Host trees partially explain the complex bacterial communities of two threatened saproxylic beetles
Bibliographic record
Abstract
ABSTRACT Microorganisms are integral to ecosystem functioning and host adaptation, yet the understanding of microbiomes in diverse beetle taxa remains limited. We conducted a comprehensive study to investigate the microbial composition of two red flat bark beetle species, Cucujus haematodes and C. cinnaberinus , and assessed the influence of host taxonomic relatedness and host tree species on their microbiomes. Moreover, we summarize the available data on the microbiome of saproxylic beetles as a reference. We sampled 67 larvae of two Cucujus taxa taken from eleven host tree species. 16S rRNA V4 fragment sequencing revealed distinct microbial communities associated with each Cucujus species, with host tree species significantly influencing microbiome composition. Alpha and beta diversity metrics indicated significant differences between microbial communities in both, beetle and host tree species. Principal Component Analysis indicated distinct clustering based on host tree species but not for beetle species. This overlap could be attributed to the similar ecology of both Cucujus species. The detection of various bacteria, among which some have already been reported in saproxylophagous beetles, suggests that the red flat bark beetles ingest the bacteria via foraging on other wood-dwelling invertebrates. Our findings show the complex interplay between host taxonomy, microhabitat, and microbial composition in Cucujus , providing insights into their ecological roles and conservation implications. This research helps to fill the gap in understanding the microbial dynamics of saproxylic beetles, sheds light on factors shaping their microbiomes, and highlights the importance of considering both host species and environmental conditions when studying insect-microbe interactions in forest ecosystems.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".