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Article

Assessing Shrub Patch Characteristics and Soil Nutrient Distribution Patterns of Four Typical Alpine Shrub Plants in the Eastern Qilian Mountains

by
Jinmei Zhao
*,
Benjamin Adu
,
Jingnan Wang
and
Yuhang Fan
College of Forestry, Gansu Agricultural University, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(4), 1547; https://doi.org/10.3390/su16041547
Submission received: 27 October 2023 / Revised: 5 February 2024 / Accepted: 7 February 2024 / Published: 12 February 2024
Browse Figures
Versions Notes

Abstract

:
Shrub patches have an impact on soil fertility and vegetation, influencing species composition and diversity. The unique context of the Eastern Qilian Mountains provides insights into alpine ecosystems’ responses to environmental challenges. This study aimed to evaluate the physical characteristics and soil nutrient contents of shrub patches for four different shrub species (Salix oritrepha (SO), Spiraea alpina (SA), Rhododendron capitatum (RC), and Potentilla fruticosa (PF)). We assessed their patch characteristics and soil nutrients at different depths within three patch microsites (the center (CS), the edge (ES), and the midpoint between the center and the edge of the shrub patch (BC)). Soil samples were collected and analyzed for organic matter, nitrogen, phosphorus, and potassium content. Statistical analyses were conducted to evaluate differences among shrub species and locations within the shrub patches. Our results showed that the shrub species exhibited variations in patch characteristics and soil nutrient distribution. Soil nutrient content varied by depth and location within the shrub patches, with higher concentrations at the center. The relative interaction intensity (RII) revealed nutrient aggregation or dispersion trends. The study highlighted the complex interactions between shrub characteristics and soil nutrients, emphasizing their influence on nutrient cycling, vegetation dynamics, and soil properties. These findings contribute to our understanding of alpine ecosystem dynamics and inform conservation, sustainability, and management strategies.

1. Introduction

Shrub patch configuration can be defined by spatial plant characteristics, such as cover, size, density, height, shape, biomass distribution, and ratios to adjacent bare ground or grass patches [1]. Globally, shrublands are widespread, covering an area of 8.5 × 106 km2, and consist of a mosaic of shrub patches interspersed with grass patches [2]. Global climate changes and human interventions, along with their complex interactions, have led to the encroachment of indigenous shrubs in arid and semi-arid grassland regions, resulting in intricate connections with vegetation dynamics [3,4,5,6]. These shrub patches and their associated fertility islands are crucial self-organized systems, serving as “indicators” of ecosystem responses to climate change or human activities. The presence of shrub patches acts as an early signal of ecosystem changes in response to these factors [7]. Numerous studies have investigated the characteristics of shrub patterns and their underlying mechanisms. It has been observed that the features of patches, labyrinths, gaps, and stripes (among others) result from the redistribution of soil water and competition for limiting nutrients [8,9]. The “fertility island effect” describes the competitive advantage that shrub patches have concerning nutrient availability. Compared to adjacent grass patches, shrub patches exhibit significantly higher soil nutrient content, particularly in organic matter, nitrogen, phosphorus, and potassium [10]. This phenomenon underscores the capacity of shrubs to influence biogeochemical cycles, enriching the soil within the patches compared to the surrounding areas [11]. Shrub patches not only affect soil fertility but also have a substantial impact on vegetation dynamics. They often support different plant communities, influencing species composition, diversity, and distribution [12]. The spatial distribution of shrubs in the Eastern Qilian Mountains can vary according to species and local soil characteristics, further highlighting the complex relationships between shrubs, soil, and vegetation [13]. Soil characteristics also influence shrub plants and the patches they form. Soil nutrients, temperature, and moisture control the processes of shrub invasion, plant diversity, and the formation of shrub patches [14]. According to Berdugo et al. (2019), the effects and impacts of different shrubland and grassland ecosystems on soil properties were distinct due to differences in habitat, vegetation, and soil properties [15].
The Eastern Qilian Mountains provide a unique context for studying fertility islands in alpine ecosystems. The term “fertility islands” typically refers to localized areas within an ecosystem that have higher soil fertility compared to the surrounding areas. In the context of alpine ecosystems, fertility islands may arise due to various factors, such as the accumulation of organic matter, the presence of certain plant species, or specific geological features. These islands of enhanced fertility can have a significant impact on the distribution and growth of plants in the otherwise nutrient-poor alpine environment. This high-altitude region is characterized by harsh environmental conditions, including low temperatures and limited water availability. Various alpine shrub species, such as S. oritrepha, S. alpina, R. capitatum, and P. fruticosa, have adapted to these challenging circumstances. Each of these species exhibits distinct characteristics that enable them to thrive in this unique environment [16,17]. The study of fertility islands provides insights into how alpine ecosystems function in response to these environmental challenges [18]. Therefore, the analysis of shrubland characteristics, soil properties, and relationships among different vegetation types plays an important role in better understanding the vegetation structure, landscape pattern, and feedback relationships within the alpine grassland shrubs of the alpine vegetation ecosystem [19]. This study aimed to evaluate the physical characteristics and soil nutrient contents of shrub patches at different depths within three patch microsites (center, edge, and midpoint between the two) for four different species of shrubs. We specifically determined the shrub patch characteristics and soil nutrient distribution of various shrub types. Additionally, we analyzed the relationship between the shrub patch characteristics and soil nutrients to reveal the soil nutrient enrichment characteristics among shrub species.

2. Materials and Methods

2.1. Study Area

We conducted field experiments in the alpine grasslands of the Eastern Qilian Mountains in Daiqian Village (37°14′34″ N, 102°35′38″ E; elevation 1950–4848 m) in the Tianzhu Tibetan Autonomous County of Gansu Province of China. The region experiences a characteristic alpine climate, characterized by cold temperatures and frequent precipitation throughout much of the year. The region features environmental conditions such as a low air density with reduced oxygen levels, intense solar radiation, and elevated ultraviolet radiation. The average annual temperature in the area is −0.1 °C, with an accumulated temperature of 1380 °C (average monthly minimum and maximum temperatures of −18.3 °C and 12.7 °C, respectively), annual sunshine hours of 2600 h, annual precipitation of 416 mm (mostly concentrated in July, August, and September), and annual evaporation of 1592 mm [20]. The planting period spans approximately 120 days, extending from May to September. The soil in the area is a typical alpine Chernozem (a dark soil with a chernic horizon, starting ≤50 cm below the lower limit of the mollic horizon (pH > 6) and, if present, above a petrocalcic horizon, a layer with protocalcic properties, ≥5 cm-thick, or a calcic horizon, as well as a base saturation (by 1 M NH4OAc, pH 7) of ≥50% from the mineral soil surface to the layer with protocalcic properties or to the calcic horizon, throughout [21]), is found in high-altitude alpine environments. This suggests that despite the challenging conditions of alpine ecosystems, there are areas with soils that exhibit characteristics similar to fertile Chernozem soils, which are more commonly associated with lower elevations. It exhibits a bulk density of approximately 0.73 g cm−3, an organic carbon content of around 138.45 g kg−1, total nitrogen levels of about 4.31 g kg−1, and total phosphorus content of roughly 0.65 g kg−1. Figure 1 presents a map showing the description and altitude of the study area. SO, SA, RC, and PF dominate the shrubby grassland ecosystem, with heights of 50–120 cm [22]. All these alpine shrub species play important roles in maintaining the biodiversity and stability of their respective ecosystems. They provide food and shelter for various animal species and help prevent soil erosion and retain moisture in the soil.

2.2. Field Investigation

This study was conducted from July to August 2021 and was based on the principles of uniform distribution of shrub patches, elevation, and slope to avoid biases from site heterogeneity. Four shrublands were formed by four typical alpine shrubs: SO, SA, RC, and PF, which were widely distributed in the Eastern Qilian Mountain. Randomly chosen quadrats within the herbaceous community beneath shrub plots and grass plots adjoining shrubland were selected for the plant community survey. Each sample involved three 10 m × 10 m shrub patches to investigate the characteristics of the sample plot. Plant species, height, shrub area, litter depth, and biomass were measured in all patches in the sample plots. In each quadrat, all aboveground shrub parts were cut, collected, and placed into separate labeled envelopes. To assess the belowground biomass, we employed the comprehensive excavation method, which involves retrieving the entire root system from the soil [23]. The roots and the aboveground shrub parts were washed and oven-dried at 65 °C for 24 h to a constant weight for the determination of biomass. The process involves taking multiple (six) shrub height measurements within a quadrat using a height stick and then calculating the average height to provide a more representative assessment of the shrub height in that specific area [24].

