Microbiomes of willow-galling sawflies: effects of host plant, gall type, and phylogeny on community structure and function
Bibliographic record
Abstract
While free-living herbivorous insects are thought to harbor microbial communities composed of transient bacteria derived from their diet, recent studies indicate that insects that induce galls on plants may be involved in more intimate host–microbe relationships. We used 16S rDNA metabarcoding to survey larval microbiomes of 20 nematine sawfly species that induce bud or leaf galls on 13 Salix species. The 391 amplicon sequence variants (ASVs) detected represented 69 bacterial genera in six phyla. Multi-variate statistical analyses showed that the structure of larval microbiomes is influenced by willow host species as well as by gall type. Nevertheless, a “core” microbiome composed of 58 ASVs is shared widely across the focal galler species. Within the core community, the presence of many abundant, related ASVs representing multiple distantly related bacterial taxa is reflected as a statistically significant effect of bacterial phylogeny on galler–microbe associations. Members of the core community have a variety of inferred functions, including degradation of phenolic compounds, nutrient supplementation, and production of plant hormones. Hence, our results support suggestions of intimate and diverse interactions between galling insects and microbes and add to a growing body of evidence that microbes may play a role in the induction of insect galls on plants.
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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) | 0.000 | 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".