Riparian plant-soil-microbial C: N: P stoichiometry: are they conserved at plant functional group level?

doi:10.21203/rs.3.rs-3988840/v1

As a consequence of the tight linkages between plants, soil, and microorganisms, we hypothesized the variations in plant species would change soil and microbial stoichiometry. Here, we examined the plant leaf C: N: P ratios of nine species coming from three plant functional groups (PFGs) in the riparian zones of Hulunbuir steppe during near-peak biomass. The soil C: N: P, MBC: MBN, and extracellular enzyme’s C: N: P were also assessed using the soils from each species. We found that plant tissue, soil nutrients, microbial, and enzyme activities stoichiometry significantly differed among different PFGs. Plant leaf and soil nutrient ratios tended to similar (p > 0.05) between different species within the same PFGs. The variations in leaf C: N: P significantly correlated with the changes in soil C: N: P and MBC: MBN ratios. The homeostatic coefficients (H) < 1 suggested the relationships between plants and their resources C: N: P ratios might be non-homeostatic in the examined riparian zone. By assessing plant tissue and its soils nutrient stoichiometry, this study provided a perspective to understand the linkages of plant communtity, soil nutrient and microbial characteristics.

riparian ecological stoichiometry

homeostasis

plant-soil feedback

plant functional groups

Hulunbuir steppe

Ecological stoichiometry is a powerful theoretical framework for understanding the complex relationships between ecosystem function and biogeochemical cycle using organism’s and environment elements ratios and the concept of stoichiometric invariance (Bertrand et al. 2019, Sardans et al. 2012a, Sinsabaugh et al. 2009).

At the global and ecosystem levels, previous studies have documented soil stoichiometric characteristics vary widely depending upon changes in plant community types and species composition (Cleveland &Liptzin 2007, Sardans et al. 2012b, Wang et al. 2020). The nutrient requirements for N and P of the plants, especially during the vigorous growth phase, furtherly can expand the differences in soil stoichiometry (Goldberg et al. 2017, Lambers et al. 2008, Zhou et al. 2018). Moreover, some evidence has also shown that plants control the nutrient availability and stoichiometry in the soil by interacting with soil microorganisms that produce the enzymes for the degradation of soil organic matter and then mineralize soil nutrients for plants and microbial uptake (Gougoulias et al. 2014, Jacoby et al. 2017, Jin et al. 2017). Most previous research has mainly focused on the global and ecosystem scales. It still remains unclear if these characteristics exist at smaller scales such as the riparian ecosystems with less concerned (Yao et al. 2019, Zhang et al. 2012).

Riparian areas, the ecotones between terrestrial and aquatic ecosystems, are the active zones for biogeochemical cycles and hydrological processes due to the distinct hydrology, vegetation, and soil characteristics (Larson et al. 2019). It often has regional biodiversity across the full spectrum from microbes, and plants to vertebrate animals (Hauer et al. 2016), and also has diverse plant-soil-microorganism nutrients biogeochemical cycles (Li et al. 2019, Miralles 2018). For example, previous studies have reported that, in the direction perpendicular to the rivers channel, the active exchange for energy and materials between rivers and their floodplains caused by river flow and flood pulses facilitated the diversity of plant community types, habitats, and organism-environment biogeochemical processes (Geng et al. 2017, Shrestha et al. 2014, Yang et al. 2019). The areas near or far away from the river channel are generally dominated by hygrophyte or xerophyte PFGs, respectively, whereas between these two areas, the dominated plants are mesophyte PFGs (Stromberg et al. 2017). In the hygrophyte zones, the mineralization and decomposition of plant litter and soil organic matter are slower than in the xerophyte zones due to the low soil porosity, oxygen contents, and microbial activity (Garssen et al. 2015, Li et al. 2019, Shrestha et al. 2014). At small scales, such as the riparian scale, previous studies have focused on plant composition, soil bio-physicochemical properties, and plant-soil interactions, there is less clear if the changes in plant stoichiometry can affect soil stoichiometry, including soil nutrient ratio, microbial biomass, and enzymes stoichiometry.

In general, at smaller scales, the changes in species or community commonly produce differences in plant tissue nutrient concentrations, stoichiometric ratios, and even a series of plant ecological functions, due to different species tending to have different nutrients demand and survival strategies (Bell et al. 2014, Sardans et al. 2012a). These differences in plants could influence their tissues C: N: P stoichiometry and decomposition rate (Sardans et al. 2012a, Zechmeister-Boltenstern et al. 2015), which is also greatly associated with the differences in soil organic matter concentrations, soil fertility, and nutrients C: N: P ratios (Chen et al. 2019, Geng et al. 2017, Li et al. 2019). For example, hydrophyte plant tissues generally have higher water content, N: P, and C: P but lower N and P concentrations than xerophyte plants (Moe et al. 2019), which results in the differences in soil and litters nutrients stoichiometry (Yao et al. 2019, Zhang et al. 2012). Meanwhile, the stoichiometric relationships of hydrophyte exhibit a relatively higher change in plant tissues stoichiometry compared with resources stoichiometry (soil nutrients). These nutrient feedbacks and stoichiometric relationships between plants and their living environments can use homeostasis theory to understand and quantify by assessing the ability of plants to maintain internal stoichiometric ratios stability under external interference (Cleveland &Liptzin 2007, Jeyasingh et al. 2017, Sterner &Elser 2002).

