Application of Herbaceous plant CSR strategy responses to four kinds of habitats in the Qinling Mountain for plant community design

Application of Herbaceous plant CSR strategy responses to four kinds of habitats in the Qinling Mountain for plant community design | 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 Application of Herbaceous plant CSR strategy responses to four kinds of habitats in the Qinling Mountain for plant community design Fei Wang, Peilu Huang, Mingyu Jiang, Qiongwen Zhang, Manyu Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3991265/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 Context Urban habitats have been severely degenerated or destroyed due to construction activities and consequent human interference, which have threatened the urban ecosystem, especially plant species richness and diversity. The interactive relationship between plants and habitats is an outcome of long-time evolution. Exploring the relationship can provide an insight for improving the sustainability of urban greenspace. Objectives We attempted to 1) build a relationship between individual plants and the whole community based on CSR theory, and 2) explore ecological function of communities can be achieved better by a suitable combination of individual functional traits. Methods This study referred to Grime's CSR (C: competitor, S: stress tolerance, R: ruderal) theory to analyze Qinling mountain herbaceous communities in typical habitats (roadside, riverside, forest margin, and understory). Species composition in communities of different habitats was recorded. Then dominant and non-dominant species were identified and analyzed emphatically. Results (1) In four habitats, the CWMs of CSR ecological strategies showed that C-scores of riverside communities were the highest, while understory communities were the lowest. S-scores of understory communities were the highest, while roadside communities were the lowest. Lastly, R-scores of roadside communities were the highest, while those of understory communities were the lowest. (2) In terms of CWMs of leaf traits, LDMC gradually increased along the disturbance gradient, but SLA was on the contrary. (3) Dominant species were more profoundly shaped by environmental circumstances than non-dominant species, which can effectively indicate their habitat characteristics. For example, C-scores of dominant species and subdominant species in riverside were significantly higher than in other habitats; S-scores of dominant species in understory habitats were significantly higher than others; and R-scores of dominant species in roadside habitats were significantly higher than others. Conclusions This study suggested that the strategy of dominant species is the main factor that determines the effect of various habitats on plant compositions. This rule verified that the CSR model could help select the cultivated species for urban green space. Also, it can help predict the effect of climate change on herbaceous communities, which has great potential for the planting design of urban herbaceous communities. CSR strategy dominant species habitat type herbaceous community leaf trait planting design Qinling Mountains Figures Figure 1 Figure 2 Figure 3 Introduction Urban green infrastructure has been leading an increasingly critical role in dealing with ecological threats since the 21st century (Hoyle et al. 2017 ).In the setting, perennial urban meadows have become trendy and practical in urban landscape design (Hitchmough and de la Fleur 2006 ; Hitchmough et al. 2017 ). Perennial herbs are available in a wide range of selections as design tools characterized by their relatively short growth periods, rich variation in phenotype, and low construction cost (Guo et al. 2023 ). The naturalistic design approach which is informed by these traits with creative and multifunctional applications has been seen as a fashionable design tool for ecological benefits as well as reducing maintenance costs (Hang 2017 ). Notably, several planting designers (e.g. Piet Oudolf, Nigel Dunnett, and James Hitchmough) were the main proponents reflecting their design philosophies of such in different types of projects such as The High Line New York and Queen Elizabeth Olympic Park, London (Stalter 2004 ). Key ecological theories applied in those plant communities include niche, resource and competition (Chefaoui et al. 2016 ), and these ideas evolved the conventional horticulture practice in many parts of the world (Jing 2017 ). Arlington and Whitehall (2021) argued that herbaceous planting design is supposed to manage the relationship between plants and habitats, plants and humans, as well as between plants and plants (Arlington and White Hall 2021 ). The understanding of the interaction between plants and habitats is normally the initial step to the success of a planting design scheme. In the 1870s, Richard Hansen proposed that plant communities could be combined artificially based on species with similar original habits and growth conditions (Hansen and Stahl 1993 ). Abiotic factors including light, temperature, water and soil conditions were also critical. These were managed to different levels via intense maintenance such as fertilizing, watering and clipping in order to meet the needs of plants (Ignatieva and Hedblom 2018 ; Qian et al. 2016 ). However, issues such as degeneration, slow growth and weak adaptability can still diminish a planting scheme in the longer term. This very often becomes a major bottleneck in herbaceous community design, and then inevitably leads to intense maintenance(which was the original problem for conventional horticulture practice and had been increasingly questioned since it was not sustainable and didn’t conform to the needs of ecological principles and cost control) (Köppler and Hitchmough 2015 ). Therefore, this study infers that there may be other key influencing factors other than nutrient availability and climate effectively determining the relationship between plants and habitats, and the persistence of a semi-natural community. This study aims to build a framework to identify such factors and to interpret the relationship between plants and habitats from a planting design point of view. The CSR (i.e triangular model, Competitor – Stress-tolerator – Ruderal) proposed by Grime was one of the most representative models in describing plant response to the environment (Grime and Pierce 2012a ; Grime 1974 )- an effective method to analyze the relationship between plants and habitats. The CSR strategy represents the adaptive response of one plant in a habitat with a certain level of stress and disturbance. This relevance can be supported by community investigation of various circumstances all over the world (Grime and Pierce 2012a ). For example, ruderals and competitors can grow better in many city habitats with typically high disturbance and humidity (Pierce et al. 2007 ). However, stress-tolerators were more fitted in conditions such as green roofs with poor soil and high temperature (Catalano et al. 2016 ; Köhler 2006 ). If functional traits of many plants could be clearly classified by CSR strategy, the complicated interactions between plants and habitats would be more predictable, which would help designers choose more suitable species regarding the habitats. However, the application of CSR strategy in planting design has rarely been explored for many habitat types, especially in China, whereas precedent studies have mainly focused on ecological topics (Good et al. 2019 ; Wen et al. 2022 ). Thus, this study takes the perspective of landscape design., looking into the feasibility of CSR strategy on design applications (i.e. plant community design practice). The development of CSR theory was rooted in plant functional traits. Functional traits indicated the adaption of plants to circumstances more objectively (Grime 1977 ; Myers-Smith et al. 2019 ). Functional traits also owned important ecological and evolutional values. Moreover, among functional traits, as leaf traits were most closely related to resource acquisition and utilization (Wright et al. 2004 ), it could greatly reflect the response of a plant species to certain habitat changes (Vendramini et al. 2002 ). For example, SLA (Specific Leaf Area) of psammophyte (plants in poor and drought habitats) is often smaller in order to increase resource retention capacity and reduce water loss (Rosado and de Mattos 2017 ), and thus their strategy tended to S-selection. However, plants in greater water and higher temperature conditions usually gained more growth, with larger leaf area to take more resources of light (Pierce et al. 2014 ), and this typically represented C-selection. These outcomes showed that all plant functional traits were strongly influenced by environmental factors, thus ecological strategy of plants also changed synchronously (Pierce et al. 2017 ). Qinling Mountains traverse the central area of China and mark the boundary between the north and south environmental systems of China. They also strengthen the natural ecological safety barrier in central China and become one of the biodiversity hotspots in East Asia (Chen 1998 ; Liu et al. 2016 ). Environmental factors and specific genes commonly have the ability to affect the growth of plants, which leads to the appearance of abundant phenotypes and ecotypes to acclimatize themselves to diverse habitats (Canino-Koning et al. 2019 ). For example, topographic, soil factors and climate factors jointly affected the phenotypes (fruit weight and shape) of Schisandra sphenanthera Rehd. et Wil in the Qinling Mountains(Wang et al. 2022 ). Twenty-five leaf traits of two Quercus species also exhibited different ecological strategies along an altitudinal gradient (Chai et al. 2015 ). This would be the key to building a sustainable herbaceous community in urban settings (Fang et al. 2015 ). Generally, dominant species with the most population, widest distribution and most abundant biomass should particularly be paid more attention to, because they were recognized as one of the greatest populations impacting community composition in a certain habitat (Avolio et al. 2019 ). The identity of dominant species was ecologically relevant because species compositions of the community varied between locations. For example, extreme stress-tolerator Carex spp. was the dominant species in relatively disturbance-free meadow communities in the eastern European Alps (Pierce et al. 2007 ). However, competitors and ruderals were popular in habitats with high stress and disturbance in South Korea and Turkey (Elmas 2017 ). Meanwhile, the assembly of different species in nature did not appear in a certain place spontaneously (Tabassum et al. 2020 ). It is usually the functional traits of a plant that selects its living environment. The environment affects the functional traits and ecological strategy of the plant in return (Catorci et al. 2011 ). Thus, evaluating the ecological strategy of plants is the cornerstone of the further investigation about plant adaptation and ecology progress (Grime 2001 ; Negreiros et al. 2014 ; Pierce et al. 2017 ). The ecological strategy of dominant species is of much more importance. If the composition of CSR strategy and the strategic change of dominant species in natural herbaceous communities of different habitats can be widely analyzed, theoretical and practical guidance can be provided for species combination of herbaceous communities in urban planting design. In this study, we investigated the species composition of herbaceous communities in different