Reliable monitoring of wild populations is the cornerstone of effective wildlife conservation and management but estimating key population parameters, such as population size and density, remains a fundamental challenge. All survey methods are subject to bias and therefore it is critical that wildlife researchers apply statistical correction techniques to raw survey counts to obtain precise and unbiased estimates. Robust population estimates are especially important for managers of harvested populations, as an understanding of population size, demographic composition, and temporal dynamics is necessary to set sustainable harvest quotas. Mule deer are one of the few remaining species of large herbivores in southwestern North America and serve important ecological roles as browsers of woody plants, dispersers of seeds and nutrients, and as a primary prey source for mountain lions. Southern mule deer are a subspecies of mule deer that are endemic to southern California and the Baja Peninsula and are managed both as a harvested species and a species of conservation concern. There has been limited research regarding population parameters of this subspecies. To fill in this knowledge gap, we tested a variety of survey data types and statistical correction techniques to determine the most effective ways to survey southern mule deer and achieve the best population estimates. In particular, we used joint spatial capture- recapture resource selection function (SCR-RSF) models to compare population parameters estimated with detection data from non-invasive fecal surveys only and integrated non- invasive detection data and GPS telemetry data for a population of mule deer in southern California between 2018 and 2019. Additionally, we applied a simultaneous double count correction method called double-observer modeling and a GPS-collar version of mark-resight called sightability modeling to mule deer survey data from helicopter aerial surveys. We found that the integrated SCR-RSF and sightability models produced the most reliable estimates of mule deer population size and density. Implementing these methods in future surveys will improve the availability of population parameter estimates for southern mule deer in southern California.