Table of Contents
1 Cover
4 Introduction I.1. Hidden variables in ecology I.2. Hidden variables in statistical modeling I.3. Statistical methods I.4. Approach and structure of our work I.5. Directions for further perspectives I.6. References
5 1 Trajectory Reconstruction and Behavior Identification Using Geolocation Data 1.1. Introduction 1.2. Hierarchical models of movement 1.3. Case study: masked booby, Sula dactylatra (originals) 1.4. References
6 2 Detection of Eco-Evolutionary Processes in the Wild: Evolutionary Trade-Offs Between Life History Traits 2.1. Context 2.2. The correlative approach to detecting evolutionary trade-offs in natural settings: problems 2.3. Case study 2.4. References
7 3 Studying Species Demography and Distribution in Natural Conditions: Hidden Markov Models 3.1. Introduction 3.2. Overview of HMMs 3.3. HMM and demography 3.4. HMM and species distribution 3.5. Discussion 3.6. Acknowledgments 3.7. References
8 4 Inferring Mechanistic Models in Spatial Ecology Using a Mechanistic-Statistical Approach 4.1. Introduction 4.2. Dynamic systems in ecology 4.3. Estimation 4.4. Examples 4.5. References
9 5 Using Coupled Hidden Markov Chains to Estimate Colonization and Seed Bank Survival in a Metapopulation of Annual Plants 5.1. Introduction 5.2. Metapopulation model for plants: introduction of a dormant state 5.3. Dynamics of weed species in cultivated parcels 5.4. Discussion and conclusion 5.5. Acknowledgments 5.6. References
10 6 Using Latent Block Models to Detect Structure in Ecological Networks 6.1 Introduction 6.2. Formalism 6.3. Probabilistic mixture models for networks 6.4. Statistical inference 6.5. Application 6.6. Conclusion 6.7. References
11 7 Latent Factor Models: A Tool for Dimension Reduction in Joint Species Distribution Models 7.1. Introduction 7.2. Joint species distribution models 7.3. Dimension reduction with latent factors 7.4. Inference 7.5. Ecological interpretation of latent factors 7.6. On the interpretation of JSDMs 7.7. Case study 7.8. Conclusion 7.9. References
12 8 The Poisson Log-Normal Model: A Generic Framework for Analyzing Joint Abundance Distributions 8.1. Introduction 8.2. The Poisson log-normal model 8.3. Data analysis: marine species 8.4. Discussion 8.5. Acknowledgments 8.6. References
13
9 Supervised Component-Based Generalized Linear Regression: Method and Extensions
9.1.