Having the ‘right’ microbiome matters for host trait expression and the strength of mutualism between duckweeds and microbes
Bibliographic record
Abstract
Abstract An organism’s phenotypes and fitness often depend on interactive effects of its genome ( G host ), microbiome ( G microbe ), and environment ( E ). These G x G, G x E , and G x G x E effects fundamentally shape host-microbiome (co)evolution and may be widespread, but are rarely compared within a single experiment. We collected and cultured Lemna minor (duckweed) and its associated microbiome from 10 sites across an urban-to-rural ecotone. We factorially manipulated host genotype and microbiome in two environments (low and high zinc, an urban aquatic stressor) in an experiment with 200 treatments: 10 host genotypes × 10 microbiomes × 2 environments. Host genotype explained the most variation in L. minor fitness and traits, while microbiome effects depended on host genotype ( G x G ) or environment ( G x E ). Hosts had higher fitness and microbes grew fastest when tested microbiomes more closely matched field microbiomes, suggesting some local adaptation between hosts and their microbiota. High microbiome similarity also led to more predictable host trait expression. In contrast, although zinc decreased host fitness, we observed no local adaptation of urban duckweed or microbes to high-zinc conditions. Thus, we found that host fitness and trait expression are contingent on microbiome composition, with implications for microbiome engineering and host-microbiome evolution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".