Hookworm prevalence in ocelots in Costa Rica is inconsistent with spillover from domestic dogs despite high overlap
Bibliographic record
Abstract
Abstract Spatial overlap between wildlife and related domestic animals can lead to disease transmission, with substantial evidence for viral and bacterial spillover. Domestic and wild animals can also share potentially harmful helminth parasites, many of which have environmental transmission stages that do not require direct contact between hosts. We used camera traps, fecal sampling, and mathematical modeling to evaluate the potential for hookworm parasites to spillover from domestic dogs to wild cats in the Osa Peninsula, Costa Rica. Traditional microscopy was found to be more sensitive than DNA‐based diagnostics for parasites, though the methods were complementary. We found high hookworm (Ancylostoma spp.) prevalence in domestic dogs (74.2%, 95% CI: 67.0%–80.7%, N = 155), and considerable spatial overlap with ocelots (Leopardus pardalis) and pumas (Puma concolor), particularly on trails and dirt roads. Pumas had hookworm prevalence of 36.4% (18.6%–57.2%, N = 22), and ocelots had 27.3% (7.6%–56.5%, N = 11); however, molecular identification of these parasites was inconclusive. We developed a macroparasite transmission model to infer the likelihood of spillover, compared with separate parasite cycles, or different parasite species in each host. According to the model, spillover of hookworm from dogs would lead to a prevalence of less than 10% in wild hosts. Low presumed compatibility between wild hosts and parasites adapted to domestic species limits the prevalence that could be reached in wild species, even under potentially higher overlap. The prevalence observed was more consistent with a model that assumes hookworms in wild cats in the Osa are a cat‐specific parasite. The combination of parasitology, molecular diagnostics, and mathematical modeling used here could complement wildlife disease monitoring programs worldwide to shed light on understudied helminth–host dynamics at the domestic–wild animal interface.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".