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High and rising economic costs of biological invasions worldwide

Abstract

Biological invasions are responsible for substantial biodiversity declines as well as high economic losses to society and monetary expenditures associated with the management of these invasions1,2. The InvaCost database has enabled the generation of a reliable, comprehensive, standardized and easily updatable synthesis of the monetary costs of biological invasions worldwide3. Here we found that the total reported costs of invasions reached a minimum of US$1.288 trillion (2017 US dollars) over the past few decades (1970–2017), with an annual mean cost of US$26.8 billion. Moreover, we estimate that the annual mean cost could reach US$162.7 billion in 2017. These costs remain strongly underestimated and do not show any sign of slowing down, exhibiting a consistent threefold increase per decade. We show that the documented costs are widely distributed and have strong gaps at regional and taxonomic scales, with damage costs being an order of magnitude higher than management expenditures. Research approaches that document the costs of biological invasions need to be further improved. Nonetheless, our findings call for the implementation of consistent management actions and international policy agreements that aim to reduce the burden of invasive alien species.

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Fig. 1: Temporal trend of global invasion costs (in millions of 2017 US dollars) between 1970 and 2017.
Fig. 2: Temporal trends of global damage and management costs (in millions of 2017 US dollars) based on both mean annual costs for each decade and model prediction between 1970 and 2017.
Fig. 3: Cumulative and mean annual costs observed in the database and predicted by linear regression.
Fig. 4: Geographical distribution of the cost estimates (in millions of 2017 US dollars) available in the most robust subset of the original database for the period of 1970–2017.
Fig. 5: The 10 costliest taxa from the most robust subset of the original database for both cumulative damage and management costs (in billions of 2017 US dollars) between 1970 and 2017.

Data availability

The original InvaCost database3 considered here is openly accessible at https://doi.org/10.6084/m9.figshare.12668570.v1 and provided as Supplementary Data 1.

Code availability

We carried out all data processing and analyses with the ‘invacost’ R package (available on the Comprehensive R Archive Network at https://cran.r-project.org/package=invacost). The analytical framework has been described in detail previously44. A step-by-step tutorial for this framework is also available at https://www.github.com/Farewe/invacost. The code used to generate the graphs and analyses for this paper is available at http://borisleroy.com/invacost/global_invasion_costs_scripts.html.

References

  1. 1.

    Born, W., Rauschmayer, F. & Bräuer, I. Economic evaluation of biological invasions—a survey. Ecol. Econ. 55, 321–336 (2005).

    Article  Google Scholar 

  2. 2.

    Jackson, T. Addressing the economic costs of invasive alien species: some methodological and empirical issues. Int. J. Sustain. Soc. 7, 221–240 (2015).

    Article  Google Scholar 

  3. 3.

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

    CAS  Article  Google Scholar 

  4. 4.

    Bellard, C., Cassey, P. & Blackburn, T. M. Alien species as a driver of recent extinctions. Biol. Lett. 12, 20150623 (2016).

    Article  Google Scholar 

  5. 5.

    Kumschick, S. et al. Ecological impacts of alien species: quantification, scope, caveats, and recommendations. Bioscience 65, 55–63 (2015).

    Article  Google Scholar 

  6. 6.

    Ogden, N. H. et al. Emerging infectious diseases and biological invasions: a call for a One Health collaboration in science and management. R. Soc. Open Sci. 6, 181577 (2019).

    Article  ADS  Google Scholar 

  7. 7.

    Jones, B. A. Invasive species impacts on human well-being using the life satisfaction index. Ecol. Econ. 134, 250–257 (2017).

    Article  Google Scholar 

  8. 8.

    Bradshaw, C. J. A. et al. Massive yet grossly underestimated global costs of invasive insects. Nat. Commun. 7, 12986 (2016).

    CAS  Article  ADS  Google Scholar 

  9. 9.

    Seebens, H. et al. Projecting the continental accumulation of alien species through to 2050. Glob. Change Biol. 27, 970–982 (2020).

    Article  ADS  Google Scholar 

  10. 10.

    Essl, F. et al. Drivers of future alien species impacts: an expert-based assessment. Glob. Change Biol. 26, 4880–4893 (2020).

