Metabolites limiting predator growth wane with prey biodiversity
Bibliographic record
Abstract
Predator-prey interactions are a major driver of microbiome dynamics, but remain difficult to predict. While several prey traits potentially impact resistance to predation, their effects in a multispecies context remain unclear. Here, we leverage synthetic bacterial communities of varying complexity to identify traits driving palatability for nematodes, a main consumer of bacteria in soil. We assessed trophic interactions between four nematode species and 122 bacterial isolates, across a gradient of prey biodiversity ranging from single species to 50 species. Nematode size, a proxy for prey palatability, varied strongly with prey community composition and could be predicted by metabolic and morphological properties of the prey. However, the influence of prey traits on predators depended on biodiversity. Secondary metabolites drove palatability in monoculture, but this effect vanished under increasing prey biodiversity, where prey size became the dominant predictors of nematode size. Although idiosyncratic properties are often emphasized in the literatures, our results suggest that in biodiverse assemblages, the composition of available prey and their traits are more reliable predictors of predator-prey interactions. This study offers valuable insights into microbial ecology in the context of predator-prey interactions, as cryptic microbial responses can be guided by deductions based on generalizable biological traits.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".