Grasshoppers are highly destructive pests, and their outbreak can directly damage livestock development. Grasshopper outbreaks can be monitored and forecasted through dynamic analysis of their potential geographic distribution and main influencing factors. By integrating vegetation, edaphic, meteorological, topography, and other geospatial data, this study simulated the grasshopper suitability index in Hulunbuir grassland using maximum entropy species distribution modeling (Maxent). The Maxent model showed high accuracy, with the training area under the curve (AUC) value ranging from 0.897 to 0.973 and the testing AUC ranging from 0.853 to 0.971 for the past 13 years. The results showed that suitable areas, including the most suitable area and moderately suitable area, accounted for a small proportion and were mainly located in the eastern and southern parts of the study area. According to model analysis based on 51 environmental factors, not all factors played a significant role in the grasshopper cycle. Moreover, differences in environmental factors drive the spatial variability of suitable areas for grasshoppers. The monitoring and prediction of potential outbreak areas can be improved by identifying major environmental factors having large variability between suitable and unsuitable areas. Future trends in grasshopper suitability indices are likely to contradict past trends in most of the study area, with only approximately 33% of the study area continuing the past trend. The results are expected to guide future monitoring and prediction of grasshoppers in Hulunbuir grassland.

Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland.

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry.

Grasshoppers can significantly disrupt agricultural and livestock management because they reproduce and develop quickly in friendly environments. Xilingol League is the region most severely affected by grasshopper infestations. The region’s extensive grasslands are considered valuable, a critical component of the local ecosystem, a vital resource for the region’s key economic activity of livestock farming, and crucial for supporting diverse flora and fauna, carbon sequestration, and climate regulation. Oedaleus decorus asiaticus (O. d. asiaticus) is highly harmful in Xilingol League in the Inner Mongolia Autonomous Region of China. Therefore, early warning is crucial for projecting O. d. asiaticus’s regional spread and detecting the impacts of critical environmental elements. We systematically identified 26 major contributing elements by examining four categories of environmental factors—meteorology, vegetation, soil, and topography—encompassing the three growth phases of grasshoppers. Furthermore, the MaxEnt and frequency ratio (FR) approaches, coupled with multisource remote sensing data, were used to predict a potentially appropriate distribution (habitat suitability) of O. d. asiaticus in Xilingol League. The research found nine key habitat factors influencing O. d. asiaticus distribution: the mean specific humidity during the adult stage (ASH), vegetation type (VT), above-ground biomass during the nymph stage (NAB), soil sand content (SSAND), mean precipitation during the egg stage (EP), mean precipitation during the nymph stage (NP), soil bulk density (SBD), elevation, and soil type (ST). Additionally, our analysis revealed that the most suitable and moderately suitable habitats for O. d. asiaticus are predominantly located in the southern and eastern parts of Xilingol League, with significant concentrations in West Ujumqin, East Ujumqin, Xilinhot, Zhenglan, Zheng Xiangbai, Duolun, and Taipusi. Based on the suitable habitat results, policymakers may make judgments about future management actions to preserve the ecological security of grasslands and their sustainable growth. This study indicates that the Maxent approach exhibited superior accuracy (receiver operating characteristic) compared to the FR approach for assessing the habitat suitability for O. d. asiaticus in Xilingol League.

Oedaleus decorus asiaticus is one of the dominant harmful pests in central Inner Mongolia, China. Large-scale outbreaks of this pest create many serious problems in animal husbandry and agriculture. Therefore, understanding the underlying mechanisms between plant losses and Odecorus at different density levels and growth stages can guide the development of monitoring and prediction measures to reduce damage. In this study, an unmanned aerial vehicle (UAV) carrying a camera was employed to collect multi-spectral data. Further, nine vegetation indices (VIs) were analyzed to explore the most suitable indices for estimating plant loss caused by O. decorus in different growth stages. The following results were obtained: (1) The second instar nymphs of O. decorus could promote vegetation growth. As the density level in each cage increased, the biomass of each cage increased (nymph density < 30 nymphs/m²) and then decreased (nymph density ≥ 30 nymphs/m²). When nymph density was greater than 60 nymphs/m², the biomass in those cages decreased significantly. (2) With respect to the control group, large damage began to emerge during the third instar nymphal stage. In particular, the largest vegetation loss was caused by fourth nymphal larvae. (3) The ratio vegetation index (RVI) appeared as the most excellent index for reflecting Leymus chinensis loss caused by O. decorus at different growth stages. Nevertheless, the difference vegetation index (DVI) was better than the RVI in the fifth instar nymphal stage.

Grasshoppers pose a significant threat to both natural grassland vegetation and crops. Therefore, comprehending the relationship between environmental factors and grasshopper occurrence is of paramount importance. This study integrated machine learning models (Maxent) using the kuenm package to screen MaxEnt models for grasshopper species selection, while simultaneously fitting remote sensing data of major grasshopper breeding areas in Inner Mongolia, China. It investigated the spatial distribution and key factors influencing the occurrence of typical grasshopper species in grassland ecosystems. The modelling results indicate that a typical steppe has a larger suitable area. The soil type, above biomass, altitude, and temperature, predominantly determine the grasshopper occurrence in typical steppes. This study explicitly delineates the disparate impacts of key environmental factors (meteorology, vegetation, soil, and topography) on grasshopper occurrence in typical steppes. Furthermore, it provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be instrumental in formulating future management measures to guarantee grass ecological environment security and the sustainable development of grassland.