AbstractAim The value of biodiversity informatics rests upon the capacity to assess data quality. Yet as these methods have developed, investigating the quality of the underlying specimen data has largely been neglected. Using an exceptionally large, densely sampled specimen data set for non‐flying small mammals of Utah, I evaluate measures of uncertainty associated with georeferenced localities and illustrate the implications of uncritical incorporation of data in the analysis of patterns of species richness and species range overlap along elevational gradients.Location Utah, USA, with emphasis on the Uinta Mountains.Methods Employing georeferenced specimen data from the Mammal Networked Information System (MaNIS), I converted estimates of areal uncertainty into elevational uncertainty using a geographic information system (GIS). Examining patterns in both areal and elevational uncertainty measures, I develop criteria for including localities in analyses along elevational gradients. Using the Uinta Mountains as a test case, I then examine patterns in species richness and species range overlap along an elevational gradient, with and without accounting for data quality.Results Using a GIS, I provide a framework for post‐hoc 3‐dimensional georeferencing and demonstrate collector‐recorded elevations as a valuable technique for detecting potential errors in georeferencing. The criteria established for evaluating data quality when analysing patterns of species richness and species range overlap in the Uinta Mountains test case reduced the number of localities by 44% and the number of associated specimens by 22%. Decreasing the sample size in this manner resulted in the subsequent removal of one species from the analysis. With and without accounting for data quality, the pattern of species richness along the elevational gradient was hump‐shaped with a peak in richness at about mid‐elevation, between 2300 and 2600 m. In contrast, the frequencies of different pair‐wise patterns of elevational range overlap among species differed significantly when data quality was and was not accounted for.Main conclusions These results indicate that failing to assess spatial error in data quality did not alter the shape of the observed pattern in species richness along the elevational gradient nor the pattern of species’ first and last elevational occurrences. However, it did yield misleading estimates of species richness and community composition within a given elevational interval, as well as patterns of elevational range overlap among species. Patterns of range overlap among species are often used to infer processes underlying species distributions, suggesting that failure to account for data quality may alter interpretations of process as well as perceived patterns of distribution. These results illustrate that evaluating the quality of the underlying specimen data is a necessary component of analyses incorporating biodiversity informatics.
