Temperature-mediated climatic factors dominate intraspecific variation and ecological adaptation strategies for traits of Phragmites australis rather than soil properties

Temperature-mediated climatic factors dominate intraspecific variation and ecological adaptation strategies for traits of Phragmites australis rather than soil properties | 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 Temperature-mediated climatic factors dominate intraspecific variation and ecological adaptation strategies for traits of Phragmites australis rather than soil properties Zhichao XU, Huamin Liu, Lu Wen, Jinghui Zhang, Xiaoyun Xin, Jinpeng Hu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2103651/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims Widespread species of Phragmites australis , has a high degree of intraspecific variation in functional traits during external climatic and environmental changes. However, the underlying mechanism of the environmental gradient changes at regional scale on intraspecific variation and adaptation strategies of species functional traits are still not well understood. Methods Morphological traits, nutrient contents, and stoichiometric ratios of P. australis in lakeshore wetlands of semi-arid and arid regions in the Inner Mongolia Plateau were investigated to reveal the variability of functional traits at different regional scales and the influencing factors and to reveal the ecological adaptation strategies of P. australis in different regions through plant economic spectrum. Results The functional traits of P. australis varied significantly within the species at different scales, and the variation has a significant latitude pattern. Climatic factors determine the intraspecific variation of the functional traits of P. australis , and the influence of soil properties is small. Plant economic spectrum theory is also applicable to the functional traits of various organs and whole plants of P. australis , and different ecological adaptation strategies are confirmed across arid and semi-arid regions. Conclusions Intraspecific variation of functional traits of P. australis originates from temperature-mediated climatic differences brought about by sampling geographic locations, rather than the soil properties of the sampling locations. The utilization and assimilation of resources of P. australis are conservative in arid regions, while in semi-arid regions it is an acquisition strategy. functional traits intraspecific variation lakeshore wetland plant economics spectrum Phragmites australis spatial scales Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Plant functional traits are adaptive and effect traits that affect plant survival, growth and reproduction, reflecting plant responses to the environment and measurable traits that can influence ecosystem function (Liu and Ma, 2015). It has proved useful for predicting plant community assembly (Lavorel and Garnier, 2010 ), which can respond strongly to environmental and climate change and can serve as an excellent ecological indicator role (Joswig et al., 2022 ). Functional traits are morphological, physiological, phenological and behavioral characteristics that respond to the environment and directly or indirectly affect plant adaptation and related ecosystem properties (Violle et al., 2007 , Moore et al., 2018 ). Variation in plant functional traits is the result of natural selection and feedback from plants to their environment and reflects variation in the relative of plant’s adaptive mechanisms (Liu et al., 2010 , Meyerson et al., 2016 ). Quantifying the variation pattern of critical traits related to morphological traits, nutrient contents and stoichiometric ratios at each of multiple spatial scales and driving factors could provide an opportunity to evaluate the relative importance of these drivers for species evolutionary adaptation (Ackerly and Cornwell, 2007 , Joswig et al., 2022 ). The cosmopolitan species Phragmites australis is a tall wetland grass with high intraspecific variation, making it a suitable model species for studying the underlying mechanisms of intraspecific trait variation (Kueffer et al., 2013 , Eller et al., 2017 ). The species occurs over an extensive latitudinal range and in a broad range of ecological niches and habitats(Packer et al., 2017 ). Species with general distributions tend to be more traits variable and have high intraspecific variation and show a continuous geographic gradient, especially across different latitudes as a consequence of acclimation across a broad range of environmental conditions (Ren et al., 2020 , Siefert et al., 2015 ). Although plant traits have been extensively studied under different treatments (e.g. grazing, nutrient addition, etc.), intraspecific variation at the regional scale under natural conditions have rarely been considered (Pigliucci, 2010 ). Many studies have shown that large-scale variation in individual plant traits is associated with environmental gradients. Early plant biogeographers believed that climate and soil together determined the form and function of plants (Bruelheide et al., 2018 , Simpson et al., 2016). However, the study of plant functional trait variability characteristics and their adaptation to external conditions is significant for understanding plant community construction in different regions and their response mechanisms to habitats (Suding et al., 2008 , Kong et al., 2021 ). The concept of economic spectrum of plant leaves has attracted wide attention from ecologists. By applying the "investment-benefit trade-off" theory of economics to the study of plant resource allocation, the interrelationships among plant functional traits and ecological adaptation strategies can be quantitatively analyzed (Osnas et al., 2013 , Niinemets, 2015 ). Wright et al. ( 2004 ) defined a continuously varying spectrum of leaf functional trait combinations on a global scale, the "leaf economic spectrum (LES)", to illustrate trade-off strategies between vascular plant resource acquisition and storage, which can be expressed through the range of variation in trait indicators and their quantitative relationships (Wright et al., 2004 ). Since then, leaf economic spectrum studies have been extended to stems (SES), roots (RES) and whole plants (WPES), community structures and ecosystem types at different levels by related ecologists (Li et al., 2019 , Pérez-Ramos et al., 2012, Kong et al., 2019 ). One end of the economic spectrum represents the "fast investment-gain" strategy of plants, where species with this feature have cheap tissue investment and fast return on investment (acquisition strategy); while the other end represents the "slow investment-gain" strategy of plants, where species with this feature have expensive tissue investment and slow return on investment (conservative strategy) (Wright et al., 2004 , Reich and Cornelissen, 2014 ). As a kind of organ-level trade-off strategy spectrum, plant economic spectrum studies can better describe and generalize plant traits and functional types, providing new thinking perspectives and explore avenues for researching plant functional traits and ecological adaptation strategies of species. The Inner Mongolia Plateau is located in the semi-arid and arid climate region of northern China, with a large longitude span, and the climate from east to the west shows the transition characteristics from semi-arid to the arid region. Most of the lakes in the region are inland-type lakes, and the species and community structure of the lakeshore wetlands are significantly influenced by the geographical location and climate environment. As a dominant species in the lakeshore wetlands of the arid and semi-arid region of Inner Mongolia, P. australis provides suitable conditions for studying possible latitudinal variation in morphology, growth and phenological traits in population distribution and climate response. In this study, the variation in morphological, nutrient and stoichiometry traits of P. australis under geographic location and the drivers of trait variation were analyzed, and the ecological adaptation strategies under different regional characteristics were elucidated by economic spectrum analysis. We surveyed reed community sample sites in 13 lakeshore wetlands and monitored 26 plant functional traits including phenotypic traits, nutrient content and stoichiometric ratios. These traits were from plant organs (leaves, stems and roots) that are particularly sensitive to environmental change, and essential for plant growth and reproduction capacity. We specifically assessed the following hypotheses: (1) The functional traits of P. australis are variable at different scales. At the local scale, at a broadly similar climate regime, we expect edaphically driven heterogeneity in moisture availability to primarily influence variation in morphological and nutrient economy traits of species in lakeshore wetlands, while under the latitudinal gradient dominated by climatic factors. (2) The economic spectrum theory also applies to leaves, stems, roots and whole plant individuals at the population level of reeds, and the ecological adaptation strategies for plant traits are different in semi-arid than in arid regions. Materials And Methods Study Area, experiment design and sampling This study was conducted on the lakeshore wetland ecosystem along the lakes in the semi-arid and arid regions of Inner Mongolia, northern China (Fig. S1 and Table S1). Within the transect, contains 13 sampling sites in the lakeshore wetlands of 11 lakes (There are 3 sampling sites of XHK, MNAB and GLD in Hulun Lake), distributed in arid and semi-arid regions. In this study, two distinct climatic regions were identified, referred to as aridity index, semi-arid (AI ≥ 0.2) and arid (AI < 0.2) (Trabucco and Zomer, 2018 ). There are four of eleven study sampling localities in the semi-arid region (Hulun lake, BEL, ZGST and HJN) and seven in the arid region (CGNE, NLH, TG, BG, BDD, BDX and JYH) (Fig. S1). Within these lakeshore wetlands, one of the most dominant species, P. australis , was selected as the main plant community for the study. Dominant species, represent roughly 80–90% of the total vascular plant biomass of