Host genetics and larval host plant modulate microbiome structure and evolution underlying the intimate insect–microbe–plant interactions in <i>Parnassius</i> species on the <scp>Qinghai‐Tibet</scp> Plateau
Bibliographic record
Abstract
Abstract Insects harbor a remarkable diversity of gut microbiomes critical for host survival, health, and fitness, but the mechanism of this structured symbiotic community remains poorly known, especially for the insect group consisting of many closely related species that inhabit the Qinghai‐Tibet Plateau. Here, we firstly analyzed population‐level 16S rRNA microbial dataset, comprising 11 Parnassius species covering 5 subgenera, from 14 populations mostly sampled in mountainous regions across northwestern‐to‐southeastern China, and meanwhile clarified the relative importance of multiple factors on gut microbial community structure and evolution. Our findings indicated that both host genetics and larval host plant modulated gut microbial diversity and community structure. Moreover, the effect analysis of host genetics and larval diet on gut microbiomes showed that host genetics played a critical role in governing the gut microbial beta diversity and the symbiotic community structure, while larval host plant remarkably influenced the functional evolution of gut microbiomes. These findings of the intimate insect–microbe–plant interactions jointly provide some new insights into the correlation among the host genetic background, larval host plant, the structure and evolution of gut microbiome, as well as the mechanisms of high‐altitude adaptation in closely related species of this alpine butterfly group.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".