DOI: 10.5061/dryad.wstqjq2zh
title: Patterns and drivers of range filling of alien mammals in Europe

description: Biological invasions are major drivers of biodiversity change. Alien
 mammals are particularly concerning in Europe, where their expansion
 remains unabated, though the driving factors are still unclear.
 Well-documented introductions and distributions in this continent provide
 a unique opportunity to understand how human activities influenced this
 expansion. We modelled the potential alien ranges of 46 established alien
 mammals in Europe using species’ introduction localities, residence time,
 dispersal ability, generation length, and climatic suitability. We
 compared potential and observed ranges through three range indices: range
 filling (portion of potential distribution occupied), overfilling (portion
 of observed distribution unexpectedly occupied), and unfilling (portion of
 potential distribution currently unoccupied), and we investigated the
 effects of native range size, introduction pathways (species’ sum of the
 known pathways of introduction across the European alien range, spanning
 1492–2020), and socio-economic variables (spanning 1980–2017) on uncovered
 patterns. We show that the median range overfilling value was high (22%),
 suggesting that alien mammals are substantially spreading outside expected
 distribution areas. Conversely, median values of range filling (14%) and
 unfilling (17%) were lower, suggesting recorded introductions inadequately
 explain alien mammals’ distributions. Range patterns were strongly shaped
 by human population density, which positively influenced all three range
 indices, driving range patterns and influencing alien mammals’
 introduction and establishment. Contrary, roads and railways were
 negatively related to range overfilling and unfilling, as was the number
 of introduction pathways to range filling and overfilling. Ultimately, the
 role of these socio-economic factors depends on human behaviour rather
 than environmental characteristics or species’ ecology. We confirm human
 agency as an important driver of alien mammals’ distribution and spread in
 Europe, highlighting that modifying human attitudes and regulations
 towards these taxa is key to limiting further spread.
description: # Patterns and drivers of range filling of alien mammals in Europe Dataset
 DOI: [10.5061/dryad.wstqjq2zh](10.5061/dryad.wstqjq2zh) ## Description of
 the data and file structure Supplementary Data - Patterns and drivers of
 range filling of alien mammals in Europe ### Files and variables ####
 File: Supplementary\_Data\_2025\_05\_06.xlsx **Description:**  This
 supplementary dataset is in Excel format (.xlsx) which can be explored and
 manipulated with programs like Microsoft Office, OpenOffice Calc,
 LibreOffice Calc, or imported into Google Sheets. The following text
 describes the information contained in its twenty-two sheets. **Legend:**
 contains names and affiliations of all authors, and a description of the
 supplementary data. **Species_variables:** Species’ traits used in the
 calculation of the potential alien ranges, species-specific variables used
 to explain the different range patterns in the GLMMs, their rationale, and
 the sources, for each study species (n = 46). The sheet consists of: * 48
 columns: 2 columns for the variables, plus 46 columns for the species. *
 22 rows: 2 rows for the taxonomic order and species' name, plus 20
 rows for the variables. Variables description: * *Order*: the taxonomic
 order of the species, following the PHYLACINE 1.2 taxonomy (Faurby et al.
 2018).  * *Binomial*: the scientific name of the species, following the
 PHYLACINE 1.2 taxonomy (Faurby et al. 2018).  * *Median and maximum
 dispersal*: respectively, the median and maximum distance between species’
 birth and breeding site (both in km). Median dispersal is derived from the
 COalesced Mammal dataBase of INtrinsic and Extrinsic traits (COMBINE;
 Soria et al. 2021), which follows the methodology described by Santini et
 al. (2013). We calculated maximum dispersal following the same approach as
 described by Santini et al. (2013). * *Generation length*: the average age
 of parents of a cohort (in days). Generation length is derived from
 COMBINE (Soria et al. 2021). * *Age at first reproduction*: the age at
 which females give birth to their first litter or their young attach to
 teats (in days). Age at first reproduction is derived from COMBINE (Soria
 et al. 2021). * *Residence time:* the number of days since the first
 successful introduction in Europe (Biancolini et al. 2021, Melone et al.
