Centre de documentation
scientifique

CAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data / Cyrille B.K. Rathgeber in Tree Physiology, vol 38 n° 8 (August 2018)  

Public [article]  inTree Physiology > vol 38 n° 8 (August 2018) . - pp 1246 - 1260 Titre : CAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data Type de document : Article/Communication Auteurs : Cyrille B.K. Rathgeber, Auteur ; Philippe Santenoise, Auteur ; Henri E. Cuny , Auteur Année de publication : 2018 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : pp 1246 - 1260 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cerne [Termes IGN] données allométriques [Termes IGN] dynamique de la végétation [Termes IGN] forêt boréale [Termes IGN] forêt tempérée [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Loi de Gompertz [Termes IGN] phénologie [Termes IGN] Pinophyta [Termes IGN] R (langage) [Termes IGN] régression logistique [Termes IGN] visualisation de données [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) In the last decade, the pervasive question of climate change impacts on forests has revived investigations on intra-annual dynamics of wood formation, involving disciplines such as plant ecology, tree physiology and dendrochronology. This resulted in the creation of many research groups working on this topic worldwide and a rapid increase in the number of studies and publications. Wood-formation-monitoring studies are generally based on a common conceptual model describing xylem cell formation as the succession of four differentiation phases (cell division, cell enlargement, cell wall thickening and mature cells). They generally use the same sampling techniques, sample preparation methods and anatomical criteria to separate between differentiation zones and discriminate and count forming xylem cells, resulting in very similar raw data. However, the way these raw data are then processed, producing the elaborated data on which statistical analyses are performed, still remains quite specific to each individual study. Thereby, despite very similar raw data, wood-formation-monitoring studies yield results that are still quite difficult to compare. CAVIAR—an R package specifically dedicated to the verification, visualization and manipulation of wood-formation-monitoring data—can help to improve this situation. Initially, CAVIAR was built to provide efficient algorithms to compute critical dates of wood formation phenology for conifers growing in temperate and cold environments. Recently, we developed it further to check, display and process wood-formation-monitoring data. Thanks to new and upgraded functions, raw data can now be consistently verified, standardized and modelled (using logistic regressions and Gompertz functions), in order to describe wood phenology and intra-annual dynamics of tree-ring formation. We believe that CAVIAR will help strengthening the science of wood formation dynamics by effectively contributing to the standardization of its concepts and methods, making thereby possible the comparison between data and results from different studies. Numéro de notice : A2018-657 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/treephys/tpy054 Date de publication en ligne : 19/05/2018 En ligne : https://doi.org/10.1093/treephys/tpy054 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93813 [article]