Joint effects of shrub encroachment and aridity on herbaceous elemental traits and biogeochemical coordination in northern grasslands

Joint effects of shrub encroachment and aridity on herbaceous elemental traits and biogeochemical coordination in northern grasslands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Joint effects of shrub encroachment and aridity on herbaceous elemental traits and biogeochemical coordination in northern grasslands Peng He, Jiang Chen, Josep Peñuelas, Jordi Sardans, Lina Xie, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8468769/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims Shrub encroachment is projected to intensify under increasing drought in arid and semi-arid grasslands, potentially altering species interactions and ecosystem processes. Although leguminous shrubs are known to redistribute soil resources, their influence on the foliar elementomes and biogeochemical niches of coexisting herbs remains poorly understood. Methods Here, we conducted a large-scale transect survey across the Inner Mongolian steppe, measuring leaf macro- and micro-elements of Caragana shrubs and associated herbaceous species, alongside key soil properties. We developed a multidimensional framework that integrates biogeochemical niches with foliar elemental networks to evaluate shrub-herbaceous interactions. Results We found that Caragana generally reduced elemental accumulation in understory herbs, but this inhibition weakened with increasing aridity, supporting the stress-gradient hypothesis. Shrub presence and aridity jointly influenced herbaceous elemental traits, with grasses and forbs showing contrasting responses. The biogeochemical niche distance between forbs and grasses increased with aridity, indicating that shrubs may promote their resource-use partitioning. Meanwhile, we found that shrub encroachment increased nodes and edges while reducing clustering and edge density in foliar elemental networks of understory plants, suggesting that the herbs may form less integrated networks as a trade-off to lower their construction costs. Structural equation models further revealed that shrub height and soil fertility indirectly affected herbaceous productivity by weakening network connectivity, implying reliance on specific elemental phenotypes under stress. Conclusions Our findings highlight the utility of multidimensional elemental frameworks for understanding grassland responses to climate change and woody encroachment, providing novel mechanistic insights into biogeochemical cycling in degraded northern grasslands. woody encroachment drought stress-gradient hypothesis niche theory elemental composition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Shrub encroachment, the increasing density, cover, and biomass of native woody plants, has become a dominant vegetation shift in arid and semi-arid grasslands worldwide (Van Auken 2009 , Stevens et al. 2017 ). This transformation is driven by several interacting factors such as overgrazing, fire suppression, and climate change (Archer et al. 2017 ). Since water availability is the main limiting factor in drylands, intensifying drought is expected to accelerate shrub expansion, as shrubs typically have deeper roots, higher water-use efficiency, and greater drought tolerance than co-occurring herbs (Ploughe et al. 2019 , Maestre et al. 2021). Understanding how aridity and shrub encroachment jointly regulate grassland structure and function is therefore crucial for predicting ecosystem responses under climate change (Zhu et al. 2022 ). Shrub encroachment can strongly reshape community composition and ecosystem processes, with cascading effects (Eldridge et al. 2011 , Zhou et al. 2019 ). It has long been associated with land degradation, such as reduced herbaceous cover, lowered soil infiltration efficiency, and declines in ecosystem function, ultimately contributing to processes of desertification (Eldridge and Soliveres 2015 ). However, in recent decades, shrub-grass relationships have become a classic paradigm for studying interspecific interactions (Maestre et al. 2009 , Cui et al. 2023 ). Increasing evidence shows that shrubs may provide net benefits by facilitating understory species through diverse mechanisms (Ding and Eldridge 2023 , Aweto 2024 ). The positive effects of shrubs on understory vegetation are largely attributed to their capacity to ameliorate microhabitat conditions and to provide physical refuge for understory species (Soliveres et al. 2011 ). First, shrubs can reduce herbivory pressure through their taller and denser canopies (Saixiyala et al. 2017 ). Second, by intercepting rainfall and trapping weathered sediments, shrubs promote localized resource accumulation (Moro et al. 1997 ). They can buffer abiotic stress by reducing solar radiation and evapotranspiration (Liu et al. 2021 ). Furthermore, shrubs typically possess deep, laterally extensive root systems that could enhance water and nutrient availability through hydraulic lift and associations with microbial symbionts (Wang et al. 2017 ). Leguminous shrubs, in particular, can alleviate nitrogen (N) limitation via symbiotic N fixation (Michalet et al. 2015 , Zhao et al. 2023 ). Together, these processes increase spatial heterogeneity and enrich resources beneath canopies, forming well-known 'fertile islands' (Schlesinger et al. 1996 , Reynolds et al. 1999 , Li et al. 2019 ). While the fertile island effect is widely documented, most evidence is local or regional (Ward et al. 2018 , Jia et al. 2022 ), and its magnitude and drivers across broad environmental gradients remain poorly understood (but see Ding and Eldridge 2021 , Velasco et al. 2024 ). Shrubs frequently act as nurse plants that facilitate vegetation recovery, with their positive effects often intensifying under harsh conditions (Gómez-Aparicio 2009 ). This pattern aligns with the stress-gradient hypothesis (SGH), which predicts that the frequency, intensity, and importance of facilitation increase monotonically with environmental severity (Bertness and Callaway 1994 , Brooker et al. 2005 ). However, the generality of the SGH in shrub-encroached ecosystems remains contested because shrub effects are highly context dependent (Maestre et al. 2005 ). Shrubs may buffer microclimatic stress, improve soil fertility, and enhance herbaceous recruitment, yet they can also impose strong competition for resources, especially in arid environments (Soliveres et al. 2011 ). Increasing evidence indicates that shrub-grass interactions often follow a unimodal pattern: facilitation peaks under intermediate stress but declines or shifts to competition under extreme conditions (Michalet et al. 2014 , Noumi et al. 2016 ). Such discrepancies may depend on the traits of the interacting species, the parameters used to evaluate plant performance, as well as the type and intensity of the stress imposed (He et al. 2013 , Yang et al. 2022 ). Therefore, facilitation and competition commonly coexist in shrub dominant ecosystems, with their balance shifting along environmental gradients. In parallel, shrub canopies can modify soil moisture regimes, further regulating nutrient availability beneath their cover (Reynolds et al. 1999 ). Collectively, these processes position shrubs as key regulators of local biogeochemical cycling (Du et al. 2024 ). Comparing multi-elemental composition ( i.e ., elementomes) between shrubs and their associated herbaceous species may deepen our understanding of nutrient-mediated interspecific interactions and offer novel empirical tests of the SGH. The foliar elementome integrates essential macro- and microelements that regulate plant structural development, metabolism, photosynthesis, and stress tolerance. It can act both as an effect trait, reflecting species-specific resource-use strategies, and as a response trait, indicating plant fitness and adaptation under biotic and abiotic stresses. Because each species has distinct elemental requirements for structural and physiological functions, the elementome is increasingly recognized as a key functional dimension for defining trophic niches and interspecific interactions in a quantifiable way (Fernández-Martínez 2022 ). Building on Hutchinson's (1957) n -dimensional hypervolume niche concept, recent studies have proposed the biogeochemical niche hypothesis (Peñuelas et al. 2019 ) and the multidimensional stoichiometric niche framework (González et al. 2017 ), which posit that species occupy discrete positions in a multivariate elemental space defined by tissue concentrations of macro- and microelements. These approaches allow quantification of niche volume and overlap across distinct functional groups, such as woody versus herbaceous species (He et al. 2025 ). However, despite substantial empirical support (Sardans et al. 2021 , Zhang et al. 2022 ), it remains unknown how shrub encroachment and drought reshape the biogeochemical niches of co-occurring herbaceous plants, or whether differences in functional group identity led to divergent responses. Meanwhile, multi-element network analysis has emerged as a complementary approach for characterizing species' ecological strategies within high-dimensional elemental space (Zhang et al. 2021 , Zuo et al. 2024 ). This method extends plant trait network analysis by treating individual elements as nodes and their pairwise correlations as edges (He et al. 2020 , Li et al. 2022 ). A suite of network topology metrics can then summarize structural properties of the network, describing how nodes are arranged and interconnected (Ye et al. 2024 ). Commonly used metrics include connectivity (the degree of association among elements), modularity (the extent to which elements form tightly linked clusters), and centrality (the relative influence of a given element within the network). These metrics can provide insights into how tightly species are linked through elemental composition and how changes in one species may cascade through the community. Previous studies have demonstrated that shifts in network topology are often linked to species' stress tolerance, with both elemental connectivity and modularity exhibiting plastic responses to increasing stress intensity (Rao et al. 2023 , Medeiros et al. 2024 ). Notably, because of its scalability across taxa and spatiotemporal scales, this approach holds strong potential as a framework for disentangling shrub-grass interaction mechanisms under environmental change. In this study, we conducted a large-scale transect survey across the Inner Mongolian steppes to investigate how shrub encroachment alters the foliar elementome of understory grasses and its implications for community structure. We first proposed a multidimensional elemental framework that integrates biogeochemical niche analysis with multi-element network construction to elucidate the mechanisms of shrub-grass interactions. The biogeochemical niche quantifies the multidimensional space of foliar elemental concentrations, reflecting species-specific resource acquisition strategies, while the multi-element network captures correlations among species' elemental profiles, where high connectivity indicates strong coordination and high modularity indicates functional divergence. We anticipate that shrub encroachment and drought will jointly reshape the multidimensional elemental composition of co-occurring herbaceous species, as illustrated in a hypothetical schematic diagram (Fig. 1 ). Moreover, owing to differences in ecological strategies among functional groups, grasses and forbs are expected to exhibit distinct responses. Specifically, we hypothesize that: (I) the leguminous shrub Caragana Fabr. may facilitate elemental accumulation in its understory by forming fertile islands, with such facilitation intensifying linearly with increasing aridity and declining soil fertility; (II) shrub presence and aridity will jointly shape the elemental niches of understory herbaceous species, enhancing niche partitioning between functional groups and reducing network connectivity under environmental stress; and (III) shrub traits and environmental variables may indirectly affect community structure and function by modifying the elemental composition of herbaceous species, thereby reshaping community organization. Materials and methods Study sites We selected eight study sites along a ~ 1600 km east–west transect across the Inner Mongolian grasslands in northern China (Fig. 2 ). These sites were chosen to span a broad aridity gradient from semiarid to arid zones, while keeping other factors (e.g., grazing intensity, land-use history) as comparable as possible. Site selection followed two criteria: ( i ) grasslands where shrub encroachment by Caragana species was well established and representative of local vegetation dynamics, and ( ii ) locations with accessible long-term climatic data. Six of the sites were encroached by Caragana stenophylla , and two by C. microphylla . Both are symbiotic nitrogen-fixing legumes that are phylogenetically close and share similar physiological traits (Zhang et al. 2009 ). The transect also supported a diverse assemblage of herbaceous species, encompassing both grass and forb functional groups. Comprehensive information on the geographic coordinates, dominant vegetation, and soil classifications of the eight sampling sites were provided in Table 1 . To quantify aridity, we extracted the Standardized Precipitation Evapotranspiration Index (SPEI) from a high-resolution drought dataset for mainland China (Zhang et al. 2025 ). Mean 6-month SPEI values were calculated for the past 20 years at each site, as this time scale reliably captures ecosystem-level drought trends. Higher SPEI values indicate wetter conditions, whereas lower values indicate higher aridity. Across sites, SPEI values ranged from − 0.26 in the arid west to 0.39 in the more mesic east (Table 1 ). Field survey and sampling Fieldwork was conducted in July-August 2022. At each site, six 20 × 20 m plots were established within homogeneous grasslands encroached by Caragana shrubs, with a minimum spacing of 50 m to ensure spatial independence. Within each plot, one healthy adult shrub was randomly selected as the focal shrub, hereafter referred