Mediterranean coastal dunes have undergone substantial transformations over the last 70 years due to increasing anthropogenic pressure and environmental change. However, most studies on dune vegetation dynamics have been conducted at local scales, limiting our understanding of long-term plant diversity trends across broader regions. Here, we present the first national-scale assessment of long-term vegetation changes in Italian coastal dunes, based on ReSurveyDunes, a collaborative resurvey initiative. We analysed 519 vegetation plots originally surveyed on average 30 years ago and resampled in 2023-2024 along the entire Italian coastline. We quantified temporal changes in species richness and community composition, with a focus on ecological guilds, and analysed habitat transitions over time across three key dune habitats: upper beach, shifting dunes, and dune grasslands. Species richness increased across all habitats. However, this trend masked a marked decline of habitat-specialist psammophilous species, particularly in early-successional habitats. Upper beach and shifting dunes showed strong reductions in occurrence and cover of diagnostic species, accompanied by increases in ruderal taxa and species typical of more stabilised or inland habitats. These patterns reflect a redistribution of species along the coastal zonation gradient. Accordingly, nearly one-third of plots changed EUNIS habitat type or disappeared, indicating the coexistence of inland-directed succession and stabilisation with localised degradation and habitat loss, especially in foredune habitats. Our results show that apparent increases in species richness can conceal profound compositional and habitat-level changes. This highlights the importance of long-term, large-scale resurveys and of complementing richness-based metrics with compositional and habitat-level indicators when evaluating vegetation changes in dynamic coastal dune ecosystems.

Mediterranean forests are complex ecosystems shaped by human activity, with early-spring flowering geophytes playing a key role. This study focuses on Crocus etruscus, an early-flowering geophyte endemic to central Italy. We investigated its ecological strategy in relation to surrounding deciduous forests and environmental factors across 12 sites along a coast-to-inland gradient. Vegetation surveys, and measurements of leaf traits (Leaf Area, Leaf Dry Matter Content, and Specific Leaf Area) were performed to estimate the Competitive-Stress tolerant-Ruderal strategies of both C. etruscus and forest communities. Statistical analyses revealed intraspecific trait variation in C. etruscus, though its CSR strategy remained consistent across populations. In contrast, the ecological strategies of forest communities showed great variability. Specifically, altitude, distance from the coast, pH, nitrogen content, and carbon nitrogen ratio significantly influenced the ecological strategies of both C. etruscus and plant communities. In conclusion, this study shows that C. etruscus can tolerate low levels of human disturbance (i.e., in chestnut woods) and stress factors, such as nutrient scarcity, limited water availability, and high temperatures (i.e., in oak woods). Nonetheless, the research underscores the importance of sustainable forest management to preserve this endemic species and support forest biodiversity and function.

Human activities are driving simultaneous native extinctions and alien naturalizations, reshaping global tree diversity with major implications for ecosystem structure and function. Here we analysed functional traits and environmental niches of 31,001 tree species worldwide, comparing naturalized, threatened and non-threatened species to assess current patterns and project future shifts under intensified extinction and naturalization. Future tree-rich ecosystems are projected to become increasingly dominated by fast-growing, high-resource-use species with acquisitive traits, while slow-growing, conservative species face greater extinction risk. Although group means along the main functional axes do not differ significantly, naturalized species occupy broader functional and environmental spaces and thrive in colder and more variable climates, whereas threatened species are more specialized to warm, stable and nutrient-rich environments, with non-threatened species intermediate. Projected naturalizations expand local functional diversity, but their acquisitive strategies could reduce long-term ecosystem stability, while extinctions cause pronounced contractions of functional and environmental trait space, especially in climatically variable regions. Overall, our findings reveal an accelerating global shift towards faster-growing tree communities, with likely consequences for carbon storage and biodiversity, underscoring the need to safeguard slow-growing species and limit the dominance of acquisitive trees.

