Keeping trees in the ground: How property management and socio-ecological factors affect retention of trees planted through a backyard planting initiative
Bibliographic record
Abstract
Tree planting initiatives have been adopted across North American cities to grow the urban forest. They are driven by the numerous benefits trees provide and many focus specifically on residential properties. While these initiatives are successfully planting trees, with existing research focusing on where or by whom, post-planting monitoring is rarely undertaken, particularly for trees planted in residential backyards. Given that trees need to reach maturity to fully provide the benefits they are planted for, it is important to understand their retention over the short and long-term and the factors that affect it. We examined if tree, human community, surrounding environment, and property management factors were associated with the retention of trees planted through a backyard tree planting initiative in Toronto (Ontario, Canada) to better understand why trees are retained or removed. We used logistic and spatial regressions to examine factors significantly associated with tree retention five-years post-planting and in a mixed age population in 2022. The results show a relatively high retention rate for backyard trees five years after planting (83.2%), with odds of retention decreasing for every change in homeownership and if the tree was planted in the spring. The retention rate for the mixed-age population in 2022 was lower (59.5%) and was associated with additional tree and human community factors. Our findings suggest that while several variables contribute to increased backyard tree retention, targeted outreach to new homeowners, as well as emphasing the importance of stewardship for spring plantings, may be most impactful. • Tree planting programs often target residential property • Examined factors related to retention of trees planted in backyards • Lower retention odds when house sold for establishment and mixed-age cohort samples • Tree size and human community factors associated with retention in mixed-age cohort • Overall retention was high but need for outreach with new homeowners
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".