Tree species growth response to climate warming varies by forest canopy position in boreal and temperate forests
Bibliographic record
Abstract
Reports of forest sensitivity to climate change are based largely on the study of overstory trees, which contribute significantly to forest growth and wood supply. However, juveniles in the understory are also critical to predict future forest dynamics and demographics, but their sensitivity to climate remains less known. In this study, we applied boosted regression tree analysis to compare the sensitivity of understory and overstory trees for the 10 most common tree species in eastern North America using growth information from an unprecedented network of nearly 1.5 million tree records from 20,174 widely distributed, permanent sample plots across Canada and the United States. Fitted models were then used to project the near-term (2041-2070) growth for each canopy and tree species. We observed an overall positive effect of warming on tree growth for both canopies and most species, leading to an average of 7.8%-12.2% projected growth gains with climate change under RCP 4.5 and 8.5. The magnitude of these gains peaked in colder, northern areas for both canopies, while growth declines are projected for overstory trees in warmer, southern regions. Relative to overstory trees, understory tree growth was less positively affected by warming in northern regions, while displaying more positive responses in southern areas, likely driven by the buffering effect of the canopy from warming and climate extremes. Observed differences in climatic sensitivity between canopy positions underscore the importance of accounting for differential growth responses to climate between forest strata in future studies to improve ecological forecasts. Furthermore, latitudinal variation in the differential sensitivity of forest strata to climate reported here may help refine our comprehension of species range shift and changes in suitable habitat under climate change.
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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 | 0.000 | 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 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".