Reviewer: Christansen CT4
Measurement unit: µmol m-2 sec-1; Measurement scale: plot; Equipment costs: €€-€€€; Running costs: €-€€; Installation effort: medium to high; Maintenance effort: medium; Knowledge need: medium to high; Measurement mode: manual and/or data logger
Soil CO2 efflux, often denoted as soil respiration, is one of the largest carbon fluxes in terrestrial ecosystems and therefore regularly assessed in climate-manipulation studies. In vegetated ecosystems, CO2 efflux from soil comes from two primary sources: heterotrophic (decomposition) and autotrophic (root and rhizosphere) respiration (e.g. Harris & van Bavel, 1957; Högberg et al., 2001). A third CO2 source can arise during carbonate dissolution/formation. Such abiotic CO2 fluxes usually play a negligible role (Schindlbacher et al., 2015), but can become significant in arid or semi-arid ecosystems (Serrano-Ortiz et al., 2010).
It is not yet fully resolved how global warming, rising atmospheric CO2 concentrations, and changing precipitation patterns will affect future soil CO2 efflux (e.g. Vicca et al., 2014; Romero-Olivares et al., 2017). In particular, the longer-term responses of soil CO2 efflux to climate change remain unclear (Crowther et al., 2016). Since the major sources of soil CO2 efflux arise from C pools of different residence time (autotrophic: short lived; heterotrophic: longer lived), it is critical to assess how the different sources are affected by climate change. Assessment of CO2 efflux in climate manipulation experiments or observational studies can therefore become a complex task. The interaction of changing climate with other global-change phenomena such as nitrogen deposition or land-use change will add even more complexity to the response of soil CO2 fluxes (Janssens et al. 2010; Sheng et al. 2010).
Besides CO2, soils can also be important sources or sinks of the greenhouse gases CH4 and N2O (Schulze et al., 2009). CH4 and N2O measurements can be combined with those of CO2 (depending on the analyser) and are therefore also integrated into this protocol. The measurement principles of reactive short-lived trace gases such as NOx are not specifically addressed in this protocol. A description of thorough NOx flux assessment can be found in, for example, Pape et al. (2009).
22.214.171.124 What and how to measure?
Soil CO2 efflux is defined as the flow of CO2 through the soil surface. The common expression is µmol m‑2 sec-1, or Tonnes C ha-1 and kg C m-2 when reported on an annual basis. The most commonly used methods for soil CO2 efflux determination are chamber measurements. Chambers are placed on the soil surface, and air circulates between the closed chamber headspace and a CO2 detector (closed-dynamic or non-steady state chamber), or the detector is located directly in the chamber headspace. The increase in headspace CO2 concentration over time allows the soil CO2 efflux to be inferred. Soil CO2 efflux can be measured manually with transportable measuring devices (spatial replication) or by using automated systems operating at higher temporal resolution (temporal replication).
Most commonly, infrared gas analysers (IRGA) are used for CO2 detection. There is an array of different IRGAs and IRGA-chamber combinations for soil CO2 efflux measurements on the market. An assessment of various systems can be found in Pumpanen et al. (2004), but companies are constantly improving their products. In addition to IRGAs, there is a new generation of (also portable) laser-based technologies entering the market. These instruments enable simultaneous measurements of CO2, CH4, and N2O. On the downside, they are costly and heavy to carry, but the development of new lasers proceeds fast. The possibility to measure these gases alongside CO2 and at a similar temporal resolution means that similar protocols as described here for CO2 may be applied when using these gas analysers.
Gas samples can also be obtained from closed (closed-static) chambers and analysed later on in the laboratory, for example by gas chromatography (GC). Gas samples are repeatedly taken with a syringe and collected in glass vials. This static approach is frequently applied when CH4 or N2O fluxes are assessed by GC. In comparison to the dynamic method described above, sample collection is labour-intensive and has a higher potential of bias during vial preparation, sample collection, storage, and transport. Long chamber closing times (often necessary for N2O / CH4; see Pihlatie et al., 2013) should be strictly avoided for CO2 efflux measurements as they result in substantially underestimated soil CO2 efflux rates. On the positive side, the method offers the opportunity to sample a larger set of chambers simultaneously, thereby saving time and creating a potential for higher spatial replication. Due to the various sources of bias, we only recommend this method when no IRGA or laser is available.
