Sexual selection, speciation and constraints on geographical range overlap in birds
Bibliographic record
Abstract
The role of sexual selection as a driver of speciation remains unresolved, not least because we lack a clear empirical understanding of its influence on different phases of the speciation process. Here, using data from 1306 recent avian speciation events, we show that plumage dichromatism (a proxy for sexual selection) does not predict diversification rates, but instead explains the rate at which young lineages achieve geographical range overlap. Importantly, this effect is only significant when range overlap is narrow (< 20%). These findings are consistent with a 'differential fusion' model wherein sexual selection reduces rates of fusion among lineages undergoing secondary contact, facilitating parapatry or limited co-existence, whereas more extensive sympatry is contingent on additional factors such as ecological differentiation. Our results provide a more mechanistic explanation for why sexual selection appears to drive early stages of speciation while playing a seemingly limited role in determining broad-scale patterns of diversification.
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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 | 0.001 | 0.001 |
| 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".