In the present study, we selected the riparian zones on the Hulunbuir steppe (the best-preserved steppe in the eastern Eurasian Steppe) as the research object. Due to the continuous and flat topography of Hulunbuir steppe, the riparian zones on here are relatively flat, wide, and 200 to 300 meters on average (Li et al. 2019). Meanwhile, the riparian zones of Hulunbuir steppe also have high biodiversity and obviously changed plant community types and functional groups perpendicular to the rivers channel (Li et al. 2019, Wu &Liu 2015). The variations in plant types might greatly influence the plant-soil-microbe biogeochemical processes, and nutrients stoichiometric characteristics, as well as the the homeostatic relationships between plants and their resources stoichiometry (Bezemer et al. 2006, van der Putten et al. 2013, Zak et al. 2003). However, there is few literature regarding plant-soil-microbial C: N: P stoichiometry of different plant types in the riparian zones of Hulunbuir steppe (Li et al. 2019). It is also still unclear about the homeostatic relationships between plants and their resources stoichiometry.

Therefore, based on the differences in plant types caused by the hydrological characteristics of riparian zones perpendicular to the rivers channel, we selected nine plant species from three PFGs (i.e., hygrophyte, mesophyte, xerophyte; for each PFG, selected three plants) to examine the C: N: P stoichiometry of plants, soil, soil microbial biomass, and soil enzymes acquisition activities, and their relationship at species and PFGs levels, and addressed the following question: whether the plant-soil-microbial stoichiometry is relatively conserved at PFG levels or not? And either the relationships between consumers (plants) and resources (soils and soil microorganisms ) are homeostatic or not at species and PFG levels? We hypothesized that, due to the different habitats and soil properties formed by the frequently varied hydrological characteristics, the riparian plant-soil-microbial stoichiometry will differ among different PFGs. However, within the same PFGs, the plant-soil-microbial stoichiometry might be similar due to the similar hydrological characteristics and soil properties which would develop similar plant, microbial community, even similar coupling relationship among plant, soil, and microorganisms (Schnitzer et al. 2011, Zak et al. 2003). We also hypothesized that the homeostasis relationship of the riparian could be non-homeostatic because of the long-term fluctuating hydrological factors and anthropogenic disturbance such as grazing. We expected that examing C: N: P stoichiometry and the homeostasis relationships among organisms and resources could be an indicator to assess the health and nutrient limitation in the plant-soil-microbial system of riparian zones in the Hulunbuir steppe.

2.1 Study site description

The present study was conducted in the core areas of Hui-river National Nature Reserve (latitude: 48° 10′ to 48° 57′ N, longitude: 118° 40′ to 119° 45′ E) (Fig. 1), Hulunbuir steppe, China. This site is influenced by the temperate continental monsoon climate and has the annual average precipitation and temperature of 375 mm and –2.4 ℃ to 2.2 ℃, respectively. The soils of Hui-river riparian zones are Fluvisols and Kastanozems (WRB 2015). In 2007, this river was included in the China Biosphere Reserve Network (CBRN), and become one of the 151 critical areas for biodiversity protection. Hui-river also provides the important breeding sites and habitats for migratory birds from East Asia‒Australia (https://www.iucnredlist.org). The riparian zone of Hui-river is less disturbed bceuase its location in the core areas of the Hui-river National Nature Reserve, and has high species diversity. The dominant plant species belong to Cyperaceae, Gramineae, and Asteraceae family (Li et al. 2019).

2.2 Experimental design and sampling process

Due to the obvious variations in submergence frequency in the transversal direction (i.e., perpendicular to the river channel), the soil properties and vegetation types sharply change in this direction. For example, the areas of riparian near the river are frequently submerged, and conducive to hygrophyte plant growth (Fig. 1). However, the areas of riparian far away the river are rarely submerged or unsubmerged, and dominanted by xerophyte. According to this natural vegetation type differences in the transverse direction of the riparian zones, three plant functional groups (PFGs) were selected within a 50 m × 200 m sample area in July 2021. They were hygrophyte, mesophyte, and xerophyte. In each PFG, three plant species were selected based on the method that dominance is greater than 50% (e.g., in the 2 m × 2 m sample quadrat, if the dominance of A species is greater than 50%, we will define this observed quadrat as the A plant patch, and select plant A as an investigate object) (Sawchik et al. 2003). In total, nine plant species were selected (Table 1). For each species, three plant patches were investigated as the repetition. And, in each observed plant patch, three plants were selected, totally 81 plants were investigated. Three soil samples were collected at the depth of 0-20 cm around each plant (the black points in Fig. 1) after harvesting the plant leaf samples in July 2021, totally 243 soil samples were harvested. These soils and plant leaf samples were immediately stored at 4 ℃ in cooler boxes and shipped to the laboratory for examining the soil C, N, P concentrations, soil pH, plant leaf C, N, P concentrations, soil microbial biomass C, N and soil enzymatic activity.