to as the 'shrub patch'. A paired 'grass patch' was then established at least 2 m away from the shrub canopy, in adjacent open grassland. This distance was chosen to ensure that the grass patch was outside the direct influence of the focal shrub's canopy ( e.g ., shade, litter deposition, root competition) and to minimize interference from neighboring shrubs, providing a valid reference for comparison. Overall, each site contained six pairs of shrub-herb patches, with a total of 48 pairs across all sampling sites. Within each shrub-grass pair, two 50 × 50 cm quadrats were established: one beneath the canopy of the focal shrub and one in the paired open grass patch. In each quadrat, all vascular plant species were identified, counted, and recorded. Reproductive height of each species was measured, and all herbaceous individuals were harvested at ground level to determine aboveground biomass. To characterize shrub traits, we measured the height as well as the maximum and minimum canopy diameters of each focal shrub. All harvested herbaceous plants were sorted by species, placed into labeled envelopes, and assigned to two functional groups: grasses and forbs. Aboveground biomass was determined by oven-drying the samples at 65°C to a constant weight. To assess soil physicochemical properties, five soil cores (0–10 cm depth) were randomly collected from each quadrat using a 3 cm diameter auger and combined into a single composite sample per quadrat. The composite samples were homogenized and passed through a 2-mm sieve to remove stones and plant debris. Each sieved sample was then divided into two subsamples: one stored at 4°C for the determination of soil NH 4 + -N and NO 3 − -N concentrations, and the other air-dried naturally for subsequent chemical analyses. Chemical analysis In the laboratory, all oven-dried plant samples were shredded and ground using a ball mill (Retsch MM200, Germany). Foliar nitrogen (N) concentrations were determined using a C/N analyzer (Vario Micro Cube, Germany). Foliar phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) concentrations were determined using inductively coupled plasma mass spectrometry (ICP-MS, PerkinElmer OPTIMA 3000 DV) after digestion with a mixed-acid solution consisting of 10 mL HNO 3 and 5 mL HClO 4 . Soil ammonium (NH 4 ⁺-N) and nitrate (NO 3 ⁻-N) concentrations were extracted using 2 M KCL, and the mixtures were stirred, filtered, and analyzed using a continuous flow analyzer (Scalar SAN Plus Segmented Flow Analyzer, Netherlands). Soil total carbon (C) and nitrogen (N) were determined using the C/N analyzer. Soil available phosphorus (AP) was determined spectrophotometrically (Mapada Corporation, China) after extraction with 0.5 mol L⁻¹ NaHCO 3 . Soil available potassium (AK) was determined by flame photometry following extraction with 1 M ammonium acetate. Soil organic matter (SOM) concentration was quantified colorimetrically after oxidation with a mixture of potassium dichromate and sulfuric acid (He et al. 2025 ). Data analysis All data processing, statistical analyses, and visualizations were performed in R version 4.3.1 ( R Core Team, 2023). Variables were assessed for normality, and logarithmic transformations were applied when necessary to satisfy distributional assumptions. Quantification of biogeochemical niches We characterized the biogeochemical niches of shrub and herbaceous plants at each site using foliar concentrations of ten macro- and microelements. A principal component analysis (PCA) was performed using the ' stats ' package to reduce dimensionality, and the first three principal components (PC1-PC3) were retained. Subsequently, the three PCA scores were used to construct multidimensional hypervolumes for each functional group with the ' hypervolume ' package (Blonder et al. 2018 ). To assess niche differentiation between grasses and forbs, we calculated the Euclidean distance between the centroids of their respective hypervolumes and quantified niche similarity using the Jaccard similarity index (Jaccard 1901 ). Construction of Multi-elemental Networks To explore patterns of elemental coordination, we constructed multi-element networks following established trait network approaches (Li et al. 2022 , Zuo et al. 2024 ). Each foliar element represented a node, and statistically significant pairwise correlations (| r | > 0.2, P < 0.05) formed the edges, calculated at the functional group level using mean element concentrations. An adjacency matrix was generated to represent significant connections. We then calculated network topology metrics, including connectivity, modularity, centrality, network size, and complexity, using the ' igraph ' package ( Supplementary Table S3 ). These metrics quantify the structure and integration of foliar elemental networks, providing insight into inter-element relationships and functional coordination among species. Relative Interaction Index (RII) for Shrub Effects The influence of shrubs on herbaceous plant traits and soil properties was quantified using the Relative Interaction Index ( RII ; Armas et al., 2004 ): \(\:RII=({X}_{s}-{X}_{g})/({X}_{s}＋{X}_{g})\) 1 Where X s and X g represent measurements under shrub canopies and in adjacent open grass patches, respectively. Positive RII values indicate enhancement by shrubs, whereas negative values indicate suppression. Response variables included herbaceous biomass, species richness, foliar elemental concentrations, network topology metrics, biogeochemical niche volume, and soil physicochemical properties. RII values for plant traits were averaged at the functional group level. Statistical significance of shrub effects was assessed by whether the 95% confidence intervals overlapped zero using the 'rmisc' package. Linear Mixed-Effects Models (LMMs) for Soil and Foliar Elemental Drivers We fitted LMMs to evaluate the effects of aridity and shrub encroachment on soil physicochemical properties (NH₄⁺-N, NO₃⁻-N, SOM, A-P, A-K, pH). Fixed effects included SPEI indices, shrub presence, as well as their interaction, while site identity was included as a random effect to account for spatial heterogeneity. Subsequently, we performed a PCA on six soil physicochemical variables to summarize variations in soil properties, with the first two principal components (PC1 and PC2) retained for further analyses. To examine drivers of herbaceous foliar elemental composition, additional LMMs were fitted with foliar elemental concentrations as the response variables. Fixed effects included SPEI, soil PC1 and PC2, and shrub traits (morphological and chemical), while site identity was treated as a random effect. Notably, all predictors were standardized (z-transformed, µ = 0, σ = 1) to facilitate comparison of effect sizes. Model selection was based on the Akaike Information Criterion (AIC), with the model exhibiting the lowest AIC value deemed most parsimonious. To examine the bivariate associations among the RII values of foliar elemental concentrations, soil properties, and shrub traits, we conducted Spearman's rank correlation analyses. Subsequently, analysis of variance (ANOVA) was used to evaluate differences in elemental network topology metrics and biogeochemical niche volume between plant functional groups (grasses vs. forbs) and microsites (shrub patches vs. open grass patches). Structural Equation Modeling (SEM) We used piecewise SEM (' piecewiseSEM '' package; Lefcheck, 2016 ) to disentangle the direct and indirect effects of aridity, soil properties, and shrub traits on herbaceous species richness and aboveground biomass. The a priori model was constructed based on empirical knowledge and observed bivariate relationships, incorporating pathways linking environmental drivers, foliar elementome, network topology, biogeochemical niche metrics, and community-level attributes. Model fit was evaluated using Fisher’s C statistic, with non-significant P values ( P > 0.05) indicating adequate fit. We iteratively simplified models by removing non-significant pathways and selected the most parsimonious models based on AIC values. Results Changes in soil physiochemical properties Shrub encroachment significantly affected soil physicochemical properties (Table 2 ). Soils beneath shrub canopies exhibited elevated concentrations of NH₄⁺-N, NO₃⁻-N, SOM, and AP, alongside reduced pH levels, compared to adjacent grass-dominated patches ( estimates = -0.01 ~ 0.26, all P < 0.05). Among these variables, only soil AP increased with higher SPEI values, indicating that wetter conditions promote phosphorus availability ( estimate = 1.00, P < 0.05). However, shrub encroachment and SPEI interacted negatively on soil AP ( estimate = -0.52, P < 0.001), suggesting that the presence of shrubs may partially offset the positive effects of water availability on soil phosphorus. Principal component analysis of six soil physicochemical properties identified two principal components (PC1 and PC2) that cumulatively accounted for approximately 65% of the total variance across study sites (Supplementary Table S1 ). PC1 (42.4% of the variation explained) was predominantly positively influenced by soil NO₃⁻-N, SOM, and AP concentrations, whereas PC2 (21.7% of the variation explained) was primarily positively correlated with NH₄⁺-N concentrations. These components captured the major gradients in soil nutrient availability and were thus retained as integrated proxies of site-level edaphic fertility in further analyses. Determinants of foliar elementome in plants The best-fitting models indicated that variation in herbaceous foliar elemental composition was primarily driven by aridity and plant functional group (Supplementary Table S2 ). Foliar concentrations of Fe, Mn, and Cu in grasses, as well as N and Mg in forbs, declined with increasing SPEI (Fig. 3 , estimates = -2.81 to -0.72, all P < 0.05). Across all conditions, grasses exhibited consistently lower foliar elemental concentrations than forbs (all P < 0.05). Herbaceous foliar elemental concentrations were also positively associated with soil PC2 scores, with estimates ranging from 0.07 to 0.13 (all P < 0.05). Shrub encroachment reduced herbaceous elemental concentrations, as indicated by negative correlations between herbaceous foliar N, K, Mg, and Zn and the corresponding shrub foliar concentrations within shrub patches (estimates = -0.20 to -0.11, all P < 0.05). In contrast, shrub foliar concentrations increased with SPEI, with estimates ranging from 0.48 to 0.88 (all P < 0.05), suggesting that shrubs accumulated more nutrients under wetter conditions. Moreover, shrub size, including canopy area and height, was positively associated with shrub foliar elemental concentrations (estimates = 0.02 to 0.11, all P < 0.05). Herbaceous plants from different sites showed clear biogeochemical niche differentiation, as defined by their foliar elementomes (Supplementary Figure S1 ). Although aridity did not significantly influence biogeochemical niche characteristics, niche volume tended to decline following shrub encroachment. Shrub encroachment also markedly altered the topology of herbaceous foliar elemental networks (Fig. 4 ). In shrub patches, herbaceous plants exhibited significantly higher node and edge counts, network diameter, and average path length than those in grass patches (all P < 0.05). By contrast, edge density and clustering coefficient were significantly reduced under shrub encroachment (all P < 0.05). Collectively, these results indicate a shift toward more diffuse and less interconnected elemental networks of herbaceous plants in the presence of shrubs. Factors affecting the intensity of shrub-grass interactions Relative interaction indices revealed that the effects of shrubs on herbaceous foliar element accumulation were element- and site-specific, and could be positive, negative, or neutral. Overall, foliar elemental accumulation differed significantly between functional groups in response to shrub presence (Fig. 5 ). Additionally, there were significant interactions between functional group and SPEI on foliar N, P, and Cu concentrations (χ² = 4.88–19.97, all P < 0.05). We found that the RIIs of foliar N and P in forbs, and foliar Zn in grasses, decreased with increasing SPEI values, indicating that shrub facilitation weakened under less arid conditions (Fig. 5 ). Spearman rank correlations yielded consistent results, showing significant negative associations between SPEI and the RIIs of foliar Ca, Fe, and Zn (Fig. 6 , all P < 0.05). Conversely, the RIIs of foliar Fe, Mn, and Zn increased with the RIIs of soil available phosphorus and NO₃⁻-N (all P < 0.05), indicating that enhanced soil fertility strengthened shrub effects on herbaceous nutrient accumulation. We also found that the RII of biogeochemical niche distances between grasses and forbs decreased with increasing SPEI, implying stronger niche differentiation between functional groups under more severe aridity (Supplementary Figure S2 ). Drivers of community aboveground biomass and species richness The piecewise SEM revealed that aridity, soil physicochemical properties, and shrub traits influenced herbaceous community biomass and structure by modulating foliar elemental composition and network architecture (Fig. 7 ). Specifically, both the SPEI index and shrub height directly enhanced herbaceous aboveground biomass ( std. estimates = 0.43 ~ 0.77, all P < 0.05), while higher soil fertility (as defined by PC1 scores) and shorter shrubs could promote species richness ( std. estimates = -0.37 and 0.22, both P < 0.01). Aridity also exerted indirect effects by intensifying the negative relationships between foliar elemental concentrations and biomass accumulation (Fig. 7 a, std. estimate = -0.40, P < 0.001). Moreover, shrub height indirectly enhanced aboveground biomass by increasing edge density and reducing the clustering coefficients of elemental networks (Fig. 7 b, std. estimates = 0.79 and − 0.81, both P < 0.001). Likewise, soil fertility indirectly promoted biomass through its positive associations with both edge density and clustering (Fig. 7 b, std. estimates = 0.24 and 0.44, both P < 0.05). Notably, the biogeochemical niche volume of herbaceous plants had no significant direct or indirect effects