Promoting diversity in ecological systems through toxin production
Bibliographic record
Abstract
Abstract Toxins are used by microbes to kill and compete, and hence, unlike cross-feeding from secondary metabolites, they are less often considered as a candidate to promote diversity in a microbial community. In contrast, some natural communities hint at the possibility that the presence of toxin genes and externally supplied antibiotics may support higher diversity. We address this gap with a model that shows how toxins in small and medium-sized communities can indeed promote diversity. The central idea of the model emerges from the combination of bacterial growth laws of resource partitioning and recent experiments suggesting a trade-off between toxin sensitivity and nutrient efficiency. The second puzzle in natural data is the presence of both negative and positive regulation of toxin production under similar environmental stress. Our model shows how this diversity in regulation of toxin production can promote toxin-producer fitness. In general, we provide a framework to integrate the role of toxins in microbial communities. Significance statement Structured organization of microbial communities into functional groups (consumers and producers) has been a framework to understand diverse populations. This framework leaves out the impact of toxin producers, which kill rather than provide the others with benefits, on diversification. Surprisingly, natural microbial communities show a correlation between the number of toxin genes in a community and its diversity. Our work shows how physiological constraints and trade-offs for individual bacteria can lead to a diverse, structured population where toxin-producers serve as a keystone species. This provides a pathway towards toxin production being established as a viable strategy in a diverse population: releasing toxins allows the survival of the less competitive strains, which would otherwise be driven towards extinction by competitive exclusion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".