Cascading effects of predators on algal size structure
Bibliographic record
Abstract
The presence of edible and inedible prey species in a food web can influence the strength that nutrients (bottom-up) or herbivores (top-down) have on primary production. In boreal peatlands, wetter more nutrient-rich conditions associated with ongoing climate change are expanding consumer access to aquatic habitat and promoting sources of primary production (i.e., algae) that are susceptible to trophic regulation. Here, we used an in situ mesocosm experiment to evaluate the consequences of enhanced nutrient availability and food-web manipulation (herbivore and predator exclusion) on algal assemblage structure in an Alaskan fen. Owing to the potential for herbivores to selectively consume edible algae (small cells) in favor of more resistant forms, we predicted that the proportion of less-edible algae (large cells) would determine the strength of top-down or bottom-up effects. Consistent with these expectations, we observed an increase in algal-cell size in the presence of herbivores (2-tiered food web) that was absent in the presence of a trophic cascade (3-tiered food web), suggesting that predators indirectly prevented morphological changes in the algal assemblage by limiting herbivory. Increases in algal-cell size with herbivory were driven by a greater proportion of filamentous green algae and nitrogen-fixing cyanobacteria, whose size and morphological characteristics mechanically minimize consumption. While consumer-driven shifts in algal assemblage structure were significant, they did not prevent top-down regulation of biofilm development by herbivores. Our findings show that increasing wet periods in northern peatlands will provide new avenues for trophic regulation of algal production, including directly through consumption and indirectly via a trophic cascade.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".