Relationship between Plant Strategy Types and Soil Characteristics in Backdunes and Foredunes

Relationship between Plant Strategy Types and Soil Characteristics in Backdunes and Foredunes | 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 Relationship between Plant Strategy Types and Soil Characteristics in Backdunes and Foredunes Emire Elmas, Sevda Türkiş, Barış Bani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5327986/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Mar, 2025 Read the published version in Estuaries and Coasts → Version 1 posted 5 You are reading this latest preprint version Abstract Dune ecosystems harbor a limited array of plant species, thriving despite their challenging habitats. This study aims to explore the distribution of plant strategy types in foredune and backdune regions and their correlation with soil variables. CSR strategies and seasonal soil variables were analysed, encompassing soil moisture, organic matter, TN, NH 4 -N, NO 3 , P, pH, Na, K, Cl, Ca, CaCO 3 , Mg, EC, and CEC. The dissimilarity between foredune and backdune areas was assessed using the Bray–Curtis similarity matrix and SIMPER. CCA was used to examine the relationships between plant strategies and soil variables. The dissimilarity rate between two sites in terms of the distribution of strategy types was 67.04%. All soil variables, except P for both areas and CaCO 3 for the backdune, exhibited significant seasonal variations ( p < 0.05). The C/CR and SC strategy types was in positive correlations with pH, salinity, EC, and CaCO 3 , and negative ones with TN, P, K, saturation, organic matter, moisture, and CEC. The R/CR, R/CSR, SR/CSR, S/SR, SC/CSR and CR/CSR strategy types correlated positively with TN, P, K, saturation, organic matter, moisture, and CEC. Similarly, negative correlations were detected between CR/CSR, R/CSR, SR/CSR, and S/SR strategy types and pH, salinity, EC, and CaCO 3 . This study underscores the spatial dynamics involved in reaching the climax stage within dune ecosystems, showcasing the resilience of species that adapt to stressful environments. Even in the absence of disturbance, species uniquely suited to these conditions can thrive, marking the culmination of succession in such ecosystems. Dune plant functional types CSR strategies soil succession Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Coastal dunes serve as a dynamic interface, bridging marine, terrestrial, and atmospheric processes. They also act as an ecotone, a transitional zone between aquatic and terrestrial ecosystems. Their structure is not static but fluctuates, reflecting their dynamic nature (Schwarz et al. 2018 ; Carboni et al. 2009 ). These ecosystems are shaped by a multitude of biotic and abiotic factors. They present a challenging environment characterized by stress conditions such as salt spray, drought, nutrient deficiency, high temperature, intense light, strong wind, inundation, and substrate mobility. Plants have evolved various strategies to thrive in these stressful environments, including leaf roll, salt bladders, physiological adaptations, diverse light use strategies, germination strategies, various life cycles, and morphological changes (Hesp 1991 ; García-Mora et al. 1999 ; Bermúdez and Retuerto 2014 ). Given the extreme conditions of coastal dune environments, only certain plants capable of surviving these conditions can establish themselves. Over time, plants develop life strategies in response to different environmental conditions, reflecting their adaptation to both abiotic and biotic constraints. Environmental circumstances act as a filter for species characteristics (Keddy 1992 ), resulting in coexisting species exhibiting similar characteristics and ecological tolerances, irrespective of their phylogenetic lineage (Cornwell et al. 2006 ; Mayfield et al. 2005 ). In this context, species can be categorized into functional plant types (PFTs) based on shared characteristics. These PFTs can then be used to elucidate community and ecosystem structures (Dı́az and Cabido 2001 ; Lavorel et al. 1997 ). Grouping plants based on the similarities of their functional traits simplifies the interpretation of plant communities. This method is practical for assessing ecosystem function, as different plant life strategies contribute differently to matter and energy processes (Díaz and Cabido 1997 ). Grime identified stress and disturbance as two primary environmental factors that limit plant life. The CSR (competitor, stress tolerator and ruderal) model outlines three fundamental life strategies: competitor species that thrive under low stress and low disturbance conditions, adapting to rapid resource utilization and long-term site occupation; stress-tolerant species that survive under high stress and low disturbance conditions, settling in environments with scarce resources; and ruderal species that adapt to low stress and high disturbance conditions by growing and reproducing rapidly. Vascular plant species can be classified into CSR strategies using seven functional characteristics, including specific leaf area (SLA), canopy height, dry matter content, leaf dry weight, lateral spread, flowering time, and flowering period (Grime 1974 ; Grime et al. 1997 ; Hodgson et al. 1999 ). The CSR classification illustrates the impact of stress and disturbance factors on plants throughout the evolutionary process and provides insights into plant strategies that have evolved in response to environmental conditions (Bornhofen et al. 2011 ). The CSR model is powerful because of its broad applicability and theoretical simplicity. Unlike other methods, such as the Leaf-Height-Seed (LHS) strategy, which is limited to specific characters, or StrateFy, which bases CSR classification on a fixed set of traits, the CSR Triangle method allows for greater flexibility in character selection, which enables ecologists to adapt the structure to different plant species and ecosystems (Grime 1977 ; Pierce et al. 2017 ). Furthermore, the method provides a clear conceptual structure based on ecological theory, allowing researchers to manually interpret CSR strategies based on mixed ecological characters and environmental context. This makes it particularly useful for landscape and complex ecological systems where automated methods oversimplify plant-environment interactions. Stress and disturbance are anticipated to play significant roles in dune plant succession. Other critical stress factors that limit plant production include high temperatures, high irradiance, nutrient availability, and water scarcity. Conversely, the most crucial disturbance factors are strong wind, seawater inundation, grazing, and anthropogenic effects (Hesp 1991 ; Maun 2009 ). Coastal dune plants adapt to these ecological conditions through morphological and physiological adaptations such as leaf hypertrophy, spray tolerance, life span, growth habit, and growth timing (Barbour et al. 1985 ; Maun 2009 ). The CSR model holds particular significance in dynamic dune ecosystems. This model provides insight into how different strategies coexist in dune ecosystems with plant communities affected by both natural and anthropogenic disturbances (Maun 2009 ; Acosta et al. 2005 ; Lammerts et al. 2004 ) and helps ecologists to understand the distribution and diversity of plant species in these fragile environments, guiding management and conservation efforts. Soil, as the second most significant abiotic factor influencing plant settlement, plays a crucial role after climate. The nutrient input in coastal dunes differs from that in terrestrial areas, with meteorological inputs, particularly aerosols in spray and fog, surpassing the input from substrate weathering. Aerosols, bulk precipitation, seawater inundation, detritus deposition, and sand movement all contribute to nutrient inputs. In coastal dunes, certain nutrients (such as Na, K, Cl, and Mg) reach high and extreme levels, while others (i.e., N, P, organic matter) are present in very low concentrations (Barbour et al. 1985 ). The relationship between soil and plant species is reciprocal: dune soil conditions control plant settlement, growth, and reproduction, while plant species contribute to sand accumulation and dune formation (Moreno-Casasola 1986 ). Coastal dunes, being dynamic systems, serve as practical models for ecological research, particularly on succession. Studying functional characters is crucial to understanding the processes involved in the formation of plant communities under natural stress conditions (Cicarelli and Bona 2022; Faegin and Wu 2007; Gallego-Fernández and Martinez 2011; Chelli et al. 2019 ). While several studies have explored plant adaptations by examining functional groups (Díaz Barradas et al. 1999 ; García-Mora et al. 1999 ; Acosta et al. 2006 ; Feagin and Wu 2007 ; Novo et al. 2004 ; Gallego-Fernández and Martínez 2011 ; Bermúdez and Retuerto 2014 ), only a few have scrutinized Grime’s CSR strategy scheme and analysed the relationships between edaphic factors and the distribution of CSR (Macedo et al., 2010 ; Brunbjerg et al., 2012 ; Ciccarelli 2015 ; Son et al. 2020 ; Lee et al. 2020 ). The investigation relationships between CSR strategies and soil variables is important to understand plant adaptation and ecosystem functions in coastal dunes. These regions pose significant challenges for plant survival by fluctuating condition such as high salinity, extreme temperatures, nutrient poor soils, and drought (Hesp 1991 ). The CSR strategy model offers a framework to classify plant species based on their adaptive responses to stress and disturbance. It provides insights into how species interact with their environment and how they may shift along successional gradients (Grime 1974 ). This study seeks to clarify how these factors affect the distribution and success of different CSR strategy types in both foredune and backdune areas by analysing soil characteristics, such as pH, organic matter, nutrient content, and moisture, Grime’s CSR framework has been shown to be effective to understand that how plants cope with stress and disturbance in ecosystems that be subject of constant environmental change (Brunbjerg et al. 2012 ). In the present study, use of soil variables such as nutrient contents, electrical conductivity (EC), and cation change capacity (CEC), with the application of CSR classification, provide a deeper understanding of the complex interaction between plant species and their abiotic environment (Grime et al. 1997 ; Lee et al. 2020 ). Understanding these relationships between CSR strategies and soil variables is crucial both for theoretical ecology and practical conservation and management. Buffering inland areas from erosion and saltwater intrusion, and supporting biodiversity are some of important ecosystem services of coastal dune ecosystems (Hesp and Martinez 2007). Determining CSR strategies that are most successful under varying soil conditions provide better predictions on how these ecosystems may respond to future environmental changes, such as climate change or human disturbance (Defeo et al. 2009 ). Additionally, linking CSR strategies with soil variables provide insight about how ecosystems evolve over time by contributing to a broader understanding of plant community assembly and succession (Pugnaire and Luque 2001 ; Macedo et al. 2010 ). In this study, we hypothesize that the distribution of plant strategy types (CSR strategies: competitor, stress tolerator, and ruderal) is significantly influenced by variations in soil characteristics between foredune and backdune areas. Specifically, we propose that soil properties such as pH, moisture, organic matter, and nutrient content (e.g., total nitrogen, phosphorus) play a crucial role in determining the success and dominance of different CSR strategy types. We expect that stress-tolerant species will be more prevalent in foredune areas characterized by higher salinity and electrical conductivity (EC), while ruderal and competitive species will dominate in backdune areas with lower salinity and higher nutrient availability. Additionally, we hypothesize that seasonal variations in soil conditions will further influence the abundance and distribution of these plant strategies, with backdune areas exhibiting greater species richness and diversity due to more stable and favorable soil conditions. Finally, we anticipate that plant-soil feedbacks in backdune areas will lead to modifications in soil properties over time, further promoting the success of ruderal and competitive species in these regions. 2. MATERIALS and METHODS 2.1. Study area The study area, located near the Aksaz Wetland, is subject to significant human impact. It features two streams, Sırakaraağaçlar and Karasu, along with several drainage channels that open to the sea. These streams connect with the Blacksea during autumn, winter, and early spring and close in late spring and summer. The study area encompasses a foredune of 27 ha and a backdune of 13 ha (4 km long and of variable width, between 42°01´64.2´´N–34°04´55.8´´E and 44°02´33.3´´N–35°02´39.9´´E). The dune area lacks zonation. According to a decade’s worth of climatic data (2005–2015) from the Sinop Meteorological Service, the mean annual temperature is 14.96° C and the total annual precipitation is 720.15 mm. A drought period occurs from mid-May to the end of August. 2.2. Evaluating the distribution of CSR strategies For the foredune and backdune areas, 20 permanent sample plots were established to assess plant diversity. The plot size was determined using the minimal area method, resulting in 50 m 2 plots for both sites. The percentage cover of each plant species was recorded monthly over a 2-year period. Plant species were identified according to the “Flora of Turkey and the East Aegean Islands” (Davis 1965–1985; Davis et al. 1988 ; Güner et al. 2000 ), and the taxonomic nomenclature adhered to that of Güner et al. ( 2012 ). CSR strategies were evaluated following Hodgson et al.’s ( 1999 ) method, which uses canopy height, leaf dry matter content, flowering period, flowering initiation, leaf dry weight, lateral spread, and SLA ( see Hodgson et al. 1999 ). For subsequent phenological observations, at least ten individuals of each species were marked, and observations were made at 15-day intervals. Canopy height was measured using a measuring tape, and the lateral spread of the plant was visually estimated. To determine leaf traits, at least three mature leaves per plant were collected from three individuals of each species. Upon collection, the leaves were wrapped in damp paper and refrigerated overnight at 4°C. Fresh leaves were weighed on a digital analytic balance (Schimadzu AW320) to the nearest 0.0001 g and then pressed. The leaf area was measured using a CI202 Leaf Area Meter (CID Inc., NW Camas, WA, USA). The leaves were dried at 60°C until they reached a constant weight and then weighed again. The dry matter proportion (%) was calculated by dividing the leaf mean dry weight by the leaf mean fresh weight and multiplying by 100. The specific leaf area (SLA) was calculated by dividing the leaf mean area by the leaf mean dry weight. The CSR strategies of the studied species were determined according to Hodgson et al. ( 1999 ). The cover data of the CSR types underwent a transformation into log 10 (x + 1), and the Bray–Curtis similarity matrix was utilized to determine similarity/dissimilarity. A nonparametric two-way similarity analysis (ANOSIM) was conducted to determine if the differences among the floristic composition of communities were significant using a similarity matrix. The similarity percentage analysis (SIMPER) was employed to demonstrate the contribution of strategy types to differences among communities. SIMPER analysis was used to identify the CSR types that most contribute to similarity/dissimilarity among different communities. In this analysis, the mean abundance of each CSR type (MA), mean similarity of sampled sites (MS), contribution of each CSR type to community similarity (R), percentage contribution of each CSR type to community similarity (CoP), total contribution of CSR types to similarity (CP), mean differences in abundance between communities (MD), ratio of mean differences to standard deviation (D/SD), the contribution of CSR types to dissimilarity among communities (C), and sum of the contribution of CSR types to dissimilarity (CP) were computed. Thus, the average percentages of similarity/dissimilarity between communities were calculated, and it was revealed which species contributes the most to these differences. 2.3. Evaluating the soil variables Soil variables influence plant community and ecosystem dynamics by shaping the conditions under different plant strategy types thrive or struggle. For example, pH affects the nutrient availability, and high or low pH can limit the uptake of essential nutrients while cation exchange capacity reflects soil’s ability to retain and supply positively charged ions. Likewise organic matter serves as reservoir of nutrients and supports beneficial microbial activity. To determine the soil properties of the study area, soil samples were collected from the permanent plot areas during November, May, August, and February. The roots of most of plant species distributed in the dune area extend to a depth of 50–60 cm, while the roots of the species in the backdune are at a depth of 15–20 cm. Therefore, soil sampling was carried out at depths of 50–60 cm for the foredune and 15–20 cm for the backdune, where roots are dense. These samples were air-dried and sifted using a 2-mm sieve before being sent to the laboratory for analysis. The analysis determined soil moisture, organic matter, total nitrogen (N), ammonium (NH 4 -N), nitrate (NO 3 ), phosphorus (P), pH, sodium (Na), potassium (K), chlorin (Cl), calcium (Ca), magnesium (Mg), electrical conductivity (EC), and cation exchange capacity (CEC). Following the transformation of the data according to log(x + 1), an independent sample T-test was employed to identify differences between the two areas in each season and between seasons for each study area. The methods that were used in soil analysis was summarised in Table 1 . Table 1 The methods that were used in soil analysis Soil Variable Method of Measurement Literature Soil moisture Gravimetric method Topp and Ferré. 