habitat types (roadside, riverside, forest margin, understory) in the Qinling Mountains (1080 ~ 1260m). This included the height and coverage of species, and determined CSR strategies of species by estimating LA(Leaf Area), LDMC(Leaf Dry Matter Content) and SLA(Specific Leaf Area), mainly to answer the following questions: i) In the Qinling Middle elevation, do the community weighted mean (CWM) C-, S- and R-scores show significant differences with different habitat types? What are the differences in main trade-offs for herbaceous plants in different habitat types? ii) what are the key differences in the CSR strategies between dominant and non-dominant species in the same habitat type? Thus, we expected i) stresses and disturbances differ in different habitats. So the ecological strategies of plants could show significant differences. Because of high disturbance in roadside habitats, these plants might show a tendency towards R-selection, while the understory plants are affected by light and cold stress, showing a tendency towards S-selection. The other two habitats fall between these categories. ii) Due to the highly ecological adaptability of dominant species, which largely determines environmental conditions within the community, we predicted that the CSR strategies of dominant and non-dominant species in the same habitat are significantly different. The ecological strategies of dominant species can be used to indicate habitat characteristics. Material and methods Study Areas Our study was conducted in the Qinling Mountains, which had an altitude ranging from 1080-1260m. There were 6 sampling sites selected for this study and recorded by GPS (Navigation Satellite Timing And Ranging Global Position System): Heihe Forest Park(33°40′N, 107°51′E)、Liangfeng valley (33°31′N, 107°59′E)、Zhu valley (34°08′N, 108°02′E)、Cuifeng Mountain (34°07′N, 108°04′E)、Foping Health theme Park (33°52′N, 107°55′E)、Luo valley (34°05′N, 108°06′E). These sites typically consisted of low water retention rate, low nutrient and infertile organic matter content in soil, which were ascribed to quick and permeable drainage in the topsoil layer. These sampling sites were located in about 40–60 km south of the Wei River and 40-60km north of the Han River. Experimental design and habitat classification In our study, herbaceous communities were divided by habitat types. Comparative study of various plants’ growth and distribution in Flora of Qinling Mountains and common city habitat classification in the Guanzhong area demonstrate that 1) roadside, 2) riverside, 3) understory and 4) forest margin are the most typical habitats. The fieldwork was conducted in April and May when the vegetation in Qinling was relatively established in the year. Study areas sat in valleys in Qinling with even topography that was divided into four kinds of habitats (roadside, riverside, understory and forest margin) based on different sunlight and moisture levels. Community structure and species composition were surveyed in 4 habitats×4 sites×4 plots making a total of 64 plots. Each plot was 1m×1m. Every species name that existed in plots was then recorded, as well as the maximum height and coverage of each species and the whole community, and dominant and other species were separately recorded. All plants were classified by the Cronquist System. Functional traits and CSR strategy Ten completely expanded fresh leaves were randomly sampled from each species. This was to estimate their functional traits in each plot (the average of ten duplicates represents the value of each species). When the micro leaves were estimated, duplicates were added to reduce systematic error. Fresh leaves were flattened and put into the scanner (Cannon LiDE300), for leaf area estimation using Image. Leaf fresh weight (LFW) was obtained from saturated leaves, then leaf dry weight (LDW) was obtained after leaving 48h in a 65°C drying cabinet when the leaves reached a relatively constant weight. C, S, and R proportions of each species were calculated based on three leaf traits (LA, LDMC and SLA) in Table ‘StrateFy’, which can calculate CSR strategy proportion by leaf traits of principal component analysis axis regression according to multivariate analysis in plants’ leaf traits all over the world (Pierce et al. 2017 ). Statistical analysis We use maximum height and coverage as indicators to calculate the importance value of one species. Relative importance value was then calculated based on importance value. The formulas are as previously described (Ohsawa 1984 ): Iv = H*C \(D=\text{I}\text{v}/(\sum \text{I}\text{v}\text{*}100\text{%}\) ) In formulas, Iv is the importance value; H refers to the maximum height of one species, C refers to the coverage of this species, and D refers to the degree of dominance for one species in a plot. Based on the contribution of herbaceous plants in the community, we select 1–3 plants as dominant species with D ≥ 0.6, sub-dominant species with 0.5 ≤ D < 0.6, companion species with 0.2 ≤ D < 0.5, and rare species with D < 0.2. The relative proportions of C, S, and R strategy were calculated by LA, LDMC, and SLA of each species in Table ‘StrateFy’ (Pierce et al. 2017 ). This category showed the extent of C-, S-, and R- selection via trade-off between three leaf traits (LA, LDMC, and SLA). For each relevé, community weighted mean was estimated (CWM, i.e., weighting the mean by the relative cover of each species in the relevé) with LA, LDMC and SLA, as well as of C-, S- and R-scores, using the R package ‘FD’ (Laliberté et al. 2014 ). Before running the ANOVA, we checked for normality of plant strategy scores by means of the Shapiro-Wilk test and, accordingly, transformed LA and SLA by square root (sqrt[x]) and logarithmic (log[x + 1]) transformation, respectively, while no transformation was required for LDMC and strategy scores. We applied the ANOVA with a Tukey post hoc comparison test to identify significant differences among strategies scores between different habits combining the functions ‘aov’ and ‘HSD.test’ of R packages ‘ stats ’ and ‘ agricolae ’, respectively (Laliberté et al. 2014 ). Results The CWMs of CSR ecological strategies The Various habitats differed significantly in terms of space occupation of CSR ternary and showed a robust variation along the disturbance gradient, which related to either niches characterized by frequent disturbances to individuals (R-selection) or unproductive niches (S-selection) (Fig. 1 ). All the habitats significantly differed in terms of CWM C-, S- and R-scores (Fig. 2 ); however, they showed a clear pattern only restricted to S-gradients. C-selection and R-selection were the less discriminative; nonetheless, significant differences were still present. The most competitive communities (highest C-scores) were in riverside, whereas understory communities were the less competitive (lowest C-scores). However, understory communities were the most stress-tolerant (highest S-scores), in contrast to roadside communities which showed the lowest degree of S-selection. Forest margin owned intermediate values of S-scores, which were slightly higher than that of riverside. Lastly, roadside species were the most ruderal (highest of R-scores) and understory species were the least ruderal (lowest of R-scores). The CWMs of plant traits The CWMs of LA, LDMC and SLA showed significant differences between habitats. LDMC showed a gradual increase along the disturbance gradient (Fig. 2 ). With regard to LA, riverside communities exhibited plants with the largest leaves, while forest margin communities included plants with the smallest. In less stable roadside communities, leaves were smaller compared to the relatively stable understory communities. Roadside displayed the lowest leaf construction investment (lowest LDMC), while grasslands with low disturbance understory exhibited the highest leaf construction investment (highest LDMC). The forest margin species generally tended to have intermediate values of LDMC, and they were significantly higher than that of riverside. Besides, SLA shows roughly the opposite pattern to LDMC along the interference gradients. Both roadside and riverside possessed species with the most acquisitive leaves (highest mean values of SLA), as opposed to understory species which showed the most conservative leaves (lowest mean values of SLA). Forest margin species showed intermediate SLA values among other grassland communities. Variations of environmental factors, species composition and CSR strategies In the four types of habitats of the Qinling Mountains, the CSR strategy of the dominant species was significantly different from subordinate species in a community. Meanwhile, the CSR strategy of companion and rare species showed larger variability in comparison with dominant and subdominant species (Fig. 3 ). For example, in the four types of habitats, there was a tiny difference for C-scores of companions and rare species in the herbaceous community. In contrast, C-scores of dominant and subordinate species in riverside were significantly higher than in other habitats. However, their S-scores were significantly lower. S-scores of dominant species in understory communities were significantly higher than in other habitats, but their R-scores were significantly lower (P < 0.05). In addition, S-scores of dominant species in roadside communities were significantly lower than in other habitats. However, their R-scores were significantly higher. Discussion Differences in CWMs for CSR ecological strategies in various habitat types This study provides an overview of how CWMs of CSR ecological strategies and plant traits are influenced by habitat types in the Qinling Mountains (Fig. 1 ). This strategy spectrum suggests that niche segregation and coexistence within Qinling Mountain Communities are mediated by a strong functional divergence with response to differential local stress and disturbance (Pierce et al. 2007 ; Zanzottera et al. 2020 ). Different habitats with different ecological factors such as light, water, soil and different intensity of disturbance often create distinct germination, growth and propagation opportunities for various species, and this is the result of mutual selection between plants and habitats (Morris et al. 2016 ). According to the hypothesis, compared to the other three habitat types, roadside habitats exhibit ruderal characteristics (rapid completion of the life cycle and selection of regenerative traits). These plants mainly with R, R/CR and CR as a response to their long-term exposure to disturbance. In contrast, species in understory habitats are dominated by S/CS, CS/CSR, SR/CSR, S/SR, S and the strong stress-tolerant characteristics (low productivity and conservative adaptations), which can help them grow and thrive in poor conditions. Riverside habitats favored competitive ruderal characteristics with rapid growth and abundant biomass. It was evident that the strategies of these plants were mainly CR, R/CR, SR, CSR, and CR/CSR benefiting by occupying light, water and nutrients to the maximum by quick growth in a relatively high productivity environment. Forest margin species have mediate characteristics among the above three habitats, which can facilitate the growth of CSR species. The strategies in forest margin were mostly R/CSR, CSR, SR/CSR, and R/SR. This is in accordance with the hypothesis that habitat heterogeneity is the main reason for most plants to exhibit two or more strategies, which helps them to adapt to diverse habitats (Hiebeler 2007 ). Differences in CWMs for plant traits in various