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Regularized regression: A new tool for investigating and predicting tree growth / Stuart I. Graham in Forests, vol 12 n° 9 (September 2021)  

Public [article]  inForests > vol 12 n° 9 (September 2021) . - n° 1283 Titre : Regularized regression: A new tool for investigating and predicting tree growth Type de document : Article/Communication Auteurs : Stuart I. Graham, Auteur ; Ariel Rokem, Auteur ; Claire Fortunel, Auteur ; Nathan J.B. Kraft, Auteur ; Janneke Hille Ris Lambers, Auteur Année de publication : 2021 Article en page(s) : n° 1283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] croissance des arbres [Termes IGN] inférence statistique [Termes IGN] interpolation [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] placette d'échantillonnage [Termes IGN] régression Résumé : (auteur) Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction. Numéro de notice : A2021-720 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12091283 En ligne : https://doi.org/10.3390/f12091283 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98636 [article]

Modeling in forestry using mixture models fitted to grouped and ungrouped data / Eric K. Zenner in Forests, vol 12 n° 9 (September 2021)  

Public [article]  inForests > vol 12 n° 9 (September 2021) . - n° 1196 Titre : Modeling in forestry using mixture models fitted to grouped and ungrouped data Type de document : Article/Communication Auteurs : Eric K. Zenner, Auteur ; Mahdi Teimouri, Auteur Année de publication : 2021 Article en page(s) : n° 1196 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] algorithme espérance-maximisation [Termes IGN] complexité [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] distribution de Weibull [Termes IGN] distribution, loi de [Termes IGN] dynamique de la végétation [Termes IGN] estimation par noyau [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] modélisation de la forêt [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) The creation and maintenance of complex forest structures has become an important forestry objective. Complex forest structures, often expressed in multimodal shapes of tree size/diameter (DBH) distributions, are challenging to model. Mixture probability density functions of two- or three-component gamma, log-normal, and Weibull mixture models offer a solution and can additionally provide insights into forest dynamics. Model parameters can be efficiently estimated with the maximum likelihood (ML) approach using iterative methods such as the Newton-Raphson (NR) algorithm. However, the NR algorithm is sensitive to the choice of initial values and does not always converge. As an alternative, we explored the use of the iterative expectation-maximization (EM) algorithm for estimating parameters of the aforementioned mixture models because it always converges to ML estimators. Since forestry data frequently occur both in grouped (classified) and ungrouped (raw) forms, the EM algorithm was applied to explore the goodness-of-fit of the gamma, log-normal, and Weibull mixture distributions in three sample plots that exhibited irregular, multimodal, highly skewed, and heavy-tailed DBH distributions where some size classes were empty. The EM-based goodness-of-fit was further compared against a nonparametric kernel-based density estimation (NK) model and the recently popularized gamma-shaped mixture (GSM) models using the ungrouped data. In this example application, the EM algorithm provided well-fitting two- or three-component mixture models for all three model families. The number of components of the best-fitting models differed among the three sample plots (but not among model families) and the mixture models of the log-normal and gamma families provided a better fit than the Weibull distribution for grouped and ungrouped data. For ungrouped data, both log-normal and gamma mixture distributions outperformed the GSM model and, with the exception of the multimodal diameter distribution, also the NK model. The EM algorithm appears to be a promising tool for modeling complex forest structures. Numéro de notice : A2021-721 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12091196 En ligne : https://doi.org/10.3390/f12091196 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98639 [article]