The joint influence of photoperiod and temperature during growth cessation and development of dormancy in white spruce (<i>Picea glauca</i>)
Bibliographic record
Abstract
Timely responses to environmental cues enable the synchronization of phenological life-history transitions essential for the health and survival of north-temperate and boreal tree species. While photoperiodic cues will remain persistent under climate change, temperature cues may vary, contributing to possible asynchrony in signals influencing developmental and physiological transitions essential to forest health. Understanding the relative contribution of photoperiod and temperature as determinants of the transition from active growth to dormancy is important for informing adaptive forest management decisions that consider future climates. Using a combination of photoperiod (long = 20 h or short = 8 h day lengths) and temperature (warm = 22 °C/16 °C and cool = 8 °C/4 °C day/night, respectively) treatments, we used microscopy, physiology and modeling to comprehensively examine hallmark traits of the growth-dormancy transition-including bud formation, growth cessation, cold hardiness and gas exchange-within two provenances of white spruce [Picea glauca (Moench) Voss] spanning a broad latitude in Alberta, Canada. Following exposure to experimental treatments, seedlings were transferred to favorable conditions, and the depth of dormancy was assessed by determining the timing and ability of spruce seedlings to resume growth. Short photoperiods promoted bud development and growth cessation, whereas longer photoperiods extended the growing season through the induction of lammas growth. In contrast, cool temperatures under both photoperiodic conditions delayed bud development. Photoperiod strongly predicted the development of cold hardiness, whereas temperature predicted photosynthetic rates associated with active growth. White spruce was capable of attaining endodormancy, but its release was environmentally determined. Dormancy depth varied substantially across experimental treatments suggesting that environmental cues experienced within one season could affect growth in the following season, which is particularly important for a determinate species such as white spruce. The joint influence of these environmental cues points toward the importance of including local constant photoperiod and shifting temperature cues into predictive models that consider how climate change may affect northern forests.
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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".