Trait-based, spatial, and temporal variation in leaf temperature of tropical trees.
Bibliographic record
Abstract
Current estimates of temperature effects on plants are usually based on air temperature (Tair), although it is well known that leaf temperature (Tleaf) can deviate considerably from Tair. In some studies, to overcome the problem of Tair often being a poor proxy of Tleaf, measurements of canopy temperature (Tcan) have been used instead. However, Tcan data do not capture the spatial variation in Tleaf among leaves with different thermoregulatory traits. This may be particularly problematic for highly diverse and heterogeneous tropical forest canopies. In this study, we used infrared thermometers to study Tleaf and Tcan in multispecies tropical tree plantations established at three sites along an elevation gradient from 2,400 m a.s.l. (17.1°C mean daytime temperature) to 1,300 m a.s.l. (24.0°C) in Rwanda. Measurements of chlorophyll fluorescence were also conducted to study the photosynthetic heat tolerance of these species. Our results showed high Tleaf (up to ~50°C) and leaf-to-air temperature differences (ΔTleaf; on average 8-10°C and up to 24°C) of sun-exposed leaves. Both leaf size and stomatal conductance were important traits in controlling Tleaf. The Tleaf (and thus ΔTleaf) of sun-exposed leaves greatly exceeded the simultaneously measured values of Tcan (and ΔTcan). Photosynthetic heat tolerance partially acclimated to increased growth temperature; on average 0.31°C increase in heat tolerance per 1°C increase in growth temperature. Consequently, thermal safety margins were narrower for species at the warmer, lower-elevation sites. Our findings highlight the importance of leaf traits for leaf thermoregulation and show that monitoring of canopy temperature is not enough to capture the peak temperatures and heat stress experienced by individual leaves in diverse tropical forest canopies. They also suggest that tropical trees have limited abilities to thermally acclimate to increasing temperatures.Keywords: Canopy temperature, elevation gradient, fluorescence, heat tolerance, leaf area, leaf temperature, stomatal conductance, thermoregulation, tropical forest.
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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 | 0.001 |
| 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".