the ecosystem (Cornelissen et al., 2003 ). Due to hydrological conditions, lakeshore wetlands have a pattern of moisture gradients that radiate outward along the water surface in a band, forming wetland plant communities under different moisture gradients. Within these sampling localities, XHK, GLD, MNAB, ZGST, CGNE, NLH, BG and JYH, we use different distances from the water surface to define the moisture gradients and the existence of two moisture gradients in the P. australis community (High and Low). In contrast, P. australis in the remaining lakeshore wetlands are present in only one moisture gradient. In mid-August 2019–2020, all fresh samples were collected at 13 sampling sites in lakeshore wetlands, 5 quadrats (1×1 m, as 5 blocks) were randomly selected in each P. australis community and 8–10 intact plants with consistent growth and no pests or diseases were selected in each quadrat and dug up with the roots and brought back to the laboratory for decomposition as a study of functional traits in P. australis . Soil samples (0–10 cm depth) were collected from three randomly selected sample quadrat within the dug P. australis five soil cores were collected using a 7 cm diameter drill from each quadrat and then were mixed into one composite sample and brought back to the laboratory for analysis of the soil physical and chemical properties. Functional traits measurements Twenty-six traits of P. australis plants were measured directly or indirectly. The height (High) was measured in the field and then all the selected individuals containing the root system were excavated. Then, the roots, stems and leaves of the plants are separated. Roots, stems and leaves were scanned individually at 300 dpi resolution for each plant (Epson Perfection V850 Pro scanner, Dell USA) for measuring leaf area and root diameter infield before being dried at 80°C for 48 h and weighed for biomass to the nearest 10 − 3 g, and calculated specific leaf area (SLA), leaf dry matter content (LDMC), and specific root length (SRL) followed Cornelissen et al. (Cornelissen et al., 2003 ). Scanned leaves and root pictures were analyzed using Photoshop software ( http://www.ps.lhfei.cn/ ) and winRHIZO software ( http://www.regentinstruments.com/assets/winrhizo_about.html ), respectively. Leaf thickness (LTH), stem diameter (SDI) and root diameter (RD) were measured directly using vernier calipers. Stem density (SDE) was calculated from dry stem weight and stem volume. The carbon/nitrogen concentrations of these samples (include leaf nitrogen content (LN), leaf carbon content (LC), stem nitrogen content (SN), stem carbon content (SC), root nitrogen content (RN) and root carbon content (RC)) was measured with an elemental analyzer (Elementar Vario EL Ⅲ, Germany). The total phosphorus concentration (include leaf phosphorus content (LP), stem phosphorus content (SP) and root phosphorus content (RP)) was measured by the H 2 SO 4 -HClO 4 fusion method. The stoichiometric ratios of roots, stems and leaves (including leaf C:N, C:P and N: P ratios, stem C:N, C:P and N: P ratios, root C:N, C:P and N:P ratios) were calculated based on their carbon, nitrogen and phosphorus nutrient contents (Wang et al., 2020 ). Soil properties measurements and climate data collection Soil samples were sieved through a 2 mm mesh sieve to remove roots and small rocks, and divided into two parts, one for a natural drying and one for the fresh soil samples were stored in a refrigerator at 4°C. The fresh soil (10 g) was used to measure ammonium concentrations (NH4) with 50 ml of 2 M KCl-extractable with a continuous flow spectrophotometer (FIAstar 5000, Foss Tecator, Denmark). Soil available phosphorus (AP) was extracted with 0.5M NaHCO3 (pH 8.5) and analyzed using the molybdenum blue-ascorbic acid method. Soil pH was measured in 1:5 soil/water suspension and soil electrical conductivity (EC) was measured in 1:2.5 soil/water suspension using a glass electrode. Soil total carbon (TC) and nitrogen (TN) were measured with an elemental analyzer (Elementar Vario EL Ⅲ, Germany). The climate factor of mean annual temperature (MAT), mean annual precipitation (MAP), temperature annual range (TAR), precipitation seasonality (PS), annual potential evapotranspiration (PET) and aridity index (AI) were used in this study. MAT, MAP, TAR and PS data with a resolution of 30 s × 30 s were obtained from WorldClim global climate database using the geographic coordinates of each plot ( http://www.worldclim.org ). PET data with a resolution of 30 s × 30 s were extracted from CGIAR-CSI ( http://www.cgiar-csi.org ). Then AI was calculated as the ratio of MAP to PET (AI = MAP/PET). Statistical analysis Nested ANOVAs, based on the decomposition of type I sum-of-squares were used to quantify the variation in P. australis morphological traits, nutrient contents and stoichiometric ratios among different moisture gradients, sampling locations and (semi-) arid regions. All data were log-transformed before analysis. The moisture gradients response of P. australis morphological traits, nutrients content and stoichiometric ratios were estimated with the log response ratio. The response ratio of traits under two moisture gradients was determined by the ratio of traits in the high moisture gradient to those in the low moisture gradient. Thus, a positive log response ratio value indicates that high moisture gradients increase traits of P. australis . One way ANOVA analysis was used to quantify the variation in P. australis morphological traits, nutrients and stoichiometric ratios under different moisture gradients. The linear regression modeling tests the relationship between latitude or longitude and functional traits in the sampling localities, which used to reveal intraspecific variation on a large scale. Redundancy analysis was used to relate trait–environment covariation (R package ‘vegan’), and the explanation rate of environmental factors was confirmed by package ‘rdacca.hp’. Principal component analysis was used to rank the leaf traits, stem traits, root traits, and whole-plant traits of P. australis , and the calculated PC1 and PC2 explained a higher proportion of the variance and were used as a proxy for LES, SES, RES and WPES, which used to reveal its ecological adaptation strategy. The relative contribution of each trait to PC1 and PC2 was estimated using correlation analysis of each trait index with PC1 and PC2 scores. The F-test was used to analyze the differences between PC1 and PC2 scores for leaf, stem, root and whole plant traits in arid and semi-arid regions. All the statistical analyses were performed in R 4.1.1. Results Intraspecific variations of traits in spatial scales The results of the nested ANOVA showed that contributions of the different scales to intraspecific variation of traits varied depending on the trait considered (Fig. 1 ). Among the morphological traits, the moisture gradient explained over 50% of the variance in High, SDI and SDE, while LTH, LDMC, SLA, RD and SRL were mainly influenced by geographical location. For nutrient traits, geographic location explained the variation in nitrogen content of roots, stems, and leaves, and the region had the most significant effect on phosphorus content. About stoichiometric ratios, the stems and leaves were mainly influenced by moisture gradient and geographical location, while roots were more influenced by geographical location and region. For intraspecific variation of P. australis functional traits under different moisture gradients, see Appendix S2 in Supporting Information. Most functional traits of P. australis showed significant variation in latitudinal patterns ( P ≤ 0.05), albeit minor to moderately explained ( R 2 ≤ 0.537, Fig. 2 – 4 ). High, LDMC, SDI and RD morphological traits significantly increased towards higher latitudes, and SLA and SRL decreased towards higher latitudes ( P < 0.05, Fig. 2 ). The carbon and phosphorus contents of stems and leaves and the phosphorus content of roots significantly increased towards higher latitudes ( P < 0.05), the nitrogen content of stems decreased towards higher latitudes ( P < 0.001), and the nitrogen content of leaves and roots did not correlate with latitude (Fig. 3 ). The root, stem and leaf C:N increased with increasing latitudes, and the C:P and N:P decreased towards higher latitudes ( P < 0.05, Fig. 4 ). The variation in functional traits of P. australis in the lakeshore wetlands on a large scale is mainly derived from climatic factors based on temperature and is less related to soil properties (Fig. 5 ). Among morphological traits, TAR, PET, AI, MAT and MAP as the main climatic factor significantly influenced the variability of traits, soil properties were significant only for NH4 and C:N (Fig. 5 A, 5 B). PET, MAT, and PS were the main climatic factors affecting the variability of nutrient content, in addition to soil pH which significantly affected the variability of nutrient content ( P < 0.05, Fig. 5 C, 5 D). PET, MAT, PS, TAR and MAP were the main factors influencing the variability of stoichiometric ratios, while there was a weak correlation between soil PH, C:N and EC ( P < 0.1, Fig. 5 E, 5 F). The economic spectrum was used to quantify ecological adaptation strategies of Phragmites australis Based on economic spectrum theory, traits of P. australis showed an ecological adaptation strategy of conservation in arid regions while acquisition in semi-arid regions (Fig. 6 , See Appendix S3 in Supporting Information). The results of principal component analysis for leaf traits showed that PC1 and PC2 occupied 66.56% and 17.06% of the variance explained, respectively (Fig. 6 A). Most of the P. australis leaf traits in the arid region are clustered on the conservative side of the LES and the semi-arid region is clustered on the acquisitive side, and the scores of the PC1 axis were significantly higher in the arid region than in the semi-arid region ( p < 0.001, Table S4). The results of principal component analysis for stem traits showed that PC1 and PC2 occupied 50.32% and 34.2% of the variance explained, respectively (Fig. 6 B). Arid and semi-arid region P. australis were distributed on both conserved and acquired sides of the SES representation, and