2002 Organic matter Loss-on-ignition method Nelson and Sommers 1996 Total nitrogen (TN) Kjeldahl method Bremner and Mulvaney 1982 Ammonium (NH4-N) Colorimetric method Mulvaney 1996 Nitrate (NO3) Ion-selective electrode method Keeney and Nelson, 1982 Phosphorus (P) Olsen's method (colorimetric) Olsen et al. 1954 pH Electrometric method using pH meter McLean 1982 Sodium (Na) Flame photometry Richards 1954 Potassium (K) Flame photometry Richards 1954 Chlorine (Cl) Titration method Richards 1954 Calcium (Ca) Titration method with EDTA Lanyon and Heald 1982 Magnesium (Mg) Atomic absorption spectrophotometry Isaac and Johnson 1975 Electrical conductivity (EC) Conductometry Rhoades 1996 Cation exchange capacity (CEC) Ammonium acetate extraction method Chapman 1965 2.4. Statistical analysis A canonical correspondence analysis (CCA) analysis was performed to analyse the relationship between the distribution of CSR strategy types and environmental data as the gradient length exceeded 3 standard deviations, indicating unimodal species-environment relationships. The analysis was conducted using Canoco v4.5 package program, incorporating the percentage coverage of each CSR type. 3. RESULTS 3.1. Distribution of CSR strategies In the foredune, 34 species were identified, while in the backdune, the count was significantly higher at 171 species. The backdune exhibited 17 distinct CSR strategy types, compared to 13 in the foredune. The Bray–Curtis analysis revealed a similarity percentage of 65.38% among the sample plots in the dune area and 75.93% in the backdune area (Figure 1). The ANOSIM results indicated that the differences were statistically significant at p < 0.1% (global R: 0.989). The dissimilarity percentage between the foredune and backdune areas was found to be 67.04%. In the foredune area, the strategy types C/CR, R, and R/CR contributed the most to similarity, with values of 54.94%, 15.20%, and 10.38%, respectively. In contrast, in the backdune area, the strategy types R/CR, R, and R/SR contributed 26.1%, 14.57%, and 12.29% to similarity, respectively (Table 2). The strategy types that contributed the most to dissimilarity between the foredune and backdune areas were R/CR, S/SR, R, R/SR, CR/CSR, and C/CR, with contributions of 19.59%, 11.45%, 9.69%, 8.87%, 8.75%, and 7.43%, respectively (Table 3). Figure 1. Dendrogram illustrating the study areas (F: Foredune, B: Backdune) Table 2 . CSR strategy types contributing to similarity among sample plots within each study area (MA: Mean abundance, MS: Mean similarity, R: Rate, C: contribution, CoP: Contribution percentage, CP: Cumulative percentage) Species MA MS R CoP CP Foredune (Average similarity: 65.38%) C/CR 25.02 35.92 2.89 54.94 54.94 R 1.21 9.93 3.94 15.20 70.13 R/CR 0.95 6.78 2.52 10.38 80.51 Backdune area (Average similarity: 75.93%) R/CR 44.21 19.82 5.05 26.10 26.10 R 10.90 11.07 3.88 14.57 40.67 R/SR 5.88 9.33 5.59 12.29 52.96 Table 3. CSR s trategy types contributing to dissimilarity between foredune and backdune areas (MA1: Mean abundance in foredune, MA2: Mean abundance in backdune, MD: Mean differences, D/SD: Differences/standard deviation, C: Contribution, CP: Cumulative percentage) Species MA1 MA2 MD D/SD C (%) CP Group foredune-backdune (Average dissimilarity: 67.04%) R/CR 0.95 44.21 13.13 3.56 19.59 19.59 S/SR 0.09 7.68 7.68 2.19 11.45 31.04 R 1.21 10.90 6.50 2.63 9.69 40.74 R/SR 0.58 5.88 5.95 3.44 8.87 49.61 CR/CSR 0.00 4.07 5.87 2.31 8.75 58.36 C/CR 25.02 5.78 4.98 1.29 7.43 65.79 3.2. Results of soil analysis The mean annual soil moisture was 7.24% for the foredune and 13.67% in the backdune area. The foredune soil was identified as strong alkaline and mild saline loamy, whereas mild alkaline and salt-free clay loamy in the backdune, according to the results (Table 4). Table 4. Classification of soil types for the study areas. Foredune Backdune Value (F) Class Value (T) Class pH 8.68 ±0.25 Strong alkaline 8.00 ±0.10 Mild alkaline EC 5.96 ±1.96 Mild saline 0.30 ±0.08 Salt-free Salinity 0.14 ±0.09 Mild saline 0.01 ±0.003 Salt-free Saturation 39.89 ±2.05 Loamy 47.73 ±4.47 Clay-loamy CEC 7.47 ±1.80 Rich 42.02 ±8.20 Rich The differences in organic matter and pH between the two areas were statistically significant for all seasons ( p < 0.05). Ca showed significant differences at p < 0.05 in winter, spring, and summer, while CaCO 3 differed in winter, summer, and autumn. CEC varied in winter, spring, and autumn; soil moisture in winter and spring, saturation in winter and summer; salinity and electrical conductivity in summer; Mg in winter, and P in spring between the foredune and backdune areas. The results indicated that all soil variables, except for P for both areas and CaCO 3 for the backdune area, exhibited significant seasonal variations in each area ( p < 0.05) (Figure 2). Figure 2. Seasonal variations in variables within the study areas and differences observed within and between communities (A: autumn, W: winter, Sp: spring, S: summer, F: foredune, and B: backdune). Capital letters indicate differences between communities, while lowercase letters denote differences between seasons within the community. Different letters indicate statistically significant differences ( p < 0.05). 3.3. The relationships between CSR strategy types and soil variables A canonical correspondence analysis (CCA) was performed to evaluate the relationship between the distribution of CSR strategy types and soil variables. The ordination plot separates the foredune and backdune areas along the environmental gradients, illustrating the distinct soil conditions that define these two areas. The first two axes of the CCA explained 66.6% of the relationships between strategy types and environmental factors (Table 5). The first canonical axis was statistically significant according to the Monte-Carlo Test ( p < 0.02, F = 0.764). The ordination analysis differentiated the study areas based on TN, P, K, saturation, organic matter, moisture, Ca, CaCO 3 , Mg, pH, salinity, EC, and CEC (Figure 3). Strong positive correlations were observed between the C/CR and SC strategy type and pH, salinity, EC, and CaCO 3 . This suggests that species employing a competitor/stress-tolerator strategy thrive in the foredune areas, where these soil conditions are prominent. On the other hand, R/CR, R/CSR, SR/CSR, S/SR, SC/CSR and CR/CSR strategy types showed strong correlations with TN, P, K, saturation, organic matter, moisture, and CEC. These variables are more abundant in the backdune areas, indicating that ruderal and stress-tolerant species dominate in environments with greater nutrient availability and moisture. Conversely, negative correlations were found between the C/CR strategy type and TN, P, K, saturation, organic matter, moisture, and CEC, and between the CR/CSR, R/CSR, SR/CSR, and S/SR strategy types and pH, salinity, EC, and CaCO 3 (Figure 3). Table 5. Results of canonical correspondence analysis (CCA) of community variables. Axes 1 2 3 4 Total inertia Eigenvalues : 0.289 0.143 0.061 0.050 0.667 Species-environment correlations : 1.000 0.969 1.000 0.986 Cumulative percentage variance of species data : 43.3 64.7 73.8 81.3 of species-environment relation : 44.6 66.6 73.0 83.6 Sum of all eigenvalues 0.667 Sum of all canonical eigenvalues 0.648 4. DISCUSSION Dune areas, which constitute 20% of the earth’s surface and 99% of desert areas (Tsoar 2005 ), are of significant importance despite their small coverage. They are among the most threatened ecosystems due to their high levels of ecological diversity and the variety of ecosystem services they provide (Defeo et al. 2009 ; Everard et al. 2010 ; Hesp and Martinez 2007). Coastal ecosystems buffer stressful conditions such as soil erosion and saltwater intrusion, providing crucial support for inland areas by reducing stress (Hanley et al. 2020 ). In these ecosystems, developing effective conservation strategies requires an understanding of their ecosystem dynamics. Identifying functional groups of vegetation components reduces complexity and enables better comparison of vegetation types. CSR strategy types are considered one of the most suitable methods for interpreting plant communities (Zelnik and Čarni 2008 ). In our study area, the representation of intermediate strategies rather than main strategies (CSR) appears to be due to the inability to reach the optimal strategy in all characters due to allocation processes and trade-offs in the life cycle (Mustard et al. 2003 ). This suggests that there isn’t a single evolutionary solution for various environmental conditions (Frenetto-Dussaoult et al. 2012; Cicarelli 2015). Conversely, the distribution of CSR strategies was more homogeneous in the backdune area than foredune, with a similarity rate of 75.93% vs. 65.38%. This could be attributed to increased human activities or decreased dune flexibility (Garcia-Mora et al. 2000). Other studies have also demonstrated that plant distribution in mobile dunes is more heterogeneous and exhibits a random pattern (Conti et al. 2017 ). The strategy types R/CR, S/SR, R, R/SR, CR/CSR, and C/CR contributed the most to the dissimilarity between the foredune and backdune areas. It appears that environments with stress conditions can either reduce competition and foster facilitative interactions or intensify competition (Bertnes and Calaway 1994; Berkowitz et al. 1995 ; Brunjberg et al. 2012; Conti et al. 2017 ). According to Hart and Marshall ( 2013 ), species are less tolerant of competition in harsh environments, leading to more straightforward competitive exclusion. In our study area, the higher number of competitive species in the foredune area, which presents more challenging conditions, supports this perspective. The heterogeneity in stressful environments is a key factor in the competition or facilitation relationships between plants (Zuo et al. 2009 ). The heterogeneity of the foredune area in our study is likely due to the spatial distribution of soil resources or disturbance. However, the fact that at least 25% of the plants in the foredune exhibit competitive primary strategies suggests that the level of disturbance, if present, is low. This heterogeneity may be attributed to the presence of rivers flowing into the sea in the area, as well as differences in the spatial effects of ecological factors such as wind and salt spray in dune areas. Spatial variations in soil properties along successional gradients play a crucial role in vegetation cover change, and in turn, changes in vegetation cover are likely to influence the spatial heterogeneity of soil properties (Olff et al. 1993 ; Zuo et al. 2009 ). Spatial heterogeneity of soil nutrient pools at different scales is a common characteristic of arid and semi-arid ecosystems (Hook et al. 1991 ; Schlesinger and Pilmanis 1998 ). Therefore, soil heterogeneity appears to be more influential. Almost all of the soil variables studied exhibited temporal variation. However, OM, moisture, CaCO 3 , Ca, pH, salinity, EC, and CEC were identified as the most influential soil factors on the distribution of CSR strategy types along the gradient. OM, moisture, and Ca were higher in the backdune, while pH, salinity, EC, CEC, and CaCO 3 were higher in the foredune. Although many studies have shown that nitrogen is a significant limiting factor in plant growth (Willis et al. 1959 ; Willis 1963 ; Kachi and Hirose 1983 ; Dougherty et al. 1990; Olf et al. 1993), no significant difference was found between the two regions in the present study. However, a statistically significant difference was found between the foredune and backdune in terms of OM. This suggests that the amount of OM is not synonymous with nutrient availability, as shown in other studies (Sýkora et al. 2004 ; Lane et al. 2008 ). The lack of difference in terms of nitrogen forms, despite a significant difference in terms of OM, may be due to the rapid uptake of nitrogen forms from the soil by plant species in the backdune area or to faster mineralization in the foredune. In their study, Zhu et al. ( 2021 ) found that the presence of less water in coarse-textured loamy soils increased microbial diversity by reducing competition between bacteria and providing more niches for microbial associations. They also stated that the presence of lime in the soil slows down decomposition processes because it provides larger surfaces for nutrients. Considering that nitrogen-rich environments favor competitive species (Vitousek and Walker 1987 ; Berendse 1990 ), the higher representation of competitive strategy in the foredune seems to support the rapid mineralization in this area. However, this issue warrants further research. Conversely, soil texture exerts a significant influence on pH. While some studies report an increase in pH as species diversity increases (Isermann 2005 ), others show a tendency for pH to decrease in areas with higher species richness (Lane et al. 2008 ). In the present study, pH tended to decrease toward the backdune, where species richness was higher. The clay-loamy soil structure in the backdune leads to a lower pH environment due to the rapid leaching of carbonate and salt. The contrasting variation of CaCO 3 and pH throughout the year suggests that CaCO 3 in the foredune favours a higher pH due to its buffering effect. According to our study and others, OM and acidification are significant drivers of succession (Grime 1973 ; Berendse et al. 1998 ; Sýkora et al. 2004 ; Lane et al. 2008 ; Angiolini et al. 2018 ). Unlike CaCO 3 , Ca is more abundant in the backdune. As one of the most important exchangeable cations in growth substrates, calcium can affect nutrient availability. Therefore, it plays a crucial role in nutrient uptake and could favour plant settlement (Pausas and Austin 2001 ). The seasonal and spatial dynamics observed by Feagin and Wu ( 2007 ) on the Gulf of Mexico’s coastal dunes. They reported that temporal changes in soil moisture and salinity led to shifts in plant strategy dominance across the dune ecosystem. Similarly, in our study, seasonal fluctuations in soil variables such as moisture, salinity, and organic matter influenced the distribution of CSR strategies, with competitive species remaining dominant in foredunes during drier seasons, while ruderal species thrived in backdunes during wetter periods. These findings demonstrate how temporal environmental changes drive plant community dynamics in coastal dune systems worldwide. This study highlights significant differences in the distribution of plant strategy types (CSR strategies: competitor, stress tolerator, and ruderal) between foredune and backdune areas of a coastal dune ecosystem. In the foredune, competitive and stress-tolerant species (C/CR and SC strategies) dominated, strongly correlating with high salinity, pH, electrical conductivity (EC), and CaCO₃ levels. Conversely, in the backdune, ruderal and competitive species (R/CR, R/CSR, SR/CSR, S/SR, SC/CSR and CR/CSR strategies) were more prevalent due to higher nutrient availability, moisture, and organic matter. The Canonical Correspondence Analysis (CCA) revealed that environmental gradients, particularly soil properties such as pH, salinity, organic matter, and moisture, played a crucial role in influencing the spatial distribution of these strategies. Seasonal variations in soil properties further impacted species distribution, with ruderal species thriving in backdunes during wetter periods and competitive and stress-tolerant species in foredunes adapting to fluctuations in salinity and moisture. Additionally, the study suggests that plant species in backdunes contribute to soil modifications over time, increasing organic matter, which favors the success of ruderal and competitive species. These findings emphasize the importance of both environmental stress and nutrient availability in shaping plant communities in coastal dune ecosystems. The results of this study support our initial hypotheses that soil characteristics play a critical role in determining the distribution and success of different CSR strategy types across foredune and backdune areas. As hypothesized, stress-tolerant species were more prevalent in the foredune areas where soil conditions, including higher salinity and electrical conductivity, create a more stressful environment for plant growth. Conversely, backdune areas, characterized by lower salinity and more nutrient-rich soil, supported a greater abundance of ruderal, stress tolerant and competitive species. This aligns with our expectation that nutrient availability and soil stability would foster species richness and diversity in the backdunes. Additionally, seasonal variations in soil moisture and organic matter further influenced the distribution of plant strategies, reinforcing the importance of temporal dynamics in these ecosystems. The observed plant-soil feedbacks in the backdune, where plant communities contributed to the enrichment of soil organic matter, also provide evidence for the hypothesized positive feedback loop that promotes the establishment of ruderal and competitive species over time. These findings highlight the complex interactions between soil properties and plant strategies, emphasizing the role of both abiotic stress and disturbance in shaping plant community composition in coastal dune ecosystems. Salinity is a key factor influencing environmental gradients in dune ecosystems. High salinity conditions can also subject plants to drought stress due to elevated osmotic pressure (Maun 2009 ). Soil moisture in the foredune diminishes with the onset of the growing season, while salinity and EC peak in the summer. These two variables were found to strongly correlated with the C/CR strategy. Similar results were reported by Son et al. ( 2020 ). The more barren areas in the foredune lead to greater water loss through evaporation due to the higher temperature effect (Ishikawa et al. 1995 ; Álvarez-Rogel et al. 2006 ), resulting in increased salinity. Thus, salinity acts as a crucial filter in the foredune area (Gallego-Fernández and Martinez 2011). However, these variables remain constant throughout the year in the backdune. The higher vegetation cover in the backdune could be a primary reason for this, as it prevents water loss by evaporation (Liu et al. 2010 ; Zheng et al. 2015 ). The elevated EC during the growing season in the foredune may be attributed to the decomposition and accumulation of soluble salts during this period (Su et al. 2002 ; Dong et al. 2009 ). In conclusion, the foredune area presents a more dynamic scenario in terms of soil reactions. Initially, salinity, EC, CaCO 3 , and pH play a significant role in the formation of plant communities in the gradient from marine to terrestrial environments. However, OM, moisture and nutrient become influential as we move toward inland environments. An increase in competitive species in terrestrial environments is recognized as the climax stage of succession. Our study reveals that spatial effects play a part in reaching this climax stage. Even in the absence of disturbance, the climax stage can be achieved in areas defined as stressful environments, with the settlement of species that best adapt to the area’s dynamics in the final stage of succession. Cicarelli (2015) in Mediterranean and Brunbjerg et al. ( 2012 ) in Denmark dune ecosystems found that stress tolerant species adapted to extreme conditions were dominant in foredunes. Pugnaire and Luque ( 2001 ) noted that facilitation rather than competition tends to dominate in high stress environments, allowing for the coexistence of multiple stress-tolerant species. In contrast, our study shows that competitive species are more prevalent in the foredune area, despite the high-stress conditions. This discrepancy may be due to regional differences in soil properties or species pools. Specifically, the relatively high pH and salinity levels in our study area may create conditions that allow competitive species to thrive, a finding also observed by Son et al. ( 2020 ) and Lee et al. ( 2020 ), where competitor species were found in higher abundance in areas with elevated pH and salinity. While some studies report that physical stress and disturbance conditions limit the development of C-strategy-dominated plant communities (Cicarelli 2015), others show that competitors dominate in the foredune and ruderality increases in the backdune (Lee et al. 2020 ). In this study, we determined a shift from a competitive strategy to a ruderal and stress-tolerant strategy in the foredune-backdune gradient. The fact that our study area is located in the Black Sea Region, where sea salt stress is less, may be one of the reasons for the development of a plant community where the C-strategy is prioritized. This indicates that the relative importance of destruction and stress under different environmental conditions varies according to local environmental conditions (Petrů et al. 2006 ). The presence of more species in the backdune area suggests that facilitative effects increase in stressful environments (Montesinos 2015 ) and that the direction of interaction changes from competitive to facilitative (Pugnaire and Luque 2001 ). Species employing the C/CR strategy, which are most prevalent in the foredune, are characterized by a larger leaf area and significantly lower leaf dry matter content compared to species with other strategies. These species exhibit high nitrogen uptake capacity, high plasticity in aboveground/underground allocation, and can endure harsh conditions such as salt spray and repeated soil degradation. Limited by abiotic factors, foredune plant communities form a distinct dominant hierarchy (Mustard et al. 2003 ; Del Vecchio et al. 2021 ). Dominant species create favorable ecological conditions for other species and contribute to ecosystem function and the stabilization of ecosystem processes (Ellison et al. 2005 ; Fantinato et al. 2018 ). They can also mitigate stressful conditions due to marine impact, rendering the backdune area more temperate for plants (Borsje et al. 2011 ; De Battisti and Griffin 2020 ). These burial-resistant species possess deep root systems and play a crucial role in the structuring and monitoring of dune areas (Garcia-Mora et al. 1999; Garcia-Mora et al. 2000). The over-representation of R/CR, R/CSR, SR/CSR, S/SR, SC/CSR and CR/CSR strategies in the backdune area indicates that disturbance is a significant factor in this area. Ruderals and stress-tolerant species have evolved to maintain high levels of aboveground nitrogen by partially limiting their aboveground subsoil allocation. This is because they need to maximize seed quantity to ensure reproduction in these areas (Mustard et al. 2003 ). The broader ecological and conservation implications of the findings from this study are significant for managing and preserving coastal dune ecosystems. The study’s results highlight