A detection probability model for aerial surveys of mule deer

Population estimates derived from aerial surveys of ungulates are biased by imperfect detection, where probability of sighting groups is influenced by variables specific to terrain features and vegetation communities. Therefore, methods for bias-correction must be validated for the region to which they will be applied. Our objectives were to quantify factors affecting detection probability of mule deer (Odocoileus hemionus) during helicopter surveys in Texas, USA, rangelands, and develop a detection probability model to reduce bias in deer population estimates. We placed global positioning system (GPS) collars on 215 deer on 6 sites representative of mule deer range in the southern Great Plains and the Chihuahuan Desert during 2008-2010. We collected data during aerial surveys in January-March and fit logistic regression models to predict detection probability of mule deer based on ecological and behavioral covariates. We evaluated the model using independent estimates of population size derived from a markresight procedure. Detection of mule deer was negatively related to distance from the transect, increasing brush cover, sunlight, and increasing terrain ruggedness (P