Functional traits and individual tree growth relationship weakens with stand development but strengthens with increasing temperature
Bibliographic record
Abstract
Abstract Understanding spatiotemporal variation in individual tree growth–functional traits relationships (GTRs) is crucial to predicting forest growth responses to changing environments, aiding in long‐term forest planning and sustainability. Although weak GTRs have been frequently observed within individual forest site or across sites of similar climates and development stages, GTRs at large spatial scales remain uncertain. We hypothesize that GTRs at large scales are regulated by stand development and regional climate through their effects on competition intensity and tree mortality rates. We used forest inventory data in the United States (9828 plots and 228,981 trees) to investigate how stand age and regional climate (temperature and climate moisture index) regulate GTRs. We found an overall positive relationship between relative tree growth rate and stem traits associated with acquisitive strategies (greater height and lower wood density). However, leaf traits associated with acquisitive strategies (higher specific leaf area, leaf nitrogen and phosphorus content) exhibited divergent effects, promoting growth in angiosperms but reducing growth in gymnosperms. Importantly, GTRs weakened with stand age but strengthened with increasing mean annual temperature. Structural equation modelling showed that stand age indirectly weakened GTRs by increasing stand basal area and tree mortality. Synthesis . Our findings suggest that current efforts focusing on planting acquisitive tree species for rapid carbon sequestration may become less effective as forests mature, especially in conservation forests aimed at providing long‐term ecosystem services. Therefore, mixing conservative trees with longer growth cycles alongside acquisitive species could be a forest management strategy to enhance carbon sequestration over the long term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".