Migration path of American golden-plovers breeding across the Nearctic tundra
Bibliographic record
Abstract
Many populations of migratory birds are currently declining. Understanding of space use throughout the entire annual cycle, as well as migratory connectivity (i.e., geographic linkage of individuals and populations across different stages of the annual cycle), can improve our ability to identify factors driving population declines and influencing extinction risk. The main objectives of our study were to i) document the space use and phenology of migration during the non-breeding period and ii) quantify the degree of migratory connectivity across the range of the American Golden-Plover (Pluvialis dominica) breeding across the North American Arctic. American Golden-Plovers breed across their entire breeding range (northern North America) and migrate up to their main wintering site located in South America. We used archival light-level geolocators to track the migration. We quantified migratory connectivity based on the non-breeding range spread of all individuals and the breeding population spread. We used Mantel tests to evaluate whether the relative spatial configuration of the sampled breeding area was preserved on the non-breeding ground. We identified 13 and 7 stopover sites used during the fall (post-breeding, southbound) and spring (pre-breeding, northbound) migrations, respectively, and one main site used during the wintering period. We highlight stopover sites that were previously unknown and show the transatlantic and transpacific routes used by plovers during migration. We found that individuals breeding in proximity tended to be closer to each other during brief and highly limited portions of the non-breeding period. Broadly, individuals from different breeding populations were well mixed during the wintering period and throughout most of the spring and fall migrations. Overall, the migratory connectivity of American Golden-Plovers is relatively low for most of the non-breeding period, suggesting that breeding populations separated by large distances should be similarly affected by disturbances and changes encountered at some migratory stopovers and on the wintering area.
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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".