Foraging habitat and diet of Song Sparrows (<i>Melospiza melodia</i>) nesting in farmland: a stable isotope approach
Bibliographic record
Abstract
Agricultural intensification has been linked to declines in farmland and grassland bird populations in Europe and in North America. One factor thought to be behind these declines is reduced invertebrate food abundance in the breeding season, leading to reduced reproductive success. However, little is known about foraging habitat or diet of farmland birds in North America. We used stable isotopes to study foraging habitat and diet of Song Sparrows ( Melospiza melodia (A. Wilson, 1810)), a common hedgerow-breeding bird, by collecting claw clippings of Song Sparrow nestlings from farms in eastern Ontario, Canada. Mean values of δ13C and δ15N were compared between claws and invertebrate food sources from adjacent land-cover types, using a Bayesian mixing model. The portion of nestling diet that came from seminatural and forage habitats was higher than expected, based on the proportion of seminatural and forage cover at the nest. This was particularly evident at nests with low seminatural cover, where hedgerows were the only noncrop habitat. The most important food sources for Song Sparrow nestlings were hoppers (Auchenorrhyncha) and caterpillars (Lepidoptera). Management actions to increase invertebrate abundance in hedgerows may benefit Song Sparrows and other farmland birds with minimal impact on crop yield.
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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".