Alternative prey use affects helminth parasite infections in grey wolves
Bibliographic record
Abstract
Predators affect prey populations not only through direct predation, but also by acting as definitive hosts for their parasites and completing parasite life cycles. Understanding the affects of parasitism on prey population dynamics requires knowing how their predators' parasite community is affected by diet and prey availability. Ungulates, such as moose (Alces americanus) and white-tailed deer (Odocoileus virginianus), are often important prey for wolves (Canis lupus), but wolves also consume a variety of alternative prey, including beaver (Castor canadensis) and snowshoe hare (Lepus americanus). The use of alternative prey, which may host different or fewer parasites than ungulates, could potentially reduce overall abundance of ungulate parasites within the ecosystem, benefiting both wolves and ungulate hosts. We examined parasites in wolf carcasses from eastern Manitoba and estimated wolf diet using stable isotope analysis. Taeniidae cestodes were present in most wolves (75%), reflecting a diet primarily comprised of ungulates, but nematodes were unexpectedly rare. Cestode abundance was negatively related to the wolf's δ(13) C value, indicating diet affects parasite abundance. Wolves that consumed a higher proportion of beaver and caribou (Rangifer tarandus), estimated using Bayesian mixing models, had lower cestode abundance, suggesting the use of these alternative prey can reduce parasite loads. Long-term consumption of beavers may lower the abundance of adult parasites in wolves, eventually lowering parasite density in the region and ultimately benefiting ungulates that serve as intermediate hosts. Thus, alternative prey can affect both predator-prey and host-parasite interactions and potentially affect food web dynamics.
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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 | 0.000 | 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".