Metabolic characteristics of the annual ephemeral plant Tetracme quadricornis in heterogeneous habitats

Metabolic characteristics of the annual ephemeral plant Tetracme quadricornis in heterogeneous habitats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabolic characteristics of the annual ephemeral plant Tetracme quadricornis in heterogeneous habitats Zhengwei Heng, Lingwei Zhang, Huiliang Liu, Zhengwang Gao, Yu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7625560/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background and Aims The complex metabolic regulation mechanisms of plants enable them to maintain an orderly metabolic process despite varying external conditions, thereby supporting normal growth and development. Yet, the response of metabolites in annual ephemeral plants to changing desert environments is still poorly understood. Methods We utilized plant metabolomics in tandem with multivariate statistical analysis to delve into the metabolic adaptation strategies exhibited by Tetracme quadricornis, a quintessential annual ephemeral plant, within three distinct desert habitats: sandy desert, gravel desert, and saline-alkali desert. Results Analysis of metabolic profiles revealed distinct habitat-specific patterns in Tetracme quadricornis, with terpenoids demonstrating the highest relative abundance in sandy desert habitats, while fatty acids predominated in both gravel desert and saline-alkali desert environments. The diversity of metabolites in both roots and flowers varied significantly across different habitats. Moreover, even within the same habitat, metabolite profiles exhibited notable organ-specific variability. The highest metabolite diversity was observed in stems in sandy desert habitats, roots in gravel desert, and flowers in saline-alkali desert. Metabolite diversity in Tetracme quadricornis was significantly positively correlated with several key ecological factors, including soil electrical conductivity, altitude, and longitude. Conclusions The annual ephemeral plant Tetracme quadricornis employs organ-specific metabolic plasticity to adapt to heterogeneous desert environments. This adaptive strategy is driven by environmental factors and manifested through dynamic nutrient reallocation and a growth–defense trade-off, ultimately enhancing its ecological fitness in arid ecosystems. Habitat heterogeneity Annual ephemeral plants Tetracme quadricornis metabolome Ecological adaptation strategies Figures Figure 1 Figure 2 Figure 3 1 Introduction Desert ecosystems are characterized by extreme aridity (Zang et al. 2020 ), high temperatures (Alsharif et al. 2020 ), poor soil quality (Gutierrez and Whitford 1987 ), salinity and alkalinity stress (Zhang et al. 2019 ), and intense radiation, rendering them among the most demanding terrestrial habitats on Earth. Plants in desert environments encounter numerous adversities, including prolonged droughts that lead to cellular dehydration and osmotic imbalance, deficiencies in essential nutrients such as carbon, nitrogen, and phosphorus in the soil, which restrict plant growth, and excessive accumulation of ions like Na⁺ and Cl⁻, which disrupts ion homeostasis. These factors pose significant challenges to plant physiology, growth, survival, and reproduction (Zhang et al. 2025 ; Gong et al. 2020 ). Despite the harsh living conditions in desert ecosystems, hundreds of thousands of plant species thrive in these environments worldwide. Long-term exposure to adverse environmental conditions triggers a series of physiological, biochemical, and molecular responses in plants, enabling them to resist and tolerate stress (Rossnerova et al. 2020 ). When the growth environment of a plant—such as temperature, water availability, salinity, and nutrient levels—changes, its metabolic balance is disrupted. In response, plants adjust their metabolism to meet physiological requirements, achieve a new equilibrium, and adapt to the complex and fluctuating external environment (Plaxton and Tran 2011 ; Khan et al. 2019 ). This process results in the biosynthesis of a diverse range of metabolic compounds, the abundance of which demonstrates a direct and significant correlation with the plant’s adaptive resistance mechanisms(Cao et al. 2024 ). Plant metabolites not only regulate growth rhythms (Kamboj et al. 2024 ) and enhance stress tolerance (Kumar et al. 2022 ), but they also reflect the plants' responses to their living environments and characterize their ecological strategies (Díaz and Cabido 2001). Furthermore, plant metabolomics is frequently employed to monitor growth and development under biotic stresses (such as microorganisms, insects, and herbivores) (Kumaraswamy et al. 2011 ) and abiotic stresses (such as temperature fluctuations, drought, and ultraviolet light) (Wang et al. 2020 ). It is also used to identify and breed resistant varieties, as well as to discover bioactive metabolites (Rinschen et al. 2019 ). By analyzing the metabolites of plants in different habitats and exploring the impact of the environment on plant metabolites, we can discover the regional distribution patterns of plants and explain the formation of plants' special ecological adaptation strategies. Plant metabolomics, as a means of analyzing plant metabolites, has the technical advantages of high throughput, no bias, and comprehensive analysis (Yuan et al. 2025 ). It can identify all metabolites in plants and provide technical support for fully revealing the metabolic mechanisms under stress conditions. Annual ephemeral plants employ drought escape strategies to complete their life cycle within two to three months, thereby avoiding the high temperatures of summer by transitioning into a seed form. This unique adaptation enables them to endure harsh environments over extended periods (Qiu et al 2018 ; Xiao et al 2024 ). This group of plants has thrived in desert ecosystems for a considerable time, developing numerous growth and developmental characteristics tailored to extreme conditions. They play a crucial role in the formation of desert plant communities and vegetation succession (Lan and Zhang 2008 ). The Brassicaceae family is one of the most prevalent groups in the early spring ephemeral flora, comprising approximately 15% of all species. Tetracme quadricornis is a prominent annual ephemeral plant, exhibiting a frequency of 70% and an importance value of 24%, thus fulfilling a significant ecological role (Liu et al. 2011 ; Zhan and Liu 2012). Previous studies have demonstrated that variations in soil nutrient levels across the habitats of Tetracme quadricornis in the Junggar Desert region lead to differences in individual morphology, nutrient distribution, and photosynthetic pigments (Peng et al. 2022 ). Consequently, how does Tetracme quadricornis adapt its metabolic strategies to thrive in such harsh environments? Furthermore, how do environmental factors influence the development of these strategies? Therefore, this study took the annual ephemeral plant Tetracme quadricornis as the research object, used non-targeted metabolomics technology to conduct a comprehensive detection of Tetracme quadricornis in three desert habitats, and combined with environmental factors, in order to preliminarily reveal the metabolic homeostasis mechanism of Tetracme quadricornis in the face of environmental changes from the perspective of plant metabolomics, thereby providing an effective research strategy for analyzing the ecological adaptation of annual ephemeral plants. 2 Materials and Methods 2.1 Overview of the study area This study area is situated in the northwest of the Junggar Basin in Xinjiang. The Junggar Basin is geographically positioned between the Tianshan Mountains and the Altai Mountains, bordered to the west by the western Junggar Mountains and to the center by the Gurbantunggut Desert. Its unique geographical features have resulted in a diverse array of desert types. The region experiences a typical temperate continental desert climate characterized by hot, dry summers and long, cold winters. The average annual temperature ranges from 6 to 10°C, with average annual precipitation approximately 150 mm and average annual evaporation exceeding 2000 mm (Wang et al. 2011 ). The basin exhibits a variety of soil types, predominantly comprising aeolian sandy soil, brown calcareous soil, and desert gray calcareous soil. Additionally, cracked soil, meadow soil, and saline-alkali soil can be found in certain areas (Du et al. 2021 ). This region serves as the primary habitat for ephemeral plant species in China, which are highly sensitive to environmental changes. Among which the main ones are Tetracme quadricornis , Strigosella africana , and Erodium oxyrhinchum . 2.2 Experimental design and sample collection In this investigation, a spatially stratified sampling methodology (Stevens and Olsen 2004 ) was implemented to establish experimental plots in contiguous areas encompassing three characteristic desert types—sandy, gravelly, and saline—within the Junggar Desert ecosystem. Following the principle of ecological typicality, four large sampling quadrats (10 m × 10 m) were systematically positioned along representative environmental gradients within each desert type, yielding a total of 12 major quadrats with a minimum inter-plot separation of 20 m. Within each major quadrat, five 1 m × 1 m subquadrats were arranged according to standardized five-point sampling protocol to capture fine-scale spatial heterogeneity. Sampling quadrats were selected according to the following criteria: (1) widespread and homogeneous distribution of the target plant species; (2) sufficiently large quadrat size to incorporate a 10–20 m peripheral buffer zone; and (3) preference for flat or uniformly sloped terrain, while avoiding abrupt topographic transitions such as ridge crests, valley bases, and fragmented microtopographic features (Fang et al. 2009 ). Samples were collected during the peak flowering period (late April) of the annual ephemeral plant, Tetracme quadricornis . From each small plot, uniform-sized plants were selected, and the entire plant was harvested using the whole-plant excavation method. Plant samples from the five small plots were combined to form a replicate. A total of 40–60 plants were selected from each plot and divided into four parts: roots, stems, leaves, and flowers. The samples were washed with deionized water, wrapped in tin foil, labeled, and immediately placed in liquid nitrogen for rapid freezing before being transferred to a − 80°C environment in the laboratory for metabolomics measurements. Given that annual ephemeral plants possess shallow root systems, a soil drill was employed to randomly collect the top 20 cm of soil from each small sample plot. The soil from each large sample plot was mixed to create a composite soil sample, which was placed in a sealed bag, with gravel and residual roots removed. The sample was then sieved through a 2 mm sieve and air-dried for the determination of soil physical and chemical properties. 2.3 Sample determination Metabolomics data were extracted from samples using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Soil pH and electrical conductivity (EC) were measured using the potentiometric method, with water-to-soil ratios of 1:2.5 and 1:5, respectively. Soil water content (SWC) was determined using the oven-drying method, while soil particle size was analyzed using a laser particle size analyzer. Soil organic carbon (SOC) and total nitrogen (TN) contents were quantified using chromatography with an elemental analyzer (EA3100, Italy). Total phosphorus (TP) content was measured using the ammonium molybdate colorimetric method. A handheld GPS (eTrex H) was employed to record the altitude, longitude, and latitude of each sampling site. Mean annual precipitation (MAP) and mean annual temperature (MAT) data were sourced from the World Climate global climate database ( http://www.worldclim.org ) based on the geographic coordinates of each sampling site, with a resolution of 30 arc minutes. 2.4 Data processing and statistical analysis SPSS 26.0 software was employed to perform a one-way analysis of variance on the climate, geography, and soil physical and chemical properties across the three habitats. Duncan's test was utilized for multiple comparisons to assess the significance of differences in climate, geography, and soil properties among the various habitats, with a significance level set at 0.05. Raw UHPLC-MS/MS data were analyzed using the Global Natural Products Society (GNPS) database (Wang et al. 2016 ). Based on the GNPS output, we annotated all compounds according to the biosynthetic pathways outlined in NPClassifier (Kim et al. 2021 ). The classification of these pathways includes "amino acids and peptides," "carbohydrates," and "fatty acids," which represent primary metabolites, as well as "alkaloids," "polyketides," "shikimate-phenylpropanoid compounds," and "terpenes," which represent secondary metabolites. In the metabolite annotation process, we also incorporated the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. After normalizing the plant compounds (0–1) using the "vegan" package, we calculated the Shannon-Wiener diversity index (α diversity) of plant metabolites using the abundance matrix. The Shannon diversity index integrates the richness and evenness of phytochemicals into a single metric, assigning less weight to compounds with lower abundance in the overall diversity estimate, thereby providing more ecologically relevant insights into phytochemical complexity. Following the exclusion of factor autocorrelation, redundancy analysis (RDA) and Mantel tests were conducted to analyze the responses of the metabolite diversity characteristics of each organ of Tetracme quadricornis to environmental factors. 3 Results 3.1 Soil Physical and Chemical Properties in Different Desert Habitats According to the standards for desert landform, surface composition, and soil classification (Ditzler 2016; Wu et al. 2013 ), particles with a size of less than 0.002 mm are classified as clay, those between 0.002 mm and 0.05 mm as silt, particles ranging from 0.05 mm to 2 mm as sand, and particles exceeding 2 mm as gravel. Gravel deserts are defined as having a gravel content greater than 10%, while sand deserts are characterized by a sand content exceeding 90%. Saline-alkali deserts are typically low-lying, flat areas where the surface composition includes various salt components, primarily consisting of silt and clay. As shown in Fig. 1 (a), the sandy desert habitat was dominated by sand particles, accounting for 96.28% of the soil composition. The gravel desert habitat contained 13.63% gravel, exceeding the 10% gravel-content threshold. In contrast, the saline-alkali desert habitat was primarily characterized by silt and clay particles, with an average soil pH of 7.87, indicating alkaline conditions. Soil electrical conductivity (EC) was significantly higher in the saline desert than in the other habitats (Fig. 1 g, h), consistent with substantial accumulation of soluble salts and alkaline compounds. Sandy desert, gravel desert, and saline-alkali desert all belong to desert ecosystems, which are characterized by extreme aridity, sparse precipitation, and high evaporation. As illustrated in Fig. 1 , the elevation, annual average rainfall, and soil moisture content of the gravel desert habitat are significantly higher than those of the other habitats (Fig. 1 d, f, and i), whereas the annual average temperature is lower than that of the other habitats (Fig. 1 e). Soil nutrients and pH levels exhibit variability depending on habitat and soil type. According to the soil nutrient abundance and deficiency indices (Sun et al. 2019 ; Wang et al. 2022a ; Gao et al. 2023), SOC content below 6 g/kg is classified as very low, TN content below 0.5 g/kg is classified as severely deficient, and TP content below 0.44 g/kg is classified as very low. Figure 1 (j, k, and l) indicates that, among the three habitats examined, the SOC and TN contents in the sandy desert and saline-alkali desert habitats are extremely low. Additionally, TP content is severely deficient across all three habitats. Furthermore, the TN content in the sandy desert habitat, measuring at 0.18 g/kg, is also critically low in nutrients. The SOC, TN, and TP contents in the gravel desert habitat are significantly higher than those in the other habitats, while the soil pH and electrical conductivity in the saline-alkali desert habitat are markedly elevated compared to the other habitats. 3.2 Composition and diversity of metabolites of Tetracme quadricornis in different habitats The metabolome of various organs of Tetracme quadricornis across different habitats was examined using non-targeted metabolomics technology. A total of 53,082 peaks were detected, with 1,535 identified as known metabolites while the remainder were classified as unknown metabolites. The metabolite composition of each organ of Tetracme quadricornis in diverse habitats is illustrated in Fig. 2 (a). Notably, Tetracme quadricornis comprises primary metabolites such as amino acids, peptides, carbohydrates, and fatty acids, in addition to secondary metabolites including alkaloids, polyketides, shikimates, phenylpropanoids, and terpenoids. Notably, terpenoids exhibited the highest relative abundance among metabolites in desert habitats, with a particular concentration in floral tissues. Conversely, fatty acids emerged as the predominant metabolite class in both gravel desert and saline-alkali desert environments. This suggests that while the types and quantities of metabolites present in the organs of Tetracme quadricornis across the three habitats are comparable, distinct differences exist in the distribution and accumulation of each metabolite. Alpha diversity is indicative of species richness and diversity. This study employed the Shannon index to quantify metabolite diversity in Tetracme quadricornis . As illustrated in Fig. 2 (b), the plant metabolite diversity in the sandy desert habitat exhibited a pronounced hump-shaped pattern across roots, stems, leaves, and flowers, with the stems displaying the highest metabolite diversity; however, this difference was not statistically significant when compared to stem metabolite diversity in other habitats. In the gravel desert habitat, a decreasing trend in plant metabolite diversity was observed among roots, stems, leaves, and flowers, with roots demonstrating the highest metabolite diversity, significantly exceeding that of roots in other habitats. Conversely, in saline-alkali desert habitat, an increasing trend in plant metabolite diversity was noted across roots, stems, leaves, and flowers, with flowers exhibiting the highest metabolite diversity, significantly surpassing that found in both sandy desert and gravel desert habitats. Nonetheless, no significant differences were observed in the metabolite diversity of leaves of Tetracme quadricornis across the three habitats. Overall, Tetracme quadricornis exhibited the greatest metabolite diversity in the saline-alkali desert habitat. 