Invasive species’ environmental impacts are more severe in the Global South
Data files
Jul 30, 2026 version files 15.93 MB
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Code_Data_Bacher-etal2026.zip
15.89 MB
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README.md
37.78 KB
Abstract
Invasive alien species (IAS) are a major threat to biodiversity, yet their impact distribution remains poorly understood. Global biodiversity assessments suggest the highest impacts in wealthy countries of the Global North, but this is only inferred from IAS numbers, reflecting research bias. Using a new global database of standardised impact measures, we calculated average impact severity per country and found that it is higher in the Global South despite more than twice as many reports in the Global North. Weak governance and limited management capacity are the main drivers of high impact severity. Emerging economies with rapid economic growth but poor governance are particularly vulnerable. Failure to recognise the Global South as facing the highest IAS impacts diverts attention away from the most threatened regions.
output:
pdf_document: default
html_document: default
Article citation: Bacher, Sven, Pysek, Petr & Seebens, Hanno (2026) Invasive species’
environmental impacts are more severe in the Global South. Science 393, 376-380,
DOI: 10.1126/science.aed0603
Authors of Readme:
Hanno Seebens, Justus-Liebig University Giessen, Giessen, Germany
Sven Bacher, University of Fribourg, Fribourg, Switzerland
Description of the data and file structure
The files listed below were used to conduct the statistical analyses underlying the
manuscript by Sven Bacher et al., Invasive species’ environmental impacts are more
severe in the Global South. Science 393, 376-380 (2026)
(DOI: 10.1126/science.aed0603) and to produce the figures in the main manuscript.
The files are organised in the sub-folders "Data", "Figures" (only required if
figures are produced) and "R", the latter containing the R scripts to create the figures.
Zip file
Code_Data_Bacher-etal2026.zip: Main file containing all other files.
The zip file contains the required folder structure and all required files listed
below. The folder structure is required when executing the code.
Data
Input datasets
GIDIAS_20250417_IAS_ImpactAnalyses.csv: GIDIAS dataset.
This table represents an extract from the dataset of impact records of invasive alien species (IAS)
as published by Bacher et al.
Sci Data 12, 832 (2025), (https://doi.org/10.1038/s41597-025-05184-5). It forms
the basis for the analysis related to impacts of invasive alien species.
More details about the GIDIAS dataset is provided in the original publication.
To obtain the table used here, the GIDIAS dataset has been merged with the list
of harmonised locations used in this study and irrelevant columns have been
deleted.
- UniqueID: Unique identifier for impact record
- IAS.Species.Name: Taxon name as provided in the original publication
- Verified.Name.GBIF.Taxon: Taxon name verified through the GBIF backbone taxonomy
- GBIF.scientificName.with.author:
- Phylum: Phylum of taxon
- Kingdom: Kingdom of taxon
- IAS.Taxon: Classification into vertebrate, invertebrate, plant or microorganism
- Reference: Citation of original publication
- Year: Year of report publication
- Region: Name of region
- Country.Location: Name of location as provided in GIDIAS
- Location: Name of location as used in this study
- ISO3: ISO code of country names if available
- Continent: Name of continent
- GlobalSouth: Indication whether location is considered as a member of the Global South (1: yes; 0: no)
- Island.k: Indication whether location is an island (1: island; 0: no island)
- magnitude.Nature: Magnitude of impact severity for impacts on nature
- Severe.impact: Indication whether this is a severe impact (1: yes; 0: no) ???
SInAS_AlienSpeciesDB_2.5.csv: Table of non-native species' distributions
This table is the original dataset of the distribution of alien species (called
SInAS) as published
by Seebens et al. NeoBiota 59: 39–59. (DOI: https://doi.org/10.5281/zenodo.10038256).
It forms the basis for the analysis related to numbers of alien species.
Colums are separated by comma and empty cells indicated by "NA".
More details about the dataset is provided in the original publication.
- Location: Name of location
- locationID: Internal ID for location
- Taxon: Name of taxon
- scientificName: Scientific name of taxon
- taxonID: Internal ID of taxon
- eventDate: Year of first record of taxon in a given location
- habitat: Habitat type suitable for taxon if information is available
- occurrenceStatus: Status of occurrence of taxon in the location if information is available; terms follow Darwin Core terminology
- establishmentMeans: Means of establishment of taxon in the location if information is available; terms follow Darwin Core terminology
- degreeOfEstablishment: Degree of establishment of taxon in the location if information is available; terms follow Darwin Core terminology
- origDB: Original database providing the information
- references: Reference of the provided information
- isInvasive: Indication whether the taxon is considered invasive in the location according to the database GRIIS
- Group: Higher taxonomic group of taxon
- FirstRecord_orig: Original presentation of the year of first record
AllLocations_AlienImpacts.csv: Table of location names.
