Vegetation patch-level characteristics as key drivers of coastal dune elevation: optimizing nature-based coastal defences

Vegetation patch-level characteristics as key drivers of coastal dune elevation: optimizing nature-based coastal defences | 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 Vegetation patch-level characteristics as key drivers of coastal dune elevation: optimizing nature-based coastal defences Lucía Rodríguez-Arias, Aina M. Alemany, Teresa Alcoverro, Sara Pons Mateu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7309660/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Coastal dune vegetation is increasingly recognised as a nature-based solution for buffering shoreline dynamics, particularly by reducing the risk of inland inundation during storms. This protective function depends on the capacity of vegetated dunes to maintain or build elevation. To identify the ecological attributes influencing sediment elevation, we examined how patch size, canopy structure, dominant species identity, species composition, and plant traits shape elevation patterns across multiple coastal sites with similar dune plant assemblages. We used centimetric-precision differential GPS to measure sediment elevation inside and immediately outside vegetated patches. To our knowledge, this is the first field-based, multi-site study to directly test the effect of vegetation patch size on sediment elevation in coastal dune systems. While patch size is assumed to influence sediment dynamics, previous work has either focused on mesocosms or treated vegetation as a homogeneous cover layer, without explicitly addressing patch-scale heterogeneity. Our results show that patch size, species composition, and dominant species identity significantly influence dune elevation. Larger and mixed-species patches promoted higher sediment elevation than monospecific ones, and patches dominated by Ammophila arenaria were particularly effective. No single plant trait consistently explained elevation patterns, but our findings suggest that combinations of traits– likely related to root architecture– may enhance sediment accumulation. These results highlight key ecological attributes that support elevation gain in coastal dunes and offer insights for conservation and restoration strategies aimed at enhancing natural defences against storm-driven inundation in dynamic shoreline environments. coastal management coastal protection dune restoration functional diversity natural coastal barrier Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 INTRODUCTION Coastal dunes are dynamic landscapes that play a fundamental role in shaping shorelines and buffering inland ecosystems from coastal change. These landforms act as natural barriers against storm surges and rising sea levels while also serving as reservoirs of windblown sand that sustain coastal sediment budgets (Martínez et al., 2013; Otvos, 2012). Dune ecosystems also support specialised plant communities adapted to shifting sands and harsh environmental gradients, contributing to coastal biodiversity (Reijers et al., 2019). However, despite their ecological and protective functions, dune ecosystems are increasingly under pressure from coastal development, sea level rise, and storm intensification, with some European regions having experienced between 70–90% reductions in dune extent in the last century (Gallego‑Fernández et al., 2011; Garcia-Lozano & Pintó, 2018) Coastal vegetation plays a critical role in the formation and persistence of dunes by modifying sediment transport and accumulation (Hacker et al., 2012; Psuty, 1989). The presence of vegetation increases the sand-trapping efficiency of dunes by 10–50% (Keijsers et al., 2015) and reduces soil erosion by up to 45% (Dahl et al., 1975). However, dune vegetation is not uniformly distributed; rather, it forms discrete patches influenced by environmental gradients, disturbance regimes, and species interactions (e.g., Ciccarelli, 2015; Doing, 1985). In turn, these vegetation patches strongly influence where dunes form and how they evolve (Barbier et al., 2008; Bonte et al., 2021; Seabloom et al., 2013). While previous studies have examined how plant density, dune width, clonal growth patterns and patch shape influence dune dynamics (Reijers et al., 2019, Kobayashi et al., 2013; Bouma et al., 2010; Charbonneau et al., 2017; Silva et al., 2016), the role of patch size in controlling sediment elevation in dunes remains poorly understood. In other coastal vegetated ecosystems, such as seagrasses and saltmarshes, studies suggest that hydrodynamic forcing (i.e., waves and currents) decreases from the edge towards the interior of vegetation belts or patches (Bouma et al., 2009; Möller et al., 2014; Peterson et al., 2004), promoting sediment deposition. However, these ecosystems are permanently or frequently submerged, whereas dunes are primarily shaped by wind-driven sediment transport and are only exposed to waves during storms. Furthermore, most research on the role of vegetation in sediment dynamics has been conducted in controlled flume experiments rather than in natural field settings. As a result, empirical field studies directly linking vegetation patch size to sediment elevation in coastal dunes remain scarce, leaving a critical gap in our understanding of dune-building processes. Beyond patch size, dune vegetation exhibits a wide variation in plant traits, such as aboveground structure, flexibility, biomass, and belowground root architecture– all known to influence sediment binding capacity (Figlus et al., 2017). Differences in plant functional traits among dominant dune species can shape sand elevation patterns, with aboveground biomass reducing wave energy and water velocity, while belowground structures stabilise sediment and reduce soil erodibility (Coops et al., 1996). Root systems further enhance dune-building by dissipating current velocity (Koch et al., 2006) and trapping wind-blown sediment, facilitating continuous dune growth (Sigren et al., 2014). Importantly, species that integrate both structural components– above- and belowground– tend to provide the most effective erosion control compared to those relying on either component alone (Figlus et al., 2017). However, the extent to which patch-scale vegetation attributes, particularly patch size, interact with these functional traits to drive sediment accumulation remains poorly quantified. Understanding how vegetation modulates sediment elevation is critical for assessing coastal vulnerability and informing nature-based coastal management strategies. As managers move away from hard-engineering solutions, interest in revegetation schemes has grown. However, these efforts often lack a mechanistic understanding of how specific plant traits and vegetation structures contribute most effectively to dune resilience. Identifying key attributes that enhance sediment elevation and retention would help optimise dune revegetation programmes and improve coastal protection (Maximiliano‑Cordova et al., 2019). Thus, providing field-based evidence to bridge that knowledge gap is essential to support the design strategies that maximize the protective functions of coastal vegetation. In this study, we explicitly test whether patch size is a key determinant of sediment elevation, alongside species composition, canopy height, and dominant plant traits. To address this gap, we use differential GPS measurements to quantify ground elevation inside and outside vegetated patches across multiple coastal sites. By directly measuring patch-scale sediment dynamics, this study offers novel insights into how vegetation structure influences dune-building processes under natural conditions, with direct implications for dune restoration and coastal management. 2 MATERIALS AND METHODS To assess the role of patch-level attributes and species traits on sand elevation, we surveyed 480 individual vegetated patches varying in size, species richness, and distance from the shore across sites with a similar species assemblage. These ecosystems included beaches, dunes, and transitional zones leading to salt marshes. Site selection was based on identifying locations with comparable species composition, similar slopes, and environmental conditions but including a range of patch sizes and plant species diversity. The study included 10 sites across the Iberian Peninsula, spanning the Mediterranean Sea, the Cantabrian Sea, and the Atlantic Ocean (Fig. 1 ). Sampling was conducted from September 2022 to September 2023. 2.1 VEGETATION DATA COLLECTION We quantified elevation change in vegetated patches by measuring the difference in elevation (cm) between the central point inside the patch and the immediate area outside the patch. To minimise confounding effects of topography, we selected patches in relatively flat areas with minimal slope (Fig. S1 ). We measured ground elevation using a differential GPS (Emlid Reach RS2 + RTK mode) with a vertical accuracy of ± 0.14 millimetres (Fig. 2 ). In addition to elevation, we recorded several vegetation and morphometric attributes for each patch. Vegetation variables were (i) patch species composition and (ii) dominant species within each patch. In terms of patch species composition, we defined patches with a single species as monospecific, while those containing multiple species were categorised as multispecific (mixed patches). To determine the dominant species, we estimated species cover using a 40x40 cm quadrat within each patch. In terms of morphometric variables, we measured patch size (width and length) and canopy structure using a tape measure with ± 0.01 metres accuracy. Canopy structure included maximum canopy height, defined as the horizontal width of the aboveground portion of the vegetation patch. A total of 12 dominant plant species were recorded across all vegetation patches (Table S1 and S2). 