2.3. Sampling and Measurement of Soil Properties

Each shrub patch was approximated as a square, and the center of the square was considered the center of the shrub patches. Three patch microsites (the center (CS), the edge (ES), and the midpoint between the center and the edge of the shrub patch (BC)) were established in each of the four directions: east, south, west, and north. At each site, soil samples were collected from beneath the organic layer after removing the litter. These samples were individually stored in aluminum boxes. Simultaneously, undisturbed core samples were extracted at various depths using standard core steel samplers with a 55 mm internal diameter and 45 mm height. Soil was sampled at a time interval of 10 days at 3 depths: 0–20 cm, 20–40 cm, and 40–60 cm at CS, ES, and BC. Samples were taken at the same layer and location points were then mixed to create a single sample. The soil samples underwent air-drying at room temperature over one week and were subsequently sieved through a 2 mm sieve to eliminate stones, roots, and coarse debris. The soil passing through the sieve was then used to assess the soil organic matter content (SOM), determined using the dichromate oxidation method [25]. Total nitrogen (TN) was determined using Kjeldahl analysis [26]. For total phosphorus (TP) and total potassium (TK), NaOH fusion was employed, and measurements were conducted using molybdenum antimony anti-colorimetric and flame photometry methods, respectively.

2.4. Data Analysis

The characteristics of shrubs and the soil chemical properties of various types of shrub-encroached grassland were analyzed using one-way ANOVA and the least significant difference (LSD) test. Additionally, the Pearson correlation was employed to examine the correlation between shrub characteristics and soil chemical properties. The relative interaction intensity (RII) was utilized to estimate the magnitude of the shrub patch effect on SOM, TN, TK, and TP [27].
The relative interaction intensity (RII) was calculated as:
RII = (Xn − Xi)/(Xn − Xi)
where X n and X i represent values for soil nutrient content at different position points and depths of shrub patch type, respectively. The index ranged from 1 to −1, and with RII values > 0, the positive and negative values indicate an increasing and decreasing effect on soil nutrients, respectively.

3. Results

3.1. Characteristics of Shrub Patch

We conducted measurements on four patches characterized by a single species of shrub (Table 1). The encroachment of shrubs into grassland areas gives rise to the development of noticeable shrub patches, defined by areas predominantly covered with shrub vegetation. The features of these patches exhibit variations contingent on the specific shrub species and the extent of their encroachment. Among the species investigated, SO displayed the most substantial values for patch area, shrub height, litter depth, and shoot biomass; however, it exhibited the lowest root biomass. In contrast, R. capitatum showcased the highest root biomass. PF, on the other hand, displayed the lowest shrub height and shoot biomass, while SA exhibited the smallest patch area and lowest litter depth. A noteworthy distinction was observed between SA and RC, as they tend to form dense patches characterized by a high canopy cover and stem density. In contrast, SA and PF tend to create more dispersed and less densely populated patches.

3.2. Heterogeneity of Soil Nutrient Distribution and the Shrub Types

We analyzed the soil nutrient profiles at a soil depth of 60 cm within various shrub patches situated at microsites within the patches (Figure 2). Across the 0–60 cm soil layer, which includes the ES and the CS, we observed that SOM reached its highest concentration in the PF shrub patch and its lowest in the SA shrub patch. Contrarily, in BC, the highest SOM content was observed in the RC shrub patch, while the SA shrub patch displayed the lowest. Concerning total potassium levels in the three different microsites within the shrub plot, we found the highest concentration in the SO shrub patch and the lowest in the RC patch. In the 60 cm soil depth, we also assessed total nitrogen levels within the four different shrub types across the three patch microsites. The trend observed across this 60 cm range was consistent, with the highest concentration in the SO shrub patch, followed by SA, PF, and finally RC, in descending order. Total phosphorus levels followed a different pattern, with the highest concentration occurring in the PF shrub patch, followed by RC, SO, and SA, in that order.

3.3. Distribution Characteristics of Shrub Patch Soil Nutrients in Different Soil Layers

The levels of SOM, TN, TK, and TP within the various shrub patches and the different locations within the shrub plot were consistently higher in the 40–60 cm soil layer compared to the 0–20 cm soil layer (Table 2). Through one-way analysis of variance (ANOVA), it was determined that the SOM and TN values within the SO, SA, and RC shrub patches exhibited significant differences between the 0–20 cm and 40–60 cm soil layers (p < 0.05). In contrast, the SOM and TN values of PF shrub patches, as well as the TK and TP values within all shrub patches, did not display significant differences between the 0–20 cm and 40–60 cm soil layers (p > 0.05).

3.4. Characteristics of Soil Nutrient Distribution in Different Locations of Shrub Patches

A total of 216 soil samples were collected for the analysis of soil properties across various shrub types, encompassing soil depths from 0 to 60 cm. The summary of these soil properties, including their probability distribution and notable differences observed among them in different patch microsites, is presented in Table 2. The findings revealed noteworthy changes in SOM, TN, TK, and TP, with respective increases of 8.74%, 9.21%, and 17.12%, and a decrease of 7.04%. This alteration in soil nutrient distribution was notably dependent on the location of the shrub patch (Table 2). At the edge of the shrub patches (ES), the mean values for SOM, TN, TK, and TP were recorded as 67.51 g/kg, 0.99 g/kg, 1.06 g/kg, and 0.73 g/kg, respectively. Conversely, at the center of the shrub patch (CS), the mean values for SOM, TN, TK, and TP were notably higher, at 73.97 g/kg, 1.09 g/kg, 1.28 g/kg, and 0.68 g/kg, respectively. This analysis indicates a tendency for SOM, TN, and TK to exhibit an increase at the center of the shrub patches, while TP tended to decrease in concentration at these locations.

3.5. Characteristics of Soil Nutrient Enrichment Rate in Shrub Patches

We examined the soil nutrient enrichment characteristics across various shrub patches, spanning from the edge of the shrub patch (ES) to the center of the shrub patch (CS), utilizing the RII calculation method (as presented in Table 3). What follows is a summary of the observed phenomena. In the SO shrub patch, soil nutrients, such as SOM, TN, and TK, exhibited an aggregation effect, with SOM demonstrating the highest aggregation effect (0.235) and TN displaying the lowest (0.049). Within the SA shrub patch, SOM, TN, and TK exhibited aggregation effects as well, with the RII values ranking as SOM > TN > TK. In the case of the RC shrub patch, the TK and TP demonstrated clustering effects, with respective clustering indices (RII) of 0.087 and 0.094 as one moves from the edge to the center of the patch. For the PF shrub patch, the TN, TK, and TP displayed aggregation characteristics, with corresponding aggregation indices (RII) of 0.050, 0.088, and 0.004, respectively. Interestingly, an aggregation effect contrary to the general trend was also noted, where soil exhibited an aggregation effect from the edge to the center of the shrub patches. The average RII values from the edge of the shrub to the center were found to be −0.105 and −0.260 in the SO and SA shrub patch types, respectively, for TP. Meanwhile, RC displayed average RII values of −0.048 and −0.156 for SOM and TN, respectively. PF in SOM exhibited an average RII value of −0.084. Negative RII values indicated a gradual decrease in soil nutrient content from the edge to the center of the shrub patches, signifying a lack of nutrient aggregation in the soil. Conversely, soil nutrients increased from the center of the shrub patch to the edge, reflecting a pattern of soil nutrient accumulation.
We employed the RII calculation method to assess the accumulation patterns of soil nutrients across different soil layers, extending from the edge of the shrub to the center (Table 3). Our findings revealed variations in RII values for soil nutrients within the same shrub, signifying differences in the soil’s nutrient aggregation potential. In the SO shrub patch, both SOM and TK displayed positive RII values across the 0–60 cm soil layer. Notably, the highest RII values for both SOM and TK were observed within the 20–40 cm soil layer. This pattern suggests that the 0–60 cm soil layer, extending from the edge of the SO shrub patch to the center, exhibited significant nutrient enrichment, particularly within the 20–40 cm soil layer. Across the soil nutrients of the SO, SA, and RC shrub patches, there was an observable trend of aggregation from the edge of the shrub patch to the center. However, for TP in the SO and SA shrub patches, negative RII values indicated an absence of nutrient enrichment from the edge to the center of these shrub patches. This phenomenon was most pronounced within the 0–20 cm and 40–60 cm soil layers. In the SA, RC, and PF shrub patches, the RII values exhibited both positive and negative values within the 0–60 cm range. This indicated that soil nutrients aggregated in the soil layers with positive RII values, while the opposite was true for those with negative RII values, signifying a lack of aggregation within these layers in the research direction.

