Seasonal dynamics and ecological correlates of population abundance of birds inhabiting green areas within a megacity
Bibliographic record
Abstract
The expansion of cities has had severe effects on biodiversity. However, within cities, green areas are suitable habitats for diverse species, both native and exotic. This study quantified the population abundances and temporal variations of 16 native and four exotic species of birds that inhabit urban green areas of Mexico City, which is considered a megacity. We used N-mixture models to estimate the abundance of these species. These demographic models also estimate their detection probabilities. We found that six species of birds (three native and three exotic) had remarkably higher abundances than the rest of the species. Eight species had seasonal changes in their abundances, with six of these species increasing in numbers during the dry season and the other two species being more abundant during the rainy season. Some native bird species had higher population numbers in parks with large trees of diverse species, abundant flowers, less urban cover, and low noise levels. In contrast, exotic species were more abundant in parks with intense human activity, where they can easily find anthropogenic food sources. Our findings provide fundamental information about the ecological traits of urban green areas that allow native and exotic birds to thrive in urban ecosystems.
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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 | 0.000 | 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) | 0.001 | 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".