• This record comes from PubMed

Using species ranges and macroeconomic data to fill the gap in costs of biological invasions

. 2025 Jun ; 9 (6) : 1021-1030. [epub] 20250526

Language English Country Great Britain, England Media print-electronic

Document type Journal Article

Links

PubMed 40419738
DOI 10.1038/s41559-025-02697-5
PII: 10.1038/s41559-025-02697-5
Knihovny.cz E-resources

Biological invasions threaten global biodiversity, human well-being and economies. Many regional and taxonomic syntheses of monetary costs have been produced recently but with important knowledge gaps owing to uneven geographic and taxonomic research intensity. Here we combine species distribution models, macroeconomic data and the InvaCost database to produce the highest resolution spatio-temporal cost estimates currently available to bridge these gaps. From a subset of 162 invasive species with 'highly reliable' documented costs at the national level, our interpolation focuses on countries that have not reported any costs despite the known presence of invasive species. This analysis demonstrates a substantial underestimation, with global costs potentially estimated to be 1,646% higher for these species than previously recorded. This discrepancy was uneven geographically and taxonomically, respectively peaking in Europe and for plants. Our results showed that damage costs were primarily driven by gross domestic product, human population size, agricultural area and environmental suitability, whereas management expenditure correlated with gross domestic product and agriculture areas. We also found a lag time for damage costs of 46 years, but management spending was not delayed. The methodological predictive approach of this study provides a more complete view of the economic dimensions of biological invasions and narrows the global disparity in invasion cost reporting.

AMURE University of Western Brittany Plouzané France

Bieler School of Environment McGill University Montreal Quebec Canada

CAMB Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Mubarak Al Abdullah Kuwait

CEE M University of Montpellier CNRS INRAE Instit Agro Montpellier France

Department of Biology McGill University Montreal Quebec Canada

Department of Botany Institute of Ecology and Earth Science University of Tartu Tartu Estonia

Department of River Ecology and Conservation Senckenberg Research Institute and Natural History Museum Frankfurt Gelnhausen Germany

Department of Sociology Environmental and Business Economics University of Southern Denmark Esbjerg Denmark

Estación Biológica de Doñana CSIC Seville Spain

Faculty of Fisheries and Protection of Waters South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses University of South Bohemia in České Budějovice Vodňany Czech Republic

GEOMAR Helmholtz Zentrum für Ozeanforschung Kiel Kiel Germany

Graduate Program in Conservation and Ecotourism Federal University of Rio de Janeiro State Rio de Janeiro Brazil

Institute for Biochemistry and Biology University of Potsdam Potsdam Germany

Institute for Global Food Security School of Biological Sciences Queen's University Belfast Belfast UK

Institute of Biology Freie Universität Berlin Berlin Germany

Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Germany

Marine Policy Center Woods Hole MA USA

School of Biological Sciences Faculty of Science Monash University Clayton Victoria Australia

Unité Biologie des Organismes et Ecosystèmes Aquatiques Muséum national d'Histoire naturelle Sorbonne Université Université de Caen Normandie CNRS IRD Université des Antilles Paris France

Université Paris Saclay CNRS AgroParisTech Ecologie Société Evolution Gif sur Yvette France

Woods Hole Oceanographic Institution Falmouth MA USA

See more in PubMed

Pyšek, P. et al. Scientists’ warning on invasive alien species. Biol. Rev. 95, 1511–1534 (2020). PubMed DOI

Blackburn, T. M., Bellard, C. & Ricciardi, A. Alien versus native species as drivers of recent extinctions. Front. Ecol. Environ. 17, 203–207 (2019). DOI

Roy, H. E., Pauchard, A., Stoett, P. & Renard Truong, T. Thematic Assessment Report on Invasive Alien Species and their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). Zenodo https://doi.org/10.5281/zenodo.7430682 (2023).

Turbelin, A. J. et al. Biological invasions are as costly as natural hazards. Perspect. Ecol. Conserv. 21, 143–150 (2023).

