A dataset on invasive alien plants of European Union concern

Abstract

We present PREVALIEN, a dataset created under the project Prevention and Early Detection of the Invasive Alien Plants of European Union Concern in the Italian Protected Areas, an innovative tool supporting prevention and early detection of Invasive Alien Plants. Developed on PostgreSQL, it integrates ecological and spatial data from authoritative European and global sources. This dataset covers 41 vascular plant and algal species, with detailed information on taxonomy, traits, introduction pathways, impacts, and management strategies. Presence and absence records for Italians’ protected areas were obtained from the Italian Institute for Environmental Protection and Research databases and included only if collected within National Park boundaries. Data underwent expert verification, including cross-checking with risk assessments and consultation with park authorities. Structured into 34 relational tables, PREVALIEN supports multi-scale ecological analysis and the development of spatially explicit risk models. As an open-access resource, it fills critical data gaps, enhances national IAS monitoring, and provides a foundation for evidence-based policies and actions against biological invasions, strengthening protection of Europe’s most vulnerable and biodiversity-rich landscapes.

Background & Summary

Invasive Alien Species (IAS) are a critical and growing threat to biodiversity, ecosystem functions, and socio-economic systems globally¹. In the European Union alone, the estimated annual cost of IAS impacts amounts to approximately €12 billion^2–6. The increasing trend in IAS introductions, predominantly unintentional^7–9, calls for robust and harmonized monitoring systems capable of supporting timely management and policy responses. Consequently, multiple datasets and databases and information portals have been developed at global, European, and national levels to enhance knowledge dissemination and inform management strategies. At the European scale, the EU Biodiversity Strategy 2020 and Regulation (EU) No 1143/2014 on the prevention and management of IAS have established a legal and institutional framework for coordinated actions. The Regulation defines a three-tiered approach: prevention, early detection and rapid eradication, and control of established IAS, requiring collaboration between Member States, EU institutions, and stakeholders. Central to this strategy is the List of invasive alien species of Union concern, which is continuously updated through risk-based prioritisation, with support from scientific assessments. Citizen science initiatives (e.g., apps for recording species’ occurrences) can provide complementary evidence on distribution dynamics, although they do not directly drive the updating process¹⁰. To effectively implement such strategies, access to accurate and up-to-date IAS distribution and trait data is essential¹¹. The European Alien Species Information Network (EASIN)^12,13 was established to integrate and harmonize alien species data across terrestrial, freshwater, and marine ecosystems, linking to systems such as the Delivering Alien Invasive Species Inventories for Europe (DAISIE)^14,15, the North European and Baltic Network on Invasive Alien Species (NOBANIS)^16,17 and the East and South European Network for Invasive Alien Species (ESENIAS)¹⁸. EASIN provides detailed information on species taxonomy, pathways of introduction, spatial distribution, impacts, and diagnostic tools^19,20. However, it has been noted that existing datasets lack recent updates, do not include plant traits and omit a significant portion of IAS occurrence records²¹. At the global level, one of the most relevant efforts is the Global Invasive and Alien TrAits and Records (GIATAR)²² dataset integrates dated occurrence records and biological traits for 46,666 alien taxa across 249 countries from major sources such as the European and Mediterranean Plant Protection Organization Geo-Database (EPPO-GD)²³, CABI Compendium Invasive Species (CABI-ISC)²⁴, Standardising and Integrating Alien Species (SinAS)²⁵, and the Global Biodiversity Information Facility (GBIF)²⁶. GIATAR facilitates standardized, machine-readable access to crucial information and is designed for regular updates and seamless integration into ongoing research and policy development. Another global resource worth mentioning is the Global Naturalized Alien Flora (GloNAF) database^27,28, which provides comprehensive data on the distribution of naturalized alien vascular plants across 844 regions worldwide. It catalogues diversity patterns, invasion hotspots, taxonomic and phylogenetic trends, and life-history traits, highlighting regional differences in naturalization rates and drivers, such as climate, socioeconomic factors, and biogeographic isolation. GloNAF identifies key taxa and geographic areas affected by plant invasions and serves as a critical tool for informing global strategies for data collection and IAS management²⁹. Complementing these resources, the Global Register of Introduced and Invasive Species (GRIIS)³⁰ delivers harmonised country-level checklists of naturalized and invasive species. Designed as a maintainable platform to support national governments, GRIIS provides standardised information suitable for policy and reporting, thereby facilitating transparent and repeatable analyses. Complementary initiatives further illustrate the importance of regional and national-scale efforts. In China, Yang et al.