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Record W2570142701 · doi:10.1073/pnas.1604072114

Higher rates of sex evolve during adaptation to more complex environments

2017· article· en· W2570142701 on OpenAlexafffund
Pepijn Luijckx, Eddie K. H. Ho, Majid Gasim, Suyang Chen, Andrijana Stanić, Connor Yanchus, Yun Seong Kim, Aneil F. Agrawal

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsAdaptation (eye)PsychologyComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

A leading hypothesis for the evolutionary maintenance of sexual reproduction proposes that sex is advantageous because it facilitates adaptation. Changes in the environment stimulate adaptation but not all changes are equivalent; a change may occur along one or multiple environmental dimensions. In two evolution experiments with the facultatively sexual rotifer Brachionus calyciflorus, we test how environmental complexity affects the evolution of sex by adapting replicate populations to various environments that differ from the original along one, two, or three environmental dimensions. Three different estimates of fitness (growth, lifetime reproduction, and population density) confirmed that populations adapted to their new environment. Growth measures revealed an intriguing cost of complex adaptations: populations that adapted to more complex environments lost greater amounts of fitness in the original environment. Furthermore, both experiments showed that B. calyciflorus became more sexual when adapting to a greater number of environmental dimensions. Common garden experiments confirmed that observed changes in sex were heritable. As environments in nature are inherently complex these findings help explain why sex is maintained in natural populations.

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 categoriesnone
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 score0.556
Threshold uncertainty score0.169

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 science0.0010.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.327
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations45
Published2017
Admission routes2
Has abstractyes

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