When can ecological speciation be detected with neutral loci?
Bibliographic record
Abstract
It is not yet clear under what conditions empirical studies can reliably detect progress toward ecological speciation through the analysis of allelic variation at neutral loci. We use a simulation approach to investigate the range of parameter space under which such detection is, and is not, likely. We specifically test for the conditions under which divergent natural selection can cause a 'generalized barrier to gene flow' that is present across the genome. Our individual-based numerical simulations focus on how population divergence at neutral loci varies in relation to recombination rate with a selected locus, divergent selection on that locus, migration rate and population size. We specifically test whether genetic differences at neutral markers are greater between populations in different environments than between populations in similar environments. We find that this expected signature of ecological speciation can be detected under part of the parameter space, most consistently when divergent selection is strong and migration is intermediate. By contrast, the expected signature of ecological speciation is not reliably detected when divergent selection is weak or migration is low or high. These findings provide insights into the strengths and weaknesses of using neutral markers to infer ecological speciation in natural systems.
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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 | 0.013 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".