This table is used as a reference for harmonising location names across datasets.
It has been created for the study based on the country standardised coding ISO 3166-1
and extended to accomodate sub-national units, which were useful for this study.
Empty cells are indicated by "NA".
Columns are separated by comma.
Character entries are quoted.
- ISO3: The ISO code of each location if available
- Location: Name of location
- tdwg1_name: Name of associated biogeographic region following the World Geographical Scheme for Recording Plant Distributions (https://www.tdwg.org/standards/wgsrpd/)
- island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- EconDevelop_strict: Classification of location by economic development following the suggestion of UNCTAD (United Nations Conference on Trade and Development (UNCTAD), Data Hub (2026);
https://unctadstat.unctad.org/EN/Index.html.) - EconDevelop_relaxed: Classification of location by economic development following the suggestion of UNCTAD; in addition, locations not listed by UNCTAD were classified according to geographic location
GIDIAS_20250417_agg_regions.csv: Core metrics underlying analyses and figures.
This table is created by running the script PrepareImpactData_Bacher-etal.R and
includes a set of metrics related to the number of non-native species and impacts
of invasive alien species per location, which were used for analysing the distribution
of impacts. The information on impacts originates from the GIDIAS dataset
(Bacher et al. Sci Data 12, 832 (2025), https://doi.org/10.1038/s41597-025-05184-5).
Empty cells are indicated by "".
- Location: Name of location
- n_high_impacts: Number of recorded high impacts per location
- n_impacts: Total number of impact records per location
- prop_highimpacts: Proportion of high impacts among all recorded impacts per location
- n_impactreports: Total number of impact reports per location
- n_highestimpact: Number of high impacts per location
- n_IAS: Number of invasive alien species as reported by GIDIAS
- mean_impact: Mean of impact severity across all impacts per location
- ISO3: ISO code for location if available
- n_highImp2Reports: Proportion of the number of high impacts to the number of impact reports
Temporal_severe_impacts.csv: Table of impact records.
This dataset is an extract from the full GIDIAS (Bacher et al. Sci Data 12, 832
(2025), https://doi.org/10.1038/s41597-025-05184-5) used for analysing potential
temporal effects of impact reporting.
- rowID: Internal ID for row number
- UniqueID: Unique identifier for impact record
- Verified.Name.GBIF.Taxon: Taxon name verified through the GBIF backbone taxonomy
- Phylum: Phylum of taxon
- Kingdom: Kingdom of taxon
- IAS.Taxon: Classification into vertebrate, invertebrate, plant or microorganism
- Year: Year of report publication
- Region: Name of region
- Country.Location: Name of location as provided by GIDIAS
- GlobalSouth: Indication whether location is considered as a member of the Global South (1: yes; 0: no)
- Island.k: Indication whether location is an island (1: island; 0: no island)
- magnitude.Nature: Magnitude of impact severity for impacts on nature
- Severe.impact: Indication whether this is a severe impact (1: yes; 0: no)
Species_agg_regs.csv: Measures of alien species numbers
This table contains measures of the number of alien species per location calculated
from the SInAS dataset. These measures provide the basis for the analysis of alien
species distributions in Bacher et al. (2026).
- Location: Name of location
- n_alien: Number of alien species per location
- n_invasive: Number of invasive alien species per location
- prop_invasive: Proportion of invasive alien species among all alien species per location
- ISO3: ISO codes of location if available
- Continent: Continent name of location
- Island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- Area: Size of location area in square meters
- EconGrowth: Classification of location by economic development (same as EconDevelop_strict in dataset CompleteVariablesRegression_impacts_250919.csv
- nReports_alien: Number of reports of alien species per location
Input datasets for regression analysis
CompleteVariablesRegression_impacts_250919.csv: Variables for regression analyses of all taxa and habitats.
This table contains all variables used for the regression analyses in Bacher et al.
