Data Paper

A multi-proxy dataset of plant functional traits in Italian semi-natural grasslands under grazing exclusion and simulated drought*

Eugenia Siccardi^‡, Virginia Amanda Volanti^‡, Andrea Coppi^‡, Andrea Scartazza^§, Eleonora Peruzzi^§, Valerio Lazzeri^§, Serena Doni^§, Leonardo Latilla^|, Federica D’Alò^¶, Carlotta Volterrani^¶, Olga Gavrichkova^¶, Lorenzo Lazzaro^‡

‡ Department of Biology, University of Florence, Firenze, Italy

§ Research Institute on Terrestrial Ecosystems (IRET), National Research Council (CNR), Pisa, Italy

| Research Institute on Terrestrial Ecosystems (IRET), National Research Council (CNR), Montelibretti, Italy

¶ Research Institute on Terrestrial Ecosystems (IRET), National Research Council (CNR), Porano, Italy

Corresponding author: Eugenia Siccardi ( eugenia.siccardi@unifi.it )

Academic editor: Stefano Chelli

© 2026 Eugenia Siccardi, Virginia Amanda Volanti, Andrea Coppi, Andrea Scartazza, Eleonora Peruzzi, Valerio Lazzeri, Serena Doni, Leonardo Latilla, Federica D’Alò, Carlotta Volterrani, Olga Gavrichkova, Lorenzo Lazzaro.

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation: Siccardi E, Volanti VA, Coppi A, Scartazza A, Peruzzi E, Lazzeri V, Doni S, Latilla L, D’Alò F, Volterrani C, Gavrichkova O, Lazzaro L (2026) A multi-proxy dataset of plant functional traits in Italian semi-natural grasslands under grazing exclusion and simulated drought. Vegetation Ecology and Diversity 63: e191956. https://doi.org/10.3897/ved.191956

Abstract

Semi-natural grasslands are high-value biodiversity hotspots that provide critical ecosystem services, yet land-use changes and shifting precipitation patterns increasingly threaten them. Understanding the ecological stability of these habitats requires high-resolution data integrating taxonomic and functional perspectives. This paper presents a multi-proxy dataset (https://doi.org/10.5281/zenodo.19052795) collected within the “CAROLINA: Impact of land-use change on climate resilience of semi-natural grasslands” project, focusing on the short-term impacts of grazing exclusion and simulated drought across three distinct Italian climatic zones: Mediterranean plains (San Rossore), Central Apennine hills (San Venanzo), and Alpine mountains (Tesino). The experimental design employs a manipulative approach using three treatment levels: grazed, as a control, grazing exclusion, and grazing exclusion + drought, with the latter simulated via rain-out shelters following the International Drought-Net protocol. The dataset integrates taxonomic diversity from floristic-vegetational surveys (88 plot × 164 species), leaf morphological functional traits for 39 species (leaf area, LA, specific leaf area, SLA, leaf dry matter content, LDMC, and plant height, H), and ecophysiological functional traits based on carbon and nitrogen elemental and stable isotope composition (δ¹³C, δ¹⁵N) from 37 surveyed species across the three sites. This comprehensive repository provides a foundational resource for long-term monitoring and ecological modelling of grassland responses to global change, supporting the development of effective conservation practices.

Keywords

Climate resilience, ecophysiological diversity, functional traits, semi-natural grassland

