Evidence map›Paper›PMID 31970528›Full record

ArticleBulletin of mathematical biology2020

Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models.

Giovanni Laudanno, Bart Haegeman, Rampal S Etienne

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Giovanni LaudannoGroningen Institute for Evolutionary Life Sciences, Box 11103, 9700 CC, Groningen, The Netherlands. glaudanno@gmail.com.
Bart HaegemanTheoretical and Experimental Ecology Station, CNRS and Paul Sabatier University, Moulis, France.
Rampal S EtienneGroningen Institute for Evolutionary Life Sciences, Box 11103, 9700 CC, Groningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular phylogenies have been increasingly recognized as an important source of information on species diversification. For many models of macroevolution, analytical likelihood formulas have been derived to infer macroevolutionary parameters from phylogenies. A few years ago, a general framework to numerically compute such likelihood formulas was proposed, which accommodates models that allow speciation and/or extinction rates to depend on diversity. This framework calculates the likelihood as the probability of the diversification process being consistent with the phylogeny from the root to the tips. However, while some readers found the framework presented in Etienne et al. (Proc R Soc Lond B Biol Sci 279(1732):1300-1309, 2012) convincing, others still questioned it (personal communication), despite numerical evidence that for special cases the framework yields the same (i.e., within double precision) numerical value for the likelihood as analytical formulas do that were independently derived for these special cases. Here we prove analytically that the likelihoods calculated in the new framework are correct for all special cases with known analytical likelihood formula. Our results thus add substantial mathematical support for the overall coherence of the general framework.

Indexed as

BiodiversityModels, BiologicalAnimalsBiological EvolutionComputer SimulationExtinction, BiologicalGenetic SpeciationGenetic VariationLikelihood FunctionsMathematical ConceptsModels, GeneticPhylogenyBirth–death processDiversificationLikelihood modelMacroevolutionPhylogenetic trees

Identifiers

PMID31970528
PMCPMC6976549

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.