Hydro-saline synergy regulates ecosystem multifunctionality via microbial biomass in semi-arid grasslands, China

doi:10.1016/j.jaridl.2026.03.009

Journal of Arid Land

2026, Vol. 18

Issue (3): 524-546 DOI: 10.1016/j.jaridl.2026.03.009 CSTR: 32276.14.JAL.20250372

Research article

Hydro-saline synergy regulates ecosystem multifunctionality via microbial biomass in semi-arid grasslands, China

HU Jinpeng, HE Yuanyuan, LI Yuanhong, ZHANG Yuewei, ZHANG Jinlin^*(

)

State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Engineering Research Center of Grassland Industry, Ministry of Education; Center for Grassland Microbiome; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730000, China

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Abstract

Soil water content and salinity critically regulate soil microbial composition, plant community structure, and ecosystem multifunctionality (EMF) in semi-arid grasslands. However, the mechanisms through which drought (D), saline-alkaline (SA), and their combined (DSA) stress influence these ecological components remain poorly understood. This study investigated these mechanisms along natural gradients in a semi-arid grassland of China by analyzing soil physical-chemical properties, microbial communities, and vegetation characteristics. The results showed that as the environmental stress shifted from the D group to the DSA group and then to the SA group, soil electrical conductivity significantly increased, while urease and phosphatase activities significantly decreased. Soil organic carbon, total nitrogen, total phosphorus, and microbial biomass carbon and nitrogen were lower in the D and SA groups than in the DSA group. Meanwhile, plant biomass showed an increasing trend along the treatment gradient, primarily driven by dominant species, while plant diversity did not exhibit significant differences. Further analysis identified the soil water content and salinity as the key determinants of soil microbial diversity and community complexity. Soil enzyme activities exhibited contrasting relationships with microbial composition, correlating positively with the richness of bacterial amplicon sequence variants (ASVs) but negatively with the richness of fungal ASVs. Notably, microbial biomass, which varied significantly across different groups, emerged as a key predictor of changes in EMF, with its critical role confirmed through structural equation modeling. These findings collectively elucidate the responses of ecological communities to synergistic soil hydro-saline stress in semi-arid ecosystems, while highlighting the critical role of microbial biomass in maintaining EMF.

Key words： hydro-saline soil microbial enzyme activity ecosystem multifunctionality semi-arid grassland

Received: 11 August 2025 Published: 31 March 2026

Corresponding Authors: *ZHANG Jinlin (E-mail: jlzhang@lzu.edu.cn)

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Cite this article:

HU Jinpeng, HE Yuanyuan, LI Yuanhong, ZHANG Yuewei, ZHANG Jinlin. Hydro-saline synergy regulates ecosystem multifunctionality via microbial biomass in semi-arid grasslands, China. Journal of Arid Land, 2026, 18(3): 524-546.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.03.009 OR http://jal.xjegi.com/Y2026/V18/I3/524

Fig. 1 Soil properties and soil microbial biomass in different groups. (a), pH; (b), SWC (soil water content); (c), EC (electrical conductivity); (d), SOC (soil organic carbon); (e), TN (total nitrogen); (f), TP (total phosphorus); (g), MBC (microbial biomass carbon); (h), MBN (microbial biomass nitrogen); (i), MBP (microbial biomass phosphorus). D, drought; SA, saline-alkaline; DSA, combined stress of D and SA. Boxes indicate the IQR (interquartile range, 75^th to 25^th of the data). The median value is shown as a line within the box. Outlier is shown as black point. Whiskers extend to the most extreme value within 1.5×IQR. Different lowercase letters indicate significant differences at P<0.050 level among different groups.

Table 1 Soil ions content and soil enzyme activities in different groups

Fig. 2 Plant community features and their relationship with SWC and EC. (a), Venn diagram showing the number of unique and shared plant species; (b), vegetation cover; (c), CWM (community-weighted mean value) of height; (d), total biomass; (e1-e4), relationships between plant community features and SWC; (f1-f4), relationships between plant community features and EC. Different lowercase letters in Figure 2b-d indicate significant differences at P<0.050 level among different groups. Shaded area in Figure 2e1-e4 and f1-f4 indicates the 95.00% confidence interval.