both were not significant on the PC1 axis ( p > 0.05, Table S4). The results of principal component analysis for root traits showed that the economic spectrum of P. australis roots was not present (Fig. 6 C, Table S3). The results of principal component analysis for whole-plant traits showed that PC1 and PC2 occupied 32.23% and 30.41% of the variance explained, respectively (Fig. 6 D). All traits except LDMC and RC contributed significantly to the PC1 axis (Table S3). Most of the P. australis traits in semi-arid regions were distributed on the acquisition side and those in arid regions on the conservative side, and the PC1 axis scores for P. australis traits were significantly higher in the arid regions than in the semi-arid regions ( p < 0.001, Table S4). Discussion In this study, we investigated functional traits of the cosmopolitan grass P. australis , which were grown in 11 lakeshore wetlands of semi-arid and arid regions of Inner Mongolia in northern China. We aimed was detect the relative effects of spatial scale on trait variation in P. australis , and the economic spectrum of leaves, stems and roots reveal their ecological adaptation strategies. We found considerable intraspecific trait variation of P. australis across the moisture gradients, among geographical location characteristics, and showed similarities and differences in morphological traits, nutrient contents and stoichiometric ratios. On lakeshore wetlands, traits responding to plant morphology increased toward higher latitudes, and traits responding to plant nutrient uptake strategies decreased along higher latitudes. Intraspecific variation in traits of P. australis is the result of natural selection caused mainly by climatic factors at the regional scale. Through the analysis of the plant economic spectrum, it was confirmed that the economic spectrum of P. australis plants is not only present in the traits of a single organ, but acts on the whole plant. And the traits of P. australis showed an ecological adaptation strategy of conservation in arid regions while acquisition in semi-arid regions. Intraspecific trait variation along with location characteristic in climate-regulated For species with a wide distribution area, differences in stand and climatic environment of the natural growth site will inevitably lead to differentiation in physiological and ecological characteristics of plants, resulting in significant differences in functional traits of plants (Roscher et al., 2018 ). Most morphological traits increased toward higher latitudes, while SLA and SRL decreased toward latitudes. Specific leaf area and dry matter content are important indicators of plant resource utilization strategies (Joswig et al., 2022 ). In general, the lower the specific leaf area, the thicker the leaf, making the water diffusion from the inside of the leaf more resistant to prevent water dissipation, more advantageous from withstanding cold and dry conditions (Wright et al., 2002 ). Plants adapted to harsh environments often have increased dry matter content to enhance stress tolerance and retention of nutrients. It is evident that P. australis adapt to high latitudes by reducing specific leaf area and increasing leaf dry matter content, which is consistent with most studies on functional traits of plant leaves (Ren et al., 2020 ). As important nutrients for plant growth and development, the content of carbon (C), nitrogen (N) and phosphorus (P) and their stoichiometric characteristics can not only reflect the ability of plants to produce assimilated products and nutrient utilization efficiency, but also determine the limiting elements affecting plant growth and development (Zhang et al., 2020 ). The elemental concentrations in plant tissues differed among the geographical location characteristics, most likely reflecting the effect of different nutrient requirements and assimilative capacity under different climatic growth conditions (Heilmeier, 2019 ). In this study, the nitrogen content of leaves and roots did not show a latitudinal pattern, which may result from the unrestricted availability of nitrogen in the lakeshore wetlands of the geographic pattern. The phosphorus content of P. australis leaves, stems, and roots increased toward latitudinal pattern, which indicates that lakeshore wetlands in arid regions are more phosphorus-limited than semi-arid regions. The plant carbon to nitrogen ratio tended to increase with latitudinal gradient, indicating that the uptake and assimilation capacity of P. australis for carbon and nitrogen increased with higher latitudes (Wang et al., 2018 ). Both plant C:P and N:P showed a decreasing trend under the latitudinal gradient, indicating that phosphorus was reduced by phosphorus limitation from arid to semi-arid lakeshore wetlands, and the supply of phosphorus was greater than that of carbon and nitrogen. C:P and N:P can be used to determine the availability of plant nutrients in the soil and are widely used to determine the limiting pattern of C, N and P nutrients in plant-soil systems (Luo et al., 2017 ). Variation in plant functional traits depends mainly on plant evolutionary differences and environmental factor constraints (Reich et al., 1997 ). Differences in traits between species are the result of divergence in the process of passing traits between major plant lineages to progeny taxa during evolution (evolutionary convergence), while differences in traits between locations for the same species are the result of plastic responses of species to different environments (adaptive evolution) (Reich et al., 2003 ). LTH, LDMC, SLA, LN and LP, SDI and SRL, all show broad-scale correlations with climate or soil, and it has also been reported that many of these traits show latitudinal patterns (Wright et al., 2017 ). In this study, Climate factors based on temperature were the dominant factor in the variation of functional traits of P. australis at the regional scale, and were less related to soil properties. Climatic factors such as temperature, potential evapotranspiration and precipitation have strong effects not only on growth and biomass but also on reproduction and phenology (Cavender-Bares et al., 2016 ). The study could report latitudinal clines in morphological traits, nutrient contents and stoichiometric ratios, proving that the investigated functional traits of P. australis are preserved and expressed according to their adaptation to the climatic origin, especially MAT (Ren et al., 2020 ). Several previous studies of P. australis have found that the variation is due to climate change in latitudinal and longitudinal patterns (Lin et al., 2010 ). The high and frequent precipitation, the abundance of water vapor, and the suitable temperature should satisfy the basic requirements of plant physiology. In addition, these conditions promote the weathering of soil minerals, provide suitable conditions for microbial activity, contribute to the rapid turnover of organic matter, and support the supply of nutrients; in short, they represent conditions that allow plants to grow fast and tall in the race for light (Slessarev et al., 2016 ). Plant economic spectrum and ecological adaptation strategies The study of the economic spectrum provides new theories and methods for analyzing the effects of global climate change on plants and their adaptation mechanisms, and has become one of the hot issues in ecological research (Sakschewski et al., 2015 ). The economic spectrum theory summarizes the change patterns of functional traits of plants under environmental perturbations and their interrelationships, which is the performance of plants to allocate, compensate and balance their resources according to habitat conditions to adapt to the negative effects of environmental changes on them, and quantifies the resource utilization capacity and trade-off strategies of plants(Wright et al., 2004 ). The results of studies on a single plant P. australis population showed that the economic spectrum theory is equally applicable to different traits of the same species in different habitats, and further points out the characteristics of different plant functional trait combinations and the interrelationships among traits, reflecting that plant in different habitats will adopt different environmental adaptation strategies through trade-offs among traits. The economic spectrum theory was applied to leaf, stem and whole plant traits of P. australis , and there were significant differences between LES and WPES in arid and semi-arid regions. The P. australis in the arid region are distributed on the conservative side of the LES and WPES representatives, with small SLA, LN, L_C:P and L_N:P, and large LDMC, LP and L_C:N, while the semi-arid zone is distributed on the acquisition side. This suggests that P. australis have constant or consistent conservative or acquisition strategies at the organ and whole plant levels in arid and semi-arid regions (Joswig et al., 2022 ). Plant of P. australis in arid zones usually have thicker leaves, stronger stalks, allocate more biomass to mechanical support, and thus exhibit a conservative strategy in order to withstand adverse environmental conditions (Pérez-Ramos et al., 2012). However, in semi-arid zones, where environmental conditions are more suitable for P. australis growth, an acquisition-based strategy is favored to meet the higher nutrient content required. In the lakeshore wetlands of Inner Mongolia, LES, SES and WPES of P. australis all exhibited significant differences between arid and semi-arid regions, while RES was not present in this climatic region. This may be due to the significant variation in root traits themselves, most of which have a high coefficient of variation and are more sensitive to soil texture as well as nutrient content, and different regions of the P. australis have their survival strategies. Thus, despite the importance of the respective selection of plant traits under different environmental conditions, the coordination of plant acquisition or conservative strategies among traits, organs and resources still converge under different habitat conditions(Reich and Cornelissen, 2014 ). Conclusions The results of a study on functional traits of