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Sci Data
Sci Data
Scientific Data
2052-4463
Nature Publishing Group UK London

39261508
3824
10.1038/s41597-024-03824-w
Data Descriptor
GIATAR: a Spatio-temporal Dataset of Global Invasive and Alien Species and their Traits
http://orcid.org/0000-0002-7920-6997
Saffer Ariel asaffer@ncsu.edu

Worm Thom tworm@ncsu.edu

Takeuchi Yu
http://orcid.org/0000-0002-1247-6212
Meentemeyer Ross
https://ror.org/04tj63d06 grid.40803.3f 0000 0001 2173 6074 Center for Geospatial Analytics, North Carolina State University, Raleigh, North Carolina USA
11 9 2024
11 9 2024
2024
11 99115 12 2023
23 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Monitoring and managing the global spread of invasive and alien species requires accurate spatiotemporal records of species presence and information about the biological characteristics of species of interest including life cycle information, biotic and abiotic constraints and pathways of spread. The Global Invasive and Alien Traits And Records (GIATAR) dataset provides consolidated dated records of invasive and alien presence at the country-scale combined with a suite of biological information about pests of interest in a standardized, machine-readable format. We provide dated presence records for 46,666 alien taxa in 249 countries constituting 827,300 country-taxon pairs in locations where the taxon’s invasive status is either alien, invasive, or unknown, joined with additional biological information for thousands of taxa. GIATAR is designed to be quickly updateable with future data and easy to integrate into ongoing research on global patterns of alien species movement using scripts provided to query and analyze data. GIATAR provides crucial data needed for researchers and policymakers to compare global invasion trends across a wide range of taxa.

Subject terms

Invasive species
Biogeography
issue-copyright-statement© Springer Nature Limited 2024
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pmcBackground & Summary

The global, human-mediated introduction of alien species, that is, the range expansions of species into regions where they are not native, continues to increase1,2 as a consequence of increasing global trade and air travel3–5. These species are considered invasive when they have documented negative economic, ecological and societal impacts in their novel geographic range6. Here, we refer to both alien and invasive species and their range expansions as “invaders” and “invasions”. Border interception and proactive early-detection of invasions are substantially more cost effective than eradicating established invasive populations7–9. However, effective border surveillance is costly and time consuming and efficient surveillance requires an understanding of the risk of specific country-commodity pathways for invaders of interest10.

Stopping invasions before they occur, through management and surveillance requires detailed information about identities and locations of invaders. More than 13,000 plant species5 and as many as 24,000 species in total11 are known invaders. Invader presence records can come from a variety of sources ranging from direct reports from members of the public12, news and other media13,14, and individual reports of species absence in a country15, to country-wide16 and international compendia and checklists17,18. In addition to presence records, effective preventative measures, invasion control and modeling rely on information on preferred commodity pathways, life cycle, native range, impacts, hosts, ecological constraints and other biological information19,20. Like invasion records, data on these biological characteristics also come from many different sources and are subject to similar biases in study based on the economic importance of the pest, taxonomy, visibility etc. While literature on the biological parameters of invaders, particularly those of economic importance, exists, it is frequently contained in publications from many different disciplines or in government reports or other “gray literature”21,22.

There is reason to believe that many invasion records in existing datasets are delayed, missing, or incomplete, and that these issues are biased towards specific taxa and geographies. Coverage in digital biodiversity databases has been shown to be geographically biased10,23–28, likely due to the same geographic and socioeconomic biases that impact much of the scientific literature: representation is greatest for wealthy, English-speaking nations, especially those geographically close to the owner of the database10,24,25. This is likely true for invasive species as well. Particularly for taxa that are actively spreading, delays are expected in the time to detect and report these new invasive species29,30 and estimates of unreported crop pests in some countries reach as high as 300 species31. Further, many records of presence are consolidated without the date of observation, losing temporal information that is critical to reconstructing a species’ trajectory of spread32 and to better understand the timing and scope of global invasion as a multi-species process.

