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.