Open-access biodiversity repositories such as the Global Biodiversity Information Facility (GBIF) are central to contemporary conservation research, yet their heterogeneous data sources introduce quality issues and spatial sampling biases that may compromise conservation analyses. This study evaluates the spatial and taxonomic quality of GBIF occurrence data for five protected species representing mammals and vascular plants in Central Europe. A transparent data-cleaning workflow was applied, including coordinate validation, removal of duplicate and erroneous locations, and taxonomic harmonization. Spatial sampling bias was quantified using nearest neighbor distance-based metrics, enabling comparison of clustering patterns before and after data cleaning. Raw datasets exhibited strong spatial clustering across all species, with nearest neighbor ratios (NNR) ranging from 0.06 to 0.73. Data cleaning reduced the number of retained records by approximately 15–57% and increased NNR values to 0.27–0.80, indicating the removal of extreme spatial artifacts. However, NNR values remained well below unity after cleaning, demonstrating persistent non-random spatial sampling structure related to uneven sampling effort. The strongest relative improvements were observed for plant species derived from herbarium records. These results highlight the need to distinguish between data quality errors and structural spatial bias in open biodiversity data and underscore the necessity of bias-aware methods beyond standard data-cleaning procedures in conservation analyses.
Seweryn Lipiński (2026) studied this question.