Seasonal effects in gastrointestinal parasite prevalence, richness and intensity in vervet monkeys living in a semi‐arid environment
Bibliographic record
Abstract
Abstract Parasite and pathogen incidence and prevalence is driven by both periodic variation in environmental conditions and host characteristics. Given the increasing risk of zoonotic transmission to humans, and the close phylogenetic relationship between humans and non‐human primates, understanding this variation in parasite dynamics is becoming essential for epidemiologists and conservationists alike. The extreme seasonal temperatures coupled with declining annual rainfall and severe periodic drought of the semi‐arid Karoo poses distinct challenges to both hosts and pathogens and serves as a window into how animals confront climate change‐induced environmental changes. Here we quantified annual variation in gastrointestinal parasite prevalence, intensity and richness in three troops of wild vervet monkeys (Chlorocebus pygerythrus) and determined what climatic variables were driving these changes. Further, we assessed whether there is long‐term temporal dependence in intra‐individual faecal egg counts. We found variation in the prevalence of five genera of helminths identified in the study population, but little variation in parasite richness across the year. Such variation was driven primarily by precipitation and maximum daily temperature. Finally, we found structure in faecal egg counts, suggesting that contrary to previous findings, egg shedding of Trichostrongylus sp. and ?Protospirura sp. are not stochastic processes and may serve as an indicator of individual levels of infection in our population. Combined, these results provide the first report of seasonal effects in gastrointestinal parasites of vervet monkeys living in an extreme environment.
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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 | 0.001 |
| 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 | 0.000 | 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".