The contribution of sexual selection to ecological and mutation-order speciation
Bibliographic record
Abstract
Abundant evidence supports a role for sexual selection in the evolution of reproductive isolation, and it is thus unsurprising that much attention has been given, both conceptually and empirically, to understanding its role in speciation. In doing so, debate has arisen on how sexual selection fits within the much used ecological versus mutation-order classification of speciation mechanisms, with sexual selection often presented as a distinct third alternative. We argue that models of speciation by sexual selection include a fundamental role of divergent selection between environments or mutation order in initiating the process. Rather than representing a unique mechanism, sexual selection layers a coevolutionary process between males and females on top of the classic mechanisms such that the evolution of each sex can now be driven by divergent selection, mutation order, and selection arising from interactions with the other sex. In addition to blurring the distinction between ecological and mutation-order speciation, this coevolutionary process is likely to speed divergence. Sexual selection is not unique in this way; coevolutionary processes can similarly arise from ecological interactions between populations or species, with similar results. Ultimately, understanding the contribution of sexual selection to speciation will require identifying the processes that drive the divergence of mating biases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".