Dense aquatic vegetation can reduce parasite transmission to amphibians
Bibliographic record
Abstract
• Aquatic vegetation can physically or chemically affect host-parasite interactions. • Macrophyte density and complexity may impede parasite host finding or host evasion. • Vegetation complexity did not affect tadpole infection by pathogenic trematodes. • Extreme macrophyte density reduced trematode infection success. • Aquatic vegetation inconsistently affected trematode parasitism across 12 studies. Submerged aquatic vegetation (macrophytes) can provide prey with refuges from predators and may perform a similar role for interactions with other natural enemies such as parasites. This could occur by interfering with the ability of free-swimming infectious parasite stages to locate or move towards hosts, reducing infections. Alternatively, infections may increase if macrophytes reduce host anti-parasite behaviours such as detection or evasion. Both scenarios could be affected by macrophyte density and structural complexity. Here we investigated whether experimental infection of tadpoles ( Rana sylvatica and Rana pipiens ) by parasitic flatworms (the trematodes Ribeiroia ondatrae and Echinostoma spp. was affected by the presence of artificial vegetation with varying density and complexity (simple versus branching), as well as tadpole activity under these conditions. Macrophyte presence significantly reduced tadpole infection loads only in the highest density treatment, but there was no effect of structural complexity. Related to this, tadpoles spent significantly more time near aquatic vegetation when it was dense but showed no preference for either structural type. Our results indicate that aquatic vegetation can reduce parasite transmission in certain scenarios, with further studies needed to explore how structural complexity in natural systems can affect host-parasite interactions, considering the massive physical alterations possible through eutrophication and the introduction of invasive plant species.
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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) | 0.001 | 0.001 |
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".