Assessing the effect of seasonal agriculture on the condition and winter survival of a migratory songbird in Mexico
Bibliographic record
Abstract
Migratory birds can spend 8 months of the year on their wintering grounds and the conversion of natural habitats to agriculture in Latin America has been implicated in population declines of several Neotropical migrants. Despite this, few studies have directly assessed the value of agricultural habitat for wintering migrants. We compared the condition and survival of Yellow Warblers ( Setophaga petechia ) occupying natural (riparian forest, scrub‐mangrove) and agricultural habitat (annually cropped sorghum, corn, and chili‐peppers separated by hedgerow) in western Mexico. We assessed condition with five metrics (daily and seasonal changes in size‐adjusted body mass, leukocyte profiles, rectrix regrowth rate, rectrix quality, and dates of departure on spring migration). We used Cormack–Jolly–Seber models fitted to mark‐resighting data collected over 4 years (2012–2015) to estimate January–May monthly survival rates. We found that birds occupying agricultural habitat and riparian forest had higher monthly apparent survival between January and May than birds in scrub‐mangrove. Birds in agricultural habitat also grew higher quality feathers (i.e., rectrices with a higher barbule density) than those in natural habitat. In contrast, birds in agricultural habitat were lighter than those in riparian habitat. We found no detectable effect of winter habitat use on daily or season changes in size‐adjusted mass and H/L ratios, although the effect of winter habitat use on departure rates differed for males and females. Our results demonstrate that agricultural habitat may provide suitable winter habitat for a long‐distance migrant and suggest that feather quality can be an indicator of winter habitat quality.
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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".