Landscape influences non-breeding performance of a Nearctic-Neotropical migratory songbird
Bibliographic record
Abstract
During their stationary, non-breeding period, Nearctic-Neotropical migratory songbirds using habitats within agricultural working landscapes may be affected by both immediate site conditions as well as those of the surrounding landscape. We evaluated whether body condition and apparent non-breeding survival of Wilson’s Warblers (Cardellina pusilla) and body condition of Wood Thrushes (Hylocichla mustelina) were influenced by site and landscape contexts during two non-breeding seasons in a coffee-growing region in Honduras. At the site scale, we tested whether the coffee farm management system (i.e., shade coffee farm, land-sparing farm, sun coffee farm) influenced performance. At the landscape scale, we derived two independent composite metrics, from 250 m radius land cover maps around a centroid estimated from survey sites within each farm. The first metric represented landscapes with more open habitats (such as sun coffee, early successional cover types, and pastureland/croplands) relative to shade coffee. The second represented landscapes with more mature and advanced second-growth forests with high edge density relative to shade coffee. Wilson’s Warblers at shade coffee farms had higher condition than those occupying land-sparing farms. At the landscape scale, we found opposing effects in which Wilson’s Warblers’ body condition was lower but apparent non-breeding survival was higher in forest-dominated, high edge-density landscapes. For Wood Thrushes, we did not find evidence that site or landscape context influenced body condition, and we had insufficient data to examine apparent non-breeding survival. We underscore the necessity of considering landscape context in relation to non-breeding songbird performance, concluding that in coffee-growing landscapes, shade coffee and forest habitat may benefit different aspects of performance.
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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".