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National-level prediction & extrapolation of invasive species costs based on socio-economic traits of countries

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InvaCost Damage Cost:Management Expenditure Ratio

DOI insect damage icon

National-level assessement of the drivers of invasive-species costs (ratio of damage costs:management expenditure & rate of ratio change) based on socio-economic traits of countries (cost data derived from the InvaCost database)


Prof Corey J. A. Bradshaw
Global Ecology | Partuyarta Ngadluku Wardli Kuu, Flinders University, Adelaide, Australia
April 2023; updated January 2024
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contributors: Phil Hulme, Emma Hudgins, Brian Leung, Melina Kourantidou, Pierre Courtois, Anna Turbelin, Shana McDermott, Katie Lee, Danish Ahmed, Guillaume Latombe, Alok Bang, Thomas Bodey, Phillip Haubrock, Frédérik Saltré, Franck Courchamp

Accompanies paper:

Bradshaw, CJA, PE Hulme, EJ Hudgins, B Leung, M Kourantidou, P Courtois, AJ Turbelin, SM McDermott, K Lee, DA Ahmed, G Latombe, A Bang, TW Bodey, PJ Haubrock, F Saltré, F Courchamp. 2024. Damage costs from invasive species exceed management expenditure in nations experiencing lower economic activity. Ecological Economics doi:10.1016/j.ecolecon.2024.108166

Abstract

While data on biological invasions and their economic toll are increasingly available, drivers of susceptibility to damage and cost-effectiveness of management in reducing long-term costs remain poorly understood. We used data describing the damage costs of, and management expenditure on, invasive species among 56 nations between 2000 and 2020 reported in the InvaCost database to test the overarching hypothesis that higher-income nations and those with higher trade volume have a higher efficiency to limit the damage incurred by invasive species by spending relatively more on management. We also tested whether nations with (i) more corruption have a reduced capacity to manage invasive species, leading to relatively higher damage costs, (ii) more educated citizens or greater technological and scientific output allow for improved incentives and ability to manage invasive species, thereby reducing relative damage costs, and (iii) economies based on higher primary resource dependencies (e.g., agriculture) are at greater risk of incurring high costs of invasive species, and so all other conditions being equal, have higher relative damage costs compared to management expenditure. By focusing on the ratio between damage costs and management expenditure, we analyse the willingness of countries to invest in management as a function of the extent of the damage suffered. We show that economic activity, measured by the volume of trade, is the main determinant of this ratio — the greater the volume, the smaller the ratio. We also found a higher rate of increase in the damage:management ratio as a country’'s proportion of total land area devoted to agriculture increased, suggesting that a higher economic dependency on agriculture predisposes a country to greater damage costs from invasive species over time. When considering the proportion of total costs identified as damage-related, results indicated that higher government investment in education produced higher proportional damage, and lower corruption and lower trade volume both reduced proportional damage. Our overall results suggest that wealthier nations with high per-capita imports of goods and services are more susceptible to damage, but also have a greater capacity to reduce it, and are therefore less threatened by biological invasions than countries with fewer resources and lower imports.

Based on (now out-of-date) preprint:

Bradshaw, CJA, PE Hulme, EJ Hudgins, B Leung, M Kourantidou, P Courtois, AJ Turbelin, SM McDermott, K Lee, DA Ahmed, G Latombe, A Bang, TW Bodey, PJ Haubrock, F Saltré, F Courchamp. Weaker economies experience higher relative damage costs arising from biological invasions. SSRN doi:10.2139/ssrn.4587717

  • ntlScaleCostDiffInvaCostGithub2024.R: main R code for analysis
  • new_lmer_AIC_tables3.R: source code for information-theoretic algorithms
  • r.squared.R: source code for calculating goodness-of-fit for linear models (including mixed-effects models)
  • GDPpc.csv: per capita gross domestic product by country (source: World Bank)
  • CPI.csv: corruption perception index (source: Transparency International)
  • govexpedu.csv: government expenditure on all education (% of GDP; source: World Bank)
  • faoag.csv: value added proportion of GDP from agriculture, fisheries, and forestry (source: Food and Agriculture Organization of the United Nations)
  • GHSI2022.csv: global health security index (source: Global Health Security Index)
  • pcAgrLand.csv: % land surface area devoted to agriculture (source: World Bank)
  • importGS.csv: imports of goods and services (source: World Bank)
  • stjarticles.csv: scientific & technical journal articles (source: World Bank)
  • pop2021.csv: 2021 national population size (source: World Bank)
  • continent.countryINVACOST.csv & fao.cntry.code.csv are code files for merging countries and regions across different datasets

Required R packages

invacost, lme4, dismo, gbm, boot, VIM, mice, performance, sjPlot, rworldmap, rgeos, SpatialEpi, nlme, rcompanion,

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