³¹ developed a high-resolution, county-level database for 400 invasive alien plant species (IAPs), integrating over 246,000 georeferenced records from local herbaria, literature, and global databases. This dataset provides more occurrence data for these taxa in the area than GBIF alone, offering insights into invasion dynamics over 400 years. Similarly, in Russia, Vinogradova et al.³² compiled the first national inventory of invasive flora, linking species richness to climatic, socio-economic, and anthropogenic drivers. In Greece, Arianoutsou et al.³³ produced a comprehensive inventory of IAS, including taxa which are known to occur, established, or likely to arrive within the next decade. This work contributed to the formation of the national HELLAS-ALIENS list, aligned with EU Regulation and tailored to the Greek context. Such efforts highlight the need for datasets that are harmonized for interoperability yet locally informed to reflect national specificities, ensuring effective IAS risk assessment and management. Beyond occurrence records, understanding the pathways of introduction and spread is essential for implementing prevention strategies. Jansson and Ebenhard³⁴ analysed pathways for 101 IAS relevant to Sweden: 88 of Union concern and 13 of national interest. Their dataset, covering Sweden’s land, coastal, and economic zones, includes current and potential pathways based on the Convention on Biological Diversity (CBD) classification framework³⁵. Sources included national and EU risk assessments as well as databases such as NOBANIS, CABI, DAISIE, Global Invasive Species Database (GISD)³⁶, EPPO, EASIN, and the Swedish (SLU) Species Information Centre. The study assessed alien species’ introduction pathways abroad, in confined use, and in natural environments in Sweden, incorporating SLU risk analyses on invasion potential and impacts. Future scenarios under increased trade and climate change highlight the value of integrated spatial and ecological data for guiding national priorities. Despite this progress, IAS databases remain fragmented. A comparative evaluation of four major IAS platforms (CABI, EASIN, GISD, and NOBANIS) revealed substantial discrepancies in data quality, coverage, and citation use³⁷. While CABI scored highest for information quality overall, EASIN was found to provide the most complete distribution data, highlighting the complementarity, but also the need for integration of these resources. Moreover, the impacts of IAS extend beyond ecological damage. As emphasized by Bacher et al.³⁸, invasive species can significantly affect ecosystem services and human livelihoods. Their Global Impacts Dataset of Invasive Alien Species (GIDIAS) compiles over 22,000 records across taxa and habitats, categorized by type of impact according to standardized Environmental Impact Classification for Alien Taxa (EICAT) and Socio‐Economic Impact Classification of Alien Taxa (SEICAT) frameworks. This dataset allows a deeper understanding of how IAS impact not only biodiversity but also citizens’ well-being. Altogether, the development and harmonization of IAS databases, from local to global scales, are essential for improving surveillance, informing policy decisions, and guiding management actions. The integration of spatial, temporal, and biological data, supported by interoperable systems and citizen engagement, forms the backbone of an effective strategy against biological invasions in the 21^st century³⁹.

In this context, the PREVALIEN dataset (Prevention and Early Detection of the Invasive Alien Plants of European Union Concern in the Italian Protected Areas) was developed as the first major output of Work Package 1 of the PRIN-PREVALIEN 2022 project. The initiative addresses the need to counter the increasing pressure of Invasive Alien Plants of Union Concern (IAPUC) in Italy’s Protected Areas (PAs), which are both highly susceptible to invasions and crucial for safeguarding native biodiversity and ecosystem functioning^40,41. The project aims to identify the key ecological and environmental drivers of IAPUC invasions, assess the vulnerability of Italian Protected Areas, map invasion hotspots and priority species, develop innovative management strategies, and enhance public awareness through communication and citizen science⁶. The PREVALIEN dataset provides an up-to-date resource to support these goals by offering detailed information on the taxonomy, distribution, and invasion potential of IAPUC in Italian PAs. It contributes to filling critical knowledge gaps and supports data-driven risk assessments. Furthermore, the PREVALIEN project endeavours to deepen the understanding of introduction and spread pathways, identify the most vulnerable ecosystems and species, and develop a spatially explicit risk model tailored to Italian PAs. This model will follow open science and open-access principles, ensuring transparency, interoperability, and broad applicability to newly listed IAPUC and potentially to other PAs at the European scale. It will be regularly updated and expanded to incorporate new data, species, and risk scenarios, supporting long-term applicability and responsiveness to emerging invasion challenges.

