Analysis of biodiversity data suggests that mammal species are hidden in predictable places

Research in the biological sciences is hampered by the Linnean shortfall,
which describes the number of hidden species that are suspected of
existing without formal species description. Using machine learning and
species delimitation methods, we built a predictive model that
incorporates some 5.0 × 105 data points for 117 species traits, 3.3 × 106
occurrence records, and 9.1 × 105 gene sequences from 4,310 recognized
species of mammals. Delimitation results suggest that there are hundreds
of undescribed species in class Mammalia. Predictive modeling indicates
that most of these hid- den species will be found in small-bodied taxa
with large ranges characterized by high variability in temperature and
precipitation. As demonstrated by a quantitative analysis of the
literature, such taxa have long been the focus of taxonomic research. This
analysis supports taxonomic hypotheses regarding where undescribed
diversity is likely to be found and highlights the need for investment in
taxonomic research to overcome the Linnean shortfall.