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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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. The continuously updated version of the database is permanently stored at https://doi.org/10.6084/m9.figshare.12668570. As the database is constantly updated, researchers as well as stakeholders and decision makers need to be kept informed with the most up-to-date information on cost dynamics and distribution. In addition, the breakdown of costs shown here highlighted an actual or potential deficiency in cost information for some taxa (e.g., plants) or regions (e.g., Africa), which resulted in an even more significant cost underestimation for those taxa and regions. Therefore, we have published an interactive ‘living figure’ which displays the most recent cost figures and categories of cost data at different taxonomic (Figs. 3 and 5 of the original article) and geographic (Fig. 4 in the original article) scales, as the database is updated over time, and accessible at https://borisleroy.com/invacost/invacost_livingfigure.html. Still, we should keep in mind that cost figures and numbers displayed in this living figure intrinsically remain an underestimation of the true cost of invasions, given they represent only the part of known costs that are actually estimated and reported.
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.
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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.
The authors declare no competing interests.
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.
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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.
The blue line represents the average trend fitted with locally estimated scatterplot smoothing. The surrounding bands represent the 95% confidence interval (Supplementary Methods 1).
This file contains bases and avenues for cost data improvement of the ten costliest taxa.
Detailed rationale and procedures for modelling the temporal trend of costs over 1970-2017.
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.
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).
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.
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.
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).
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 592, 571–576 (2021). https://doi.org/10.1038/s41586-021-03405-6
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