A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Mixture of inhomogeneous matrix models for species‐rich ecosystems
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. Copyright © 2014 John Wiley & Sons, Ltd.
Mixture of inhomogeneous matrix models for species‐rich ecosystems
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. Copyright © 2014 John Wiley & Sons, Ltd.
Mixture of inhomogeneous matrix models for species‐rich ecosystems
Mortier, Frédéric (author) / Ouédraogo, Dakis‐Yaoba (author) / Claeys, Florian (author) / Tadesse, Mahlet G. (author) / Cornu, Guillaume (author) / Baya, Fidèle (author) / Benedet, Fabrice (author) / Freycon, Vincent (author) / Gourlet‐Fleury, Sylvie (author) / Picard, Nicolas (author)
Environmetrics ; 26 ; 39-51
2015-02-01
13 pages
Article (Journal)
Electronic Resource
English
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