Next Article in Journal
Correction: Chan et al. Analysis of the Synergies of Cutting Air Pollutants and Greenhouse Gas Emissions in an Integrated Iron and Steel Enterprise in China. Sustainability 2023, 15, 13231
Previous Article in Journal
Walkable and Sustainable City Centre Greenway Planning
Previous Article in Special Issue
Revealing the Exacerbated Drought Stress Impacts on Regional Vegetation Ecosystems in Karst Areas with Vegetation Indices: A Case Study of Guilin, China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 7
10.3390/su17072899
sustainability-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
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Ecological Adaptation Strategies of Desert Plants in the Farming–Pastoral Zone of Northern Tarim Basin

by
Baohua Han
1,2,
Liyang Cui
3,
Mengting Jin
3 and
Hegan Dong
1,2,*
1
Production and Construction Corps Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Shihezi University, North 4 Rd., Shihezi 832003, China
2
College of Life Sciences, Shihezi University, North 4 Rd., Shihezi 832003, China
3
China Geological Survey Urumqi Comprehensive Survey Center on Natural Resources, Urumqi 830057, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(7), 2899; https://doi.org/10.3390/su17072899
Submission received: 16 February 2025 / Revised: 23 March 2025 / Accepted: 23 March 2025 / Published: 25 March 2025
(This article belongs to the Special Issue Impact and Adaptation of Climate Change on Natural Ecosystems)
Browse Figures
Versions Notes

Abstract

:
Plant functional traits are indicative of the long-term responses and adaptations of plants to their environment. However, the specific mechanisms by which desert plant functional groups (PFGs) adjust their ecological adaptation strategies to cope with harsh environments remain unclear, particularly in ecologically fragile farming–pastoral zones. To address this gap, this study investigates and analyzes the morphological and chemical characteristics of 13 desert plant species in the farming–pastoral zone of the northern Tarim Basin. Through cluster analysis, these desert plants were categorized into distinct PFGs to elucidate their ecological response strategies at a higher organizational level. The results were as follows: (1) Based on plant functional traits, the 13 desert plant species were classified into acquisitive, medium, and conservative PFGs. These groups exhibited significant differences in chemical element content and proportion, as well as morphological adjustments (p < 0.05). (2) The acquisitive functional group maintained high resource acquisition and turnover through high specific leaf area and leaf phosphorus content; the medium functional group occupied limited resources through greater plant height and canopy width, whereas the conservative functional group exhibited low growth rates but high morphological investment to ensure survival. Moreover, these differences in ecological adaptation strategies led to the selection of divergent central traits by different PFGs. (3) Low soil nutrient availability and soil salinization, rather than groundwater depth, were identified as the primary environmental factors driving the differentiation of PFGs in the farming–pastoral zone. These findings suggest that desert plants in arid regions employ diverse ecological adaptation strategies to cope with environmental pressures. This research study provides valuable insights and recommendations for the conservation and restoration of desert plant communities.

1. Introduction

Water availability is the primary limiting factor for plant growth and reproduction in dryland ecosystems [1]. Owing to differences in evolutionary history and environmental selection pressures, desert plants have evolved diverse ecological adaptation strategies to cope with extreme drought conditions [2]. Understanding the interspecific differences among plants is crucial for elucidating the ecological response strategies of desert plants. The “leaf economic spectrum” (LES) describes a continuous functional continuum of leaf traits, characterized by two contrasting strategies: “fast investment–return” and “slow investment–return” plants [3]. The former strategy is typified by shorter leaf lifespans, lower leaf carbon investment, and higher leaf nitrogen (N) and phosphorus (P) contents, which result in higher photosynthetic and respiratory rates. These traits enable plants to efficiently acquire resources and achieve rapid turnover, thereby enhancing their competitiveness in resource-limited environments [3,4]. Based on the LES framework, Diaz et al. [5] proposed a global spectrum of plant form and function strategies which integrates leaf economic traits with other key plant morphological attributes such as plant height and canopy width. This integrative approach provides a more comprehensive and objective framework for understanding plant adaptation strategies. However, previous studies on plant ecological adaptation strategies have predominantly focused on the species level [6,7,8], with limited exploration of how these findings can be extended to the community scale. This gap in knowledge hinders our ability to effectively simulate, assess, and predict plant community dynamics under current and future environmental conditions, thereby limiting the applicability of these findings to contemporary research needs.
The ecological adaptation strategies employed by plants can be effectively reflected through plant functional traits, which have garnered considerable attention in recent decades [9,10]. Plant functional traits encompass a suite of core attributes that are closely linked to colonization, survival, growth, and mortality, thereby significantly influencing vegetation responses to environmental changes [11,12]. For instance, a high specific leaf area (SLA) enhances carbon assimilation in plants [13], while high wood density confers resistance to external physical damage [14]. Additionally, a well-developed root system facilitates the uptake of water and nutrients [15]. Plant chemical traits, on the other hand, refer to the content and proportion of various chemical components and metabolites within plants. Among these, nitrogen (N) and phosphorus (P) content are particularly important [16]. Generally, fast-growing plants tend to maintain higher N and P contents but exhibit lower N:P ratios [17]. Given the limited availability of resources, plants must balance growth and reproduction, often resulting in trade-offs among functional traits [18]. For example, desert plants typically exhibit small but thick leaves [19], high water use efficiency and hydraulic safety margins [20], elevated root-to-shoot ratios [21], and substantial accumulation of osmoregulatory substances [22]. Therefore, research on plant functional traits provides valuable insights into the ecological strategies employed by plants to adapt to their environments.
As associations of vegetation with similar functions in ecosystems, plant functional groups (PFGs) have garnered significant attention from ecologists [23,24]. This interest stems from the fact that PFGs offer a more intuitive framework for comparing the effects of different plant strategies on community structure and productivity in real-world ecosystems [25]. Relevant studies have demonstrated that PFGs, in addition to species diversity, can enhance the cycling of materials and energy within ecosystems [26]. Moreover, PFGs may be more closely related to ecological processes than species richness alone [27]. This is likely because PFGs help to reduce the noise associated with interspecific relationships, thereby providing clearer insights into community dynamics [28]. Additionally, PFGs may offer more explicit indications of community structure, stability, and productivity than analyses focused solely on the individual species level [29]. Building on the concept of PFGs, numerous studies have explored various plant communities, including evergreen and deciduous tree species in tropical dry forests [30], dominant herbaceous plants in desertified grasslands [31], and deciduous, semideciduous, and evergreen tree species in semiarid riparian forests [32]. However, despite these advances, research on desert plant trait differences and ecological strategies from the perspective of PFGs remains limited. This gap in knowledge hinders a comprehensive understanding of how plant communities adapt and respond to global climate change.
The northern margin of the Tarim Basin is one of the most arid regions in China, characterized by scarce precipitation, intense evaporation, and high ecological sensitivity [2]. The plant community structure in this area is relatively simple, with vegetation that is predominantly adapted to tolerate salinity, alkalinity, cold, drought, wind, and sand. These adaptations are crucial for improving saline soils [33], maintaining soil fertility [34], and mitigating desertification. In recent years, frequent human activities have led to a sharp decline in the ecological carrying capacity of the farming–pastoral zone in the upper reaches of the Tarim River. This decline is manifested through secondary salinization, degradation of grazing grasslands, decreased groundwater levels, and soil desertification [2,35,36]. To ensure the sustainability of this region, it is essential to understand the ecological strategies employed by desert plants and to identify the primary environmental drivers shaping these strategies. Such information can provide a scientific basis for vegetation recovery, ecological restoration, and effective management in extreme arid environments.
To address these questions, we investigated 13 common desert plant species in the farming–pastoral zone of the northern Tarim Basin, measuring and analyzing their morphological and chemical characteristics. We hypothesized that: (1) different plant species can be classified into the same PFGs based on their similar functional traits; (2) the ecological adaptation strategies employed by different PFGs are distinct; and (3) habitat degradation, including soil salinization, desertification, and groundwater depth decline are key drivers of PFG differentiation in this region.

