Assessing local adaptation vs. plasticity under different resource conditions in seedlings of a dominant boreal tree species
Why this work is in the frame
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Bibliographic record
Abstract
Under changing climate conditions, understanding local adaptation of plants is crucial to predicting the resilience of ecosystems. We selected black spruce (Picea mariana), the most dominant tree species in the North American boreal forest, in order to evaluate local adaptation vs. plasticity across regions experiencing some of the most extreme climate warming globally. Seeds from three provenances across the latitudinal extent of this species in northwestern Canada were planted in a common garden study in growth chambers. Two levels of two resource conditions were applied (low/high nutrient and ambient/elevated CO 2 ) in a fully factorial design and we measured physiological traits, allocational traits, growth and survival. We found significant differences in height, root length and biomass among populations, with southern populations producing the largest seedlings. However, we did not detect meaningful significant differences among nutrient or CO 2 treatments in any traits measured, and there were no consistent population-level differences in physiological traits or allocation patterns. We found that there was greater mortality after simulated winter in the high nutrient treatment, which may reflect an important shift in seedling growth strategies under increased resource availability. Our study provides important insight into how this dominant boreal tree species might respond to the changing climate conditions predicted in this region.
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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