Stochastic Forces in Microbial Community Assembly: Founding Community Size Governs Divergent Ecological Trajectories
Bibliographic record
Abstract
ABSTRACT Biological community dynamics arise from both deterministic and stochastic processes. While species' responses to environmental factors define attractors of community structure, stochasticity, particularly during early assembly, can redirect ecological trajectories. However, quantifying such roles of stochasticity in community assembly has remained challenging. We tracked community assembly in two multi‐replicated experimental systems, each with four levels of founding community size, analysing > 3000 samples across four time points. Stronger initial stochasticity led to greater divergence of both population‐ and community‐level consequences. Strikingly, conspicuous differentiation into alternative trajectories of community assembly occurred when the absolute number of founding prokaryotic cells was less than the order of 10 4 . Thus, quantitative differences in stochasticity produced qualitative differences in community fate. These results demonstrate that early stochastic events can have enduring impacts on ecological dynamics. Deeper quantitative insights into stochasticity will reorganise our views on biological invasions, agroecosystem microbiome management, and therapeutics of human‐associated microbiomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".