2.3 Sample analysis

The fresh leaves of each plant species were oven-dried at 105 ℃ for 20 minutes, and then at 60 ℃ for 48 hours. These dried leaf samples were ground and passed through 0.15 mm sieve for determining the nutrients of plant leaf–TOC, leaf–TN, and leaf–TP. Plant leaf–TOC was measured using the vario TOC (vario TOC select, Elementar Analysensysteme GmbH, Elementar-Straße 1, 63505 Langenselbold, Germany). Leaf–TN was measured by the Kjeldahl method, and the digestion solution was examined using the FOSS Kjeltec ^TM 8420 (Kjeltec ^TM 8420, FOSS. Foss Allé 1, DK-3400 Hillerød, Denmark) to examine the N concentrations (Bao 2000). Leaf–TP was determined by the H₂SO₄-HClO₄ ammonium molybdate method (Bao 2000).

In the laboratory, each soil sample was divided into two parts. One was stored at 4 ℃ to determine soil MBC, MBN, and seven soil enzyme activities after passing through a 2 mm sieve. Another soil sample was air-dried to measure soil physicochemical properties. SOC was determined by the vario TOC. Soil TN and TP were measured using the Kjeldahl method and H₂SO₄-HClO₄method, respectively. Their digestion solutions were examined using the SEAL Auto Analyzer 3 (SEAL Analytical, Inc., 6501 W Donges Bay Rd, Mequon, WI 53092, United States) to determine the concentrations of N and P. Soil pH was measured in a 1: 2.5 soil-DI H₂O suspension using a Sartorius pH meter (PB-10, Sartorius Corporate Administration GmbH, Goettingen, Germany).

Soil MBC and MBN were determined by the chloroform (CHCl₃) fumigation-extraction method (Brookes et al. 1985). Briefly, two fresh soil samples were weighed (each of 20 g), one was fumigated with CHCl₃for 12 h at 25 ℃. Then, the fumigated soil was extracted with 80 ml 0.5 mol·L^-1 K₂SO₄ and shaken for 30 min at 200 rpm on an oscillator. Another soil sample (non-fumigated) was directly extracted as discussed above. The supernatants of fumigated and non-fumigated soil samples were filtered to determine fumigated and non-fumigated soil MBC and MBN. The MBC and MBN in the filtrate were measured using the elementar vario TOC.

The potential activities of seven extracellular hydrolytic enzymes were determined by the standard fluorimetric techniques using a TECAN Spark^® multimode microplate reader (TECAN Spark^®, Tecan Group Ltd., Seestrasse 103, 8708 Männedorf, Switzerland ) setting excitation-wavelength to 365 nm and emission-wavelength to 450 nm (Bell et al. 2013, Li et al. 2020). These enzymes degrade the common constituent of organic matter and help microorganisms and plants to acquire C, N, and P nutrients (Li et al. 2020). They are C–acquisition enzymes:αG, βG, βDC, and βX, N–acquisition enzymes: NAG and LAP, and P–acquisition enzyme: aP.

2.4 Statistical analysis

We performed Shapiro-Wilk and Levene’s test to analyze normality and equal variances of our data by R ‘stats’ and ‘car’ packages, respectively, using R: The R Project for Statistical Computing (Bell et al. 2014, R-Core-Team 2013). To improve the homoscedasticity and normality for the subsequent analysis, some data were natural-log transformed such as enzyme C: N = ln (αG + βG + βDC + βX): ln (NAG + LAP), enzyme N: P = ln (NAG + LAP): ln (aP), and enzyme C: P = ln (αG + βG + βDC + βX) : ln (aP). An IBM–SPSS Statistics for Windows version 25.0 (IBM Corp., Armonk, NY, USA) was used to calculate the means and standard errors for all parameters in this study.

We used the distance-based redundancy analysis (dbRDA) as a statistical-criterion to evaluate the overall difference in the plant-soil-microbial C: N: P stoichiometric ratios among plant species and PFGs by the ‘vegan’ package in the R (R-Core-Team 2013). The dbRDA is an excellent nonlinear distance-metric model with the multidimensional resolution, which has been generally accepted by ecologists (Soong et al. 2016). This model is an ordination approach with 3 steps to assess the effect of the environmental variables (i.e. stoichiometric ratio) on the defined group (i.e. plant species, PFGs). Briefly, a dissimilarity or distance matrix was calculated for different plan species and PFGs using the Bray–Curtis dissimilarity (nonlinear) to obtain the dissimilarity or distance matrices for plant species and PFGs (Soong et al. 2016). In steps 2 and 3 of the dbRDA model, a Principal Component Analysis (PCA) was calculated based on the distance matrices, from which the eigenvalues were applied to a Redundancy Analysis (RDA) (Soong et al. 2016). The results of dbRDA were visualized using R: ‘ggplot2’ package.

