Changes in morphological and physiological traits of urban trees in response to elevated temperatures within an Urban Heat Island
Bibliographic record
Abstract
Urban heat islands (UHIs) are a common phenomenon in metropolitan areas worldwide where the air temperature is significantly higher in urban areas than in surrounding suburban, rural or natural areas. Mitigation strategies to counteract UHI effects include increasing tree cover and green spaces to reduce heat. The successful application of these approaches necessitates a deep understanding of the thermal tolerances in urban trees and their susceptibility to elevated urban temperatures. We evaluated how the photosynthetic thermal optimum (Topt), photosynthetic heat tolerance (T50) and key leaf thermoregulatory morphological traits (leaf area [LA], specific leaf area, leaf width, thickness and leaf dry matter content) differ between conspecific trees growing in 'hot' (UHI) vs 'cool' parts of Montreal, Canada (with a difference of 3.4 °C in air temperature), to assess the ability of seven common tree species to acclimation to higher temperatures. We hypothesized that individuals with hotter growing temperatures would exhibit higher Topt and T50, as well as leaf thermoregulatory morphological traits aligned with conservative strategies (e.g., reduced LA and increased leaf mass) compared with their counterparts in the cooler parts of the city. Contrary to our a priori hypotheses, LA increased with growing temperatures and only four of the seven species had higher T50 and only three had higher Topt values in the hotter area. These results suggest that many tree species cannot acclimate to elevated temperatures and that the important services they provide, such as carbon capture, can be negatively affected by high temperatures caused by climate change and/or the UHI effect. The ability vs inability of tree species to acclimate to high temperatures should be considered when implementing long term tree planting programs in urban areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".