Plasticity and implications of water‐use traits in contrasting tropical tree species under climate change
Bibliographic record
Abstract
Abstract Plants face a trade‐off between hydraulic safety and growth, leading to a range of water‐use strategies in different species. However, little is known about such strategies in tropical trees and whether different water‐use traits can acclimate to warming. We studied five water‐use traits in 20 tropical tree species grown at three different altitudes in Rwanda (RwandaTREE): stomatal conductance (gs), leaf minimum conductance (gmin), plant hydraulic conductance (Kplant), leaf osmotic potential (ψo) and net defoliation during drought. We also explored the links between these traits and growth and mortality data. Late successional (LS) species had low Kplant, gs and gmin and, thus, low water loss, while low ψo helped improve leaf water status during drought. Early successional (ES) species, on the contrary, used more water during both moist and dry conditions and exhibited pronounced drought defoliation. The ES strategy was associated with lower mortality and more pronounced growth enhancement at the warmer sites compared to LS species. While Kplant and gmin showed downward acclimation in warmer climates, ψo did not acclimate and gs measured at prevailing temperature did not change. Due to distinctly different water use strategies between successional groups, ES species may be better equipped for a warmer climate as long as defoliation can bridge drought periods.
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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 | 0.001 | 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) | 0.001 | 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".