A Test of Founder Effect Speciation Using Multiple Loci in the Auklets (Aethia spp.)
Bibliographic record
Abstract
Whether speciation results more frequently from the genetic consequences of founder events or from gradual genetic divergence of large populations is a matter of debate. In this study, multiple analyses were applied to data from three loci (cytochrome b, alpha-enolase intron VIII, and MHC class II B) to test for founder effects associated with speciation in Aethia (Aves: Alcidae), a genus of seabirds thought to have undergone a rapid founder-induced radiation. Effective population sizes (N(e)) were derived from estimators of based on allelic diversity and the coalescent and from data on trans-species polymorphism. Results indicated that N(e) has been on the order of 10(5)-10(6) individuals throughout the evolutionary histories of least and crested auklets (A. pusilla and A. cristatella, respectively) and that N(e) of the ancestral species was at least 16,000 individuals. Computer simulations of MHC evolution indicated that a single-generation bottleneck at speciation could not have involved <85 individuals for each species. More moderate simulation scenarios indicated that population size could not have dropped below 2000 individuals at the time of species founding. Demographic history appears to have been stable for the auklets throughout the past several million years, and a founder effect associated with their speciation is unlikely.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".