Methods

List of species

The management of invasive alien species (IAS) within the European Union has evolved significantly over recent decades, driven by the increasing recognition of their severe impacts on biodiversity, ecosystem services, and socio-economic activities. Regulation (EU) No 1143/2014 established a unified framework for invasive alien species, including the List of invasive alien species of Union concern (“Union List”). The process of including species in this Union List is based on rigorous scientific risk assessments and involves input from Member States, the European Commission, and expert groups. Species are evaluated according to their invasiveness, ecological and socio-economic impacts, and potential for management or eradication. The Union List is dynamic, with periodic updates through implementing regulations to reflect new scientific knowledge and emerging threats. The most recent update was adopted through Commission Implementing Regulation (EU) 2025/1422 of 17 July 2025, which amended Implementing Regulation (EU) 2016/1141 to include newly identified species of Union concern. In this context, our dataset is conceived as a dynamic resource, with dedicated updating procedures to incorporate future additions to the Union List (Fig. 1).

Fig. 1.

Timeline showing the effective implementation of Regulation (EU) No 1143/2014 on the prevention and management of the introduction and spread of invasive alien species (IAS), resulting in the periodical updating of the Union List of IAS. Key milestones and legislative amendments are indicated.

The PREVALIEN dataset includes 40 taxa of vascular plants and one alga listed in the European Union List of invasive alien species and its subsequent updates (Table 1).

Table 1.

List of the 41 plant and algal species included in the PREVALIEN dataset, with information on their taxonomic family and the specific EU Implementing Regulation under which each species was first added to the Union List of invasive alien species of Union concern (year of inclusion).