3.3 Relationship between metabolite diversity of various organs of Tetracme quadricornis and environment in different habitats RDA analysis was conducted to evaluate the environmental characteristics of three habitats and the metabolites present in the roots, stems, leaves, and flowers of Tetracme quadricornis . The metabolites of each organ were treated as response variables, while the environmental factors served as explanatory variables. As illustrated in Fig. 3 (a), axes 1 and 2 collectively account for over 90% of the variability in the data. Following the removal of collinearity among the factors, the results of the envfit function test (Fig. 3 b) indicated that longitude, average annual precipitation, soil conductivity, and altitude significantly influence the metabolite diversity of various organs of Tetracme quadricornis ( p < 0.05). Through Mantel tests (Fig. 3 c) and correlation analyses (Fig. 3 d), we revealed the root metabolite diversity exhibited a significant positive correlation with pH, electrical conductivity, organic carbon, total nitrogen, altitude, average annual precipitation, and longitude ( p < 0.05). Stem metabolite diversity was significantly and positively correlated with soil moisture, altitude, and longitude ( p < 0.05). Leaf metabolite diversity demonstrated a significant positive correlation with electrical conductivity, longitude, and latitude ( p < 0.05). Finally, flower metabolite diversity was significantly positively correlated with electrical conductivity, altitude, average annual precipitation, and longitude ( p < 0.05). Moreover, metabolite diversity across all organs of Tetracme quadricornis showed a significant positive correlation with longitude ( p < 0.05). Mantel tests (Fig. 3 c) and correlation analyses (Fig. 3 d) revealed distinct organ-specific patterns of metabolite diversity in Tetracme quadricornis in response to abiotic gradients. Root metabolite diversity exhibited significant positive correlations with soil pH, EC, SOC, STN, altitude, mean annual precipitation (MAP), and longitude ( p < 0.05). Stem metabolites showed stronger associations with soil water content (SWC), altitude, and longitude ( p < 0.05), while leaf metabolite diversity correlated positively with EC, longitude, and latitude ( p < 0.05). Flower metabolites demonstrated significant relationships with EC, altitude, MAP, and longitude ( p < 0.05). Notably, metabolite diversity across all plant organs was consistently positively correlated with longitude ( p < 0.05). 4. Discussion 4.1 Changes in metabolite diversity in various organs of Tetracme quadricornis under different habitats Plants adapt to various ecological stresses by synthesizing a wide range of metabolites. These metabolites are numerous, structurally diverse, and highly variable in composition and content across time and space (Rai et al. 2017 ; Fang et al. 2019 ; Yang et al. 2020 ). The diversity of plant metabolites plays a crucial role in determining both environmental fitness and ecosystem functions and services (Rosenthal and Berenbaum 1991 ; Hunter 2016 ). Plant metabolomes exhibit variability among different species (Perkowski et al. 2012 ), among varieties of the same species (Lin et al. 2014 ), and even among different tissues of the same individual (Dong et al. 2014 ). In this study, Tetracme quadricornis demonstrated not only differences in metabolite composition among various organs but also variability in metabolite diversity across different habitats. In sandy desert environments, the highest metabolite diversity was observed in the stem of Tetracme quadricornis , whereas the lowest was found in the flower. Given that sandy desert soils primarily consist of sand and exhibit strong fluidity, storms and sandstorms can impair plant photosynthesis and disrupt the transport and storage of energy. Consequently, to ensure survival, plants allocate more resources to withstand wind and sand, resulting in reduced investment in reproduction (Fan et al. 2018 ). In contrast, Tetracme quadricornis in saline-alkali desert habitats tends to allocate more resources to reproduction. Among its various organs, the floral parts exhibit the highest diversity of metabolites, significantly surpassing that found in other habitats and showing a positive correlation with electrical conductivity. Salt desert soils are characterized by high levels of saline-alkali substances and extreme nutrient deficiency, which trigger stress responses in the plant. Nonetheless, the alkaline pH of the soil promotes the growth of reproductive structures. In environments with favorable soil conditions, plants typically invest more resources in the mother plant's growth and development. Conversely, in nutrient-poor habitats, plants prioritize survival by allocating more resources to their progeny, thereby producing a greater number of reproductive structures with improved dispersal capabilities (Lemoine et al. 2017 ; Lu et al. 2021 ). Compared to sandy desert and saline-alkali desert habitats, the surface of gravel desert soils is predominantly covered with black gravel, which effectively reduces soil water evaporation, resulting in relatively lower water loss in the topsoil (Zhang et al. 2021 ). Gravel deserts are formed through the alluvial deposition of mountain gravel and are typically found in high-altitude areas, where rainfall tends to increase with elevation (Hu et al. 2017 ; Li et al. 2013 ; Sun et al. 2015 ). In arid and semi-arid regions, precipitation serves as the primary source of soil moisture (Yang et al. 2018 ), and regions with higher precipitation levels also exhibit greater soil nutrient availability (Xiao et al. 2024 ). Consequently, the gravel desert examined in this study not only possesses higher soil water content but also demonstrates improved soil nutrient status (SOC, STN, and STP). However, the increased soil compaction and gravel density in gravel deserts impede root penetration and restrict the absorption of infiltrated water by roots (Wang et al. 2024 ). To survive, plants must enable their root systems to absorb water from the soil, necessitating a greater allocation of resources to the roots. Furthermore, to ensure normal growth, development, and fruiting under stressful conditions, plants need to simultaneously engage in both root-based resource expansion and leaf-based resource conservation (Sheffer et al. 1987). This leads to the observation that the root metabolite diversity of Tetracme quadricornis in the gravel desert is the highest, while its leaf metabolite diversity is comparatively lower. 4.2 Changes in metabolite composition of various organs of Tetracme quadricornis under different habitats The accumulation of plant metabolites is influenced by various factors, including genomic evolution, genetic diversity, and environmental stimuli (Taylor and Briggs 1990 ; Hectors et al. 2014 ; Verma and Shukla 2015 ; Fang and Luo 2019 ). Research indicates that plants subjected to higher stress conditions exhibit increased fatty acid content (Magni et al. 2023 ). The annual ephemeral plant Tetracme quadricornis , which inhabits desert ecosystems, contains a significant amount of fatty acids. Lipids represent a crucial class of compounds in plants, serving not only as signaling molecules and energy sources to initiate defense responses under stress but also in regulating cell osmotic pressure to maintain membrane stability, thus mitigating stress damage to plants (Liu et al. 2023 ). Caragana tibetica , a xerophytic, dwarf, cushion-like shrub found in arid ecosystems, exhibits similar traits, with its metabolites primarily consisting of steroids and lipids (He et al. 2024 ). Desert plants frequently experience salinity stress, which adversely impacts plant growth through ion toxicity and osmotic stress (Skliros et al. 2018 ). Tetracme quadricornis , found in saline-alkali desert habitats, endures both salt and alkaline stress; however, our research indicates a significant increase in the levels of fatty acids and amino acids in its tissues compared to plants from other habitats. Amino acids play crucial roles in maintaining osmotic balance (Mibei et al. 2018 ) and providing antioxidant defense (Gagné-Bourque et al. 2016 ). Research on Poa pratensis and Glycine max has also demonstrated that plant adaptation to salinity and alkaline stress is primarily associated with the accumulation of amino acids, carbohydrates, fatty acids, and organic acids (Hu et al. 2015 ; Yang et al. 2017 ). In response to environmental changes, plants enhance not only their energy metabolism but also various secondary metabolic pathways, thereby mitigating oxidative damage through the scavenging of reactive oxygen species (ROS) (Bowne et al. 2012 ). In tropical rainforest ecosystems, trees are characterized by an abundance of alkaloids, followed by shikimate-phenylpropanoids and terpenoids (Wang et al. 2023 ). Potentilla fruticosa L., a typical deciduous shrub in the Qinghai-Tibet Plateau ecosystem, is particularly rich in secondary metabolites (Liu et al. 2016 ). This study is conducted in an arid desert area where the dry climate and intense light radiation lead to significant evaporation of surface soil moisture, resulting in low soil moisture content. Generally, drought stress tends to increase amino acid content in plants (Bowne et al. 2012 ; Witt et al. 2012 ). However, in sandy desert habitats characterized by low average rainfall and soil moisture, Tetracme quadricornis exhibits a notably low amino acid content while containing a substantial amount of terpenoids. Research indicates that moderate drought may promote an increase in plant terpenoid content (Delfine et al. 2005), which may serve to protect plants from drought stress (Ormeño et al. 2007 ). 4.3 The relationship between different habitats and metabolite diversity of Tetracme quadricornis There exists a close correlation between plants and soil nutrients (Ordoñez et al. 2009 ). Plants absorb soil nutrients through their roots, and the presence of essential nutrients in the soil can significantly affect the content and function of various metabolites within plants (Deng et al. 2019 ; Balcke et al. 2017 ; De Long et al. 2016 ). Different habitats exhibit distinct geographical, climatic, and edaphic conditions. However, climatic conditions across various locations may indirectly influence plant metabolite production by affecting changes in soil nutrients (Tu et al. 2018 ). Research has demonstrated that soil organic carbon and total nitrogen content tend to increase with increasing longitude (Li et al. 2020 ). In this study, the metabolite diversity of the roots, stems, leaves, and flowers of Tetracme quadricornis was found to be significantly positively correlated with longitude, with root metabolite diversity showing a notable positive correlation with soil organic carbon and total nitrogen. In forest ecosystems, plant metabolite diversity also tends to increase with higher soil nutrient levels (Wang et al. 2023 ). Water serves as the primary limiting factor for plant growth within desert ecosystems (Teng et al. 2011 ). Research indicates that plants subjected to drought stress exhibit reduced growth and development while accumulating higher concentrations of secondary metabolites compared to those grown under well-watered conditions (Kleinwächter and Selmar 2015 ; Liao et al. 2017 ). Notably, the metabolite diversity in the roots and flowers of Tetracme quadricornis was positively correlated with average annual precipitation, while the metabolite diversity in stems correlated positively with soil moisture content. We hypothesize that this correlation arises from the study's location in an arid desert region. As an early spring annual short-lived plant, Tetracme quadricornis has evolved adaptations to thrive in extreme drought environments, making it more responsive to water availability compared to other plant groups (Fan et al. 2022 ). This may also elucidate the extremely low amino acid content observed in Tetracme quadricornis within arid desert habitats. Consequently, during precipitation events, the roots of Tetracme quadricornis will strive to absorb water from the soil, subsequently transporting energy through the stems to fulfill its nutritional requirements. Similar findings were reported in a study of Triadica sebifera , where all leaf and root secondary metabolites varied by location, with precipitation significantly influencing the regulation of leaf and root metabolites; notably, phytochemical content was higher in areas with elevated precipitation (Xiao et al. 2024 ). Plant metabolite diversity is influenced by altitude gradients (Portella et al. 2021 ), as plants actively defend against high-altitude stressors such as elevated UV radiation, strong winds, and low temperatures (Zhu et al. 2022 ). Subalpine regions exhibit greater plant metabolite diversity compared to tropical regions, particularly in terms of alkaloids and carbohydrates, which tend to increase with altitude in subalpine areas (Zhang et al. 2024 ). The metabolite diversity of the roots, stems, and flowers of Tetracme quadricornis also demonstrates a positive correlation with altitude. Moreover, in gravel desert habitats at higher elevations, the carbohydrate content of this species is greater than that found in other habitats. Conversely, alkaloid content is highest in lower-altitude sandy desert environments. Research on the perennial herb Aconitum pendulum indicates that both the content and variety of alkaloids are more abundant at lower altitudes compared to higher elevations (Wang et al. 2022b ). 5. Conclusion This study integrates plant metabolomes with various environmental factors to investigate the metabolite adaptation characteristics of the annual ephemeral plant Tetracme quadricornis across three distinct desert habitats: sandy desert, gravel desert, and saline-alkali desert. Our findings demonstrate a habitat-specific metabolic allocation in Tetracme quadricornis , with terpenoids exhibiting the highest relative abundance in desert environments, while fatty acids predominated in both gravel desert and saline-alkali desert habitats. Furthermore, we observed significant metabolic divergence not only across different habitat types but also among distinct plant organs within identical environmental conditions. Specifically, stem metabolite diversity was highest in sandy desert, root metabolite diversity was most pronounced in gravel deserts, and flower metabolite diversity peaked in saline-alkali deserts. These patterns are primarily attributed to environmental factors such as longitude, average annual precipitation, soil conductivity, and altitude. The findings suggest that organ-specific metabolic plasticity enables the annual ephemeral plant Tetracme quadricornis to adapt to heterogeneous desert habitats. This research enhances our understanding of the adaptation mechanisms of the annual ephemeral plant Tetracme quadricornis to heterogeneous desert habitats from a metabolic perspective and offers novel insights for analyzing the ecological adaptation of desert ephemeral plants. Given the intricate and multifaceted nature of various factors within desert ecosystems, future research should further investigate the relationship between annual ephemeral plants and their environments, thereby deepening our understanding of survival and adaptation mechanisms. Declarations Competing Interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the National Natural Science Foundation of China (32160256, 32560263), the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2024D01A72), and the Graduate Research Innovation Project of Xinjiang Agricultural University (XJAUGRI2025029). Author Contributions The research was planned and designed by Zhengwei Heng, Lingwei Zhang and Huiliang Liu. Data analysis and manuscript writing were carried out by Zhengwei Heng and Lingwei Zhang. Sample collection involved Zhengwei Heng and Zhengwang Gao, while experiments were conducted by Zhengwei Heng and Yu Wang. Acknowledgements We are grateful for the group mates’support and help in collecting samples. We thank Kangwei Jiang for his guidance and advice on data analysis. References Alsharif W, Saad MM, Hirt H (2020) Desert Microbes for Boosting Sustainable Agriculture in Extreme Environments. Front Microbiol 11:1666. https://doi.org/10.3389/fmicb.2020.01666 Balcke GU, Bennewitz S, Bergau N, Athmer B, Henning A, Majovsky P, Jiménez-Gómez JM, Hoehenwarter W, Tissier A (2017) Multi-Omics of Tomato Glandular Trichomes Reveals Distinct Features of Central Carbon Metabolism Supporting High Productivity