P. australis in lakes and lakeshore wetlands in arid and semi-arid regions of Inner Mongolia Plateau show that the functional traits of P. australis showed a significant latitudinal gradient pattern across the latitudinal gradient, and the variation in traits across the latitudinal gradient was mainly dominated by temperature-mediated climatic factors and less related to soil heterogeneity. The economic spectrum of reed populations in terms of leaf, stem traits and the whole-plant are present, and the "investment-gain" strategy axis of the economic spectrum of P. australis in arid and semi-arid regions is characterized by divergence along with two different directions. The arid region shows a conservative strategy, while the semi-arid region shows an acquisitive strategy. This provides strong evidence to explore the WPES of a single species in the context of plant economic spectrum, and further enriches the integration of intraspecific variation and plant economic spectrum in different climatic regions, which has important theoretical and practical significance for understanding ecological niche differentiation and ecological adaptation strategies of species. Declarations Authors' contributions Z.X. and H.L. contributed to analysis and interpretation of data, prepared all figures and tables and writing the original draft; L.W., J.Z. and D.W. contributed to collected the data of the study; X.X., J.H., Y.Z. and X.K. field sampling and data curation; L.W. conceived the idea and designed methodology and contributed in reviewing & editing the paper. All authors collected the field data, substantially contributed to data interpretation, critically revised the manuscript and gave final approval for publication. Acknowledgments The authors gratefully thank Jiangtao Peng for his assistance in the laboratory experiment. The research was supported by National Natural Science Fund, P.R. China (No. 32160279， 3211101852 and 31960249) and the Science and Technology Major Project of Inner Mongolia (No. ZDZX2018054, 2021ZD0011). Data availability The dataset generated during this study are available from the corresponding author on reasonable request. Competing interests We declare there is no competing financial interest. References ACKERLY D, CORNWELL W (2007) A trait-based approach to community assembly: Partitioning of species trait values into within- and among-community components. Ecol Lett 10:135–145 BRUELHEIDE H, LENOIR DENGLERJPURSCHKEO, JANDT U (2018) Global trait–environment relationships of plant communities. 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Ecol Lett 18:1406–1419 SIMPSON AH, LAUGHLIN DC (2016) Soil-climate interactions explain variation in foliar, stem, root and reproductive traits across temperate forests. Glob Ecol Biogeogr 25:964–978 SLESSAREV EW, LIN Y, BINGHAM NL, DAI JOHNSONJE, CHADWICK OA (2016) Water balance creates a threshold in soil pH at the global scale. Nature 540:567–569 SUDING KN, CORNELISSEN LAVORELS III, D AZ J, GOLDBERG SGARNIERE, NAVAS ML (2008) Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants. Glob Change Biol 14:1125–1140 TRABUCCO A, ZOMER RJ (2018) Global Aridity Index and Potential Evapo-Transpiration (ET0) Climate Database v2. CGIAR Consortium for Spatial Information(CGIAR-CSI). Published online, available from the CGIAR-CSI GeoPortal at https://cgiarcsi.community VIOLLE C, NAVAS ML, VILE D, KAZAKOU E, FORTUNEL C, HUMMEL I, GARNIER E (2007) Let the concept of trait be functional! Oikos 116:882–892 WANG J, WANG Y, HE N, YE Z, CHEN C, FENG ZANGR, LU Y, LI J (2020) Plant functional traits regulate soil bacterial diversity across temperate deserts. Sci Total Environ 715:136976 WANG W, WANG SARDANSJ, ZENG C, TONG C, BARTRONS C, ASENSIO M (2018) Shifts in plant and soil C, N and P accumulation and C:N:P stoichiometry associated with flooding intensity in subtropical estuarine wetlands in China. Estuar Coast Shelf Sci 215:172–184D. & PE UELAS WRIGHT I, WESTOBY M, REICH P (2002) Convergence towards higher leaf mass per area in dry and nutrient-poor habitats has different consequences for leaf life span. J Ecol 90:534–543 WRIGHT IJ, DONG N, PRENTICE MAIREV, WESTOBY IC, GALLAGHER MDIAZS, JACOBS R, LEISHMAN BKOOYMANRLAWEA, REICH MNIINEMETSÜ, SACK P, WANG LVILLARR, WILF P (2017) Global climatic drivers of leaf size. Science 357:917–921 WRIGHT IJ, WESTOBY REICHPB, BARUCH MACKERLYDD, BONGERS Z, CAVENDER-BARES F, CHAPIN J et al (2004) The worldwide leaf economics spectrum. Nature 428:821–827 ZHANG XJ, SONG K, PAN YJ, GAO ZW, CIERAAD E (2020) Responses of leaf traits to low temperature in an evergreen oak at its upper limit. Ecol Res 35:900–911 Supplementary Files SupportingInformation.docx Cite Share Download PDF Status: Posted Version 1 posted 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-2103651","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":142916149,"identity":"d82b5aba-2984-434f-8829-6ea88b40db51","order_by":0,"name":"Zhichao XU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACCcYGZgiL+QADg4EFAxuYw0aUFrYEoBYJYrQAjYeweAwgXEJa5Gc3N34uqLFhMLiR83XDjwIJBj6xMwYMH8oOM/DPbsCqhXHOwWbpGcfSGCRn5G672QNymHSOAeOMc4cZJO4cwKqFWSKxjZmHDWimRO62GzxQLcy8bYeB/krAqoUNrOXfYSAj59nNPzAtf/Fo4QFpAZnJL5HDdhtuCyMeLRISic3SvH1Av/Q8M7stYyDBwyadVnCw51w6j8QN7FrkZ6Q//MzzDRhix5Of3Xzzx0ZOfnbyxgc/yqzl+Gdg1wID9Q0wl4KIAzDGKBgFo2AUjALyAACl7k9LYNV1FgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4751-3036","institution":"Inner Mongolia University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhichao","middleName":"","lastName":"XU","suffix":""},{"id":142916150,"identity":"47cd514d-d603-4d63-b6f2-0ed345cb140d","order_by":1,"name":"Huamin Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huamin","middleName":"","lastName":"Liu","suffix":""},{"id":142916151,"identity":"636b6060-546e-4ddc-91d1-0527a06faa74","order_by":2,"name":"Lu Wen","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Wen","suffix":""},{"id":142916152,"identity":"e3766473-fcd0-4999-b864-4de02eff39f6","order_by":3,"name":"Jinghui Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinghui","middleName":"","lastName":"Zhang","suffix":""},{"id":142916153,"identity":"1dd54cfa-0d62-48fb-909e-6a0790e1ce7c","order_by":4,"name":"Xiaoyun Xin","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Xin","suffix":""},{"id":142916154,"identity":"4df03c7f-318c-4c11-bd08-ae16745a1fa9","order_by":5,"name":"Jinpeng Hu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinpeng","middleName":"","lastName":"Hu","suffix":""},{"id":142916155,"identity":"1cd513f2-eef0-48d8-bc7c-91efe856297f","order_by":6,"name":"Xin Kou","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Kou","suffix":""},{"id":142916156,"identity":"16940ac2-5094-4980-958d-acab4e065eee","order_by":7,"name":"Dongwei Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongwei","middleName":"","lastName":"Liu","suffix":""},{"id":142916157,"identity":"ec74c893-e3e4-4251-8a25-0846e3ec2efc","order_by":8,"name":"Yi Zhuo","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhuo","suffix":""},{"id":142916158,"identity":"85348204-2632-4505-8d86-4ed904d2b384","order_by":9,"name":"Lixin Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lixin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2022-09-26 08:31:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2103651/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2103651/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27679577,"identity":"2e9a8411-c1b4-4a99-ae66-e4d5e4283602","added_by":"auto","created_at":"2022-10-12 14:54:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":292922,"visible":true,"origin":"","legend":"\u003cp\u003eIntraspecific variance components of morphological traits, nutrients contents and stoichiometric ratios in \u003cem\u003ePhragmites australis\u003c/em\u003e based on nested ANOVA across spatial scales including (semi-) arid regions, sampling localities and moisture gradients.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2103651/v1/494bf804d177aad4119bbda5.jpg"},{"id":27679581,"identity":"1a0a655e-f111-41d7-b8ac-5686f0db0c05","added_by":"auto","created_at":"2022-10-12 14:54:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":996859,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between latitude and intraspecific variation in morphological traits of\u003cem\u003e Phragmites australis\u003c/em\u003e. Blue lines are the fitted lines from OLS regressions. Grey shadings represent 95% confidence intervals. Significance (\u003cem\u003ep\u003c/em\u003e-value) is shown in parentheses. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e describes the proportion of variation explained by each model.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2103651/v1/3d126825dd3f21524682a6bb.png"},{"id":27680667,"identity":"9a7813da-129a-4132-b2c3-b7473a89344e","added_by":"auto","created_at":"2022-10-12 15:04:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1038307,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between latitude and nutrient contents of\u003cem\u003e Phragmites australis \u003c/em\u003eleaves, stems and roots.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2103651/v1/d67e631274e6f285c5c96928.png"},{"id":27679579,"identity":"067d5fe0-f181-45c4-ab64-f515d8541771","added_by":"auto","created_at":"2022-10-12 14:54:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1143500,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between latitude and stoichiometric ratios of\u003cem\u003e Phragmites australis \u003c/em\u003eleaves, stems and roots.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2103651/v1/9e66dc33b1621700ba142e72.png"},{"id":27680278,"identity":"bdbec63a-e35e-4fe9-a208-0665d35e63eb","added_by":"auto","created_at":"2022-10-12 14:59:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":258475,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis (RDA) ordination for morphological traits, nutrient contents, and stoichiometric ratios of \u003cem\u003ePhragmites australis\u003c/em\u003e and climate and contribution of climatic and soil factors to trait variability in lakeshore wetlands. The box in the figure A, C, E represents the lakeside wetland in the semi-arid regions, while the circle represents the arid regions.