The Role of Urban Ecological Networks on Health from a One Health Perspective: A Systematic Review
Bibliographic record
Abstract
Introduction: Ecological networks (ENs) are critical frameworks designed to protect biodiversity, enhance habitat connectivity, and provide ecosystem services in fragmented landscapes. Urban ecological networks (UENs) adapt this concept to address the challenges posed by urbanization, habitat fragmentation, and climate change. Methods: This systematic review follows the PRISMA methodology, with the search strategy applied across PubMed, Scopus, and Web of Science. Articles published until 29 July 2025, were evaluated based on their alignment with One Health domains: human, animal, and ecosystem health. The included studies underwent independent review and quality assessment using the Newcastle–Ottawa Scale. Results: Only nine of the 228 articles that were found satisfied the requirements for inclusion. These studies examined UENs’ effects on biodiversity, species migration, and climate resilience but lacked direct evaluation of human health impacts. Key findings highlighted the role of ecological corridors in improving habitat connectivity, promoting biodiversity, and mitigating climate-related fragmentation. Conclusions: While UENs show significant potential to enhance biodiversity and urban resilience, their direct impacts on human health remain underexplored. Future interdisciplinary research should focus on quantifying these links and integrating UENs into urban planning to address ecological and Public Health challenges under a One Health framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".