how soil factors such as pH, CEC, organic matter, and salinity play a crucial role in determining the distribution of plant strategy types (CSR strategies). These insights are particularly important for ecosystem management, as they demonstrate that different parts of a dune ecosystem (foredune vs. backdune) require different conservation approaches based on soil characteristics. For example, the dominance of stress-tolerant species in high-salinity, low-nutrient foredune areas suggests that these regions may be more vulnerable to environmental disturbances like climate change or human activities, which could further exacerbate stress conditions. Understanding that competitive species thrive in richer backdune soils with higher organic matter and moisture helps guide restoration efforts in degraded areas, as management strategies can focus on improving soil fertility to promote biodiversity and resilience. The comparison with similar studies also emphasizes the importance of considering regional differences, such as pH's role in limiting or supporting species diversity. Conservation strategies must, therefore, be tailored to the specific environmental conditions of each dune system. The findings reinforce the need for targeted interventions that consider both soil health and plant adaptation strategies to maintain the stability and function of coastal dunes. Furthermore, this research can inform predictive models for how dune ecosystems might respond to future environmental changes, helping to mitigate biodiversity loss and protect ecosystem services such as erosion control and habitat provision. The paper provides a thorough analysis of plant-soil interactions in coastal dune ecosystems, highlighting how environmental gradients like salinity, pH, and nutrient availability affect the distribution of CSR plant strategies. The use of Canonical Correspondence Analysis (CCA) strengthens the results by clearly linking soil characteristics to plant strategies. Additionally, the inclusion of seasonal and spatial variability offers a dynamic view of these ecosystems over time. The study is highly relevant for understanding plant adaptations in stressed environments, and its insights into plant-soil feedbacks in backdunes are particularly valuable for long-term ecological understanding. The geographic focus is narrow, limiting the generalizability of the findings to other ecosystems. The study also relies on correlation-based analyses without experimental manipulation, making it difficult to establish causality between soil characteristics and plant strategies. Additionally, the focus is limited to CSR strategies, without considering a broader range of functional traits that could provide further insights into plant adaptation. Future research should expand to include other coastal dune ecosystems to test the generalizability of the findings. Experimental manipulation of soil variables, such as salinity and nutrients, would help establish causality. A broader analysis of plant functional traits and long-term monitoring of plant-soil dynamics would provide deeper insights into ecological processes. Investigating the impacts of climate change on these interactions and linking findings to ecosystem services, such as dune stabilization, would also increase the practical value of the research for conservation and management. Declarations The authors declare the following financial interests/personal relationships which may be considered as potential competing interests Funding: This study was supported from the TUBITAK (The Scientific and Technological Research Council of Turkey, project no. 114O796). References Acosta, A. T. R., M. L. Carranza, and C. F. Izzi. 2005. Are there habitats that contribute best to plant species diversity in coastal dunes? Biodiversity and Conservation 14(11): 2609–2626. https://doi.org/10.1007/s10531-008-9421-5 Acosta, A., C. F. Izzi, and A. Stanisci. 2006. Comparison of native and alien plant traits in Mediterranean coastal dunes. Community Ecology 7(1): 35–41. https://doi.org/10.1556/ComEcJ.2006.1.4 Álvarez-Rogel, J., J. J. Martínez-Sánchez, L. C. Blázquez, and C. M. M. Semitiel. 2006. A conceptual model of salt marsh plant distribution in coastal dunes of southeastern The Society of Wetland Scientist. 26(3): 703–717. https://doi.org/10.1672/0277-5212(2006)26 Angiolini, C., G. Bonari, and M. Landi. 2018. Focal plant species and soil factors in Mediterranean coastal dunes: an undisclosed liaison? Estuarine, Coastal and Shelf Science. 211: 248–258. https://doi.org/10.1016/j.ecss.2017.06.001 Barbour, M. G., T. M. De Jong, and B. M. Pavlik. 1985. Marine beach and dune plant communities. In: Physiological Ecology of North American Plant Communities , eds. Chabot, B.F., Mooney, H.A., 296–322. Dordrecht, Netherlands,.Springer. Berendse, F. 1990. Organic matter accumulation and nitrogen mineralization during secondary succession in heathland ecosystems. Journal of Ecology 78(2): 413–427. https://doi.org/10.2307/2261121 Berendse, F., E. J. Lammerts, and H. Olff. 1998. Soil organic matter accumulation and its implications for nitrogen mineralization and plant species composition during succession in coastal dune slacks. Plant Ecology 137(1): 71–78. https://doi.org/10.2307/2261121 Bermúdez, R., and R. Retuerto. 2014. Together but different: co-occurring dune plant species differ in their water-and nitrogen-use strategies. Oecologia 174(3): 651–663. https://doi.org/10.1007/s00442-013-2820-7 Berkowitz, A. R., C. D. Canha, and V. R. Kelly. 1995. Competition vs. facilitation of tree seedling growth and survival in early successional communities. Ecology 76(4): 1156–1168. https://doi.org/10.2307/1940923 Bertness, M. D., and R. Callaway. 1994. Positive interactions in communities. TREE 9(5): 191–193. https://doi.org/10.1016/0169-5347(94)90088-4 Borsje, B. W., B. K. van Wesenbeeck, F. Dekke, P. Paalvast, T. J. Bouma, M. M. van Katwijk, and M. B. de Vries. 2011. How ecological engineering can serve in coastal protection. Ecological Engineering 37(2): 113–122. https://doi.org/10.1016/j.ecoleng.2010.11.027 Bornhofen, S., S. Barot, and C. Lattaud. 2011. The evolution of CSR life-history strategies in a plant model with explicit physiology and architecture. Ecological Modelling 222(1): 1–10. https://doi.org/10.1016/j.ecolmodel.2010.09.014 Bremner, J. M., and C. S. Mulvaney. 1982. Nitrogen-Total. In: Methods of Soil Analysis: Part 2 - Chemical and Microbiological Properties , eds. Page, A.L., Miller, R.H., Keeney, D.R., 595–624. Madison, WI., American Society of Agronomy. Brunbjerg, A. K., F. Borchsenius, W. L. Eiserhardt, R. Ejrnæs, and J. C. Svenning. 2012. Disturbance drives phylogenetic community structure in coastal dune vegetation. Journal of Vegetation Science 23(6): 1082–1094. https://doi.org/10.1111/j.1654-1103.2012.01433.x Carboni, M., M. L. Carranza, and A. Acosta. 2009. Assessing conservation status on coastal dunes: a multiscale approach. Lands and Urban Planning 91(1): 17–25. https://doi.org/10.1016/j.landurbplan.2008.11.004 Chapman, H. D. 1965. Cation-exchange capacity. In Methods of Soil Analysis: Part 2 - Chemical and Microbiological Properties , ed. C. A. Black. 891–901. Madison, WI.: American Society of Agronomy. Chelli, S., M. Marignani, E. Barni, A. Petraglia, G. Puglielli, and C. Wellstein et al. 2019. Plant–environment interactions through a functional traits perspective: a review of Italian studies. Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology 153(6): 853–869. https://doi.org/10.1080/11263504.2018.1559250 Ciccarelli, D. 2015. Mediterranean coastal dune vegetation: are disturbance and stress the key selective forces that drive the psammophilous succession? Estuarine, Coastal and Shelf Science. 165: 247–253. https://doi.org/10.1016/j.ecss.2015.05.023 Ciccarelli, D., and C. Bona. 2022. Exploring the functional strategies adopted by coastal plants along an ecological gradient using morpho-functional traits. Estuarine Coastal and Shelf Science 45(1): 114–129. https://doi.org/10.1007/s12237-021-00945-y Conti, L., F. de Bello, J. Lepš, A. T. R. Acosta, and M. Carboni. 2017. Environmental gradients and micro-heterogeneity shape fine‐scale plant community assembly on coastal dunes. Journal of Vegetation Science 28(4): 762–773. https://doi.org/10.1111/jvs.12533 Cornwell, W. K., k D.W. Schwil, and D. D. Ackerly. 2006. A trait-based test for habitat filtering: convex hull volume. Ecology 87(6): 1465–1471. https://doi.org/10.1890/0012-9658(2006)87 Davis, P. 1965–1985. Flora of Turkey and the East Aegean Islands . Volume 1–9 Edinburgh University Press. Edinburg. Davis, P. H., R. R. Mill, and K. Tan. 1988. Flora of Turkey and the East Aegean Islands . vol. 10 Edinburgh: Edinburgh University. De Battisti, D., and J. N. Griffin. 2020. Below-ground biomass of plants, with a key contribution of buried shoots, increases foredune resistance to wave swash. Annals of Botany 125(2): 325–334. https://doi.org/10.1093/aob/mcz125 Defeo, O., A. McLachlan, D. S. Schoeman, T. A. Schlacher, J. Dugan, A. Jones, M. Lastra, and F. Scapini. 2009. Threats to sandy beach ecosystems: a review. Estuarine Coastal and Shelf Science 81(1): 1–12. https://doi.org/10.1016/j.ecss.2008.09.022 Del Vecchio, S., E. Mattana, T. Ulian, and G. Buffa. 2021. Functional seed traits and germination patterns predict species coexistence in Northeast Mediterranean foredune communities. Annals of Botany 127(3): 361–370. https://doi.org/10.1093/aob/mcaa186 Díaz Barradas, M. C., M. Zunzunegui, R. Tirado, F. Ain-Lhout, and F. García Novo. 1999. Plant functional types and ecosystem function in Mediterranean shrubland. Journal of Vegetation Science 10(5): 709–716. https://doi.org/10.2307/3237085 Díaz, S., and M. Cabido. 1997. Plant functional types and ecosystem function in relation to global change. Journal of Vegetation Science 8(4): 463–474. https://doi.org/10.2307/3237198 Dı́az, S., and M. Cabido. 2001. Vive la différence: plant functional diversity matters to ecosystem processes. TREE 16(11): 646–655. https://doi.org/10.1016/S0169-5347(01)02283-2 Dong, X. W., X. K. Zhang, X. L. Bao, and J. K. Wang. 2009. Spatial distribution of soil nutrients after the establishment of sand-fixing shrubs on sand dune. Plant Soil and Environment 55(7): 288–294. https://doi.org/10.17221/13/2009-PSE Dougherthy, K. M., .IA. Mendelsohn, and F. J. Monteferrante. 1990. Effect of nitrogen, phosphorus and potassium additions on plant biomass and soil nutrient content of a swale barrier strand community in Louisiana. Annals of Botany 66(3): 265–271. https://doi.org/10.1093/oxfordjournals.aob.a088024 Ellison, A. M., M. S. Bank, and B. D. Clinton et al. 2005. Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. Frontiers in Ecology and Environment 3(9): 479–486. https://doi.org/10.1890/1540-9295(2005)003 Everard, M., L. Jones, and B. Watts. 2010. Have we neglected the societal importance of sand dunes? An ecosystem services perspective. Aquatic Conservation 20(4): 476–487. https://doi.org/10.1002/aqc.1114 Fantinato, E., S. Del Vecchio, G. Silan, and G. Buffa. 2018. Pollination networks along the sea-inland gradient reveal landscape patterns of keystone plant species. Scientific Reports 8: 1–9. https://doi.org/10.1038/s41598-018-33652-z Feagin, R. A., and X. B. Wu. 2007. The spatial patterns of functional groups and successional direction in a coastal dune community. Rangeland Ecology and Management 60(4): 417–425. https://doi.org/10.2111/1551-5028(2007)60 Frenette-Dussault, C., B. Shipley, J. F. Léger, D. Meziane, and Y. Hingrat. 2012. Functional structure of an arid steppe plant community reveals similarities with Grime's C‐S‐R theory. Journal of Vegetation Science 23(2): 208–222. https://doi.org/10.1111/j.1654-1103.2011.01350.x Gallego-Fernández, J. B., and M. L. Martínez. 2011. Environmental filtering and plant functional types on Mexican foredunes along the Gulf of Mexico. Ecoscience 18(1): 52–62. https://doi.org/10.2980/18-1-3376 García-Mora, M. R., J. B. Gallego‐Fernández, and F. García‐Novo. 1999. Plant functional types in coastal foredunes in relation to environmental stress and disturbance. Journal of Vegetation Science 10(1): 27–34. https://doi.org/10.2307/3237157 García-Mora, M. R., J. B. Gallego-Fernández, and F. García-Novo. 2000. Plant diversity as a suitable tool for coastal dune vulnerability assessment. Journal of Coastal Research 16(4): 990–995. Grime, J. P. 1973. Competitive exclusion in herbaceous vegetation. Nature 242(5396): 344–347. https://doi.org/10.1038/242344a0 Grime, J. P. 1974. Vegetation classification by reference to strategies. Nature 250(5461): 26–31. https://doi.org/10.1038/250026a0 Grime, J. P. 1977. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. American Naturalist 111(982): 1169–1194. https://doi.org/10.1086/283244 Grime, J. P. 2001. Plant Strategies, Vegetation Processes, and Ecosystem Properties . 2nd Edition. Wiley. Grime, J. P., K. Thompson, R. Hunt, J. G. Hodgson, and J. H. C. Cornelissen et al. 1997. Integrated screening validates primary axes of specialisation in plants. Oikos 79(2): 259–281. https://doi.org/10.2307/3546011 Güner, A., N. Özhatay, T. Ekim, and K. H. C. Başer. 2000. Flora of Turkey and the East Aegean Islands . vol. 11 Edinburgh: Edinburgh University. Güner, A., S. Aslan, T. Ekim, M. Bural, and M. T. Babaç. 2012. Türkiye Bitkileri Listesi (Damarlı Bitkiler) . İstanbul, Turkey, Nezahat Gökyiğit Botanik Bahçesi ve Flora Araştırmaları Derneği. Hanley, M. E., T. J. Bouma, and H. L. Mossman. 2020. The gathering storm: optimizing management of coastal ecosystems in the face of a climate-driven threat. Annals of Botany 125(2): 197–212. https://doi.org/10.1093/aob/mcz204 Hart, S. P., and D. J. Marshall. 2013. Environmental stress, facilitation, competition, and coexistence. Ecology 94(12): 2719–2731. https://doi.org/10.1890/12-0804.1 Hesp, P. A. 1991. Ecological processes and plant adaptations on coastal dunes. Journal of Arid Environment. 21(2): 165–191. https://doi.org/0.1016/S0140-1963(18)30681-5. Hesp, P. A., and M. L. Martínez. 2007. Disturbance processes and dynamics in coastal dunes. In Plant Disturbance Ecology: The Process and The Response , ed. E. A. Johnson, and K. Miyanishi. 215–247. USA, Elsevier. Hodgson, J. G., P. J. Wilson, R. Hunt, J. P. Grime, and K. Thompson. 1999. Allocating CSR plant functional types: a soft approach to a hard problem. Oikos 85(2): 282–294. https://doi.org/10.2307/3546494 Hook, P. B., I. C. Burke, and W. K. Lauenroth. 1991. Heterogeneity of soil and plant N and C associated with individual plants and openings in North American shortgrass steppe. Plant and Soil 138(2): 247–256. https://doi.org/10.1007/BF00012252 Isaac, R. A., and W. C. Johnson. 1975. Determination of calcium, magnesium by atomic absorption. In Soil Science Society of America Journal: Analytical Methods for Soil and Plant Tissue , ed. L. M. Walsh. 489–510. Madison, WI.: Soil Science Society of America. Isermann, M. 2005. Soil pH and species diversity in coastal dunes. Plant Ecology 178(1): 111–120. https://doi.org/10.1007/s11258-004-2558-8 Ishikawa, S. I., A. Furukawa, and T. Oikawa. 1995. Zonal plant distribution and edaphic and micrometeorological conditions on a coastal sand dune. Ecological Research 10(3): 259–266. https://doi.org/10.1007/BF02347851 Kachi, N., and T. Hirose. 1983. Limiting nutrients for plant growth in coastal sand dune soils. Journal of Ecology 71(3): 937–944. https://doi.org/https://doi.org/10.2307/2259603 Keddy, P. A. 1992. Assembly and response rules: two goals for predictive community ecology. Journal of Vegetation Science 3(2): 157–164. https://doi.org/https://doi.org/10.2307/3235676 Keeney, D. R., and D. W. Nelson. 1982. Nitrogen-Inorganic forms. In Methods of Soil Analysis: Part 2 - Chemical and Microbiological Properties , ed. R. H. Miller, and D. R. Keeney. 643–698. Madison, WI.: American Society of Agronomy. Pa g e, A.L.. Lammerts, E. J., A. P. Grootjans, and H. G. J. Vander Hagen. 2004. Coastal Dune Vegetation Dynamics: The Impact of Erosion and Deposition on Stabilized and Unstabilized Dunes. Applied Vegetation Science 7(1): 23–30. Lane, C., S. J. Wright, J. Roncal, and J. Maschinski. 2008. Characterizing environmental gradients and their influence on vegetation zonation in a subtropical coastal sand dune system. Journal of Coastal Research 24(3): 213–224. https://doi.org/https://doi.org/10.2112/07-0853.1 Lanyon, L. E., and W. R. Heald. 1982. Magnesium and calcium determination. In Methods of Soil Analysis: Part 2 - Chemical and Microbiological Properties , ed. R. H. Miller, and D. R. Keeney. 247–262. Madison, WI.: American Society of Agronomy. Pa g e, A.L.. Lavorel, S., S. McIntyre, J. Landsberg, and T. D. A. Forbes. 1997. Plant functional classifications: from general groups to specific groups based on response to disturbance. TREE 12(12): 474–478. https://doi.org/10.1016/S0169-5347(97)01219-6 Lee, J. S., D. H. Son, S. H. Lee, H. H. Myeong, J. S. Cho, J. C. Lee, J. Y. Lee, C. S. Park, and J. W. Kim. 2020. Canonical correspondence analysis ordinations and competitor, stress tolerator, and ruderal strategies of coastal dune plants in South Korea. Journal of Coastal Research 36(3): 528–535. https://doi.org/10.2112/JCOASTRES-D-19-00106.1 Liu, Z., M. Notaro, and R. Gallimore. 2010. Indirect vegetation-soil moisture feedback with application to Holocene North Africa climate. Global Change Biology 16(6): 1733–1743. https://doi.org/10.1111/j.1365-2486.2009.02087.x Macedo, J. A., P. Alves, Â. Lomba, J. Vicente, R. Henriques, H. Granja, and J. Honrado. 2010. On the interest of plant functional classifications to study community-level effects of increased disturbance on coastal dune vegetation. Acta Botanica Gallica 157(2): 305–315. https://doi.org/10.1080/12538078.2010.10516208 Maun, M. A. 2009. The biology of coastal sand dunes . New York: Oxford University Press. Mayfield, M. M., M. F. Boni, G. C. Daily, and D. Ackerly. 2005. Species and functional diversity of native and human-dominated plant communities. Ecology 86(9): 2365–2372. https://doi.org/10.1890/05-0141 McLean, E. O. 1982. Soil pH and lime requirement. In Methods of Soil Analysis: Part 2 - Chemical and Microbiological Properties , ed. R. H. Miller, and D. R. Keeney. 199–224. Madison, WI.: American Society of Agronomy. Pa g e, A.L.. Montesinos, D. 2015. Plant–plant interactions: from competition to facilitation. Web Ecology 15(1): 1–2. https://doi.org/10.5194/we-15-1 Moreno-Casasola, P. 1986. Sand movement as a factor in the distribution of plant communities in a coastal dune system. Vegetatio 65(2): 67–76. https://doi.org/10.1007/BF00044876 Mulvaney, R. L. 1996. Nitrogen-Inorganic forms. In Methods of Soil Analysis: Part 3 - Chemical Methods , 1123–1184. Madison, WI.: American Society of Agronomy. Mustard, M. J., D. B. Standing, M. J. Aitkenhead, D. Robinson, and A. J. S. McDonald. 2003. The emergence of primary strategies in evolving virtual-plant populations. Evolutionary Ecology Research 5(7): 1067–1081. Nelson, D. W., and L. E. Sommers. 1996. Total carbon, organic carbon, and organic matter. In Methods of Soil Analysis: Part 3 - Chemical Methods , ed. D. L. Sparks. 961–1010. Madison, WI.: American Society of Agronomy. Novo, F. G., M. D. Barradas, M. Zunzunegui, R. G. Mora, and J. G. Fernández. 2004. Plant functional types in coastal dune habitats. In: Coastal Dunes: Ecology and Conservation, Ecological Studies , eds. Martínez, M.L., Psuty, N.P., 155–169. Vol 171, Berlin, Springer. https://doi.org/10.1007/978-3-540-74002-5-10 Olff, H., J. Huisman, and B. F. Van Tooren. 1993. Species dynamics and nutrient accumulation during early primary succession in coastal sand dunes. Journal of Ecology 81(4): 693–706. https://doi.org/10.2307/2261667 Olsen, S. R., C. V. Cole, F. S. Watanabe, and L. A. Dean. 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate . vol. 939 1–19. U.S. Department of Agriculture Circular. Pausas, J. G., and M. P. Austin. 2001. Patterns of plant species richness in relation to different environments: an appraisal. Journal of Vegetation Science 12(2): 153–166. https://doi.org/10.2307/3236601 Petrů, M., K. Tielbörger, R. Belkin, M. Sternberg, and F. Jeltsch. 2006. Life history variation in an annual plant under two opposing environmental constraints along an aridity gradient. Ecography 29(1): 66–74. https://doi.org/10.1111/j.2005.0906-7590.04310.x Pierce, S., A. Luzzaro, M. Caccianiga, R. M. Ceriani, and B. Cerabolini. 2017. The StrateFy tool: A new method to calculate CSR ecological strategies globally. Functional Ecology 31(2): 444–457. https://doi.org/10.1111/1365-2435.12722 Pugnaire, F. I., and M. T. Luque. 