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Regional Sustainability >

2024 , Vol. 5 >Issue 3: 100162

DOI: https://doi.org/10.1016/j.regsus.2024.100162

Full Length Article

Characteristics and drivers of the soil multifunctionality under different land use and land cover types in the drylands of China

  • SONG Boyi a, b, c, d ,
  • ZHANG Shihang b, c, d, e ,
  • LU Yongxing b, c, d ,
  • GUO Hao b, c, d ,
  • GUO Xing b, c, d, e ,
  • WANG Mingming b, c, d ,
  • ZHANG Yuanming b, c, d ,
  • ZHOU Xiaobing b, c, d ,
  • ZHUANG Weiwei , a, *
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  • aCollege of Life Sciences, Xinjiang Normal University, Urumqi, 830054, China
  • bState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
  • cXinjiang Key Laboratory of Biodiversity Conservation and Application in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
  • dXinjiang Field Scientific Observation Research Station of Tianshan Wild Fruit Forest Ecosystem, Ili Botanical Garden, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ili, 844900, China
  • eUniversity of Chinese Academy of Sciences, Beijing, 100049, China
* E-mail address: (ZHUANG Weiwei).

Received date: 2024-01-28

  Revised date: 2024-06-16

  Accepted date: 2024-08-23

  Online published: 2025-08-14

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Abstract

The drylands of China cover approximately 6.6×106 km2 and are home to approximately 5.8×108 people, providing important ecosystem services for human survival and development. However, dryland ecosystems are extremely fragile and sensitive to external environmental changes. Land use and land cover (LULC) changes significantly impact soil structure and function, thus affecting the soil multifunctionality (SMF). However, the effect of LULC changes on the SMF in the drylands of China has rarely been reported. In this study, we investigated the characteristics of the SMF changes based on soil data in the 1980s from the National Tibetan Plateau Data Center. We explored the drivers of the SMF changes under different LULC types (including forest, grassland, shrubland, and desert) and used structural equation modeling to explore the main driver of the SMF changes. The results showed that the SMF under the four LULC types decreased in the following descending order: forest, grassland, shrubland, and desert. The main driver of the SMF changes under different LULC types was mean annual temperature (MAT). In addition to MAT, pH in forest, soil moisture (SM) and soil biodiversity index in grassland, SM in shrubland, and aridity index in desert are crucial factors for the SMF changes. Therefore, the SMF in the drylands of China is regulated mainly by MAT and pH, and comprehensive assessments of the SMF in drylands need to be performed regarding LULC changes. The results are beneficial for evaluating the SMF among different LULC types and predicting the SMF under global climate change.

Key words： Soil multifunctionality (SMF); Land use and land cover (LULC) changes; Structural equation modeling (SEM); Climate change; Dryland ecosystems

Cite this article

SONG Boyi , ZHANG Shihang , LU Yongxing , GUO Hao , GUO Xing , WANG Mingming , ZHANG Yuanming , ZHOU Xiaobing , ZHUANG Weiwei . Characteristics and drivers of the soil multifunctionality under different land use and land cover types in the drylands of China[J]. Regional Sustainability, 2024 , 5(3) : 100162 . DOI: 10.1016/j.regsus.2024.100162