Species	Family	EU Regulation of first inclusion (year)
Acacia saligna (Labill.) H.L.Wendl.	Fabaceae	Reg. (EU) 2019/1262 (2019)
Ailanthus altissima (Mill.) Swingle	Simaroubaceae	Reg. (EU) 2019/1262 (2019)
Alternanthera philoxeroides (Mart.) Griseb.	Amaranthaceae	Reg. (EU) 2017/1263 (2017)
Andropogon virginicus L.	Poaceae	Reg. (EU) 2019/1262 (2019)
Asclepias syriaca L.	Apocynaceae	Reg. (EU) 2017/1263 (2017)
Baccharis halimifolia L.	Asteraceae	Reg. (EU) 2016/1141 (2016)
Cabomba caroliniana Gray	Cabombaceae	Reg. (EU) 2016/1141 (2016)
Cardiospermum grandiflorum Sw.	Sapindaceae	Reg. (EU) 2019/1262 (2019)
Celastrus orbiculatus Thunb.	Celastraceae	Reg. (EU) 2022/1203 (2022)
Cortaderia jubata (Lemoine ex Carrière) Stapf	Poaceae	Reg. (EU) 2019/1262 (2019)
Ehrharta calycina Sm.	Poaceae	Reg. (EU) 2019/1262 (2019)
Eichhornia crassipes (Martius) Solms *	Pontederiaceae	Reg. (EU) 2016/1141 (2016)
Elodea nuttallii (Planch.) St. John	Hydrocharitaceae	Reg. (EU) 2017/1263 (2017)
Gunnera tinctoria (Molina) Mirbel	Gunneraceae	Reg. (EU) 2017/1263 (2017)
Gymnocoronis spilanthoides (D.Don ex Hook. & Arn.) DC.	Asteraceae	Reg. (EU) 2019/1262 (2019)
Hakea sericea Schrad. & J.C. Wendl. *	Proteaceae	Reg. (EU) 2022/1203 (2022)
Heracleum mantegazzianum Sommier & Levier	Apiaceae	Reg. (EU) 2017/1263 (2017)
Heracleum persicum Fischer	Apiaceae	Reg. (EU) 2016/1141 (2016)
Heracleum sosnowskyi Mandenova	Apiaceae	Reg. (EU) 2016/1141 (2016)
Humulus scandens (Lour.) Merr.	Cannabaceae	Reg. (EU) 2019/1262 (2019)
Hydrocotyle ranunculoides L. f.	Araliaceae	Reg. (EU) 2016/1141 (2016)
Impatiens glandulifera Royle	Balsaminaceae	Reg. (EU) 2017/1263 (2017)
Koenigia polystachya (Wall. ex Meisn.) T.M. Schust. & Reveal	Polygonaceae	Reg. (EU) 2022/1203 (2022)
Lagarosiphon major (Ridley) Moss	Hydrocharitaceae	Reg. (EU) 2016/1141 (2016)
Lespedeza cuneata (Dum.Cours.) G.Don (Lespedeza juncea var. sericea (Thunb.) Lace & Hauech)	Fabaceae	Reg. (EU) 2019/1262 (2019)
Ludwigia grandiflora (Michx.) Greuter & Burdet	Onagraceae	Reg. (EU) 2016/1141 (2016)
Ludwigia peploides (Kunth) P.H. Raven	Onagraceae	Reg. (EU) 2016/1141 (2016)
Lygodium japonicum (Thunb.) Sw.	Lygodiaceae	Reg. (EU) 2019/1262 (2019)
Lysichiton americanus Hultén & St. John	Araceae	Reg. (EU) 2016/1141 (2016)
Microstegium vimineum (Trin.) A. Camus	Poaceae	Reg. (EU) 2017/1263 (2017)
Myriophyllum aquaticum (Vell.) Verdc.	Haloragaceae	Reg. (EU) 2016/1141 (2016)
Myriophyllum heterophyllum Michaux	Haloragaceae	Reg. (EU) 2017/1263 (2017)
Parthenium hysterophorus L.	Asteraceae	Reg. (EU) 2016/1141 (2016)
Pennisetum setaceum (Forssk.) Chiov. *	Poaceae	Reg. (EU) 2017/1263 (2017)
Pistia stratiotes L.	Araceae	Reg. (EU) 2022/1203 (2022)
Persicaria perfoliata (L.) H. Gross (Polygonum perfoliatum L.)	Polygonaceae	Reg. (EU) 2016/1141 (2016)
Prosopis juliflora (Sw.) DC.	Fabaceae	Reg. (EU) 2019/1262 (2019)
Pueraria montana (Lour.) Merr. var. lobata (Willd.) (Pueraria lobata (Willd.) Ohwi)	Fabaceae	Reg. (EU) 2016/1141 (2016)
Rugulopteryx okamurae (E.Y. Dawson) I.K. Hwang, W.J. Lee & H.S. Kim	Dictyotaceae	Reg. (EU) 2022/1203 (2022)
Salvinia molesta D.S. Mitch.	Salviniaceae	Reg. (EU) 2019/1262 (2019)
Triadica sebifera (L.) Small	Euphorbiaceae	Reg. (EU) 2019/1262 (2019)

As a result of widely accepted changes to the nomenclatural references of three invasive alien plant species of Union concern, it is necessary to change the names or other elements of the full nomenclature for the following invasive alien species listed in the Annex to Implementing Regulation (EU) 2016/1141, denoted by an asterisk (*) in the table: Eichhornia crassipes (Mart.) Solms is replaced by Pontederia crassipes Mart; Hakea sericea Schrad. & J.C.Wendl. is replaced by Hakea sericea Schrad. & J.C.Wendl. s.l.; Pennisetum setaceum (Forssk.) Chiov. is replaced by Cenchrus setaceus (Forssk.) Morrone (Pennisetum setaceum (Forssk.) Chiov.).