One-way Analysis of Variance (ANOVA) was used to check the differences in plant leaf stoichiometry (leaf–TOC, leaf–TN, leaf–TP, leaf C: N, leaf N: P, and leaf C: P), soil stoichiometry (SOC, TN, TP, soil C: N, soil N: P, and soil C: P), soil microbial biomass stoichiometry (MBC, MBN, and MBC: MBN), and enzyme acquisition activities stoichiometry (activities of αG, βG, βDC, βX, NAG, LAP, and P, enzyme C: N, enzyme N: P, and enzyme C: P) at different plant species levels and PFGs levels, respectively, using the IBM–SPSS v. 25.0. Fisher’s least significant difference (LSD) test to compare the mean values for all univariate analysis. The results of one-way ANOVA were visualized by Origin Pro v. 2018 (Origin Pro for Windows, v. 2018, Origin Lab Corporation, Northampton, MA, USA).

To assess the relationships between consumer stoichiometry (plant leaf C: N: P) and their resource stoichiometry (i.e. soil C: N: P, MBC: MBN, and enzyme C: N: P) of the riparian zones in Hulunbuir steppe, a regression analysis was calculated in R. To adhere to the conventions of stoichiometric analysis and to normalize variance because of the data of enzymes stoichiometry has been natural-log transformed, other stoichiometric data were also natural-log transformed before performing the regression analysis (Sterner &Elser 2002). The results of regression analysis were visualized using R: ‘ggplot2’ package. Furthermore, the homeostatic index (H) with the corresponding r² between consumer stoichiometry and resource stoichiometry was calculated by the nonlinear equation y = cx^1/H as Bell et al. (2014) described using Origin Pro v. 2018. In this equation, y represents consumer, and x represents resources (Sterner &Elser 2002). H > 1 indicates a lower change in consumer stoichiometry than in resource stoichiometry, which implies there is homeostatic. However, H ≤ 1 indicates non-homeostatic relationships among consumer and resource stoichiometry because there are consistent changes or higher changes in consumer stoichiometry comparing with resource stoichiometry (Bell et al. 2014, Cleveland &Liptzin 2007, Sterner &Elser 2002). Through assessing the homeostasis relationship, we can indirectly evaluate the nutrients limitation among plant, soil, and microorganisms (Sperfeld et al. 2017).

3.1 The overall differences in plant-soil-microbial C: N: P stoichiometry of Hulunbuir steppe riparian zones

Overall, riparian zones plant-soil-microbial C: N: P stoichiometry were significantly different (p < 0.05) (Fig. 2). At species levels (Fig. 2a and b), the C: N: P stoichiometry of each species obviously scattered among different PFGs, while grouped together within the same PFGs (Fig. 2a). Cumulative variation of 74.9% was explained by Axis 1 and Axis 2 among different species (Fig. 2a) (F = 6.99, p = 0.001). At PFGs levels (Fig. 2c and d), C: N: P stoichiometry significantly differed between different PFGs (Fig. 2c). 89.6% of cumulative variation was explained by Axis 1 and Axis 2 among different PFGs (Fig. 2c) (F = 4.80, p = 0.022). Soil C: N and C: P, MBC: MBN, enzyme C: N had remarkable contribution for the variations at PFGs and species levels (p < 0.05) (Fig. 2b and 2d).

3.2 Plants, soils, microbes, and enzyme activities C: N: P stoichiometric characteristics among different species and PFGs

At species levels, one-way ANOVA indicated plant leaf C: N: P (Fig. 3a, 3c, and 3e) and soil C: N: P (Fig. 4a, 4c, and 4e) tended to similar (p > 0.05) among different species of the same PFGs. However, soil MBC: MBN (Fig. 5a) and enzymes C: N: P (Fig. 6a, 6c, and 6e) significantly differed between different species of the same PFGs (p < 0.05). At PFGs levels, the discrepancies of leaf C: N: P (Fig. 3b, 3d, and 3f), soil C: N: P (Fig. 4b, 4d, and 4f), enzymes activity N: P (Fig. 6d) and C: P (Fig. 6f) ratio were significant among different PFGs (p < 0.05). But, the differences in soil MBC: MBN (Fig. 5b) and enzyme C: N (Fig. 6b) between different PFGs were non-significant (p > 0.05). It was noteworthy that although the resources stoichiometric ratios (soil C: N: P) differed between different PFGs (p < 0.05), the differences in the corresponding xerophyte leaf C: N: P stoichiometry were non-significant (p > 0.05).

Overall, the variations in the concentration of C, N, and P in plant leaf were less than the changes in soil and soil microbial biomass no matter at PFGs or species levels (Table 2). The activities of C, N, and P acquisition enzymes were non-significantly (p > 0.05) at the PFGs levels but slight-obviously (p < 0.05) different at species levels.

3.3 Correlations between consumer (plant) and resource (soil, microbe, and enzyme) stoichiometry

Our results also revealed that consumer stoichiometry (i.e., plant leaf C: N: P) correlated their corresponding resource stoichiometry (i.e., soil C: N: P, MBC: MBN, and enzyme C: N: P) of each species in the riparian zones of Hulunbuir steppe (Fig. 7). Leaf C: N: P ratios were significant-negatively correlated with soil C: N, N: P, and C: P (r = -0.67, p < 0.001, Fig. 7a; r = -0.53, p < 0.01, Fig. 7b; r = -0.52, p < 0.01, Fig. 7c, respectively). Soil enzyme C: N and enzyme C: P poorly related to their consumer stoichiometry (plant leaf C: N, r = 0.14, and C: P, r = 0.20, respectively) (Fig. 7e and 7g), but enzyme N: P negatively correlated with leaf N: P (r = -0.57, p < 0.01, Fig. 7f).

