Leveraging GPS Location Tracking to Forecast Cattle Preference for Enhanced Rangeland Management in South-central British Columbia

This study modelled cattle habitat use intensity across three connected fenced pastures of varying environmental conditions and sizes in British Columbia, Canada, using GPS eartag tracking data from two growing seasons (April-September 2024 and 2025). Unlike traditional presence-absence approaches, we employed a continuous use intensity framework treating log-transformed GPS fix counts per 100-meter grid cell as the response variable, avoiding pseudo-absence bias where GPS fix absence does not indicate habitat avoidance.

Two complementary models, a Random Forest (RF) regressor and a Generalized Additive Model (GAM), were compared using spatial cross-validation, independent holdout validation, and interannual validation across years. Ten environmental predictors were included: elevation, slope, aspect (cosine and sine transformed), topographic position index (TPI), distance to water, distance to road, crown closure, and monthly median NDVI and EVI.