Combining conventional tree-ring measurements with wood anatomy and strontium isotope analyses enables dendroprovenancing at the local scale
Bibliographic record
Abstract
Dendroprovenancing provides critical information regarding the origin of wood, allowing further insights into economic exploitation strategies and source regions of timber products. Traditionally, dendroprovenancing relies on pattern-matching of tree rings, but its spatial resolution is limited by the geographical coverage of species-specific chronologies available for crossdating and, in the case of short-distance trades, by scarce environmental variability. Here, we present an approach to provenance timber with high spatial resolution from forested areas that have been exploited intensively throughout history, with the aim to understand the sustainability of the various woodland management practices used to supply timber products. To this end, we combined tree-ring width (TRW), wood anatomical and geochemical analyses in addition to multivariate statistical validation procedures to trace the origin of living oak trees (Quercus robur) sampled in four stands located within a 30-km radius around the city of Limoges (Haute-Vienne, France). We demonstrate that TRW and wood anatomical variables (and in particular cell density) robustly discriminate the eastern from the western site, while failing to trace the origin of trees from the northern and southern sites. Here, strontium isotopic ratios (87Sr/86Sr) and Ca concentrations identify clusters of trees which could not be identified with TRW or wood anatomy. Ultimately, our study demonstrates that the coupling of wood anatomy with geochemical signatures allows to correctly pinpoint the origin of trees. Given the small geographic scale of our study and the limited differences in elevation and climate between study sites, our results are particularly promising for future dendroprovenancing studies. We thus conclude that the combination of multiple approaches will not only increase the accuracy of dendroprovenancing studies at local scales, but could also be implemented at much larger scales to identify trends in historic timber supply throughout Europe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".