Mixture of Generalized Linear Regression Models for Species-Rich Ecosystems

Mortier Frédéric. 2017. Mixture of Generalized Linear Regression Models for Species-Rich Ecosystems. In : ENAR 2017 Spring Meeting abstracts. Eastern North American Region International Biometric Society. Washington : Eastern North American Region International Biometric Society, Résumé, 263. ENAR 2017 Spring Meeting, Washington, États-Unis, 12 Mars 2017/15 Mars 2017.

Résumé : Understanding how climate change could impact population dynamics is of primary importance for species conservation. In species-rich ecosystems with many rare species, the small population sizes hinder a good fit of species-specific models. We propose a mixture of regression models with variable selection allowing the simultaneous clustering of species into groups according to vital rate information (recruitment, growth, and mortality) and the identification of group-specific explicative environmental variables. We illustrate the effectiveness of the method on data from a tropical rain forest in the Central African Republic and demonstrate the accuracy of the model in successfully reproducing stand dynamics and classifying tree species into well-differentiated groups with clear ecological interpretations.

Classification Agris : P01 - Conservation de la nature et ressources foncières
U10 - Informatique, mathématiques et statistiques
K01 - Foresterie - Considérations générales
P40 - Météorologie et climatologie

Auteurs et affiliations

• Mortier Frédéric, CIRAD-ES-UPR BSef (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/586791/)

Métriques

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