Disentangling the impact of nested sources of variability on species growth processes: A mixture of multilevel mixed model approach

Moudjieu Leumbe Etienne Fabrice, Mortier Frédéric, Takam Soh Patrice, Picard Nicolas, Allah-Barem Félix, Baya Fidèle, Tadesse Mahlet G., Rossi Vivien. 2024. Disentangling the impact of nested sources of variability on species growth processes: A mixture of multilevel mixed model approach. Stat, 13 (2):e695, 14 p.

Résumé : The understanding of tree growth processes is crucial for promoting sustainable forest management strategies. This is a challenging task in highly biodiverse ecosystems where many tree species are observed on very few individuals and the small sample sizes hinder a good fit of species-specific models. We propose the use of finite mixture of random coefficient regression models with multilevel nested random effects to infer guild specific fixed and random effects while evaluating the relative importance of the nested sources of variability on goodness-of-fit. This approach extends finite mixture of linear mixed model used for longitudinal or single group structured data contexts. A dedicated expectation–maximisation algorithm is introduced for parameter estimation. Simulations are performed for the evaluation of the misspecification of nested-grouping structures. This work has been motivated by data collected biennially in Central African rainforests from 1986 to 2010. We show the accuracy of the proposed approach in successfully reproducing individual growth processes and classifying tree species into well-differentiated clusters with clear ecological interpretations. Moreover, results confirm that interindividual variability appears as the most important factor to explain tropical tree species growth process variability from Central Africa forests.

Mots-clés Agrovoc : forêt tropicale humide, modèle de simulation, séquestration du carbone, croissance, méthode statistique, modèle mathématique, forêt tropicale

Mots-clés libres : African rainforest, Expectation–maximisation algorithm, Joint models,, Multilevels, Random coefficient regression, Tree growth models

Classification Agris : K01 - Foresterie - Considérations générales
U10 - Informatique, mathématiques et statistiques
F40 - Écologie végétale

Champ stratégique Cirad : CTS 5 (2019-) - Territoires

Agences de financement hors UE : Agence Nationale de la Recherche

Projets sur financement : (FRA) Nouvelles avancées dans la modélisation de la biodiversité et des services écosystémiques : améliorations statistiques et pertinences écologiques des modèles de distribution multi-espèces, (FRA) Partenariat Public-Privé pour gérer durablement les Forêts d'Afrique centrale

Auteurs et affiliations

• Moudjieu Leumbe Etienne Fabrice, CIRAD-ES-UPR Forêts et sociétés (FRA) - auteur correspondant
• Mortier Frédéric, CIRAD-ES-UPR Forêts et sociétés (FRA)
• Takam Soh Patrice, University of Yaounde 1 (CMR)
• Picard Nicolas, Center GIP ECOFOR (FRA)
• Allah-Barem Félix, Institut centrafricain de la recherche agronomique (CAF)
• Baya Fidèle, Ministère des eaux et forêts, chasses, pêches et tourisme (République centrafricaine) (CAF)
• Tadesse Mahlet G., Georgetown University (USA)
• Rossi Vivien, CIRAD-ES-UPR Forêts et sociétés (CMR) ORCID: 0000-0001-5458-1523

Source : Cirad-Agritrop (https://agritrop.cirad.fr/609605/)

Métriques

[ Page générée et mise en cache le 2024-08-08 ]