Based on distribution data and environmental variables from Hulun Buir, this study applied an optimized MaxEnt model to assess grasshopper habitat suitability under current and future climates. The model performed well (AUC: 0.869/0.829), identifying western regions as currently highly suitable due to cold-season precipitation, thermal stability, terrain–vegetation heterogeneity, and human activity. Projections under SSP126, SSP245, and SSP585 scenarios indicate habitat expansion correlated with CO2 increases, illustrating how climate warming and rising CO2 jointly drive distribution shifts. These findings support early warning and adaptive management of grasshopper outbreaks in the region.

Global warming has significantly altered the spatiotemporal distribution of floods, leading to substantial variations in human adaptation patterns. Identifying the potential drivers of these changes and the underlying mechanisms of disaster adaptation is essential for formulating effective flood risk strategies. Based on observed streamflow records from 9,531 hydrological stations and data from 910 major flood events worldwide, this study reveals that most regions globally exhibit synchronized trends in drought and flood flows, with 28.14% showing a simultaneous increase and 33.36% showing a simultaneous decrease. To mitigate flood risk, residents in 53% of countries—most notably in the Middle East—demonstrate a tendency to migrate away from flood-prone areas. This retreat has significantly reduced flood-related mortality and forced displacement. Conversely, in regions with robust flood protection infrastructure, residents tend to maintain shorter migration distances. Further analysis of the drivers behind floodplain migration indicates that in developing nations, flood-induced mortality and displacement are the primary catalysts for relocation. In these contexts, the psychological memory of destruction or the urgent need for resources often compels residents to either flee or, paradoxically, migrate toward flood-prone zones. Under climate-driven pressures, the extent of flood inundation is a more significant determinant of migration patterns in regions such as Australia. Notably, in countries like the Philippines and Kenya, the mitigation of compound drought-flood extremes has encouraged further settlement in flood-prone areas, highlighting the complexity of multi-hazard interactions. This study systematically deciphers the mechanisms underlying flood adaptation strategies and attributes their primary drivers, providing a robust scientific framework for enhancing flood risk management and regional resilience.

The southern slopes of the Himalayas, with their significant altitudinal gradients and diverse climates and habitats, harbour exceptional biodiversity and serve as crucial habitats for endangered pheasant species in Asia. However, increasing human disturbances pose severe threats to local ecosystems. Understanding the distribution patterns of endangered pheasants and their responses to environmental changes is vital for their conservation. This study utilised infrared camera traps combined with the Joint Species Distribution Model and the MaxEnt model to analyse the spatial distribution patterns of Satyr Tragopan (Tragopan satyra) in the southern Himalayas and predict its future responses to climate change. The results indicate that Satyr Tragopan is primarily distributed in riverine forests at altitudes between 2700m and 3600m. Its occupancy probability was significantly influenced by altitude, human activity disturbances, and forest cover proportion. Species co-occurrence patterns revealed a significant positive correlation between Satyr Tragopan and Hill Partridge, Mainland Serow, and Chinese Goral, suggesting similar habitat requirements or mutualistic relationships. MaxEnt model predictions indicated that, under future climate scenarios, the suitable habitat for Satyr Tragopan is projected to increase, particularly expanding westwards into Nepal under the SSP245 scenario. However, owing to its limited dispersal ability and poor habitat connectivity, the survival of Satyr Tragopan remains threatened. This study emphasizes the importance of enhancing habitat connectivity, establishing new protected areas, and reducing habitat fragmentation, thus providing a scientific basis for the long-term conservation of pheasants on the southern slopes of the Himalayas.

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, as pernicious pests, pose a formidable threat to the advancement of agriculture and animal husbandry. Their presence can elicit a cascade of environmental challenges, underscoring the pressing need for effective control measures. However, grasshopper development is an intricate process influenced by diverse environmental factors with varying weights across regions, making it difficult to prevent and control. Therefore, this study focused on the prevalent infestation region, Xilingol, and selected Dasyhippus barbipes as the research subject because of its highest density and largest damaged area. Initially, according to the development mechanisms of D. barbipes, 31 habitat factors from five categories (meteorology, vegetation, soil, topography, and ecology) were selected; then, difference tests, correlation analysis, importance tests, and principal component analysis were applied to construct representative indicators for monitoring the habitat of D. barbipes (HDB). Subsequently, employing the occurrence data of D. barbipes from 2018 to 2023, a spatial pattern analysis was conducted to explore the hotspot aggregation area (HAA) and spatiotemporal characteristics of D. barbipes. Finally, considering landscape and spatial heterogeneity, the Landscape-based Geographically Weighted Logistic Regression (L-GWLR) model for HDB was constructed to achieve adaptive changes in factor weights across regions. The indicators included minimum temperature during the egg stage, precipitation, and soil temperature during the spawning stage, slope, fractional vegetation coverage in the nymph stage, soil moisture in the 1st to 3rd nymph instar, patch area, and gyration radius. The spatial pattern analysis revealed a significant spatial autocorrelation in the distribution of D. barbipes at a 90 % confidence interval (z > 1.65 and p < 0.1), and HAAs were concentrated in West Ujimqin, XilinHot, and ZhengLan. The habitat monitoring results demonstrated the superior performance of the L-GWLR model over models neglecting landscape or spatial heterogeneity. These findings provide essential support for the environmentally friendly scientific control of grasshoppers, contributing significantly to the sustainable development of agriculture and animal husbandry.

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.

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.

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 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.

Autism spectrum disorder (ASD), a group of neurodevelopmental disorders characterized by social communication deficits and stereotyped behaviors, may be associated with changes to the gut microbiota. However, how gut commensal bacteria modulate brain function in ASD remains unclear. Here, we used chromodomain helicase DNA-binding protein 8 (CHD8) haploinsufficient mice as a model of ASD to elucidate the pathways through which the host and gut microbiota interact with each other. We found that increased levels of amino acid transporters in the intestines of the mouse model of ASD contribute to the high level of serum glutamine and the increased excitation/inhibition (E/I) ratio in the brain. In addition, elevated α-defensin levels in the haploinsufficient mice resulted in dysregulation of the gut microbiota characterized by a reduced abundance of Bacteroides. Furthermore, supplementation with Bacteroides uniformis improved the ASD-like behaviors and restored the E/I ratio in the brain by decreasing intestinal amino acid transport and the serum glutamine levels. Our study demonstrates associations between changes in the gut microbiota and amino acid transporters, and ASD-like behavioral and electrophysiology phenotypes, in a mouse model.

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.