Mutualism outcome across plant populations, microbes, and environments in the duckweed <i>Lemna minor</i>
Bibliographic record
Abstract
Abstract The picture emerging from the rapidly growing literature on host-associated micro-biota is that host traits and fitness often depend on complex and interactive effects of host genotype, microbial interactions, and abiotic environment. However, testing these main and interactive effects typically requires large, multi-factorial experiments and thus remains challenging in many systems. Furthermore, most studies of plant microbiomes focus on terrestrial hosts and microbes. Aquatic habitats may confer unique properties to plant micriobiomes. We grew different populations of duck-weed ( Lemna minor ), a floating aquatic plant of increasing popularity in freshwater phytoremediation, in three microbial treatments (adding no, “home”, or “away” microbes) at two levels of zinc, a common water contaminant in urban areas. Thus, we simultaneously manipulated plant source population, microbial community, and the abiotic environment, and measured both plant and microbial performance as well as plant traits. Although we found little evidence of interactive effects, we found strong main effects of plant source, microbial treatment, and zinc on both duckweed and microbial growth, with significant variation among both duckweed and microbial communities. Despite strong growth alignment between duckweed and microbes, zinc consistently decreased plant growth, but increased microbial growth. Furthermore, as in recent studies of terrestrial plants, microbial interactions altered a duckweed phenotype (frond aggregation). Our results suggest that the duckweed source population, its associated microbiome, and the contaminant environment may all need to be considered in real-world phytoremediation efforts. Lastly, we propose that duckweed microbes offer a robust experimental system for study of host-microbiota interactions under a range of environmental stresses.
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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 | 0.000 |
| 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".