Storage of soil phytoliths and phytolith-occluded carbon along a precipitation gradient in grasslands of northern China

Geoderma 364 (2020) 114200

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Storage of soil phytoliths and phytolith-occluded carbon along a precipitation gradient in grasslands of northern China

T

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Xiaodong Zhanga, Zhaoliang Songa, , Qian Haoa, Changxun Yub, Hongyan Liuc, Chunmei Chena, Karin Müllerd, Hailong Wange,f a

Institute of the Surface-Earth System Science, Tianjin University, Tianjin, China Department of Biology and Environmental Science, Linnaeus University, SE-39182 Kalmar, Sweden c College of Urban and Environmental Sciences, Peking University, Peking 100871, China d The NZ Institute for Plant & Food Research Limited, Ruakura Research Centre, Private Bag 3123, Hamilton, New Zealand e School of Environmental and Chemical Engineering, Foshan University, Foshan, Guangdong 528000, China f School of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Soil phytolith Mean annual precipitation Stability Mongolian Plateau

Climatic factors including mean annual precipitation (MAP) significantly influence the carbon (C) cycle in terrestrial ecosystems and Earth overall. Phytolith-occluded carbon (PhytOC) is an important C sequestration mechanism and as such plays a vital role in global long-term C sequestration. Understanding the spatial variability in the storage of soil phytoliths and PhytOC and its relationship with climate is critical for evaluating the impact of global climate change on terrestrial ecosystem functions. However, little is known about the responses of soil phytoliths and PhytOC to MAP in grassland ecosystems. This study sampled soil from 24 natural, semi-arid steppe sites along a 2,500 km transect with a precipitation gradient of 243–481 mm yr−1 in northern China. We investigated the influence of precipitation on the spatial distributions of soil phytoliths and PhytOC storage. Storage of soil phytoliths in bulk soil (0–100 cm depth) ranged from 21.3 ± 0.4 to 88.4 ± 20.3 t ha−1 along the precipitation gradient. Amounts of soil phytoliths and PhytOC storage were significantly and positively correlated with MAP. Multiple regression analysis revealed that phytolith storage in bulk soil was best predicted by MAP (R = 0.5) and soil organic carbon (SOC, R = 0.4), with these two variables accounting for about 58% of the total variation observed. Considering the forecasted increase in MAP in the Inner Mongolian steppe due to climate change, and the strong influence of MAP on the annual net primary productivity (ANPP) and related soil PhytOC input from litter decomposition in this region, we expect that ecosystem primary productivity will increase from deserts to meadow steppe and thereby promote soil PhytOC storage. These findings have important implications for understanding the dynamics of soil phytoliths, and predicting the impacts of global climate change on ecosystem functions and management practices in the East Asian steppe ecosystems.

1. Introduction Terrestrial ecosystems are important carbon (C) sinks and absorb about 30% of the total anthropogenic carbon dioxide (CO2) emitted to the atmosphere (Tang et al., 2018). Soil is the largest C sink in terrestrial ecosystems and holds more than twice as much C as aboveground vegetation. This means that slight changes in soil C storage may cause large fluctuations in the atmospheric CO2 level and the global C cycle (Johnson et al., 2007; Yang et al., 2010; Chen et al., 2015). Therefore, understanding the spatial and temporal dynamics of soil organic C is particularly important. Contents of soil phytoliths in different terrestrial

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ecosystems usually range from 2% to 5%, and some of organic C in the form of amino acids is occluded and protected by silica structure (Alexandre et al., 1997, 2016; Blecker et al., 2006). Recently, many studies indicated that phytolith-occluded C (PhytOC) is an important terrestrial C sink mechanism and that it plays a key role in the global C cycle (Parr and Sullivan, 2005; Street-Perrott and Barker, 2008; Song et al, 2016). Although a fraction of soil phytoliths can be dissolved over time, many studies have demonstrated that in general, most of phytoliths are extremely stable and can be conserved in soil profiles for thousands of years, thereby potentially contributing to enhancing atmospheric CO2 sequestration in terrestrial ecosystems (Song et al.,

Corresponding author at: Institute of the Surface-Earth System Science Research, Tianjin University, No. 92 Weijin Road Nankai District, Tianjin 300072, China. E-mail address: [email protected] (Z. Song).

https://doi.org/10.1016/j.geoderma.2020.114200 Received 27 October 2019; Received in revised form 16 January 2020; Accepted 17 January 2020 0016-7061/ © 2020 Elsevier B.V. All rights reserved.