2.2 FUNCTIONAL TRAITS We collected plant samples from nine species in June of 2023 to assess functional traits. Sampling followed the protocols described in Kleyer et al. (2019). For each species, we extracted three replicate core samples using 15 cm diameter × 40 cm length corers. To preserve sample integrity, we kept plants on ice during fieldwork and measured all traits promptly upon returning to the laboratory or field base. Once in the laboratory, we assessed functional traits using direct morphometric measurements and biomass data. Aboveground traits included maximum leaf length, the number of shoots, and leaf spread. Belowground components were separated into belowground stems, rhizomes, and roots, and their structure was analysed in detail. We recorded the total rhizome length and the number of roots, along with root architecture metrics such as total length, maximum size, mean size and root spread (Fig. S2). To evaluate root concentration and spatial distribution in the soil, we calculated several root traits, including root tissue density (RTD, g/cm³), density root length (DLR, cm/cm²), specific root length (SRL, cm/g), root length ratio (RLR, cm/g) and the relative root weight (RWR), alongside aboveground biomass (AGB). We also determined the root to shoot ratio (RS) to assess biomass allocation between above- and belowground structures. Additionally, we recorded the overall plant size, measured from base to tip. After completing the measurements, we dried all above- and belowground components in an oven at 70°C for 48 h and weighed to determine dry biomass (Lemein et al., 2015). Further species characteristics, such as family classification and phenology (annual vs. perennial), were obtained from the literature (Table S3). 2.3 SEDIMENT ANALYSIS To characterize the sediment properties at each site, we collected surface sediment samples (top 5 cm) from all dune sites using a 4.8 cm Ø core. At each site, we randomly collected three replicate cores and froze them at − 20°C until further processing. We determined organic matter content (% dry weight) using loss on ignition (LOI), heating samples at 450 ◦C for 4 hours (Heiri et al., 2001) after homogenising them in a mortar grinder (Touch et al., 2017). Grain size was classified based on the Udden-Wentworth scale, ranging from 1000 µm, using a Malvern Laser Particle Sizer 2000, after digesting ~ 5 g of dried sediment with 30% hydrogen peroxide at 60°C to remove organic matter (conducted at the Centre for Advanced Studies of Blanes, CEAB-CSIC). 2.4 STATISTICAL ANALYSIS 2.4.1 Drivers of patch elevation (stepwise linear regression) To investigate the drivers of elevation change across vegetation patches, we fitted Generalised Linear Mixed effects Models (GLMMs) using the lme4 (Bates et al., 2015), lmerTest (Kuznetsova et al., 2017), and nlme (Pinheiro et al., 2022) packages in R. Before analysis, we identified and removed instrumental outliers from the dataset, which were attributed to DGPS measurement errors – specifically, when the device was operating in less accurate “float” mode instead of “fix” mode, resulting in implausible values (e.g., elevation change > 1 m). The response variable, 'elevation change' (log-transformed), was calculated as the difference in elevation between the inside and outside of each patch (see previous section). We modelled ‘elevation change’ as a function of the following fixed explanatory variables: 'dominant plant species identity' (categorical with 12 levels), 'patch composition' (categorical with 2 levels: monospecific vs. multispecific patches), 'species richness', 'patch canopy', 'patch area', 'patch distance to the coastline', and sediment properties (‘organic matter content’ and ‘grain size’). To account for site-level variability, we included 'site' as a random effect, which was supported by Akaike Information Criterion (AIC) comparisons and log-likelihood ratio tests (Zuur et al., 2009). We used a stepwise approach for model selection, systematically dropping non-significant fixed effects and selecting the best model based on the AIC and the likelihood ratio tests (Zuur et al., 2009). For each best-selected model, we performed marginal F-tests with univariate analysis of deviance to assess the effects of the remaining explanatory variables. Tukey Honestly Significant Difference (HSD) post hoc tests were performed using the package multcomp (Hothorn et al., 2008) to determine level-specific differences within model variables. We validated model performance and checked model assumptions by inspecting residuals and fitted values using a simulation-based approach with the DHARMa package (Hartig, 2022). We further evaluated model fit using the performance package (Lüdecke et al., 2021). 2.4.2 Principal Component Analysis (PCA) of plant functional traits To explore whether functional traits defined a single or group of plant species, we performed a Principal Component Analysis on 9 species, each characterised by 20 traits (Table S3). Species selection for this analysis was based on the most common species in the dataset, ensuring sufficient representation of trait variation. The PCA was conducted on scaled data (given the variety of trait units) and the principal components were retained after inspecting scree plots. 2.4.3 Partial Least Squares Regression (PLSR) We used Partial Least Squares Regression (PLSR) to examine how plant traits influence the response variable ‘elevation change’. This multivariate method generates latent variables (PLS components) as linear combinations of predictors, maximising the explained variance of the response variable. PLSR is particularly effective when the number of predictor variables is high (21 in this study) and when predictors exhibit high collinearity, both common characteristics of trait data (Carrascal et al., 2009). We assessed PLSR models based on variance explained ( R 2 ), loadings of predictor variables on PLS components, and Variable Importance in Projection (VIP). Loadings indicate the strength and direction of relationships between predictors and the response variable (‘elevation change’), while VIP values provide a summary of the importance of each predictor, with a threshold of VIP > 1 indicating significance (Farrés et al., 2015). To evaluate PLSR performance, we split the dataset into training (80%) and test (20%) sets. PLSR was conducted on the training set, and the resulting model was validated using the test data. To improve robustness, we repeated this process 100 times, generating distributions of VIP values and loadings for each variable. We visualised VIP and loadings results using boxplots and barplots with error bars. PLSR analyses were carried out using pls (Mevik et al., 2011) and caret (Kuhn, 2021) packages in R. 3 RESULTS 3.1 Drivers of patch elevation (stepwise linear regression) Patch-level characteristics played a key role in determining sediment elevation across coastal dune ecosystems (Table 1 , Fig. 3 a). We found very strong evidence that patch size and species composition influenced sediment elevation inside patches, with larger and multispecific patches showing higher values (Table 1 , Fig. 3 b). Despite intraspecific variation, we also found very strong evidence that species identity was a driver of sediment elevation, with A. arenaria forming patches with greater inner elevation than other species, particularly Salicornia europaea and Cakile maritima (Table 1 , Fig. 3 c). In contrast, sediment properties (organic matter content and grain size), patch canopy height and distance to the shore showed little or no evidence of influencing patch elevation. Due to collinearity between species richness and patch composition (mono- vs. multispecific patches), we could not test the independent effect of species richness on elevation change. Patch area, patch composition and species identity together explained 39% of the variance in sediment elevation (R 2 marginal = 0.389). Including the random effect ‘site’ increased the explained variance to 46% (R 2 conditional = 0.462), highlighting the combined influence of patch characteristics and local environmental factors on sediment dynamics. The remaining unexplained variance suggests that additional untested factors contribute to dune patch elevation change. Table 1 Results from the stepwise linear mixed-effects models (GLMM) for the main drivers of elevation change. Response Fixed effects d.f. X 2 p -value log(elevation) log(patch area) Patch composition Species identity 1 1 11 169.137 23.210 31.327 0.001*** 0.001*** 0.001*** d.f..: degrees of freedom. ***, p -value < 0.001, strong evidence for the effect influencing the response variable. The LOI450 (%DW) values varied significantly across localities, indicating differences in sediment composition and potential organic accumulation (Fig. S4). Sediment particle distribution followed a consistent pattern across most sites, with coarse sand (250–1000 µm) being the dominant fraction, whereas silt, clay, and fine sand were present in smaller proportions (Fig. S5). 