    Article  ADS  Google Scholar 

  11. 11.

    Courchamp, F. et al. Invasion biology: specific problems and possible solutions. Trends Ecol. Evol. 32, 13–22 (2017).

    Article  Google Scholar 

  12. 12.

    Lenzner, B. et al. A framework for global twenty-first century scenarios and models of biological invasions. Bioscience 69, 697–710 (2019).

    Article  Google Scholar 

  13. 13.

    Heger, T. et al. Conceptual frameworks and methods for advancing invasion ecology. Ambio 42, 527–540 (2013).

    Article  Google Scholar 

  14. 14.

    Larson, D. L. et al. A framework for sustainable invasive species management: environmental, social, and economic objectives. J. Environ. Manage. 92, 14–22 (2011).

    Article  Google Scholar 

  15. 15.

    Hoffmann, B. D. & Broadhurst, L. M. The economic cost of managing invasive species in Australia. NeoBiota 31, 1–18 (2016).

    Article  Google Scholar 

  16. 16.

    Pimentel, D. et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agric. Ecosyst. Environ. 84, 1–20 (2001).

    Article  Google Scholar 

  17. 17.

    Paini, D. R. et al. Global threat to agriculture from invasive species. Proc. Natl Acad. Sci. USA 113, 7575–7579 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Latombe, G. et al. A vision for global monitoring of biological invasions. Biol. Conserv. 213, 295–308 (2017).

    Article  Google Scholar 

  19. 19.

    Diagne, C., Catford, J. A., Essl, F., Nuñez, M. A. & Courchamp, F. What are the economic costs of biological invasions? A complex topic requiring international and interdisciplinary expertise. NeoBiota 63, 25–37 (2020).

    Article  Google Scholar 

  20. 20.

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

    CAS  Article  ADS  Google Scholar 

  21. 21.

    Bishop, M. J. et al. Effects of ocean sprawl on ecological connectivity: impacts and solutions. J. Exp. Mar. Biol. Ecol. 492, 7–30 (2017).

    Article  Google Scholar 

  22. 22.

    Bacher, S. et al. Socio‐Economic Impact Classification of Alien Taxa (SEICAT). Methods Ecol. Evol. 9, 159–168 (2018).

  23. 23.

    Angulo, E. et al. Non-English languages enrich scientific knowledge: the example of economic costs of biological invasions. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.144441 (2021).

  24. 24.

    Colautti, R. I. & MacIsaac, H. J. A neutral terminology to define ‘invasive’ species. Divers. Distrib. 10, 135–141 (2004).

    Article  Google Scholar 

  25. 25.

    Dana, E. D., Jeschke, J. M. & García-de-Lomas, J. Decision tools for managing biological invasions: existing biases and future needs. Oryx 48, 56–63 (2014).

    Article  Google Scholar 

  26. 26.

    Jeschke, J. M., Keesing, F. & Ostfeld, R. S. Novel organisms: comparing invasive species, GMOs, and emerging pathogens. Ambio 42, 541–548 (2013).

    Article  Google Scholar 

  27. 27.

    Jones, B. A. Tree shade, temperature, and human health: evidence from invasive species-induced deforestation. Ecol. Econ. 156, 12–23 (2019).

    Article  Google Scholar 

  28. 28.

    Jones, B. A. & McDermott, S. M. Health impacts of invasive species through an altered natural environment: assessing air pollution sinks as a causal pathway. Environ. Resour. Econ. 71, 23–43 (2018).

    Article  Google Scholar 

  29. 29.

    Pyšek, P. et al. Geographical and taxonomic biases in invasion ecology. Trends Ecol. Evol. 23, 237–244 (2008).

    Article  Google Scholar 

  30. 30.

    Clout, M. N. & Russell, J. C. The invasion ecology of mammals: a global perspective. Wildl. Res. 35, 180–184 (2008).

    Article  Google Scholar 

  31. 31.

    Early, R. et al. Global threats from invasive alien species in the twenty-first century and national response capacities. Nat. Commun. 7, 12485 (2016).

    CAS  Article  ADS  Google Scholar 

  32. 32.

