Abstract
<jats:p>The increasing volume and sampling frequency of telemetry data require a critical evaluation of algorithms for estimating animal home range (HR), especially for highly mobile species with complex territorial behavior. In this study, we conducted a comprehensive comparative analysis of the performance of 6 HR estimation algorithms using tracking data from Golden Eagles (Aquila chrysaetos). Model evaluation was performed using aggregated multi-criteria decision analysis (MCDA), spatial indices (SQI, M1/M2), predictive power metrics (AUC, LL), and the two-sample Kolmogorov-Smirnov test to assess behavioral realism. The analysis was conducted on both original high-frequency data and artificially rarefied (downsampled) tracks (up to 1 location per 6 hours). The results revealed fundamental differences in the spatial topology of the models. Classical approaches (KDE h ref, BBMM) demonstrated excessive oversmoothing of the HR core area (50%). In contrast, dynamic Brownian Bridge Movement Models (dBBMM) were highly sensitive to sampling density. On high-frequency data, the algorithm falls into spatial overfitting, classifying over 90% of actual transit locations as statistical noise. Extreme downsampling of tracks revealed a paradox of algorithm convergence and an inversion of their scale nesting. Also, it led to a critical loss of behavioral microstructure across all interpolative models. Based on the aggregation of all metrics, the AKDE method was recognized as the absolute leader. By integrating the temporal autocorrelation structure (OU/OUF models), AKDE proved to be the only algorithm that preserved the reliable spatial extent of the HR and predictive accuracy under sparse data conditions. This study demonstrates that for highly mobile avian predators, route interpolation becomes ecologically inadequate, and it is precisely AKDE that allows the transformation of discrete transit locations into a biologically robust HR model.</jats:p>