Sublethal effects of parasitism on ruminants can have cascading consequences for ecosystems
Bibliographic record
Abstract
Parasitic infections are common, but how they shape ecosystem-level processes is understudied. Using a mathematical model and meta-analysis, we explored the potential for helminth parasites to trigger trophic cascades through lethal and sublethal effects imposed on herbivorous ruminant hosts after infection. First, using the model, we linked negative effects of parasitic infection on host survival, fecundity, and feeding rate to host and producer biomass. Our model, parameterized with data from a well-documented producer–caribou–helminth system, reveals that even moderate impacts of parasites on host survival, fecundity, or feeding rate can have cascading effects on ruminant host and producer biomass. Second, using meta-analysis, we investigated the links between helminth infections and traits of free-living ruminant hosts in nature. We found that helminth infections tend to exert negative but sublethal effects on ruminant hosts. Specifically, infection significantly reduces host feeding rates, body mass, and body condition but has weak and highly variable effects on survival and fecundity. Together, these findings suggest that while helminth parasites can trigger trophic cascades through multiple mechanisms, overlooked sublethal effects on nonreproductive traits likely dominate their impacts on ecosystems. In particular, by reducing ruminant herbivory, pervasive helminth infections may contribute to a greener world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".