3.6. The Correlation between Shrub Patch Characteristics and Soil Nutrients

The correlation analysis revealed highly significant relationships between the characteristics of shrub patches and soil nutrients, as depicted in Figure 3. The depth of the color reflects the strength of the correlation, where darker hues indicate stronger associations, while lighter shades suggest weaker ones. Significant correlations were observed between the characteristics of the shrub patch and the following soil nutrient parameters: SOM, TN, and TP in the CS, SOM beneath the crown (BC), and TK and TP at the edge of the shrub patch (ES) (p < 0.01). Additionally, a significant correlation was identified between the height of the shrub patches and TK beneath the crown (BC), as well as TP within the center of the shrub patches (CS) (p < 0.05). The shoot biomass of the shrub patches exhibited a significant correlation with TN and TP beneath the crown (BC), and TN at the edge of the shrub patches (ES) (p < 0.05). A significant correlation was also established between the root biomass of the shrub patches and TK beneath the crown (BC), and TP within the center of the shrub patches (CS) (p < 0.05). These findings underscore the intricate relationships between shrub patch characteristics and soil nutrient parameters, shedding light on the interconnected dynamics in this ecosystem.

4. Discussion

The findings of this study can reflect the effect on shrub growth status or properties. In water-limited environments, enhanced productivity and diversity often result from the patch distribution of soil resources [28], contributing to sustained ecological balance. Changes in plant distribution characteristics reflect the effects of environmental factors on plant growth [29]. The species composition and distributional characteristics of alpine shrub plants in the study area reflect the Eastern Qilian Mountains’ long-term adaptation to various factors, including climate, soil, water, topography, and plant biological characteristics [12,30]. Fan et al. described the spread of shrubby plant aggregates with shrubby patch characteristics as a long-term evolutionary response to environmental factors [31]. The shrub patches in the study area are mainly a group of shade-tolerant and cold-tolerant shrubs adapted to the high-altitude environment, and their vegetation is mainly characterized by tussocks, mats, and low plants [32]. Under the long-term impact of the environmental conditions of the study area and the biological characteristics of the plants, different shrub patch characteristics have been formed. The results showed that the shrub patch characteristics of SO, SA, RC, and PF were different. SO had the highest patch area, height, litter depth, and shoot biomass, and R. capitatum had the highest root biomass (Table 1), which suggested that the growth of SO was influenced by patch microsite conditions. Firstly, the low palatability of shrubs poses a challenge for livestock foraging and concurrently offers shelter for understory herbs beneath the shrub canopy [33]. Secondly, the shrub canopy exerts a notable positive impact on understory herbaceous vegetation, enhancing the microenvironment, cover, biomass, and species diversity [34].
Moreover, shrub patches exhibit elevated biomass and litter compared to the surrounding grassland patches, contributing to increased soil nutrient inputs [35]. Additionally, this may potentially be due to their elevation differences, which may lead to differentiation in phenology and growth [36]. SO possesses certain traits or adaptations that give it a competitive advantage over other plant species in the ecosystem [17]. These traits include efficient resource utilization, superior growth rates, or the ability to outcompete other species for light, nutrients, and water. SO is well suited to the specific environmental conditions and adaptations that allow it to thrive in the prevailing climate, soil conditions, or other ecological factors. These sustained adaptations enhance its growth, reproduction, and overall biomass production. SO has a high reproductive capacity, allowing it to produce abundant offspring and establish larger patches. This could result in a higher overall patch area compared to other species. SO has efficient mechanisms for nutrient acquisition and utilization [37]. It has deep root systems that access nutrients from lower soil layers or symbiotic relationships with beneficial microorganisms that enhance nutrient uptake. This improved nutrient acquisition contributes to its increased shoot biomass and overall growth. SO adapted to respond positively to disturbances or changes in the ecosystem [38]. If the study area experiences disturbances, such as fire or grazing, SO has mechanisms to recover and grow rapidly, leading to an increased patch area, height, and biomass. The July temperature is a common climate factor driving the growth of SO across the latitudinal gradient of the Eastern Qilian Mountains [39]. It is assumed that low temperatures drive the onset of alpine shrub growth [40]. Furthermore, warm summer temperatures could benefit new wood tissue formation, and more than half of the ring width was completed in July [40]. This phenomenon reflects the strongest suitability of the SO shrub patch type to the natural environment and climatic and soil conditions. In a recent compilation, it was discovered that summer temperature serves as the primary climate factor influencing shrub growth across various taxa and locations in the tundra biome [41]. RC possesses a well-developed and extensive root system that allows for efficient nutrient and water uptake [42]. Its root architecture includes deep penetrating roots, numerous lateral roots, or a dense root network that facilitates resource acquisition and storage. This robust root system enables RC to accumulate higher biomass compared to other plant species [43]. RC has efficient mechanisms for nutrient acquisition and utilization. It possesses traits such as enhanced nutrient absorption capacity, increased root exudation, or symbiotic associations with beneficial microorganisms that enhance nutrient uptake [44]. It out-competes other species for limited resources, including nutrients, water, and space, which can cause shrub encroachment. Shrub encroachment can alter the composition and structure of plant communities, potentially leading to changes in biodiversity. Some shrub species may outcompete or shade out other plant species, reducing plant diversity. On the other hand, shrubs can provide new habitat niches for different wildlife species, contributing to increased biodiversity at the animal level. The impacts of shrub encroachment can be both positive and negative. On one hand, shrubs can provide habitat and food sources for wildlife, stabilize slopes, and contribute to soil fertility. However, excessive shrub encroachment can have negative effects on the ecosystem. It may reduce the availability of resources for other plant species, alter water and nutrient cycles, increase the risk of wildfires, and affect the biodiversity of the region. The ability of RC to effectively utilize resources could result in increased root growth and biomass. These adaptations would enable R. capitatum to access and utilize available nutrients more effectively, leading to increased root biomass. In the Qilian Mountains, summer temperature is also a primary limiting factor for the radial growth of alpine rhododendron shrubs [45]. Competition among roots for water in arid regions could also influence the size of patches and the spacing between them [31].
Erfanzadeh discovered that the type of shrub played a crucial role in influencing the productivity and species diversity of shrub understory herbaceous communities in semi-arid regions [46]. For instance, the height of shrubs, leaf area, and canopy structure influence the redistribution of rainfall [47]. PF had the lowest shrub height and shoot biomass. Research conducted in the northern grasslands of China has indicated that the encroachment of shrubs diminishes herbaceous abundance and aboveground biomass, with shrub type and climate identified as predominant factors [48]. According to other research, the early growth season soil moisture content influences willow shrub growth variation [49]. PF naturally exhibits a compact or dwarfed growth habit [50]. Some plant species, including certain shrubs, are genetically predisposed to have shorter heights and smaller overall sizes. This growth habit may result in a lower shrub height and shoot biomass compared to species with more vigorous growth patterns. PF experiences limitations in accessing essential resources, such as light, nutrients, or water. The Eastern Qilian Mountains have resource-poor conditions, and PF must allocate limited resources to various physiological processes, resulting in reduced growth and biomass production. PF faces intense competition from other plant species in the ecosystem. The other three species are more efficient at resource acquisition or have traits that provide them with a competitive advantage, resulting in PF experiencing restricted access to vital resources. This competition can limit its growth and biomass accumulation. PF is more susceptible to environmental stress factors, such as drought, extreme temperatures, or poor soil conditions [51]. These stressors can negatively impact its growth and development, leading to a reduced shrub height and shoot biomass. Additionally, PF is prone to herbivory or grazing by animals, which hinders its growth and biomass production [52]. Continuous browsing or grazing pressure can limit its ability to recover and allocate resources toward shoot growth. Within the species, there can be natural phenotypic variation. Some individuals or populations of PF might inherently exhibit lower heights and shoot biomass compared to others. This variation could be a result of genetic factors, local adaptation, or historical ecological interactions.