of Specialized Metabolites. Plant Cell 29(5):960–983. https://doi.org/10.1105/tpc.17.00060 Bowne JB, Erwin TA, Juttner J, Schnurbusch T, Langridge P, Bacic A, Roessner U (2012) Drought responses of leaf tissues from wheat cultivars of differing drought tolerance at the metabolite level. Mol Plant 5(2):418–429. https://doi.org/10.1093/mp/ssr114 Cao P, Yang J, Xia L, Zhang Z, Wu Z, Hao Y, Liu P, Wang C, Li C, Yang J, Lai J, Li X, Deng M, Wang S (2024) Two gene clusters and their positive regulator SlMYB13 that have undergone domestication-associated negative selection control phenolamide accumulation and drought tolerance in tomato. Mol Plant 17(4):579–597. https://doi.org/10.1016/j.molp.2024.02.003 De Long JR, Sundqvist MK, Gundale MJ, Giesler R, Wardle DA (2016) Effects of elevation and nitrogen and phosphorus fertilization on plant defence compounds in subarctic tundra heath vegetation. Funct Ecol 30(2):314–325. https://doi.org/10.1111/1365-2435.12493 Deng B, Li Y, Xu D, Ye Q, Liu G (2019) Nitrogen availability alters flavonoid accumulation in Cyclocarya paliurus via the effects on the internal carbon/nitrogen balance. Sci Rep 9(1):2370. https://doi.org/10.1038/s41598-019-38837-8 Dı́az S, Cabido M (2001) Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol Evol 16(11):646–655. https://doi.org/10.1016/S0169-5347(01)02283-2 Ditzler CA (2017) Soil Properties and Classification (Soil Taxonomy). In: West L, Singer M, Hartemink A (eds) The Soils of the USA. World Soils Book Series. Springer, Cham, pp 29–41. https://doi.org/10.1007/978-3-319-41870-4_3 Dong X, Chen W, Wang W, Zhang H, Liu X, Luo J (2014) Comprehensive profiling and natural variation of flavonoids in rice. J Integr Plant Biol 56(9):876–886. https://doi.org/10.1111/jipb.12204 Du BCX, Cheng YX, Wu L (2021) Analysis of negative correlation between vegetation and soil salinization in Junggar Basin. Acta Ecol Sin 23:1–13. https://doi.org/10.5846/stxb202009242485 Fan B, Zhao C, Zhang X, Sun K (2018) Impacts of sand burial and wind erosion on regeneration and growth of a desert clonal shrub. Front Plant Sci 9:1696. https://doi.org/10.3389/fpls.2018.01696 Fan L, Li Y, Ma J, Mao J, Wang L (2022) Snow and rainfall independently affect the density, composition and productivity of ephemerals in a temperate desert. Sci Total Environ 807(Pt3):151033. https://doi.org/10.1016/j.scitotenv.2021.151033 Fang C, Fernie AR, Luo J (2019) Exploring the diversity of plant metabolism. Trends Plant Sci 24(1):83–98. https://doi.org/10.1016/j.tplants.2018.09.006 Fang C, Luo J (2019) Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism. Plant journal: cell Mol biology 97(1):91–100. https://doi.org/10.1111/tpj.14097 Fang JY, Wang XP, Shen ZH, Tang ZY, He JS, Yu D, Jiang Y, Wang ZH, Zheng CY, Zhu JL, Guo ZD (2009) Methods and protocols for plant community inventory. Biodivers Sci 17(6):533. https://www.biodiversity-science.net/EN/ 10.3724/SP.J.1003.2009.09253 Gagné-Bourque F, Bertrand A, Claessens A, Aliferis KA, Jabaji S (2016) Alleviation of drought stress and metabolic changes in timothy ( Phleum pratense L.) colonized with Bacillus subtilis B26. Front Plant Sci 7:584. https://doi.org/10.3389/fpls.2016.00584 Gong Z, Xiong L, Shi H, Yang S, Herrera-Estrella LR, Xu G, Chao DY, Li J, Wang PY, Qin F, Li J, Ding Y, Shi Y, Wang Y, Yang Y, Guo Y, Zhu JK (2020) Plant abiotic stress response and nutrient use efficiency. Sci China Life Sci 63(5):635–674. https://doi.org/10.1007/s11427-020-1683-x Gutierrez JR, Whitford WG (1987) Chihuahuan desert annuals: importance of water and nitrogen. Ecology 68(6):2032–2045. https://doi.org/10.2307/1939894 He M, Han Y, Gao Y, Han M, Duan L (2024) Decoding the metabolomic responses of Caragana tibetica to livestock grazing in fragile ecosystems. Front Plant Sci 15:1339424. https://doi.org/10.3389/fpls.2024.1339424 Hectors K, Van Oevelen S, Geuns J, Guisez Y, Jansen MA, Prinsen E (2014) Dynamic changes in plant secondary metabolites during UV acclimation in Arabidopsis thaliana . Physiol Plant 152(2):219–230. https://doi.org/10.1111/ppl.12168 Hu J, Lv YH, Fu BJ, Sun JF (2017) Soil hydrothermal variation and rainfall pulses along altitudinal gradient in pailugou watershed in the Qilian Mountain. Arid Zone Res 34(1):151–160. https://www.researchgate.net/publication/329528501 Hu LX, Zhang PP, Jiang Y, Fu JM (2015) Metabolomic analysis revealed differential adaptation to salinity and alkalinity stress in Kentucky bluegrass ( Poa pratensis ). Plant Mol biology Report 33(1):56–68. https://doi.org/10.1007/s11105-014-0722-4 Hunter MD (2016) The phytochemical landscape: linking trophic interactions and nutrient dynamics. Princeton University Press, Princeton. https://doi.org/10.1515/9781400881208 Kamboj A, Sangha MK, Devi V, Kumar P, Pathak M, Singla D (2024) Enhancing germination and growth in wild okra genotypes through gibberellic acid priming. Sci Hort 334:113332. https://doi.org/10.1016/j.scienta.2024.113332 Khan N, Bano A, Rahman MA, Rathinasabapathi B, Babar MA (2019) UPLC-HRMS-based untargeted metabolic profiling reveals changes in chickpea ( Cicer arietinum ) metabolome following long-term drought stress. Plant Cell Environ 42(1):115–132. https://doi.org/10.1111/pce.13195 Kim HW, Wang M, Leber CA, Nothias LF, Reher R, Kang KB, van der Hooft JJJ, Dorrestein PC, Gerwick WH, Cottrell GW (2021) NPClassifier:a deep neural network-based structural classification tool for natural products. J Nat Prod 84(11):2795–2807. https://doi.org/10.1021/acs.jnatprod.1c00399 Kleinwächter M, Selmar D (2015) New insights explain that drought stress enhances the quality of spice and medicinal plants: potential applications. Agron Sustain Dev 35:121–131. https://doi.org/10.1007/s13593-014-0260-3 Kumar S, Shah SH, Vimala Y, Jatav HS, Ahmad P, Chen Y, Siddique KHM (2022) Abscisic acid: Metabolism, transport, crosstalk with other plant growth regulators, and its role in heavy metal stress mitigation. Front Plant Sci 13:972856. https://doi.org/10.3389/fpls.2022.972856 Kumaraswamy KG, Kushalappa AC, Choo TM, Dion Y, Rioux S (2011) Mass spectrometry based metabolomics to identify potential biomarkers for resistance in barley against fusarium head blight ( Fusarium graminearum ). J Chem Ecol 37(8):846–856. https://doi.org/10.1007/s10886-011-9989-1 Lan HY, Zhang FC (2008) Reviews on special mechanisms of adaptability of early-spring ephemeral plants to desert habitats in Xinjiang. Acta Bot Boreali-Occidentalia Sinica 28(7):1478–1485 Lemoine NP, Doublet D, Salminen JP, Burkepile DE, Parker JD (2017) Responses of plant phenology, growth, defense, and reproduction to interactive effects of warming and insect herbivory. Ecology 98(7):1817–1828. https://doi.org/10.1002/ecy.1855 Li CC, Zeng QC, Jia PL, An SS (2020) Characteristics of soil aggregate stability and corrosion resistance longitude change in the Loess Plateau. Acta Ecol Sin 40:2039–2048. https://doi.org/10.5846/stxb201903020388 Li HD, Shen WH, Zou CX, Jiang J, Fu LN, She GH (2013) Spatio-temporal variability of soil moisture and its effect on vegetation in a desertified aeolian riparian ecotone on the Tibetan Plateau, China. J Hydrol 479:215–225. https://doi.org/10.1016/j.jhydrol.2012.12.002 Li M, Xu J, Wang X, Fu H, Zhao M, Wang H, Shi L (2018) Photosynthetic characteristics and metabolic analyses of two soybean genotypes revealed adaptive strategies to low-nitrogen stress. J Plant Physiol 229:132–141. https://doi.org/10.1016/j.jplph.2018.07.009 Liao P, Liu D, Xu TR, Yang Y, Cui XM (2017) Soil water stress attenuate the growth and development but enhance the saponin synthesis of Panax notogesing during flowering stage. Ind Crops Prod 108:95–105. https://doi.org/10.1016/j.indcrop.2017.05.052 Lin H, Rao J, Shi J, Hu C, Cheng F, Wilson ZA, Zhang D, Quan S (2014) Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars. J Integr Plant Biol 56(9):826–836. https://doi.org/10.1111/jipb.12228 Liu W, Yin D, Li N, Hou X, Wang D, Li D, Liu J (2016) Influence of environmental factors on the active substance production and antioxidant activity in Potentilla fruticosa L. and its quality assessment. Sci Rep 6:28591. https://doi.org/10.1038/srep28591 Liu Y, Zhang LN, Liu XH, Huang XM, Jia RX (2023) Research progress from individual plant physiological response to ecological model prediction under drought stress. Acta Ecol Sin 43:10042–10053. https://link.cnki.net/doi/ 10.20103/j.stxb.202304070703 Liu ZQ, Liu T, Zhang R, Cheng HL (2011) Species diversity and spatial differentiation of ephemeral plant community in southern Gurbantunggut Desert. Chin J Ecol 30(1):45–52. https://www.cje.net.cn/CN/Y2011/V30/I01/45 Lu J, Gan L, Tan D, Baskin CC, Baskin JM (2021) Effects of the habitat-soil factor on transgenerational plasticity in a diaspore-polymorphic cold desert annual. Plant Soil 461:355–367. https://doi.org/10.1007/s11104-020-04792-x Magni NN, Veríssimo ACS, Silva H, Pinto DCGA (2023) Metabolomic profile of Salicornia perennis plant’s organs under diverse in situ stress : the ria de Aveiro salt marshes case. Metabolites 13(2):280. https://doi.org/10.3390/metabo13020280 Mibei EK, Owino WO, Ambuko J, Giovannoni JJ, Onyango AN (2018) Metabolomic analyses to evaluate the effect of drought stress on selected African Eggplant accessions. J Sci Food Agric 98(1):205–216. https://doi.org/10.1002/jsfa.8458 Moura JCMS, Bonine CAV, de Oliveira Fernandes Viana J, Dornelas MC, Mazzafera P (2010) Abiotic and biotic stresses and changes in the lignin content and composition in plants. J Integr Plant Biol 52(4):360–376. https://doi.org/10.1111/j.1744-7909.2010.00892.x Ordoñez JC, Van Bodegom PM, Witte JPM, Wright IJ, Reich PB, Aerts R (2009) A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob Ecol Biogeogr 18(2):137–149. https://doi.org/10.1111/j.1466-8238.2008.00441.x Ormeño E, Mévy JP, Vila B, Bousquet-Mélou A, Greff S, Bonin G, Fernandez C (2007) Water deficit stress induces different monoterpene and sesquiterpene emission changes in Mediterranean species. Relationship between terpene emissions and plant water potential. Chemosphere 67(2):276–284. https://doi.org/10.1016/j.chemosphere.2006.10.029 Peng M, He H, Wang Z, Li G, Lv X, Pu X, Zhuang L (2022) Responses and comprehensive evaluation of growth characteristics of ephemeral plants in the desert-oasis ecotone to soil types. J Environ Manage 316:115288. https://doi.org/10.1016/j.jenvman.2022.115288 Perkowski J, Stuper K, Buśko M, Góral 2, Kaczmarek A, Jeleń H (2012) Differences in metabolomic profiles of the naturally contaminated grain of barley, oats and rye. J Cereal Sci 56(3):544–551. https://doi.org/10.1016/j.jcs.2012.07.012 Plaxton WC, Tran HT (2011) Metabolic adaptations of phosphate-starved plants. Plant Physiol 156(3):1006–1015. https://doi.org/10.1104/pp.111.175281 Portella RO, Cordeiro EMG, Marques APS, Ming LC, Zucchi MI, Lima MP, Martins ER, Hantao LW, Sawaya ACHF, Semir J, Pinheiro JB, Marques MOM (2021) Evidence of altitudinal gradient modifying genomic and chemical diversity in populations of Lychnophora pinaster Mart. Phytochemistry 192:112898. https://doi.org/10.1016/j.phytochem.2021.112898 Qiu Y, Liu T, Zhang C, Liu B, Pan B, Wu S, Chen X (2018) Mapping Spring Ephemeral Plants in Northern Xinjiang. China Sustain 10(3):804. https://doi.org/10.3390/su10030804 Rai A, Saito K, Yamazaki M (2017) Integrated omics analysis of specialized metabolism in medicinal plants. Plant journal: cell Mol biology 90(4):764–787. https://doi.org/10.1111/tpj.13485 Rinschen MM, Ivanisevic J, Giera M, Siuzdak G (2019) Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol 20(6):353–367. https://doi.org/10.1038/s41580-019-0108-4 Rosenthal GA, Berenbaum MR (1991) Herbivores: their interactions with secondary plant metabolites: volume I: the chemical participants. Academic Rossnerova A, Izzotti A, Pulliero A, Bast A, Rattan SIS, Rossner P (2020) The molecular mechanisms of adaptive response related to environmental stress. Int J Mol Sci 21(19):7053. https://doi.org/10.3390/ijms21197053 Sheffer KM, Dunn JH, Minner DD (2022) Summer drought response and rooting depth of three cool-season turfgrasses. HortScience 296–297. https://doi.org/10.21273/hortsci.22.2.296 Skliros D, Kalloniati C, Karalias G, Skaracis GN, Rennenberg H, Flemetakis E (2018) Global metabolomics analysis reveals distinctive tolerance mechanisms in different plant organs of lentil ( Lens culinaris ) upon salinity stress. Plant Soil 429(1):451–468. https://doi.org/10.1007/s11104-018-3691-9 Stevens DL, Olsen AR (2004) Spatially balanced sampling of natural resources. J Am Stat Assoc 94(1–3):1–8. https://doi.org/10.1198/016214504000000250 Sun F, Lü Y, Wang J, Hu J, Fu B (2015) Soil moisture dynamics of typical ecosystems in response to precipitation: A monitoring-based analysis of hydrological service in the Qilian Mountains. CATENA 129:63–75. https://doi.org/10.1016/j.catena.2015.03.001 Sun H, Zhang J, Jiang L, Lv Y, Wang Y (2019) Study on the abundance-deficiency index of soil available P and K and appropriate nutrient application rates of P and K for rice in China. China Rice 51(05):895–902. http://www.zgdm.net/CN/ 10.3969/j.issn.1006-8082.2018.05.001 Taylor LP, Briggs WR (1990) Genetic regulation and photocontrol of anthocyanin accumulation in maize seedlings. Plant Cell 2(2):115–127. https://doi.org/10.1105/tpc.2.2.115 Teng ZQ, Fu HQ, Jia SH, Meng WW, Dai RJ, Deng YL (2011) Review of current progress in the metabolomics for plant response to abiotic stress. Chin J Plant Ecol 35(1):110–118. https://www.plant-ecology.com/EN/ 10.3724/SP.J.1258.2011.00110 Tu KY, Tsai SF, Guo TW, Lin HH, Yang ZW, Liao CT, Chuang WP (2018) The Role of Plant Abiotic Factors on the Interactions Between Cnaphalocrocis medinalis (Lepidoptera: Crambidae) and its Host Plant. Environ Entomol 47(4):857–866. https://doi.org/10.1093/ee/nvy066 Verma N, Shukla S (2015) Impact of various factors responsible for fluctuation in plant secondary metabolites[J]. J Appl Res Med aromatic plants 2(4):105–113. https://doi.org/10.1016/j.jarmap.2015.09.002 Wang JJ, Lou HY, Liu Y, Han HP, Ma FW, Pan WD, Chen Z (2022b) Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics. Phytochemistry 195:113047. https://doi.org/10.1016/j.phytochem.2021.113047 Wang L, Ma KB, Lu ZG, Ren SX, Jiang HR, Cui JW, Chen G, Teng NJ, Lam HM, Jin B (2020) Differential physiological, transcriptomic and metabolomic responses of Arabidopsis leaves under prolonged warming and heat shock. BMC Plant Biol 20(1):86. https://doi.org/10.1186/s12870-020-2292-y Wang LJ, Sun DY, Zhao CY, Li JY, Sheng Y (2011) Plant architecture characteristics of Haloxylon ammodendron and Haloxylon persicum in Zhungar Basin. Acta Ecol Sin 31(17):4952–4960. https://www.sciengine.com/AES/doi/ 10.0000/j.1000-0933.2011311249524960 Wang LX, Li ZW, Wang DY, Liao SS, Nie XD, Liu YJ (2022a) Factors controlling soil organic carbon with depth at the basin scale. CATENA 217:106478. https://doi.org/10.1016/j.catena.2022.106478 Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, Nguyen DD, Watrous J, Kapono CA, Luzzatto-Knaan T, Porto C, Bouslimani A, Melnik AV, Meehan MJ, Liu WT, Crüsemann M, Boudreau PD, Esquenazi E, Sandoval-Calderón M, Kersten RD et al (2016) Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 34(8):828–837. https://doi.org/10.1038/nbt.3597 Wang T, Liu B, Zhang X, Wang M, Tan D (2024) Variations in root architecture traits and their association with organ mass fraction of common annual ephemeral species in the desert of northern Xinjiang. Ecol Evol 14(2):e10908. https://doi.org/10.1002/ece3.10908 Wang X, He Y, Sedio BE, Jin L, Ge X, Glomglieng S, Cao M, Yang J, Swenson NG, Yang J (2023) Phytochemical diversity impacts herbivory in a tropical rainforest tree community. Ecol Lett 26(11):1898–1910. https://doi.org/10.1111/ele.14308 Witt S, Galicia L, Lisec J, Cairns J, Tiessen A, Araus JL, Palacios-Rojas N, Fernie AR (2012) Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress. Mol Plant 5(2):401–417. https://doi.org/10.1093/mp/ssr102 Witt S, Galicia L, Lisec J, Cairns J, Tiessen A, Araus JL, Palacios-Rojas N, Fernie AR (2012) Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress. Mol Plant 5(2):401–417. https://doi.org/10.1093/mp/ssr102 Wu JL, Yang FX, Zhou J, Zhang ZW (2013) Desert types and characteristics in the Qira River Basin. Arid Land Geogr 36(5):803–811. http://alg.xjegi.com/CN/Y2013/V36/I5/803 Xiao L, Huang W, Carrillo J, Ding JQ, Siemann E (2024) Interactive effects of soils, local environmental conditions and herbivores on secondary chemicals in tallow tree. J Plant Ecol 17(4):rtae062. https://www.jpe.ac.cn/EN/ 10.1093/jpe/rtae062 Xiao TW, Song F, Vu DQ, Feng Y, Ge XJ (2024) The evolution of ephemeral flora in Xinjiang, China: insights from plastid phylogenomic analyses of Brassicaceae. BMC Plant Biol 24(1):111. https://doi.org/10.1186/s12870-024-04796-0 Yang C, Yang H, Xu Q, Wang Y, Sang Z, Yuan H (2020) Comparative metabolomics analysis of the response to cold stress of resistant and susceptible Tibetan hulless barley ( Hordeum distichon ). Phytochemistry 174:112346. https://doi.org/10.1016/j.phytochem.2020.112346 Yang DS, Zhang J, Li MX, Shi LX (2017) Metabolomics analysis reveals the salt-tolerant mechanism in Glycine soja . J Plant Growth Regul 36(2):460–471. https://doi.org/10.1007/s00344-016-9654-6 Yang L, Zhang H, Chen L (2018) Identification on threshold and efficiency of rainfall replenishment to soil water in semi-arid loess hilly areas. Sci China Earth Sci 61(3):292–301. https://doi.org/10.1007/s11430-017-9140-0 Yuan X, Smith NSS, Moghe GD (2025) Analysis of plant metabolomics data using identification-free approaches. Appl plant Sci 13(4):e70001. https://doi.org/10.1002/aps3.70001 Zang YX, Min XJ, de Dios VR, Ma JY, Sun W (2020) Extreme drought affects the productivity, but not the composition, of a desert plant community in Central Asia differentially across microtopographies. Sci Total Environ 