on either community biomass or species richness. Discussion In arid and semi-arid grasslands, vegetation is typically co-limited by multiple and intercorrelated essential elements, underscoring the importance of a multidimensional elemental framework for assessing interspecific interactions and ecosystem functioning (Moreno-Jiménez et al. 2019 , Osborne et al. 2022 , Zuo et al. 2024 ). Understanding how shrub encroachment and aridity influence foliar elemental differences between shrubs and understory herbaceous species is critical for assessing the biogeochemical consequences of shrub encroachment and the mechanisms underlying shrub-grass interactions (Du et al. 2024 ). In this study, we found that the encroachment of the leguminous shrub Caragana Fabr. significantly altered soil fertility, herbaceous foliar elementome, community structure, and aboveground biomass. These results suggest that both biotic interactions and climatic stressors together regulate key below- and aboveground ecosystem processes in northern grasslands, although the degree of influence may vary depending on local environmental conditions. Our findings indicate that shrub encroachment significantly altered soil physicochemical properties, notably by increasing concentrations of soil NH₄⁺-N, NO₃⁻-N, SOM, and A-P, while reducing soil pH. These results align with the widely accepted view that shrubs can enhance resource heterogeneity and promote fertile island formation beneath their canopies (Filazzola and Lortie 2014 , Ale et al. 2023 ). The underlying mechanisms of these positive effects are likely driven by multiple abiotic and biotic processes. First, shrub canopies can contribute to resource accumulation within shrub patches by intercepting aeolian sediments, modifying litter inputs, and redistributing stemflow-mediated nutrients, whereas simultaneously increasing evapotranspiration and nutrient loss in the adjacent interspaces (Schlesinger et al. 1996 , Eldridge et al. 2011 ). Second, shrubs can further enhance local nutrient enrichment by stimulating key biotic processes, such as root turnover, biological nitrogen fixation, and hydraulic lift (Moro et al. 1997 , Zhang et al. 2018 ). Our research supports these mechanisms, showing that Caragana species, as nitrogen-fixing legumes, possess root nodules that augment the availability of plant-accessible nitrogen (Zhao et al. 2023 ). This mechanism is responsible for the sustained increase in soil NH₄⁺-N and NO₃⁻-N concentrations under shrub canopies. Moreover, shrub encroachment may promote the activity of soil micro- and macro-organisms, thereby enhancing nutrient cycling through processes such as decomposition, mineralization, and mycorrhizal interactions (Maestre et al. 2021, Zhang et al. 2023 ). In a previous study conducted along the same transect, we observed higher soil nematode abundance beneath shrubs compared to adjacent grass patches (Xie et al. 2021 ). Notably, we found that shrub-induced increases in soil A-P were more pronounced at xeric sites, suggesting that shrub encroachment may alleviate phosphorus limitation under water stress. This effect could be driven by the rhizosphere exudation of organic acids from leguminous shrubs, which enhance acid phosphatase activity and promote phosphorus solubilization (Shen et al. 2004 ). Additionally, elevated soil organic carbon under shrub canopies may increase the release of organic anions that displace phosphate from sorption sites, thereby enhancing its bioavailability (Jia et al. 2022 ). Despite the substantial enhancement of soil fertility beneath shrub canopies, we observed generally negative correlations between the foliar elemental concentrations of shrubs and co-occurring herbaceous species, particularly for N, K, Mg, and Zn. These results suggest that competitive or inhibitory interactions between shrubs and herbaceous species may limit nutrient uptake or accumulation in neighboring plants. This pattern aligns with a previous study by Ward et al. ( 2018 ), who found that grasses and forbs did not benefit from increased nutrient availability beneath savanna shrub species. However, our results also suggest that nutrient competition between shrubs and grasses may be more pronounced at mesic sites, likely due to the greater availability of water and nutrients, which intensify competitive interactions. This is further supported by the Relative Interaction Index ( RII ), which showed that the RIIs of foliar P, Ca, Fe, and Zn were generally neutral or positive at xeric sites. This finding aligns with the SGH hypothesis, indicating that shrubs may enhance resource availability for neighboring plants only under extreme drought conditions (Bertness and Callaway 1994 , He et al. 2013 , Ale et al. 2023 ). Notably, the mobility of P, Ca, Fe, and Zn in soil in soil primarily depends on mass flow and diffusion processes, which are particularly sensitive to declining soil water potential (He et al. 2024 ). Therefore, the facilitation of shrubs in arid habitats may be attributed to their root hydraulic lifting mechanism, through which they can alleviate nutrient limitations for understory herbaceous plants. Consistent with our hypothesis II, we found that shrub presence and aridity jointly shape the multidimensional elemental traits of herbaceous species, with distinct responses observed between two functional groups. In general, forbs exhibited higher foliar elemental concentrations than grasses, a pattern that has been consistently reported in previous studies and likely stems from inherent differences in stomatal and root traits (Kaspari et al. 2021 , Tian et al. 2021 , Zhou et al. 2024 ). Specifically, grasses possess dumbbell-shaped stomata and fibrous root systems, which generally result in lower transpiration rates and nutrient uptake efficiency. In contrast, forbs typically have kidney-shaped stomata and thicker roots with higher carboxylate exudation, which can facilitate more effective mobilization of mineral-bound nutrients under resource-limited conditions (Li et al. 2017 , Chen et al. 2023 ). These findings suggest that forbs may have a competitive advantage over grasses in nutrient acquisition, likely due to their deeper root systems and greater mycorrhizal dependency (Wieczorkowski and Lehmann 2022 ). This adaptation may enable forbs to more effectively acquire and mobilize nutrients under the combined stresses of shrub encroachment and drought. Our results also support significant biogeochemical niche partitioning between forbs and grasses, as evidenced by the increasing distance between their respective hypervolume centroids along the aridity gradient. This pattern may reflect an indirect facilitative effect of shrubs, whereby the fertile island effect reduces competition for essential elements among co-occurring herbaceous species of different functional groups. Such facilitation may promote complementary resource utilization within the habitat. Similar findings were reported in the semi-arid Patagonian steppe, where facilitation by the nurse shrub Adesmia volckmannii outweighed competitive interactions among beneficiary species, thereby enhancing their coexistence beneath shrub canopies (Armas et al. 2008 ). These results extend the biogeochemical niche hypothesis, which posits that coexisting species tend to minimize similarity in elemental composition to reduce competition (Peñuelas et al. 2019 , Sardans et al. 2021 ). Our study provides empirical evidence that this assumption also applies to shrub-herbaceous assemblages and that the effect becomes more pronounced under increasingly arid conditions. Additionally, we observed a significant decrease in the hypervolume of herbaceous plants following shrub encroachment, suggesting that shrubs may facilitate the expansion of the realized niche of understory grasses. Collectively, we hypothesize that shrubs may promote the coexistence and fitness of herbaceous species by both expanding their community niche occupancy and enhancing niche partitioning (Bruno et al. 2003 , He and Bertness 2014 ). We found that shrub encroachment substantially restructures the multi-element network of herbaceous species. Specifically, shrub presence increased the number of nodes and edges in herbaceous foliar elemental networks, while reducing the clustering coefficient and edge density. This reconfiguration suggests a transition toward a looser, less integrated elemental network, which may indicate weakened coordination among elements and a decoupling of underlying physiological processes (He et al. 2020 , Zhang et al. 2021 ). This pattern also implies a reduction in network connectivity of understory herbaceous species, suggesting that they may respond to the localized microenvironment within shrub patches by constraining their foliar multi-elemental space and restricting inter-element synergism. Similar patterns have been reported across diverse ecosystems. For example, a transect study in arid Californian ecosystems showed that increasing aridity led to less interconnected and more simplified trait network architectures in plant communities (Medeiros et al. 2024 ). Likewise, a manipulation experiment demonstrated that low light stress significantly reduced trait network connectivity in Potamogeton maackianus (Rao et al. 2023 ). Furthermore, Zuo et al. ( 2024 ) observed that correlations among bio-elements in wetland plants exhibited lower connectivity, complexity, and stability under cold and saline conditions. Collectively, these studies indicate that plant trait networks vary systematically with resource availability and environmental stress, reflecting species' adaptive strategies. Because maintaining stable and complex multi-elemental correlations often entails high construction costs, we thus assume that shrub-induced loosening of network connectivity may represent an adaptive response that enhances the ability of herbaceous species to persist in stressful habitats (Alon 2003 , Flores-Moreno et al. 2019, Siwicka et al. 2020 ). The SEM analysis provides novel evidence that aridity, soil fertility, and shrub traits, both directly and indirectly, influence the aboveground biomass and species richness of understory grasses by shaping their multidimensional elemental characteristics. This finding is consistent with our hypothesis III, underscoring the mechanistic importance of nutrient coordination patterns in mediating key ecosystem-level processes. Notably, we observed a direct positive effect of shrub height on herbaceous aboveground biomass, supporting the facilitative role of shrubs in promoting understory growth. In contrast, herbaceous species richness was more strongly associated with soil fertility and the presence of smaller shrubs, suggesting that nutrient-rich, low-competition microhabitats created by these smaller shrubs may enhance understory diversity. These seemingly contrasting effects of shrub size reinforce previous findings that nurse effects are context-dependent and vary with the performance metrics of beneficiary species (Pugnaire and Luque 2001 , Ding and Eldridge 2023 ). More importantly, we found that shrub height and soil fertility influenced herb productivity through negative and positive effects on multi-element network connectivity, respectively. This result supports our earlier hypothesis that herbaceous plants beneath shrub canopies may enhance growth by relying on specific elemental phenotypes with reduced inter-elemental integration, potentially buffering them against environmental variability. However, the relatively small number of elements analyzed in this study (10 in total) limits our ability to resolve dynamic changes in hub elements or shifts in network modularity of herbaceous plants under shrub encroachment and environmental gradients. This highlights the need for further studies to include a broader spectrum of essential elements and to use individual-level measurements to improve the resolution of elemental network analyses (Zhang et al. 2021 ). Conclusions In this study, we found that the leguminous shrub Caragana Fabr. shapes community productivity and diversity by regulating the biogeochemical niches and elemental coordination of herbaceous species. The presence of shrubs could enhance nutrient accumulation in their understory, with facilitation increasing under greater aridity and lower soil fertility. They can also restructure herbaceous multi-element networks, promoting niche partitioning and reducing network connectivity. Furthermore, we also observed that shrub traits, particularly height, and soil fertility influenced community structure and functioning, indirectly enhancing herbaceous productivity under environmental stress. Overall, our findings highlight the multifaceted role of shrubs in regulating nutrient dynamics and in structuring community assembly. The multidimensional elemental framework can offer a more integrative and mechanistic perspective on shrub-grass interactions than traditional stoichiometry, providing a robust tool to investigate functional differentiation and ecosystem responses to environmental stress. Declarations Author contribution Peng He : Conceptualization; Investigation; Formal analysis; Visualization; Writing-original draft; Writing-review & editing. Jiang Chen : Vegetation data curation; field investigation. Josep Peñuelas : Conceptualization; Writing-review & editing. Jordi Sardans : Conceptualization; Methodology; Writing-review & editing. Lina Xie : Manuscript revisions; Soil data curation. Heyong Liu : Soil data curation; Field investigation. Liang Man : Species identification, Feld investigation. Chengcang Ma : Resources; Manuscript revisions. Xingguo Han : Conceptualization; Writing-review & editing. Yong Jiang : Conceptualization; Funding acquisition; Supervision; Writing-review & editing. Maihe Li : Supervision; Writing-review & editing. Acknowledgements This work was supported by the Natural Science Foundation of Tianjin (23JCQNJC01140) and National Natural Science Foundation of China (32001147), and the Open Foundation of Kunyu Mountain Station of National Ecological Quality Comprehensive Monitoring Centre (NIES-KYS-202402). We also acknowledge the help for the fieldwork by Professor Hongyu Guo. References Ale R, Zhang L, Bahadur Raskoti B, Cui G, Pugnaire FI, Luo T (2023) Leaf carbon isotope tracks the facilitation pattern of legume shrubs shaped by water availability and species replacement along a large elevation gradient in Trans-Himalayas. Ann Botany 132:429–442 Alon U (2003) Biological Networks: The Tinkerer as an Engineer. Science 301:1866–1867 Archer SR, Andersen EM, Predick KI, Schwinning S, Steidl RJ, Woods SR (2017) Woody plant encroachment: causes and consequences. 