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Record W2004601902 · doi:10.1371/journal.pone.0053539

Sympatric Speciation: When Is It Possible in Bacteria?

2013· article· en· W2004601902 on OpenAlexfundno aff
Jonathan Friedman, Eric J. Alm, B. Jesse Shapiro

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsSympatric speciationBiologyReproductive isolationEcological nicheAdaptation (eye)Genetic algorithmEvolutionary biologyEcological speciationIncipient speciationAssortative matingSympatryGene flowNicheGenomeRecombinationEcologyGeneticsMatingGeneGenetic variationPopulationHabitat

Abstract

fetched live from OpenAlex

According to theory, sympatric speciation in sexual eukaryotes is favored when relatively few loci in the genome are sufficient for reproductive isolation and adaptation to different niches. Here we show a similar result for clonally reproducing bacteria, but which comes about for different reasons. In simulated microbial populations, there is an evolutionary tradeoff between early and late stages of niche adaptation, which is resolved when relatively few loci are required for adaptation. At early stages, recombination accelerates adaptation to new niches (ecological speciation) by combining multiple adaptive alleles into a single genome. Later on, without assortative mating or other barriers to gene flow, recombination generates unfit intermediate genotypes and homogenizes incipient species. The solution to this tradeoff may be simply to reduce the number of loci required for speciation, or to reduce recombination between species over time. Both solutions appear to be relevant in natural microbial populations, allowing them to diverge into ecological species under similar constraints as sexual eukaryotes, despite differences in their life histories.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.217
Teacher spread0.196 · 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 designBench or experimental
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

Citations63
Published2013
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicEvolution and Genetic DynamicsFrench-language works237,207