This study used the ITS approach based on Illumina MiSeq sequencing to assess the endosphere and rhizosphere fungal communities in healthy and diseased faba bean plants. The findings indicate that the most predominant phyla in all samples were Ascomycota (49.89–99.56%) and Basidiomycota (0.33–25.78%). In healthy endosphere samples, Glomeromycota (0.08–1.17%) was the only predominant phylum. In diseased endosphere samples, Olpidiomycota (0.04–1.75%) was the only predominant phylum. At the genus level, Penicillium (0.47–35.21%) was more abundant in rhizosphere soil, while Paraphoma (3.48–91.16%) was predominant in the endosphere roots of faba bean plants. Significant differences were observed in the alpha diversity of rhizosphere samples from different germplasm resources (p < 0.05). The fungal community structures were clearly distinguished between rhizosphere and endosphere samples and between healthy and diseased endosphere samples (p < 0.05). Saccharomyces was significantly enriched in diseased endosphere samples, whereas Apiotrichum was enriched in healthy endosphere samples. Vishniacozyma and Phialophora were enriched in diseased rhizosphere samples, while Pseudogymnoascus was enriched in healthy rhizosphere samples. Diseased samples displayed more strongly correlated genera than healthy samples. Saprotrophs accounted for a larger proportion of the fungal microbes in rhizosphere soil than in endosphere roots. This study provides a better understanding of the composition and diversity of fungal communities in the rhizosphere and endosphere of faba bean plants as well as a theoretical guidance for future research on the prevention or control of faba bean root rot disease.

Elsholtzia densa and Avena fatua are known as two of the most aggressive weeds, causing severe economic, environmental, human and animal health problems in China and around the world. In this study, seven strains of pathogenic fungi (i.e. DT-YSB1, DT-04A2, DT-DYLC, DT-XRKA, DT-08C, DT-14A2, and DT-QKBD004A) were isolated from weeds plants with infections symptoms. Pathogenicity test was performed and found that strain DT-XRKA exhibited strong herbicidal activity against E. densa, while strain DT-QKBD004A was highly pathogenic to A. fatua. On the basis of cultural, morphological and molecular characteristics, these two strains were identified as Alternaria alternata and Fusarium avenaceum, respectively. The safety assessment indicated that the spore suspension (10⁴ spore/ml) of strain DT-XRKA was generally safe for rapeseed, cabbage, tomato, cucumber, and pepper among the 12 tested crop species, though some non-target infections were observed. Strain DT-QKBD004A with same concentration was found to be safe for broad beans, corn, cabbage, tomato, cucumber, and pepper. Therefore, A. alternata and F. avenaceum can be selected for further studies to development mycoherbicides for control of these two weeds in rapeseed and broad bean fields.

Chenopodium album L. is one of the most important threat weeds affecting crops productivity in the fields. Control of this weed is complex and currently, lies in the use of chemical methods, although this method has not proven to be fully effective. The utilization of microorganisms has emerged as a means of simultaneously controlling this weed with high-efficiency, and friendly to the environment. In this regard, this study used LC-MS/MS and RNA-Seq technology to gain insights into the molecular herbicidal mechanisms underlying strain Aureobasidium pullulans PA-2 on C. album. Physiological and biochemical tests showed that compared with the control group (CK), the content of chlorophyll, soluble protein, soluble sugar and phenylalanine ammonia-lyase (PAL) activity in C. album leaves in the pot show a decreasing trend under the infection of PA-2. Transmission electron microscopy (TEM) observation revealed that abnormal shapes of chloroplast, incomplete intracellular structure and gradual disintegration of the outer membrane in the cells of C. album are observed at the third day after inoculation. A total of 69,404 unigene was obtained, among which 35,950 were differentially expressed genes (DEGs), and most of them were enriched plant secondary metabolite biosynthesis, phytohormone signaling, and carotenoid biosynthesis. Moreover, the analysis of 8 candidate genes showed that the content of photosynthesis indices was significantly decreased, which was resulted from the down-regulation of photosynthesis-related genes expression levels after PA-2 infection. During the PA-2 infection phase, a total of 14,521 and 13,211 differentially accumulated metabolites (DAMs) were identified using the ESI+ and ESI− modes, respectively. Significant differences were observed in the content of DAMs at the five stages of PA-2 infection, especially photosynthesis, purine metabolism, and carotenoid biosynthesis. Further correlation analysis of major DAMs and DEGs showed that 19 key DEGs were involved in photosynthesis, 10 key DEGs in carotenoid biosynthesis, and 3 key DEGs in purine metabolism. These findings have paved way in further functional characterization of candidate genes and subsequently can be better understanding of molecular mechanism of PA-2 infection on C. album.

Species distribution models (SDMs) grapple with uncertainty. To address this, a parameter-optimized MaxEnt model was used to predict habitat suitability for Elsholtzia densa, a predominant agricultural weed on the Tibetan Plateau. Through multiparameter optimization with 149 occurrence points and three climate variable sets, we systematically evaluated how the three MaxEnt extrapolation approaches (Free Extrapolation, Extrapolation with Clamping, No Extrapolation) influenced model outputs. The results showed the following: (1) Model optimization using the Kuenm R package version (1.1.10) identified seven critical bioclimatic variables (Feature Combinations = LQTH, Regularization Multipliers = 2.5), with optimized models demonstrating high accuracy (Area Under Curve > 0.9). (2) Extrapolation approaches exhibited negligible effects on variable selection, though four bioclimatic variables “bio1 (annual mean temperature)”, “bio12 (annual precipitation)”, “bio2 (mean diurnal range)”, and “bio7 (temperature annual range)” predominantly drove model predictions. (3) Current high-suitability areas are clustered in the eastern and southern regions of the Tibetan Plateau, and with Free Extrapolation yielding the broadest current distribution. Climate change projections suggest habitat expansion, particularly under conditions of No Extrapolation. (4) Multivariate Environmental Similarity Surface (MESS) and Most Dissimilar Variable (MoD) are not affected by the extrapolation method, and extrapolation risk analyses indicate that future climate anomalies are mainly concentrated in the western and southern parts of the Tibetan Plateau and that future warming will further increase the unsuitability of these regions. (5) Variance analysis showed that the extrapolation methods did not significantly affect the 10-replicate results but influenced the parameter and emission scenarios, with No Extrapolation methods showing minimal variance changes. Our findings validate that multiparameter optimization improves species distribution model robustness, systematically characterizes extrapolation impacts on distribution projections, and provides a conceptual framework and early warning systems for agricultural weed management on the Tibetan Plateau.