Female and male song exhibit both parallel and divergent patterns of cultural evolution: A long-term study of song structure and diversity in tropical wrens
Bibliographic record
Abstract
Abstract Animal culture changes over time through processes that include drift, immigration, selection, and innovation. Cultural change has been particularly well-studied for animal vocalizations, especially for the vocalizations of male animals in the temperate zone. Here we examine the cultural change in the vocalizations of tropical Rufous-and-white Wrens (Thryophilus rufalbus), quantifying temporal variation in song structure, song type diversity, and population-level distribution of song types in both males and females. We use data from 10 microsatellite loci to quantify patterns of immigration and neutral genetic differentiation over time, to investigate whether cultural diversity changes with rates of immigration. Based on 11 yr of data, we show that the spectro-temporal features of several widely-used persistent song types maintain a relatively high level of consistency for both males and females, whereas the distribution and frequency of particular song types change over time for both sexes. Males and females exhibit comparable levels of cultural diversity (i.e. the diversity of song types across the population), although females exhibit greater rates of cultural change over time. We found that female changes in cultural diversity increased when immigration is high, whereas male cultural diversity did not change with immigration. Our study is the first long-term study to explore cultural evolution for both male and female birds and suggests that cultural patterns exhibit notable differences between the sexes.
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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 | 0.000 |
| 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".