1. Introduction

Ecosystems provide a wide range of human survival services and perform various functions, such as element cycling, climate regulation, and energy flow (Meyer et al., 2018; Migliavacca et al., 2021). Thus, ecosystems are inherently multifunctional, and an integrated assessment of ecosystem functions is crucial. Soils, as nutrient reservoirs of ecosystems, are closely related to vegetation and convey feedback from each other. Specifically, soil indicators can reflect a variety of ecosystem functions, such as carbon (C), nitrogen (N), and phosphorus (P) storage, soil and water conservation, and the carrying capacity of wildlife (Soliveres et al., 2015; Valencia et al., 2015). Therefore, soil variables are commonly used to evaluate and quantify the ecosystem multifunctionality (EMF). The study of the soil multifunctionality (SMF) will be beneficial for obtaining a comprehensive and deep understanding of soil-integrated services (Ding and Wang, 2021), providing a basis for further research on the EMF.
Changes in the SMF have also been a research hotspot. Human activities, such as urban expansion and agricultural reclamation, have led to a reduction in forest, grassland, and other ecological land, which has further resulted in the degradation of certain ecosystems (Cabral et al., 2016; Peng et al., 2017; Pickard et al., 2017; Hu et al., 2018). Drylands are an important component of terrestrial ecosystems (Huang et al., 2016; Lian et al., 2021), covering approximately 41.5% of the Earth’s land surface (Maestre et al., 2012; Bastin et al., 2017) and living as the largest terrestrial biota on the Earth (Wang et al., 2014; Hoover et al., 2020). The extreme fragility and low stability of dryland ecosystems make them among the most sensitive terrestrial ecosystems to climate change and human activities. Changes in land use and land cover (LULC) types (e.g., deforestation, agricultural expansion, afforestation, cropland intensification, and urbanization) can result in increased soil erosion and land degradation, which will inevitably affect ecosystem services (Huang et al., 2016; Hasan et al., 2020; Stavi et al., 2022). LULC changes closely relate to ecological processes, such as water, atmospheric, and soil cycles, and are key to the interactions between human activities and natural environments (Jost et al., 2021; Wang et al., 2021). LULC changes are usually recognized as a major driver of desertification (Song et al., 2018; Yang et al., 2021). Irrational LULC planning has triggered a series of environmental problems worldwide, such as an increase in greenhouse gases (Mooney et al., 2013), soil erosion (Chi et al., 2019), and a decrease in C storage (Adelisardou et al., 2022). Therefore, LULC changes play a crucial role in surface material cycling, and biological processes influence the SMF in ecosystems (Fu and Zhang, 2014; Lawler et al., 2014; Peng et al., 2020).
China has the largest dryland area in the world (Li et al., 2021a), serving 5.8×108 people living in these areas. There are 12 major deserts in the drylands of China, and desertification is exacerbated by human activities and climate change (Chi et al., 2019). Previous studies indicated that the area of drylands in China increased from 5.8×106 km2 in 1980 to 6.6×108 km2 in 2020, with an average growth rate of 1.9×104 km2/10a (Zhang et al., 2023), which in turn exacerbates the challenges of water supply, food security, and the reduction of ecosystem C pools (Wang et al., 2008; Stringer et al., 2021). The direct economic losses caused by desertification in the drylands of China negatively affect sustainable development (Ci and Yang, 2010). The major LULC types in the drylands of China include desert, grassland, and forest (Zhang et al., 2023). Due to socioeconomic development and urbanization, a series of ecological and environmental problems, such as soil erosion, loss of biodiversity, deterioration of soil quality, reduction of water resources, and expansion of saline and alkaline land, have emerged, further accelerating LULC changes (Jiang et al., 2015). Previous study has analyzed the processes of LULC changes in the drylands of Kashi Prefecture, China (Maimaitiaili et al., 2018), but the SMF changes under different LULC types in the drylands of China have not been explored (Wu et al., 2014). Therefore, research on LULC changes and its drivers in the drylands of China needs to be conducted to improve the management of ecosystems under global climate change.
The main objectives of this study were to investigate the relationship of the SMF with climate factors (including mean annual temperature (MAT) and aridity index (AI)), soil factors (including pH, soil moisture (SM), and soil biodiversity index (SBI)), and vegetation (i.e., normalized difference vegetation index (NDVI)) and to explore the spatial variation characteristics of the SMF and its driving factors in the drylands of China. Because soil function depends on the environmental context and varies with the environmental gradient (Hu et al., 2021), we hypothesized that the SMF varies greatly under different LULC types in the drylands of China. In addition, our previous study showed that MAT drives the increase in dryland areas in China (Zhang et al., 2023). We further hypothesized that the main driver of the SMF changes under different LULC types in the drylands of China is MAT, while the effects would vary under different LULC types.

2. Methods and data sources

2.1. Study area

Drylands are areas with an AI less than 0.65 (Safriel et al., 2005). Large drylands in China have high ecosystem service values (Prăvălie, 2016). Dryland areas are characterized by low and highly variable annual precipitation, high potential evapotranspiration (Chen et al., 2015), loose and nutrient-poor soil texture (Ci and Yang, 2010), sparse vegetation, and low annual productivity (Huang et al., 2017; Smith et al., 2019). The structure and functioning of ecosystems in the drylands of China involve complex, dynamic, and interacting processes. The mean annual precipitation (MAP) in the drylands of China is 304.0 mm, which is much lower than the annual potential evapotranspiration (814.9 mm) (Li et al., 2021a). According to the calculations of AI of the TerraClimate 1958-2015 dataset (Abatzoglou et al., 2018), in China, the dryland areas expanded by 8.3% during 1980-2015, grassland and desert areas decreased by 6.3×104 and 1.4×104 km2 during 1980-2015, respectively, and forest area decreased by 2.5×104 km2 during 1980-2000 (Li et al., 2021a).