 2021). - *Pathways of introduction*: species' known pathways of
 introduction across the European alien range and across the different
 pathways, spanning 1492–2020, from Biancolini et al. (2021). In Biancolini
 et al. (2021), they are described as follows: * *Biological Control:* the
 release of alien mammals to serve as control agents for undesirable
 species and pests. * *Conservation:* introduction carried out to support
 species recovery, including Assisted Colonization programs following the
 IUCN Guidelines for Conservation Translocations (IUCN 2013). * *Farming:*
 introduction of alien mammals used as working animals or as a food source
 in captivity or left under limited control. * *Fauna Improvement:*
 introduction of alien mammals to modify the landscape species composition
 and improve its aesthetics, e.g. efforts carried out by the
 Acclimatization Societies, species used as tourism attractions or to
 populate city parks. * *Fur Farming:* alien mammals breed in captivity for
 fur production that subsequently escaped or have been released into the
 wild. * *Hunting:* introduction of alien mammals as game species in the
 wild or hunting enclosures. * *Ornamental Purposes:* alien mammals kept in
 captivity as ornamental species that subsequently escaped or have been
 released into the wild. * *Pet:* alien mammals traded and kept in
 captivity as domestic pet animals that subsequently escaped or have been
 released into the wild. * *Research:* alien mammals used in medical or
 ecological research kept in captivity or released into the wild. *
 *Stowaway:* unintentional introduction of alien mammals present in cargo
 generally transported by ship. * *Wild Fur:* purposeful introduction of
 alien mammals to create a wild population of fur- bearers. * *Zoo:* alien
 mammals used in zoological gardens as public display that escaped or have
 been released into the wild.  * *TOTAL*: total number of introduction
 pathways for each species is also indicated.  - *Native range extent*:
 from IUCN (2023) (number of 10 x 10 km cells).  - *Points of
 introduction:* sum of the species' known (successful and unsuccessful
 introductions at the administrative area level) are compiled by Blackburn
 et al. (2017), with additional literature from Biancolini et al. (2021),
 and Melone et al. (2021). **GLMM_variables:** Variables used in the
 Generalised Linear Mixed Models (GLMMs). Expected influence on each range
 pattern, rationale, time span, measurement unit, original variable’s
 resolution, processing methods, processed data resolution, statistical
 processing, and source are indicated. Variables available for several
 individual years were averaged. Variables description: * *Number of
 introduction pathways:* the species’ sum of the known introduction pathway
 across the European alien range, spanning 1492–2020, from Biancolini et
 al. (2021). * *Native range size: *from IUCN (2023) (number of 10 x 10 km
 cells).  * *Human population density*: data was downloaded from the
 History Database of the Global Environment (HYDE) 3.1 (Klein Goldewijk et
 al. 2011), for the decades 1980-2000, and the year 2005. Data were in the
 form of raster maps, with each decade represented as inhabitants/grid
 cell. We log-transformed population density, since it was strongly skewed.