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Africa (Blecker et al., 2006; Alexandre et al., 2011; White et al., 2012), a comprehensive understanding of the spatial and temporal distributions of soil phytoliths in relation to MAP in arid and semi-arid grassland ecosystems at the global scale is lacking. Therefore, in this study, 24 natural arid and semi-arid grassland ecosystems located in the Inner Mongolian Plateau were selected based on the variations in MAP. This region represents the Eurasian steppe and extends over 8,000 km from Mongolia to Hungary. These grasslands are quite different from African savannas and North American prairies in terms of soils, due to having different dominant plants and climates (Qian et al., 2007; Bai et al., 2008). The specific objectives of this study were to evaluate the spatial and temporal distribution patterns of soil phytoliths along a precipitation gradient of 243–481 mm yr−1 and investigate their implications on soil PhytOC storage. Such research has significant implications for management practices in arid and semi-arid grassland ecosystems and for accurately predicting long-term (stable) C sequestration and its response to global climate change (Song et al., 2012b; Pan et al., 2017a).

2012a; Zhang et al., 2016). Silicon (Si) is taken up by terrestrial plants from the soil solution in the form of monosilicic acid (H4SiO4), and is then deposited in transpiration sites (e.g., cell walls, cell lumina, and intercellular spaces of the cortex) as hydrated amorphous silica (SiO2·nH2O), where it forms phytogenic Si (also called opal-A; phytolith) (Jones and Handreck, 1965; Piperno, 1988; Puppe and Leue, 2018; Kaczorek et al., 2019). Phytolith content in plants (expressed as % dry matter) ranges from ≤0.5% to ≥15% (dry matter) depending on plant species (Song et al., 2016). Phytolith sizes vary significantly among plant species, approximately from 1 μm to 250 μm (Lu et al., 2002; Strömberg, 2004; Strömberg, 2005). Moreover, since phytoliths usually take the shape of their host plant’s cell structures, their characteristic shapes are often determined by the plant species (Piperno, 1988; Piperno, 2006). During the formation of phytoliths, organic C can be occluded within the phytoliths to form PhytOC. When the external organic materials on the surfaces of phytoliths are completely removed and the phytolith-occluded carbon has not been oxidized during the analysis, measurements of PhytOC contents are considered to be precise (Parr and Sullivan, 2014). Many studies have demonstrated that the carbon contents of phytoliths varies from ≤0.1% to ≥10%, but mostly ranges from 0.2% to 5.8% dry matter (Parr and Sullivan, 2005, 2010; Corbineau et al., 2013; Alexandre et al., 2016; Santos et al., 2018; Hodson, 2019). After plants die and decay, the phytoliths that have been formed in different plant tissues, are released into soils or sediments through microbial decomposition of organic matter. Parr and Sullivan (2005) reported a PhytOC accumulation rate of 7.2–8.8 kg ha−1 yr−1 in soils in the subtropic and tropics, which could compose 37% of the global longterm C sequestration rate. Furthermore, Blecker et al. (2006) estimated the turnover time of soil phytoliths based on the storage of soil phytolith and related phytoliths’ return fluxes and found that the soil phytoliths’ turnover time in temperate grasslands ranges from 1,300 ± 800 years in short grass systems to 250 ± 70 years in tall grass systems along with annual precipitation increases (344–1,110 mm), which is significantly longer than the turnover time of soil organic matter in the same systems, estimated to average four years (Song et al. 2016). In summary, PhytOC is stable because it is protected by phytoliths against microbial decomposition and is estimated to persist in soils or sediments for millennia (Jones and Handreck, 1965; Parr and Sullivan, 2005; Zuo et al., 2014). Meanwhile, the storage and distribution patterns of soil phytoliths and PhytOC are affected by a wide array of abiotic and biotic factors, including soil physiochemical properties, nutrient availability, precipitation, and intertwining biotic interactions (Bartoli, 1985; Fraysse et al., 2006; Song et al., 2016). Furthermore, the dissolution kinetics of soil phytoliths in various terrestrial ecosystems show significant differences, and these are mainly caused by the variations in physicochemical surface properties and elemental compositions of soil phytoliths (Bartoli and Wilding 1980; Puppe and Leue, 2018). Grassland, as one of the most widely distributed ecosystems, plays a critical role in the global C cycle (Fang et al., 2010). China’s grasslands, which comprise an important component of global grassland ecosystems and are mainly distributed in arid and semi-arid regions. They cover an area of 4.0 × 108 ha, accounting for 41.7% of China’s land area (Kang et al., 2007). Precipitation plays a key role in controlling plant diversity and ecosystem functioning in terrestrial ecosystems, especially in arid and semi-arid regions, which cover about ~45% of the global land area (Bai et al., 2008). Many previous studies have indicated that ANPP, soil C dynamics and trace gas fluxes in grassland ecosystems are strongly influenced by mean annual precipitation (MAP) at the regional and global scales (Baumann et al., 2009; Niu et al., 2009; Hsu et al., 2012). In addition, some researchers have found that different grassland ecosystems (i.e., grasslands with different degrees of degradation) have significant effects on soil phytolith storage (Pan et al., 2017a, 2017b). Although a few studies have investigated the responses of soil phytoliths to MAP in grasslands in North America and