2. Materials and Methods

2.1. Study Sites and Plant Inventory Data

The study area is located in the Aksu District (80.71–81.23° E, 40.25–41.56° N) in the upper reaches of the Tarim River. This region is characterized by a typical warm temperate continental arid desert climate, with abundant light and heat resources. The mean annual precipitation is less than 50 mm, while the mean annual evaporation exceeds 2000 mm [2]. The annual temperature range is approximately 58 °C, varying from a maximum of 40 °C to a minimum of −18 °C [2]. Additionally, the region exhibits significant diurnal temperature variations. The dominant soil types include brown desert soil, saline soil, and aeolian sand soil. Representative plant species in this region are Tamarix ramosissima, Halocnemum strobilaceum, Halostachys caspica, Lycium ruthenicum, and Alhagi sparsifolia [2], all of which are common forage plants. Water scarcity is a critical factor in this region, leading to a pronounced conflict between agricultural water use and ecological water requirements [35].
In July 2022, following a comprehensive survey of vegetation and soil in the study area, a 200 km transect was established along the farming–pastoral zone from north to south, passing through Wensu County, Aksu City, and Alar City. The transect comprised 25 long-term vegetation observation sites, each covering an area of 1 km2 (Figure 1). An observation well was drilled at each site to monitor groundwater depth during the growing season.
During the growing season of 2024 (June–September), three 20 × 20 m shrub quadrats were randomly established at each of the 25 sites. All plant species within the quadrats were identified and recorded, with a total of 13 desert plant species being investigated (Table 1). For each species, plant height and canopy width were measured (canopy width = (length of major axis of canopy + length of minor axis of canopy)/2), with a minimum of four replicates per species. At the end of the field investigation, leaves were collected from each species, ensuring a minimum of four replicates (four individual plants) per species at each site. The plant samples were immediately stored in an icebox and transported to the laboratory for further analysis. To elucidate the effects of environmental disturbances such as soil nutrient and salinization on vegetation, surface soil (0–20 cm) was also collected from each shrub quadrat. Specifically, a minimum of three replicates of surface soil were taken. Prior to sampling, litter and gravel on the soil surface were removed, and the mixed soil was used as the representative sample for each quadrat.

2.2. Determination of Leaf Traits and Environmental Predictors

Leaf area (LA), leaf dry matter content (LDMC), and specific leaf area (SLA; calculated as SLA = LA/LDMC) were measured using a representative sample of plant leaves. After scanning the leaves, LA was calculated using ImageJ software (1.8.0v for PC, W. Rasband, National Institute of Health, Bethesda, MD, USA), while LDMC was determined by drying the scanned leaves to constant weight at 60 °C. The remaining leaf samples were used to measure organic carbon (LC), total nitrogen (LN), and total phosphorus (LP) content, following the methods described in Soil Agrochemical Analysis [37]. The ratios of C to N (LC:LN) and N to P (LN:LP) were also calculated.
The soil samples were air-dried and subsequently passed through a 2 mm sieve. Soil organic carbon (SOC), total nitrogen (STN), total phosphorus (STP), available nitrogen (SAN), and available phosphorus (SAP) were measured using standard methods from Soil Agrochemical Analysis [37]. Soil pH was determined using a 5:1 water-to-soil ratio, while total soil salinity (STS) was assessed using the drying method. Soil clay (Clay) and bulk density (Bulk) were obtained from the World Soil Database [38].

2.3. Data Statistics and Analysis

Data sorting and analysis were conducted using Excel and R (version 4.4.1; R Core Team). Descriptive statistics included the mean ± standard deviation, maximum, minimum, median, and coefficient of variation. Cluster analysis was performed using the factoextra package in R, based on the mean values of plant functional traits. All plant species were classified into three functional groups using hierarchical clustering with the maximum linkage method and Canberra distance as the distance metric. Following data standardization (z-score transformation), principal component analysis (PCA) was employed to assess the loadings and trade-offs of each plant functional trait along the first (PC1) and second (PC2) principal components. The distribution of each functional group was visualized using 95% confidence ellipses. To elucidate the trait associations within each PFG, a plant trait network analysis was conducted using the igraph package. Central traits were identified based on network parameters, including degree, closeness, betweenness, and clustering coefficient. Finally, to examine the influence of environmental factors on PFGs, separate matrices for environmental factors and plant traits were constructed. Canonical correspondence analysis (CCA) was then performed using the vegan package to identify the key environmental drivers shaping PFG differentiation.

3. Results

3.1. Functional Trait Characteristics of Desert Plants in Farming–Pastoral Zone

Among the nine plant functional traits examined, LA exhibited the greatest variation among species, whereas LC displayed the smallest variation (Table 2). In terms of leaf stoichiometry, LC was the highest, while LN and LP contents were similar. LN:LP was 5.19, indicating a pronounced nitrogen limitation. Regarding plant morphology, desert plants generally had relatively small canopy widths and heights, but these traits exhibited high interspecific variation.

3.2. Classification of Desert Plants into Functional Groups in Farming–Pastoral Zone

Through the integration of cluster analysis and principal component analysis (PCA), desert plants were categorized into three distinct functional groups: acquisitive (PFG1), medium (PFG2), and conservative (PFG3) (Figure 2 and Figure 3). The PCA loading matrix revealed that the first (PC1) and second (PC2) principal components accounted for 41.24% and 24.29% of the total variation, respectively, cumulatively explaining 65.53% of the total variation (Table 3). This substantial proportion of explained variance indicates the reliability of the results (Figure 3). PC1 was positively correlated with SLA, LP, LN, and LC:LN, but negatively correlated with the LN:LP and canopy width. PC2 exhibited a positive correlation with LN:LP but was negatively correlated with canopy width, plant height, and LC. The 95% confidence ellipses of the three functional groups showed overlap, suggesting the presence of functional redundancy among these groups.
Among PFGs, plant functional traits showed great differences. For example, the acquisitive functional group exhibited strong resource acquisition capabilities, characterized by high SLA, LP, LN, and LC:LN. This functional group primarily consists of perennial herbs such as Sophora alopecuroides, Karelinia caspia, and Artemisia desertorum, as well as some shrubs like Lycium ruthenicum and Alhagi camelorum. The medium functional group acts as the constructive species within plant communities, featuring relatively high plant height and canopy width, coupled with moderate resource acquisition ability (high LA). This group consisted of species such as Tamarix chinensis, Kalidium foliatum, Ephedra equisetina, and Calligonum mongolicum. The conservative functional group adopted a slow-growth strategy, characterized by high LN:LP. This group included species such as Salsola arbuscula, Reaumuria songonica, Halostachys caspica, and Halocnemum strobilaceum.

