The Influence of Abiotic Factors on Street Tree Condition and Mortality in a Commercial-Retail Streetscape
Bibliographic record
Abstract
It is challenging to successfully grow trees in highly-urbanized areas, such as downtown commercial-retail districts. As part of a streetscape revitalization project, initiated in 2010, 133 London planetrees (Platanus × acerifolia) were planted in structural soil cells along the downtown, commercial district of Bloor Street in Toronto, Ontario, Canada. After most trees experienced severe decline, with many dying, all trees were removed and replaced in 2015. This research reports on an investigation of multiple abiotic factors that may have contributed to the decline and mortality of the Bloor Street trees. Researchers collected cross-sectional data on soil texture, soil compaction, soil chemistry, built-environment characteristics (e.g., proximity to road intersections, pit or bed planter), sunlight availability, and historic data on tree condition and mortality, and analyzed them with multivariate statistical techniques (e.g., correlation, MANOVA, contingent, and ANOVA tests) to investigate the potential for relationships to tree mortality (mortality rate of 46.6% before removal) and tree condition. Results indicate that trees that were alive and demonstrated better structural and foliar condition before removal in 2015 had significantly lower levels of soil salinity and alkalinity, sunlight exposure, and signs of physical damage, suggesting co-occurring and cumulative impact of these variables on tree performance. Modification to streetscape design can ameliorate tree decline in the long term, while education targeted at raising awareness about de-icing salt application and irrigation practices will lessen tree stressors immediately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".