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.
1. Trajectory Reconstruction and Behavior Identification Using Geolocation Data, Marie-Pierre Etienne and Pierre Gloaguen.
2. Detection of Eco-Evolutionary Processes in the Wild: Evolutionary Trade-Offs Between Life History Traits, Valentin Journé, Sarah Cubaynes, Julien Papaïx and Mathieu Buoro.
3. Studying Species Demography and Distribution in Natural Conditions: Hidden Markov Models, Olivier Gimenez, Julie Louvrier, Valentin Lauret and Nina Santostasi.
4. Inferring Mechanistic Models in Spatial Ecology Using aMechanistic-Statistical Approach, Julien Papaïx, Samuel Soubeyrand, Olivier Bonnefon, Emily Walker,
Julie Louvrier, Etienne Klein and Lionel Roques.
5. Using Coupled Hidden Markov Chains to Estimate Colonization and Seed Bank Survival in a Metapopulation of Annual Plants, Pierre-Olivier Cheptou, Stéphane Cordeau, Sebastian Le Coz and Nathalie Peyrard.
6. Using Latent Block Models to Detect Structure in Ecological Networks, Julie Aubert, Pierre Barbillon, Sophie Donnet and Vincent Miele.
7. Latent Factor Models: A Tool for Dimension Reduction in Joint Species Distribution Models, Daria Bystrova, Giovanni Poggiato, Julyan Arbel and Wilfried Thuiller.
8. The Poisson Log-Normal Model: A Generic Framework for Analyzing Joint Abundance Distributions, Julien Chiquet, Marie-Josée Cros, Mahendra Mariadassou, Nathalie Peyrard and Stéphane Robin.
9. Supervised Component-Based Generalized Linear Regression: Method and Extensions, Frédéric Mortier, Jocelyn Chauvet, Catherine Trottier, Guillaume Cornu and Xavier Bry.
10. Structural Equation Models for the Study of Ecosystems and Socio-Ecosystems, Fabien Laroche, Jérémy Froidevaux, Laurent Larrieu and Michel Goulard.
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.
Table of Contents
PDF File 58 Kb