Consumer‐mediated recycling and cascading trophic interactions
Bibliographic record
Abstract
Cascading trophic interactions mediated by consumers are complex phenomena, which encompass many direct and indirect effects. Nonetheless, most experiments and theory on the topic focus uniquely on the indirect, positive effects of predators on producers via regulation of herbivores. Empirical research in aquatic ecosystems, however, demonstrate that the indirect, positive effects of consumer-mediated recycling on primary producer stocks may be larger than the effects of herbivore regulation, particularly when predators have access to alternative prey. We derive an ecosystem model with both recipient- and donor-controlled trophic relationships to test the conditions of four hypotheses generated from recent empirical work on the role of consumer-mediated recycling in cascading trophic interactions. Our model predicts that predator regulation of herbivores will have larger, positive effects on producers than consumer-mediated recycling in most cases but that consumer-mediated recycling does generally have a positive effect on producer stocks. We demonstrate that herbivore recycling will have larger effects on producer biomass than predator recycling when turnover rates and recycling efficiencies are high and predators prefer local prey. In addition, predictions suggest that consumer-mediated recycling has the largest effects on primary producers when predators prefer allochthonous prey and predator attack rates are high. Finally, our model predicts that consumer-mediated recycling effects may not be largest when external nutrient loading is low. Our model predictions highlight predator and prey feeding relationships, turnover rates, and external nutrient loading rates as key determinants of the strength of cascading trophic interactions. We show that existing hypotheses from specific empirical systems do not occur under all conditions, which further exacerbates the need to consider a broad suite of mechanisms when investigating trophic cascades.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".