USE OF SPATIAL FEATURES BY FORAGING INSECTIVOROUS BATS IN A LARGE URBAN LANDSCAPE
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) We analyzed the patterns of habitat use by insectivorous bats in Mexico City, one of the largest and most populated cities of the world. We tested the hypotheses that richer patches of food, expected in more vegetated areas, have higher bat activity levels, and that fast-flying species benefit most from urbanization. We compared activity of insectivorous species and relative abundance of insects in 5 habitats (large parks, small parks, illuminated open areas, residential areas, and natural forest). Sampling of bat activity and insects was conducted every 2 weeks in 12 sites per habitat during summer 2002. Measures of bat activity were based on 3,600 oneminute sequences of sound that were recorded and analyzed. The average number of taxa per site was significantly higher in the natural forest than in urban habitats, but overall bat activity was significantly higher in large parks and illuminated open areas than in small parks, residential areas and natural forest. Vespertilionid bats (Eptesicus fuscus, Myotis, and an unidentified species), along with Eumops perotis, occurred almost exclusively in extensive green areas (large parks or natural forest). The molossid Nyctinomops macrotis made the broadest use of the urban–natural mosaic, whereas Tadarida brasiliensis used urban sites (illuminated areas and large parks) more intensively. Insect abundance was higher in large parks and natural forest, and it was significantly correlated with overall bat activity and with the number of taxa recorded per site. The observed patterns of habitat use and foraging can be explained by considering the flight and echolocation performance of species. Although some species successfully exploited highly urbanized sites, large areas with vegetation are needed to maintain the most diverse insectivorous bat fauna in Mexico City.
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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 | 0.001 | 0.001 |
| 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".