habitat types As a key insight into the relationship between plants and habitats, functional traits can comprehensively interpret the formation of plant strategy (Di Biase et al. 2021 ). This can provide guidance for exploring mutual interaction mechanisms between plants and surrounding environments (da Costa et al. 2020 ; Yu et al. 2022 ). Our results indicated that the CWMs of traits were relevant to the leaf economics spectrum (LA, LDMC and SLA) (Reich 2014 ; Wright et al. 2004 ; Zanzottera et al. 2020 ). The change was consistent with variations in the different habitat types. Specifically, SLA is negatively related to leaf lifespans and positively related to relative growth rate(Reich 2014 ; Zanzottera et al. 2020 ). High SLA can overcome frequent and certain intensities of human activity (e.g. trampling, etc.). Roadside habitats are typically colonized by species with trait attributes that optimize carbon acquisition: fast-growing and low-cost leaves with a short lifespan and high SLA(Wright et al. 2004 ). LDMC reflects the resource acquisition and nutrient maintenance ability of a plant (Pierce et al. 2012 ), which was often significantly negatively related to SLA (Zanzottera et al. 2020 ). Because the dense canopy of upper forest blocks the sunlight, understory herbaceous vegetation usually suffers stress from low light, high humidity and cold temperatures (Al-Namazi and Bonser 2020 ). These plants mainly invest their resources into vegetative tissues, which to resist adverse conditions via long-lived plant organs (Myers-Smith et al. 2019 ). This can optimize the use of plant resources, to adapt to the environment to survive and propagate more effectively (Guo et al. 2018 ). In addition, LA reflects the resource utilization of plants (Reich et al. 1998 ). Roadside habitats were abundant in water and light resources. This helped plants distribute the available resources more effectively among various traits. This helped achieve trade-offs appropriately between vegetative and reproductive growth(Zhou et al. 2021 ). The growth can help occupy the dominant niche via rapid reproduction, growth and relatively huge plant organs (in other words, high LA and high SLA). Therefore, it is of critical importance for the basic theory that understanding the rule and effect of functional traits on species composition is beneficial for studying comprehensive interactions between plants and environments (Di Biase et al. 2021 ). Agreeing with many previous studies, this finding can more comprehensively explain the formation of plant ecological strategies (Faccion et al. 2021 ; Grime 1977 ; Guo et al. 2018 ; Myers-Smith et al. 2019 ). The changes in the CSR ecological strategies with dominant species and non-dominant species Previous studies have predicted that at intermediate productivity levels and/or in more heterogeneous habitats, a wider range of CSR strategies could co-occur, and CSR strategies could therefore be a better predictor of the coexistence and relative dominance of species (Cerabolini et al. 2016 ; de Paula et al. 2015 ; Rosado and de Mattos 2017 ). Our results were in agreement with the prediction that in herbaceous communities of moderate altitude Qinling Mountains, plant species biodiversity was higher and the community was close to intermediate productivity (Grime 1974 , 1977 ). Therefore, plant strategies were all scattered in four habitats, but CSR strategy was significantly different between dominant species and non-dominant species (Fig. 3 ). However, it is worth noticing that the interactive relationship between plants and habitats is yet to be fully understood (Hitchmough et al. 2004 ; Jiang and Yuan 2017 ). There is no absolute correspondence between habitat type and strategy of dominant species. Researchers found that in the coastal sandy plain of the Restinga de Jurubatiba National Park, all plants with S/CS strategy, and S-scores of dominant species were not the highest values (Rosado and de Mattos 2017 ). Building upon this earlier work, it revealed that in environments which were extremely in favor of C, S, or R strategies, ecological strategy no longer indicates plant niches. Comprehensive traits are important for understanding community assembly and the pattern of species dominance (Grime and Pierce 2012b ; Rosado and de Mattos 2017 ). In this study, dominant species mainly consisted of one or several similar strategies (Fig. 3 ). They occupied basic niches in the community, and were more adaptable to environmental change (Glenn et al. 2001 ). For instance, dominant species of roadside habitat were mainly R-selection, such as Equisetum pratense (C: S: R = 11: 0: 89%), and Corydalis racemosa (C: S: R = 46: 0: 54%). These plants always were mostly short and clumped, which were resistant to trampling, short-lived and possessed various reproduction ways (Grime 1979 ). In contrast, dominant species of understory habitats were mainly S-selection, such as several Carex plants: Carex minxianica (C: S: R = 25: 69: 6%), and Carex pediformis (C: S: R = 12: 76: 12%) etc. They were short, shade-resistant, and owned high stoichiometric homeostasis (Yu et al. 2010 ). On the other hand, ecological strategies of non-dominant species distributed more widely, and there was no significant difference among the four kinds of habitats. For example, non-dominant species of riverside habitats included CS/CSR (C: S: R = 40: 37:23%) strategy of Argentina anserina , SR (C: S: R = 7: 54: 39%) strategy of Poa pratensis , and R (C: S: R = 0: 0: 100%) strategy of Stellaria media . These non-dominant species often co-exist with dominant species, but remain the subordinate role. All results suggested that dominant species were more susceptible to the influence of environment in comparison with non-dominant species (Arnillas and Cadotte 2019 ). The ecological difference between them can indicate their niche in the community, which also proves the mass ratio hypothesis (Grime 1998 ). There is indirect evidence for certain connections between several dominant species in a community obtained where environmental filtration determined some species to grow healthier in a site. Several thriving dominant species therefore may share some critical traits or ecological strategies (Cadotte 2017 ; Mayfield and Levine 2010 ). Based on CSR theory, how do different habitats construct herbaceous planting designs? Conventional horticulture practice tends to consider abiotic factors of for example light, water and soil nutrients more than the fitness measure between plant survival strategies and the original habitats. This very often leads to a failure in the design and management of a semi-natural community. The study of Grime revealed that C-strategy plants are suitable to habitats with low stress and disturbance; S-strategy plants are suitable to habitats with high stress and low disturbance; and R-strategy plants are suitable to habitats with low stress and high disturbance (Grime 1974 ). In our study, the results of four types of habitats have broadened and refined this theory. In high disturbance habitats (i.e. roadside), dominant species were mainly R-selected plants, which were short-lived, resistant to trample, and owned various reproductive ways, such as Corydalis racemosa (C: S: R = 46: 0: 54%), Corydalis incisa (C: S: R = 34: 0: 66%), and Geranium carolinianum (C: S: R = 28: 19: 53%). Riverside habitats with rich resources were dominant by thriving C, R-selected plants, such as Hemerocallis fulva (C: S: R = 56: 0: 44%), Vicia bungei (C: S: R = 38: 0: 62%), and Glechoma longituba (C: S: R = 31: 26: 43%). Plants in forest margin habitats with high heterogeneity always inhibited two or more strategies, such as Thalictrum aquilegiifolium (C: S: R = 27: 17: 56%), Carex lanceolata (C: S: R = 14: 14: 72%) and Medicago ruthenica (C: S: R = 5: 56: 39%). Understory habitats were dominated by S-selection plants with great stress tolerance, such as Sedum lineare (C: S: R = 1: 78: 21%), Carex pediformis (C: S: R = 12:76: 12%) and Melica scabrosa (C: S: R = 7: 65: 28%). Furthermore, Piet and Noel ( 2013 ) indicated that in fertile habitats, one of the critical design principles is not to mix up plants with different adaption traits to stress to maintain the sustainability of natural community contexts for a long time (Piet and Noel 2013 ). In contrast, in poor resource or more heterogeneous habitats, practitioners can select plants with more diversity strategies, because it will not decrease the biodiversity of the community (Piet and Noel 2013 ). This study argues that CSR theory can supplement conventional planting design by interpreting the relationship between plants and habitats with ecological theories. This study has investigated the relationship between habitats and the composition of plant CSR strategies in the Qinling herbaceous communities with a fitness measurement between habitat traits (stress/disturbance) and plant survival strategy. This investigation can explore practical guidance for herbaceous planting design in urban landscapes. The study also proved CSR theory and model can not only predict the effect of environmental change on herbaceous communities with ecological principles, but also can be a conceptual framework to select plants suitably which are suitable for city habitats (Wang et al. 2024 ). This ecological principle informed process of species selection can predict the adaptivity and robustness of plants at the desktop study stage for longer term sustainability, such as enhancing the species coexistence and reducing maintenance costs. However, there was a limitation that the sampling areas were geographically restricted in the Qinling Mountains, China., but more relevant studies are required to further prove the theory. Besides, not only the relationship between plants and habitats are critical, but interactions between plants in a community also need to be further explored. The contribution of natural herbaceous community strategy and the difference of functional traits trade-offs can be clarified further. This can help choose suitable species and can help estimate the ratio of them in a mix for plant community design. Last but not least, CSR theory still stands for a theoretical model in many situations, especially urban landscape design (Grime and Pierce 2012b ). Due to the different processes of plant community establishment between nature conservation and urban planting design (Jiang and Hitchmough 2022 ), the practicability is yet to be enhanced with further research with long-term data. Conclusion Based on CSR theory, this work studied herbaceous communities in the Qinling Mountains for reviewing and summarizing different compositions of plant strategies in four types of habitats, which could be a framework for plant communities in urban habitats. Plants belonged to different strategies due to mutual selection of the interaction between plants and habitats. Roadside and riverside habitats exhibited the most acquisitive leaves (highest CWMs of SLA), opposed to understory habitats which showed the most conservative leaves. Strong stress-tolerant characteristics (low productivity and conservative adaptations) can help plants in understory habitat grow and thrive in unproductive conditions. Besides, plants in forest margin often exhibited two or more strategies to deal with a high diversity microenvironment and heterogeneity in forest margin. Results also confirmed Grime's (reference) mass ratio hypothesis, since dominant species were more susceptible to environmental impacts than non-dominant species. As basic species in a community, the ecological strategy of dominant species is closely related to community property and environmental change, which is the critical factor in habitat evaluation of community ecology. Declarations Acknowledgements Thanks to Gao Tian and Qiu Ling (Northwest A&F University) for the support of our experimental project：herbaceous plant survey in Qinba Mountain understory environment, including transportation and laboratory condition support. Author contributions FW, PLH, and CSL contributed to the conceptualization and design of the study. Material preparation, data collection, and analysis were performed by FW, PLH, QWZ, MYZ. PLH and MYJ advised and assisted with the statisti-cal analysis. FW wrote the first draft of the manuscript. All authors commented on previous manuscript versions, contributed critically to the drafts, and gave final approval for publication. Funding The work was supported by Shaanxi Provincial Natural Science Foundation Youth Project: plant community design model of urban floral lawn in Guanzhong based on the theory of biodiversity (K3030121084). Data availability The data that support this study will be shared upon reasonable request to the corresponding author. Competing Interests The authors declare no conflicts of interest. References Al-Namazi AA, Bonser SP (2020) Plant strategies in extremely stressful environments: are the effects of nurse plants positive on all understory species? Journal of Plant Interactions 15(1):233-240 Arlington V, White Hall M (2021) Planting in a post-wild world : desgining plant communities for resilient landscapes. 