Correcting Home-Range Models for Observation Bias

Home-range models implicitly assume equal observation rates across the study area. Because this assumption is frequently violated, we describe methods for correcting home-range models for observation bias. We suggest corrections for 3 general types of home-range models including those for which parameters are estimated using least-squares theory, models utilizing maximum likelihood for parameter estimation, and models based on kernel smoothing techniques. When applied to mule deer (Odocoileus hemionus) location data, we found that uncorrected estimates of the utilization distribution were biased low by as much as 18.4% and biased high by 19.2% when compared to corrected estimates. Because the magnitude of bias is related to several factors, future research should determine the relative influence of each of these factors on home-range bias.