Next Article in Journal
STI Knowledge in Berlin Adolescents
Previous Article in Journal
Parents’ Perspectives on Family Sexuality Communication from Middle School to High School
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 15
Issue 1
10.3390/ijerph15010109
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Community Characteristics and Leaf Stoichiometric Traits of Desert Ecosystems Regulated by Precipitation and Soil in an Arid Area of China

by
Xiaolong Zhang
1,2,
Tianyu Guan
1,2,
Jihua Zhou
1,2,
Wentao Cai
1,2,
Nannan Gao
1,2,
Hui Du
1,
Lianhe Jiang
1,
Liming Lai
1 and
Yuanrun Zheng
1,*
1
Key Laboratory of Plant Resources, West China Subalpine Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing 100093, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(1), 109; https://doi.org/10.3390/ijerph15010109
Submission received: 25 October 2017 / Revised: 7 January 2018 / Accepted: 8 January 2018 / Published: 10 January 2018
(This article belongs to the Section Environmental Science and Engineering)
Browse Figures
Versions Notes

Abstract

:
Precipitation is a key environmental factor determining plant community structure and function. Knowledge of how community characteristics and leaf stoichiometric traits respond to variation in precipitation is crucial for assessing the effects of global changes on terrestrial ecosystems. In this study, we measured community characteristics, leaf stoichiometric traits, and soil properties along a precipitation gradient (35–209 mm) in a desert ecosystem of Northwest China to explore the drivers of these factors. With increasing precipitation, species richness, aboveground biomass, community coverage, foliage projective cover (FPC), and leaf area index (LAI) all significantly increased, while community height decreased. The hyperarid desert plants were characterized by lower leaf carbon (C) and nitrogen/phosphorus (N/P) levels, and stable N and P, and these parameters did not change significantly with precipitation. The growth of desert plants was limited more by N than P. Soil properties, rather than precipitation, were the main drivers of desert plant leaf stoichiometric traits, whereas precipitation made the biggest contribution to vegetation structure and function. These results test the importance of precipitation in regulating plant community structure and composition together with soil properties, and provide further insights into the adaptive strategy of communities at regional scale in response to global climate change.

1. Introduction

Water availability is the most important factor affecting ecosystem structure and function [1,2,3]. In arid and semi-arid regions, especially, biophysical activity is tightly coupled to water availability, and water deficit exerts a profound influence on ecosystem dynamics [4,5]. Precipitation is a key environmental factor that determines water availability in desert ecosystems and regulates the responses of plant communities and entire ecosystems [6,7,8]. The response and adaptation of desert plant community characteristics and their leaf stoichiometric traits to precipitation are of the utmost importance, as community characteristics and leaf traits are fundamental elements in understanding the structure and function of ecosystems [9,10,11]. Precipitation affects community characteristics and leaf traits in complex ways, especially in a changing climate. It may directly influence plant activity and ecological processes, and also indirectly mediates these through interactions with abiotic factors such as soil water content, supplementary water, soil nutrition, and evaporation [1,7,12,13]. Exactly how precipitation affects community characteristics and leaf stoichiometric traits across a precipitation gradient remains largely unclear and needs further research at the interface of ecology and hydrology, especially in arid regions [8].
Dryland ecosystems, which cover nearly 40% of terrestrial habitats and provide critical ecosystem services to biota, are particularly suitable for investigating the role of precipitation on ecological communities [2]. These ecosystems are defined by high seasonal and annual variation in precipitation [4]. In arid regions, sparse and variable precipitation exerts strong control over plant community composition, life histories, physiological properties, and resource availability, thereby impacting eco-hydrological processes [6,14]. The response of plants to precipitation regimes had been analyzed in some studies, within individual species, and within communities and ecosystems [1,14,15]. The research on the response of individual plants to precipitation regimes had focused mainly on biophysiological traits including water use efficiency, photosynthesis, sap flow, respiration and evapotranspiration [14,16,17]. Community characteristics including species diversity, species composition, and biomass are the main parameters of the ecological processes that are highly sensitive to precipitation [1,5,18,19]. Precipitation plays a crucial role in shaping vegetation distribution within arid environments, and understanding responses of community to precipitation is critical to maintain desert ecosystems sustainability [4,14].
Leaf traits are an important determinant of plant growth and production in plant communities, and their nutrient traits are closely associated with the structure and function of terrestrial ecosystems [9,10,20]. Elser et al. [21,22] revealed that terrestrial plants or plant communities grow in a wider range of nutrient conditions and the C/N/P stoichiometry of terrestrial plants could reflect how plant species adjusted to the local growth conditions. Ecological stoichiometry, as an integrative approach, can yield new insights for studying how precipitation affects the balance of essential elements including C, N, and P at different trophic levels and wide spatial scales [20,23,24,25]. Precipitation may change the leaf C, N, P stoichiometry through a cascade of plant-soil feedbacks, and may also alter the species composition and leaf C, N, P stoichiometry due to differences in species’ nutrition stoichiometry contents [13,26,27,28]. Currently, there has been increasing research on the leaf stoichiometry of plant species in relation to variations in geographical and climatic factors [20,25,26,28]. However, few studies have focused on the effects of precipitation regimes on the leaf stoichiometric traits of desert plants, especially the relationship between precipitation and nutrient stoichiometry in field environments across a range of precipitation gradients in arid regions.
The middle and lower reaches of the Heihe River, a typical arid inland river desert ecosystem, occur in an extremely arid inland region where the ecological environment is fragile [29]. The primary landscapes are peripheral desert, riparian forest and a central oasis, with desert playing a crucial role in maintaining a stable ecological environment and the oasis in maintaining agricultural production. Over the past years, human activity (e.g., grazing and agriculture) and global climate change has led to the destruction of desert vegetation in this region [29,30,31,32]. Currently, some studies on vegetation-environment relationships have been carried out in several riparian and oasis zones [33,34,35,36]. However, little is known about the effects of precipitation on natural desert vegetation and soil properties at a regional scale, especially leaf stoichiometric traits. With a large range of annual precipitation from 29 to 447 mm, the middle and lower reaches of the Heihe River are a suitable study site for investigating the responses of natural desert ecosystems to precipitation.
Seven sites with natural desert vegetation in the middle and lower reaches of the Heihe River were used to explore patterns of community structure and leaf stoichiometric traits along a natural precipitation gradient. Specifically, the study aims to: (1) characterize the pattern of plant community characteristics, leaf stoichiometric traits and soil properties along precipitation gradient; (2) evaluate the relationship between plant community characteristics and soil properties, leaf stoichiometric traits and soil properties; and (3) determine major factors affecting community characteristics and leaf stoichiometric traits along a precipitation gradient. We hypothesized that precipitation play a major role on plant community structure, while soil properties had key influence on leaf stoichiometric traits. These findings can be useful to maintain the sustainability of natural desert ecosystems.

2. Materials and Methods

2.1. Study Area and Site Description

The Heihe River Basin (Figure 1) is the second largest inland river basin in Northwest China, with a length of 821 km in its main stream and a catchment area of 14.29 × 104 km2. The river originates from the middle of the Qilian Mountains, on the northern Tibet Plateau, then flows through Qinghai Province, Gansu Province, and the Inner Mongolia Autonomous Region, and terminates at the north end of Juyan Lake in Ejin county, Inner-Mongolia [30]. The Heihe River Basin has a varied topography, with elevations between about 900 and 5500 m (calculated from ASTER GDEM, http://westdc.westgis.ac.cn/), and the integrated topographic landscape can be divided into: a glaciology and geocryology zone, an alpine vegetation zone, a piedmont oasis zone, and a desert zone. The desert zone accounts for more than 75% of the total land area. The upper reaches are covered with thick vegetation and have well-developed glaciology and geocryology, which means they form the main runoff generating region [37]. The middle and lower reaches have a great deal of farmland and desert, and have become the primary runoff consumption region [30].
The study was conducted in the vast natural desert, located in the middle and lower reaches of the Heihe River. The region is characterized by a typical continental arid climate, which is dominated by a warm-humid summer and a cold-dry winter. The mean annual precipitation (from 1950 to 2000) was 29–447 mm (calculated from the WORLDCLIM dataset, www.worldclim.org) with a high spatial and temporal variability. The majority (more than 75%) of precipitation falls from July to August, and pan evaporation is relatively high, especially in lower reaches, evaporation is 100 times greater than the precipitation [17,30]. The mean annual temperature is 5–10 °C. The soils have developed from gray-brown desert soil [30]. Natural desert vegetation accounts for 87.02% of the total area [38]. In the oasis, the primary vegetation is Populus euphratica Oliv. and Tamarix ramosissima Lebed. Outside of the oasis, the primary vegetation is temperate desert shrubland. Desert shrub plants (Artemisia desertorum Spreng., Kalidium gracile Fenzl, Salsola passerina Bunge, Kalidium cuspidatum (Ung. Sternb.) Grub., and Haloxylon ammodendron (C. A. Mey.) Bunge are the dominant species and major primary producers, and herb plant appeared in the form of companion species [30]. The list of plant species in the seven sampling sites were shown in Table A1.