Introduction

Semi-natural grasslands are environments with a long history of traditional, low-intensity pastoral and agricultural activities (Gorris et al. 2025). These environments have largely developed as a result of various agricultural land-use practices that replaced forest areas – the local potential natural vegetation (Poschlod and WallisDeVries 2002). It is widely documented that seminatural grasslands possess high ecological value and serve as global biodiversity hotspots, providing essential ecosystem services ranging from food production to carbon sequestration (Katoch et al. 2025; Milić et al. 2024; Shipley et al. 2024). The interaction between management practices, like extensive grazing, and abiotic factors, such as precipitation patterns, determines the ecological stability of these habitats (McSherry and Ritchie 2013). Despite their importance, these ecosystems are increasingly threatened by land-use change, abandonment or intensification, and by climate change, both leading to species loss and degradation of grassland ecosystem (Lv et al. 2024) with a detrimental effect on the vital ecosystem services that they provide (Prangel et al. 2024). Understanding how grasslands respond to these stressors requires high-resolution data on plant community composition and plant functional traits. Research indicates that grasslands undergo rapid compositional reorganisation in response to drought and temperature shifts (Zhu et al. 2024). To isolate these effects, manipulative experiments using standardised protocols, such as the International Drought Experiment (IDE) framework, are essential for simulating altered precipitation regimes (Knapp et al. 2017). The dataset presented here was collected within the two-year project “CAROLINA: Impact of land-use change on climate resilience of semi-natural grasslands”. An important part of the research focuses on the short-term impact of abandonment, and variation in precipitation regime on soil biochemical properties and on taxonomic and functional diversity of vascular plants across different climatic zones. Based on the Köppen-Geiger climate classification (Beck et al. 2023, https://koppen.earth/), the selected areas encompass: hot-summer Mediterranean coastal plains (Csa), the hilly transition between Mediterranean and warm temperate climates in the Central Apennines (Csa/Cwa), and the high-altitude gradient of the Alps, ranging from humid continental (Dfb) to subarctic (Dfc) conditions. The project employs a field-based manipulative approach to simulate grazing exclusion (mimicking grazing abandonment) and precipitation reduction (mimicking future drought scenarios). The presented dataset integrates two dimensions of plant biodiversity: i) taxonomic, and ii) functional (including leaf morphological and plan ecophysiological traits). This paper provides a comprehensive repository of data collected as baseline measurements during the initial characterisation of three experimental sites (between 2024 and 2025) and one year after treatment application for two of them (2025). The dataset includes: i) Taxonomic diversity data, reported as floristic-vegetational surveys; ii) Plant functional diversity data, reported as leaf morphological functional traits, and carbon and nitrogen isotopes measurements.

The dataset aims to take an important step towards understanding the dynamics linking the two diversity layers and identifying the best management and conservation practices for these valuable grassland habitats and their characteristic plant communities. By providing a comprehensive multidimensional framework of plant structural and performance-based differences, these data offer a foundational resource for long-term monitoring and ecological modelling of Mediterranean and temperate grassland responses to global change.

Study area and methodology

The experimental phase of the project was designed to study the short-term effects of grazing suppression through fencing, as well as the impact of reduced precipitation, which was simulated using rain shelters. The experiment was conducted across three sites. San Rossore (43°44'06.5"N, 10°19'31.9"E, 5 m a.s.l.), located in the Migliarino, San Rossore, Massaciuccoli Regional Park extending along the coast of the Tuscany region (Northwestern Italy), is characterised by a mild and dry climate (mean annual temperature of 16.2 °C and approximately 540 mm of rainfall, Csa climate zone of the Köppen-Geiger climate classification), and lays on a recent alluvial geological substrate composed of sandy and silty deposits from the Serchio and Arno rivers. Within this setting, the alternation of wetlands and sandy ridges supports hygrophilous and mesophilous herbaceous vegetation, maintained through extensive rotational cattle grazing. San Venanzo (42°49'38.6"N, 12°15'52.6"E, 500 m a.s.l.), which is found on private pastures in the Umbrian Apennines (Central Italy), is characterised by a temperate continental climate (mean annual temperature of 12.9 °C and 801 mm of rainfall) as it is situated in a transition zone between hot-summer Mediterranean (Csa) and more temperate climates (Cwa), and it rests on a unique Pleistocene volcanic substrate known for the presence of rare rocks like Venanzite. This geological setting, shaped by the San Venanzo volcanic complex, supports a landscape mosaic where Turkey oak (Quercus cerris) forests alternate with open pastures, which were restored in the 1980s for extensive cattle grazing. Tesino (46°06'51.7"N, 11°42'11.7"E, 1667 m a.s.l.) is located in the Passo Brocon area (Cinte Tesino), within the Lagorai mountain range of the Eastern Alps in the Trentino-Alto Adige region at an elevation of 1667 m a.s.l. The site features a harsh subalpine climate spanning the humid continental (Dfb) and subarctic (Dfc) zones (mean annual temperature of 5.5 °C and 1866 mm of rainfall), and sits upon a geological substrate of metamorphic rocks, specifically the quartz phyllites of the Lagorai chain. These acidic, nutrient-poor soils support subalpine grasslands dominated by Nardus stricta and Norway spruce (Picea abies) forests, maintained through seasonal cattle grazing managed under local municipal leases. Climate data are provided by the Euro-Mediterranean Centre on Climate Change (CMCC) (Adinolfi et al. 2021; Raffa et al. 2023).