Diagne, C. et al. High and rising economic costs of biological invasions worldwide. Nature 592, 571–576 (2021). PubMed DOI

Ahmed, D. A. et al. Recent advances in availability and synthesis of the economic costs of biological invasions. Bioscience 73, 560–574 (2023). PubMed DOI PMC

Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017). PubMed DOI PMC

Haubrock, P. J. et al. Geographic and taxonomic trends of rising biological invasion costs. Sci. Total Environ. 817, 152948 (2022). PubMed DOI

Diagne, C. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci. Data 7, 277 (2020). PubMed DOI PMC

Crystal-Ornelas, R. et al. Economic costs of biological invasions within North America. NeoBiota 67, 485–510 (2021). DOI

Cuthbert, R. N. et al. Global economic costs of aquatic invasive alien species. Sci. Total Environ. 775, 145238 (2021). PubMed DOI

Angulo, E. et al. Non-English languages enrich scientific knowledge: the example of economic costs of biological invasions. Sci. Total Environ. 775, 144441 (2021). PubMed DOI

Nuñez, M. A., Chiuffo, M. C., Pauchard, A. & Zenni, R. D. Making ecology really global. Trends Ecol. Evol. 36, 766–769 (2021). PubMed DOI

Elith, J. In Invasive Species: Risk Assessment and Management (eds. Robinson, A. P. et al.) (Cambridge Univ. Press, 2017).

Della Venezia, L., Samson, J. & Leung, B. The rich get richer: invasion risk across North America from the aquarium pathway under climate change. Divers. Distrib. 24, 285–296 (2018). DOI

Parker, I. M. et al. Impact: toward a framework for understanding the ecological effects of invaders. Biol. Invasions 1, 3–19 (1999). DOI

Essl, F. et al. Potential sources of time lags in calibrating species distribution models. J. Biogeogr. 51, 89–102 (2023). PubMed DOI PMC

Henry, M. et al. Unveiling the hidden economic toll of biological invasions in the European Union. Environ. Sci. Eur. 35, 1–16 (2023). DOI

Courtois, P., Figuieres, C., Mulier, C. & Weill, J. A cost–benefit approach for prioritizing invasive species. Ecol. Econ. 146, 607–620 (2018). DOI

Newman, R. & Noy, I. The global costs of extreme weather that are attributable to climate change. Nat. Commun. 14, 6103 (2023). PubMed DOI PMC

Briski, E., Drake, D. A. R., Chan, F. T., Bailey, S. A. & MacIsaac, H. J. Variation in propagule and colonization pressures following rapid human‐mediated transport: implications for a universal assemblage‐based management model. Limnol. Oceanogr. 59, 2068–2076 (2014). DOI

Turbelin, A. J. et al. Biological invasions as burdens to primary economic sectors. Glob. Environ. Change 87, 102858 (2024). DOI

Boscutti, F., Sigura, M., De Simone, S. & Marini, L. Exotic plant invasion in agricultural landscapes: a matter of dispersal mode and disturbance intensity. Appl. Veg. Sci. 21, 250–257 (2018). DOI

Soto, I. et al. The wild cost of invasive feral animals worldwide. Sci. Total Environ. 912, 169281 (2024). PubMed DOI

Wang, S., Deng, T., Zhang, J. & Li, Y. Global economic costs of mammal invasions. Sci. Total Environ. 857, 159479 (2023). PubMed DOI

Essl, F. et al. Socioeconomic legacy yields an invasion debt. Proc. Natl Acad. Sci. USA 108, 203–207 (2011). PubMed DOI

Ahmed, D. A. et al. Managing biological invasions: the cost of inaction. Biol. Invasions 24, 1927–1946 (2022). DOI

Haubrock, P. J., Cuthbert, R. N., Ricciardi, A., Diagne, C. & Courchamp, F. Economic costs of invasive bivalves in freshwater ecosystems. Diversity Distrib. 28, 1010–1021 (2022). DOI

Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020). PubMed DOI

Hughes, A. C. et al. Sampling biases shape our view of the natural world. Ecography 44, 1259–1269 (2021). DOI

Bradshaw, C. J. et al. Damage costs from invasive species exceed management expenditure in nations experiencing lower economic activity. Ecol. Econ. 220, 108166 (2024). DOI

Hudgins, E. J. et al. Unevenly distributed biological invasion costs among origin and recipient regions. Nat. Sustain. 6, 1113–1124 (2023). DOI

Novoa, A. et al. Global costs of plant invasions must not be underestimated. NeoBiota 69, 75–78 (2021). DOI

Zhang, C. & Boyle, K. J. The effect of an aquatic invasive species (Eurasian watermilfoil) on lakefront property values. Ecol. Econ. 70, 394–404 (2010). DOI

Lazzaro, L. et al. Invasive alien plant impacts on human health and well-being. in Invasive Species and Human Health (eds. Mazza, G. & Tricario, E.) pp. 16–33 (CAB International, 2018).

Cuthbert, R. et al. Economic impact disharmony in global biological invasions. Sci. Total Environ. 913, 169622 (2024). PubMed DOI

Heringer, G. et al. Economic costs of invasive non-native species in urban areas: an underexplored financial drain. Sci. Total Environ. 917, 170336 (2024). PubMed DOI

Soto, I. et al. Global economic costs of herpetofauna invasions. Sci. Rep. 12, 10829 (2022). PubMed DOI PMC

Parkes, J. P. & Panetta, F. D. Eradication of invasive species: progress and emerging issues in the 21st century. in Invasive Species Management. A Handbook of Principles and Techniques. 47–60 (Oxford Univ. Press, 2009).

Nguyen, D. & Leung, B. How well do species distribution models predict occurrences in exotic ranges? Glob. Ecol. Biogeogr. 31, 1051–1065 (2022). DOI

Kourantidou, M. et al. The economic costs, management and regulation of biological invasions in the Nordic countries. J. Environ. Manag. 324, 116374 (2022). DOI

Leroy, B. et al. Analysing economic costs of invasive alien species with the invacost R package. Methods Ecol. Evol. 13, 1930–1937 (2022). DOI

Global Biodiversity Information Facility (GBIF, 2023); https://www.gbif.org

Phillips, S. J. et al. Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data. Ecol. Appl. 19, 181–197 (2009). PubMed DOI

Barbet‐Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo‐absences for species distribution models: how, where and how many? Methods Ecol. Evolution 3, 327–338 (2012). DOI

Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017). DOI

Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 1–15 (2018). DOI

Karger, D. N., Wilson, A. M., Mahony, C., Zimmermann, N. E. & Jetz, W. Global daily 1 km land surface precipitation based on cloud cover-informed downscaling. Sci. Data 8, 307 (2021). PubMed DOI PMC

CIESIN. Global Roads Open Access Data Set, Version 1 (gROADSv1) (NASA Socioeconomic Data and Applications Center, 2013); https://doi.org/10.7927/H4VD6WCT

Mainali, K. P. et al. Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling. Glob. Change Biol. 21, 4464–4480 (2015). DOI

Naimi, B., Skidmore, A. K., Groen, T. A. & Hamm, N. A. S. On uncertainty in species distribution modelling. ITC dissertation, Univ. Twente (2015).

Wood, S. mgcv: Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation and GAMMs by REML/PQL (Chapman and Hall/CRC, 2017).

Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open‐source release of Maxent. Ecography 40, 887–893 (2017). DOI

Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019). PubMed DOI PMC

Merow, C. et al. What do we gain from simplicity versus complexity in species distribution models? Ecography 37, 1267–1281 (2014). DOI

Briski, E. et al. Does non-native diversity mirror earth’s biodiversity. Glob. Ecol. Biodivers. 33, 48–62 (2024). DOI

World Development Indicators (World Bank, 2023); www.data.worldbank.org

R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2023); https://www.R-project.org/

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...