3.2 Principal Component Analysis (PCA) of plant functional traits The first two principal components (PCs) explained 61% of the variance in dune species traits, with PC1 accounting for 33.9% and PC2 explaining 27.1%. PC1 primarily differentiated species based on biomass allocation strategies, specifically root vs. shoot dominance and was dominated by relative traits (ratios) that are independent of plant size. Traits related to root allocation, such as root weight ratio (RWR), root to shoot ratio (RS), and root length ratio (RLR), had high positive loadings, while shoot-dominated traits, including aboveground biomass (AGB), above-to-belowground biomass ratio, and stem biomass, had negative loadings (Fig. 4 , arrows). Species were widely distributed across the PCA plot, indicating high functional diversity. Species on the positive side of PC1, such as A. arenaria and Echinophora spinosa , exhibited strong root dominance, while those on the negative side, such as Sarcornia fruticosum and Salsola kali were associated with greater aboveground investment. PC2 primarily captured differences in plant size and was dominated by absolute trait values that scale with plant size. Traits such as total root length and leaf length had high positive loadings, meaning larger species like A. arenaria were positioned on the positive side of PC2. In contrast, smaller species such as Limonium vigoi , Suaeda maritima and S. europaea aligned with the negative side, reflecting their overall smaller size and shorter root and leaf structures. 3.3 Partial Least Squares Regression (PLSR) PLSR did not identify a single key functional trait associated with sediment elevation across dune plant species. Instead, the variable importance in projection (VIP) highlighted that only patch-level attributes, specifically patch area and patch canopy (although near the VIP > 1 threshold), were the most influential factors. This suggests that patch-level attributes play a greater role in driving sediment elevation than the suite of species-level functional traits measured. Furthermore, PLSR results suggest that rather than a single dominant plant trait, a combination of functional traits might determine elevation capabilities across species (Fig. 5 c). Overall, PLS1 explained 35.3% of the variance in predictors but only 11.4% of the variance in elevation, indicating that additional factors contribute to sediment elevation. Root traits, including mean root length, RLR and DLR, were among the positively weighted variables on PLS1 (Fig. 5 b), though their VIP values remained low. Interestingly, PLS1 was similar to PC1 from Fig. 4 , with root-associated traits dominating the positive side and aboveground traits loading negatively (Fig. 5 b). The final PLS regression model with 2 PLS components explained 19.8% of the variation in elevation and 46.7% of the predictor variance. The final PLS regression model with 3 PLS components explained 21.3% of the variation in elevation and 55.6% of the predictor variance. Results have been written translating p-values into evidence language, as recommended by (Muff et al., 2021). 4 DISCUSSION Ecologists have traditionally focused on how dune vegetation adapts to dune topography and environmental conditions, while geomorphologists have studied the changing properties of dune architecture in response to physical rather than biological factors. However, a holistic approach that integrates both perspectives is needed to fully understand the broader drivers of how vegetation influences sediment elevation, which is essential to enhance coastal dune resilience as natural flood barriers. Our findings show that patch-level characteristics– particularly patch area, canopy and species composition– are the primary drivers of sediment elevation across coastal dune ecosystems. While species identity significantly influenced elevation outcomes, no single plant trait dominated. Instead, our results suggest that a combination of functional traits, likely those related to root architecture traits (e.g., root density, total root length, and root weight ratio), might play a role. Sand dunes and their vegetation interact dynamically, each modifying the conditions that shape the other. Vegetation controls sediment deposition and accumulation (Nyman et al., 2006), typically increasing dune height and volume (Durán & Moore, 2013; Feagin et al., 2015). Our results confirm that vegetation consistently enhances sediment elevation, with patch size as the primary driver (Fig. 3 a, Table 1 ). Elevation increases with patch size but plateau beyond a threshold, likely constrained by plant growth dynamics and environmental factors such as sediment availability, burial rates, or hydrodynamic forces (Hesp, 1991; Wang et al., 2006). Larger and denser patches reduce coastal erosion and maximize sand-trapping efficiently than sparse ones (Charbonneau et al., 2017; Kobayashi et al., 2013; Reijers et al., 2019; Silva et al., 2016). Our study provides empirical validation from the field to some of these results, which mainly arise from flume experiments or artificial plant mimics (Feagin et al., 2023; Figlus et al., 2022). Vegetation patches vary in biophysical structure– shaped by plant density, distribution, morphology, as well as species composition and trait interactions between species (Ciccarelli et al., 2023; Maximiliano‑Cordova et al., 2019)–, which influences their ‘roughness form’ and potential for sediment trapping and elevation capacity (Bouma et al., 2010; Gillies et al., 2014). These factors significantly alter their ecological functions, particularly their soil-building ability. Although most sampled patches were monospecific, our results show that multispecific (mixed) patches exhibited greater soil elevation capacity (Fig. 3 b and c, Table 1 ), likely due to their greater structural complexity, creating functionally richer environments (Acosta et al., 2009). While sand can accumulate around any living or non-living structure, dune plants grow in size, replicate, and are stimulated in response to burial (Feagin et al., 2023), which ultimately facilitates elevation processes. Thus, multispecific patches may better adapt physiologically to dynamic conditions and actively contribute to the development of natural sand barriers, functioning not merely as passive elements but as key agents of coastal resilience (Feagin et al., 2023; Zarnetske et al., 2012). Our results reveal significant variation in the sand elevation capacity of different plant species (Fig. 3 c). In psammophilous plants, burial acts as a selective force determining processes of vegetation assembly (Brown & Zinnert, 2018), filtering out species when burial exceeds their survival threshold (Maun, 2004), while stimulating the growth and vitality of other species (Stallins, 2005). Dune-building species like A. arenaria play an important role in dune development (Bonte et al., 2021), contributing the most to sediment elevation through extensive root systems and tall canopies (Fig. 3 and Fig. 5 ) (e.g., Bouma et al., 2001; Figlus et al., 2022). They adjust growth patterns to rapidly produce vertical sediment elevation, enhancing anchoring and sand capture efficiency in response to environmental signals (Reijers et al., 2019). Despite belowground investment is widely assumed to play a role in dune-building processes and resilience to disturbances (Charbonneau et al., 2017; Ciccarelli et al., 2023; Figlus et al., 2022), our results surprisingly did not provide strong evidence that root traits are key drivers of sediment elevation (VIP < 1, Fig. 5 a). Since the data were collected during a stationary sampling period under calm conditions, future studies should assess if root structures show a greater soil stabilization role after a disturbance event, such as a storm. Additionally, not all species significantly contribute to dune elevation (Fig. 3 c and Fig. 5 c). Some plants, like E. paralia, S. maritima or C. maritima , are burial-tolerant stabilizers that respond positively to burial, but do not promote vertical dune growth, instead providing resistance to overwash (Zinnert et al., 2016). These species appear to invest more in aboveground growth (traits negatively aligned with PC1, ABG, and stem biomass, Fig. 4 ). In back dune zones, burial-intolerant stabilizers such as S. fructicosum, S. europaea , or L. vigoi , are well-adapted to flood-prone areas, with short lateral roots that respond to anoxic and hypoxic conditions (Bouma et al., 2001), enabling them to tolerate saline flooding, regenerate through sediment deposition, and thrive in wetter soils (Silander & Antonovics, 1979). While maximizing dune elevation is central to securing coastal futures, preserving other key functions like sand stabilisation or salt tolerance is equally important as sea levels rise and storms intensify. Since no single species guarantees full coastal protection (Barbier et al., 2008), conserving the entire dune vegetation diversity is essential (Standish & Parkhurst, 2024). Identifying which key trait adaptations drive elevation dynamics can guide effective revegetation efforts, though our results suggest no single trait dominates. Further investigation is needed to clarify whether elevation is driven by species richness per se or by specific combinations of functional traits. All the evidence presented above underscores the challenges and limitations of coastal restoration projects, particularly in dune ecosystems, which remain poorly addressed in the literature despite their ecological importance (Lithgow et al., 2013). Despite substantial investments averaging $ 1600000/ha (2010 price levels) (Bayraktarov et al., 2016), approximately 40% of attempts fail or achieve only partial success in fully rehabilitating target species (Suding, 2011). This is often due to limited local-scale ecological data or the lack of information about functional traits and physiological adaptations to dynamic environmental conditions (Gallego‑Fernández et al., 2011). Additionally, despite the common assumption that vegetation’s