Range-wide patterns of geographic variation in songs of Golden-crowned Sparrows (<i>Zonotrichia atricapilla</i>)
Bibliographic record
Abstract
Discrete geographic variation, or dialects, in songs of songbirds arise as a consequence of complex interactions between ecology and song learning. Four of the five species of Zonotrichia sparrows, including the model species White-crowned Sparrow (Z. leucophrys), have been studied with respect to the causes and consequences of geographic variation in song. Within White-crowned Sparrows, subspecies that migrate farther have larger range size of dialects. Here, we assessed geographic patterns of song variation in the fifth species of this genus, the Golden-crowned Sparrow (Z. atricapilla). We analyzed field-recorded songs from 2 sampling periods (1996–1998 and 2006–2013) covering most of its breeding range in western North America. All songs began with a descending whistle and most songs consisted of 3–4 phrases that contained combinations of whistles, buzzes, and trills. We identified 13 discrete song types based on unique sequences of phrase types and frequency changes between phrases. Over 90% of individuals sang 1 of 5 song types, and we found clear dialect structure composed of these 5 common song types. The geographic range of dialects spanned large distances (500 to 1,700 km), resembling the geographic structure of dialects in the long-distance migrant Gambel's White-crowned Sparrow (Z. l. gambelli), though locations of dialect boundaries differ between species. Because both Golden-crowned Sparrows and Gambel's White-crowned Sparrows migrate similarly long distances, our study provides support to the hypothesis that dialect range size correlates with migration distance. We found little evidence of change in dialect composition in 4 populations that were sampled 15 years apart, which suggests that the dialect structure is stable across multiple generations. Our study opens the door for further comparisons to investigate links between ecology and the emergence of song dialects in this well-studied genus.
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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".