Peer reviewed eScholarship.org Powered by the California Digital Library University of California Highlights We measured drought and N impact on litter enzyme activities and microbial biomass. Despite semi-arid conditions, the litter was bacterially dominated. Enzyme efficiencies declined with drought. Enzyme efficiencies in the N treatment suggest microbes adapt to local environment. Measuring enzyme efficiencies could help predict in-situ enzyme activity. a b s t r a c t Microbial enzymes play a fundamental role in ecosystem processes and nutrient mineralization. Therefore understanding enzyme responses to anthropogenic environmental change is important for predicting ecosystem function in the future. In a previous study, we used a reciprocal transplant design to examine the direct and indirect effects of drought and nitrogen (N) fertilization on litter decomposition in a southern California grassland. This work showed direct and indirect negative effects of drought on decomposition, and faster decomposition by N-adapted microbial communities in N-fertilized plots than in non-fertilized plots. Here we measured microbial biomass and the activities of nine extracellular enzymes to examine the microbial and enzymatic mechanisms underlying litter decomposition responses to drought and N. We hypothesized that changes in fungal biomass and potential extracellular enzyme activity (EEA) would relate directly to litter decomposition responses. We also predicted that fungal biomass would dominate the microbial community in our semi-arid study site. However, we found that the microbial community was dominated by bacterial biomass, and that bacteria responded negatively to drought treatment. In contrast to patterns in decomposition, fungal biomass and most potential EEA increased in direct response to drought treatment. Potential EEA was also decoupled from the decomposition response to N treatment. These results suggest that drought and N alter the effi-ciencies of EEA, defined as the mass of target substrate lost per unit potential EEA. Enzyme efficiencies declined with drought treatment, possibly because reduced water availability increased enzyme immobilization and reduced diffusion rates. In the N experiment, the efficiencies of b-glucosidase, b-xylosidase, and polyphenol oxidase were greater when microbes were transplanted into environments from which they originated. This increase in enzymatic efficiency suggests that microbial enzymes may adapt to their local environment. Overall, our results indicate that drought and N addition may have predictable impacts on the efficiencies of extracellular enzymes, providing a means of linking enzyme potentials with in-situ activities.

The temperature sensitivity of soil processes is of major interest, especially in light of climate change. Originally formulated to explain the temperature dependence of chemical reactions, the Arrhenius equation, and related Q 10 temperature coefficient, has a long history of application to soil biological processes. However, empirical data indicate that Q 10 and Arrhenius model are often poor metrics of temperature sensitivity in soils. In this opinion piece, we aim to (a) review alternative approaches for characterizing temperature sensitivity, focusing on macromolecular rate theory (MMRT); (b) provide strategies and tools for implementing a new temperature sensitivity framework; (c) develop thermal adaptation hypotheses for the MMRT framework; and (d) explore new questions and opportunities stemming from this paradigm shift. Microbial ecologists should consider developing and adopting MMRT as the basis for predicting biological rates as a function of temperature. Improved understanding of temperature sensitivity in soils is particularly pertinent as microbial response to temperature has a large impact on global climate feedbacks. K E Y W O R D S activation energy, Arrhenius, macromolecular rate theory, Q 10 , soil microbes, temperature sensitivity, thermal adaptation

Traits-based approaches in microbial ecology provide a valuable way to abstract organismal interaction with the environment and to generate hypotheses about community function. Using macromolecular rate theory (MMRT), we recently identified that temperature sensitivity can be characterized as a distinct microbial trait. As temperature is fundamental in controlling biological reactions, variation in temperature sensitivity across communities, organisms, and processes has the potential to vastly improve understanding of microbial response to climate change. These microbial temperature sensitivity traits include the heat capacity (ΔCP‡), temperature optimum (T ), and point of maximum temperature sensitivity (TS ), each of which provide unique insights about organismal response to changes in temperature. In this meta-analysis, we analyzed the distribution of these temperature sensitivity traits from bacteria, fungi, and mixed communities across a variety of biological systems (e.g., soils, oceans, foods, wastewater treatment plants) in order to identify commonalities in temperature responses across these diverse organisms and reaction rates. Our analysis of temperature sensitivity traits from over 350 temperature response curves reveals a wide distribution of temperature sensitivity traits, with T and TS well within biological relevant temperatures. We find that traits vary significantly depending on organism type, microbial diversity, source environment, and biological process, with higher temperature sensitivity found in fungi than bacteria and in less diverse systems. Carbon dioxide production was found to be less temperature sensitive than denitrification, suggesting that changes in temperature will have a potentially larger impact on nitrogen-related processes. As climate changes, these results have important implications for basic understanding of the temperature sensitivity of biological reactions and for ecological understanding of species' trait distributions, as well as for improved treatment of temperature sensitivity in models.

The activity of soil microbial extracellular enzymes is strongly controlled by temperature, yet the degree to which temperature sensitivity varies by microbe and enzyme type is unclear. Such information would allow soil microbial enzymes to be incorporated in a traits-based framework to improve prediction of ecosystem response to global change. If temperature sensitivity varies for specific soil enzymes, then determining the underlying causes of variation in temperature sensitivity of these enzymes will provide fundamental insights for predicting nutrient dynamics belowground. In this study, we characterized how both microbial taxonomic variation as well as substrate type affects temperature sensitivity. We measured β-glucosidase, leucine aminopeptidase, and phosphatase activities at six temperatures: 4, 11, 25, 35, 45, and 60°C, for seven different soil microbial isolates. To calculate temperature sensitivity, we employed two models, Arrhenius, which predicts an exponential increase in reaction rate with temperature, and Macromolecular Rate Theory (MMRT), which predicts rate to peak and then decline as temperature increases. We found MMRT provided a more accurate fit and allowed for more nuanced interpretation of temperature sensitivity in all of the enzyme × isolate combinations tested. Our results revealed that both the enzyme type and soil isolate type explain variation in parameters associated with temperature sensitivity. Because we found temperature sensitivity to be an inherent and variable property of an enzyme, we argue that it can be incorporated as a microbial functional trait, but only when using the MMRT definition of temperature sensitivity. We show that the Arrhenius metrics of temperature sensitivity are overly sensitive to test conditions, with activation energy changing depending on the temperature range it was calculated within. Thus, we propose the use of the MMRT definition of temperature sensitivity for accurate interpretation of temperature sensitivity of soil microbial enzymes.

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.