Local adaptation shapes functional traits and resource allocation in black spruce
Bibliographic record
Abstract
Climate change is rapidly altering weather patterns, resulting in shifts in climatic zones. The survival of trees in specific locations depends on their functional traits. Local populations exhibit trait adaptations that ensure their survival and accomplishment of growth and reproduction processes during the growing season. Studying these traits offers valuable insights into species responses to present and future environmental conditions, aiding the implementation of measures to ensure forest resilience and productivity. This study investigates the variability in functional traits among five black spruce (Picea mariana (Mill.) B.S.P.) provenances originating from a latitudinal gradient along the boreal forest, and planted in a common garden in Quebec, Canada. We examined differences in bud phenology, growth performance, lifetime first reproduction, and the impact of a late-frost event on tree growth and phenological adjustments. The findings revealed that trees from northern sites exhibit earlier budbreak, lower growth increments, and reach reproductive maturity earlier than those from southern sites. Late-frost damage affected growth performance, but no phenological adjustment was observed in the successive year. Local adaptation in the functional traits may lead to maladaptation of black spruce under future climate conditions or serve as a potent evolutionary force promoting rapid adaptation under changing environmental conditions.
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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".