Analysis of Spatiotemporal Variation in Habitat Suitability for Oedaleus decorus asiaticus Bei-Bienko on the Mongolian Plateau Using Maxent and Multi-Source Remote Sensing Data

Insects. 2023 May 24;14(6):492. doi: 10.3390/insects14060492.

Abstract

O. decorus asiaticus is a major grasshopper species that harms the development of agriculture on the Mongolian Plateau. Therefore, it is important to enhance the monitoring of O. decorus asiaticus. In this study, the spatiotemporal variation in the habitat suitability for O. decorus asiaticus on the Mongolian Plateau was assessed using maximum entropy (Maxent) modeling along with multi-source remote sensing data (meteorology, vegetation, soil, and topography). The predictions of the Maxent model were accurate (AUC = 0.910). The key environmental variables affecting the distribution of grasshoppers and their contribution were grass type (51.3%), accumulated precipitation (24.9%), altitude (13.0%), vegetation coverage (6.6%), and land surface temperature (4.2%). Based on the assessment results of suitability by Maxent model, the model threshold settings, and the formula for calculating the inhabitability index, the 2000s, 2010s, and 2020s inhabitable areas were calculated. The results show that the distribution of suitable habitat for O. decorus asiaticus in 2000 was similar to that in 2010. From 2010 to 2020, the suitability of the habitat for O. decorus asiaticus in the central region of the Mongolian Plateau changed from moderate to high. The main factor contributing to this change was accumulated precipitation. Few changes in the areas of the habitat with low suitability were observed across the study period. The results of this study enhance our understanding of the vulnerability of different regions on the Mongolian Plateau to plagues of O. decorus asiaticus and will aid the monitoring of grasshopper plagues in this region.

Keywords: Maxent model; Mongolian Plateau; O. decorus asiaticus; grasshopper; habitat suitability; spatiotemporal characteristics.

Grants and funding

This research was funded by the Director of the International Research Center for Big Data for Sustainable SDG, No. CBAS2022 DF001; the Hainan Provincial Natural Science Foundation of China, No. 420 QN293; the Youth Innovation Promotion Association CAS, No. 2021119; and the Future Star Talent Program of Aerospace Information Research Institute, Chinese Academy of Sciences, No. 2020 KTYWLZX08.