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Record W2109312919 · doi:10.1086/512690

The Shapes of Phylogenetic Trees of Clades, Faunas, and Local Assemblages: Exploring Spatial Pattern in Differential Diversification

2007· article· en· W2109312919 on OpenAlexaff
Stephen B. Heard, Graham H. Cox

Bibliographic record

VenueThe American Naturalist · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of New Brunswick
FundersNational Science Foundation
KeywordsCladePhylogenetic treeDiversification (marketing strategy)FaunaBiologyDifferential (mechanical device)Evolutionary biologyEcologyGeography

Abstract

fetched live from OpenAlex

Life on Earth is characterized by strong diversity skewness: related lineages typically show pronounced variation in diversification success, and clades contain hyperdiverse and depauperate subclades. Previous studies have documented diversity skewness only for entire (global) clades. We demonstrate methods for measurement and significance testing of diversity skewness of local assemblages and regional biotas; we illustrate this with an analysis of geographic structure in diversity skewness of primate assemblages. For primates, continental faunas differ in diversity skewness from expectations based on the global phylogeny: South American faunas have significantly low skewness and African faunas have significantly high skewness. However, no local assemblage has diversity skewness different from that expected based on sampling the continental fauna. We also document a latitudinal gradient in diversity skewness for the African assemblages and test for (but do not find) associations of skewness with longitude, local species richness, and net primary productivity. Our data suggest that continental-scale biogeographic events rather than local-scale processes have shaped diversity skewness in modern primate faunas.

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 score0.158
Threshold uncertainty score0.996

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 studies0.0000.001
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 head0.015
GPT teacher head0.242
Teacher spread0.226 · 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

Citations39
Published2007
Admission routes1
Has abstractyes

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