Distance sampling is a survey technique for estimating the abundance or density of wild animal populations. Detection probabilities of animals inherently differ by species, age class, habitats, or sex. By incorporating the change in an observer's ability to detect a particular class of animals as a function of distance, distance sampling leads to density estimates that are comparable across different species, ages, habitats, sexes, etc. Increasing interest in evaluating the effects of management practices on animal populations in an experimental context has led to a need for suitable methods of analysis of distance sampling data. We outline a two-stage approach for analyzing distance sampling data from designed experiments, in which a two-step bootstrap is used to quantify precision and identify treatment effects. The approach is illustrated using data from a before-after control-impact experiment designed to assess the effects of large-scale prescribed fire treatments on bird densities in ponderosa pine forests of the southwestern United States.