Responses of Plant and Microbial Respiration Sources to Changing Cold Season Climate Drivers in the East River Watershed
Authors
Mariah Carbone1* (mariah.carbone@nau.edu), Andrew Richardson1, Austin Simonpietri1, Ben Lucas2, Adrianna Foster3, Will Wieder3
Institutions
1Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ; 2Department of Math and Statistics, Northern Arizona University, Flagstaff, AZ; 3National Center for Atmospheric Research, Boulder, CO
URLs
Abstract
Global change is altering cold season climate in the western U.S. mountains. Snowpack is declining, snowmelt is occurring earlier in the spring, and growing seasons are becoming longer. This project will quantify the changing cold season climate effects on the soil carbon dioxide (CO2) flux, and its plant and microbial sources in the East River Watershed, near Crested Butte, CO. Researchers are building on an exploratory grant, which established a network of soil CO2 flux stations along an elevation gradient of different forest cover types. These measurements provide the foundation to use a model experiment (ModEx) approach to improve representation of belowground processes in mechanistic models. The team will install new automated soil CO2 flux sensors designed to operate underneath the snowpack to expand measurements in cold seasons. Radiocarbon (14C) partitioning methods will be applied to determine how much of the soil CO2 flux is coming from plant-root metabolism and microbial decomposition. Quantitative DNA stable isotope probing (qSIP) will be applied to quantify microbial community dynamics. Both 14C and qSIP sampling will be purposefully scheduled in time and space to reduce the largest data-model uncertainties. Results from field studies will be applied to improve model parameterization and mechanistic representation of the soil CO2 flux through plant and microbial mechanisms in FATES and MIMICS, respectively. The work is motivated by an overarching hypothesis that changing cold season climate drivers will impact belowground plant and microbial processes separately, and thus quantifying these influences is necessary to robustly predict how the East River Watershed ecosystems will respond to future environmental change. Researchers initial progress leverages long-term continuous dataset and machine learning approaches to assess how variability in snowpack, snowmelt timing, growing season length, and monsoon rain inputs influence the total soil CO2 flux.