Species distribution models (SDMs) are one of the prominent methods for studying biodiversity conservation, and have been used to identify the locations of rare species, suggest reserve sites and project future distributions under climate change. The importance of incorporating movement in SDMs has been noted recently in review articles; however the specifics of how to achieve this are often absent. Broad-scale movement processes such as migration and dispersal have been more frequently addressed in SDM studies, while fine-scale regular (i.e. daily) movement has been rarely included, despite limitations associated with the resulting SDM prediction maps generated for mobile species. This paper presents a novel framework for incorporating regular movement in SDMs that is comprised of an agent-based model which combines step-selection function (SSF) and memory to simulate regular movements in a dynamic landscape. SSF analysis of oilbirds in Venezuela was delineated into six two hour periods of a day, and showed that oilbirds were most likely to move through evergreen forests between midnight and six in the morning. Movement simulations were run within all the 1o presence grids in the SDM output for South America. Five of these presence grids were considered unsustainable under current conditions, highlighting the potential for this method to quickly identify over-predicted distributions by the SDM. The integration of daily movements with SDM is unique and this paper has shown that by incorporating the two together, SDM interpretation can be improved substantially.