Species coexistence in simple microbial communities: unravelling the phenotypic landscape of co‐occurring <scp> <i>M</i> </scp> <i>etschnikowia</i> species in floral nectar
Bibliographic record
Abstract
Identifying the ecological processes that underlie the distribution and abundance of species in microbial communities is a central issue in microbial ecology and evolution. Classical trade-off based niche theories of resource competition predict that co-occurrence in microbial communities is more likely when the residing species show trait divergence and complementary resource use. We tested the prediction that niche differentiation explained the co-occurrence of two yeast species (Metschnikowia reukaufii and M. gruessii) in floral nectar. Assessment of the phenotypic landscape showed that both species displayed a significantly different physiological profile. Comparison of utilization profiles in single versus mixed cultures indicated that these two species did not compete for most carbon and nitrogen sources. In mixed cultures, M. reukaufii grew better in sucrose solutions and in the presence of the antimicrobial compound digitonin than when grown as pure culture. M. gruessii, on the other hand, grew better in mixed cultures in glucose and fructose solutions. Overall, these results provide clear evidence that M. reukaufii and M. gruessii frequently co-occur in nectar and that they differ in their phenotypic response to variation in environmental conditions, suggesting that niche differentiation and resource partitioning are important mechanisms contributing to species co-occurrence in nectar yeast communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".