| Description |
1 online resource (294 p.) |
| Series |
Chapman and Hall/CRC Applied Environmental Statistics Series |
|
Chapman and Hall/CRC Applied Environmental Statistics Series
|
| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- List of Figures -- List of Tables -- I. Introductory -- 1. Statistical Models for Individual-based Processes -- 1.1. Ecological Dynamics and Individual-based Models -- 1.2. Time-to-event Model for Individual Timelines -- 1.3. Spatial Point Process for Habitat Utilization -- 1.4. Generalized Linear Models -- 1.5. Likelihood Estimation of Generalized Linear Models -- 1.6. Estimation using Template Model Builder -- 1.7. Evaluating Model Fit -- 1.8. Chapter Summary -- 1.9. Exercise |
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2. Hierarchical Models and Laplace Approximation -- 2.1. Why Hierarchical Models are Necessary -- 2.1.1. Example: Compound distributions -- 2.1.2. A Quick Derivation of Random Effects -- 2.2. Introducing the Laplace Approximation -- 2.3. Estimating Heterogeneity using a Generalized Linear Mixed Model -- 2.4. Inner Hessian Sparsity and Conditional Independence -- 2.5. Addressing Common Problems During Inner or Outer Optimization -- 2.5.1. Inner Hessian failure -- 2.5.2. Outer Hessian failure -- 2.6. Chapter Summary -- 2.7. Exercises -- II. Basic -- 3. Population Dynamics and State-space Models |
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3.1. Population Models and Applied Ecology -- 3.2. Gompertz Model for Population Dynamics -- 3.3. Semivariance and Correlation Functions -- 3.4. State-Space Population Model -- 3.5. Conditional vs. Joint Covariance Modeling -- 3.6. Seasonal/missing Data and Forecasting -- 3.7. Chapter Summary -- 3.8. Exercise -- 4. Individual Movement -- 4.1. Movement Ecology -- 4.2. Defining Diffusion and Taxis -- 4.3. Track Reconstruction -- 4.4. Specifying Covariance Matrices -- 4.4.1. Factor Models -- 4.4.2. Structural Equation Models -- 4.5. Comparing Factor and Structural Equation Models for Group Movement |
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4.6. Chapter Summary -- 4.7. Exercise -- 5. Spatial Models -- 5.1. Exogenous and Endogenous Drivers of Spatial Patterns -- 5.2. Basis Expansion and Splines -- 5.3. Tensor Splines and Generalized Additive Models -- 5.4. Separable Correlation and Sparsity -- 5.5. Models for Irregular Shaped Spatial Domains -- 5.5.1. Conditional Autoregressive Model -- 5.5.2. SPDE Method -- 5.6. Chapter Summary -- 5.7. Exercise -- 6. Spatial Sampling Designs and Analysis -- 6.1. Ecological Inference and Field Sampling -- 6.2. Spatial Integration and Weighting -- 6.2.1. Estimating Uncertainty in Derived Quantities |
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6.2.2. Expansion Weights -- 6.2.3. Epsilon Bias-correction -- 6.3. Spatial Sampling Designs -- 6.4. Preferential Sampling -- 6.5. Multi-stage Sampling -- 6.5.1. Estimators for Multi-stage Sampling -- 6.5.2. Occupancy Models and Demographic Closure -- 6.6. Chapter Summary -- 6.7. Exercises -- 7. Covariates Affecting Densities and Detectability -- 7.1. Reasons to Include Covariates -- 7.2. Predicting the Effect of System Changes -- 7.2.1. Do-calculus and Causal Inference -- 7.2.2. Structural Equation Models -- 7.3. Benefits of Density Covariates -- 7.4. Density and Detectability Covariates |
| Summary |
This book introduces a minimal set of principles and numerical techniques for spatio-temporal statistics that can be used to implement a wide range of real-world ecological analyses regarding animal movement, population dynamics, community composition, causal attribution, and spatial dynamics |
| Notes |
Description based upon print version of record |
|
7.5. Chapter Summary |
| Genre/Form |
Electronic books
|
| Form |
Electronic book
|
| Author |
Kristensen, Kasper
|
| ISBN |
9781003851868 |
|
100385186X |
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