3.3. Trait Variation Among Plant Functional Groups in Farming–Pastoral Zone

One-way analysis of variance revealed significant differences in plant functional traits among the three PFGs (Figure 4). Specifically, the acquisitive functional group exhibited significantly higher values for SLA, LP, LN, and LC:LN compared to the other two functional groups (p < 0.05). The canopy width of the medium functional group was significantly greater than that of the acquisitive functional group (p < 0.05), but no significant difference was observed when compared to the conservative functional group (p > 0.05). Plant height was significantly higher in the medium functional group than in the other two functional groups (p < 0.05). The conservative functional group had the lowest LA and LC content (p < 0.05). Within each functional group, considerable interspecific variation in traits was also observed. For example, Halocnemum strobilaceum exhibited high variability in LA, SLA, and LN:LP; Tamarix chinensis showed significant variation in canopy width and height; and Halostachys caspica displayed notable differences in SLA.

3.4. Correlation of Plant Functional Traits Within Each Functional Group in Farming–Pastoral Zone

Based on the trait network analysis of nine plant functional traits, we quantified the trait trade-offs within each functional group (Figure 5). Among the three functional groups, the conservative functional group exhibited the highest edge density and modularity. In the acquisitive functional group, LC, canopy width, and SLA were identified as the top three central traits. Specifically, LC and SLA were negatively correlated with canopy width, indicating that acquisitive species prioritize resource acquisition at the expense of morphological construction. For the medium functional group, height, LC:LN, and LN were the top three central traits. Specifically, height was positively correlated with LC:LN but negatively correlated with LN. This suggests that medium plants allocate more resources to C storage and the synthesis of structural substances to enhance morphological construction, while sacrificing growth rate. In the conservative functional group, the LN:LP, LN, and LP were the top three central traits. Specifically, LN:LP was negatively correlated with both LN and LP. Combined with the results from Figure 3 and Figure 5, we reaffirm that the “slow-growth” strategy of conservative plants is driven by nutrient limitation.

3.5. Environmental Driven Niche Separation of Plant Functional Groups in Farming–Pastoral Zone

The first (CCA1) and second (CCA2) axes of the canonical correspondence analysis explained 69.09% and 21.84% of the total variation in plant functional traits, respectively, with the combined explanatory power of these two axes reaching 90.93% (Figure 6a). The spatial distribution ranges of the three functional groups were ordered as conservative > acquisitive > medium, indicating that the conservative functional group exhibited the broadest environmental adaptability. Environmental factors, including SAN, SAP, SOC, STN, and STP, significantly influenced the spatial distribution of desert plants, with their effects decreasing in the order listed (Figure 6b; p < 0.05).

4. Discussion

4.1. Differences in Plant Functional Traits of Desert Plant Functional Groups in Farming–Pastoral Zone

Nitrogen and P are essential nutrients that form the structural and metabolic basis of plant cells, playing a critical role in their growth and development [39]. Plants in the acquisitive functional group exhibited high leaf N and P contents, indicating an active resource acquisition strategy and rapid photosynthesis to sustain high growth rates. These plants occupy the “fast-return” end of the plant economic spectrum [40,41] and are characterized as “fast investment-return” species. Such species typically maintain a high SLA to enhance the exchange of water and carbon dioxide on the leaf surface, thereby increasing photosynthetic efficiency [7]. Leaf N:P reflects the nutrient restriction of plants: with N:P < 14, plants are limited by N; with N:P > 16, plants are limited by P; with 14 < N:P < 16, plants are limited by both N and P [42]. In this study, the N:P ratios of the acquisitive, medium, and conservative functional groups were 1.25, 3.19, and 4.10, respectively, indicating that the growth of desert plants in extreme arid areas were limited by N. The growth rate hypothesis holds that with the increase in growth rate, N:P and C:P in plants tend to decrease, while P content tends to increase [43]. In this study, the conservative functional group has the highest leaf N:P, a relatively weak morphological construction, and the lowest resource acquisition ability, belonging to the “slow investment-return” type species.
Plant morphology is a key indicator of an individual’s capacity to occupy and utilize resources. For instance, plants with large canopy widths and heights typically exhibit strong light competition and water interception abilities, often serving as the dominant or constructive species within communities [41,44]. In this study, the medium functional group played a crucial role in nutrient turnover and community stability maintenance by maintaining relatively high plant height and canopy width. In contrast, the acquisitive functional group exhibited the lowest plant height and canopy width, suggesting that under resource-limited conditions, these plants prioritize rapid resource acquisition over extensive morphological development. In harsh environments, efficient resource allocation is critical for plant survival and reproduction, inevitably leading to niche differentiation among different plant species [45,46]. However, this study revealed a partial overlap in the niches of the acquisitive, medium, and conservative functional groups. This overlap may be attributed to functional redundancy resulting from low environmental heterogeneity [47,48].
Desert plants in different arid regions employ diverse ecological strategies to cope with adverse conditions. For instance, in the Taklamakan Desert, Populus euphratica not only develops deep roots to access soil water but also features a waxy leaf cuticle that reduces water evaporation and increased leaf thickness and spongy tissue to enhance drought resistance [49]. In the desert–oasis transition zone, Bassia dasyphylla combats drought by reducing stem length and leaf area [50]. In contrast, plants in the Mediterranean arid region typically have shallow root systems that efficiently utilize surface soil moisture during brief rainy seasons [51], unlike Haloxylon ammodendron in the southern margin of the Junggar Basin, which develops deep root systems to access deep soil water during droughts while uptaking surface soil water during wet season [52]. In Australia, Eucalyptus urophylla reduces water loss through thick leaf cuticles, small leaf areas, and vertical leaf orientation to minimize direct sunlight [53]. In the Sahara Desert, annual herbaceous plants complete their growth and reproduction rapidly during short rainy seasons and then enter dormancy [54]. Comparing these multidimensional ecological adaptations of vegetation in different arid regions can enhance our understanding of global biodiversity patterns.

