Presence of microbiome decreases fitness and modifies phenotype in the aquatic plant <i>Lemna minor</i>
Bibliographic record
Abstract
Abstract Plants live in close association with microbial organisms that inhabit the environment in which they grow. Much recent work has aimed to characterize these plant–microbiome interactions, identifying those associations that increase growth. Although most work has focused on terrestrial plants, Lemna minor, a floating aquatic angiosperm, is increasingly used as a model in host–microbe interactions and many bacterial associations have been shown to play an important role in supporting plant fitness. However, the ubiquity and stability of these interactions as well as their dependence on specific abiotic environmental conditions remain unclear. Here, we assess the impact of a full L. minor microbiome on plant fitness and phenotype by assaying plants from eight natural sites, with and without their microbiomes, over a range of abiotic environmental conditions. We find that the microbiome systematically suppressed plant fitness, although the magnitude of this effect varied among plant genotypes and depended on the abiotic environment. Presence of the microbiome also resulted in phenotypic changes, with plants forming smaller colonies and producing smaller fronds and shorter roots. Differences in phenotype among plant genotypes were reduced when the microbiome was removed, as were genotype by environment interactions, suggesting that the microbiome plays a role in mediating the plant phenotypic response to the environment.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".