Science 393, 376-380 (2026). The individual variables were obtained from various
providers as stated in the list below. In each case, the location names were
harmonised using ISO3 codes where possible to match the location names used in
the study. Variable entries were not available for each location, which resulted
in missing values indicated by "NA".
- Location: Name of location
- ISO3: ISO code of each location if available
- Continent: Name of continent
- Area: Size of location area in square meters
- Island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- EconDevelop_strict: Classification of location by economic development following the suggestion of UNCTAD (United Nations Conference on Trade and Development (UNCTAD), Data Hub (2026);
https://unctadstat.unctad.org/EN/Index.html.) - EconDevelop_relaxed: Classification of location by economic development following the suggestion of UNCTAD; in addition, locations not listed by UNCTAD were classified according to geographic location
- reactive: Metric of reactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- proactive: Metric of proactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- pop2000: Size of human population of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDP: Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDPpc: per capita Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- temp_annual: Annual mean temperature of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in degree celsius
- precip_annual: Annual mean precipitation of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in millimeters
- human_footprint: Human footprint index per location (obtained from Venter et al. (2016) Scientific Data 3*:160067)
- WGI: World Governance Indicator of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- corrupt_CPI2018: Corruption Perception Index provided by Transparency International for the year 2018 (obtained from https://ourworldindata.org)
- n_high_impacts: Number of high impact records (obtained from GIDIAS_20250417_agg_regions.csv)
- n_impacts: Total number of impact records per location (obtained from GIDIAS_20250417_agg_regions.csv)
CompleteVariablesRegression_19092025_freshwater_final.csv: Variables for regression analyses of impacts by freshwater taxa
This table contains all variables used for the regression analyses of impacts
posed by freshwater species as presented in Bacher et al.
Science 393, 376-380 (2026). The individual variables were obtained from various
providers as stated in the list below. In each case, the location names were
harmonised using ISO3 codes where possible to match the location names used in
the study.
Columns are separated by comma.
- Location: Name of location
- ISO3: ISO code of each location if available
- Continent: Name of continent
- Island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- EconGrowth: Classification of location by economic development (same as EconDevelop_strict in dataset CompleteVariablesRegression_impacts_250919.csv
- reactive: Metric of reactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- proactive: Metric of proactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- pop2000: Size of human population of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDP: Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDPpc: per capita Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- temp_annual: Annual mean temperature of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in degree celsius
- precip_annual: Annual mean precipitation of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in millimeters
- WGI: World Governance Indicator of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- human_footprint: Human footprint index per location (obtained from Venter et al. (2016) Scientific Data 3*:160067)
- n_high_impacts: Number of high impact records (obtained from GIDIAS_20250417_agg_regions.csv)
- n_impacts: Total number of impact records per location (obtained from GIDIAS_20250417_agg_regions.csv)
- Area: Size of location area in square meters
CompleteVariablesRegression_19092025_marine_final.csv: Variables for regression analyses of impacts by marine taxa
This table contains all variables used for the regression analyses of impacts
posed by marine species as presented in Bacher et al.
Science 393, 376-380 (2026). The individual variables were obtained from various
providers as stated in the list below. In each case, the location names were
harmonised using ISO3 codes where possible to match the location names used in
the study. Variable entries were not available for each location, which resulted
in missing values indicated by "NA".
- Location: Name of location
- ISO3: ISO code of each location if available
- Continent: Name of continent
- Island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- EconGrowth: Classification of location by economic development (same as EconDevelop_strict in dataset CompleteVariablesRegression_impacts_250919.csv
- reactive: Metric of reactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- proactive: Metric of proactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- pop2000: Size of human population of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDP: Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDPpc: per capita Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- temp_annual: Annual mean temperature of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in degree celsius
- precip_annual: Annual mean precipitation of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in millimeters
- WGI: World Governance Indicator of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- human_footprint: Human footprint index per location (obtained from Venter et al. (2016) Scientific Data 3*:160067)
- n_high_impacts: Number of high impact records (obtained from GIDIAS_20250417_agg_regions.csv)
- n_impacts: Total number of impact records per location (obtained from GIDIAS_20250417_agg_regions.csv)
- Area: Size of location area in square meters
CompleteVariablesRegression_19092025_terrestrial_final.csv: Variables for regression analyses of impacts by terrestrial taxa
This table contains all variables used for the regression analyses of impacts
posed by terrestrial species as presented in Bacher et al.
