Evidence map›Paper›PMID 38346860›Full record

ReviewCold Spring Harbor perspectives in biology2024

Predictability, an Orrery, and a Speciation Machine: Quest for a Standard Model of Speciation.

Marius Roesti, Hannes Roesti, Ina Satokangas, Janette Boughman, Samridhi Chaturvedi, Jochen B W Wolf, R Brian Langerhans

Abstract readReview
In one paragraph

Review in Cold Spring Harbor perspectives in biology, 2024. 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

7 authors.

Marius Roesti *Division of Evolutionary Ecology, Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland marius.roesti@unibe.ch.
Hannes Roesti *Department of Biology, Alte Kantonsschule Aarau, 5001 Aarau, Switzerland.
Ina SatokangasOrganismal & Evolutionary Biology Research Programme, University of Helsinki, 00014 Helsinki, Finland.ORCID 0000-0002-2553-1487
Janette BoughmanDepartment of Integrative Biology, Ecology, Evolution, and Behavior Program, Michigan State University, Lansing, Michigan 48824, USA.
Samridhi ChaturvediDepartment of Ecology and Evolutionary Biology, Tulane University, New Orleans, Louisiana 70118, USA.
Jochen B W WolfDivision of Evolutionary Biology, Faculty of Biology, LMU Munich Biozentrum Martinsried, D-82152 Planegg-Martinsried, Germany.
R Brian LangerhansDepartment of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate predictions are commonly taken as a hallmark of strong scientific understanding. Yet, we do not seem capable today of making many accurate predictions about biological speciation. Why? What limits predictability in general, what exactly is the function and value of predictions, and how might we go about predicting new species? Inspired by an orrery used to explain solar eclipses, we address these questions with a thought experiment in which we conceive an evolutionary speciation machine generating new species. This experiment highlights complexity, chance, and speciation pluralism as the three fundamental challenges for predicting speciation. It also illustrates the methodological value of predictions in testing and improving conceptual models. We then outline how we might move from the hypothetical speciation machine to a predictive standard model of speciation. Operationalizing, testing, and refining this model will require a concerted shift to large-scale, integrative, and interdisciplinary efforts across the tree of life. This endeavor, paired with technological advances, may reveal apparently stochastic processes to be deterministic, and promises to expand the breadth and depth of our understanding of speciation and more generally, of evolution.

Indexed as

Genetic SpeciationAnimalsBiological EvolutionModels, Biological

Identifiers

PMID38346860
PMCPMC11146309

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.