2.2. Experimental Design and Data Collection

This study was conducted in temperate desert shrubland through the middle and lower reaches of Heihe River Basin. In August 2015, seven shrubland sampling sites along a precipitation gradient were prepared; vegetation and soil sampling were finished at the same time within one week when aboveground biomass reached the peak [30]. The sites were in an open, flat, undisturbed desert Gobi area, far from the river (more than 10 km) and other water resources. In each sampling site, three shrub quadrats (5 m × 5 m) were established randomly as three replicates. The number of species, coverage, plant height, leaf area index (LAI), basal diameter, and width of canopy were recorded individually. Four herb quadrats (1 m × 1 m) were established at each corner of the shrub quadrat to collect data of number of herb species, coverage, and height. Compared to community coverage, foliage projective cover only records green leaves coverage [39]. A 30 m sample lines were set up near each shrub quadrat, and foliage projective cover (FPC) was measured with a simple FPC measuring tube [39]. Aboveground biomass was determined by the harvest method. Harvested materials were oven-dried at 80 °C to constant weight, and then the weight was recorded. The geographic coordinates and elevation of each plot were recorded using a global positioning system. LAI was measured with a LAI-2200 Plant Canopy Analyzer (LI-COR, Lincoln, NE, USA), by using one sensor with a 90° view cap. Measurements were made near sunset [40].
At each site, intact soil cores were collected using a cutting ring (volume of 100 cm3) from five soil depths (0–10, 10–20, 20–30, 30–40, and 40–50 cm) in each shrub quadrat after removing any rocks and litter, with three replicates. Soil samples were sealed in an ice chest and were transported to laboratory. Soil samples were air-dried and passed through a 2-mm sieve. Soil pH and electrical conductivity (EC) were measured in 1:1 soil-water and 1:5 soil-water suspensions (Multiline F/SET-3, WTW, Weilheim, Germany), respectively [41]. Soil total C and N were measured using a C/H/N analyzer (Vario EL III, Elementar, Hanau, Germany) [42], soil available P was measured by the Olsen method, and soil available K was obtained with 1 M ammonium acetate and measured by atomic absorption spectroscopy [43]. Soil bulk density and gravimetric soil water content (SWC) was measured by collecting soil cores from each soil layer using a stainless-steel cutting ring (100 cm3) at 0–10, 10–20, 20–30, 30–40, and 40–50 cm depths in each shrub quadrat, and then were oven dried at 105°C to a constant weight.

2.3. Leaf Stoichiometric Traits

Sun-exposed and fully expanded mature leaves (or assimilating shoots) were collected from three individuals of the dominant species at each shrub quadrat. The leaves from same species were put together and grouped in paper envelopes. Leaf stoichiometric traits were analyzed with three replicates for same sample, and presented in mass basis (%). The total C and N concentrations in the leaves were measured using a C/H/N analyzer (Vario EL III, Elementar, Hanau, Germany). Leaf P and K concentrations were measured using an inductively coupled plasma optical emission spectrometer (iCAP 6300, Thermo Scientific, Waltham, MA, USA) [44].

2.4. Statistical Analysis

Species richness was determined from the total species numbers in each plot. Species importance value was calculated as (RD + RC + RF)/3 to indicate the dominant species (Table A2 and Table 1), where RD, RC, and RF are the relative density, relative coverage, and relative frequency, respectively, of each species in each plant community [45]. Gravimetric soil water content data were averaged across three soil layers of 0–10, 10–30, and 30–50 cm; other soil data were averaged across 0–50 cm soil depth.
All data were log 10 transformed to meet the homogeneity of variance and normality. One-way ANOVA analysis of variance was applied to compare the differences in community characteristics, leaf stoichiometric traits, and soil properties in different sites. If significant differences were found, Tukey’s test was used to determine the differences (Table A3 and Table A4). Regression analyses were used to detect relationship among plant community characteristics, leaf stoichiometric traits, and soil properties along precipitation gradient. Appropriate regression equations were selected based on level of significance and high R2 value. Pearson correlation was used to determine the strengths of possible relationships between community characteristics, leaf stoichiometric traits, and environmental factors. Significant differences were evaluated at the level of p < 0.05. Statistical analyses were carried out using SPSS Version 18.0 (SPSS, Chicago, IL, USA).
Environment variables included precipitation and 11 soil properties including gravimetric soil water content (0–10 cm), gravimetric soil water content (10–30 cm), gravimetric soil water content (30–50 cm), soil bulk density, soil total nitrogen, soil total carbon, soil C/N, soil available P, soil available K, soil pH, and soil electrical conductivity were used to separate key environmental factors for variation of community characteristics, the marginal and conditional effects of the variables were calculated through forward selection in redundancy analysis (RDA) that directly showed the significance and percentage of the explained factors [46]. Statistical test for each added variable was conducted with Monte Carlo permutation tests (9999 permutations). Marginal effects showed the effects of the environmental variables on community characteristics, and conditional effects showed the effects of the environment variables on community characteristics after the anterior variable was eliminated by the forward selection method [31,32,46]. The forward selection method was performed to exclude variables that did not contribute significantly (p > 0.05) to variation, and the redundant variables were eliminated and a group of key variables was determined. Both precipitation variable and soil properties were included in the group of key variables, variation partitioning was used to separate the variation in the community characteristics between two groups of significant predictors: precipitation and soil properties. The independent effects of each factor and the interactive effects between factors were included in the final model [47]. Either precipitation variable or all of soil properties was not included in the group of key variables, variation partitioning procedure was not performed. Leaf stoichiometric traits data was analyzed in same process. The forward selection, Monte Carlo test, and variation partitioning were conducted using CANOCO for Windows program (version 5.0) [46].

3. Results

3.1. Changes in Community Characteristics along the Precipitation Gradient

Species richness (F = 38.79, p < 0.001), aboveground biomass (F = 8.75, p < 0.001), community height (F = 18.51, p < 0.001), community coverage (F = 62.05, p < 0.001), FPC (F = 14.80, p < 0.001) and LAI (F = 76.44, p < 0.001) were significantly different among sites with different annual precipitation (Table 1 and Table A3).
Species richness, aboveground biomass, community coverage, FPC, and LAI significantly increased with increasing precipitation and could be described by linear equations, while community height significantly decreased with increasing precipitation and could also be described by linear equations (Table 1, Figure 2).

3.2. Changes in Leaf Stoichiometric Traits along the Precipitation Gradient

For all species, the mean leaf C, N, P, and K contents, and C/N, C/P, and N/P ratios were 301.22 mg g−1, 18.81 mg g−1, 1.74 mg g−1, 17.59 mg g−1, 15.88, 199.68, and 12.27, respectively (Table 2). Leaf C (F = 175.76, p < 0.001), leaf N (F = 109.19, p < 0.001), leaf P (F = 456.43, p < 0.001), leaf K (F = 253.59, p < 0.001), leaf C/N (F = 319.26, p < 0.001), leaf C/P (F = 306.01, p < 0.001), and leaf N/P (F = 241.23, p < 0.001) were significantly different among sites with different precipitation (Table 2 and Table A3). Leaf C, leaf N, and leaf C/N had no significant trend with increasing precipitation. Leaf K decreased significantly with increasing precipitation and could be described by linear equation, while Leaf P showed a hump-shaped pattern, increasing and then decreasing rapidly with increasing precipitation, and could be described by quadratic curve (Figure 3). Leaf C/P and leaf N/P had the opposite trend to leaf P (Figure 3).

3.3. Changes in Soil Properties along the Precipitation Gradient

Gravimetric soil water content in the 0–10 cm soil layer (GSWC10) (F = 3.24, p = 0.033), gravimetric soil water content in 10–30 cm soil layer (GSWC30) (F = 12.62, p < 0.001), gravimetric soil water content in 30–50 cm soil layer (GSWC50) (F = 18.01, p < 0.001), soil bulk density (F = 24.39, p < 0.001), soil total N (F = 22.26, p < 0.001), soil total C (F = 56.56, p < 0.001), soil C/N ratio (F = 28.68, p < 0.001), soil available P (F = 23.76, p < 0.001), soil available K (F = 24.02, p < 0.001), soil pH (F = 2.33, p = 0.41), and soil EC (F = 25.36, p < 0.001) varied significantly among sites with different precipitation (Table 3 and Table A4).
Gravimetric soil water content at 0–10 cm and at 10–30 cm showed a significantly increasing trend with increasing precipitation, and could be described by linear equations, but this trend was not significant at 30–50 cm soil depths (Figure 4). Soil bulk density and soil pH remained relatively constant with increasing precipitation (Figure 4). Soil total N and total C significantly increased with precipitation and could be described by linear equations, while soil available K significantly decreased with precipitation and could also be described by logarithmic equation (Figure 4). Soil C/N, soil available P, and soil electrical conductivity did not significantly vary with increasing precipitation (Figure 4).

