Mixture of Generalized Linear Regression Models for Species-Rich Ecosystems
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.
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Format: | conference_item biblioteca |
Language: | eng |
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Eastern North American Region International Biometric Society
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Subjects: | P01 - Conservation de la nature et ressources foncières, U10 - Informatique, mathématiques et statistiques, K01 - Foresterie - Considérations générales, P40 - Météorologie et climatologie, |
Online Access: | http://agritrop.cirad.fr/586791/ http://agritrop.cirad.fr/586791/1/ID586791.pdf |
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Summary: | 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. |
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