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Coordinates MAP (mm) MAT (°C) Elevation (m) SPEI index Soil type Dominant grasses Dominant forbs SNTY 42°21′ N 112°57′ E 199 6.14 1144 -0.260 Mollisols Eragrostis pilosa Stipa capillata Cleistogenes songorica Convolvulus ammannii Peganum harmala Lipschitzia divaricata ALSZ 38°24′ N 105°38′ E 225 9.50 1150 -0.181 Entisols Setaria viridis Enneapogon borealis Tribulus terrester Peganum harmala Cynanchum komarovii DMQ 41°19′ N 111°14′ E 294 5.71 1367 0.011 Mollisols Leymus chinensis Stipa capillata Cleistogenes squarrosa Artemisia frigida Convolvulus ammannii Sibbaldianthe bifurca QPJ 39°22′ N 107°40′ E 286 8.06 1150 0.065 Aridisols Stipa glareosa Enneapogon borealis Setaria viridis Salsola tragus Geranium wilfordii Allium mongolicum HJQ 39°50′ N 108°40′ E 430 7.72 1012 0.129 Aridisols Stipa glareosa Leymus chinensis Calamagrostis pseudaphragmites Tribulus terrester Polygala tenuifolia Allium mongolicum SNTZ 43°48′ N 113°37′ E 285 2.84 1000 0.185 Aridisols Stipa capillata Enneapogon borealis Setaria viridis Tribulus terrester Allium mongolicum Salsola tragus XLHT 43°56′ N 115°53′ E 293 3.58 1059 0.284 Mollisols Leymus chinensis Stipa capillata Cleistogenes squarrosa Asparagus cochinchinensis Allium mongolicum Carex korshinskyi XWZMQ 44°37′ N 117°39′ E 348 2.44 1024 0.390 Mollisols Leymus chinensis Cleistogenes squarrosa Agropyron cristatum Artemisia scoparia Artemisia frigida Carex korshinskyi Table 2 Summary of linear mixed-effects models testing the interactive effects of aridity (SPEI) and shrub encroachment on soil physicochemical properties along an aridity gradient in Inner Mongolia. Abbreviations: A-K, soil available potassium; A-P, soil available phosphorus; SOM, soil organic matter; NH₄⁺–N, soil ammonium nitrogen; NO₃⁻–N, soil nitrate nitrogen. SOM pH NH 4 + -N NO 3 + -N A-P A-K SPEI Estimate 0.914 0.006 0.445 -0.591 0.995 -0.234 t value 1.619 0.225 2.248 -1.214 2.826 -1.433 P 0.133 0.827 < 0.05 0.254 < 0.05 0.182 Shrub Estimate 0.165 -0.006 0.087 0.256 0.255 0.015 t value 2.042 -2.436 2.890 8.860 9.064 0.820 P < 0.05 < 0.05 < 0.01 < 0.001 < 0.001 0.414 SPEI×Shrub Estimate -0.408 -0.014 -0.135 -0.001 -0.517 -0.118 t value -1.127 -1.298 -1.004 -0.008 -4.107 -1.433 P 0.262 0.197 0.318 0.994 < 0.001 0.155 Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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13:44:52","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":218273,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/e3a91fd65774fe116c8f34bb.html"},{"id":100242930,"identity":"f3e9a773-4709-4bd6-abd6-49e81e0b76db","added_by":"auto","created_at":"2026-01-14 13:44:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":801111,"visible":true,"origin":"","legend":"\u003cp\u003ethe present study, which depicts the expected effects of shrub encroachment and drought on biogeochemical niche and inter-elemental coordination of understory herbaceous species. Based on prior knowledge, both shrub encroachment and drought are expected to enhance the biogeochemical niche differentiation between two functional groups (\u003cstrong\u003eFigure 1a\u003c/strong\u003e; increasing distance between their respective hypervolume centroids), thereby promoting their complementary resource utilization. However, the biogeochemical niche volume occupied by understory herbs (the range of multidimensional elemental space) may shrink under the combined effects of drought filtering and shrub competition (\u003cstrong\u003eFigure 1a, c\u003c/strong\u003e). Furthermore, shrub encroachment and drought may loosen multi-elemental networks by reducing connectivity (edge density), weakening inter-elemental coordination, and increasing modularity to enhance functional independence (\u003cstrong\u003eFigure 1b, c\u003c/strong\u003e). As maintain stable and complex multi-elemental correlations entails high construction costs for plants under stress. Importantly, due to differences in physiological and ecological traits, grasses and other herbaceous plants may exhibit distinct responses (\u003cstrong\u003eFigure 1c\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/6840b6904840abfbd897faf9.png"},{"id":100371625,"identity":"133d7630-cd68-4d63-a041-f113257f000e","added_by":"auto","created_at":"2026-01-16 08:10:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2962180,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study region showing eight sampling sites across semiarid and arid grasslands in Inner Mongolia. Sites are distributed along an aridity gradient, with blue indicating lower aridity and red indicating higher aridity. Photographs depict landscapes of three sampling sites with varying levels of shrub encroachment: (a) XWZMQ; (b) SNTZ; (c) ALSZ. Detailed site information is provided in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/9ef482f9ee72b1c9af2c3f97.png"},{"id":100371686,"identity":"acaa7e33-106c-493e-852c-241b50efddc6","added_by":"auto","created_at":"2026-01-16 08:10:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2351560,"visible":true,"origin":"","legend":"\u003cp\u003eInteractive effects of drought index (SPEI) and functional groups (FG) on foliar elemental concentrations, as estimated by linear mixed‐effects models. Results of type II Wald χ² tests are shown in each panel, with significance levels indicated as \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05 (*), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 (**), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 (**), and \u003cem\u003eP\u003c/em\u003e\u0026gt; 0.05 (\u003cem\u003ens\u003c/em\u003e). Only significant regression lines (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) are displayed. All elemental concentrations were log‐transformed to meet distributional assumptions.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/f818a2828c4b259d367240fa.png"},{"id":100370771,"identity":"aaca4347-5a25-4794-afbf-c444e3a6f05f","added_by":"auto","created_at":"2026-01-16 08:08:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":464553,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in topological parameters of the herbaceous foliar elemental networks between shrub and grass patches (without shrubs). Significance levels are as follows: * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; \u003cem\u003ens\u003c/em\u003e, \u003cem\u003eP\u003c/em\u003e ≥ 0.05.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/060da097d9da76572a575cb5.png"},{"id":100371657,"identity":"85b28aff-25dd-4c6a-9789-2609ba348b12","added_by":"auto","created_at":"2026-01-16 08:10:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1422003,"visible":true,"origin":"","legend":"\u003cp\u003eInteractive effects of drought index (SPEI) and functional groups (FG) on relative interaction intensity (RII) values for foliar elemental concentrations, as estimated by linear mixed‐effects models. Results of type II Wald χ² tests are shown in each panel, with significance levels indicated as \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (*), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 (**), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 (**), and \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 (\u003cem\u003ens\u003c/em\u003e). Only significant regression lines (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) are retained. Points above, across, and below the zero line represent positive, neutral, and negative effects of shrubs, respectively.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/8ff6211711d378511a1cd09a.png"},{"id":100371136,"identity":"51292847-3dd7-441a-93a1-8222f2956700","added_by":"auto","created_at":"2026-01-16 08:09:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":466808,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation coefficients among relative interaction intensity (RII) values of herbaceous foliar elemental concentrations, soil physicochemical properties, and shrub traits. Asterisks indicate significant correlations (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Abbreviations: Soil A-K, available potassium; Soil A-P, available phosphorus; Soil SOM, soil organic matter; Soil NH₄⁺–N, ammonium nitrogen; Soil NO₃⁻–N, nitrate nitrogen; FI index, fertile island index.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/dd09dd17db050a66ccafd91d.png"},{"id":100242938,"identity":"1804b066-31ce-4586-aa07-e51afcdfc1ec","added_by":"auto","created_at":"2026-01-14 13:44:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":686343,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model illustrating the direct and indirect effects of aridity (SPEI), soil physicochemical properties (as indicated by soil PC1 scores), and shrub height on herbaceous (a) foliar elementome and (b) foliar elemental network structure and biogeochemical niche, and their subsequent impacts on species richness and aboveground biomass. Loadings of individual soil variables on soil PC1 are detailed in Supplementary \u003cstrong\u003eTable S1\u003c/strong\u003e. Solid black arrows denote positive linear relationships; dashed arrows denote negative linear relationships. Arrow width is proportional to the strength of the relationship. Standardized path coefficients next to the arrows indicate effect sizes. \u003cem\u003eR²\u003c/em\u003e values within boxes represent the proportion of variance explained for each endogenous variable. Significance levels are as follows: * \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, and *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Non-significant paths are omitted for clarity.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/979af1243d75ef16679147e4.png"},{"id":103505028,"identity":"6319a260-8b73-4c3c-8e71-b4a0df864e32","added_by":"auto","created_at":"2026-02-26 13:22:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10084202,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/4decf053-68b9-4848-a63e-94d683583faa.pdf"},{"id":100371418,"identity":"426ba0b7-3635-4a8f-99a9-a191fc499c85","added_by":"auto","created_at":"2026-01-16 08:10:04","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":427916,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8468769/v1/39d1f1f7b61165134a3b9f9c.docx"}],"financialInterests":"","formattedTitle":"Joint effects of shrub encroachment and aridity on herbaceous elemental traits and biogeochemical coordination in northern grasslands","fulltext":[{"header":"Introduction","content":"\u003cp\u003eShrub encroachment, the increasing density, cover, and biomass of native woody plants, has become a dominant vegetation shift in arid and semi-arid grasslands worldwide (Van Auken \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Stevens et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This transformation is driven by several interacting factors such as overgrazing, fire suppression, and climate change (Archer et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Since water availability is the main limiting factor in drylands, intensifying drought is expected to accelerate shrub expansion, as shrubs typically have deeper roots, higher water-use efficiency, and greater drought tolerance than co-occurring herbs (Ploughe et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Maestre et al. 2021). Understanding how aridity and shrub encroachment jointly regulate grassland structure and function is therefore crucial for predicting ecosystem responses under climate change (Zhu et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eShrub encroachment can strongly reshape community composition and ecosystem processes, with cascading effects (Eldridge et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Zhou et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It has long been associated with land degradation, such as reduced herbaceous cover, lowered soil infiltration efficiency, and declines in ecosystem function, ultimately contributing to processes of desertification (Eldridge and Soliveres \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, in recent decades, shrub-grass relationships have become a classic paradigm for studying interspecific interactions (Maestre et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Cui et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Increasing evidence shows that shrubs may provide net benefits by facilitating understory species through diverse mechanisms (Ding and Eldridge \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Aweto \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe positive effects of shrubs on understory vegetation are largely attributed to their capacity to ameliorate microhabitat conditions and to provide physical refuge for understory species (Soliveres et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). First, shrubs can reduce herbivory pressure through their taller and denser canopies (Saixiyala et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Second, by intercepting rainfall and trapping weathered sediments, shrubs promote localized resource accumulation (Moro et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). They can buffer abiotic stress by reducing solar radiation and evapotranspiration (Liu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, shrubs typically possess deep, laterally extensive root systems that could enhance water and nutrient availability through hydraulic lift and associations with microbial symbionts (Wang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Leguminous shrubs, in particular, can alleviate nitrogen (N) limitation via symbiotic N fixation (Michalet et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Zhao et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Together, these