Is local the best? Phenotypic plasticity vs local adaptation in a reciprocal transplant experiment with white spruce in Alaska
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it