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Record W4387128726 · doi:10.1126/science.ade2833

Cycles of fusion and fission enabled rapid parallel adaptive radiations in African cichlids

2023· article· en· W4387128726 on OpenAlexaff
Joana I. Meier, Matthew D. McGee, David A. Marques, Salome Mwaiko, Mary A. Kishe, S.B. Wandera, Dirk Neumann, Hillary D. J. Mrosso, Lauren J. Chapman, Colin A. Chapman, Les Kaufman, Anthony Taabu‐Munyaho, Catherine E. Wagner, Rémy Bruggmann, Laurent Excoffier, Ole Seehausen

Bibliographic record

VenueScience · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsVancouver Island UniversityMcGill University
Fundersnot available
KeywordsAdaptive radiationLineage (genetic)PopulationAdaptation (eye)BiologyFissionEvolutionary biologyEcologyNeutronPhysicsPhylogeneticsDemography

Abstract

fetched live from OpenAlex

Although some lineages of animals and plants have made impressive adaptive radiations when provided with ecological opportunity, the propensities to radiate vary profoundly among lineages for unknown reasons. In Africa's Lake Victoria region, one cichlid lineage radiated in every lake, with the largest radiation taking place in a lake less than 16,000 years old. We show that all of its ecological guilds evolved in situ. Cycles of lineage fusion through admixture and lineage fission through speciation characterize the history of the radiation. It was jump-started when several swamp-dwelling refugial populations, each of which were of older hybrid descent, met in the newly forming lake, where they fused into a single population, resuspending old admixture variation. Each population contributed a different set of ancient alleles from which a new adaptive radiation assembled in record time, involving additional fusion-fission cycles. We argue that repeated fusion-fission cycles in the history of a lineage make adaptive radiation fast and predictable.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.135

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)0.0000.000

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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 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

Citations111
Published2023
Admission routes1
Has abstractyes

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