Data from: Multi-variable approach pinpoints origin of oak wood with higher precision

Aim: Spatial variations of environmental conditions translate into biogeographic growth patterns of tree growth. This fact is used to identify the origin of timber by means of dendroprovenancing. Yet, dendroprovenancing attempts are based on ring-widths measurements, and neglect additional tree-ring parameters. To explore the effect of including additional variables in dendroprovenancing, we investigate whether and, if so, why the incorporation of wood-anatomical parameters can increase the precision of identifying the origin of oak wood. Since such features reflect environmental conditions of different periods – which vary between source regions – we hypothesize that their inclusion allows more precise dendroprovenancing. Location: Europe, Spain. Taxon: Quercus robur L., Quercus petraea (Matt.) Liebl., Quercus faginea Lam., Quercus pyrenaica Willd. Methods: We sampled four oak species resembling a longitudinal and an elevational/continental gradients. We measured multiple tree-ring variables to (1) extract meaningful variables, (2) represent statistical relations among variables, (3) analyse regional-specific growth patterns in individual time series and (4) determine underlying climate-growth relationships. Leave-one-out analyses were used to test whether a combination of selected variables allows dendroprovenancing of a randomly selected tree within the area. Results: A combination of latewood width and earlywood vessels size can be used to pin-point the origin of oak wood with higher precision than latewood width only. Variation in latewood widths appointed the wood to areas across the longitudinal gradient, whereas variation in vessels assigned wood to locations along a latitudinal/topographic gradient. The climatic factors behind these gradients are respectively an East-West gradient in June-July temperature, and a North-South gradient in winter/ spring temperatures. The leave-one-out analyses supported the robustness of the results. Main conclusions: Integration of multiple tree-ring variables in combination with multivariate techniques leads to higher precision in the dendroprovenancing of ring-porous oak species.