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Record W4409051780 · doi:10.1098/rsos.242088

Diversity–disease relationships in natural microscopic nematode communities

2025· article· en· W4409051780 on OpenAlexafffund
Robbert van Himbeeck, Jessica N. Sowa, Hala Tamim El Jarkass, Wenjia Wu, Job Oude Vrielink, Joost A. G. Riksen, Lisa van Sluijs

Bibliographic record

VenueRoyal Society Open Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchKoninklijke Nederlandse Akademie van Wetenschappen
KeywordsBiologyNematodeDiversity (politics)DiseaseEcologyEvolutionary biology

Abstract

fetched live from OpenAlex

Host diversity can affect parasite prevalence, a phenomenon widely studied in macroscopic organisms. However, data from microscopic communities are lacking, despite their essential role in ecosystem functioning and the unique experimental opportunities microscopic organisms offer. Here, we study diversity–disease effects in wild nematode communities by profiting from the molecular tools available in the well-studied model nematode Caenorhabditis elegans . Nanopore sequencing was used to characterize nematode community diversity and composition, whereas parasites were identified using nine distinct experimental assays based on fluorescent staining or fluorescent reporter strains. Our results indicate that biotic stress is abundant in wild nematode communities. Moreover, in two assays, diversity–disease relations were observed: microsporidia and immune system activation were more often detected in relatively species-poor communities. Other assays, targeting different parasites, were without diversity–disease relations. Together, this study provides the first demonstration of diversity–disease effects in microbial communities and establishes the use of nematode communities as model systems to study disease–diversity relationships.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.295
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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