Evidence map›Paper›PMID 39047110›Full record

ArticleScience advances2024

Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks.

Torsten Hauffe, Juan L Cantalapiedra, Daniele Silvestro

Abstract read
In one paragraph

Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Fossils improve extinction-rate estimates under state-dependent diversification models.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Article
  7. Review
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.

Torsten HauffeDepartment of Biology, University of Fribourg and Swiss Institute of Bioinformatics, 1700 Fribourg, Switzerland.ORCID 0000-0001-5711-9457
Juan L CantalapiedraDepartamento de Paleobiología, Museo Nacional de Ciencias Naturales, Consejo Superior de Investigaciones Científicas, 28006 Madrid, Spain.ORCID 0000-0003-0913-7735
Daniele SilvestroDepartment of Biology, University of Fribourg and Swiss Institute of Bioinformatics, 1700 Fribourg, Switzerland.ORCID 0000-0003-0100-0961

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Species life-history traits, paleoenvironment, and biotic interactions likely influence speciation and extinction rates, affecting species richness over time. Birth-death models inferring the impact of these factors typically assume monotonic relationships between single predictors and rates, limiting our ability to assess more complex effects and their relative importance and interaction. We introduce a Bayesian birth-death model using unsupervised neural networks to explore multifactorial and nonlinear effects on speciation and extinction rates using fossil data. It infers lineage- and time-specific rates and disentangles predictor effects and importance through explainable artificial intelligence techniques. Analysis of the proboscidean fossil record revealed speciation rates shaped by dietary flexibility and biogeographic events. The emergence of modern humans escalated extinction rates, causing recent diversity decline, while regional climate had a lesser impact. Our model paves the way for an improved understanding of the intricate dynamics shaping clade diversification.

Indexed as

Bayes TheoremExtinction, BiologicalFossilsGenetic SpeciationNeural Networks, ComputerAnimalsBiodiversityHumans

Identifiers

PMID39047110
PMCPMC11268411

What OpenQuestion holds

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LicenceCC BY
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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.