Assessing local adaptation vs. plasticity under different resource conditions in seedlings of a dominant boreal tree species
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 CO2) 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 CO2 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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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 | 0.000 | 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 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".