At each location, an area of 135 m² was fenced off to prevent cattle from grazing. Specifically, at the San Rossore site, grazing management involves 15 head of cattle occupying an area of 3.4 hectares. The San Venanzo pasture covers 60 hectares and supports a herd of 25 cattle, while in the Tesino subalpine pastures, a herd of 51 cattle grazes an area of 15 hectares. Three treatment levels were established, each with three replicate blocks (2 × 3 m): Control (G as per ‘grazed’), which was located outside the fence and exposed to ambient precipitation and extensive grazing; Grazing Exclusion (GE), which was located inside the fence and exposed to ambient precipitation; and Grazing Exclusion + Drought (GED). A schematic figure of the experimental design is available in the Suppl. material 1. Following the international Drought-Net protocol, rain-out shelters were used to simulate extreme drought, targeting the 1^st percentile of long-term local precipitation. This resulted in site-specific expected reductions in rainfall of 57% (308 mm) for San Rossore, 50% (400 mm) for San Venanzo, and 33% (616) for Tesino. The sites’ details are summarised in Table 1. Figure 1 shows the location of the sites on a map. The sampling design and methodology employed to collect the presented data are outlined in the following paragraphs.

Figure 1.

Geographic location and environmental characteristics of the three study sites in Italy. The map indicates the positions of San Rossore (coastal), San Venanzo (hilly), and Tesino (alpine). The table summarises the elevation (m a.s.l.) and the Köppen-Geiger climate classification for each site, highlighting the environmental gradient across the study areas.

Table 1.

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XLSX

Climatic characteristics, specific rainfall reduction, soil properties, and stocking density across the three study sites (San Rossore, San Venanzo, and Tesino). MAT: Mean Annual Temperature; MAP: Mean Annual Precipitation. Specific rain reduction under the shelter in mm and in %.

Site	MAT, °C	MAP, mm	Expected rain reduction, mm	Expected rain reduction, %	Soil texture	Stocking density, head/ha
San Rossore	16.2	540	308	57	Sandy loam	4.4
San Venanzo	12.9	801	400	50	Loam	0.4
Tesino	5.5	1866	616	33	Sandy loam	3.4

Vegetation data

Vegetation surveys were conducted at San Rossore in late April 2024, San Venanzo in mid-May 2024, and Tesino in early July 2025. These dates were specifically selected to align with the peak vegetative season of each site, accounting for regional climatic variations to ensure optimal sampling results. Vegetation surveys were conducted in two 1 × 1 m subplots, one at each of the opposite corners of the main plot. Within the GED plots, the subplot was delineated 50 cm towards the centre of the plot to avoid the margin effect and ensure the entire subplot was covered by the rain shelter. Due to unexpected mowing at San Rossore shortly before sampling took place, the number of control replicates for that site was reduced to two. For each subplot, the present species were recorded and assigned a relative abundance with a percentage scale. Total cover could exceed 100% to allow for overlap. Most species were identified in situ; however, those that were difficult to identify were collected and determined in the laboratory. Taxonomic nomenclature follows the Portal to the Flora of Italy, version 2024.3 (http://dryades.units.it/floritaly/), derived from Checklists of the native floras of Italy (Bartolucci et al. 2024).

Morphological and physiological functional traits

To assess functional diversity, we targeted the most abundant species in each plot following the 80% cumulative cover criterion (Pérez-Harguindeguy et al. 2013). However, full trait coverage for this threshold was achieved through a combination of newly collected measurements and existing data sources. In particular, trait values for some species were obtained from the TRY database (Kattge et al. 2020) and from an internal trait database when direct field sampling was not feasible. In total, we directly measured leaf functional traits for 39 species, which represent the subset of species for which original measurements are provided in this dataset. The remaining species contributing to the 80% cover threshold are not included here, as their trait values were derived from external sources. Ecophysiological (isotopic) analyses were conducted on a subset of individuals from the same set for which leaf traits were measured, with minor exceptions. Due to sample loss during laboratory processing, two species were not analysed for isotopic composition. Plant height was recorded in the field, and leaf samples were processed in the laboratory within 24 hours of collection. For each individual, five healthy leaves were weighed and scanned (using an EPSON Perfection V370 Photo at 300 dpi), and these scans were analysed using ImageJ software (Schneider et al. 2012) to determine leaf area in mm² (LA). After drying at 70 °C for 48 hours, the leaves were weighed again (using an analytical balance with an accuracy of 0.01 mg with an AS 60/220.R2, Radwag). We calculated specific leaf area (SLA, mm²/mg) as the ratio of leaf area to leaf dry mass, and leaf dry matter content (LDMC, mg/g) as the ratio of oven-dry mass to water-saturated leaf fresh mass (Cornelissen et al. 2003; Pérez-Harguindeguy et al. 2013). Dried leaves were then ground into a homogeneous powder using a 4-ball mill (MM 400, Retsch). An aliquot of leaf was weighed and analysed for C and N concentration (dry mass basis, %) and isotope composition (δ¹³C, δ¹⁵N). Analyses were performed with an elemental analyser (model NA 1500, Carlo Erba, Milan, Italy) coupled with a continuous-flow isotope ratio mass spectrometer (ISOPRIME, GV Instruments, Manchester, UK).

