1_Zacháry.indd 3Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19.DOI: 10.15201/hungeobull.68.1.1 Hungarian Geographical Bulletin 68 2019 (1) 3–19. Introduction The application of stable isotope analysis has proved to be an extremely useful tool for tracking various changes in the Earth’s sys- tems. Since the discovery of isotopes in the 1910s, stable isotope geochemistry has provid- ed essential information for geosciences, first for chemistry and geochemistry and later for biochemistry and ecology (Dawson, T.E. and Siegwolf, R.T.W. 2007). With the help of stable isotopes, paleo-environmental reconstruction became an achievable tool (Epstein, S. et al. 1953), as did the study of the atmosphere and the hydrological cycle via the isotopic signa- ture of precipitation (Dansgaard, W. 1964). Stable isotopes also help the more precise identification of extinction events (Pálfy, J. et al. 2001). These are just a few examples of the possible application of stable isotopes in geochemical questions. Today, stable isotope analyses cover almost the entire spectrum of geoscience research and in some areas their application is mandatory (Demény, A. 2004). The carbon isotope composition of organ- ic and inorganic compounds alters in the course of exchange processes in the vegeta- tion-soil-atmosphere cycle, leaving an isotop- ic imprint on plant, soil and atmospheric car- bon pools and fluxes (Werner, C. et al. 2012). These isotopic imprints allow, for example, the tracking of newly assimilated C incor- Applications of stable carbon isotopes in soil science with special attention to natural 13C abundance approach Dóra ZACHÁRY1,2 Abstract Since the invention of the isotope ratio mass spectrometer in the late 1930s, isotope analysis has shed light on many key processes in the Earth’s ecosystems. Stable isotope analysis was first applied in the field of chemistry and geochemistry in the 1940s, while the use of isotopic fractionation for various biochemical reactions was elaborated later. The knowledge gained from isotope research led to a better understanding of the dynamics of the biosphere and to the more efficient study of interactions between the geosphere and biosphere. In soil research, stable isotopes are ideally suited to provide a wider insight into the element cycles in soil ecosystems. Stable carbon isotopes, in particular, have been in the focus of soil research, since soil organic matter (SOM) plays an important role not only in soil fertility, soil water management and many other physical, chemical and biological soil functions, but also in the global carbon cycle. If processes connected with these soil functions are isotopically labelled with stable carbon isotopes, the key reactions of C input, exchange and output in the soil and other soil organic matter functions can be studied accurately. The 13C abundance approach is one of the useful methods applying natural stable carbon isotope differences in the atmosphere-plant-soil system to track the stability of organic carbon in these reservoirs. The turnover of SOM, particularly the rate of decomposition and the partitioning of C between the different soil CO2 efflux sources are in the focus of soil science research, which can be studied in detail with the help of natural 13C abundance method. Thus, analysing the isotopic composition of CO2 exchange between the soil and the atmosphere not only helps to gain more information about the impact and role of SOM and its various forms but also to predict ecosystem responses to global changes. Keywords: stable C isotopes, 13C natural labelling, soil organic carbon turnover, isotope fractionation 1 Geographical Institute, Research Centre for Astronomy and Earth Sciences, Hungarian Academy of Sciences, H-1112 Budapest, Budaörsi út 45. E-mail: zachary.dora@csfk.mta.hu 2 Institute for Nuclear Research, Hungarian Academy of Sciences. H-4026 Debrecen, Bem tér 18/c. Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19.4 porated in plants, then migrating to the soil, stored within the soil ecosystem or lost to the atmosphere (Brüggemann, N. et al. 2011). Paleo-environmental studies (Schwartz, D. et al. 1986; Cerling, T.E. et al. 1989; Fox, D.L. and Koch, P.L. 2004; Barta, G. et al. 2018) use stable carbon isotopes in soils or paleosols to track past changes in vegetation and cli- mate. Stable carbon isotope methods related to landscape evolution, land use change and erosion studies (Paul, S. et al. 2008a; Häring, V. et al. 2013; Alewell, C. et al. 2016; Brandt, C. et al. 2016, 2018) make it possible to trace the origin and stability of carbon in different systems during diverse processes. Today, stable isotope information permits scientists to address issues that seemed in- tractable using other methods. The stable isotope data generated with these methods have provided insights into a wide range of complex processes on temporal and re- gional scales from seconds to millennia and from cells to net ecosystem flux partitioning (Dawson, T.E. et al. 2002; Dawson, T.E. and Siegwolf, R.T.W. 2007). This review provides the theoretical back- ground of stable isotope research, using ex- amples of major processes resulting in car- bon isotope fractionation to illustrate various types of isotope fractionation and highlight- ing the stable carbon isotope variation in the Earth’s main reservoirs, focusing in particular on soil ecosystems. It details the