 * *Anthropic land-use*: data was downloaded from the History Database of
 the Global Environment (HYDE) 3.1 (Klein Goldewijk et al. 2011), for the
 decades 1980-2000, and the year 2005. We selected only categories of
 anthropic environments (pasture, cropland, and urban area), excluding
 natural land, and produced a raster per each category with the area
 covered per cell in km2. We then summed all categories to obtain a single
 anthropic land-use variable describing the amount of anthropized
 environments per cell. * *Roads and railways*: were selected from Williams
 et al. (2020) for the year 2017. **ODMAP:** Overview, Data, Model,
 Assessment and Prediction (Zurell et al. 2020) protocol for SDMs
 **single_models_statistics:** Single models statistics for the 4,140 SDMs
 models (six algorithms x three runs x five pseudo-absences sets x 46
 species) of the study. The sheet consists of: * 9 columns:
 *Species* (species name in the format Genusspecies), *PA* (pseudo-absence
 set, from 1 to 5) , *run* (run, from 1 to 5), *algo* (the six algorithms
 used for the ensemble modeling; acronyms as follows: Generalised Linear
 Models GLM, Multivariate Adaptive Regression Splines MARS, Flexible
 Discriminant Analysis FDA, Generalised Boosting Models GBM, Artificial
 Neural Network ANN, Random Forest RF), *metric.eval* (evaluation metric
 used; acronyms as follows: True Skill Statistics TSS, Cohen's kappa
 KAPPA, receiver operating characteristic curve ROC), *Testing.data* (value
 of the testing data), *Cutoff* (cutoff value), *Sensitivity* (sensitivity
 value), and *Specificity* (specificity value) for each model are
 indicated. * 12409 rows.  **OAR_Range_ove_climatic_suitab:** the sheet
 consist of: * 7 columns: *Binomial* (scientific species' name),
 *Range_overfilling* (number of 10x10 raster cells for range overfilling),
 *Range_overfilling_suit* (number of 10x10 raster cells within the
 climatically suitable areas projected by the SDMs for the range
 overfilling), *Range_overfilling_suit_perc* (proportion of the raster
 cells occupied within the climatically suitable areas projected by the
 SDMs for the range overfilling), *OAR* (number of 10x10 raster cells for
 the Observed Alien Range OAR), *OAR_suit* (number of 10x10 raster cells
 within the climatically suitable areas projected by the SDMs for the OAR),
 *OAR_suit_perc* (proportion of the raster cells occupied within the
 climatically suitable areas projected by the SDMs for the OAR). * 47 rows.
 **Disp_gen_correlations:** the sheet contains the correlation matrix of
 the variables for the three (one for each filling pattern: range filling,
 overfilling, and unfilling) Generalised Linear Mixed Models, for the
 median dispersal and generation length (**Disp_gen**) scenario. Variables
 are as follows: * *Pathways_tot:* represents the species’ sum of the known
 *pathways of introduction* across the European alien range, spanning
 1492–2020, from Biancolini et al. (2021). * *Native_range:* represents the
 native range extent from IUCN (2023) (number of 10 x 10 km cells) *
 *PopdensityAvg_3035_average:* Human population density data was downloaded
 from the History Database of the Global Environment (HYDE) 3.1 (Klein
 Goldewijk et al. 2011), for the decades 1980-2000, and the year 2005. Data
 were in the form of raster maps, with each decade represented as
 inhabitants/grid cell. We log-transformed population density, since it was
 strongly skewed. * *landuseHAvg_3035_average:* Anthropic land-use data was
 downloaded from the History Database of the Global Environment (HYDE) 3.1
 (Klein Goldewijk et al. 2011), for the decades 1980-2000, and the year
 2005. We selected only categories of anthropic environments (pasture,
 cropland, and urban area), excluding natural land, and produced a raster
 per each category with the area covered per cell in km2. We then summed
 all categories to obtain a single anthropic land-use variable describing
 the amount of anthropized environments per cell. *
 *infrastructuresAvg_3035_average:* Roads and railways were selected from
 Williams et al. (2020) for the year 2017. NB: The same variables are
 repeated in the sheets
 **Disp_age_correlations, MaxDisp_gen_correlations**,
 and **MaxDisp_age_correlations.** **Disp_age_correlations:** the sheet
 contains the correlation matrix of the variables for the three (one for
 each filling pattern: range filling, overfilling, and unfilling)
 Generalised Linear Mixed Models, for the median dispersal and age at first
 reproduction (**Disp_age**) scenario. **MaxDisp_gen_correlations:** the
 sheet contains the correlation matrix of the variables for the three (one
 for each filling pattern: range filling, overfilling, and unfilling)
