Assessing species-level gaps and biases in occurrence data

Gaps in data may hamper good species distribution modelling. When assessing quality of species occurrence data, an often suggested (but rarely tested) cause of species-level variation and bias is, that species attributes, e.g. body size and diurnal activity, affect detection and collection. In this study, researchers overlaid GBIF-mediated occurrences of 3,625 mammalian species with expert-drawn range maps, and assessed occurrences of species and higher taxonomic groups according to record count, range coverage and geographical bias. Relating these to species attributes, range geometry and socio-economic factors, the researchers found that primates stand out for below average record counts, and carnivores for below average range coverage. Marsupials, however, score high in both measures. On a global scale, coverage is, not surprisingly, positively correlated with record count. However, the role of species attributes is remarkably minor. The study concludes by recommending increased data mobilization in institutions near data gaps and added focus on understudied species.