Diversity and resilience of the wood‐feeding higher termite <i>Mironasutitermes shangchengensis</i> gut microbiota in response to temporal and diet variations
Bibliographic record
Abstract
Abstract Termites are considered among the most efficient bioreactors, with high capacities for lignocellulose degradation and utilization. Recently, several studies have characterized the gut microbiota of diverse termites. However, the temporal dynamics of the gut microbiota within a given termite with dietary diversity are poorly understood. Here, we employed 16S rDNA barcoded pyrosequencing analysis to investigate temporal changes in bacterial diversity and richness of the gut microbiota of wood‐feeding higher termite Mironasutitermes shangchengensis under three lignocellulose content‐based diets that feature wood, corn stalks, and filter paper. Compositions of the predominant termite gut residents were largely constant among the gut microbiomes under different diets, but each diet caused specific changes in the bacterial composition over time. Notably, microbial communities exhibited an unexpectedly strong resilience during continuous feeding on both corn stalks and filter paper. Members of five bacterial phyla, that is, Spirochaetes, Firmicutes, Actinobacteria, Tenericutes, and Acidobacteria, were strongly associated with the resilience. These findings provide insights into the stability of the gut microbiota in higher termites and have important implications for the future design of robust bioreactors for lignocellulose degradation and utilization.
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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 | 0.001 | 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".