2. Materials and methods 2.1. Study sites and soil sampling Our study area (41.1°–49.5° N, 114.3°–120.9° E) is located in the Inner Mongolian region of northern China (Fig. 1). The natural grasslands in the Inner Mongolia belong to the Eurasia Steppe region and cover 78.8 million ha, which accounts for 66% of the region’s total land area (Chinese Academy of Sciences: Integrative Expedition Team to Inner Mongolia and Ningxia 1985). Topographically, the study area consists of many gently rolling hills with altitudes ranging between 290 m and 1,473 m. The climate can be characterized as an arid to semi-arid continental climate. Based on long-term meteorological data (1981–2010), MAP ranges from 243 mm to 481 mm, and about 80% of which falls between May and August. The mean annual temperature (MAT) ranges from − 2.3 °C to 7.6 °C with the lowest and highest mean monthly temperatures occurring in January and July, respectively. The main vegetation types distributed along this transect of 2,500 km length from the west to the east are desert steppe, typical steppe, and meadow steppe. At the eastern end of the transect, dominating species in the meadow steppe are Stipa baicalensis, Filifolium sibiricum and Carex pediformis. Both, plant species richness (25 species per m2) and ANPP (> 200 g m−2 yr−1) are the highest in the meadow steppe along the transect. Typical steppe is dominated by Stipa grandis, Stipa krylovii and Artemisia sacrorum, and has moderate ANPP and plant species richness. Desert steppe is dominated by Cleistogenes squarrosa, Agropyron mongolicum and Carex duriuscula, and has the lowest ANPP (< 60 g m−2 yr−1) and plant species richness (3 species per m2) along the transect (Table 1). The growing season in the Inner Mongolian grasslands extends from April to September. The soil types are mainly Kastanozems, according to the Food and Agriculture Organization’s soil classification system (Cheng et al., 2009). 2.2. Sample collection and analysis Soil sampling was performed during July and August 2016. To quantify the distribution of soil phytoliths and PhytOC storage along the transect, 24 sampling sites were systemically placed along it. The sites were located in mature native grasslands on the Inner Mongolia Plateau that had minimal disturbance and different MAP levels (Fig. 1 and Table 1). Elevation and location of each sampling site were recorded by GPS. At each sampling site, three quadrats of 2 × 2 m within an area of 0.25 km2 were randomly selected. Plant species, as well as the abundance, height and cover of each species at each sampling site were recorded and all living vascular plants were clipped at ground level within each quadrat to estimate annual community biomass. Aboveground biomass was oven-dried at 85 °C for 48 h and weighed. To capture the spatial heterogeneity in soil properties, three plots were 2

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Fig. 1. The distribution of Chinese grassland types and precipitation isopleths (200–600 mm), with the locations of sampling sites across the precipitation gradient.

density (BD, g cm−3) (Bai et al., 2008). SOC contents were determined using an Elementar Vario EL III (Elementar Analysensysteme GmbH, Germany) after removing soil inorganic C with 1 mol L-1 hydrochloric acid (Midwood and Boutton, 1998). Soil pH and EC were measured using a pH-conductivity meter (Thermo, USA) with a soil/water mass ratio of 1:2.5. Soil phytoliths were extracted by heavy liquid suspension (ZnBr2, 2.36 g cm−3) after wet oxidation (Li et al., 2013a; Zuo et al., 2014). The extracted phytoliths were purified following the WalkleyBlack method to ensure that all organic materials were removed

randomly selected within an area of 0.25 km2 at each sampling site. At each plot, samples were collected at depths of 0–10, 10–20, 20–40, 40–60, 60–80 and 80–100 cm. These were used to determine contents of soil phytoliths and PhytOC, as well as a range of basic soil properties. An additional intact soil core of 200 cm3 was collected from each depth for measuring soil bulk density. All bulk soil samples were air-dried. The samples were sieved (< 2.0 mm), and fine roots and organic debris were removed prior to analysis. The intact soil cores were oven-dried at 105 °C to measure soil bulk

Table 1 Information on location, climate and major species composition for the sampling sites. Sites

Longitude (E)

Latitude (N)

Altitude (m)

MAT (°C)

MAP (mm)

