Stability and persistence of food webs with omnivory: Is there a general pattern?
Bibliographic record
Abstract
The relationship between omnivory and stability has been the subject of a longstanding debate in ecology. Early theory predicted that omnivory would decrease the probability of food webs being stable. While early empirical data appeared to support the prediction that omnivory should be rare, detailed study of food webs later revealed that omnivory is ubiquitous across ecosystems and taxa. Recent years have seen renewed interest in the omnivory‐stability debate, and advances in mechanistic non‐equilibrium models demonstrated that omnivory can both increase and decrease stability. Current efforts have therefore focused on identifying biological mechanisms that promote the persistence of food webs with omnivory. We synthesize recent evidence that omnivory often stabilizes food webs when it occurs as life‐history omnivory, when prey experience reduced predation rates due to refuges or adaptive antipredator defences, and when omnivores interfere with each other or feed adaptively. Empirical research has lagged behind theory and there remains a shortage of studies directly measuring the stability of diverse natural communities that vary in the number and strength of omnivorous interactions. Early microcosm experiments indicated a narrow range of conditions for the persistence of simple omnivorous modules, while studies of omnivory embedded within larger natural networks have demonstrated its stabilizing effects. These new findings alter our view of food web dynamics and show that rather than looking for a simple and general omnivory‐stability relationship, we should focus on identifying conditions under which omnivory is a stabilizing feature of more complex natural systems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".