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Record W4367172408 · doi:10.1093/beheco/arad034

Symbiotic microbiota vary with breeding group membership in a highly social joint-nesting bird

2023· article· en· W4367172408 on OpenAlexafffund
Leanne A. Grieves, Gregory B. Gloor, James S. Quinn

Bibliographic record

VenueBehavioral Ecology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsWestern UniversityMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyMicrobiomeFeatherCooperative breedingEcologyInclusive fitnessSocial groupMicrobial population biologyZoologySocial animalAffect (linguistics)GeneticsCommunication

Abstract

fetched live from OpenAlex

Abstract Symbiotic microbes affect the health, fitness, and behavior of their animal hosts, and can even affect the behavior of non-hosts. Living in groups presents numerous benefits and challenges to social animals, including exposure to symbiotic microbes, which can mediate both cooperation and competition. In social mammals, individuals from the same social group tend to share more similar microbes and this social microbiome, the microbial community of all hosts in the same social group, can shape the benefits and costs of group living. In contrast, little is known about the social microbiome of group living birds. We tested the predictions that communally breeding smooth-billed anis (Crotophaga ani) belonging to the same breeding group share more similar microbes and that microbial community composition differs between body regions. To test this, we used 16S rRNA gene sequencing to characterize the preen gland and body feather microbiota of adult birds from 16 breeding groups at a long-term study site in southwestern Puerto Rico. As predicted, individuals from the same breeding group shared more similar microbiota than non-group members and preen gland and body feathers harbored distinct microbial communities. Future research will evaluate whether this social microbiome affects the behavior of group living birds.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.075
GPT teacher head0.261
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2023
Admission routes2
Has abstractyes

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