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Complex histories of speciation and dispersal in communities: a re‐analysis of some Australian bird data using BPA

2002· article· en· W2082016666 on OpenAlexafffund
Deborah A. McLennan, Daniel R. Brooks

Bibliographic record

VenueJournal of Biogeography · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsVicarianceSympatric speciationBiological dispersalEndemismBiologyEcologyLineage (genetic)Allopatric speciationGenetic algorithmBiogeographyEvolutionary biologyCladePhylogeneticsPopulationDemography

Abstract

fetched live from OpenAlex

Aim Demonstrate the utility of Brooks Parsimony Analysis (BPA) in showing that communities of species characterizing areas of endemism may have complex histories rather than simple histories of vicariance, and that the non‐vicariant influences can be discovered. Location Australia. Methods Primary and secondary BPA of eight clades of Australian birds inhabiting ten areas of endemism. Results While supporting previous general conclusions that the evolution of those members of the Australian avifauna has been influenced by two different vicariant elements, BPA also discovered substantial non‐vicariant elements, including one non‐response to a vicariance event, five instances of peripheral isolates speciation, four episodes of extinction, seven cases of post‐speciation dispersal and three lineage duplications, which could represent ancient episodes of sympatric speciation. The result of including all evolutionary events in the analysis is that seven of the ten areas have reticulate histories. Main conclusions Areas of endemism are not necessarily the result of simple histories of vicariance. They may also be evolutionary hot spots, places where multiple evolutionary events have occurred over a significant period of time. This produces communities heavily influenced by a variety of evolutionary processes affecting the same areas at different times.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.150
GPT teacher head0.288
Teacher spread0.138 · 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

Citations31
Published2002
Admission routes2
Has abstractyes

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