The overlooked role of individual variability in autumn xylem phenology and carbon sequestration
Bibliographic record
Abstract
Accurate modeling of carbon sequestration by forests requires scaling wood formation processes from trees to the landscape. The quantification of growth and carbon dynamics requires deep knowledge of the variability in xylem phenology among individuals. This study presents a comprehensive assessment of seasonal and individual variability in xylem phenology based on more than 800 balsam firs ( Abies balsamea (L.) Mill.) monitored weekly across 33 plots from 2018 to 2022 in Montmorency Forest, Quebec, Canada. Wood microcores were collected from April to October to quantify the timings of cambial activity and xylem development on anatomical sections observed at high magnification under the microscope. The first enlarging cells appeared between late May and early June (day of the year (DOY) 153–167), and cell-wall thickening ended in late August (DOY 223–238), resulting in a growing season of 63–79 days. Xylem production ranged from 27.4 to 47.9 radial cells. While the onset of xylogenesis was well synchronized among individuals, within 2 weeks, the cessation of growth showed a greater variability, reaching up to 3 weeks. This autumnal variability was positively correlated with wood production, as higher cambial activity increases the accumulation of xylem cells to be differentiated. Our findings provide empirical evidence that individual variability in growth cessation reflects the underlying heterogeneity in cambial activity among trees of the same stand. Our results demonstrate the role of xylem phenology, especially during the autumn, in shaping forest growth. The assessment of both seasonal and individual variability in phenology is an essential step to improve the representation of autumn processes in forest carbon models, which can help to reduce the uncertainty in predictions of boreal forest growth under current or future climate scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 source (direct Gemma or distilled Codex), 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".