How Important Is Variation in Extrinsic Reproductive Isolation to the Process of Speciation?
Bibliographic record
Abstract
The strength of reproductive isolation (RI) between two or more lineages during the process of speciation can vary by the ecological conditions. However, most speciation research has been limited to studying how ecologically dependent RI varies among a handful of broadly categorized environments. Very few studies consider the variability of RI and its effects on speciation at finer scales-that is, within each environment due to spatial or temporal environmental heterogeneity. Such variation in RI across time and/or space may inhibit speciation through leaky reproductive barriers or promote speciation by facilitating reinforcement. To investigate this overlooked aspect of speciation research, we conducted a literature review of existing studies of variation in RI in the field and then conducted individual-based simulations to examine how variation in hybrid fitness across time and space affects the degree of gene flow. Our simulations indicate that the presence of variation in hybrid fitness across space and time often leads to an increase in gene flow compared to scenarios where hybrid fitness remains static. This observation can be attributed to the convex relationship between the degree of gene flow and the strength of selection on hybrids. Our simulations also show that the effect of variation in RI on facilitating gene flow is most pronounced when RI, on average, is relatively low. This finding suggests that it could serve as an important mechanism to explain why the completion of speciation is often challenging. While direct empirical evidence documenting variation in extrinsic RI is limited, we contend that it is a prevalent yet underexplored phenomenon. We support this argument by proposing common scenarios in which RI is likely to exhibit variability and thus influence the process of speciation.
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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 | 0.003 | 0.017 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".