4.2. Correlations Among Plant Functional Traits of Desert Plant Functional Groups in Farming–Pastoral Zone

Screening central traits is crucial for elucidating trait synergies and their associated functional modules. However, previous studies have predominantly focused on identifying a single central trait [55,56], which is insufficient for practical applications. This limitation arises because the combination of plant morphological characteristics and leaf economic traits reflects the trade-offs plants make in a multi-dimensional functional space, encompassing growth, survival, competition, and reproduction [57,58]. Recent studies have shown that integrating plant morphological traits with the “leaf economic spectrum” can explain over 75% of the variation in plant functional traits [59,60]. Therefore, selecting multiple central traits is essential. As anticipated, our results revealed that for acquisitive and medium species, both morphological and nutrient-related functional traits emerged as central traits, with trade-offs observed between these two modules. This outcome is consistent with the notion that under resource-limited conditions, plants cannot simultaneously maintain all functional traits at high levels, necessitating optimal allocation strategies [61]. For instance, high wood density indicates greater investment in morphological construction, enhancing resistance to herbivory and wind damage and thereby increasing survival. However, this comes at the expense of reduced water conduction and photosynthetic efficiency [41,62]. In contrast, plants at the “fast-return” end of the leaf economic spectrum promote nutrient acquisition and turnover through high SLA and elevated N and P content, thereby enhancing ecosystem productivity [62,63]. For conservative species, the N and P nutrient modules were identified as central traits driving functional differentiation and niche separation. This pattern may be attributed to the slow growth rates of conservative plants, which necessitate maintaining high leaf N:P ratios [43]. The high leaf N:P ratio in conservative plants may also result from low soil-available P [64]. In this study, the leaf N:P ratio of conservative plants was 4.10, indicating N limitation.
Among the three functional groups, plant height exhibited a positive correlation with canopy width (p < 0.05), with the highest correlation coefficient observed in the acquisitive functional group. This pattern can be attributed to the fact that, during growth, plants compete for light, air, and nutrients. Increasing plant height enhances light capture, while horizontal crown expansion strengthens and stabilizes competitive advantages [65,66]. Additionally, LA was significantly and positively correlated with SLA across all functional groups (p < 0.05). In extreme arid environments, plants typically reduce leaf area to improve water use efficiency, and many species experience severe leaf degradation. For example, Haloxylon ammodendron utilizes its annual branches as photosynthetic organs in place of leaves [67]. Despite such adaptations, this study demonstrated a general synergistic relationship between LA and SLA at the functional group level, highlighting the persistence of certain functional trait associations even in the face of severe leaf degradation in drylands.

4.3. Effects of Environmental Driving Factors on Ecological Adaptation of Plant Functional Groups in Farming–Pastoral Zone

The ecological strategies adopted by plants reflect their long-term responses and adaptations to environmental conditions [68]. In this study, we found that desert plants belonging to the conservative functional group exhibit strong adaptability and stability in the ecologically fragile farming–pastoral zone. Previous research has reported similar findings: Jing et al. [40] observed that in temperate grasslands, reductions in precipitation led to significant declines in both species and functional diversity of the acquisitive functional group, while the conservative functional group remained largely unaffected. Similarly, Du et al. [41] demonstrated that in Northwest China, the conservative functional group of desert shrubs had a broader spatial distribution and greater resilience to environmental pressures compared to the acquisitive functional group. The medium functional group, in contrast, invests more resources in morphological construction to secure resources and complete their life cycles in extreme environments, as shown by Du et al. [41]. These findings are corroborated by the results of Wu et al. [2] and are consistent with our current study.
Soil salinization, nutrient depletion, desertification, and declining groundwater levels are critical environmental factors influencing the survival of desert plants [1,69]. For instance, nutrient-poor soils lead to slow plant growth and smaller plant sizes, thereby reducing ecosystem productivity [70]. Soil salinization inhibits plant growth and reduces species diversity through ion toxicity, decreased seed germination, and damage to chlorophyll structure [71,72]. Declining groundwater levels force plants to allocate excessive resources to root development while reducing investment in aboveground biomass, potentially leading to carbon starvation [73,74]. In this study, we found that low availability of N and P, lack of soil organic C, and total N, as well as soil salinization—rather than declining groundwater depth—were the primary environmental drivers of desert plant survival and distribution in the farming–pastoral zone. This is likely attributable to the relatively shallow average groundwater depth in the region (3.27 m), which is within a range accessible for water uptake by the majority of desert plants. Tracing the linkages between soil nutrient depletion and human activities, such as overgrazing and irrational cultivation, is essential for elucidating the ecological adaptation strategies of desert plants in the farming–pastoral zone of the northern Tarim Basin. However, given the limited spatial of sampling (a 200 km transect) and the restricted number of sampled species (13 desert plant species), the generalizability of the conclusions drawn in this study remains to be verified. This is because drylands are characterized by significant environmental heterogeneity [1,74]. For instance, oases, which are the primary distribution areas for plants and water, contrast sharply with the sparse vegetation cover in desert areas. Additionally, another study on the adaptation strategies of desert shrubs (seven species) and perennial herbaceous plants (eight species) in the upper reaches of the Tarim Basin has shown that herbaceous plants also constitute a substantial proportion of the vegetation in this region [2]. Therefore, expanding the sampling range and reducing scale effects in future research could enhance the generalizability of the findings.

5. Conclusions

Based on morphological and chemical characteristics, 13 desert plant species were classified into three distinct functional groups. These groups exhibited significantly different ecological adaptation strategies to cope with environmental pressures. Specifically, the acquisitive functional group maintained high resource acquisition and turnover through elevated SLA and LP. The medium functional group occupied limited resources by maximizing plant height and canopy width. In contrast, the conservative functional group exhibited a slow growth rate but invested heavily in morphological construction to ensure survival under harsh conditions. Furthermore, each functional group selected divergent central traits to optimize their adaptation to the environment. Nevertheless, while categorizing plants in arid regions into functional groups in understanding their ecological adaptation strategies, it is essential to recognize that, compared to those in humid regions, the vast majority of plants in arid regions still belong to conservative strategy functional groups. Low soil nutrient availability and salinization emerged as the primary environmental factors constraining plant growth and vegetation maintenance in the farming–pastoral zone. This study elucidates the divergent ecological adaptation strategies of PFGs and their underlying environmental drivers in this ecologically fragile region. These findings enhance our understanding of how desert plants adapt at the community scale and provide a scientific basis for the management and restoration of desert plant communities in the face of climate change. Examples include optimizing species selection in vegetation restoration, choosing salt-tolerant plants to ameliorate soil salinization, selecting drought-extreme plants to enhance carbon sequestration, and exploring the role of soil microbes, such as mycorrhizae, in soil improvement.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17072899/s1, Figure S1: Mean annual precipitation of the study region; Figures S2–S4: Pearson correlation matrix of plant functional traits within each plant functional group; Table S1: Statistical analysis on plant functional traits of each species; Table S2: F-test results of plant functional traits among three functional groups.

