Assessing Rodent-Induced Ecological Disturbance in Natural Grasslands Using Multi-Source Spatial Data.

High-density rodent populations cause severe habitat degradation and ecological imbalance in natural grasslands through intense foraging and burrowing activities. However, dynamically monitoring these small mammals and assessing their large-scale damage using traditional ground surveys alone is challenging. In this study, we evaluated rodent damage severity in alpine meadows and typical steppe by proposing an integrated framework that combines ground, unmanned aerial vehicle (UAV), and satellite data. Using data from 36 plots per grassland type, we extracted a suite of ecological parameters, including aboveground biomass, vegetation cover, community height, rodent burrow density, and plant diversity metrics, to construct a plot-scale Rodent Damage Index (RDI). This RDI was then linked with a satellite-derived Remote Sensing Ecological Index (RSEI) to model and map damage severity at the regional scale. Separate linear regression models were developed for the two grassland types. The alpine meadow model exhibited better model fit and predictive performance (fitting R2 = 0.762, RMSE = 0.136; LOOCV R2 = 0.729, RMSE = 0.145, 95% CI: 0.580-0.886) than the typical steppe model (fitting R2 = 0.574, RMSE = 0.198; LOOCV R2 = 0.478, RMSE = 0.214, 95% CI: 0.279-0.787), highlighting that the predictive relationship model performance differs significantly between grassland types. Our findings demonstrate that integrating multi-source, cross-scale spatial data is an effective approach for assessing rodent damage. Furthermore, these results indicate that rodent damage assessment should be grassland-type-specific to ensure accuracy and support targeted rodent damage management planning.