Wildlife management is an adaptive process that requires accurate, precise, and frequent information on animal populations, particularly for species of conservation risk or in areas experiencing rapid environmental change. However, the information currently collected in many jurisdictions rarely meets these three criteria. Technological advances have led to increased use of camera traps to survey wildlife populations, a potentially cost-effective non-invasive alternative to standard survey methods. With the advent of spatial capture-recapture analyses as an emerging method of estimating population density it is critical to understand how different methods compare, especially when land managers may only have access to one survey method. This study used concurrent genetic, photographic and/or telemetry data of multiple mid and large sized mammal species to determine how parameter precision varied when using single and multiple data source spatial-capture recapture models. We found that sex-specific genetic spatial-capture recapture (maximum likelihood) models were quick and easy to run, yielding precise density estimates. Spatial count (Bayesian) models of unmarked populations were computationally intensive and frequently did not converge, even after high numbers of MCMC iterations, making it difficult to produce a reliable density estimate. Integrating multiple data sources in spatial-capture recapture models produced the most precise density estimates. This study highlights the challenge of applying complex models to low-density mammal populations, and stresses the need for continued evaluation of the most effective analytical approaches and survey designs, to better inform ecological conclusions and conservation management.