The dynamics of top‐down and bottom‐up effects in food webs of varying prey diversity, composition, and productivity
Bibliographic record
Abstract
Prey diversity is thought to mediate the strength of top‐down and bottom‐up effects, but few experiments directly test this hypothesis. I assembled food webs of bacteria and bacterivorous protist prey in laboratory microcosms with all combinations of five productivity levels, two top predator treatments (present or absent), and three prey compositions. Depauperate food chains contained one of two edible prey species, while more diverse food webs contained both edible prey species plus two additional less‐edible/inedible prey. Equilibrium theory predicts that prey diversity should weaken the top‐down and bottom‐up effects on trophic level biomasses, due to density compensation among prey species. Top‐down effects should increase with productivity in food chains, but decrease with productivity in food webs. Results revealed highly dynamic top‐down effects, the strength of which varied more over time than among treatments. Further, top‐down effects did not merely vary in absolute strength over time, but also in relative strength across different prey compositions and productivity levels. It might be expected that equilibrium models would qualitatively reproduce time‐averaged results. However, time‐averaged data largely failed to support equilibrium predictions. This failure may reflect strong temporal variability in treatment effects combined with nonlinear density dependence of species’ per‐capita growth rates. Strong temporal variability in the strength of top‐down effects has not previously been demonstrated, but likely is common in nature as well.
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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 | 0.001 |
| 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) | 0.000 | 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".