717:137251. https://doi.org/10.1016/j.scitotenv.2020.137251 Zhang HX, Jia RL, Zhao PS, Zhao X, Cui XY (2025) A review of the stress physiology and ecology of desert plants. J Desert Res 45(3):72. http://www.desert.ac.cn/EN/ 10.7522/j.issn.1000-694X.2025.00060 Zhang JJ, Bai ZK, Yang BY (2021) Gravel curtain layer in the desert open-pit mining area of Xinjiang: Ecological damage and reconstruction method. Earth Sci Front 28(4):142. https://link.cnki.net/doi/ 10.13745/j.esf.sf.2020.10.15 Zhang K, Shi Y, Cui X, Yue P, Li K, Liu X, Tripathi BM, Chu H (2019) Salinity is a key determinant for soil microbial communities in a desert ecosystem. Msystems 4(1):e00225–e00218. https://doi.org/10.1128/mSystems.00225-18 Zhang R, Liu T (2012) Plant species diversity and community classification in the southern Gurbantunggut Desert. Acta Ecol Sin 32(19):6056–6066. https://doi.org/10.5846/stxb201109021289 Zhang Y, Worthy SJ, Xu S, He Y, Wang X, Song X, Cao M, Yang J (2024) Phytochemical diversity and their adaptations to abiotic and biotic pressures in fine roots across a climatic gradient. Sci Total Environ 927:172051. https://doi.org/10.1016/j.scitotenv.2024.172051 Zhu L, Pietiäinen M, Kontturi J, Turkkelin A, Elomaa P, Teeri TH (2022) Polyketide reductases in defense-related parasorboside biosynthesis in Gerbera hybrida share processing strategies with microbial polyketide synthase systems. New Phytol 236(1):296–308. https://doi.org/10.1111/nph.18328 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 15 Oct, 2025 Editor invited by journal 17 Sep, 2025 Editor assigned by journal 17 Sep, 2025 First submitted to journal 15 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7625560","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530356402,"identity":"789ef338-4514-40e5-a143-782a6c2f4b2e","order_by":0,"name":"Zhengwei Heng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoklEQVRIiWNgGAWjYBACgwNgyoaHn7+BNC1pMpIzDpCm5bCNQUMCsVpu5JhJ/vhznseA4QDjh485RGixB2qRkOC5zWPO3MAsOXMbMbbcTks2MJC4zWPZcICNmZdoLQkG53gMDiQQrSX54IMDCQdI0XL/8cGHDQeSeSRnHGwm0i9nDjYc/PHHzp6fv/ngh4/EaEECjA2kqR8Fo2AUjIJRgBsAAHtbOSzuUDIgAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0008-9924-6268","institution":"Xinjiang Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Zhengwei","middleName":"","lastName":"Heng","suffix":""},{"id":530356403,"identity":"32c41d39-fc4b-47a1-bab5-b8b26f3c9f77","order_by":1,"name":"Lingwei Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lingwei","middleName":"","lastName":"Zhang","suffix":""},{"id":530356404,"identity":"9b1a0dc9-6c97-4bb4-a930-9000b78753d8","order_by":2,"name":"Huiliang Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Huiliang","middleName":"","lastName":"Liu","suffix":""},{"id":530356405,"identity":"3e352bc5-93c8-42ea-b05e-3103feb6f936","order_by":3,"name":"Zhengwang Gao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhengwang","middleName":"","lastName":"Gao","suffix":""},{"id":530356406,"identity":"54e49f9c-6257-468b-82b6-616fae13ccb7","order_by":4,"name":"Yu Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-09-16 04:02:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7625560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7625560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94824091,"identity":"3b2bddce-d7f7-4dc7-9c27-12c62fdcb629","added_by":"auto","created_at":"2025-10-31 06:48:27","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9336,"visible":true,"origin":"","legend":"","description":"","filename":"plsoPLSOD2503536.xml","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/e453edbaf0bb4d87c0b372ce.xml"},{"id":94748322,"identity":"052afe4a-f4d3-4658-a0ab-273cfb7c61f6","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1093,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD250353663420.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/47365a36a7febf6d6cb1f57d.xml"},{"id":94824192,"identity":"535eaa34-cbf3-416e-8866-e69409661406","added_by":"auto","created_at":"2025-10-31 06:48:37","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":790,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD2503536Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/79200c7d9c5ea6a7b97f1f11.xml"},{"id":94748323,"identity":"3d4c78a9-4ad6-4dcd-ad93-b1e334f456c8","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197598,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD25035360enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/fc00a85c03e92db96a8685bd.xml"},{"id":94748333,"identity":"0cf775cf-a95c-4bf8-a81b-0340c75daac3","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"jpg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":851282,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/d0869ff79ed7fb3fd8420b81.jpg"},{"id":94748324,"identity":"69013801-c88f-4519-b18d-593cbe734ef3","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"jpg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4876407,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/9c29319224d7728ea979007d.jpg"},{"id":94748332,"identity":"ab09ffe8-e340-4109-aff4-1c6a5e679c3e","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1123546,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/e0079bcce141525da718fc1c.jpg"},{"id":94823229,"identity":"b7fa1d3a-942d-42b5-be94-1c6b3a47b5f1","added_by":"auto","created_at":"2025-10-31 06:46:49","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115805,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/fb106f6356399fb3e5de7baa.jpeg"},{"id":94748334,"identity":"4817fdde-92ef-48aa-a143-d7b86f2e51e3","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87139,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/7a1ca393e860d9eff9840f26.jpeg"},{"id":94748338,"identity":"e1fae72d-fb45-4367-ab85-5f3691ea46ab","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154481,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/2bb5cbc55f28ebcb41b63dd6.jpeg"},{"id":94823249,"identity":"0c7ff97b-d121-4f10-8239-f6a04fee62b0","added_by":"auto","created_at":"2025-10-31 06:46:52","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":390323,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/577b78fb02b3002778986a9a.png"},{"id":94748331,"identity":"abb7f82c-5fd9-48e4-987f-dc7b2c23bdfa","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":483625,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/69ee9eac19d179c2614b4d78.png"},{"id":94748329,"identity":"51fa33c8-8398-413c-9bb3-c6e632fbcb1e","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":455574,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/a5a0ab01a6db27c65aed2c35.png"},{"id":94748336,"identity":"d8cbe3ad-e760-48c4-a05d-a27ae701f620","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56566,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/359f64dbdbad126f7ec4b7a2.png"},{"id":94824258,"identity":"8607029b-ed7d-41b1-bc07-564802b5a047","added_by":"auto","created_at":"2025-10-31 06:48:42","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46363,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/237af1e29db5e560ba356b4e.png"},{"id":94748337,"identity":"107eb0e3-365e-4d44-adf5-70d5de6d3f84","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87602,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/e50ae3436025bb68688f4823.png"},{"id":94748340,"identity":"7f661757-900d-4e79-ae29-26bfa429dcb5","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195903,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD25035360structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/dc83c1bbbb2bdd439972abdb.xml"},{"id":94748335,"identity":"67dfca69-5d42-430f-81cc-024c69ee839b","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":205910,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/23ca4dc9544b26cdbbdf3c90.html"},{"id":94748319,"identity":"ff1a2727-f30f-421d-b6af-94d141cb4cc2","added_by":"auto","created_at":"2025-10-30 09:56:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":851282,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of climate, geographical and soil characteristics of sandy desert, gravel desert and saline-alkali desert habitats. Each sub-graph represents a different ecological parameter, with data expressed as mean value ± standard error. Different lowercase letters above the bars indicate significant differences among desert habitats for a given property according to Tukey's HSD test (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05). (a) Soil particle size composition: Shows the percentage distribution of sand, silt, clay, and gravel. (b) Geographic longitude of the three desert habitats. (c) Geographic latitude of the three desert habitats. (d) Altitude above sea level for each desert habitat. (e) Average annual temperature for the three desert habitats. (f) Average annual precipitation levels for each desert habitat. (g) Soil pH values across the three desert habitats. (h) The conductivity of soil in each desert habitat. (i) Moisture content of the soil in the three desert habitats. (j) Organic carbon concentration in the soil of each desert habitat. (k) Total nitrogen levels in the soil across the three desert habitats. (l) Total phosphorus concentration in the soil for each desert habitat.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/900e3eb4f06dee6e6d06b67d.jpg"},{"id":94822918,"identity":"84b68223-9e92-4a2d-a92b-9e34b26e054b","added_by":"auto","created_at":"2025-10-31 06:45:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4876407,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of metabolites composition and diversity of different plant organs and habitats of \u003cem\u003eTetracme quadricornis\u003c/em\u003e. (a) Stacked bar charts show the relative abundance of metabolites in different habitats and plant tissues of \u003cem\u003eTetracme quadricornis\u003c/em\u003e. A, B, C indicates different locations; A: sandy desert, B: gravel desert, C: saline-alkali desert. F, L, R, S indicates different organs or tissues of the plant; R: root, S: stem, L: leaves, F: flowers. Metabolite classes include Shikimates and Phenylpropanoids, Amino acids and Peptides, Carbohydrates, Terpenoids, Polyketides, Fatty acids, Alkaloids, and unclassified compounds (NA). Values are presented as mean percentages. (b) Box plots show Shannon diversity indexes for plant metabologroups of different organs (roots, stems, leaves and flowers) and habitats (sand, gravel and salt). The boxes represent the interquartile range (IQR), the line inside the box indicates the median, and the whiskers extend to the minimum and maximum values. Capital letters indicate significant differences (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) among organs within the same habitat, while lowercase letters indicate significant differences among habitats within the same organ, as determined by a two-way ANOVA followed by Tukey's HSD post-hoc test.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/99d1b2950238c0464cb89e8b.jpg"},{"id":94748328,"identity":"68794b9d-47dc-4d47-8fc5-d741cc963707","added_by":"auto","created_at":"2025-10-30 09:56:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1123546,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of metabolic diversity and ecological factors of different plant organs in \u003cem\u003eTetracme quadricornis\u003c/em\u003e. (a) Redundancy analysis (RDA) triplot displaying the constraints of environmental factors on the metabolomic diversity of root, stem, leaf, and flower tissues across three habitats (Sandy, Gravel, Salt). The first two RDA axes explain 52.33% and 37.95% of the total variance, respectively. The length and angle of each vector indicate the strength and direction of its relationship with the ordination axes. (b) Bar plot showing the explanatory power (R²) of each environmental factor derived from the RDA model. Factors are ordered by their contribution to the constrained variance. Asterisks denote statistical significance (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). (c) Mantel test results assessing the correlation between metabolomic diversity matrices of each organ and the matrix of environmental factors. The network uses lines to connect related variables, with green lines indicats correlations and gray lines indicats uncorrelated (Mantel's p). Line thickness represents correlation strength (Mantel's r), and color intensity in the heatmap denotes correlation magnitude (Pearson's r). (d) The heatmap on the right provides a detailed matrix of correlations, with color gradients representing the strength and direction of relationships. Darker colors indicate stronger correlations, either positive (green) or negative (red). Abbreviations for environmental factors: pH, soil acidity/alkalinity; EC, electrical conductivity; SWC, soil water content; SOC, soil organic carbon; TN, total nitrogen; TP, total phosphorus; ELE, elevation; MAP, mean annual precipitation; MAT, mean annual temperature; LAT, latitude; LONG, longitude; R, root metabolite diversity; S, stem metabolite diversity; L, leaf metabolite diversity; F, flower metabolite diversity.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/b59795bf11d09265d84b03e9.jpg"},{"id":94827227,"identity":"ae5e9ae0-867f-4c52-bdf5-34e3e46d762b","added_by":"auto","created_at":"2025-10-31 06:56:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7719159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7625560/v1/c01c2e2c-d29c-4e34-9c0b-2529c173c7f7.pdf"}],"financialInterests":"","formattedTitle":"Metabolic characteristics of the annual ephemeral plant Tetracme quadricornis in heterogeneous habitats","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDesert ecosystems are characterized by extreme aridity (Zang et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), high temperatures (Alsharif et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), poor soil quality (Gutierrez and Whitford \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), salinity and alkalinity stress (Zhang et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and intense radiation, rendering them among the most demanding terrestrial habitats on Earth. Plants in desert environments encounter numerous adversities, including prolonged droughts that lead to cellular dehydration and osmotic imbalance, deficiencies in essential nutrients such as carbon, nitrogen, and phosphorus in the soil, which restrict plant growth, and excessive accumulation of ions like Na⁺ and Cl⁻, which disrupts ion homeostasis. These factors pose significant challenges to plant physiology, growth, survival, and reproduction (Zhang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gong et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite the harsh living conditions in desert ecosystems, hundreds of thousands of plant species thrive in these environments worldwide.\u003c/p\u003e\u003cp\u003eLong-term exposure to adverse environmental conditions triggers a series of physiological, biochemical, and molecular responses in plants, enabling them to resist and tolerate stress (Rossnerova et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). When the growth environment of a plant\u0026mdash;such as temperature, water availability, salinity, and nutrient levels\u0026mdash;changes, its metabolic balance is disrupted. In response, plants adjust their metabolism to meet physiological requirements, achieve a new equilibrium, and adapt to the complex and fluctuating external environment (Plaxton and Tran \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e ; Khan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This process results in the biosynthesis of a diverse range of metabolic compounds, the abundance of which demonstrates a direct and significant correlation with the plant\u0026rsquo;s adaptive resistance mechanisms(Cao et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Plant metabolites not only regulate growth rhythms (Kamboj et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and enhance stress tolerance (Kumar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but they also reflect the plants' responses to their living environments and characterize their ecological strategies (D\u0026iacute;az and Cabido 2001). Furthermore, plant metabolomics is frequently employed to monitor growth and development under biotic stresses (such as microorganisms, insects, and herbivores) (Kumaraswamy et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and abiotic stresses (such as temperature fluctuations, drought, and ultraviolet light) (Wang et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is also used to identify and breed resistant varieties, as well as to discover bioactive metabolites (Rinschen et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By analyzing the metabolites of plants in different habitats and exploring the impact of the environment on plant metabolites, we can discover the regional distribution patterns of plants and explain the formation of plants' special ecological adaptation strategies. Plant metabolomics, as a means of analyzing plant metabolites, has the technical advantages of high throughput, no bias, and comprehensive analysis (Yuan et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It can identify all metabolites in plants and provide technical support for fully revealing the metabolic mechanisms under stress conditions.