3.4. Relationships among Community Characteristics, Leaf Stoichiometric Traits and Environmental Factors

Species richness, aboveground biomass, community coverage, FPC, and LAI were significantly and positively related to soil water content at 0–10 cm. Species richness, FPC, and LAI was positively correlated with soil water content at 10–30 cm, and LAI was positively correlated with soil water content at 30–50 cm. There were other positive or negative correlations among community characteristics or leaf stoichiometric traits and soil properties in these natural desert communities (Table A5).

3.5. Controlling Factors of Community Characteristics and Leaf Stoichiometric Traits

In the Monte Carlo test of forward selection (p < 0.05) for community characteristics, precipitation, soil C/N, GSWC50, soil total nitrogen and soil available P passed the test (Table 4). Variation partitioning showed that precipitation and soil properties jointly explained 76.9% of the variation of community characteristics; precipitation had the largest contribution (34.1%), next was soil properties (24.3%), and then the interaction of precipitation and soil properties (Figure 5). In the Monte Carlo test of forward selection (p < 0.05) for leaf stoichiometric traits, GSWC50, soil C/N, soil bulk density, GSWC30, soil electrical conductivity and soil available K passed the test (Table 5).
Precipitation had the largest contribution to variations in community characteristics, whereas soil properties had significant effect on the variations in leaf stoichiometric traits (Table 4 and Table 5).

4. Discussion

4.1. Community Characteristics and the Precipitation Gradient

Vegetation dynamics are tightly coupled with hydrological processes in arid and semi-arid ecosystems [2]. Previous studies have reported that precipitation plays an important role in regulating plant community structure and composition, with consequent influences on ecosystem functioning and potential feedback [1,4,48]. Our results showed that precipitation was the major driving force for variation of community characteristics and supported our hypothesis. In this arid region, the majority (more than 75%) of precipitation falls in July and August, shrub plants could use surface rainfall for survival in summer [30,49], and precipitation might be the main water resource for shrub plants. In addition, herb plants increased the community coverage, foliage projective cover, and species richness in rainy season. Therefore, water limitation might explain why single shrub-dominated community existed in S1–S5, while herb plant appeared in S6 and S7 (Table A1 and Table A2). Community height was significantly and negatively related to precipitation in our results, which was contrary to results in an alpine wetland ecosystem [50]. Potential mechanisms for this result may be that plants with greater height use more soil water at deeper depths than plants of smaller individual in hyperarid regions [51].
Our results showed that species richness and aboveground biomass increased linearly along the precipitation gradient (Figure 2), however, the maximum species richness and aboveground biomass did not appear in S7 with highest precipitation (Table 1). Decreased species richness and aboveground biomass appeared to be caused largely by the dominant shrub plant (Kalidium cuspidatum) [52]. Because Kalidium cuspidatum is a typical salt-secreting halophytic shrub, highly saline habitats with salt crust can develop [52], and distribution and growth of other plants were limited, therefore, species richness was low. Our results showed that species richness was significantly positive correlated with GSWC10 and GSWC30 (Table A5), which was contrary to results obtained in a previous study in an alpine wetland ecosystem [50], but was consistent with the results in arid and semiarid regions [3,53]. This difference might stem from the relatively small effects of interspecific competition in arid regions, whereas high species density in humid environments leads to greater interspecific competition and decreased species diversity [53]. These findings suggest that there is a positive interaction effect for plant diversity and upper soil water content in arid regions [54]. Although our results were consistent with previous conclusions obtained in arid and semi-arid regions, our results were derived from one-time-filed observations; the relationship between plant communities and precipitation may change in different seasons, long-term study are necessary in future.

4.2. Leaf Stoichiometric Traits and the Precipitation Gradient

Leaf stoichiometric traits along a natural precipitation gradient in an arid desert habitat might be different from those reported at regional scales [35,55,56]. Our results showed that the mean of leaf C was 301.22 mg g−1, which was significantly lower than in other arid regions and lower than the average of global flora [21,25,55,56]. This difference might be because drought and salt stress inhibit desert plant photosynthesis by reducing stomatal conductance and water potential, and drought and salt stress lead to increased metabolic costs and decreasing C fixation [55,57]. The mean leaf N was 18.81 mg g−1, significantly lower than that in other arid regions [35,55,56], but nearly equivalent with Chinese flora and global flora [20,21,25]. Killingbeck et al. [58] reported that the average of leaf N was 22.0 mg g−1 based on 78 species of desert plant leaves. In our study, the fact that the leaf N of dominant species tended to be relatively low was largely due to the lower soil N and a relative lack of symbiotic N fixer [35].
Previous studies have reported that P is considered the major growth-constraining nutrients in plant communities in China compared with the global average [20]. However, some studies reported that soil P content have large variation across China and show an increasing trend from humid region to arid region [59]. In our study, the mean leaf P was 1.74 mg g−1, higher than that of the Loess Plateau and the average of Chinese flora [20,35], but nearly equivalent with that in other arid regions and the average of global flora [21,25,55,57]. Relative high leaf P content appeared to be caused largely by high soil P content, due to leaf P being tightly coupled with soil P [20]. An N/P ratio less than 14 indicates N constraint, while an N/P ratio more than 16 indicates P constraint. With an N/P ratio between 14 and 16, either or both N or P constrain plant growth [60]. In the present study, the leaf N/P ratio in this region was 12.7, indicating that plant growth was largely constrained by N. This is consistent with some previous studies in desert ecosystems [55,61], but differs from other studies in grassland and woodland [62,63]. This difference indicated that P might play an important role in plant distribution patterns in relative humid ecosystems in China. The relative deficiency in soil N content and the relatively adequate soil P content could possibly explain why N content is the key limiting factor for the plant N/P pattern in desert ecosystems [61].
Some studies on leaf stoichiometric traits-climate-soil relationships have been carried out at local, regional, or global scales providing further understanding of the mechanisms of vegetation dynamics in response to global climate change [20,25,55,64]. Reich and Oleksyn et al. [25] observed that leaf N, leaf P and N/P ratio were significantly related to latitude and mean annual temperature at a global scale. However, Kerkhoff et al. [64] reported that leaf N and leaf P were not related to the latitude, but leaf N/P ratio significantly decreased with increased latitude based on 1054 worldwide plant species. Han et al. [20] reported that leaf N and P of 753 plant species in China were significantly related with latitude and mean annual temperature, but leaf N/P ratio was not related to latitude. This discrepancy may be attributed to the different nutrient limitations in the different regions [28]. Our results showed that there was no significant linear relationship between leaf C/N/P stoichiometry and precipitation at the regional scale (Figure 3), and further proved leaf stoichiometric traits of different plant functional groups fluctuated significantly and the climate varied relatively little at a regional scale, so that variations of leaf stoichiometric traits modulated by the climate are non-significant [35]. Our results showed that leaf K was significantly and negatively related to precipitation (Figure 3), similar results were reported that ability of resisting drought and absorbing water for Erica multiflora L. depend on obtaining more K element in arid environment [65]. Our sampling captured a relatively narrow range of precipitation, given the complex relationships between precipitation and vegetation, detail field investigations in different seasons at large scale in arid region should be conducted to elucidate the responses of different functional groups or community-level leaf stoichiometric traits to precipitation in next research.

4.3. Soil Properties and the Precipitation Gradient

Soil properties play important roles in regulating plant community structure and composition in arid and semi-arid ecosystem, especially the non-phreatophyte species [1]. Our result showed that upper soil water content (0–30 cm) showed a significantly increasing trend with increasing precipitation (Figure 4), and was consistent with results obtained in arid and semi-arid region [3,7]. In our study, with increasing rainfall and decreasing evaporation in summer, soil moisture at soil surface from S1 to S7 increased. Some studies reported that soil with high bulk density has low water holding capacity in the surface soil and might induce drought stress in the surface soil in arid regions [66,67]. However, soil bulk density in our study did not show a significant decrease with increasing precipitation, and soil pH remained relatively constant with increasing precipitation (Figure 4). These results appeared to be caused by soil parent material that mainly composes of sand [30]. Thomey et al. [68] and Noy-Meir [4] found that large rainfall events result in a significantly high pulse-response in the upper soil water content (0–16 cm) in arid regions, and deep soil water content change controlled by precipitation are non-significant due to relative small precipitation and high evaporation. Our results also showed that deeper soil water content (30–50 cm) did not show a significant increase with increasing precipitation. However, the largest soil water content appeared in S1 (Figure 4). In addition, high soil electrical conductivity in S1 indicated that the soil water content (30–50 cm) may be affected by groundwater.
Previous studies have reported that precipitation can directly and indirectly affect soil properties via improved plant-soil feedback responses [1,48]. Zhou et al. [7] reported that precipitation may regulate plant production and decomposition and then affect soil C and soil N. Some studies have observed that soil C and N increased with increasing precipitation [69,70], and this was consistent with our results that soil total N and total C significantly increased with precipitation (Figure 4). The loss of soil water would increase organic matter decomposition rates and affect net N mineralization in hyperarid regions, which leads to losses of soil C and N content [71].

