Seasonal Variations in the Microbiome of Hyalomma excavatum Ticks in Algeria
Bibliographic record
Abstract
Ticks are key vectors of zoonotic diseases, with their microbiomes playing a critical role in tick physiology, survival, and vector competence. This study presents the first investigation of the microbiome in Hyalomma excavatum ticks from Algeria, focusing on seasonal variations in bacterial diversity, community composition, and pathogen interactions. Using next-generation sequencing (NGS), the microbiome of 21 female ticks collected during spring, summer, and autumn was analyzed. Beta diversity analysis revealed significant seasonal shifts in microbial community structure, while alpha diversity metrics showed no significant differences in richness and evenness. Co-occurrence network analysis demonstrated seasonal shifts in microbial interactions, particularly between symbionts and pathogens, highlighting Francisella as a key taxon in tick survival and pathogen dynamics. Rickettsia presence varied by season, influencing microbial network stability. These findings underscore the ecological determinants shaping the microbiome and its potential role in pathogen transmission. Understanding seasonal microbiome shifts provides valuable insights for managing tick-borne diseases and could inform the development of targeted, season-specific vector control strategies.
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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 | 0.000 |
| 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".