Is local the best? Phenotypic plasticity vs local adaptation in a reciprocal transplant experiment with white spruce in Alaska
Bibliographic record
Abstract
Abstract Key message Provenances show a high phenotypic plasticity and the ability to grow beyond the cold treeline. Local is best can still be applied. Abstract Boreal forests situated in high latitudes face heightened susceptibility to climate extremes and global warming. Understanding the relative influence of adaptation mechanisms like phenotypic plasticity or local adaptation on key traits is crucial to better understand and project species distribution, forest growth and vitality. To address this, we conducted a reciprocal transplant experiment featuring two white spruce (Picea glauca [Moench] Voss) provenances in Alaska, representing cold and dry treelines. Trees from each provenance were reciprocally transplanted across a gradient spanning from dry bluff sites, dry treelines via old-growth forests to cold-limited treelines and beyond. From 2015 to 2022, we monitored survival, vitality, growth, and various needle morphology traits. Results showed that the dry provenance had a superior performance in its home environment. Whereas both provenances performed similarly at the cold site. Survival and vitality rates indicated that elevated temperatures favoured tree growth. Seedling survival and growth are possible beyond the current cold treeline. Further, needle morphology traits were more influenced by the current environment than by origin, thus showing a high phenotypic plasticity. Nevertheless, significant differences in needle morphology among provenances hinted at a genetic base of these traits. Results suggested that local is best can still be applied.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".