Overall, the riparian zones’ plant tissue stoichiometry (leaf C: N: P) were predominantly non-homeostatic (H ≤ 1) relationships with corresponding resource stoichiometry (soil, microbial, and enzyme activity) (Table 3). However, part of the species were homeostatic (H > 1) in their parts of nutrients stoichiometry such as SK and AC were homeostatic in leaf–soil N: P and C: P (H = 14.67, r² = 0.63, H = 4.84, r² = 0.52, respectively). The majority of the homeostatic relationships (H > 1) were between leaf C: N: P and soil C: N: P stoichiometry (Table 3). At PFGs levels, only xerophyte displayed the homeostatic relationships (H > 1) in leaf–soil C: P (H = 3.40, r² = 0.40), which might suggest that xerophyte had a higher ecological tolerance to external disturbance and environment changes than hygrophyte and mesophyte, in Hulunbuir steppe riparian zones.

4.1 Plant-soil-microbial C: N: P stoichiometry among different species and PFGs

Consistent with the hypothesis, the variations in plant functional groups caused by the differences in riparian hydrological characteristics obviously affect the biogeochemical cycles between plants, soil, and microbes (Garssen et al. 2015, Kercheva et al. 2017, Li et al. 2019), and lead to plant-soil-microbial C: N: P ratios differ among different plant functional groups. These findings were aslo supported by previous research. That was, different plant functional groups generally have different rhizosphere environments and soil properties (Kimberley et al. 2012, Saint-Laurent et al. 2010), which may cause them to coexist with different microorganisms and a series of different plant-soil-microbial biogeochemical processes (Hu et al. 2020, Wang et al. 2019, Wang et al. 2020, Zhou et al. 2018). These different processes might be the key reasons causing riparian plant-soil-microbial C: N: P stoichiometry differed among different plant functional groups. On the contrary, within the same hydrological environment, the plants tends to have similar survival strategy and nutrients ratios (Lambers et al. 2008), which may develop similar rhizosphere properties and plant-resources nutrient coupling processes, as well as similar plant-soil-microbial C: N: P ecological stoichiometry (Fan et al. 2019, Yu &Ehrenfeld 2010). This might be the reason why plant-soil-microbial C: N: P stoichiometry tends to similar within the same plant functional groups.

For the individual riparian C: N: P ecological stoichiometry, plant leaf and the corresponding soil exhibited similarity between different species of the same plant functional group but leaf N: P of CS and MV, leaf C: P of RC and AG showed significant differences. These exceptions can be explained by species differences (Yang et al. 2018). Although both CS (Carex stricta) and MV (Myriophyllum verticillatum) are hygrophytes, CS belongs to the Cyperaceae family, and MV from the Haloragaceae family. Different families indicated they had different morphological structures, metabolic and respiratory characteristics (Hess 1970, Lambers et al. 2008). These suggested that CS and MV might have different nutrient demands (Lambers et al. 2008), which were the key drivers resulting in the differences in leaf N: P of CS and MV (Yang et al. 2018). Likewise, the differences in leaf C: P between RC (Rumex crispus, family: Polygonaceae) and AG (Agrostis gigantea, family: Gramineae) might be also due to species differences (Yang et al. 2018). These phenomena suggested that plants can maintain their species specificity despite of their growth in the same or similar environment (Hess 1970). Furthermore, the species specificity indicated that different plant species might coexist with different soil microbial communities (Fan et al. 2019, Muneer 2019, Stefanowicz et al. 2019). This could be indirectly proved by the different soil microbial biomass and enzymes C: N: P stoichiometry of different species within the same plant functional groups (Fig. 5 and Fig. 6). Compared with plants, microorganisms are more sensitive to the changes in habitat environment (Cavicchioli et al. 2019), thus we found that the differences in soil microbial and enzyme stoichiometric characteristics were higher than that in plant leaf and soil. Moreover, microbes also have strong host specificity (Guttman et al. 2014), which indicates that the plant species differences may greatly affect the coexisted microbial community compositions and structures because of the different correlations , even the species coming from the same family or genus (Drogue et al. 2012, Hahl et al. 2020). These may explain why soil microbial biomass and enzyme activities C: N: P differ among different species even within the same plant functional group.

4.2 Correlations between plant, soil, microbe, and enzyme stoichiometry

Consistent with previous studies (Bell et al. 2014, Yang et al. 2018), we found that the plant tissue stoichiometry tightly related to the below-ground stoichiometry. For example, plant leaf C: N: P stoichiometry obvious-negatively correlated with soil C: N: P ratios (Fig. 7), which suggested that the rise in plant tissue C: N: P might decline soil C: N: P. In this study, leaf N: P ratio was between 5.16 and 10.97 (Fig. 3) and the average leaf N: P ratio was 7.43, significantly lower than 14 (Yang et al. 2018). In terrestrial plants, Koerselman andMeuleman (1996) found that when leaf N: P ratio > 16, the plant community was P-limited, whereas the N: P ratio < 14 indicated N limitation. Thus, we found that the exmined riparian zone might be N-limited (7.43 < 14). Based on the homeostasis theory that an organism can maintain a stable internal state such as conserved C: N: P elemental ratios (Cleveland &Liptzin 2007). To decreased the N limitation, and increased the N: P when leaf N: P ratio < 14, plant might absorb more N from soil to keep the N: P ratio stable (Sperfeld et al. 2017). This might be the reason why the high leaf N: P ratios corresponded to the low soil N: P ratios, and why they were negatively related. Plant or microorganisms could regulate soil enzyme activity through physiological and biochemical processes, which promoted the mineralization and decomposition of soil nutrients, and increased nutrient concentrations (Liu et al. 2020, Yang et al. 2018). This might explain that the activity of the LAP enzyme (N-acquisition) was high when the leaf N: P was low.

