Joint 118th Annual Cordilleran/72nd Annual Rocky Mountain Section Meeting - 2022

Paper No. 23-5
Presentation Time: 8:30 AM-6:00 PM

SOIL DEPTH DICTATE’S FUNCTION: A REVISED PERSPECTIVE ON CONTROL VOLUME AND PLANT-MICROBE ASYNCHRONY IN WATER-LIMITED ECOSYSTEMS


HUBER, David, Northwest Watershed Research Center, US Department of Agriculture - Agricultural Research Service, 251 E Front Street, Suite 400, Boise, ID 83702; Earth, Environmental and Resource Sciences, University of Texas at El Paso, 500 W University, El Paso, TX 79902; Department of Geosciences, Boise State University, Environmental Research Building 1160, MS1535, Boise, ID 83725, LOHSE, Kathleen A., Biological Sciences, Idaho State University, 921 S 8th Ave, MSC 8007, Pocatello, ID 83209 and GERMINO, Matthew J., Forest and Rangeland Ecosystem Science Center, US Geological Survey, 970 S Lusk Street, Boise, ID 83706

Soil depth to bedrock or other impermeable layers can vary greatly in drylands, adding complexity to an already heterogeneous landscape. Although often neglected or poorly quantified, soil depth has been shown to strongly affect primary production and soil water storage. Here we characterize how soil moisture, carbon (C), and nitrogen (N) dynamics in surface soils (0-10 cm depth) of a cold desert ecosystem respond to a long-term experimental manipulation of depth to impermeable layer, hereafter referred to as the “control volume” to emphasize the importance of soil depth on storage capacity of limiting resources. We highlight the spatiotemporal variability in these limiting resources within and between shallow (≤0.5 m) vs. deep (≥2 m) control volumes and evaluate the interactions of control volume with shifts in seasonal water additions (i.e., spring vs. summer) and plant functional types (sagebrush vs. crested wheatgrass). We conclude by presenting an extension of dryland ecohydrological theory to better include biogeochemical cycling in surface soils and propose how this revised framework may aid rangeland management and conservation and improve predictive modeling of ecosystem response to agents of disturbance such as shifts in climate, plant community composition, and wildfire.