Biofilm thickness controls the relative importance of stochastic and deterministic processes in microbial community assembly in moving bed biofilm reactors
Bibliographic record
Abstract
Deterministic and stochastic processes are believed to play a combined role in microbial community assembly, though little is known about the factors determining their relative importance. We investigated the effect of biofilm thickness on community assembly in nitrifying moving bed biofilm reactors using biofilm carriers where maximum biofilm thickness is controlled. We examined the contribution of stochastic and deterministic processes to biofilm assembly in a steady state system using neutral community modelling and community diversity analysis with a null-modelling approach. Our results indicate that the formation of biofilms results in habitat filtration, causing selection for phylogenetically closely related community members, resulting in a substantial enrichment of Nitrospira spp. in the biofilm communities. Stochastic assembly processes were more prevalent in biofilms of 200 µm and thicker, while stronger selection in thinner (50 µm) biofilms could be driven by hydrodynamic and shear forces at the biofilm surface. Thicker biofilms exhibited greater phylogenetic beta-diversity, which may be driven by a variable selection regime caused by variation in environmental conditions between replicate carrier communities, or by drift combined with low migration rates resulting in stochastic historical contingency during community establishment. Our results indicate that assembly processes vary with biofilm thickness, contributing to our understanding of biofilm ecology and potentially paving the way towards strategies for microbial community management in biofilm systems.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".