Invasive plants can modify the diversity and taxonomical structure of soil microbiomes. However, it is difficult to generalize the underlying factors as their influence often seems to depend on the complex plant-soil-microbial interactions. In this paper, we investigated how Quercus rubra impacts on the soil microbiome across two soil horizons in relation to native woodland. Five paired adjacent invaded vs native vegetation plots in a managed forest in southern Poland were investigated. Soil microbial communities were assessed along with soil enzyme activities and soil physicochemical parameters, separately for both organic and mineral horizons, as well as forest stand characteristics to explore plant-soil-microbe interactions. Although Q. rubra did not significantly affect pH, organic C, total N, available nutrients nor enzymatic activity, differences in soil abiotic properties (except C to N ratio) were primarily driven by soil depth for both vegetation types. Further, we found significant differences in soil microbiome under invasion in relation to native vegetation. Microbial richness and diversity were lower in both horizons of Q. rubra vs control plots. Moreover, Q. rubra increased relative abundance of unique amplicon sequence variants in both horizons and thereby significantly changed the structure of the core soil microbial communities, in comparison to the control plots. In addition, predicted microbial functional groups indicated a predominant soil depth effect in both vegetation plots with higher abundance of aerobic chemoheterotrophic bacteria and endophytic fungi in the organic horizon and greater abundance of methanotrophic and methylo-trophic bacteria, and ectomycorrhizal fungi in the mineral horizon. Overall, our results indicate strong associ-ations between Q. rubra invasion and changes in soil microbiome and associated functions, a finding that needs to be further investigated to predict modifications in ecosystem functioning caused by this invasive species.

Covering mine tailings with uncontaminated soil is a common strategy to mitigate environmental impacts and promote ecosystem recovery during post-mining land reclamation. Topsoil is often stockpiled for future use as a capping layer, but prolonged storage alters its physical, chemical, and microbial properties, often hindering revegetation. This study evaluated soil health parameters within the 28-meter depth profile of a 14-year-old copper mine topsoil stockpile to identify key indicators of revegetation success in semi-arid ecosystem. Using non-invasive root phenotyping in rhizoboxes, filled with soils collected from different depth layers, we monitored plant growth and assessed how biochemical variability in stockpile materials affects germination and early establishment. Machine learning models integrating soil properties, plant responses, and sequenced soil bacterial/archaeal and fungal DNA, identified key indicators influencing plant performance. Results revealed significant heterogeneity in soil quality across depths, with distinct biochemical and microbial profiles shaping vegetation establishment. The upper 10 m exhibited greater potential for supporting growth, with seedling survival reaching 95 %, whereas deeper layers showed drastically reduced survival, sometimes as low as 0 %, due to microbial shifts to anoxic conditions and elevated Fe and Mn toxicity. Fungal communities played a dominant role in germination, while archaea were more influential during later plant establishment. Soil parameter comparisons before and after the experiment indicated recovery processes initiated by plant-soil feedback, including fungal community renewal. These findings highlight the role of stockpile formation in preserving soil health attributes critical for ecological recovery and provide practical insights for optimizing land reclamation in semi-arid ecosystems.

Self-sustaining vegetation in metal-contaminated areas is essential for rebuilding the ecological resilience and community stability in degraded lands. Metal-tolerant plants originating from contaminated post-mining areas may hold the key to successful plant establishment and growth. Yet, little is known about the impact of metal toxicity on reproductive strategies, metal accumulation and allocation patterns at the seed stage. Our research focused on metal tolerant Atriplex lentiformis, examining the effects of toxic metal(loid) concentration in soils on variability in its reproductive strategies, including germination patterns, elemental uptake, and allocation within the seeds. We employed advanced imaging techniques like synchrotron X-ray Fluorescence Microscopy (XFM; 2D scans and 3D tomograms) combined with ICP-MS to reveal significant differences in metal(loid) concentration and distribution within the seed structures of A. lentiformis from contrasting habitats. Exclusive Zn hotspots of high concentrations were found in the seeds of the metallicolous accession, primarily in the sensitive tissues of shoot apical meristems and root zones of the seed embryos. The findings of this study offer novel insights into phenotypic variability, metal tolerance and accumulation in plants from extreme environments. This knowledge can be applied to enhance plant survival and performance in land restoration efforts.

Automated leaf segmentation pipelines must balance accuracy, scalability, and usability to be readily adopted in plant research. We present an end-to-end deep learning pipeline designed for practical use in plant phenotyping, which we developed and evaluated during a real-world plant growth experiment using Atriplex lentiformis . The pipeline integrates a fine-tuned Mask Region-based Convolutional Neural Network (Mask R-CNN) segmentation model trained on 176 plant images and achieves high performance despite the small training data set (Dice coefficient = 0.781). We quantitatively compare the fine-tuned Mask R-CNN model to Meta AI’s Segment Anything Model (SAM) and evaluate natural language prompts using Grounded SAM and the Leaf-Only SAM post-processing pipeline for refining segmentation outputs. Our findings highlight that transfer learning on a specialized data set can still outperform a large foundation model in domain-specific tasks. In addition, we integrate QR codes for automated sample identification and benchmark multiple QR code decoding libraries, evaluating their robustness under real-world imaging conditions like distortion and lighting variation. To ensure accessibility, we deploy the pipeline as a user-friendly Streamlit web application, allowing researchers to analyze images without deep learning expertise. By focusing on practical deployment in addition to model performance, this study provides an open-source, scalable framework for plant science applications and addresses real-world challenges in automation and usability by the end-researcher.