Using ensemble forecasting to examine how climate change promotes worldwide invasion of the golden apple snail (Pomacea canaliculata)
The golden apple snail, Pomacea canaliculata, is one of the world’s 100 most notorious invasive alien species. Knowledge about the critical climate variables that limit the global distribution range of the snail, as well as predictions of future species distributions under climate change, is very helpful for management of snail. In this study, the climatically suitable habitats for this kind of snail under current climate conditions were modeled by biomod2 and projected to eight future climate scenarios (2 time periods [2050s, 2080s] × 2 Representative Concentration Pathways [RCPs; RCP2.6, RCP8.5] × 2 atmospheric General Circulation Models [GCMs; Canadian Centre for Climate Modelling and Analysis (CCCMA), Commonwealth Scientific and Industrial Research Organisation (CSIRO)]). The results suggest that the lowest temperature of coldest month is the critical climate variable to restrict the global distribution range of P. canaliculata. It is predicted that the climatically suitable habitats for P. canaliculata will increase by an average of 3.3% in 2050s and 3.8% in 2080s for the RCP2.6 scenario, while they increase by an average of 8.7% in 2050s and 10.3% in 2080s for the RCP8.5 scenario. In general, climate change in the future may promote the global invasion of the invasive species. Therefore, it is necessary to take proactive measures to monitor and preclude the invasion of this species.
KeywordsInvasive alien species Pomacea canaliculata Species distribution model biomod2
The authors are grateful to Tiago S. Vasconcelos, Morgane Barbet-Massin, and anonymous reviewers for valuable comments and suggestions on the manuscript. This research was sponsored by Qing Lan Project of Jiangsu Province, Natural Science Foundation of Jiangsu Province (grant no. BK20131087), Natural Science Foundation of Jiangsu Higher Education Institutions of China (grant no. 15KJB180004), and Doctoral Scientific Research Fund of Jiangsu Second Normal University (grant no. JSNU2015BZ04).
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