The presence of shrub cover frequently leads to soil heterogeneity, particularly in nutrient accumulation within shrubland. Numerous studies have demonstrated that shrubs can create ‘fertile islands’ by accumulating carbon (C) and nitrogen (N) [53]. The impact of shrub encroachment on soil nutrients varies across the landscape, ranging from positive to negative or neutral effects [54]. On the contrary, certain shrubs have a positive impact on the soil conditions of their understory at the patch level [55]. This study determined the distribution of soil nutrients in different soil depths and the position points of different shrub species. Our study showed that soil nutrients in the shallow (0–20 cm) soil depths were higher than those in the deeper (20–40 cm and 40–60 cm) soil depths (Table 2). This may result from organic matter accumulation, biological activity, root activity, leaching and erosion, weathering, and mineralization. Organic matter, including decomposed plant and animal material, accumulates near the soil surface. As organic matter decomposes, it releases nutrients into the soil, enriching the shallow soil layers with higher concentrations of nutrients [56]. Over time, the accumulation of organic matter in the topsoil contributes to higher nutrient levels in the shallow soil depths. Most soil microorganisms, including bacteria and fungi, are concentrated in the upper soil layers due to the availability of organic matter and aeration. The vital function of these microorganisms lies in their contribution to nutrient cycling and the processes of decomposition, releasing nutrients from organic matter and making them available for plant uptake. Consequently, their activity is more prominent in shallow soil depths, leading to higher nutrient concentrations. The majority of plant roots are concentrated in the upper soil layers, primarily in shallow depths of 0–20 cm [33].
In the xeric site, shrubs exclusively form tap roots in the upper layers, while they develop tap roots, lateral roots, and fine roots concurrently in the upper soil profile under more favorable moisture conditions [57]. Thus, shrubs uptake nutrients from the soil through their roots, and their activity in the shallow soil layers promotes nutrient uptake and accumulation in this region [58]. As a result, nutrient concentrations tend to be higher in the topsoil where root density is greatest. Nutrients are subject to leaching, where water carries dissolved nutrients from the topsoil down into the deeper soil layers. Leaching can lead to lower nutrient concentrations in the deeper soil depths [59]. Additionally, erosion processes can remove nutrient-rich topsoil, further depleting the nutrient content in the deeper soil layers [60]. Soil weathering processes and mineralization of parent materials occur primarily in the upper soil layers [61]. These processes release nutrients from minerals and rocks, contributing to higher nutrient concentrations in shallow soil depths. The variation in litter composition among the shrubs suggests that the increased nitrogen input resulted in an enhanced microbial turnover of organic matter [62]. Plants rapidly assimilate the mineralized nitrogen shortly after [63]. This decomposition model aligns with the desert model proposed by Barnes et al. (2015), suggesting that freshly senesced plant material undergoes high rates of photodegradation when in a standing dead state. During this initial stage, litter decomposition occurs slowly. The soil–litter matrix is established as standing lifeless plant material falls to the soil surface. Subsequently, the influence of photodegradation diminishes, and microbial decomposition intensifies, partially due to the erosive impact of wind. Overall decomposition rates reach a peak owing to swift losses of easily decomposable chemical components in the litter [64]. Organic matter in the soil and plant litter characterized by lower carbon and nitrogen ratios tends to undergo decomposition at a faster rate compared to those with higher carbon and nitrogen ratios [65]. The size of shrubs represents another potential factor influencing soils, as indicated by several studies. Large shrubs, characterized by their greater biomass returning to the soil, have been demonstrated to exert significantly more impact on the soil compared to smaller shrubs [66,67]. The larger shrub is likely to release a greater amount of root exudates compared to smaller plants [68].
RC had the largest SOM, while SA had the lowest SOM. The order of content is R. capitatum > P. fruticosa > S. oritrepha > S. alpina. This may be attributed to litter quality, rhizosphere effects, mycorrhizal associations, and microbial activity. R. capitatum produces leaf litter with a higher organic matter content or slower decomposition rates compared to the other plant species in the Eastern Qilian Mountains [69]. Leaf litter with a higher organic matter content takes longer to decompose, resulting in a greater accumulation of SOM in the soil [70]. The rhizosphere—the soil region influenced by plant roots—plays a significant role in SOM dynamics. R. capitatum has a more extensive root system, higher root exudation rates, or a stronger influence on the soil microbial community compared to the other species [71]. These factors can enhance the microbial decomposition of organic matter, leading to increased SOM levels. It is known to form mycorrhizal associations with beneficial soil fungi. Mycorrhizal fungi contribute to nutrient cycling and organic matter decomposition, potentially leading to higher SOM levels in the presence of Rhododendron capitatum [72]. R. capitatum supports higher microbial biomass and activity in the rhizosphere, resulting in increased decomposition of organic matter and subsequent SOM accumulation [73].
Characteristics of soil nutrient distribution in different locations of shrub patches were analyzed. The results showed that soil organic matter, total nitrogen, and total potassium tended to increase by 8.74%, 9.21%, and 17.12%, respectively, across the three position points (ES, BC, and CS) of the shrub species, and total phosphorus tended to decrease by 7.04%. The results indicated that shrub species influenced the distribution pattern of soil nutrients, which is in line with the results of Tuomisto et al. [74]. There was also an assessment of soil nutrient enrichment from various study area position points. The analysis suggested that the causes of this phenomenon are closely related to the distribution, composition, and biomass of shrubs, nutrient uptake through the root, and the redeposition of plant debris on the soil because of organic residues. Due to the differences in the patch area, height, litter depth, and shoot biomass (Table 1), the soil nutrient enrichment characteristics and the degree of absorption and utilization of nutrients will also differ, indicating an inconsistency in the soil nutrient enrichment rate. One intriguing aspect to explore is whether the encroachment of shrubs has positive effects on soil fertility, potentially giving rise to the formation of “islands of fertility” [10,75]. The study also found that there were differences in the area, height, litter depth, and shoot and root biomass of shrub patches (Table 1). Therefore, the characteristics of soil nutrient enrichment in different shrub patches are not the same, and the degree of absorption and utilization of different nutrients and nutrient return by shrub vegetation to the soil are also different, leading to the phenomenon that the enrichment rate of different soil nutrients is inconsistent. The presence of shrubs might result in positive effects that trigger a feedback loop, involving additional nutrient deposition in these shrub islands of fertility. This, in turn, could contribute to heightened shrub growth and reproduction [76].
Soil nutrients in the shrub patches across the position points had enrichment characteristics, i.e., a “fertility island” effect, while some aggregation effects had no “fertility island” effect [77]. This showed that some soil nutrients gradually accumulated in the central position point of the shrub patches, while others had no or were less disturbed by the shrub, which cannot affect the distribution of soil elements and develop aggregation characteristics. This was due to shrub patch characteristics having different degrees of influence on soil nutrients due to the different shrub species, resulting in the formation of soil nutrient aggregation or non-aggregation characteristics. Correlation analysis showed that shrub patch characteristics were closely related to soil nutrient factors (Figure 3). The area, height, litter depth, and biomass of shrub patches had a positive correlation with SOM, TN, and TP in the CS, while SOM below the crown (BC), TK, and TP at the edge of the patch (ES) had a very significant positive correlation (p < 0.01). This phenomenon showed that shrub patch characteristics had the closest interaction between soil nutrients in the center of the shrub patch (CS) and further explained that the mutual adaptation characteristics between shrub plants and soil nutrients were formed under long-term interaction.

5. Conclusions

The concept of fertility islands in shrub patch characteristics and soil nutrient distribution patterns within the Eastern Qilian Mountains highlights the complex interactions between shrubs and their surrounding environments. Our study showed that different shrub patches were significantly affected by the plant species and displayed great differences. Overall, the patches of the four different species exhibited differing above- and below-ground morphological characteristics (e.g., height, shoot, and root biomass). Soil organic matter (SOM) and macronutrient (N, P, and K) concentrations varied by shrub patch species, soil depth, and distance from the patch center. The soil nutrient content of the shrub patches gradually decreased with the deepening of the soil layer, while the soil nutrient content was significantly higher in the center of the shrub, showing a more significant soil nutrient aggregation phenomenon. The complex interactions between the shrub patch characteristics and soil nutrients investigated in this study make it difficult to determine the level of influence of each factor. A relative interaction intensity variable that compared SOM and macronutrient concentrations from the center to the edges of shrub patches was calculated for each shrub species and at different depths, and the results revealed that the phenomenon of the index of soil nutrient aggregation differs across soil depths of the same shrub patch type. Finally, correlations between shrub patch characteristics and soil nutrients were calculated across all shrub patch types, and the results revealed that there was a highly significant correlation between patch characteristics and soil nutrients at the three patch microsites of the patch types. Their impact on nutrient cycling, vegetation dynamics, and soil properties underscores their significance in understanding ecological processes in these unique landscapes. This study explored the intricate relationships between soil properties, shrub characteristics, and nutrient cycling in the context of alpine ecosystems in the Eastern Qilian Mountains, contributing to our broader understanding of ecosystem dynamics and the development of effective conservation and management strategies.