The large gaps in publicly available, standardized information about invasive species have long been recognized as a major impediment to predicting and controlling invasions22 and, more generally, the lack of standardized information on the traits of insects, plants and other taxa is recognized as a major impediment to making ecological comparisons between taxa. An effort to standardize biodiversity information, Essential Biodiversity Variables (EBVs) are “measurements required for the study, reporting, and management of biodiversity change”33. At their core, EBVs synthesize a species’ distributions (the “where”) with characteristics of that species’ unique biology (the “what”). Latombe et al.32 extended this framework to focus on the essential variables required for invasion monitoring, including presence records, species impacts, pathways of spread, affected environments and other biological information. They identify a continuum of alien species records ranging in spatiotemporal resolution from undated species checklists through dated presence records at the national level, to spatially-explicit reporting of species presence/absence. Standardized workflows for reporting and collecting the distribution and EBVs for alien species are offered by Seebens et al.34, including guidelines on the harmonization of taxonomic names and standardization of geographic locations.

Prior efforts

There have been numerous prior efforts to collect and report invasive species distribution information. However, many of these prior efforts lack the global geographic scope35–37 or the temporal specificity18 needed to quantify the rate and scale of global invasions. Compilations of alien species distribution data that are not directly accessible as pre-formatted, machine-readable data are similarly difficult to utilize without substantial pre-processing17. The Standardising and Integrating Alien Species (SInAS) workflow and dataset34 offers more comprehensive spatiotemporal and taxonomic scope for presence records by standardizing dated first records (i.e., the earliest dated report for a given location and species) for species presence at the national scale from several static datasets with specific taxonomic scopes into a single dataset. Importantly, the SInAS dataset is designed to be updated automatically (i.e., using scripts with limited input from maintainers).

Our work is motivated by the desire to automatically collect, standardize, and update spatio-temporal invader presence records and Essential Biodiversity Variables from multiple sources into one location, with easy-to-use tools to retrieve data about many invasive species, to track emerging invaders and support comparative study of invasion trends across space, time and taxa. Building on work consolidating multiple taxonomy-focused invasion datasets, we created the Global Invasive and Alien Traits And Records (GIATAR) dataset by combining invasive and alien species records in diverse digital formats from global and regional invasion databases, monthly biosecurity reporting, and abundant citizen science records that can be obtained in near-real time17,34–36,38. Our dataset consolidates 827,300 dated first records in locations where the taxon’s invasive status is either alien, invasive, or unknown, at the national scale for 46,666 alien and invasive taxa, considerably improving on the geographic, taxonomic, and temporal accuracy and completeness of available first records in any individual data source. These records are combined with what is, to our knowledge, the largest assemblage of native range information collected for invaders (6,520 taxa) and trait data related to invader biology (e.g., climate tolerances, hosts and lifecycle information) and pathways of spread. We provide easy-to-use tools to retrieve data about many invasive species, to track emerging invaders and support comparative study of invasion trends across space, time and taxa.

Methods

To produce the Global Invasive and Alien Traits And Records (GIATAR) dataset39, we acquired and consolidated data from multiple data sources with distinct formats (web page, application programming interface [API], spreadsheet), naming standards, and update frequency (Fig. 1, Table 1). We standardize taxon names, location names, and event dates in line with the SInAS workflow34. Detailed metadata for all data files are provided in Appendix A. Code to replicate this process and update the dataset with more recent records and information is provided in a stable Zenodo repository40. Future modifications to these scripts will be released in our project repository on Github41.Fig. 1 Illustrative workflow of the GIATAR data consolidation process. (a) The dataset is constructed by harmonizing the identities of invasive and alien species across species lists. These species identities are mapped to traits. Species observations are consolidated across multiple sources. Descriptive locations are standardized to ISO3 codes and dates to four-digit year format. The earliest dated occurrence of a species at each location is consolidated as a first record. All processes are automated using Python scripts. (b) Query functions allow users to choose between R or Python to extract data across tables within the dataset.

Table 1 Data sources, update frequency, and types of data they provide to the GIATAR dataset.