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Chinese Landscape Architecture 31(07):59-63 Glenn EP, Waugh WJ, Moore D, McKeon C, Nelson SG (2001) Revegetation of an abandoned uranium millsite on the Colorado Plateau, Arizona. Journal of Environmental Quality 30(4):1154-1162 Good M, Morgan JW, Venn S, Green P (2019) Timing of snowmelt affects species composition via plant strategy filtering. Basic and Applied Ecology 35:54-62 Grime J (2001) Plant Strategies, Vegetation Processes, and Ecosystem Properties. Grime J, Pierce S (2012a) The Evolutionary Strategies That Shape Ecosystems Grime JP (1974) Vegetation classification by reference to strategies. Nature 250:26-31 Grime JP (1977) Evidence for the Existence of Three Primary Strategies in Plants and Its Relevance to Ecological and Evolutionary Theory. The American Naturalist 111:1169 - 1194 Grime JP (1979) Plant Strategies and Vegetation Processes. Sons, Ltd., Chichester-New York-Brisbane-Toronto Grime JP (1998) Benefits of plant diversity to ecosystems: immediate, filter and founder effects. Journal of Ecology 86(6):902-910 Grime JP, Pierce S (2012b) From Adaptive Strategies to Communities. The Evolutionary Strategies that Shape Ecosystems. pp. 105-162 Guo H, Zhou XB, Tao Y et al (2023) Perennial herb diversity contributes more than annual herb diversity to multifunctionality in dryland ecosystems of North-western China. Frontiers in Plant Science 14 Guo WY, van Kleunen M, Winter M et al (2018) The role of adaptive strategies in plant naturalization. Ecology Letters 21(9):1380-1389 Hang Y (2017) Development and Application of Herbaceous Vegetation in New Naturalistic Ecological Planting Design. 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Urban Forestry & Urban Greening 27:117-126 Hoyle H, Hitchmough J, Jorgensen A (2017) All about the 'wow factor'? The relationships between aesthetics, restorative effect and perceived biodiversity in designed urban planting. Landscape and Urban Planning 164:109-123 Ignatieva M, Hedblom M (2018) An alternative urban green carpet. Science 362(6411):148-149 Jiang M, Hitchmough JD (2022) Can sowing density facilitate a higher level of forb abundance, biomass, and richness in urban, perennial "wildflower" meadows? Urban Forestry & Urban Greening 74 Jiang Y, Yuan T (2017) Public perceptions and preferences for wildflower meadows in Beijing, China. Urban Forestry & Urban Greening 27:324-331 Jing Z (2017) Groundcovers Cultivation and Water Saving Technology in Northern Shaanxi Garden Based on Green Economy. Agro Food Industry Hi-Tech 28(1):3389-3392 Köhler M (2006) Long-Term Vegetation Research on Two Extensive Green Roofs in Berlin. Urban Habitats 4 Köppler M-R, Hitchmough JD (2015) Ecology good, aut-ecology better; improving the sustainability of designed plantings. Journal of Landscape Architecture 10(2):82-91 Laliberté E, Legendre P, Shipley B (2014) FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1:0-12 Liu X-z, Gao C-c, Su Q, Zhang Y, Song Y (2016) Altitudinal trends in δ13C value, stomatal density and nitrogen content of Pinus tabuliformis needles on the southern slope of the middle Qinling Mountains, China. Journal of Mountain Science 13(6):1066-1077 Mayfield MM, Levine JM (2010) Opposing effects of competitive exclusion on the phylogenetic structure of communities. Ecology Letters 13(9):1085-1093 Morris DW, Lundberg P, Brown JS (2016) On strategies of plant behaviour: evolutionary games of habitat selection, defence, and foraging. Evolutionary Ecology Research 17(5):619-636 Myers-Smith IH, Thomas HJD, Bjorkman AD (2019) Plant traits inform predictions of tundra responses to global change. New Phytologist 221(4):1742-1748 Negreiros D, Le Stradic S, Wilson Fernandes G, Renno HC (2014) CSR analysis of plant functional types in highly diverse tropical grasslands of harsh environments. Plant Ecology 215(4):379-388 Ohsawa M (1984) Differentiation of vegetation zones and species strategies in the subalpine region of Mt. Fuji. Vegetatio 57(1):15-52 Pierce S, Brusa G, Sartori M, Cerabolini BEL (2012) Combined use of leaf size and economics traits allows direct comparison of hydrophyte and terrestrial herbaceous adaptive strategies. Annals of Botany 109(5):1047-1053 Pierce S, Luzzaro A, Caccianiga M, Ceriani RM, Cerabolini B (2007) Disturbance is the principal alpha-scale filter determining niche differentiation, coexistence and biodiversity in an alpine community. Journal of Ecology 95(4):698-706 Pierce S, Negreiros D, Cerabolini BEL et al (2017) A global method for calculating plant CSR ecological strategies applied across biomes world-wide. Functional Ecology 31(2):444-457 Pierce S, Vagge I, Brusa G, Cerabolini BEL (2014) The intimacy between sexual traits and Grime's CSR strategies for orchids coexisting in semi-natural calcareous grassland at the Olive Lawn. Plant Ecology 215(5):495-505 Piet O, Noel K (2013) Beauty of Wildernessb-Naturalistic planting design Timber Press, Portland, Oregon Qian S, Qi M, Huang L, Zhao L, Lin D, Yang Y (2016) Biotic homogenization of China’s urban greening: A meta-analysis on woody species. Urban Forestry & Urban Greening 18:25-33 Reich PB (2014) The world-wide 'fast-slow' plant economics spectrum: a traits manifesto. Journal of Ecology 102(2):275-301 Reich PB, Walters MB, Tjoelker MG, Vanderklein D, Buschena C (1998) Photosynthesis and respiration rates depend on leaf and root morphology and nitrogen concentration in nine boreal tree species differing in relative growth rate. Functional Ecology 12(3):395-405 Rosado BHP, de Mattos EA (2017) On the relative importance of CSR ecological strategies and integrative traits to explain species dominance at local scales. Functional Ecology 31(10):1969-1974 Stalter R (2004) The Flora on the High Line, New York city, New York. Journal of the Torrey Botanical Society 131(4):387-393 Tabassum S, Ossola A, Manea A, Cinantya A, Winzer LF, Leishman MR (2020) Using ecological knowledge for landscaping with plants in cities. Ecological Engineering 158 Vendramini F, xed, az S et al (2002) Leaf Traits as Indicators of Resource-Use Strategy in Floras with Succulent Species. The New Phytologist 154(1):147-157 Wang F, Zhang Q, Huang P, Li C, Li Y (2024) CSR strategy composition and leaf traits for herbaceous plants in garden design. Ecological Indicators 158:111173 Wang XR, Yan M, Wang XX et al (2022) The phenotypic diversity of Schisandra sphenanthera fruit and SVR model for phenotype forecasting. Industrial Crops and Products 186 Wen YB, Chen C, He BH, Lu XH (2022) CSR Ecological Strategies and Functional Traits of the Co-Existing Species along the Succession in the Tropical Lowland Rain Forest. Forests 13(8) Wright IJ, Reich PB, Westoby M et al (2004) The worldwide leaf economics spectrum. Nature 428(6985):821-827 Yu JL, Hou G, Zhou TC, Shi PL, Zong N, Sun J (2022) Variation of plant CSR strategies across a precipitation gradient in the alpine grasslands on the northern Tibet Plateau. Science of the Total Environment 838 Yu Q, Quansheng C, Elser J et al (2010) Linking stoichiometric homeostasis with ecosystem structure, functioning and stability. Ecology letters 13:1390-9 Zanzottera M, Fratte MD, Caccianiga M, Pierce S, Cerabolini BEL (2020) Community-level variation in plant functional traits and ecological strategies shapes habitat structure along succession gradients in alpine environment. Community Ecology 21(1):55-65 Zhou DY, Ni YH, Yu XN et al (2021) Trait-based adaptability of Phragmites australis to the effects of soil water and salinity in the Yellow River Delta. Ecol Evol 11(16):11352-11361 Additional Declarations No competing interests reported. Supplementary Files S1.xlsx 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. 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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-3991265","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278336754,"identity":"6ebfa9a4-6b59-486b-bc4e-331c441d8fa4","order_by":0,"name":"Fei Wang","email":"","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Wang","suffix":""},{"id":278336755,"identity":"422d3026-107e-43b2-900c-8f8530e4f050","order_by":1,"name":"Peilu Huang","email":"","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Peilu","middleName":"","lastName":"Huang","suffix":""},{"id":278336756,"identity":"6ba5cedf-5f34-4d2a-aeb3-ea2d46053750","order_by":2,"name":"Mingyu Jiang","email":"","orcid":"","institution":"Scotland's Rural College","correspondingAuthor":false,"prefix":"","firstName":"Mingyu","middleName":"","lastName":"Jiang","suffix":""},{"id":278336757,"identity":"5c23c9db-4a42-44d5-b3c7-9952aead4236","order_by":3,"name":"Qiongwen Zhang","email":"","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Qiongwen","middleName":"","lastName":"Zhang","suffix":""},{"id":278336758,"identity":"ad00086f-839b-405f-a9c7-d754d66d90f6","order_by":4,"name":"Manyu Zhang","email":"","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Manyu","middleName":"","lastName":"Zhang","suffix":""},{"id":278336759,"identity":"3af36c0f-f283-4662-82ed-a04a4d296c8b","order_by":5,"name":"Cangshuan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACefmDjY9/8Pzn4WdvIFKL4QzmZmMGGWYZyZ4DxFpzg71NmsGG2cZgRgKROhhnNzZIF+Sw8RhIPt54g6HGJpqgFnaZgw3GM87w8JhLpxVbMBxLy20gaEtDYkMCb48Ej+XsHDMJxobDhLUwHEhsOMD7z4DH4OYZYrXcSGxs5uFJ4DG4wUOkFsOeg82MM3gO8Ej2AP2SQIxf5Nnbn//4wHPAnp/98MYbH2psiHAYEjCQSCBFOUQLqTpGwSgYBaNgZAAAh3k+6P6evJQAAAAASUVORK5CYII=","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Cangshuan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-02-26 15:12:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3991265/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3991265/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52625574,"identity":"e293010a-1733-4bda-b5d7-e286e08ee9af","added_by":"auto","created_at":"2024-03-13 17:40:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":382539,"visible":true,"origin":"","legend":"\u003cp\u003eCommunity weighted means (CWMs) of each habitat.