EXPLORING THE DIVERSITY AND HEALTH OF URBAN STREET TREES. THE CASE OF THE CITY OF BRAMPTON
Bibliographic record
Abstract
Street trees make a significant component of urban forest and are critical for supporting urban forest functions. Forest researchers and professionals have attributed the increasing decline in the urban forest canopy cover partially to the composition and low diversity of street trees. Limited diversity and composition of street trees have been associated with high susceptibility of urban forest to disturbances such as pests and disease invasions. To increase biodiversity, minimize the loss of urban forest cover, and improve the resilience of the urban forests canopy, it has been suggested that the urban forest species, genera and family composition should be 5-10-30 (Moll, 1989). This threshold enables to cap species, genus and family composition to a maximum of 5%, 10% and 30% of the total tree species population respectively. This study examined diversity and health index of urban street trees in three residential areas developed in 1971, 1991 and 2011 time periods within the City of Brampton. The specific objectives of the study were to: (i) Determine Street tree species diversity by assessing the species composition, richness and evenness (relative abundance) across three residential areas in the city; and (ii) Assess street tree health across different size classes, species and residential areas. The study utilized a 2019 street tree inventory data provided by the City of Brampton, Parks and Forestry Division. Simpson’s diversity index was used to measure the diversity of tree species across selected residential areas. Nonparametric One-Way ANOVA (the Wilcoxon Signed-rank test) and descriptive statistics were used to analyze and evaluate tree size (diameter data in cm) and condition rating (fail, poor, fair, good, very good, and excellent) The results show that street tree composition is more homogeneous than the 5-10-30 rule (Moll, 1989), and that street tree population has low species richness of 1.4%, 2.3% and 2.6% for the 1971, 1991 and 2011 residential areas respectively. Again, the study finds significant differences in the overall tree size across the residential areas, and that significant proportions (84%) of the street tree species are in fair condition. The study recommends that the City develops a medium to long term urban tree management plan to improve urban tree diversity, maintenance and monitoring to enhance urban tree conditions; adopt the use of community-based objective inventory such as Neighbourwoods© to gather more detailed and accurate scientific data to make informed management and monitoring decisions etc.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".