Host specialization in ticks and transmission of tick-borne diseases: a review
Bibliographic record
Abstract
Determining patterns of host use, and the frequency at which these patterns change, are of key importance if we are to understand tick population dynamics, the evolution of tick biodiversity, and the circulation and evolution of associated pathogens. The question of whether ticks are typically host specialists or host generalists has been subject to much debate over the last half-century. Indeed, early research proposed that morphological diversity in ticks was linked to host specific adaptations and that most ticks were specialists. Later work disputed this idea and suggested that ticks are largely limited by biogeographic conditions and tend to use all locally available host species. The work presented in this review suggests that the actual answer likely lies somewhere between these two extremes. Although recent observational studies support the view that phylogenetically diverse host species share ticks when found on similar ecological ranges, theory on host range evolution predicts that host specialization should evolve in ticks given their life history characteristics. Contemporary work employing population genetic tools to examine host-associated population structure in several tick systems support this prediction and show that simple species records are not enough to determine whether a parasite is a true host generalist; host specialization does evolve in ticks at local scales, but may not always lead to speciation. Ticks therefore seem to follow a pattern of being global generalists, local specialists. Given this, the notion of host range needs to be modified from an evolutionary perspective, where one simply counts the number of hosts used across the geographic distribution, to a more ecological view, where one considers host use at a local scale, if we are to better understand the circulation of tick-borne pathogens and exposure risks for humans and livestock.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".