Spatial Distribution and Pathogen Profile of <i>Dermacentor reticulatus</i> Ticks in Southeastern Poland: A Genetic and Environmental Analysis
Bibliographic record
Abstract
In recent years, significant changes have been observed in the distribution and abundance of local Dermacentor reticulatus populations. However, changes in D. reticulatus dynamics have not been studied in southeastern Poland. Our objective was to enhance our understanding of the environmental factors influencing the occurrence and density of D. reticulatus in this area. Additionally, we sought to investigate the genetic diversity of the tick population and the prevalence of tick‐borne pathogens (TBPs). To this end, we established 45 study sites in the Subcarpathian province. Ticks were collected during their peak activity in both spring and autumn. A subset of randomly selected specimens underwent molecular analysis for TBPs screening, using high‐throughput microfluidic real‐time PCR. Positive amplicons were then sequenced, and phylogenetic analyses were conducted. Our findings confirmed the presence of D. reticulatus ticks in 24 surveyed sites, primarily concentrated in the northern and eastern parts of the region. The mean density of D. reticulatus ticks in their compact range was 5.8 ± 6.4 specimens/100 m 2 . Notably, air temperature and altitude emerged as significant factors influencing the species’ activity. We also identified a high prevalence of Rickettsia raoultii infections in adult D. reticulatus , reaching up to 84.21%. Additionally, 9.52% of ticks were found to be infected with R. helvetica and 4.76% with Anaplasma phagocytophilum . Furthermore, our genetic analyses confirmed the identity of D. reticulatus in the Subcarpathian region, aligning with haplotypes found in other regions of Poland, Czechia, Croatia, and Portugal. In conclusion, our study suggests that the surveyed region represents the current boundary of the compact range of D. reticulatus in Poland in which this tick species exhibits low genetic diversity and a narrow spectrum of detected TBPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".