Where to start
Pape et al. (2009); Pihlatie et al. (2013); Pumpanen et al. (2004); Romero-Olivares et al. (2017); Vicca et al. (2014)
Installation, field operation, maintenance, interpretation
The handbook “Soil Carbon Dynamics – An Integrated Methodology” (Kutsch et al., 2009) provides detailed information on the principles of soil CO2 efflux measurements, the constraints and limitations of different methods, flux-upscale, and experimental design. We highly recommend this book to anybody dealing with CO2 efflux measurements. Here, we specifically concentrate on practical advice with regard to climate-manipulation experiments.
Manual soil CO2 efflux measurements (+ cheap, easy to apply, – low temporal coverage-to-workload ratio)
Depending on the studied ecosystem and the specific research question, a system suitable for soil CO2 efflux measurements has to be chosen according to its size and design. It is up to the researcher to decide whether a commercially available analyser/chamber combination meets their needs, or whether they shall construct their own individual chamber system. The measurement principle of soil CO2 efflux is relatively simple and homemade chambers can do a good job if they are thoroughly constructed (e.g. larger (diameter > 20–30 cm) chambers should contain an appropriate fan for proper air mixing; a simple vent can avoid over-pressure during chamber closure).
When choosing a chamber, several criteria need to be considered:
- Colour: to avoid photosynthetic activity of small plants or algae, chambers for soil CO2 efflux are typically dark. It is advisable to use bright or reflecting exterior material/colour to avoid unwanted air/soil warming inside (especially in an open field). Under specific climate manipulation treatments, such as infrared warming, the heat conductivity of the collar/ chamber material should be tested and the appropriate material selected in order to avoid potential overheating.
- Size: commercially available chambers frequently cover only a relatively small soil surface area (e.g. 10 cm diameter). Under heterogeneous soil conditions, as in many forests, the application of such chambers can result in highly variable measurements of soil CO2 efflux rates within a short distance. As a result, a correspondingly high number of individual chamber measurements is required to cover spatial heterogeneity to make manipulation treatments comparable. The application of larger diameter chambers (covering more soil and thereby reducing variability) can be helpful in heterogeneous systems. On the other hand, small diameter chambers may fit within narrow bare-soil spaces between or under the plants, thus reducing bias due to the cutting or removal of plants. Chamber height is commonly between 10 and 20 cm to ensure a good balance between being low enough to enable a considerable increase in headspace CO2 in a few minutes but still high enough to avoid excessive turbulence (created by the fan).
- Collar: chambers are typically placed on permanently installed collars or the whole chamber is permanently installed and closed with a lid during measurements. Collar/chamber insertion depth should be as shallow as possible to avoid root cutting (Wang et al., 2005); usually 1–2 cm insertion depth is sufficient to gain a sealing effect between soil and collar/chamber.
- Chamber air mixing: sufficient mixing of the chamber atmosphere using a fan, for example, provides a clearer signal of headspace CO2 over time. However, chamber wind speed should be carefully considered depending on chamber size and design. Too low or too high air mixing may cause boundary layer formation or pumping of air from the soil to the chamber, respectively, both leading to biased flux estimates (Hooper et al., 2002).
- Soil CO2 efflux can show distinctive temporal/daily variations (due to changes in soil temperature or autotrophic contribution). Therefore, the sequence of soil respiration measurements should ideally be fully random in order to avoid any interference with the effect of the climate manipulation being tested. Performing, for instance, all CO2 measurements at control plots in the morning and all measurements at drought treatment plots in the afternoon could induce serious bias. Also note that calm wind conditions (i.e. low atmospheric turbulence), which may itself have a specific diel course, may cause biases in flux estimations (Braendholt et al., 2017).
Automated systems (+ high temporal coverage-to-workload ratio, – expensive)
Automated chamber systems can provide year-round high temporal resolution soil CO2 efflux data. They usually consist of a single CO2 analyser and one or more auto-chambers, which are operated one after another. Auto-chambers (at least commercial ones) are costly and it is the researcher’s task to weigh the pros and cons of the investment. For a mere comparison of annual soil respiration among gradual manipulation treatments (e.g. warming), auto-chamber measurements are mostly not necessary. However, if the climate manipulation has an event character (e.g. heavy rainfall), auto-chambers can provide the needed resolution to capture important flux events. A further advantage is that auto-chamber systems can easily be attached to lasers or field mass spectrometers, thereby allowing high-resolution stable isotope measurements with a series of potential applications (e.g. isotope labelling). Generally, the number of auto-chambers per treatment can be kept as low as possible. Auto-chambers capture the temporal trend in soil CO2 efflux (spatial variation is captured by periodic manual measurements). Keeping the number of auto-samplers low reduces costs and work-time as these chambers require maintenance and produce large amounts of data to handle. Furthermore, the lower the number of auto-chambers, the higher the measurement frequency of each individual chamber.