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2103651/v1/a5e1779852a1e7da61125e8d.png"},{"id":27679578,"identity":"6cfac9bc-556d-41cb-817b-e67a80b83709","added_by":"auto","created_at":"2022-10-12 14:54:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":321145,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of leaf, stem, root system and whole-plant traits. A, PCA of leaf traits. B, PCA of stem traits. C, PCA of root traits. D, PCA of whole-plant traits. The solid lines represent 95% confidence intervals for functional traits in arid regions. Dashed lines represent 95% confidence intervals for functional traits in semi-arid regions.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2103651/v1/99e2e2379cf000a7fc63942a.png"},{"id":29983702,"identity":"b94e2de6-8852-46ca-b6d8-5ebbf7070def","added_by":"auto","created_at":"2022-12-06 20:18:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1650794,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2103651/v1/86c1df8c-26e3-478e-b32f-1ff7c86fee4d.pdf"},{"id":27680277,"identity":"9dcd4900-155f-45b6-9be8-378039fa78b3","added_by":"auto","created_at":"2022-10-12 14:59:30","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1196562,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2103651/v1/9638b27bc6cd1d21ead3398e.docx"}],"financialInterests":"","formattedTitle":"Temperature-mediated climatic factors dominate intraspecific variation and ecological adaptation strategies for traits of Phragmites australis rather than soil properties","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlant functional traits are adaptive and effect traits that affect plant survival, growth and reproduction, reflecting plant responses to the environment and measurable traits that can influence ecosystem function (Liu and Ma, 2015). It has proved useful for predicting plant community assembly (Lavorel and Garnier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which can respond strongly to environmental and climate change and can serve as an excellent ecological indicator role (Joswig et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Functional traits are morphological, physiological, phenological and behavioral characteristics that respond to the environment and directly or indirectly affect plant adaptation and related ecosystem properties (Violle et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Moore et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Variation in plant functional traits is the result of natural selection and feedback from plants to their environment and reflects variation in the relative of plant\u0026rsquo;s adaptive mechanisms (Liu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Meyerson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Quantifying the variation pattern of critical traits related to morphological traits, nutrient contents and stoichiometric ratios at each of multiple spatial scales and driving factors could provide an opportunity to evaluate the relative importance of these drivers for species evolutionary adaptation (Ackerly and Cornwell, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Joswig et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe cosmopolitan species \u003cem\u003ePhragmites australis\u003c/em\u003e is a tall wetland grass with high intraspecific variation, making it a suitable model species for studying the underlying mechanisms of intraspecific trait variation (Kueffer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Eller et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The species occurs over an extensive latitudinal range and in a broad range of ecological niches and habitats(Packer et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Species with general distributions tend to be more traits variable and have high intraspecific variation and show a continuous geographic gradient, especially across different latitudes as a consequence of acclimation across a broad range of environmental conditions (Ren et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Siefert et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although plant traits have been extensively studied under different treatments (e.g. grazing, nutrient addition, etc.), intraspecific variation at the regional scale under natural conditions have rarely been considered (Pigliucci, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Many studies have shown that large-scale variation in individual plant traits is associated with environmental gradients. Early plant biogeographers believed that climate and soil together determined the form and function of plants (Bruelheide et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Simpson et al., 2016). However, the study of plant functional trait variability characteristics and their adaptation to external conditions is significant for understanding plant community construction in different regions and their response mechanisms to habitats (Suding et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Kong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe concept of economic spectrum of plant leaves has attracted wide attention from ecologists. By applying the \"investment-benefit trade-off\" theory of economics to the study of plant resource allocation, the interrelationships among plant functional traits and ecological adaptation strategies can be quantitatively analyzed (Osnas et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Niinemets, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Wright et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) defined a continuously varying spectrum of leaf functional trait combinations on a global scale, the \"leaf economic spectrum (LES)\", to illustrate trade-off strategies between vascular plant resource acquisition and storage, which can be expressed through the range of variation in trait indicators and their quantitative relationships (Wright et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Since then, leaf economic spectrum studies have been extended to stems (SES), roots (RES) and whole plants (WPES), community structures and ecosystem types at different levels by related ecologists (Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, P\u0026eacute;rez-Ramos et al., 2012, Kong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). One end of the economic spectrum represents the \"fast investment-gain\" strategy of plants, where species with this feature have cheap tissue investment and fast return on investment (acquisition strategy); while the other end represents the \"slow investment-gain\" strategy of plants, where species with this feature have expensive tissue investment and slow return on investment (conservative strategy) (Wright et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Reich and Cornelissen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As a kind of organ-level trade-off strategy spectrum, plant economic spectrum studies can better describe and generalize plant traits and functional types, providing new thinking perspectives and explore avenues for researching plant functional traits and ecological adaptation strategies of species.\u003c/p\u003e \u003cp\u003eThe Inner Mongolia Plateau is located in the semi-arid and arid climate region of northern China, with a large longitude span, and the climate from east to the west shows the transition characteristics from semi-arid to the arid region. Most of the lakes in the region are inland-type lakes, and the species and community structure of the lakeshore wetlands are significantly influenced by the geographical location and climate environment. As a dominant species in the lakeshore wetlands of the arid and semi-arid region of Inner Mongolia, \u003cem\u003eP. australis\u003c/em\u003e provides suitable conditions for studying possible latitudinal variation in morphology, growth and phenological traits in population distribution and climate response. In this study, the variation in morphological, nutrient and stoichiometry traits of \u003cem\u003eP. australis\u003c/em\u003e under geographic location and the drivers of trait variation were analyzed, and the ecological adaptation strategies under different regional characteristics were elucidated by economic spectrum analysis. We surveyed reed community sample sites in 13 lakeshore wetlands and monitored 26 plant functional traits including phenotypic traits, nutrient content and stoichiometric ratios. These traits were from plant organs (leaves, stems and roots) that are particularly sensitive to environmental change, and essential for plant growth and reproduction capacity. We specifically assessed the following hypotheses: (1) The functional traits of \u003cem\u003eP. australis\u003c/em\u003e are variable at different scales. At the local scale, at a broadly similar climate regime, we expect edaphically driven heterogeneity in moisture availability to primarily influence variation in morphological and nutrient economy traits of species in lakeshore wetlands, while under the latitudinal gradient dominated by climatic factors. (2) The economic spectrum theory also applies to leaves, stems, roots and whole plant individuals at the population level of reeds, and the ecological adaptation strategies for plant traits are different in semi-arid than in arid regions.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area, experiment design and sampling\u003c/h2\u003e \u003cp\u003eThis study was conducted on the lakeshore wetland ecosystem along the lakes in the semi-arid and arid regions of Inner Mongolia, northern China (Fig. S1 and Table S1). Within the transect, contains 13 sampling sites in the lakeshore wetlands of 11 lakes (There are 3 sampling sites of XHK, MNAB and GLD in Hulun Lake), distributed in arid and semi-arid regions. In this study, two distinct climatic regions were identified, referred to as aridity index, semi-arid (AI\u0026thinsp;\u0026ge;\u0026thinsp;0.2) and arid (AI\u0026thinsp;\u0026lt;\u0026thinsp;0.2) (Trabucco and Zomer, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There are four of eleven study sampling localities in the semi-arid region (Hulun lake, BEL, ZGST and HJN) and seven in the arid region (CGNE, NLH, TG, BG, BDD, BDX and JYH) (Fig. S1).