Which Tree Species Best Withstand Urban Stressors? Ask the Experts
Bibliographic record
Abstract
Abstract Background The importance of urban trees and their benefits to society are increasingly recognized. However, cities are a challenging environment for trees to grow and thrive. Current knowledge on tree vulnerabilities to existing urban stressors remains scarce and available only for a limited number of species and specific stressors. Methods Using the Delphi method with urban forestry experts familiar with the studied area and a closed-ended questionnaire, we sought to elucidate the tolerance of commonly planted urban tree species in northeastern North America to multiple urban stressors—air pollution, soil compaction, de-icing salts, insects and diseases, strong winds, ice storms, snow, drought, and extreme temperatures—as well as to assess which characteristics may capture a species’ ability to cope with these stressors. Results Ginkgo biloba, Gleditsia triacanthos, Quercusspp., andUlmusspp. were rated by urban forestry professionals as the most tolerant species in northeastern North America to the studied stressors. No species was listed as tolerant to all stressors. Furthermore, respondents disagreed on how a given species was likely to be affected by or respond to a given stressor. Conclusions Our study provides a powerful approach to gaining difficult-to-obtain information on trees’ vulnerabilities to environmental stressors and identifying the gaps that remain unaddressed. Our findings fill some of the gaps in our knowledge of city trees’ vulnerabilities, which makes the approach useful in practice to inform the choice of tree species that could be planted across our cities to build more resilient urban 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".