The strength of trophic cascades across ecosystems: predictions from allometry and energetics
Bibliographic record
Abstract
Summary Top‐down control of trophic structure is often highly variable both within and among ecosystems. We explored the roles of relative body sizes of predators and prey, their metabolic types, the production‐to‐biomass ratio (P : B) of plants, and system productivity in determining the strength of the indirect effects of predators on plants. We used a well‐studied food chain model with three trophic levels that is parameterized based on allometric relationships for rates of ingestion and metabolic efficiency. The model predicts that invertebrate and ectotherm predators and herbivores should propagate cascades to a greater degree than vertebrates and endotherms because of their higher metabolic efficiency. Increasing the herbivore‐to‐plant body‐size ratio strengthened the effects of cascades, while predator body‐size was predicted to have no effect. Increasing system productivity or plant P : B magnified cascades. Because herbivore : plant body size ratios and plant P : B are both generally greater in aquatic than terrestrial systems (especially those with unicellular producers), the model predicts stronger cascades in water than on land. This prediction is supported by a recent cross‐system comparison of trophic cascade experiments. We discuss features of natural systems that are not incorporated into the model and their implications for the intensity of trophic cascades across 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".