Species distribution models (SDMs) are widely used in ecology to learn about the species-habitat relationship and abundance across space and time. Distance sampling (DS) and capture-recapture (CR) are two widely collected data types to learn about species abundance; still, they are seldomly used in SDMs due to the lack of spatial coverage. However, in contrast to using one data source alone, jointly modeling the two data sources using data fusion can reduce parameter uncertainty associated with limitations such as lack of spatial coverage. We develop a model-based approach that fuses DS and CR data, making assumptions about the missing value mechanism to account for missing data issues. We evaluate the performance of our modeling approach and compare it to existing approaches using a simulated experiment. Our results show that our method significantly increased the precision and efficiency of parameter estimates than the approaches that use ad-hoc approaches to account for missing data issues. We demonstrate the approaches using data collected for Grasshopper Sparrows on Konza Prairie Biological Station, Kansas.