DIVERGENT ENVIRONMENTS AND POPULATION BOTTLENECKS FAIL TO GENERATE PREMATING ISOLATION IN DROSOPHILA PSEUDOOBSCURA
Bibliographic record
Abstract
While the feasibility of bottleneck-induced speciation is in doubt, population bottlenecks may still affect the speciation process by interacting with divergent selection. To explore this possibility, I conducted a laboratory speciation experiment using Drosophila pseudoobscura involving 78 replicate populations assigned in a two-way factorial design to both bottleneck (present vs. absent) and environment (ancestral vs. novel) treatments. Populations independently evolved under these treatments and were then tested for assortative mating and male mating success against their common ancestor. Bottlenecks alone did not generate any premating isolation, despite an experimental design that was conducive to bottleneck-induced speciation. Premating isolation also did not evolve in the novel environment treatment, neither in the presence nor absence of bottlenecks. However, male mating success was significantly reduced in the novel environment treatment, both as a plastic response to this environment and as a result of environment-dependent inbreeding effects in the bottlenecked populations. Reduced mating success of derived males will hamper speciation by enhancing the mating success of immigrant, ancestral males. Novel environments are generally thought to promote ecological speciation by generating divergent natural selection. In the current experiment, however, the novel environment did not cause the evolution of any premating isolation and it reduced the likelihood of speciation through its effects on male mating success.
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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 | 0.000 |
| 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".