Year: 2016
Type: article
Abstract: 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.
Source: Ecosphere
DOI: 10.1002/ecs2.1241
Institutions: Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, Ecologie, Société, Evolution, Sorbonne Université, Biologie des Organismes et Écosystèmes Aquatiques, Muséum national d'Histoire naturelle, Institut de Recherche pour le Développement, PatriNat, Université Pierre-et-Marie-Curie, Université de Rennes, Laboratoire d'Écologie Alpine, Université Grenoble Alpes, Zoological Society of London, École Polytechnique, Laboratoire de Météorologie Dynamique
Language: English
Subfield: Ecological Modeling
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: gold
Estimated APC paid: $2,430
OpenAlex ID: W2306488032
