Predicting the Distribution of the Invasive Species Leptocybe invasa: Combining MaxEnt and Geodetector Models
College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China
*
Authors to whom correspondence should be addressed.
Insects 2021, 12(2), 92; https://doi.org/10.3390/insects12020092
Received: 25 December 2020
/
Revised: 18 January 2021
/
Accepted: 19 January 2021
/
Published: 21 January 2021
(This article belongs to the Section Insect Ecology, Diversity and Conservation)
Simple Summary
Leptocybe invasa is a global eucalyptus plantation invasive pest and the second alien invasive species in China. In this study, based on the current distribution data of L. invasa in China, combined with a geographic detector model and MaxEnt model, the main environmental variables were selected, and potential suitable growth areas of L. invasa in China in 2030 and 2050 were predicted. The results show that under the future climate change scenario, the potential distribution core areas of L. invasa in China will be located in Yunnan, Guangxi, Guangdong, and Hainan, and tend to spread to high latitudes (Hubei, Anhui, Zhejiang, Jiangsu, and other regions). Combined with the results of predicting the potential suitable zone in this study, we can clearly identify its diffusion trend, which has important theoretical significance for curbing the growth and development of L. invasa and formulating effective control measures.
Leptocybe invasa is a globally invasive pest of eucalyptus plantations, and is steadily spread throughout China. Predicting the growth area of L. invasa in China is beneficial to the establishment of early monitoring, forecasting, and prevention of this pest. Based on 194 valid data points and 21 environmental factors of L. invasa in China, this study simulated the potential distribution area of L. invasa in China under three current and future climate scenarios (SSPs1–2.5, SSPs2–3.5, and SSPs5–8.5) via the MaxEnt model. The study used the species distribution model (SDM) toolbox in ArcGIS software to analyze the potential distribution range and change of L. invasa. The importance of crucial climate factors was evaluated by total contribution rate, knife-cut method, and environmental variable response curve, and the area under the receiver operating characteristic (ROC) curve was used to test and evaluate the accuracy of the model. The results showed that the simulation effect of the MaxEnt model is excellent (area under the ROC curve (AUC) = 0.982). The prediction showed that L. invasa is mainly distributed in Guangxi, Guangdong, Hainan, and surrounding provinces, which is consistent with the current actual distribution range. The distribution area of the potential high fitness zone of L. invasa in the next three scenarios increases by between 37.37% and 95.20% compared with the current distribution. Climate change affects the distribution of L. invasa, with the annual average temperature, the lowest temperature of the coldest month, the average temperature of the driest season, the average temperature of the coldest month, and the precipitation in the wettest season the most important. In the future, the core areas of the potential distribution of L. invasa in China will be located in Yunnan, Guangxi, Guangdong, and Hainan. They tend to spread to high latitudes (Hubei, Anhui, Zhejiang, Jiangsu, and other regions).
View Full-Text
Keywords:
L. invasa; suitable growth area; MaxEnt; climate change
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Zhang, H.; Song, J.; Zhao, H.; Li, M.; Han, W. Predicting the Distribution of the Invasive Species Leptocybe invasa: Combining MaxEnt and Geodetector Models. Insects 2021, 12, 92. https://doi.org/10.3390/insects12020092
AMA Style
Zhang H, Song J, Zhao H, Li M, Han W. Predicting the Distribution of the Invasive Species Leptocybe invasa: Combining MaxEnt and Geodetector Models. Insects. 2021; 12(2):92. https://doi.org/10.3390/insects12020092
Chicago/Turabian StyleZhang, Hua, Jinyue Song, Haoxiang Zhao, Ming Li, and Wuhong Han. 2021. "Predicting the Distribution of the Invasive Species Leptocybe invasa: Combining MaxEnt and Geodetector Models" Insects 12, no. 2: 92. https://doi.org/10.3390/insects12020092
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
