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Record W2946281943 · doi:10.1101/637678

Assortative mating in hybrid zones is remarkably ineffective in promoting speciation

2019· preprint· en· W2946281943 on OpenAlexaff
Darren E. Irwin

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAssortative matingReproductive isolationBiologyCline (biology)MatingHybrid zoneGenetic algorithmEvolutionary biologyHybridMating preferencesEcological speciationEcologyMate choiceGeneticsGene flowGeneGenetic variationPopulation

Abstract

fetched live from OpenAlex

Abstract Assortative mating and other forms of partial prezygotic isolation are often viewed as being more important than partial postzygotic isolation (low fitness of hybrids) early in the process of speciation. Here I simulate secondary contact between two populations (‘species’) to examine effects of pre- and postzygotic isolation in preventing blending. A small reduction in hybrid fitness (e.g., 10%) produces a narrower hybrid zone than a strong but imperfect mating preference (e.g., 10x stronger preference for conspecific over heterospecific mates). This is because, in the latter case, rare F1 hybrids find each other attractive (due to assortative mating), leading to the gradual buildup of a full continuum of intermediates between the two species. The cline is narrower than would result from purely neutral diffusion over the same number of generations, largely due to the frequency-dependent mating disadvantage of individuals of rare mating types. Hybrids tend to pay this cost of rarity more than pure individuals, meaning there is an induced postzygotic isolation effect of assortative mating. These results prompt a questioning of the concept of partial prezygotic isolation, since it is not very isolating unless there is also postzygotic isolation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.218
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2019
Admission routes1
Has abstractyes

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