Is a seasonally reduced growth potential a convergent strategy to survive drought and frost in plants?
Bibliographic record
Abstract
BACKGROUND: Plants have adapted to survive seasonal life-threatening frost and drought. However, the timing and frequency of such events are impacted by climate change, jeopardizing plant survival. Understanding better the strategies of survival to dehydration stress is therefore timely and can be enhanced by the cross-fertilization of research between disciplines (ecology, physiology), models (woody, herbaceous species) and types of stress (drought, frost). SCOPE: We build upon the 'growth-stress survival' trade-off, which underpins the identification of global plant strategies across environments along a 'fast-slow' economics spectrum. Although phenological adaptations such as dormancy are crucial to survive stress, plant global strategies along the fast-slow economic spectrum rarely integrate growth variations across seasons. We argue that the growth-stress survival trade-off can be a useful framework to identify convergent plant ecophysiological strategies to survive both frost and drought. We review evidence that reduced physiological activity, embolism resistance and dehydration tolerance of meristematic tissues are interdependent strategies that determine thresholds of mortality among plants under severe frost and drought. We show that complete dormancy, i.e. programmed growth cessation, before stress occurrence, minimizes water flows and maximizes dehydration tolerance during seasonal life-threatening stresses. We propose that incomplete dormancy, i.e. the programmed reduction of growth potential during the harshest seasons, could be an overlooked but major adaptation across plants. Quantifying stress survival in a range of non-dormant versus winter- or summer-dormant plants, should reveal to what extent incomplete to complete dormancy could represent a proxy for dehydration tolerance and stress survival. CONCLUSIONS: Our review of the strategies involved in dehydration stress survival suggests that winter and summer dormancy are insufficiently acknowledged as plant ecological strategies. Incorporating a seasonal fast-slow economics spectrum into global plant strategies improves our understanding of plant resilience to seasonal stress and refines our prevision of plant adaptation to extreme climatic events.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".