Can we derive macroecological patterns from primary Global Biodiversity Information Facility data?

Abstract Aim To determine whether the method used to build distributional maps from raw data influences the representation of two principal macroecological patterns: the latitudinal gradient in species richness and the latitudinal variation in range sizes ( R apoport's rule). Location World‐wide. Methods All available distribution data from the Global Biodiversity Information Facility ( GBIF) for those fish species that are members of orders of fishes with only marine representatives in each order were extracted and cleaned so as to compare four different procedures: point‐to‐grid ( GBIF maps), range maps applying an α‐shape [ GBIF‐ extent of occurrence ( EOO) maps], the MaxEnt method of species distribution modelling ( GBIF ‐MaxEnt maps) and the MaxEnt method but restricted to the area delimited by the α‐shape ( GBIF ‐MaxEnt‐restricted maps). Results The location of hotspots and the latitudinal gradient in species richness or range sizes are relatively similar in the four procedures. GBIF‐EOO maps and most GBIF ‐MaxEnt‐maps provide overestimations of species richness when compared with those present in a priori well‐surveyed cells. GBIF‐EOO maps seem to provide more reasonable world macroecological patterns. MaxEnt can erroneously predict the presence of species in environmentally similar cells of another hemisphere or in other regions that lie outside the range of the species. Limiting this overpredictive capacity, as in the case of GBIF ‐MaxEnt‐restricted maps, seems to mimic the frequency of observations derived from a simple point‐to‐grid procedure, with the utility of this procedure consequently being limited. Main conclusions In studies of macroecological patterns at a global scale, the simple α‐shape method seems to be a more parsimonious option for extrapolating species distributions from primary data than are distribution models performed indiscriminately and automatically with MaxEnt. GBIF data may be used in macroecological patterns if original data are cleaned, autocorrelation is corrected and species richness figures do not constitute obvious underestimations. Efforts therefore should focus on improving the number and quality of records that can serve as the source of primary data in macroecological studies.