    Jarić, I. et al. Crypticity in biological invasions. Trends Ecol. Evol. 34, 291–302 (2019).

    Article  Google Scholar 

  33. 33.

    Pascual, U. et al. in The Economics of Ecosystems and Biodiversity: Ecological and Economic Foundations (ed. Kumar, P.) 183–256 (Earthscan, 2010).

  34. 34.

    Spangenberg, J. H. & Settele, J. Precisely incorrect? Monetising the value of ecosystem services. Ecol. Complex. 7, 327–337 (2010).

    Article  Google Scholar 

  35. 35.

    Kallis, G., Gómez-Baggethun, E. & Zografos, C. To value or not to value? That is not the question. Ecol. Econ. 94, 97–105 (2013).

    Article  Google Scholar 

  36. 36.

    Meinard, Y., Dereniowska, M. & Gharbi, J.-S. The ethical stakes in monetary valuation methods for conservation purposes. Biol. Conserv. 199, 67–74 (2016).

    Article  Google Scholar 

  37. 37.

    Faulkner, K. T., Robertson, M. P. & Wilson, J. R. U. Stronger regional biosecurity is essential to prevent hundreds of harmful biological invasions. Glob. Change Biol. 26, 2449–2462 (2020).

    Article  ADS  Google Scholar 

  38. 38.

    Pagad, S., Genovesi, P., Carnevali, L., Schigel, D. & McGeoch, M. A. Introducing the Global Register of Introduced and Invasive Species. Sci. Data 5, 170202 (2018).

    Article  Google Scholar 

  39. 39.

    Holden, M. H., Nyrop, J. P. & Ellner, S. P. The economic benefit of time‐varying surveillance effort for invasive species management. J. Appl. Ecol. 53, 712–721 (2016).

    Article  Google Scholar 

  40. 40.

    Hulme, P. E. et al. Integrating invasive species policies across ornamental horticulture supply chains to prevent plant invasions. J. Appl. Ecol. 55, 92–98 (2018).

    Article  Google Scholar 

  41. 41.

    Chaffin, B. C. et al. Biological invasions, ecological resilience and adaptive governance. J. Environ. Manage. 183, 399–407 (2016).

    Article  Google Scholar 

  42. 42.

    Sardain, A., Sardain, E. & Leung, B. Global forecasts of shipping traffic and biological invasions to 2050. Nat. Sustain. 2, 274–282 (2019).

    Article  Google Scholar 

  43. 43.

    Seebens, H. Invasion ecology: expanding trade and the dispersal of alien species. Curr. Biol. 29, R120–R122 (2019).

    CAS  Article  Google Scholar 

  44. 44.

    Leroy, B. et al. Analysing global economic costs of invasive alien species with the invacost R package. Preprint at https://doi.org/10.1101/2020.12.10.419432 (2020).

Download references

Acknowledgements

We acknowledge the French National Research Agency (ANR-14-CE02-0021) and the BNP-Paribas Foundation for providing supporting funds to the InvaCost project, and the Biodiversa Eranet for the AlienScenario programme and AXA Research Fund for the Invasion Biology Chair. A.-C.V., B.L., D.R., F.C., J.-M.S. and R.E.G. were mainly funded by their salaries as French agents affiliated with public institutions. Work by I.J. was supported by the J. E. Purkyně Fellowship of the Czech Academy of Sciences. We thank L. Nuninger and C. Assailly for their work in the initial project; S. Pagad and F. Simard for their assistance and advice during all of this work; S. Milborrow, N. Dubos and A. A. Kramer for their statistical recommendations; E. Angulo, C. Bellard, L. Ballesteros-Mejia, E. Bonnaud, H. Dole, M. Perron and A. Turbelin for their constructive feedback on our work; and T. Kneib, S. McDermott and M. Springborn for comments that improved our initial manuscript.

