royle, J. Andrew USGS Patuxent Wildlife Research Center North Carolina State University Sollmann, Rahel |
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| DOI | 10.1016/C2012-0-01222-7 |
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| Discipline | Ecology Biology |
| EISBN | 9780124071520 012407152X |
| Edition | 1 |
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| Notes | Includes bibliographical references (pages 545-568) and index Transferred to Digital Printing in 2013 "Space plays a vital role in virtually all ecological processes (Tilman and Kareiva, 1997; Hanski, 1999; Clobert et al., 2001). The spatial arrangement of habitat can influence movement patterns during dispersal, habitat selection, and survival. The distance between an organism and its competitors and prey can influence activity patterns and foraging behavior. Further, understanding distribution and spatial variation in abundance is necessary in the conservation and management of populations"-- Provided by publisher |
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| PublicationDate | c2014 2013 2013-08-27 |
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| PublicationPlace | Amsterdam |
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| PublicationYear | 2014 2013 |
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| Snippet | Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and... |
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| SubjectTerms | Animal populations -- Mathematical models Ecology Spatial behavior in animals - Research Spatial ecology -- Research |
| TableOfContents | 6.4 Likelihood Analysis of the Wolverine Camera Trapping Data -- 6.4.1 Sensitivity to integration grid and state-space buffer -- 6.4.2 Using a habitat mask (restricted state-space) -- 6.5 DENSITY and the R Package secr -- 6.5.1 Encounter device types and detection models -- 6.5.2 Analysis using the secr package -- 6.5.3 Likelihood analysis in the secr package -- 6.5.4 Multi-session models in secr -- 6.5.5 Some additional capabilities of secr -- 6.5.5.1 Alternative observation models -- 6.5.5.2 Summary statistics -- 6.5.5.3 State-space buffer -- 6.5.5.4 Model selection and averaging -- 6.5.5.5 Population closure test -- 6.5.5.6 Density mapping and effective sample area -- 6.5.5.7 Covariate models -- 6.6 Summary and outlook -- 7 Modeling Variation in Encounter Probability -- 7.1 Encounter Probability Models -- 7.1.1 Bayesian analysis with bear.JAGS -- 7.1.2 Bayesian analysis of encounter probability models -- 7.2 Modeling Covariate Effects -- 7.2.1 Date and time -- 7.2.2 Trap-specific covariates -- 7.2.3 Behavior or trap response by individual -- 7.2.4 Individual covariates -- 7.3 Individual Heterogeneity -- 7.3.1 Models of heterogeneity -- 7.3.2 Heterogeneity induced by variation in home range size -- 7.4 Likelihood Analysis in secr -- 7.4.1 Notes for fitting standard models -- 7.4.2 Sex effects -- 7.4.3 Individual heterogeneity -- 7.4.4 Model selection in secr using AIC -- 7.5 Summary and Outlook -- 8 Model Selection and Assessment -- 8.1 Model Selection by AIC -- 8.1.1 AIC analysis of the wolverine data -- 8.2 Bayesian Model Selection -- 8.2.1 Model selection by DIC -- 8.2.2 DIC analysis of the wolverine data -- 8.2.3 Bayesian model-averaging with indicator variables -- 8.2.3.1 Analysis of the wolverine data -- 8.2.4 Choosing among detection functions -- 8.3 Evaluating Goodness-of-Fit -- 8.4 The Two Components of Model Fit Intro -- Half Title -- Title Page -- Copyright -- Contents -- Foreword -- Preface -- Acknowledgments -- PART I: Background and Concepts -- 1 Introduction -- 1.1 The study of populations by