Evidence map›Paper›PMID 34603491›Full record

ReviewEvolutionary applications2021

The evolution of barriers to exploitation: Sometimes the Red Queen can take a break.

Jonathan R Goodman, Paul W Ewald

Open access · goldAbstract readReview
In one paragraph

Review in Evolutionary applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 18% of its field
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

4 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. 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

2 authors at 2 institutions in 2 countries.

Jonathan R GoodmanLeverhulme Centre for Human Evolutionary Studies University of Cambridge Cambridge UK.ORCID https://orcid.org/0000-0001-8217-364X
Paul W EwaldDepartment of Biology University of Louisville Louisville Kentucky USA.ORCID https://orcid.org/0000-0003-3979-0460
University of Cambridge · GBUniversity of Louisville · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a general barrier theory as an evolutionary framework for understanding coevolutionary effects of conflicts of interest in natural and human systems. It is generalized from the barrier theory of cancer, which describes how cancer develops through the evasion of mechanisms, that block unregulated cellular reproduction and survival. Barriers are naturally evolved or artificially implemented mechanisms for blocking exploitation; restraints are mechanisms that impede but do not block exploitation. When conflicts of interest arise, selection will favor exploiters that are capable of overcoming barriers and restraints. When barriers are in place, they halt, at least temporarily, coevolutionary arms races (the Red Queen can stop running). Barriers occur in a broad spectrum of interactions characterized by conflicts of interest: barriers to cellular survival (apoptosis) and reproduction (cell cycle arrest) may block a virus from replicating its genome through reproduction of its host cell. Vaccines may completely protect against targeted pathogens. A plant may escape herbivory by evolving defensive chemicals that block herbivory. Obligate mutualisms may evolve when barriers to horizontal transmission favor symbionts that increasingly lose mechanisms that contribute to horizontal transmission. Here, we show how the barrier theory applies across a spectrum of natural and social systems.

Indexed as

barrier theorycoevolutionevolutionary ecologyevolutionary medicineexploitationRed Queen

Identifiers

PMID34603491
PMCPMC8477591
OpenAlexW3185575826

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.