Recalcitrant carbon controls the magnitude of soil organic matter mineralization in temperate forests of northern China

Study site and soil sampling

This study has been carried out in the Taiyueshan Long-Term Forest Ecology Research Station, which is located at the National Forest Park of Lingkongshan (36°33′–36°42′ N, 111°59′–112°07′ E; elevation ranging from 1100 to 1950 m a.s.l.). This forest park is located about 200 km southwest of Taiyuan in the Shanxi Province of northern China. The geographic and meteorological conditions and species composition of the forest communities for this study area had already been introduced in detail by (Zhou et al. 2013). The tree species extensively distributed across this forest park are Pinus tabulaeformis Carrière, Quercus wutaishanica Mayr, Betula dahurica Pall, Betula platyphylla Suk, Juglans mandshurica Maxim, Tilia mongolica Maxim, Populus davidiana Dode, Malus baccata Borkh. The major shrub species are Corylus mandshurica Maxim, Corylus heterophylla Fisch, Acer ginnala Maxim, Lespedeza bicolor Turcz, Philadelphus incanus Koehne, Rosa bella Rehd. The herbaceous community mainly consists of Carex lanceolata Boott, Spodiopogon sibiricus Trin, Rubia chinensis Regel et Maack, Thalictrum petaloideum Linn, Melica pappiana Hempel.

Five forest plots of 20 m × 20 m were established in 2013, for each of three forest types: a Pinus tabulaeformis forest, a Quercus wutaishanica forest, and a mixed forest of P. tabulaeformis and Q. wutaishanica. The plots were laid out along the topography of the Xiaoshegou catchment area, at a distance of 3 km west of the research station. Five soil samples at 20 cm depth were taken using a 4-cm diameter soil auger to assess the soil condition of each forest plot in late August; these samples were then mixed and combined into a single sample after passing through a 2 cm sieve screen to separate rocks and debris. Each composite soil sample was separated into two sub-samples. One sub-sampled was transported in an ice-cooled container to the Key Laboratory for Forest Resources & Ecosystem Processes of the Beijing Forestry University. Soil physicochemical properties were analyzed with air-dried soil of 20 g being ground and passed through a 0.18-mm sieve screen. SOC content was analyzed via the standard Mebius method (Nelson and Sommers 1982). TN content was analyzed following the Kjeldahl digestion procedure and ammonium nitrogen (NH₄ - N) was colorimetrically measured by the alkali method with a Tector KJeltec 1025 Distilling system (Gallaher et al. 1976). The soil pH was measured in deionized H₂O with a water to soil ratio of 2.5:1 using the Sartorius AG method (PB-10, Sartorius, Germany). The water holding capacity (WHC) was determined by saturating 20 g fresh soil above a filter paper in a 10-cm glass funnel, and then permitting the water to drain for 4–6 h before being weighed (n = 5). The saturated soils were then oven-dried at 105 °C to a constant mass, until the WHC was equal to the percentage of water retained after several hours’ drainage (for 100% WHC). Soil microbial biomass carbon and nitrogen contents were measured using the chloroform-fumigation method with a mean calibration factor k_C of 0.38 (Vance et al. 1987a, 1987b). Concurrently with soil sampling, soil bulk density was measured by dividing the mass of oven-dried soil (at 105 °C) by the cylinder volume (100 cm³) after subtracting the detritus volume for each forest plot.

Incubation and measurement of SOC mineralization rate

The second sub-sampled soil was immediately (< 2 d) delivered in a cooler with blue ice to the Key Laboratory of Ecosystem Network Observation and Modeling, Chinese Academy of Sciences. The sub-sampled soil of each forest plot was divided into eighteen 40 g equivalent aliquots; the soil moisture content (SMC) of every six aliquots was individually adjusted to 30, 60 and 90% WHC. The moisture-adjusted soil samples were placed in 200 cm³ plastic flasks with lids perforated to allow gas diffusion, and connected directly to the CO₂ measurement system. After getting static and equilibrated for 5 days, six soil samples separately at 30, 60 and 90% of WHC for each forest plot were respectively incubated for 387 days in a microcosm with temperatures of 5 °C, 10 °C, 15 °C, 20 °C, 25 °C and 30 °C and a stable air moisture content (n = 5 per incubator). To prevent anaerobiosis, the perforated hole of the flask lid was not sealed until 24 h before the measurement of soil organic carbon mineralization rate. Throughout the whole incubation period, the flask was periodically weighed and the required proportion of deionized water was added to maintain soil moisture content at 30%, or 60%, or 90% of WHC.

Calculation of soil carbon fractions

In order to get the reasonable parameters from functions (3) and (4), several criteria were set up: that (i) k₀ and k₁ were larger than 0, and that (ii) k₁ was larger than k₂, and that (iii) C₀, C₁ and C₂ were larger than zero. Additionally, one assumption was followed that (C₁ + C₂) was equal to 1000 mg C∙g^− 1 SOC (i.e. the two carbon fractions add up to the total amount of initial organic carbon in the sample). Based on the functions (2), (3) and (4) and the estimations of different carbon pools, the quantity of mineralized recalcitrant carbon was assumed to be the difference between the total amount of mineralized carbon and that of C₀ (potential mineralizable C) or C₁ (labile carbon fraction). In the present study, C₀ was used to estimate the amount of mineralized recalcitrant carbon, as most C₁ could not meet the criterion (iii) at a moisture content of 30% for all forest types (Additional file 1).

