Small snails, high productivity? Larval output of parasites from an abundant host
Bibliographic record
Abstract
Abstract How energy is transformed and distributed within ecosystems is a fundamental question in ecology. Parasites have been shown to play an essential role in these processes. In particular, the larval stages of trematodes, that is, cercariae, appear to contribute significantly to biomass and productivity in aquatic systems. Overall, ecosystem‐wide studies on parasite productivity remain scarce and have typically investigated systems with large hosts and high parasite infection rates. These studies may thus represent isolated cases of exceptionally high parasite contribution to ecosystem energetics, potentially overestimating the importance of parasite biomass. Here, we quantified the productivity of trematode cercariae from a small but hyper‐abundant snail intermediate host with only moderate trematode prevalence (i.e., proportion of infected individuals) in an entire lake ecosystem. We assessed individual larval output from snails and calculated the overall trematode productivity in the ecosystem. Average output of individual trematode species ranged from 3 to 62 cercariae per snail per day and correlated negatively with individual cercarial size. Cercarial productivity was not uniformly distributed across trematode taxa, but dominated by the most common species that accounted for more than 80% of the productivity. Total cercarial productivity amounted to 1.85 g m−2, which falls within the ranges of previous studies from freshwater systems. Small but abundant snail populations may thus support a considerable productivity of parasites. However, total annual cercarial productivity in the study system amounted to 5.9 kg, which constituted just 1.2% of the standing stock snail biomass, suggesting that intermediate host populations are potentially underexploited by their parasites. Moreover, comparisons with previous studies revealed contrasting patterns of parasite productivity and biomass contribution across different habitats, showing that impacts of parasites on ecosystem energetics can vary widely. Overall, we are still far away from having a complete picture of the dynamics of parasite productivity and biomass in many ecosystems. It therefore remains critical to quantify the contribution of parasites to the flow and distribution of energy and nutrients within and across habitats, to better understand their impacts on fundamental ecological principles, such as food‐web structure and ecosystem energetics.
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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 | 0.001 | 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".