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Record W4417106858 · doi:10.1186/s42523-025-00496-8

Passive environmental and group-level processes drive gut microbiome composition in a wild corvid

2025· article· en· W4417106858 on OpenAlexafffund
Eleonore Lebeuf‐Taylor, Andrea Meltzer, Saverio Lubrano, Karl Cottenie, Michael Griesser

Bibliographic record

VenueAnimal Microbiome · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsMicrobiomeGut microbiomeMetagenomicsGut floraEcosystemComposition (language)

Abstract

fetched live from OpenAlex

BACKGROUND: The gut microbiome is known from laboratory studies to be essential to host function and sociality, yet comparatively little is known about this association in wild animals. In wild birds, the gut microbiome seems to be broadly driven by environmental factors, and there is mixed evidence for a link with sociality. Here, we describe the gut microbiome composition of the Siberian jay (Perisoreus infaustus), a highly social group-living and food-caching corvid of the Eurasian boreal forest. RESULTS: We present evidence of potential environment-related variation in the gut microbiome of wild Siberian jays. Environmental acquisition of microbes may be an important process shaping their gut microbiome composition based on similarities to the local environmental microbial community, for which we propose an environment–oral–gut route as a potential underlying mechanism. We also identify an unexpected group-level convergence, wherein social horizontal transmission of gut microbes may be an incidental consequence of reciprocal cache pilfering among group members. CONCLUSIONS: While the ecological significance of gut microbiome variation in Siberian jays is still unclear, our results paint a picture of passive microbiome assembly resulting from a combination of environmental acquisition and social transmission in a wild bird species.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes2
Has abstractyes

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