Source	Source update frequency	GIATAR update frequency	How obtained	Country first reports	Other data types	Citation	
Invasive Species Factsheet Compendium, Center For Agriculture and Bioscience International	Ongoing	Static	Web scrape	Country-scale presence reports, dated and undated	Descriptive data tables (e.g., pathways, impact, symptoms, environmental thresholds)	17	
European and Mediterranean Plant Protection Organization Global Database	Ongoing	Updated (monthly)	API and web scrape	Dated country-scale first reports	Multi-lingual species names, hosts, quarantine category	35	
Global Biological Information Facility species presence records	Ongoing	Updated (monthly)	API	Geolocated and dated point observations	Taxonomic backbone	38	
Standardising and Integrating Alien Species (SInAS) dataset	Last updated: October 2023	Updated (periodic)	Zenodo download	Country-scale dated first reports	None	34	
Inventory of Invasive Species in Europe	Static (no longer maintained)	Static	Github	N/A	Native range locations	36	
Recent Immigrant Insect Fauna — Another Look at a Classic Analysis	Single manuscript	Static	Downloaded from manuscript supplementary data	N/A	Native range locations	44	
Original literature review of additional native ranges	Single manuscript	Static	Literature search for native ranges of select species	N/A	Native range locations	39	

We obtained and consolidated data from several major publicly available international databases of invasive species: the European and Mediterranean Plant Protection Organization Global Database (EPPO-GD), CABI’s Invasive Species Compendium (CABI-ISC; now the CABI Compendium), the SInAS dataset, and the Delivering Alien Invasive Species Inventories for Europe (DAISIE) species checklist dataset17,34–36,42,43. We selected these datasets and web resources to integrate records with distinct geographic (i.e., global and regional) and disciplinary (i.e., biosecurity, agricultural, and ecological) mandates. We also incorporate species observations from the Global Biodiversity Information Facility (GBIF)38. These records are consolidated from a broad range of formal scientific data sources (e.g., national species checklists, biodiversity atlases, and digitized herbaria), as well as volunteered by citizen scientists.

List of invaders

The EPPO-GD includes information about a variety of agriculturally relevant species beyond invasive species and pests. We therefore included species with a regulatory categorization (indicating that the species has been listed as a Quarantine Pest, Regulated Non-Quarantine Pest, Biological Control Agent, Regulated Invasive Species, Invasive Alien Species of Concern, Emergency, or Alert Lists; 4,419 species; species list downloaded from the EPPO data services dashboard and species Categorization queried using the EPPO API) in our dataset (20,775 species; species list downloaded from the EPPO data services dashboard and species Categorization queried using the EPPO API). The CABI-ISC17 likewise includes datasheets for non-invasive species. We included all species with datasheet type “Pest” or “Invasive species” in our dataset (9,263 species) in our dataset. The original SInAS42 and DAISIE43 datasets are restricted to invasive and alien species. We therefore included all taxa listed in each dataset (SInAS: 39,363 taxa; DAISIE: 11,395 taxa).

To homologize species names across datasets, we matched all taxa names from the CABI-ISC, EPPO-GD, and DAISIE datasets to the GBIF backbone taxonomy using the GBIF Species API. We relied on the GBIF usageKey as the primary key used to identify organisms in the GBIF backbone to integrate taxonomic information and consolidate data and records across sources. The SInAS dataset provides the GBIF usageKey for all included taxa. For names that did not produce matches, or were matched to higher taxonomic ranks, we additionally searched 8 supplementary taxonomic backbone providers (complete list in Appendix B) and searched GBIF again for matches to species synonyms or to matches at a higher rank (e.g., at the genus level for unmatched species names). This approach provided an additional 1,226 matches, including 550 matches for taxa previously missing taxonomy in the SInAS dataset.

For the remaining 1,990 taxa that did not match the GBIF backbone or other taxonomy (particularly hybrids), we produced unique identifiers, beginning with “XX”. Due to their differing disciplinary and geographic foci, each data source captured a different subset of species (Fig. 2a, Invasive taxa).Fig. 2 Summary of biological traits, invasive taxa, native ranges, and first records in GIATAR. (a) shows the source of the data and number of species (traits and invasive taxa) or number of species-geographies (native ranges, first records). “Multiple sources” refers to species and records that appear in more than one of the previously listed sources, e.g., CABI and EPPO; SInAS, GBIF, and DAISIE. (b) shows the distribution of the number of records per species for species with at least 1 record. Names, Hosts and Vectors, and Ecology consolidate data from multiple tables using the GIATAR query functions (Fig. 3). The example queries in (c) demonstrate the data types available in each table. Tuta absoluta graphic drawn by Laura Tateosian.

These data are included in link files (SINAS_link.csv, CABI_link.csv, EPPO_link.csv, DAISIE_link.csv, all_usageKeys.csv).

