Dependence of stream predators on terrestrial prey fluxes: food web responses to subsidized predation
Bibliographic record
Abstract
Resource subsidies in the form of energy, materials, and organisms can support the productivity of recipient ecosystems. When subsidies increase the abundance of top predators, theory predicts that top‐down interactions will be strengthened. However, the degree to which subsidies intensify predation should be constrained by the strength of interactions between predators and their prey. To test the potential for subsidies to drive strong top‐down control by two stream predators, steelhead (Oncorhynchus mykiss) and Pacific giant salamander (Dicamptodon tenebrosus) we reduced terrestrial prey and manipulated the presence of predators in 32 stream reaches. Prey subsidies supported elevated growth of predatory steelhead in our study system and in the absence of allochthonous prey steelhead experienced a 187% reduction in growth. Despite the high biomass of subsidized predators, there was little support for strong top‐down control of herbivore biomass, or a trophic cascade as measured by changes in AFDM and chlorophyll‐a. This result was consistent across subsidy treatments, suggesting that predatory steelhead are unable to increase exploitation of aquatic prey in the absence of terrestrial prey subsidies. The potential for top‐down control was apparently limited by the fact that most (82%) herbivores in our study system were armored and relatively invulnerable to predation. These results demonstrate the potential importance of behavioral and morphological adaptations that can temper predator prey interactions in highly subsidized ecosystems.
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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 | 0.000 |
| 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".