Major species composition

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

120.8 119.5 118.5 117.3 116.8 115.9 114.8 115.1 116.6 116.9 118.2 119.4 120.9 119.5 119.8 119.7 117.9 118.9 120.6 120.9 114.7 114.3 114.7 114.9

43.4 43.2 43.4 43.2 43.5 43.9 44.0 43.9 44.3 45.5 45.7 45.4 46.3 47.4 48.3 49.0 49.5 49.4 49.3 49.1 41.1 42.0 42.7 42.8

290 472 724 1217 1276 1102 1126 1047 1100 834 837 937 534 876 880 629 545 633 650 711 1473 1465 1159 1180

7.3 7.0 6.6 1.3 1.5 2.4 2.5 2.2 0.8 1.6 1.0 1.0 0.5 −2.3 −1.7 −0.9 0.6 −0.6 −1.9 −1.9 3.4 3.2 3.4 3.4

353.0 356.2 363.5 404.4 369.3 280.9 243.3 264.2 329.4 269.0 313.1 395.6 480.5 434.6 390.4 349.2 279.9 310.5 404.4 404.4 405.6 301.6 283.0 283.0

Tribulus terrester, Bupleurum chinense Bassia dasyphylla, Salsola collina, Echinops sphaerocephalus Caragana microphylla, Melissilus ruthenicus, Bassia dasyphylla Agropyron cristatum, Scutellaria baicalensis, Filipendula Palmata Cleistogenes squarrosa, Carex lanceolata Carex lanceolata, Leymus chinensis., Calystegia hederacea, Cleistogenes squarrosa Stipa capillata, Leymus chinensis, Melissitus ruthenica Agropyron cristatum, Heteropappus hispidus, Stipa capillata Achnatherum splendens, Leymus chinensis Nitraria tangutorum, Reaumuria songonica Leymus chinensis, Cleistogenes squarrosa, Agropyron cristatum Cleistogenes squarrosa, Agropyron cristatum Leymus chinensis, Agropyron cristatum Carex lanceolata, Leymus chinensis Leymus chinensis, Agropyron cristatum Leymus chinensis, Carex duriuscula Achnatherum splendens, Agropyron cristatum Leymus chinensis, Agropyron cristatum Agropyron cristatum, Poa annua Poa annua, Leymus chinensis Leymus chinensis, Agropyron mongolicum, Cleistogenes squarrosa Cleistogenes squarrosa, Agropyron mongolicum, Carex duriuscula Carex duriuscula, Leymus chinensis Psammochloa villosa, Salsola collina

Note MAT and MAP represent the mean annual temperature and mean annual precipitation, respectively. 3

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88.4 ± 20.3 t ha−1 in the meadow steppe (Table 2), and was positively related to MAP and SOC content (Figs. 3 and 4). Soil PhytOC storage displayed a similar pattern along the transect and precipitation gradient (Figs. 3 and 4). Although soil phytolith storage was significantly and negatively correlated with MAT (Table 3), stepwise multiple regression analysis showed that MAT could not explain the variation in soil phytoliths, because MAT was highly correlated with MAP. The best multiple regression model for phytolith storage included the variables MAP (R = 0.5) and SOC (R = 0.4), while BD, pH, EC, MAT and altitude were excluded from the model (Table 4). These two variables accounted for 58% of the total variation observed for phytolith storage. Furthermore, while soil phytolith storage differed significantly with precipitation (p < 0.05), soil PhytOC storage generally increased with increasing MAP, but these differences were not significant (Fig. 5).

(Walkley and Black, 1934; Li et al., 2013b). The resulting soil phytoliths were oven-dried to a constant weight at 60 °C for 48 h, and the concentration of C occluded within phytoliths was determined using an Elementar Vario EL III. The precision of the soil physicochemical properties was > 5%, monitored using a standard soil reference sample (GBW07405). 2.3. Calculations and statistical analysis We calculated the storage of soil phytoliths and PhytOC amounts (t ha−1) at each site using the following equations:

Soil phytoliths storage n

=

∑ Ti × BDi

× (soil phytoliths content )i ×

i=1

(1 − Ci ) 100

3.3. Turnover of soil phytoliths n

(1 − Ci ) Soil PhytOC storage = ∑ Ti × BDi × (soil PhytOC content )i × 100 i=1

Assuming soil phytolith storage in the plant-soil system to be at equilibrium, the results indicate that the soil phytolith turnover time averaged 1,975 ± 687 years in the 200–300 mm precipitation range, 1,088 ± 346 years in the 300–400 mm precipitation range ,and 737 ± 196 years in the 400–500 mm precipitation range (Fig. 6). The soil phytolith turnover time decreased with increasing MAP and differed significantly between precipitation ranges (p < 0.05).

