Temperature and breeding success for Cliff Swallows (<i>Petrochelidon pyrrhonota</i>) nesting on man-made structures: ecological traps?
Bibliographic record
Abstract
When an environmental cue that previously signaled a suitable habitat leads an animal to use an unsuitable site, individual fitness can decrease, ultimately leading to population declines. Such “ecological traps” may be particularly likely for birds that use human infrastructure for nesting. Here we tested whether high nest temperatures and the physical properties of barns are associated with lower breeding success for a declining population of Cliff Swallows (Petrochelidon pyrrhonota (Vieillot, 1817)). We monitored nests under barn eaves below wood and metal roofs to determine nestling survival and mass, and recorded temperature under barn eaves, to relate ambient temperature to eave temperature. We found that eave temperature increased with ambient temperatures and was higher at high temperatures and lower at cool temperatures under metal roofs than wood roofs. Nestling survival was lower during periods with higher ambient temperatures, and both survival and mass were lower under metal roofs. Our findings suggest that barn eaves, especially those with metal roofs, may be an ecological trap for Cliff Swallows, where a seemingly suitable nesting site early in the breeding season results in low breeding success. Furthermore, warming temperatures may lead to ecological traps for other bird species, particularly those nesting in man-made structures.
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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 | 0.001 |
| 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".