Dataset organization

The PREVALIEN dataset has been developed using PostgreSQL⁴², an open-source relational database management system, and is structured into ten information groups, each comprising a set of interconnected tables (Supplementary Information). These modules reflect the core components of the project, such as taxonomy, species distribution, habitat preference, introduction pathways, environmental and anthropogenic drivers, and management/control strategies (Fig. 2). Within each group, tables are linked through primary and foreign keys, enabling complex queries and integrated analyses across biological, ecological, and spatial dimensions. For example, species-level tables are linked to habitat descriptions, Protected Areas metadata, introduction records, and risk evaluations, ensuring consistency and enabling multi-layered data exploration. This architecture supports data integrity, scalability, and interoperability with external sources (e.g., EPPO, GBIF), and is designed to be easily extensible as new data becomes available (Figure S1 – Supplementary Information). Future work may include mapping to Darwin Core⁴³ concepts to further align the dataset with widely adopted biodiversity data standards. The PREVALIEN dataset serves as the central infrastructure for the project’s data-driven analyses, including risk modelling and vulnerability assessments.

Fig. 2.

Overview of the main information groups included in the PREVALIEN dataset. The diagram summarizes the structure and thematic content of the dataset.

The primary data sources used to populate the PREVALIEN dataset were the official Pest Risk Analyses (PRAs) and Risk Assessments (RAs) developed for the European Commission in support of the Union List under Regulation (EU) No. 1143/2014, which currently provide standardized, comprehensive, and readily accessible coverage for these taxa. This resource provided us with a practical and reliable basis for designing both the methodology and the overall structure of the dataset. Based on consistent evidence, we ensured that the initial development of our approach was based on solid and comparable data, while remaining adaptable for future expansion. When data gaps were identified in the PRAs and RAs, supplementary data were retrieved from authoritative international and European databases. In particular, synonyms and alternative scientific names were retrieved for each species by filtering the GIATAR dataset²² to extract EPPO-related records corresponding to the 41 IAPUC considered in PREVALIEN. These records were used to enrich the taxonomy tables and ensure accurate species identification across data sources. For global distribution data and information on naturalization status, a customized query was submitted to the GloNAF database²⁸. The query was filtered for the subset of 41 IAPUC included in PREVALIEN, and only records classified as ‘naturalized’ were retained. These data provided a global overview of each species’ invasion status and were subsequently integrated with distribution data extracted from PRAs, RAs, as well as the CABI Invasive Species Compendium²⁴ and the EPPO Global Database²³. In cases where specific information was lacking in these sources, supplementary evidence was drawn from peer-reviewed literature to ensure completeness and reliability of the dataset (Fig. 3).

Fig. 3.

Global distribution of a subset of plant species included in the PREVALIEN dataset, categorized by biogeographical status (indigenous = native, alien-cultivated, alien-casual, alien-naturalized, alien-invasive, cryptogenic = uncertain native/alien status) across their regions of occurrence. Data were compiled from GloNAF, PRAs, RAs, EPPO, and the CABI database. The map illustrates the variability of species’ status across different regions worldwide.

The data, particularly those on species’ geographic distribution, exhibit a ‘funnel effect of spatial resolution’⁴⁴, spanning scales from the global level, through Europe and Italy, down to Italian PAs. Spatial data on the occurrence of IAPUC in Italian PAs were obtained from the Italian National Institute for Environmental Protection and Research (ISPRA) platform. The dataset consists of a grid of 10 × 10 km cells covering the national territory, with each cell indicating the confirmed presence of one or more IAPUC, based on verified observational records. To assess the distribution of IAPUC within Italian PAs, we collected spatial boundaries for national and regional parks. Whenever available, these were retrieved from the World Database on Protected Areas (WDPA)⁴⁵. In cases where the WDPA did not provide up-to-date or complete data, we obtained the boundaries directly from the official websites of individual park authorities or extracted the relevant geometries using the Overpass Turbo⁴⁶ (https://overpass-turbo.eu/), a powerful web-based data mining tool that allows for complex queries and retrieval of vector data directly from the OpenStreetMap basemap. Both the IAPUC grid and the protected area boundaries were projected using the IGNF:EPSG:7794 coordinate reference system. The IAPUC grid was intersected with the polygon boundaries of the protected areas. For each park, we identified all grid cells that overlapped with its boundaries, and for each cell, we recorded the binary presence (1) or absence (0) of alien species. These data were used to construct presence–absence matrices for all parks. From these matrices, we calculated the relative frequency of IAPUC within each protected area, expressed as the proportion of grid cells within the park containing at least one IAPUC. These values were used to categorise parks according to the intensity of IAPUC occurrences. All spatial analyses, including intersection operations and frequency calculations, were performed using R (version 4.5.0)⁴⁷, employing “sf”^48–52 package for geospatial data manipulation. Cartographic outputs were produced using QGIS (version 3.40.8)⁵³ (Fig. 4).