Consolidating first records

We extracted and combined species records at the year and country scale from several sources: the EPPO-GD Distribution and Reporting pages, the CABI-ISC Distribution table, the SInAS dataset, the DAISIE distribution dataset, and the GBIF Occurrence API. We included all geolocated and dated records of taxa considered invaders by at least one or more of our species list sources (i.e., SInAS, DAISIE, EPPO-GD, or CABI-ISC). Each record includes a field “Native” which indicates “False” if the location is known to be alien or invaded to the species, “True” if it is known to be within the species’ native range, and “NA” if this information is unavailable.

We obtained first record dates and new reports from the EPPO-GD by web scraping the Distribution and Reporting pages for each species included and processing the text to extract a table of places and years reported (detailed in Appendix C). We set “Native” to “False” for records from EPPO-GD Reporting pages, and “NA” for records from EPPO-GD Distribution pages. From the CABI-ISC Distribution tables, we used country-scale reports (omitting subregions). We set “Native” to “True”, “False” or “NA” according to the information provided in the Distribution table. For records from both EPPO-GD, CABI-ISC, and DAISIE we used the First Reported date, if provided. If no report date was provided, we indicate the year of the earliest included Reference. Undated records with no dated reference in DAISIE were assigned the year of data publication (i.e., 2019). Records from SInAS include a year and standard country location provided by the authors. We set “Native” as “False” for records from DAISIE and SInAS. For all invaders included across sources, we queried the GBIF Occurrence API for presence records for each year from 1970 to present, aggregated to the country-scale. We set “Native” to “NA” (unknown status) for records from GBIF. We summarized the counts across years to determine a first reported year for each country. From all sources, location names were matched to standardized ISO3 codes describing countries, territories, and geographic areas using exact matches to existing data crosswalks (i.e., datasets with country names paired to their ISO3 code) and the Python package pycountry for fuzzy matching when no exact match was available. This included 18,464 reports from EPPO (17,210 from the Distribution pages, 1,254 from Reporting pages), 38,806 reports from CABI, 782,671 reports from GBIF, 204,641 reports from SInAS, and 54,385 reports from DAISIE.

To produce first records, we selected the earliest year reported for each taxon-country pair across sources, recording its source(s) and any provided literature references (Fig. 2a,b, First records). When consolidating records across sources, we conserved the “Native” status (“True”/“False”) of a location if it was known from any individual source.

These data are included in the occurrence files (all_records.csv, first_records.csv).

Documenting native ranges

Species native-range locations are sparsely documented across most of the original sources in the dataset. We did not extract native range locations from EPPO-GD and GBIF because the observation records do not systematically differentiate between native and invaded locations. The CABI-ISC Distribution table included 10,043 native range locations for 568 species. The DAISIE Donor Area table included 17,324 native range locations for 5,856 species. We additionally incorporated native range information from Takeuchi et al.44 (411 native range locations for 367 species) and 562 locations for 380 species derived from literature review for the purpose of this dataset (detailed in Appendix C; Fig. 2a,b, Native range).

Native range location is not always documented at the country scale for all species. When a native location was provided listed as a bioregion (e.g., Western Palearctic Region, Neotropical Region; 871 locations) that did not directly map to country-scale ISO3 codes, we preserved the original description. A crosswalk between ISO3 codes and Biogeographic regions was lightly modified from the bioregions crosswalk provided by AntWiki45 by adding a handful of minor outlying islands that appeared in GIATAR records. Native ranges as listed in DAISIE were variable, sometimes referring to countries, biogeographic regions and other geonyms (e.g., Sahel, Indochina, Hindu Kush). Original geonyms were preserved and a crosswalk was added to map DAISIE geonyms to their corresponding biogeographic zones. All geographic harmonization follows the SInAS methodology34.

These data are integrated in the occurrence files described above.

Integrating descriptive traits

The CABI-ISC contains information beyond species distributions describing species biology, means of dispersal and transport, and invasive impacts. We extracted and consolidated 28 sections that provided data in a tabular format across all invasive species. Of these, we discarded 8 tables (naturalFoodSources, preventionAndControl, impactEnvironmental, biologyAndEcology, diagnosis, description, hostAnimals, principalSource, Pictures) that provided sparse data for only a few species. Using the EPPO-GD API, we extracted multi-language names, host species, and biosecurity categorization for each invasive species. From the DAISIE species checklist dataset, we included files on distribution, donor area, habitat, pathways, vectors and vernacular names. We discarded species synonyms because those are ideally handled by the GBIF backbone and other Darwin core taxonomic backbones and species_profile, which listed simple eco-functional groups of taxa (e.g. terrestrial plants, insects) which are better accessed using the taxonomy of the target organism.