2.2. Data sources

2.2.1. Soil data

Soil data in the 1980s, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP), available potassium (AK), and pH, were collected from the National Tibetan Plateau Data Center (https://doi.org/10.11888/Soil.tpdc.270281). SM data in 2017 were obtained from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) dataset published by the National Aeronautics and Space Administration (NASA; McNally et al., 2022). SBI data in 2016 were obtained from the Global Soil Biodiversity Atlas (https://esdac.jrc.ec.europa.eu/content/global-soil-biodiversity-atlas#tabs-0-description=0). In this study, we used raster-level sampling based on cells (10 km×10 km), totaling approximately 49,827 sampling points. The sampling points were 7689 for forest, 2684 for shrubland, 22,038 for grassland, and 17,416 for desert.

2.2.2. Environmental data

Environmental data were obtained from various databases. AI data during 1970‒2000 were obtained from the Global Aridity Index and Potential Evapotranspiration Climate database (https://cgiarcsi.community/). MAT data in 2018 were obtained from the MOD11A1 Version 6.1 data product (https://lpdaac.usgs.gov/products/mod11a1v006/), which provides daily surface temperature and emissivity values on a 1-km grid (Rodell et al., 2004). MAP data in 2016 were obtained from the Terra Climate Dataset published by the University of Idaho, which included global land surface monthly average climate and climate water balance datasets, as well as solar radiation data (Abatzoglou et al., 2018). NDVI data in 2019 came from the 2000-2020 China 30-m annual maximum NDVI dataset (http://www.nesdc.org.cn/sdo/detail?id=60f68d757e28174f0e7d8d49). LULC data in 2010 were obtained from the European Space Agency (ESA) Climate Change Initiative database at a spatial resolution of 300 m (https://climate.esa.int/en/). The LULC types in the study area included forest, grassland, shrubland, desert, water body, etc.

2.3. Calculation of the soil multifunctionality (SMF)

Eleven key soil function indicators in the drylands were selected for the SMF calculations in this study, including SOC, TN, TP, TK, AN, AP, AK, SOC:TN, SOC:TP, TN:TP, and AN:AP. These indicators are closely related to the cycling of C, N, and P in ecosystems (Sanderson et al., 2004; Maestre et al., 2012; Yan et al., 2020) and can reflect multiple ecosystem functions to a greater extent (Soliveres et al., 2014; Valencia et al., 2015). In this study, two methods (i.e., averaging and multiple-threshold) were used to calculate the SMF. Averaging, which synthesizes multifunctionality into an index that estimates the average level of the measured function, is the most widely used method. This method represents the EMF by calculating the average standardized score (Z score) of different soil functions; it is useful for quantifying the simultaneous maintenance of multiple soil functions and provides a straightforward and easily interpretable method, however, this method does not consider the interrelationships among different soil functions (Byrnes et al., 2014). The multiple-threshold not only considers the relationships among different soil functions but also obtains the weights of different soil functions by clustering, which can effectively avoid bias in the calculation results (Craven et al., 2018). In this study, cluster analysis revealed that the eight soil function indicators could be clustered into four categories (Fig. 1a). As shown in Figure 1b, the first category was SOC:TP, the second was the sum of TN, TP, TK, TN:TP, SOC, AP, SOC:TN, and AN:AP, the third was AN, and the fourth was AK. Based on these clustering results, the weight of each soil function was calculated, and the multiple-threshold was subsequently used to calculate the EMF. In this study, three thresholds (30.0%, 55.0%, and 80.0%) were set to calculate the EMF, and the mean value was subsequently taken as the final result (Yan et al., 2020).
Fig. 1. Clustering diagram of soil function indicators. (a), determining the optimal number of clusters (k); (b), a dendrogram of soil function indicators showing four main clusters. SOC, soil organic carbon; TN, total nitrogen; TP, total phosphorus; TK, total potassium; AN, available nitrogen; AP, available phosphorus; AK, available potassium.
In this study, the results obtained using averaging and multiple-threshold methods were very close (R2=0.95), and both showed significant positive correlations with the 11 single soil function indicators (Table 1). Here, we focused on the EMF calculated using the multiple-threshold method and used it for subsequent analysis.
Table 1 Correlations between soil function indicators and the soil multifunctionality (SMF).
Soil organic carbon
(SOC)		 Total nitrogen
(TN)		 Total phosphorus
(TP)		 Total potassium
(TK)		 SOC:TN SOC:TP TN: TP Available nitrogen
(AN)		 Available phosphorus
(AP)		 Available potassium
(AK)		 AN:AP
MF1 0.82** 0.96** 0.56** 0.69** 0.91** 0.43* 0.53** 0.78** 0.57** 0.59** 0.77**
MF2 0.77** 0.83** 0.61** 0.75** 0.84** 0.51** 0.55** 0.66** 0.76** 0.62** 0.90**