processes increase spatial heterogeneity and enrich resources beneath canopies, forming well-known 'fertile islands' (Schlesinger et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Reynolds et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While the fertile island effect is widely documented, most evidence is local or regional (Ward et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Jia et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and its magnitude and drivers across broad environmental gradients remain poorly understood (but see Ding and Eldridge \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Velasco et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eShrubs frequently act as nurse plants that facilitate vegetation recovery, with their positive effects often intensifying under harsh conditions (G\u0026oacute;mez-Aparicio \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This pattern aligns with the stress-gradient hypothesis (SGH), which predicts that the frequency, intensity, and importance of facilitation increase monotonically with environmental severity (Bertness and Callaway \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1994\u003c/span\u003e, Brooker et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, the generality of the SGH in shrub-encroached ecosystems remains contested because shrub effects are highly context dependent (Maestre et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Shrubs may buffer microclimatic stress, improve soil fertility, and enhance herbaceous recruitment, yet they can also impose strong competition for resources, especially in arid environments (Soliveres et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Increasing evidence indicates that shrub-grass interactions often follow a unimodal pattern: facilitation peaks under intermediate stress but declines or shifts to competition under extreme conditions (Michalet et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Noumi et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such discrepancies may depend on the traits of the interacting species, the parameters used to evaluate plant performance, as well as the type and intensity of the stress imposed (He et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Yang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, facilitation and competition commonly coexist in shrub dominant ecosystems, with their balance shifting along environmental gradients. In parallel, shrub canopies can modify soil moisture regimes, further regulating nutrient availability beneath their cover (Reynolds et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Collectively, these processes position shrubs as key regulators of local biogeochemical cycling (Du et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Comparing multi-elemental composition (\u003cem\u003ei.e\u003c/em\u003e., elementomes) between shrubs and their associated herbaceous species may deepen our understanding of nutrient-mediated interspecific interactions and offer novel empirical tests of the SGH.\u003c/p\u003e \u003cp\u003eThe foliar elementome integrates essential macro- and microelements that regulate plant structural development, metabolism, photosynthesis, and stress tolerance. It can act both as an effect trait, reflecting species-specific resource-use strategies, and as a response trait, indicating plant fitness and adaptation under biotic and abiotic stresses. Because each species has distinct elemental requirements for structural and physiological functions, the elementome is increasingly recognized as a key functional dimension for defining trophic niches and interspecific interactions in a quantifiable way (Fern\u0026aacute;ndez-Mart\u0026iacute;nez \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Building on Hutchinson's (1957) \u003cem\u003en\u003c/em\u003e-dimensional hypervolume niche concept, recent studies have proposed the biogeochemical niche hypothesis (Pe\u0026ntilde;uelas et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the multidimensional stoichiometric niche framework (Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which posit that species occupy discrete positions in a multivariate elemental space defined by tissue concentrations of macro- and microelements. These approaches allow quantification of niche volume and overlap across distinct functional groups, such as woody versus herbaceous species (He et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, despite substantial empirical support (Sardans et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it remains unknown how shrub encroachment and drought reshape the biogeochemical niches of co-occurring herbaceous plants, or whether differences in functional group identity led to divergent responses.\u003c/p\u003e \u003cp\u003eMeanwhile, multi-element network analysis has emerged as a complementary approach for characterizing species' ecological strategies within high-dimensional elemental space (Zhang et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Zuo et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This method extends plant trait network analysis by treating individual elements as nodes and their pairwise correlations as edges (He et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A suite of network topology metrics can then summarize structural properties of the network, describing how nodes are arranged and interconnected (Ye et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Commonly used metrics include connectivity (the degree of association among elements), modularity (the extent to which elements form tightly linked clusters), and centrality (the relative influence of a given element within the network). These metrics can provide insights into how tightly species are linked through elemental composition and how changes in one species may cascade through the community. Previous studies have demonstrated that shifts in network topology are often linked to species' stress tolerance, with both elemental connectivity and modularity exhibiting plastic responses to increasing stress intensity (Rao et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Medeiros et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, because of its scalability across taxa and spatiotemporal scales, this approach holds strong potential as a framework for disentangling shrub-grass interaction mechanisms under environmental change.\u003c/p\u003e \u003cp\u003eIn this study, we conducted a large-scale transect survey across the Inner Mongolian steppes to investigate how shrub encroachment alters the foliar elementome of understory grasses and its implications for community structure. We first proposed a multidimensional elemental framework that integrates biogeochemical niche analysis with multi-element network construction to elucidate the mechanisms of shrub-grass interactions. The biogeochemical niche quantifies the multidimensional space of foliar elemental concentrations, reflecting species-specific resource acquisition strategies, while the multi-element network captures correlations among species' elemental profiles, where high connectivity indicates strong coordination and high modularity indicates functional divergence. We anticipate that shrub encroachment and drought will jointly reshape the multidimensional elemental composition of co-occurring herbaceous species, as illustrated in a hypothetical schematic diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, owing to differences in ecological strategies among functional groups, grasses and forbs are expected to exhibit distinct responses. Specifically, we hypothesize that: (I) the leguminous shrub \u003cem\u003eCaragana\u003c/em\u003e Fabr. may facilitate elemental accumulation in its understory by forming fertile islands, with such facilitation intensifying linearly with increasing aridity and declining soil fertility; (II) shrub presence and aridity will jointly shape the elemental niches of understory herbaceous species, enhancing niche partitioning between functional groups and reducing network connectivity under environmental stress; and (III) shrub traits and environmental variables may indirectly affect community structure and function by modifying the elemental composition of herbaceous species, thereby reshaping community organization.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sites\u003c/h2\u003e \u003cp\u003eWe selected eight study sites along a\u0026thinsp;~\u0026thinsp;1600 km east\u0026ndash;west transect across the Inner Mongolian grasslands in northern China (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These sites were chosen to span a broad aridity gradient from semiarid to arid zones, while keeping other factors (e.g., grazing intensity, land-use history) as comparable as possible. Site selection followed two criteria: (\u003cem\u003ei\u003c/em\u003e) grasslands where shrub encroachment by \u003cem\u003eCaragana\u003c/em\u003e species was well established and representative of local vegetation dynamics, and (\u003cem\u003eii\u003c/em\u003e) locations with accessible long-term climatic data. Six of the sites were encroached by \u003cem\u003eCaragana stenophylla\u003c/em\u003e, and two by \u003cem\u003eC. microphylla\u003c/em\u003e. Both are symbiotic nitrogen-fixing legumes that are phylogenetically close and share similar physiological traits (Zhang et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The transect also supported a diverse assemblage of herbaceous species, encompassing both grass and forb functional groups. Comprehensive information on the geographic coordinates, dominant vegetation, and soil classifications of the eight sampling sites were provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo quantify aridity, we extracted the Standardized Precipitation Evapotranspiration Index (SPEI) from a high-resolution drought dataset for mainland China (Zhang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Mean 6-month SPEI values were calculated for the past 20 years at each site, as this time scale reliably captures ecosystem-level drought trends. Higher SPEI values indicate wetter conditions, whereas lower values indicate higher aridity. Across sites, SPEI values ranged from \u0026minus;\u0026thinsp;0.26 in the arid west to 0.39 in the more mesic east (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eField survey and sampling\u003c/em\u003e Fieldwork was conducted in July-August 2022.\u003c/p\u003e \u003cp\u003eAt each site, six 20 \u0026times; 20 m plots were established within homogeneous grasslands encroached by \u003cem\u003eCaragana\u003c/em\u003e shrubs, with a minimum spacing of 50 m to ensure spatial independence. Within each plot, one healthy adult shrub was randomly selected as the focal shrub, hereafter referred to as the 'shrub patch'. A paired 'grass patch' was then established at least 2 m away from the shrub canopy, in adjacent open grassland. This distance was chosen to ensure that the grass patch was outside the direct influence of the focal shrub's canopy (\u003cem\u003ee.g\u003c/em\u003e., shade, litter deposition, root competition) and to minimize interference from neighboring shrubs, providing a valid reference for comparison. Overall, each site contained six pairs of shrub-herb patches, with a total of 48 pairs across all sampling sites. Within each shrub-grass pair, two 50 \u0026times; 50 cm quadrats were established: one beneath the canopy of the focal shrub and one in the paired open grass patch. In each quadrat, all vascular plant species were identified, counted, and recorded. Reproductive height of each species was measured, and all herbaceous individuals were harvested at ground level to determine aboveground biomass. To characterize shrub traits, we measured the height as well as the maximum and minimum canopy diameters of each focal shrub.\u003c/p\u003e \u003cp\u003eAll harvested herbaceous plants were sorted by species, placed into labeled envelopes, and assigned to two functional groups: grasses and forbs. Aboveground biomass was determined by oven-drying the samples at 65\u0026deg;C to a constant weight. To assess soil physicochemical properties, five soil cores (0\u0026ndash;10 cm depth) were randomly collected from each quadrat using a 3 cm diameter auger and combined into a single composite sample per quadrat. The composite samples were homogenized and passed through a 2-mm sieve to remove stones and plant debris. Each sieved sample was then divided into two subsamples: one stored at 4\u0026deg;C for the determination of soil NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N concentrations, and the other air-dried naturally for subsequent chemical analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChemical analysis\u003c/h3\u003e\n\u003cp\u003eIn the laboratory, all oven-dried plant samples were shredded and ground using a ball mill (Retsch MM200, Germany). Foliar nitrogen (N) concentrations were determined using a C/N analyzer (Vario Micro Cube, Germany). Foliar phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) concentrations were determined using inductively coupled plasma mass spectrometry (ICP-MS, PerkinElmer OPTIMA 3000 DV) after digestion with a mixed-acid solution consisting of 10 mL HNO\u003csub\u003e3\u003c/sub\u003e and 5 mL HClO\u003csub\u003e4\u003c/sub\u003e. Soil ammonium (NH\u003csub\u003e4\u003c/sub\u003e⁺-N) and nitrate (NO\u003csub\u003e3\u003c/sub\u003e⁻-N) concentrations were extracted using 2 M KCL, and the mixtures were stirred, filtered, and analyzed using a continuous flow analyzer (Scalar SAN Plus Segmented Flow Analyzer, Netherlands). Soil total carbon (C) and nitrogen (N) were determined using the C/N analyzer. Soil available phosphorus (AP) was determined spectrophotometrically (Mapada Corporation, China) after extraction with 0.5 mol L⁻\u0026sup1; NaHCO\u003csub\u003e3\u003c/sub\u003e. Soil available potassium (AK) was determined by flame photometry following extraction with 1 M ammonium acetate. Soil organic matter (SOM) concentration was quantified colorimetrically after oxidation with a mixture of potassium dichromate and sulfuric acid (He et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eAll data processing, statistical analyses, and visualizations were performed in \u003cem\u003eR\u003c/em\u003e version 4.3.1 (\u003cem\u003eR\u003c/em\u003e Core Team, 2023). Variables were assessed for normality, and logarithmic transformations were applied when necessary to satisfy distributional assumptions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuantification of biogeochemical niches\u003c/h3\u003e\n\u003cp\u003eWe characterized the biogeochemical niches of shrub and herbaceous plants at each site using foliar concentrations of ten macro- and microelements. A principal component analysis (PCA) was performed using the '\u003cem\u003estats\u003c/em\u003e' package to reduce dimensionality, and the first three principal components (PC1-PC3) were retained. Subsequently, the three PCA scores were used to construct multidimensional hypervolumes for each functional group with the '\u003cem\u003ehypervolume\u003c/em\u003e' package (Blonder et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To assess niche differentiation between grasses and forbs, we calculated the Euclidean distance between the centroids of their respective hypervolumes and quantified niche similarity using the Jaccard similarity index (Jaccard \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1901\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eConstruction of Multi-elemental Networks\u003c/h3\u003e\n\u003cp\u003eTo explore patterns of elemental coordination, we constructed multi-element networks following established trait network approaches (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zuo et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Each foliar element represented a node, and statistically significant pairwise correlations (|\u003cem\u003er\u003c/em\u003e| \u0026gt; 0.