 Generalised Linear Mixed Models, for the maximum dispersal and generation
 length (**MaxDisp_gen**) scenario. **MaxDisp_age_correlations:** the sheet
 contains the correlation matrix of the variables for the three (one for
 each filling pattern: range filling, overfilling, and unfilling)
 Generalised Linear Mixed Models, for the maximum dispersal and age at
 first reproduction (**MaxDisp_age**) scenario. **Disp_gen_Range_filling:**
 this sheet contains, for the median dispersal and generation length
 scenario, the following information for range filling: * 12 columns:
 *Order* (species' taxonomic order), *Binomial* (scientific
 species' name), *Observed_range* (number of 10x10 km cell for
 observed range), *Potential_range* (number of 10x10 km cell for potential
 range), *POAR* (number of 10x10 km cell for Potential and Observed Alien
 Range POAR), *Range_filling* (number of 10x10 raster cells for range
 filling), *Filling_percentage_range_filling* (filling percentage of range
 filling). The last 5 columns contain polygon-level values for the
 variables considered in the GLMMs: *Pathways_tot* (represents the species’
 sum of the known *pathways of introduction* across the European alien
 range, spanning 1492–2020, from Biancolini et al. (2021), *Native_range*
 (represents the native range extent from IUCN (2023); number of 10 x 10 km
 cells), *PopdensityAvg_3035_average* (Human population density data was
 downloaded from the History Database of the Global Environment (HYDE) 3.1
 (Klein Goldewijk et al. 2011), for the decades 1980-2000, and the year
 2005. Data were in the form of raster maps, with each decade represented
 as inhabitants/grid cell. We log-transformed population density, since it
 was strongly skewed), *landuseHAvg_3035_average* (Anthropic land-use data
 was downloaded from the History Database of the Global Environment (HYDE)
 3.1 (Klein Goldewijk et al. 2011), for the decades 1980-2000, and the year
 2005. We selected only categories of anthropic environments (pasture,
 cropland, and urban area), excluding natural land, and produced a raster
 per each category with the area covered per cell in km2. We then summed
 all categories to obtain a single anthropic land-use variable describing
 the amount of anthropized environments per cell),
 *infrastructuresAvg_3035_average* (Roads and railways were selected from
 Williams et al. (2020) for the year 2017). - 365 rows. NB: The same
 polygon-level variables are repeated in the
 sheets **Disp_gen_Range_overfilling, Disp_gen_Range_unfilling,
 Disp_age_Range_filling,
 Disp_age_Range_overfilling, Disp_age_Range_unfilling,
 MaxDisp_gen_Range_filling, MaxDisp_gen_Range_overfilling, MaxDisp_gen_Range_unfilling, MaxDisp_age_Range_filling, MaxDisp_age_Range_overfilling, MaxDisp_age_Range_unfilling.** **Disp_gen_Range_overfilling**: this sheet contains, for the median dispersal and generation length scenario, the following information for range overfilling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_overfilling* (number of 10x10 raster cells for range overfilling), *Filling_percentage_range_overfilling* (filling percentage of range overfilling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 638 rows. **Disp_gen_Range_unfilling:** this sheet contains, for the median dispersal and generation length scenario, the following information for range unfilling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_unfilling* (number of 10x10 raster cells for range unfilling), *Filling_percentage_range_unfilling* (filling percentage of range unfilling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 298 rows. **Disp_age_Range_filling:** this sheet contains, for the median dispersal and age at first reproduction scenario, the following information for range filling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_filling* (number of 10x10 raster cells for range filling), *Filling_percentage_range_filling* (filling percentage of range filling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 486 rows. **Disp_age_Range_overfilling:** this sheet contains, for the median dispersal and age at first reproduction scenario, the following information for range overfilling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_overfilling* (number of 10x10 raster cells for range overfilling), *Filling_percentage_range_overfilling* (filling percentage of range overfilling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 1059 rows. **Disp_age_Range_unfilling:** this sheet contains, for the median dispersal and age at first reproduction scenario, the following information for range unfilling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_unfilling* (number