Prey Vulnerability Limits Top-Down Control and Alters Reciprocal Feedbacks in a Subsidized Model Food Web
Bibliographic record
Abstract
Resource subsidies increase the productivity of recipient food webs and can affect ecosystem dynamics. Subsidies of prey often support elevated predator biomass which may intensify top-down control and reduce the flow of reciprocal subsidies into adjacent ecosystems. However, top-down control in subsidized food webs may be limited if primary consumers posses morphological or behavioral traits that limit vulnerability to predation. In forested streams, terrestrial prey support high predator biomass creating the potential for strong top-down control, however armored primary consumers often dominate the invertebrate assemblage. Using empirically based simulation models, we tested the response of stream food webs to variations in subsidy magnitude, prey vulnerability, and the presence of two top predators. While terrestrial prey inputs increased predator biomass (+12%), the presence of armored primary consumers inhibited top-down control, and diverted most aquatic energy (∼75%) into the riparian forest through aquatic insect emergence. Food webs without armored invertebrates experienced strong trophic cascades, resulting in higher algal (∼50%) and detrital (∼1600%) biomass, and reduced insect emergence (-90%). These results suggest prey vulnerability can mediate food web responses to subsidies, and that top-down control can be arrested even when predator-invulnerable consumers are uncommon (20%) regardless of the level of subsidy.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".