Tropical cities as windows into the ecosystems of our present and future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it