The isotope ratios of C (R = ¹³C/¹²C) and N (R = ¹⁵N/¹⁴N) were measured to calculate δ¹³C and δ¹⁵N (‰) referring to the Vienna Pee Dee Belemnite (VPDB) and atmospheric N₂ standards, respectively, as:

$δ^{13} C or δ^{15} N = (\frac{R_{s a m p l e}}{R_{s t d}} - 1) \times 1000$

Where R_sample is the isotope ratio of the sample, and R_std is the isotope ratio of the international standard. For δ¹³C calibration, IAEA-CH-7 Polyethylene Foil (δ¹³C = −32.15‰), IAEA-CH6 sucrose (δ¹³C = −10.43‰), and IAEA-600 caffeine (δ¹³C = −27.5‰) were used to scale the measurements to the Vienna Pee Dee Belemnite (VPDB) international standard. For δ¹⁵N calibration, IAEA-600 caffeine (δ¹⁵N = +1‰), USGS40 L-Glutamic Acid (δ¹⁵N = −4.5‰), and IAEA-NO-3 potassium nitrate (δ¹⁵N = +4.7‰) were used to normalise results to the atmospheric N₂ standard (Portarena et al. 2024). For both δ¹³C and δ¹⁵N, the standard deviation of replicate measurements for each standard and sample was ± 0.1‰. For C concentrations, instrument precision of standard measurements was ± 1.0%, for N ± 0.04%.

Data description, database structure and content

The dataset presented has been deposited in the open-access Zenodo repository, which was selected for its long-term preservation policy, Digital Object Identifier (DOI) assignment, and compliance with the FAIR principles (Findable, Accessible, Interoperable, Reusable). It contains seven separate tabs, which are described below. Overall, the database includes data sampled in 88 treatment plots: 18 at San Venanzo and a further 18 plots at San Rossore, where surveys were repeated for two years (2024 and 2025). However, two plots could not be surveyed in 2024 due to unexpected mowing at San Rossore. Finally, there are 18 plots at Tesino, which were surveyed just once in 2025 due to the inability to construct the experimental design during the first year. There are three types of treatment plots (G, GE, and GED) at each site. Each type has three repetition plots, with two subplots placed at opposite corners of each plot (tab ENV in the database). A total of 164 plant species were recorded during the two-year floristic survey (tab SURVEY), while 358 individuals belonging to 39 species were measured for leaf functional traits (tab LEAF TRAITS PER LEAF). Average leaf traits measurements were then calculated at the species level (tab LEAF TRAITS PER SPECIES). Some species were measured at more than one site depending on their abundance. Ecophysiological analyses of carbon and nitrogen elemental and stable isotope composition were conducted on 283 individuals from 37 species at the individual level. Of these, 75 individuals from 9 species were measured at San Rossore, 141 individuals from 21 species at San Venanzo, and 67 individuals from seven species at Tesino (tab ISOTOPES PER INDIVIDUAL). Average isotope measurements were then calculated at the species level (tab ISOTOPES MEAN PER SPECIES). The metadata were carefully structured, with a description of the data contained in each column of each tab provided in the first tab (tab METADATA).

