Black spruce growth under climate extremes: Genetic insights for managing a key resource production species
Bibliographic record
Abstract
Understanding the influence of intraspecific genetic variation on the response of tree species to stress events—like heatwaves, droughts, and growing season frost—is crucial. This genetic variation is linked to species adaptive potential and plays a pivotal role in shaping the resilience and long-term adaptability of species to climate change. Furthermore, genetic variation can affect populations’ responses to stress events, thereby influencing forest productivity and carbon sequestration potential. We combined dendroecological and genomic approaches to analyze the growth response of 61 black spruce ( Picea mariana ) populations, grown for over 40 years in four common gardens, to daily extreme vapor pressure, soil moisture deficits, and growing season frosts. Our objectives were to 1) assess the effects of stress events defined from physiological thresholds on annual biomass production and 2) explore the potential influence of standing genetic variation on trees’ responses to stress events at the population scale. The growth response of black spruce to those events was site-specific but with an important influence of soil or atmospheric drought at most sites. This response was also nonlinear, with a negative effect on growth occurring only after several stress events a year. Additionally, an interaction between this response and the geographic origin of the population indicated an influence of standing genetic variation, notably linked to local adaptation. These results may provide insight into tree resilience to stress events and help estimate the adaptive potential of the Canadian boreal forest. Moreover, this knowledge can help guiding forest management, for example assisted gene flow. • Growth response of black spruce to extreme weather events is site-specific. • Extreme weather events only impact biomass after a threshold of several events. • Standing genetic variation linked to local adaptation influences growth response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".