Note: MF1, the SMF obtained via the averaging method; MF2, the SMF obtained via the multiple-threshold method; *, P<0.05; **, P<0.01.

2.4. Statistical analysis

We used the Shapiro‒Wilk test to examine the normality of the data. Non-normal variables were log-transformed. All of data analyses were performed in R 4.4.1 software (R Foundation for Statistical Computing, Vienna, Austria). We used the “reshape2” package to compare the significance of differences in the SMF under different LULC types and plotted box plots using “ggplot2” package. The SMF and environmental factors under different LULC types were polynomially fitted using the “lm4” package and visualized using the “geom_smooth()” function of the standardized major axis (SMA) regression SMART package to test whether the relationships between the SMF and environmental factors differed under different LULC types. To mechanistically understand the spatial variability of the SMF in the drylands, we employed structural equation modeling (SEM), an analytical method widely used in the ecological sciences (Hershberger, 2001; Grace and Bollen, 2008). We constructed an SEM to assess the direct and indirect effects of climate factors (AI and MAT), soil factors (SBI, pH, and SM), and vegetation (NDVI) on the SMF. We selected the model based on an overall goodness-of-fit test (Keith et al., 2003; Eldridge et al., 2018). Variance decomposition analyses were conducted using “vegan” package (Tian et al., 2021) to determine the relative importance of climate factors, soil factors, and vegetation to spatial variability of the SMF.