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3991265/v1/747e5ea08936913b9c4d5600.jpg"},{"id":52624942,"identity":"74b1e3ec-2973-4e55-8d65-0952b4fe92dc","added_by":"auto","created_at":"2024-03-13 17:32:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93418,"visible":true,"origin":"","legend":"\u003cp\u003eMean values and standard deviation of community weighted means (CWMs) of\u003cstrong\u003e a) \u003c/strong\u003eC-scores, \u003cstrong\u003eb)\u003c/strong\u003e S-scores,\u003cstrong\u003e c)\u003c/strong\u003e R-scores,\u003cstrong\u003e d\u003c/strong\u003e) leaf area (LA),\u003cstrong\u003e e)\u003c/strong\u003e leaf dry matter content (LDMC), and \u003cstrong\u003ef)\u003c/strong\u003e specific leaf area (SLA) for each habitat (i.e., vegetation communities discernible within each Habitat type). Small letters (a, b, and c) indicate the results of the post hoc comparisons. Results of the ANOVA are also shown below each plot, indicating F-values.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3991265/v1/26908baecc8141ae34bcf3a3.png"},{"id":52624945,"identity":"05b39f73-2a68-4af9-ba44-eb64ea48a398","added_by":"auto","created_at":"2024-03-13 17:32:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":631878,"visible":true,"origin":"","legend":"\u003cp\u003eThe C-, S-, and R-strategy values of the community composition in four types of Qinling grasslands. (a) Roadside; (b) Riverside; (c) Forest margin; (d) Understory. Box-and-whisker plots showing changes in C-, S- and R-strategy of species composition in the communities of Roadside, Riverside, Forest margin and Understory. (e) C-strategy; (f) S-strategy; and (g) R-strategy (Tukey's test, P \u0026lt; 0.005).\u003c/p\u003e","description":"","filename":"Fig.31.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3991265/v1/81a4bcbff61b5cac1680fd84.jpg"},{"id":52826403,"identity":"090a978c-dfa9-4d53-8755-4c5220fbfe95","added_by":"auto","created_at":"2024-03-17 01:22:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":530711,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3991265/v1/e13fc8ed-ea46-4b78-8b34-90f0db39891b.pdf"},{"id":52624943,"identity":"6e12ffc4-a78d-4f84-9c2b-c92735dc2c7b","added_by":"auto","created_at":"2024-03-13 17:32:14","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32979,"visible":true,"origin":"","legend":"","description":"","filename":"S1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3991265/v1/80a9c0f3ae78358b1123876d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of Herbaceous plant CSR strategy responses to four kinds of habitats in the Qinling Mountain for plant community design","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrban green infrastructure has been leading an increasingly critical role in dealing with ecological threats since the 21st century (Hoyle et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).In the setting, perennial urban meadows have become trendy and practical in urban landscape design (Hitchmough and de la Fleur \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hitchmough et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Perennial herbs are available in a wide range of selections as design tools characterized by their relatively short growth periods, rich variation in phenotype, and low construction cost (Guo et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The naturalistic design approach which is informed by these traits with creative and multifunctional applications has been seen as a fashionable design tool for ecological benefits as well as reducing maintenance costs (Hang \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Notably, several planting designers (e.g. Piet Oudolf, Nigel Dunnett, and James Hitchmough) were the main proponents reflecting their design philosophies of such in different types of projects such as The High Line New York and Queen Elizabeth Olympic Park, London (Stalter \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Key ecological theories applied in those plant communities include niche, resource and competition (Chefaoui et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and these ideas evolved the conventional horticulture practice in many parts of the world (Jing \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArlington and Whitehall (2021) argued that herbaceous planting design is supposed to manage the relationship between plants and habitats, plants and humans, as well as between plants and plants (Arlington and White Hall \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The understanding of the interaction between plants and habitats is normally the initial step to the success of a planting design scheme. In the 1870s, Richard Hansen proposed that plant communities could be combined artificially based on species with similar original habits and growth conditions (Hansen and Stahl \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Abiotic factors including light, temperature, water and soil conditions were also critical. These were managed to different levels via intense maintenance such as fertilizing, watering and clipping in order to meet the needs of plants (Ignatieva and Hedblom \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Qian et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, issues such as degeneration, slow growth and weak adaptability can still diminish a planting scheme in the longer term. This very often becomes a major bottleneck in herbaceous community design, and then inevitably leads to intense maintenance(which was the original problem for conventional horticulture practice and had been increasingly questioned since it was not sustainable and didn\u0026rsquo;t conform to the needs of ecological principles and cost control) (K\u0026ouml;ppler and Hitchmough \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, this study infers that there may be other key influencing factors other than nutrient availability and climate effectively determining the relationship between plants and habitats, and the persistence of a semi-natural community. This study aims to build a framework to identify such factors and to interpret the relationship between plants and habitats from a planting design point of view.\u003c/p\u003e \u003cp\u003eThe CSR (i.e triangular model, Competitor \u0026ndash; Stress-tolerator \u0026ndash; Ruderal) proposed by Grime was one of the most representative models in describing plant response to the environment (Grime and Pierce \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e; Grime \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1974\u003c/span\u003e)- an effective method to analyze the relationship between plants and habitats. The CSR strategy represents the adaptive response of one plant in a habitat with a certain level of stress and disturbance. This relevance can be supported by community investigation of various circumstances all over the world (Grime and Pierce \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e). For example, ruderals and competitors can grow better in many city habitats with typically high disturbance and humidity (Pierce et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, stress-tolerators were more fitted in conditions such as green roofs with poor soil and high temperature (Catalano et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; K\u0026ouml;hler \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIf functional traits of many plants could be clearly classified by CSR strategy, the complicated interactions between plants and habitats would be more predictable, which would help designers choose more suitable species regarding the habitats. However, the application of CSR strategy in planting design has rarely been explored for many habitat types, especially in China, whereas precedent studies have mainly focused on ecological topics (Good et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, this study takes the perspective of landscape design., looking into the feasibility of CSR strategy on design applications (i.e. plant community design practice).\u003c/p\u003e \u003cp\u003eThe development of CSR theory was rooted in plant functional traits. Functional traits indicated the adaption of plants to circumstances more objectively (Grime \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Myers-Smith et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Functional traits also owned important ecological and evolutional values. Moreover, among functional traits, as leaf traits were most closely related to resource acquisition and utilization (Wright et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), it could greatly reflect the response of a plant species to certain habitat changes (Vendramini et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For example, SLA (Specific Leaf Area) of psammophyte (plants in poor and drought habitats) is often smaller in order to increase resource retention capacity and reduce water loss (Rosado and de Mattos \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and thus their strategy tended to S-selection. However, plants in greater water and higher temperature conditions usually gained more growth, with larger leaf area to take more resources of light (Pierce et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and this typically represented C-selection. These outcomes showed that all plant functional traits were strongly influenced by environmental factors, thus ecological strategy of plants also changed synchronously (Pierce et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQinling Mountains traverse the central area of China and mark the boundary between the north and south environmental systems of China. They also strengthen the natural ecological safety barrier in central China and become one of the biodiversity hotspots in East Asia (Chen \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Environmental factors and specific genes commonly have the ability to affect the growth of plants, which leads to the appearance of abundant phenotypes and ecotypes to acclimatize themselves to diverse habitats (Canino-Koning et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, topographic, soil factors and climate factors jointly affected the phenotypes (fruit weight and shape) of \u003cem\u003eSchisandra sphenanthera Rehd. et Wil\u003c/em\u003e in the Qinling Mountains(Wang et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Twenty-five leaf traits of two Quercus species also exhibited different ecological strategies along an altitudinal gradient (Chai et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This would be the key to building a sustainable herbaceous community in urban settings (Fang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerally, dominant species with the most population, widest distribution and most abundant biomass should particularly be paid more attention to, because they were recognized as one of the greatest populations impacting community composition in a certain habitat (Avolio et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The identity of dominant species was ecologically relevant because species compositions of the community varied between locations. For example, extreme stress-tolerator \u003cem\u003eCarex spp.