Light-level geolocation reveals moderate levels of migratory connectivity for declining and stable populations of Black-throated Blue Warblers ( Setophaga caerulescens )
Bibliographic record
Abstract
Black-throated blue warbler (Setophaga caerulescens) populations have been declining at the southern edge of the breeding range in North Carolina over the past two decades. Determining the causes of population declines in migratory species requires knowledge of the threats faced throughout the entire annual cycle, necessitating accurate information about the migratory routes and non-breeding areas used by birds. We used light-level geolocators to identify the fall migratory routes and non-breeding distributions of adults breeding at the southern edge of the range in North Carolina (n = 5), where populations are declining, and at the core of the range in New Hampshire (n = 8), where populations are stable. The strength of migratory connectivity was moderate (mean = 0.42). New Hampshire birds used non-breeding areas broadly distributed across the Caribbean, whereas North Carolina birds used a restricted non-breeding area largely in the Dominican Republic. Suitable forest cover declined at a higher rate from 2000 to 2019 in the Dominican Republic than in other Caribbean countries (8.4% vs. 2–4% loss), exposing birds from the trailing edge to significantly higher suitable habitat loss on the non-breeding grounds compared with range-core birds. Birds from the two study populations also exhibited differing migratory routes, with North Carolina birds migrating south through Florida and many New Hampshire birds performing an overwater flight from the Carolinas to the Caribbean. Our results suggest the possibility that, at least for this species, forest loss on the island of Hispaniola could be exacerbating population declines at the southern edge of the breeding range in North Carolina.
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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 | 0.000 |
| 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".