Machine Learning Model Analysis of Myrmeleotettix palpalis Habitat Distribution Driven by Topography-Climate-Surface Parameter Coupling

This study systematically identified the key drivers of Myrmeleotettix palpalis habitat distribution using integrated machine learning models and ecological mechanism analysis. Through Pearson correlation matrix-based feature selection and hyperparameter optimization, we retained 15 environmental variables across topographic, climatic, and surface parameter dimensions. Random Forest and MaxEnt models were employed to quantify variable importance and stability, revealing a “tripartite regulatory framework”: (1) Topographic factors (Aspect, DEM) mediate microclimate through solar radiation redistribution; (2) Climatic factors (Bio18: warmest quarter precipitation, Bio4: temperature seasonality) serve as core energy-water suppliers; (3) Surface parameters (soil sand content, land use) constitute the resource foundation. The optimized MaxEnt model exhibited excellent performance (AUC = 0.966), identifying optimal habitats concentrated in the central-eastern Inner Mongolia steppe (3.67 × 105 km2) with a critical precipitation threshold (Bio18 > 320 mm). Our composite feature evaluation framework (importance-weighted 0.6 + stability-weighted 0.4) reliably prioritized ecologically robust drivers (Aspect composite score = 0.94, Bio18 = 0.81). These findings provide scientific targets for precision locust control and ecological intervention in grassland ecosystems.