The power of species sorting: Local factors drive bacterial community composition over a wide range of spatial scales
Bibliographic record
Abstract
There is a vivid debate on the relative importance of local and regional factors in shaping microbial communities, and on whether microbial organisms show a biogeographic signature in their distribution. Taking a metacommunity approach, spatial factors can become important either through dispersal limitation (compare large spatial scales) or mass effects (in case of strongly connected systems). We here analyze two datasets on bacterial communities [characterized by community fingerprinting through denaturing gradient gel electrophoresis (DGGE)] in meso- to eutrophic shallow lakes to investigate the importance of spatial factors at three contrasting scales. Variation partitioning on datasets of both the bacterial communities of 11 shallow lakes that are part of a strongly interconnected and densely packed pond system <1 km apart, three groups of shallow lakes approximately 100 km apart, as well as these three groups of shallow lakes combined that span a large part of a North-South gradient in Europe (>2,500 km) shows a strong impact of local environmental factors on bacterial community composition, with a marginal impact of spatial distance. Our results indicate that dispersal is not strongly limiting even at large spatial scales, and that mass effects do not have a strong impact on bacterial communities even in physically connected systems. We suggest that the fast population growth rates of bacteria facilitate efficient species sorting along environmental gradients in bacterial communities over a very broad range of dispersal rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".