Overall, we found the majority of obvious correlations between plant tissue and soil stoichiometry but only weak relationships between plant and microbial or enzyme stoichiometry, which might suggest that the relationships between plant growth metabolism, and soil physicochemical properties were tighter than soil microbes. This was in agreement with previous studies (Bell et al. 2014, Yang et al. 2018). However, these strong or weak relationships among plant tissue, soil, and soil microbial stoichiometry cannot provide a clear explanation for the interactions among the C: N: P stoichiometry in the plant-soil-microbial system. Therefore, future research should focus on the C:N:P stoichiometry in the plant-soil-micronbial system in the riparian ecosystem of grassland.

4.3 Homeostasis relationships among consumer (plant tissue) and resource (soil, microbial, and enzyme) stoichiometry

Plant tissue stoichiometry may be tightly constrained by below-ground stoichiometry in this grassland riparian zones, as there were many non-homeostatic relationships (H <1, i.e. slopes = 1/H > 1) observed among plants, soil, and microbial components (Table 3). The slope > 1 suggests the plant might not keep its internal state stable and would change with the soil physical and biochemical environment (Mitteldorf 2004, Sterner &Elser 2002), which implied that this riparian zone might be a weak resilience and unhealthy compared with the homeostatic systems (Reynolds 2002). Homeostasis reflects the ability that organisms resist changes in the external environment, and may also be regarded as robustness in an ecological context, while non-homeostasis generally implies that an ecosystem featured poor resistance and resilience to withstand the natural or anthropogenic stresses (Sperfeld et al. 2017, Sterner &Elser 2002). Elliott andQuintino (2007) reported that the riparian ecosystems with weak resistance and resilience (i.e. non-homeostatic) might be more prone to degradation under the unreasonable anthropogenic interferences. In this study, the overall non-homeostasis relationships among plant and their resources stoichiometry implied that the riparian zone might be a weak resistance and resilience, and combined with the N-limited finding (leaf N:P < 14), this investigated riparian zone might be unhealthy.

However, on the other hand, at plant functional groups levels we found that only xerophyte plants displayed some homeostatic relationships (H > 1), which might suggest that xerophytes have a stronger capability to maintain internal body stability, resist external disturbances and climate changes than hygrophyte and mesophyte in Hulunbuir steppe riparian (Sterner &Elser 2002). This finding could also explain why plant tissue C: N: P trend to similar (p > 0.05) (Fig. 3) among different species within xerophyte plant functional group even the corresponding soil C: N: P significantly differed (Fig. 4). Overall, our results proposed that the xerophytes have higher ecological tolerance than the hygrophyte and mesophyte plant functional groups in Hulunbuir steppe.

Our findings suggested that the C: N: P stoichiometry of plant-soil-microbial was not conserved at plant functional group levels in the riparian zones, while the C: N: P ratios of plant tissue and soil in different species tended to similar within the same plant functional group. The variations in plant tissue stoichiometry tightly related to their below-ground stoichiometry, especially soil C: N: P ratios. Also, the overall non-homeostatic relationships among plants and their resource C: N: P stoichiometry implied that the riparian zone might be a weak resistance and resilience to external environment changing, and combined with the N-limited finding (leaf N: P < 14), our results suggested that the riparian zone might be unhealthy. Furthermore, our results also demonstrated that xerophytes have a stronger capability to maintain internal body stability and resist external disturbances than hygrophyte and mesophyte in the riparian zones of the Hulunbuir steppe.

Overall, this study preliminarily clarified the variation characteristics and relationships of plant-soil-microbial stoichiometry at plant functional group levels and species levels in a grassland riparian zone of the Hulunbuir steppe, provided a synthesized insight into identifying the nutrient limitation and healthy for riparian zones by assessing both organism and resource C: N: P stoichiometry and their homeostasis relationships.