\u003c/p\u003e \u003cp\u003eWithin these lakeshore wetlands, one of the most dominant species, \u003cem\u003eP. australis\u003c/em\u003e, was selected as the main plant community for the study. Dominant species, represent roughly 80\u0026ndash;90% of the total vascular plant biomass of the ecosystem (Cornelissen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Due to hydrological conditions, lakeshore wetlands have a pattern of moisture gradients that radiate outward along the water surface in a band, forming wetland plant communities under different moisture gradients. Within these sampling localities, XHK, GLD, MNAB, ZGST, CGNE, NLH, BG and JYH, we use different distances from the water surface to define the moisture gradients and the existence of two moisture gradients in the \u003cem\u003eP. australis\u003c/em\u003e community (High and Low). In contrast, \u003cem\u003eP. australis\u003c/em\u003e in the remaining lakeshore wetlands are present in only one moisture gradient.\u003c/p\u003e \u003cp\u003eIn mid-August 2019\u0026ndash;2020, all fresh samples were collected at 13 sampling sites in lakeshore wetlands, 5 quadrats (1\u0026times;1 m, as 5 blocks) were randomly selected in each \u003cem\u003eP. australis\u003c/em\u003e community and 8\u0026ndash;10 intact plants with consistent growth and no pests or diseases were selected in each quadrat and dug up with the roots and brought back to the laboratory for decomposition as a study of functional traits in \u003cem\u003eP. australis\u003c/em\u003e. Soil samples (0\u0026ndash;10 cm depth) were collected from three randomly selected sample quadrat within the dug \u003cem\u003eP. australis\u003c/em\u003e five soil cores were collected using a 7 cm diameter drill from each quadrat and then were mixed into one composite sample and brought back to the laboratory for analysis of the soil physical and chemical properties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFunctional traits measurements\u003c/h2\u003e \u003cp\u003eTwenty-six traits of \u003cem\u003eP. australis\u003c/em\u003e plants were measured directly or indirectly. The height (High) was measured in the field and then all the selected individuals containing the root system were excavated. Then, the roots, stems and leaves of the plants are separated. Roots, stems and leaves were scanned individually at 300 dpi resolution for each plant (Epson Perfection V850 Pro scanner, Dell USA) for measuring leaf area and root diameter infield before being dried at 80\u0026deg;C for 48 h and weighed for biomass to the nearest 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e g, and calculated specific leaf area (SLA), leaf dry matter content (LDMC), and specific root length (SRL) followed Cornelissen et al. (Cornelissen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Scanned leaves and root pictures were analyzed using Photoshop software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ps.lhfei.cn/\u003c/span\u003e\u003cspan address=\"http://www.ps.lhfei.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and winRHIZO software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.regentinstruments.com/assets/winrhizo_about.html\u003c/span\u003e\u003cspan address=\"http://www.regentinstruments.com/assets/winrhizo_about.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), respectively. Leaf thickness (LTH), stem diameter (SDI) and root diameter (RD) were measured directly using vernier calipers. Stem density (SDE) was calculated from dry stem weight and stem volume. The carbon/nitrogen concentrations of these samples (include leaf nitrogen content (LN), leaf carbon content (LC), stem nitrogen content (SN), stem carbon content (SC), root nitrogen content (RN) and root carbon content (RC)) was measured with an elemental analyzer (Elementar Vario EL Ⅲ, Germany). The total phosphorus concentration (include leaf phosphorus content (LP), stem phosphorus content (SP) and root phosphorus content (RP)) was measured by the H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e-HClO\u003csub\u003e4\u003c/sub\u003e fusion method. The stoichiometric ratios of roots, stems and leaves (including leaf C:N, C:P and N: P ratios, stem C:N, C:P and N: P ratios, root C:N, C:P and N:P ratios) were calculated based on their carbon, nitrogen and phosphorus nutrient contents (Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSoil properties measurements and climate data collection\u003c/h2\u003e \u003cp\u003eSoil samples were sieved through a 2 mm mesh sieve to remove roots and small rocks, and divided into two parts, one for a natural drying and one for the fresh soil samples were stored in a refrigerator at 4\u0026deg;C. The fresh soil (10 g) was used to measure ammonium concentrations (NH4) with 50 ml of 2 M KCl-extractable with a continuous flow spectrophotometer (FIAstar 5000, Foss Tecator, Denmark). Soil available phosphorus (AP) was extracted with 0.5M NaHCO3 (pH 8.5) and analyzed using the molybdenum blue-ascorbic acid method. Soil pH was measured in 1:5 soil/water suspension and soil electrical conductivity (EC) was measured in 1:2.5 soil/water suspension using a glass electrode. Soil total carbon (TC) and nitrogen (TN) were measured with an elemental analyzer (Elementar Vario EL Ⅲ, Germany).\u003c/p\u003e \u003cp\u003eThe climate factor of mean annual temperature (MAT), mean annual precipitation (MAP), temperature annual range (TAR), precipitation seasonality (PS), annual potential evapotranspiration (PET) and aridity index (AI) were used in this study. MAT, MAP, TAR and PS data with a resolution of 30 s \u0026times; 30 s were obtained from WorldClim global climate database using the geographic coordinates of each plot (\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). PET data with a resolution of 30 s \u0026times; 30 s were extracted from CGIAR-CSI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cgiar-csi.org\u003c/span\u003e\u003cspan address=\"http://www.cgiar-csi.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Then AI was calculated as the ratio of MAP to PET (AI\u0026thinsp;=\u0026thinsp;MAP/PET).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNested ANOVAs, based on the decomposition of type I sum-of-squares were used to quantify the variation in \u003cem\u003eP. australis\u003c/em\u003e morphological traits, nutrient contents and stoichiometric ratios among different moisture gradients, sampling locations and (semi-) arid regions. All data were log-transformed before analysis. The moisture gradients response of \u003cem\u003eP. australis\u003c/em\u003e morphological traits, nutrients content and stoichiometric ratios were estimated with the log response ratio. The response ratio of traits under two moisture gradients was determined by the ratio of traits in the high moisture gradient to those in the low moisture gradient. Thus, a positive log response ratio value indicates that high moisture gradients increase traits of \u003cem\u003eP. australis\u003c/em\u003e. One way ANOVA analysis was used to quantify the variation in \u003cem\u003eP. australis\u003c/em\u003e morphological traits, nutrients and stoichiometric ratios under different moisture gradients. The linear regression modeling tests the relationship between latitude or longitude and functional traits in the sampling localities, which used to reveal intraspecific variation on a large scale. Redundancy analysis was used to relate trait\u0026ndash;environment covariation (R package \u0026lsquo;vegan\u0026rsquo;), and the explanation rate of environmental factors was confirmed by package \u0026lsquo;rdacca.hp\u0026rsquo;. Principal component analysis was used to rank the leaf traits, stem traits, root traits, and whole-plant traits of \u003cem\u003eP. australis\u003c/em\u003e, and the calculated PC1 and PC2 explained a higher proportion of the variance and were used as a proxy for LES, SES, RES and WPES, which used to reveal its ecological adaptation strategy. The relative contribution of each trait to PC1 and PC2 was estimated using correlation analysis of each trait index with PC1 and PC2 scores. The F-test was used to analyze the differences between PC1 and PC2 scores for leaf, stem, root and whole plant traits in arid and semi-arid regions. All the statistical analyses were performed in R 4.1.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eIntraspecific variations of traits in spatial scales\u003c/h2\u003e\n\u003cp\u003eThe results of the nested ANOVA showed that contributions of the different scales to intraspecific variation of traits varied depending on the trait considered (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the morphological traits, the moisture gradient explained over 50% of the variance in High, SDI and SDE, while LTH, LDMC, SLA, RD and SRL were mainly influenced by geographical location. For nutrient traits, geographic location explained the variation in nitrogen content of roots, stems, and leaves, and the region had the most significant effect on phosphorus content. About stoichiometric ratios, the stems and leaves were mainly influenced by moisture gradient and geographical location, while roots were more influenced by geographical location and region. For intraspecific variation of \u003cem\u003eP. australis\u003c/em\u003e functional traits under different moisture gradients, see Appendix S2 in Supporting Information.