physiological role is to stabilize sand during extreme events, initial evidence provided by Feagin et al. (2023) suggest this assumption may be incorrect if it does not include good ecological knowledge, potentially leading to negative consequences. For example, the introduction of non-indigenous species (e.g. Casuarina , Ammophila , Spartina , or Tamarix ) in impacted areas has displaced native plants, often resulting in reduced dune accumulation and lower elevations in stabilization projects worldwide, ultimately increasing erosion over longer time scales (Gao et al., 2020). Overlooking the ability of psammophilous species to adapt functionally to variable coastal conditions may accelerate ecosystem degradation and associated community loss during perturbations. Low-cost nature-based solutions, such as strategic revegetation planting, offer a sustainable alternative to the commonly used hard structures (Grafals‑Soto, 2012). We propose prioritizing patch-level attributes– particularly patch size (already discussed above), canopy height (Fig. 4 and Fig. 5 ), and multispecies composition– as key considerations for restoration programmes. Given the morphological variability of dune plant communities, this study offers a step toward integrating ecological knowledge to refine species selection in dune restoration. Declarations ACKNOWLEDGEMENTS The authors belong to the research group 2021 SGR 00405 funded by the Generalitat de Catalunya (AGAUR). This project received financial support from the Spanish Agency of Research (AEI-MICINN), grants STORM (PID2020-113745RB-I00) and DYNCOAST (PID2023-151732OA-I00), funded by MCIN/AEI/10.13039/501100011033 and the FES+. LRA was supported by an FPI fellowship (PRE2021-099061) and JFP by the Ramón y Cajal programme (RYC2022-036196-I), both funded by MCIN/AEI/10.13039/501100011033 and the FES+. The authors thank Jordi Boada, Jesús Zarcero, Inés Mazarrasa, Fernando García González, Mario Minguito-Frutos, and Luca Di Vita for their contribution, and the administrations of the Ebro Delta and Aiguamolls de l’Empordà Natural Park for research permits and logistical support. CONTRIBUTIONS All authors contributed to the study design. LRA, AMA, and SPM led fieldwork and prepared and analysed the samples. LRA and JFP analysed the data. LRA, TA, RA, and JFP led the writing of the manuscript, with contributions from all the authors. All authors contributed to the article and approved the submitted version. CORRESPONDING AUTHOR Correspondence to Lucía Rodríguez-Arias. ETHICS DECLARATIONS Competing interests The authors have no financial or non-financial competing interests to declare. DATA AVAILABILITY Data are available, as well as the R scripts used to run all of the analyses reported, in a GitHub repository (https://github.com/lucciluRdrigz/dunes). References Acosta, A., Carranza, M. L., & Izzi, C. F. (2009). Are there habitats that contribute best to plant species diversity in coastal dunes? Biodiversity and Conservation , 18 (4), 1087–1098. https://doi.org/10.1007/s10531-008-9454-9 Barbier, E. B., Koch, E. W., Silliman, B. R., Hacker, S. D., Wolanski, E., Primavera, J., Granek, E. F., Polasky, S., Aswani, S., Cramer, L. A., Stoms, D. M., Kennedy, C. J., Bael, D., Kappel, C. V., Perillo, G. M. E., & Reed, D. J. (2008). Coastal ecosystem-based management with nonlinear ecological functions and values. 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New York: Springer. https://doi.org/10.1007/978-0-387-87458-6 Supplementary Files GrapAbstract.docx Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 17 Aug, 2025 Editor invited by journal 06 Aug, 2025 Editor assigned by journal 06 Aug, 2025 First submitted to journal 06 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7309660","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501443217,"identity":"4f832b1a-a402-4be0-aff9-00f25680065e","order_by":0,"name":"Lucía Rodríguez-Arias","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-8400-0360","institution":"Centro de Estudios Avanzados de Blanes: Centre d'Estudis Avancats de Blanes","correspondingAuthor":true,"prefix":"","firstName":"Lucía","middleName":"","lastName":"Rodríguez-Arias","suffix":""},{"id":501443218,"identity":"bce27659-6ba1-46d6-8136-19eebc4f0ce3","order_by":1,"name":"Aina M. Alemany","email":"","orcid":"","institution":"Centre d’Estudis Avançats de Blanes: Centre d'Estudis Avancats de Blanes","correspondingAuthor":false,"prefix":"","firstName":"Aina","middleName":"M.","lastName":"Alemany","suffix":""},{"id":501443219,"identity":"d0b3fe56-38dc-4789-9ed2-94b04bbc5f16","order_by":2,"name":"Teresa Alcoverro","email":"","orcid":"","institution":"Centre d’Estudis Avançats de Blanes: Centre d'Estudis Avancats de Blanes","correspondingAuthor":false,"prefix":"","firstName":"Teresa","middleName":"","lastName":"Alcoverro","suffix":""},{"id":501443220,"identity":"e5a6dfd4-4fbf-4ceb-a6fc-f0cc56b8aa24","order_by":3,"name":"Sara Pons Mateu","email":"","orcid":"","institution":"Centre d’Estudis Avançats de Blanes: Centre d'Estudis Avancats de Blanes","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"Pons","lastName":"Mateu","suffix":""},{"id":501443221,"identity":"91e6888b-8773-45ea-b9d5-b02af2f76d75","order_by":4,"name":"Rohan Arthur","email":"","orcid":"","institution":"Nature Conservation Foundation","correspondingAuthor":false,"prefix":"","firstName":"Rohan","middleName":"","lastName":"Arthur","suffix":""},{"id":501443222,"identity":"a0fa4151-1c73-428a-8faf-fc902c801bfd","order_by":5,"name":"Jordi F. Pagès","email":"","orcid":"","institution":"Centre d’Estudis Avançats de Blanes: Centre d'Estudis Avancats de Blanes","correspondingAuthor":false,"prefix":"","firstName":"Jordi","middleName":"F.","lastName":"Pagès","suffix":""}],"badges":[],"createdAt":"2025-08-06 12:17:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7309660/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7309660/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90035721,"identity":"c73ccfa8-d5ef-4fa8-8652-546629762841","added_by":"auto","created_at":"2025-08-27 15:49:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85082,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution of the 10 coastal dune sites sampled across the Mediterranean Sea, Cantabrian Sea, and Atlantic Ocean in this study.\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-7309660/v1/866312c75e5b0fd8253788fc.png"},{"id":90034905,"identity":"0b267e50-ebfb-4a8a-9d97-295ecda151e7","added_by":"auto","created_at":"2025-08-27 15:41:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":225704,"visible":true,"origin":"","legend":"\u003cp\u003eHigh precision multi-band RTK GNSS receiver (Emlid Reach RS2+) used for ground elevation measurement both outside and inside vegetation.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7309660/v1/141bce42ed4b4b05f249625a.png"},{"id":90034896,"identity":"5923f4c3-692c-4cd7-86a8-25a7074262b2","added_by":"auto","created_at":"2025-08-27 15:41:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108461,"visible":true,"origin":"","legend":"\u003cp\u003eDrivers of patch elevation change. a) Relationship between vegetation patch area (m²) and elevation change (cm). The fitted line represents predictions from a linear model, the shaded area indicates confidence intervals and the points are the raw data. b) Effect of patch composition on sediment elevation change (cm), comparing monospecific and multispecific patches. c) Effect of dominant plant species on sediment elevation change (cm). Different lower-case letters above each box indicate significant differences (p-value \u0026lt; 0.05, Tukey HSD post-hoc tests). The number of patches per species is shown below each letter. To visualise the predictions of the General Linear Mixed effects model, see Fig. S3.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7309660/v1/b5a573f3b90cb932d083b5e3.png"},{"id":90034898,"identity":"1a05bbc6-ff80-4e85-9e8b-36ef623e5338","added_by":"auto","created_at":"2025-08-27 15:41:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":153061,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) biplot of dune plant species traits. Each point represents a plant species, while arrows indicate functional trait loadings on the first and second principal components. The length and direction of the arrows illustrate the strength and contribution of each trait to the PCA axes. The angle between arrows reflects their correlations, with smaller angles indicating strong correlations and perpendicular arrows suggesting no correlation. Species that are closer together in the plot occupy a similar trait space, meaning they share similar functional trait profiles.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7309660/v1/b7c257c70e9205a603d7ce08.png"},{"id":90034897,"identity":"62c112c2-7a41-4ca5-b6e6-a1123e5340e0","added_by":"auto","created_at":"2025-08-27 15:41:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":204609,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between sediment elevation, patch attributes and dune species traits, as shown by Partial Least Squares Regression (PLSR). (a) Variable Importance in Projection (VIP) scores, highlighting the most influential variables in determining sediment elevation. Variables with VIP\u0026gt;1 (dark blue) were considered the most important predictors. (b) Variable loadings on PLS1, showing their relative contributions to this component. Positive loadings indicate a positive association with sediment elevation. (c) Relationship between sediment elevation and PLS1 scores. The x-axis represents a latent variable– a combination of the predictors with higher loadings on PLS1. Higher PLS1 scores (points further to the right) correspond to species with well-developed root systems, which tend to have higher inner patch elevations (y-axis).