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.

China’s extensive grasslands are ecologically and economically vital but are increasingly degraded by grasshopper outbreaks. Traditional monitoring approaches are too limited for large-scale management. This study developed an advanced monitoring framework for the Xilingol League by integrating multi-source remote sensing, a degree-day model, and machine learning (ML). Field survey data from 2018 to 2023 were combined with 29 environmental variables aligned to grasshopper life stages. Four ML algorithms—Random Forest (RF), XGBoost, Multilayer Perceptron (MLP), and Logistic Regression (LR)—were evaluated for predictive performance. RF consistently outperformed other models, achieving the highest accuracy and robustness. Spatial autocorrelation analysis (Global Moran’s I) confirmed that grasshopper distributions were persistently clustered across all years, highlighting non-random outbreak patterns. Suitability mapping showed highly suitable habitats concentrated in East Ujumqin, West Ujumqin, and Xilinhot, with pronounced interannual variability, including a peak in 2022. Variable importance analysis identified soil type and vegetation type as dominant universal drivers, while precipitation, soil texture, and humidity exerted region-specific effects. These findings demonstrate that coupling biologically informed indicators with integrated learning provides ecologically interpretable and scalable predictions of outbreak risk. The framework offers a robust basis for early warning and targeted management, advancing sustainable pest control and grassland conservation.

The common leopard (Panthera pardus) in Azad Jammu and Kashmir (AJ and K), Pakistan, is increasingly threatened by habitat fragmentation and climate change. This study employs a dual-model approach, integrating Maximum Entropy (MaxEnt) and Random Forest algorithms with multi-source remote sensing data to evaluate leopard habitat suitability. Our analysis identifies land cover (LC), fractional vegetation cover (FVC), elevation, temperature seasonality (bio4), and distance to roads (Dist_road) as the most influential habitat predictors. Leopards exhibit a strong preference for mixed forests at elevations between 1000 and 3000 m, with a suitability index of 0.83. The study identifies several unsuitable conditions including: road proximity (<0.08 km), low elevation zones ( 8 °C), and non-forested land cover types. MaxEnt demonstrated superior habitat prediction accuracy over Random Forest (AUC = 0.912 vs. 0.827). The results highlight a distinct north-to-south suitability gradient, with optimal habitats concentrated in the northern districts (Muzaffarabad, Hattian, Neelum, Bagh, Haveli, Poonch, Sudhnutti) and declining suitability in human-dominated southern areas. Based on these findings, this study underscores the urgency of targeted conservation efforts in the northern districts of AJ and K, where optimal leopard habitats are identified. The findings emphasize the need for habitat connectivity and protection measures to mitigate the impacts of habitat fragmentation and climate change. Future conservation strategies should prioritize the preservation of mixed forests and the establishment of buffer zones around roads to ensure the long-term survival of the common leopard in this region.

Remote sensing technologies provide valuable geospatial data for analyzing environmental conditions and for supporting spatial ecological modeling across large, heterogeneous landscapes. In this study, multi-source remote sensing–derived environmental variables were integrated with ensemble machine learning techniques to model the habitat suitability of the common leopard (Panthera pardus) in Azad Jammu and Kashmir (AJ&K), Pakistan. Environmental predictors derived from satellite observations included land cover, vegetation condition, terrain attributes, and climate-related indicators. To ensure model reliability, multicollinearity among predictors was evaluated, and spatial clustering patterns of leopard occurrence records were examined using global spatial autocorrelation analysis. Two complementary machine learning algorithms, Maximum Entropy (MaxEnt) and Random Forest (RF), were implemented and integrated through a weighted ensemble approach to improve predictive accuracy and robustness. The ensemble model achieved high predictive performance with an area under the curve (AUC) value of 0.942, outperforming individual algorithms. The resulting habitat suitability map indicates that approximately 30% of the study region is highly suitable habitat, primarily in the northern and central districts, including Muzaffarabad, Neelum, Hattian, Poonch, and Sudhnutti. Variable importance analysis identified remotely sensed land cover, elevation, vegetation cover, slope, and temperature seasonality as the dominant predictors of habitat suitability, whereas anthropogenic indicators such as proximity to roads and population density had secondary effects in fragmented areas. The results demonstrate the potential of integrating remote sensing data and ensemble machine learning for spatial habitat modeling and wildlife conservation planning in mountainous ecosystems.