The Use of Basal Area Increment to Preserve the Multi-Decadal Climatic Signal in Shrub Growth Ring Chronologies: A Case Study of Betula glandulosa in a Rapidly Warming Environment
Bibliographic record
Abstract
Climate warming at high latitudes has contributed to the growing interest in shrub tree-ring analysis. Shrub architecture presents new challenges for dendrochronology, such as the seemingly lower and inconsistent climatic sensitivity of stems vs. root collars. Shrub stems may thus be considered as sub-optimal to study climate–growth relationships. In this paper, we propose that the lower climatic sensitivity of stems could be caused by the use of unsuitable detrending methods for chronologies spanning decades rather than centuries. We hypothesize that the conversion of the ring width (RW) to basal area increment (BAI) is better suited than traditional detrending methods to removing age/size-related trends without removing multi-decadal climate signals. Using stem and root collar samples collected from three sites in the forest–tundra ecotone of eastern Canada, we compared the climate–growth relationships of these two approaches for stems and root collars using mixed-effects models. The climate sensitivity was, on average, 4.9 and 2.7 times higher with BAI than with detrended (mean-centered) RW chronologies for stems and root collars, respectively. The climatic drivers of radial growth were identical for stems and root collars when using BAI (July temperature and March precipitation), but were inconsistent when using detrended RW series (root collars: July temperature and March precipitation at all sites; stems: April and June temperature, depending on the site). Although the use of BAI showed promising results for studying long-term climate signals in shrub growth chronologies, further studies focusing on different species and locations are needed before the use of BAI can become broadly used in shrub dendrochronology.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".