Wheat Rhizosphere Bacterial Community Response to Bromus tectorum (L.) and Fusarium pseudograminearum Crown Rot

Annual crop yield losses due to plant diseases and weeds can be substantial. In the northern Great Plains, Bromus tectorum (L.) (also known as cheatgrass or downy brome) and Fusarium pseudograminearum (causing crown rot) form a multi-trophic pest complex threatening wheat production sustainability. This study assessed the impact of these pests on the wheat rhizosphere bacterial community. Field trials were conducted over four site-years in plots inoculated with F. pseudograminearum using a randomized split-plot design with two seeding and nitrogen fertilizer rates and B. tectorum presence/absence. A seed fungicide treatment was also used to evaluate its effect on F. pseudograminearum abundance. Rhizosphere bacterial communities were analyzed using full-length 16 S rRNA sequencing on the Oxford Nanopore platform, followed by diversity analysis, structural equation modeling (SEM), and co-occurrence network analysis. Alpha and beta diversity were significantly different between location-years. The SEM results showed a negative relationship (β = -0.180, p = 0.002) between F. pseudograminearum presence and rhizosphere bacterial community alpha and beta diversity. Effects of B. tectorum presence, seeding rate, nitrogen fertilizer, and fungicide treatment were not significant. Correlation analysis identified specific bacterial taxa responsive to F. pseudograminearum presence, including putatively beneficial species belonging to the genera Massilia, Bacillus, and Neobacillus, which were positively correlated with pathogen presence, suggesting a stress response mechanism. Network analysis revealed that F. pseudograminearum presence reduced network cohesion, and connectivity measures compared to treatments with lower pathogen load. These findings demonstrate that fungal pathogen presence can impact rhizosphere bacterial networks even when overall diversity metrics show minimal changes, highlighting the importance of network-based approaches in understanding plant-microbe-pathogen interactions in agricultural systems.