All trait measurements were collected by trained botanists following standardised protocols (see the ‘Morphological and physiological functional traits’ section), and taxonomic identification was harmonised according to national floristic references. Data quality control (QC) was primarily based on expert visual inspection. All records were screened to identify evident transcription or digitalisation errors (e.g., inconsistent units, misplaced decimal points, or clearly implausible values). When such errors were detected, they were corrected by referring to the original field records, when available, or in few cases, measurements were repeated, when possible, to verify and correct the original values. No formal statistical outlier detection procedure was applied, and no observations were removed based on predefined outlier criteria. The dataset is structured in long format, where each row represents a single trait measurement. The absence of records for a given species–trait–replicate combination reflects that no measurement is available. No imputation of missing observations was performed. Metadata were carefully structured to facilitate integration with other vegetation databases and to ensure reusability for ecological analyses and conservation planning at different spatial scales.

Application and future perspective

This multi-proxy dataset provides robust data with which to investigate the interactions between land-use abandonment and climate stressors in semi-natural grasslands. By integrating taxonomic and functional layers, the dataset serves as a foundational resource for ecological modelling and predicting the climate resilience of plant communities in Mediterranean and temperate regions. While this dataset offers a multidimensional perspective on grassland resilience (Figures 2, 3, 4, 5), it is important to acknowledge its limitations, including the short-term nature of the post-treatment observations and the geographical restriction to three Italian sites. These limitations may prevent the dataset from capturing the full range of environmental variability across the Mediterranean and Alpine biomes. Nevertheless, this comprehensive repository could support the development of evidence-based management practices and serve as a critical asset for long-term monitoring and global meta-analyses.

Figure 2.

The histograms show the dominant plant families across the three study sites (San Rossore, San Venanzo, and Tesino), based on their frequency of occurrence in the sampling plots. For clarity, only the five most frequent families at each site are displayed. The y-axis represents the total occurrences, defined as the cumulative number of individual species records per family across all sampling plots within each site. The taxonomic structure reveals a clear dominance of Poaceae and Asteraceae across all locations, which is characteristic of Mediterranean and temperate herbaceous communities. However, the internal ranking varies, reflecting different climatic and soil conditions.

Figure 3.

The boxplots illustrate the distribution of species richness (number of species per plot), with the horizontal line representing the median. Individual data points (plots) are overlaid and distinguished by treatment: G = grazing, GE = grazing exclusion, and GED = grazing exclusion and drought. Colours and shapes of the points correspond to the different experimental treatments, while the box colours represent the sites. Median richness was similar between San Rossore and Tesino, while the highest values were observed at San Venanzo. The spread of the boxplots and the position of the whiskers further indicate the degree of vegetation homogeneity. A compact boxplot suggests a more uniform plant community, whereas a wider spread reflects greater environmental heterogeneity within the site. Accordingly, Tesino exhibited the most homogeneous vegetation.

Figure 4.

The density plots for leaf traits show how values for four key functional traits are distributed, considering the means calculated for each measured species. These are Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Area (LA), and Plant Height (H). These plots show how often values occur for species in the database. The distributions of SLA and LDMC are crucial indicators of the leaf economics spectrum. A peak in high SLA values suggests dominance by ‘acquisitive’ (fast-growing) species, while a wide LDMC distribution indicates a variety of strategies for nutrient retention and leaf toughness. Most species have relatively small leaves and are low-growing, with a few ‘functional outliers’ (large-leaved or tall species).

Figure 5.

Density distributions of elemental and isotopic composition are shown. The distributions of δ¹³C and δ¹⁵N are suitable indicators of intrinsic water-use efficiency and nitrogen source/cycling, respectively. The plots illustrate the frequency distribution of carbon and nitrogen concentrations and their respective stable isotope compositions across the sampled plant individuals (n = 283). The upper panels display (from left to right): C (%), leaf carbon concentration; C/N, carbon-to-nitrogen ratio; and N (%), leaf nitrogen concentration. The lower panels show the isotopic composition: δ¹³C (‰), relative to the VPDB standard; and δ¹⁵N (‰), relative to atmospheric N₂.

Acknowledgements

Researchers would like to thank the local institutions and people who made the establishment and maintenance of the experimental sites possible: Mr. Angelo Gualdana for the San Venanzo site; the Migliarino, San Rossore, Massaciuccoli Regional Park Authority for the San Rossore site; and the Municipality of Cinte Tesino and Mr. Carlo Piazza for the Tesino site. We acknowledge Michele Mattioni, Luca Sessa, and Daniele Piastrelloni for their contribution to the site set-up, and Luciano Spaccino and Irene Tunno for their help with isotopic analyses.