5. Conclusions

This study comprehensively characterized plant community characteristics, leaf stoichiometric traits, and soil properties along a precipitation gradient in an arid area of China. The analysis indicated that precipitation had a positive effect on species richness, aboveground biomass, community coverage, FPC, and LAI, but it significantly decreased community height, and precipitation was an important factor that affected soil properties, including soil water and soil nutrition. Whereas, soil properties, rather than precipitation, were drivers of desert plant leaf stoichiometric traits. The growth of desert plants might be more limited by N rather than P in this region. Given the different roles of precipitation and soil properties in leaf stoichiometric traits and community characteristics, these environmental factors should be involved in biogeochemical simulation models and degraded ecosystem restoration in arid areas.

Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 91425301). We also greatly appreciate the academic editor and anonymous reviewers for the insightful comments.

Author Contributions

Yuanrun Zheng and Lianhe Jiang conceived and designed the study; Xiaolong Zhang and Tianyu Guan carried out the calculation and result analysis; Xiaolong Zhang took the lead in writing the manuscript; Xiaolong Zhang, Tianyu Guan, Jihua Zhou, Wentao Cai, Nannan Gao, Du Hui and Liming Lai conducted field investigations. All authors gave their approval of the version submitted for publication.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. List of plant species in the seven sampling sites.
Table A1. List of plant species in the seven sampling sites.
Sampling SitesFamilySpecies
S1 (1 species including 1 shrub)ChenopodiaceaeHaloxylon ammodendron (C. A. Mey.) Bunge
S2 (1 species including 1 shrub)ZygophyllaceaeNitraria sibirica Pall.
S3 (2 species including 2 shrubs)ZygophyllaceaeNitraria praevisa Bobr.
SolanaceaeLycium ruthenicum Murr.
S4 (3 species including 3 shrubs)AsteraceaeArtemisia desertorum Spreng.
EphedraceaeEphedra przewalskii Stapf.
PolygonaceaeCalligonum mongolicum Turcz.
S5 (3 species including 3 shrubs)ChenopodiaceaeKalidium gracile Fenzl
ChenopodiaceaeSalsola passerina Bunge
ChenopodiaceaeSympegma regelii Bunge
S6 (7 species including 5 shrubs and 2 herbages)ChenopodiaceaeKalidium gracile Fenzl
ChenopodiaceaeSalsola passerina Bunge
ChenopodiaceaeSympegma regelii Bunge
LeguminosaeCaragana roborovskyi Kom.
ZygophyllaceaeNitraria roborowskii Kom.
ChenopodiaceaeAgriophyllum squarrosum (L.) Moq.
ChenopodiaceaeHalogeton glomeratus (Bieb.) C. A. Mey.
S7 (4 species including 3 shrubs and 1 herbage)ChenopodiaceaeKalidium cuspidatum (Ung. Sternb.) Grub.
ChenopodiaceaeSalsola passerina Bunge
TamaricaceaeReaumuria songarica (Pall.) Maxim.
ZygophyllaceaeZygophyllum fabago L.
Table
Table A2. Information of sampling sites. Shrub layer: HA, Haloxylon ammodendron; NS, Nitraria sibirica; NP, Nitraria praevisa; AD, Artemisia desertorum; KG, Kalidium gracile; SP, Salsola passerina; SR, Sympegma regelii; KC, Kalidium cuspidatum. Herb layer: AS, Agriophyllum squarrosum; HG, Halogeton glomeratus; ZF, Zygophyllum fabago.
Table A2. Information of sampling sites. Shrub layer: HA, Haloxylon ammodendron; NS, Nitraria sibirica; NP, Nitraria praevisa; AD, Artemisia desertorum; KG, Kalidium gracile; SP, Salsola passerina; SR, Sympegma regelii; KC, Kalidium cuspidatum. Herb layer: AS, Agriophyllum squarrosum; HG, Halogeton glomeratus; ZF, Zygophyllum fabago.
SiteAnnual Rainfall (mm)Important Value of Major Species in Shrub LayerImportant Value of Major Species in Herb Layer
HANSNPADKGSPSRKCASHGZF
S1351.00
S239 1.00
S362 0.76
S482 0.72
S5149 0.510.31
S6162 0.610.22 0.850.15
S7209 0.26 0.69 1
Table
Table A3. One-way ANOVA of community characteristics and leaf stoichiometric traits among the sampling sites across the middle and lower reaches of Heihe River Basin. *** indicates significant difference at p < 0.001.
Table A3. One-way ANOVA of community characteristics and leaf stoichiometric traits among the sampling sites across the middle and lower reaches of Heihe River Basin. *** indicates significant difference at p < 0.001.
Community Characteristics/Leaf Stoichiometric TraitsSum of SquaresdfMean SquareFSig.
Species richness1.15360.19238.79<0.001 ***
Aboveground biomass1.60160.2678.75<0.001 ***
Community coverage1.14860.19118.51<0.001 ***
Community height2.18760.36462.05<0.001 ***
Foliage projective cover1.40360.23414.80<0.001 ***
Leaf area index2.12760.35476.44<0.001 ***
Leaf C0.43060.072175.76<0.001 ***
Leaf N0.24660.041109.19<0.001 ***
Leaf P0.65160.108456.43<0.001 ***
Leaf K0.48760.081253.59<0.001 ***
Leaf C/N0.09860.016319.26<0.001 ***
Leaf C/P0.81560.136306.01<0.001 ***
Leaf N/P0.56460.094241.23<0.001 ***
Table
Table A4. One-way ANOVA of soil properties among the sampling sites across the middle and lower reaches of Heihe River Basin. *, ** indicate significant difference at p < 0.05 and p < 0.01, respectively.
Table A4. One-way ANOVA of soil properties among the sampling sites across the middle and lower reaches of Heihe River Basin. *, ** indicate significant difference at p < 0.05 and p < 0.01, respectively.
Soil PropertiesSum of SquaresdfMean SquareFSig.
Gravimetric soil water content (0–10 cm)2.59660.4333.24<0.033 *
Gravimetric soil water content (10–30 cm)4.14460.69112.62<0.001 **
Gravimetric soil water content (30–50 cm)3.86260.64418.01<0.001 **
Soil bulk density0.25160.04224.39<0.001 **
Soil total nitrogen2.03460.33922.26<0.001 **
Soil total carbon6.79361.13256.56<0.001 **
Soil C/N4.75160.79228.68<0.001 **
Soil available P 3.66560.61123.76<0.001 **
Soil available K4.21560.70224.02<0.001 **
Soil pH0.00760.0012.330<0.041 *
Soil electrical conductivity19.36063.22725.36<0.001 **
Table
Table A5. Pearson’s correlation coefficients (r) among community characteristics, leaf stoichiometric traits, and environmental factors in different sites along a precipitation gradient in the Heihe River Basin. Significant correlations at p < 0.05 and p < 0.01 are shown in bold and in bold with underline, respectively. Abbreviations: SR, species richness; AGB, aboveground biomass; COVER, community coverage; Height, community height; FPC, foliage projective cover; LAI, leaf area index. Other abbreviations are described in Table 3.
Table A5. Pearson’s correlation coefficients (r) among community characteristics, leaf stoichiometric traits, and environmental factors in different sites along a precipitation gradient in the Heihe River Basin. Significant correlations at p < 0.05 and p < 0.01 are shown in bold and in bold with underline, respectively. Abbreviations: SR, species richness; AGB, aboveground biomass; COVER, community coverage; Height, community height; FPC, foliage projective cover; LAI, leaf area index. Other abbreviations are described in Table 3.
Community Characteristics and Leaf Stoichiometric TraitsGSWC10GSWC30GSWC50SBDSTNSTCC/NSAPSAKpHEC
SR0.7420.6420.370−0.1520.7520.700−0.0400.179−0.132−0.1440.100
AGB0.4490.3420.130−0.1670.3860.7300.273−0.155−0.375−0.044−0.218
COVER0.5370.2900.077−0.0590.5600.7390.1930.017−0.3450.077−0.158
HEIGHT−0.2480.1890.414−0.131−0.539−0.1770.469−0.1790.628−0.2490.477
FPC0.6780.4630.226−0.1490.6980.7520.003−0.046−0.359−0.136−0.157
LAI0.5730.5810.674−0.1960.1030.6230.496−0.2850.112−0.2320.133
Leaf C0.0670.1220.1700.071−0.2420.0750.517−0.0330.429−0.0690.333
Leaf N0.0370.0900.180−0.245−0.0700.0640.3830.4160.534−0.2420.649
Leaf P−0.407−0.518−0.548−0.126−0.191−0.1220.2430.315−0.0110.1590.027
Leaf K−0.541−0.524−0.3570.122−0.607−0.3660.375−0.0960.2000.1100.022
Leaf C/N0.1710.1730.1480.431−0.2570.1140.413−0.5380.1040.147−0.170
Leaf C/P0.5960.7240.7460.1430.1450.3260.174−0.2570.340−0.2460.288
Leaf N/P0.5830.7480.8130.0350.1720.2860.104−0.1400.411−0.3350.413

