Grasshoppers have profound effects on both grassland ecosystems and livestock production. Despite commendable efforts made by China in grasshopper control, completely eradicating or preventing them still remains a distant prospect. This study aims to analyze the ecological distribution and patterns of grasshopper occurrences in order to provide more accurate monitoring techniques and preventive measures. By considering four types of environmental determinants—meteorology, vegetation, soil, and topography—we systematically identified 18 key influencing factors. These factors encompass various developmental stages of grasshoppers, including variables such as temperature, precipitation, vegetation coverage, vegetation type, soil moisture, soil salinity, soil type, and terrain characteristics. The MaxEnt model is employed in this study to comprehensively capture complex ecological interactions. Omission curves, Receiver Operating Characteristic curves (ROC curves), and the Area Under the Curve (AUC values) demonstrate the robustness and high accuracy of the MaxEnt model. Our research results indicate that meteorological factors are the primary influencing factors for the distribution of grasshoppers, surpassing the effects of vegetation, soil, and terrain. Precipitation and vegetation type emerge as key factors shaping their distributional patterns. Integrating the Sen-MK trend method, our findings identify the epicenter of damage primarily within the central, southern, and northeastern regions, notably affecting locales such as New Barag East County and the Ewenki Autonomous Banner. While their impact in 2012 was particularly severe, temporal trends indicate a decreasing risk of grasshoppers in specific regions, with escalated activity observed in other areas. The empirical insights from this study lay a solid foundation for the development of monitoring and control strategies concerning grasshoppers. Furthermore, the derived theoretical framework serves as a valuable foundation for future research endeavors addressing grasshopper infestations.

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 (O. decorus) is a significant pest in the grasslands of Inner Mongolia, posing considerable challenges to the development of animal husbandry. To understand the key factors influencing the population distribution of O. decorus, field surveys were conducted from 2018 to 2020, during which the population count, growth stage, and location information of O. decorus were recorded. Daily soil moisture (SM) data and daily land surface temperature (LST) data were obtained from the National Tibetan Plateau Data Center, and a Generalized Additive Model (GAM) was constructed. Our findings indicate that the SM (S8) in August of the previous year is the most critical factor, with an F-value of 27.422, followed by the LST (L10) in October of the previous year, the LST (L6) in June of the survey year, the SM (S9) in September of the previous year, the LST (L3) in March of the survey year, and the LST (L5) in May of the survey year, with F-values of 7.848, 7.223, 5.823, 4.919, and 3.547, respectively. S8 and S9 can be regarded as vital indicators for predicting and monitoring the occurrence of O. decorus. However, the contributions of S8 and S9 to O. decorus density differ considerably. S8 is negatively correlated with O. decorus density, while S9 values below 0.29 m³/m³ can promote the growth of O. decorus. A higher LST during early overwintering correlates with increased O. decorus density. During the survey year, LST emerged as the primary factor affecting grasshopper density. Additionally, it plays a more complex role during incubation periods. This study clearly identifies SM and LST as the major factors influencing the occurrence of O. decorus, which will aid in predicting and monitoring its density.

Grasshopper outbreaks severely threaten pastoral agriculture and grassland ecosystems in Inner Mongolia, China. Identifying suitable habitats and understanding their monthly dynamics are critical for early warning and targeted control. This study integrated multi-source remote sensing data (Moderate Resolution Imaging Spectroradiometer land surface temperature (MODIS LST), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI)), meteorological data (precipitation and temperature), soil and topographic variables, and field survey data (May–July 2023) to construct a coupled Random Forest (RF) and Maximum Entropy (MaxEnt) model for the dominant grasshopper species Dasyhippus barbipes. Key objectives were to identify environmental variables at different developmental stages, map monthly suitable habitats, quantify inter-monthly spatiotemporal-shift patterns of suitable habitats using a suitability class transition matrix, and analyze association patterns of NDVI, precipitation, and LST with suitability transitions. The results showed the following: (1) Key drivers varied monthly—vegetation/soil types, precipitation, and FVC dominated in May; FVC, SOC, slope, and NDVI increased in June; and soil, vegetation, aspect, and Max. The temperature of Warmest Month (Bio5) dominated in July. (2) Highly suitable areas expanded eastward from central Xilingol League and Chifeng City, following west-to-east and center-to-north-and-south diffusion patterns. (3) Vegetation green-up, precipitation (as a limiting moisture driver), and LST (threshold effects on development rates) jointly formed a vegetation–water–heat synergistic system. (4) The coupled Random Forest (RF) and Maximum Entropy (MaxEnt) model achieved high accuracy (AUC > 0.91), with variable importance matching observed spatiotemporal patterns. This study elucidates the monthly scale evolution of D. barbipes-suitable habitats and multi-factor associations between environmental predictors and habitat suitability, providing a scientific basis for refined monitoring, early warning, and green control in Inner Mongolia’s typical steppes.