Distribution maps depicting the range of a species have traditionally been produced by developing a minimum convex polygon around presence data (e.g., museum specimens, visual observations) with the assumption that gaps in the data are regarded to have the species present, provided that there is suitable habitat. While the polygons rely on presence data, they also rely on expert opinion. Biases are inherent in this process. Biases increase with sample size and are affected by the complexity of the habitat, the error associated with recording a specimen's location, and the spatial and temporal distribution of the sampling effort.

Dot distribution maps use presence data to depict the occurrence of a species across the region without making assumptions regarding sampling gaps. Biases also exist, but are confined to the magnitude of the extent error associated with the location of each point. A locality recorded with a handheld gps has a relatively small error (~3 m), while historical descriptive localities converted to a latitude and longitude usually have a larger error (~1 km). Other errors can also mislead our understanding of a distribution and include a misidentified specimen, a mis-assignment of a location with a commonly used place name (e.g., which Agua Caliente are they referring to?), and mistakes made when georeferencing.