2001. Changes in plant interactions along a gradient of environmental stress. Oikos 93(1): 42–49. https://doi.org/10.1034/j.1600- 0706.2001.930104.x Rhoades, J. D. 1996. Electrical conductivity. In Methods of Soil Analysis: Part 3 - Chemical Methods , ed. D. L. Sparks. 417–435. Madison, WI.: American Society of Agronomy. Richards, L. A. 1954. Diagnosis and improvement of saline and alkali soils. (USDA Handbook No. 60) . Washington, U.S.: Department of Agriculture. Schlesinger, W. H., and A. M. Pilmanis. 1998. Plant-soil interactions in deserts. Biogeochemistry 42(1): 169–187. https://doi.org/10.1023/A:1005939924434 Schwarz, C., J. Brinkkemper, and G. Ruessink. 2018. Feedbacks between biotic and abiotic processes governing the development of foredune blowouts: a review. Journal of Marine Science and Engineering 7(1): 2. https://doi.org/10.3390/jmse7010002 Son, D. H., J. S. Lee, S. H. Lee, S. K. Hong, H. H. Myeong, and J. S. Cho et al. 2020. Importance of Biotic and Soil Factors in Determining the Distribution Strategies of Coastal Salt Marsh Plants. JMIC 9(2): 2–13. https://doi.org/10.21463/jmic.2020.09.2.01 Su, Y., H. Zhao, and T. Zhang. 2002. Influencing mechanism of several shrubs and subshrubs on soil fertility in Keerqin sandy land. Ying Yong Sheng Tai Xue Bao. Journal of Applied Ecology 13(7): 802–806. Sýkora, K. V., J.C. van den Bogert, and F. Berendse. 2004. Changes in soil and vegetation during dune slack succession. Journal of Vegetation Science 15(2): 209–218. https://doi.org/10.1111/j.1654-1103.2004.tb02256.x Tsoar, H. 2005. Sand dunes mobility and stability in relation to climate. Physica A: Statistical Mechanics and its Applications 357(1): 50–56. https://doi.org/10.1016/j.physa.2005.05.067 Topp, G. C., and P. A. Ferré. 2002. Methods for measurement of soil water content: Thermogravimetric method. In Methods of Soil Analysis: Part 4 - Physical Methods , ed. J. H. Dane, and G. C. Topp. 417–445. Madison, WI, Soil Science Society of America. Vitousek, P. M., and L. R. Walker. 1987. Colonization, succession and resource availability: ecosystem-level inter- actions. In Colonization, Succession and Stability , ed. A. J. Gray, M. J. Crawley, and P. J. Edwards. 207–224. Oxford: Blackwell Scientific. Willis, A. J., B. F. Folkes, J. F. Hope-Simpson, and E. W. Yemm. 1959. Braunton Burrows: The dune system and its vegetation. Journal of Ecology 47(1): 1–24. https://doi.org/10.2307/2257245 Willis, A. J. 1963. Braunton Burrows: the effects on the vegetation of the addition of mineral nutrients to the dune soils. Journal of Ecology 51(2): 353–374. https://doi.org/10.2307/2257690 Zelnik, I., and A. Čarni. 2008. Distribution of plant communities, ecological strategy types and diversity along a moisture gradient. Community Ecology 9(1): 1–9. https://doi.org/10.1556/ComEc.9.2008.1.1 Zheng, H., J. Gao, Y. Teng, C. Feng, and M. Tian. 2015. Temporal variations in soil moisture for three typical vegetation types in inner Mongolia, Northern China. Plos One 10(3): e0118964. https://doi.org/10.1371/journal.pone.0118964 Zhu, Y., B. Guo, C. Liu, Y. Lin, Q. Fu, N. Li, and H. Li. 2021. Soil fertility, enzyme activity, and microbial community structure diversity among different soil textures under different land use types in coastal saline soil. Journal of Soil and Sediment 21(11): 2240–2252. https://doi.org/10.1007/s11368-021-02916-z Zuo, X., X. Zhao, H. Zhao, T. Zhang, Y. Guo, Y. Li, and Y. Huang. 2009. Spatial heterogeneity of soil properties and vegetation–soil relationships following vegetation restoration of mobile dunes in Horqin Sandy Land, Northern China. Plant and Soil 318(1): 153–167. https://doi.org/10.1007/s11104-008-9826-7 Cite Share Download PDF Status: Published Journal Publication published 01 Mar, 2025 Read the published version in Estuaries and Coasts → Version 1 posted Reviewers agreed at journal 13 Nov, 2024 Reviewers invited by journal 12 Nov, 2024 Editor invited by journal 04 Nov, 2024 Editor assigned by journal 25 Oct, 2024 First submitted to journal 24 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5327986","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377384191,"identity":"91922e6b-4ce1-4b09-8c12-a4879562d11e","order_by":0,"name":"Emire Elmas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYFAC5gYgYcPABuURo4URpCUNSQsbcVoOw+0krIWfvbF1w48/5+35pJuPPWCosE5skO99gFeLZM/Btpu9bbcT22SOpRswnElPbGBjN8CrxeBGYtsN3obbCWwSOWYSjG2HgVoIuMz+/sO2m3/+nLOHaPlHhBYDoMm3edgOMLaBtTQQoUXiTGLbbdm25MQ2ibR0g4Rj6cZtbGn4tfC3Hz52880fO3v5GcnHHnyosZbtZz6GXwsyYGNIYCAmJlG0jIJRMApGwSjABgAsqkJDAT8wqQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5620-1798","institution":"Sinop University: Sinop Universitesi","correspondingAuthor":true,"prefix":"","firstName":"Emire","middleName":"","lastName":"Elmas","suffix":""},{"id":377384192,"identity":"dcb26d57-e77e-401b-8f43-1b93701d99aa","order_by":1,"name":"Sevda Türkiş","email":"","orcid":"","institution":"Ordu University: Ordu Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Sevda","middleName":"","lastName":"Türkiş","suffix":""},{"id":377384193,"identity":"ef535be3-229e-4be4-9ab2-6d86eb469659","order_by":2,"name":"Barış Bani","email":"","orcid":"","institution":"Kastamonu Üniversitesi: Kastamonu Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Barış","middleName":"","lastName":"Bani","suffix":""}],"badges":[],"createdAt":"2024-10-24 19:00:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5327986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5327986/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12237-025-01512-5","type":"published","date":"2025-03-01T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70552712,"identity":"188e88af-61d9-4d91-a50b-f49d58159a6c","added_by":"auto","created_at":"2024-12-04 10:25:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":274867,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram illustrating the study areas (F: Foredune, B: Backdune)\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5327986/v1/e7cc7bdfdabdaa48830d769a.jpg"},{"id":70552713,"identity":"766fa05b-cfac-4e5a-9971-4297380ef7b7","added_by":"auto","created_at":"2024-12-04 10:25:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2455517,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variations in variables within the study areas and differences observed within and between communities (A: autumn, W: winter, Sp: spring, S: summer, F: foredune, and B: backdune). Capital letters indicate differences between communities, while lowercase letters denote differences between seasons within the community. Different letters indicate statistically significant differences (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5327986/v1/46431f82a078c08e415f38a7.jpg"},{"id":70552932,"identity":"d5f2c16c-ac52-407d-9501-d730139e379d","added_by":"auto","created_at":"2024-12-04 10:33:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":647550,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot of the canonical correspondence analysis (CCA) ordination for the study areas.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5327986/v1/69a0b0adc9978f1ab65ae7a8.jpg"},{"id":77622453,"identity":"805e5341-1261-4202-9be4-1ae56182dfd6","added_by":"auto","created_at":"2025-03-03 16:07:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4275320,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5327986/v1/a43de013-3436-4384-9725-c6188f6d5299.pdf"}],"financialInterests":"","formattedTitle":"Relationship between Plant Strategy Types and Soil Characteristics in Backdunes and Foredunes","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eCoastal dunes serve as a dynamic interface, bridging marine, terrestrial, and atmospheric processes. They also act as an ecotone, a transitional zone between aquatic and terrestrial ecosystems. Their structure is not static but fluctuates, reflecting their dynamic nature (Schwarz et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Carboni et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These ecosystems are shaped by a multitude of biotic and abiotic factors. They present a challenging environment characterized by stress conditions such as salt spray, drought, nutrient deficiency, high temperature, intense light, strong wind, inundation, and substrate mobility. Plants have evolved various strategies to thrive in these stressful environments, including leaf roll, salt bladders, physiological adaptations, diverse light use strategies, germination strategies, various life cycles, and morphological changes (Hesp \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Garc\u0026iacute;a-Mora et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Berm\u0026uacute;dez and Retuerto \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the extreme conditions of coastal dune environments, only certain plants capable of surviving these conditions can establish themselves. Over time, plants develop life strategies in response to different environmental conditions, reflecting their adaptation to both abiotic and biotic constraints. Environmental circumstances act as a filter for species characteristics (Keddy \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), resulting in coexisting species exhibiting similar characteristics and ecological tolerances, irrespective of their phylogenetic lineage (Cornwell et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mayfield et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In this context, species can be categorized into functional plant types (PFTs) based on shared characteristics. These PFTs can then be used to elucidate community and ecosystem structures (Dı́az and Cabido \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lavorel et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGrouping plants based on the similarities of their functional traits simplifies the interpretation of plant communities. This method is practical for assessing ecosystem function, as different plant life strategies contribute differently to matter and energy processes (D\u0026iacute;az and Cabido \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Grime identified stress and disturbance as two primary environmental factors that limit plant life. The CSR (competitor, stress tolerator and ruderal) model outlines three fundamental life strategies: competitor species that thrive under low stress and low disturbance conditions, adapting to rapid resource utilization and long-term site occupation; stress-tolerant species that survive under high stress and low disturbance conditions, settling in environments with scarce resources; and ruderal species that adapt to low stress and high disturbance conditions by growing and reproducing rapidly. Vascular plant species can be classified into CSR strategies using seven functional characteristics, including specific leaf area (SLA), canopy height, dry matter content, leaf dry weight, lateral spread, flowering time, and flowering period (Grime \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Grime et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Hodgson et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The CSR classification illustrates the impact of stress and disturbance factors on plants throughout the evolutionary process and provides insights into plant strategies that have evolved in response to environmental conditions (Bornhofen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The CSR model is powerful because of its broad applicability and theoretical simplicity. Unlike other methods, such as the Leaf-Height-Seed (LHS) strategy, which is limited to specific characters, or StrateFy, which bases CSR classification on a fixed set of traits, the CSR Triangle method allows for greater flexibility in character selection, which enables ecologists to adapt the structure to different plant species and ecosystems (Grime \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Pierce et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, the method provides a clear conceptual structure based on ecological theory, allowing researchers to manually interpret CSR strategies based on mixed ecological characters and environmental context. This makes it particularly useful for landscape and complex ecological systems where automated methods oversimplify plant-environment interactions.\u003c/p\u003e \u003cp\u003eStress and disturbance are anticipated to play significant roles in dune plant succession. Other critical stress factors that limit plant production include high temperatures, high irradiance, nutrient availability, and water scarcity. Conversely, the most crucial disturbance factors are strong wind, seawater inundation, grazing, and anthropogenic effects (Hesp \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Maun \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Coastal dune plants adapt to these ecological conditions through morphological and physiological adaptations such as leaf hypertrophy, spray tolerance, life span, growth habit, and growth timing (Barbour et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Maun \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The CSR model holds particular significance in dynamic dune ecosystems. This model provides insight into how different strategies coexist in dune ecosystems with plant communities affected by both natural and anthropogenic disturbances (Maun \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Acosta et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lammerts et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and helps ecologists to understand the distribution and diversity of plant species in these fragile environments, guiding management and conservation efforts.\u003c/p\u003e \u003cp\u003eSoil, as the second most significant abiotic factor influencing plant settlement, plays a crucial role after climate. The nutrient input in coastal dunes differs from that in terrestrial areas, with meteorological inputs, particularly aerosols in spray and fog, surpassing the input from substrate weathering. Aerosols, bulk precipitation, seawater inundation, detritus deposition, and sand movement all contribute to nutrient inputs. In coastal dunes, certain nutrients (such as Na, K, Cl, and Mg) reach high and extreme levels, while others (i.e., N, P, organic matter) are present in very low concentrations (Barbour et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). The relationship between soil and plant species is reciprocal: dune soil conditions control plant settlement, growth, and reproduction, while plant species contribute to sand accumulation and dune formation (Moreno-Casasola \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1986\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoastal dunes, being dynamic systems, serve as practical models for ecological research, particularly on succession. Studying functional characters is crucial to understanding the processes involved in the formation of plant communities under natural stress conditions (Cicarelli and Bona 2022; Faegin and Wu 2007; Gallego-Fern\u0026aacute;ndez and Martinez 2011; Chelli et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While several studies have explored plant adaptations by examining functional groups (D\u0026iacute;az Barradas et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Garc\u0026iacute;a-Mora et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Acosta et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Feagin and Wu \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Novo et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gallego-Fern\u0026aacute;ndez and Mart\u0026iacute;nez \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Berm\u0026uacute;dez and Retuerto \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), only a few have scrutinized Grime\u0026rsquo;s CSR strategy scheme and analysed the relationships between edaphic factors and the distribution of CSR (Macedo et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Brunbjerg et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ciccarelli \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Son et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The investigation relationships between CSR strategies and soil variables is important to understand plant adaptation and ecosystem functions in coastal dunes. These regions pose significant challenges for plant survival by fluctuating condition such as high salinity, extreme temperatures, nutrient poor soils, and drought (Hesp \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The CSR strategy model offers a framework to classify plant species based on their adaptive responses to stress and disturbance. It provides insights into how species interact with their environment and how they may shift along successional gradients (Grime \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). This study seeks to clarify how these factors affect the distribution and success of different CSR strategy types in both foredune and backdune areas by analysing soil characteristics, such as pH, organic matter, nutrient content, and moisture,\u003c/p\u003e \u003cp\u003eGrime\u0026rsquo;s CSR framework has been shown to be effective to understand that how plants cope with stress and disturbance in ecosystems that be subject of constant environmental change (Brunbjerg et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the present study, use of soil variables such as nutrient contents, electrical conductivity (EC), and cation change capacity (CEC), with the application of CSR classification, provide a deeper understanding of the complex interaction between plant species and their abiotic environment (Grime et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding these relationships between CSR strategies and soil variables is crucial both for theoretical ecology and practical conservation and management. Buffering inland areas from erosion and saltwater intrusion, and supporting biodiversity are some of important ecosystem services of coastal dune ecosystems (Hesp and Martinez 2007). Determining CSR strategies that are most successful under varying soil conditions provide better predictions on how these ecosystems may respond to future environmental changes, such as climate change or human disturbance (Defeo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, linking CSR strategies with soil variables provide insight about how ecosystems evolve over time by contributing to a broader understanding of plant community assembly and succession (Pugnaire and Luque \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Macedo et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we hypothesize that the distribution of plant strategy types (CSR strategies: competitor, stress tolerator, and ruderal) is significantly influenced by variations in soil characteristics between foredune and backdune areas. Specifically, we propose that soil properties such as pH, moisture, organic matter, and nutrient content (e.g., total nitrogen, phosphorus) play a crucial role in determining the success and dominance of different CSR strategy types. We expect that stress-tolerant species will be more prevalent in foredune areas characterized by higher salinity and electrical conductivity (EC), while ruderal and competitive species will dominate in backdune areas with lower salinity and higher nutrient availability. Additionally, we hypothesize that seasonal variations in soil conditions will further influence the abundance and distribution of these plant strategies, with backdune areas exhibiting greater species richness and diversity due to more stable and favorable soil conditions. Finally, we anticipate that plant-soil feedbacks in backdune areas will lead to modifications in soil properties over time, further promoting the success of ruderal and competitive species in these regions.\u003c/p\u003e"},{"header":"2. MATERIALS and METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study area\u003c/h2\u003e\n \u003cp\u003eThe study area, located near the Aksaz Wetland, is subject to significant human impact. It features two streams, Sırakaraağa\u0026ccedil;lar and Karasu, along with several drainage channels that open to the sea. These streams connect with the Blacksea during autumn, winter, and early spring and close in late spring and summer. The study area encompasses a foredune of 27 ha and a backdune of 13 ha (4 km long and of variable width, between 42\u0026deg;01\u0026acute;64.2\u0026acute;\u0026acute;N\u0026ndash;34\u0026deg;04\u0026acute;55.8\u0026acute;\u0026acute;E and 44\u0026deg;02\u0026acute;33.3\u0026acute;\u0026acute;N\u0026ndash;35\u0026deg;02\u0026acute;39.9\u0026acute;\u0026acute;E). The dune area lacks zonation. According to a decade\u0026rsquo;s worth of climatic data (2005\u0026ndash;2015) from the Sinop Meteorological Service, the mean annual temperature is 14.96\u0026deg; C and the total annual precipitation is 720.15 mm. A drought period occurs from mid-May to the end of August.