Linking microbial community structure and microbial processes: an empirical and conceptual overview
Bibliographic record
Abstract
A major goal of microbial ecology is to identify links between microbial community structure and microbial processes. Although this objective seems straightforward, there are conceptual and methodological challenges to designing studies that explicitly evaluate this link. Here, we analyzed literature documenting structure and process responses to manipulations to determine the frequency of structure-process links and whether experimental approaches and techniques influence link detection. We examined nine journals (published 2009-13) and retained 148 experimental studies measuring microbial community structure and processes. Many qualifying papers (112 of 148) documented structure and process responses, but few (38 of 112 papers) reported statistically testing for a link. Of these tested links, 75% were significant and typically used Spearman or Pearson's correlation analysis (68%). No particular approach for characterizing structure or processes was more likely to produce significant links. Process responses were detected earlier on average than responses in structure or both structure and process. Together, our findings suggest that few publications report statistically testing structure-process links. However, when links are tested for they often occur but share few commonalities in the processes or structures that were linked and the techniques used for measuring them.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".