Niche Evolution, Trophic Structure, and Species Turnover in Model Food Webs
Bibliographic record
Abstract
The features that govern the stability and persistence of species interaction networks, such as food webs, remain elusive, but recent work suggests that the distribution and strength of trophic links play an important role. Potential omnivory-stability relationships have been investigated and debated extensively, but we still have a relatively poor understanding of how levels of omnivory relate to the stability of diverse food webs. Here, we use an evolutionary assembly model to investigate how different trade-offs in resource use influence both food web structure and dynamic stability during the assembly process. We build on a previous model by allowing speciation along with the evolution of two traits: body size and feeding-niche width. Across a wide range of conditions, the level of omnivory in a food web is positively related to its dynamic instability (variability and species turnover). Parameter values favoring omnivory also allow a wider range of phenotypes to invade, often displacing existing species. This high species turnover leaves signatures in reconstructed phylogenies, with shorter branches connecting extant species in more omnivorous food webs. Our findings suggest that features of the environment may influence both trophic structure and dynamic stability, leading to emergent omnivory-stability relationships.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".