Biology subjects

Plessis, C.

Publications and source records attributed to Plessis, C..

2 recordsLinked to original sources

bioRxiv2026

Ecological Impacts of Additive-Enriched LDPE Microplastics in Agricultural Soils: Single and Multi-Species Assessments

Low-density polyethylene (LDPE) microplastics (MPs) are the most frequently sampled type of microplastic in agricultural soils, potentially threatening the soil environment. The majority of MPs that have been investigated are produced from standard polymer formulations, for which the nature of the added compounds is often unknown. Furthermore, standard ecotoxicity tests performed on model species are insufficient for assessing the ecological consequences of MPs contamination in soil. This study examined the responses of multiple keystone species to exposure to MPs in interaction with various additives. No significant effects on their growth were observed when organisms were exposed to MPs alone. However, significant reductions in growth occurred when organisms interacted within uncontaminated soil: the introduction of plants reduced potworm biomass by 49 {+/-} 4.1 % while the introduction of potworms reduced earthworm biomass by 41 {+/-} 5.2%. In MP-contaminated soil containing plants, the average individual biomass of potworm increased significantly from 1.16 {+/-} 0.09 mg in uncontaminated conditions to 2.01 {+/-} 0.27 mg. This suggests that MPs limited the negative effects of interactions. Similar patterns were observed for the potworm-earthworm interaction. MPs containing the highest concentrations of additives induced the strongest biological responses. Analysis of soil parameters revealed that these impacts are likely linked to the disruption of nitrogen cycling. Therefore, it is imperative to comprehensively address the interactions between soil organisms and the influence of additives on plastic ecotoxicity in order to better assess the ecological risk posed by MPs.

ecology↗

bioRxiv2026

Easy to use and low cost leaf disease quantification workflow using Ilastik

Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.

plant biology↗