3. Results

3.1. Variability of the SMF in the drylands of China under different land use and land cover (LULC) types

The mean values of SOC, TN, TP, TK, AN, AP, AK, NDVI, MAP, and MAT were the highest in forest, the lowest in desert, and slightly fluctuated in grassland and shrubland (Table 2). Soil pH decreased in the order of desert>shrubland>forest>grassland, and AI decreased in the order of desert>forest>grassland>shrubland. For the coefficient of variation (CV), in forest, TN had the greatest spatial variation (2.73), the CV of SOC, AN, and AP was 0.74, 0.75, and 0.48, respectively, and TK had the smallest CV (0.12). In grassland, the CV of TN was the highest (2.16), and the CV of TK was the lowest (0.13). In shrubland, TN had the largest CV (2.53), followed by TP (1.04). In desert, TN had the greatest spatial variation, with a CV of 4.46, followed by AN (2.31), and TK had the smallest spatial variation (0.10).
Table 2 Descriptive statistics of soil factors, vegetation, and climate factors in the drylands of China under different LULC types.
LULC type Parameter SOC (g/kg) TN
(g/kg)		 TP
(g/kg)		 TK
(g/kg)		 AN
(mg/kg)		 AP
(mg/kg)		 AK
(mg/kg)		 pH NDVI MAP
(mm)		 MAT
(°C)		 AI
Forest Mean 8.12 0.41 0.53 2.01 190.16 8.53 157.41 6.71 0.36 550.12 7.09 0.41
Min 5.53 0.26 0.33 1.72 83.34 4.59 49.36 4.41 0.31 125.16 -4.33 0.28
Max 27.27 1.08 0.69 2.65 518.69 15.01 277.13 8.51 0.41 985.71 15.33 0.65
SD 5.99 1.12 0.42 0.25 143.29 4.13 69.89 0.92 0.11 238.84 5.99 0.11
CV 0.74 2.73 0.79 0.12 0.75 0.48 0.44 0.14 0.31 0.43 0.84 0.27
Grassland Mean 8.05 0.43 0.59 2.12 188.72 8.72 147.39 6.06 0.28 514.77 6.44 0.33
Min 4.98 0.17 0.29 1.17 79.88 5.51 54.12 3.70 0.22 110.98 -5.13 0.18
Max 22.18 1.12 0.77 2.71 534.90 14.98 285.56 8.81 0.34 996.75 16.12 0.59
SD 6.16 0.93 0.37 0.27 161.25 4.04 71.18 1.01 0.10 229.26 4.57 0.13
CV 0.77 2.16 0.63 0.13 0.85 0.46 0.48 0.17 0.36 0.45 0.71 0.39
Shrubland Mean 6.61 0.38 0.47 1.88 175.16 6.83 162.38 7.05 0.25 389.41 6.18 0.45
Min 4.45 0.29 0.31 1.53 66.37 13.37 55.36 4.92 0.17 155.74 -4.49 0.22
Max 25.69 0.92 0.65 2.77 451.13 15.07 343.39 8.63 0.28 885.14 14.54 0.61
SD 5.28 0.96 0.49 0.23 144.57 3.98 62.58 0.79 0.08 213..63 3.93 0.16
CV 0.80 2.53 1.04 0.12 0.83 0.58 0.39 0.11 0.32 0.55 0.64 0.36
Desert Mean 2.18 0.13 0.32 1.84 25.19 5.11 144.72 8.13 0.04 120.15 3.61 0.13
Min 0.15 0.02 0.23 1.68 10.02 1.35 42.11 6.99 0.01 7.87 -11.00 0.01
Max 6.93 0.52 0.41 2.53 87.55 7.78 205.95 9.00 0.08 212.36 19.93 0.22
SD 2.58 0.58 0.22 0.19 58.14 3.93 59.96 0.35 0.02 189.16 6.89 0.09
CV 1.18 4.46 0.69 0.10 2.31 0.77 0.41 0.04 0.50 1.57 1.91 0.69

Note: LULC, land use and land cover; NDVI, normalized difference vegetation index; MAP, mean annual precipitation; MAT, mean annual temperature; AI, aridity index; Min, minimum; Max, maximum; SD, standard deviation; CV, coefficient of variation.

The SMF varied considerably under different LULC types (Fig. 2). Overall, the SMF decreased in the order of forest>grassland>shrubland>desert. The SMF in desert was significantly lower than that in forest, grassland, and shrubland (P<0.001). No significant differences in the SMF were found between grassland and shrubland. The SMF in forest was significantly greater than that in shrubland but was not significantly different from that in grassland.
Fig. 2. Soil multifunctionality (SMF) in the drylands of China under different land use and land cover (LULC) types. The dot represents the data value; the top, middle, and bottom lines of the box represent the upper quartile, median, and lower quartile, respectively; the upper whisker represents the upper quartile+1.5IQR (interquartile range); and the lower whisker represents the lower quartile-1.5IQR. Different lowercase letters indicate significant differences among different LULC types at P<0.05 level.

3.2. Environmental factors and their relationships with the SMF

In this study, the correlations between the SMF and environmental factors were derived by the linear fitting (Table 3). Pearson’s correlation analysis revealed that the SMF was correlated (P<0.05) with climate factors (MAT and AI), soil factors (pH, SM, and SBI), and vegetation (NDVI) (Table 3), with climate factors explaining 66.0% of the variation in the SMF in forest, 79.0% in grassland, and 60.0% in desert (Fig. 3). Soil factors explained 47.0% of the variation in the SMF in shrubland (Fig. 3c). Specifically, the SMF was significantly negatively correlated with MAT (P<0.001) but was significantly positively correlated with SBI under the four LULC types (P<0.01). NDVI positively affected the SMF in forest and grassland but did not affect the SMF in shrubland and desert. The SMF in forest, grassland, and desert showed highly significant positive correlations with AI (P<0.001). SM had no significant effect on the SMF in forest but had a positive effect on the SMF in shrubland (P<0.001), grassland (P<0.01), and desert (P<0.01).
Table 3 Correlations between the SMF and environmental factors under different LULC types.
LULC type NDVI pH SM SBI MAT AI
Forest 0.356*** -0.297** 0.145 0.279** -0.682** 0.243**
Grassland 0.214** 0.143 0.347** 0.326** -0.650*** 0.399***
Shrubland 0.150 -0.432*** 0.480*** 0.381*** -0.428*** 0.117
Desert 0.091 -0.386*** 0.231** 0.469*** -0.368*** 0.586***