Fig. 3 Composition of plant communities and species attributes. (a), species cover; (b), Shannon-Wiener index; (c), Simpson index; (d), non-metric multidimensional scaling (NMDS) analysis; (e1-e3), plant density; (f1-f3), importance value (IV); (g1-g3) biomass. A. mongolicum, Allium mongolicum Regel; A. capillaris, Artemisia capillaris Thunb.; A. gobicus, Asparagus gobicus Ivanova ex Grubov; C. album, Chenopodium album L.; K. gracile, Kalidium gracile Fenzl.; L. angustus, Leymus angustus (Trin.) Pilg.; N. tangutorum, Nitraria tangutorum Bobrov; P. harmala; Peganum harmala L.; R. songarica, Reaumuria songarica (Pall.) Maxim; Z. mucronatum, Zygophyllum mucronatum Maxim. Different lowercase letters indicate significant differences at P<0.050 level among different groups or plant species. Bars in Figure 3e1-e3, f1-f3, and g1-g3 are standard errors.

Fig. 4 Amplicon sequence variants (ASVs), alpha diversity, NMDS, and relationships of dissimilarity distance with SWC and EC. (a1-a3), bacterial ASVs and alpha diversity; (b1-b3), fungal ASVs and alpha diversity; (c), NMDS analysis for bacteria; (d), NMDS analysis for fungi; (e1 and e2), relationships of dissimilarity distance with SWC and EC for bacteria, respectively; (f1 and f2), relationships of dissimilarity distance with SWC and EC for fungi, respectively. Different lowercase letters in Figure 4a1-a3 and b1-b3 indicate significant differences at P<0.050 level among different groups. ACE, abundance-based coverage estimation.

Fig. 5 Co-occurrence networks and topological properties of networks. (a1-a3), bacterial networks in different groups; (b1-b3), fungal networks in different groups; (c1-c3), ratio of links to nodes, average degree, and density in different groups; (d1-d3), within-module connectivity (Zi) and among-module connectivity (Pi) plots showing the distribution of ASVs based on their topological roles and ability to predict keystone ASVs in networks, with red dashed lines on the x-axis and y-axis representing Pi=0.62 and Zi=2.5, respectively. In Figure a1-a3 and b1-b3, nodes represent ASVs. The node color represents the top 6 phyla, and node size indicates the degree.

Table 2 Topological properties of correlation networks for bacteria and fungi in different groups

Fig. 6 Drivers of ecosystem multifunctionality (EMF). (a), SEM (structural equation modeling) showing the direct and indirect effects of factors; (b), contributions of biotic and abiotic factors to soil microbial ASVs and EMF based on Spearman's correlation and regression model; (c), PCA (principal component analysis) result. GFI, comparative fit index; RMSEA, root mean square error of approximation; PC, principal component; UE, urease; NP, neutral phosphatase; AKP, alkaline phosphatase; β-GC, β-glucosidase; NAG, N-acetyl-glucosaminidase; α-GC, α-glucosidase. ^*, P<0.050 level; ^***, P<0.001 level.

Table S1 Distribution of plants in each group

Fig. S1 Traits of the dominant plant species (R. songarica). (a), leaf length; (b), chlorophyll a; (c), chlorophyll b; (d), total chlorophyll; (e), Na⁺ content; (f), K⁺ content. D, drought; SA, saline-alkaline; DSA, combined stress of D and SA; DW, dry weight. Boxes indicate the IQR (interquartile range, 75^th to 25^th of the data). The median value is shown as a line within the box. Whiskers extend to the most extreme value within 1.5×IQR. Different lowercase letters indicate significant differences at P<0.050 level among different groups.

Fig. S2 Relative abundance of microbial communities in different soil groups. (a and b), bacterial and fungal relative abundance at phylum level, respectively; (c and d), bacterial and fungal relative abundance at genus level, respectively.

Table S2 Classification of nodes to identify keystone taxa in bacterial and fungal networks

Table S3 Distribution of keystone taxa in bacterial and fungal networks

Fig. S3 Soil carbon (C; a), nitrogen (N; b), and phosphorus (P; c) cycles and ecosystem multifunctionality (EMF; d). Different lowercase letters indicate significant differences at P<0.050 level among different groups.

Fig. S4 Structural equation modeling (SEM) showing the direct and indirect effects of drivers of soil microbial amplicon sequence variants (ASVs). (a), bacteria; (b), fungi. EC, electrical conductivity; SWC, soil water content; SOC, soil organic carbon; CWM, community-weighted means; GFI, goodness-of-fit index; RMSEA, root mean square error of approximation; ^**, P<0.010 level; ^***, P<0.001 level.


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