Stabilizing mechanisms in a food web with an introduced omnivore
Bibliographic record
Abstract
Intraguild predation (IGP) is an omnivorous food web configuration in which the top predator consumes both a competitor (consumer) and a second prey that it shares with the competitor. This omnivorous configuration occurs frequently in food webs, but theory suggests that it is unstable unless stabilizing mechanisms exist that can decrease the strength of the omnivore and consumer interaction. Although these mechanisms have been documented in native food webs, little is known about whether they operate in the context of an introduced species. Here, we study a marine mussel aquaculture system where the introduction of omnivorous mussels should generate an unstable food web that favors the extinction of the consumer, yet it persists. Using field and laboratory approaches, we searched for stabilizing mechanisms that could reduce interaction strengths in the food web. While field zooplankton counts suggested that mussels influence the composition and abundance of copepods, stable isotope results indicated that life-history omnivory and cannibalism facilitated the availability of prey refugia, and reduced competition and the interaction strength between the mussel omnivore and zooplankton consumers. In laboratory experiments, however, we found no evidence of adaptive feeding which could weaken predator-consumer interactions. Our food web study suggests that the impact of an introduced omnivore may not only depend on its interaction with native species but also on the availability of stabilizing mechanisms that alter the strength of those interactions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".