Author Contributions

Conceptualization: H.D.; methodology: H.D. and L.C.; investigation: B.H. and L.C.; formal analysis: B.H. and M.J.; writing—original draft preparation: B.H.; writing—review and editing: H.D.; funding acquisition: H.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (32460352); the Corps Guided Science and Technology Program Project (2023ZD051); and the Shihezi University High level Talent Research Launch Project (RCZK202365).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liu, H.; Liu, Y.; Chen, Y.; Fan, M.; Chen, Y.; Gang, C.; You, Y.; Wang, Z. Dynamics of global dryland vegetation were more sensitive to soil moisture: Evidence from multiple vegetation indices. Agric. For. Meteorol. 2023, 331, 109327. [Google Scholar] [CrossRef]
  2. Wu, X.; Li, Z.; Gong, L.; Li, R.; Zhang, X.; Zheng, Z. Ecological adaptation strategies of plant functional groups in the upper reaches of the Tarim River based on leaf functional traits. Environ. Exp. Bot. 2023, 215, 105490. [Google Scholar] [CrossRef]
  3. Wright, I.J.; Reich, P.B.; Westoby, M.; Ackerly, D.D.; Baruch, Z.; Bongers, F.; Cavender-Bares, J.; Chapin, T.; Cornelissen, J.H.C.; Diemer, M.; et al. The worldwide leaf economics spectrum. Nature 2004, 428, 821–827. [Google Scholar] [PubMed]
  4. Zhang, P.; Ding, J.; Wang, Q.; McDowell, N.G.; Kong, D.; Tong, Y.; Yin, H. Contrasting coordination of non-structural carbohydrates with leaf and root economic strategies of alpine coniferous forests. New Phytol. 2024, 243, 580–590. [Google Scholar]
  5. Diaz, S.; Kattge, J.; Cornelissen, J.H.C.; Wright, I.J.; Lavorel, S.; Dray, S.; Reu, B.; Kleyer, M.; Wirth, C.; Prentice, I.C.; et al. The global spectrum of plant form and function. Nature 2016, 529, 167–171. [Google Scholar]
  6. Han, X.; Xu, Y.; Huang, J.; Zang, R. Species diversity regulates ecological strategy spectra of forest vegetation across different climatic zones. Front. Plant Sci. 2022, 13, 807369. [Google Scholar]
  7. Liu, X.; Yi, S.; Wu, P.; Wang, N.; Li, Q. Why are widely distributed species widely distributed? understanding from a quantified investment acquisition strategy. Ecol. Evol. 2024, 14, e70581. [Google Scholar]
  8. Tapolczai, K.; Bouchez, A.; Stenger-Kovacs, C.; Padisak, J.; Rimet, F. Trait-based ecological classifications for benthic algae: Review and perspectives. Hydrobiologia 2016, 776, 1–17. [Google Scholar] [CrossRef]
  9. Anderegg, L.D.L. Why can’t we predict traits from the environment? New Phytol. 2023, 237, 1998–2004. [Google Scholar]
  10. Gross, N.; Le Bagousse-Pinguet, Y.; Liancourt, P.; Berdugo, M.; Gotelli, N.J.; Maestre, F.T. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 2017, 1, 132. [Google Scholar] [CrossRef]
  11. Bruelheide, H.; Dengler, J.; Purschke, O.; Lenoir, J.; Jimenez-Alfaro, B.; Hennekens, S.M.; Botta-Dukat, Z.; Chytry, M.; Field, R.; Jansen, F.; et al. Global trait-environment relationships of plant communities. Nat. Ecol. Evol. 2018, 2, 1906–1917. [Google Scholar] [PubMed]
  12. Kraft, N.J.B.; Godoy, O.; Levine, J.M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl. Acad. Sci. USA 2015, 112, 797–802. [Google Scholar] [PubMed]
  13. Agudelo, C.M.; Benavides, A.M.; Taylor, T.; Feeley, K.J.; Duque, A. Functional composition of epiphyte communities in the Colombian Andes. Ecology 2019, 100, e02858. [Google Scholar]
  14. Silva, M.d.S.; Apgaua, D.M.G.; Silva, C.C.S.; da Silva, L.B.; Tng, D.Y.P. Expanding the wood anatomy economics spectrum: The correlates of vessel element lengths and pit apertures sizes in tropical forest trees. Plant Ecol. Divers. 2021, 14, 279–291. [Google Scholar]
  15. Pohl, M.; Stroude, R.; Buttler, A.; Rixen, C. Functional traits and root morphology of alpine plants. Ann. Bot. 2011, 108, 537–545. [Google Scholar]
  16. Ling-Ling, S.; Qing, T.; Guang, L.; Zong-Xing, L.; Meng-Qing, L.; Bin, X. Response of soil properties to C, N, and P stoichiometry of plants in Qilian Mountains, China. Ecol. Indic. 2023, 150, 110245. [Google Scholar]
  17. Niklas, K.J.; Cobb, E.D. N, P, and C stoichiometry of Eranthis hyemalis (Ranunculaceae) and the allometry of plant growth. Am. J. Bot. 2005, 92, 1256–1263. [Google Scholar]
  18. Wang, Y.; Yang, D.; Vargas, G.G.; Hao, G.; Powers, J.S.; Ke, Y.; Wang, Q.; Zhang, Y.; Zhang, J. Leaf habit differentiation explains trait tradeoffs across savanna woody plants. For. Ecosyst. 2024, 11, 100190. [Google Scholar] [CrossRef]
  19. Valencia, E.; Maestre, F.T.; Le Bagousse-Pinguet, Y.; Luis Quero, J.; Tamme, R.; Boerger, L.; Garcia-Gomez, M.; Gross, N. Functional diversity enhances the resistance of ecosystem multifunctionality to aridity in Mediterranean drylands. New Phytol. 2015, 206, 660–671. [Google Scholar]
  20. Burridge, J.D.; Grondin, A.; Vadez, V. Optimizing crop water use for drought and climate change adaptation requires a multi-scale approach. Front. Plant Sci. 2022, 13, 824720. [Google Scholar]
  21. Zhao, W.; Ji, X.; Du, Z.; Jin, B.; Zhao, L.; Chen, R.; Zhou, H. Variation and coordination among the plant functional traits of three coexisting shrub species in arid conditions. Environ. Exp. Bot. 2024, 226, 105925. [Google Scholar]
  22. Abreha, K.B.; Enyew, M.; Carlsson, A.S.; Vetukuri, R.R.; Feyissa, T.; Motlhaodi, T.; Ng’uni, D.; Geleta, M. Sorghum in dryland: Morphological, physiological, and molecular responses of sorghum under drought stress. Planta 2022, 255, 20. [Google Scholar]
  23. Song, H.; Cao, Y.; Zhao, L.; Zhang, J.; Li, S. Review: WRKY transcription factors: Understanding the functional divergence. Plant Sci. 2023, 334, 111770. [Google Scholar] [PubMed]
  24. Wen, Z.; Li, H.; Shen, Q.; Tang, X.; Xiong, C.; Li, H.; Pang, J.; Ryan, M.H.; Lambers, H.; Shen, J. Tradeoffs among root morphology, exudation and mycorrhizal symbioses for phosphorus-acquisition strategies of 16 crop species. New Phytol. 2019, 223, 882–895. [Google Scholar]
  25. Li, W.; Knops, J.M.H.; Brassil, C.E.; Lu, J.; Qi, W.; Li, J.; Liu, M.; Chang, S.; Li, W. Functional group dominance and not productivity drives species richness. Plant Ecol. Divers. 2016, 9, 141–150. [Google Scholar]
