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 as the research subject because of its highest density and largest damaged area. Initially, according to the development mechanisms of , 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 (HDB). Subsequently, employing the occurrence data of from 2018 to 2023, a spatial pattern analysis was conducted to explore the hotspot aggregation area (HAA) and spatiotemporal characteristics of . 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 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.

中文翻译：

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利用多源数据对蝗虫栖息地进行时空监测：结合景观和空间异质性

蝗虫作为一种恶性害虫，对农业和畜牧业的发展构成巨大威胁。它们的存在可能引发一系列环境挑战，凸显了有效控制措施的迫切需要。然而，蝗虫的发育过程复杂，受不同环境因素影响，各地区权重不同，防治难度较大。因此，本研究以虫害流行区锡林郭勒为研究对象，因其密度最高、受灾面积最大而被选定为研究对象。初步根据其发展机制，筛选出气象、植被、土壤、地形、生态5类31个生境因子；然后运用差异性检验、相关性分析、重要性检验和主成分分析等方法构建HDB栖息地监测代表性指标。随后，利用2018年至2023年的发生数据，进行空间格局分析，探讨了热点聚集区（HAA）和时空特征。最后，考虑景观和空间异质性，构建HDB基于景观的地理加权Logistic回归（L-GWLR）模型，实现跨区域因子权重的自适应变化。指标包括卵期最低气温、降水量、产卵期土壤温度、坡度、若虫期植被覆盖度、若虫1～3龄土壤湿度、斑块面积、回转半径等。空间格局分析显示，90%置信区间的分布存在显着的空间自相关性（z > 1.65，p < 0.1），HAA集中在西乌珠穆沁、锡林浩特和正兰。栖息地监测结果表明，L-GWLR 模型的性能优于忽略景观或空间异质性的模型。这些研究结果为蝗虫的环保科学防治提供了重要支撑，为农牧业可持续发展做出了重要贡献。