Temperature sensitivity of soil microbial communities: An application of macromolecular rate theory to microbial respiration

Abstract

There is compelling evidence that microbial communities vary widely in their temperature sensitivity and may adapt to warming through time. To date, this sensitivity has been largely characterized using a range of models relying on versions of the Arrhenius equation, which predicts an exponential increase in reaction rate with temperature. However, there is growing evidence from laboratory and field studies that observe nonmonotonic responses of reaction rates to variation in temperature, indicating that Arrhenius is not an appropriate model for quantitatively characterizing temperature sensitivity. Recently, Hobbs et al. (2013) developed macromolecular rate theory (MMRT), which incorporates thermodynamic temperature optima as arising from heat capacity differences between isoenzymes. We applied MMRT to measurements of respiration from soils incubated at different temperatures. These soils were collected from three grassland sites across the U.S. Great Plains and reciprocally transplanted, allowing us to isolate the effects of microbial community type from edaphic factors. We found that microbial community type explained roughly 30% of the variation in the CO2 production rate from the labile C pool but that temperature and soil type were most important in explaining variation in labile and recalcitrant C pool size. For six out of the nine soil × inoculum combinations, MMRT was superior to Arrhenius. The MMRT analysis revealed that microbial communities have distinct heat capacity values and temperature sensitivities sometimes independent of soil type. These results challenge the current paradigm for modeling temperature sensitivity of soil C pools and understanding of microbial enzyme dynamics.

Temperature Sensitivity as a Microbial Trait Using Parameters from Macromolecular Rate Theory

“…In a previous study (Alster et al, 2016), we found that temperature sensitivity varied among soil microbial communities. We applied both Arrhenius and MMRT to our data to compare the effectiveness of each of these models and demonstrate how Arrhenius estimates of temperature sensitivity may not be sufficient, even within in situ representative temperature ranges.…”

“…In order to quantitatively characterize temperature responses of each of the 21 isolate × enzyme combinations, we plotted the natural log of the reaction rate against temperature and fitted both the Arrhenius and MMRT equations using an analytic Gauss-Newton for Arrhenius and a numerical Gauss-Newton for MMRT in JMP Pro 11 (Schipper et al, 2014; Alster et al, 2016). Parameters E A and Δ${\mathrm{C}}_{\mathrm{p}}^{\mathrm{‡}}$, along with their uncertainty, were reported by the software.…”

“…Parameters E A and Δ${\mathrm{C}}_{\mathrm{p}}^{\mathrm{‡}}$, along with their uncertainty, were reported by the software. The optimum temperature ( T opt ) and point of maximum temperature sensitivity (TS max ) from the MMRT curve fits were calculated by taking the derivative of the MMRT equation with respect to temperature (Alster et al, 2016). We used a Monte Carlo Simulation to estimate the standard error for T opt and TS max .…”

A meta‐analysis of temperature sensitivity as a microbial trait

“…Recently, we (Alster, Baas, Wallenstein, Johnson, & Fischer, ) introduced a mechanism for characterizing temperature sensitivity as a microbial trait. We found that temperature sensitivity can be characterized as a microbial trait itself with distinct temperature sensitivity trait values, influenced by genetic and environmental variation (Alster, Baas, et al, ; Alster, Koyama, Johnson, Wallenstein, & Fischer, ). Defining temperature sensitivity as a microbial trait with measurable and intercomparable characteristics adds another technique to predict microbial community assemblage and creates an important tool to evaluate microbial response to climate change and impact on ecosystem function.…”

Soil Organic Matter Temperature Sensitivity Cannot be Directly Inferred From Spatial Gradients

“…At warm sites, microbial enzyme activity was closer to the predicted maximum activity based on the heat capacity of enzymes (Figure S3). As a result, the model variants predicted smaller SOC stock losses with warming because the increased encounter rate between molecules when warmed (Davidson et al, ; Rodrigo et al, ) was compensated by the decreased ability of proteins (such as enzymes) to bind substrates at high temperatures (Alster et al, ; Ratkowsky et al, ; Schipper et al, ).…”