  26. Hooper, D.U.; Vitousek, P.M. The Effects of Plant Composition and Diversity on Ecosystem Processes. Science 1997, 277, 1302–1305. [Google Scholar]
  27. Semenova, G.V.; van der Maarel, E. Plant functional types—A strategic perspective. J. Veg. Sci. 2000, 11, 917–922. [Google Scholar]
  28. Zhang, S.; Zang, R.; Sheil, D. Rare and common species contribute disproportionately to the functional variation within tropical forests. J. Environ. Manag. 2022, 304, 114332. [Google Scholar] [CrossRef]
  29. Cruz, M.; Lasso, E. Insights into the functional ecology of paramo plants in Colombia. Biotropica 2021, 53, 1415–1431. [Google Scholar]
  30. Alvarez-Yepiz, J.C.; Burquez, A.; Martinez-Yrizar, A.; Teece, M.; Yepez, E.A.; Dovciak, M. Resource partitioning by evergreen and deciduous species in a tropical dry forest. Oecologia 2017, 183, 607–618. [Google Scholar]
  31. Ning, Z.; Li, Y.; Zhao, X.; Han, D.; Zhan, J. Comparison of leaf and fine root traits between annuals and perennials, implicating the mechanism of species changes in desertified grasslands. Front. Plant Sci. 2022, 12, 778547. [Google Scholar] [CrossRef] [PubMed]
  32. da Silva E Teodoro, E.D.M.; da Silva, A.P.A.; da Silva Brito, N.D.; Nogueira Rodal, M.J.; Shinozaki-Mendes, R.A.; Alves de Lima, A.L. Functional traits determine the vegetative phenology of woody species in riparian forest in semi-arid Brazil. Plant Ecol. 2022, 223, 1137–1153. [Google Scholar] [CrossRef]
  33. Duan, Z.; Chen, C.; Ni, C.; Xiong, J.; Wang, Z.; Cai, J.; Tan, W. How different is the remediation effect of biochar for cadmium contaminated soil in various cropping systems? A global meta-analysis. J. Hazard. Mater. 2023, 448, 130939. [Google Scholar] [CrossRef] [PubMed]
  34. Demo, A.H.; Asefa Bogale, G. Enhancing crop yield and conserving soil moisture through mulching practices in dryland agriculture. Front. Agron. 2024, 6, 1361697. [Google Scholar] [CrossRef]
  35. Zhao, J.; Gong, L.; Chen, X. Relationship between ecological stoichiometry and plant community diversity in the upper reaches of Tarim River, northwestern China. J. Arid Land 2020, 12, 227–238. [Google Scholar] [CrossRef]
  36. Yue, H.; Zhao, L.; Yang, D.; Zhang, M.; Wu, J.; Zhao, Z.; Xing, X.; Zhang, L.; Qin, Y.; Guo, F.; et al. Comparative analysis of the endophytic bacterial diversity of Populus euphratica Olive. in environments of different salinity intensities. Microbiol. Spectr. 2022, 10, e00500-22. [Google Scholar] [CrossRef]
  37. Bao, S. Soil Agrochemical Analysis, 3rd ed.; China Agriculture Press: Beijing, China, 2000. [Google Scholar]
  38. Batjes, N.H. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma 2016, 269, 61–68. [Google Scholar] [CrossRef]
  39. Luo, M.; Moorhead, D.L.; Ochoa-Hueso, R.; Mueller, C.W.; Ying, S.C.; Chen, J. Nitrogen loading enhances phosphorus limitation in terrestrial ecosystems with implications for soil carbon cycling. Funct. Ecol. 2022, 36, 2845–2858. [Google Scholar] [CrossRef]
  40. Jing, H.; Xiong, X.; Jiang, F.; Pu, X.; Ma, W.; Li, D.; Liu, Z.; Wang, Z. Climate change filtered out resource-acquisitive plants in a temperate grassland in Inner Mongolia, China. Sci. China Life Sci. 2024, 67, 403–413. [Google Scholar] [CrossRef]
  41. Du, L.; Tian, S.; Zhao, N.; Zhang, B.; Mu, X.; Tang, L.; Zheng, X.; Li, Y. Intraspecific trait variation regulates biodiversity and community productivity of shrublands in drylands. Forests 2024, 15, 911. [Google Scholar] [CrossRef]
  42. Sabine, G. N:P ratios in terrestrial plants: Variation and functional significance. New Phytol. 2004, 164, 243–266. [Google Scholar]
  43. Niklas, K.J.; Owens, T.; Reich, P.B.; Cobb, E.D. Nitrogen/phosphorus leaf stoichiometry and the scaling of plant growth. Ecol. Lett. 2005, 8, 636–642. [Google Scholar] [CrossRef]
  44. Zhao, G.; Chen, F.; Yuan, C.; Yang, J.; Shen, Y.; Zhang, S.; Yang, J.; Ayemele, A.G.; Li, X.; Xu, J. Response strategies of woody seedlings to shading and watering over time after topsoil translocation in dry-hot karst region of China. For. Ecol. Manag. 2022, 519, 120319. [Google Scholar]
  45. Yu, H.; Chen, Y.; Zhou, G.; Xu, Z. Coordination of leaf functional traits under climatic warming in an arid ecosystem. BMC Plant Biol. 2022, 22, 439. [Google Scholar]
  46. Prieto, I.; Leon-Sanchez, L.; Nicolas, E.; Nortes, P.; Querejeta, J.I. Warming reduces both photosynthetic nutrient use efficiency and water use efficiency in Mediterranean shrubsWarming reduces nutrient use efficiency. Environ. Exp. Bot. 2023, 210, 105331. [Google Scholar] [CrossRef]
  47. Du, L.; Tian, S.; Sun, J.; Zhang, B.; Mu, X.-H.; Tang, L.; Zheng, X.; Li, Y. Ecosystem multifunctionality, maximum height, and biodiversity of shrub communities affected by precipitation fluctuations in Northwest China. Front. Plant Sci. 2023, 14, 1259858. [Google Scholar]
  48. Le Bagousse-Pinguet, Y.; Soliveres, S.; Gross, N.; Torices, R.; Berdugo, M.; Maestre, F.T. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl. Acad. Sci. USA 2019, 116, 8419–8424. [Google Scholar] [CrossRef]
  49. Yao, S.; Wang, J.; Huang, W.; Jiao, P.; Peng, C.; Li, Y.; Song, S. Adaptation Strategies of Populus euphratica to Arid Environments Based on Leaf Trait Network Analysis in the Mainstream of the Tarim River. Forests 2024, 15, 437. [Google Scholar] [CrossRef]
  50. Guo, W.; Wang, G.; Gou, Q.; Liu, J. Module growth and biomass allocation of three typical Chenopodiaceae annuals in a typical desert-oasis ecotone of the Hexi Corridor in Gansu Province, China. Acta Prataculturae Sin. 2022, 31, 25–38. [Google Scholar]
  51. Balao, F.; Medrano, M.; Bazaga, P.; Paun, O.; Alonso, C. Long-term methylome changes after experimental seed demethylation and their interaction with recurrent water stress in Erodium cicutarium (Geraniaceae). Plant Biol. 2024, 26, 1199–1212. [Google Scholar]
  52. Xiao, J.; Eziz, A.; Zhang, H.; Wang, Z.; Tang, Z.; Fang, J. Responses of four dominant dryland plant species to climate change in the Junggar Basin, northwest China. Ecol. Evol. 2019, 9, 13596–13607. [Google Scholar] [PubMed]
