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The New Dominator of the World: Modeling the Global Distribution of the Japanese Beetle under Land Use and Climate Change Scenarios

Author

Listed:
  • Francesca Della Rocca

    (Department of Earth and Environmental Sciences, University of Pavia, Via Ferrata 9, 27100 Pavia, Italy)

  • Pietro Milanesi

    (Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland)

Abstract

The spread of invasive species is a threat to global biodiversity. The Japanese beetle is native to Japan, but alien populations of this insect occur in North America, and recently, also in southern Europe. This beetle was recently included on the list of priority species of European concern, as it is a highly invasive agricultural pest. Thus, in this study, we aimed at (i) assessing its current distribution range, and identifying areas of potential invasion, and (ii) predicting its distribution using future climatic and land-use change scenarios for 2050. We collected species occurrences available on the citizen science platform iNaturalist, and we combined species data with climatic and land-use predictors using a Bayesian framework, specifically the integrated nested Laplace approximation, with a stochastic partial differential equation. We found that the current distribution of the Japanese beetle was mainly, and positively, driven by the percentage of croplands, the annual range of temperature, habitat diversity, percentage of human settlements, and human population density; it was negatively related to the distance to airports, elevation, mean temperature diurnal range, wetlands, and waters. As a result, based on current conditions, the Japanese beetle is likely to occur in 47,970,200 km 2 , while its distribution will range from between 53,418,200 and 59,126,825 km 2 , according to the 2050 climatic and land-use change scenarios. We concluded that the Japanese beetle is a high-risk invasive species, able to find suitable conditions for its colonization in several regions around the globe, especially in light of ongoing climatic change. Thus, we strongly recommend strict biosecurity checks and quarantines, as well as regular pest management surveys, in order to reduce its spread.

Suggested Citation

  • Francesca Della Rocca & Pietro Milanesi, 2022. "The New Dominator of the World: Modeling the Global Distribution of the Japanese Beetle under Land Use and Climate Change Scenarios," Land, MDPI, vol. 11(4), pages 1-17, April.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:4:p:567-:d:791929
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    References listed on IDEAS

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    1. Anonymous, 1961. "World Meteorological Organization," International Organization, Cambridge University Press, vol. 15(1), pages 197-198, January.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Corey J. A. Bradshaw & Boris Leroy & Céline Bellard & David Roiz & Céline Albert & Alice Fournier & Morgane Barbet-Massin & Jean-Michel Salles & Frédéric Simard & Franck Courchamp, 2016. "Massive yet grossly underestimated global costs of invasive insects," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
    4. Fleming, Walter E., 1972. "Biology of the Japanese Beetle," Technical Bulletins 171890, United States Department of Agriculture, Economic Research Service.
    5. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
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    Cited by:

    1. Giuseppe Pulighe & Flavio Lupia & Valentina Manente, 2025. "Climate-Driven Invasion Risks of Japanese Beetle ( Popillia japonica Newman) in Europe Predicted Through Species Distribution Modelling," Agriculture, MDPI, vol. 15(7), pages 1-14, March.
    2. Edgar Santos‐Fernandez & Julie Vercelloni & Aiden Price & Grace Heron & Bryce Christensen & Erin E. Peterson & Kerrie Mengersen, 2024. "Increasing Trust in New Data Sources: Crowdsourcing Image Classification for Ecology," International Statistical Review, International Statistical Institute, vol. 92(1), pages 43-61, April.
    3. Pietro Milanesi & Felice Puopolo & Florian Zellweger, 2022. "Landscape Features, Human Disturbance or Prey Availability? What Shapes the Distribution of Large Carnivores in Europe?," Land, MDPI, vol. 11(10), pages 1-15, October.

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