CP: Carex pediformis	SOC: soil organic C
CS: Carex stricta	TN: total N
MV: Myriophyllum verticillatum	TP: total P
RC: Rumex crispus	Soil C: N: SOC: TN
AG: Agrostis gigantea	Soil N: P: TN: TP
PS: Poa subfastigiata	Soil C: P: SOC: TP
CD: Carex duriuscula	Leaf–TOC: leaf total organic C
AC: Agropyron cristatum	Leaf–TN: leaf total N
SK: Stipa krylovii	Leaf–TP: leaf total P
PFG: plant functional group	Leaf C: N: Leaf–TOC: Leaf–TN
αG: α-glucosidase	Leaf N: P: Leaf–TN: Leaf–TP
βG: β-1,4-glucosidase	Leaf C: P: Leaf–TOC: Leaf–TP
βDC: β-D-cellubiosidase	MBC: microbial biomass C
βX: β-xylosidase	MBN: microbial biomass N
NAG: β-1,4-N-acetylgucosaminidase	Enzyme C: N: ln (αG + βG + βDC + βX): ln (NAG + LAP)
LAP: leucine aminopeptidase	Enzyme N: P: ln (NAG + LAP): ln (aP)
aP: acid phosphatase	Enzyme C: P: ln (αG + βG + βDC + βX): ln (aP)

Acknowledgements

We thank the staff from the Academy of Animal Husbandry and Veterinary Science, Qinghai University, for offering help and experimental equipment for sample analyses.

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contributions

Xingfu Li Conceptualization, Methodology, Writing - original draft. Yu Liu: Sample collection, Sample analysis. Dan Chen: Writing - review & editing. Chenxiang Ding: Provision of sample testing instruments. Pu Ma: Sample collection, Sample analysis. Jin He: Writing - review & editing and Funding acquisition. Derong Su: Writing - review & editing.

Funding

This work was supported by National Natural Science Foundation of China (Grant No. 32201335).

Compliance with Ethical Standards: accepted principles of ethical and professional conduct have been followed.

Consent to Participate

This study did not involve human participants or animals.

Consent to Publish

This study did not involve human participants or animals.

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Table 1

Species names, their abbreviations, and plant functional groups for the nine investigated plant species in this study

Species names	Abbreviations	Plant functional groups
Carex pediformis	CP	hygrophyte
Carex stricta	CS
Myriophyllum verticillatum	MV
Rumex crispus	RC	mesophyte
Agrostis gigantea	AG
Poa subfastigiata	PS
Carex duriuscula	CD	xerophyte
Agropyron cristatum	AC
Stipa krylovii	SK

Table 2

The concentrations of the plant-soil-microbial C, N, P, and soil enzyme acquisition activities of the riparian zones in the Hulunbuir steppe, corresponding to species and PFGs levels, respectively.

Species	Leaf–TOC g·kg^-1	Leaf–TN g·kg^-1	Leaf–TP g·kg^-1	SOC g·kg^-1	TN g·kg^-1	TP g·kg^-1	MBC mg·kg^-1	MBN mg·kg^-1
CP	434.57±29.04a	11.4±0.25a	1.15±0.06a	115.44±4.09ab	12.08±0.91a	1.12±0.06b	244.74±4.56c	29.24±1.23b
CS	433.48±10.3a	12.77±0.64a	1.16±0.03a	130.60±4.16a	14.81±0.64a	1.47±0.06a	382.07±13.51a	65.7±0.61a
MV	437.88±56.91a	11.1±0.45a	1.33±0.06a	101.98±7.21b	11.11±1.95a	1.20±0.05b	280.15±8.99b	28.52±1.48b
RC	440.71±14.79a	10.67±0.67b	1.62±0.05b	155.30±3.03b	27.78±2.19a	1.57±0.01a	452.66±13.29b	59.8±0.19b
AG	413.04±25.11a	11.13±0.5b	2.16±0.22a	196.98±5.36a	30.06±0.04a	1.48±0.02b	605.97±2.70ab	67.9±0.61a
PS	442.52±10.27a	13.4±0.44a	2.45±0.05a	136.06±5.98c	22.34±1.02b	1.33±0.01c	713.07±85.96a	63.02±2.31b
CD	388.73±31.59b	14.33±0.23b	2.43±0.42a	13.27±0.87a	1.01±0.05a	0.09±0.00a	196.98±3.61a	21.57±0.61a
AC	468.29±18.27a	16.43±0.41a	2.28±0.12a	5.97±0.84b	0.81±0.07a	0.04±0.00c	171.03±8.98b	19.35±0.61b
SK	413.72±5.9ab	14.93±0.09b	2.05±0.04a	4.63±0.35b	0.91±0.07a	0.05±0.00b	212.67±0.88a	16.98±0.77c
hygrophyte	435.31±11.32a	11.76±0.51b	1.21±0.06b	114.14±8.38b	12.73±1.17b	1.26±0.11a	302.32±41.16b	41.07±12.31ab
mesophyte	432.09±9.54a	11.73±0.84b	2.08±0.25a	168.78±17.98a	26.73±2.29a	1.46±0.07a	590.57±75.59a	63.58±2.36a
xerophyte	423.58±23.49a	15.23±0.62a	2.25±0.11a	7.95±2.68c	0.9±0.05c	0.06±0.02b	193.56±12.14b	19.3±1.32b
Species	αG	βG	βX	CBH	NAG	LAP	aP	pH
CP	6.87±0.26c	44.28±3.72b	3.39±0.16a	2.05±0.17a	17.52±0.8b	18.61±3.85a	149.13±14.14c		6.57±0.01a
CS	11.58±0.15b	31.36±4.46b	2.42±0.08b	1.52±0.08b	20.42±1.75b	13.39±1.61a	192.61±4.65b		6.5±0.03ab
MV	16.89±1.84a	68.16±4.74a	3.52±0.19a	1.63±0.19ab	27.51±0.4a	14.16±4.26a	261.02±5.7a		6.43±0.04b
RC	10.54±0.51a	41.54±2.02a	3.16±0.15a	1.69±0.01ab	18.76±0.38a	45.82±2.82a	173.47±7.81a		6.34±0.02c
AG	12.49±1.93a	42.55±5.37a	2.09±0.05a	2.74±0.67ab	13.3±2.14b	28.75±1.11b	123.66±3.6b		7.22±0.08b
PS	13.74±0.57a	31.56±0.28b	3.62±0.84a	1.92±0.04b	10.51±0.07c	32.27±2.63b	101.47±5.98c		9.52±0.03a
CD	6.11±0.10b	50.89±0.7a	2.30±0.02b	3.91±0.17a	18.84±1.07a	99.83±16.09a	297.27±4.67a		6.65±0.04a
AC	10.93±0.22a	37.35±0.48b	3.99±0.6a	1.91±0.03c	13.51±0.65b	50.96±7.46b	272.97±48.32a		6.57±0.03ab
SK	12.35±0.92a	39.73±6.92ab	2.14±0.13b	2.57±0.04b	19.55±1.37a	68.29±3.73b	292.88±4.12a		6.52±0.01b
hygrophyte	11.78±2.89a	47.93±10.78a	3.11±0.35a	1.73±0.16a	21.82±2.97a	17.05±3.29b	267.59±97.53a		6.50±0.04a
mesophyte	12.25±0.93a	31.88±10.17a	2.96±0.45a	1.78±0.53a	11.86±5.6a	38.95±8.5ab	112.87±38.49a		7.69±0.95a
xerophyte	9.80±1.89a	42.66±4.17a	2.81±0.59a	2.13±0.91a	15.97±3.23a	63.03±20.02a	287.71±7.48a		6.58±0.04a