Note: SM, soil moisture; SBI, soil biodiversity index. *, **, *** indicate significant differences at P<0.05, P<0.01, and P<0.001 levels, respectively.

Fig. 3. Impacts of climate factors (MAT and AI), soil factors (pH, SM, and SBI), and vegetation (NDVI) on the SMF under different LULC types. (a), forest; (b), grassland; (c), shrubland; (d), desert. The pie chart reflects the relative importance of climate factors, soil factors, and vegetation to the SMF. NDVI, normalized difference vegetation index; AI, aridity index; MAT, mean annual temperature; SM, soil moisture; SBI, soil biodiversity index; *, P<0.05; **, P<0.01; ***, P<0.001. Error bar represents the standard deviation.
SEM analysis revealed that the model explained the SMF of 49.0% in forest, grassland, and desert, of 42.0% in shrubland. In forest, the SMF was most influenced by MAT, with a loading value of -0.64, followed by pH (-0.18). SBI, NDVI, and AI significantly affected the SMF (Fig. 4a). SM indirectly affected the SMF by significantly influencing NDVI and pH. The driving factor in grassland was MAT, followed by SM and SBI. Soil pH affected the SMF in grassland by significantly influencing SBI (Fig. 4b). NDVI and AI can also have large effects on the SMF in grassland. In shrubland, SM, MAT, pH, and SBI were the main factors affecting the SMF, while the effect of NDVI was not direct (Fig. 4c). In desert, MAT and AI were the main factors affecting the SMF, followed by pH, SBI, and SM, and NDVI did not directly impact the SMF (Fig. 4d).
Fig. 4. Structural equation modeling (SEM) showing the impacts of climate factors, soil factors, and vegetation on the SMF under different LULC types. (a), forest; (b), grassland; (c), shrubland; (d), desert. The red and blue lines indicate positive and negative relationships, respectively. The thickness of the line is proportional to the size of the normalized path coefficient and indicates the strength of the relationship. The arrow represents the direction of the effect. The value on the arrow indicates the effect size. *, P<0.05; **, P<0.01; ***, P<0.001.