applicability of stable carbon isotope research in soil sci- ences, with special attention to the 13C natural labelling approach. The natural abundance of stable carbon isotopes has been widely used to probe the turnover of SOM and to differentiate the diverse sources of CO2 efflux from the soil. These results provide a clearer picture of the fate of organic carbon in the vegetation-soil-atmosphere cycle. Isotope nomenclature and fractionation Determining the absolute abundance of iso- topes is difficult, because absolute variations in isotopic abundance based on physical and biological factors are small (to the order of a few percent) (Ehleringer, J.R. and Rundel, P.W. 1989), so relative isotope abundance is conventionally calculated as follows: R = rare isotope abundant isotope where R is the ratio of the rare isotope to the abundant isotope. The ratio R of a sample is generally compared to that of a known standard material, which provides high pre- cision and repeatability over the long-term. Because of the small variations present in nature between the isotopic compositions of the sample and the standard material, the ratios are expressed using the conventional δ notation, introduced by Craig, H. (1953), in parts per thousand: δ (‰) = Rsample Rstandard – 1 (x 103) The unit of δ is “‰” or “permil” (also per mill). The worldwide standards for the six con- ventional elements are V-SMOW (Vienna- Standard Mean Ocean Water) for H, V-PDB (Vienna-PDB, a replacement standard for the original calcium carbonate found in Belemnitella americana in the Cretaceous PeeDee formation in South Carolina, USA) for C, AIR N2 for N, V-SMOW for O, V-CDT (Troilite from the Canyon Diablo iron meteorite) for S and NBS-28 (quartz sand) for Si (Hoefs, J. 2009). The basis for isotope geochemistry is the fractionation of isotopes, i.e. ’the partition- ing of isotopes between two phases of the same substance with different isotope ratios’ (Hoefs, J. 2009), which results in different isotopes of the same element having differ- ent distribution patterns in the environment. The fractionation factor (α) is the difference in the ratio of the product isotope ratio (RP) to the reactant isotope ratio (RR): α = RP RR , (1) (2) (3) 5Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19. In general, isotope effects are small, α ≈ 1, so it has become common practice in recent years to replace the fractionation factor α by the de- viation of α from 1, referred to as the ε-value: ε = α – 1 (x 103) The ε-value represents the enrichment (ε > 0) or depletion (ε < 0) of the rare isotope in the product compared to the reactant iso- tope and approximates the fractionation in parts per thousand, making it similar to the δ value (Mook, W.G. 2001; Hoefs, J. 2009). Isotope fractionation is often referred to as ‘discrimination’ in biological systems, meaning that specific enzymes discriminate against the heavier and favour the lighter isotope (Dawson, T.E. et al. 2002). Isotope fractionation is caused by mass-de- pendent and mass-independent mechanisms, of which the latter is less frequent. Isotope exchange reactions (or equilibrium isotope distribution) and kinetic processes are the main mass-dependent processes. Equilibrium isotope fractionation The distribution of isotopes is controlled by the lowest energy state of the system (Ohkouchi, N. et al. 2015; Trumbore, S.E. et al. 2016). The energy state of a molecule is based differences in translation, rotation and vibration energy, among which differences in vibrational energy are predominant. There- fore, this is the source of isotope partitioning (Hoefs, J. 2009). The vibrational energy of a molecule depends inversely on the masses of the atoms in the molecule (Bigeleisen, J. 1965). As a consequence, isotopes partition different- ly for various types of chemical bonds and for the phases of the same molecule (e.g. for H2O as vapour, liquid or ice). The heavier isotope prefers molecules with stronger bonds and phases with less entropy (e.g. a solid versus a liquid versus a gas) (Trumbore, S.E. et al. 2016). Equilibrium isotope fractionation occurs in nature especially between the phases of the CO2 – H2O – H2CO3 – CaCO3 system. One typical ex- ample is the isotope equilibrium between gase- ous CO2 and dissolved bicarbonate (HCO3 −): 13CO2(gas) + H12CO3 - ↔ 12CO2(gas) + H13CO3 - (5) In this fractionation the 13R (see above) is 0.0111421 for CO2 gas (g) and 0.0112372 for bicarbonate (b) at 20 °C (Mook, W.G. 2001), so the fractionation factors are 13αg/b = 0.9915 and 13εg/b = −8.46‰. Figure 1. illustrates the different ε-values for different phases of the CO2 – H2O – H2CO3 – CaCO3 system. This fig- ure also shows the temperature dependence of isotope fractionation. In general, isotope fractionation is higher at a lower temperature, while it becomes zero at a very high tempera- ture, based on the different vibrational fre- quencies of the molecules (Hoefs, J. 2009). Another example of equilibrium fractiona- tion is the precipitation of calcium carbonate from water. In this case the heavier C isotope will partition into the calcium carbonate, which has fewer degrees of freedom because it is solid. The δ13C of C in calcium carbonate will be enriched to a greater extent (~10‰) (4) Fig. 1. Temperature-dependent equilibrium isotope fractionation for the different phases of the CO2 – H2O – H2CO3 – CaCO3 system. – a = dissolved CO2; b = dis- solved HCO3 −; c = dissolved carbonate ions; g = gaseous CO2; s = solid carbonate. The different phases are shown with respect to dissolved HCO3 −. Source: redrawn from Mook, W.G. 2001. Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19.6 than that of atmospheric CO2 in equilibrium with water from which the calcium carbonate is precipitated (Mook, W.G. 2001; Trumbore, S.E. et al. 2016). Kinetic isotope fractionation In comparison with equilibrium fractiona- tion, kinetic fractionation occurs in non- equilibrium conditions when a reaction is irreversible, such as evaporation, diffu- sion, dissociation or biologically mediated reactions (Bigeleisen, J. and Wolfsberg, M. 1958; Hoefs, J. 2009; Ohkouchi, N. et al. 2015). Kinetic processes depend primarily on differences in the reaction rates of isotopic molecules: the lighter isotope will react and diffuse faster than the heavier isotope at a given temperature (Hoefs, J. 2009). As a consequence, the preferential enrichment of the lighter isotope is observed in the reac- tion products compared to the heavier iso- tope (Mook, W.G. 2001; Michener, R.H. and Lajtha, K. 2007; Hoefs, J. 2009; Ohkouchi, N. et al. 2015; Trumbore, S.E. et al. 2016). One prominent example of stable C isotope fractionation is the process of photosynthesis: 6CO2 + 6H2O + light → C6H12O6 + 6O2 , (6) where the 13R of the reactant (atmospheric CO2) = 0.9926, and the 13R of the product (plant material) = 0.9724 (Trumbore, S.E. et al. 2016), giving fractionation factors of 13ε = 0.9796 and 13α = −20.4‰. Another kinetic process is the mineraliza- tion (bacterial decomposition) of soil organic matter to methane, resulting in an ε-value of about −55‰. Although natural processes are not purely kinetic or irreversible, they are of- ten referred to as non-equilibrium fractiona- tions (Mook, W.G. 2001). Mass-independent fractionation Some fractionation processes do not exhibit the mass-dependent effects described above. Mass-independent fractionation was observed in meteorites by Clayton, R.N. et al. (1973) with the use of oxygen isotope diagrams and was interpreted by Thiemens, M.H. (1999). In this kind of fractionation, Allègre, C.J. (2008) reported that isotope differences do not de- pend on the mass difference but on the sym- metry of the molecule. Mauersberger, K. et al. (1999), however, demonstrated experimental- ly that it is not the symmetry of the molecule which is responsible for fractionation but the difference in its geometry. New research in- dicates that mass-independent isotope frac- tionations are more abundant than originally thought and serve as a novel form of the iso- topic fingerprint (Hoefs, J. 2009). Stable carbon isotope variation in the Earth’s reservoirs Of the three naturally occurring C isotopes, 12C and 13C are stable, representing 98.89% and 1.11% of the C atoms on Earth, respective- ly (Meija, J. et al. 2016). Both stable isotopes were originally created by nucleosynthesis in stars and their abundance has remained constant since their synthesis (Trumbore, S.E. et al. 2016). However, the relative abundance of stable C isotopes may vary in the Earth’s various carbon reservoirs (atmosphere, bio- sphere, hydrosphere, lithosphere), resulting in naturally occurring variations greater than 120‰, from heavy marine carbonates (δ13C values +20‰) to light methane (δ13C values –110‰, Figure 2). The systematic differences in the δ13C values of various carbon reservoirs have been known since the work of Nier, A.O. and Gulbransen, E.A. (1939). Stable carbon isotope studies in soil science Norman, A.G. and Werkman, C.H. (1943) con- ducted the first soil tracer study on 15N-labelled soybean residues, examining their decomposi- tion in the soil. Since then many types of re- search have used tracers to track the fate of SOM constituents and dynamics in soils. Stable 7Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19. carbon isotope measurements in soil science studies have become more and more signifi- cant in the past decades, as soil plays an im- portant role in the global carbon cycle. The significance of stable carbon isotope research in soil science was summarized by Brüggemann, N. et al. (2011), who provided a comprehensive overview of the complex network of carbon transformation and trans- port processes in the plant-soil-atmosphere continuum and demonstrated that research using C isotopes makes it possible to track the fate of C molecules and to integrate in- formation on physical, chemical and biologi- cal processes in ecosystems across space and time. Kuzyakov, Y. (2011) stated that isotopic tracers are the most frequently applied and most powerful tracers because of the nearly identical chemical and biochemical proper- ties of isotopes of a single element. Isotope labelling in soils is based on the fact that biological, chemical and physical Fig. 2. δ13C variations in selected carbon-bearing materials. Source: redrawn from Meija, J. et al. 2016. Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19.8 fractionation processes in nature are unique- ly δ13C labelled, and that this labelling is in- herited in the soil. This labelling happens naturally. Another technique for understand- ing C dynamics in soils is to artificially alter the C isotope content of assimilated C using enriched stable (13C) or radioactive (14C) C compounds (CO2, whole plant residues or plant monomers and polymers) in short pulses (pulse labelling) or over long periods (continuous labelling) (Kuzyakov, Y. 2006). Stable C isotope fractionation processes in the atmosphere-plant-soil system The atmospheric CO2 photosynthesis of plants and the different mechanisms involved were reported by Bender, M.M. (1971), who was the first to describe differences in the δ13C values of various plant species. Reviews published from the 1980s onwards (e.g. O’Leary, M.H. 1981; Farquhar, G.D. et al. 1989; Hayes, J.M. 2001) provided the biochemical background of carbon isotope fractionation during pho- tosynthesis. It was concluded that there are three different mechanisms of photosynthetic CO2 fixation: the C3 (Calvin-Benson) pathway, the C4 (Hatch-Slack) pathway and the crassu- lacean acid metabolism (CAM). Plant photo- synthesis strongly discriminates against the heavier carbon isotope, so the uptake of this isotope by C3 and C4 plants averages 19‰ and 4‰ less, respectively, than the atmospheric ambient δ13C (Figure 3) (Boutton, T.W. 1996; Hoefs, J. 2009), which is −8‰ compared to the V-PDB standard (see Figure 2 and 3). The CAM pathway is a modification of photosynthetic carbon fixation resulting in δ13C values rang- ing from –10 to –28‰ (Boutton, T.W. 1996). After CO2 photosynthetic fixation by plants, further fractionation processes take place, resulting in different δ13C values for different compounds in plants (Park, R. and Epstein, S. 1960). Lignin, lipids and cellulose are depleted, while sugars, amino acids and hemicelluloses are enriched in 13C relative to the bulk plant material (Boutton, T.W. 1996). Therefore, within a single plant δ13C differences between substances may be as much as 9‰ for C3 plants and 10.3‰ for C4 plants (Hobbie, E.A. and Werner, R.A. 2004). Kinetic isotope effects seem to be the cause of these 13C differences (Hoefs, J. 2009). Fig. 3. Isotopic composition of C3 and C4 plants compared to atmospheric CO2 and the C isotope ratio measure- ment standard. Source: redrawn from Ehleringer, J.R. and Cerling, T.E. 2002. 0 50 100 150 200 Fr eq ue nc y –40 –30 –20 –10 0 10 C plants₃ C plants₄ At m os ph er ic C O₂ St an da rd V- PD B –4‰ Δ C=–19‰�� δ C ( )�� ‰ 9Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19. In addition, microbes in the soil also dis- criminate isotopes. The term ’preferential substrate utilization’ or ’preferential decom- position’ refers to the phenomenon whereby microorganisms select certain individual sub- stances in plant residues and decompose them to CO2 (Werth, M. and Kuzyakov, Y. 2010). Microbes, especially the bacteria prefer easily decomposable substances (e.g. glucose, su- crose) enriched in 13C rather than lignin and lipids. This preferential substrate utilization is more significant than the 13C-depletion effect of the metabolism (CO2 from microbial respi- ration is 13C-depleted compared to the sub- strate from which it is derived) (Šantrůčková, H. et al. 2000). As a consequence, the CO2 emit- ted during decomposition is enriched in 13C, while 13C-depleted SOM remains in the soil, as the preferential utilization of the 13C-enriched SOM fractions means that 13C is lost more rap- idly than 12C (Ågren, G.I. 1996). Application of natural C isotope fractionation The differences in δ13C values between C3 and C4 plants have great importance for soil science since the δ13C of SOM in the steady- state system is nearly identical to that of the source vegetation from which the organic matter was derived (Boutton, T.W. 1996). This is the basis for numerous stable carbon isotopic applications in soil science. The natural labelling or δ13C natural abun- dance method is based on 1) the above-men- tioned physiological difference in the photo- synthetic fixation of CO2 in C3 and C4 plants and 2) the assumption that the δ13C natural abundance signature of SOM is identical to the δ13C natural abundance signature of the plants from which it is derived, because the isotopic difference between C3 and C4 plants is much larger than the isotopic changes occurring dur- ing SOM decay (Balesdent, J. and Mariotti, A. 1996). Thus, growing C4 plants on a C3 soil or vice versa can be considered as in situ label- ling. With this method the rate of loss of the C derived from the original vegetation and the incorporation of C derived from the new veg- etation can be estimated (Balesdent, J. et al. 1987). As a consequence, the natural labelling approach makes it possible 1) to calculate the turnover rate of C derived from the original vegetation (Six, J. and Jastrow, J. 2002) and 2) to separate the different sources of soil CO2 efflux (Kuzyakov, Y. 2006). Fig. 4. The basis of C3–C4 vegetation change for natural abundance 13C labelling. The figure represents the replacement of SOM derived from previous vegetation A by the new vegetation B. Source: redrawn from Balesdent, J. and Mariotti, A. 1996. So il C co nt en t t₀ t CB CA δA₀ δB δA δvegA δvegB Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19.10 In natural labelling experiments, in contrast to artificial labelling, the isotope differences are smaller, but they have the advantage that no artificially enriched compounds are re- quired. For example, as described by Gunina, A. and Kuzyakov, Y. (2014), the δ13C natural abundance approach is able to estimate C flows under steady-state conditions without applying artificial tracers. In addition, the dis- tribution of 13C between the