Size and variation in individual growth rates among food web modules
Bibliographic record
Abstract
Abstract The complexity of food webs can be reduced to fundamental modules of trophic interactions that repeat to form reticulate webs. Omnivory, defined as feeding on more than one trophic level, is a module that is found in food webs more often than predicted by chance, likely because it confers stability. Yet, little is known about how omnivore and other food web modules affect individuals in the food web. Here, we constructed four different experimental food web modules using a blue mussel predator, zooplankton consumer, and phytoplankton resource. By manipulating the predator's access to the consumer and providing resource subsidies, we produced exploitative competition, food chain and omnivory food web modules, and a consumer‐resource interaction. We used RNA:DNA ratios to measure the growth rate of the consumer in each food web. In 24‐h experiments, growth rates of the consumer in the omnivory food web were significantly higher and more variable than in the other modules. Our results suggest that higher growth rates and variation at the individual scale may weaken the strength of predator–consumer interactions and help explain the ubiquity of omnivory in real food webs.
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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 | 0.001 | 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".