Author information

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Authors

Contributions

F.C., C.D., B.L. and C.J.A.B. conceived and refined the initial project. C.D. designed the study strategy with inputs from B.L. and F.C. C.D., B.L., A.-C.V., I.J., J.-M.S., R.E.G., D.R. and F.C. collected and standardized all data. C.D., B.L. and A.-C.V. processed the final database. B.L. and C.D. implemented the analyses with inputs from A.C.V., R.E.G. and D.R. C.D. wrote the first draft of the manuscript with reviews from all contributing authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Christophe Diagne or Franck Courchamp.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Thomas Kneib, Shana McDermott, Michael Springborn and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data figures and tables

Extended Data Fig. 1 The lag between cost occurrence and year of publication based on the most robust subset of the database.

Further information is included in the Methods, ‘Dataset and processing steps’ section. The box-and-whisker plot shows the median of the distribution (centre), box boundaries corresponding to the first and third quartiles and whiskers that extend to up to 1.5× the interquartile range. The few occurrences of publications before economic impacts corresponded to planned budgets over specific periods expanding beyond the publication year.

Extended Data Fig. 2 Temporal trend (1970–2017) of global invasion costs (in millions of 2017 US dollars) predicted based on different modelling techniques.

OLS, ordinary least-squares model; GAM, generalized additive model; MARS, multiple adaptive regression splines. The linear trend over time is considered the best way to estimate the mean annual cost of invasions over time (see Supplementary Methods 1 for details). Results are those obtained when considering models calibrated with at least 25% data completeness (calibration interval 1970–2015). We log10-transformed cost estimates (from the ‘Cost estimate per year 2017 USD exchange rate’ column of Supplementary Data 1).

Extended Data Fig. 3 Temporal trend (1970–2017) of global costs (in millions of 2017 US dollars) according to the type of cost.

The type of cost included damage (economic losses due to direct and/or indirect impacts of invaders) and management (economic resources allocated to actions to avoid or limit invasion impacts). a, Predicted trend for damage costs. b, Predicted trend for management costs. c, Observed trends for both damage and management costs. The horizontal bars indicate the total time span over which decadal mean costs were calculated. Results were obtained when considering models calibrated with at least 25% data completeness (calibration interval, 1970–2015). Note that the error bands (a, b) represent the 95% confidence intervals for all models except MARS, for which they represent 95% prediction intervals (as confidence intervals cannot be estimated using MARS). We log10-transformed cost estimates (from the ‘Cost estimate per year 2017 USD exchange rate’ column of Supplementary Data 1).

Extended Data Fig. 4 Temporal trend (1970–2017) in global costs (in millions of 2017 US dollars) for the taxonomic groups plants, invertebrates and vertebrates.

A, All plants (Aa) and classes for which sufficient data were available (Liliopsida (Ab) and Magnoliopsida (Ac)). B, All invertebrates (Ba) and classes for which sufficient data were available (Insecta (Bb)). C, All vertebrates (Ca) and classes for which sufficient data were available (Amphibia (Cb), Aves (Cc) and Mammalia (Cd)). Given that some subsets of the taxonomic groups were also heavily affected by outliers, we also decided to focus exclusively on robust regressions (see Supplementary Methods 1 for details). Note that the error bands represent the 95% confidence intervals for all models except MARS, for which they represent 95% prediction intervals (as confidence intervals cannot be estimated using MARS). Results are those obtained when considering models calibrated with at least 25% data completeness (calibration interval, 1970–2015). We log10-transformed cost estimates (from the ‘Cost estimate per year 2017 USD exchange rate’ column of Supplementary Data 1).

Extended Data Fig. 5 Temporal trends (1970–2017) based on the cumulative and mean costs (in millions of 2017 US dollars) in different geographical regions.

Geographical regions include Africa, Asia, Central America, Europa, North America, Oceania and the Pacific Islands, and South America. The horizontal bars indicate the total time span over which the decadal mean costs were calculated.

Extended Data Fig. 6 Temporal trend (1970–2017) of global invasion costs (in millions of 2017 US dollars) predicted based on different modelling techniques.

