Microbial Grazers May Aid in Controlling Infections Caused by the Aquatic Zoosporic Fungus Batrachochytrium dendrobatidis
Bibliographic record
Abstract
Free-living eukaryotic microbes may reduce animal diseases. We evaluated the dynamics by which micrograzers (primarily protozoa) apply top-down control on the chytrid Batrachochytrium dendrobatidis (Bd) a devastating, panzootic pathogen of amphibians. Although micrograzers consumed zoospores (∼3 μm), the dispersal stage of chytrids, not all species grew monoxenically on zoospores. However, the ubiquitous ciliate Tetrahymena pyriformis, which likely co-occurs with Bd, grew at near its maximum rate (r = 1.7 d–1). A functional response (ingestion vs. prey abundance) for T. pyriformis, measured using spore-surrogates (microspheres) revealed maximum ingestion (Imax) of 1.63 × 103 zoospores d–1, with a half saturation constant (k) of 5.75 × 103 zoospores ml–1. Using these growth and grazing data we developed and assessed a population model that incorporated chytrid-host and micrograzer dynamics. Simulations using our data and realistic parameters obtained from the literature suggested that micrograzers could control Bd and potentially prevent chytridiomycosis (defined as 104 sporangia host–1). However, simulated inferior micrograzers (0.7 × Imax and 1.5 × k) did not prevent chytridiomycosis, although they ultimately reduced pathogen abundance to below levels resulting in disease. These findings indicate how micrograzer responses can be applied when modeling disease dynamics for Bd and other zoosporic fungi.
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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".