Fig. 4.

Spatial distribution of IAPUC occurrences within Italian protected areas, represented as a gradient from green (0%) to red (100%) based on their relative presence across national, interregional and regional sites. The map uses the RDN2008/Italy zone (E-N) reference system (EPSG:7794), scale of 1:7500000.

Regarding dispersal mode, a key trait influencing the spread and establishment of invasive alien species – the classification of seed and propagule dispersal mechanisms – is based on a synthesis of classical and functional ecological literature. General categories and subtypes are derived from van der Pijl (1982)⁵⁴, Howe & Smallwood (1982)⁵⁵, and Sádlo et al.⁵⁶, with further refinements informed by functional trait databases^57,58. Specific subtypes, such as endozoochory, ornithochory, or myrmecochory, are distinguished when relevant to assessing a species’ dispersal potential, particularly in the context of risk analysis^59–61 (Table S35 -Supplementary Information).

Data Records

The PREVALIEN dataset is available as a series of 34 flat.csv files with complete table descriptions and metadata available in the Supplementary Information. Each table includes citations to the original data sources. The full dataset, encompassing all tables and references, is publicly available on Zenodo [10.5281/zenodo.17937403]⁶². Metadata will follow the Darwin Core Archive (DwC-A)⁴³ standard, facilitated by the new tool ChatIPT⁶³, a chatbot designed to transform spreadsheets into standardized, GBIF-ready datasets. The dataset is provided in machine-readable CSV format (UTF-8 encoding, preserving special characters where applicable). In the next phase, the dataset will be integrated into the Italian NBFC Digital Platform (https://nbfc.platform.cineca.it/landing) and the Biodiversity Gateway platform (www.biodiversitygateway.it/en/), ensuring long-term accessibility, enhanced interoperability with other biodiversity datasets, and alignment with national and international data infrastructures.

Technical Validation

An expert-reviewed approach was adopted to ensure the quality, reliability, and scientific validity of the PREVALIEN dataset. The validation process was carried out using an expert-based assessment, leveraging the collective expertise of the PREVALIEN consortium (www.prevalien.it/), which includes researchers from seven Italian academic institutions: University of Sassari, University of Milano-Bicocca, University of Turin, University of Molise, University of Trieste, Sapienza University of Rome and University of Cagliari. These teams collectively represent national excellence in the fields of invasion ecology, plant taxonomy, biogeography, conservation planning, and biodiversity informatics. To guarantee consistency across datasets, all data entries, particularly those derived from Pest Risk Analyses (PRAs), Risk Assessments (RAs), and external databases such as EPPO, CABI, and GloNAF, were cross-checked by members of the respective thematic groups within the project. Each group applied standardized protocols for data extraction and formatting, ensuring that variables adhered to controlled vocabularies and relational integrity within the dataset architecture. In addition to expert-based validation, a systematic technical quality assurance workflow was implemented. All records were managed using standardized tabular formats (UTF-8 CSV files and PostgreSQL relational tables) designed around predefined data schemas. These schemas specified data types, mandatory fields, controlled vocabularies, and foreign-key constraints linking taxa, regulatory information, source types, and spatial units. Automated validation scripts were run at each data import step to identify missing values, duplicated records, invalid formats, and taxonomic inconsistencies. Lookup tables were used to enforce standardized categorical values, while referential integrity was guaranteed through dataset-level constraints (e.g., PRIMARY KEY, UNIQUE, and FOREIGN KEY rules). All operations (imports, corrections, merges) were tracked through version control (date-stamped updates and changelogs), ensuring full reproducibility and traceability of all data modifications. Particular attention was given to taxonomic harmonization. Scientific names were checked across multiple sources (e.g., GBIF backbone taxonomy, Portal to the Flora of Italy⁶⁴, EPPO Global Database), and synonyms were extracted and verified using filtered queries from the GIATAR dataset. Discrepancies and unresolved cases were discussed among taxonomy experts and resolved manually when necessary, following a double-checking system across institutional teams. Species occurrence records were further verified through direct consultation with national PAs. A structured questionnaire-interview survey was developed and disseminated to managers of Italian Protected Areas to confirm the presence of IAPUC within their territories. These responses served as a ground-truthing tool to verify literature-based and secondary-source data, and to improve spatial accuracy and data completeness. This multi-level validation approach, combined expert review, automated technical checks, taxonomic harmonization, and field-based confirmation, ensures that the PREVALIEN dataset meets high standards of accuracy, completeness, and interoperability, thus supports future ecological research, conservation planning, and policy implementation related to invasive alien species in PAs.

