Agriculture is the most dominant land use globally and is projected to increase in the future to support a growing human population but also threatens ecosystem structure and services. Bacteria mediate numerous biogeochemical pathways within ecosystems. Therefore, identifying linkages between stressors associated with agricultural land use and responses of bacterial diversity is an important step in understanding and improving resource management. Here, we use the Mississippi Alluvial Plain (MAP) ecoregion, a highly modified agroecosystem, as a case study to better understand agriculturally-associated drivers of stream bacterial diversity and assembly mechanisms. In the MAP, we found that planktonic bacterial communities were strongly influenced by salinity. Tolerant taxa increased with increasing ion concentrations, likely driving homogenous selection which accounted for ~90% of assembly processes. Sediment bacterial phylogenetic diversity increased with increasing agricultural land use and was influenced by sediment particle size, with assembly mechanisms shifting from homogenous to variable selection as differences in median particle size increased. Within individual streams, sediment heterogeneity was correlated with bacterial diversity and a subsidy-stress relationship along the particle size gradient was observed. Planktonic and sediment communities within the same stream also diverged as sediment particle size decreased. Nutrients including carbon, nitrogen, and phosphorus, which tend to be elevated in agroecosystems, were also associated with detectable shifts in bacterial community structure. Collectively, our results establish that two understudied variables, salinity and sediment texture, are the primary drivers of bacterial diversity within the studied agroecosystem, while nutrients are secondary drivers. Although numerous macrobiological communities respond negatively, we observed increasing bacterial diversity in response to agricultural stressors including salinization and sedimentation. Elevated taxonomic and phylogenetic bacterial diversity likely increases the probability of detecting community responses to stressors. Thus, bacteria community responses may be more reliable for establishing water quality goals within highly modified agroecosystems that have experienced shifting baselines.

Prior to analyses, sediments were dried, ground, and pre-sieved through a 2 mm sieve to remove any coarse material. We measured sediment P (Sed P) concentrations using a Melich-3 extraction method. Briefly, 1 g of dried sediment was digested using an extract composed of 0.2 M acetic acid, 0.25 M ammonium nitrate, 0.015 M ammonium fluoride, 0.013 M nitric acid, and 0.001 M ethylene diamine tetraacetic acid. Phosphorus content of extracted sediment samples was then determined using the standard ascorbic-ammonium molybdate method using a MULTISKAN SkyHigh microplate reader (Thermo Scientific). We determined percent carbon (C) and N of 1 g of dried sediment using a CN analyzer (Elementar). We determined sediment texture and particle size distribution using a digital hydrometer and the integral suspension pressure method (ISP+) (Pario, METER AG, Munich) following manufacture protocols (see Durner and Iden 2021 for theory). Briefly, dried sediments were shaken overnight in 5% Sodium Hexametaphosphate to disperse aggregates. The dispersed sample was transferred to a 1-L graduated cylinder with a valve for draining effluent. We homogenized the sample for one minute prior to inserting the digital hydrometer into the sample tube. The density of the sediment solution was continuously measured for 2.5 hours. After 2.5 hours, we drained the top portion of the effluent containing the clay fraction through the valve into a pre-weighed, 250 mL beaker to determine the mass of the clay fraction in the sample. We wet-sieved the sand fraction through 500, 250, 125, and 63 µm sieves to determine the mass of different sand fractions. Based on the mass of the clay fraction in the effluent and the mass of different sand fractions, an inverse model of suspension pressure was fit using the Pario software to determine the continuous size distribution and percent sand, silt, and clay.