Assessing urban tree taxonomic diversity, composition and structure across public and private green space types: a community-based tree inventory
Bibliographic record
Abstract
The urban forest is a crucial component of the city landscape, providing communities with countless benefits we refer to as ecosystem services. Trees improve urban air quality, decrease city temperatures, provide spaces for recreation and promote mental wellbeing. To properly quantify the benefits the urban forest provides, we require a strong baseline understanding of forest structure, diversity, and composition. To date, fine-scale work considering urban forest diversity has been commonly limited to trees on public land, considering only one or two green space types. However, the governance of green spaces in cities means tree species composition is being influenced by management decisions at various levels, including by institutions, municipalities, and individual landowners responsible for their care. Using a mixed-method approach combining a traditional field-inventory and community science project, I inventoried the urban forest in the residential neighbourhood of Notre-Dame-de-Grȃce, Montreal. I assessed four green space types in the public and private domain: parks, institutions, street rights of way and private yards to quantify how tree diversity, composition and structure varies across multiple land management types at local scales. I additionally considered how patterns of service-traits (traits related to managers preference and ecosystem services) differed across green space types, with implications for the distribution of ecosystem services across the urban landscape. I found that green space types displayed meaningful differences in both tree diversity and structure. For example, the inclusion of private trees contributed an additional 52 species (30% of total species) not found in the local public tree inventory, and private land was dominated by smaller trees compared to the public domain. I found patterns of richness, size and abundance extend to differences in tree composition and service-traits at local-scales, particularly in the street right-of way and private yards. Composition varied considerably across street blocks; however, blocks were very similar in terms of mean service-based traits. Contrastingly, species composition was similar from yard to yard, however, yards differed significantly in mean service-trait values. Overall, my work emphasizes that public tree inventories are unlikely to be fully representative of urban forest composition and structure, with implications for urban forest management at larger spatial scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".