References

  1. Yang, H.J.; Wu, M.Y.; Liu, W.X.; Zhang, Z.; Zhang, N.L.; Wan, S.Q. Community structure and composition in response to climate change in a temperate steppe. Glob. Chang. Biol. 2011, 17, 452–465. [Google Scholar] [CrossRef]
  2. Wang, L.; D’Odorico, P.; Evans, J.P.; Eldridge, D.J.; McCabe, M.F.; Caylor, K.K.; King, E.G. Dryland ecohydrology and climate change: Critical issues and technical advances. Hydrol. Earth Syst. Sci. 2012, 16, 2585–2603. [Google Scholar] [CrossRef] [Green Version]
  3. Deng, L.; Wang, K.B.; Li, J.P.; Zhao, G.W.; Shangguan, Z.P. Effect of soil moisture and atmospheric humidity on both plant productivity and diversity of native grasslands across the Loess Plateau, China. Ecol. Eng. 2016, 94, 525–531. [Google Scholar] [CrossRef]
  4. Noy-Meir, I. Desert ecosystems: Environment and producers. Annu. Rev. Ecol. Syst. 1973, 4, 25–51. [Google Scholar] [CrossRef]
  5. Arndt, S.K. Integrated research of plant functional traits is important for the understanding of ecosystem processes. Plant Soil 2006, 285, 1–3. [Google Scholar] [CrossRef]
  6. Huxman, T.E.; Cable, J.M.; Ignace, D.D.; Eits, J.A.; English, N.B.; Weltzin, J.; Williams, D.G. Response of net ecosystem gas exchange to a simulated precipitation pulse in a semi-arid grassland: The role of native versus non-native grasses and soil texture. Oecologia 2004, 141, 295–305. [Google Scholar] [CrossRef] [PubMed]
  7. Zhou, X.H.; Talley, M.; Luo, Y.Q. Biomass, litter, and soil respiration along a precipitation gradient in Southern Great Plains, USA. Ecosystems 2009, 12, 1369–1380. [Google Scholar] [CrossRef]
  8. Smith, M.D.; Wilcox, K.R.; Power, S.A.; Tissue, D.T.; Knapp, A.K. Assessing community and ecosystem sensitivity to climate change-toward a more comparative approach. J. Veg. Sci. 2017, 28, 235–237. [Google Scholar] [CrossRef]
  9. Wright, I.J.; Reich, P.B.; Westoby, M.; Ackerly, D.D.; Baruch, Z.; Bongers, F.; Cavender-Bares, J.; Chapin, T. The worldwide leaf economics spectrum. Nature 2004, 428, 821–827. [Google Scholar] [CrossRef] [PubMed]
  10. Güsewell, S. N:P ratios in terrestrial plants: Variation and functional significance. New Phytol. 2004, 164, 243–266. [Google Scholar] [CrossRef]
  11. Song, L.L.; Fan, J.W.; Harris, W.; Wu, S.H.; Zhong, H.P.; Zhou, Y.C.; Wang, N.; Zhu, X.D. Adaptive characteristics of grassland community structure and leaf traits along an altitudinal gradient on a subtropical mountain in Chongqing, China. Plant Ecol. 2012, 213, 89–101. [Google Scholar] [CrossRef]
  12. Heisler, J.L.; Weltzin, J.F. Variability matters: Towards a perspective on the influence of precipitation on terrestrial ecosystems. Effects of precipitation change on ecosystems (EPRECOT)—A Terrestrial Ecosystem Response to Atmospheric and Climatic Change (TERACC) and European Commission sponsored precipitation workshop, Elsinore, Denmark, May 2006. New Phytol. 2006, 172, 189–192. [Google Scholar] [CrossRef] [PubMed]
  13. Ordoñez, J.C.; Van Bodegom, P.M.; Witte, J.M.; Wright, I.J.; Reich, P.B.; Aerts, R. A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 2009, 18, 137–149. [Google Scholar] [CrossRef]
  14. Xu, H.; Li, Y. Water-use strategy of three central Asian desert shrubs and their responses to rain pulse events. Plant Soil 2006, 285, 5–17. [Google Scholar] [CrossRef]
  15. Knapp, A.K.; Fay, P.A.; Blair, J.M.; Collins, S.L.; Smith, M.D.; Carlisle, J.D.; Harper, C.W.; Danner, B.T.; Lett, M.S.; McCarron, J.K. Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science 2002, 298, 2202–2205. [Google Scholar] [CrossRef] [PubMed]
  16. Misson, L.; Gershenson, A.; Tang, J.; Mckay, M.; Cheng, W.; Goldstein, A. Influences of canopy photosynthesis and summer rain pulses on root dynamics and soil respiration in a young ponderosa pine forest. Tree Physiol. 2006, 26, 833–844. [Google Scholar] [CrossRef] [PubMed]
  17. Li, W.; Yu, T.F.; Li, X.Y.; Zhao, C.Y. Sap flow characteristics and their response to environmental variables in a desert riparian forest along lower Heihe River Basin, Northwest China. Environ. Monit. Assess. 2016, 188, 561. [Google Scholar] [CrossRef] [PubMed]
  18. Giladi, L.; Ziv, Y.; May, F.; Jeltsch, F. Scale-dependent determinants of plant species richness in a semi-arid fragmented agro-ecosystem. J. Veg. Sci. 2011, 22, 983–996. [Google Scholar] [CrossRef]
  19. Fry, E.L.; Manning, P.; Power, S.A. Ecosystem functions are resistant toextreme changes to rainfall regimes in a mesotrophic grassland. Plant Soil 2014, 381, 351–365. [Google Scholar] [CrossRef]
  20. Han, W.X.; Fang, J.Y.; Guo, D.L.; Zhang, Y. Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. New Phytol. 2005, 168, 377–385. [Google Scholar] [CrossRef] [PubMed]
  21. Elser, J.J.; Fagan, W.F.; Denno, R.F.; Dobberfuhl, D.R.; Folarin, A.; Huberty, A.; Interlandi, S.; Kilham, S.S.; McCauley, E.; Schulz, K.L.; et al. Nutritional constraints in terrestrial and freshwater food webs. Nature 2000, 408, 578–580. [Google Scholar] [CrossRef] [PubMed]
  22. Elser, J.J.; Fagan, W.F.; Kerkhoff, A.J.; Swenson, N.G.; Enquist, B.J. Biological stoichiometry of plant production: Metabolism, scaling and ecological response to global change. New Phytol. 2010, 186, 593–608. [Google Scholar] [CrossRef] [PubMed]
  23. Elser, J.J.; Sterner, R.W.; Gorokhova, E.; Fagan, W.F.; Markow, T.A.; Cotner, J.B.; Harrison, J.F.; Hobbie, S.E.; Odell, G.M.; Weider, L.J. Biological stoichiometry from genes to ecosystems. Ecol. Lett. 2000, 3, 540–550. [Google Scholar] [CrossRef]
  24. Sterner, R.W.; Elser, J.J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere; Princeton University Press: Princeton, NJ, USA, 2002. [Google Scholar]
  25. Reich, P.B.; Oleksyn, J. Global patterns of plant leaf N and P in relation to temperature and latitude. Proc. Natl. Acad. Sci. USA 2004, 101, 11001–11006. [Google Scholar] [CrossRef] [PubMed]
  26. Han, W.X.; Fang, J.Y.; Reich, P.B.; Woodward, F.I.; Wang, Z.H. Biogeography and variability of eleven mineral elements in plant leaves across gradients of climate, soil and plant functional type in China. Ecol. Lett. 2011, 14, 788–796. [Google Scholar] [CrossRef] [PubMed]
  27. Posada, J.M.; Schuur, E.A.G. Relationships among precipitation regime, nutrient availability, and carbon turnover in tropical rain forests. Oecologia 2011, 165, 783–795. [Google Scholar] [CrossRef] [PubMed]
  28. Zhao, N.; He, N.P.; Wang, Q.F.; Zhang, X.Y.; Wang, R.L.; Xu, Z.W.; Yu, G.R. The altitudinal patterns of leaf C:N:P stoichiometry are regulated by plant growth form, climate and soil on Changbai Mountain, China. PLoS ONE 2014, 9, e95196. [Google Scholar] [CrossRef]
  29. Zhu, Y.H.; Chen, Y.N.; Ren, L.L.; Lü, H.S.; Zhao, W.Z.; Yuan, F.; Xu, M. Ecosystem restoration and conservation in the arid inland river basins of Northwest China: Problems and strategies. Ecol. Eng. 2016, 94, 629–637. [Google Scholar] [CrossRef]
  30. Cheng, G.D. Study on the Integrated Management of the Water-Ecology-Economy System of Heihe River Basin; Science Press: Beijing, China, 2009. [Google Scholar]
  31. Zhu, J.T.; Yu, J.J.; Wang, P.; Zhang, Y.C.; Yu, Q. Interpreting the groundwater attributes influencing the distribution patterns of groundwater-dependent vegetation in northwestern China. Ecohydrology 2012, 5, 628–636. [Google Scholar] [CrossRef]
  32. Ding, J.Y.; Zhao, W.W.; Daryanto, S.; Wang, L.X.; Fan, H.; Feng, Q.; Wang, Y.P. The spatial distribution and temporal variation of desert riparian forests and their influencing factors in the downstream Heihe River basin, China. Hydrol. Earth Syst. Sci. 2017, 21, 2405–2419. [Google Scholar] [CrossRef]
  33. Zheng, Y.R.; Xie, Z.X.; Jiang, L.H.; Shimizu, H.; Rimmington, G.M.; Zhou, G.S. Vegetation responses along environmental gradients on the Ordos plateau, China. Ecol. Res. 2006, 21, 396–404. [Google Scholar] [CrossRef]
  34. Weltzin, J.F.; Loik, M.E.; Schwinning, S.; Williams, D.G.; Fay, P.A.; Haddad, B.M.; Harte, J.; Huxman, T.E.; Knapp, A.K.; Lin, G.H.; et al. Assessing the response of terrestrial ecosystems to potential changes in precipitation. Bioscience 2003, 53, 941–952. [Google Scholar] [CrossRef]
  35. Zheng, S.X.; Shuangguan, Z.P. Spatial patterns of leaf nutrient traits of the plants in the Loess Plateau of China. Trees 2007, 21, 357–370. [Google Scholar] [CrossRef]
  36. Wu, T.G.; Dong, Y.; Yu, M.K.; Wang, G.G.; Zeng, D.H. Leaf nitrogen and phosphorus stoichiometry of quercus species across China. For. Ecol. Manag. 2012, 284, 116–123. [Google Scholar] [CrossRef]