\u003c/p\u003e\u003cp\u003eAnnual ephemeral plants employ drought escape strategies to complete their life cycle within two to three months, thereby avoiding the high temperatures of summer by transitioning into a seed form. This unique adaptation enables them to endure harsh environments over extended periods (Qiu et al \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xiao et al \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This group of plants has thrived in desert ecosystems for a considerable time, developing numerous growth and developmental characteristics tailored to extreme conditions. They play a crucial role in the formation of desert plant communities and vegetation succession (Lan and Zhang \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The Brassicaceae family is one of the most prevalent groups in the early spring ephemeral flora, comprising approximately 15% of all species. \u003cem\u003eTetracme quadricornis\u003c/em\u003e is a prominent annual ephemeral plant, exhibiting a frequency of 70% and an importance value of 24%, thus fulfilling a significant ecological role (Liu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhan and Liu 2012). Previous studies have demonstrated that variations in soil nutrient levels across the habitats of \u003cem\u003eTetracme quadricornis\u003c/em\u003e in the Junggar Desert region lead to differences in individual morphology, nutrient distribution, and photosynthetic pigments (Peng et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, how does \u003cem\u003eTetracme quadricornis\u003c/em\u003e adapt its metabolic strategies to thrive in such harsh environments? Furthermore, how do environmental factors influence the development of these strategies? Therefore, this study took the annual ephemeral plant \u003cem\u003eTetracme quadricornis\u003c/em\u003e as the research object, used non-targeted metabolomics technology to conduct a comprehensive detection of \u003cem\u003eTetracme quadricornis\u003c/em\u003e in three desert habitats, and combined with environmental factors, in order to preliminarily reveal the metabolic homeostasis mechanism of \u003cem\u003eTetracme quadricornis\u003c/em\u003e in the face of environmental changes from the perspective of plant metabolomics, thereby providing an effective research strategy for analyzing the ecological adaptation of annual ephemeral plants.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Overview of the study area\u003c/h2\u003e\u003cp\u003eThis study area is situated in the northwest of the Junggar Basin in Xinjiang. The Junggar Basin is geographically positioned between the Tianshan Mountains and the Altai Mountains, bordered to the west by the western Junggar Mountains and to the center by the Gurbantunggut Desert. Its unique geographical features have resulted in a diverse array of desert types. The region experiences a typical temperate continental desert climate characterized by hot, dry summers and long, cold winters. The average annual temperature ranges from 6 to 10\u0026deg;C, with average annual precipitation approximately 150 mm and average annual evaporation exceeding 2000 mm (Wang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The basin exhibits a variety of soil types, predominantly comprising aeolian sandy soil, brown calcareous soil, and desert gray calcareous soil. Additionally, cracked soil, meadow soil, and saline-alkali soil can be found in certain areas (Du et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This region serves as the primary habitat for ephemeral plant species in China, which are highly sensitive to environmental changes. Among which the main ones are \u003cem\u003eTetracme quadricornis\u003c/em\u003e, \u003cem\u003eStrigosella africana\u003c/em\u003e, and \u003cem\u003eErodium oxyrhinchum\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Experimental design and sample collection\u003c/h2\u003e\u003cp\u003eIn this investigation, a spatially stratified sampling methodology (Stevens and Olsen \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) was implemented to establish experimental plots in contiguous areas encompassing three characteristic desert types\u0026mdash;sandy, gravelly, and saline\u0026mdash;within the Junggar Desert ecosystem. Following the principle of ecological typicality, four large sampling quadrats (10 m \u0026times; 10 m) were systematically positioned along representative environmental gradients within each desert type, yielding a total of 12 major quadrats with a minimum inter-plot separation of 20 m. Within each major quadrat, five 1 m \u0026times; 1 m subquadrats were arranged according to standardized five-point sampling protocol to capture fine-scale spatial heterogeneity. Sampling quadrats were selected according to the following criteria: (1) widespread and homogeneous distribution of the target plant species; (2) sufficiently large quadrat size to incorporate a 10\u0026ndash;20 m peripheral buffer zone; and (3) preference for flat or uniformly sloped terrain, while avoiding abrupt topographic transitions such as ridge crests, valley bases, and fragmented microtopographic features (Fang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSamples were collected during the peak flowering period (late April) of the annual ephemeral plant, \u003cem\u003eTetracme quadricornis\u003c/em\u003e. From each small plot, uniform-sized plants were selected, and the entire plant was harvested using the whole-plant excavation method. Plant samples from the five small plots were combined to form a replicate. A total of 40\u0026ndash;60 plants were selected from each plot and divided into four parts: roots, stems, leaves, and flowers. The samples were washed with deionized water, wrapped in tin foil, labeled, and immediately placed in liquid nitrogen for rapid freezing before being transferred to a \u0026minus;\u0026thinsp;80\u0026deg;C environment in the laboratory for metabolomics measurements. Given that annual ephemeral plants possess shallow root systems, a soil drill was employed to randomly collect the top 20 cm of soil from each small sample plot. The soil from each large sample plot was mixed to create a composite soil sample, which was placed in a sealed bag, with gravel and residual roots removed. The sample was then sieved through a 2 mm sieve and air-dried for the determination of soil physical and chemical properties.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sample determination\u003c/h2\u003e\u003cp\u003eMetabolomics data were extracted from samples using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Soil pH and electrical conductivity (EC) were measured using the potentiometric method, with water-to-soil ratios of 1:2.5 and 1:5, respectively. Soil water content (SWC) was determined using the oven-drying method, while soil particle size was analyzed using a laser particle size analyzer. Soil organic carbon (SOC) and total nitrogen (TN) contents were quantified using chromatography with an elemental analyzer (EA3100, Italy). Total phosphorus (TP) content was measured using the ammonium molybdate colorimetric method. A handheld GPS (eTrex H) was employed to record the altitude, longitude, and latitude of each sampling site. Mean annual precipitation (MAP) and mean annual temperature (MAT) data were sourced from the World Climate global climate database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org\u003c/span\u003e\u003cspan address=\"http://www.worldclim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on the geographic coordinates of each sampling site, with a resolution of 30 arc minutes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data processing and statistical analysis\u003c/h2\u003e\u003cp\u003eSPSS 26.0 software was employed to perform a one-way analysis of variance on the climate, geography, and soil physical and chemical properties across the three habitats. Duncan's test was utilized for multiple comparisons to assess the significance of differences in climate, geography, and soil properties among the various habitats, with a significance level set at 0.05. Raw UHPLC-MS/MS data were analyzed using the Global Natural Products Society (GNPS) database (Wang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Based on the GNPS output, we annotated all compounds according to the biosynthetic pathways outlined in NPClassifier (Kim et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The classification of these pathways includes \"amino acids and peptides,\" \"carbohydrates,\" and \"fatty acids,\" which represent primary metabolites, as well as \"alkaloids,\" \"polyketides,\" \"shikimate-phenylpropanoid compounds,\" and \"terpenes,\" which represent secondary metabolites. In the metabolite annotation process, we also incorporated the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. After normalizing the plant compounds (0\u0026ndash;1) using the \"vegan\" package, we calculated the Shannon-Wiener diversity index (α diversity) of plant metabolites using the abundance matrix. The Shannon diversity index integrates the richness and evenness of phytochemicals into a single metric, assigning less weight to compounds with lower abundance in the overall diversity estimate, thereby providing more ecologically relevant insights into phytochemical complexity. Following the exclusion of factor autocorrelation, redundancy analysis (RDA) and Mantel tests were conducted to analyze the responses of the metabolite diversity characteristics of each organ of \u003cem\u003eTetracme quadricornis\u003c/em\u003e to environmental factors.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Soil Physical and Chemical Properties in Different Desert Habitats\u003c/h2\u003e\u003cp\u003eAccording to the standards for desert landform, surface composition, and soil classification (Ditzler 2016; Wu et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), particles with a size of less than 0.002 mm are classified as clay, those between 0.002 mm and 0.05 mm as silt, particles ranging from 0.05 mm to 2 mm as sand, and particles exceeding 2 mm as gravel. Gravel deserts are defined as having a gravel content greater than 10%, while sand deserts are characterized by a sand content exceeding 90%. Saline-alkali deserts are typically low-lying, flat areas where the surface composition includes various salt components, primarily consisting of silt and clay. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a), the sandy desert habitat was dominated by sand particles, accounting for 96.28% of the soil composition. The gravel desert habitat contained 13.63% gravel, exceeding the 10% gravel-content threshold. In contrast, the saline-alkali desert habitat was primarily characterized by silt and clay particles, with an average soil pH of 7.87, indicating alkaline conditions. Soil electrical conductivity (EC) was significantly higher in the saline desert than in the other habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg, h), consistent with substantial accumulation of soluble salts and alkaline compounds. Sandy desert, gravel desert, and saline-alkali desert all belong to desert ecosystems, which are characterized by extreme aridity, sparse precipitation, and high evaporation.\u003c/p\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the elevation, annual average rainfall, and soil moisture content of the gravel desert habitat are significantly higher than those of the other habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, f, and i), whereas the annual average temperature is lower than that of the other habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Soil nutrients and pH levels exhibit variability depending on habitat and soil type. According to the soil nutrient abundance and deficiency indices (Sun et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Gao et al. 2023), SOC content below 6 g/kg is classified as very low, TN content below 0.5 g/kg is classified as severely deficient, and TP content below 0.44 g/kg is classified as very low. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (j, k, and l) indicates that, among the three habitats examined, the SOC and TN contents in the sandy desert and saline-alkali desert habitats are extremely low. Additionally, TP content is severely deficient across all three habitats. Furthermore, the TN content in the sandy desert habitat, measuring at 0.18 g/kg, is also critically low in nutrients. The SOC, TN, and TP contents in the gravel desert habitat are significantly higher than those in the other habitats, while the soil pH and electrical conductivity in the saline-alkali desert habitat are markedly elevated compared to the other habitats.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Composition and diversity of metabolites of \u003cem\u003eTetracme quadricornis\u003c/em\u003e in different habitats\u003c/h2\u003e\u003cp\u003eThe metabolome of various organs of \u003cem\u003eTetracme quadricornis\u003c/em\u003e across different habitats was examined using non-targeted metabolomics technology. A total of 53,082 peaks were detected, with 1,535 identified as known metabolites while the remainder were classified as unknown metabolites. The metabolite composition of each organ of \u003cem\u003eTetracme quadricornis\u003c/em\u003e in diverse habitats is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a). Notably, \u003cem\u003eTetracme quadricornis\u003c/em\u003e comprises primary metabolites such as amino acids, peptides, carbohydrates, and fatty acids, in addition to secondary metabolites including alkaloids, polyketides, shikimates, phenylpropanoids, and terpenoids. Notably, terpenoids exhibited the highest relative abundance among metabolites in desert habitats, with a particular concentration in floral tissues. Conversely, fatty acids emerged as the predominant metabolite class in both gravel desert and saline-alkali desert environments. This suggests that while the types and quantities of metabolites present in the organs of \u003cem\u003eTetracme quadricornis\u003c/em\u003e across the three habitats are comparable, distinct differences exist in the distribution and accumulation of each metabolite.\u003c/p\u003e\u003cp\u003eAlpha diversity is indicative of species richness and diversity. This study employed the Shannon index to quantify metabolite diversity in \u003cem\u003eTetracme quadricornis\u003c/em\u003e. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b), the plant metabolite diversity in the sandy desert habitat exhibited a pronounced hump-shaped pattern across roots, stems, leaves, and flowers, with the stems displaying the highest metabolite diversity; however, this difference was not statistically significant when compared to stem metabolite diversity in other habitats. In the gravel desert habitat, a decreasing trend in plant metabolite diversity was observed among roots, stems, leaves, and flowers, with roots demonstrating the highest metabolite diversity, significantly exceeding that of roots in other habitats. Conversely, in saline-alkali desert habitat, an increasing trend in plant metabolite diversity was noted across roots, stems, leaves, and flowers, with flowers exhibiting the highest metabolite diversity, significantly surpassing that found in both sandy desert and gravel desert habitats. Nonetheless, no significant differences were observed in the metabolite diversity of leaves of \u003cem\u003eTetracme quadricornis\u003c/em\u003e across the three habitats. Overall, \u003cem\u003eTetracme quadricornis\u003c/em\u003e exhibited the greatest metabolite diversity in the saline-alkali desert habitat.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Relationship between metabolite diversity of various organs of \u003cem\u003eTetracme quadricornis\u003c/em\u003e and environment in different habitats\u003c/h2\u003e\u003cp\u003eRDA analysis was conducted to evaluate the environmental characteristics of three habitats and the metabolites present in the roots, stems, leaves, and flowers of \u003cem\u003eTetracme quadricornis\u003c/em\u003e. The metabolites of each organ were treated as response variables, while the environmental factors served as explanatory variables. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a), axes 1 and 2 collectively account for over 90% of the variability in the data. Following the removal of collinearity among the factors, the results of the envfit function test (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) indicated that longitude, average annual precipitation, soil conductivity, and altitude significantly influence the metabolite diversity of various organs of \u003cem\u003eTetracme quadricornis\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eThrough Mantel tests (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) and correlation analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), we revealed the root metabolite diversity exhibited a significant positive correlation with pH, electrical conductivity, organic carbon, total nitrogen, altitude, average annual precipitation, and longitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Stem metabolite diversity was significantly and positively correlated with soil moisture, altitude, and longitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Leaf metabolite diversity demonstrated a significant positive correlation with electrical conductivity, longitude, and latitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Finally, flower metabolite diversity was significantly positively correlated with electrical conductivity, altitude, average annual precipitation, and longitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, metabolite diversity across all organs of \u003cem\u003eTetracme quadricornis\u003c/em\u003e showed a significant positive correlation with longitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eMantel tests (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) and correlation analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) revealed distinct organ-specific patterns of metabolite diversity in \u003cem\u003eTetracme quadricornis\u003c/em\u003e in response to abiotic gradients. Root metabolite diversity exhibited significant positive correlations with soil pH, EC, SOC, STN, altitude, mean annual precipitation (MAP), and longitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Stem metabolites showed stronger associations with soil water content (SWC), altitude, and longitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while leaf metabolite diversity correlated positively with EC, longitude, and latitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Flower metabolites demonstrated significant relationships with EC, altitude, MAP, and longitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, metabolite diversity across all plant organs was consistently positively correlated with longitude (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Changes in metabolite diversity in various organs of \u003cem\u003eTetracme quadricornis\u003c/em\u003e under different habitats\u003c/h2\u003e\u003cp\u003ePlants adapt to various ecological stresses by synthesizing a wide range of metabolites. These metabolites are numerous, structurally diverse, and highly variable in composition and content across time and space (Rai et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The diversity of plant metabolites plays a crucial role in determining both environmental fitness and ecosystem functions and services (Rosenthal and Berenbaum \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Hunter \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Plant metabolomes exhibit variability among different species (Perkowski et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), among varieties of the same species (Lin et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and even among different tissues of the same individual (Dong et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this study, \u003cem\u003eTetracme quadricornis\u003c/em\u003e demonstrated not only differences in metabolite composition among various organs but also variability in metabolite diversity across different habitats. In sandy desert environments, the highest metabolite diversity was observed in the stem of \u003cem\u003eTetracme quadricornis\u003c/em\u003e, whereas the lowest was found in the flower. Given that sandy desert soils primarily consist of sand and exhibit strong fluidity, storms and sandstorms can impair plant photosynthesis and disrupt the transport and storage of energy. Consequently, to ensure survival, plants allocate more resources to withstand wind and sand, resulting in reduced investment in reproduction (Fan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, \u003cem\u003eTetracme quadricornis\u003c/em\u003e in saline-alkali desert habitats tends to allocate more resources to reproduction. Among its various organs, the floral parts exhibit the highest diversity of metabolites, significantly surpassing that found in other habitats and showing a positive correlation with electrical conductivity. Salt desert soils are characterized by high levels of saline-alkali substances and extreme nutrient deficiency, which trigger stress responses in the plant. Nonetheless, the alkaline pH of the soil promotes the growth of reproductive structures. In environments with favorable soil conditions, plants typically invest more resources in the mother plant's growth and development. Conversely, in nutrient-poor habitats, plants prioritize survival by allocating more resources to their progeny, thereby producing a greater number of reproductive structures with improved dispersal capabilities (Lemoine et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCompared to sandy desert and saline-alkali desert habitats, the surface of gravel desert soils is predominantly covered with black gravel, which effectively reduces soil water evaporation, resulting in relatively lower water loss in the topsoil (Zhang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gravel deserts are formed through the alluvial deposition of mountain gravel and are typically found in high-altitude areas, where rainfall tends to increase with elevation (Hu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In arid and semi-arid regions, precipitation serves as the primary source of soil moisture (Yang et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and regions with higher precipitation levels also exhibit greater soil nutrient availability (Xiao et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, the gravel desert examined in this study not only possesses higher soil water content but also demonstrates improved soil nutrient status (SOC, STN, and STP). However, the increased soil compaction and gravel density in gravel deserts impede root penetration and restrict the absorption of infiltrated water by roots (Wang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To survive, plants must enable their root systems to absorb water from the soil, necessitating a greater allocation of resources to the roots. Furthermore, to ensure normal growth, development, and fruiting under stressful conditions, plants need to simultaneously engage in both root-based resource expansion and leaf-based resource conservation (Sheffer et al. 1987). This leads to the observation that the root metabolite diversity of \u003cem\u003eTetracme quadricornis\u003c/em\u003e in the gravel desert is the highest, while its leaf metabolite diversity is comparatively lower.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Changes in metabolite composition of various organs of \u003cem\u003eTetracme quadricornis\u003c/em\u003e under different habitats\u003c/h2\u003e\u003cp\u003eThe accumulation of plant metabolites is influenced by various factors, including genomic evolution, genetic diversity, and environmental stimuli (Taylor and Briggs \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Hectors et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Verma and Shukla \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fang and Luo \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Research indicates that plants subjected to higher stress conditions exhibit increased fatty acid content (Magni et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The annual ephemeral plant \u003cem\u003eTetracme quadricornis\u003c/em\u003e, which inhabits desert ecosystems, contains a significant amount of fatty acids. Lipids represent a crucial class of compounds in plants, serving not only as signaling molecules and energy sources to initiate defense responses under stress but also in regulating cell osmotic pressure to maintain membrane stability, thus mitigating stress damage to plants (Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eCaragana tibetica\u003c/em\u003e, a xerophytic, dwarf, cushion-like shrub found in arid ecosystems, exhibits similar traits, with its metabolites primarily consisting of steroids and lipids (He et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDesert plants frequently experience salinity stress, which adversely impacts plant growth through ion toxicity and osmotic stress (Skliros et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eTetracme quadricornis\u003c/em\u003e, found in saline-alkali desert habitats, endures both salt and alkaline stress; however, our research indicates a significant increase in the levels of fatty acids and amino acids in its tissues compared to plants from other habitats. Amino acids play crucial roles in maintaining osmotic balance (Mibei et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and providing antioxidant defense (Gagn\u0026eacute;-Bourque et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Research on \u003cem\u003ePoa pratensis\u003c/em\u003e and \u003cem\u003eGlycine\u003c/em\u003e max has also demonstrated that plant adaptation to salinity and alkaline stress is primarily associated with the accumulation of amino acids, carbohydrates, fatty acids, and organic acids (Hu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn response to environmental changes, plants enhance not only their energy metabolism but also various secondary metabolic pathways, thereby mitigating oxidative damage through the scavenging of reactive oxygen species (ROS) (Bowne et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In tropical rainforest ecosystems, trees are characterized by an abundance of alkaloids, followed by shikimate-phenylpropanoids and terpenoids (Wang et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003ePotentilla fruticosa\u003c/em\u003e L., a typical deciduous shrub in the Qinghai-Tibet Plateau ecosystem, is particularly rich in secondary metabolites (Liu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This study is conducted in an arid desert area where the dry climate and intense light radiation lead to significant evaporation of surface soil moisture, resulting in low soil moisture content. Generally, drought stress tends to increase amino acid content in plants (Bowne et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Witt et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, in sandy desert habitats characterized by low average rainfall and soil moisture, \u003cem\u003eTetracme quadricornis\u003c/em\u003e exhibits a notably low amino acid content while containing a substantial amount of terpenoids. Research indicates that moderate drought may promote an increase in plant terpenoid content (Delfine et al. 2005), which may serve to protect plants from drought stress (Orme\u0026ntilde;o et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3 The relationship between different habitats and metabolite diversity of \u003cem\u003eTetracme quadricornis\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eThere exists a close correlation between plants and soil nutrients (Ordo\u0026ntilde;ez et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Plants absorb soil nutrients through their roots, and the presence of essential nutrients in the soil can significantly affect the content and function of various metabolites within plants (Deng et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Balcke et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; De Long et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Different habitats exhibit distinct geographical, climatic, and edaphic conditions. However, climatic conditions across various locations may indirectly influence plant metabolite production by affecting changes in soil nutrients (Tu et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Research has demonstrated that soil organic carbon and total nitrogen content tend to increase with increasing longitude (Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, the metabolite diversity of the roots, stems, leaves, and flowers of \u003cem\u003eTetracme quadricornis\u003c/em\u003e was found to be significantly positively correlated with longitude, with root metabolite diversity showing a notable positive correlation with soil organic carbon and total nitrogen. In forest ecosystems, plant metabolite diversity also tends to increase with higher soil nutrient levels (Wang et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWater serves as the primary limiting factor for plant growth within desert ecosystems (Teng et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Research indicates that plants subjected to drought stress exhibit reduced growth and development while accumulating higher concentrations of secondary metabolites compared to those grown under well-watered conditions (Kleinw\u0026auml;chter and Selmar \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liao et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Notably, the metabolite diversity in the roots and flowers of \u003cem\u003eTetracme quadricornis\u003c/em\u003e was positively correlated with average annual precipitation, while the metabolite diversity in stems correlated positively with soil moisture content. We hypothesize that this correlation arises from the study's location in an arid desert region. As an early spring annual short-lived plant, \u003cem\u003eTetracme quadricornis\u003c/em\u003e has evolved adaptations to thrive in extreme drought environments, making it more responsive to water availability compared to other plant groups (Fan et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This may also elucidate the extremely low amino acid content observed in \u003cem\u003eTetracme quadricornis\u003c/em\u003e within arid desert habitats. Consequently, during precipitation events, the roots of \u003cem\u003eTetracme quadricornis\u003c/em\u003e will strive to absorb water from the soil, subsequently transporting energy through the stems to fulfill its nutritional requirements. Similar findings were reported in a study of \u003cem\u003eTriadica sebifera\u003c/em\u003e, where all leaf and root secondary metabolites varied by location, with precipitation significantly influencing the regulation of leaf and root metabolites; notably, phytochemical content was higher in areas with elevated precipitation (Xiao et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePlant metabolite diversity is influenced by altitude gradients (Portella et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), as plants actively defend against high-altitude stressors such as elevated UV radiation, strong winds, and low temperatures (Zhu et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Subalpine regions exhibit greater plant metabolite diversity compared to tropical regions, particularly in terms of alkaloids and carbohydrates, which tend to increase with altitude in subalpine areas (Zhang et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The metabolite diversity of the roots, stems, and flowers of \u003cem\u003eTetracme quadricornis\u003c/em\u003e also demonstrates a positive correlation with altitude. Moreover, in gravel desert habitats at higher elevations, the carbohydrate content of this species is greater than that found in other habitats. Conversely, alkaloid content is highest in lower-altitude sandy desert environments. Research on the perennial herb \u003cem\u003eAconitum pendulum\u003c/em\u003e indicates that both the content and variety of alkaloids are more abundant at lower altitudes compared to higher elevations (Wang et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study integrates plant metabolomes with various environmental factors to investigate the metabolite adaptation characteristics of the annual ephemeral plant \u003cem\u003eTetracme quadricornis\u003c/em\u003e across three distinct desert habitats: sandy desert, gravel desert, and saline-alkali desert. Our findings demonstrate a habitat-specific metabolic allocation in \u003cem\u003eTetracme quadricornis\u003c/em\u003e, with terpenoids exhibiting the highest relative abundance in desert environments, while fatty acids predominated in both gravel desert and saline-alkali desert habitats. Furthermore, we observed significant metabolic divergence not only across different habitat types but also among distinct plant organs within identical environmental conditions. Specifically, stem metabolite diversity was highest in sandy desert, root metabolite diversity was most pronounced in gravel deserts, and flower metabolite diversity peaked in saline-alkali deserts. These patterns are primarily attributed to environmental factors such as longitude, average annual precipitation, soil conductivity, and altitude. The findings suggest that organ-specific metabolic plasticity enables the annual ephemeral plant \u003cem\u003eTetracme quadricornis\u003c/em\u003e to adapt to heterogeneous desert habitats. This research enhances our understanding of the adaptation mechanisms of the annual ephemeral plant \u003cem\u003eTetracme quadricornis\u003c/em\u003e to heterogeneous desert habitats from a metabolic perspective and offers novel insights for analyzing the ecological adaptation of desert ephemeral plants. Given the intricate and multifaceted nature of various factors within desert ecosystems, future research should further investigate the relationship between annual ephemeral plants and their environments, thereby deepening our understanding of survival and adaptation mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32160256, 32560263), the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2024D01A72), and the Graduate Research Innovation Project of Xinjiang Agricultural University (XJAUGRI2025029).\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eThe research was planned and designed by Zhengwei Heng, Lingwei Zhang and Huiliang Liu. Data analysis and manuscript writing were carried out by Zhengwei Heng and Lingwei Zhang. Sample collection involved Zhengwei Heng and Zhengwang Gao, while experiments were conducted by Zhengwei Heng and Yu Wang.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe are grateful for the group mates\u0026rsquo;support and help in collecting samples. We thank Kangwei Jiang for his guidance and advice on data analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlsharif W, Saad MM, Hirt H (2020) Desert Microbes for Boosting Sustainable Agriculture in Extreme Environments. Front Microbiol 11:1666. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2020.01666\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2020.01666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalcke GU, Bennewitz S, Bergau N, Athmer B, Henning A, Majovsky P, Jim\u0026eacute;nez-G\u0026oacute;mez JM, Hoehenwarter W, Tissier A (2017) Multi-Omics of Tomato Glandular Trichomes Reveals Distinct Features of Central Carbon Metabolism Supporting High Productivity of Specialized Metabolites. Plant Cell 29(5):960\u0026ndash;983. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1105/tpc.17.00060\u003c/span\u003e\u003cspan address=\"10.1105/tpc.17.00060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBowne JB, Erwin TA, Juttner J, Schnurbusch T, Langridge P, Bacic A, Roessner U (2012) Drought responses of leaf tissues from wheat cultivars of differing drought tolerance at the metabolite level. Mol Plant 5(2):418\u0026ndash;429. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/mp/ssr114\u003c/span\u003e\u003cspan address=\"10.1093/mp/ssr114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao P, Yang J, Xia L, Zhang Z, Wu Z, Hao Y, Liu P, Wang C, Li C, Yang J, Lai J, Li X, Deng M, Wang S (2024) Two gene clusters and their positive regulator SlMYB13 that have undergone domestication-associated negative selection control phenolamide accumulation and drought tolerance in tomato. Mol Plant 17(4):579\u0026ndash;597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molp.2024.02.003\u003c/span\u003e\u003cspan address=\"10.1016/j.molp.2024.02.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Long JR, Sundqvist MK, Gundale MJ, Giesler R, Wardle DA (2016) Effects of elevation and nitrogen and phosphorus fertilization on plant defence compounds in subarctic tundra heath vegetation. Funct Ecol 30(2):314\u0026ndash;325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.12493\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.12493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng B, Li Y, Xu D, Ye Q, Liu G (2019) Nitrogen availability alters flavonoid accumulation in Cyclocarya paliurus via the effects on the internal carbon/nitrogen balance. Sci Rep 9(1):2370. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-38837-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-38837-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDı́az S, Cabido M (2001) Vive la diff\u0026eacute;rence: plant functional diversity matters to ecosystem processes. Trends Ecol Evol 16(11):646\u0026ndash;655. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0169-5347(01)02283-2\u003c/span\u003e\u003cspan address=\"10.1016/S0169-5347(01)02283-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDitzler CA (2017) Soil Properties and Classification (Soil Taxonomy). In: West L, Singer M, Hartemink A (eds) The Soils of the USA. World Soils Book Series. Springer, Cham, pp 29\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-41870-4_3\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-41870-4_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong X, Chen W, Wang W, Zhang H, Liu X, Luo J (2014) Comprehensive profiling and natural variation of flavonoids in rice. J Integr Plant Biol 56(9):876\u0026ndash;886. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jipb.12204\u003c/span\u003e\u003cspan address=\"10.1111/jipb.12204\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDu BCX, Cheng YX, Wu L (2021) Analysis of negative correlation between vegetation and soil salinization in Junggar Basin. Acta Ecol Sin 23:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5846/stxb202009242485\u003c/span\u003e\u003cspan address=\"10.5846/stxb202009242485\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan B, Zhao C, Zhang X, Sun K (2018) Impacts of sand burial and wind erosion on regeneration and growth of a desert clonal shrub. Front Plant Sci 9:1696. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2018.01696\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2018.01696\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan L, Li Y, Ma J, Mao J, Wang L (2022) Snow and rainfall independently affect the density, composition and productivity of ephemerals in a temperate desert. Sci Total Environ 807(Pt3):151033. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.151033\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.151033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang C, Fernie AR, Luo J (2019) Exploring the diversity of plant metabolism. Trends Plant Sci 24(1):83\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tplants.2018.09.006\u003c/span\u003e\u003cspan address=\"10.1016/j.tplants.2018.09.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang C, Luo J (2019) Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism. Plant journal: cell Mol biology 97(1):91\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/tpj.14097\u003c/span\u003e\u003cspan address=\"10.1111/tpj.14097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang JY, Wang XP, Shen ZH, Tang ZY, He JS, Yu D, Jiang Y, Wang ZH, Zheng CY, Zhu JL, Guo ZD (2009) Methods and protocols for plant community inventory. Biodivers Sci 17(6):533. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biodiversity-science.net/EN/\u003c/span\u003e\u003cspan address=\"https://www.biodiversity-science.net/EN/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3724/SP.J.1003.2009.09253\u003c/span\u003e\u003cspan address=\"10.3724/SP.J.1003.2009.09253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGagn\u0026eacute;-Bourque F, Bertrand A, Claessens A, Aliferis KA, Jabaji S (2016) Alleviation of drought stress and metabolic changes in timothy (\u003cem\u003ePhleum pratense\u003c/em\u003e L.) colonized with Bacillus subtilis B26. Front Plant Sci 7:584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2016.00584\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2016.00584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGong Z, Xiong L, Shi H, Yang S, Herrera-Estrella LR, Xu G, Chao DY, Li J, Wang PY, Qin F, Li J, Ding Y, Shi Y, Wang Y, Yang Y, Guo Y, Zhu JK (2020) Plant abiotic stress response and nutrient use efficiency. Sci China Life Sci 63(5):635\u0026ndash;674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11427-020-1683-x\u003c/span\u003e\u003cspan address=\"10.1007/s11427-020-1683-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGutierrez JR, Whitford WG (1987) Chihuahuan desert annuals: importance of water and nitrogen. Ecology 68(6):2032\u0026ndash;2045. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1939894\u003c/span\u003e\u003cspan address=\"10.2307/1939894\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe M, Han Y, Gao Y, Han M, Duan L (2024) Decoding the metabolomic responses of \u003cem\u003eCaragana tibetica\u003c/em\u003e to livestock grazing in fragile ecosystems. Front Plant Sci 15:1339424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2024.1339424\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2024.1339424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHectors K, Van Oevelen S, Geuns J, Guisez Y, Jansen MA, Prinsen E (2014) Dynamic changes in plant secondary metabolites during UV acclimation in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. Physiol Plant 152(2):219\u0026ndash;230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ppl.12168\u003c/span\u003e\u003cspan address=\"10.1111/ppl.12168\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu J, Lv YH, Fu BJ, Sun JF (2017) Soil hydrothermal variation and rainfall pulses along altitudinal gradient in pailugou watershed in the Qilian Mountain. Arid Zone Res 34(1):151\u0026ndash;160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/329528501\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/329528501\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu LX, Zhang PP, Jiang Y, Fu JM (2015) Metabolomic analysis revealed differential adaptation to salinity and alkalinity stress in Kentucky bluegrass (\u003cem\u003ePoa pratensis\u003c/em\u003e). Plant Mol biology Report 33(1):56\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11105-014-0722-4\u003c/span\u003e\u003cspan address=\"10.1007/s11105-014-0722-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHunter MD (2016) The phytochemical landscape: linking trophic interactions and nutrient dynamics. Princeton University Press, Princeton. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/9781400881208\u003c/span\u003e\u003cspan address=\"10.1515/9781400881208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKamboj A, Sangha MK, Devi V, Kumar P, Pathak M, Singla D (2024) Enhancing germination and growth in wild okra genotypes through gibberellic acid priming. Sci Hort 334:113332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scienta.2024.113332\u003c/span\u003e\u003cspan address=\"10.1016/j.scienta.2024.113332\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan N, Bano A, Rahman MA, Rathinasabapathi B, Babar MA (2019) UPLC-HRMS-based untargeted metabolic profiling reveals changes in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e) metabolome following long-term drought stress. Plant Cell Environ 42(1):115\u0026ndash;132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pce.13195\u003c/span\u003e\u003cspan address=\"10.1111/pce.13195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim HW, Wang M, Leber CA, Nothias LF, Reher R, Kang KB, van der Hooft JJJ, Dorrestein PC, Gerwick WH, Cottrell GW (2021) NPClassifier:a deep neural network-based structural classification tool for natural products. J Nat Prod 84(11):2795\u0026ndash;2807. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jnatprod.1c00399\u003c/span\u003e\u003cspan address=\"10.1021/acs.jnatprod.1c00399\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKleinw\u0026auml;chter M, Selmar D (2015) New insights explain that drought stress enhances the quality of spice and medicinal plants: potential applications. Agron Sustain Dev 35:121\u0026ndash;131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13593-014-0260-3\u003c/span\u003e\u003cspan address=\"10.1007/s13593-014-0260-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar S, Shah SH, Vimala Y, Jatav HS, Ahmad P, Chen Y, Siddique KHM (2022) Abscisic acid: Metabolism, transport, crosstalk with other plant growth regulators, and its role in heavy metal stress mitigation. Front Plant Sci 13:972856. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.972856\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.972856\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumaraswamy KG, Kushalappa AC, Choo TM, Dion Y, Rioux S (2011) Mass spectrometry based metabolomics to identify potential biomarkers for resistance in barley against fusarium head blight (\u003cem\u003eFusarium graminearum\u003c/em\u003e). J Chem Ecol 37(8):846\u0026ndash;856. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10886-011-9989-1\u003c/span\u003e\u003cspan address=\"10.1007/s10886-011-9989-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLan HY, Zhang FC (2008) Reviews on special mechanisms of adaptability of early-spring ephemeral plants to desert habitats in Xinjiang. Acta Bot Boreali-Occidentalia Sinica 28(7):1478\u0026ndash;1485\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLemoine NP, Doublet D, Salminen JP, Burkepile DE, Parker JD (2017) Responses of plant phenology, growth, defense, and reproduction to interactive effects of warming and insect herbivory. Ecology 98(7):1817\u0026ndash;1828. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecy.1855\u003c/span\u003e\u003cspan address=\"10.1002/ecy.1855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi CC, Zeng QC, Jia PL, An SS (2020) Characteristics of soil aggregate stability and corrosion resistance longitude change in the Loess Plateau. Acta Ecol Sin 40:2039\u0026ndash;2048. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5846/stxb201903020388\u003c/span\u003e\u003cspan address=\"10.5846/stxb201903020388\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi HD, Shen WH, Zou CX, Jiang J, Fu LN, She GH (2013) Spatio-temporal variability of soil moisture and its effect on vegetation in a desertified aeolian riparian ecotone on the Tibetan Plateau, China. J Hydrol 479:215\u0026ndash;225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2012.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2012.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi M, Xu J, Wang X, Fu H, Zhao M, Wang H, Shi L (2018) Photosynthetic characteristics and metabolic analyses of two soybean genotypes revealed adaptive strategies to low-nitrogen stress. J Plant Physiol 229:132\u0026ndash;141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jplph.2018.07.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jplph.2018.07.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiao P, Liu D, Xu TR, Yang Y, Cui XM (2017) Soil water stress attenuate the growth and development but enhance the saponin synthesis of \u003cem\u003ePanax notogesing\u003c/em\u003e during flowering stage. Ind Crops Prod 108:95\u0026ndash;105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.indcrop.2017.05.052\u003c/span\u003e\u003cspan address=\"10.1016/j.indcrop.2017.05.052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin H, Rao J, Shi J, Hu C, Cheng F, Wilson ZA, Zhang D, Quan S (2014) Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars. J Integr Plant Biol 56(9):826\u0026ndash;836. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jipb.12228\u003c/span\u003e\u003cspan address=\"10.1111/jipb.12228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu W, Yin D, Li N, Hou X, Wang D, Li D, Liu J (2016) Influence of environmental factors on the active substance production and antioxidant activity in \u003cem\u003ePotentilla fruticosa\u003c/em\u003e L. and its quality assessment. Sci Rep 6:28591. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep28591\u003c/span\u003e\u003cspan address=\"10.1038/srep28591\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Zhang LN, Liu XH, Huang XM, Jia RX (2023) Research progress from individual plant physiological response to ecological model prediction under drought stress. Acta Ecol Sin 43:10042\u0026ndash;10053. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.cnki.net/doi/\u003c/span\u003e\u003cspan address=\"https://link.cnki.net/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20103/j.stxb.202304070703\u003c/span\u003e\u003cspan address=\"10.20103/j.stxb.202304070703\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu ZQ, Liu T, Zhang R, Cheng HL (2011) Species diversity and spatial differentiation of ephemeral plant community in southern Gurbantunggut Desert. Chin J Ecol 30(1):45\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cje.net.cn/CN/Y2011/V30/I01/45\u003c/span\u003e\u003cspan address=\"https://www.cje.net.cn/CN/Y2011/V30/I01/45\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu J, Gan L, Tan D, Baskin CC, Baskin JM (2021) Effects of the habitat-soil factor on transgenerational plasticity in a diaspore-polymorphic cold desert annual. Plant Soil 461:355\u0026ndash;367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-020-04792-x\u003c/span\u003e\u003cspan address=\"10.1007/s11104-020-04792-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMagni NN, Ver\u0026iacute;ssimo ACS, Silva H, Pinto DCGA (2023) Metabolomic profile of \u003cem\u003eSalicornia perennis\u003c/em\u003e plant\u0026rsquo;s organs under diverse \u003cem\u003ein situ stress\u003c/em\u003e: the ria de Aveiro salt marshes case. Metabolites 13(2):280. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/metabo13020280\u003c/span\u003e\u003cspan address=\"10.3390/metabo13020280\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMibei EK, Owino WO, Ambuko J, Giovannoni JJ, Onyango AN (2018) Metabolomic analyses to evaluate the effect of drought stress on selected African Eggplant accessions. J Sci Food Agric 98(1):205\u0026ndash;216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jsfa.8458\u003c/span\u003e\u003cspan address=\"10.1002/jsfa.8458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoura JCMS, Bonine CAV, de Oliveira Fernandes Viana J, Dornelas MC, Mazzafera P (2010) Abiotic and biotic stresses and changes in the lignin content and composition in plants. J Integr Plant Biol 52(4):360\u0026ndash;376. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1744-7909.2010.00892.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1744-7909.2010.00892.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrdo\u0026ntilde;ez JC, Van Bodegom PM, Witte JPM, Wright IJ, Reich PB, Aerts R (2009) A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob Ecol Biogeogr 18(2):137\u0026ndash;149. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1466-8238.2008.00441.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1466-8238.2008.00441.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrme\u0026ntilde;o E, M\u0026eacute;vy JP, Vila B, Bousquet-M\u0026eacute;lou A, Greff S, Bonin G, Fernandez C (2007) Water deficit stress induces different monoterpene and sesquiterpene emission changes in Mediterranean species. Relationship between terpene emissions and plant water potential. Chemosphere 67(2):276\u0026ndash;284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chemosphere.2006.10.029\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2006.10.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng M, He H, Wang Z, Li G, Lv X, Pu X, Zhuang L (2022) Responses and comprehensive evaluation of growth characteristics of ephemeral plants in the desert-oasis ecotone to soil types. J Environ Manage 316:115288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jenvman.2022.115288\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2022.115288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerkowski J, Stuper K, Buśko M, G\u0026oacute;ral 2, Kaczmarek A, Jeleń H (2012) Differences in metabolomic profiles of the naturally contaminated grain of barley, oats and rye. J Cereal Sci 56(3):544\u0026ndash;551. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcs.2012.07.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jcs.2012.07.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePlaxton WC, Tran HT (2011) Metabolic adaptations of phosphate-starved plants. Plant Physiol 156(3):1006\u0026ndash;1015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.111.175281\u003c/span\u003e\u003cspan address=\"10.1104/pp.111.175281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePortella RO, Cordeiro EMG, Marques APS, Ming LC, Zucchi MI, Lima MP, Martins ER, Hantao LW, Sawaya ACHF, Semir J, Pinheiro JB, Marques MOM (2021) Evidence of altitudinal gradient modifying genomic and chemical diversity in populations of \u003cem\u003eLychnophora pinaster\u003c/em\u003e Mart. Phytochemistry 192:112898. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phytochem.2021.112898\u003c/span\u003e\u003cspan address=\"10.1016/j.phytochem.2021.112898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiu Y, Liu T, Zhang C, Liu B, Pan B, Wu S, Chen X (2018) Mapping Spring Ephemeral Plants in Northern Xinjiang. China Sustain 10(3):804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su10030804\u003c/span\u003e\u003cspan address=\"10.3390/su10030804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRai A, Saito K, Yamazaki M (2017) Integrated omics analysis of specialized metabolism in medicinal plants. Plant journal: cell Mol biology 90(4):764\u0026ndash;787. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/tpj.13485\u003c/span\u003e\u003cspan address=\"10.1111/tpj.13485\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRinschen MM, Ivanisevic J, Giera M, Siuzdak G (2019) Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol 20(6):353\u0026ndash;367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41580-019-0108-4\u003c/span\u003e\u003cspan address=\"10.1038/s41580-019-0108-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenthal GA, Berenbaum MR (1991) Herbivores: their interactions with secondary plant metabolites: volume I: the chemical participants. Academic\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRossnerova A, Izzotti A, Pulliero A, Bast A, Rattan SIS, Rossner P (2020) The molecular mechanisms of adaptive response related to environmental stress. Int J Mol Sci 21(19):7053. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms21197053\u003c/span\u003e\u003cspan address=\"10.3390/ijms21197053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheffer KM, Dunn JH, Minner DD (2022) Summer drought response and rooting depth of three cool-season turfgrasses. HortScience 296\u0026ndash;297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21273/hortsci.22.2.296\u003c/span\u003e\u003cspan address=\"10.21273/hortsci.22.2.296\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkliros D, Kalloniati C, Karalias G, Skaracis GN, Rennenberg H, Flemetakis E (2018) Global metabolomics analysis reveals distinctive tolerance mechanisms in different plant organs of lentil (\u003cem\u003eLens culinaris\u003c/em\u003e) upon salinity stress. Plant Soil 429(1):451\u0026ndash;468. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-018-3691-9\u003c/span\u003e\u003cspan address=\"10.1007/s11104-018-3691-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStevens DL, Olsen AR (2004) Spatially balanced sampling of natural resources. J Am Stat Assoc 94(1\u0026ndash;3):1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1198/016214504000000250\u003c/span\u003e\u003cspan address=\"10.1198/016214504000000250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun F, L\u0026uuml; Y, Wang J, Hu J, Fu B (2015) Soil moisture dynamics of typical ecosystems in response to precipitation: A monitoring-based analysis of hydrological service in the Qilian Mountains. CATENA 129:63\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.catena.2015.03.001\u003c/span\u003e\u003cspan address=\"10.1016/j.catena.2015.03.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun H, Zhang J, Jiang L, Lv Y, Wang Y (2019) Study on the abundance-deficiency index of soil available P and K and appropriate nutrient application rates of P and K for rice in China. China Rice 51(05):895\u0026ndash;902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.zgdm.net/CN/\u003c/span\u003e\u003cspan address=\"http://www.zgdm.net/CN/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3969/j.issn.1006-8082.2018.05.001\u003c/span\u003e\u003cspan address=\"10.3969/j.issn.1006-8082.2018.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaylor LP, Briggs WR (1990) Genetic regulation and photocontrol of anthocyanin accumulation in maize seedlings. Plant Cell 2(2):115\u0026ndash;127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1105/tpc.2.2.115\u003c/span\u003e\u003cspan address=\"10.1105/tpc.2.2.115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeng ZQ, Fu HQ, Jia SH, Meng WW, Dai RJ, Deng YL (2011) Review of current progress in the metabolomics for plant response to abiotic stress. Chin J Plant Ecol 35(1):110\u0026ndash;118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.plant-ecology.com/EN/\u003c/span\u003e\u003cspan address=\"https://www.plant-ecology.com/EN/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3724/SP.J.1258.2011.00110\u003c/span\u003e\u003cspan address=\"10.3724/SP.J.1258.2011.00110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTu KY, Tsai SF, Guo TW, Lin HH, Yang ZW, Liao CT, Chuang WP (2018) The Role of Plant Abiotic Factors on the Interactions Between \u003cem\u003eCnaphalocrocis medinalis\u003c/em\u003e (Lepidoptera: Crambidae) and its Host Plant. Environ Entomol 47(4):857\u0026ndash;866. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ee/nvy066\u003c/span\u003e\u003cspan address=\"10.1093/ee/nvy066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerma N, Shukla S (2015) Impact of various factors responsible for fluctuation in plant secondary metabolites[J]. J Appl Res Med aromatic plants 2(4):105\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jarmap.2015.09.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jarmap.2015.09.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang JJ, Lou HY, Liu Y, Han HP, Ma FW, Pan WD, Chen Z (2022b) Profiling alkaloids in \u003cem\u003eAconitum pendulum\u003c/em\u003e N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics. Phytochemistry 195:113047. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phytochem.2021.113047\u003c/span\u003e\u003cspan address=\"10.1016/j.phytochem.2021.113047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L, Ma KB, Lu ZG, Ren SX, Jiang HR, Cui JW, Chen G, Teng NJ, Lam HM, Jin B (2020) Differential physiological, transcriptomic and metabolomic responses of Arabidopsis leaves under prolonged warming and heat shock. BMC Plant Biol 20(1):86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12870-020-2292-y\u003c/span\u003e\u003cspan address=\"10.1186/s12870-020-2292-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang LJ, Sun DY, Zhao CY, Li JY, Sheng Y (2011) Plant architecture characteristics of \u003cem\u003eHaloxylon ammodendron\u003c/em\u003e and \u003cem\u003eHaloxylon persicum\u003c/em\u003e in Zhungar Basin. Acta Ecol Sin 31(17):4952\u0026ndash;4960. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciengine.com/AES/doi/\u003c/span\u003e\u003cspan address=\"https://www.sciengine.com/AES/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.0000/j.1000-0933.2011311249524960\u003c/span\u003e\u003cspan address=\"10.0000/j.1000-0933.2011311249524960\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang LX, Li ZW, Wang DY, Liao SS, Nie XD, Liu YJ (2022a) Factors controlling soil organic carbon with depth at the basin scale. CATENA 217:106478. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.catena.2022.106478\u003c/span\u003e\u003cspan address=\"10.1016/j.catena.2022.106478\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, Nguyen DD, Watrous J, Kapono CA, Luzzatto-Knaan T, Porto C, Bouslimani A, Melnik AV, Meehan MJ, Liu WT, Cr\u0026uuml;semann M, Boudreau PD, Esquenazi E, Sandoval-Calder\u0026oacute;n M, Kersten RD et al (2016) Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 34(8):828\u0026ndash;837. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nbt.3597\u003c/span\u003e\u003cspan address=\"10.1038/nbt.3597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang T, Liu B, Zhang X, Wang M, Tan D (2024) Variations in root architecture traits and their association with organ mass fraction of common annual ephemeral species in the desert of northern Xinjiang. Ecol Evol 14(2):e10908. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.10908\u003c/span\u003e\u003cspan address=\"10.1002/ece3.10908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, He Y, Sedio BE, Jin L, Ge X, Glomglieng S, Cao M, Yang J, Swenson NG, Yang J (2023) Phytochemical diversity impacts herbivory in a tropical rainforest tree community. Ecol Lett 26(11):1898\u0026ndash;1910. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.14308\u003c/span\u003e\u003cspan address=\"10.1111/ele.14308\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWitt S, Galicia L, Lisec J, Cairns J, Tiessen A, Araus JL, Palacios-Rojas N, Fernie AR (2012) Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress. Mol Plant 5(2):401\u0026ndash;417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/mp/ssr102\u003c/span\u003e\u003cspan address=\"10.1093/mp/ssr102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWitt S, Galicia L, Lisec J, Cairns J, Tiessen A, Araus JL, Palacios-Rojas N, Fernie AR (2012) Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress. Mol Plant 5(2):401\u0026ndash;417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/mp/ssr102\u003c/span\u003e\u003cspan address=\"10.1093/mp/ssr102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu JL, Yang FX, Zhou J, Zhang ZW (2013) Desert types and characteristics in the Qira River Basin. Arid Land Geogr 36(5):803\u0026ndash;811. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://alg.xjegi.com/CN/Y2013/V36/I5/803\u003c/span\u003e\u003cspan address=\"http://alg.xjegi.com/CN/Y2013/V36/I5/803\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao L, Huang W, Carrillo J, Ding JQ, Siemann E (2024) Interactive effects of soils, local environmental conditions and herbivores on secondary chemicals in tallow tree. J Plant Ecol 17(4):rtae062. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jpe.ac.cn/EN/\u003c/span\u003e\u003cspan address=\"https://www.jpe.ac.cn/EN/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jpe/rtae062\u003c/span\u003e\u003cspan address=\"10.1093/jpe/rtae062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao TW, Song F, Vu DQ, Feng Y, Ge XJ (2024) The evolution of ephemeral flora in Xinjiang, China: insights from plastid phylogenomic analyses of Brassicaceae. BMC Plant Biol 24(1):111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12870-024-04796-0\u003c/span\u003e\u003cspan address=\"10.1186/s12870-024-04796-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang C, Yang H, Xu Q, Wang Y, Sang Z, Yuan H (2020) Comparative metabolomics analysis of the response to cold stress of resistant and susceptible Tibetan hulless barley (\u003cem\u003eHordeum distichon\u003c/em\u003e). Phytochemistry 174:112346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phytochem.2020.112346\u003c/span\u003e\u003cspan address=\"10.1016/j.phytochem.2020.112346\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang DS, Zhang J, Li MX, Shi LX (2017) Metabolomics analysis reveals the salt-tolerant mechanism in \u003cem\u003eGlycine soja\u003c/em\u003e. J Plant Growth Regul 36(2):460\u0026ndash;471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00344-016-9654-6\u003c/span\u003e\u003cspan address=\"10.1007/s00344-016-9654-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang L, Zhang H, Chen L (2018) Identification on threshold and efficiency of rainfall replenishment to soil water in semi-arid loess hilly areas. Sci China Earth Sci 61(3):292\u0026ndash;301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11430-017-9140-0\u003c/span\u003e\u003cspan address=\"10.1007/s11430-017-9140-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan X, Smith NSS, Moghe GD (2025) Analysis of plant metabolomics data using identification-free approaches. Appl plant Sci 13(4):e70001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/aps3.70001\u003c/span\u003e\u003cspan address=\"10.1002/aps3.70001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZang YX, Min XJ, de Dios VR, Ma JY, Sun W (2020) Extreme drought affects the productivity, but not the composition, of a desert plant community in Central Asia differentially across microtopographies. Sci Total Environ 717:137251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2020.137251\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2020.137251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang HX, Jia RL, Zhao PS, Zhao X, Cui XY (2025) A review of the stress physiology and ecology of desert plants. J Desert Res 45(3):72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.desert.ac.cn/EN/\u003c/span\u003e\u003cspan address=\"http://www.desert.ac.cn/EN/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7522/j.issn.1000-694X.2025.00060\u003c/span\u003e\u003cspan address=\"10.7522/j.issn.1000-694X.2025.00060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang JJ, Bai ZK, Yang BY (2021) Gravel curtain layer in the desert open-pit mining area of Xinjiang: Ecological damage and reconstruction method. Earth Sci Front 28(4):142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.cnki.net/doi/\u003c/span\u003e\u003cspan address=\"https://link.cnki.net/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13745/j.esf.sf.2020.10.15\u003c/span\u003e\u003cspan address=\"10.13745/j.esf.sf.2020.10.15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang K, Shi Y, Cui X, Yue P, Li K, Liu X, Tripathi BM, Chu H (2019) Salinity is a key determinant for soil microbial communities in a desert ecosystem. Msystems 4(1):e00225\u0026ndash;e00218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/mSystems.00225-18\u003c/span\u003e\u003cspan address=\"10.1128/mSystems.00225-18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang R, Liu T (2012) Plant species diversity and community classification in the southern Gurbantunggut Desert. Acta Ecol Sin 32(19):6056\u0026ndash;6066. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5846/stxb201109021289\u003c/span\u003e\u003cspan address=\"10.5846/stxb201109021289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Worthy SJ, Xu S, He Y, Wang X, Song X, Cao M, Yang J (2024) Phytochemical diversity and their adaptations to abiotic and biotic pressures in fine roots across a climatic gradient. Sci Total Environ 927:172051. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2024.172051\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2024.172051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu L, Pieti\u0026auml;inen M, Kontturi J, Turkkelin A, Elomaa P, Teeri TH (2022) Polyketide reductases in defense-related parasorboside biosynthesis in \u003cem\u003eGerbera hybrida\u003c/em\u003e share processing strategies with microbial polyketide synthase systems. New Phytol 236(1):296\u0026ndash;308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.18328\u003c/span\u003e\u003cspan address=\"10.1111/nph.18328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Habitat heterogeneity, Annual ephemeral plants, Tetracme quadricornis, metabolome, Ecological adaptation strategies","lastPublishedDoi":"10.21203/rs.3.rs-7625560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7625560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background and Aims\n\nThe complex metabolic regulation mechanisms of plants enable them to maintain an orderly metabolic process despite varying external conditions, thereby supporting normal growth and development. Yet, the response of metabolites in annual ephemeral plants to changing desert environments is still poorly understood.\n\nMethods\n\nWe utilized plant metabolomics in tandem with multivariate statistical analysis to delve into the metabolic adaptation strategies exhibited by Tetracme quadricornis, a quintessential annual ephemeral plant, within three distinct desert habitats: sandy desert, gravel desert, and saline-alkali desert.\n\nResults\n\nAnalysis of metabolic profiles revealed distinct habitat-specific patterns in Tetracme quadricornis, with terpenoids demonstrating the highest relative abundance in sandy desert habitats, while fatty acids predominated in both gravel desert and saline-alkali desert environments. The diversity of metabolites in both roots and flowers varied significantly across different habitats. Moreover, even within the same habitat, metabolite profiles exhibited notable organ-specific variability. The highest metabolite diversity was observed in stems in sandy desert habitats, roots in gravel desert, and flowers in saline-alkali desert. Metabolite diversity in Tetracme quadricornis was significantly positively correlated with several key ecological factors, including soil electrical conductivity, altitude, and longitude.\n\nConclusions\n\nThe annual ephemeral plant Tetracme quadricornis employs organ-specific metabolic plasticity to adapt to heterogeneous desert environments. This adaptive strategy is driven by environmental factors and manifested through dynamic nutrient reallocation and a growth–defense trade-off, ultimately enhancing its ecological fitness in arid ecosystems.","manuscriptTitle":"Metabolic characteristics of the annual ephemeral plant Tetracme quadricornis in heterogeneous habitats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 09:55:49","doi":"10.21203/rs.3.rs-7625560/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-16T03:14:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2025-09-17T14:02:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T10:31:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-09-16T00:01:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b41934ae-4f8f-43b3-a4ba-c4565d7c3897","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-30T09:55:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 09:55:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7625560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7625560","identity":"rs-7625560","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}