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) formed the edges, calculated at the functional group level using mean element concentrations. An adjacency matrix was generated to represent significant connections. We then calculated network topology metrics, including connectivity, modularity, centrality, network size, and complexity, using the '\u003cem\u003eigraph\u003c/em\u003e' package (\u003cb\u003eSupplementary Table S3\u003c/b\u003e). These metrics quantify the structure and integration of foliar elemental networks, providing insight into inter-element relationships and functional coordination among species.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRelative Interaction Index (RII) for Shrub Effects\u003c/h2\u003e \u003cp\u003eThe influence of shrubs on herbaceous plant traits and soil properties was quantified using the Relative Interaction Index (\u003cem\u003eRII\u003c/em\u003e; Armas et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:RII=({X}_{s}-{X}_{g})/({X}_{s}＋{X}_{g})\\)\u003c/span\u003e \u003c/span\u003e 1\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e represent measurements under shrub canopies and in adjacent open grass patches, respectively. Positive \u003cem\u003eRII\u003c/em\u003e values indicate enhancement by shrubs, whereas negative values indicate suppression. Response variables included herbaceous biomass, species richness, foliar elemental concentrations, network topology metrics, biogeochemical niche volume, and soil physicochemical properties. \u003cem\u003eRII\u003c/em\u003e values for plant traits were averaged at the functional group level. Statistical significance of shrub effects was assessed by whether the 95% confidence intervals overlapped zero using the \u003cem\u003e'rmisc'\u003c/em\u003e package.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLinear Mixed-Effects Models (LMMs) for Soil and Foliar Elemental Drivers\u003c/h3\u003e\n\u003cp\u003eWe fitted LMMs to evaluate the effects of aridity and shrub encroachment on soil physicochemical properties (NH₄⁺-N, NO₃⁻-N, SOM, A-P, A-K, pH). Fixed effects included SPEI indices, shrub presence, as well as their interaction, while site identity was included as a random effect to account for spatial heterogeneity. Subsequently, we performed a PCA on six soil physicochemical variables to summarize variations in soil properties, with the first two principal components (PC1 and PC2) retained for further analyses. To examine drivers of herbaceous foliar elemental composition, additional LMMs were fitted with foliar elemental concentrations as the response variables. Fixed effects included SPEI, soil PC1 and PC2, and shrub traits (morphological and chemical), while site identity was treated as a random effect. Notably, all predictors were standardized (z-transformed, \u003cem\u003e\u0026micro;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0, \u003cem\u003eσ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) to facilitate comparison of effect sizes. Model selection was based on the Akaike Information Criterion (AIC), with the model exhibiting the lowest AIC value deemed most parsimonious. To examine the bivariate associations among the \u003cem\u003eRII\u003c/em\u003e values of foliar elemental concentrations, soil properties, and shrub traits, we conducted Spearman's rank correlation analyses. Subsequently, analysis of variance (ANOVA) was used to evaluate differences in elemental network topology metrics and biogeochemical niche volume between plant functional groups (grasses vs. forbs) and microsites (shrub patches vs. open grass patches).\u003c/p\u003e\n\u003ch3\u003eStructural Equation Modeling (SEM)\u003c/h3\u003e\n\u003cp\u003eWe used piecewise SEM ('\u003cem\u003epiecewiseSEM\u003c/em\u003e'' package; Lefcheck, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to disentangle the direct and indirect effects of aridity, soil properties, and shrub traits on herbaceous species richness and aboveground biomass. The \u003cem\u003ea priori\u003c/em\u003e model was constructed based on empirical knowledge and observed bivariate relationships, incorporating pathways linking environmental drivers, foliar elementome, network topology, biogeochemical niche metrics, and community-level attributes. Model fit was evaluated using Fisher\u0026rsquo;s C statistic, with non-significant \u003cem\u003eP\u003c/em\u003e values (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) indicating adequate fit. We iteratively simplified models by removing non-significant pathways and selected the most parsimonious models based on AIC values.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eChanges in soil physiochemical properties\u003c/h2\u003e \u003cp\u003eShrub encroachment significantly affected soil physicochemical properties (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Soils beneath shrub canopies exhibited elevated concentrations of NH₄⁺-N, NO₃⁻-N, SOM, and AP, alongside reduced pH levels, compared to adjacent grass-dominated patches (\u003cem\u003eestimates\u003c/em\u003e = -0.01\u0026thinsp;~\u0026thinsp;0.26, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these variables, only soil AP increased with higher SPEI values, indicating that wetter conditions promote phosphorus availability (\u003cem\u003eestimate\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, shrub encroachment and SPEI interacted negatively on soil AP (\u003cem\u003eestimate\u003c/em\u003e = -0.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the presence of shrubs may partially offset the positive effects of water availability on soil phosphorus.\u003c/p\u003e \u003cp\u003ePrincipal component analysis of six soil physicochemical properties identified two principal components (PC1 and PC2) that cumulatively accounted for approximately 65% of the total variance across study sites (Supplementary \u003cb\u003eTable S1\u003c/b\u003e). PC1 (42.4% of the variation explained) was predominantly positively influenced by soil NO₃⁻-N, SOM, and AP concentrations, whereas PC2 (21.7% of the variation explained) was primarily positively correlated with NH₄⁺-N concentrations. These components captured the major gradients in soil nutrient availability and were thus retained as integrated proxies of site-level edaphic fertility in further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eDeterminants of foliar elementome in plants\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe best-fitting models indicated that variation in herbaceous foliar elemental composition was primarily driven by aridity and plant functional group (Supplementary \u003cb\u003eTable S2\u003c/b\u003e). Foliar concentrations of Fe, Mn, and Cu in grasses, as well as N and Mg in forbs, declined with increasing SPEI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, estimates = -2.81 to -0.72, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Across all conditions, grasses exhibited consistently lower foliar elemental concentrations than forbs (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Herbaceous foliar elemental concentrations were also positively associated with soil PC2 scores, with estimates ranging from 0.07 to 0.13 (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Shrub encroachment reduced herbaceous elemental concentrations, as indicated by negative correlations between herbaceous foliar N, K, Mg, and Zn and the corresponding shrub foliar concentrations within shrub patches (estimates = -0.20 to -0.11, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, shrub foliar concentrations increased with SPEI, with estimates ranging from 0.48 to 0.88 (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that shrubs accumulated more nutrients under wetter conditions. Moreover, shrub size, including canopy area and height, was positively associated with shrub foliar elemental concentrations (estimates\u0026thinsp;=\u0026thinsp;0.02 to 0.11, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eHerbaceous plants from different sites showed clear biogeochemical niche differentiation, as defined by their foliar elementomes (Supplementary \u003cb\u003eFigure S1\u003c/b\u003e). Although aridity did not significantly influence biogeochemical niche characteristics, niche volume tended to decline following shrub encroachment. Shrub encroachment also markedly altered the topology of herbaceous foliar elemental networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In shrub patches, herbaceous plants exhibited significantly higher node and edge counts, network diameter, and average path length than those in grass patches (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By contrast, edge density and clustering coefficient were significantly reduced under shrub encroachment (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Collectively, these results indicate a shift toward more diffuse and less interconnected elemental networks of herbaceous plants in the presence of shrubs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFactors affecting the intensity of shrub-grass interactions\u003c/h2\u003e \u003cp\u003eRelative interaction indices revealed that the effects of shrubs on herbaceous foliar element accumulation were element- and site-specific, and could be positive, negative, or neutral. Overall, foliar elemental accumulation differed significantly between functional groups in response to shrub presence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Additionally, there were significant interactions between functional group and SPEI on foliar N, P, and Cu concentrations (χ\u0026sup2; = 4.88\u0026ndash;19.97, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We found that the \u003cem\u003eRIIs\u003c/em\u003e of foliar N and P in forbs, and foliar Zn in grasses, decreased with increasing SPEI values, indicating that shrub facilitation weakened under less arid conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Spearman rank correlations yielded consistent results, showing significant negative associations between SPEI and the \u003cem\u003eRIIs\u003c/em\u003e of foliar Ca, Fe, and Zn (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, the \u003cem\u003eRIIs\u003c/em\u003e of foliar Fe, Mn, and Zn increased with the \u003cem\u003eRIIs\u003c/em\u003e of soil available phosphorus and NO₃⁻-N (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that enhanced soil fertility strengthened shrub effects on herbaceous nutrient accumulation. We also found that the \u003cem\u003eRII\u003c/em\u003e of biogeochemical niche distances between grasses and forbs decreased with increasing SPEI, implying stronger niche differentiation between functional groups under more severe aridity (Supplementary \u003cb\u003eFigure S2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDrivers of community aboveground biomass and species richness\u003c/h2\u003e \u003cp\u003eThe piecewise SEM revealed that aridity, soil physicochemical properties, and shrub traits influenced herbaceous community biomass and structure by modulating foliar elemental composition and network architecture (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Specifically, both the SPEI index and shrub height directly enhanced herbaceous aboveground biomass (\u003cem\u003estd. estimates\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43\u0026thinsp;~\u0026thinsp;0.77, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while higher soil fertility (as defined by PC1 scores) and shorter shrubs could promote species richness (\u003cem\u003estd. estimates\u003c/em\u003e = -0.37 and 0.22, both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Aridity also exerted indirect effects by intensifying the negative relationships between foliar elemental concentrations and biomass accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, \u003cem\u003estd. estimate\u003c/em\u003e = -0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, shrub height indirectly enhanced aboveground biomass by increasing edge density and reducing the clustering coefficients of elemental networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, \u003cem\u003estd. estimates\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.79 and \u0026minus;\u0026thinsp;0.81, both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Likewise, soil fertility indirectly promoted biomass through its positive associations with both edge density and clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, \u003cem\u003estd. estimates\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24 and 0.44, both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the biogeochemical niche volume of herbaceous plants had no significant direct or indirect effects on either community biomass or species richness.