pools is more uni- form than in artificial pulse labelling methods (Kuzyakov, Y. 2005). One of the strengths of the method is the easy application under field conditions, because there is no need for artifi- cial labelling equipment or isolation from the atmosphere (Kuzyakov, Y. 2006). Therefore, this technique is one of the best for the study of field soil dynamics (Paul, E.A. 2016). Nevertheless, the method has some short- comings (Kuzyakov, Y. 2006): (i) C3 plant/C4 soil pairs or vice versa are rare under field conditions; (ii) the maximum δ13C variation of CO2 between C3 and C4 plants is only about 14‰; (iii) there is a 13C discrimination by plants caused by temperature, water avail- ability, air humidity, N supply, light intensity and plant properties (root length, plant sex). Figure 4 illustrates the basis of the C3–C4 vegetation change method representing the two photosynthetic pathways A and B (Balesdent, J. and Mariotti, A. 1996). At the time of the vegetation change (t0) SOM has an isotopic composition δA0 close to that of the original vegetation. As this SOM from vegetation A progres- sively decays, it is partially replaced by SOM derived from the new vegetation B. At a given time t, the total SOM content can be expressed as C = CA + CB and the isotope com- position δAB of SOM under mixed vegetation is the following: δAB (CA + CB) = δAB (C) = δACA + δBCB where CA and CB stand for the amount of SOM from the old (A) and new (B) vegeta- tion, respectively, and δA and δB are the δ13C values of SOM derived from vegetation A and B, respectively. As CA = C – CB, Eq. (7) can be rewritten as follows (Amelung, W. et al. 2008): δAB = δBCB + δA (C–CB) = δBCB + δA (1 – CB) (8) C C C C Hence, the contribution of plant B to the total C content can be calculated as fol- lows (Balesdent, J. and Mariotti, A. 1996; Amelung, W. et al. 2008): F = CB = (δAB – δA) / (δB – δA) C expressed as the fraction of new carbon in the soil (F). Because δA and δB cannot be measured di- rectly in the mixed cropping system, they must be estimated. The natural labelling method assumes that δB is equivalent to the isotopic composition of the new vegetation (δVEG B, see Figure 4), and δA to the initial δ13C of the soil or of the control soil remaining un- der the initial vegetation (δREF A). Hence, the new portions of vegetation B are estimated as follows (Balesdent, J. and Mariotti, A. 1996; Amelung, W. et al. 2008): F = (δAB – δREF A) / (δVEG B – δREF A) Many studies (Balesdent, J. and Balabane, M. 1992; Six, J. et al. 1999; Dignac, M.F. et al. 2005; Paul, E.A. et al. 2008b; Pausch, J. and Kuzyakov, Y. 2012; Schiedung, H. et al. 2017; Poeplau, C. et al. 2018) have applied the 13C natural abundance approach to calculate the proportion of C derived from the new vegetation/fresh organic input. Based on Eq. (10), this technique allows also the percent- age of C derived from different treatments and amendments to be calculated. For ex- ample, Lynch, D.H. et al. (2006) estimated the percentage of C derived from different C4 compost treatments and the retention of compost C in a temperate grassland (C3) soil in Nova Scotia. Measurements took place one and two years after the application of the compost treatments (corn silage, dairy manure and sewage sludge) and showed that , (7) (9) (10) 11Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19. the fraction of SOM derived from compost was around 33% for most of the treatments. The results indicated that the fraction of com- post retained in the soil was the highest for corn silage compost one and two years after the treatment (~ 95% and 90%, respectively). Another possible application of the 13C natural abundance method is connected with land use change studies (e.g. Yamashita, T. et al. 2006; Jakab, G. et al. 2018a; Zhang, Q. et al. 2018). Agricultural land use disturbs the natural SOM system, e.g. by affecting the ag- gregate size and stability of SOM (Bilandžija, D. et al. 2017; Jakab, G. et al. 2018b). With the help of 13C natural abundance these effects can be examined in more detail, as stable carbon isotopes widen the scope of land use research. For instance, the effect of land use changes on the aggregate systems in the soil or how different land use types influence the fraction of C derived from the new veg- etation can be examined with the help of 13C natural abundance approach (John, B. et al. 2005; Yamashita, T. et al. 2006; Paul, S. et al. 2008a,b; Liu, Y. et al. 2018). In addition, 13C natural abundance has been successfully applied to trace sediment and SOM transfer during erosion (Papanicolaou, A.N. et al. 2003; Alewell, C. et al. 2008; Schaub, M. and Alewell, C. 2009; Zollinger, B. et al. 2014). The source of eroded soil sedi- ments or suspended organic matter and the rate of soil erosion and redistribution can be monitored by the δ13C signature of soils. Turnbull, L. et al. (2008) used the δ13C sig- nals of eroded material of soils over a C4 grass to C3 shrub transition. They concluded that variations in δ13C values of SOM in bulk eroded sediment can be used to trace changes in erosion dynamics over events of different magnitudes and over different vegetation types. Jacinthe, P.A. et al. (2009) determined the amount and source of eroded soil organic carbon retained in C3 grass filters receiving runoff from areas supporting C4 vegetation. Novara, A. et al. (2015) measured the δ13C values of