Stability of multi-species consortia during microbial metabolic evolution
Bibliographic record
Abstract
Explaining multi-genic adaptations is a major objective of evolutionary theory. Metabolic pathways require multiple functional enzymes to generate a phenotype, and their evolution in microbes remains underexplored. In particular, sites polluted with manmade chemicals or "xenobiotics", like plastic or pesticides, provide evidence for the rapid adaptation of novel metabolic pathways in microbes, which degrade these xenobiotics into utilizable nutrients. Decades of microbiological studies revealed that these pathways often are not consolidated within a single microbial species but are rather distributed across several different species cooperatively degrading xenobiotics. These diverse consortia are remarkably stable in the laboratory, but the determinants of this stability have not been hereto addressed. In this study, we predict barriers to stable co-existence arising from the metabolic roles each species plays in the novel metabolic pathway. Then, we show that ecological variation in microbial life history overcomes these barriers and explains stable co-existence in mathematical models of exemplary consortia growing on a xenobiotic as the sole source of a limiting nutrient. Stability hinges on an "ecological matching" between a species' metabolic role and its nutrient utilization strategy, which, if satisfied, can greatly accelerate the evolution of metabolic pathways in both field and laboratory.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".