Thu, Aug 05, 2021:On Demand
Background/Question/Methods
In both aquatic and terrestrial ecosystems, bacteria and archaea play a foundational role in ecosystem processes. Connectivity between the two habitats is governed by variations in precipitation and stream hydrology, that encompass differences in surface waters sourced from either overland flow or soil infiltration to groundwaters. Although chemical and physical (i.e. erosion) contributions of soil to waterways is well studied, less work has explored how microbes themselves may move between these systems. We worked to identify common microbiome signatures between soil and freshwater systems across the natural rainfall gradient of the Central Great Plains: Soil sampling was conducted in sites with discretely variable land use: undisturbed prairie sites, conventional row-crop agriculture, or post-agricultural restored prairie, with mineral soil cores collected to 1-m depth. Stream surface water and benthos was sampled across grassland-to-agricultural land-use gradients in catchments with different levels of intermittency. Using this wide scope, we addressed the predictions that microbiota from streams with more intermittent flow regimes would contain a greater proportion of surface soil-sourced taxa, while streams from more perennial catchments would carry more taxa first identified from deeper in the soil profiles, but would carry a terrestrial land-use signal further downstream due to lower water residence time.
Results/Conclusions Our ongoing data analyses are linking the two microbiome datasets together. In initial analyses, the largest predictor of differences in soil microbiomes was depth (P <0.001), which interacted independently with the average annual precipitation (P<0.01) or land use (P<0.01). Both interactions show that soil surface microbiomes (0-5cm) are most different from those in the deepest layers sampled (75cm+), and that these depth differences decline in sites with greater precipitation and with increasing disturbance from agricultural (prairie>post-ag>ag). Stream water microbiome showed significant effects of both precipitation level and stream order (P<0.0001, R2=0.073, 0.196; respectively), and in support of one prediction, proportion of row-crop agricultural land-use predicted community variation in mesic catchments, but not arid catchments (P<0.0001, R2=0.444; n.s.; respectively). Benthic microbial communities did not show a strong response to land-use or aridity gradient, but we do expect them to reflect a stronger colonization by terrestrial-sourced microbial taxa.
Results/Conclusions Our ongoing data analyses are linking the two microbiome datasets together. In initial analyses, the largest predictor of differences in soil microbiomes was depth (P <0.001), which interacted independently with the average annual precipitation (P<0.01) or land use (P<0.01). Both interactions show that soil surface microbiomes (0-5cm) are most different from those in the deepest layers sampled (75cm+), and that these depth differences decline in sites with greater precipitation and with increasing disturbance from agricultural (prairie>post-ag>ag). Stream water microbiome showed significant effects of both precipitation level and stream order (P<0.0001, R2=0.073, 0.196; respectively), and in support of one prediction, proportion of row-crop agricultural land-use predicted community variation in mesic catchments, but not arid catchments (P<0.0001, R2=0.444; n.s.; respectively). Benthic microbial communities did not show a strong response to land-use or aridity gradient, but we do expect them to reflect a stronger colonization by terrestrial-sourced microbial taxa.