Separating autotrophic and heterotrophic soil CO2 efflux
As mentioned above, separating flux components is often necessary to assess climate manipulation effects on different C pools. There is an array of different methods (Hanson et al., 2000; Kuzyakov et al., 2006; Subke et al., 2006): their applicability depends on the manipulation and the ecosystem. More simple approaches, such as trenching or girdling aim at mechanically excluding autotrophic soil respiration, while more sophisticated approaches such as stable isotope labelling leave plants and soil intact and minimise disturbance. Although the least-destructive method should be the method of choice in climate-manipulation experiments, applicability is often restricted due to financial or methodological constraints. The most common methods are:
- Trenching (+ easy to apply; – destructive): trenching incurs the disruption of roots along a trench around a defined plot (from dm2 to m2). In the trenched plot, only heterotrophic soil respiration from the decomposition of soil organic matter is anticipated. Autotrophic respiration can be calculated as the difference between control plot respiration and trenched plot respiration. The trench is usually sealed with a plastic foil to prevent root in-growth. Using different sized meshes instead of foil allows the differentiation between root and mycorrhizal respiration (Heinemeyer et al., 2007). Besides the easy and cheap treatment, trenched plots are relatively easy to manipulate. Trenching is therefore frequently used in warming studies. Due to its intrusive nature, the method has several drawbacks such as preventing transpiration from trenched plots, enhanced decomposition of dead roots, and gradually changing soil chemistry due to missing nutrient uptake by roots. All these factors have to be accounted for to produce a reliable estimate of source contribution (Diaz-Pines et al., 2010; Comstedt et al., 2011) making the method more labour-intensive and sophisticated than at first sight.
- Tree Girdling (+ easy; – destructive): as with trenching, girdling is based on the suppression of autotrophic soil respiration. A strip of bark and phloem is removed from the stem to cut-off labile C transport to roots, whereas water uptake is not hampered and roots as well as trees can survive for a while. This method has been shown to provide striking results when applied on a large scale (Högberg et al., 2001), but as most trees die after 1–3 years, the method is less useful in manipulation experiments, which are usually set-up for a longer term. A potential alternative may be stem compression (Henriksson et al., 2015), but this remains to be tested for trees other than pine.
- Isotope techniques (+ non-destructive; – expensive and complex): a non-destructive approach in flux partitioning is the use of stable C isotopes (13C) or radiocarbon (14C). Naturally abundant 13C can be used as a measure of autotrophic contribution during distinctive weather events or when C3 plants grow on C4 soil or vice versa (Ekblad & Högberg, 2001; Kuzyakov, 2006). Alternatively, 13C labelling allows the fate of the carbon taken up during photosynthesis to be traced (Högberg et al., 2008). Labelling studies are the most elegant way of assessing C allocation, but they are more difficult in higher vegetation (trees) that do not easily fit into a chamber. The flux partitioning is also complex. Moreover, the radiocarbon technique is based on different 14C signatures of ambient CO2 (autotrophic respiration) and respired SOM (heterotrophic). The method is relatively labour-intensive as the 14C signatures of heterotrophic and root respiration need to be assessed several times, and are costly (14C measurements) but can provide good estimates as well (Borken et al., 2006) (not recommended on carbonate soil!).
- Modelling heterotrophic efflux (+ non-destructive; – based on simple model): another non-destructive approach is the use of soil C concentrations/stocks and the relationship of heterotrophic respiration to soil temperature and moisture (assessed during lab incubation) as surrogates to model the annual heterotrophic soil CO2 Autotrophic efflux is the difference between measured total efflux and modelled heterotrophic efflux. Although this method has proven to produce reliable estimates (Kutsch et al., 2010; Wangdi et al., 2017), it is not recommended for climate manipulation studies where temperature and moisture are the targeted variables during manipulation.
