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Record W2494089698 · doi:10.1098/rspb.2016.0556

Continental cichlid radiations: functional diversity reveals the role of changing ecological opportunity in the Neotropics

2016· article· en· W2494089698 on OpenAlexafffund
Jessica H. Arbour, Hernán López‐Fernández

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoUniversity of California, DavisUniversity of WashingtonDrexel UniversityAuburn University
KeywordsCichlidAdaptive radiationEcologyBiologyDiversification (marketing strategy)Ecological nicheLineage (genetic)NicheSpecies richnessMacroevolutionPhylogeneticsFish <Actinopterygii>FisheryHabitat

Abstract

fetched live from OpenAlex

Adaptive radiations have been hypothesized to contribute broadly to the diversity of organisms. Models of adaptive radiation predict that ecological opportunity and ecological release, the availability of empty ecological niches and the response by adapting lineages to occupy them, respectively, drive patterns of phenotypic and lineage diversification. Adaptive radiations driven by 'ecological opportunity' are well established in island systems; it is less clear if ecological opportunity influences continent-wide diversification. We use Neotropical cichlid fishes to test if variation in rates of functional evolution is consistent with changing ecological opportunity. Across a functional morphological axis associated with ram-suction feeding traits, evolutionary rates declined through time as lineages diversified in South America. Evolutionary rates of ram-suction functional morphology also appear to have accelerated as cichlids colonized Central America and encountered renewed opportunity. Our results suggest that ecological opportunity may play an important role in shaping patterns of morphological diversity of even broadly distributed lineages like Neotropical cichlids.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.043
GPT teacher head0.221
Teacher spread0.178 · 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

Citations66
Published2016
Admission routes2
Has abstractyes

Explore more

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