where Ti, BDi, Ci are the thickness of each soil depth (cm), bulk density (g cm−3) and volume percentage of the fraction (> 2 mm) at depth i, respectively. The results were multiplied by 100 to transform them from g cm−2 to t ha−1. The turnover time of soil phytoliths was evaluated as follows:

Soil phytolith turnover time =

soil phytolith storage phytolith input flux

4. Discussion

where soil phytolith turnover time, soil phytolith storage and phytolith input flux are given in units of years, t ha−1, and t ha−1 year−1, respectively. Phytolith input flux is the total amount of phytoliths returned to the soil by litter decomposition per area and year, and were obtained from Song et al. (2012b). The results of each site are the means and standard deviations of three replicates. SPSS (19.0) and Excel software were used for statistical analyses. One-way analysis of variance (ANOVA) and least square difference (LSD) tests were used to identify whether the sites along the transect and respective MAP gradient were significantly different. Furthermore, statistical methods including t-tests, linear regression, Pearson’s correlation test and stepwise multiple linear regression were conducted to determine the effects of physicochemical soil properties and the respective MAP gradient on the storage of soil phytoliths and PhytOC storage along the transect. All data passed normality and homoscedasticity tests.

4.1. Climatic and soil factors controlling soil phytolith storage Phytolith storage was positively correlated with MAP, suggesting that precipitation is an important driver of soil phytolith storage in the Inner Mongolian grassland ecosystem (Fig. 3). Decomposition of plant tissues was the main pathway for input of phytoliths into the soils, as indicated by the similar depth distributions of soil phytoliths and SOC contents with depth in most of the soil profiles, highest values in the surface layers and they decreased sharply with depth (Fig. 2). We suspect that this pattern is mainly caused by the ANPP and plant species richness at each grassland site, which is controlled by MAP in arid and semi-arid areas of northern China (Barrett et al., 2002; Zhou et al., 2002). For example, Bai et al. (2008) similarly indicated that ANPP increased significantly with increasing MAP across different grassland ecosystems, with MAP accounting for 76% of ANPP variation. This is generally consistent with other studies (Hooper and Johnson, 1999; Lauenroth et al., 2000; Guo et al., 2006). In addition, another study indicated that the variation in soil phytolith storage among grassland sites with different degrees of anthropogenic degradation was mainly attributed to the litter fall return flux (Pan et al., 2017a). The grassland steppes in our study area mainly consisted of meadow steppe, typical steppe and desert steppe, and the plant species diversity in each grassland steppe varied greatly. Song et al. (2012b) estimated the phytolith production fluxe of different grassland steppes in China, finding it to be significantly higher in meadow steppe (9.0 g m−2 yr−1) than in typical steppe (3.8 g m−2 yr−1) and desert steppe (1.6 g m−2 yr−1). This was mainly attributed to different ANPP and phytolith contents of diverse plant species. In addition, the soil phytolith contents observed in our study were affected by other factors such as soil erosion, land use change, and parent materials. Wind erosion is more likely to drive the loss of fine soil particles, which consequently results in the loss of soil phytoliths (Zhang and Liu, 2010; Yan et al., 2013). Previous studies have found that the strength and occurrence frequency of wind erosion can be exacerbated by increasing aridity (Wang et al., 2006; Wang et al., 2016). The decline of vegetation coverage in arid and semi-arid ecosystems increases the vulnerability of soils to wind erosion, which may decrease their soil phytolith content, especially in topsoils. Particulate phytoliths in topsoils, especially in arid and semi-arid regions, are easily

3. Results 3.1. Soil physicochemical properties along the precipitation gradient The average soil pH at each grassland site along the sampling transect ranged from 6.5 to 9.8, and were highest in the desert steppe and lowest in the meadow steppe (Table 2). Soil bulk density ranged from 1.1 ± 0.2 g cm−3 to 1.7 ± 0.1 g cm−3, and decreased gradually with increasing MAP along the transect. Soil pH and BD showed no consistent trend with depth at any of the sites. Soil EC ranged from 22.2 ± 1.6 to 1,799.6 ± 23.4 μs cm−1 and decreased with increasing MAP. In general, SOC storage differed along the transect, and was significantly and positively related to MAP. As expected, the SOC content decreased with increasing depth at each sampling site (Fig. 2). 3.2. Contents and storage of soil phytoliths and PhytOC along the MAP gradient In general, soil phytolith content decreased with soil depth, and differed significantly among all grassland sites at the same depth (p < 0.05) (Fig. 2). Across the whole transect, soil phytolith storage to 100 cm depth ranged from 21.3 ± 0.4 t ha−1 in the desert steppe to 4