  37. Wang, Y.H.; Yang, H.B.; Yang, D.W.; Qin, Y.; Gao, B.; Cong, Z.T. Spatial interpolation of daily precipitation in a high mountainous watershed based on gauge observations and a regional climate model simulation. J. Hydrometeorol. 2017, 18, 845–862. [Google Scholar] [CrossRef]
  38. Zhang, X.L.; Zhou, J.H.; Zheng, Y.R. 1:100,000 Vegetation Map of Heihe River Basin (Version 2.0); Cold and Arid Regions Science Data Cente: Lanzhou, China, 2016. [Google Scholar]
  39. Jiang, L.H.; Wang, J.Z.; Zheng, Y.R. Foliage projective cover, effective tool for describing structure of plant communities. Acta Bot. Yunnanica 2004, 26, 166–172. [Google Scholar] [CrossRef]
  40. Zhu, Y.H.; Ren, L.L.; Skaggs, T.H.; Lü, H.S.; Yu, Z.B.; Wu, Y.Q.; Fang, X.Q. Simulation of Populus euphratica root uptake of groundwater in an arid woodland of the Ejina Basin, China. Hydrol. Process. 2009, 23, 2460–2469. [Google Scholar] [CrossRef]
  41. Zuo, X.A.; Wang, S.K.; Lv, P.; Zhou, X.; Zhao, X.Y.; Zhang, T.H.; Zhang, J. Plant functional diversity enhances associations of soil fungal diversity with vegetation and soil in the restoration of semiarid sandy grassland. Ecol. Evol. 2016, 6, 318–328. [Google Scholar] [CrossRef] [PubMed]
  42. Köppler, M.R.; Kowarik, I.; Kühn, N.; Lippe, M.V. Enhancing wasteland vegetation by adding ornamentals: Opportunities and constraints for establishing steppe and prairie species on urban demolition sites. Landsc. Urban Plan. 2014, 126, 1–9. [Google Scholar] [CrossRef]
  43. ISSCAS (Institute of Soil Sciences, Chinese Academy of Sciences). Physical and Chemical Analysis Methods of Soils; Shanghai Science Technology Press: Shanghai, China, 1978. [Google Scholar]
  44. Seymour, C.L.; Milewski, A.V.; Mills, A.J.; Joseph, G.S.; Cumming, G.S.; Cumming, D.H.M.; Mahlangu, Z. Do the large termite mounds of macrotermes, concentrate micronutrients in addition to macronutrients in nutrient-poor African savannas? Soil Biol. Biochem. 2014, 68, 95–105. [Google Scholar] [CrossRef]
  45. Zhang, J.T.; Dong, Y. Factors affecting species diversity of plant communities and the restoration process in the Loess area of China. Ecol. Eng. 2010, 36, 345–350. [Google Scholar] [CrossRef]
  46. Ter Braak, C.J.F.; Smilauer, P. Canoco Reference Manual and User’s Guide: Software for Ordination, Version 5.0; Microcomputer Power, Ithaca: Wageningen, The Netherlands, 2012. [Google Scholar]
  47. Heikkinen, R.K.; Luoto, M.; Kuussaari, M.; Pöyry, J. New insights into Butterfly-environment relationships using partitioning methods. Proc. Biol. Sci. 2005, 272, 2203–2210. [Google Scholar] [CrossRef] [PubMed]
  48. Kardol, P.; Campany, C.E.; Souza, L.; Norby, R.J.; Weltzin, J.F.; Classen, A.T. Climate change effects on plant biomass alter dominance patterns and community evenness in an experimental old-field ecosystem. Glob. Chang. Biol. 2010, 16, 2676–2687. [Google Scholar] [CrossRef]
  49. Fu, A.H.; Chen, Y.N.; Li, W.H. Water use strategies of the desert riparian forest plant community in the lower reaches of Heihe River Basin, China. Sci. China Earth Sci. 2014, 57, 1293–1305. [Google Scholar] [CrossRef]
  50. Wu, G.L.; Ren, G.H.; Wang, D.; Shi, J.H.; Warrington, D. Above- and below-ground response to soil water change in an alpine wetland ecosystem on the Qinghai-Tibetan Plateau, China. J. Hydrol. 2013, 476, 120–127. [Google Scholar] [CrossRef]
  51. Ferreira, J.N.; Bustamante, M.; Garcia-Montiel, D.C.; Caylor, K.K.; Davidson, E.A. Spatial variation in vegetation structure coupled to plant available water determined by two-dimensional soil resistivity profiling in a Brazilian savanna. Oecologia 2007, 153, 417–430. [Google Scholar] [CrossRef] [PubMed]
  52. Zhang, Y.C.; Zhang, J.N. Anatomical Structure and Analysis on leaves of Kalidium culpidatum (Umg-sternb) Grub. J. Agric. Sci. 2004, 25, 43–45. [Google Scholar] [CrossRef]
  53. Wu, G.L.; Zhang, Z.N.; Wang, D.; Shi, J.H.; Zhu, Y.J. Interactions of soil water content heterogeneity and species diversity patterns in semi-arid steppes on the Loess Plateau of China. J. Hydrol. 2014, 519, 1362–1367. [Google Scholar] [CrossRef]
  54. Bezemer, T.M.; Lawson, C.S.; Hedlund, K.; Edwards, A.R.; Brook, A.J.; Lgual, J.M.; Mortimer, S.R.; Van der Putten, W.H. Plant species and functional group effects on abiotic and microbial soil properties and plant-soil feedback responses in two grasslands. J. Ecol. 2006, 4, 893–904. [Google Scholar] [CrossRef]
  55. Wang, L.L.; Zhao, G.X.; Li, M.; Zhang, M.T.; Zhang, L.F.; Zhang, X.F.; An, L.Z.; Xu, S.J. C:N:P stoichiometry and leaf traits of halophytes in an arid saline environment, Northwest China. PLoS ONE 2015, 10, e0119935. [Google Scholar] [CrossRef] [PubMed]
  56. Li, Y.L.; Mao, W.; Zhao, X.Y.; Zhang, T.H. Leaf nitrogen and phosphorus stoichiometry in typical desert and desertified regions, North China. Environ. Sci. 2010, 31, 1716–1725. [Google Scholar] [CrossRef]
  57. McCree, K. Whole-plant carbon balance during osmotic adjustment to drought and salinity stress. Funct. Plant Biol. 1986, 13, 33–43. [Google Scholar] [CrossRef]
  58. Killingbeck, K.T.; Whitford, W.G. High foliar nitrogen in desert shrubs: An important ecosystem trait or defective desert doctrine. Ecology 1996, 77, 1728–1737. [Google Scholar] [CrossRef]
  59. Wang, T.; Yang, Y.H.; Ma, W.H. Storage, patterns and environmental controls of soil phosphorus in China. Acta Sci. Nat. Univ. Pekin. 2008, 44, 945–952. [Google Scholar] [CrossRef]
  60. Koerselman, W.; Meuleman, A.F.M. The vegetation N:P ratio: A new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 1996, 33, 1441–1450. [Google Scholar] [CrossRef]
  61. Vitousek, P.M.; Howarth, R.W. Nitrogen limitation on land and in the sea: How can it occur? Biogeochemistry 1991, 13, 87–115. [Google Scholar] [CrossRef]
  62. He, J.S.; Wang, L.; Flynn, D.F.B.; Wang, X.; Ma, W.; Fang, J.Y. Leaf nitrogen: Phosphorus stoichiometry across Chinese grassland biomes. Oecologia 2008, 155, 301–310. [Google Scholar] [CrossRef] [PubMed]
  63. Kang, H.Z.; Zhuang, H.L.; Wu, L.L.; Liu, Q.L.; Shen, G.G.; Berg, B.; Man, R.Z.; Liu, C.J. Variation in leaf nitrogen and phosphorus stoichiometry in Picea abies across Europe: An analysis based on local observations. For. Ecol. Manag. 2011, 261, 195–202. [Google Scholar] [CrossRef]
  64. Kerkhoff, A.J.; Enquist, B.J.; Elser, J.J.; Fagan, W.F. Plant allometry, stoichiometry and the temperature-dependence of primary productivity. Glob. Ecol. Biogeogr. 2005, 14, 585–598. [Google Scholar] [CrossRef]
  65. Rivas-Ubach, A.; Sardans, J.; Péreztrujillo, M.; Estiarte, M.; Peñuelas, J. Strong relationship between elemental stoichiometry and metabolome in plants. Proc. Natl. Acad. Sci. USA 2012, 109, 4181–4186. [Google Scholar] [CrossRef] [PubMed]
  66. Stirzaker, R.J.; Passioura, J.B.; Wilms, Y. Soil structure and plant growth: Impact of bulk density and biopores. Plant Soil 1996, 185, 151–162. [Google Scholar] [CrossRef]
  67. Ravi, S.; Breshears, D.D.; Huxman, T.E.; D’Odorico, P. Land degradation in drylands: Interactions among hydrologic-aeolian erosion and vegetation dynamics. Geomorphology 2010, 116, 236–245. [Google Scholar] [CrossRef]
  68. Thomey, M.L.; Collins, S.L.; Vargas, R.; Johnson, J.E.; Brown, R.F.; Natvig, D.O.; Friggens, M.T. Effect of precipitation varia bility on net primary production and soil respiration in a Chihuahuan Desert grassland. Glob. Chang. Biol. 2011, 17, 1505–1515. [Google Scholar] [CrossRef]
  69. Burke, I.C.; Lauenroth, W.K.; Parton, W.J. Regional and temporal variation in net primary production and nitrogen mineralization in grasslands. Ecology 1997, 78, 1330–1340. [Google Scholar] [CrossRef]
  70. Zhou, G.; Wang, Y.; Wang, S. Responses of grassland ecosystems to precipitation and land use along the Northeast China Transect. J. Veg. Sci. 2002, 13, 361–368. [Google Scholar] [CrossRef]
  71. Jones, M.L.; Sowerby, M.A.; Williams, D.L.; Jones, R.E. Factors controlling soil development in sand dunes: Evidence from a coastal dune soil chronosequence. Plant Soil 2008, 307, 219–234. [Google Scholar] [CrossRef] [Green Version]
Ijerph 15 00109 g001 550
Figure 1. The Heihe River Basin in northwestern China and the locations of the sampling sites.
Figure 1. The Heihe River Basin in northwestern China and the locations of the sampling sites.
Ijerph 15 00109 g001
Ijerph 15 00109 g002 550
Figure 2. Changes of community characteristics with the precipitation gradient. (A) Species richness; (B) Aboveground biomass (g/m2); (C) Community height (cm); (D) Community coverage (%); (E) Foliage projective cover (%); (F) Leaf area index.