These data are included in the EPPO data files (EPPO_hosts.csv, EPPO_names.csv, EPPO_categorization.csv), the CABI tables files (20.csv files, see Appendix A for full list), and the DAISIE data files (DAISIE_vernacular_names.csv, DAISIE_vectors.csv, DAISIE_donor_area.csv, DAISIE_donor_pathways.csv, DAISIE_habitat.csv, DAISIE_distribution.csv).

Data Records

GIATAR39 is available as a series of 40 flat.csv files with complete table descriptions and metadata available in the supplementary material (Appendix A). Occurrences are linked to trait information and taxonomy via unique taxa identifiers in the usageKey column. We provide functions in Python and R to query data easily across tables.

File structure

GIATAR is structured around a series of folders (Fig. 3). The data includes link files that map each source of data to a common taxonomic backbone (e.g., EPPO_link.csv, DAISIE_link.csv), occurrence records consolidated to first records by contributing source and across sources (all_records.csv and first_records.csv, respectively), and 30 descriptive data files related to EBVs and means of dispersal, sourced from EPPO (4 tables), CABI (20 tables), and DAISIE (6 tables). Traits are stored according to their source database in folders (i.e., CABI data, EPPO data, GBIF data and DAISIE data, Fig. 3). We also provide all files needed to recreate the dataset if they are not available for download from their original sources.Fig. 3 File structure and query functions in the GIATAR dataset. The query functions (in R and Python, available in the file repository and via Github40,41) simplify the process of joining and querying the tables in the dataset for the most common uses. Queries return a dataframe or a dictionary of dataframes. The lines in the figure connect the data files (left) to the query function (right) that can be used to easily extract data for an invader.

Data querying

The query functions align similar datasets from different sources and map between species names and usageKeys (Fig. 3, example in Fig. 2c). For example, the user can provide a species name to get_common_names() to obtain values from both the EPPO_names and DAISIE_vernacular_names tables or use get_species_list() with desired taxonomic levels (e.g. Class = Insecta or Family = Pinaceae) to get a list of all matching taxa. An example Jupyter notebook and Rmarkdown demonstrating the functionality of the query tools are available in the Zenodo repository.

Representation in GIATAR

Plants make up a substantial portion of the overall number of taxa included in GIATAR (Fig. 4), comprising 46% of taxa while insects, birds and fungi comprise 31%, 3.9% and 4.6% respectively. These figures likely reflect the substantially increased attention given to surveillance and reporting of plants and insects compared to microorganisms like fungi and nematodes20. The total number of plant taxa is also inflated by the presence of hybrids and horticultural varieties (e.g., Eucalyptus cloeziana x Eucalyptus portuensis) which some of our sources tracked independently.Fig. 4 Overview of GIATAR taxa diversity. In (a) branch tips represent individual species and higher-order nodes represent respective genera, families, etc. All possible taxa were matched to taxa in the National Center for Biotechnology Information (NCBI) database, representing 19,332 of 46,666 taxa. Not all taxa could be matched for a variety of reasons, e.g., unmatched taxa were hybrids, cultivars or viruses referred to by their common name. Despite this, this figure provides a visual overview of general trends of abundance in our dataset. The largest group of invaders are plants (Viridiplantae). Viruses (DNA and RNA viruses as defined by the NCBI) and Fungi make up relatively few overall species, representing the relative lack of knowledge around the invasiveness of these taxa with respect to their overall diversity49,50. Insects make up nearly one quarter of the invaders in the GIATAR dataset. Unlabeled taxa in the top left quadrant most represent a mixed bag of many other animal (Animalia) taxa, particularly nematodes. Unlabeled taxa in the top right represent mites and their allies. In (b), counts of the number of taxa by Phylum are shown for the top 10 Phyla. Counts represent the number of GBIF-matched taxa in a particular phylum in GIATAR.