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Table 2 Selected basic physicochemical characteristics of the studied soil profiles (100 cm). Sites

SOC (t ha−1)

pH

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

48.3 ± 2.1 24.3 ± 3.1 48.9 ± 1.3 48.8 ± 1.8 164.3 ± 6.2 77.9 ± 2.9 56.0 ± 4.2 44.0 ± 0.6 175.1 ± 1.9 42.2 ± 0.5 67.6 ± 2.6 101.2 ± 5.3 215.0 ± 8.4 326.7 ± 6.9 184.3 ± 12.0 280.2 ± 8.5 124.6 ± 9.4 77.4 ± 2.7 192.7 ± 2.1 367.1 ± 6.1 137.4 ± 9.2 89.8 ± 7.6 38.1 ± 1.1 16.6 ± 0.8

7.7 7.0 6.9 6.8 7.8 8.1 8.1 8.6 9.3 8.9 9.8 9.3 6.5 6.6 7.1 6.7 6.9 7.0 9.5 7.0 7.4 6.7 8.5 8.6

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.1 0.1 0.0 0.1 0.1 0.1 0.2 0.0 0.1 0.1 0.0 0.2 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.1 0.0

EC (µs cm−1)

BD (g cm−3)

Phytolith storage (t ha−1)

PhytOC storage (g m−2)

48.5 ± 2.9 22.2 ± 1.6 42.8 ± 0.6 25.3 ± 2.0 93.4 ± 5.1 105.3 ± 2.1 151.2 ± 0.9 68.8 ± 1.2 1799.6 ± 23.4 85.0 ± 0.9 83.2 ± 5.6 299.5 ± 8.6 47.9 ± 2.3 56.4 ± 0.4 81.5 ± 2.6 54.3 ± 4.9 61.0 ± 0.8 45.3 ± 7.2 563.0 ± 13.8 130.4 ± 11.3 146.2 ± 9.2 72.0 ± 6.6 106.8 ± 5.1 63.7 ± 1.3

1.6 1.7 1.6 1.6 1.4 1.6 1.6 1.7 1.4 1.5 1.5 1.5 1.5 1.5 1.4 1.5 1.6 1.4 1.5 1.5 1.5 1.1 1.7 1.7

39.3 27.4 49.3 74.7 50.4 34.4 24.5 25.9 69.4 22.1 21.3 39.8 78.1 51.4 44.8 46.9 54.5 30.2 63.9 88.4 41.1 36.0 32.5 27.3

18.2 ± 0.2 10.3 ± 0.1 28.2 ± 0.5 30.2 ± 0.4 38.9 ± 2.5 32.5 ± 0.4 32.1 ± 0.4 44.4 ± 0.4 47.1 ± 2.1 18.5 ± 0.1 30.4 ± 0.5 23.8 ± 1.4 68.1 ± 1.9 66.4 ± 4.4 51.1 ± 2.5 35.2 ± 2.5 25.8 ± 0.6 27.5 ± 0.8 55.7 ± 3.2 38.7 ± 8.6 4.3 ± 0.6 20.3 ± 3.6 21.9 ± 0.3 15.8 ± 0.1

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.1 0.0 0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.1 0.1 0.0 0.0 0.1 0.0 0.2 0.0 0.2 0.0 0.1

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.4 0.2 1.0 0.9 3.3 0.5 0.3 0.2 2.6 0.2 0.4 2.2 3.5 3.7 2.4 3.2 1.5 0.9 4.0 20.3 6.3 6.7 0.4 0.1

Soil organic carbon (SOC), pH-H2O, electric conductivity (EC), bulk density (BD) and phytolith storage. All data are presented as means ± standard deviation.

relationships between the two parameters on different continents.

