Accurately quantifying species’ area requirements is a prerequisite for effective area-based conser-vation. This typically involves collecting tracking data on species of interest and then conducting home-rangeanalyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated.Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a rela-tionship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals,we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied blockcross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg wereunderestimated by a mean approximately15%, and species weighing approximately100 kg were underestimatedby approximately50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias thatwas worse for large species. Combined with the fact that extinction risk increases as body mass increases, theallometric scaling of bias we observed suggests the most threatened species are also likely to be those with theleast accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informedhome-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning requiredan approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, nota viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurateestimates irrespective of mass. When relating body mass to home range size, we detected that correcting forautocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changedsubstantially at the upper end of the mass spectrum.