Species distribution models (SDMs) are widely used to predict species’ habitat suitability, yet they often rely primarily on abiotic predictors, potentially limiting their ecological realism. This limitation may be particularly relevant for habitat-specialist species whose distribution is strongly shaped by vegetation structure and biotic context. In this study, we evaluated the contribution of ecologically informed biotic proxies to SDMs developed for Crocus etruscus, a nearly-threatened early-spring flowering geophyte endemic to Mediterranean forests of central Italy. We compared a baseline model based solely on abiotic variables with models incorporating land cover–derived dominant tree composition and remotely sensed NDVI. Models including dominant tree composition markedly outperformed both abiotic-only and NDVI-based models, achieving higher discrimination ability and improved model fit. However, NDVI provided additional information, when combined with land cover data. This study highlights the importance of integrating species-specific ecological knowledge into predictor selection and supports the use of biotic proxies to enhance the ecological relevance of SDMs applied to habitat-specialist plant species.

Dataset containing species and trait data used in the manuscript "Multiple environmental filters reduce between-species trait diversity but foster intraspecific variability for specific leaf area". The dataset contains also the R file with the codes used to run the analysis.This dataset is a subset of large published dataset:Chelli, S., Bricca, A., Petruzzellis, F., Tordoni, E., Calvia, G., Acosta, A. T. R., Bacaro, G., Beccari, E., Bernardo, L., Bonari, G., Bolpagni, R., Boscutti, F., Campetella, G., Cancellieri, L., Canullo, R., Carbognani, M., Carboni, M., Carranza, M. L., Castellani, M. B., … Puglielli, G. (2025). Dataset for: ITV-net: a dataset of intraspecific leaf traits data across major Italian habitats [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15642699 For more information see Chelli et al. (2025). Please do not use this reference but the original ones (i.e., Chelli et al., 2025).

Coastal ecosystems play a critical role in shoreline protection and support coastal communities by providing essential ecological functions and natural resources. Along the Brazilian coastline, restinga environments form heterogeneous habitat mosaics characterized by high floristic diversity and challenging environmental conditions. Within the restinga of the Parque Natural Municipal das Dunas da Lagoa da Conceição (PNMDLC), in Florianópolis, southern Brazil, the endangered endemic species Noticastrum hatschbachii Zardini occurs across a range of distinct microhabitats. This study investigated intraspecific variation in leaf and root functional traits of N. hatschbachii across four contrasting microhabitats (Front, Back, Plain, and Slack) and examined whether such variation is influenced by edaphic factors. Additionally, we investigated whether the coordination between above- and belowground traits changed across different microhabitats. Univariate and multivariate analyses, including Non-Metric Multidimensional Scaling, revealed clear functional differentiation among microhabitats, indicating environmentally driven phenotypic variability. Trait variation included thicker root tissues in the Front, lower leaf dry matter content (LDMC) in the Back, higher LDMC in the Plain and Slack, and thicker cuticles in the Slack. However, coordination between leaf and root traits varied markedly among microhabitats, indicating that above- and belowground organs respond independently to local environmental conditions. Rather than expressing a single whole-plant strategy, N. hatschbachii exhibited multiple combinations of leaf and root traits, highlighting the context-dependent nature of functional coordination. This functional flexibility probably underlies the broad ecological amplitude of N. hatschbachii and highlights its potential for restoration and conservation of heterogeneous restinga ecosystems.

Surveying vegetation is essential for documenting plant diversity, especially for coastal vegetation that results among the most threatened ecosystems globally. To support conservation and management programs, we developed the SALt-affected vegeTatIon dataset of Tuscany coaStal Habitats (SALTISH). This dataset comprises 734 newly sampled vegetation plots of 4 m 2 (2 m × 2 m) from the Tuscany region in central Italy, including 569 sand dune plots and 165 salt marsh plots, recorded between 2018 and 2023. In total, the dataset contains 4,541 occurrences of vascular plant taxa. Overall, it comprehends 257 vascular plant taxa belonging to 165 genera and 56 families. The Poaceae family is the most diverse, represented by 50 taxa, while the most represented genus is Juncus , with seven species. Species richness within individual plots ranges from one to 55 species, with 622 plots (84%) containing fewer than 10 species. Juniperus macrocarpa emerges as the most frequent and dominant species in the dataset. Helichrysum stoechas , Festuca fasciculata , and Medicago littoralis are present in over 20% of the plots, whereas 157 taxa are recorded in fewer than 1% of plots. The dataset includes noteworthy taxa: four Italian endemics ( Centaurea aplolepa subsp. subciliata , Limonium etruscum , L. multiforme , and Solidago virgaurea subsp. litoralis ), eight taxa listed as threatened in the Italian Red List, and 18 archaeophyte and neophyte alien species. SALTISH provides critical data for monitoring and conserving threatened coastal habitats in Tuscany. This resource will facilitate comparisons of biodiversity status and vegetation changes over time and will aid in identifying habitats harboring rare and endangered plant species.

Over the last decades plant ecology has greatly benefited from open data on functional traits. Nowadays, several national and international trait databases are available, but trait data from Southern European countries are generally missing or underrepresented. In addition, most of the available databases lack detailed trait information at the intraspecific level, an important source of trait variation linked to species potential for adaptation. Data were gathered from 21 Italian research groups. Individual measurements of leaf area (LA) and specific leaf area (SLA) for the most abundant vascular plant species were provided at the plot level using standardised protocols of trait measurement. The ITV-net dataset includes 8,518 records of leaf area (LA) and specific leaf area (SLA) in 1,043 georeferenced plots spanning most of the Italian peninsula and eight EUNIS habitat types. Individual LA and SLA measurements are available for 709 native and alien species (77 families, 353 genera). The ITV-net dataset is freely available in the Zenodo repository and will contribute to expanding research on a largely underrated source of trait variation and on its ecological consequences.

We present ReSurveyDunes — the first database of Italian coastal dune vegetation plots, developed through a collaborative network of Italian vegetation scientists. This paper describes the scope of the initiative, provides an overview of the available data, and highlights its key features, research applications, and future potential. ReSurveyDunes currently comprises 972 vegetation plots distributed across 11 Italian regions (Abruzzo, Basilicata, Calabria, Campania, Emilia-Romagna, Lazio, Molise, Sardinia, Sicily, Tuscany, and Veneto). The original plots were surveyed between 1974 and 2009, with a primary focus on herbaceous psammophilous coastal zonation (habitats 1210, 2110, 2120, 2130*, 2210, 2230, and 2240, as defined by the 92/43/EEC Habitats Directive). Resurveys were conducted in 2023 and 2024. Each plot dataset includes (i) a complete list of vascular plant species with cover estimates; (ii) geographic coordinates (latitude and longitude); (iii) sampling dates; and (iv) plot size. Designed to analyze fine-scale temporal trends in Italian coastal vegetation, ReSurveyDunes is a versatile tool for diverse ecological studies. It represents a valuable resource for evidence-based decision-making, enabling targeted conservation and management actions informed by up-to-date ecological data.

Coastal dunes are dynamic ecosystems vulnerable to human impact. Traditional monitoring relies on costly field surveys, but high‐resolution satellite imagery offers an efficient alternative. This study integrates remote sensing (RS) and field data to analyze vegetation and landscape changes over 25 years in the highly protected Castelporziano Presidential Estate. We examined three habitat groups—Herbaceous Dune Vegetation (HDV), Woody Dune Vegetation (WDV), and Broadleaf Mixed Forest (BMF)—using 58 resurveyed plots and land cover maps. Landscape dynamics and vegetation compositional changes were assessed, and temporal patterns were calculated for three buffer sizes (25, 75, and 125 m), using Bray–Curtis dissimilarity and differences in landscape metrics. Random forest models evaluated the relationship between landscape and vegetation compositional changes. The results revealed a reduction in artificial surfaces, greater vegetation encroachment, and clear signs of natural succession. HDV exhibited a shift toward grassland species, reflecting ongoing changes in vegetation composition. WDV experienced the most pronounced compositional change, while BMF showed signs of structural homogenization. Habitat proportion emerged as the strongest predictor of compositional changes, especially at the finest scale. These findings confirm the value of combining RS and field data for long‐term monitoring and provide useful insights for managing coastal dune habitats.

SummaryRemote sensing is a fundamental tool to monitor biodiversity over large spatial extents. However, it is still not clear whether spectral diversity (SD - variation of spectral response across a set of pixels) may represent a fast and reliable proxy for different biodiversity facets such as taxonomic (TD) and functional diversity (FD) across different spatial scales.We used fine resolution (3 cm) multispectral imagery on coastal dune communities in Italy to explore SD patterns across spatial scales and assess SD relationships with TD and FD along the environmental gradient.We measured TD as species richness, while SD and FD were computed using probability densities functions based on pixels and species position in multivariate spaces based on pixel values and traits, respectively. We assessed how SD is related to TD and FD, we compared SD and FD patterns in multivariate space occupation, and we explored diversity patterns across spatial scales using additive partitioning (i.e., plot, transect, and study area).We found a strong correspondence between the patterns of occupation of the functional and spectral spaces and significant relationships were found along the environmental gradient. TD showed no significant relationships with SD. However, TD and SD showed higher variation at broader scale while most of FD variation occurred at plot level.By measuring FD and SD with a common methodological framework, we demonstrate the potential of SD in approximating functional patterns in plant communities. We show that SD can retrieve information about FD at very small scale, which would otherwise require very intensive sampling efforts. Overall, we show that SD retrieved using high resolution images is able to capture different aspects of FD, so that the occupation of the spectral space is analogous to the occupation of the functional space. Studying the occupation of both spectral and functional space brings a more comprehensive understanding of the factors that influence the distribution and abundance of plant species across environmental gradients.

Coastal dunes play a crucial role in mitigating sea -related impacts and safeguarding coastlines. However, increasing human influence and natural factors such as sea -level rise underscore the need for effective coastal risk assessment methodologies. This study introduces a comprehensive coastal risk index covering 24 km of the Italian coastline within the protected area of San Rossore Park (Tuscany, Italy). The study area, distinguished by its notable coastal dune ecosystems, holds naturalistic, cultural, and economic importance. Nevertheless, diverse uses, zoning, and human impact variables pose challenges. By incorporating geological, socioeconomic, cultural, and ecological parameters, the index integrates a range of data sources and field observations. This research focused on developing and applying a vegetation -based risk index (VRI) within a geographic information system (GIS) framework, recognizing the ecological importance of dune vegetation in mitigating coastal erosion. Analysis: revealed varying risk levels within the study area. Half of the San Rossore Park coastline exhibited low risk values, 37.5% had moderate risk values, and 12.5% had high risk values. The publicly accessible northernmost section displays excellent preservation of dune habitats but faces heightened risk due to anthropogenic impacts. Conversely, the central -southern portion, inaccessible to the public, registers high -risk levels linked to variables associated with coastal erosion. Furthermore, the results highlight areas with heightened cultural and ecological vulnerabilities aligned with elevated risk levels. The index facilitates clear and intuitive cartographic representations of coastal risk, identifying variable categories that substantially influence on risk determination. Tailored strategies, including mitigating human pressure in the northern sector and implementing erosion management in the central -southern region, are recommended. In summary, this study not only provides a practical tool for assessing and managing coastal areas and directing attention to specific threats but also supports stakeholders in informed decision -making. The VRI enhances global sustainable coastal conservation, deepening our understanding of coastal risks and providing valuable insights for effective management strategies.

Trait-based ecology has already revealed main independent axes of trait variation defining trait spaces that summarize plant adaptive strategies, but often ignoring intraspecific trait variability (ITV). By using empirical ITV-level data for two independent dimensions of leaf form and function and 167 species across five habitat types (coastal dunes, forests, grasslands, heathlands, wetlands) in the Italian peninsula, we found that ITV: (i) rotated the axes of trait variation that define the trait space; (ii) increased the variance explained by these axes and (iii) affected the functional structure of the target trait space. However, the magnitude of these effects was rather small and depended on the trait and habitat type. Our results reinforce the idea that ITV is context-dependent, calling for careful extrapolations of ITV patterns across traits and spatial scales. Importantly, our study provides a framework that can be used to start integrating ITV into trait space analyses. By using empirical data for two independent dimensions of leaf form and function and 167 species across five habitat types, we show that including intraspecific trait variability in a trait space: (i) rotates the axes of trait variation of the target trait space, (ii) increases the variance explained by these axes and (iii) modifies the functional structure of the trait space. However, these effects were rather small and strongly trait- and habitat-dependent.image

Biological invasions threaten biodiversity and cause significant economic and ecological costs. Effective management of invasive species is crucial, as highlighted by the European Community's Regulation 1143/2014 on Invasive Alien Species (IAS). This study focuses on coastal dune ecosystems, particularly assessing the time and cost-effectiveness of three monitoring methods for detecting and mapping alien plants: photointerpretation, machine learning classification, and field monitoring. Yucca gloriosa L., an invasive species in Regional Park of Migliarino-San Rossore-Massaciuccoli (Tuscany, Italy), served as the target species. Using RGB DJI Phantom 4 Pro v. 2.0 and DJI P4 Multispectral drones, images were analyzed via photointerpretation and machine learning. Photointerpretation, though precise, was time-consuming and subjective. Machine learning minimized human effort but required extensive computing. Field monitoring produced accurate maps but was labor-intensive and limited by accessibility issues. This study concludes that UAV-based monitoring of Y. gloriosa is optimal for balancing cost and time efficiency in coastal dune ecosystems.

Effective monitoring and early detection of invasive alien plant species (IAPs) are crucial for mitigating their spread and safeguarding native habitats. Unmanned Aerial Vehicles (UAVs) offer a cost-efficient solution, providing high resolution images. In this study, we aimed to develop a semi-automated methodology using a machine learning algorithm, spatial metrics, and clustering techniques on UAV images to monitor, map, and suggest management measures to counteract Yucca gloriosa, an invasive plant colonizing coastal fixed dunes in central Italy. UAV flights were conducted using two drones: one for the visible spectrum and the other for multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared) along with a Digital Surface Model (DSM). Derived vegetation indices were also utilized. For mapping Y. gloriosa distribution, a Geographic Object Based Image Analysis (GEOBIA) approach was applied to the orthophoto segmentation, followed by a Random Forest algorithm in a training phase, considering three variable combinations (DSM + vegetation indices, DSM + spectral bands, DSM + mixed variables). The most accurate Y. gloriosa map was used to suggest management measures combining the spatial pattern of invaded patches (size, height, isolation level, and aggregation degree) and a mixed clustering approach (hierarchical and partitioning). The results highlighted that the most accurate prediction map was based on the DSM + mixed variables dataset, showing the important role of using a combination of spectral bands and vegetation indices. In all three cases, the DSM emerged as the pivotal variable for discriminating Y. gloriosa from the surrounding environment. Additionally, our results demonstrate the advantages of incorporating vegetation indices in discerning the target invasive alien plant (IAP) from the broader environment, particularly considering its distinctive photosynthesis process and biomass production. From a managerial standpoint, our pilot study indicates that the UAV-based mapping methodology represents an optimal balance between field efforts and costs. This approach allows for the precise identification of containment and removal areas of Y. gloriosa, without compromising the accuracy of the method. The generated prediction maps also hold potential significance for the conservation of coastal dune ecosystems, providing a promising tool for the effective management of invasive species and biodiversity conservation by suggesting management measures for Y. gloriosa.

Understanding the relationship between biodiversity and ecosystem functioning (BEF) is crucial to predicting the consequences of ongoing global biodiversity loss. However, what drives BEF relationships in natural ecosystems under globally changing conditions remains poorly understood. To address this knowledge gap, we applied a trait-based approach to data from coastal dune plant communities distributed along a natural environmental stress gradient. Specifically, we compared the relative importance of below-ground and above-ground traits in predicting productivity, decomposition, water regulation, carbon stock and nutrient pools, and tested how these BEF relationships were modulated by environmental stress and the presence of rare species that are typically excluded from experimental systems. Below-ground traits were just as important as above-ground traits in driving ecosystem functioning. Moreover, despite having low abundances, rare species positively influenced ecosystem multifunctionality (EMF). However, most biodiversity effects became weaker as environmental stress increased. Our study shows that to understand variation in ecosystem functioning we must consider below-ground traits as much as above-ground ones. Moreover, it highlights the importance of conserving rare species for maintaining EMF. However, our findings also suggest that rapid global change could dampen the positive effects of diversity on ecosystem functioning.Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog.image

Invasive Alien Plants (IAPs) represent a severe threat to biodiversity and the functioning of crucial ecosystems such as coastal dunes. In this work, the Yucca gloriosa invasion along Italy’s Mediterranean coastal dunes was used as a case study to explore the potential of Unmanned Aerial Vehicles (UAVs). High-resolution images were collected during different seasons (spring: pre-flowering and fall: flowering stage) for detecting, mapping, and managing this IAP. Due to its peculiar foliar characteristics, Y. gloriosa is particularly suitable for assessing this UAV technique. We suggest springtime for data collection because light conditions are ideal and shading effects are minimal. Moreover, long-term dynamics of the Y. gloriosa invasion after plant removal over the last 10–15 years were investigated. The information presented here is the first step in the future development of an early detection program to manage with this IAP problem in the study area.

Plant trait-based functional spectra are crucial to assess ecosystem functions and services. Whilst most research has focused on aboveground vegetative traits (leaf economic spectrum, LES), contrasting evidence on any coordination between the LES and root economic spectrum (RES) has been reported. Studying spectra variation along environmental gradients and accounting for species' phylogenetic relatedness may help to elucidate the strength of coordination between above- and belowground trait variation. We focused on leaf and root traits of 39 species sampled in three distinct habitats (front, back and slack) along a shoreline-inland gradient on coastal dunes. We tested, within a phylogenetic comparative framework, for the presence of the LES and RES, for any coordination between these spectra, and explored their relation to variation in ecological strategies along this gradient. In each habitat, three-quarters of trait variation is captured in two-dimensional spectra, with species' phylogenetic relatedness moderately influencing coordination and trade-off between traits. Along the shoreline-inland gradient, aboveground traits support the LES in all habitats. Belowground traits are consistent with the RES in the back-habitat only, where the environmental constraints are weaker, and a coordination between leaf and root traits was also found, supporting the whole-plant spectrum (PES). This study confirms the complexity when seeking any correlation between the LES and RES in ecosystems characterized by multiple environmental pressures, such as those investigated here. Changes in traits adopted to resist environmental constraints are similar among species, independent of their evolutionary relatedness, thus explaining the low phylogenetic contribution in support of our results.

Using UAV imagery is a powerful method for monitoring invasive alien plant species (IAPs), particularly when combined with automatic image analysis conducted by artificial intelligence. To this end, we conducted a pilot study on Yucca gloriosa, an invasive species of coastal dunes spread in central Italy. Specifically, we assessed the agreement in quantifying Y. gloriosa cover between field-based sampling and human visual screening of UAV images captured at different altitudes. Additionally, we examined the concordance among different operators both before and after a training procedure, comparing a simpler and quicker approach (referred to as the "envelope" method) against a seemingly more precise but time-consuming method (referred to as the "leaf by leaf" method). In our current study, we discovered a good concordance not only between operators and field sampling but also among operators, particularly when using the "envelope" method. Furthermore, we assessed the performance of deep learning in identifying Y. gloriosa plants in UAV images compared to visual identification by human operators, achieving an overall accuracy of 96 % for images taken at an altitude of 35 m. Our findings suggest that UAV imagery may serve as a valid alternative to field-based sampling for monitoring IAPs, especially when dealing with plants like Y. gloriosa, which have distinctive morphological characteristics that facilitate identification. Consequently, mapping Y. gloriosa on Mediterranean coastal dunes can be effectively accomplished using UAV images, even though an automated machine-based approach, thereby expediting and enhancing the reliability of alien species monitoring and management.