eroded by rainfall and runoff (Durr et al., 2009). Zuo et al. (2014) estimated the phytolith flux with soil erosion from the Chinese Loess Plateau into the Yellow River to be 2.5 Mt per year. Moreover, parent materials and land use changes (e.g., intensified grazing and tillage) play important roles in controlling soil formation processes (Barthold et al., 2013), which lead to different soil types with varying physiochemical properties along precipitation gradients. Soil properties, such as soil pH, nutrient contents and metal ion content, are important for regulating the stability of soil phytoliths, thereby further affecting soil phytolith storage and the global Si biogeochemical cycle (Bartoli, 1985; Blecker et al., 2006; Fraysse et al., 2006; Li et al., 2014; Song et al., 2016). In the present study, soil phytolith storage was negatively correlated with soil pH along the transect (Table 3). This likely reflects the high solubility of soil phytoliths under alkaline conditions, as evidenced by the fact that the dissolution rate of phytoliths increased with soil pH, making phytoliths are better preserved in acidic soils (Li et al., 2019; Nguyen et al., 2014; Song et al., 2016). Aridity has been identified as one of the most important parameters affecting bacterial diversity, taxon abundance and community composition in the grassland ecosystems of northern China (Wang et al., 2014), which can in turn alter the soil phytolith decomposition rates observed in the soil profiles sampled across the precipitation gradient. The relationship between soil phytoliths and soil microbial community compositions is beyond the scope of this study but need to be studied in the future. Stepwise multiple regression analysis was used to quantify the simultaneous effects of climatic factors and physicochemical soil properties on bulk soil phytolith storage. Considering the impacts of MAP on ANNP and physiochemical soil properties, we concluded that MAP is better than MAT for estimating the variation in soil phytolith storage in the Inner Mongolian steppe. However, the positive response of soil phytolith storage to in the Inner Mongolia steppe, contrasts the pattern found in the Central Great Plains of North America, where soil phytolith storage decreases with increasing MAP from the short grass steppe to the tall grass steppe (Blecker et al., 2006). These opposite trends reflect the different responses of soil phytoliths to MAP on the two continents. Therefore, further research should be conducted to explore potential driving factors (e.g., species composition and soil physicochemical properties) behind the different relationships between soil phytoliths and MAP observed on different continents as well as to analyze the

4.2. Phytolith stability and turnover In most terrestrial ecosystems, soil phytolith storage is approximately 500–1000 times greater than that in the aboveground biomass (Alexandre et al., 1997; Conley, 2002; Blecker et al., 2006). However, soil phytolith storage varies between terrestrial ecosystems, mainly due to differences in plant litter input and the geochemical stability of soil phytoliths (Song et al., 2016; Zhang et al., 2016). Therefore, the stability and turnover of soil phytoliths in different terrestrial ecosystems play important role in long-term (stable) C sequestration. The stability of soil phytolith stocks is mainly controlled by an inherent balance between their mass input and output, which feeds back to climate conditions and various soil physiochemical properties. Climate, plant type and element composition of phytoliths are the main controlling factors driving the dissolution and solubility of phytoliths (Blecker et al., 2006). For example, the solubility and dissolution rate of soil phytoliths increase with temperature, and phytoliths from grass and deciduous species are more soluble than those formed in conifers because of differences in surface area, Si/Al ratio and water content (Bartoli and Wilding, 1980; Bartoli, 1985; Fraysse et al., 2006). Although various approaches have been used to evaluate the solubility and mean residence time of soil phytoliths, determining their dynamics remains very challenging, mainly because (i) the large quantity of particles, (ii) an absolute differentiation between phytoliths and minerogenic or microcrystalline Si structures/forms in soils via non-destructive gravimetric extraction is difficult, and (iii) the physicochemical surface properties of phytoliths determine their residence times in soils (Puppe and Leue, 2018). However, previous studies report that the residence time of soil phytoliths can be used to explain their general turnover processes (Blecker et al., 2006; Song et al., 2016; Zhang et al., 2016). In this study, we estimated soil phytolith turnover times along the precipitation gradient by calculating the ratio of soil phytolith storage to annual phytolith input by litter fall. The turnover time was significantly higher at lower precipitation ranges (Fig. 6). With higher MAP, water availability also increases in grassland ecosystems in northern China, which enhances aboveground biomass production and soil phytolith dissolution, ultimately resulting in shorter turnover times. 5

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Fig. 2. Depth distributions of phytoliths and soil organic carbon in soil profiles with MAP increasing from 200 to 300 mm (a, b) to 300–400 mm (c, d) and 400–500 mm (e, f).

620 ± 280 years. Although California grassland is also temperate, soil phytolith turnover rates in the two grassland ecosystems differed greatly. Very few studies have been conducted on soil phytolith turnover, therefore, future studies investigating turnover rates of soil phytoliths in different climatic zones and with different vegetation and soil physiochemical properties are required to improve our understanding of the mechanisms and controlling factors for soil phytoliths’ stabilization.

The turnover rate of soil phytoliths in our study area was generally slower than that determined in grass savannas (average 352 ± 139 years) (Alexandre et al., 2011), which was most likely caused by lower temperatures and different plant species in our study area. Besides, Blecker et al. (2006) reported that soil phytolith turnover times in the Central Great Plains ranged from 1,300 ± 800 years to 250 ± 70 years with increasing MAP. These are generally faster than those determined in our study, possibly due to the relatively higher MAP in the Central Great Plains (350–1,100 mm). White et al. (2012) reported that soil phytolith turnover time in California grassland was 6

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Fig. 3. Correlations of MAP with the storage of phytoliths (a) and PhytOC (b) in all sampled soil profiles.