The GIATAR dataset includes first records for all 249 countries with ISO 3166-assigned alpha-3 codes (ISO3, Fig. 5). GBIF was the source of the first record (either the earliest or the only observation of a given invader in each location) for 706,617 records (Fig. 2a, First records), and the source with the most first records for all countries. Incorporating this source adds a vast amount of new and earlier spatiotemporal data to the existing most comprehensive data (SInAS) and datasets with agricultural (CABI, EPPO) and geographic (EPPO, DAISIE) mandates. Instead of relying on an individual source of records, there is therefore benefit in consolidating records from multiple sources with different geographic and disciplinary priorities.Fig. 5 Global distribution and timeline of first records. The sum of dated first records is shown by country in (a) and by year in (b). Because we collected records from GBIF as of 1970, we exclude the first 10 years of these records and earlier records from (b), as well as undated records.

The GIATAR dataset includes additional trait data describing EBVs as well as human-mediated modes of dispersal relevant to global invasions for 13,294 distinct taxa. Common names (the names used to refer to the invasive taxa in multiple languages), pathways and hosts (categorizing the common means of dispersal and transport of the invasive taxa, e.g., horticulture, crop production, hitchhiking; the direct means of local and long distance dispersal, e.g., land vehicles, mail, ship ballast water, wind, vector organism; and the species the invader depends on for its life cycle), and habitats (environments known to host the invasive taxa, e.g., cultivated, disturbed areas, natural forests) link data across multiple data sources (Fig. 2a,b, Traits).

Technical Validation

Taxonomic harmonization between data sources was performed using GBIF backbone taxonomy (see Methods). Crosswalks of DAISIE geonyms to biogeographic regions and country ISO3 codes to biogeographic regions were manually validated by multiple researchers.

A majority of first records are sourced from GBIF (Fig. 2a, First records), which provides documentation on its data validation procedures46. Most records from CABI, EPPO and SInAS are associated with references to the peer-reviewed publications and are stored with their associated records in GIATAR.

Usage Notes

While a majority of taxa are identified to the species level, records for genera, varieties, crosses, subspecies, species complexes and other hierarchical classifications are also present, which account for the total number of taxa exceeding estimates of the total number of invasive species18. The proportion of species with no taxonomic mapping was 4.4%, but these taxa are likely not evenly spread across taxonomic groups. We noted that these taxa disproportionately include pathogenic and parasitic microorganisms like viruses and bacteria.

Though checks are in place in GBIF (e.g., including only iNaturalist research-grade observations47), occurrence records derived from citizen-science data are known to contain occasional errors of misidentification, incorrect geolocation or incorrect dates. Although these errors are expected to represent a small proportion of the full data records, we recommend users screen for outliers when applying them in management decisions.

The geographic distribution of records in GIATAR is consistent with the findings of other researchers that the global distribution of biodiversity data likely reflects uneven research effort10,23,24,27,28. Though global in scope, the four data sources providing first records included in GIATAR are all European in origin. Additional ongoing efforts to digitize and integrate data generated and managed in other locations, particularly the Global South, may therefore improve the quality of global data coverage.

Our dataset includes presence records for species known to be invasive somewhere. Because knowledge of the native range and the invasive status of a species can change through time, we have opted to include presence records from all locations where the species has been observed (i.e., countries within a species’ currently described native range, countries where species may have been intentionally or accidentally introduced, and countries where the invasive status of the species is unknown). Thus, presence records do not necessarily indicate that a species is invasive to the recording country, however, this issue can be remedied as additional native range data becomes available. For species and locations with native or invasive status information, we provide this information (as “True” or “False”) with the occurrence record. Researchers pursuing specific questions (e.g., the number of invasive versus introduced taxa present in a region) should apply additional information as available.

With the global costs of biological invasion estimated at more than US$423 billion a year48, there is a need to predict and prevent high-impact invasive species movements to facilitate safe global trade and air travel. The GIATAR dataset automatically collects, standardizes, and organizes geographical data, traits, and taxonomy of invasive species. This data is valuable for integrating invasion history and biological knowledge towards the study of global invader movement. We envision it being used to analyze, understand, and visualize global scale species movements, evaluate movement trends and identify likely biological invasion pathways to minimize damages to crop and natural resources by invasive species.

Supplementary information

Appendices

Supplementary information

The online version contains supplementary material available at 10.1038/s41597-024-03824-w.

Author contributions

Authors A.S. and T.W. contributed equally to this work.

Code availability

All code used to create and query the dataset is available and maintained in the project’s GitHub repository41. A version archived at the time of publication is available on Zenodo40.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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