Where to start
Borken et al. (2006), Brændholt et al. (2017), Comstedt et al. (2011), Díaz-Pinés et al. (2010), Ekblad & Högberg (2001), Hanson et al. (2000), Heinemeyer et al. (2007), Henriksson et al. (2015), Högberg et al. (2001, 2008), Hooper et al. (2002), Kutsch et al. (2009, 2010), Kuzyakov (2006), Subke et al. (2006), Wang et al. (2005), Wangdi et al. (2017)
126.96.36.199 Special cases, emerging issues, and challenges
Greenhouse gas efflux through snow
Climate change will affect the magnitude and duration of snow cover and hence wintertime soil microclimate. Cold season soil C dynamics can play an important role and the number of snow-manipulation studies is increasing in temperate to arctic ecosystems (e.g. Wipf & Rixen, 2010; Li et al., 2016).
The preferred approach for measuring greenhouse gas effluc (GHG) fluxes through snow is the concentration gradient method (Sommerfeld et al., 1993). GHG concentrations are measured from the snow to the soil surface. Under uniform snow characteristics, GHG concentrations will linearly decrease from the soil to the snow surface. This concentration gradient, together with specific snow characteristics (porosity and tortuosity) can be used to calculate the GHG efflux. CO2 concentrations above and in the snow can be easily measured by attaching a straight rod/pipe (inner diameter 2–5 mm) directly to a CO2 analyser. The rod should have an easy-to-read scale, so that one knows how deep the rod-tip is inserted into the snow. The CO2 concentration of a specific snow depth can be recorded within a few seconds after inserting the rod into the snow. This way, detailed CO2 concentration profiles can be measured through deeper snow within a couple of minutes. With shallow snow cover, a single measurement slightly above the snow surface and another measurement at the soil surface can be enough for a flux calculation. For the calculation of the GHG flux, characterisation of the snow profile is necessary. Primarily, snow depth and snow density need to be determined (density by simply weighing a known volume of snow). The gradient method provides accurate flux estimates under less turbulent atmospheric conditions. Under turbulence (wind pumping), a thorough flux estimate can become highly complex (Massmann & Frank, 2006; Seok et al., 2009). The occurrence of ice layers in snow packs can block gas diffusion and complicate flux calculations as well.
Chamber measurements on the snow surface can work well, but can underestimate GHG fluxes, especially when snow is lightly textured (Mast et al., 1998; McDowell et al., 2000; Schindlbacher et al., 2007). Excavating chambers from snow can be an option with shallow snow cover (Björkman et al., 2010). However, excavating chambers into deep snow creates preferred flow paths, which leads to lateral transport of CO2 and overestimation of the flux. Therefore, excavation, and perhaps even snow disturbance, should be avoided.
It should be noted that most GHG analysers are not built for measurements at temperatures far below freezing: it is therefore recommended that the devices be properly insulated when measuring in the field.
GHG concentrations in the soil profile
Like in snow, soil GHG gradients can be used to calculate the diffusive GHG efflux from the soil surface. The advantage of this approach is that a set of CO2 sensors, which are installed at different soil depths, can be operated simultaneously, thereby generating soil CO2 gradients (and fluxes) at extremely high temporal resolution (Maier & Schack-Kirchner, 2014). The method, however, has its drawbacks since the soil characteristics which are needed for a thorough flux calculation (porosity, tortuosity) are much more difficult to assess and change over time with, for example, soil moisture content. Therefore, the CO2 gradient method is comparatively rarely applied in soil. GHGs such as N2O usually do not show a linear concentration profile in soil because they are produced and consumed in soil layers at various depths. Accordingly, a flux calculation is mostly not feasible.
Knowledge of GHG concentrations in the soil profile can, nevertheless, be of advantage in order to improve the mechanistic understanding of climate manipulation effects on soil processes (e.g. how drought affects N2O production and consumption at different soil depths). Soil GHG concentrations can be easily accessed by inserting capillaries at different soil depths and drawing samples directly with a gas analyser or a syringe for further storage/measurement.
Theory, significance, and large datasets
Bond-Lamberty & Thomson (2010, 2014), Bond-Lamberty et al. (2018)
More on methods and protocols
Pihlatie et al. (2013), Pumpanen et al. (2004)
Björkman, M. P., Morgner, E., Cooper, E. J., Elberling, B., Klemedtsson, L., & Björk, R. G. (2010). Winter carbon dioxide effluxes from Arctic ecosystems: an overview and comparison of methodologies. Global Biogeochemical Cycles, 24(3), GB3010.