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Evaluating the distribution of CSR strategies\u003c/h2\u003e\n \u003cp\u003eFor the foredune and backdune areas, 20 permanent sample plots were established to assess plant diversity. The plot size was determined using the minimal area method, resulting in 50 m\u003csup\u003e2\u003c/sup\u003e plots for both sites. The percentage cover of each plant species was recorded monthly over a 2-year period. Plant species were identified according to the \u0026ldquo;Flora of Turkey and the East Aegean Islands\u0026rdquo; (Davis 1965\u0026ndash;1985; Davis et al. \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e; G\u0026uuml;ner et al. \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), and the taxonomic nomenclature adhered to that of G\u0026uuml;ner et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eCSR strategies were evaluated following Hodgson et al.\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) method, which uses canopy height, leaf dry matter content, flowering period, flowering initiation, leaf dry weight, lateral spread, and SLA (\u003cem\u003esee\u003c/em\u003e Hodgson et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). For subsequent phenological observations, at least ten individuals of each species were marked, and observations were made at 15-day intervals. Canopy height was measured using a measuring tape, and the lateral spread of the plant was visually estimated. To determine leaf traits, at least three mature leaves per plant were collected from three individuals of each species. Upon collection, the leaves were wrapped in damp paper and refrigerated overnight at 4\u0026deg;C. Fresh leaves were weighed on a digital analytic balance (Schimadzu AW320) to the nearest 0.0001 g and then pressed. The leaf area was measured using a CI202 Leaf Area Meter (CID Inc., NW Camas, WA, USA). The leaves were dried at 60\u0026deg;C until they reached a constant weight and then weighed again. The dry matter proportion (%) was calculated by dividing the leaf mean dry weight by the leaf mean fresh weight and multiplying by 100. The specific leaf area (SLA) was calculated by dividing the leaf mean area by the leaf mean dry weight. The CSR strategies of the studied species were determined according to Hodgson et al. (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe cover data of the CSR types underwent a transformation into log\u003csub\u003e10\u003c/sub\u003e (x\u0026thinsp;+\u0026thinsp;1), and the Bray\u0026ndash;Curtis similarity matrix was utilized to determine similarity/dissimilarity. A nonparametric two-way similarity analysis (ANOSIM) was conducted to determine if the differences among the floristic composition of communities were significant using a similarity matrix. The similarity percentage analysis (SIMPER) was employed to demonstrate the contribution of strategy types to differences among communities. SIMPER analysis was used to identify the CSR types that most contribute to similarity/dissimilarity among different communities. In this analysis, the mean abundance of each CSR type (MA), mean similarity of sampled sites (MS), contribution of each CSR type to community similarity (R), percentage contribution of each CSR type to community similarity (CoP), total contribution of CSR types to similarity (CP), mean differences in abundance between communities (MD), ratio of mean differences to standard deviation (D/SD), the contribution of CSR types to dissimilarity among communities (C), and sum of the contribution of CSR types to dissimilarity (CP) were computed. Thus, the average percentages of similarity/dissimilarity between communities were calculated, and it was revealed which species contributes the most to these differences.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Evaluating the soil variables\u003c/h2\u003e\n \u003cp\u003eSoil variables influence plant community and ecosystem dynamics by shaping the conditions under different plant strategy types thrive or struggle. For example, pH affects the nutrient availability, and high or low pH can limit the uptake of essential nutrients while cation exchange capacity reflects soil\u0026rsquo;s ability to retain and supply positively charged ions. Likewise organic matter serves as reservoir of nutrients and supports beneficial microbial activity. To determine the soil properties of the study area, soil samples were collected from the permanent plot areas during November, May, August, and February. The roots of most of plant species distributed in the dune area extend to a depth of 50\u0026ndash;60 cm, while the roots of the species in the backdune are at a depth of 15\u0026ndash;20 cm. Therefore, soil sampling was carried out at depths of 50\u0026ndash;60 cm for the foredune and 15\u0026ndash;20 cm for the backdune, where roots are dense. These samples were air-dried and sifted using a 2-mm sieve before being sent to the laboratory for analysis. The analysis determined soil moisture, organic matter, total nitrogen (N), ammonium (NH\u003csub\u003e4\u003c/sub\u003e-N), nitrate (NO\u003csub\u003e3\u003c/sub\u003e), phosphorus (P), pH, sodium (Na), potassium (K), chlorin (Cl), calcium (Ca), magnesium (Mg), electrical conductivity (EC), and cation exchange capacity (CEC). Following the transformation of the data according to log(x\u0026thinsp;+\u0026thinsp;1), an independent sample T-test was employed to identify differences between the two areas in each season and between seasons for each study area. The methods that were used in soil analysis was summarised in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe methods that were used in soil analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoil Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethod of Measurement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil moisture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGravimetric method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTopp and Ferr\u0026eacute;. 2002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrganic matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoss-on-ignition method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNelson and Sommers \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal nitrogen (TN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKjeldahl method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBremner and Mulvaney \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmmonium (NH4-N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColorimetric method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulvaney \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrate (NO3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon-selective electrode method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKeeney and Nelson, \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus (P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOlsen\u0026apos;s method (colorimetric)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOlsen et al. \u003cspan class=\"CitationRef\"\u003e1954\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrometric method using pH meter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMcLean \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium (Na)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlame photometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichards \u003cspan class=\"CitationRef\"\u003e1954\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium (K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlame photometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichards \u003cspan class=\"CitationRef\"\u003e1954\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChlorine (Cl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTitration method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichards \u003cspan class=\"CitationRef\"\u003e1954\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium (Ca)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTitration method with EDTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLanyon and Heald \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium (Mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtomic absorption spectrophotometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsaac and Johnson \u003cspan class=\"CitationRef\"\u003e1975\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrical conductivity (EC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConductometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRhoades \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCation exchange capacity (CEC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmmonium acetate extraction method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChapman \u003cspan class=\"CitationRef\"\u003e1965\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e\n \u003cp\u003eA canonical correspondence analysis (CCA) analysis was performed to analyse the relationship between the distribution of CSR strategy types and environmental data as the gradient length exceeded 3 standard deviations, indicating unimodal species-environment relationships. The analysis was conducted using Canoco v4.5 package program, incorporating the percentage coverage of each CSR type.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDistribution of CSR strategies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the foredune, 34 species were identified, while in the backdune, the count was significantly higher at 171 species. The backdune exhibited 17 distinct CSR strategy types, compared to 13 in the foredune. The Bray\u0026ndash;Curtis analysis revealed a similarity percentage of 65.38% among the sample plots in the dune area and 75.93% in the backdune area (Figure 1). The ANOSIM results indicated that the differences were statistically significant at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.1% (global R: 0.989). The dissimilarity percentage between the foredune and backdune areas was found to be 67.04%.\u003c/p\u003e\n\u003cp\u003eIn the foredune area, the strategy types C/CR, R, and R/CR contributed the most to similarity, with values of 54.94%, 15.20%, and 10.38%, respectively. In contrast, in the backdune area, the strategy types R/CR, R, and R/SR contributed 26.1%, 14.57%, and 12.29% to similarity, respectively (Table 2). The strategy types that contributed the most to dissimilarity between the foredune and backdune areas were R/CR, S/SR, R, R/SR, CR/CSR, and C/CR, with contributions of 19.59%, 11.45%, 9.69%, 8.87%, 8.75%, and 7.43%, respectively (Table 3).\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;1. Dendrogram illustrating the study areas (F: Foredune, B: Backdune)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. CSR strategy types contributing to similarity among sample plots within each study area (MA: Mean abundance, MS: Mean similarity, R: Rate, C: contribution, CoP: Contribution percentage, CP: Cumulative percentage)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"384\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForedune (Average similarity: 65.38%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eC/CR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e25.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e35.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e54.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e54.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e9.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e15.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e70.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eR/CR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e80.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackdune area (Average similarity: 75.93%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eR/CR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e44.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e19.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e26.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e26.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e14.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e40.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eR/SR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e12.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e52.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eCSR\u003cstrong\u003e\u0026nbsp;s\u003c/strong\u003etrategy types contributing to dissimilarity between foredune and backdune areas (MA1: Mean abundance in foredune, MA2: Mean abundance in backdune, MD: Mean differences, D/SD: Differences/standard deviation, C: Contribution, CP: Cumulative percentage)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"448\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMA2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD/SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup foredune-backdune (Average dissimilarity: 67.04%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003eR/CR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e44.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e13.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e19.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e19.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003eS/SR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e7.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e7.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e31.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e9.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e40.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003eR/SR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e8.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e49.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003eCR/CSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e58.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003eC/CR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e25.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e65.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Results of soil analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean annual soil moisture was 7.24% for the foredune and 13.67% in the backdune area. The foredune soil was identified as strong alkaline and mild saline loamy, whereas mild alkaline and salt-free clay loamy in the backdune, according to the results (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4.\u0026nbsp;Classification of soil types for the study areas.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForedune\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackdune\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue (F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue (T)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e8.68 \u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eStrong alkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e8.00 \u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eMild alkaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e5.96 \u0026plusmn;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eMild saline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.30 \u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSalt-free\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSalinity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.14 \u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eMild saline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.01 \u0026plusmn;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSalt-free\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSaturation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e39.89 \u0026plusmn;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eLoamy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e47.73 \u0026plusmn;4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eClay-loamy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCEC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e7.47 \u0026plusmn;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e42.02 \u0026plusmn;8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe differences in organic matter and pH between the two areas were statistically significant for all seasons (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Ca showed significant differences at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 in winter, spring, and summer, while CaCO\u003csub\u003e3\u003c/sub\u003e differed in winter, summer, and autumn. CEC varied in winter, spring, and autumn; soil moisture in winter and spring, saturation in winter and summer; salinity and electrical conductivity in summer; Mg in winter, and P in spring between the foredune and backdune areas. The results indicated that all soil variables, except for P for both areas and CaCO\u003csub\u003e3\u003c/sub\u003e for the backdune area, exhibited significant seasonal variations in each area (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (Figure 2).\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;2. Seasonal variations in variables within the study areas and differences observed within and between communities (A: autumn, W: winter, Sp: spring, S: summer, F: foredune, and B: backdune). Capital letters indicate differences between communities, while lowercase letters denote differences between seasons within the community. Different letters indicate statistically significant differences (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. The relationships between CSR strategy types and soil variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA canonical correspondence analysis\u0026nbsp;(CCA) was\u0026nbsp;performed\u0026nbsp;to evaluate the relationship between\u0026nbsp;the\u0026nbsp;distribution of CSR strategy\u0026nbsp;types\u0026nbsp;and soil variables.\u0026nbsp;The ordination plot separates the foredune and backdune areas along the environmental gradients, illustrating the distinct soil conditions that define these two areas.\u0026nbsp;The first two axes of\u0026nbsp;the\u0026nbsp;CCA explained 66.6% of\u0026nbsp;the relationships between\u0026nbsp;strategy types and environmental\u0026nbsp;factors\u0026nbsp;(Table 5). The first canonical axis was statistically significant according to\u0026nbsp;the\u0026nbsp;Monte-Carlo Test (\u003cem\u003ep\u003c/em\u003e \u0026lt;\u0026nbsp;0.02, F\u0026nbsp;= 0.764).\u0026nbsp;The ordination analysis\u0026nbsp;differentiated\u0026nbsp;the study areas\u0026nbsp;based on\u0026nbsp;TN, P, K, saturation, organic matter, moisture, Ca, CaCO\u003csub\u003e3\u003c/sub\u003e, Mg, pH, salinity, EC,\u003csub\u003e\u0026nbsp;\u003c/sub\u003eand CEC (Figure 3).\u0026nbsp;Strong positive correlations\u0026nbsp;were\u0026nbsp;observed\u0026nbsp;between the C/CR and SC strategy type and pH, salinity, EC, and CaCO\u003csub\u003e3\u003c/sub\u003e.\u0026nbsp;This suggests that species employing a competitor/stress-tolerator strategy thrive in the foredune areas, where these soil conditions are prominent.\u0026nbsp;On the other hand, R/CR, R/CSR, SR/CSR, S/SR, SC/CSR and CR/CSR strategy types showed strong correlations with TN, P, K, saturation, organic matter, moisture, and CEC.\u0026nbsp;These variables are more abundant in the backdune areas, indicating that ruderal and stress-tolerant species dominate in environments with greater nutrient availability and moisture.\u0026nbsp;Conversely, negative correlations were\u0026nbsp;found\u0026nbsp;between the C/CR strategy type and TN, P, K, saturation, organic matter, moisture, and CEC,\u0026nbsp;and between the CR/CSR, R/CSR, SR/CSR, and S/SR strategy types and pH, salinity, EC, and CaCO\u003csub\u003e3\u0026nbsp;\u003c/sub\u003e(Figure\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Results of\u0026nbsp;canonical correspondence analysis\u0026nbsp;(CCA)\u0026nbsp;of\u0026nbsp;community variables.