different soil profiles sampled along a Sicilian vineyard slope and quantified the rates of erosion. Estimation of the turnover rate of C pools The carbon turnover rate is the rate of C cycling from one pool to another. If the system is in the steady-state condition (i.e. input into the pool is equal to the output), the value of the turnover rate is the ratio of the input amount per time unit to the total pool amount. In this case, the mean residence time (i.e. the mean period of residence of C in the given pool) is the inverse of the turnover rate (Kuzyakov, Y. 2006). Based on the simplest assumption, SOM consists of a homogeneous, single C pool, which decomposes exponentially following first-order kinetics (Stanford, G. and Smith, S.J. 1972). For the amounts of SOM from the old vegetation: CA = (CA + CB) exp ( – kt) where CA and CB stand for the amount of SOM from the old (A) and new (B) vegeta- tion, k is the decay rate constant and t is the time since vegetation change. The mean resi- dence time (MRT) can be calculated as the inverse of the decay rate constant as follows (Amelung, W. et al. 2008): MRT = 1 = –t/ln (1 – F) k SOM pools dominated by turnover times ranging from a year to several hundreds of years have been calculated with the help of the natural labelling approach (Balesdent, J. and Mariotti, A. 1996). Carbon turnover time is not just an impor- tant indicator of SOM dynamics, but is a key parameter in coupled climate-carbon cycle models (e.g. Earth System Models). Hence, there is an urgent need to accurately estimate the turnover times of SOM to predict the fu- ture sizes of the terrestrial C sinks and sources and to obtain a better understanding of cli- mate-carbon feedback (Carvalhais, N. et al. 2014; He, Y. et al. 2016; Wang, J. et al. 2018). Balesdent, J. et al. (1987) were the first to use the natural 13C abundance method on two French sites which originally had C3 type veg- , (11) (12) Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19.12 etation. They cultivated maize (C4 type veg- etation) to achieve a C3/C4 vegetation change. An organic carbon turnover rate of 22% was calculated from the δ13C values of soils sam- pled at one experimental site after 13 years of maize cultivation, with different annual rates. This suggested that the decay of SOM can- not be described using a single carbon pool model. A turnover time of 36 years was cal- culated for this site assuming an exponential decay. At another experimental site, where continuous maize cultivation for 23 years was applied after pine forest clearing, two treatments were used: in the first, leaves and stalks were incorporated back into the soil, while in the second, leaves and stalks were removed for the last 17 years. The percentage of organic carbon derived from maize was calculated for different particle size fractions in the topsoil (0−30 cm) and subsoil (30−40 cm) horizons. The turnover of the coarse sand fraction (200−2,000 mm) was found to be the most rapid, while the fine clay fraction (<0.2 mm) contained most of the SOM. Since then, a number of studies have used the 13C natural abundance method for SOM turnover rate calculations for different pur- poses, but the work of Balesdent, J. et al. (1987) forecast the major questions of SOM research which have since been studied with this method. These are the 1) estimation of the turnover time of different physically and/or chemically separated SOM fractions repre- senting distinct SOM fractions connected to different soil textures or minerals (Martin, A. et al. 1990; Bonde, T.A. et al. 1992; Balesdent, J. et al. 1998; Shang, C. and Tiessen, H. 2000; Liao, J.D. et al. 2006; Dalal, R.C. et al. 2013); 2) estimation of the turnover time of dif- ferent SOM pools (Bernoux, M. et al. 1998; Derrien, D. and Amelung, W. 2011); 3) esti- mation of the turnover time of SOM derived from different treatments or land uses (Six, J. and Jastrow, J. 2002; Zach, A. et al. 2006; Novara, A. et al. 2013); 4) comparison of the turnover time of SOM at different soil depths (Bernoux, M. et al. 1998; Flessa, H. et al. 2017). A combination of these topics is embed- ded in many other studies. For example, Collins, H.P. et al. (1999) investigated the soil C dynamics in the Corn Belt region of the central USA. They calculated the per cent of C derived from corn after conversion to a monoculture of C4 corn and the MRTs of the C3 soils. The proportion of corn-derived C decreased with soil depth and was minimal in the 50–100 cm depth increments of fine- textured soils. The mean residence time of non-corn C (C3) ranged from 36 to 108 years at the surface and up to 769 years at the sub- soil depth. It was shown that clay minerals effectively protected the organic matter in the case of older C3-derived C (longer MRTs), while no such protection was observed for the younger C4-derived C (shorter MRTs). John, B. et al. (2005) estimated the turnover times of different density fractions of SOM (free particulate organic matter with a den- sity <1.6 g cm−3, light occluded particulate organic matter with a density of <1.6 g cm−3, dense occluded particulate organic matter with a density of 1.6−2.0 g cm−3 and mineral- associated SOM with a density >2 g cm−3) and of SOM from different depths. They calculated turnover times of 54, 144 and 223 years for the 0−30 cm, 30−45 cm and 45−60 cm horizons, respectively. The mean turnover times for the