Usage Notes

The dataset was designed to support research on the distribution and traits of invasive alien plant species in Italy and is particularly relevant for national-level analyses and for studies across European countries. The majority of the tables are structured using a binary format, with values coded as 0 = FALSE and 1 = TRUE, facilitating their use in quantitative analyses and integration with statistical software. Missing values are present in 17% of the dataset, which reflect either the lack of available information or taxonomic uncertainty at the time of compilation. We encourage users to handle missing data according to the specific needs of their analyses, and to transparently report any filtering criteria applied. Data sharing among research groups is essential for improving the completeness and quality of information on invasive alien plants, especially within national biodiversity monitoring frameworks. Ideally, impact data should be structured consistently across global, European, and Italian levels, reflecting the approach adopted for habitat preferences and pathways of entry and spread. However, the availability of reliable impact information remains very limited, particularly for Italy. Although some national-level data are available thanks to previous research^65,66 efforts and a dedicated questionnaire addressed to experts, empirical assessments of impact are still missing for many species. This limitation certainly affects the completeness of our dataset, and we explicitly acknowledge it as an area requiring further development. By bringing attention to this gap, we aim to encourage more systematic, geographically comprehensive evaluations of invasive species impacts. The public availability of this dataset is intended to foster collaboration, reproducibility, and methodological transparency. Future work includes the integration of species occurrence data with spatially explicit georeferenced records, using spatial extensions in PostgreSQL (e.g., PostGIS). This will enable more advanced spatial analyses and support applications in ecological modelling, risk assessment, and invasive species management. Users should be aware that presence in the dataset does not imply invasive status in a given location. While efforts have been made to include available information on native and invasive status, this classification may vary over time and should be interpreted cautiously. At the time of finalizing this data-paper (July 2025), a new update of the List of invasive alien species of Union concern took place, adding the following species: Acacia mearnsii [as ‘mearnsi’] De Wild., Pl. Bequaert. 3: 61 (1925); Broussonetia papyrifera (L.) L’Hér ex Vent; Crassula helmsii (Kirk) Cockayne; Delairea odorata Lem; Nanozostera japonica (Ascherson & Graebner) Tomlinson & Posluszny, 2001; Reynoutria japonica Houtt.; Reynoutria sachalinensis (F. Schmidt) Nakai; Reynoutria × bohemica Chrtek & Chrtková. The structure of the PREVALIEN dataset is designed to accommodate new entries and to allow continuous updates of information for all existing records, ensuring the dataset remains current and relevant. The dataset may benefit from being complemented with the most up-to-date regional or taxonomic expertise accessible to researchers.

Author contributions

L.A.S., V.L., G.B. conceived the idea. L.A.S., V.L., G.B., M.L.C., M.F., A.S. compiled the data and curated the database. V.L., L.C.-G., E.B., D.B., N.S., S.C. and C.M. contributed species occurrence data. L.A.S. and M.F. analysed the data, L.A.S. and A.S. drafted the manuscript. All authors commented on the manuscript draft and approved its submission.

Data availability

The dataset is available at 10.5281/zenodo.17937403.

Code availability

There is no custom code produced during the collection and validation of this dataset.

Competing interests

The authors declare no competing interests.

Supplementary information

The online version contains supplementary material available at 10.1038/s41597-026-06932-x.