Science 393, 376-380 (2026). The individual variables were obtained from various
providers as stated in the list below. In each case, the location names were
harmonised using ISO3 codes where possible to match the location names used in
the study. Variable entries were not available for each location, which resulted
in missing values indicated by "NA".
- Location: Name of location
- ISO3: ISO code of each location if available
- Continent: Name of continent
- Island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- EconGrowth: Classification of location by economic development (same as EconDevelop_strict in dataset CompleteVariablesRegression_impacts_250919.csv
- reactive: Metric of reactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- proactive: Metric of proactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- pop2000: Size of human population of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDP: Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDPpc: per capita Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- temp_annual: Annual mean temperature of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in degree celsius
- precip_annual: Annual mean precipitation of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in millimeters
- WGI: World Governance Indicator of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- human_footprint: Human footprint index per location (obtained from Venter et al. (2016) Scientific Data 3*:160067)
- n_high_impacts: Number of high impact records (obtained from GIDIAS_20250417_agg_regions.csv)
- n_impacts: Total number of impact records per location (obtained from GIDIAS_20250417_agg_regions.csv)
- Area: Size of location area in square meters
CompleteVariablesRegression_19092025_plants_final.csv: Variables for regression analyses of impacts by plants
This table contains all variables used for the regression analyses of impacts
posed by vascular plants as presented in Bacher et al.
Science 393, 376-380 (2026). The individual variables were obtained from various
providers as stated in the list below. In each case, the location names were
harmonised using ISO3 codes where possible to match the location names used in
the study. Variable entries were not available for each location, which resulted
in missing values indicated by "NA".
- Location: Name of location
- ISO3: ISO code of each location if available
- Continent: Name of continent
- Island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- EconGrowth: Classification of location by economic development (same as EconDevelop_strict in dataset CompleteVariablesRegression_impacts_250919.csv
- reactive: Metric of reactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- proactive: Metric of proactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- pop2000: Size of human population of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDP: Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDPpc: per capita Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- temp_annual: Annual mean temperature of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in degree celsius
- precip_annual: Annual mean precipitation of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in millimeters
- WGI: World Governance Indicator of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- human_footprint: Human footprint index per location (obtained from Venter et al. (2016) Scientific Data 3*:160067)
- n_high_impacts: Number of high impact records (obtained from GIDIAS_20250417_agg_regions.csv)
- n_impacts: Total number of impact records per location (obtained from GIDIAS_20250417_agg_regions.csv)
- Area: Size of location area in square meters
CompleteVariablesRegression_19092025_invertebrates_final.csv: Variables for regression analyses of impacts by invertebrates
This table contains all variables used for the regression analyses of impacts
posed by invertebrate species as presented in Bacher et al.
Science 393, 376-380 (2026). The individual variables were obtained from various
providers as stated in the list below. In each case, the location names were
harmonised using ISO3 codes where possible to match the location names used in
the study. Variable entries were not available for each location, which resulted
in missing values indicated by "NA".
- Location: Name of location
- ISO3: ISO code of each location if available
- Continent: Name of continent
- Island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- EconGrowth: Classification of location by economic development (same as EconDevelop_strict in dataset CompleteVariablesRegression_impacts_250919.csv
- reactive: Metric of reactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- proactive: Metric of proactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- pop2000: Size of human population of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDP: Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDPpc: per capita Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- temp_annual: Annual mean temperature of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in degree celsius
- precip_annual: Annual mean precipitation of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in millimeters
- WGI: World Governance Indicator of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- human_footprint: Human footprint index per location (obtained from Venter et al. (2016) Scientific Data 3*:160067)
- n_high_impacts: Number of high impact records (obtained from GIDIAS_20250417_agg_regions.csv)
- n_impacts: Total number of impact records per location (obtained from GIDIAS_20250417_agg_regions.csv)
- Area: Size of location area in square meters
CompleteVariablesRegression_19092025_vertebrates_final.csv: Variables for regression analyses of impacts by vertebrates
This table contains all variables used for the regression analyses of impacts
posed by vertebrate species as presented in Bacher et al.
Science 393, 376-380 (2026). The individual variables were obtained from various
providers as stated in the list below. In each case, the location names were
harmonised using ISO3 codes where possible to match the location names used in
the study. Variable entries were not available for each location, which resulted
in missing values indicated by "NA".
