Automated analysis of intra-annual density fluctuations reveals climate-sensitive and genetically variable wood traits
Bibliographic record
Abstract
Understanding how trees adjust their wood structure to increasing climate variability is critical for predicting forest resilience. In this study, we examined intra-annual density fluctuations (IADFs) in the boreal black spruce as indicators of plastic and genetic responses to water stress. IADFs are abrupt deviations in the density of wood formed within a growth ring and reflect temporary disruptions in cambial activity. We assessed their occurrence in 24-year-old trees from controlled-cross families planted in two climatically distinct common gardens in eastern Québec. Using an automated method, we quantified both the frequency and the structural characteristics of IADFs, including their width, height, and area. Our results show that earlywood IADF frequency was strongly correlated with local climate indices related to water deficit, indicating that IADFs are sensitive biomarkers of both current-year and lagged drought conditions. IADF frequency was positively associated with radial growth but negatively with earlywood and total wood density, suggesting a physiological trade-off between maintaining growth under water stress and investing in wood structural properties. Moreover, moderate levels of genetic control indicated that these traits are partially heritable. By integrating IADFs, climatic, and genetic data, our approach provides new insights into how a major boreal tree species modulate wood structure in response to environmental stress. It thus represents a promising framework for studying drought-response and support the selection of more climate-resilient trees.
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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 | 0.000 | 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 | 0.000 | 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".