Thermodynamic theory explains the temperature optima of soil microbial processes and high Q₁₀ values at low temperatures

Abstract

Our current understanding of the temperature response of biological processes in soil is based on the Arrhenius equation. This predicts an exponential increase in rate as temperature rises, whereas in the laboratory and in the field, there is always a clearly identifiable temperature optimum for all microbial processes. In the laboratory, this has been explained by denaturation of enzymes at higher temperatures, and in the field, the availability of substrates and water is often cited as critical factors. Recently, we have shown that temperature optima for enzymes and microbial growth occur in the absence of denaturation and that this is a consequence of the unusual heat capacity changes associated with enzymes. We have called this macromolecular rate theory -MMRT (Hobbs et al., 2013, ACS Chem. Biol. 8:2388). Here, we apply MMRT to a wide range of literature data on the response of soil microbial processes to temperature with a focus on respiration but also including different soil enzyme activities, nitrogen and methane cycling. Our theory agrees closely with a wide range of experimental data and predicts temperature optima for these microbial processes. MMRT also predicted high relative temperature sensitivity (as assessed by Q 10 calculations) at low temperatures and that Q 10 declined as temperature increases in agreement with data synthesis from the literature. Declining Q 10 and temperature optima in soils are coherently explained by MMRT which is based on thermodynamics and heat capacity changes for enzyme-catalysed rates. MMRT also provides a new perspective, and makes new predictions, regarding the absolute temperature sensitivity of ecosystems -a fundamental component of models for climate change.

Antagonistic Effects of Ocean Acidification and Rising Sea Surface Temperature on the Dissolution of Coral Reef Carbonate Sediments

“…Q 10 for R in the temperature only incubation was high (7.4-13.0) when typically, Q 10 values for biological systems are no greater than 2.5 (Valiela, 1995;Davidson et al, 2006). However, Q 10 has been shown to vary based on the temperatures that it is calculated at with higher Q 10 values at lower temperatures (Schipper et al, 2014). The lower Q 10 for GPP compared to R explains the change in P/R driven by warming.…”

The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment

“…It is very different from the other models because it does not require enzyme denaturation to account for the shapes of the TPCs for individual proteins (Schipper et al, 2014). To understand the theory, it is necessary to think about the steps via which an enzyme catalyzes the conversion from substrate to product.…”

Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter

“…These drivers include the bioavailability of C and energy (Linton and Stephenson 1978;Bremer and Kuikman 1994;Fonte et al 2013), the sensitivity of growth rates to environmental fluctuations in, for example, temperature (Apple et al 2006;Amado et al 2013;Frey et al 2013;Schipper et al 2014), consumer-substrate stoichiometric balance (Rousk and Baath 2007;Creamer et al 2014;Mooshammer et al 2014), and microbial community dynamics Lipson et al 2009;Blagodatskaya et al 2014). Numerous approaches to measuring community-scale carbon use efficiency (CUE C ) exist using a ratio of biomass production and substrate uptake, where uptake can be approximated by the sum of production and respiration (Manzoni et al 2012).…”

“…For example, variation in CUE has been linked to substrate biochemistry (Linton and Stephenson 1978;Payne and Wiebe 1978;Lemee et al 2002), thermodynamic and genetic capacity of the cell (Roller and Schmidt 2015), the environmental sensitivity of microbial physiology (Apple et al 2006;Schipper et al 2014), consumersubstrate stoichiometric balance (Creamer et al 2014;Mooshammer et al 2014), and microbial community structure and activity Blagodatskaya et al 2014). Other approaches have meanwhile explored CUE from food web and ecosystem perspectives, such as the effects of altered efficiency on availability of resources to higher trophic levels [i.e., Lindeman's ecological efficiency (1942)] and the mediation of ecosystem services like C sequestration (Frey et al 2013).…”