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn arid and semi-arid grasslands, vegetation is typically co-limited by multiple and intercorrelated essential elements, underscoring the importance of a multidimensional elemental framework for assessing interspecific interactions and ecosystem functioning (Moreno-Jim\u0026eacute;nez et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Osborne et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zuo et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Understanding how shrub encroachment and aridity influence foliar elemental differences between shrubs and understory herbaceous species is critical for assessing the biogeochemical consequences of shrub encroachment and the mechanisms underlying shrub-grass interactions (Du et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, we found that the encroachment of the leguminous shrub \u003cem\u003eCaragana\u003c/em\u003e Fabr. significantly altered soil fertility, herbaceous foliar elementome, community structure, and aboveground biomass. These results suggest that both biotic interactions and climatic stressors together regulate key below- and aboveground ecosystem processes in northern grasslands, although the degree of influence may vary depending on local environmental conditions.\u003c/p\u003e \u003cp\u003eOur findings indicate that shrub encroachment significantly altered soil physicochemical properties, notably by increasing concentrations of soil NH₄⁺-N, NO₃⁻-N, SOM, and A-P, while reducing soil pH. These results align with the widely accepted view that shrubs can enhance resource heterogeneity and promote fertile island formation beneath their canopies (Filazzola and Lortie \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Ale et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The underlying mechanisms of these positive effects are likely driven by multiple abiotic and biotic processes. First, shrub canopies can contribute to resource accumulation within shrub patches by intercepting aeolian sediments, modifying litter inputs, and redistributing stemflow-mediated nutrients, whereas simultaneously increasing evapotranspiration and nutrient loss in the adjacent interspaces (Schlesinger et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Eldridge et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Second, shrubs can further enhance local nutrient enrichment by stimulating key biotic processes, such as root turnover, biological nitrogen fixation, and hydraulic lift (Moro et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our research supports these mechanisms, showing that \u003cem\u003eCaragana\u003c/em\u003e species, as nitrogen-fixing legumes, possess root nodules that augment the availability of plant-accessible nitrogen (Zhao et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This mechanism is responsible for the sustained increase in soil NH₄⁺-N and NO₃⁻-N concentrations under shrub canopies. Moreover, shrub encroachment may promote the activity of soil micro- and macro-organisms, thereby enhancing nutrient cycling through processes such as decomposition, mineralization, and mycorrhizal interactions (Maestre et al. 2021, Zhang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In a previous study conducted along the same transect, we observed higher soil nematode abundance beneath shrubs compared to adjacent grass patches (Xie et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, we found that shrub-induced increases in soil A-P were more pronounced at xeric sites, suggesting that shrub encroachment may alleviate phosphorus limitation under water stress. This effect could be driven by the rhizosphere exudation of organic acids from leguminous shrubs, which enhance acid phosphatase activity and promote phosphorus solubilization (Shen et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Additionally, elevated soil organic carbon under shrub canopies may increase the release of organic anions that displace phosphate from sorption sites, thereby enhancing its bioavailability (Jia et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the substantial enhancement of soil fertility beneath shrub canopies, we observed generally negative correlations between the foliar elemental concentrations of shrubs and co-occurring herbaceous species, particularly for N, K, Mg, and Zn. These results suggest that competitive or inhibitory interactions between shrubs and herbaceous species may limit nutrient uptake or accumulation in neighboring plants. This pattern aligns with a previous study by Ward et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who found that grasses and forbs did not benefit from increased nutrient availability beneath savanna shrub species. However, our results also suggest that nutrient competition between shrubs and grasses may be more pronounced at mesic sites, likely due to the greater availability of water and nutrients, which intensify competitive interactions. This is further supported by the Relative Interaction Index (\u003cem\u003eRII\u003c/em\u003e), which showed that the \u003cem\u003eRIIs\u003c/em\u003e of foliar P, Ca, Fe, and Zn were generally neutral or positive at xeric sites. This finding aligns with the SGH hypothesis, indicating that shrubs may enhance resource availability for neighboring plants only under extreme drought conditions (Bertness and Callaway \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1994\u003c/span\u003e, He et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Ale et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, the mobility of P, Ca, Fe, and Zn in soil in soil primarily depends on mass flow and diffusion processes, which are particularly sensitive to declining soil water potential (He et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, the facilitation of shrubs in arid habitats may be attributed to their root hydraulic lifting mechanism, through which they can alleviate nutrient limitations for understory herbaceous plants.\u003c/p\u003e \u003cp\u003eConsistent with our hypothesis II, we found that shrub presence and aridity jointly shape the multidimensional elemental traits of herbaceous species, with distinct responses observed between two functional groups. In general, forbs exhibited higher foliar elemental concentrations than grasses, a pattern that has been consistently reported in previous studies and likely stems from inherent differences in stomatal and root traits (Kaspari et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Tian et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Zhou et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specifically, grasses possess dumbbell-shaped stomata and fibrous root systems, which generally result in lower transpiration rates and nutrient uptake efficiency. In contrast, forbs typically have kidney-shaped stomata and thicker roots with higher carboxylate exudation, which can facilitate more effective mobilization of mineral-bound nutrients under resource-limited conditions (Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Chen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings suggest that forbs may have a competitive advantage over grasses in nutrient acquisition, likely due to their deeper root systems and greater mycorrhizal dependency (Wieczorkowski and Lehmann \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This adaptation may enable forbs to more effectively acquire and mobilize nutrients under the combined stresses of shrub encroachment and drought. Our results also support significant biogeochemical niche partitioning between forbs and grasses, as evidenced by the increasing distance between their respective hypervolume centroids along the aridity gradient. This pattern may reflect an indirect facilitative effect of shrubs, whereby the fertile island effect reduces competition for essential elements among co-occurring herbaceous species of different functional groups. Such facilitation may promote complementary resource utilization within the habitat. Similar findings were reported in the semi-arid Patagonian steppe, where facilitation by the nurse shrub \u003cem\u003eAdesmia volckmannii\u003c/em\u003e outweighed competitive interactions among beneficiary species, thereby enhancing their coexistence beneath shrub canopies (Armas et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These results extend the biogeochemical niche hypothesis, which posits that coexisting species tend to minimize similarity in elemental composition to reduce competition (Pe\u0026ntilde;uelas et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Sardans et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our study provides empirical evidence that this assumption also applies to shrub-herbaceous assemblages and that the effect becomes more pronounced under increasingly arid conditions. Additionally, we observed a significant decrease in the hypervolume of herbaceous plants following shrub encroachment, suggesting that shrubs may facilitate the expansion of the realized niche of understory grasses. Collectively, we hypothesize that shrubs may promote the coexistence and fitness of herbaceous species by both expanding their community niche occupancy and enhancing niche partitioning (Bruno et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, He and Bertness \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that shrub encroachment substantially restructures the multi-element network of herbaceous species. Specifically, shrub presence increased the number of nodes and edges in herbaceous foliar elemental networks, while reducing the clustering coefficient and edge density. This reconfiguration suggests a transition toward a looser, less integrated elemental network, which may indicate weakened coordination among elements and a decoupling of underlying physiological processes (He et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This pattern also implies a reduction in network connectivity of understory herbaceous species, suggesting that they may respond to the localized microenvironment within shrub patches by constraining their foliar multi-elemental space and restricting inter-element synergism. Similar patterns have been reported across diverse ecosystems. For example, a transect study in arid Californian ecosystems showed that increasing aridity led to less interconnected and more simplified trait network architectures in plant communities (Medeiros et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Likewise, a manipulation experiment demonstrated that low light stress significantly reduced trait network connectivity in \u003cem\u003ePotamogeton maackianus\u003c/em\u003e (Rao et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, Zuo et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed that correlations among bio-elements in wetland plants exhibited lower connectivity, complexity, and stability under cold and saline conditions. Collectively, these studies indicate that plant trait networks vary systematically with resource availability and environmental stress, reflecting species' adaptive strategies. Because maintaining stable and complex multi-elemental correlations often entails high construction costs, we thus assume that shrub-induced loosening of network connectivity may represent an adaptive response that enhances the ability of herbaceous species to persist in stressful habitats (Alon \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Flores-Moreno et al. 2019, Siwicka et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe SEM analysis provides novel evidence that aridity, soil fertility, and shrub traits, both directly and indirectly, influence the aboveground biomass and species richness of understory grasses by shaping their multidimensional elemental characteristics. This finding is consistent with our hypothesis III, underscoring the mechanistic importance of nutrient coordination patterns in mediating key ecosystem-level processes. Notably, we observed a direct positive effect of shrub height on herbaceous aboveground biomass, supporting the facilitative role of shrubs in promoting understory growth. In contrast, herbaceous species richness was more strongly associated with soil fertility and the presence of smaller shrubs, suggesting that nutrient-rich, low-competition microhabitats created by these smaller shrubs may enhance understory diversity. These seemingly contrasting effects of shrub size reinforce previous findings that nurse effects are context-dependent and vary with the performance metrics of beneficiary species (Pugnaire and Luque \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Ding and Eldridge \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). More importantly, we found that shrub height and soil fertility influenced herb productivity through negative and positive effects on multi-element network connectivity, respectively. This result supports our earlier hypothesis that herbaceous plants beneath shrub canopies may enhance growth by relying on specific elemental phenotypes with reduced inter-elemental integration, potentially buffering them against environmental variability. However, the relatively small number of elements analyzed in this study (10 in total) limits our ability to resolve dynamic changes in hub elements or shifts in network modularity of herbaceous plants under shrub encroachment and environmental gradients. This highlights the need for further studies to include a broader spectrum of essential elements and to use individual-level measurements to improve the resolution of elemental network analyses (Zhang et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we found that the leguminous shrub \u003cem\u003eCaragana\u003c/em\u003e Fabr. shapes community productivity and diversity by regulating the biogeochemical niches and elemental coordination of herbaceous species. The presence of shrubs could enhance nutrient accumulation in their understory, with facilitation increasing under greater aridity and lower soil fertility. They can also restructure herbaceous multi-element networks, promoting niche partitioning and reducing network connectivity. Furthermore, we also observed that shrub traits, particularly height, and soil fertility influenced community structure and functioning, indirectly enhancing herbaceous productivity under environmental stress. Overall, our findings highlight the multifaceted role of shrubs in regulating nutrient dynamics and in structuring community assembly. The multidimensional elemental framework can offer a more integrative and mechanistic perspective on shrub-grass interactions than traditional stoichiometry, providing a robust tool to investigate functional differentiation and ecosystem responses to environmental stress.