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Record W2033804168 · doi:10.1080/10635150290102528

Using Tree Shape

2002· review· en· W2033804168 on OpenAlexaff
Arne Ø. Mooers, Stephen B. Heard

Bibliographic record

VenueSystematic Biology · 2002
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsUniversity of New BrunswickSimon Fraser University
Fundersnot available
KeywordsPhylogenetic treeTaxonTree (set theory)CladeBiologyEvolutionary biologyDiversification (marketing strategy)Tree of life (biology)Variation (astronomy)GenealogyPhylogeneticsEcologyHistoryMathematicsCombinatorics

Abstract

fetched live from OpenAlex

Although the primary purpose of phylogenies is to depict evolutionary relationships among taxa, they have other interesting properties. One of the most interesting is their shape, some quantifiable measure of how they look. Such measures come in two general flavors: diversity variation among contemporaneous clades (e.g., when averaged over the whole tree, this is tree balance) and distribution of splitting times (e.g., lineages-through-time plots). These measures represent variation in diversification rates among taxa at a given time and within a taxon through time, respectively. As such, tree shape is the signature of the forces that produce biodiversity, and its study informs one of the major areas in evolutionary biology. It has been over a decade since Craig Guyer and Joseph Slowinski (Slowinski and Guyer, 1989; Guyer and Slowinski, 1991, 1993) began the current era of tree shape work (for a history and general review, see Mooers and Heard, 1997). The concept was elegant: compare shapes of published trees with expectations from reasonable null models, deal with uninteresting technical confounds, and interpret what remains with an eye to explaining past and (perhaps) predicting future biodiversification (though we might not have called it that a decade ago). A decade on, it is well established that trees shapes contain interesting and important information about the evolutionary process (see, e.g., Gaston and Spicer, 1998; Schluter, 2000; Felsenstein, in press). For the 2001 joint SSB/SSE/ASN meeting, we were fortunate to be able to organize a symposium titled Developing uses for phylogenetic tree shape in the study of evolution, an opportunity to organize the decade's work and foster collaboration for the future. Collected here are papers representing many of the contributing authors and others who presented related material in other sessions of that 2001 meeting. We gratefully acknowledge the support of the Society of Systematic Biologists, which both sponsored the symposium and agreed to review these papers for publication. These papers are an excellent, if incomplete, update on the decade's work on tree shape. As expected, the descriptive aspect has become more sophisticated, with new software tools and ever better statistical power (contributions in this issue by Agapow and Purvis, and Chan and Moore; see also McKenzie and Steel, 2000; Stam, 2002). We are getting better at incorporating and analyzing branch length information (the latest update and extension is by Pybus et al., this issue). There are also some new observations: perhaps the most intriguing is the idea that species-level and higher level phylogenies might actually differ in shape (Purvis and Agapow, this issue). Along with these changes is a welcome expansion into new fields. Two sampled here are comparative methods (a potentially powerful way to summarize tree shapes is presented by Martins and Housworth, this issue) and community structure (Webb and Pitman, this issue). An important distinction between neontological and paleontological phylogenies has been recognized, and tools for dealing with the latter have been developed (Harcourt-Brown, this issue). Finally, Alan de Queiroz submits a provocative essay on the complexity of the question of why some taxonomic groups might diversify more than others. But we have still just begun. The patterns are certainly real and are becoming better documented (Purvis and Agapow, and Savolainen et al., this issue; Stam, 2002), and the evolutionary models are inching towards increased realism (Heard and Mooers, this issue). However, present explanations for nonrandom tree shapes are incomplete, and the application of tree-shape concepts in related disciplines is still in its infancy (see, e.g., Heard and Mooers, 2000; von Euler, 2001). We expect to see major advances made with

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)0.005
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.039
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0080.009
Science and technology studies0.0020.002
Scholarly communication0.0130.015
Open science0.0040.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0300.019

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 head0.252
GPT teacher head0.355
Teacher spread0.103 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2002
Admission routes1
Has abstractyes

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