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CRATER LAKE COLONIZATION BY NEOTROPICAL CICHLID FISHES

2012· article· en· W2125844174 on OpenAlexfundno aff
Kathryn R. Elmer, Topi K. Lehtonen, Shaohua Fan, Axel Meyer

Bibliographic record

VenueEvolution · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversität KonstanzTurun YliopistoDeutsche Forschungsgemeinschaft
KeywordsCichlidBiologyColonizationCrater lakeImpact craterEcologyHabitatFish <Actinopterygii>FisheryAstrobiology

Abstract

fetched live from OpenAlex

Volcanic crater lakes are isolated habitats that are particularly well suited to investigating ecological and evolutionary divergence and modes of speciation. However, the mode, frequency, and timing of colonization of crater lakes have been difficult to determine. We used a statistical comparative phylogeographic approach, based on a mitochondrialDNA dataset, to infer the colonization history of two Nicaraguan crater lakes by populations of genetically and ecologically divergent cichlid lineages: Midas (Amphilophus cf. citrinellus complex) and moga (Hypsophrys nematopus). We compared estimates of diversity among populations within the two cichlid lineages and found that Midas were the most genetically diverse. From an approximate Bayesian computation analysis, we inferred that the crater lakes were each founded by both cichlid lineages in single waves of colonization: Masaya 5800 ± 300 years ago and Xiloá 5400 ± 750 years ago. We conclude that natural events are likely to have a dominant role in colonization of the crater lakes. Further, our findings suggest that the higher species richness and more rapid evolution of the Midas species complex, relative to other lineages of fishes in the same crater lakes, cannot be explained by earlier or more numerous colonization events.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.999

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)0.0050.002

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

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

Citations35
Published2012
Admission routes1
Has abstractyes

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