Guild Dynamics and Pathogen Interactions in <i>Hyalomma</i> Ticks From Algerian Cattle
Bibliographic record
Abstract
Ticks are pivotal in transmitting a variety of pathogens that affect both humans and animals. These pathogens often occur in guilds, groups of species that exploit similar resources in similar ways. Although the composition of tick‐borne pathogen (TBP) guilds is well‐documented, the interactions among pathogens within these guilds remain poorly understood. We hypothesized that abiotic and biotic factors significantly influence the patterns of occurrence and interactions among pathogens within these guilds. To investigate this, we analyzed microfluidic‐based high‐throughput data on microorganisms from 166 Hyalomma excavatum ticks (94 male and 72 female) collected across different seasons from cattle in the central Algerian steppe using network analysis to uncover complex pathogen–pathogen interaction patterns. We found that female ticks had a higher infection rate (63.9%) with common pathogens such as Rickettsia slovaca (26.4%), unclassified Apicomplexa (22.2%), and Borrelia afzelii (19.4%). Male ticks showed a 56.4% infection rate, with Rickettsia (31.1%) and R. slovaca (16%) being the most prevalent. Notable pathogen–pathogen interactions within guilds were identified, with positive associations such as between R. slovaca and Rickettsia conorii in males, and B. afzelii and Borrelia spielmanii in females, indicating cooperative interactions. Conversely, negative associations, such as between Anaplasma phagocytophilum and Francisella tularensis, suggested competitive exclusion. The observed variation in interaction patterns under different conditions indicates that ecological determinants, both biotic and abiotic, influence pathogen association dynamics within guilds. These findings have significant implications for understanding disease transmission and developing 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 | 0.001 |
| 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".