Statistical analyses

The data presented in the tables and figures represent the mean value under different incubation conditions for each forest type. The differences of soil carbon pools for various incubation conditions and forest types were tested using one way ANOVA or multi-factor analysis of variance. Equations (3) and (4) were fitted using non-linear regression analysis (‘nls’ of R language). The starting value for each parameter was obtained from the published literature. Canonical correspondence analysis (CCA) was applied using the function ‘cca’ in the vegan package of R. All of the statistical analyses were done using the software R3.4.0. The figures in this paper were compiled using SigmaPlot 10.0 and R software.

Cumulative mineralized carbon

The cumulative carbon mineralized (C _cum , C respired per g initial SOC) over the incubation period (387 days) significantly varied with forest type, incubation temperature and soil moisture content, but no significant interaction effect was found on C _cum among these three variables (Table 1). Under the same incubation temperature, C _cum showed an increasing trend with SMC from 30 to 60 to 90% for each forest type; the forest of Q. wutaishanica had a higher C _cum value than those of the P. tabulaeformis forest and the mixed forest, under similar incubation conditions except at 5 °C; a parabolic correlation existed between C _cum and incubation temperature at the same SMC gradient for each forest type. The highest value of C _cum was 96.74, 109.20, and 100.08 mg C∙g^− 1 SOC respectively for the P. tabulaeformis forest, the Q. wutaishanica forest, and the mixed forest under an incubation condition of 15 °C and 90% SMC (Fig. 1; Additional file 2).

Variable	Df	Sum Sq	Mean Sq	F	P
F	2	5989	2994	7.066	0.0011
M	2	87,270	43,635	102.98	<2e-16
T	5	15,736	3147	7.427	1.89e-06
F × M	4	3387	847	1.998	0.959
F × T	10	3683	368	0.869	0.563
M × T	10	5403	540	1.275	0.246
F × M × T	20	3041	152	0.359	0.995

F, M and T represent forest type, moisture content, and temperature, respectively

Potential mineralizable carbon

We only presented the data of C₁, k₁ and k₂ at 60%, 90% SMC as supportive materials, since the first-order kinetic two-compartment model did not fit well the data of C _cum at 30% SMC for all forest types. C₁ was on average lower than C₀ by one unit.

Contribution of recalcitrant carbon to the cumulative mineralized carbon

Soil biophysical properties and their effects on carbon mineralization

The results of the canonical correspondence analysis indicated that the recalcitrant carbon mineralization was affected jointly by forest type, SOC, SBD, SMBN and soil pH, and its 68.33% variance was mainly ascribed to these five factors (P = 0.003). SOC and SBD had a converse effect and clustered along the first axis explaining 50.42% of the total variance, and SMBN and pH along the second axis comprising 20.02% of the total variance. SBD played an important role in determining the recalcitrant carbon mineralization in the P. tabulaeformis forest, SOC and pH were important factors in the Q. wutaishanica forest, and SMBN and pH had a great function in the mixed forest (Fig. 2).

The magnitude of SOC mineralization and its fraction

Although invariant correlations were not detected between the amount of mineralized SOC and some incubation variables, there existed a constant and gradually increasing trend of cumulative mineralized SOC with incubation time in the present study. The longer the incubation time period, the larger the amount of mineralized SOC. At the end of this incubation experiment, the broadleaved forest at 15 °C and 90% SMC was found to have the highest value of mineralized SOC at 109.20 mg C∙g^− 1 SOC, which was about four times higher than the lowest value of the mixed forest soil at 30 °C and 30% SMC. The total amount of the mineralized SOC in the present study averaged 58.37 mg C∙g^− 1 SOC across all incubation conditions, which was in agreement with other results of Rey et al. (2005, 2008) and of the permafrost peatland in northeast China (Wang et al. 2010). This mean value of total mineralized SOC also fell well within the range of mineralized carbon compiled by Hamdi et al. (2013). The results of the present study agree with other research results where SOC had been mineralized under similar incubation conditions.

The cumulative mineralized carbon had a curvilinear relationship with the incubation time, and was fitted to the first order kinetic one-compartment or two-compartment model to estimate the soil carbon fractions for each forest type. Only the estimates from the one-compartment model were presented in this study, as the two-compartment model did not fit well the data of cumulative carbon mineralized at 30% SMC. The potential mineralizable carbon (C₀) averaged 4.00 mg C∙g^− 1 SOC, contributing on average 8.36% to the total mineralized carbon across all incubation conditions, and taking over 0.4% of soil carbon. Its decomposition constants (k₀) varied around 1.23 × 10^− 2 day^− 1 for all incubation conditions. As recalcitrant carbon was assumed to be SOC or cumulative mineralized carbon minus potential mineralizable carbon, it contributed more than 90% to the SOC pool and almost 99% to the total mineralized carbon. The lowest proportion of labile SOC was also found by the studies of Yang et al. (2006), Rey and Jarvis (2006), but k₀ was slightly lower in the report of Rey and Jarvis (2006). This variation in k₀ value may be ascribed to the different moisture content during the incubation process.

