Effects of soil moisture on the temperature sensitivity of heterotrophic respiration vary seasonally in an old‐field climate change experiment

Abstract

Microbial decomposition of soil organic matter produces a major flux of CO2 from terrestrial ecosystems and can act as a feedback to climate change. Although climate‐carbon models suggest that warming will accelerate the release of CO2 from soils, the magnitude of this feedback is uncertain, mostly due to uncertainty in the temperature sensitivity of soil organic matter decomposition. We examined how warming and altered precipitation affected the rate and temperature sensitivity of heterotrophic respiration (Rh) at the Boston‐Area Climate Experiment, in Massachusetts, USA. We measured Rh inside deep collars that excluded plant roots and litter inputs. In this mesic ecosystem, Rh responded strongly to precipitation. Drought reduced Rh, both annually and during the growing season. Warming increased Rh only in early spring. During the summer, when Rh was highest, we found evidence of threshold, hysteretic responses to soil moisture: Rh decreased sharply when volumetric soil moisture dropped below ~15% or exceeded ~26%, but Rh increased more gradually when soil moisture rose from the lower threshold. The effect of climate treatments on the temperature sensitivity of Rh depended on the season. Apparent Q10 decreased with high warming (~3.5 °C) in spring and fall. Presumably due to limiting soil moisture, warming and precipitation treatments did not affect apparent Q10 in summer. Drought decreased apparent Q10 in fall compared to ambient and wet precipitation treatments. To our knowledge, this is the first field study to examine the response of Rh and its temperature sensitivity to the combined effects of warming and altered precipitation. Our results highlight the complex responses of Rh to soil moisture, and to our knowledge identify for the first time the seasonal variation in the temperature sensitivity of microbial respiration in the field. We emphasize the importance of adequately simulating responses such as these when modeling trajectories of soil carbon stocks under climate change scenarios.

Variation of Q10values in a fenced and a grazed grassland on the loess plateau, northwestern China

“…Our study indicated that short-term apparent Q 10 of SR and MR had a clear seasonality, which is consistent with the recent results of Jia et al (2013) and Suseela et al (2012). Jia et al (2013) found that short-term Q 10 was higher in the beginning of the growing season in a Chinese pine (Pinus tabulaeformis) plantation.…”

“…Despite numerous studies, the temperature sensitivity of MR remains controversial Kirschbaum 2006;Hakkenberg et al 2008). For example, Suseela et al (2012) found the temperature sensitivity of heterotrophic respiration was unresponsive to warming treatments; however, a reduction of the temperature sensitivity of soil organic matter decomposition with sustained temperature increasing was reported by Craine et al (2013). The causes of this controversy may stem from confounding controlling factors such as soil moisture and substrate availability Kirschbaum 2006).…”

“…Jia et al (2013) found that short-term Q 10 was higher in the beginning of the growing season in a Chinese pine (Pinus tabulaeformis) plantation. Similarly, Suseela et al (2012) indicated that Q 10 values in summer were lower than in fall and spring. Short-term Q 10 of SR and MR decreased with increasing soil temperature (Fig.…”

“…1) was selected to determine the relationships between CO 2 flux and soil temperature (Suseela et al 2012):…”

Diffusion based modelling of temperature and moisture interactive effects on carbon fluxes of mineral soils

“…Moisture and temperature interactions have been observed in a number of experimental studies (Craine and Gelderman, 2011;Rey et al, 2005;Suseela et al, 2012;Wickland and Neff, 2008), but neither consistent trends 30 nor general explanatory theories have been identified. Improving our understanding of these interactions is a crucial step in increasing confidence in models and for interpreting modelling and experimental results (Crowther et al, 2016;Tang and Riley, 2014).…”