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Record W2165386977 · doi:10.1086/518963

Null Models of Geographic Range Size Evolution Reaffirm Its Heritability

2007· article· en· W2165386977 on OpenAlexaff
Anthony Waldron

Bibliographic record

VenueThe American Naturalist · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRange (aeronautics)Null modelAllopatric speciationBiologyEvolutionary biologyHeritabilityNull hypothesisGenetic algorithmPhylogenetic treeCharacter evolutionMost recent common ancestorStatisticsEcologyCladeGeneticsMathematicsPopulationDemography

Abstract

fetched live from OpenAlex

Most models of allopatric speciation predict that the two daughter species will have range sizes different from each other's and potentially from that of their common ancestor. However, I find that this difference is less than that expected under a variety of null models of range evolution. Sister species' range values may therefore become more similar in the time following speciation. Greater-than-expected similarity (symmetry) has also been treated as a form of range size heritability. I therefore compare the results of this symmetry approach to a test for phylogenetic signal, using the range sizes of North American birds. I find that range size is heritable under both tests. I suggest that null models for range size heritability should be informed by an explicit model of evolution. Comparative methods may give erroneous results if they fail to take the unusual form of inheritance of range size into account.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.004
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.245
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations91
Published2007
Admission routes1
Has abstractyes

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