Research overview: autocorrelation and home-range estimator bias

Classic home-range estimators assume that successive locations are statistically independent, an assumption that high-frequency GPS data violate flagrantly, since fixes minutes apart are highly correlated. This overview explains how that mismatch causes conventional kernel and polygon estimators to underestimate home-range area, sometimes severely, as more frequent sampling paradoxically shrinks the apparent range. Researchers historically tried to dodge the problem by thinning data to near-independence, discarding hard-won information. The modern solution embraces autocorrelation by modeling the movement process directly, as in continuous-time methods and the autocorrelated kernel density estimator. The overview frames autocorrelation as a central methodological hazard in felid telemetry, one that distinguishes naive from rigorous estimates and explains discrepancies across the literature.