Linking growth dynamics and intra-annual density fluctuations to late-summer precipitation in humid subtropical China
Bibliographic record
Abstract
Global warming has intensified extreme rainfall events and prolonged droughts, significantly impacting tree growth and wood formation. This study investigates the effects of late-summer precipitation variability on the intra-annual growth dynamics of Cunninghamia lanceolata and Cryptomeria fortunei in humid subtropical China. Microcores were collected from 12 trees at 7–10 days intervals between March and December from 2021 to 2023 in the Gushan Mountains. Typically, high temperatures and rainfall deficits in July induce cambial dormancy, while subsequent rainfall in August and September reactivates growth, resulting in a bimodal growth pattern. However, in 2022, an unprecedented drought (August–October rainfall 77% below average) shortened the growing season, causing an early cessation of growth and a rare unimodal growth pattern. In contrast, persistent rainfall in 2023 accelerated cell enlargement to 7 μm d-¹ and significantly increased latewood intra-annual density fluctuations (L-IADFs). Notably, despite abundant late-summer rainfall in 2021, L-IADFs did not form, indicating a nonlinear and inconsistent relationship between rainfall and L-IADFs. These findings highlight the critical role of late-summer precipitation variability in shaping tree growth patterns and wood density in southeastern China. Given the expected increase in precipitation variability under climate change, regional forest ecosystems may become more vulnerable. This study provides valuable insights for forest management strategies to enhance resilience and mitigate climate-related risks.
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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 | 0.000 |
| 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) | 0.000 | 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 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".