Parasite-mediated allochthonous input: Do hairworms enhance subsidized predation of stream salmonids on crickets?
Bibliographic record
Abstract
Energy and nutrients flow in diverse pathways across heterogeneous landscapes and tightly link the discrete food webs in local habitats. However, parasitism that enhances allochthonous resource input has not been previously documented. In a well-known example of parasite manipulation of host behaviour, crickets infected by mature hairworms (Nematomorpha) seek and jump into water when the worms reach the free-living stage. We found that a large number of trout (22%–61%), an aquatic predator, preyed on camel crickets (genera Diestrammena Brunner von Wattenwyl, 1888 and Tachycines Adelung, 1902) in September in five Japanese mountain streams where this host–parasite system exists. Trout (Kirikuchi charr, Salvelinus leucomaenis japonicus (Oshima, 1961); red-spotted masu salmon, Oncorhynchus masou ishikawae Jordan and McGregor, 1925) that preyed on crickets frequently ingested hairworms, whereas trout that did not prey on crickets did not ingest hairworms. Our results strongly suggest that hairworms enhance stream salmonid predation on camel crickets. This is the first documentation of parasitism enhancing allochthonous resource input in nature. Trout ingested a greater mass of crickets than other prey species in September, and this energy influx may play an important role in food-web dynamics in headwater streams.
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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 | 0.000 | 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) | 0.002 | 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".