Infection of filamentous phytoplankton by fungal parasites enhances herbivory in pelagic food webs
Bibliographic record
Abstract
Abstract Chytrid fungal parasites are ubiquitous in aquatic ecosystems and infect a wide array of aquatic organisms, including all phytoplankton groups. In addition to their role as parasites, chytrids serve as food to zooplankton, thereby establishing an alternative trophic link between primary and secondary production in pelagic food webs, the so‐called mycoloop. We hypothesized that, in addition to the mycoloop, chytrid infection facilitates grazing of filamentous phytoplankton by rendering it more edible to zooplankton consumers through infection‐induced fragmentation. We undertook grazing assays to compare the ability of the key zooplankter Daphnia to graze on a filamentous cyanobacterium in the presence or absence of chytrid infection. A near doubling in mean clearance rates was consistently recorded when Daphnia were fed with infected cultures of the cyanobacterium as compared to uninfected ones. Infected filaments were shorter than noninfected ones, indicating that infection‐induced fragmentation undermines resistance of filamentous phytoplankton to grazing. We propose an extended conceptualization of the mycoloop that includes both direct effects (i.e., transfer via grazing of chytrid zoospores) and indirect effects (i.e., trophic upgrading and facilitated grazing on phytoplankton via fragmentation) of chytrid infection on trophic transfer at the base of pelagic food webs.
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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) | 0.001 | 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".