How to diversify native trees in cities: a trait-based approach in the Atlantic Forest in southern Brazil
Bibliographic record
Abstract
Enhancing tree diversity is a key strategy for building climate-resilient cities. In Brazil, however, urban forests, except in urban forest remnants, are often dominated by a few species, mostly exotic, despite the country's immense native biodiversity. This study presents a rapid and simple method to support planners in designing functionally diverse urban forests using native trees. We grouped 77 native species from the Dense Ombrophilous Forest (DOF) in southern Brazil into six functional groups based on four traits: seed mass, wood density, height, and leaf persistence. These groups represent distinct ecological strategies and can guide tree selection tailored to local conditions. By offering a low-cost, replicable tool grounded in functional ecology, this approach promotes the use of native species, enhances ecosystem resilience, and fosters a stronger local ecological identity. The method can be adapted for use in other formations and biomes and is particularly relevant for small and mid-sized cities that usually lack the technical capacity for ecological planning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".