density fractions were found to be the following: 22 years for the free par- ticulate organic matter, 49 years for the dense occluded particulate organic matter, 63 years for mineral-associated SOM and 83 years for light occluded particulate organic matter. Lisboa, C.C. et al. (2009) calculated the turnover time of different SOM fractions (>250 mm, 53−250 mm, 2−53 mm, <2 mm) applying a two-pool (active and slow decom- position rate) exponential model for a forest- to-pasture chrono-sequence in the Brazilian Amazon. Except for the >250 mm fraction no difference was detected between the frac- tions in the active pool phase, whereas in the slow pool phase the fractions were separated according to their turnover rates: the clay- associated SOM (fraction <2 mm) had the greatest turnover rate (>2,500 years), the mi- croaggregate and silt-associated SOM had medium turnover rates (498 and 210 years, 13Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19. respectively) and the particulate organic matter (>250 mm fraction) had the smallest turnover rate (~ 1 year). Panettieri, M. et al. (2017) studied the differ- ent turnover times of fractionated water-stable aggregates (larger macro-aggregates with 2.0−7.1 mm, macro-aggregates with 0.200−2.00 mm, microaggregates with 0.050−0.200 mm and silt + clay fraction with <0.050 mm) of perma- nent cropland and temporary grassland plots after nine and three years of maize cultivation, respectively. The calculated turnover times for the two land uses were similar for the micro- and macro-aggregates but different for the silt + clay fraction. Namely, the MRT of the silt + clay fraction of grassland soil was twice as of the cropland soil confirming that this smallest fraction is affected to the greatest extent by land use practices, and particularly tillage. It could be explained by the increased degradation of SOM due to higher aeration caused by tillage. Soil CO2 efflux source determination Besides the estimation of SOM turnover time, the 13C natural abundance method is well ap- plicable to partition the CO2 fluxes from the soil (Cheng, W. 1996). The evaluation of the contribution made by different C sources to soil CO2 efflux is also a key parameter in de- termining whether the soil is a net source or sink of atmospheric CO2 (Kuzyakov, Y. and Larionova, A.A. 2005). According to Kuzyakov, Y. (2006) there are five main sources of soil CO2 efflux (Figure 5): 1) microbial decomposition of SOM (termed basal respiration), 2) microbial decomposi- tion of SOM affected by recent input of rhizo- deposits and/or fresh undecomposed plant residues (termed priming effect), 3) microbial decomposition of partly decomposed dead plant remains, 4) microbial decomposition of rhizo-deposits of living roots (termed rhizomicrobial respiration) and 5) root res- piration (respiration of assimilates by roots of autotrophic plants). These CO2 effluxes represent different C pools with different turnover rates and MRTs (see Figure 5). The pedogenic or anthropogenic acidi- fication of soils containing CaCO3 is also a source of CO2 efflux in the soil, but its con- tribution is only significant on the geological time scale and not on the sub-annual to dec- adal time scales used in soil research studies (Kuzyakov, Y. 2006). With the help of the 13C natural labelling approach, it is possible to separate the sourc- es of soil respiration. Growing C4 plants on a C3 soil or vice versa and tracing the δ13C value of CO2 efflux from the soil allows the separation of SOM-derived from plant-de- rived CO2 (see Figure 5). If additional data on the δ13C values of microbial biomass and roots is available, root and rhizomicrobial respiration can also be partitionated, as can the separation of SOM-derived basal respi- ration from the microbial decomposition of plant residues. Natural C3/C4 vegetation differences are also used to partition the autotrophic (root respiration) and heterotrophic (other 5 res- piration sources in Figure 5) soil respira- tion in many studies (e.g. Rochette, P. and Flanagan, L.B. 1997; Giardina, C.P. et al. 2004; Millard, P. et al. 2008). Millard, P. et al. (2010) were the first to quantify the proportion of SOM-derived CO2 in a forest soil using 13C natural abundance discrimination, with carbon input derived solely from C3 photosynthesis. For this, meas- ured δ13C values of root respiration (−27.60 ± 0.51‰) and SOM-derived respiration (−25.10 ± 0.88‰) were used as the end points of a two-component mixing model using the small isotopic difference between them. The calculated mean percentage of SOM-derived CO2 was 0.61 ± 0.28. By adding C4 plant residues to a C3 soil or vice versa and measuring their contribu- tion to the total CO2 efflux it is also possible to separate the CO2 originating from plant residues and that derived from the microbial decomposition of SOM. In addition, by com- paring soil with added residues to control soil with no residue addition, the priming effect can be calculated using the 13C natural labelling approach (Kuzyakov, Y. 2006). For Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19.14 example, Kuzyakov, Y. and Cheng, W. (2001) applied the 13C natural abundance method to partition the soil-derived and root-derived (root respiration plus rhizomicrobial respi- ration) CO2 from C4 prairie soil planted with C3 wheat in a 7-day laboratory experiment. Photosynthesis was greatly reduced and, on