  53. Anderegg, L.D.L.; Loy, X.; Markham, I.P.; Elmer, C.M.; Hovenden, M.J.; HilleRisLambers, J.; Mayfield, M.M. Aridity drives coordinated trait shifts but not decreased trait variance across the geographic range of eight Australian trees. New Phytol. 2021, 229, 1375–1387. [Google Scholar] [PubMed]
  54. Javier Herrera-Sanchez, F.; Lopez, O.; Rodriguez-Siles, J.; Angel Diaz-Portero, M.; Arredondo, A.; Manuel Saez, J.; Alvarez, B.; Cancio, I.; de Lucas, J.; Perez, J.; et al. Feeding Ecology of the Cuvier’s Gazelle (Gazella cuvieri, Ogilby, 1841) in the Sahara Desert. Animals 2023, 13, 567. [Google Scholar] [CrossRef] [PubMed]
  55. Mu, X.; Du, L.; Zheng, X.; Tang, L.; Ma, J.; Li, Y. Population regeneration of two Haloxylon species in central Asian deserts as affected by groundwater depth. Environ. Exp. Bot. 2024, 225, 105834. [Google Scholar]
  56. Qian, H.; Tong, J.; Li, S.; Xie, J.; Wang, Z.; Li, Y.; Lu, S. Plant trait networks reveal the ecological strategies of Arabidopsis thaliana along ontogeny. Ecosphere 2025, 16, e70180. [Google Scholar]
  57. Kramer-Walter, K.R.; Bellingham, P.J.; Millar, T.R.; Smissen, R.D.; Richardson, S.J.; Laughlin, D.C. Root traits are multidimensional: Specific root length is independent from root tissue density and the plant economic spectrum. J. Ecol. 2016, 104, 1299–1310. [Google Scholar]
  58. Reich, P.B. The world-wide ‘fast-slow’ plant economics spectrum: A traits manifesto. J. Ecol. 2014, 102, 275–301. [Google Scholar]
  59. Siefert, A.; Violle, C.; Chalmandrier, L.; Albert, C.H.; Taudiere, A.; Fajardo, A.; Aarssen, L.W.; Baraloto, C.; Carlucci, M.B.; Cianciaruso, M.V.; et al. A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. Ecol. Lett. 2015, 18, 1406–1419. [Google Scholar]
  60. Violle, C.; Enquist, B.J.; McGill, B.J.; Jiang, L.; Albert, C.H.; Hulshof, C.; Jung, V.; Messier, J. The return of the variance: Intraspecific variability in community ecology. Trends Ecol. Evol. 2012, 27, 244–252. [Google Scholar]
  61. Poorter, H.; Lambers, H.; Evans, J.R. Trait correlation networks: A whole-plant perspective on the recently criticized leaf economic spectrum. New Phytol. 2014, 201, 378–382. [Google Scholar]
  62. Zheng, Z.; Zhang, Y.; Dong, F.; Chen, L.; Li, Z. Linkage between leaf anatomical structure and key leaf economic traits across co-existing species in temperate forests. Plant Soil 2024. [Google Scholar] [CrossRef]
  63. Oram, N.J.; De Deyn, G.B.; Bodelier, P.L.E.; Cornelissen, J.H.C.; van Groenigen, J.W.; Abalos, D. Plant community flood resilience in intensively managed grasslands and the role of the plant economic spectrum. J. Appl. Ecol. 2020, 57, 1524–1534. [Google Scholar] [CrossRef]
  64. Liu, Z.; Shi, X.; Yuan, Z.; Lock, T.R.; Kallenbach, R.L. Plant nutritional adaptations under differing resource supply for a dryland grass Leymus chinensis. J. Arid Environ. 2020, 172, 104037. [Google Scholar] [CrossRef]
  65. Mao, L.; Dong, Y.; Xing, B.; Chen, Y.; Dennett, J.; Bater, C.; Stadt, J.J.; Nielsen, S.E. Maximum canopy height is associated with community phylogenetic structure in boreal forests. J. Plant Ecol. 2023, 16, rtac104. [Google Scholar] [CrossRef]
  66. Moudry, V.; Gabor, L.; Marselis, S.; Pracna, P.; Bartak, V.; Prosek, J.; Navratilova, B.; Novotny, J.; Potuckova, M.; Gdulova, K.; et al. Comparison of three global canopy height maps and their applicability to biodiversity modeling: Accuracy issues revealed. Ecosphere 2024, 15, e70026. [Google Scholar] [CrossRef]
  67. Feng, X.; Liu, R.; Li, C.; Li, M.; Wang, Y.; Li, Y. Multi-level physiological and morphological adjustment of Haloxylon ammodendron related to groundwater drawdown in a desert ecosystem. Agric. For. Meteorol. 2022, 324, 109096. [Google Scholar] [CrossRef]
  68. Akram, M.A.; Zhang, Y.; Wang, X.; Shrestha, N.; Malik, K.; Khan, I.; Ma, W.; Sun, Y.; Li, F.; Ran, J.; et al. Phylogenetic independence in the variations in leaf functional traits among different plant life forms in an arid environment. J. Plant Physiol. 2022, 272, 153671. [Google Scholar] [CrossRef]
  69. Wang, L.; Jiao, W.; MacBean, N.; Rulli, M.C.; Manzoni, S.; Vico, G.; D’Odorico, P. Dryland productivity under a changing climate. Nat. Clim. Change 2022, 12, 981–994. [Google Scholar]
  70. Eldridge, D.J.; Maestre, F.T.; Koen, T.B.; Delgado-Baquerizo, M. Australian dryland soils are acidic and nutrient-depleted, and have unique microbial communities compared with other drylands. J. Biogeogr. 2018, 45, 2803–2814. [Google Scholar] [CrossRef]
  71. Litalien, A.; Zeeb, B. Curing the earth: A review of anthropogenic soil salinization and plant-based strategies for sustainable mitigation. Sci. Total Environ. 2020, 698, 134235. [Google Scholar] [CrossRef]
  72. Ondrasek, G.; Rathod, S.; Manohara, K.K.; Gireesh, C.; Anantha, M.S.; Sakhare, A.S.; Parmar, B.; Yadav, B.K.; Bandumula, N.; Raihan, F.; et al. Salt stress in plants and mitigation approaches. Plants 2022, 11, 717. [Google Scholar] [CrossRef] [PubMed]
  73. Bai, Y.; Liu, Y.; Kueppers, L.M.; Li, E.; Zhang, C.; Yu, K.; Yang, X.; Li, X. Hydraulic sensitivity and stomatal regulation of two desert riparian species. J. Geophys. Res. Biogeosci. 2022, 127, e2022JG006971. [Google Scholar] [CrossRef]
  74. Li, M.; Petrie, M.D.; Chen, H.; Zeng, F.; Ahmed, Z.; Sun, X. Effects of groundwater and seasonal streamflow on the symbiotic nitrogen fixation of deep-rooted legumes in a dryland floodplain. Geoderma 2023, 434, 116490. [Google Scholar] [CrossRef]
Sustainability 17 02899 g001
Figure 1. Spatial distribution of sampling plots. Each point represents an individual sampling plot. The map was edited based on standard national boundary (GS(2023)2767), and the boundary was not modified.
Figure 1. Spatial distribution of sampling plots. Each point represents an individual sampling plot. The map was edited based on standard national boundary (GS(2023)2767), and the boundary was not modified.
Sustainability 17 02899 g001
Sustainability 17 02899 g002
Figure 2. The cluster analysis divided 13 desert plants into three PFGs using 9 plant functional traits. All the plants were classified into PFG1 (acquisitive functional group), PFG2 (medium functional group), and PFG3 (conservative functional group). The cluster analysis method was maximum linkage clustering, and the distance coefficient was the Canberra distance.
