Adaptation of lodgepole pine and interior spruce to climate: implications for reforestation in a warming world
Bibliographic record
Abstract
We investigated adaptation to climate in populations of two widespread tree species across a range of contrasting environments in western Canada. In a series of common garden experiments, bud phenology, cold hardiness, and seedling growth traits were assessed for 254 populations in the interior spruce complex (Picea glauca, P. engelmannii, and their hybrids) and for 281 populations of lodgepole pine (Pinus contorta). Complex multitrait adaptations to different ecological regions such as boreal, montane, coastal, and arid environments accounted for 15-20% of the total variance. This population differentiation could be directly linked to climate variables through multivariate regression tree analysis. Our results suggest that adaptation to climate does not always correspond linearly to temperature gradients. For example, opposite trait values (e.g., early versus late budbreak) may be found in response to apparently similar cold environments (e.g., boreal and montane). Climate change adaptation strategies may therefore not always be possible through a simple shift of seed sources along environmental gradients. For the two species in this study, we identified a relatively small number of uniquely adapted populations (11 for interior spruce and nine for lodgepole pine) that may be used to manage adaptive variation under current and expected future climates.
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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 | 0.000 | 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".