Multi-city analysis of synergies and trade-offs between urban bird diversity and carbon storage to inform decision-making
Bibliographic record
Abstract
Abstract Cities are particularly vulnerable to the impacts of biodiversity loss and climate change. Urban greenspaces are important ecosystems that can conserve biodiversity and help offset the carbon footprint of urban areas. However, despite large-scale tree planting and restoration initiatives in cities, it is not well known where trees or vegetation should be planted or restored to achieve multiple benefits. We considered urban greenspaces as nature-based solutions for urban climate mitigation and biodiversity conservation planning. Using bivariate mapping, we examined the spatial synergies and trade-offs between bird functional diversity and carbon storage in ten Canadian cities spanning a gradient of geography and population, and modelled the relationships between vegetation attributes and both bird diversity and amount of carbon. We found carbon and biodiversity are weakly positively correlated across the ten cities, however, this relationship varied in strength, direction and significance. Our maps highlight areas within our target cities where greenspaces could be managed, restored, or protected to maximize carbon storage and conserve biodiversity. Nationwide, our results also show that forest management strategies that promote increases in canopy cover and the proportion of needle-leaved species in urban greenspaces are potential win-win strategies for biodiversity and carbon. Our study shows NbS strategies are not always generalizable across regions. National policies should guide municipalities and cities using regional priorities and science advice, since a NbS promoting biodiversity in one region may, in fact, reduce it in another.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".