Forest structure but not tree diversity differs among urban woodlands with differing conservation status
Bibliographic record
Abstract
While biodiversity conservation in urban areas is a topic of great interest, few studies have focused on the role that urban conservation areas have for preserving biodiversity. Urban woodlands, which are patches of forest habitat confined within the city’s boundaries, offer a promising approach to evaluate the importance of conservation areas within cities. Here, we examined the relationship between conservation status, forest structure and composition across 11 urban woodlands in Montréal, Canada. We used field surveys to assess biodiversity, canopy cover, and structural complexity for urban woodlands with a conservation status and those without. We found that Montréal’s urban woodlands fostered similar levels of biodiversity regardless of conservation status. Similarly, all urban woodlands supported high proportions of native tree species despite differences in conservation status and associated management. Our results suggest that both conservation areas and non-status woodlands play an important role in safeguarding urban biodiversity. Woodlands with a conservation status, however, had higher canopy cover and vegetative complexity, but also contained higher average proportions of invasive trees, particularly Rhamnus cathartica (Common Buckthorn). While the high complexity in vegetation layers observed may provide habitat to native wildlife, these benefits may be limited by the high proportion of invasive trees.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".