Dissecting the Space-Time Structure of Tree-Ring Datasets Using the Partial Triadic Analysis

• Autores: Rossi, Jean Pierre; Nardin, Maxime; Godefroid, Martin; Ruiz Diaz, Manuela; Sergent, Anne Sophie; Martinez Meier, Alejandro; Paques, Luc; Rozenberg, Philippe
• Año de publicación: 2014
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: TREES; DENDROCHRONOLOGY; CLIMATE CHANGE
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Tree-ring datasets are used in a variety of circumstances, including archeology, climatology, forest ecology, and wood technology. These data are based on microdensity profiles and consist of a set of tree-ring descriptors, such as ring width or early/latewood density, measured for a set of individual trees. Because successive rings correspond to successive years, the resulting dataset is a ring variables × trees × time datacube. Multivariate statistical analyses, such as principal component analysis, have been widely used for extracting worthwhile information from ring datasets, but they typically address two-way matrices, such as ring variables × trees or ring variables × time. Here, we explore the potential of the partial triadic analysis (PTA), a multivariate method dedicated to the analysis of three-way datasets, to apprehend the space-time structure of tree-ring datasets. We analyzed a set of 11 tree-ring descriptors measured in 149 georeferenced individuals of European larch (Larix decidua Miller) during the period of 1967–2007. The processing of densitometry profiles led to a set of ring descriptors for each tree and for each year from 1967–2007. The resulting three-way data table was subjected to two distinct analyses in order to explore i) the temporal evolution of spatial structures and ii) the spatial structure of temporal dynamics. We report the presence of a spatial structure common to the different years, highlighting the inter-individual variability of the ring descriptors at the stand scale. We found a temporal trajectory common to the trees that could be separated into a high and low frequency signal, corresponding to inter-annual variations possibly related to defoliation events and a long-term trend possibly related to climate change. We conclude that PTA is a powerful tool to unravel and hierarchize the different sources of variation within tree-ring datasets.
Fil: Rossi, Jean Pierre. Institut National de la Recherche Agronomique; Francia
Fil: Nardin, Maxime. Institut National de la Recherche Agronomique; Francia
Fil: Godefroid, Martin. Institut National de la Recherche Agronomique; Francia
Fil: Ruiz Diaz, Manuela. Universidad Nacional de Misiones; Argentina
Fil: Sergent, Anne Sophie. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez Meier, Alejandro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; Argentina
Fil: Paques, Luc. Institut National de la Recherche Agronomique; Francia
Fil: Rozenberg, Philippe. Institut National de la Recherche Agronomique; Francia