capture-recapture -- 1.2 Lions and Tigers and Bears, oh my: Genesis of Spatial -- 1.2.1 Camera trapping -- 1.2.2 DNA sampling -- 1.2.3 Acoustic sampling -- 1.2.4 Search-encounter methods -- 1.3 Capture-Recapture for Modeling Encounter Probability -- 1.3.1 Example: Fort Drum bear study -- 1.3.2 Inadequacy of non-spatial capture-recapture -- 1.4 Historical Context: a Brief Synopsis -- 1.4.1 Buffering -- 1.4.2 Temporary emigration -- 1.5 Extension of Closed Population Models -- 1.5.1 Toward spatial explicitness: Efford's formulation -- 1.5.2 Abundance as the aggregation of a point process -- 1.5.3 The activity center concept -- 1.5.4 The state-space -- 1.5.5 Abundance and density -- 1.6 Characterization of SCR Models -- 1.7 Summary and Outlook -- 2 Statistical Models and SCR -- 2.1 Random Variables and Probability Distributions -- 2.1.1 Stochasticity in ecology -- 2.1.2 Properties of probability distributions -- 2.2 Common Probability Distributions -- 2.2.1 The binomial distribution -- 2.2.2 The Bernoulli distribution -- 2.2.3 The multinomial and categorical distributions -- 2.2.4 The Poisson distribution -- 2.2.5 The uniform distribution -- 2.2.6 Other distributions -- 2.3 Statistical Inference and Parameter Estimation -- 2.4 Joint, Marginal, and Conditional Distributions -- 2.5 Hierarchical Models and Inference -- 2.6 Characterization of SCR Models -- 2.7 Summary and Outlook -- 3 GLMs and Bayesian Analysis -- 3.1 GLMs and GLMMs -- 3.2 Bayesian Analysis -- 3.2.1 Bayes' rule -- 3.2.2 Principles of Bayesian inference -- 3.2.3 Prior distributions -- 3.2.4 Posterior inference -- 3.2.5 Small sample inference 8.4.1 Testing uniformity or spatial randomness -- 8.4.1.1 Sensitivity to bin size -- 8.4.1.2 Sensitivity to state-space extent -- 8.4.2 Assessing fit of the observation model -- 8.4.3 Does the SCR model fit the wolverine data? -- 8.5 Quantifying Lack-of-Fit and Remediation -- 8.6 Summary and Outlook -- 9 Alternative Observation Models -- 9.1 Poisson Observation Model -- 9.1.1 Poisson model of space usage -- 9.1.2 Poisson relationship to the Bernoulli model -- 9.1.3 A cautionary note on modeling encounter frequencies -- 9.1.4 Analysis of the Poisson SCR model in BUGS -- 9.1.5 Simulating data and fitting the model -- 9.1.6 Analysis of the wolverine study data -- 9.1.7 Count detector models in the secr package -- 9.2 Independent Multinomial Observations -- 9.2.1 Multinomial resource selection models -- 9.2.2 Simulating data and analysis using JAGS -- 9.2.3 Multinomial relationship to the Poisson -- 9.2.4 Avian mist-netting example -- 9.2.4.1 Multiple sample sessions -- 9.2.4.2 Analysis using JAGS -- 9.2.4.3 Analysis using secr -- 9.3 Single-Catch Traps -- 9.3.1 Inference for single-catch systems -- 9.3.2 Analysis of Efford's possum trapping data -- 9.4 Acoustic Sampling -- 9.4.1 The signal strength model -- 9.4.2 Implementation in secr -- 9.4.3 Implementation in BUGS -- 9.4.4 Other types of acoustic data -- 9.5 Summary and Outlook -- 10 Sampling Design -- 10.1 General Considerations -- 10.1.1 Model-based not design-based -- 10.1.2 Sampling space or sampling individuals? -- 10.1.3 Focal population vs. state-space -- 10.2 Study Design for (Spatial) Capture-Recapture -- 10.3 Trap Spacing and Array Size Relative to Animal Movement -- 10.3.1 Black bears from Pictured Rocks National Lakeshore -- 10.4 Sampling Over Large Areas -- 10.5 Model-Based Spatial Design -- 10.5.1 Statement of the design problem -- 10.5.2 Model-based Design for SCR 10.5.3 An optimal design criterion for SCR 3.3 Characterizing Posterior Distributions by MCMC Simulation -- 3.3.1 What goes on under the MCMC hood -- 3.3.2 Rules for constructing