Mixed strongyle parasite infections vary across host age and space in a population of feral horses
Bibliographic record
Abstract
Abstract Identifying factors that drive among-individual variation in mixed parasitic infections is fundamental to understanding the ecology and evolution of host–parasite interactions. However, a lack of non-invasive diagnostic tools to quantify mixed infections has restricted their investigation for host populations in the wild. This study applied DNA metabarcoding on parasite larvae cultured from faecal samples to characterize mixed strongyle infections of 320 feral horses on Sable Island, Nova Scotia, Canada, in 2014 to test for the influence of host (age, sex and reproductive/social status) and environmental (location, local density and social group membership) factors on variation. Twenty-five strongyle species were identified, with individual infections ranging from 3 to 18 species with a mean richness (±1 s.d. ) of 10.8 ± 3.1. Strongyle eggs shed in faeces were dominated by small strongyle (cyathostomins) species in young individuals, transitioning to large strongyles ( Strongylus spp.) in adults. Egg counts were highest in young individuals and in the west or centre of the island for most species. Individuals in the same social group had similar parasite communities, supporting the hypothesis that shared environment may drive parasite assemblages. Other factors such as local horse density, sex, date and reproductive/social status had minimal impacts on infection patterns. This study demonstrates that mixed infections can be dynamic across host ontogeny and space and emphasizes the need to consider species-specific infection patterns when investigating mixed infections.
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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".