Distance sampling is widely used to estimate the abundance or density of wildlife populations. Methods to estimate wildlife mortality rates have developed largely independently from distance sampling, despite the conceptual similarities between estimation of cumulative mortality and the population density of living animals. Conventional distance sampling analyses rely on the assumption that animals are distributed uniformly with respect to transects and thus require randomized placement of transects during survey design. Because mortality events are rare, however, it is often not possible to obtain precise estimates in this way without infeasible levels of effort. A great deal of wildlife data, including mortality data, is available via road-based surveys. Interpreting these data in a distance sampling framework requires accounting for the non-uniformity sampling. Additionally, analyses of opportunistic mortality data must account for the decline in carcass detectability through time. We develop several extensions to distance sampling theory to address these problems.We build mortality estimators in a hierarchical framework that integrates animal movement data, surveillance effort data, and motion-sensor camera trap data, respectively, to relax the uniformity assumption, account for spatiotemporal variation in surveillance effort, and explicitly model carcass detection and disappearance as competing ongoing processes.Analysis of simulated data showed that our estimators were unbiased and that their confidence intervals had good coverage.We also illustrate our approach on opportunistic carcass surveillance data acquired in 2010 during an anthrax outbreak in the plains zebra of Etosha National Park, Namibia.The methods developed here will allow researchers and managers to infer mortality rates from opportunistic surveillance data.