Bond-Lamberty, B. P., & Thomson, A. M. (2010). A global database of soil respiration measurements, Biogeosciences 7, 1321-1344
Bond-Lamberty, B. P., & Thomson, A. M. (2014). A Global Database of Soil Respiration Data, Version 3.0. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. doi: 10.3334/ORNLDAAC/123
Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M., & Vargas, R. (2018). Globally rising soil heterotrophic respiration over recent decades. Nature, 560, 80-83.
Borken, W., Savage, K., Davidson, E. A., & Trumbore, S. E. (2006). Effects of experimental drought on soil respiration and radiocarbon efflux from a temperate forest soil. Global Change Biology, 12(2), 177-193.
Brændholt, A., Steenberg Larsen, K., Ibrom, A., & Pilegaard, K. (2017). Overestimation of closed-chamber soil CO2 effluxes at low atmospheric turbulence. Biogeosciences, 14(6), 1603-1616.
Comstedt, D., Boström, B., & Ekblad, A. (2011). Autotrophic and heterotrophic soil respiration in a Norway spruce forest: estimating the root decomposition and soil moisture effects in a trenching experiment. Biogeochemistry, 104(1-3), 121-132.
Crowther, T. W., Todd-Brown, K. E. O., Rowe, C. W., Wieder, W. R., Carey, J. C., Machmuller, M. B., … Blair, J. M. (2016). Quantifying global soil carbon losses in response to warming. Nature, 540(7631), 104-108.
Díaz-Pinés, E., Schindlbacher, A., Pfeffer, M., Jandl, R., Zechmeister-Boltenstern, S., & Rubio, A. (2010). Root trenching: a useful tool to estimate autotrophic soil respiration? A case study in an Austrian mountain forest. European Journal of Forest Research, 129(1), 101-109.
Ekblad, A., & Högberg, P. (2001). Natural abundance of 13C in CO2 respired from forest soils reveals speed of link between tree photosynthesis and root respiration. Oecologia, 127, 305-308.
Hanson, P. J., Edwards, N. T., Garten, C. T., & Andrews, J. A. (2000). Separating root and soil microbial contributions to soil respiration: a review of methods and observations. Biogeochemistry, 48(1), 115-146.
Harris, D. G., & van Bavel, C. H. M. (1957). Root respiration of tobacco, corn, and cotton plants. Agronomy Journal, 49(4), 182-184.
Heinemeyer, A., Hartley, I. P., Evans, S. P., Carreira de la Fuente, J. A., & Ineson, P. (2007). Forest soil CO2 flux: uncovering the contribution and environmental responses of ectomycorrhizas. Global Change Biology, 13(8), 1786-1797.
Henriksson, N., Tarvainen, L., Lim, H., Tor-Ngern, P., Palmroth, S., Oren, R., … Näsholm, T. (2015). Stem compression reversibly reduces phloem transport in Pinus sylvestris trees. Tree Physiology, 35(10), 1075-1085.
Högberg, P., Nordgren, A., Buchmann, N., Taylor, A. F., Ekblad, A., Högberg, M. N., … Read, D. J. (2001). Large-scale forest girdling shows that current photosynthesis drives soil respiration. Nature, 411(6839), 789-792.
Högberg, P., Högberg, M. N., Göttlicher, S. G., Betson, N. R., Keel, S. G., Metcalfe, D. B., … Linder, S. (2008). High temporal resolution tracing of photosynthate carbon from the tree canopy to forest soil microorganisms. New Phytologist, 177(1), 220-228.
Hooper, D. U., Cardon, Z. G., Chapin, F., & Durant, M. (2002). Corrected calculations for soil and ecosystem measurements of CO2 flux using the LI-COR 6200 portable photosynthesis system. Oecologia, 132(1), 1-11.
Janssens, I. A., Dieleman, W., Luyssaert, S., Subke, J. A., Reichstein, M., Ceulemans, R., … Papale, D. (2010). Reduction of forest soil respiration in response to nitrogen deposition. Nature Geoscience 3(5), 315-322.
Kutsch, W. L., Bahn, M., & Heinemeyer, A. (2009). Soil Carbon Dynamics: An Integrated Methodology. Cambridge: Cambridge University Press.
