An integrative urban tree risk index as a novel framework for risk assessment: A case study of Montreal, Canada
Bibliographic record
Abstract
Urban trees provide essential ecosystem services in cities, but are increasingly at risk from climate extremes, urban stressors, and pests and diseases. Assessing and managing these risks is vital to maintaining the benefits urban forests offer to communities. These challenges highlight the need for robust, integrative tools to assess species-specific vulnerability and support evidence-based urban forest management. Here, we introduce the Urban Tree Risk Index ( UTRI ), a novel integrative framework designed to evaluate the vulnerability of urban tree species. The UTRI incorporates five components: climatic safety margins (reflecting exposure to climates outside the species’ tolerance limits), species abundance (affecting risk of disease spread), tree size and age (both affecting risk of mortality), and four key ecophysiological traits (associated with tolerance or resistance to stressors: leaf nitrogen content, seed mass, specific leaf area, and wood density). We assessed the urban forest of Montreal, Canada, and our results suggest substantial variability in safety margins across species, with many already experiencing conditions near or beyond their climatic thresholds. Notably, the five most abundant species, including Acer platanoides and A. saccharinum , constitute nearly half of Montreal’s urban forest and rank among the most vulnerable species according to UTRI , due to their high abundance, low climate resilience, and high climate vulnerability. By analysing with combined climate, demographic and ecophysiological trait data, the UTRI provides an integrative risk assessment, highlighting vulnerable species and locations, and supporting strategic urban forest management to enhance resilience under future climate challenges. • The Urban Tree Risk Index quantifies tree vulnerability to climate stress. • The index integrates climate and demographic metrics with trait data. • The index can identify resilient tree species and prioritise species monitoring. • The index enhances decision-making and can support sustainable urban forestry.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".