The linear trend over time is considered the best way to estimate the mean annual cost of invasions over time (see Supplementary Methods 1 for details). Note that the error bands represent the 95% confidence intervals for all models except MARS, for which they represent 95% prediction intervals (as confidence intervals cannot be estimated using MARS). Results are those obtained when considering models calibrated with at least 25% data completeness (calibration interval, 1970–2015). We log10-transformed cost estimates (from the ‘Cost estimate per year 2017 USD exchange rate’ column of Supplementary Data 1). We considered that the duration time of costs for which no period of impact was specified was higher than those considered in our conservative strategy when completing missing data on the temporal dynamics. For this purpose, we considered as occurring until 2017 every cost that could be repeated over several years, but for which we had no information on the exact duration.

Extended Data Fig. 7 Relationship between annual cost and number of estimates.

The blue line represents the average trend fitted with locally estimated scatterplot smoothing. The surrounding bands represent the 95% confidence interval (Supplementary Methods 1).

Supplementary information

Supplementary Discussion

This file contains bases and avenues for cost data improvement of the ten costliest taxa.

Reporting Summary

Supplementary Methods 1

Detailed rationale and procedures for modelling the temporal trend of costs over 1970-2017.

Supplementary Methods 2

Types of costs (damage versus management). Damage corresponds to financial losses due to direct and/or indirect impacts of invaders (such as yield loss, health injury, land alteration, infrastructure damage, or income reduction); management corresponded to financial resources allocated to mitigate the impacts of invaders (such as prevention, control, research, long-term management, eradication). We assigned costs to ‘mixed’ when they could not be assigned to only one or the other type, and unspecified when the nature of cost was not defined.

Supplementary Methods 3

Synthesis of the structure and content of InvaCost, a public database of the economic costs of biological invasions worldwide (version 1.0; Diagne et al. 2020 Scientific Data).

Supplementary Data 1

Version of the InvaCost database considered in this study. The three spreadsheets represent, respectively the original database (version 1.0 from Diagne et al. 20203; ‘Original_database’ spreadsheet), the roust subset we used following our data processing (‘Robust_subset’ spreadsheet), and the list of individual references recorded in the original version of InvaCost (‘Reference_list’ spreadsheet). The last five columns (headlines highlighted in orange) represent information we added to the original dataset for our analysis. The two cost entries highlighted in yellow in the ‘Original_database’ spreadsheet were removed (as being not related to biological invasions) prior to analysis.

Supplementary Table 1

Quantitative summary of the cost estimates used for the approach based on available estimates (costs are provided in 2017 USD millions) over time. Expanded cost estimates (see Methods) that are repeated over several years are accounted for each year of cost occurrence.

Supplementary Table 2

Predicted average global costs (in 2017 USD millions) from models analyzing the temporal trend of invasion costs between 1970 and 2017. We considered models calibrated and fitted with at least 75% of cost data completeness from the most robust dataset (see Supplementary Methods 1).

Supplementary Table 3

Statistical summary of the outputs from the different models used for analyzing the temporal trends of global invasion costs between 1970 and 2017. We considered models calibrated and fitted with at least 75% of cost data completeness from the dataset (see Supplementary Methods 1). The statistical significance of estimates was tested using Student’s t-test (for Ordinary Least Square regressions and robust regressions) or Wald test (for Generalized Additive Models).

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Diagne, C., Leroy, B., Vaissière, AC. et al. High and rising economic costs of biological invasions worldwide. Nature (2021). https://doi.org/10.1038/s41586-021-03405-6

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Further reading

  • Non-English languages enrich scientific knowledge: The example of economic costs of biological invasions

    • Elena Angulo
    • , Christophe Diagne
    • , Liliana Ballesteros-Mejia
    • , Tasnime Adamjy
    • , Danish A. Ahmed
    • , Evgeny Akulov
    • , Achyut K. Banerjee
    • , César Capinha
    • , Cheikh A.K.M. Dia
    • , Gauthier Dobigny
    • , Virginia G. Duboscq-Carra
    • , Marina Golivets
    • , Phillip J. Haubrock
    • , Gustavo Heringer
    • , Natalia Kirichenko
    • , Melina Kourantidou
    • , Chunlong Liu
    • , Martin A. Nuñez
    • , David Renault
    • , David Roiz
    • , Ahmed Taheri
    • , Laura N.H. Verbrugge
    • , Yuya Watari
    • , Wen Xiong
    •  & Franck Courchamp

    Science of The Total Environment (2021)

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