The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Major drivers of invasion risks throughout the world
Open accessAbstract
In this paper, we investigate how climate, land use, habitat characteristics, and socioeconomic activities contribute to predict the current potential distributions of the “100 among the world's worst invasive alien species”. We calculated the predictive power of each of the 41 variables for the 95 species including a large number of plants, vertebrates and invertebrates. We then calibrated the species distribution models with a set of appropriate variables for each invasive alien species to predict the potential distribution of these species and identify the major regions of origin of the invasive alien species. We found that climate variables were primarily predictors of the distribution of the global invaders studied. In addition, the habitat characteristics were also important predictors following by the socioeconomic variables such as the nearest distance to airports, seaports and human population density. We show that the potential areas at the highest risk of invasions from these species are located in Western Europe, Eastern United States, Central America, the eastern coast of Australia, and some Indonesian islands. We argue that these potential hotspots of invasions should be monitored in priority to prevent new invasions from these species. This study provides evidence of the importance of considering both habitat characteristics, socioeconomic and climate change factors for the current and future predictions of biological invasions.
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Cites
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Impacts of biological invasions: what's what and the way forward
Simberloff, D., Martin, J.-L., Genovesi, P., Maris, V., Wardle, D. A., Aronson, J., Courchamp, F., Galil, B., García-Berthou, E., Pascal, M., et al. (2013) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2012.07.013 · Cited by 3,344
Self-citationTrade, transport and trouble: managing invasive species pathways in an era of globalization
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A Unified Classification of Alien Species Based on the Magnitude of their Environmental Impacts
Blackburn, T. M., Essl, F., Evans, T., Hulme, P. E., Jeschke, J. M., Kühn, I., Kumschick, S., Marková, Z., Mrugała, A., Nentwig, W., et al. (2014) · PLoS Biology · doi:10.1371/journal.pbio.1001850 · Cited by 959
Historical Accumulation of Nonindigenous Forest Pests in the Continental United States
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Biological invasions: a field synopsis, systematic review, and database of the literature
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Cited by
Early, R., Bradley, B. A., Dukes, J. S., Lawler, J. J., Olden, J. D., Blumenthal, D. M., Gonzalez, P., Grosholz, E. D., Ibañez, I., Miller, L. P., et al. (2016) · Nature Communications · doi:10.1038/ncomms12485 · Cited by 1,377
Global rise in emerging alien species results from increased accessibility of new source pools
Seebens, H., Blackburn, T. M., Dyer, E. E., Genovesi, P., Hulme, P. E., Jeschke, J. M., Pagad, S., Pyšek, P., van Kleunen, M., Winter, M., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719429115 · Cited by 692
Complex responses of global insect pests to climate warming
Lehmann, P., Ammunét, T., Barton, M., Battisti, A., Eigenbrode, S. D., Jepsen, J. U., Kalinkat, G., Neuvonen, S., Niemelä, P., Terblanche, J. S., et al. (2020) · Frontiers in Ecology and the Environment · doi:10.1002/fee.2160 · Cited by 474
Metadata
- Authors
- Bellard, C., Leroy, B., Thuiller, W., Rysman, J. ‐., & Courchamp, F.
- Venue
- Ecosphere
- Publisher
- Wiley
- Published
- 2016-03
- Volume
- 7
- Issue
- 3
- ISSN
- 2150-8925 (ISSN-L)
- OpenAlex
- W2306488032
- Cited by
- 174
- References
- 9
- FWCI
- 7.01
- Type
- journal article
- Language
- en
- Open access
- gold
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- Bellard, C., Leroy, B., Thuiller, W., Rysman, J. ‐., & Courchamp, F. (2016). Major drivers of invasion risks throughout the world. Ecosphere. https://doi.org/10.1002/ecs2.1241
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