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"555\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Total inertia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp;Eigenvalues\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e: 0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp;Species-environment correlations\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e: 1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 403px;\"\u003e\n \u003cp\u003e\u0026nbsp;Cumulative percentage variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp; of species data\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e: 43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp; 64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp; 73.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp; 81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp; of species-environment relation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e: 44.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp; 66.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp; 73.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp; 83.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp;Sum of all eigenvalues\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp;Sum of all canonical eigenvalues\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eDune areas, which constitute 20% of the earth\u0026rsquo;s surface and 99% of desert areas (Tsoar \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), are of significant importance despite their small coverage. They are among the most threatened ecosystems due to their high levels of ecological diversity and the variety of ecosystem services they provide (Defeo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Everard et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hesp and Martinez 2007). Coastal ecosystems buffer stressful conditions such as soil erosion and saltwater intrusion, providing crucial support for inland areas by reducing stress (Hanley et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In these ecosystems, developing effective conservation strategies requires an understanding of their ecosystem dynamics. Identifying functional groups of vegetation components reduces complexity and enables better comparison of vegetation types. CSR strategy types are considered one of the most suitable methods for interpreting plant communities (Zelnik and Čarni \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study area, the representation of intermediate strategies rather than main strategies (CSR) appears to be due to the inability to reach the optimal strategy in all characters due to allocation processes and trade-offs in the life cycle (Mustard et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This suggests that there isn\u0026rsquo;t a single evolutionary solution for various environmental conditions (Frenetto-Dussaoult et al. 2012; Cicarelli 2015). Conversely, the distribution of CSR strategies was more homogeneous in the backdune area than foredune, with a similarity rate of 75.93% vs. 65.38%. This could be attributed to increased human activities or decreased dune flexibility (Garcia-Mora et al. 2000). Other studies have also demonstrated that plant distribution in mobile dunes is more heterogeneous and exhibits a random pattern (Conti et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The strategy types R/CR, S/SR, R, R/SR, CR/CSR, and C/CR contributed the most to the dissimilarity between the foredune and backdune areas. It appears that environments with stress conditions can either reduce competition and foster facilitative interactions or intensify competition (Bertnes and Calaway 1994; Berkowitz et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Brunjberg et al. 2012; Conti et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Hart and Marshall (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), species are less tolerant of competition in harsh environments, leading to more straightforward competitive exclusion. In our study area, the higher number of competitive species in the foredune area, which presents more challenging conditions, supports this perspective.\u003c/p\u003e \u003cp\u003eThe heterogeneity in stressful environments is a key factor in the competition or facilitation relationships between plants (Zuo et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The heterogeneity of the foredune area in our study is likely due to the spatial distribution of soil resources or disturbance. However, the fact that at least 25% of the plants in the foredune exhibit competitive primary strategies suggests that the level of disturbance, if present, is low. This heterogeneity may be attributed to the presence of rivers flowing into the sea in the area, as well as differences in the spatial effects of ecological factors such as wind and salt spray in dune areas.\u003c/p\u003e \u003cp\u003eSpatial variations in soil properties along successional gradients play a crucial role in vegetation cover change, and in turn, changes in vegetation cover are likely to influence the spatial heterogeneity of soil properties (Olff et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Zuo et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Spatial heterogeneity of soil nutrient pools at different scales is a common characteristic of arid and semi-arid ecosystems (Hook et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Schlesinger and Pilmanis \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Therefore, soil heterogeneity appears to be more influential. Almost all of the soil variables studied exhibited temporal variation. However, OM, moisture, CaCO\u003csub\u003e3\u003c/sub\u003e, Ca, pH, salinity, EC, and CEC were identified as the most influential soil factors on the distribution of CSR strategy types along the gradient. OM, moisture, and Ca were higher in the backdune, while pH, salinity, EC, CEC, and CaCO\u003csub\u003e3\u003c/sub\u003e were higher in the foredune. Although many studies have shown that nitrogen is a significant limiting factor in plant growth (Willis et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1959\u003c/span\u003e; Willis \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1963\u003c/span\u003e; Kachi and Hirose \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Dougherty et al. 1990; Olf et al. 1993), no significant difference was found between the two regions in the present study. However, a statistically significant difference was found between the foredune and backdune in terms of OM. This suggests that the amount of OM is not synonymous with nutrient availability, as shown in other studies (S\u0026yacute;kora et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lane et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The lack of difference in terms of nitrogen forms, despite a significant difference in terms of OM, may be due to the rapid uptake of nitrogen forms from the soil by plant species in the backdune area or to faster mineralization in the foredune. In their study, Zhu et al. (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that the presence of less water in coarse-textured loamy soils increased microbial diversity by reducing competition between bacteria and providing more niches for microbial associations. They also stated that the presence of lime in the soil slows down decomposition processes because it provides larger surfaces for nutrients. Considering that nitrogen-rich environments favor competitive species (Vitousek and Walker \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Berendse \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), the higher representation of competitive strategy in the foredune seems to support the rapid mineralization in this area. However, this issue warrants further research.\u003c/p\u003e \u003cp\u003eConversely, soil texture exerts a significant influence on pH. While some studies report an increase in pH as species diversity increases (Isermann \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), others show a tendency for pH to decrease in areas with higher species richness (Lane et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the present study, pH tended to decrease toward the backdune, where species richness was higher. The clay-loamy soil structure in the backdune leads to a lower pH environment due to the rapid leaching of carbonate and salt. The contrasting variation of CaCO\u003csub\u003e3\u003c/sub\u003e and pH throughout the year suggests that CaCO\u003csub\u003e3\u003c/sub\u003e in the foredune favours a higher pH due to its buffering effect. According to our study and others, OM and acidification are significant drivers of succession (Grime \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Berendse et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; S\u0026yacute;kora et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lane et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Angiolini et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Unlike CaCO\u003csub\u003e3\u003c/sub\u003e, Ca is more abundant in the backdune. As one of the most important exchangeable cations in growth substrates, calcium can affect nutrient availability. Therefore, it plays a crucial role in nutrient uptake and could favour plant settlement (Pausas and Austin \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe seasonal and spatial dynamics observed by Feagin and Wu (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) on the Gulf of Mexico\u0026rsquo;s coastal dunes. They reported that temporal changes in soil moisture and salinity led to shifts in plant strategy dominance across the dune ecosystem. Similarly, in our study, seasonal fluctuations in soil variables such as moisture, salinity, and organic matter influenced the distribution of CSR strategies, with competitive species remaining dominant in foredunes during drier seasons, while ruderal species thrived in backdunes during wetter periods. These findings demonstrate how temporal environmental changes drive plant community dynamics in coastal dune systems worldwide.\u003c/p\u003e \u003cp\u003eThis study highlights significant differences in the distribution of plant strategy types (CSR strategies: competitor, stress tolerator, and ruderal) between foredune and backdune areas of a coastal dune ecosystem. In the foredune, competitive and stress-tolerant species (C/CR and SC strategies) dominated, strongly correlating with high salinity, pH, electrical conductivity (EC), and CaCO₃ levels. Conversely, in the backdune, ruderal and competitive species (R/CR, R/CSR, SR/CSR, S/SR, SC/CSR and CR/CSR strategies) were more prevalent due to higher nutrient availability, moisture, and organic matter. The Canonical Correspondence Analysis (CCA) revealed that environmental gradients, particularly soil properties such as pH, salinity, organic matter, and moisture, played a crucial role in influencing the spatial distribution of these strategies. Seasonal variations in soil properties further impacted species distribution, with ruderal species thriving in backdunes during wetter periods and competitive and stress-tolerant species in foredunes adapting to fluctuations in salinity and moisture. Additionally, the study suggests that plant species in backdunes contribute to soil modifications over time, increasing organic matter, which favors the success of ruderal and competitive species. These findings emphasize the importance of both environmental stress and nutrient availability in shaping plant communities in coastal dune ecosystems.\u003c/p\u003e \u003cp\u003eThe results of this study support our initial hypotheses that soil characteristics play a critical role in determining the distribution and success of different CSR strategy types across foredune and backdune areas. As hypothesized, stress-tolerant species were more prevalent in the foredune areas where soil conditions, including higher salinity and electrical conductivity, create a more stressful environment for plant growth. Conversely, backdune areas, characterized by lower salinity and more nutrient-rich soil, supported a greater abundance of ruderal, stress tolerant and competitive species. This aligns with our expectation that nutrient availability and soil stability would foster species richness and diversity in the backdunes. Additionally, seasonal variations in soil moisture and organic matter further influenced the distribution of plant strategies, reinforcing the importance of temporal dynamics in these ecosystems. The observed plant-soil feedbacks in the backdune, where plant communities contributed to the enrichment of soil organic matter, also provide evidence for the hypothesized positive feedback loop that promotes the establishment of ruderal and competitive species over time. These findings highlight the complex interactions between soil properties and plant strategies, emphasizing the role of both abiotic stress and disturbance in shaping plant community composition in coastal dune ecosystems.\u003c/p\u003e \u003cp\u003eSalinity is a key factor influencing environmental gradients in dune ecosystems. High salinity conditions can also subject plants to drought stress due to elevated osmotic pressure (Maun \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Soil moisture in the foredune diminishes with the onset of the growing season, while salinity and EC peak in the summer. These two variables were found to strongly correlated with the C/CR strategy. Similar results were reported by Son et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The more barren areas in the foredune lead to greater water loss through evaporation due to the higher temperature effect (Ishikawa et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; \u0026Aacute;lvarez-Rogel et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), resulting in increased salinity. Thus, salinity acts as a crucial filter in the foredune area (Gallego-Fern\u0026aacute;ndez and Martinez 2011). However, these variables remain constant throughout the year in the backdune. The higher vegetation cover in the backdune could be a primary reason for this, as it prevents water loss by evaporation (Liu et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The elevated EC during the growing season in the foredune may be attributed to the decomposition and accumulation of soluble salts during this period (Su et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, the foredune area presents a more dynamic scenario in terms of soil reactions. Initially, salinity, EC, CaCO\u003csub\u003e3\u003c/sub\u003e, and pH play a significant role in the formation of plant communities in the gradient from marine to terrestrial environments. However, OM, moisture and nutrient become influential as we move toward inland environments. An increase in competitive species in terrestrial environments is recognized as the climax stage of succession. Our study reveals that spatial effects play a part in reaching this climax stage. Even in the absence of disturbance, the climax stage can be achieved in areas defined as stressful environments, with the settlement of species that best adapt to the area\u0026rsquo;s dynamics in the final stage of succession.\u003c/p\u003e \u003cp\u003eCicarelli (2015) in Mediterranean and Brunbjerg et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in Denmark dune ecosystems found that stress tolerant species adapted to extreme conditions were dominant in foredunes. Pugnaire and Luque (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) noted that facilitation rather than competition tends to dominate in high stress environments, allowing for the coexistence of multiple stress-tolerant species. In contrast, our study shows that competitive species are more prevalent in the foredune area, despite the high-stress conditions. This discrepancy may be due to regional differences in soil properties or species pools. Specifically, the relatively high pH and salinity levels in our study area may create conditions that allow competitive species to thrive, a finding also observed by Son et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Lee et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), where competitor species were found in higher abundance in areas with elevated pH and salinity. While some studies report that physical stress and disturbance conditions limit the development of C-strategy-dominated plant communities (Cicarelli 2015), others show that competitors dominate in the foredune and ruderality increases in the backdune (Lee et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, we determined a shift from a competitive strategy to a ruderal and stress-tolerant strategy in the foredune-backdune gradient. The fact that our study area is located in the Black Sea Region, where sea salt stress is less, may be one of the reasons for the development of a plant community where the C-strategy is prioritized. This indicates that the relative importance of destruction and stress under different environmental conditions varies according to local environmental conditions (Petrů et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The presence of more species in the backdune area suggests that facilitative effects increase in stressful environments (Montesinos \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and that the direction of interaction changes from competitive to facilitative (Pugnaire and Luque \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecies employing the C/CR strategy, which are most prevalent in the foredune, are characterized by a larger leaf area and significantly lower leaf dry matter content compared to species with other strategies. These species exhibit high nitrogen uptake capacity, high plasticity in aboveground/underground allocation, and can endure harsh conditions such as salt spray and repeated soil degradation. Limited by abiotic factors, foredune plant communities form a distinct dominant hierarchy (Mustard et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Del Vecchio et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Dominant species create favorable ecological conditions for other species and contribute to ecosystem function and the stabilization of ecosystem processes (Ellison et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fantinato et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). They can also mitigate stressful conditions due to marine impact, rendering the backdune area more temperate for plants (Borsje et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; De Battisti and Griffin \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These burial-resistant species possess deep root systems and play a crucial role in the structuring and monitoring of dune areas (Garcia-Mora et al. 1999; Garcia-Mora et al. 2000). The over-representation of R/CR, R/CSR, SR/CSR, S/SR, SC/CSR and CR/CSR strategies in the backdune area indicates that disturbance is a significant factor in this area. Ruderals and stress-tolerant species have evolved to maintain high levels of aboveground nitrogen by partially limiting their aboveground subsoil allocation. This is because they need to maximize seed quantity to ensure reproduction in these areas (Mustard et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe broader ecological and conservation implications of the findings from this study are significant for managing and preserving coastal dune ecosystems. The study\u0026rsquo;s results highlight how soil factors such as pH, CEC, organic matter, and salinity play a crucial role in determining the distribution of plant strategy types (CSR strategies). These insights are particularly important for ecosystem management, as they demonstrate that different parts of a dune ecosystem (foredune vs. backdune) require different conservation approaches based on soil characteristics.\u003c/p\u003e \u003cp\u003eFor example, the dominance of stress-tolerant species in high-salinity, low-nutrient foredune areas suggests that these regions may be more vulnerable to environmental disturbances like climate change or human activities, which could further exacerbate stress conditions. Understanding that competitive species thrive in richer backdune soils with higher organic matter and moisture helps guide restoration efforts in degraded areas, as management strategies can focus on improving soil fertility to promote biodiversity and resilience.