4. Discussion

The SMF is fundamental to realizing ecosystem functions and services (Zheng et al., 2019). In this study, the SMF varied greatly under different LULC types in the drylands of China, supporting the first hypothesis of this study. Different LULC types will be different in the process to modify ecological processes (Ochoa-Hueso et al., 2018). For example, forest characterized by tall stems, large crowns, and well-developed root systems has a greater ability to ameliorate environmental stresses (Belsky et al., 1989) and is more resistant to climate change (e.g., water scarcity and temperature extremes) (Hodgkinson, 1992). Thus, forest had the highest SMF. In contrast, grassland, shrubland, and desert are relatively sensitive to environmental factors because wide, dense tree canopies less protect them (Belsky et al., 1989), resulting in a rapid decrease in nutrient cycling (e.g., unstable C); subsequently, a decreasing SMF was observed in grassland, shrubland, and desert. There are fewer nutrient sources in grassland and shrubland than in forest, which leads to a relatively lower SMF. In desert ecosystems, although vascular plants are rare, the SMF can be maintained by increasing the cover of biocrusts, which plays an important role in stabilizing soils, increasing hydrological functions, providing habitats for microorganisms, and mitigating the negative impacts of climate change (Delgado-Baquerizo et al., 2017; Gao et al., 2017).
Human activities have led to extensive LULC changes (Goldewijk et al., 2017). Human activities have impacted more than 70.0% of the world’s ice-free land surface (Jia, 2020). Several ecological and environmental protection measures have been implemented in China, including converting arable land to forest and grassland (Liu et al., 2020), which can increase the SMF in the drylands to some extent. However, the SMF needs to be evaluated along with economic development, which might increase the amount of land for construction and arable land. The relationship between LULC types and the SMF in this study suggested that promoting LULC types compatible with local natural environmental conditions and with the regional economic and social development stage is critical (Long et al., 2022).
LULC changes are both a driver and a consequence of global environmental change (Alkama and Cescatti, 2016; Roy et al., 2022). This study revealed that MAT was the strongest driver of the SMF under the four LULC types, with a direct negative effect on the SMF (Fig. 4), while decreasing effects occurred from forest, grassland, and shrubland to desert. These results were consistent with the second hypothesis of this study that MAT was the main driver of the SMF changes and the effects varied under different LULC types. Durán et al. (2018) reported that temperature change is one of the main drivers of spatial variability in the SMF changes in global drylands, which is similar to the results of this study. Many studies have shown that soil nutrients (e.g., C and N) are negatively correlated with temperature at a regional scale (Chen et al., 2015; Xu et al., 2022). The mechanism for the effects of temperature on the SMF under the four LULC types can be summarized as follows. Firstly, elevated temperatures increase the loss of soluble C, N, and P from soils, leading to a lower SMF in the drylands (Yan et al., 2020). Secondly, increased MAT also leads to a shift in microbial-mediated N cycling from anabolic to catabolic reactions, which in turn increases the rate of nitrification and denitrification in soils, further decreasing soil TN (Dai et al., 2020). Therefore, an increase in MAT decreases the soil nutrient content, leading to a decrease in the SMF under different LULC types in the drylands. Thirdly, the physiological characteristics of soil microorganisms are also sensitive to temperature. For example, fungi grow optimally in the temperature range of 25°C-30°C (Pietikäinen et al., 2005), and extracellular enzymes (e.g., cellulase) are also sensitive to high temperatures (Sinsabaugh et al., 2008). Fourthly, temperature also affects most physiological and biochemical processes in plants (Jankju, 2013). An increase in temperature may exacerbate drought, which is detrimental to plant survival, nutrient accumulation, and ecosystem stability (Reich and Oleksyn, 2004; Jankju, 2013). Lastly, warming will also lead to a decrease in available habitat space and potential ecological niche complementarity for plants (Searle and Chen, 2020), which will reduce resource inputs to belowground communities (Catford et al., 2020), ultimately leading to a reduction in the SMF.
The effect of pH on the SMF in this study showed a significant negative correlation in forest, shrubland, and desert (Fig. 4). Soil pH directly or indirectly affects soil nutrient levels and is the most important determinant of soil function (Ding and Wang, 2021). Many researchers have previously reported the occurrence of soil acidification in Chinese forests (Liu et al., 2010). Except for the Northwest China, forest soils in China were significantly acidified between 1981 and 1985 and between 2006 and 2010 (Huang et al., 2014). In this study, the mean pH in forest was 6.71, similar to that in the previous study (Wu and Liu, 2019). Increased soil acidification may negatively affect timber production and lead to biodiversity loss and reduced ecosystem stability (Chen et al., 2012; Azevedo et al., 2013; Ceulemans et al., 2013). To maintain forest productivity and ecological functions, more attention should be given to forest soil acidification and its negative impacts on China’s forests. Soil pH is also an important driver of the SMF in shrubland and desert, where soil alkalization typically disrupts nutrient uptake by roots and reduces soil physical properties due to high salt stress, leading to soil nutrient loss. In general, soil pH in shrubland and desert is lower than that in forest and grassland, which may reduce the interannual variability in community biomass (Wang and Qin, 2017). However, the effect of pH on the SMF is not always negative. The result of this study showed that, in grassland, the SMF was positively correlated with pH but not significant (Table 3). These correlation results are consistent with the findings that increasing soil pH promotes the accumulation of SOC and N in semiarid grassland soils (Zhang et al., 2021a). Therefore, a slight increase in soil pH of grassland (weakly acidic to weakly alkaline) optimizes the grassland soil environment and allows plants to grow better.
SM is often recognized as a limiting factor for many terrestrial ecosystem processes and is a key factor for plant growth and soil microbial activity (Moyano et al., 2013). Increased precipitation improves plant productivity and N inputs to the soils (Meng et al., 2023). In this study, SM was the strong driving factor of the SMF in grassland. It may be related to the larger root-to-shoot ratio in arid and semiarid grasslands (Mokany et al., 2006). Approximately 50%-90% of the annual net primary productivity of grassland occurs in roots (Mokany et al., 2006; Bi et al., 2020), and root production, senescence, and decomposition are highly influential processes in nutrient cycling in grassland ecosystems (Li et al., 2021b). However, SM was the second most important predictor of the SMF in shrubland. SM affects vegetation development, C and N cycling, and microbial processes, especially in arid ecosystems (Li et al., 2021b; Yu et al., 2021). In addition, the presence in shrubland depletes SM and reduces herbaceous plant diversity (Zhao et al., 2021). Therefore, a positive contribution of SM to the SMF in shrubland was also shown in this study, where increased SM alleviated water limitations in shrubland. Similar studies reported that precipitation affects the elemental content in shrubland ecosystems (Jiang et al., 2017; Liu et al., 2023). It should be noted that, in this study, the main factors driving the SMF in forest differed from those in grassland, shrubland, and desert. In particular, SM did not have a significant direct effect on the SMF in forest but indirectly affected the SMF by influencing soil pH and NDVI. This is largely because most forest ecosystems have abundant SM, so SM is not a limiting factor. Microbial diversity has a positive role in maintaining the SMF (Jia et al., 2022). Consistent with the findings of previous study (Delgado-Baquerizo et al., 2020), we found that SBI positively impacted the SMF under the four LULC types.
Aridity affects a wide range of ecosystem structures and functional attributes, such as nutrient cycling, plant productivity, and microbial communities (Müller and Bahn, 2022). In this study, AI was a key factor affecting the SMF in desert. Increased aridity may lead to rapid changes in soil microbial composition, which in turn may trigger changes in plant‒microbe interactions, leading to changes in nutrient cycling and plant community composition (Xu et al., 2024). Recent study has shown that when AI is 0.46, 0.30, and 0.20, there is a sudden decline in plant productivity, soil fertility, and ecosystem richness, respectively (Berdugo et al., 2020). In this study, desert had an AI value of 0.13, the lowest of the four LULC types; thus, the SMF in desert was more sensitive to the response of AI. Therefore, it is important to focus on AI in desert to minimize the negative impact of aridity on ecosystem services.
The above factor analysis revealed that difference in background conditions of each LULC type led to the difference in the SMF (Cheng et al., 2021; Yang et al., 2023). The SMF itself varies, and the main factors that drive changes in the SMF may also change. Attention needs to be given to the different types of work involved in the SMF assessment and ecosystem management. For example, efforts to restore degraded land and improve biodiversity need to be carried out in a way that is adapted to local conditions, taking into account, in particular, changes in MAT under different LULC types.