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7309660/v1/2c8075d3df4d717e2fc4d644.png"},{"id":90037981,"identity":"51fdad47-df52-4c91-bdf2-5dd1ffb8ebf7","added_by":"auto","created_at":"2025-08-27 16:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1333918,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7309660/v1/c0dd9e03-2cc7-40a2-b4de-d6162756e0bc.pdf"},{"id":90034901,"identity":"6066e6ad-39b3-47db-a1c7-abbb0b9a30d6","added_by":"auto","created_at":"2025-08-27 15:41:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":298485,"visible":true,"origin":"","legend":"","description":"","filename":"GrapAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-7309660/v1/4a18bd31f0bb6972fe5d8ea2.docx"},{"id":90034914,"identity":"6692f8cf-3760-4472-83d4-cef29bc4b8bb","added_by":"auto","created_at":"2025-08-27 15:41:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8234396,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7309660/v1/3bce72b48202723bec6ffc1a.docx"}],"financialInterests":"","formattedTitle":"Vegetation patch-level characteristics as key drivers of coastal dune elevation: optimizing nature-based coastal defences","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eCoastal dunes are dynamic landscapes that play a fundamental role in shaping shorelines and buffering inland ecosystems from coastal change. These landforms act as natural barriers against storm surges and rising sea levels while also serving as reservoirs of windblown sand that sustain coastal sediment budgets (Mart\u0026iacute;nez et al., 2013; Otvos, 2012). Dune ecosystems also support specialised plant communities adapted to shifting sands and harsh environmental gradients, contributing to coastal biodiversity (Reijers et al., 2019). However, despite their ecological and protective functions, dune ecosystems are increasingly under pressure from coastal development, sea level rise, and storm intensification, with some European regions having experienced between 70\u0026ndash;90% reductions in dune extent in the last century (Gallego‑Fern\u0026aacute;ndez et al., 2011; Garcia-Lozano \u0026amp; Pint\u0026oacute;, 2018)\u003c/p\u003e\u003cp\u003eCoastal vegetation plays a critical role in the formation and persistence of dunes by modifying sediment transport and accumulation (Hacker et al., 2012; Psuty, 1989). The presence of vegetation increases the sand-trapping efficiency of dunes by 10\u0026ndash;50% (Keijsers et al., 2015) and reduces soil erosion by up to 45% (Dahl et al., 1975). However, dune vegetation is not uniformly distributed; rather, it forms discrete patches influenced by environmental gradients, disturbance regimes, and species interactions (e.g., Ciccarelli, 2015; Doing, 1985). In turn, these vegetation patches strongly influence where dunes form and how they evolve (Barbier et al., 2008; Bonte et al., 2021; Seabloom et al., 2013).\u003c/p\u003e\u003cp\u003eWhile previous studies have examined how plant density, dune width, clonal growth patterns and patch shape influence dune dynamics (Reijers et al., 2019, Kobayashi et al., 2013; Bouma et al., 2010; Charbonneau et al., 2017; Silva et al., 2016), the role of patch size in controlling sediment elevation in dunes remains poorly understood. In other coastal vegetated ecosystems, such as seagrasses and saltmarshes, studies suggest that hydrodynamic forcing (i.e., waves and currents) decreases from the edge towards the interior of vegetation belts or patches (Bouma et al., 2009; M\u0026ouml;ller et al., 2014; Peterson et al., 2004), promoting sediment deposition. However, these ecosystems are permanently or frequently submerged, whereas dunes are primarily shaped by wind-driven sediment transport and are only exposed to waves during storms. Furthermore, most research on the role of vegetation in sediment dynamics has been conducted in controlled flume experiments rather than in natural field settings. As a result, empirical field studies directly linking vegetation patch size to sediment elevation in coastal dunes remain scarce, leaving a critical gap in our understanding of dune-building processes.\u003c/p\u003e\u003cp\u003eBeyond patch size, dune vegetation exhibits a wide variation in plant traits, such as aboveground structure, flexibility, biomass, and belowground root architecture\u0026ndash; all known to influence sediment binding capacity (Figlus et al., 2017). Differences in plant functional traits among dominant dune species can shape sand elevation patterns, with aboveground biomass reducing wave energy and water velocity, while belowground structures stabilise sediment and reduce soil erodibility (Coops et al., 1996). Root systems further enhance dune-building by dissipating current velocity (Koch et al., 2006) and trapping wind-blown sediment, facilitating continuous dune growth (Sigren et al., 2014). Importantly, species that integrate both structural components\u0026ndash; above- and belowground\u0026ndash; tend to provide the most effective erosion control compared to those relying on either component alone (Figlus et al., 2017). However, the extent to which patch-scale vegetation attributes, particularly patch size, interact with these functional traits to drive sediment accumulation remains poorly quantified.\u003c/p\u003e\u003cp\u003eUnderstanding how vegetation modulates sediment elevation is critical for assessing coastal vulnerability and informing nature-based coastal management strategies. As managers move away from hard-engineering solutions, interest in revegetation schemes has grown. However, these efforts often lack a mechanistic understanding of how specific plant traits and vegetation structures contribute most effectively to dune resilience. Identifying key attributes that enhance sediment elevation and retention would help optimise dune revegetation programmes and improve coastal protection (Maximiliano‑Cordova et al., 2019). Thus, providing field-based evidence to bridge that knowledge gap is essential to support the design strategies that maximize the protective functions of coastal vegetation.\u003c/p\u003e\u003cp\u003eIn this study, we explicitly test whether patch size is a key determinant of sediment elevation, alongside species composition, canopy height, and dominant plant traits. To address this gap, we use differential GPS measurements to quantify ground elevation inside and outside vegetated patches across multiple coastal sites. By directly measuring patch-scale sediment dynamics, this study offers novel insights into how vegetation structure influences dune-building processes under natural conditions, with direct implications for dune restoration and coastal management.\u003c/p\u003e"},{"header":"2 MATERIALS AND METHODS","content":"\u003cp\u003eTo assess the role of patch-level attributes and species traits on sand elevation, we surveyed 480 individual vegetated patches varying in size, species richness, and distance from the shore across sites with a similar species assemblage. These ecosystems included beaches, dunes, and transitional zones leading to salt marshes. Site selection was based on identifying locations with comparable species composition, similar slopes, and environmental conditions but including a range of patch sizes and plant species diversity. The study included 10 sites across the Iberian Peninsula, spanning the Mediterranean Sea, the Cantabrian Sea, and the Atlantic Ocean (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sampling was conducted from September 2022 to September 2023.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 VEGETATION DATA COLLECTION\u003c/h2\u003e\u003cp\u003eWe quantified elevation change in vegetated patches by measuring the difference in elevation (cm) between the central point inside the patch and the immediate area outside the patch. To minimise confounding effects of topography, we selected patches in relatively flat areas with minimal slope (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We measured ground elevation using a differential GPS (Emlid Reach RS2\u0026thinsp;+\u0026thinsp;RTK mode) with a vertical accuracy of \u0026plusmn;\u0026thinsp;0.14 millimetres (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to elevation, we recorded several vegetation and morphometric attributes for each patch. Vegetation variables were (i) patch species composition and (ii) dominant species within each patch. In terms of patch species composition, we defined patches with a single species as monospecific, while those containing multiple species were categorised as multispecific (mixed patches). To determine the dominant species, we estimated species cover using a 40x40 cm quadrat within each patch. In terms of morphometric variables, we measured patch size (width and length) and canopy structure using a tape measure with \u0026plusmn;\u0026thinsp;0.01 metres accuracy. Canopy structure included maximum canopy height, defined as the horizontal width of the aboveground portion of the vegetation patch. A total of 12 dominant plant species were recorded across all vegetation patches (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 FUNCTIONAL TRAITS\u003c/h2\u003e\u003cp\u003eWe collected plant samples from nine species in June of 2023 to assess functional traits. Sampling followed the protocols described in Kleyer et al. (2019). For each species, we extracted three replicate core samples using 15 cm diameter \u0026times; 40 cm length corers. To preserve sample integrity, we kept plants on ice during fieldwork and measured all traits promptly upon returning to the laboratory or field base.\u003c/p\u003e\u003cp\u003eOnce in the laboratory, we assessed functional traits using direct morphometric measurements and biomass data. Aboveground traits included maximum leaf length, the number of shoots, and leaf spread. Belowground components were separated into belowground stems, rhizomes, and roots, and their structure was analysed in detail. We recorded the total rhizome length and the number of roots, along with root architecture metrics such as total length, maximum size, mean size and root spread (Fig. S2). To evaluate root concentration and spatial distribution in the soil, we calculated several root traits, including root tissue density (RTD, g/cm\u0026sup3;), density root length (DLR, cm/cm\u0026sup2;), specific root length (SRL, cm/g), root length ratio (RLR, cm/g) and the relative root weight (RWR), alongside aboveground biomass (AGB). We also determined the root to shoot ratio (RS) to assess biomass allocation between above- and belowground structures. Additionally, we recorded the overall plant size, measured from base to tip.\u003c/p\u003e\u003cp\u003eAfter completing the measurements, we dried all above- and belowground components in an oven at 70\u0026deg;C for 48 h and weighed to determine dry biomass (Lemein et al., 2015). Further species characteristics, such as family classification and phenology (annual vs. perennial), were obtained from the literature (Table S3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 SEDIMENT ANALYSIS\u003c/h2\u003e\u003cp\u003eTo characterize the sediment properties at each site, we collected surface sediment samples (top 5 cm) from all dune sites using a 4.8 cm \u0026Oslash; core. At each site, we randomly collected three replicate cores and froze them at \u0026minus;\u0026thinsp;20\u0026deg;C until further processing. We determined organic matter content (% dry weight) using loss on ignition (LOI), heating samples at 450 ◦C for 4 hours (Heiri et al., 2001) after homogenising them in a mortar grinder (Touch et al., 2017). Grain size was classified based on the Udden-Wentworth scale, ranging from \u0026lt;\u0026thinsp;63 \u0026micro;m to \u0026gt;\u0026thinsp;1000 \u0026micro;m, using a Malvern Laser Particle Sizer 2000, after digesting\u0026thinsp;~\u0026thinsp;5 g of dried sediment with 30% hydrogen peroxide at 60\u0026deg;C to remove organic matter (conducted at the Centre for Advanced Studies of Blanes, CEAB-CSIC).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 STATISTICAL ANALYSIS\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Drivers of patch elevation (stepwise linear regression)\u003c/h2\u003e\u003cp\u003eTo investigate the drivers of elevation change across vegetation patches, we fitted Generalised Linear Mixed effects Models (GLMMs) using the lme4 (Bates et al., 2015), lmerTest (Kuznetsova et al., 2017), and nlme (Pinheiro et al., 2022) packages in R. Before analysis, we identified and removed instrumental outliers from the dataset, which were attributed to DGPS measurement errors \u0026ndash; specifically, when the device was operating in less accurate \u0026ldquo;float\u0026rdquo; mode instead of \u0026ldquo;fix\u0026rdquo; mode, resulting in implausible values (e.g., elevation change\u0026thinsp;\u0026gt;\u0026thinsp;1 m).\u003c/p\u003e\u003cp\u003eThe response variable, 'elevation change' (log-transformed), was calculated as the difference in elevation between the inside and outside of each patch (see previous section). We modelled \u0026lsquo;elevation change\u0026rsquo; as a function of the following fixed explanatory variables: 'dominant plant species identity' (categorical with 12 levels), 'patch composition' (categorical with 2 levels: monospecific vs. multispecific patches), 'species richness', 'patch canopy', 'patch area', 'patch distance to the coastline', and sediment properties (\u0026lsquo;organic matter content\u0026rsquo; and \u0026lsquo;grain size\u0026rsquo;).\u003c/p\u003e\u003cp\u003eTo account for site-level variability, we included 'site' as a random effect, which was supported by Akaike Information Criterion (AIC) comparisons and log-likelihood ratio tests (Zuur et al., 2009). We used a stepwise approach for model selection, systematically dropping non-significant fixed effects and selecting the best model based on the AIC and the likelihood ratio tests (Zuur et al., 2009). For each best-selected model, we performed marginal F-tests with univariate analysis of deviance to assess the effects of the remaining explanatory variables. Tukey Honestly Significant Difference (HSD) post hoc tests were performed using the package multcomp (Hothorn et al., 2008) to determine level-specific differences within model variables. We validated model performance and checked model assumptions by inspecting residuals and fitted values using a simulation-based approach with the DHARMa package (Hartig, 2022). We further evaluated model fit using the performance package (L\u0026uuml;decke et al., 2021).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Principal Component Analysis (PCA) of plant functional traits\u003c/h2\u003e\u003cp\u003eTo explore whether functional traits defined a single or group of plant species, we performed a Principal Component Analysis on 9 species, each characterised by 20 traits (Table S3). Species selection for this analysis was based on the most common species in the dataset, ensuring sufficient representation of trait variation. The PCA was conducted on scaled data (given the variety of trait units) and the principal components were retained after inspecting scree plots.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 Partial Least Squares Regression (PLSR)\u003c/h2\u003e\u003cp\u003eWe used Partial Least Squares Regression (PLSR) to examine how plant traits influence the response variable \u0026lsquo;elevation change\u0026rsquo;. This multivariate method generates latent variables (PLS components) as linear combinations of predictors, maximising the explained variance of the response variable. PLSR is particularly effective when the number of predictor variables is high (21 in this study) and when predictors exhibit high collinearity, both common characteristics of trait data (Carrascal et al., 2009).\u003c/p\u003e\u003cp\u003eWe assessed PLSR models based on variance explained (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e), loadings of predictor variables on PLS components, and Variable Importance in Projection (VIP). Loadings indicate the strength and direction of relationships between predictors and the response variable (\u0026lsquo;elevation change\u0026rsquo;), while VIP values provide a summary of the importance of each predictor, with a threshold of VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 indicating significance (Farr\u0026eacute;s et al., 2015).\u003c/p\u003e\u003cp\u003eTo evaluate PLSR performance, we split the dataset into training (80%) and test (20%) sets. PLSR was conducted on the training set, and the resulting model was validated using the test data. To improve robustness, we repeated this process 100 times, generating distributions of VIP values and loadings for each variable. We visualised VIP and loadings results using boxplots and barplots with error bars. PLSR analyses were carried out using pls (Mevik et al., 2011) and caret (Kuhn, 2021) packages in R.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Drivers of patch elevation (stepwise linear regression)\u003c/h2\u003e\u003cp\u003ePatch-level characteristics played a key role in determining sediment elevation across coastal dune ecosystems (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). We found very strong evidence that patch size and species composition influenced sediment elevation inside patches, with larger and multispecific patches showing higher values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Despite intraspecific variation, we also found very strong evidence that species identity was a driver of sediment elevation, with \u003cem\u003eA. arenaria\u003c/em\u003e forming patches with greater inner elevation than other species, particularly \u003cem\u003eSalicornia europaea\u003c/em\u003e and \u003cem\u003eCakile maritima\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eIn contrast, sediment properties (organic matter content and grain size), patch canopy height and distance to the shore showed little or no evidence of influencing patch elevation. Due to collinearity between species richness and patch composition (mono- vs. multispecific patches), we could not test the independent effect of species richness on elevation change.\u003c/p\u003e\u003cp\u003ePatch area, patch composition and species identity together explained 39% of the variance in sediment elevation (R\u003csup\u003e2\u003c/sup\u003emarginal\u0026thinsp;=\u0026thinsp;0.389). Including the random effect \u0026lsquo;site\u0026rsquo; increased the explained variance to 46% (R\u003csup\u003e2\u003c/sup\u003econditional\u0026thinsp;=\u0026thinsp;0.462), highlighting the combined influence of patch characteristics and local environmental factors on sediment dynamics. The remaining unexplained variance suggests that additional untested factors contribute to dune patch elevation change.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from the stepwise linear mixed-effects models (GLMM) for the main drivers of elevation change.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ed.f.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elog(elevation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elog(patch area)\u003c/p\u003e\u003cp\u003ePatch composition\u003c/p\u003e\u003cp\u003eSpecies identity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e169.137\u003c/p\u003e\u003cp\u003e23.210\u003c/p\u003e\u003cp\u003e31.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001***\u003c/p\u003e\u003cp\u003e0.001***\u003c/p\u003e\u003cp\u003e0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ed.f..: degrees of freedom.\u003c/p\u003e\u003cp\u003e***, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, strong evidence for the effect influencing the response variable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe LOI450 (%DW) values varied significantly across localities, indicating differences in sediment composition and potential organic accumulation (Fig. S4). Sediment particle distribution followed a consistent pattern across most sites, with coarse sand (250\u0026ndash;1000 \u0026micro;m) being the dominant fraction, whereas silt, clay, and fine sand were present in smaller proportions (Fig. S5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Principal Component Analysis (PCA) of plant functional traits\u003c/h2\u003e\u003cp\u003eThe first two principal components (PCs) explained 61% of the variance in dune species traits, with PC1 accounting for 33.9% and PC2 explaining 27.1%. PC1 primarily differentiated species based on biomass allocation strategies, specifically root vs. shoot dominance and was dominated by relative traits (ratios) that are independent of plant size. Traits related to root allocation, such as root weight ratio (RWR), root to shoot ratio (RS), and root length ratio (RLR), had high positive loadings, while shoot-dominated traits, including aboveground biomass (AGB), above-to-belowground biomass ratio, and stem biomass, had negative loadings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, arrows).\u003c/p\u003e\u003cp\u003eSpecies were widely distributed across the PCA plot, indicating high functional diversity. Species on the positive side of PC1, such as \u003cem\u003eA. arenaria\u003c/em\u003e and \u003cem\u003eEchinophora spinosa\u003c/em\u003e, exhibited strong root dominance, while those on the negative side, such as \u003cem\u003eSarcornia fruticosum\u003c/em\u003e and \u003cem\u003eSalsola kali\u003c/em\u003e were associated with greater aboveground investment.\u003c/p\u003e\u003cp\u003ePC2 primarily captured differences in plant size and was dominated by absolute trait values that scale with plant size. Traits such as total root length and leaf length had high positive loadings, meaning larger species like \u003cem\u003eA. arenaria\u003c/em\u003e were positioned on the positive side of PC2. In contrast, smaller species such as \u003cem\u003eLimonium vigoi\u003c/em\u003e, \u003cem\u003eSuaeda maritima\u003c/em\u003e and \u003cem\u003eS. europaea\u003c/em\u003e aligned with the negative side, reflecting their overall smaller size and shorter root and leaf structures.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Partial Least Squares Regression (PLSR)\u003c/h2\u003e\u003cp\u003ePLSR did not identify a single key functional trait associated with sediment elevation across dune plant species. Instead, the variable importance in projection (VIP) highlighted that only patch-level attributes, specifically patch area and patch canopy (although near the VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 threshold), were the most influential factors. This suggests that patch-level attributes play a greater role in driving sediment elevation than the suite of species-level functional traits measured. Furthermore, PLSR results suggest that rather than a single dominant plant trait, a combination of functional traits might determine elevation capabilities across species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eOverall, PLS1 explained 35.3% of the variance in predictors but only 11.4% of the variance in elevation, indicating that additional factors contribute to sediment elevation. Root traits, including mean root length, RLR and DLR, were among the positively weighted variables on PLS1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), though their VIP values remained low. Interestingly, PLS1 was similar to PC1 from Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, with root-associated traits dominating the positive side and aboveground traits loading negatively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThe final PLS regression model with 2 PLS components explained 19.8% of the variation in elevation and 46.7% of the predictor variance. The final PLS regression model with 3 PLS components explained 21.3% of the variation in elevation and 55.6% of the predictor variance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003col\u003e\u003cli\u003e\u003cspan\u003e Results have been written translating p-values into evidence language, as recommended by (Muff et al., 2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eEcologists have traditionally focused on how dune vegetation adapts to dune topography and environmental conditions, while geomorphologists have studied the changing properties of dune architecture in response to physical rather than biological factors. However, a holistic approach that integrates both perspectives is needed to fully understand the broader drivers of how vegetation influences sediment elevation, which is essential to enhance coastal dune resilience as natural flood barriers. Our findings show that patch-level characteristics\u0026ndash; particularly patch area, canopy and species composition\u0026ndash; are the primary drivers of sediment elevation across coastal dune ecosystems. While species identity significantly influenced elevation outcomes, no single plant trait dominated. Instead, our results suggest that a combination of functional traits, likely those related to root architecture traits (e.g., root density, total root length, and root weight ratio), might play a role.\u003c/p\u003e\u003cp\u003eSand dunes and their vegetation interact dynamically, each modifying the conditions that shape the other. Vegetation controls sediment deposition and accumulation (Nyman et al., 2006), typically increasing dune height and volume (Dur\u0026aacute;n \u0026amp; Moore, 2013; Feagin et al., 2015). Our results confirm that vegetation consistently enhances sediment elevation, with patch size as the primary driver (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Elevation increases with patch size but plateau beyond a threshold, likely constrained by plant growth dynamics and environmental factors such as sediment availability, burial rates, or hydrodynamic forces (Hesp, 1991; Wang et al., 2006). Larger and denser patches reduce coastal erosion and maximize sand-trapping efficiently than sparse ones (Charbonneau et al., 2017; Kobayashi et al., 2013; Reijers et al., 2019; Silva et al., 2016). Our study provides empirical validation from the field to some of these results, which mainly arise from flume experiments or artificial plant mimics (Feagin et al., 2023; Figlus et al., 2022).\u003c/p\u003e\u003cp\u003eVegetation patches vary in biophysical structure\u0026ndash; shaped by plant density, distribution, morphology, as well as species composition and trait interactions between species (Ciccarelli et al., 2023; Maximiliano‑Cordova et al., 2019)\u0026ndash;, which influences their \u0026lsquo;roughness form\u0026rsquo; and potential for sediment trapping and elevation capacity (Bouma et al., 2010; Gillies et al., 2014). These factors significantly alter their ecological functions, particularly their soil-building ability. Although most sampled patches were monospecific, our results show that multispecific (mixed) patches exhibited greater soil elevation capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and c, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), likely due to their greater structural complexity, creating functionally richer environments (Acosta et al., 2009). While sand can accumulate around any living or non-living structure, dune plants grow in size, replicate, and are stimulated in response to burial (Feagin et al., 2023), which ultimately facilitates elevation processes. Thus, multispecific patches may better adapt physiologically to dynamic conditions and actively contribute to the development of natural sand barriers, functioning not merely as passive elements but as key agents of coastal resilience (Feagin et al., 2023; Zarnetske et al., 2012).\u003c/p\u003e\u003cp\u003eOur results reveal significant variation in the sand elevation capacity of different plant species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In psammophilous plants, burial acts as a selective force determining processes of vegetation assembly (Brown \u0026amp; Zinnert, 2018), filtering out species when burial exceeds their survival threshold (Maun, 2004), while stimulating the growth and vitality of other species (Stallins, 2005). Dune-building species like \u003cem\u003eA. arenaria\u003c/em\u003e play an important role in dune development (Bonte et al., 2021), contributing the most to sediment elevation through extensive root systems and tall canopies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (e.g., Bouma et al., 2001; Figlus et al., 2022). They adjust growth patterns to rapidly produce vertical sediment elevation, enhancing anchoring and sand capture efficiency in response to environmental signals (Reijers et al., 2019). Despite belowground investment is widely assumed to play a role in dune-building processes and resilience to disturbances (Charbonneau et al., 2017; Ciccarelli et al., 2023; Figlus et al., 2022), our results surprisingly did not provide strong evidence that root traits are key drivers of sediment elevation (VIP\u0026thinsp;\u0026lt;\u0026thinsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Since the data were collected during a stationary sampling period under calm conditions, future studies should assess if root structures show a greater soil stabilization role after a disturbance event, such as a storm.\u003c/p\u003e\u003cp\u003eAdditionally, not all species significantly contribute to dune elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Some plants, like \u003cem\u003eE. paralia, S. maritima\u003c/em\u003e or \u003cem\u003eC. maritima\u003c/em\u003e, are burial-tolerant stabilizers that respond positively to burial, but do not promote vertical dune growth, instead providing resistance to overwash (Zinnert et al., 2016). These species appear to invest more in aboveground growth (traits negatively aligned with PC1, ABG, and stem biomass, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In back dune zones, burial-intolerant stabilizers such as \u003cem\u003eS. fructicosum, S. europaea\u003c/em\u003e, or \u003cem\u003eL. vigoi\u003c/em\u003e, are well-adapted to flood-prone areas, with short lateral roots that respond to anoxic and hypoxic conditions (Bouma et al., 2001), enabling them to tolerate saline flooding, regenerate through sediment deposition, and thrive in wetter soils (Silander \u0026amp; Antonovics, 1979). While maximizing dune elevation is central to securing coastal futures, preserving other key functions like sand stabilisation or salt tolerance is equally important as sea levels rise and storms intensify. Since no single species guarantees full coastal protection (Barbier et al., 2008), conserving the entire dune vegetation diversity is essential (Standish \u0026amp; Parkhurst, 2024). Identifying which key trait adaptations drive elevation dynamics can guide effective revegetation efforts, though our results suggest no single trait dominates. Further investigation is needed to clarify whether elevation is driven by species richness \u003cem\u003eper se\u003c/em\u003e or by specific combinations of functional traits.\u003c/p\u003e\u003cp\u003eAll the evidence presented above underscores the challenges and limitations of coastal restoration projects, particularly in dune ecosystems, which remain poorly addressed in the literature despite their ecological importance (Lithgow et al., 2013). Despite substantial investments averaging \u003cspan\u003e$\u003c/span\u003e1600000/ha (2010 price levels) (Bayraktarov et al., 2016), approximately 40% of attempts fail or achieve only partial success in fully rehabilitating target species (Suding, 2011). This is often due to limited local-scale ecological data or the lack of information about functional traits and physiological adaptations to dynamic environmental conditions (Gallego‑Fern\u0026aacute;ndez et al., 2011). Additionally, despite the common assumption that vegetation\u0026rsquo;s physiological role is to stabilize sand during extreme events, initial evidence provided by Feagin et al. (2023) suggest this assumption may be incorrect if it does not include good ecological knowledge, potentially leading to negative consequences. For example, the introduction of non-indigenous species (e.g. \u003cem\u003eCasuarina\u003c/em\u003e, \u003cem\u003eAmmophila\u003c/em\u003e, \u003cem\u003eSpartina\u003c/em\u003e, or \u003cem\u003eTamarix\u003c/em\u003e) in impacted areas has displaced native plants, often resulting in reduced dune accumulation and lower elevations in stabilization projects worldwide, ultimately increasing erosion over longer time scales (Gao et al., 2020).\u003c/p\u003e\u003cp\u003eOverlooking the ability of psammophilous species to adapt functionally to variable coastal conditions may accelerate ecosystem degradation and associated community loss during perturbations. Low-cost nature-based solutions, such as strategic revegetation planting, offer a sustainable alternative to the commonly used hard structures (Grafals‑Soto, 2012). We propose prioritizing patch-level attributes\u0026ndash; particularly patch size (already discussed above), canopy height (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and multispecies composition\u0026ndash; as key considerations for restoration programmes. Given the morphological variability of dune plant communities, this study offers a step toward integrating ecological knowledge to refine species selection in dune restoration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors belong to the research group 2021 SGR 00405 funded by the Generalitat de Catalunya (AGAUR). This project received financial support from the Spanish Agency of Research (AEI-MICINN), grants STORM (PID2020-113745RB-I00) and DYNCOAST (PID2023-151732OA-I00), funded by MCIN/AEI/10.13039/501100011033 and the FES+. LRA was supported by an FPI fellowship (PRE2021-099061) and JFP by the Ram\u0026oacute;n y Cajal programme (RYC2022-036196-I), both funded by MCIN/AEI/10.13039/501100011033 and the FES+. The authors thank Jordi Boada, Jes\u0026uacute;s Zarcero, In\u0026eacute;s Mazarrasa, Fernando Garc\u0026iacute;a Gonz\u0026aacute;lez, Mario Minguito-Frutos, and Luca Di Vita for their contribution, and the administrations of the Ebro Delta and Aiguamolls de l\u0026rsquo;Empord\u0026agrave; Natural Park for research permits and logistical support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study design. LRA, AMA, and SPM led fieldwork and prepared and analysed the samples. LRA and JFP analysed the data. LRA, TA, RA, and JFP led the writing of the manuscript, with contributions from all the authors. All authors contributed to the article and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCORRESPONDING AUTHOR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Luc\u0026iacute;a Rodr\u0026iacute;guez-Arias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no financial or non-financial competing interests to declare.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available, as well as the R scripts used to run all of the analyses reported, in a GitHub repository (https://github.com/lucciluRdrigz/dunes).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcosta, A., Carranza, M. L., \u0026amp; Izzi, C. F. (2009). 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New York: Springer. https://doi.org/10.1007/978-0-387-87458-6\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":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":"coastal management, coastal protection, dune restoration, functional diversity, natural coastal barrier","lastPublishedDoi":"10.21203/rs.3.rs-7309660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7309660/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal dune vegetation is increasingly recognised as a nature-based solution for buffering shoreline dynamics, particularly by reducing the risk of inland inundation during storms. This protective function depends on the capacity of vegetated dunes to maintain or build elevation. To identify the ecological attributes influencing sediment elevation, we examined how patch size, canopy structure, dominant species identity, species composition, and plant traits shape elevation patterns across multiple coastal sites with similar dune plant assemblages. We used centimetric-precision differential GPS to measure sediment elevation inside and immediately outside vegetated patches. To our knowledge, this is the first field-based, multi-site study to directly test the effect of vegetation patch size on sediment elevation in coastal dune systems. While patch size is assumed to influence sediment dynamics, previous work has either focused on mesocosms or treated vegetation as a homogeneous cover layer, without explicitly addressing patch-scale heterogeneity. Our results show that patch size, species composition, and dominant species identity significantly influence dune elevation. Larger and mixed-species patches promoted higher sediment elevation than monospecific ones, and patches dominated by \u003cem\u003eAmmophila arenaria\u003c/em\u003e were particularly effective. No single plant trait consistently explained elevation patterns, but our findings suggest that combinations of traits\u0026ndash; likely related to root architecture\u0026ndash; may enhance sediment accumulation. These results highlight key ecological attributes that support elevation gain in coastal dunes and offer insights for conservation and restoration strategies aimed at enhancing natural defences against storm-driven inundation in dynamic shoreline environments.\u003c/p\u003e","manuscriptTitle":"Vegetation patch-level characteristics as key drivers of coastal dune elevation: optimizing nature-based coastal defences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 15:41:01","doi":"10.21203/rs.3.rs-7309660/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-19T15:12:59+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-17T09:12:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Estuaries and Coasts","date":"2025-08-06T15:23:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T12:36:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Estuaries and Coasts","date":"2025-08-06T08:16:14+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":"b8df496e-6366-41ca-ac99-c284fcaaa94c","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T14:40:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 15:41:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7309660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7309660","identity":"rs-7309660","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}