\u003c/p\u003e \u003cp\u003eThe comparison with similar studies also emphasizes the importance of considering regional differences, such as pH's role in limiting or supporting species diversity. Conservation strategies must, therefore, be tailored to the specific environmental conditions of each dune system. The findings reinforce the need for targeted interventions that consider both soil health and plant adaptation strategies to maintain the stability and function of coastal dunes. Furthermore, this research can inform predictive models for how dune ecosystems might respond to future environmental changes, helping to mitigate biodiversity loss and protect ecosystem services such as erosion control and habitat provision.\u003c/p\u003e \u003cp\u003eThe paper provides a thorough analysis of plant-soil interactions in coastal dune ecosystems, highlighting how environmental gradients like salinity, pH, and nutrient availability affect the distribution of CSR plant strategies. The use of Canonical Correspondence Analysis (CCA) strengthens the results by clearly linking soil characteristics to plant strategies. Additionally, the inclusion of seasonal and spatial variability offers a dynamic view of these ecosystems over time. The study is highly relevant for understanding plant adaptations in stressed environments, and its insights into plant-soil feedbacks in backdunes are particularly valuable for long-term ecological understanding. The geographic focus is narrow, limiting the generalizability of the findings to other ecosystems. The study also relies on correlation-based analyses without experimental manipulation, making it difficult to establish causality between soil characteristics and plant strategies. Additionally, the focus is limited to CSR strategies, without considering a broader range of functional traits that could provide further insights into plant adaptation. Future research should expand to include other coastal dune ecosystems to test the generalizability of the findings. Experimental manipulation of soil variables, such as salinity and nutrients, would help establish causality. A broader analysis of plant functional traits and long-term monitoring of plant-soil dynamics would provide deeper insights into ecological processes. Investigating the impacts of climate change on these interactions and linking findings to ecosystem services, such as dune stabilization, would also increase the practical value of the research for conservation and management.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cem\u003eThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study was supported from the TUBITAK (The Scientific and Technological Research Council of Turkey, project no. 114O796).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcosta, A. T. R., M. L. Carranza, and C. F. Izzi. 2005. Are there habitats that contribute best to plant species diversity in coastal dunes? \u003cem\u003eBiodiversity and Conservation\u003c/em\u003e 14(11): 2609\u0026ndash;2626. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10531-008-9421-5\u003c/span\u003e\u003cspan address=\"10.1007/s10531-008-9421-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcosta, A., C. F. Izzi, and A. Stanisci. 2006. Comparison of native and alien plant traits in Mediterranean coastal dunes. \u003cem\u003eCommunity Ecology\u003c/em\u003e 7(1): 35\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1556/ComEcJ.2006.1.4\u003c/span\u003e\u003cspan address=\"10.1556/ComEcJ.2006.1.4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aacute;lvarez-Rogel, J., J. J. Mart\u0026iacute;nez-S\u0026aacute;nchez, L. C. Bl\u0026aacute;zquez, and C. M. M. Semitiel. 2006. A conceptual model of salt marsh plant distribution in coastal dunes of southeastern The Society of Wetland Scientist. 26(3): 703\u0026ndash;717. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1672/0277-5212(2006)26\u003c/span\u003e\u003cspan address=\"10.1672/0277-5212(2006)26\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngiolini, C., G. Bonari, and M. Landi. 2018. Focal plant species and soil factors in Mediterranean coastal dunes: an undisclosed liaison? Estuarine, Coastal and Shelf Science. 211: 248\u0026ndash;258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecss.2017.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.ecss.2017.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbour, M. G., T. M. De Jong, and B. M. Pavlik. 1985. Marine beach and dune plant communities. In: \u003cem\u003ePhysiological Ecology of North American Plant Communities\u003c/em\u003e, eds. Chabot, B.F., Mooney, H.A., 296\u0026ndash;322. Dordrecht, Netherlands,.Springer.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerendse, F. 1990. Organic matter accumulation and nitrogen mineralization during secondary succession in heathland ecosystems. \u003cem\u003eJournal of Ecology\u003c/em\u003e 78(2): 413\u0026ndash;427. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2261121\u003c/span\u003e\u003cspan address=\"10.2307/2261121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerendse, F., E. J. Lammerts, and H. Olff. 1998. Soil organic matter accumulation and its implications for nitrogen mineralization and plant species composition during succession in coastal dune slacks. \u003cem\u003ePlant Ecology\u003c/em\u003e 137(1): 71\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2261121\u003c/span\u003e\u003cspan address=\"10.2307/2261121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerm\u0026uacute;dez, R., and R. Retuerto. 2014. Together but different: co-occurring dune plant species differ in their water-and nitrogen-use strategies. \u003cem\u003eOecologia\u003c/em\u003e 174(3): 651\u0026ndash;663. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00442-013-2820-7\u003c/span\u003e\u003cspan address=\"10.1007/s00442-013-2820-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerkowitz, A. R., C. D. Canha, and V. R. Kelly. 1995. Competition vs. facilitation of tree seedling growth and survival in early successional communities. \u003cem\u003eEcology\u003c/em\u003e 76(4): 1156\u0026ndash;1168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1940923\u003c/span\u003e\u003cspan address=\"10.2307/1940923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertness, M. D., and R. Callaway. 1994. Positive interactions in communities. \u003cem\u003eTREE\u003c/em\u003e 9(5): 191\u0026ndash;193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0169-5347(94)90088-4\u003c/span\u003e\u003cspan address=\"10.1016/0169-5347(94)90088-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorsje, B. W., B. K. van Wesenbeeck, F. Dekke, P. Paalvast, T. J. Bouma, M. M. van Katwijk, and M. B. de Vries. 2011. How ecological engineering can serve in coastal protection. \u003cem\u003eEcological Engineering\u003c/em\u003e 37(2): 113\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoleng.2010.11.027\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoleng.2010.11.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBornhofen, S., S. Barot, and C. Lattaud. 2011. The evolution of CSR life-history strategies in a plant model with explicit physiology and architecture. \u003cem\u003eEcological Modelling\u003c/em\u003e 222(1): 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolmodel.2010.09.014\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolmodel.2010.09.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBremner, J. M., and C. S. Mulvaney. 1982. Nitrogen-Total. In: \u003cem\u003eMethods of Soil Analysis: Part 2 - Chemical and Microbiological Properties\u003c/em\u003e, eds. Page, A.L., Miller, R.H., Keeney, D.R., 595\u0026ndash;624. Madison, WI., American Society of Agronomy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunbjerg, A. K., F. Borchsenius, W. L. Eiserhardt, R. Ejrn\u0026aelig;s, and J. C. Svenning. 2012. Disturbance drives phylogenetic community structure in coastal dune vegetation. \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 23(6): 1082\u0026ndash;1094. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1654-1103.2012.01433.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1654-1103.2012.01433.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarboni, M., M. L. Carranza, and A. Acosta. 2009. Assessing conservation status on coastal dunes: a multiscale approach. \u003cem\u003eLands and Urban Planning\u003c/em\u003e 91(1): 17\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landurbplan.2008.11.004\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2008.11.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapman, H. D. 1965. Cation-exchange capacity. In \u003cem\u003eMethods of Soil Analysis: Part 2 - Chemical and Microbiological Properties\u003c/em\u003e, ed. C. A. Black. 891\u0026ndash;901. Madison, WI.: American Society of Agronomy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChelli, S., M. Marignani, E. Barni, A. Petraglia, G. Puglielli, and C. Wellstein et al. 2019. Plant\u0026ndash;environment interactions through a functional traits perspective: a review of Italian studies. \u003cem\u003ePlant Biosystems - An International Journal Dealing with all Aspects of Plant Biology\u003c/em\u003e 153(6): 853\u0026ndash;869. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/11263504.2018.1559250\u003c/span\u003e\u003cspan address=\"10.1080/11263504.2018.1559250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiccarelli, D. 2015. Mediterranean coastal dune vegetation: are disturbance and stress the key selective forces that drive the psammophilous succession? Estuarine, Coastal and Shelf Science. 165: 247\u0026ndash;253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecss.2015.05.023\u003c/span\u003e\u003cspan address=\"10.1016/j.ecss.2015.05.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiccarelli, D., and C. Bona. 2022. Exploring the functional strategies adopted by coastal plants along an ecological gradient using morpho-functional traits. \u003cem\u003eEstuarine Coastal and Shelf Science\u003c/em\u003e 45(1): 114\u0026ndash;129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12237-021-00945-y\u003c/span\u003e\u003cspan address=\"10.1007/s12237-021-00945-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConti, L., F. de Bello, J. Lepš, A. T. R. Acosta, and M. Carboni. 2017. Environmental gradients and micro-heterogeneity shape fine‐scale plant community assembly on coastal dunes. \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 28(4): 762\u0026ndash;773. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jvs.12533\u003c/span\u003e\u003cspan address=\"10.1111/jvs.12533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCornwell, W. K., k D.W. Schwil, and D. D. Ackerly. 2006. A trait-based test for habitat filtering: convex hull volume. \u003cem\u003eEcology\u003c/em\u003e 87(6): 1465\u0026ndash;1471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/0012-9658(2006)87\u003c/span\u003e\u003cspan address=\"10.1890/0012-9658(2006)87\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, P. 1965\u0026ndash;1985. \u003cem\u003eFlora of Turkey and the East Aegean Islands\u003c/em\u003e. Volume 1\u0026ndash;9 Edinburgh University Press. Edinburg.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, P. H., R. R. Mill, and K. Tan. 1988. \u003cem\u003eFlora of Turkey and the East Aegean Islands\u003c/em\u003e. vol. 10 Edinburgh: Edinburgh University.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Battisti, D., and J. N. Griffin. 2020. Below-ground biomass of plants, with a key contribution of buried shoots, increases foredune resistance to wave swash. \u003cem\u003eAnnals of Botany\u003c/em\u003e 125(2): 325\u0026ndash;334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aob/mcz125\u003c/span\u003e\u003cspan address=\"10.1093/aob/mcz125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDefeo, O., A. McLachlan, D. S. Schoeman, T. A. Schlacher, J. Dugan, A. Jones, M. Lastra, and F. Scapini. 2009. Threats to sandy beach ecosystems: a review. \u003cem\u003eEstuarine Coastal and Shelf Science\u003c/em\u003e 81(1): 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecss.2008.09.022\u003c/span\u003e\u003cspan address=\"10.1016/j.ecss.2008.09.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Vecchio, S., E. Mattana, T. Ulian, and G. Buffa. 2021. Functional seed traits and germination patterns predict species coexistence in Northeast Mediterranean foredune communities. \u003cem\u003eAnnals of Botany\u003c/em\u003e 127(3): 361\u0026ndash;370. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aob/mcaa186\u003c/span\u003e\u003cspan address=\"10.1093/aob/mcaa186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026iacute;az Barradas, M. C., M. Zunzunegui, R. Tirado, F. Ain-Lhout, and F. Garc\u0026iacute;a Novo. 1999. Plant functional types and ecosystem function in Mediterranean shrubland. \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 10(5): 709\u0026ndash;716. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3237085\u003c/span\u003e\u003cspan address=\"10.2307/3237085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026iacute;az, S., and M. Cabido. 1997. Plant functional types and ecosystem function in relation to global change. \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 8(4): 463\u0026ndash;474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3237198\u003c/span\u003e\u003cspan address=\"10.2307/3237198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDı́az, S., and M. Cabido. 2001. Vive la diff\u0026eacute;rence: plant functional diversity matters to ecosystem processes. \u003cem\u003eTREE\u003c/em\u003e 16(11): 646\u0026ndash;655. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0169-5347(01)02283-2\u003c/span\u003e\u003cspan address=\"10.1016/S0169-5347(01)02283-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, X. W., X. K. Zhang, X. L. Bao, and J. K. Wang. 2009. Spatial distribution of soil nutrients after the establishment of sand-fixing shrubs on sand dune. \u003cem\u003ePlant Soil and Environment\u003c/em\u003e 55(7): 288\u0026ndash;294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17221/13/2009-PSE\u003c/span\u003e\u003cspan address=\"10.17221/13/2009-PSE\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDougherthy, K. M., .IA. Mendelsohn, and F. J. Monteferrante. 1990. Effect of nitrogen, phosphorus and potassium additions on plant biomass and soil nutrient content of a swale barrier strand community in Louisiana. \u003cem\u003eAnnals of Botany\u003c/em\u003e 66(3): 265\u0026ndash;271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/oxfordjournals.aob.a088024\u003c/span\u003e\u003cspan address=\"10.1093/oxfordjournals.aob.a088024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllison, A. M., M. S. Bank, and B. D. Clinton et al. 2005. Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. \u003cem\u003eFrontiers in Ecology and Environment\u003c/em\u003e 3(9): 479\u0026ndash;486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/1540-9295(2005)003\u003c/span\u003e\u003cspan address=\"10.1890/1540-9295(2005)003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEverard, M., L. Jones, and B. Watts. 2010. Have we neglected the societal importance of sand dunes? An ecosystem services perspective. \u003cem\u003eAquatic Conservation\u003c/em\u003e 20(4): 476\u0026ndash;487. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/aqc.1114\u003c/span\u003e\u003cspan address=\"10.1002/aqc.1114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFantinato, E., S. Del Vecchio, G. Silan, and G. Buffa. 2018. Pollination networks along the sea-inland gradient reveal landscape patterns of keystone plant species. \u003cem\u003eScientific Reports\u003c/em\u003e 8: 1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-33652-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-33652-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeagin, R. A., and X. B. Wu. 2007. The spatial patterns of functional groups and successional direction in a coastal dune community. \u003cem\u003eRangeland Ecology and Management\u003c/em\u003e 60(4): 417\u0026ndash;425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2111/1551-5028(2007)60\u003c/span\u003e\u003cspan address=\"10.2111/1551-5028(2007)60\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrenette-Dussault, C., B. Shipley, J. F. L\u0026eacute;ger, D. Meziane, and Y. Hingrat. 2012. Functional structure of an arid steppe plant community reveals similarities with Grime's C‐S‐R theory. \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 23(2): 208\u0026ndash;222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1654-1103.2011.01350.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1654-1103.2011.01350.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallego-Fern\u0026aacute;ndez, J. B., and M. L. Mart\u0026iacute;nez. 2011. Environmental filtering and plant functional types on Mexican foredunes along the Gulf of Mexico. \u003cem\u003eEcoscience\u003c/em\u003e 18(1): 52\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2980/18-1-3376\u003c/span\u003e\u003cspan address=\"10.2980/18-1-3376\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Mora, M. R., J. B. Gallego‐Fern\u0026aacute;ndez, and F. Garc\u0026iacute;a‐Novo. 1999. Plant functional types in coastal foredunes in relation to environmental stress and disturbance. \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 10(1): 27\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3237157\u003c/span\u003e\u003cspan address=\"10.2307/3237157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Mora, M. R., J. B. Gallego-Fern\u0026aacute;ndez, and F. Garc\u0026iacute;a-Novo. 2000. Plant diversity as a suitable tool for coastal dune vulnerability assessment. \u003cem\u003eJournal of Coastal Research\u003c/em\u003e 16(4): 990\u0026ndash;995.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrime, J. P. 1973. Competitive exclusion in herbaceous vegetation. \u003cem\u003eNature\u003c/em\u003e 242(5396): 344\u0026ndash;347. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/242344a0\u003c/span\u003e\u003cspan address=\"10.1038/242344a0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrime, J. P. 1974. Vegetation classification by reference to strategies. \u003cem\u003eNature\u003c/em\u003e 250(5461): 26\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/250026a0\u003c/span\u003e\u003cspan address=\"10.1038/250026a0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrime, J. P. 1977. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. \u003cem\u003eAmerican Naturalist\u003c/em\u003e 111(982): 1169\u0026ndash;1194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/283244\u003c/span\u003e\u003cspan address=\"10.1086/283244\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrime, J. P. 2001. \u003cem\u003ePlant Strategies, Vegetation Processes, and Ecosystem Properties\u003c/em\u003e. 2nd Edition. Wiley.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrime, J. P., K. Thompson, R. Hunt, J. G. Hodgson, and J. H. C. Cornelissen et al. 1997. Integrated screening validates primary axes of specialisation in plants. \u003cem\u003eOikos\u003c/em\u003e 79(2): 259\u0026ndash;281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3546011\u003c/span\u003e\u003cspan address=\"10.2307/3546011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026uuml;ner, A., N. \u0026Ouml;zhatay, T. Ekim, and K. H. C. Başer. 2000. \u003cem\u003eFlora of Turkey and the East Aegean Islands\u003c/em\u003e. vol. 11 Edinburgh: Edinburgh University.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026uuml;ner, A., S. Aslan, T. Ekim, M. Bural, and M. T. Baba\u0026ccedil;. 2012. \u003cem\u003eT\u0026uuml;rkiye Bitkileri Listesi (Damarlı Bitkiler)\u003c/em\u003e. İstanbul, Turkey, Nezahat G\u0026ouml;kyiğit Botanik Bah\u0026ccedil;esi ve Flora Araştırmaları Derneği.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanley, M. E., T. J. Bouma, and H. L. Mossman. 2020. The gathering storm: optimizing management of coastal ecosystems in the face of a climate-driven threat. \u003cem\u003eAnnals of Botany\u003c/em\u003e 125(2): 197\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aob/mcz204\u003c/span\u003e\u003cspan address=\"10.1093/aob/mcz204\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHart, S. P., and D. J. Marshall. 2013. Environmental stress, facilitation, competition, and coexistence. \u003cem\u003eEcology\u003c/em\u003e 94(12): 2719\u0026ndash;2731. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/12-0804.1\u003c/span\u003e\u003cspan address=\"10.1890/12-0804.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHesp, P. A. 1991. Ecological processes and plant adaptations on coastal dunes. Journal of Arid Environment. 