• Energy Research

Stomatal conductance (gs) is a key land-surface attribute as it links transpiration, the dominant component of global land evapotranspiration, and photosynthesis, the driving force of the global carbon cycle. Despite the pivotal role of gs in predictions of global water and carbon cycle changes, a global-scale database and an associated globally applicable model of gs that allow predictions of stomatal behaviour are lacking. Here, we present a database of globally distributed gs obtained in the field for a wide range of plant functional types (PFTs) and biomes. We find that stomatal behaviour differs among PFTs according to their marginal carbon cost of water use, as predicted by the theory underpinning the optimal stomatal model1 and the leaf and wood economics spectrum2, 3. We also demonstrate a global relationship with climate. These findings provide a robust theoretical framework for understanding and predicting the behaviour of gs across biomes and across PFTs that can be applied to regional, continental and global-scale modelling of ecosystem productivity, energy balance and ecohydrological processes in a future changing climate. This research was supported by the Australian Research Council (ARC MIA Discovery Project 1433500-2012-14). A.R. was financially supported in part by The Next-Generation Ecosystem Experiments (NGEE-Arctic) project, which is supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, and through the United States Department of Energy contract No. DE-AC02-98CH10886 to Brookhaven National Laboratory. M.O.d.B. acknowledges that the Brassica data were obtained within a research project financed by the Belgian Science Policy (OFFQ, contract number SD/AF/02) and coordinated by K. Vandermeiren at the Open-Top Chamber research facilities of CODA-CERVA (Tervuren, Belgium).

Abstract Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.

Abstract Soil enzymes are central to ecosystem processes because they mediate numerous reactions that are essential in biogeochemical cycles. However, how soil enzyme activities will respond to global warming is uncertain. We reviewed the literature on mechanisms linking temperature effects on soil enzymes and microbial communities, and outlined a conceptual overview on how these changes may influence soil carbon fluxes in terrestrial ecosystems. At the enzyme scale, although temperature can have a positive effect on enzymatic catalytic power in the short term (i.e. via the instantaneous response of activity), this effect can be countered over time by enzyme inactivation and reduced substrate affinity. At the microbial scale, short‐term warming can increase enzymatic catalytic power via accelerated synthesis and microbial turnover, but shifts in microbial community composition and growth efficiency may mediate the effect of warming in the long term. Although increasing enzyme activities may accelerate labile carbon decomposition over months to years, our literature review highlights that this initial stage can be followed by the following phases: (a) a reduction in soil carbon loss, due to changing carbon use efficiency among communities or substrate depletion, which together can decrease microbial biomass and enzyme activity and (b) an acceleration of soil carbon loss, due to shifts in microbial community structure and greater allocation to oxidative enzymes for recalcitrant carbon degradation. Studies that bridge scales in time and space are required to assess whether there will be an attenuation or acceleration of soil carbon loss through changes in enzyme activities in the very long term. We conclude that soil enzymes determine the sensitivity of soil carbon to warming, but that the microbial community and enzymatic traits that mediate this effect change over time. Improving representation of enzymes in soil carbon models requires long‐term studies that characterize the response of wide‐ranging hydrolytic and oxidative enzymatic traits—catalytic power, kinetics, inactivation—and the microbial community responses that govern enzyme synthesis. Read the free Plain Language Summary for this article on the Journal blog.

Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e., a wireless sensor network (WSN), that can be part of a worldwide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scales of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime shifts in terms of forest productivity, health, and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.