Resilience to multiple stressors in an aquatic plant and its microbiome
Bibliographic record
Abstract
PREMISE: Outcomes of species interactions, especially mutualisms, are notoriously dependent on environmental context, and environments are changing rapidly. Studies have investigated how mutualisms respond to or ameliorate anthropogenic environmental changes, but most have focused on nutrient pollution or climate change and tested stressors one at a time. Relatively little is known about how mutualisms may be altered by or buffer the effects of multiple chemical contaminants, which differ fundamentally from nutrient or climate stressors and are especially widespread in aquatic habitats. METHODS: We investigated the impacts of two contaminants on interactions between the duckweed Lemna minor and its microbiome. Sodium chloride (salt) and benzotriazole (a corrosion inhibitor) often co-occur in runoff to water bodies where duckweeds reside. We tested three L. minor genotypes with and without the culturable portion of their microbiome across field-realistic gradients of salt (3 levels) and benzotriazole (4 levels) in a fully factorial experiment (24 treatments, tested on each genotype) and measured plant and microbial growth. RESULTS: Stressors had conditional effects. Salt decreased both plant and microbial growth and decreased plant survival more as benzotriazole concentrations increased. In contrast, benzotriazole did not affect microbial abundance and even benefited plants when salt and microbes were absent, perhaps due to biotransformation into growth-promoting compounds. Microbes did not ameliorate duckweed stressors; microbial inoculation increased plant growth, but not at high salt concentrations. CONCLUSIONS: Our results suggest that multiple stressors matter when predicting responses of mutualisms to global change and that beneficial microbes may not always buffer hosts against stress.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".