\u003c/em\u003e was the dominant species in relatively disturbance-free meadow communities in the eastern European Alps (Pierce et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, competitors and ruderals were popular in habitats with high stress and disturbance in South Korea and Turkey (Elmas \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Meanwhile, the assembly of different species in nature did not appear in a certain place spontaneously (Tabassum et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is usually the functional traits of a plant that selects its living environment. The environment affects the functional traits and ecological strategy of the plant in return (Catorci et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, evaluating the ecological strategy of plants is the cornerstone of the further investigation about plant adaptation and ecology progress (Grime \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Negreiros et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pierce et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The ecological strategy of dominant species is of much more importance. If the composition of CSR strategy and the strategic change of dominant species in natural herbaceous communities of different habitats can be widely analyzed, theoretical and practical guidance can be provided for species combination of herbaceous communities in urban planting design.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the species composition of herbaceous communities in different habitat types (roadside, riverside, forest margin, understory) in the Qinling Mountains (1080\u0026thinsp;~\u0026thinsp;1260m). This included the height and coverage of species, and determined CSR strategies of species by estimating LA(Leaf Area), LDMC(Leaf Dry Matter Content) and SLA(Specific Leaf Area), mainly to answer the following questions: i) In the Qinling Middle elevation, do the community weighted mean (CWM) C-, S- and R-scores show significant differences with different habitat types? What are the differences in main trade-offs for herbaceous plants in different habitat types? ii) what are the key differences in the CSR strategies between dominant and non-dominant species in the same habitat type? Thus, we expected i) stresses and disturbances differ in different habitats. So the ecological strategies of plants could show significant differences. Because of high disturbance in roadside habitats, these plants might show a tendency towards R-selection, while the understory plants are affected by light and cold stress, showing a tendency towards S-selection. The other two habitats fall between these categories. ii) Due to the highly ecological adaptability of dominant species, which largely determines environmental conditions within the community, we predicted that the CSR strategies of dominant and non-dominant species in the same habitat are significantly different. The ecological strategies of dominant species can be used to indicate habitat characteristics.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eStudy Areas\u003c/p\u003e \u003cp\u003eOur study was conducted in the Qinling Mountains, which had an altitude ranging from 1080-1260m. There were 6 sampling sites selected for this study and recorded by GPS (Navigation Satellite Timing And Ranging Global Position System): Heihe Forest Park(33\u0026deg;40\u0026prime;N, 107\u0026deg;51\u0026prime;E)、Liangfeng valley (33\u0026deg;31\u0026prime;N, 107\u0026deg;59\u0026prime;E)、Zhu valley (34\u0026deg;08\u0026prime;N, 108\u0026deg;02\u0026prime;E)、Cuifeng Mountain (34\u0026deg;07\u0026prime;N, 108\u0026deg;04\u0026prime;E)、Foping Health theme Park (33\u0026deg;52\u0026prime;N, 107\u0026deg;55\u0026prime;E)、Luo valley (34\u0026deg;05\u0026prime;N, 108\u0026deg;06\u0026prime;E). These sites typically consisted of low water retention rate, low nutrient and infertile organic matter content in soil, which were ascribed to quick and permeable drainage in the topsoil layer. These sampling sites were located in about 40\u0026ndash;60 km south of the Wei River and 40-60km north of the Han River.\u003c/p\u003e \u003cp\u003eExperimental design and habitat classification\u003c/p\u003e \u003cp\u003eIn our study, herbaceous communities were divided by habitat types. Comparative study of various plants\u0026rsquo; growth and distribution in \u003cem\u003eFlora of Qinling Mountains\u003c/em\u003e and common city habitat classification in the Guanzhong area demonstrate that 1) roadside, 2) riverside, 3) understory and 4) forest margin are the most typical habitats. The fieldwork was conducted in April and May when the vegetation in Qinling was relatively established in the year. Study areas sat in valleys in Qinling with even topography that was divided into four kinds of habitats (roadside, riverside, understory and forest margin) based on different sunlight and moisture levels. Community structure and species composition were surveyed in 4 habitats\u0026times;4 sites\u0026times;4 plots making a total of 64 plots. Each plot was 1m\u0026times;1m. Every species name that existed in plots was then recorded, as well as the maximum height and coverage of each species and the whole community, and dominant and other species were separately recorded. All plants were classified by the Cronquist System.\u003c/p\u003e \u003cp\u003eFunctional traits and CSR strategy\u003c/p\u003e \u003cp\u003eTen completely expanded fresh leaves were randomly sampled from each species. This was to estimate their functional traits in each plot (the average of ten duplicates represents the value of each species). When the micro leaves were estimated, duplicates were added to reduce systematic error. Fresh leaves were flattened and put into the scanner (Cannon LiDE300), for leaf area estimation using Image. Leaf fresh weight (LFW) was obtained from saturated leaves, then leaf dry weight (LDW) was obtained after leaving 48h in a 65\u0026deg;C drying cabinet when the leaves reached a relatively constant weight. C, S, and R proportions of each species were calculated based on three leaf traits (LA, LDMC and SLA) in Table \u0026lsquo;StrateFy\u0026rsquo;, which can calculate CSR strategy proportion by leaf traits of principal component analysis axis regression according to multivariate analysis in plants\u0026rsquo; leaf traits all over the world (Pierce et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe use maximum height and coverage as indicators to calculate the importance value of one species. Relative importance value was then calculated based on importance value. The formulas are as previously described (Ohsawa \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1984\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eIv\u0026thinsp;=\u0026thinsp;H*C\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(D=\\text{I}\\text{v}/(\\sum \\text{I}\\text{v}\\text{*}100\\text{%}\\)\u003c/span\u003e \u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn formulas, Iv is the importance value; H refers to the maximum height of one species, C refers to the coverage of this species, and D refers to the degree of dominance for one species in a plot. Based on the contribution of herbaceous plants in the community, we select 1\u0026ndash;3 plants as dominant species with D\u0026thinsp;\u0026ge;\u0026thinsp;0.6, sub-dominant species with 0.5\u0026thinsp;\u0026le;\u0026thinsp;D\u0026thinsp;\u0026lt;\u0026thinsp;0.6, companion species with 0.2\u0026thinsp;\u0026le;\u0026thinsp;D\u0026thinsp;\u0026lt;\u0026thinsp;0.5, and rare species with D\u0026thinsp;\u0026lt;\u0026thinsp;0.2.\u003c/p\u003e \u003cp\u003eThe relative proportions of C, S, and R strategy were calculated by LA, LDMC, and SLA of each species in Table \u0026lsquo;StrateFy\u0026rsquo; (Pierce et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This category showed the extent of C-, S-, and R- selection via trade-off between three leaf traits (LA, LDMC, and SLA). For each relev\u0026eacute;, community weighted mean was estimated (CWM, i.e., weighting the mean by the relative cover of each species in the relev\u0026eacute;) with LA, LDMC and SLA, as well as of C-, S- and R-scores, using the R package \u0026lsquo;FD\u0026rsquo; (Lalibert\u0026eacute; et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBefore running the ANOVA, we checked for normality of plant strategy scores by means of the Shapiro-Wilk test and, accordingly, transformed LA and SLA by square root (sqrt[x]) and logarithmic (log[x\u0026thinsp;+\u0026thinsp;1]) transformation, respectively, while no transformation was required for LDMC and strategy scores. We applied the ANOVA with a Tukey post hoc comparison test to identify significant differences among strategies scores between different habits combining the functions \u0026lsquo;aov\u0026rsquo; and \u0026lsquo;HSD.test\u0026rsquo; of R packages \u0026lsquo;\u003cem\u003estats\u003c/em\u003e\u0026rsquo; and \u0026lsquo;\u003cem\u003eagricolae\u003c/em\u003e\u0026rsquo;, respectively (Lalibert\u0026eacute; et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe CWMs of CSR ecological strategies\u003c/p\u003e \u003cp\u003eThe Various habitats differed significantly in terms of space occupation of CSR ternary and showed a robust variation along the disturbance gradient, which related to either niches characterized by frequent disturbances to individuals (R-selection) or unproductive niches (S-selection) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll the habitats significantly differed in terms of CWM C-, S- and R-scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); however, they showed a clear pattern only restricted to S-gradients. C-selection and R-selection were the less discriminative; nonetheless, significant differences were still present. The most competitive communities (highest C-scores) were in riverside, whereas understory communities were the less competitive (lowest C-scores). However, understory communities were the most stress-tolerant (highest S-scores), in contrast to roadside communities which showed the lowest degree of S-selection. Forest margin owned intermediate values of S-scores, which were slightly higher than that of riverside. Lastly, roadside species were the most ruderal (highest of R-scores) and understory species were the least ruderal (lowest of R-scores).\u003c/p\u003e \u003cp\u003eThe CWMs of plant traits\u003c/p\u003e \u003cp\u003eThe CWMs of LA, LDMC and SLA showed significant differences between habitats. LDMC showed a gradual increase along the disturbance gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). With regard to LA, riverside communities exhibited plants with the largest leaves, while forest margin communities included plants with the smallest. In less stable roadside communities, leaves were smaller compared to the relatively stable understory communities. Roadside displayed the lowest leaf construction investment (lowest LDMC), while grasslands with low disturbance understory exhibited the highest leaf construction investment (highest LDMC). The forest margin species generally tended to have intermediate values of LDMC, and they were significantly higher than that of riverside. Besides, SLA shows roughly the opposite pattern to LDMC along the interference gradients. Both roadside and riverside possessed species with the most acquisitive leaves (highest mean values of SLA), as opposed to understory species which showed the most conservative leaves (lowest mean values of SLA). Forest margin species showed intermediate SLA values among other grassland communities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVariations of environmental factors, species composition and CSR strategies\u003c/p\u003e \u003cp\u003eIn the four types of habitats of the Qinling Mountains, the CSR strategy of the dominant species was significantly different from subordinate species in a community. Meanwhile, the CSR strategy of companion and rare species showed larger variability in comparison with dominant and subdominant species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For example, in the four types of habitats, there was a tiny difference for C-scores of companions and rare species in the herbaceous community. In contrast, C-scores of dominant and subordinate species in riverside were significantly higher than in other habitats. However, their S-scores were significantly lower. S-scores of dominant species in understory communities were significantly higher than in other habitats, but their R-scores were significantly lower (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, S-scores of dominant species in roadside communities were significantly lower than in other habitats. However, their R-scores were significantly higher.