Local adaptation of balsam fir seedlings improves growth resilience to heat stress
Bibliographic record
Abstract
Increasing frequencies of heat waves and drought are expected to shift the range and growth of balsam fir ( Abies balsamea (L.) Mill.), a widely distributed cold-adapted boreal species. However, our ability to predict this species response to these climate anomalies remains limited, especially when considering how trees can exhibit delayed and persistant growth responses to these stressors, or legacy effects. Here, we assess the growth response of balsam fir seedlings from four populations following 60 treatment combinations of temperature and water deficit in the previous year. Although we observed moderate water deficit legacy effects on growth, there were no resilience or recovery responses. We did, however, observe considerable negative legacy effects on growth proportional to the level of warming, with average legacy growth declines reaching 45% under the highest warming treatment. Furthermore, the southern populations displayed a 28% higher average growth resilience to temperature stress compared with the northern populations, indicating a higher tolerance to warming. When comparing legacy effects on balsam fir populations at moderate warming conditions relative to the current local baseline climate, we report limited growth declines for southern populations and growth increases for the northern populations. While our results highlight the importance of legacy effects from heat stress in seedlings, they also provide evidence that careful selection of warm-adapted genotypes for reforestation efforts may help offset some of these legacy effects.
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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 | 0.000 | 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) | 0.001 | 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".