Range-wide post- and pre-breeding migratory networks of a declining neotropical–nearctic migratory bird, the blackpoll warbler
Bibliographic record
Abstract
Identifying the drivers of population declines in migratory species requires an understanding of how individuals are distributed between periods of the annual cycle. We built post- (fall) and pre-breeding (spring) migratory networks for the blackpoll warbler (Setophaga striata), a Neotropical-Nearctic songbird, using tracking data from 47 light-level geolocators deployed at 11 sites across its breeding range. During pre-breeding migration, two stopover nodes (regions) on the U.S. eastern seaboard received high scores in our network metrics (betweenness centrality and time-adjusted node weight), likely acting as key refuelling areas for most of the global blackpoll warbler population before their multi-day flights over the Atlantic Ocean. During post-breeding migration, highly ranked stopover nodes in the southeastern U.S. acted as a geographical bottleneck before birds dispersed to their boreal breeding destinations. Nodes located in northern Colombia and Venezuela were also ranked highly during both migrations and were likely used to prepare for (pre-breeding) and recover from (post-breeding) Atlantic flights. Blackpoll warblers showed a crosswise migration pattern, whereby individuals from western breeding populations tended to spend the nonbreeding season in the eastern part of the nonbreeding range and vice-versa. Despite this, the strength of migratory connectivity between the breeding and nonbreeding grounds ranged from moderate to low, largely because many individuals used more than one node during the 'stationary' nonbreeding period. Our results suggest that the number of breeding populations affected by a threat in the blackpoll warbler's range will strongly depend on where and when this threat occurs. Consequently, our migratory network should be key to inform future conservation planning and population monitoring efforts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".