Evidence map›Paper›PMID 40289122›Full record

ArticleNature communications2025

Biotic resistance predictably shifts microbial invasion regimes.

Xiaozhou Ye, Or Shalev, Christoph Ratzke

Abstract read
In one paragraph

Article in Nature communications, 2025. 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
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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.

Xiaozhou YeInterfaculty Institute for Microbiology and Infection Medicine Tübingen (IMIT), Cluster of Excellence EXC 2124 "Controlling Microbes to Fight Infections" (CMFI), University of Tübingen, Tübingen, Germany.ORCID http://orcid.org/0000-0002-7723-5532
Or ShalevInterfaculty Institute for Microbiology and Infection Medicine Tübingen (IMIT), Cluster of Excellence EXC 2124 "Controlling Microbes to Fight Infections" (CMFI), University of Tübingen, Tübingen, Germany.
Christoph RatzkeInterfaculty Institute for Microbiology and Infection Medicine Tübingen (IMIT), Cluster of Excellence EXC 2124 "Controlling Microbes to Fight Infections" (CMFI), University of Tübingen, Tübingen, Germany. christoph.ratzke@uni-tuebingen.de.ORCID http://orcid.org/0009-0006-2230-4779

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) 468972576Deutsche Forschungsgemeinschaft (German Research Foundation) 516931136EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 948753
6 · The paper itself

Abstract

Invading new territory is a central aspect of the microbial lifestyle. However, invading microbes rarely find novel territories uninhabited; resident microbes can interact with the newcomers and, in many cases, impede their invasion - an effect known as 'biotic resistance'. Accordingly, invasions are shaped by the interplay between dispersal and resistance. However, these two factors are difficult to disentangle or manipulate in natural systems, making their interplay challenging to understand. To address this challenge, we track microbial invasions in the lab over space and time - first in a model system of two interacting microbes, then in a multi-strain system involving a pathogen invading resident communities. In the presence of biotic resistance, we observe three qualitatively different invasion regimes: 'consistent', 'pulsed', and 'pinned', where, in the third regime, strong biotic resistance stalls the invasion entirely despite ongoing invader dispersal. These rich invasion dynamics could be qualitatively predicted with a simple, parameter-free framework that ignores individual species interactions, even for rather complex communities. Moreover, we show that this simple framework could accurately predict simulated invasions from different mechanistic models, indicating its broad applicability. Our work offers an understanding of how biotic resistance impacts invasions and introduces a predictive tool to identify invasion-resistant communities.

Indexed as

Microbial InteractionsEcosystemIntroduced SpeciesModels, Biological

Identifiers

PMID40289122
PMCPMC12034811

What Socratic 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 Socratic graph.