\u003c/p\u003e\n\u003cp\u003eMost functional traits of \u003cem\u003eP. australis\u003c/em\u003e showed significant variation in latitudinal patterns (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05), albeit minor to moderately explained (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.537, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). High, LDMC, SDI and RD morphological traits significantly increased towards higher latitudes, and SLA and SRL decreased towards higher latitudes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The carbon and phosphorus contents of stems and leaves and the phosphorus content of roots significantly increased towards higher latitudes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the nitrogen content of stems decreased towards higher latitudes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the nitrogen content of leaves and roots did not correlate with latitude (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The root, stem and leaf C:N increased with increasing latitudes, and the C:P and N:P decreased towards higher latitudes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe variation in functional traits of \u003cem\u003eP. australis\u003c/em\u003e in the lakeshore wetlands on a large scale is mainly derived from climatic factors based on temperature and is less related to soil properties (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Among morphological traits, TAR, PET, AI, MAT and MAP as the main climatic factor significantly influenced the variability of traits, soil properties were significant only for NH4 and C:N (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). PET, MAT, and PS were the main climatic factors affecting the variability of nutrient content, in addition to soil pH which significantly affected the variability of nutrient content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD). PET, MAT, PS, TAR and MAP were the main factors influencing the variability of stoichiometric ratios, while there was a weak correlation between soil PH, C:N and EC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eThe economic spectrum was used to quantify ecological adaptation strategies of Phragmites australis\u003c/h2\u003e\n\u003cp\u003eBased on economic spectrum theory, traits of \u003cem\u003eP. australis\u003c/em\u003e showed an ecological adaptation strategy of conservation in arid regions while acquisition in semi-arid regions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, See Appendix S3 in Supporting Information). The results of principal component analysis for leaf traits showed that PC1 and PC2 occupied 66.56% and 17.06% of the variance explained, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Most of the \u003cem\u003eP. australis\u003c/em\u003e leaf traits in the arid region are clustered on the conservative side of the LES and the semi-arid region is clustered on the acquisitive side, and the scores of the PC1 axis were significantly higher in the arid region than in the semi-arid region (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table S4). The results of principal component analysis for stem traits showed that PC1 and PC2 occupied 50.32% and 34.2% of the variance explained, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Arid and semi-arid region \u003cem\u003eP. australis\u003c/em\u003e were distributed on both conserved and acquired sides of the SES representation, and both were not significant on the PC1 axis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table S4). The results of principal component analysis for root traits showed that the economic spectrum of P. australis roots was not present (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC, Table S3). The results of principal component analysis for whole-plant traits showed that PC1 and PC2 occupied 32.23% and 30.41% of the variance explained, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). All traits except LDMC and RC contributed significantly to the PC1 axis (Table S3). Most of the \u003cem\u003eP. australis\u003c/em\u003e traits in semi-arid regions were distributed on the acquisition side and those in arid regions on the conservative side, and the PC1 axis scores for \u003cem\u003eP. australis\u003c/em\u003e traits were significantly higher in the arid regions than in the semi-arid regions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table S4).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated functional traits of the cosmopolitan grass \u003cem\u003eP. australis\u003c/em\u003e, which were grown in 11 lakeshore wetlands of semi-arid and arid regions of Inner Mongolia in northern China. We aimed was detect the relative effects of spatial scale on trait variation in \u003cem\u003eP. australis\u003c/em\u003e, and the economic spectrum of leaves, stems and roots reveal their ecological adaptation strategies. We found considerable intraspecific trait variation of \u003cem\u003eP. australis\u003c/em\u003e across the moisture gradients, among geographical location characteristics, and showed similarities and differences in morphological traits, nutrient contents and stoichiometric ratios. On lakeshore wetlands, traits responding to plant morphology increased toward higher latitudes, and traits responding to plant nutrient uptake strategies decreased along higher latitudes. Intraspecific variation in traits of \u003cem\u003eP. australis\u003c/em\u003e is the result of natural selection caused mainly by climatic factors at the regional scale. Through the analysis of the plant economic spectrum, it was confirmed that the economic spectrum of \u003cem\u003eP. australis\u003c/em\u003e plants is not only present in the traits of a single organ, but acts on the whole plant. And the traits of \u003cem\u003eP. australis\u003c/em\u003e showed an ecological adaptation strategy of conservation in arid regions while acquisition in semi-arid regions.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIntraspecific trait variation along with location characteristic in climate-regulated\u003c/h2\u003e \u003cp\u003eFor species with a wide distribution area, differences in stand and climatic environment of the natural growth site will inevitably lead to differentiation in physiological and ecological characteristics of plants, resulting in significant differences in functional traits of plants (Roscher et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Most morphological traits increased toward higher latitudes, while SLA and SRL decreased toward latitudes. Specific leaf area and dry matter content are important indicators of plant resource utilization strategies (Joswig et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In general, the lower the specific leaf area, the thicker the leaf, making the water diffusion from the inside of the leaf more resistant to prevent water dissipation, more advantageous from withstanding cold and dry conditions (Wright et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Plants adapted to harsh environments often have increased dry matter content to enhance stress tolerance and retention of nutrients. It is evident that \u003cem\u003eP. australis\u003c/em\u003e adapt to high latitudes by reducing specific leaf area and increasing leaf dry matter content, which is consistent with most studies on functional traits of plant leaves (Ren et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As important nutrients for plant growth and development, the content of carbon (C), nitrogen (N) and phosphorus (P) and their stoichiometric characteristics can not only reflect the ability of plants to produce assimilated products and nutrient utilization efficiency, but also determine the limiting elements affecting plant growth and development (Zhang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The elemental concentrations in plant tissues differed among the geographical location characteristics, most likely reflecting the effect of different nutrient requirements and assimilative capacity under different climatic growth conditions (Heilmeier, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, the nitrogen content of leaves and roots did not show a latitudinal pattern, which may result from the unrestricted availability of nitrogen in the lakeshore wetlands of the geographic pattern. The phosphorus content of \u003cem\u003eP. australis\u003c/em\u003e leaves, stems, and roots increased toward latitudinal pattern, which indicates that lakeshore wetlands in arid regions are more phosphorus-limited than semi-arid regions. The plant carbon to nitrogen ratio tended to increase with latitudinal gradient, indicating that the uptake and assimilation capacity of \u003cem\u003eP. australis\u003c/em\u003e for carbon and nitrogen increased with higher latitudes (Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Both plant C:P and N:P showed a decreasing trend under the latitudinal gradient, indicating that phosphorus was reduced by phosphorus limitation from arid to semi-arid lakeshore wetlands, and the supply of phosphorus was greater than that of carbon and nitrogen. C:P and N:P can be used to determine the availability of plant nutrients in the soil and are widely used to determine the limiting pattern of C, N and P nutrients in plant-soil systems (Luo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVariation in plant functional traits depends mainly on plant evolutionary differences and environmental factor constraints (Reich et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Differences in traits between species are the result of divergence in the process of passing traits between major plant lineages to progeny taxa during evolution (evolutionary convergence), while differences in traits between locations for the same species are the result of plastic responses of species to different environments (adaptive evolution) (Reich et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). LTH, LDMC, SLA, LN and LP, SDI and SRL, all show broad-scale correlations with climate or soil, and it has also been reported that many of these traits show latitudinal patterns (Wright et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, Climate factors based on temperature were the dominant factor in the variation of functional traits of \u003cem\u003eP. australis\u003c/em\u003e at the regional scale, and were less related to soil properties. Climatic factors such as temperature, potential evapotranspiration and precipitation have strong effects not only on growth and biomass but also on reproduction and phenology (Cavender-Bares et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The study could report latitudinal clines in morphological traits, nutrient contents and stoichiometric ratios, proving that the investigated functional traits of \u003cem\u003eP. australis\u003c/em\u003e are preserved and expressed according to their adaptation to the climatic origin, especially MAT (Ren et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Several