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The study of ecological systems is often impeded by components that escape perfect observation, such as the trajectories of moving animals or the status of plant seed banks. These hidden components can be efficiently handled with statistical modeling by using hidden variables, which are often called latent variables. Notably, the hidden variables framework enables us to model an underlying interaction structure between variables (including random effects in regression models) and perform data clustering, which are useful tools in the analysis of ecological data. This book provides an…mehr
The study of ecological systems is often impeded by components that escape perfect observation, such as the trajectories of moving animals or the status of plant seed banks. These hidden components can be efficiently handled with statistical modeling by using hidden variables, which are often called latent variables. Notably, the hidden variables framework enables us to model an underlying interaction structure between variables (including random effects in regression models) and perform data clustering, which are useful tools in the analysis of ecological data. This book provides an introduction to hidden variables in ecology, through recent works on statistical modeling as well as on estimation in models with latent variables. All models are illustrated with ecological examples involving different types of latent variables at different scales of organization, from individuals to ecosystems. Readers have access to the data and R codes to facilitate understanding of the model and to adapt inference tools to their own data.
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Nathalie Peyrard is a Senior Scientist at INRAE. Most of her current research focuses on computational statistics, with applications in ecology. Olivier Gimenez is a Senior Scientist at CNRS. His research focuses on animal ecology, statistical modeling and social sciences.
Inhaltsangabe
Introduction xi Nathalie PEYRARD, Stéphane ROBIN and Olivier GIMENEZ
Chapter 1. Trajectory Reconstruction and Behavior Identification Using Geolocation Data 1 Marie-Pierre ETIENNE and Pierre GLOAGUEN
1.1. Introduction 1
1.1.1. Reconstructing a real trajectory from imperfect observations 1
1.1.2. Identifying different behaviors in movement 3
1.2. Hierarchical models of movement 3
1.2.1. Trajectory reconstruction model 3
1.2.2. Activity reconstruction model 6
1.3. Case study: masked booby, Sula dactylatra (originals) 14
1.3.1. Data 14
1.3.2. Projection 15
1.3.3. Data smoothing 15
1.3.4. Identification of different activities through movement 16
1.3.5. Results 17
1.4. References 23
Chapter 2. Detection of Eco-Evolutionary Processes in the Wild: Evolutionary Trade-Offs Between Life History Traits 27 Valentin JOURNÉ, Sarah CUBAYNES, Julien PAPAÏX and Mathieu BUORO
2.1. Context 27
2.2. The correlative approach to detecting evolutionary trade-offs in natural settings: problems 28
2.2.1. Mechanistic and statistical modeling as a means of accessing hidden variables 29
2.3. Case study 31
2.3.1. Costs of maturing and migration for survival: a theoretical approach 31
2.3.2. Growth/reproduction trade-off in trees 37
2.4. References 44
Chapter 3. Studying Species Demography and Distribution in Natural Conditions: Hidden Markov Models 47 Olivier GIMENEZ, Julie LOUVRIER, Valentin LAURET and Nina SANTOSTASI
3.1. Introduction 47
3.2. Overview of HMMs 48
3.3. HMM and demography 50
3.3.1. General overview 50
3.3.2. Case study: estimating the prevalence of dog-wolf hybrids with uncertain individual identification 54
3.4. HMM and species distribution 55
3.4.1. General case 55
3.4.2. Case study: estimating the distribution of a wolf population with species identification errors and heterogeneous detection 57
3.5. Discussion 60
3.6. Acknowledgments 62
3.7. References 62
Chapter 4. Inferring Mechanistic Models in Spatial Ecology Using a Mechanistic-Statistical Approach 69 Julien PAPAÏX, Samuel SOUBEYRAND, Olivier BONNEFON, Emily WALKER, Julie LOUVRIER, Etienne KLEIN and Lionel ROQUES
4.1. Introduction 69
4.2. Dynamic systems in ecology 70
4.2.1. Temporal models 70
4.2.2. Spatio-temporal models without reproduction 74