- Location: Name of location
- ISO3: ISO code of each location if available
- Continent: Name of continent
- Island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- EconGrowth: Classification of location by economic development (same as EconDevelop_strict in dataset CompleteVariablesRegression_impacts_250919.csv
- reactive: Metric of reactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- proactive: Metric of proactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- pop2000: Size of human population of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDP: Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDPpc: per capita Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- temp_annual: Annual mean temperature of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in degree celsius
- precip_annual: Annual mean precipitation of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in millimeters
- WGI: World Governance Indicator of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- human_footprint: Human footprint index per location (obtained from Venter et al. (2016) Scientific Data 3*:160067)
- n_high_impacts: Number of high impact records (obtained from GIDIAS_20250417_agg_regions.csv)
- n_impacts: Total number of impact records per location (obtained from GIDIAS_20250417_agg_regions.csv)
- Area: Size of location area in square meters
CompleteVariablesRegression_19092025_microbes_final.csv: Variables for regression analyses of impacts by microorganisms
This table contains all variables used for the regression analyses of impacts
posed by microorganisms as presented in Bacher et al.
Science 393, 376-380 (2026). The individual variables were obtained from various
providers as stated in the list below. In each case, the location names were
harmonised using ISO3 codes where possible to match the location names used in
the study. Variable entries were not available for each location, which resulted
in missing values indicated by "NA".
- Location: Name of location
- ISO3: ISO code of each location if available
- Continent: Name of continent
- Island: Indication whether location is an island (1*: island; 0*: no island)
- GlobalSouth: Indication whether location is considered as a member of the Global South (1*: yes; 0*: no)
- EconGrowth: Classification of location by economic development (same as EconDevelop_strict in dataset CompleteVariablesRegression_impacts_250919.csv
- reactive: Metric of reactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- proactive: Metric of proactive capacity of a country (obtained from Early et al. 2016, DOI*: 10.1038/ncomms12485)
- pop2000: Size of human population of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDP: Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- GDPpc: per capita Gross Domestic Product of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- temp_annual: Annual mean temperature of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in degree celsius
- precip_annual: Annual mean precipitation of a location (obtained from Chelsa, Karger et al. (2017) Scientific Data 4, 170122) in millimeters
- WGI: World Governance Indicator of a country (obtained from World Bank, https://data.worldbank.org/, accessed, 30.09.2024)
- human_footprint: Human footprint index per location (obtained from Venter et al. (2016) Scientific Data 3*:160067)
- n_high_impacts: Number of high impact records (obtained from GIDIAS_20250417_agg_regions.csv)
- n_impacts: Total number of impact records per location (obtained from GIDIAS_20250417_agg_regions.csv)
- Area: Size of location area in square meters
Impact comparisons both GSGN.txt: Average impact severity
The table provides measures of impacts and average impact severity per region
(i.e., countries of the Global North or Global South, respectively) for species
introduced in both regions.
- IAS: Name of taxon
- all.impacts: Number of all impacts
- impacts.North: Number of all impacts imposed in countries of the Global North
- impacts.South: Number of all impacts imposed in countries of the Global South
- severe.North: Number of high impacts imposed in countries of the Global North
- notsevere.North: Number of non-high impacts imposed in countries of the Global North
- severe.South: Number of high impacts imposed in countries of the Global South
- notsevere.South: Number of non-high impacts imposed in countries of the Global South
- AIS.North: Proportion of of high to all impacts in countries of the Global North
- AIS.South: Proportion of of high to all impacts in countries of the Global South
TTestNvsS.txt: Average impact severity per species and region
The table lists measures of average impact severity per species and per region
(i.e., countries of the Global North or Global South, respectively).
- AIS: Average impact severity across all location either in countries of the Global North or Global South per taxon
- Region: Indication of the Global North or Global South region ("North" or "South")
Geographic information
Shapefiles are organised into individual files and can contain the following files
all with the same name but different endings:
- prj: The file that contains the coordinate system and map projection information.
- shp: The main geospatial data file that contains feature geometry.
- shx: The file containing the index of feature geometry.
- dbf: The dBASE that contains the attributes of features.
- cpg: The file specifying the codepage to identify the characterset.
The shapefiles are imported and processed using R version 4.4.0.
SInAS_Locations: Boundaries of locations.
