Control mechanisms of photosynthetic epibionts on zooplankton: an experimental approach
Bibliographic record
Abstract
Several top‐down and bottom‐up forces have been put forward to explain variable infestation rates of zooplankton by epibionts. Among top‐down forces, fish predation affects epibiont prevalence on zooplanktonic organisms, either by eliminating more conspicuous, heavily burdened individuals, or by reducing population size of zooplankton hosts, with consequences for substrate availability for epibionts. However, detailed experimental‐based information on the effects of top‐down forces is still lacking. Among bottom‐up forces, light can potentially control populations of photosynthetic epibionts. Therefore, both changes in light penetration in the water column and the vertical position of hosts in the water column could affect the photic conditions in which epibionts live and could thus control their population growth. We tested experimentally the hypothesis that both light limitation and fish predation affect epibiont burden on zooplankton. Moreover, we also tested the hypothesis that zooplanktivorous fish affect the prevalence and burden of the epibiotic alga Colacium sp. (Euglenida) on zooplankton not only by direct predation, but also by affecting the vertical distribution of zooplankton. We analyzed Colacium burden on two zooplankton genera that responded differently to the presence of zooplanktivorous fish by altering their daytime vertical distributions, thus exposing photosynthetic epibionts to different light conditions. Colacium burden on the two zooplankton genera was also compared between enclosures with different degrees of light limitation. Our results suggest that (1) ambient light limitation has the potential to reduce the burden of photosynthetic epibionts on zooplankton in natural conditions, and (2) zooplankton behavior (e.g., daytime refuge use to escape fish predation) can reduce the burden by exposing photosynthetic epibionts to suboptimal light conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".