Linking the green and brown worlds: the prevalence and effect of multichannel feeding in food webs
Bibliographic record
Abstract
Recent advances in food‐web ecology highlight that most real food webs (1) represent an interplay between producer‐ and detritus‐based webs and (2) are governed by consumers which are rampant omnivores; feeding on varied prey across trophic levels and resource channels. A possible avenue to unify these advances comes from models demonstrating that predators feeding on distinctly different channels may stabilize food webs. Empirical studies suggest many consumers engage in such behavior by feeding on prey items from both living‐autotroph (green) and detritus‐based (brown) webs, what we term “multichannel feeding,” yet we know little about how common such feeding is across systems and trophic levels, or its effect on system stability. Considering 23 empirical webs, we find that multichannel feeding is equally common across terrestrial, freshwater, and marine systems, with >50% of consumers classified as multichannel consumers. Multichannel feeding occurred most often at the first consumer level, indicating that most taxa at the herbivore/detritivore level are more aptly described as multichannel consumers, and that such feeding is not restricted to predators. We next developed a simple four‐compartment nutrient cycling model for consumers eating both autotrophs and detritus with separate parameter sets to represent aquatic vs. terrestrial ecosystems. Modeling results showed that, across terrestrial and aquatic ecosystems, multichannel feeding is stabilizing at low attack rates on autotrophs or when attack rates are asymmetric (moderate on autotrophs while low on detritus), but destabilizing at high attack rates on autotrophs, compared to herbivory‐ or detritivory‐only models. The set of conditions with stable webs with multichannel consumers is narrower, however, for aquatic systems, suggesting that multichannel feeding may generally be more stabilizing in terrestrial systems. Together, our results demonstrate that multichannel feeding is common across ecosystems and may be a stabilizing force in real webs that have consumers with low or asymmetric attack rates.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".