Intra-annual stem size variations converge across marginal populations of European beech
Bibliographic record
Abstract
Abstract Key message The intra-annual variability in stem size of marginal beech population from south-eastern distribution range of beech in Poland is higher within than between populations. Abstract One of the key issues of the distribution of tree species is their ability to track environmental changes. European beech (Fagus sylvaticaL.) is a species highly sensitive to extreme climatic events, because of its high phenotypic plasticity. In this study, we aim to determine the variability in stem size between and within marginal beech populations. Marginal populations of beech growing under uniform environmental conditions of provenance trial offer unique opportunity to detect adaptive differentiations driven by natural selection. In this work, we studied stem size variation recorded by automatic band dendrometers in four beech marginal populations growing in a common garden in the south-eastern distribution range of beech in Poland over the period 2016–2018. Strong climatic effects and weak provenance differences in seasonal stem size variation were observed. The provenances exhibited similar climate-related seasonal stem circumference variation. A high within-provenance variation was confirmed. Temperature of spring as well as temperature and precipitation of autumn were detected as key climatic parameters mostly for onset and end of stem size variation. Maximum stem size was mostly affected by the later end of its variation, which positively affected its duration. Climatic distance between beech provenances and provenance trial had a negligible effect on the variability in seasonal stem size variation between provenances. The evidence of weak inter-provenance and high intra-provenance variation in stem size changes observed in the south-eastern distribution range indicates that an individual-based approach could be a suitable strategy, when selecting for phenotypic plasticity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".