Figure 2. The cluster analysis divided 13 desert plants into three PFGs using 9 plant functional traits. All the plants were classified into PFG1 (acquisitive functional group), PFG2 (medium functional group), and PFG3 (conservative functional group). The cluster analysis method was maximum linkage clustering, and the distance coefficient was the Canberra distance.
Sustainability 17 02899 g002
Sustainability 17 02899 g003
Figure 3. PCA loading diagram based on mean values of species traits. Ellipse represents 95% confidence interval of each PFG.
Figure 3. PCA loading diagram based on mean values of species traits. Ellipse represents 95% confidence interval of each PFG.
Sustainability 17 02899 g003
Sustainability 17 02899 g004
Figure 4. Trait differences of the three PFGs. Brown, green, and purple boxes represent acquisitive, medium, and conservative PFGs, respectively. The error bar on the box indicates a 95% confidence interval. In each figure, different lowercase letters indicate that the trait is significantly different among PFGs (p < 0.05). The results of the F test are shown in Table S2.
Figure 4. Trait differences of the three PFGs. Brown, green, and purple boxes represent acquisitive, medium, and conservative PFGs, respectively. The error bar on the box indicates a 95% confidence interval. In each figure, different lowercase letters indicate that the trait is significantly different among PFGs (p < 0.05). The results of the F test are shown in Table S2.
Sustainability 17 02899 g004
Sustainability 17 02899 g005
Figure 5. Trait correlation networks of acquisitive (a), medium (b), and conservative (c) functional groups. Traits identified by green circles represent the top three central traits, with transparency of the color indicates relative importance. Gray and red lines indicate positive and negative relationships, respectively (p < 0.05). The Pearson correlations of plant functional traits within each functional group are shown as Figures S2–S4.
Figure 5. Trait correlation networks of acquisitive (a), medium (b), and conservative (c) functional groups. Traits identified by green circles represent the top three central traits, with transparency of the color indicates relative importance. Gray and red lines indicate positive and negative relationships, respectively (p < 0.05). The Pearson correlations of plant functional traits within each functional group are shown as Figures S2–S4.
Sustainability 17 02899 g005
Sustainability 17 02899 g006
Figure 6. Canonical correlation analysis (CCA) of the three functional groups (a) and their affecting environmental factors (b). In the left panel, different colors and areas correspond to the distribution ranges of functional groups. Significance levels were expressed as * p < 0.05.
Figure 6. Canonical correlation analysis (CCA) of the three functional groups (a) and their affecting environmental factors (b). In the left panel, different colors and areas correspond to the distribution ranges of functional groups. Significance levels were expressed as * p < 0.05.
Sustainability 17 02899 g006
Table 1. Basic information of 13 desert plants.
Table 1. Basic information of 13 desert plants.
SpeciesClassOrderFamilyLife-Form
Tamarix chinensisMagnoliopsidaCaryophyllalesTamaricaceaeShrub
Halostachys caspicaMagnoliopsidaCaryophyllalesAmaranthaceaeShrub
Halocnemum strobilaceumMagnoliopsidaCaryophyllalesAmaranthaceaeShrub
Lycium ruthenicumMagnoliopsidaSolanalesSolanaceaeShrub
Alhagi camelorumMagnoliopsidaFabalesFabaceaeShrub
Ephedra equisetinaPinopsidaEphedralesEphedraceaeShrub
Calligonum mongolicumMagnoliopsidaCaryophyllalesPolygonaceaeShrub
Salsola arbusculaMagnoliopsidaCentrospermaeAmaranthaceaeShrub
Reaumuria songonicaMagnoliopsidaParietalesTamaricaceaeShrub
Kalidium foliatumMagnoliopsidaCaryophyllalesAmaranthaceaeShrub
Sophora alopecuroidesMagnoliopsidaFabalesFabaceaeHerbaceous perennial
Karelinia caspiaMagnoliopsidaAsteralesAsteraceaeHerbaceous perennial
Artemisia desertorumMagnoliopsidaAsteralesAsteraceaeHerbaceous perennial
Table 2. Statistical analysis on plant functional traits.
Table 2. Statistical analysis on plant functional traits.
Leaf Functional TraitsMean ± SDMaxMinMedianCoefficient of Variation
LA (cm2)2.02 ± 3.0412.030.041.15150.66
SLA (cm2·g−1)54.77 ± 27.36118.0310.1051.5949.95
LC (mg·g−1)36.09 ± 8.8249.4621.4237.0424.44
LN (mg·g−1)2.05 ± 0.593.480.971.9829.25
LP (mg·g−1)1.09 ± 0.592.680.490.8954.04
LC:LN23.05 ± 12.0658.359.3723.6552.32
LN:LP5.19 ± 2.7911.931.085.0253.79
Height (m)1.16 ± 1.526.260.370.73130.94
Canopy (m)1.69 ± 2.048.070.240.95121
Note: The statistical analysis of the plant functional traits of each species is attached in Supplementary Materials as Table S1.
Table 3. PCA loading matrix for plant functional traits of 13 desert plants.
Table 3. PCA loading matrix for plant functional traits of 13 desert plants.
PC1PC2
eigenvalue3.712.19
portion of variance41.2424.29
LA−0.17−0.75
SLA0.670.17
LC0.59−0.64
LN0.75−0.01
LP0.760.14
LC:LN0.79−0.29
LN:LP−0.900.20
Height−0.18−0.83
Canopy−0.51−0.58
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Han, B.; Cui, L.; Jin, M.; Dong, H. Ecological Adaptation Strategies of Desert Plants in the Farming–Pastoral Zone of Northern Tarim Basin. Sustainability 2025, 17, 2899. https://doi.org/10.3390/su17072899

AMA Style

Han B, Cui L, Jin M, Dong H. Ecological Adaptation Strategies of Desert Plants in the Farming–Pastoral Zone of Northern Tarim Basin. Sustainability. 2025; 17(7):2899. https://doi.org/10.3390/su17072899

Chicago/Turabian Style

Han, Baohua, Liyang Cui, Mengting Jin, and Hegan Dong. 2025. "Ecological Adaptation Strategies of Desert Plants in the Farming–Pastoral Zone of Northern Tarim Basin" Sustainability 17, no. 7: 2899. https://doi.org/10.3390/su17072899

APA Style

Han, B., Cui, L., Jin, M., & Dong, H. (2025). Ecological Adaptation Strategies of Desert Plants in the Farming–Pastoral Zone of Northern Tarim Basin. Sustainability, 17(7), 2899. https://doi.org/10.3390/su17072899

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