Which phenotypic traits are under selection under warm, dry climates in black spruce?
Bibliographic record
Abstract
• Trees are increasingly at risk of maladaptation to their environment as climates change rapidly world-wide. Although adaptive evolution by natural selection is a key mechanism by which populations and species can avoid extinction in changing environments, we have limited information regarding the phenotypic traits under selection under warm and dry environments. We answer the following research questions: (1) What ecophysiological traits are under selection in warm and dry environments? (2) Will intrapopulation trait integration affect the response to selection in the warmer, drier site? (3) Is the plastic response of traits under selection adaptive? • We studied 425 trees from seven provenances across three 50-year-old Picea mariana (black spruce) provenance trials located along a spatial climate gradient across eastern Canada. We measured height growth rate as a performance metric, and 10 traits that reflect water use, thermoregulation, structural support, and photosynthetic rate. • All traits were under selection in at least one site, mostly in combination with other traits. For two trait combinations, the strength of selection gradients significantly increased from the colder, wetter site to the warmer, drier site: water use efficiency (WUE) with Huber value (HV), and carbon-to-nitrogen ratio (CN) with HV. In the warmer and drier site, trait-trait correlations among these three traits were largely absent, except for CN:HV in two provenances. The plastic response to the spatial climate gradient was adaptive for WUE but maladaptive for HV and CN. • Results suggest that adaptive evolution in response to climate change in P. mariana may favor phenotypes with fewer needles that are conservative for water and resource use. Intrapopulation trait integration should minimally impede adaptive evolution. However, the species’ plastic responses to warmer and drier conditions may constrain the expression of optimally adapted phenotypes.
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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 | 0.001 |
| 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".