The effects of temperature and moisture on SOC mineralization rate

Temperature and moisture content are two important variables manipulating the magnitude of SOC mineralization. When other factors, e.g. soil moisture and pH, are in optimal status, carbon mineralization often exponentially increases with temperature, and their correlation is frequently described by the Arrhenius function or the Michaelis-Menten model (Davidson et al. 2012). In this study, the cumulative mineralized carbon was significantly influenced by the incubation temperature, however, it reached the highest value at 15 °C under each moisture gradient for all forest types. In most cases, no significant difference was found in the total amount of mineralized carbon between 30 °C and 5 °C, except at 30 and 90% SMC for the P. tabulaeformis forest and at 30% SMC for the mixed forest.

The above discrepancy between the changing trend of cumulative mineralized carbon and incubation temperature was mainly attributed to the complexity of soil carbon fraction. In the present study, the labile carbon (C₀ or C₁) constituted a small amount of the SOC pool, and the larger proportion of mineralized carbon mainly derived from the recalcitrant carbon pool. The carbon pool mineralized during the first 60 days of the incubation period equaled the estimated labile carbon pool, and the instantaneous mineralization rate positively increased with temperature during this first incubation stage (≥ 70 days), and then the mineralization rate began decreasing and was lowest at 30 °C as the incubation time was longer than 70 days. The lower amount of cumulative mineralized carbon at 30 °C may be accounted for by the faster depletion of labile carbon during the prior incubation stage and the difficult availability of recalcitrant carbon at the late incubation stage. The reason for carbon mineralization keeping a higher value at 15 °C was mainly because the mean annual temperature fluctuates around 15 °C in this study area, and because microbial organisms will maintain a higher activity for a longer period at this temperature gradient.

The soil moisture content significantly increased carbon mineralization in the present incubation experiment. Unlike the effect of temperature, the highest moisture content, i.e. 90% SMC, led to the largest amount of cumulative mineralized carbon for all forest types. Soil moisture not only ameliorated the dormant status of microbes, but also increased the dissolution of organic or mineral resources, which in turn supplied a large quantity of substrate to microbial decomposition (Rey et al. 2005; Demoling et al. 2007). Although 60% SMC was suggested to be optimal for microbial activity (Howard and Howard 1993), the higher proportion of sandy particulate modified the aeration of incubating soil samples (Rasmussen et al. 2006), but still promoted the SOC mineralization at 90% SMC.

The effects of forest type and soil properties on SOC mineralization

Forest type is a great biotic manipulator in regulating the turnover of soil organic matter via the quantity and quality of litter input or through the modification of soil biophysical properties. Cellulose and lignin constitute the largest composition of forest ecosystem production, and are the primary determinants of SOM quality and microbial decomposition (Rasmussen et al. 2006; Rey et al. 2008). Generally, the broadleaf forest has a litter production of high quality and quantity, which enhances soil physicochemical properties. In our study, the Q. wutaishanica forest had a higher SOC ant TN content and lower soil bulk density than the P. tabulaeformis forest. Using the data of the measured soil characteristics and the cumulative mineralized carbon, the canonical correspondence analysis indicated that carbon mineralization was significantly influenced by SOC, soil pH, soil microbial biomass nitrogen, and soil bulk density. However, soil bulk density had a negative effect on carbon mineralization along the first CCA axis.

The proportion of labile and recalcitrant carbon specifies the quality of SOC, which determines the magnitude of SOC decomposition. The low quality SOC with more recalcitrant carbon needs a longer time to turnover (Leifeld and Führer 2005). Soil bulk density has a negative correlation with soil organic carbon content, and reflects the mineral soil percentage in the soil regime, the higher its value the slower the carbon mineralization. The change of soil microbial biomass nitrogen may represent the transition of microbial community composition and the substrate use strategy. After the depletion of labile carbon during the first weeks, microbial activity turned to be C-constrained (Demoling et al. 2007). The K-strategist bacteria decomposing recalcitrant carbon (Fontaine et al. 2003) or the fungi using N-rich SOM would become predominant in the microbial community during the late stage of the incubation period (Boberg et al. 2010). Soil pH not only modulates the soil micro-environment and microbial community composition (Strickland and Rousk 2010), but also governs the breakdown processes of SOC decomposition (Riffaldi et al. 1996). The soil mineralization process including the adsorption of SOC by colloids and the substitution between different multivalent cations within organo-mineral complexes (Yuan et al. 2000; Rasmussen et al. 2006) is greatly influenced by soil pH, which ultimately controlled the availability of recalcitrant carbon in the late incubation stage of our experiment.