Evidence map›Paper›PMID 42079146›Full record

ArticlebioRxiv : the preprint server for biology2026

Unlocking a flexible set of phylogenetic models for discrete and continuous trait evolution using discretized stochastic diffusion.

Liam J Revell, Laura R V Alencar, Michael E Alfaro, Jonathan Dain, Nichola J Hill, Menna Jones, Kristen M Martinet, Viviana Romero-Alarcon, Luke J Harmon

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Liam J RevellDepartment of Biology, University of Massachusetts Boston.ORCID 0000-0003-0767-4713
Laura R V AlencarDepartment of Ecology and Evolutionary Biology, Yale University.ORCID 0000-0002-6805-7682
Michael E AlfaroDepartment of Ecology and Evolutionary Biology, University of California, Los Angeles.ORCID 0000-0002-8898-8230
Jonathan DainDepartment of Biology, University of Massachusetts Boston.ORCID 0009-0006-9725-4753
Nichola J HillDepartment of Biology, University of Massachusetts Boston.
Menna JonesDepartment of Geophysical Sciences, University of Chicago.ORCID 0000-0002-1161-4492
Kristen M MartinetCollege of Information Science, University of Arizona.ORCID 0000-0002-3905-9507
Viviana Romero-AlarconDepartment of Biology, University of Massachusetts Boston.ORCID 0000-0002-2969-2384
Luke J HarmonDepartment of Biological Sciences, University of Idaho.ORCID 0000-0002-4985-5750

Funding

Tufts IRACDAK12GM133314 · NIGMS · TUFTS UNIVERSITY BOSTON · PI CLAIRE L MOORE, Jamie Lynn Maguire · 2019 to 2026
$8.4M
NIGMS NIH HHS K12 GM133314
6 · The paper itself

Abstract

The practical utility of many modern phylogenetic comparative methods can depend on how accurately mathematical models capture the evolutionary process of traits. Boucher and Démery (2016) described a new quantitative trait model, Brownian motion with reflective limits, that they anticipated might be of use in testing hypotheses about a particular sort of constraint on phenotypic character evolution. Since their analytic solution for the probability function under this bounded evolutionary scenario was not practical to evaluate for reasonably-sized trees, Boucher and Démery (2016) also identified a creative technique for computing the likelihood of their model. The basis of this methodology derives from the convergence of an equal-rates, symmetric, ordered Markov chain and continuous stochastic diffusion in the limit as the number of steps in our chain goes to

Identifiers

PMID42079146
PMCPMC13131536

What OpenQuestion holds

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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.