Altitudinal differentiation in growth and phenology among populations of temperate-zone tree species growing in a common garden
Bibliographic record
Abstract
The aim of the study was to determine whether there are genetic variations in growth and leaf phenology (flushing and senescence) among populations of six woody species ( Abies alba Mill., Acer pseudoplatanus L., Fagus sylvatica L., Fraxinus excelsior L., Ilex aquifolium L., and Quercus petraea (Matt.) Liebl.) along altitudinal gradients, using a common-garden experiment. We found (i) significant differences in phenology and growth among provenances for most species and (ii) evidence that these among-population differences in phenology were related to the annual temperature at the provenance sites for ash, beech, and oak. It is noteworthy that along the same climatic gradient, species can exhibit opposing genetic clines: beech populations from high elevations flushed earlier than those from low elevations, whereas we observed the opposite trend for ash and oak. For most species, significant altitudinal clines for growth were also revealed. Finally, we highlighted the fact that both phenology timing and growth rate were highly consistent from year to year. The results demonstrated that despite the proximity of the populations in their natural area, differences in altitude led to genetic differentiation in their phenology and growth. These adaptive capacities acting along a natural climatic gradient could allow populations to cope with current 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".