Author Contributions

J.Z., conceptualization, investigation, methodology, visualization, writing—original draft; B.A., writing—review and editing; J.W., investigation, visualization; Y.F., investigation, visualization. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Gansu Province Higher Education Institutions Industrial Support Program Project (2023CYZC-45) and the National Natural Science Foundation of China (32360378).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ravolainen, V.T.; Bråthen, K.A.; Ims, R.A.; Yoccoz, N.G.; Soininen, E.M. Shrub patch configuration at the landscape scale is related to diversity of adjacent herbaceous vegetation. Plant Ecol. Divers. 2013, 6, 257–268. [Google Scholar] [CrossRef]
  2. Whitford, W.G.; Duval, B.D. Ecology of Desert Systems; Academic Press: Cambridge, MA, USA, 2019. [Google Scholar]
  3. D’Odorico, P.; Okin, G.S.; Bestelmeyer, B.T. A synthetic review of feedbacks and drivers of shrub encroachment in arid grasslands. Ecohydrology 2012, 5, 520–530. [Google Scholar] [CrossRef]
  4. Eldridge, D.J.; Bowker, M.A.; Maestre, F.T.; Roger, E.; Reynolds, J.F.; Whitford, W.G. Impacts of shrub encroachment on ecosystem structure and functioning: Towards a global synthesis. Ecol. Lett. 2011, 14, 709–722. [Google Scholar] [CrossRef] [PubMed]
  5. Zhang, H.-Y.; Yu, Q.; Lü, X.-T.; Trumbore, S.E.; Yang, J.-J.; Han, X.-G. Impacts of leguminous shrub encroachment on neighboring grasses include transfer of fixed nitrogen. Oecologia 2016, 180, 1213–1222. [Google Scholar] [CrossRef]
  6. Wang, Y.; Liang, E.; Lu, X.; Camarero, J.J.; Babst, F.; Shen, M.; Peñuelas, J. Warming-induced shrubline advance stalled by moisture limitation on the Tibetan Plateau. Ecography 2021, 44, 1631–1641. [Google Scholar] [CrossRef]
  7. Zhao, L.-X.; Zhang, K.; Siteur, K.; Li, X.-Z.; Liu, Q.-X.; van de Koppel, J. Fairy circles reveal the resilience of self-organized salt marshes. Sci. Adv. 2021, 7, eabe1100. [Google Scholar] [CrossRef] [PubMed]
  8. Sheffer, E.; von Hardenberg, J.; Yizhaq, H.; Shachak, M.; Meron, E. Emerged or imposed: A theory on the role of physical templates and self-organisation for vegetation patchiness. Ecol. Lett. 2013, 16, 127–139. [Google Scholar] [CrossRef]
  9. Sun, G.-Q.; Wang, C.-H.; Chang, L.-L.; Wu, Y.-P.; Li, L.; Jin, Z. Effects of feedback regulation on vegetation patterns in semi-arid environments. Appl. Math. Model. 2018, 61, 200–215. [Google Scholar] [CrossRef]
  10. Cai, Y.; Yan, Y.; Xu, D.; Xu, X.; Wang, C.; Wang, X.; Chen, J.; Xin, X.; Eldridge, D.J. The fertile island effect collapses under extreme overgrazing: Evidence from a shrub-encroached grassland. Plant Soil 2020, 448, 201–212. [Google Scholar] [CrossRef]
  11. Vogel, B.; Rostagno, C.M.; Molina, L.; Antilef, M.; La Manna, L. Cushion shrubs encroach subhumid rangelands and form fertility islands along a grazing gradient in Patagonia. Plant Soil 2022, 475, 623–643. [Google Scholar] [CrossRef]
  12. Chen, L.; Li, H.; Zhang, P.; Zhao, X.; Zhou, L.; Liu, T.; Hu, H.; Bai, Y.; Shen, H.; Fang, J. Climate and native grassland vegetation as drivers of the community structures of shrub-encroached grasslands in Inner Mongolia, China. Landsc. Ecol. 2015, 30, 1627–1641. [Google Scholar] [CrossRef]
  13. Soliveres, S.; Eldridge, D.J. Do changes in grazing pressure and the degree of shrub encroachment alter the effects of individual shrubs on understorey plant communities and soil function? Funct. Ecol. 2014, 28, 530–537. [Google Scholar] [CrossRef] [PubMed]
  14. Ochoa-Hueso, R.; Eldridge, D.J.; Delgado-Baquerizo, M.; Soliveres, S.; Bowker, M.A.; Gross, N.; Le Bagousse-Pinguet, Y.; Quero, J.L.; García-Gómez, M.; Valencia, E. Soil fungal abundance and plant functional traits drive fertile island formation in global drylands. J. Ecol. 2018, 106, 242–253. [Google Scholar] [CrossRef]
  15. Berdugo, M.; Soliveres, S.; Kéfi, S.; Maestre, F.T. The interplay between facilitation and habitat type drives spatial vegetation patterns in global drylands. Ecography 2019, 42, 755–767. [Google Scholar] [CrossRef]
  16. Liu, Z.-W.; Chen, R.-S.; Song, Y.-X.; Han, C.-T. Aboveground biomass and water storage allocation in alpine willow shrubs in the Qilian Mountains in China. J. Mt. Sci. 2015, 12, 207–217. [Google Scholar] [CrossRef]
  17. Zhang, F.; Jia, W.; Zhu, G.; Zhang, Z.; Shi, Y.; Yang, L.; Xiong, H.; Zhang, M. Using stable isotopes to investigate differences of plant water sources in subalpine habitats. Hydrol. Process. 2022, 36, e14518. [Google Scholar] [CrossRef]
  18. Niu, H.; Lu, X.; Zhang, G.; Sarangi, C. Investigation of water-soluble organic constituents and their spatio-temporal heterogeneity over the Tibetan Plateau. Environ. Pollut. 2022, 302, 119093. [Google Scholar] [CrossRef]
  19. Jasechko, S.; Sharp, Z.D.; Gibson, J.J.; Birks, S.J.; Yi, Y.; Fawcett, P.J. Terrestrial water fluxes dominated by transpiration. Nature 2013, 496, 347–350. [Google Scholar] [CrossRef]
  20. Sharif, J.; Rafiq, M.; Rafiq, M.; Aziz, R.; Qayyum, A.; Saleem, A.; Nisa, W.; Jenks, M.; Li, Y. Climate change perceptions and adaptive actions by pastoral community on the Tibetan Plateau, China. Appl. Ecol. Environ. Res. 2019, 17. [Google Scholar] [CrossRef]
  21. Mantel, S.; Dondeyne, S.; Deckers, S. World reference base for soil resources (WRB). In Encyclopedia of Soils in the Environment, 2nd ed.; Michael, J., Ed.; Elsevier Science: Amsterdam, The Netherlands, 2023; pp. 206–217. [Google Scholar]
  22. Abalori, T.A.; Cao, W.; Weobong, C.A.-A.; Wang, S.; Anning, D.K.; Sam, F.E.; Liu, W.; Wang, W. Spatial variability of soil organic carbon fractions and aggregate stability along an elevation gradient in the alpine meadow grasslands of the Qilian Mountains, China. Chil. J. Agric. Res. 2022, 82, 52–64. [Google Scholar] [CrossRef]
  23. Watson, G.W.; Hewitt, A.M.; Custic, M.; Lo, M. The Management of Tree Root Systems in Urban and Suburban Settings II: A Review of Strategies to Mitigate Human Impacts. Arboric. Urban For. 2014, 40. [Google Scholar] [CrossRef]
  24. Wang, C.; Zhao, C.; Xu, Z.; Wang, Y.; Peng, H. Effect of vegetation on soil water retention and storage in a semi-arid alpine forest catchment. J. Arid Land 2013, 5, 207–219. [Google Scholar] [CrossRef]
  25. Nelson, D.W.; Sommers, L.E. Total carbon, organic carbon, and organic matter. Methods Soil Anal. Part 3 Chem. Methods 1996, 5, 961–1010. [Google Scholar]
  26. Lynch, J.M.; Barbano, D.M. Kjeldahl nitrogen analysis as a reference method for protein determination in dairy products. J. AOAC Int. 1999, 82, 1389–1398. [Google Scholar] [CrossRef] [PubMed]
