Tropical cities as windows into the ecosystems of our present and future
Bibliographic record
Abstract
Abstract Urban ecology and tropical biology have both developed rapidly in recent decades and matured into important interdisciplinary fields, with significant implications for biodiversity and human communities globally. However, urban ecosystems within the tropics remain understudied and poorly characterized despite these systems representing major hotspots for both biodiversity and human population growth. Here we review the state of the field of “tropical urban ecology.” We first evaluated and propose ecological hypotheses about how tropical versus extratropical species and ecosystems might differ from one another in how they respond to urbanization pressures. While data remain limited, we expect that tropical biodiversity should be at least as vulnerable to urbanization (and potentially more vulnerable) than extratropical biodiversity. We also examined the importance of ecosystem services in tropical cities and demonstrate the challenges in quantifying, managing, and sustaining these across diverse socioeconomic and environmental contexts. Finally, we propose an agenda for moving the field of tropical urban ecology forward through an interdisciplinary lens that synthesizes recent advances in both urban ecology and tropical biology. Specifically, advances and development in community science, Earth observation, environmental justice, One Health, and land sparing/sharing strategies could lead to major steps forward in the conservation of biodiversity in tropical cities. As the world urbanizes increasingly in biodiverse‐rich tropical ecosystems, we must have strong conceptual frameworks and increased data/attention on both the ecological and human communities most impacted by these significant global changes.
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 | 0.001 | 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 | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".