Fig. 4. Correlations of SOC storage with storage of phytoliths (a) and PhytOC (b) in all soil profiles.

Table 3 Correlation coefficients (linear regression, Pearson’s test) between storages of soil phytoliths and PhytOC (100 cm depth, N = 24) and various environmental parameters. Variables

SOC

Altitude

MAT

MAP

pH

EC

BD

Phytolith storage PhytOC storage

0.669** 0.630**

−0.152 −0.192

−0.428* −0.658**

0.689** 0.457*

−0.334 −0.081

0.314 0.238

−0.241 −0.128

Note: * indicates significant correlation between two factors (p < 0.05) and ** (p < 0.01). SOC: soil organic carbon, MAT: mean annual temperature, MAP: mean annual precipitation, EC: electric conductivity, BD: bulk density.

carbon sequestration in various terrestrial ecosystems, it is essential to differentiate between phytoliths and minerogenic or microcrystalline Si structures. However, an absolute differentiation using gravimetric extraction is unsuitable because during suspension small -scale fragile phytogenic Si is lost, and moreover, minerogenic impurities should be considered (Puppe and Leue, 2018). Although these problems have been acknowledged, gravimetric extraction still represents the standard technique for non-destructively extracting soil phytoliths (Alexandre et al., 1997; Puppe and Leue, 2018). Therefore, studies are needed to explore how to obtain more reliable measurements of purified soil phytoliths. Our results indicate that soil PhytOC storage in grassland soils overall increased with increasing MAP and SOC content (Figs. 2b, 3b and 5b). The positive relationship between MAP and soil PhytOC storage suggests that grassland soils, especially those in a semiarid climate, have a high potential for soil PhytOC storage. These findings provide a preliminary projection of global long-term changes in soil PhytOC storage in response to future climate change and have important implications for better management of arid and semi-arid grassland ecosystems in the Inner Mongolian steppe. It has been projected that MAP will increase by 30–100 mm over the next 100 years in the Inner Mongolian steppe (Ni and Zhang, 2000). Considering that the ANPP

Table 4 Stepwise multiple regression analyses of the effects of significant variables for soil phytolith storages along the precipitation gradient. Variables

Constant MAP SOC

Unstandardized coefficients B

SE

Standardized coefficients Beta

−1133.893 13.526 0.077

1650.345 5.363 0.034

0.448 0.399

t

P.

−0.687 2.522 2.246

0.500 0.020 0.036

4.3. Implication for long-term biogeochemical C sequestration Phytoliths in various plant tissues can trap 0.2–5.8% organic C (PhytOC) during their formation and play an important role in the global C cycling due to their high geochemical stability (Parr and Sullivan, 2005; Parr et al., 2010). Although the rate of soil PhytOC accumulation is relatively slow compared to that of other organic materials at the annual-decadal scale, the portion of organic compounds occluded in phytoliths is overall stable at the millennial scale and can represent up to 82% of total OC after 2000 years’ decomposition (Parr and Sullivan, 2005; Song et al., 2016). When estimating the phytolith 7

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Fig. 5. Boxplots showing the storage of phytoliths and PhytOC in soil profiles with increasing MAP.

the dynamics of soil phytoliths and PhytOC under increasing precipitation in arid and semi-arid ecosystems. Moreover, our study suggests that an increase in soil PhytOC storage is expected in Inner Mongolian grassland ecosystems along with the forecasted increase in precipitation due to climate change. This will play an important role in regulating the atmospheric CO2 concentration and mitigating global warming. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The research was supported by the National Natural Science Foundation of China (41930862, 41571130042 and 41522207) and the State’s Key Project of Research and Development Plan of China (2016YFA0601002 and 2017YFC0212703).

Fig. 6. Estimated turnover time for phytoliths in soil profiles with increasing MAP.

References: and soil PhytOC input from litter decomposition are largely constrained by precipitation in the Inner Mongolian grassland ecosystems, we expect that the forecasted increasing MAP may enhance ecosystem primary productivity and, consequently, promote soil PhytOC storage because of an increase in final phytolith storage in dry biomass matter. However, the increase in ANPP at the regional scale would be limited by several other ecosystem factors, such as nitrogen (N) limitation, water use efficiency and enhanced evapotranspiration rates associated with increased MAT. Therefore, further investigation is recommended, such as on the effect of soil water content on soil PhytOC storage under different climatic conditions. Management practices in grassland ecosystems, such as N application and prevention of overgrazing, may enhance primary productivity and, thus, soil PhytOC storage.

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