The contribution of constructed green infrastructure to urban biodiversity: A synthesis and meta‐analysis
Bibliographic record
Abstract
Abstract The development of buildings and other infrastructure in cities is viewed as a threat to local biodiversity and ecosystem functioning because natural habitat is replaced. However, there is momentum for implementing green infrastructure (GI), such as green roofs, wetland detention basins and community gardens, that partially offset these impacts and that benefit human health. GI is often designed to explicitly support ecosystem services, including implied benefits to biodiversity. The effects of GI on biodiversity have been rarely quantified, but research on this topic has increased exponentially in the last decade and a synthesis of the literature is needed. Here, we examined 1,883 published manuscripts and conducted a meta‐analysis on 33 studies that were relevant. We determined whether GI provides additional benefits to biodiversity over conventional infrastructure or natural counterparts. We also highlighted research gaps and identified opportunities to improve future applications. We determined that GI significantly improves biodiversity over conventional infrastructure equivalents, and that in some cases GI had comparable measures of biodiversity to natural counterparts. Many studies were omitted from these analyses because we found GI research has generally neglected conventional experimental design frameworks, including controls, replication or adequate sampling effort. Synthesis and applications . Our synthesis identified that taxa specificity is an important consideration for green infrastructure (GI) design relative to the more common measurements at the community level. We also identified that ignoring multi‐trophic interactions and landscape‐level patterns can limit our understanding of GI effects on biodiversity. We recommend further examination of species‐specific differences among infrastructures (i.e. green, conventional or natural equivalents) or using functional traits to improve the efficacy of GI implementation on urban biodiversity. Furthermore, we encourage policy makers and practitioners to improve the design of GI to benefit urban ecosystems because of the potential benefits for both humans and global biodiversity.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.026 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".