Abstract
Abstract
Background
Predicting the potential habitat of Phytolacca americana, a high-risk invasive species, can help provide a scientific basis for its quarantine and control strategies. Using the optimized MaxEnt model, we applied the latest climate data, CMIP6, to predict the distribution of potential risk zones and their change patterns for P. americana under current and future (SSP126, SSP245, SSP585) climate conditions, followed by invasion potential analysis.
Results
The predictions of MaxEnt model based on R language optimization were highly accurate. A significantly high area of 0.8703 was observed for working characteristic curve (AUC value) of subject and the kappa value was 0.8074. Under the current climate conditions, the risk zones for P. americana were mainly distributed in Sichuan, Chongqing, Guizhou, Hunan, and Guangxi provinces. The contribution rate of each climatic factor of P. americana was calculated using the jackknife test. The four factors with the highest contribution rate included minimum temperature of coldest month (bio6, 51.4%), the monthly mean diurnal temperature difference (bio2, 27.9%), precipitation of the driest quarter (bio17, 4.9%), and the warmest seasonal precipitation (bio12, 4.3%).
Conclusion
Under future climatic conditions, the change in the habitat pattern of P. americana generally showed a migration toward the Yangtze River Delta region and the southeastern coastal region of China. This migration exhibited an expansion trend, highlighting the strong future invasiveness of the species. Based on the predictions, targeted prevention and control strategies for areas with significant changes in P. americana were developed. Therefore, this study emphasizes the need of an integrated approach to effectively prevent the further spread of invasive plants.
Funder
Grassland Administration Science and Technology Program
Key research and development project of Ningxia Hui Autonomous Region, China
China Foundation for Poverty Alleviation
Publisher
Springer Science and Business Media LLC
Subject
Ecological Modeling,Ecology
Cited by
8 articles.
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