5. Conclusions

The results of this study revealed that the SMF varied greatly among different LULC types in the drylands of China, and the SMF in forest, grassland, and shrubland was significantly greater than that in desert. MAT had a negative direct effect on the SMF under the four LULC types, with decreasing effects from forest, grassland, and shrubland to desert. Other factors affecting the SMF of four LULC types were pH (in forest), SM and SBI (in grassland), SM (in shrubland), and AI (in desert). The effect of pH on the SMF in forest, shrubland, and desert was negative, and pH can affect the SMF both directly and indirectly by influencing SBI. This study demonstrated that the SMF was driven by different factors under different LULC types in the drylands of China, providing new insights for assessing the drivers of the SMF in global drylands.

Authorship contribution statement

SONG Boyi: formal analysis and writing - original draft; ZHANG Shihang: conceptualization and formal analysis; LU Yongxing: conceptualization and writing - review & editing; GUO Hao: methodology; GUO Xing: conceptualization; WANG Mingming: formal analysis; ZHANG Yuanming: supervision; ZHOU Xiaobing: writing - review & editing; and ZHUANG Weiwei: validation. All authors approved the manuscript.

Declaration of conflict of interest

ZHANG Yuanming is a Chief Editor of Regional Sustainability and was not involved in the editorial review or the decision to publish this article. ZHOU Xiaobing is a Young Editorial Board member of Regional Sustainability and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

Acknowledgements

This work was supported by the Tianshan Talent Training Plan of Xinjiang, China (2022TSYCLJ0058; 2022TSYCCX0001) and the National Natural Science Foundation of China (2022D01D83; 42377358).

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