Relationships between Bacterial Community Composition, Functional Trait Composition and Functioning Are Context Dependent – but What Is the Context?
Bibliographic record
Abstract
Bacterial communities are immensely diverse and drive many fundamental ecosystem processes. However, the role of bacterial community composition (BCC) for functioning is still unclear. Here we evaluate the relative importance of BCC (from 454-sequencing), functional traits (from Biolog Ecoplates) and environmental conditions for per cell biomass production (BPC; 3H-leucine incorporation) in six data sets of natural freshwater bacterial communities. BCC explained significant variation of BPC in all six data sets and most variation in four. BCC measures based on 16S rRNA (active bacteria) did not consistently explain more variation in BPC than measures based on the 16S rRNA-gene (total community), and adding phylogenetic information did not, in general, increase the explanatory power of BCC. In contrast to our hypothesis, the importance of BCC for BPC was not related to the anticipated dispersal rates in and out of communities. Functional traits, most notably the ability to use cyclic and aromatic compounds, as well as local environmental conditions, i.e. stoichiometric relationships of nutrients, explained some variation in all six data sets. In general there were weak associations between variation in BCC and variation in the functional traits contributing to productivity. This indicates that additional traits may be important for productivity as well. By comparing several data sets obtained in a similar way we conclude that no single measure of BCC was obviously better than another in explaining BPC. We identified some key functional traits for productivity, but although there was a coupling between BCC, functional traits and productivity, the strength of the coupling seems context dependent. However, the exact context is still unresolved.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".