Seasonal and biogeographical patterns of gastrointestinal parasites in large carnivores: wolves in a coastal archipelago
Bibliographic record
Abstract
Parasites are increasingly recognized for their profound influences on individual, population and ecosystem health. We provide the first report of gastrointestinal parasites in gray wolves from the central and north coasts of British Columbia, Canada. Across 60 000 km(2), wolf feces were collected from 34 packs in 2005-2008. At a smaller spatial scale (3300 km(2)), 8 packs were sampled in spring and autumn. Parasite eggs, larvae, and cysts were identified using standard flotation techniques and morphology. A subset of samples was analysed by PCR and sequencing to identify tapeworm eggs (n=9) and Giardia cysts (n=14). We detected ≥14 parasite taxa in 1558 fecal samples. Sarcocystis sporocysts occurred most frequently in feces (43·7%), followed by taeniid eggs (23·9%), Diphyllobothrium eggs (9·1%), Giardia cysts (6·8%), Toxocara canis eggs (2·1%), and Cryptosporidium oocysts (1·7%). Other parasites occurred in ≤1% of feces. Genetic analyses revealed Echinococcus canadensis strains G8 and G10, Taenia ovis krabbei, Diphyllobothrium nehonkaiense, and Giardia duodenalis assemblages A and B. Parasite prevalence differed between seasons and island/mainland sites. Patterns in parasite prevalence reflect seasonal and spatial resource use by wolves and wolf-salmon associations. These data provide a unique, extensive and solid baseline for monitoring parasite community structure in relation to environmental change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".