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDifferences in CWMs for CSR ecological strategies in various habitat types\u003c/p\u003e \u003cp\u003eThis study provides an overview of how CWMs of CSR ecological strategies and plant traits are influenced by habitat types in the Qinling Mountains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This strategy spectrum suggests that niche segregation and coexistence within Qinling Mountain Communities are mediated by a strong functional divergence with response to differential local stress and disturbance (Pierce et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zanzottera et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Different habitats with different ecological factors such as light, water, soil and different intensity of disturbance often create distinct germination, growth and propagation opportunities for various species, and this is the result of mutual selection between plants and habitats (Morris et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the hypothesis, compared to the other three habitat types, roadside habitats exhibit ruderal characteristics (rapid completion of the life cycle and selection of regenerative traits). These plants mainly with R, R/CR and CR as a response to their long-term exposure to disturbance. In contrast, species in understory habitats are dominated by S/CS, CS/CSR, SR/CSR, S/SR, S and the strong stress-tolerant characteristics (low productivity and conservative adaptations), which can help them grow and thrive in poor conditions. Riverside habitats favored competitive ruderal characteristics with rapid growth and abundant biomass. It was evident that the strategies of these plants were mainly CR, R/CR, SR, CSR, and CR/CSR benefiting by occupying light, water and nutrients to the maximum by quick growth in a relatively high productivity environment. Forest margin species have mediate characteristics among the above three habitats, which can facilitate the growth of CSR species. The strategies in forest margin were mostly R/CSR, CSR, SR/CSR, and R/SR. This is in accordance with the hypothesis that habitat heterogeneity is the main reason for most plants to exhibit two or more strategies, which helps them to adapt to diverse habitats (Hiebeler \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferences in CWMs for plant traits in various habitat types\u003c/p\u003e \u003cp\u003eAs a key insight into the relationship between plants and habitats, functional traits can comprehensively interpret the formation of plant strategy (Di Biase et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This can provide guidance for exploring mutual interaction mechanisms between plants and surrounding environments (da Costa et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our results indicated that the CWMs of traits were relevant to the leaf economics spectrum (LA, LDMC and SLA) (Reich \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wright et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zanzottera et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The change was consistent with variations in the different habitat types. Specifically, SLA is negatively related to leaf lifespans and positively related to relative growth rate(Reich \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zanzottera et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). High SLA can overcome frequent and certain intensities of human activity (e.g. trampling, etc.). Roadside habitats are typically colonized by species with trait attributes that optimize carbon acquisition: fast-growing and low-cost leaves with a short lifespan and high SLA(Wright et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLDMC reflects the resource acquisition and nutrient maintenance ability of a plant (Pierce et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which was often significantly negatively related to SLA (Zanzottera et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because the dense canopy of upper forest blocks the sunlight, understory herbaceous vegetation usually suffers stress from low light, high humidity and cold temperatures (Al-Namazi and Bonser \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These plants mainly invest their resources into vegetative tissues, which to resist adverse conditions via long-lived plant organs (Myers-Smith et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This can optimize the use of plant resources, to adapt to the environment to survive and propagate more effectively (Guo et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, LA reflects the resource utilization of plants (Reich et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Roadside habitats were abundant in water and light resources. This helped plants distribute the available resources more effectively among various traits. This helped achieve trade-offs appropriately between vegetative and reproductive growth(Zhou et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The growth can help occupy the dominant niche via rapid reproduction, growth and relatively huge plant organs (in other words, high LA and high SLA). Therefore, it is of critical importance for the basic theory that understanding the rule and effect of functional traits on species composition is beneficial for studying comprehensive interactions between plants and environments (Di Biase et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Agreeing with many previous studies, this finding can more comprehensively explain the formation of plant ecological strategies (Faccion et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Grime \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Myers-Smith et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe changes in the CSR ecological strategies with dominant species and non-dominant species\u003c/p\u003e \u003cp\u003ePrevious studies have predicted that at intermediate productivity levels and/or in more heterogeneous habitats, a wider range of CSR strategies could co-occur, and CSR strategies could therefore be a better predictor of the coexistence and relative dominance of species (Cerabolini et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; de Paula et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rosado and de Mattos \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our results were in agreement with the prediction that in herbaceous communities of moderate altitude Qinling Mountains, plant species biodiversity was higher and the community was close to intermediate productivity (Grime \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1974\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Therefore, plant strategies were all scattered in four habitats, but CSR strategy was significantly different between dominant species and non-dominant species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, it is worth noticing that the interactive relationship between plants and habitats is yet to be fully understood (Hitchmough et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Jiang and Yuan \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). There is no absolute correspondence between habitat type and strategy of dominant species. Researchers found that in the coastal sandy plain of the Restinga de Jurubatiba National Park, all plants with S/CS strategy, and S-scores of dominant species were not the highest values (Rosado and de Mattos \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Building upon this earlier work, it revealed that in environments which were extremely in favor of C, S, or R strategies, ecological strategy no longer indicates plant niches. Comprehensive traits are important for understanding community assembly and the pattern of species dominance (Grime and Pierce \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; Rosado and de Mattos \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, dominant species mainly consisted of one or several similar strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). They occupied basic niches in the community, and were more adaptable to environmental change (Glenn et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For instance, dominant species of roadside habitat were mainly R-selection, such as \u003cem\u003eEquisetum pratense\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;11: 0: 89%), and \u003cem\u003eCorydalis racemosa\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;46: 0: 54%). These plants always were mostly short and clumped, which were resistant to trampling, short-lived and possessed various reproduction ways (Grime \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). In contrast, dominant species of understory habitats were mainly S-selection, such as several \u003cem\u003eCarex\u003c/em\u003e plants: \u003cem\u003eCarex minxianica\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;25: 69: 6%), and \u003cem\u003eCarex pediformis\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;12: 76: 12%) etc. They were short, shade-resistant, and owned high stoichiometric homeostasis (Yu et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, ecological strategies of non-dominant species distributed more widely, and there was no significant difference among the four kinds of habitats. For example, non-dominant species of riverside habitats included CS/CSR (C: S: R\u0026thinsp;=\u0026thinsp;40: 37:23%) strategy of \u003cem\u003eArgentina anserina\u003c/em\u003e, SR (C: S: R\u0026thinsp;=\u0026thinsp;7: 54: 39%) strategy of \u003cem\u003ePoa pratensis\u003c/em\u003e, and R (C: S: R\u0026thinsp;=\u0026thinsp;0: 0: 100%) strategy of \u003cem\u003eStellaria media\u003c/em\u003e. These non-dominant species often co-exist with dominant species, but remain the subordinate role. All results suggested that dominant species were more susceptible to the influence of environment in comparison with non-dominant species (Arnillas and Cadotte \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The ecological difference between them can indicate their niche in the community, which also proves the mass ratio hypothesis (Grime \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). There is indirect evidence for certain connections between several dominant species in a community obtained where environmental filtration determined some species to grow healthier in a site. Several thriving dominant species therefore may share some critical traits or ecological strategies (Cadotte \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mayfield and Levine \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on CSR theory, how do different habitats construct herbaceous planting designs?