  27. Armas, C.; Ordiales, R.; Pugnaire, F.I. Measuring plant interactions: A new comparative index. Ecology 2004, 85, 2682–2686. [Google Scholar] [CrossRef]
  28. Titus, J.H.; Nowak, R.S.; Smith, S.D. Soil resource heterogeneity in the Mojave Desert. J. Arid Environ. 2002, 52, 269–292. [Google Scholar] [CrossRef]
  29. Zhang, Q.-P.; Wang, J.; Wang, Q. Effects of abiotic factors on plant diversity and species distribution of alpine meadow plants. Ecol. Inform. 2021, 61, 101210. [Google Scholar] [CrossRef]
  30. Hao, H.-M.; Lu, R.; Liu, Y.; Fang, N.-F.; Wu, G.-L.; Shi, Z.-H. Effects of shrub patch size succession on plant diversity and soil water content in the water-wind erosion crisscross region on the Loess Plateau. Catena 2016, 144, 177–183. [Google Scholar] [CrossRef]
  31. Fan, Y.; Li, X.Y.; Huang, Y.M.; Li, L.; Zhang, J.H.; Liu, Q.; Jiang, Z.Y. Shrub patch configuration in relation to precipitation and soil properties in Northwest China. Ecohydrology 2018, 11, e1916. [Google Scholar] [CrossRef]
  32. Liu, Y.-Z.; Cao, W.-X.; Wang, J.-L.; Li, W.; Xin, Y.-Q.; Wang, S.-L.; Wang, X.-J. Response of soil characteristics of different types of shrub patches to enclosure on eastern Qilian Mountain. Acta Prataculturae Sin. 2019, 28, 32. [Google Scholar]
  33. Chen, J.; Cui, H.-Y.; Jia, B.; Gang, S.; Li, Y.; Li, F.-C.; Mou, X.M.; Li, X.G. Soil sampling depth matters in assessing the impact of shrubification on soil organic carbon storage in grazed alpine meadows. Geoderma 2022, 426, 116119. [Google Scholar] [CrossRef]
  34. Howard, K.S.; Eldridge, D.J.; Soliveres, S. Positive effects of shrubs on plant species diversity do not change along a gradient in grazing pressure in an arid shrubland. Basic Appl. Ecol. 2012, 13, 159–168. [Google Scholar] [CrossRef]
  35. Aguirre, D.; Benhumea, A.E.; McLaren, J.R. Shrub encroachment affects tundra ecosystem properties through their living canopy rather than increased litter inputs. Soil Biol. Biochem. 2021, 153, 108121. [Google Scholar] [CrossRef]
  36. Vitasse, Y.; Delzon, S.; Bresson, C.C.; Michalet, R.; Kremer, A. Altitudinal differentiation in growth and phenology among populations of temperate-zone tree species growing in a common garden. Can. J. For. Res. 2009, 39, 1259–1269. [Google Scholar] [CrossRef]
  37. Cui, G.; Wei, X.; Degen, A.A.; Wei, X.; Zhou, J.; Ding, L.; Shang, Z.; Liu, S.; Long, R. Trolox-equivalent antioxidant capacity and composition of five alpine plant species growing at different elevations on the Qinghai–Tibetan Plateau. Plant Ecol. Divers. 2016, 9, 387–396. [Google Scholar] [CrossRef]
  38. Wang, J.; Li, W.; Cao, W.; Abalori, T.A.; Liu, Y.; Xin, Y.; Wang, S.; Zhang, D. Soil bacterial community responses to short-term grazing exclusion in a degraded alpine shrubland–grassland ecotone. Ecol. Indic. 2021, 130, 108043. [Google Scholar] [CrossRef]
  39. Gao, L.; Gou, X.; Deng, Y.; Yang, M.; Zhao, Z.; Cao, Z. Dendroclimatic response of Picea crassifolia along an altitudinal gradient in the eastern Qilian Mountains, northwest China. Arct. Antarct. Alp. Res. 2013, 45, 491–499. [Google Scholar] [CrossRef]
  40. Li, X.; Rossi, S.; Liang, E.; Julio Camarero, J. Temperature thresholds for the onset of xylogenesis in alpine shrubs on the Tibetan Plateau. Trees 2016, 30, 2091–2099. [Google Scholar] [CrossRef]
  41. Myers-Smith, I.H.; Elmendorf, S.C.; Beck, P.S.; Wilmking, M.; Hallinger, M.; Blok, D.; Tape, K.D.; Rayback, S.A.; Macias-Fauria, M.; Forbes, B.C. Climate sensitivity of shrub growth across the tundra biome. Nat. Clim. Chang. 2015, 5, 887–891. [Google Scholar] [CrossRef]
  42. Wu, B.; Peng, H.; Sheng, M.; Luo, H.; Wang, X.; Zhang, R.; Xu, F.; Xu, H. Evaluation of phytoremediation potential of native dominant plants and spatial distribution of heavy metals in abandoned mining area in Southwest China. Ecotoxicol. Environ. Saf. 2021, 220, 112368. [Google Scholar] [CrossRef]
  43. Makoto, K.; Kudo, G. Intraspecific differentiation in the root system of Potentilla matsumurae along a snow accumulation gradient in mid-altitude alpine environment. Arct. Antarct. Alp. Res. 2020, 52, 408–415. [Google Scholar] [CrossRef]
  44. Hales, T.C. Modelling biome-scale root reinforcement and slope stability. Earth Surf. Process. Landf. 2018, 43, 2157–2166. [Google Scholar] [CrossRef]
  45. Lu, X.; Camarero, J.J.; Wang, Y.; Liang, E.; Eckstein, D. Up to 400-year-old Rhododendron shrubs on the southeastern Tibetan Plateau: Prospects for shrub-based dendrochronology. Boreas 2015, 44, 760–768. [Google Scholar] [CrossRef]
  46. Erfanzadeh, R.; Yazdani, M.; Arani, A.M. Effect of different shrub species on their sub-canopy soil and vegetation properties in semiarid regions. Land Degrad. Dev. 2021, 32, 3236–3247. [Google Scholar] [CrossRef]
  47. Yue, K.; De Frenne, P.; Fornara, D.A.; Van Meerbeek, K.; Li, W.; Peng, X.; Ni, X.; Peng, Y.; Wu, F.; Yang, Y. Global patterns and drivers of rainfall partitioning by trees and shrubs. Glob. Change Biol. 2021, 27, 3350–3357. [Google Scholar] [CrossRef] [PubMed]
  48. Zhou, L.; Shen, H.; Chen, L.; Li, H.; Zhang, P.; Zhao, X.; Liu, T.; Liu, S.; Xing, A.; Hu, H. Ecological consequences of shrub encroachment in the grasslands of northern China. Landsc. Ecol. 2019, 34, 119–130. [Google Scholar] [CrossRef]
  49. Dearborn, K.D.; Danby, R.K. Topographic influences on ring widths of trees and shrubs across alpine treelines in southwest Yukon. Arct. Antarct. Alp. Res. 2018, 50, e1495445. [Google Scholar] [CrossRef]
  50. Hrotkó, K. Potentials in Prunus mahaleb L. for cherry rootstock breeding. Sci. Hortic. 2016, 205, 70–78. [Google Scholar] [CrossRef]
  51. Ma, L.; Zhang, Z.-H.; Yao, B.-Q.; Ma, Z.; Huang, X.-T.; Zhou, B.-R.; Xu, M.-H.; Guo, J.; Zhou, H.-K. Effects of drought and heat on the productivity and photosynthetic characteristics of alpine meadow plants on the Qinghai-Tibetan Plateau. J. Mt. Sci. 2021, 18, 2079–2093. [Google Scholar] [CrossRef]
  52. Midoko-Iponga, D.; Krug, C.B.; Milton, S.J. Competition and herbivory influence growth and survival of shrubs on old fields: Implications for restoration of renosterveld shrubland. J. Veg. Sci. 2005, 16, 685–692. [Google Scholar] [CrossRef]
  53. Li, H.; Shen, H.; Zhou, L.; Zhu, Y.; Chen, L.; Hu, H.; Zhang, P.; Fang, J. Shrub encroachment increases soil carbon and nitrogen stocks in temperate grasslands in China. Land Degrad. Dev. 2019, 30, 756–767. [Google Scholar] [CrossRef]
  54. Du, B.; Ji, H.; Peng, C.; Liu, X.; Liu, C. Altitudinal patterns of leaf stoichiometry and nutrient resorption in Quercus variabilis in the Baotianman Mountains, China. Plant Soil 2017, 413, 193–202. [Google Scholar] [CrossRef]
