Using song dialects to reveal migratory patterns of Ruby-crowned Kinglet populations
Bibliographic record
Abstract
Conservation of a migratory species requires knowledge not only of its breeding range, but also of its migratory path and non-breeding range. Except for timing, other aspects of the migration of the Ruby-crowned Kinglet (<em>Corthylio calendula</em>) remain largely unstudied, with no published data on migration routes. Breeding populations of this species in the Sierra Nevada and Cascades mountain ranges, as well as those in eastern Canada and the northeastern U.S., have experienced significant declines, whereas Rocky Mountain breeders have increased. Understanding the winter range and migratory pathways used by different breeding populations may be key to explaining these contrasting population trends. Song dialects of the Ruby-crowned Kinglet differ regionally among various breeding populations, and these dialect regions were previously mapped. Because this kinglet sings during spring migration and winter, we obtained archived, non-breeding-season recordings of song and assigned each to one of those regional song dialects. This allowed us to assess the likely winter ranges and migration pathways of different breeding populations. This approach offers some advantages over typical methods of tracking movements. Birds do not need to be captured; one can easily obtain data over large ranges and from many individuals; and it can be applied to species, such as this kinglet, that are too small to permit use of most tracking devices. We were able to assess likely winter range and spring migration routes for populations that breed in the eastern U.S. and Canada, the interior of Alaska, and for the subspecies <em>C c. grinnelli</em> that breeds along the Gulf of Alaska and western British Columbia. We found that kinglets breeding in the eastern portions of the range wintered in the southeastern and south-central U.S., and that their spring migrations occurred across a broad swath of the eastern U.S. Interior Alaska breeders wintered mostly in California, and the subspecies <em>C. c. grinnelli</em> wintered from the southernmost parts of their breeding range, south as far as northwestern California. We obtained too few winter recordings from birds using the dialects of kinglets breeding in the interior west (Rocky Mountains and the Sierra Nevada and Cascades ranges) to determine their winter range, and spring recordings were also sparse from those regions. It is likely that those interior-west breeders winter mainly in Mexico, an area with very few archived recordings. We also analyzed unpublished banding data for the Ruby-crowned Kinglet that, although providing little information about breeding-wintering range connectivity, were consistent with the migratory pathways we determined from song dialects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".