Additional information

Conflict of interest

The authors declare that they have no conflict of interest. Lorenzo Lazzaro is part of the Editorial Review Board in Vegetation Ecology and Diversity but took no part in the peer review or decision-making process for this manuscript.

Ethical statement

No ethical statement was reported.

Artificial Intelligence (AI) use

The authors accept full responsibility for the content of the manuscript, including the disclosure of any use of AI.

Regarding the use of AI in the preparation of this manuscript, the authors declare the following: Gemini 3 Flash to improve the style and readability of the text. After using the tool, the authors reviewed and corrected the content as necessary.

Funding

This study was funded by the European Union – NextGenerationEU, Mission 4 Component 2, PRIN2022PNRR project P20228JYWN – Impact of land use change on climate resilience of semi-natural grasslands (CAROLINA), CUP B53D23023480001 and B53D23023490001.

Author contributions

Eugenia Siccardi: Conceptualisation; methodology; data curation; formal analysis; visualisation; writing – original draft, review, and editing. Virginia Amanda Volanti: Data curation; writing – review and editing. Andrea Coppi: Data curation; writing – review and editing. Scartazza Andrea: Data curation; writing – review and editing. Peruzzi Eleonora: Data curation; writing – review and editing. Lazzeri Valerio: Data curation; writing – review and editing. Doni Serena: Data curation; writing – review and editing., Latilla Leonardo: Data curation; writing – review and editing. D’Alò Federica: Data curation; writing – review and editing. Volterrani Carlotta: Data curation; writing – review and editing. Gavrichkova Olga: Project administration; Data curation; writing – review and editing. Lorenzo Lazzaro: Project administration; conceptualisation; data curation; writing – review and editing.

Author ORCIDs

Eugenia Siccardi https://orcid.org/0009-0008-4738-0633

Virginia Amanda Volanti https://orcid.org/0009-0004-7851-4607

Andrea Coppi https://orcid.org/0000-0003-4760-8403

Andrea Scartazza https://orcid.org/0000-0001-5048-5112

Eleonora Peruzzi https://orcid.org/0000-0003-2523-6557

Valerio Lazzeri https://orcid.org/0009-0007-1219-0269

Serena Doni https://orcid.org/0000-0002-9428-9778

Federica D’Alò https://orcid.org/0000-0001-9604-9965

Carlotta Volterrani https://orcid.org/0009-0007-5721-3102

Olga Gavrichkova https://orcid.org/0000-0003-1818-4912

Lorenzo Lazzaro https://orcid.org/0000-0003-0514-0793

Data availability

The dataset is available as supplementary material and stored in the Zenodo repository at the following link: https://doi.org/10.5281/zenodo.19052795 (Siccardi et al. 2026). The dataset is under the Creative Commons Attribution 4.0 International license.

References

Adinolfi M, Raffa M, Reder A, Mercogliano P (2021) Evaluation and expected changes of summer precipitation at convection permitting scale with COSMO-CLM over alpine space. Atmosphere 12(1): 54. https://doi.org/10.3390/atmos12010054

Bartolucci F, Peruzzi L, Galasso G, Alessandrini A, Ardenghi, … Conti F (2024) A second update to the checklist of the vascular flora native to Italy. Plant Biosystems 158(2): 219–296. https://doi.org/10.1080/11263504.2024.2320126

Beck HE, McVicar TR, Vergopolan N, Berg A, Lutsko NJ, Miralles DG (2023) High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Scientific Data 10(1): 724. https://doi.org/10.1038/s41597-023-02549-6

Cornelissen JHC, Lavorel S, Garnier E, Díaz S, Buchmann N, … Reich PB (2003) A handbook of protocols for standardized and easy measurement of plant functional traits worldwide. Australian Journal of Botany 51(4): 335–380. https://doi.org/10.1071/BT02124

Gorris P, Bodin Ö, Giralt D, Hass AL, Reitalu T, … Westphal C (2025) Social-ecological perspective on European semi-natural grassland conservation and restoration: Key challenges and future pathways. Biological Conservation 304: 111038. https://doi.org/10.1016/j.biocon.2025.111038

Katoch R, Parmar V, Sankhyan NK, Rana RS, Mahajan R, Sharma T (2025) Grasslands in focus: Evaluating and monitoring ecosystem services. In: Mutanga O, Pandey PC, Das S, Chatterjee U (Eds) Revealing Ecosystem Services Through Geospatial Technologies: Beyond the Surface. Springer Nature Switzerland, 59–80. https://doi.org/10.1007/978-3-031-98048-0_4