\u003c/p\u003e \u003cp\u003eConventional horticulture practice tends to consider abiotic factors of for example light, water and soil nutrients more than the fitness measure between plant survival strategies and the original habitats. This very often leads to a failure in the design and management of a semi-natural community. The study of Grime revealed that C-strategy plants are suitable to habitats with low stress and disturbance; S-strategy plants are suitable to habitats with high stress and low disturbance; and R-strategy plants are suitable to habitats with low stress and high disturbance (Grime \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). In our study, the results of four types of habitats have broadened and refined this theory. In high disturbance habitats (i.e. roadside), dominant species were mainly R-selected plants, which were short-lived, resistant to trample, and owned various reproductive ways, such as \u003cem\u003eCorydalis racemosa\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;46: 0: 54%), \u003cem\u003eCorydalis incisa\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;34: 0: 66%), and \u003cem\u003eGeranium carolinianum\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;28: 19: 53%). Riverside habitats with rich resources were dominant by thriving C, R-selected plants, such as \u003cem\u003eHemerocallis fulva\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;56: 0: 44%), \u003cem\u003eVicia bungei\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;38: 0: 62%), and \u003cem\u003eGlechoma longituba\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;31: 26: 43%). Plants in forest margin habitats with high heterogeneity always inhibited two or more strategies, such as \u003cem\u003eThalictrum aquilegiifolium\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;27: 17: 56%), \u003cem\u003eCarex lanceolata\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;14: 14: 72%) and \u003cem\u003eMedicago ruthenica\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;5: 56: 39%). Understory habitats were dominated by S-selection plants with great stress tolerance, such as \u003cem\u003eSedum lineare\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;1: 78: 21%), \u003cem\u003eCarex pediformis\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;12:76: 12%) and \u003cem\u003eMelica scabrosa\u003c/em\u003e (C: S: R\u0026thinsp;=\u0026thinsp;7: 65: 28%). Furthermore, Piet and Noel (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) indicated that in fertile habitats, one of the critical design principles is not to mix up plants with different adaption traits to stress to maintain the sustainability of natural community contexts for a long time (Piet and Noel \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, in poor resource or more heterogeneous habitats, practitioners can select plants with more diversity strategies, because it will not decrease the biodiversity of the community (Piet and Noel \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study argues that CSR theory can supplement conventional planting design by interpreting the relationship between plants and habitats with ecological theories. This study has investigated the relationship between habitats and the composition of plant CSR strategies in the Qinling herbaceous communities with a fitness measurement between habitat traits (stress/disturbance) and plant survival strategy. This investigation can explore practical guidance for herbaceous planting design in urban landscapes. The study also proved CSR theory and model can not only predict the effect of environmental change on herbaceous communities with ecological principles, but also can be a conceptual framework to select plants suitably which are suitable for city habitats (Wang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This ecological principle informed process of species selection can predict the adaptivity and robustness of plants at the desktop study stage for longer term sustainability, such as enhancing the species coexistence and reducing maintenance costs.\u003c/p\u003e \u003cp\u003eHowever, there was a limitation that the sampling areas were geographically restricted in the Qinling Mountains, China., but more relevant studies are required to further prove the theory. Besides, not only the relationship between plants and habitats are critical, but interactions between plants in a community also need to be further explored. The contribution of natural herbaceous community strategy and the difference of functional traits trade-offs can be clarified further. This can help choose suitable species and can help estimate the ratio of them in a mix for plant community design. Last but not least, CSR theory still stands for a theoretical model in many situations, especially urban landscape design (Grime and Pierce \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). Due to the different processes of plant community establishment between nature conservation and urban planting design (Jiang and Hitchmough \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the practicability is yet to be enhanced with further research with long-term data.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on CSR theory, this work studied herbaceous communities in the Qinling Mountains for reviewing and summarizing different compositions of plant strategies in four types of habitats, which could be a framework for plant communities in urban habitats. Plants belonged to different strategies due to mutual selection of the interaction between plants and habitats. Roadside and riverside habitats exhibited the most acquisitive leaves (highest CWMs of SLA), opposed to understory habitats which showed the most conservative leaves. Strong stress-tolerant characteristics (low productivity and conservative adaptations) can help plants in understory habitat grow and thrive in unproductive conditions. Besides, plants in forest margin often exhibited two or more strategies to deal with a high diversity microenvironment and heterogeneity in forest margin. Results also confirmed Grime's (reference) mass ratio hypothesis, since dominant species were more susceptible to environmental impacts than non-dominant species. As basic species in a community, the ecological strategy of dominant species is closely related to community property and environmental change, which is the critical factor in habitat evaluation of community ecology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to Gao Tian and Qiu Ling (Northwest A\u0026amp;F University) for the support of our experimental project：herbaceous plant survey in Qinba Mountain understory environment, including transportation and laboratory condition support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFW, PLH, and CSL contributed to the conceptualization and design of the study. Material preparation, data collection, and analysis were performed by FW, PLH, QWZ, MYZ. PLH and MYJ advised and assisted with the statisti-cal analysis. FW wrote the first draft of the manuscript. All authors commented on previous manuscript versions, contributed critically to the drafts, and gave final approval for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by Shaanxi Provincial Natural Science Foundation Youth Project: plant community design model of urban floral lawn in Guanzhong based on the theory of biodiversity (K3030121084).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support this study will be shared upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Namazi AA, Bonser SP (2020) Plant strategies in extremely stressful environments: are the effects of nurse plants positive on all understory species? 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Ecol Evol 11(16):11352-11361\u003c/li\u003e\n\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":"CSR strategy, dominant species, habitat type, herbaceous community, leaf trait, planting design, Qinling Mountains","lastPublishedDoi":"10.21203/rs.3.rs-3991265/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3991265/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eContext\u003c/h2\u003e \u003cp\u003eUrban habitats have been severely degenerated or destroyed due to construction activities and consequent human interference, which have threatened the urban ecosystem, especially plant species richness and diversity. The interactive relationship between plants and habitats is an outcome of long-time evolution. Exploring the relationship can provide an insight for improving the sustainability of urban greenspace.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eWe attempted to 1) build a relationship between individual plants and the whole community based on CSR theory, and 2) explore ecological function of communities can be achieved better by a suitable combination of individual functional traits.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study referred to Grime's CSR (C: competitor, S: stress tolerance, R: ruderal) theory to analyze Qinling mountain herbaceous communities in typical habitats (roadside, riverside, forest margin, and understory). Species composition in communities of different habitats was recorded. Then dominant and non-dominant species were identified and analyzed emphatically.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e(1) In four habitats, the CWMs of CSR ecological strategies showed that C-scores of riverside communities were the highest, while understory communities were the lowest. S-scores of understory communities were the highest, while roadside communities were the lowest. Lastly, R-scores of roadside communities were the highest, while those of understory communities were the lowest. (2) In terms of CWMs of leaf traits, LDMC gradually increased along the disturbance gradient, but SLA was on the contrary. (3) Dominant species were more profoundly shaped by environmental circumstances than non-dominant species, which can effectively indicate their habitat characteristics. For example, C-scores of dominant species and subdominant species in riverside were significantly higher than in other habitats; S-scores of dominant species in understory habitats were significantly higher than others; and R-scores of dominant species in roadside habitats were significantly higher than others.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study suggested that the strategy of dominant species is the main factor that determines the effect of various habitats on plant compositions. This rule verified that the CSR model could help select the cultivated species for urban green space. Also, it can help predict the effect of climate change on herbaceous communities, which has great potential for the planting design of urban herbaceous communities.\u003c/p\u003e","manuscriptTitle":"Application of Herbaceous plant CSR strategy responses to four kinds of habitats in the Qinling Mountain for plant community design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 17:32:09","doi":"10.21203/rs.3.rs-3991265/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":"960da84b-cae2-401b-8f78-0accd11ed4f6","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-17T01:14:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 17:32:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3991265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3991265","identity":"rs-3991265","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}