of 10x10 raster cells for range unfilling), *Filling_percentage_range_unfilling* (filling percentage of range unfilling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 897 rows. **MaxDisp_gen_Range_filling:** this sheet contains, for the maximum dispersal and generation length scenario, the following information for range filling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_filling* (number of 10x10 raster cells for range filling), *Filling_percentage_range_filling* (filling percentage of range filling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 475 rows. **MaxDisp_gen_Range_overfilling**: this sheet contains, for the maximum dispersal and generation length scenario, the following information for range overfilling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_overfilling* (number of 10x10 raster cells for range overfilling), *Filling_percentage_range_overfilling* (filling percentage of range overfilling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 1198 rows. **MaxDisp_gen_Range_unfilling:** this sheet contains, for the maximum dispersal and generation length scenario, the following information for range unfilling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_unfilling* (number of 10x10 raster cells for range unfilling), *Filling_percentage_range_unfilling* (filling percentage of range unfilling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 1062 rows. **MaxDisp_age_Range_filling:** this sheet contains, for the maximum dispersal and age at first reproduction scenario, the following information for range filling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_filling* (number of 10x10 raster cells for range filling), *Filling_percentage_range_filling* (filling percentage of range filling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 682 rows. **MaxDisp_age_Range_overfilling:** this sheet contains, for the maximum dispersal and age at first reproduction scenario, the following information for range overfilling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range *(number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_overfilling* (number of 10x10 raster cells for range overfilling), *Filling_percentage_range_overfilling* (filling percentage of range overfilling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 1582 rows. **MaxDisp_age_Range_unfilling:** this sheet contains, for the maximum dispersal and age at first reproduction scenario, the following information for range overfilling: * 12 columns: *Order* (species' taxonomic order), *Binomial* (scientific species' name), *Observed_range* (number of 10x10 km cell for observed range), *Potential_range* (number of 10x10 km cell for potential range), *POAR* (number of 10x10 km cell for Potential and Observed Alien Range POAR), *Range_unfilling* (number of 10x10 raster cells for range unfilling), *Filling_percentage_range_unfilling* (filling percentage of range unfilling). The last 5 columns contain polygon-level values for the variables considered in the GLMMs. * 3229 rows. ## Code/software All the scripts are openly available here: [https://github.com/Lisa-Tedeschi/Range_patterns_alien_mammals/tree/main](https://github.com/Lisa-Tedeschi/Range_patterns_alien_mammals/tree/main)  Lisa Tedeschi1,2,3,*, Bernd Lenzner1, Anna Schertler1, Johannes Wessely4, Dino Biancolini2,5,6, César Capinha7,8, Beatrice Melone9,10, Carmen Soria11, Carlo Rondinini2+, Franz Essl1+   1 Division of BioInvasions, Global Change & Macroecology, Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030 Vienna, Austria. 2 Global Mammal Assessment Programme, Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Viale dell’Università 32, 00185 Rome, Italy. 3 Vienna Doctoral School of Ecology and Evolution, University of Vienna, Vienna, Austria. 4 Division of Biodiversity Dynamics and Conservation, Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030 Vienna, Austria. 5 National Research Council of Italy - Institute for Bioeconomy (CNR-IBE), Via dei Taurini 19, Rome, Italy. 6 IUCN SSC Invasive Species Specialist Group, Rome, Italy. 7 Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, Rua Branca Edmée Marques, Cidade Universitária, 1600-276 Lisboa, Portugal. 8 Associate Laboratory TERRA, Lisbon, Portugal. 9 European Commission – Joint Research Centre Unit D.02, Via Enrico Fermi 2749, 21027 Ispra, Italy. 10 FINCONS SPA, Via Torri Bianche 10, Pal. Betulla, 20871 Vimercate, Italy. 11 Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic. \* Corresponding author: Lisa Tedeschi ([lisa.tedeschi@univie.ac.at](mailto:lisa.tedeschi@univie.ac.at)) Franz Essl + and Carlo Rondinini + should be considered joint senior authors. Published in *Oikos*