Transient dynamics of competitive exclusion in microbial communities
Bibliographic record
Abstract
Microbial metabolism drives our planet's biogeochemistry and plays a central role in industrial processes. Molecular profiling in bioreactors has revealed that microbial community composition can be highly variable while maintaining constant functional performance. Furthermore, following perturbation bioreactor performance typically recovers rapidly, while community composition slowly returns to its original state. Despite its practical relevance, we still lack an understanding of the mechanisms causing the discrepancy between functional and compositional stability of microbial communities. Using a mathematical model for microbial competition, as well as simulations of a model for a nitrifying bioreactor, we explain these observations on grounds of slow non-equilibrium dynamics eventually leading to competitive exclusion. In the presence of several competing strains, metabolic niches are rapidly occupied by opportunistic populations, while subsequent species turnover and the eventual dominance of top competitors proceeds at a much slower rate. Hence, functional redundancy causes a separation of the time scales characterizing the functional and compositional stabilization of microbial communities. This effect becomes stronger with increasing richness because greater similarities between top competitors lead to longer transient population dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".