full conditional distributions -- 3.3.3 Metropolis-Hastings algorithm -- 3.4 Bayesian Analysis Using the BUGS Language -- 3.4.1 Linear regression in WinBUGS -- 3.5 Practical Bayesian Analysis and MCMC -- 3.5.1 Choice of prior distributions -- 3.5.2 Convergence and so forth -- 3.5.3 Bayesian confidence intervals -- 3.5.4 Estimating functions of parameters -- 3.6 Poisson GLMs -- 3.6.1 North American breeding bird survey data -- 3.6.2 Poisson GLM in WinBUGS -- 3.6.3 Constructing your own MCMC algorithm -- 3.7 Poisson GLM with Random Effects -- 3.8 Binomial GLMs -- 3.8.1 Binomial regression -- 3.8.2 North American waterfowl banding data -- 3.9 Bayesian Model Checking and Selection -- 3.9.1 Goodness-of-fit -- 3.9.2 Model selection -- 3.10 Summary and Outlook -- 4 Closed Population Models -- 4.1 The Simplest Closed Population Model: Model M0 -- 4.1.1 The core capture-recapture assumptions -- 4.1.2 Conditional likelihood -- 4.2 Data Augmentation -- 4.2.1 DA links occupancy models and closed population models -- 4.2.2 Model M0 in BUGS -- 4.2.3 Remarks on data augmentation -- 4.2.4 Example: Black bear study on Fort Drum -- 4.3 Temporally Varying and Behavioral Effects -- 4.4 Models with Individual Heterogeneity -- 4.4.1 Analysis of model Mh -- 4.4.2 Analysis of the Fort Drum data with model Mh -- 4.4.3 Comparison with MLE -- 4.5 Individual Covariate Models: Toward Spatial Capture-Recapture -- 4.5.1 Example: location of capture as a covariate -- 4.5.2 Example: Fort Drum black bear study -- 4.5.3 Extension of the model -- 4.5.4 Invariance of density to B -- 4.5.5 Toward fully spatial capture-recapture models -- 4.6 Distance Sampling: a Primitive SCR Model -- 4.6.1 Example: Sonoran desert tortoise study 4.7 Summary and Outlook -- PART II: Basic SCR Models -- 5 Fully Spatial Capture-Recapture Models -- 5.1 Sampling design and data structure -- 5.2 The Binomial Observation Model -- 5.2.1 Definition of home range center -- 5.2.2 Distance as a latent variable -- 5.3 The Binomial Point Process Model -- 5.3.1 The state-space of the point process -- 5.3.1.1 Prescribing the state-space -- 5.3.1.2 Invariance to the state-space -- 5.3.2 Connection to model Mh and distance sampling -- 5.4 The Implied Model of Space Usage -- 5.4.1 Bivariate normal case -- 5.4.2 Calculating space usage -- 5.4.3 Relevance of understanding space usage -- 5.4.4 Contamination due to behavioral response -- 5.5 Simulating SCR Data -- 5.5.1 Formatting and manipulating data sets -- 5.6 Fitting Model SCR0 in BUGS -- 5.7 Unknown N -- 5.7.1 Analysis using data augmentation in WinBUGS -- 5.7.1.1 Use of other BUGS engines: JAGS -- 5.7.2 Implied home range area -- 5.7.3 Realized and expected population size -- 5.8 The Core SCR Assumptions -- 5.9 Wolverine Camera Trapping Study -- 5.9.1 Practical data organization -- 5.9.2 Fitting the model in WinBUGS -- 5.9.3 Summary of the wolverine analysis -- 5.9.4 Wolverine space usage -- 5.10 Using a Discrete Habitat Mask -- 5.10.1 Evaluation of coarseness of habitat mask -- 5.10.2 Analysis of the wolverine camera trapping data -- 5.11 Summarizing Density and Activity Center Locations -- 5.11.1 Constructing density maps -- 5.11.2 Wolverine density map -- 5.11.3 Predicting where an individual lives -- 5.12 Effective Sample Area -- 5.13 Summary and outlook -- 6 Likelihood Analysis of Spatial Capture-Recapture Models -- 6.1 MLE with Known N -- 6.1.1 Implementation (simulated data) -- 6.2 MLE When N is Unknown -- 6.2.1 Integrated likelihood under data augmentation -- 6.2.2 Extensions -- 6.3 Classical model selection and assessment |
| Title | Spatial capture-recapture |
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