Addition of polyester microplastic fibers to soil alters the diversity and abundance of arbuscular mycorrhizal fungi and affects plant growth and nutrition
Bibliographic record
Abstract
Microplastics (MPs) represent a major factor in global environmental change and are increasingly found in soils. However, to what extent they can affect plants and their interactions with the soil microbiome is poorly understood. Here we test the hypothesis that increasing levels of polyester MP fibers in soil alter plant growth and nutrient acquisition responses to arbuscular mycorrhizal (AM) fungi via changes in AM fungal colonization and community composition. We used Sorghum drummondii as a model species in a fully factorial greenhouse experiment. Plants were exposed to soil treatments with 0, 0.2, 1, and 3% MP polyester fibers either in the presence or absence of an assembled AM fungal community comprising 13 species across three families with contrasting life-history strategies. We found that the 1% MP treatment promoted plant biomass irrespective of the presence of AM fungi. While no changes in macronutrient concentrations in plant tissues were seen, there was a significant increase in B and Mn when relatively low amounts of MPs were added, and this effect was modulated by AM fungi. Furthermore, there were shifts in AM fungal community composition in response to MP, favoring more competitive taxa such as Gigaspora sp. while negatively affecting ruderal taxa like Glomus sp. Overall, our data indicate that MP polyester fibers present in soil can in some cases be beneficial to plants and AM fungal interactions. However, the implications of these findings over the long-term and in the context of ecological repercussions of MP pollution in the environment remain to be seen.
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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".