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor contribution\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePeng He\u003c/b\u003e: Conceptualization; Investigation; Formal analysis; Visualization; Writing-original draft; Writing-review \u0026amp; editing. \u003cb\u003eJiang Chen\u003c/b\u003e: Vegetation data curation; field investigation. \u003cb\u003eJosep Pe\u0026ntilde;uelas\u003c/b\u003e: Conceptualization; Writing-review \u0026amp; editing. \u003cb\u003eJordi Sardans\u003c/b\u003e: Conceptualization; Methodology; Writing-review \u0026amp; editing. \u003cb\u003eLina Xie\u003c/b\u003e: Manuscript revisions; Soil data curation. \u003cb\u003eHeyong Liu\u003c/b\u003e: Soil data curation; Field investigation. \u003cb\u003eLiang Man\u003c/b\u003e: Species identification, Feld investigation. \u003cb\u003eChengcang Ma\u003c/b\u003e: Resources; Manuscript revisions. \u003cb\u003eXingguo Han\u003c/b\u003e: Conceptualization; Writing-review \u0026amp; editing. \u003cb\u003eYong Jiang\u003c/b\u003e: Conceptualization; Funding acquisition; Supervision; Writing-review \u0026amp; editing. \u003cb\u003eMaihe Li\u003c/b\u003e: Supervision; Writing-review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the Natural Science Foundation of Tianjin (23JCQNJC01140) and National Natural Science Foundation of China (32001147), and the Open Foundation of Kunyu Mountain Station of National Ecological Quality Comprehensive Monitoring Centre (NIES-KYS-202402). We also acknowledge the help for the fieldwork by Professor Hongyu Guo.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAle R, Zhang L, Bahadur Raskoti B, Cui G, Pugnaire FI, Luo T (2023) Leaf carbon isotope tracks the facilitation pattern of legume shrubs shaped by water availability and species replacement along a large elevation gradient in Trans-Himalayas. Ann Botany 132:429\u0026ndash;442\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlon U (2003) Biological Networks: The Tinkerer as an Engineer. Science 301:1866\u0026ndash;1867\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArcher SR, Andersen EM, Predick KI, Schwinning S, Steidl RJ, Woods SR (2017) Woody plant encroachment: causes and consequences. 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Functional Ecology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Y, Shen H, Akinyemi DS, Zhang P, Feng Y, Zhao M, Kang J, Zhao X, Hu H, Fang J (2022) Increased precipitation attenuates shrub encroachment by facilitating herbaceous growth in a Mongolian grassland. Funct Ecol 36:2356\u0026ndash;2366\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo Z, Reich PB, Qiao X, Zhao H, Zhang L, Yang L, Lv T, Tang Z, Yu D, Wang Z (2024) Coordination Between Bioelements Induce More Stable Macroelements Than Microelements in Wetland Plants. Ecol Lett 27:e70025\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the sampling sites along an aridity gradient in Inner Mongolia, including geographic coordinates, abiotic conditions, and the dominant species of two functional groups (grasses and forbs). Abbreviations: MAT, mean annual temperature; MAP, mean annual precipitation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAP (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAT (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSPEI index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDominant grasses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDominant forbs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNTY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u0026deg;21\u0026prime; N\u003c/p\u003e \u003cp\u003e112\u0026deg;57\u0026prime; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMollisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eEragrostis pilosa\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStipa capillata\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCleistogenes songorica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eConvolvulus ammannii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePeganum harmala\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLipschitzia divaricata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALSZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u0026deg;24\u0026prime; N\u003c/p\u003e \u003cp\u003e105\u0026deg;38\u0026prime; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEntisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSetaria viridis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eEnneapogon borealis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eTribulus terrester\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePeganum harmala\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCynanchum komarovii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026deg;19\u0026prime; N\u003c/p\u003e \u003cp\u003e111\u0026deg;14\u0026prime; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMollisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLeymus chinensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStipa capillata\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCleistogenes squarrosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eArtemisia frigida\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eConvolvulus ammannii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSibbaldianthe bifurca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQPJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u0026deg;22\u0026prime; N\u003c/p\u003e \u003cp\u003e107\u0026deg;40\u0026prime; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAridisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eStipa glareosa\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eEnneapogon borealis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSetaria viridis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSalsola tragus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eGeranium wilfordii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAllium mongolicum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHJQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u0026deg;50\u0026prime; N\u003c/p\u003e \u003cp\u003e108\u0026deg;40\u0026prime; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAridisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eStipa glareosa\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLeymus chinensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCalamagrostis pseudaphragmites\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eTribulus terrester\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePolygala tenuifolia\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAllium mongolicum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNTZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u0026deg;48\u0026prime; N\u003c/p\u003e \u003cp\u003e113\u0026deg;37\u0026prime; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAridisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eStipa capillata\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eEnneapogon borealis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSetaria viridis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eTribulus terrester\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAllium mongolicum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSalsola tragus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXLHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u0026deg;56\u0026prime; N\u003c/p\u003e \u003cp\u003e115\u0026deg;53\u0026prime; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMollisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLeymus chinensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStipa capillata\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCleistogenes squarrosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eAsparagus cochinchinensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAllium mongolicum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCarex korshinskyi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXWZMQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u0026deg;37\u0026prime; N\u003c/p\u003e \u003cp\u003e117\u0026deg;39\u0026prime; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMollisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLeymus chinensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCleistogenes squarrosa\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAgropyron cristatum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eArtemisia scoparia\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eArtemisia frigida\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCarex korshinskyi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of linear mixed-effects models testing the interactive effects of aridity (SPEI) and shrub encroachment on soil physicochemical properties along an aridity gradient in Inner Mongolia. Abbreviations: A-K, soil available potassium; A-P, soil available phosphorus; SOM, soil organic matter; NH₄⁺\u0026ndash;N, soil ammonium nitrogen; NO₃⁻\u0026ndash;N, soil nitrate nitrogen.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA-P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA-K\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.445\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.995\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.248\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.826\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShrub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.165\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.087\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.256\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.255\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.436\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.890\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e8.860\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e9.064\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPEI\u0026times;Shrub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.517\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-4.107\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"woody encroachment, drought, stress-gradient hypothesis, niche theory, elemental composition","lastPublishedDoi":"10.21203/rs.3.rs-8468769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8468769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eShrub encroachment is projected to intensify under increasing drought in arid and semi-arid grasslands, potentially altering species interactions and ecosystem processes. Although leguminous shrubs are known to redistribute soil resources, their influence on the foliar elementomes and biogeochemical niches of coexisting herbs remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eHere, we conducted a large-scale transect survey across the Inner Mongolian steppe, measuring leaf macro- and micro-elements of \u003cem\u003eCaragana\u003c/em\u003e shrubs and associated herbaceous species, alongside key soil properties. We developed a multidimensional framework that integrates biogeochemical niches with foliar elemental networks to evaluate shrub-herbaceous interactions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that \u003cem\u003eCaragana\u003c/em\u003e generally reduced elemental accumulation in understory herbs, but this inhibition weakened with increasing aridity, supporting the stress-gradient hypothesis. Shrub presence and aridity jointly influenced herbaceous elemental traits, with grasses and forbs showing contrasting responses. The biogeochemical niche distance between forbs and grasses increased with aridity, indicating that shrubs may promote their resource-use partitioning. Meanwhile, we found that shrub encroachment increased nodes and edges while reducing clustering and edge density in foliar elemental networks of understory plants, suggesting that the herbs may form less integrated networks as a trade-off to lower their construction costs. Structural equation models further revealed that shrub height and soil fertility indirectly affected herbaceous productivity by weakening network connectivity, implying reliance on specific elemental phenotypes under stress.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings highlight the utility of multidimensional elemental frameworks for understanding grassland responses to climate change and woody encroachment, providing novel mechanistic insights into biogeochemical cycling in degraded northern grasslands.\u003c/p\u003e","manuscriptTitle":"Joint effects of shrub encroachment and aridity on herbaceous elemental traits and biogeochemical coordination in northern grasslands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 13:44:46","doi":"10.21203/rs.3.rs-8468769/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"995e6cc1-7991-468d-ba65-1bdf4ec775be","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T00:22:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 13:44:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8468769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8468769","identity":"rs-8468769","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}