Experimental mapping of bacterial fitness landscapes reveals eco-evolutionary fingerprints
Bibliographic record
Abstract
Understanding microbial dynamics in natural environments remains a significant challenge. As an alternative approach, studying model bacterial strains under well-defined laboratory conditions can reveal ecological niche patterns and evolutionary consequences in a controlled setting. Here, we evaluated bacterial growth dynamics to map fitness landscapes that may reflect natural processes experimentally. Six bacterial strains from a broad phylogenetic range were cultured individually across 195 distinct media, comprising 30 components, including both pure chemical compounds and natural ingredients. This approach generated 4,680 growth rate (r) and carrying capacity (K) pairs derived from growth curves spanning a broad range of nutritional conditions. Despite variations in growth profiles among strains, both positive and negative correlations in growth were observed across different media types. Notably, patterns of growth profiles showed strong concordance with known eco-evolutionary relationships, such as phylogenetic affiliations and biogeographic traits, suggesting that microbial responses to nutrient environments are evolutionarily conserved rather than arbitrary. Moreover, the medium components influencing r and K exhibited distinct patterns of generality and specificity, independently of their chemical properties or nutritional categories. These results demonstrate that bacterial fitness landscapes, reconstructed in laboratory conditions, serve as eco-evolutionary fingerprints, offering a proof-of-concept for experimental ecology. Our findings suggest that functional traits, such as growth, can provide a framework to explore trait-phylogeny relationships and offer insights that may inform future microbial design.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".