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Mixture of inhomogeneous matrix models for species-rich ecosystems

Mortier F., Ouédraogo D.Y., Claeys F., Tadesse M.G., Cornu G., Baya F., Bénédet F., Freycon V., Gourlet-Fleury S., Picard N.. 2015. Environmetrics, 26 (1) : p. 39-51.

DOI: 10.1002/env.2320

Understanding how environmental factors could impact population dynamics is of primary importance for species conservation. Matrix population models are widely used to predict population dynamics. However, in species-rich ecosystems with many rare species, the small population sizes hinder a good fit of species-specific models. In addition, classical matrix models do not take into account environmental variability. 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 develop an inference method coupling the R packages flexmix and glmnet. We first highlight the effectiveness of the method on simulated datasets. Next, we apply it to data from a tropical rain forest in the Central African Republic. We demonstrate the accuracy of the inhomogeneous mixture matrix model in successfully reproducing stand dynamics and classifying tree species into well-differentiated groups with clear ecological interpretations.

Mots-clés : forêt tropicale humide; Écologie forestière; composition botanique; biodiversité; dynamique des populations; croissance; régénération naturelle; mortalité; méthode statistique; modèle mathématique; modèle de simulation; république centrafricaine; régression (statistique)

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