  55. Ding, L.; Wang, P.; Zhang, W.; Zhang, Y.; Li, S.; Wei, X.; Chen, X.; Zhang, Y.; Yang, F. Shrub encroachment shapes soil nutrient concentration, stoichiometry and carbon storage in an abandoned subalpine Grassland. Sustainability 2019, 11, 1732. [Google Scholar] [CrossRef]
  56. Armas, C.; Kim, J.H.; Bleby, T.M.; Jackson, R.B. The effect of hydraulic lift on organic matter decomposition, soil nitrogen cycling, and nitrogen acquisition by a grass species. Oecologia 2012, 168, 11–22. [Google Scholar] [CrossRef] [PubMed]
  57. Sarris, D.; Mazza, G. Mediterranean pine root systems under drought. In Pines and Their Mixed Forest Ecosystems in the Mediterranean Basin; Springer: Cham, Switzerland, 2021; pp. 129–140. [Google Scholar]
  58. Li, S.-X.; Wang, Z.-H.; Malhi, S.; Li, S.-Q.; Gao, Y.-J.; Tian, X.-H. Nutrient and water management effects on crop production, and nutrient and water use efficiency in dryland areas of China. Adv. Agron. 2009, 102, 223–265. [Google Scholar]
  59. Lehmann, J.; Schroth, G. Nutrient leaching. In Trees, Crops and Soil Fertility: Concepts and Research Methods; CABI Publishing: Wallingford, UK, 2002; pp. 151–166. [Google Scholar]
  60. Uhlig, D.; Amelung, W.; Von Blanckenburg, F. Mineral nutrients sourced in deep regolith sustain long-term nutrition of mountainous temperate forest ecosystems. Glob. Biogeochem. Cycles 2020, 34, e2019GB006513. [Google Scholar] [CrossRef]
  61. Kowalska, J.B.; Zaleski, T.; Józefowska, A.; Mazurek, R. Soil formation on calcium carbonate-rich parent material in the outer Carpathian Mountains—A case study. Catena 2019, 174, 436–451. [Google Scholar] [CrossRef]
  62. Wutzler, T.; Zaehle, S.; Schrumpf, M.; Ahrens, B.; Reichstein, M. Adaptation of microbial resource allocation affects modelled long term soil organic matter and nutrient cycling. Soil Biol. Biochem. 2017, 115, 322–336. [Google Scholar] [CrossRef]
  63. Radicetti, E.; Mancinelli, R.; Moscetti, R.; Campiglia, E. Management of winter cover crop residues under different tillage conditions affects nitrogen utilization efficiency and yield of eggplant (Solanum melanogena L.) in Mediterranean environment. Soil Tillage Res. 2016, 155, 329–338. [Google Scholar] [CrossRef]
  64. Barnes, P.W.; Throop, H.L.; Archer, S.R.; Breshears, D.D.; McCulley, R.L.; Tobler, M.A. Sunlight and soil–litter mixing: Drivers of litter decomposition in drylands. Prog. Bot. 2015, 76, 273–302. [Google Scholar]
  65. Cotrufo, M.F.; Wallenstein, M.D.; Boot, C.M.; Denef, K.; Paul, E. The M icrobial E fficiency-M atrix S tabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: Do labile plant inputs form stable soil organic matter? Glob. Chang. Biol. 2013, 19, 988–995. [Google Scholar] [CrossRef]
  66. Thomas, A.D.; Elliott, D.R.; Dougill, A.J.; Stringer, L.C.; Hoon, S.R.; Sen, R. The influence of trees, shrubs, and grasses on microclimate, soil carbon, nitrogen, and CO2 efflux: Potential implications of shrub encroachment for Kalahari rangelands. Land Degrad. Dev. 2018, 29, 1306–1316. [Google Scholar] [CrossRef]
  67. Ward, D.; Trinogga, J.; Wiegand, K.; du Toit, J.; Okubamichael, D.; Reinsch, S.; Schleicher, J. Large shrubs increase soil nutrients in a semi-arid savanna. Geoderma 2018, 310, 153–162. [Google Scholar] [CrossRef]
  68. Olanrewaju, O.S.; Ayangbenro, A.S.; Glick, B.R.; Babalola, O.O. Plant health: Feedback effect of root exudates-rhizobiome interactions. Appl. Microbiol. Biotechnol. 2019, 103, 1155–1166. [Google Scholar] [CrossRef] [PubMed]
  69. Wang, J.; Xu, B.; Wu, Y.; Gao, J.; Shi, F. Flower litters of alpine plants affect soil nitrogen and phosphorus rapidly in the eastern Tibetan Plateau. Biogeosciences 2016, 13, 5619–5631. [Google Scholar] [CrossRef]
  70. Córdova, S.C.; Olk, D.C.; Dietzel, R.N.; Mueller, K.E.; Archontouilis, S.V.; Castellano, M.J. Plant litter quality affects the accumulation rate, composition, and stability of mineral-associated soil organic matter. Soil Biol. Biochem. 2018, 125, 115–124. [Google Scholar] [CrossRef]
  71. López-Angulo, J.; de la Cruz, M.; Chacón-Labella, J.; Illuminati, A.; Matesanz, S.; Pescador, D.S.; Pías, B.; Sánchez, A.M.; Escudero, A. The role of root community attributes in predicting soil fungal and bacterial community patterns. New Phytol. 2020, 228, 1070–1082. [Google Scholar] [CrossRef] [PubMed]
  72. Feng, N.; Liu, D.; Li, Y.; Liu, P. Soil net N mineralization and hydraulic properties of carbonate-derived laterite under different vegetation types in Karst forests of China. Sci. Total Environ. 2023, 856, 159116. [Google Scholar] [CrossRef]
  73. Liu, W.; Pei, X.; Peng, S.; Wang, G.; Smoak, J.M.; Duan, B. Litter inputs drive increases in topsoil organic carbon after scrub encroachment in an alpine grassland. Pedobiologia 2021, 85, 150731. [Google Scholar] [CrossRef]
  74. Tuomisto, H.; Ruokolainen, K.; Yli-Halla, M. Dispersal, environment, and floristic variation of western Amazonian forests. Science 2003, 299, 241–244. [Google Scholar] [CrossRef]
  75. Qu, W.-L.; Yang, X.-P.; Zhang, C.-T.; Wei, B. Shrub-mediated “fertile island” effects in arid and semi-arid grassland. Acta Prataculturae Sin. 2015, 24, 201. [Google Scholar]
  76. Allington, G.R.; Valone, T.J. Islands of fertility: A byproduct of grazing? Ecosystems 2014, 17, 127–141. [Google Scholar] [CrossRef]
  77. Bai, Y.; She, W.; Zhang, Y.; Qiao, Y.; Fu, J.; Qin, S. N enrichment, increased precipitation, and the effect of shrubs collectively shape the plant community in a desert ecosystem in northern China. Sci. Total Environ. 2020, 716, 135379. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Illustration of the sampling location and design. Center of the shrub patch (CS), the edge of the shrub patch (ES), and the midpoint between the center and the edge of the shrub patch (BC).
Figure 1. Illustration of the sampling location and design. Center of the shrub patch (CS), the edge of the shrub patch (ES), and the midpoint between the center and the edge of the shrub patch (BC).
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Figure 2. Trends in SOM, TN, TK, and TP of the four shrub species. Center of the shrub patch (CS), the edge of the shrub patch (ES), and the midpoint between the center and the edge of the shrub patch (BC). Data are presented as Mean (±SE); different lowercase letters in the same column denote significant differences at the 0.05 level.
Figure 2. Trends in SOM, TN, TK, and TP of the four shrub species. Center of the shrub patch (CS), the edge of the shrub patch (ES), and the midpoint between the center and the edge of the shrub patch (BC). Data are presented as Mean (±SE); different lowercase letters in the same column denote significant differences at the 0.05 level.
