Full-service hotels, convenience stores, or fire escapes? Evaluating the functional role of stopover sites for Neotropical migrants following passage across the Gulf of Mexico in autumn
Bibliographic record
Abstract
Nearctic Neotropical migratory songbirds incur the highest mortality during migration. En-route, songbirds rely on a network of stopover sites to rest, refuel, and/or seek refuge during poor weather. Conservation strategies prioritize protection of sites that best meet these needs. However, the specific function of a stopover site is expected to vary in relation to factors, such as geographic location, surrounding landscape, and weather. To identify sites with the highest conservation value for migratory songbirds, a conceptual framework was independently developed to classify sites into three functional categories based on their geographic and landscape features: fire escapes, convenience stores, and full-service hotels. The few attempts to empirically validate this framework have focused on temperate stopover sites. We evaluated the framework by testing the hypothesis that a site’s geographic and landscape characteristics can predict its function. We used capture and radio-tracking data at an island and mainland site in the Yucatan Peninsula, Mexico to quantify mean and variance in daily capture rate, body condition, stopover duration, and departure behavior during autumn, with a focus on four species: Swainson’s Thrush (Catharus ustulatus), Red-eyed Vireo (Vireo olivaceus), Gray Catbird (Dumetella carolinensis), and Prothonotary Warbler (Protonotaria citrea). Our results supported our predictions that the island functions as a fire escape, providing refuge for very high numbers of birds after encountering crosswinds or headwinds over the Gulf of Mexico, and the mainland forest site serves as a full-service hotel. The framework provides valuable insight for strategic conservation planning and management of stopover sites for songbirds. We suggest future studies evaluate the framework’s application to non-forest bird species. Additionally, we encourage collaborative efforts to consolidate and integrate tracking data, capture data from migration banding stations, and radar-based bird density estimates across a broad geography to test the framework’s ability to inform conservation planning across species’ full migratory range.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".