PNNL

Abstract

The ongoing increase of atmospheric temperatures is expected to induce the loss of soil organic carbon (CS) and ultimately exacerbate the greenhouse effect. There is a great effort to understand and mathematically describe the functional relationship between temperature and the heterotrophic soil respiration rate (RSOIL) since it has significant implications for understanding current carbon cycle dynamics as well as the future evolution of the Earth system. Respiration is a biological process and thus, it depends on the size of respiring microbial biomass (CMB). When RSOIL is measured without concurrent measurement of CMB, temperature sensitivity of RSOIL could be misinterpreted since CMB can change with temperature within days or weeks of the warming experiment. Here we use a meta-analysis of 27 laboratory and field experiments conducted at different temporal scales (1 – 730 days) and under a wide range of temperatures (2 – 50 °C) and soil conditions, to examine how CMB affects the apparent temperature sensitivity of RSOIL. Across all studies, the apparent temperature sensitivity of RSOIL decreases when CMB decreases and vice versa. Observed steep decrease of CMB above optimal temperature (25.2 ± 2.4 °C) attenuates the apparent temperature sensitivity of RSOIL, an aspect which was previously explained by the existence of reaction rate temperature optima. The temperature response of microbial biomass specific respiration rate is, however, highly non-linear and soil specific. CMB explains a significant amount of variability in RSOIL across all studies, but decreases the capability of simple exponential function to fit the temperature trends of RSOIL within individual studies. Including CMB in soil biogeochemical models requires careful consideration of the variability of temperature-associated physiological changes of soil microorganisms, especially the microbial death rate. Without it, microbially explicit biogeochemical models cannot predict temperature induced loss of CS better than older, empirical models based on first order reaction kinetic.