Note: the lowercase letters behind mean ± se indicate significant-differences among different species within the same PFG using LSD test at p-values ≤ 0.05 level. pH means soil pH. The unites for these soil extracellular hydrolytic enzymes were expressed as nmol⁻¹ fluorescent substrate g⁻¹ soil·h⁻¹.

Table 3

Homeostasis values (H) with the corresponding r² between consumer stoichiometry (i.e. plant leaf C: N: P) and resource stoichiometry (i.e. soil C: N: P, MBC: MBN, enzyme C: N: P) at species levels and PFGs levels, respectively.

Consumer (y-axis)	Leaf C: N						Leaf N: P				Leaf C: P
Resource (x-axis)	Soil C: N		MBC: MBN		Enzyme C: N		Soil N: P		Enzyme N: P		Soil C: P		Enzyme C: P
species	H	r²	H	r²	H	r²	H	r²	H	r²	H	r²	H	r²
Overall	‒	0.38	‒	0.16	‒	0.03	‒	0.30	‒	0.32	‒	0.30	‒	0.04
CP	0.53	0.99	1.43	0.90	-0.63	0.49	-4.51	0.48	0.81	0.59	1.59	0.52	-0.15	0.98
CS	2.24	0.73	‒	0.06	0.45	0.74	-1.27	0.65	0.37	0.99	‒	0.27	0.07	0.55
MV	1.19	0.50	0.03	0.99	‒	0.00	1.36	0.90	0.42	0.94	‒	0.01	‒	0.15
RC	1.74	0.53	‒	0.04	0.21	0.74	‒	0.02	0.52	0.98	0.48	0.93	‒	0.03
AG	-0.35	0.72	‒	0.37	‒	0.20	‒	0.20	-0.10	0.99	-0.19	0.48	-0.01	0.57
PS	‒	0.09	‒	0.02	-1.70	0.86	1.81	0.50	‒	0.26	‒	0.30	-1.83	0.13
CD	-2.62	0.44	‒	0.09	-0.59	0.91	-0.16	0.78	-0.31	0.98	‒	0.04	0.06	0.62
AC	‒	0.00	‒	0.29	-1.74	0.72	‒	0.07	-1.42	0.97	4.84	0.52	‒	0.04
SK	-8.13	0.81	‒	< 0.001	0.55	0.98	14.67	0.63	‒	0.33	-1.78	0.72	0.77	0.86
hygrophyte	‒	0.29	‒	0.08	‒	0.08	‒	0.07	‒	0.26	‒	0.09	‒	0.01
mesophyte	‒	< 0.001	‒	0.37	‒	0.03	‒	0.05	‒	0.10	‒	0.20	-0.58	0.40
xerophyte	‒	0.14	‒	< 0.001	‒	0.08	‒	0.12	-1.38	0.50	3.40	0.40	‒	0.18

Note: only the H values with r² ≥ 0.40 were displayed, which was considered meaningful H (Bell et al. 2014). The bold letters indicate H > 1, which suggests homeostatic relationship, while H ≤ 1indicates non-homeostatic relationships among organism and resource stoichiometry (Soong et al. 2016, Sterner &Elser 2002).

Riparian plant-soil-microbial C: N: P stoichiometry: are they conserved at plant functional group level?

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Abstract

Figures

1. Introduction

2. Materials and methods