average, 75% of total CO2 efflux from the soil proved to be root-derived and 25% soil-derived. When the priming effect was compared for planted and non-planted soils a positive priming effect (42 mg C kg−1 h−1 and 33 kg C ha−1 d−1) was recorded during the first 3 days, whereas without light, the priming effect decreased and was negative due to the reduction of exudation. Werth, M. and Kuzyakov, Y. (2009) used the natural 13C labelling approach to parti- tion root respiration, rhizomicrobial respira- tion and basal respiration under field condi- tions in a loamy Haplic Luvisol in Stuttgart, Germany. They used the δ13C values of SOM, roots, microbial biomass and total CO2 efflux from the soil and applied isotopic mass bal- ance equations to calculate the contributions of the three sources of CO2 efflux. The δ13C values from a bare-fallow plot were used to calculate the 13C fractionation between SOM and CO2 and between microbial biomass and CO2 and the contribution of different CO2 sources was estimated, taking into account the 13C fractionation. The calculations revealed significant changes between the results with and with- out 13C fractionation. It was therefore sug- gested that the isotope fractionation process- es of 13C should be embedded in studies deal- ing with CO2 efflux partitioning. Werth, M. and Kuzyakov, Y. (2010) reviewed the pos- sible uncertainties connected with 13C frac- tionation in the 13C natural abundance meth- od, with special attention to the partitioning of CO2 efflux. It was concluded that even a small variation (±1.0‰) in the δ13C value of the ’endmembers’ of the mixing equations led to strong uncertainties. In addition, if significant isotope fractionation takes place, the uncertainties increase significantly. As possible solutions, they recommended vari- ous approaches to reduce uncertainties: 1) to increase the difference in δ13C value between the two ’endmembers’ (if necessary, using ar- tificial labelling); 2) to estimate the fractiona- tion of individual processes in the specific study, not using mean values estimated in other studies; 3) to analyse the δ13C values of individual substance groups or substances (i.e. compound-specific isotope analysis). Conclusions The 13C natural abundance approach occu- pies an important place among the isotope applications used in soil research, especially for the calculation of SOM turnover and the partitioning of CO2 efflux sources. The 13C natural abundance approach com- bined with other methods is a useful tool to measure the effect of different human- induced changes on organic carbon stor- age, such as land use change, erosion and soil management. Along with the traditional methods of watershed monitoring, slope Fig. 5. Main sources of soil CO2 efflux and C pools in order of turnover rates and residence times. Source: redrawn from Kuzyakov, Y. and Gavrichkova, O. 2010. 15Zacháry, D. Hungarian Geographical Bulletin 68 (2019) (1) 3–19. measurements and rainfall simulation ex- periments or tracer applications (rare earth elements or radionuclides), stable carbon isotope measurements provide additional spatial information on soil erosion dynam- ics. In addition, the 13C natural abundance approach in combination with photogram- metry or remote sensing could be useful to precisely monitor areas affected by different land use changes. With the combination of 13C natural abun- dance method and 14C labelling, the contri- bution of carbon sources to the carbon pools can be distinguished in more detail and the priming effect connected to the processes can be calculated. The measurement of the natural 14C abundance of SOM extends the timescales for C cycling to millennia supple- menting the turnover times ranging from a year to several hundreds of years calculated by the 13C natural labelling approach. The physical fractionation of soils combined with isotope labelling provides another possibility to estimate the turnover times of physically defined SOM pools. Data obtained using the 13C natural abun- dance technique provide important informa- tion on SOM dynamics, which has been in the focus of interest in recent years due to the significant role of soil in the global car- bon cycle. The determination of the turnover time and size of the active and passive soil reservoirs is essential for the evaluation of whether they serve as potential sources or sinks for atmospheric CO2. Therefore, tech- niques such as the 13C natural abundance ap- proach, not only lead to a better understand- ing of processes in the global carbon cycle but also provide fundamental information for climate change mitigation. Acknowledgements: The research was supported by the European Union and the State of Hungary, co-financed by the European Regional Development Fund in the project of GINOP-2.3.2-15-2016-00009 ‘ICER’ and by the ÚNKP-17-3 New National Excellence Program of the Ministry of Human Capacities and by the Development and Innovation Fund of Hungary (Nr. NKFIH 123953). The author is grateful to Barbara Harasztos for her help in the language editing. REFERENCES Ågren, G.I., Bosatta, E. and Balesdent, J. 1996. Isotope discrimination during decomposition of organic matter: A theoretical analysis. Soil Science Society of America Journal 60. (4): 1121–1126. Alewell, C., Birkholz, A., Meusburger, K., Schindler Wildhaber, Y. and Mabit, L. 2016. 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