21(2): 165\u0026ndash;191. https://doi.org/0.1016/S0140-1963(18)30681-5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHesp, P. A., and M. L. Mart\u0026iacute;nez. 2007. Disturbance processes and dynamics in coastal dunes. In \u003cem\u003ePlant Disturbance Ecology: The Process and The Response\u003c/em\u003e, ed. E. A. Johnson, and K. Miyanishi. 215\u0026ndash;247. USA, Elsevier.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHodgson, J. G., P. J. Wilson, R. Hunt, J. P. Grime, and K. Thompson. 1999. Allocating CSR plant functional types: a soft approach to a hard problem. \u003cem\u003eOikos\u003c/em\u003e 85(2): 282\u0026ndash;294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3546494\u003c/span\u003e\u003cspan address=\"10.2307/3546494\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHook, P. B., I. C. Burke, and W. K. Lauenroth. 1991. Heterogeneity of soil and plant N and C associated with individual plants and openings in North American shortgrass steppe. \u003cem\u003ePlant and Soil\u003c/em\u003e 138(2): 247\u0026ndash;256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00012252\u003c/span\u003e\u003cspan address=\"10.1007/BF00012252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsaac, R. A., and W. C. Johnson. 1975. Determination of calcium, magnesium by atomic absorption. In \u003cem\u003eSoil Science Society of America Journal: Analytical Methods for Soil and Plant Tissue\u003c/em\u003e, ed. L. M. Walsh. 489\u0026ndash;510. Madison, WI.: Soil Science Society of America.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsermann, M. 2005. Soil pH and species diversity in coastal dunes. \u003cem\u003ePlant Ecology\u003c/em\u003e 178(1): 111\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11258-004-2558-8\u003c/span\u003e\u003cspan address=\"10.1007/s11258-004-2558-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshikawa, S. I., A. Furukawa, and T. Oikawa. 1995. Zonal plant distribution and edaphic and micrometeorological conditions on a coastal sand dune. \u003cem\u003eEcological Research\u003c/em\u003e 10(3): 259\u0026ndash;266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02347851\u003c/span\u003e\u003cspan address=\"10.1007/BF02347851\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKachi, N., and T. Hirose. 1983. Limiting nutrients for plant growth in coastal sand dune soils. \u003cem\u003eJournal of Ecology\u003c/em\u003e 71(3): 937\u0026ndash;944. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.2307/2259603\u003c/span\u003e\u003cspan address=\"10.2307/2259603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeddy, P. A. 1992. Assembly and response rules: two goals for predictive community ecology. \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 3(2): 157\u0026ndash;164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.2307/3235676\u003c/span\u003e\u003cspan address=\"10.2307/3235676\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeeney, D. R., and D. W. Nelson. 1982. Nitrogen-Inorganic forms. In \u003cem\u003eMethods of Soil Analysis: Part 2 - Chemical and Microbiological Properties\u003c/em\u003e, ed. R. H. Miller, and D. R. Keeney. 643\u0026ndash;698. Madison, WI.: American Society of Agronomy. Pa\u003cem\u003eg\u003c/em\u003ee, A.L..\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLammerts, E. J., A. P. Grootjans, and H. G. J. Vander Hagen. 2004. Coastal Dune Vegetation Dynamics: The Impact of Erosion and Deposition on Stabilized and Unstabilized Dunes. \u003cem\u003eApplied Vegetation Science\u003c/em\u003e 7(1): 23\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLane, C., S. J. Wright, J. Roncal, and J. Maschinski. 2008. Characterizing environmental gradients and their influence on vegetation zonation in a subtropical coastal sand dune system. \u003cem\u003eJournal of Coastal Research\u003c/em\u003e 24(3): 213\u0026ndash;224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.2112/07-0853.1\u003c/span\u003e\u003cspan address=\"10.2112/07-0853.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanyon, L. E., and W. R. Heald. 1982. Magnesium and calcium determination. In \u003cem\u003eMethods of Soil Analysis: Part 2 - Chemical and Microbiological Properties\u003c/em\u003e, ed. R. H. Miller, and D. R. Keeney. 247\u0026ndash;262. Madison, WI.: American Society of Agronomy. Pa\u003cem\u003eg\u003c/em\u003ee, A.L..\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLavorel, S., S. McIntyre, J. Landsberg, and T. D. A. Forbes. 1997. Plant functional classifications: from general groups to specific groups based on response to disturbance. \u003cem\u003eTREE\u003c/em\u003e 12(12): 474\u0026ndash;478. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0169-5347(97)01219-6\u003c/span\u003e\u003cspan address=\"10.1016/S0169-5347(97)01219-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J. S., D. H. Son, S. H. Lee, H. H. Myeong, J. S. Cho, J. C. Lee, J. Y. Lee, C. S. Park, and J. W. Kim. 2020. Canonical correspondence analysis ordinations and competitor, stress tolerator, and ruderal strategies of coastal dune plants in South Korea. \u003cem\u003eJournal of Coastal Research\u003c/em\u003e 36(3): 528\u0026ndash;535. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2112/JCOASTRES-D-19-00106.1\u003c/span\u003e\u003cspan address=\"10.2112/JCOASTRES-D-19-00106.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Z., M. Notaro, and R. Gallimore. 2010. Indirect vegetation-soil moisture feedback with application to Holocene North Africa climate. \u003cem\u003eGlobal Change Biology\u003c/em\u003e 16(6): 1733\u0026ndash;1743. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2009.02087.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2009.02087.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacedo, J. A., P. Alves, \u0026Acirc;. Lomba, J. Vicente, R. Henriques, H. Granja, and J. Honrado. 2010. On the interest of plant functional classifications to study community-level effects of increased disturbance on coastal dune vegetation. \u003cem\u003eActa Botanica Gallica\u003c/em\u003e 157(2): 305\u0026ndash;315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/12538078.2010.10516208\u003c/span\u003e\u003cspan address=\"10.1080/12538078.2010.10516208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaun, M. A. 2009. \u003cem\u003eThe biology of coastal sand dunes\u003c/em\u003e. New York: Oxford University Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayfield, M. M., M. F. Boni, G. C. Daily, and D. Ackerly. 2005. Species and functional diversity of native and human-dominated plant communities. \u003cem\u003eEcology\u003c/em\u003e 86(9): 2365\u0026ndash;2372. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/05-0141\u003c/span\u003e\u003cspan address=\"10.1890/05-0141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLean, E. O. 1982. Soil pH and lime requirement. In \u003cem\u003eMethods of Soil Analysis: Part 2 - Chemical and Microbiological Properties\u003c/em\u003e, ed. R. H. Miller, and D. R. Keeney. 199\u0026ndash;224. Madison, WI.: American Society of Agronomy. Pa\u003cem\u003eg\u003c/em\u003ee, A.L..\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontesinos, D. 2015. Plant\u0026ndash;plant interactions: from competition to facilitation. \u003cem\u003eWeb Ecology\u003c/em\u003e 15(1): 1\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/we-15-1\u003c/span\u003e\u003cspan address=\"10.5194/we-15-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno-Casasola, P. 1986. Sand movement as a factor in the distribution of plant communities in a coastal dune system. \u003cem\u003eVegetatio\u003c/em\u003e 65(2): 67\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00044876\u003c/span\u003e\u003cspan address=\"10.1007/BF00044876\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulvaney, R. L. 1996. Nitrogen-Inorganic forms. In \u003cem\u003eMethods of Soil Analysis: Part 3 - Chemical Methods\u003c/em\u003e, 1123\u0026ndash;1184. Madison, WI.: American Society of Agronomy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMustard, M. J., D. B. Standing, M. J. Aitkenhead, D. Robinson, and A. J. S. McDonald. 2003. The emergence of primary strategies in evolving virtual-plant populations. \u003cem\u003eEvolutionary Ecology Research\u003c/em\u003e 5(7): 1067\u0026ndash;1081.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson, D. W., and L. E. Sommers. 1996. Total carbon, organic carbon, and organic matter. In \u003cem\u003eMethods of Soil Analysis: Part 3 - Chemical Methods\u003c/em\u003e, ed. D. L. Sparks. 961\u0026ndash;1010. Madison, WI.: American Society of Agronomy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNovo, F. G., M. D. Barradas, M. Zunzunegui, R. G. Mora, and J. G. Fern\u0026aacute;ndez. 2004. Plant functional types in coastal dune habitats. In: \u003cem\u003eCoastal Dunes: Ecology and Conservation, Ecological Studies\u003c/em\u003e, eds. Mart\u0026iacute;nez, M.L., Psuty, N.P., 155\u0026ndash;169. Vol 171, Berlin, Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-540-74002-5-10\u003c/span\u003e\u003cspan address=\"10.1007/978-3-540-74002-5-10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlff, H., J. Huisman, and B. F. Van Tooren. 1993. Species dynamics and nutrient accumulation during early primary succession in coastal sand dunes. \u003cem\u003eJournal of Ecology\u003c/em\u003e 81(4): 693\u0026ndash;706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2261667\u003c/span\u003e\u003cspan address=\"10.2307/2261667\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlsen, S. R., C. V. Cole, F. S. Watanabe, and L. A. Dean. 1954. \u003cem\u003eEstimation of available phosphorus in soils by extraction with sodium bicarbonate\u003c/em\u003e. vol. 939 1\u0026ndash;19. U.S. Department of Agriculture Circular.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePausas, J. G., and M. P. Austin. 2001. Patterns of plant species richness in relation to different environments: an appraisal. \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 12(2): 153\u0026ndash;166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3236601\u003c/span\u003e\u003cspan address=\"10.2307/3236601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrů, M., K. Tielb\u0026ouml;rger, R. Belkin, M. Sternberg, and F. Jeltsch. 2006. Life history variation in an annual plant under two opposing environmental constraints along an aridity gradient. \u003cem\u003eEcography\u003c/em\u003e 29(1): 66\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2005.0906-7590.04310.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2005.0906-7590.04310.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierce, S., A. Luzzaro, M. Caccianiga, R. M. Ceriani, and B. Cerabolini. 2017. The StrateFy tool: A new method to calculate CSR ecological strategies globally. \u003cem\u003eFunctional Ecology\u003c/em\u003e 31(2): 444\u0026ndash;457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.12722\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.12722\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePugnaire, F. I., and M. T. Luque. 2001. Changes in plant interactions along a gradient of environmental stress. \u003cem\u003eOikos\u003c/em\u003e 93(1): 42\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1034/j.1600- 0706.2001.930104.x\u003c/span\u003e\u003cspan address=\"10.1034/j.1600- 0706.2001.930104.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhoades, J. D. 1996. Electrical conductivity. In \u003cem\u003eMethods of Soil Analysis: Part 3 - Chemical Methods\u003c/em\u003e, ed. D. L. Sparks. 417\u0026ndash;435. Madison, WI.: American Society of Agronomy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards, L. A. 1954. \u003cem\u003eDiagnosis and improvement of saline and alkali soils. (USDA Handbook No. 60)\u003c/em\u003e. Washington, U.S.: Department of Agriculture.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlesinger, W. H., and A. M. Pilmanis. 1998. Plant-soil interactions in deserts. \u003cem\u003eBiogeochemistry\u003c/em\u003e 42(1): 169\u0026ndash;187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1005939924434\u003c/span\u003e\u003cspan address=\"10.1023/A:1005939924434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwarz, C., J. Brinkkemper, and G. Ruessink. 2018. Feedbacks between biotic and abiotic processes governing the development of foredune blowouts: a review. \u003cem\u003eJournal of Marine Science and Engineering\u003c/em\u003e 7(1): 2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jmse7010002\u003c/span\u003e\u003cspan address=\"10.3390/jmse7010002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon, D. H., J. S. Lee, S. H. Lee, S. K. Hong, H. H. Myeong, and J. S. Cho et al. 2020. Importance of Biotic and Soil Factors in Determining the Distribution Strategies of Coastal Salt Marsh Plants. JMIC 9(2): 2\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21463/jmic.2020.09.2.01\u003c/span\u003e\u003cspan address=\"10.21463/jmic.2020.09.2.01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu, Y., H. Zhao, and T. Zhang. 2002. Influencing mechanism of several shrubs and subshrubs on soil fertility in Keerqin sandy land. Ying Yong Sheng Tai Xue Bao. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e 13(7): 802\u0026ndash;806.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026yacute;kora, K. V., J.C. van den Bogert, and F. Berendse. 2004. Changes in soil and vegetation during dune slack succession. \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 15(2): 209\u0026ndash;218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1654-1103.2004.tb02256.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1654-1103.2004.tb02256.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsoar, H. 2005. Sand dunes mobility and stability in relation to climate. \u003cem\u003ePhysica A: Statistical Mechanics and its Applications\u003c/em\u003e 357(1): 50\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.physa.2005.05.067\u003c/span\u003e\u003cspan address=\"10.1016/j.physa.2005.05.067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTopp, G. C., and P. A. Ferr\u0026eacute;. 2002. Methods for measurement of soil water content: Thermogravimetric method. In \u003cem\u003eMethods of Soil Analysis: Part 4 - Physical Methods\u003c/em\u003e, ed. J. H. Dane, and G. C. Topp. 417\u0026ndash;445. Madison, WI, Soil Science Society of America.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitousek, P. M., and L. R. Walker. 1987. Colonization, succession and resource availability: ecosystem-level inter- actions. In \u003cem\u003eColonization, Succession and Stability\u003c/em\u003e, ed. A. J. Gray, M. J. Crawley, and P. J. Edwards. 207\u0026ndash;224. Oxford: Blackwell Scientific.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillis, A. J., B. F. Folkes, J. F. Hope-Simpson, and E. W. Yemm. 1959. Braunton Burrows: The dune system and its vegetation. \u003cem\u003eJournal of Ecology\u003c/em\u003e 47(1): 1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2257245\u003c/span\u003e\u003cspan address=\"10.2307/2257245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillis, A. J. 1963. Braunton Burrows: the effects on the vegetation of the addition of mineral nutrients to the dune soils. \u003cem\u003eJournal of Ecology\u003c/em\u003e 51(2): 353\u0026ndash;374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2257690\u003c/span\u003e\u003cspan address=\"10.2307/2257690\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZelnik, I., and A. Čarni. 2008. Distribution of plant communities, ecological strategy types and diversity along a moisture gradient. \u003cem\u003eCommunity Ecology\u003c/em\u003e 9(1): 1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1556/ComEc.9.2008.1.1\u003c/span\u003e\u003cspan address=\"10.1556/ComEc.9.2008.1.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, H., J. Gao, Y. Teng, C. Feng, and M. Tian. 2015. Temporal variations in soil moisture for three typical vegetation types in inner Mongolia, Northern China. \u003cem\u003ePlos One\u003c/em\u003e 10(3): e0118964. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0118964\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0118964\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, Y., B. Guo, C. Liu, Y. Lin, Q. Fu, N. Li, and H. Li. 2021. Soil fertility, enzyme activity, and microbial community structure diversity among different soil textures under different land use types in coastal saline soil. \u003cem\u003eJournal of Soil and Sediment\u003c/em\u003e 21(11): 2240\u0026ndash;2252. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11368-021-02916-z\u003c/span\u003e\u003cspan address=\"10.1007/s11368-021-02916-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo, X., X. Zhao, H. Zhao, T. Zhang, Y. Guo, Y. Li, and Y. Huang. 2009. Spatial heterogeneity of soil properties and vegetation\u0026ndash;soil relationships following vegetation restoration of mobile dunes in Horqin Sandy Land, Northern China. \u003cem\u003ePlant and Soil\u003c/em\u003e 318(1): 153\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-008-9826-7\u003c/span\u003e\u003cspan address=\"10.1007/s11104-008-9826-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dune, plant functional types, CSR strategies, soil, succession","lastPublishedDoi":"10.21203/rs.3.rs-5327986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5327986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDune ecosystems harbor a limited array of plant species, thriving despite their challenging habitats. This study aims to explore the distribution of plant strategy types in foredune and backdune regions and their correlation with soil variables. CSR strategies and seasonal soil variables were analysed, encompassing soil moisture, organic matter, TN, NH\u003csub\u003e4\u003c/sub\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e, P, pH, Na, K, Cl, Ca, CaCO\u003csub\u003e3\u003c/sub\u003e, Mg, EC, and CEC. The dissimilarity between foredune and backdune areas was assessed using the Bray\u0026ndash;Curtis similarity matrix and SIMPER. CCA was used to examine the relationships between plant strategies and soil variables. The dissimilarity rate between two sites in terms of the distribution of strategy types was 67.04%. All soil variables, except P for both areas and CaCO\u003csub\u003e3\u003c/sub\u003e for the backdune, exhibited significant seasonal variations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The C/CR and SC strategy types was in positive correlations with pH, salinity, EC, and CaCO\u003csub\u003e3\u003c/sub\u003e, and negative ones with TN, P, K, saturation, organic matter, moisture, and CEC. The R/CR, R/CSR, SR/CSR, S/SR, SC/CSR and CR/CSR strategy types correlated positively with TN, P, K, saturation, organic matter, moisture, and CEC. Similarly, negative correlations were detected between CR/CSR, R/CSR, SR/CSR, and S/SR strategy types and pH, salinity, EC, and CaCO\u003csub\u003e3\u003c/sub\u003e. This study underscores the spatial dynamics involved in reaching the climax stage within dune ecosystems, showcasing the resilience of species that adapt to stressful environments. Even in the absence of disturbance, species uniquely suited to these conditions can thrive, marking the culmination of succession in such ecosystems.\u003c/p\u003e","manuscriptTitle":"Relationship between Plant Strategy Types and Soil Characteristics in Backdunes and Foredunes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-04 10:25:17","doi":"10.21203/rs.3.rs-5327986/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-11-13T07:01:10+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-12T22:51:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Estuaries and Coasts","date":"2024-11-05T00:16:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-25T10:16:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Estuaries and Coasts","date":"2024-10-25T03:23:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7d562fea-bf31-4b2f-85a4-7f8a6cb17178","owner":[],"postedDate":"December 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T16:01:09+00:00","versionOfRecord":{"articleIdentity":"rs-5327986","link":"https://doi.org/10.1007/s12237-025-01512-5","journal":{"identity":"estuaries-and-coasts","isVorOnly":false,"title":"Estuaries and Coasts"},"publishedOn":"2025-03-01 15:56:55","publishedOnDateReadable":"March 1st, 2025"},"versionCreatedAt":"2024-12-04 10:25:17","video":"","vorDoi":"10.1007/s12237-025-01512-5","vorDoiUrl":"https://doi.org/10.1007/s12237-025-01512-5","workflowStages":[]},"version":"v1","identity":"rs-5327986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5327986","identity":"rs-5327986","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}