Kattge J, Bönisch G, Díaz S, Lavorel S, Prentice IC, … Wirth C (2020) TRY plant trait database – enhanced coverage and open access. Global Change Biology 26(1): 119–188. https://doi.org/10.1111/gcb.14904

Knapp AK, Avolio ML, Beier C, Carroll CJ, Collins SL, … Smith MD (2017) Pushing precipitation to the extremes in distributed experiments: Recommendations for simulating wet and dry years. Global Change Biology 23(5): 1774–1782. https://doi.org/10.1111/gcb.13504

Lv S, Huang J, Liu H, Ma S (2024) Grazing effects on species diversity across different scales are related to grassland types. BMC Plant Biology 24(1): 1103. https://doi.org/10.1186/s12870-024-05812-z

McSherry ME, Ritchie ME (2013) Effects of grazing on grassland soil carbon: A global review. Global Change Biology 19(5): 1347–1357. https://doi.org/10.1111/gcb.12144

Milić D, Rat M, Bokić B, Mudri-Stojnić S, Milošević N, … Radišić D (2024) Exploring the effects of habitat management on grassland biodiversity: A case study from northern Serbia. PLOS ONE 19(3): e0301391. https://doi.org/10.1371/journal.pone.0301391

Pérez-Harguindeguy N, Díaz S, Garnier E, Lavorel S, Poorter H, … Cornelissen JHC (2013) New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany 61(3): 167–234. https://doi.org/10.1071/BT12225

Portarena S, Farinelli D, Famiani F, Cinosi N, Traini C, … Brugnoli E (2024) Differential tolerance to summer stress conditions in two olive cultivars using the dendro-isotopic approach. Dendrochronologia 84: 126182. https://doi.org/10.1016/j.dendro.2024.126182

Poschlod P, WallisDeVries MF (2002) The historical and socioeconomic perspective of calcareous grasslands—Lessons from the distant and recent past. Biological Conservation 104(3): 361–376. https://doi.org/10.1016/S0006-3207(01)00201-4

Prangel E, Reitalu T, Neuenkamp L, Kasari-Toussaint L, Karise R, … Helm A (2024) Restoration of semi-natural grasslands boosts biodiversity and re-creates hotspots for ecosystem services. Agriculture, Ecosystems & Environment 374: 109139. https://doi.org/10.1016/j.agee.2024.109139

Raffa M, Adinolfi M, Reder A, Marras GF, … Mercogliano P (2023) Very High Resolution Projections over Italy under different CMIP5 IPCC scenarios. Scientific Data 10(1): 238. https://doi.org/10.1038/s41597-023-02144-9

Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nature Methods 9(7): 671–675. https://doi.org/10.1038/nmeth.2089

Shipley JR, Frei ER, Bergamini A, Boch S, … Rixen C (2024) Agricultural practices and biodiversity: Conservation policies for semi-natural grasslands in Europe. Current Biology 34(16): R753–R761. https://doi.org/10.1016/j.cub.2024.06.062

Siccardi E, Volanti VA, Coppi A, Scartazza A, … Lazzaro L (2026) A multi-proxy dataset of plant functional traits in Italian semi-natural grasslands under grazing exclusion and simulated drought [Data set]. Zenodo. https://doi.org/10.5281/zenodo.19052795

Zhu K, Song Y, Lesage JC, Luong JC, Bartolome JW, … Prugh LR (2024) Rapid shifts in grassland communities driven by climate change. Nature Ecology & Evolution 8(12): 2252–2264. https://doi.org/10.1038/s41559-024-02552-z

Topical Collection: Bridging vegetation and trait-based ecological research.

Supplementary material

Supplementary material 1

Schematic representation of the experimental design

Eugenia Siccardi, Virginia Amanda Volanti, Andrea Coppi, Andrea Scartazza, Eleonora Peruzzi, Valerio Lazzeri, Serena Doni, Leonardo Latilla, Federica D’Alò, Carlotta Volterrani, Olga Gavrichkova, Lorenzo Lazzaro

Data type: docx

Explanation note: The layout includes three replicated blocks for each treatment: (GED) Grazing exclusion and drought induced by rainout shelters (shaded grey areas); (GE) Grazing exclusion under ambient precipitation; (G) Grazed control plots. Blue squares represent the subplots (A and B) designated for floristic surveys within each experimental unit.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.

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