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Figure 3. The correlation between shrub patch characteristics and soil nutrients. The depth of the color reflects the strength of the correlation, where darker hues indicate stronger associations, while lighter shades suggest weaker ones. Center of the shrub patch (CS), the edge of the shrub patch (ES), and the midpoint between the center and the edge of the shrub patch (BC).
Figure 3. The correlation between shrub patch characteristics and soil nutrients. The depth of the color reflects the strength of the correlation, where darker hues indicate stronger associations, while lighter shades suggest weaker ones. Center of the shrub patch (CS), the edge of the shrub patch (ES), and the midpoint between the center and the edge of the shrub patch (BC).
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Table 1. Characteristics of shrub patch. Data are presented as Mean (±SE); different lowercase letters in the same column denote significant differences at the 0.05 level. Salix oritrepha (SO), Spiraea alpina (SA), Rhododendron capitatum (RC), and Potentilla fruticosa (PF).
Table 1. Characteristics of shrub patch. Data are presented as Mean (±SE); different lowercase letters in the same column denote significant differences at the 0.05 level. Salix oritrepha (SO), Spiraea alpina (SA), Rhododendron capitatum (RC), and Potentilla fruticosa (PF).
Patch TypePatch Area (m2)Height (cm)Litter Depth (cm)Shoot Biomass (g/m2)Root Biomass (g/m2)
SO1.66 ± 0.41a117.94 ± 3.58a3.33 ± 0.03a1098.53 ± 64.52a2284.29 ± 105.40b
SA0.08 ± 0.01b61.32 ± 3.04b0.70 ± 0.04b1062.93 ± 94.04a2581.55 ± 186.08b
RC0.37 ± 0.01b60.05 ± 1.51b0.75 ± 0.01b613.68 ± 81.33b3983.36 ± 490.31a
PF0.12 ± 0.01b43.90 ± 3.10c1.10 ± 0.01b579.53 ± 97.15b3291.81 ± 264.77a
Table 2. The characteristics of soil available nutrients in the 0–60 cm soil profile of patch microsites under four shrub patch types: Salix oritrepha (SO), Spiraea alpina (SA), Rhododendron capitatum (RC), and Potentilla fruticosa (PF). Center of the shrub patch (CS), the edge of the shrub patch (ES), and the midpoint between the center and the edge of the shrub patch (BC). Data are presented as Mean (±SE); different lowercase letters in the same column denote significant differences at the 0.05 level.
Table 2. The characteristics of soil available nutrients in the 0–60 cm soil profile of patch microsites under four shrub patch types: Salix oritrepha (SO), Spiraea alpina (SA), Rhododendron capitatum (RC), and Potentilla fruticosa (PF). Center of the shrub patch (CS), the edge of the shrub patch (ES), and the midpoint between the center and the edge of the shrub patch (BC). Data are presented as Mean (±SE); different lowercase letters in the same column denote significant differences at the 0.05 level.
Shrub Patch TypePatch MicrositesSoil Depth (cm)SOM (g/kg)TN (g/kg)TK (g/kg)TP (g/kg)
SOES0–2091.6032.2241.8720.749
20–4052.8422.1631.4440.490
40–6035.8221.6811.4110.451
mean value60.09 ± 65a2.02 ± 0.17a1.58 ± 0.15b0.56 ± 0.09a
BC0–20101.5412.6661.9930.610
20–4061.4532.1471.7030.515
40–6049.0711.5561.5950.440
mean value70.69 ± 5.83a2.12 ± 0.32a1.76 ± 0.12ab0.52 ± 0.05a
CS0–20138.9603.1822.1880.526
20–4097.2971.9292.0100.411
40–6054.2241.7741.9270.407
mean value96.83 ± 4.46a2.29 ± 0.45a2.04 ± 0.08a0.45 ± 0.04a
SAES0–2045.1321.3781.2250.647
20–4035.1801.0281.1930.591
40–6021.9600.5300.9540.417
mean value34.09 ± 6.71a0.98 ± 0.24a1.12 ± 0.09a0.55 ± 0.07a
BC0–2037.1541.3381.1430.558
20–4029.8531.2761.1220.358
40–6027.9480.8410.9780.389
mean value31.65 ± 2.81a1.15 ± 0.16a1.08 ± 0.05a0.43 ± 0.06a
CS0–2053.1971.8751.6740.441
20–4037.0991.0271.1460.379
40–6032.6800.6390.8940.191
mean value40.99 ± 6.23a1.18 ± 0.36a1.24 ± 0.23a0.34 ± 0.08a
RCES0–2096.3490.4500.7030.653
20–4088.5600.5550.7480.670
40–6058.9970.4460.7160.663
mean value81.30 ± 11.38a0.48 ± 0.03a0.72 ± 0.01b0.66 ± 0.00b
BC0–20113.6840.5200.8200.938
20–40100.8270.4660.8090.886
40–6079.3320.4970.8640.794
mean value97.95 ± 10.02a0.49 ± 0.02a0.83 ± 0.02a0.87 ± 0.04a
CS0–20120.5960.4740.9140.836
20–4060.4980.3170.8320.727
40–6051.4020.2840.8340.842
mean value77.50 ± 7.08a0.36 ± 0.06a0.86 ± 0.03a0.80 ± 0.04a
PFES0–20103.0820.5790.9001.319
20–4092.0460.4510.7970.938
40–6088.5490.4480.7841.159
mean value94.56 ± 4.38a0.49 ± 0.04b0.83 ± 0.04b1.14 ± 0.11a
BC0–2086.0400.3960.9821.148
20–4081.7430.2800.8851.091
40–6073.0260.4190.8450.928
mean value80.27 ± 3.83a0.36 ± 0.04ab0.90 ± 0.04ab1.06 ± 0.07a
CS0–2097.7480.6231.0411.124
20–4070.6970.4311.0171.206
40–6073.2820.5880.9021.082
mean value73.97 ± 9.57a1.09 ± 0.26a1.28 ± 0.15a0.68 ± 0.10a
Table 3. The relative interaction intensity (RII) values for SOM, TN, TK, and TP of the soil depths for the four shrub species: Salix oritrepha (SO), Spiraea alpina (SA), Rhododendron capitatum (RC), and Potentilla fruticosa (PF).
Table 3. The relative interaction intensity (RII) values for SOM, TN, TK, and TP of the soil depths for the four shrub species: Salix oritrepha (SO), Spiraea alpina (SA), Rhododendron capitatum (RC), and Potentilla fruticosa (PF).
Shrub Patch TypeSoil Depth (cm)SOM (g/kg)TN (g/kg)TK (g/kg)TP (g/kg)
SO0–200.2050.1770.078−0.175
20–400.296−0.0570.164−0.087
40–600.2040.0270.155−0.052
Mean Value0.2350.0490.132−0.105
SA0–200.0820.1530.155−0.189
20–400.0270.000−0.020−0.219
40–600.1960.093−0.032−0.371
Mean Value0.1020.0820.034−0.260
RC0–200.1120.0260.1300.122
20–40−0.188−0.2730.0530.041
40–60−0.069−0.2220.0760.119
Mean Value−0.048−0.1560.0870.094
PF0–20−0.0270.0370.073−0.080
20–40−0.131−0.0220.1210.125
40–60−0.0940.1350.070−0.034
Mean Value−0.0840.0500.0880.004
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Zhao, J.; Adu, B.; Wang, J.; Fan, Y. Assessing Shrub Patch Characteristics and Soil Nutrient Distribution Patterns of Four Typical Alpine Shrub Plants in the Eastern Qilian Mountains. Sustainability 2024, 16, 1547. https://doi.org/10.3390/su16041547

AMA Style

Zhao J, Adu B, Wang J, Fan Y. Assessing Shrub Patch Characteristics and Soil Nutrient Distribution Patterns of Four Typical Alpine Shrub Plants in the Eastern Qilian Mountains. Sustainability. 2024; 16(4):1547. https://doi.org/10.3390/su16041547

Chicago/Turabian Style

Zhao, Jinmei, Benjamin Adu, Jingnan Wang, and Yuhang Fan. 2024. "Assessing Shrub Patch Characteristics and Soil Nutrient Distribution Patterns of Four Typical Alpine Shrub Plants in the Eastern Qilian Mountains" Sustainability 16, no. 4: 1547. https://doi.org/10.3390/su16041547

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