4.2.3. Spatio-temporal models with reproduction 76
4.2.4. Numerical solution 77
4.3. Estimation 77
4.3.1. Estimation principle 77
4.3.2. Parameter estimation 78
4.3.3. Estimation of latent processes 80
4.3.4. Mechanistic-statistical models 82
4.4. Examples 83
4.4.1. The COVID-19 epidemic in France 83
4.4.2. Wolf (Canis lupus) colonization in southeastern France 86
4.4.3. Estimating dates and locations of the introduction of invasive strains of watermelon mosaic virus 90
4.5. References 94
Chapter 5. Using Coupled Hidden Markov Chains to Estimate Colonization and Seed Bank Survival in a Metapopulation of Annual Plants 97 Pierre-Olivier CHEPTOU, Stéphane CORDEAU, Sebastian LE COZ and Nathalie PEYRARD
5.1. Introduction 97
5.2. Metapopulation model for plants: introduction of a dormant state 99
5.2.1. Dependency structure in the model 99
5.2.2. Distributions defining the model 100
5.2.3. Parameterizing the model 101
5.2.4. Linking the parameters of the model with the ecological paramet
Introduction xi Nathalie PEYRARD, Stéphane ROBIN and Olivier GIMENEZ
Chapter 1. Trajectory Reconstruction and Behavior Identification Using Geolocation Data 1 Marie-Pierre ETIENNE and Pierre GLOAGUEN
1.1. Introduction 1
1.1.1. Reconstructing a real trajectory from imperfect observations 1
1.1.2. Identifying different behaviors in movement 3
1.2. Hierarchical models of movement 3
1.2.1. Trajectory reconstruction model 3
1.2.2. Activity reconstruction model 6
1.3. Case study: masked booby, Sula dactylatra (originals) 14
1.3.1. Data 14
1.3.2. Projection 15
1.3.3. Data smoothing 15
1.3.4. Identification of different activities through movement 16
1.3.5. Results 17
1.4. References 23
Chapter 2. Detection of Eco-Evolutionary Processes in the Wild: Evolutionary Trade-Offs Between Life History Traits 27 Valentin JOURNÉ, Sarah CUBAYNES, Julien PAPAÏX and Mathieu BUORO
2.1. Context 27
2.2. The correlative approach to detecting evolutionary trade-offs in natural settings: problems 28
2.2.1. Mechanistic and statistical modeling as a means of accessing hidden variables 29
2.3. Case study 31
2.3.1. Costs of maturing and migration for survival: a theoretical approach 31
2.3.2. Growth/reproduction trade-off in trees 37
2.4. References 44
Chapter 3. Studying Species Demography and Distribution in Natural Conditions: Hidden Markov Models 47 Olivier GIMENEZ, Julie LOUVRIER, Valentin LAURET and Nina SANTOSTASI
3.1. Introduction 47
3.2. Overview of HMMs 48
3.3. HMM and demography 50
3.3.1. General overview 50
3.3.2. Case study: estimating the prevalence of dog-wolf hybrids with uncertain individual identification 54
3.4. HMM and species distribution 55
3.4.1. General case 55
3.4.2. Case study: estimating the distribution of a wolf population with species identification errors and heterogeneous detection 57
3.5. Discussion 60
3.6. Acknowledgments 62
3.7. References 62
Chapter 4. Inferring Mechanistic Models in Spatial Ecology Using a Mechanistic-Statistical Approach 69 Julien PAPAÏX, Samuel SOUBEYRAND, Olivier BONNEFON, Emily WALKER, Julie LOUVRIER, Etienne KLEIN and Lionel ROQUES
4.1. Introduction 69
4.2. Dynamic systems in ecology 70
4.2.1. Temporal models 70
4.2.2. Spatio-temporal models without reproduction 74
4.2.3. Spatio-temporal models with reproduction 76
4.2.4. Numerical solution 77
4.3. Estimation 77
4.3.1. Estimation principle 77
4.3.2. Parameter estimation 78
4.3.3. Estimation of latent processes 80
4.3.4. Mechanistic-statistical models 82
4.4. Examples 83
4.4.1. The COVID-19 epidemic in France 83
4.4.2. Wolf (Canis lupus) colonization in southeastern France 86
4.4.3. Estimating dates and locations of the introduction of invasive strains of watermelon mosaic virus 90
4.5. References 94
Chapter 5. Using Coupled Hidden Markov Chains to Estimate Colonization and Seed Bank Survival in a Metapopulation of Annual Plants 97 Pierre-Olivier CHEPTOU, Stéphane CORDEAU, Sebastian LE COZ and Nathalie PEYRARD
5.1. Introduction 97
5.2. Metapopulation model for plants: introduction of a dormant state 99
5.2.1. Dependency structure in the model 99
5.2.2. Distributions defining the model 100
5.2.3. Parameterizing the model 101
5.2.4. Linking the parameters of the model with the ecological paramet
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