Bacterial microbiota similarity between predators and prey in a blue tit trophic network
Bibliographic record
Abstract
Trophic networks are composed of many organisms hosting microbiota that interact with their hosts and with each other. Yet, our knowledge of the factors driving variation in microbiota and their interactions in wild communities is limited. To investigate the relation among host microbiota across a trophic network, we studied the bacterial microbiota of two species of primary producers (downy and holm oaks), a primary consumer (caterpillars), and a secondary consumer (blue tits) at nine sites in Corsica. To quantify bacterial microbiota, we amplified 16S rRNA gene sequences in blue tit feces, caterpillars, and leaf samples. Our results showed that hosts from adjacent trophic levels had a more similar bacterial microbiota than hosts separated by two trophic levels. Our results also revealed a difference between bacterial microbiota present on the two oak species, and among leaves from different sites. The main drivers of bacterial microbiota variation within each trophic level differed across spatial scales, and sharing the same tree or nest box increased similarity in bacterial microbiota for caterpillars and blue tits. This study quantifies host microbiota interactions across a three-level trophic network and illustrates how the factors shaping bacterial microbiota composition vary among different hosts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".