Figure 2. Changes of community characteristics with the precipitation gradient. (A) Species richness; (B) Aboveground biomass (g/m2); (C) Community height (cm); (D) Community coverage (%); (E) Foliage projective cover (%); (F) Leaf area index.
Ijerph 15 00109 g002
Ijerph 15 00109 g003 550
Figure 3. Changes of leaf C, N, P, K, and C/N/P ratios with the precipitation gradient. (A) Leaf C (mg/g); (B) Leaf N (mg/g); (C) Leaf C/N ratio; (D) Leaf K (mg/g); (E) Leaf P (mg/g); (F) Leaf N/P ratio; (G) Leaf C/P ratio; Lines are plotted if regressions are significant at p < 0.05.
Figure 3. Changes of leaf C, N, P, K, and C/N/P ratios with the precipitation gradient. (A) Leaf C (mg/g); (B) Leaf N (mg/g); (C) Leaf C/N ratio; (D) Leaf K (mg/g); (E) Leaf P (mg/g); (F) Leaf N/P ratio; (G) Leaf C/P ratio; Lines are plotted if regressions are significant at p < 0.05.
Ijerph 15 00109 g003
Ijerph 15 00109 g004 550
Figure 4. Changes of soil properties with the precipitation gradient. (A) Gravimetric soil water content in the 0–10 cm soil layer (%); (B) Gravimetric soil water content in 10–30 cm soil layer (%); (C) Gravimetric soil water content in 30–50 cm soil layer (%); (D) Soil bulk density (g/cm3); (E) Soil total N (mg/g); (F) Soil total C (mg/g); (G) Soil C/N ratio; (H) Soil available P (mg/kg); (I) Soil available K (mg/kg); (J) Soil pH; (K) Soil EC (ms/cm). Lines are plotted if regressions are significant at p < 0.05.
Figure 4. Changes of soil properties with the precipitation gradient. (A) Gravimetric soil water content in the 0–10 cm soil layer (%); (B) Gravimetric soil water content in 10–30 cm soil layer (%); (C) Gravimetric soil water content in 30–50 cm soil layer (%); (D) Soil bulk density (g/cm3); (E) Soil total N (mg/g); (F) Soil total C (mg/g); (G) Soil C/N ratio; (H) Soil available P (mg/kg); (I) Soil available K (mg/kg); (J) Soil pH; (K) Soil EC (ms/cm). Lines are plotted if regressions are significant at p < 0.05.
Ijerph 15 00109 g004
Ijerph 15 00109 g005 550
Figure 5. Variation partitioning of precipitation, soil properties, and their interactions in accounting for the variation of community characteristics. The numbers indicate the explanation percentage of variables and their interactions for variation.
Figure 5. Variation partitioning of precipitation, soil properties, and their interactions in accounting for the variation of community characteristics. The numbers indicate the explanation percentage of variables and their interactions for variation.
Ijerph 15 00109 g005
Table
Table 1. Characteristics of plant community and mean annual rainfall in the Heihe River Basin. Values are means ± SD.
Table 1. Characteristics of plant community and mean annual rainfall in the Heihe River Basin. Values are means ± SD.
SiteLocationsAnnual Rainfall (mm)Altitude (m)Dominant SpeciesSpecies RichnessAboveground Biomass (g/m2)Coverage (%)Height (cm)Foliage Projected Cover (%)Leaf Area Index
S142°16.13′ N
101°22.46′ E		35920Haloxylon ammodendron1101.8 ± 56.65.7 ± 0.5152 ± 48.77.2 ± 3.50.8 ± 0.1
S242°30.87′ N
101°15.07′ E		39982Nitraria sibirica Pall.160.3 ± 31.47.3 ± 2.025.4 ± 3.78.9 ± 2.50.1 ± 0.0
S340°16.90′ N
98°41.03′ E		691227Nitraria praevisa Bobr.272.7 ± 23.716.0 ± 6.026.6 ± 1.115.6 ± 2.50.2 ± 0.0
S439°56.53′ N
98°59.91′ E		821326Artemisia desertorum377.6 ± 23.517.6 ± 5.062.1 ± 14.112.2 ± 3.50.4 ± 0.1
S539°56.53′ N
100°46.17′ E		1491655Kalidium gracile3230.0 ± 126.619.1 ± 0.117.5 ± 1.527.8 ± 2.50.6 ± 0.1
S638°49.09′ N
100°59.24′ E		1621714Salsola passerina5316.0 ± 69.022.2 ± 3.327.9 ± 4.324.4 ± 2.50.8 ± 0.1
S738°37.55′ N
101°5.25′ E		2092016Kalidium cuspidatum3234.8 ± 45.127.3 ± 5.514.2 ± 0.337.8 ± 5.10.9 ± 0.1
Table
Table 2. Leaf stoichiometric traits for dominant species along a precipitation gradient in the Heihe River Basin.
Table 2. Leaf stoichiometric traits for dominant species along a precipitation gradient in the Heihe River Basin.
SiteC (mg/g)N (mg/g)P (mg/g)K (mg/g)C/NC/PN/P
S1334.63 ± 11.3921.06 ± 0.741.15 ± 0.0120.29 ± 0.2315.89 ± 0.03291.03 ± 11.0518.32 ± 0.73
S2193.69 ± 3.3413.49 ± 0.211.34 ± 0.0115.68 ± 0.0914.35 ± 0.12144.63 ± 2.5210.08 ± 0.18
S3351.28 ± 28.7526.74 ± 1.442.55 ± 0.1118.43 ± 1.1413.12 ± 0.19138.18 ± 13.0610.52 ± 0.71
S4467.27 ± 2.0222.83 ± 0.452.84 ± 0.0429.04 ± 0.4320.48 ± 0.19164.78 ± 3.008.05 ± 0.09
S5176.45 ± 2.4312.86 ± 0.281.63 ± 0.0312.82 ± 0.4813.72 ± 0.11108.19 ± 3.517.89 ± 0.32
S6238.86 ± 3.6716.46 ± 0.451.83 ± 0.0818.16 ± 0.9214.51 ± 0.17130.40 ± 7.498.99 ± 0.62
S7346.35 ± 27.4318.19 ± 1.610.82 ± 0.058.70 ± 0.5219.05 ± 0.24420.55 ± 9.8322.08 ± 0.80
Mean301.22 ± 99.0518.81 ± 4.861.74 ± 0.7017.59 ± 6.0815.88 ± 2.68199.68 ± 108.6112.27 ± 5.34
Table
Table 3. Soil properties in different sites along a precipitation gradient in the Heihe River Basin. Values are means ± SD. Abbreviations: GSWC10, soil water content (0–10 cm); GSWC30, soil water content (10–30 cm); GSWC50, soil water content (30–50 cm); SBD, soil bulk density; TN, soil total nitrogen; TC, soil total carbon; C/N, soil C/N ratio; AP, soil available phosphorus, AK, soil available potassium content; pH, soil pH; EC, soil electrical conductivity.
Table 3. Soil properties in different sites along a precipitation gradient in the Heihe River Basin. Values are means ± SD. Abbreviations: GSWC10, soil water content (0–10 cm); GSWC30, soil water content (10–30 cm); GSWC50, soil water content (30–50 cm); SBD, soil bulk density; TN, soil total nitrogen; TC, soil total carbon; C/N, soil C/N ratio; AP, soil available phosphorus, AK, soil available potassium content; pH, soil pH; EC, soil electrical conductivity.
SitesGSWC10 (%)GSWC30 (%)GSWC50 (%)SBD (g/cm−3)TN (mg/g)TC (mg/g)C/NAP (mg/kg)AK (mg/kg)pHEC (ms/cm)
S11.10 ± 0.566.02 ± 2.2211.38 ± 0.591.36 ± 0.090.45 ± 0.0512.98 ± 4.6529.22 ± 11.063.82 ± 1.23312.36 ± 74.798.38 ± 0.226.51 ± 1.47
S20.22 ± 0.090.84 ± 0.181.53 ± 0.481.72 ± 0.090.57 ± 0.044.05 ± 0.997.18 ± 2.182.91 ± 0.63118.28 ± 34.898.89 ± 0.360.46 ± 0.15
S31.41 ± 0.854.23 ± 2.745.57 ± 4.061.27 ± 0.190.88 ± 0.4516.10 ± 1.6221.91 ± 11.3210.16 ± 4.23237.94 ± 106.378.52 ± 0.126.85 ± 2.14
S40.42 ± 0.261.60 ± 0.572.47 ± 0.291.58 ± 0.040.39 ± 0.0212.39 ± 1.0831.69 ± 2.932.37 ± 0.37183.90 ± 17.848.89 ± 0.761.72 ± 0.96
S50.89 ± 0.344.22 ± 0.825.02 ± 0.401.25 ± 0.040.83 ± 0.1217.69 ± 0.7421.64 ± 2.345.00 ± 1.52136.08 ± 47.988.71 ± 0.042.14 ± 0.24
S62.24 ± 0.822.86 ± 0.733.79 ± 0.391.49 ± 0.010.82 ± 0.0821.99 ± 1.4326.73 ± 0.963.60 ± 0.6068.40 ± 15.298.70 ± 0.161.26 ± 0.21
S73.33 ± 0.608.69 ± 1.2210.42 ± 0.961.54 ± 0.110.99 ± 0.7723.31 ± 1.1823.62 ± 0.553.16 ± 0.44175.05 ± 19.848.59 ± 0.073.41 ± 0.06
Table
Table 4. Marginal and conditional effects obtained from the forward selection of the Monte Carlo test for community characteristics. The abbreviations are same as Table 3.
Table 4. Marginal and conditional effects obtained from the forward selection of the Monte Carlo test for community characteristics. The abbreviations are same as Table 3.
Marginal EffectsConditional Effectsp ValueF Value
Environmental VariablesEigenvaluesEnvironmental VariablesEigenvalues
Precipitation60.5Precipitation60.50.00129.1
Soil total carbon42.5Soil C/N12.80.0018.6
Soil total nitrogen33.2GSWC505.10.0024.5
GSWC1025.1Soil total nitrogen3.90.0045.7
Soil available K18.8Soil available P3.80.0342.8
Soil C/N11.7SEC2.20.0822.2
GSWC309.3Soil bulk density1.70.1901.6
SEC6.3Soil total carbon1.50.1931.6
GSWC504.3GSWC101.20.1152.0
Soil bulk density3.4GSWC301.10.3951.0
Soil available P1.8Soil available K0.70.3911.0
Soil pH1.6Soil pH0.60.4600.9
Table
Table 5. Marginal and conditional effects obtained from the forward selection of the Monte Carlo test for leaf stoichiometric traits. The abbreviations are same as Table 3.
Table 5. Marginal and conditional effects obtained from the forward selection of the Monte Carlo test for leaf stoichiometric traits. The abbreviations are same as Table 3.
Marginal EffectsConditional Effectsp ValueF Value
Environmental VariablesEigenvaluesEnvironmental VariablesEigenvalues
GSWC5034.6GSWC5034.60.00110.1
GSWC3031.6Soil C/N11.70.0027.4
GSWC1021.9Soil bulk density16.00.0035.8
Soil available K14.7GSWC305.90.0074.5
SEC12.9SEC12.40.0105.7
Precipitation10.1Soil available K3.60.0383.5
Soil C/N9.8Soil total carbon2.60.0643.0
Soil total nitrogen7.4Precipitation2.40.1372.0
Soil total carbon6.1Soil available P1.20.3201.2
Soil available P5.7GSWC101.00.3761.0
Soil pH5.3Soil total nitrogen0.90.4490.8
Soil bulk density2.3Soil pH0.40.6580.5