previous studies of P. australis have found that the variation is due to climate change in latitudinal and longitudinal patterns (Lin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The high and frequent precipitation, the abundance of water vapor, and the suitable temperature should satisfy the basic requirements of plant physiology. In addition, these conditions promote the weathering of soil minerals, provide suitable conditions for microbial activity, contribute to the rapid turnover of organic matter, and support the supply of nutrients; in short, they represent conditions that allow plants to grow fast and tall in the race for light (Slessarev et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePlant economic spectrum and ecological adaptation strategies\u003c/h2\u003e \u003cp\u003eThe study of the economic spectrum provides new theories and methods for analyzing the effects of global climate change on plants and their adaptation mechanisms, and has become one of the hot issues in ecological research (Sakschewski et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The economic spectrum theory summarizes the change patterns of functional traits of plants under environmental perturbations and their interrelationships, which is the performance of plants to allocate, compensate and balance their resources according to habitat conditions to adapt to the negative effects of environmental changes on them, and quantifies the resource utilization capacity and trade-off strategies of plants(Wright et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The results of studies on a single plant \u003cem\u003eP. australis\u003c/em\u003e population showed that the economic spectrum theory is equally applicable to different traits of the same species in different habitats, and further points out the characteristics of different plant functional trait combinations and the interrelationships among traits, reflecting that plant in different habitats will adopt different environmental adaptation strategies through trade-offs among traits. The economic spectrum theory was applied to leaf, stem and whole plant traits of \u003cem\u003eP. australis\u003c/em\u003e, and there were significant differences between LES and WPES in arid and semi-arid regions. The \u003cem\u003eP. australis\u003c/em\u003e in the arid region are distributed on the conservative side of the LES and WPES representatives, with small SLA, LN, L_C:P and L_N:P, and large LDMC, LP and L_C:N, while the semi-arid zone is distributed on the acquisition side. This suggests that \u003cem\u003eP. australis\u003c/em\u003e have constant or consistent conservative or acquisition strategies at the organ and whole plant levels in arid and semi-arid regions (Joswig et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Plant of \u003cem\u003eP. australis\u003c/em\u003e in arid zones usually have thicker leaves, stronger stalks, allocate more biomass to mechanical support, and thus exhibit a conservative strategy in order to withstand adverse environmental conditions (P\u0026eacute;rez-Ramos et al., 2012). However, in semi-arid zones, where environmental conditions are more suitable for \u003cem\u003eP. australis\u003c/em\u003e growth, an acquisition-based strategy is favored to meet the higher nutrient content required. In the lakeshore wetlands of Inner Mongolia, LES, SES and WPES of \u003cem\u003eP. australis\u003c/em\u003e all exhibited significant differences between arid and semi-arid regions, while RES was not present in this climatic region. This may be due to the significant variation in root traits themselves, most of which have a high coefficient of variation and are more sensitive to soil texture as well as nutrient content, and different regions of the \u003cem\u003eP. australis\u003c/em\u003e have their survival strategies. Thus, despite the importance of the respective selection of plant traits under different environmental conditions, the coordination of plant acquisition or conservative strategies among traits, organs and resources still converge under different habitat conditions(Reich and Cornelissen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results of a study on functional traits of \u003cem\u003eP. australis\u003c/em\u003e in lakes and lakeshore wetlands in arid and semi-arid regions of Inner Mongolia Plateau show that the functional traits of \u003cem\u003eP. australis\u003c/em\u003e showed a significant latitudinal gradient pattern across the latitudinal gradient, and the variation in traits across the latitudinal gradient was mainly dominated by temperature-mediated climatic factors and less related to soil heterogeneity. The economic spectrum of reed populations in terms of leaf, stem traits and the whole-plant are present, and the \"investment-gain\" strategy axis of the economic spectrum of \u003cem\u003eP. australis\u003c/em\u003e in arid and semi-arid regions is characterized by divergence along with two different directions. The arid region shows a conservative strategy, while the semi-arid region shows an acquisitive strategy. This provides strong evidence to explore the WPES of a single species in the context of plant economic spectrum, and further enriches the integration of intraspecific variation and plant economic spectrum in different climatic regions, which has important theoretical and practical significance for understanding ecological niche differentiation and ecological adaptation strategies of species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eZ.X. and H.L. contributed to analysis and interpretation of data, prepared all figures and tables and writing the original draft; L.W., J.Z. and D.W. contributed to collected the data of the study; X.X., J.H., Y.Z. and X.K. field sampling and data curation; L.W. conceived the idea and designed methodology and contributed in reviewing \u0026amp; editing the paper. All authors collected the field data, substantially contributed to data interpretation, critically revised the manuscript and gave final approval for publication.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully thank Jiangtao Peng for his assistance in the laboratory experiment. The research was supported by National Natural Science Fund, P.R. China (No. 32160279， 3211101852 and 31960249) and the Science and Technology Major Project of Inner Mongolia (No. ZDZX2018054, 2021ZD0011).\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe dataset generated during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eWe declare there is no competing financial interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eACKERLY D, CORNWELL W (2007) A trait-based approach to community assembly: Partitioning of species trait values into within- and among-community components. Ecol Lett 10:135\u0026ndash;145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBRUELHEIDE H, LENOIR DENGLERJPURSCHKEO, JANDT U (2018) Global trait\u0026ndash;environment relationships of plant communities. 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Ecol Res 35:900\u0026ndash;911\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"functional traits, intraspecific variation, lakeshore wetland, plant economics spectrum, Phragmites australis, spatial scales","lastPublishedDoi":"10.21203/rs.3.rs-2103651/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2103651/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eWidespread species of \u003cem\u003ePhragmites australis\u003c/em\u003e, has a high degree of intraspecific variation in functional traits during external climatic and environmental changes. However, the underlying mechanism of the environmental gradient changes at regional scale on intraspecific variation and adaptation strategies of species functional traits are still not well understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMorphological traits, nutrient contents, and stoichiometric ratios of \u003cem\u003eP. australis\u003c/em\u003e in lakeshore wetlands of semi-arid and arid regions in the Inner Mongolia Plateau were investigated to reveal the variability of functional traits at different regional scales and the influencing factors and to reveal the ecological adaptation strategies of \u003cem\u003eP. australis\u003c/em\u003e in different regions through plant economic spectrum.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe functional traits of \u003cem\u003eP. australis\u003c/em\u003e varied significantly within the species at different scales, and the variation has a significant latitude pattern. Climatic factors determine the intraspecific variation of the functional traits of \u003cem\u003eP. australis\u003c/em\u003e, and the influence of soil properties is small. Plant economic spectrum theory is also applicable to the functional traits of various organs and whole plants of \u003cem\u003eP. australis\u003c/em\u003e, and different ecological adaptation strategies are confirmed across arid and semi-arid regions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIntraspecific variation of functional traits of \u003cem\u003eP. australis\u003c/em\u003e originates from temperature-mediated climatic differences brought about by sampling geographic locations, rather than the soil properties of the sampling locations. The utilization and assimilation of resources of \u003cem\u003eP. australis\u003c/em\u003e are conservative in arid regions, while in semi-arid regions it is an acquisition strategy.\u003c/p\u003e","manuscriptTitle":"Temperature-mediated climatic factors dominate intraspecific variation and ecological adaptation strategies for traits of Phragmites australis rather than soil properties","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-12 14:54:28","doi":"10.21203/rs.3.rs-2103651/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5851e756-0a6b-41e3-8c8e-c3abe7b784f8","owner":[],"postedDate":"October 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-19T15:01:33+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-12 14:54:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2103651","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2103651","identity":"rs-2103651","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}