Kutsch, W. L., Persson, T., Schrumpf, M., Moyano, F. E., Mund, M., Andersson, S., & Schulze, E. D. (2010). Heterotrophic soil respiration and soil carbon dynamics in the deciduous Hainich forest obtained by three approaches. Biogeochemistry, 100(1-3), 167-183.
Kuzyakov, Y. (2006). Sources of CO2 efflux from soil and review of partitioning methods. Soil Biology and Biochemistry, 38(3), 425-448.
Li, W., Wu, J., Bai, E., Jin, C., Wang, A., Yuan, F., & Guan, D. (2016). Response of terrestrial carbon dynamics to snow cover change: A meta-analysis of experimental manipulation (II). Soil Biology and Biochemistry, 103, 388-393.
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Pape, L., Ammann, C., Nyfeler-Brunner, A., Spirig, C., Hens, K., & Meixner, F. X. (2009). An automated dynamic chamber system for surface exchange measurement of non-reactive and reactive trace gases of grassland ecosystems. Biogeosciences, 6(3), 405-429.
Pihlatie, M. K., Christiansen, J. R., Aaltonena, H., Korhonena, J. F. J., Nordboa, A., Rasilod, T., …, Pumpanen, J. (2013). Comparison of static chambers to measure CH4 emissions from soils. Agricultural and Forest Meteorology, 171-172, 124-136.
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Romero-Olivares, A. L., Allison, S. D., & Treseder, K. K. (2017). Soil microbes and their response to experimental warming over time: A meta-analysis of field studies. Soil Biology and Biochemistry, 107, 32-40.
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Seok, B., Helmig, D., Williams, M. W., Liptzin, D., Chowanski, K., & Hueber, J. (2009). An automated system for continuous measurements of trace gas fluxes through snow: an evaluation of the gas diffusion method at a subalpine forest site, Niwot Ridge, Colorado. Biogeochemistry, 95(1), 95-113.
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Sheng, H. A. O., Yang, Y., Yang, Z., Chen, G., Xie, J., Guo, J. & Zuo, S. (2010). The dynamic response of soil respiration to land-use changes in subtropical China. Global Change Biology, 16(3), 1107-1121.
Sommerfeld, R. A., Mosier, A. R., & Musselman, R. C. (1993). CO2, CH4 and N2O flux through a Wyoming snowpack and implications for global budgets. Nature, 361(6408), 140-142.
Subke, J. A., Inglima, I., & Cotrufo, M. F. (2006). Trends and methodological impacts in soil CO2 efflux partitioning: a meta-analytical review. Global Change Biology, 12(6), 921-943.
Vicca, S., Bahn, M., Estiarte, M., Van Loon, E. E., Vargas, R., Alberti, G., … Borken, W. (2014). Can current moisture responses predict soil CO2 efflux under altered precipitation regimes? A synthesis of manipulation experiments. Biogeosciences, 11(11), 2991-3013.
Wang, W. J., Zu, Y. G., Wang, H. M., Hirano, T., Takagi, K., Sasa, K., & Koike, T. (2005). Effect of collar insertion on soil respiration in a larch forest measured with a LI-6400 soil CO2 flux system. Journal of Forest Research, 10(1), 57-60.
Wangdi, N., Mayer, M., Nirola, M. P., Zangmo, N., Orong, K., Ahmed, I. U., … Schindlbacher, A. (2017). Soil CO2 efflux from two mountain forests in the eastern Himalayas, Bhutan: components and controls. Biogeosciences, 14(1), 99-110.
Wipf, S., & Rixen, C. (2010). A review of snow manipulation experiments in Arctic and alpine tundra ecosystems. Polar Research, 29(1), 95-109.
Authors: Schindlbacher A1, Larsen KS2, Vicca S3
Reviewer: Christansen CT4
1 Department of Forest Ecology and Soils, Federal Research and Training Centre for Forests, Natural Hazards and Landscape, Vienna, Austria
2 Department of Geosciences and Natural Resource Management, University of Copenhagen, Fredriksberg, Denmark
3 Centre of Excellence PLECO (Plants and Ecosystems), Biology Department, University of Antwerp, Wilrijk, Belgium
4 NORCE Norwegian Research Centre and Bjerknes Centre for Climate Research, Bergen, Norway