Use of natural and anthropogenic land cover by wintering Yellow Warblers: The influence of sex and breeding origin
Bibliographic record
Abstract
Sex, age, body size, and breeding origin can influence the nonbreeding distribution of long-distance migratory birds. At continental scales, differential migration can lead to segregation of various classes or populations among different regions. At local scales, class segregation can occur among habitats of differing qualities, often due to dominance interactions related to sex, age, and body size. Conversion of natural habitats to agriculture in the Neotropics is leading to more birds wintering in modified landscapes. We examined how sex, age, size, and breeding origin influenced the use of 3 land cover types (riparian gallery forest, coastal lagoon vegetation, and agriculture) by wintering migratory Yellow Warblers (Setophaga petechia) in western Mexico. Between 2012 and 2014, we used point counts in 6 study sites to estimate Yellow Warbler densities, and we captured, sexed, and aged 205 birds to characterize individuals using each land cover type. Breeding origin was inferred using stable hydrogen isotope ratios (δ2H) in feathers. Contrary to our expectations, Yellow Warbler densities were highest in agricultural sites (5.6 ± 0.7 SE birds ha−1) and lower in sites with natural land cover (riparian gallery forest: 3.6 ± 0.5 birds ha−1; scrub mangrove: 2.0 ± 0.4 birds ha−1). The Yellow Warbler population wintering in our study sites was male-biased (64%), and the use of sites with natural vs. agricultural land cover was not influenced by sex, age, or size of individuals, suggesting that competitive interactions are not limiting access to sites with different land cover. We found that females from more northerly breeding or natal origins were more likely to winter in sites with natural land cover, while females from more southerly origins were more likely to use agricultural sites. Our results suggest that localized population declines could occur via survival differences or carryover effects if condition or subsequent reproductive success differs between birds wintering in sites with natural vs. agricultural land cover.
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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".