Males and females contribute differently to the evolution of habitat segregation driven by hybridization
Bibliographic record
Abstract
Costly heterospecific mating interactions, such as hybridization, select for prezygotic reproductive isolation. One of the potential traits responding to the selection arising from maladaptive hybridization is habitat preference, whose divergence results in interspecific habitat segregation. Theoretical studies have so far assumed that habitat preference is a sexually shared trait. However, male and female habitat preferences can experience different selection pressures. Here, by combining analytical and simulation approaches, we theoretically examine the evolution of sex-specific habitat preferences. Habitat segregation can have demographic consequences, potentially generating eco-evolutionary dynamics. We thus explicitly consider demography in the simulation model. We also vary the degrees of species discrimination to examine how mate choice influences the evolution of habitat preferences. Results show that both sexes can reduce hybridisation risk by settling in the habitats where abundant conspecific mates reside. However, when females can discriminate species, excess conspecific male aggregation intensifies male-male competition for mating opportunities, posing an obstacle to conspecific aggregation. Meanwhile, conspecific female aggregation attracts conspecific males, by offering the mating opportunity. Therefore, under effective species discrimination, females play a leading role in initiating habitat use divergence. Simulations typically result in either the coexistence with established habitat segregation or the extinction of one of the species. The former result is especially likely when the species differ to some extent in habitat preferences upon secondary contact. Our results disentangle the selection pressures acting on male and female habitat preferences, deepening our understanding of the evolutionary process of habitat segregation due to hybridization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".