Eco-evolutionary robustness of wild bacterial communities to experimental perturbation
Bibliographic record
Abstract
Most knowledge about bacterial evolution and ecological interactions comes from laboratory studies. One difference between the wild and most laboratory experiments is the diversity of bacterial taxa present. Understanding how wild bacteria respond to perturbation therefore requires consideration of how ecological sorting, colonization, and genetic changes of constituent species interact. Ecological sorting of species might reduce evolutionary rates and make communities robust to disturbance, or it could amplify selection pressures and lead to unstable co-evolutionary cascades. Even estimates of basic rates of ecological sorting, dispersal, and genetic change are rare. Here, we addressed these knowledge gaps by liming wild decomposer communities living in beech tree holes and tracking ecological and evolutionary responses for 12 weeks. Overall, tree hole communities were extremely robust to liming involving short-term pulses up to 4 pH units and long-term increases up to 2 pH units. Species diversity and composition displayed significant but small changes in treatment tree holes compared to control ones. New bacterial taxa colonized at a low rate that did not vary with liming. Genetic changes in the frequency of single nucleotide polymorphisms in metagenome assembled genomes occurred at rates that were both comparable to and correlated with ecological changes in the same metagenome assembled genomes, but the rate of genetic changes did not vary between limed and control tree holes. Analysis of rates of genetic change estimated low effective population size (~104) and generation times of roughly 1 day. Our study provides estimates of rates of ecological and evolutionary processes in wild bacterial communities, which displayed remarkable robustness to our experimental perturbation.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".