Microplastic additions modulate intraspecific variability in root traits and mycorrhizal responses across root-life history strategies
Bibliographic record
Abstract
Microplastics (MP) are recognized as a major pollutant in terrestrial environments, prompting concerns regarding their effects on plant-soil dynamics. Despite evidence of MP altering soil physicochemical properties, impacts on belowground root traits and arbuscular mycorrhizal (AM) fungi remains poorly explored. Existing research has mainly centered on a few model plant species, emphasizing root biomass, and often employs single polymer types and addition rates that surpass realistic scenarios. To investigate how environmentally relevant mixtures and concentrations of MPs impact plant growth, root trait expression and AM fungal colonization, we conducted a greenhouse experiment using six plant species chosen for their contrasting root life strategies; three species in the Amaryllidaceae family represented resource conservation root traits (Allium fistulosum (Onion), Allium tuberosum (Chive), Allium porrum (Leek)), and three from the Solanaceae family, represented plants with resource acquisitive root traits (Solanum lycopersicum (Tomato), Solanum melongena (Eggplant), Capsicum annuum (Pepper). MP treatments consisted of control (0% MP), low (0.1% w/w) and high (1% w/w) MP additions, using an environmentally relevant MP mixture of weathered polymer types and shapes. Above and belowground biomass, average root trait expression (specific root length (SRL), average root diameter (D) and root tissue density (RTD), AM fungal colonization, as well as intraspecific variability across MP addition treatments. We found that the addition of environmentally relevant additions of MPs was species specific and not determined by root life-strategy. MPs increased biomass in Leek, Eggplant and Tomato, while decreasing AM fungal colonization in Tomato. MP additions had no discernible impact on average root functional trait expression across species. However, the addition of MPs resulted in altered intraspecific variability in root traits and AM fungal colonization, indicating a mechanism for plant tolerance to MPs. To address the impacts of MP on plant functioning, our study highlights the need for future research to focus on environmentally relevant mixtures of MPs, considering various plant species' capacities to tolerate soil contamination and the potential for tipping points under real-world conditions.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".