This shapefile contains the geographic data of locations, i.e. the coordinates of
boundaries of each location. The boundaries for each location are bundled into
so-called features, and each feature is associated with additional information
stored in columns within the file (*.dbf). The columns are:
- Location: Name of location
- locationID: Internal unique identifier of a location
- island: Indication whether the location is an island (1: yes; 0: no)
- Lon: Longitude of centroid of location
- Lat: Latitude of centroid of location
- geometry: Sequence of coordinates defining the boundaries
ne_10m_ocean: Boundaries of oceans
This file contains the geographic boundaries of oceans. It is used to colour
oceans in a different colour to make them distinct from islands and mainlands.
The file contains only one row with the coordinates provided in the column "geometry".
Statistical results
Regression table_allimpacts_250919_revised.csv: Regression results for
the analysis of all impacts
This table includes the results of the regression analysis on the number of all
impacts records per location as shown in Fig. 3 of Bacher et al. (2026) Science
393, 376-380. It contains the estimates of regression coefficients and
associated significance test.
- Column1: Name of variable
- Estimate: Estimate of regression coefficient
- Adjusted SE: Adjusted standard error
- z value: z-Value of regression coefficient
- Pr(>|z|): p-Value of significance test
- _1: Indication of significance level by asterisks
Regression table_highimpacts_250919_revised.csv: Regression results for
the analysis of high impacts
This table includes the results of the regression analysis on the number of high
impacts records per location as shown in Fig. 3 of Bacher et al. (2026) Science
393, 376-380. It contains the estimates of regression coefficients and
associated significance test.
- Column1: Name of variable
- Estimate: Estimate of regression coefficient
- Adjusted SE: Adjusted standard error
- z value: z-Value of regression coefficient
- Pr(>|z|): p-Value of significance test
- _1: Indication of significance level by asterisks
Regression table_relhighimpacts_250919_revised.csv: Regression results for
the analysis of average impact severity
This table includes the results of the regression analysis on the average impact
severity per location as shown in Fig. 3 of Bacher et al. (2026) Science 393,
376-380. It contains the estimates of regression coefficients and associated
significance test.
- Column1: Name of variable
- Estimate: Estimate of regression coefficient
- Adjusted SE: Adjusted standard error
- z value: z-Value of regression coefficient
- p value: p-Value of significance test
Code
All steps of data transformation, plotting and statistical analyses are done with the open source software R version 4.4.0.
The following R packages are required: sf, RColorBrewer, shape, data.table, lme4,
MuMIn, writexl, dplyr, ggplot2, sjPlot.
The R scripts listed below need to stored in a sub-folder called "R".
The input datasets (those listed above) have to be stored in a sub-folder called "Data" at the same level like "R".
PrepareImpactData_Bacher-etal.R
The R code to prepare the main file GIDIAS_20250417_agg_regions.csv, which contains
the metrics used for most analyses and figures.
Note that the export is outcommented in the script to avoid accidental over-writing.
To produce the export the "#" at the beginning of line 93.
Fig1_Bacher-etal.R
The R code to produce Fig. 1 of Bacher et al. (2026).
This figure includes six global maps showing the different metrics of alien species
and their impacts per location.
Fig2_Bacher-etal.R
The R code to produce Fig. 2 of Bacher et al. (2026)
Note that the figure was then processed manually to remove overlaying lables.
This figure is a scatterplot showing the total number of impact records per
location on the x-axis and the relative number of severe impacts on the y-axis.
Fig3_Bacher-etal.R
The R code to produce Fig. 3 of Bacher et al. (2026)
This figure shows the estimates of the regression coefficients for all predictors
used in the regression model and the three regression models.
Fig4_Bacher-etal.R
The R code to produce Fig. 4 of Bacher et al. (2026)
The figures shows two boxplots providing the distribution of average impact
severity for regions of the Global North and regions of the Global South.
FigS1_Bacher-etal.R
The R code to produce Fig. S1 of Bacher et al. (2026)
This figure includes six scatter plots showing the metrics of alien species
numbers and impact records per location and per continent.
FigS3_Bacher-etal.R
The R code to produce Fig. S3 of Bacher et al. (2026)
This figure is a scatter plot showing the results of the analysis of a potential
temporal effect on the reporting of impacts.
Tables S2-S9_Bacher-etal.R
The R code to produce tables S2-S9 of Bacher et al. (2026)
These tables provide the estimated regression coefficients for each regression
models.
owncolleg.r
The R code to produce a colour legend for Fig. 1