Share and Cite

MDPI and ACS Style

Zhang, X.; Guan, T.; Zhou, J.; Cai, W.; Gao, N.; Du, H.; Jiang, L.; Lai, L.; Zheng, Y. Community Characteristics and Leaf Stoichiometric Traits of Desert Ecosystems Regulated by Precipitation and Soil in an Arid Area of China. Int. J. Environ. Res. Public Health 2018, 15, 109. https://doi.org/10.3390/ijerph15010109

AMA Style

Zhang X, Guan T, Zhou J, Cai W, Gao N, Du H, Jiang L, Lai L, Zheng Y. Community Characteristics and Leaf Stoichiometric Traits of Desert Ecosystems Regulated by Precipitation and Soil in an Arid Area of China. International Journal of Environmental Research and Public Health. 2018; 15(1):109. https://doi.org/10.3390/ijerph15010109

Chicago/Turabian Style

Zhang, Xiaolong, Tianyu Guan, Jihua Zhou, Wentao Cai, Nannan Gao, Hui Du, Lianhe Jiang, Liming Lai, and Yuanrun Zheng. 2018. "Community Characteristics and Leaf Stoichiometric Traits of Desert Ecosystems Regulated by Precipitation and Soil in an Arid Area of China" International Journal of Environmental Research and Public Health 15, no. 1: 109. https://doi.org/10.3390/ijerph15010109

APA Style

Zhang, X., Guan, T., Zhou, J., Cai, W., Gao, N., Du, H., Jiang, L., Lai, L., & Zheng, Y. (2018). Community Characteristics and Leaf Stoichiometric Traits of Desert Ecosystems Regulated by Precipitation and Soil in an Arid Area of China. International Journal of Environmental Research and Public Health, 15(1), 109. https://doi.org/10.3390/ijerph15010109

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop