Functional traits are measurable plant features describing the structure, functions or ecological strategies that shape species responses to biotic and abiotic environments across spatial and temporal scales and different biological complexity levels (Violle et al. 2007; Adler et al. 2014; Bruelheide et al. 2018). Increasingly, functional traits are used to understand and map plant community properties and variation across space and time (Emery and La Rosa 2019; Vanneste et al. 2019), for instance by computing communities’ functional diversity. Compared to taxonomic diversity, functional diversity has been shown to have a higher explanatory power of the links between plant communities’ ecological structure and ecosystem functioning (Tilman et al. 1997; Hillebrand and Matthiessen 2009; Díaz et al. 2013; Gonzalez et al. 2020).

Understanding the intra- and inter-specific variation in plant functional traits across space and different growth environments could provide key insights into plant species distribution patterns, community assembly mechanisms, evolutionary strategies, and ecosystem level potential responses to climate change (Westerband et al. 2021; Gremer 2023; Aguirre-Gutiérrez et al. 2025). Intraspecific trait variation is defined as the overall variability of trait values expressed by individuals belonging to the same species (Albert et al. 2011). To compute functional diversity metrics, it is widely assumed that intraspecific variation is negligible compared to that among species (Albert et al. 2012; Shipley et al. 2016), thus, averaged species-level functional trait values are commonly used (Palacio et al. 2025). However, especially at a local scale, intraspecific variation can account for more of the variation than interspecific trait variability (Kumordzi et al. 2014; Siefert et al. 2015). Consequently, intraspecific trait variation could be driving community structure and ecosystem function as much as interspecific variation (Bolnick et al. 2011; Govaert et al. 2016) and there is a growing need of incorporating such data in ecological studies or models (Funk et al. 2017).

There are multiple publicly available plant functional trait databases featuring an unprecedented amount of trait information globally, including the “Botanical Information and Ecology Network” (BIEN; http://bien.nceas.ucsb.edu/bien/, Enquist et al. 2016), the “Global Inventory of Floras and Traits for macroecology and biogeography” (GIFT, Weigelt et al. 2020) and TRY (Kattge et al. 2020). However, many species are still underrepresented in available data and information on intraspecific variation in trait values is scarce (Niinemets 2015; O’Sullivan et al. 2022). Often these species are difficult to find, highly specialized or hard to sample and or measure due to intrinsic features or peculiar living conditions.

Coastal plant species must withstand naturally and anthropogenically driven stressful environments. Natural stressors, such as salinity, drought, soils with low oxygen and nutrient content, filter species based on their functional traits values and drive the coexistence of plant communities with strong functional trait identity (i.e., large variation in dominant traits values across different communities) (Calderisi et al. 2021). Moreover, the demanding living conditions of habitats with strong environmental constraints and high temporal or spatial variability could induce plant adaptation and/or acclimation by activating trait responses and plasticity (Trotta et al. 2025), leading to high local intraspecific variability (Lammerant et al. 2025). In this context, gathering data considering the trait variation of less frequently sampled species and making the data available could provide a significant contribution in addressing existing knowledge gaps (Westerband et al. 2021) but see Puglielli et al. (2024) and Chelli et al. (2025) for intraspecific trait variability datasets spanning a variety of habitats. Currently, the majority of European coastal habitats are at high or moderate risk from land use change (Seitz et al. 2014). In Italy, over the past 50 years, dunes have suffered a decline in habitat quality and half of the current area is now categorized as vulnerable (Gigante et al. 2018; Sarmati et al. 2025). Concerning the fragile plant communities of coastal environments, providing individual-level data on scarcely sampled species could allow for a more sound and productive application of functional characterization of species assemblages and, in turn, foster more effective coastal biodiversity protection frameworks. The main objective of this data paper was to gather functional traits data for under sampled dunes and salt marshes plant species, to enhance the geographical coverage of functional data for other coastal plant species and make the dataset (named “Priorcoast”) freely available.

The study area encompasses the coasts of Tuscany and Liguria (Figure 1), two administrative regions located in the northwest and central areas of Italy facing the Ligurian and Tyrrhenian Sea (Western Mediterranean). The climate is temperate, with hot and dry summers, and it is classified by the Köppen classification (Peel et al. 2007) as Csa (Hot-summer Mediterranean climate), typical for the Mediterranean area. The yearly precipitation is concentrated from late autumn to early spring; the summer is dry, and July is the driest month. The mean annual temperature is around 15°C, with the cooler months presenting a mean temperature higher than 7°C (Baroni et al. 2020). The coast includes both rocky and sandy shores, as well as coastal lagoons and salt marshes. The sampling was carried out at 11 localities (Figure 1), selected based on the presence of habitats of interest (dunes, salt marshes or dune slacks and rocky cliffs and coasts) and/or of target species based on expert knowledge. Detailed information on sampling localities, coordinates, and main types of coastal habitats can be found in the Priorcoast dataset.

The dataset Priorcoast is available as a supplementary Microsoft Excel .xlsx file (Suppl. material 1) containing three separate sheets: “single leaf traits”, “individual traits”, and “metadata”. The “single leaf traits” hosts traits measured for each sampled leaf and contains eight columns: “Species”, compiling the species level names of sampled entities following FlorItaly (Martellos et al. 2020); “Individual” and “Leaf”, with the information on sampled individual and leaf, respectively; “Fresh_weight.g” with water saturated weights expressed in grams; “Dry_weight.g”, with oven-dried dry weights expressed in grams; “Leaf_Area.mm“, with leaf areas measurements expressed in mm²; “LDMC.mg.g” with leaf dry matter content values expressed in mg/g; “SLA.mm2.mg” with SLA values expressed in mm²/mg. The second sheet, “individual traits” hosts traits either measured for the whole individual (as in the case of height) or leaf traits values averaged to the individual level. It contains 13 columns: “Species”; “Individual”; “Height.cm”, with vegetative height values expressed in cm; “avg_Fresh_weight.g”; “avg_Fresh_weight.g_n”; “avg_Dry_weight.g”; “avg_Dry_weight.g_n”; “avg_Leaf_Area.mm”; “avg_Leaf_Area.mm_n”; “avg_LDMC.mg/g”; “avg_LDMC.mg/g_n”; “avg_SLA.mm2/mg”; “avg_SLA.mm2/mg_n”. The columns ending with “_n” indicate the number of replicas (i.e., leaves) used to calculate averaged trait values. Lastly, the “metadata” sheet contains six columns: “Species”, “Individual”, “date”, “locality”, “lat”, and “long” compiling the sampling date, locality and decimal latitude and longitude following coordinate system EPSG 4326, respectively. Throughout the dataset some missing data for dry weight, SLA or LDMC (around 3.5% of all entries at leaf level) can be found due to lack of adequate material left after drying to carry on leaf trait measurements.

The presented Priorcoast dataset contributes to furthering the knowledge of intra- and inter-specific variability in leaf functional traits and plant height of central-northern Italy coastal plants, representing a valuable resource for future meta-analysis or ecological studies addressing functional composition and diversity of coastal plant communities. The spatial and ecological scale at which intraspecific variability can be inferred from this dataset is certainly limited. However, the dataset still provides relevant information on scarcely sampled species that is suitable to be included in trait-based modeling efforts and thus functional-based conservation planning. Understanding how species are locally adapted or could adapt to changing environmental conditions based on their relative intraspecific variation could aid in assessing their resistance to global change, especially in heavily anthropized and threatened coastal systems (Kichenin et al. 2013). We are aware that our trait selection could be enhanced to include, e.g., traits related to dispersal strategies or below-ground organs that are overall scarcely studied and even more scarcely represented in available databases (Laliberté 2017; Saatkamp et al. 2019) but see La Bella et al. (2024) and Ciccarelli et al. (2023). However, even though we sampled a limited number of traits, these were selected based on well-known correlations with plant ecological strategies (Díaz et al. 2016) and are informative on how plants respond to their environment and influence ecosystem properties.