Temporal stability in songs across the breeding range of <i>Geothlypis philadelphia</i> (Mourning Warbler) may be due to learning fidelity and transmission biases
Bibliographic record
Abstract
ABSTRACT We found a stable pattern of geographic variation in songs across the breeding range of the Geothlypis philadelphia (Mourning Warbler) over a 36-year period. The Western, Eastern, Nova Scotia, and Newfoundland regiolects found in 2005 to 2009 also existed in 1983 to 1988 and 2017 to 2019. Each regiolect contained a pool of syllables that were unique and different from the other regiolects. The primary syllable types that defined each regiolect were present throughout the study, but there were changes in the frequencies of variants of these syllable types in each regiolect. We developed an agent-based model of birdsong learning within each regiolect to explore whether these frequency changes were consistent with unbiased copying or 2 forms of transmission bias: frequency bias and content bias. Strong content bias, possibly for more complex syllables, best models the temporal dynamics across regiolects. In combination with a high estimated learning fidelity, this may explain why regiolects and syllable types were stable for 36 years. We also examined whether variation in physical parameters of song over time could be attributed to acoustic adaptation to breeding habitat, using Landsat variables as a proxy for vegetation characteristics of each male’s breeding territory. The physical parameters of the songs, which changed little over time, revealed no coherent relationships with the Landsat variables and therefore little evidence for acoustic adaptation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".