Effectively and accurately mapping global biodiversity patterns for different regions and taxa

Aim To understand the representativeness and accuracy of expert range
maps, and explore alternate methods for accurately mapping species
distributions. Location Global Time period Contemporary Major taxa studied
Terrestrial vertebrates, and Odonata Methods We analyzed the biases in
50,768 animal IUCN, GARD and BirdLife species maps, assessed the links
between these maps and existing political and various non-ecological
boundaries to assess their accuracy for certain types of analysis. We
cross-referenced each species map with data from GBIF to assess if maps
captured the whole range of a species, and what percentage of occurrence
points fall within the species’ assessed ranges. In addition, we use a
number of alternate methods to map diversity patterns and compare these to
high resolution models of distribution patterns. Results On average 20-30%
of species’ non-coastal range boundaries overlapped with administrative
national boundaries. In total, 60% of areas with the highest spatial
turnover in species (high densities of species range boundaries marking
high levels of shift in the community of species present) occurred at
political boundaries, especially commonly in Southeast Asia. Different
biases existed for different taxa, with gridded analysis in reptiles,
river-basins in Odonata (except the Americas) and county-boundaries for
Amphibians in the US. On average, up to half (25-46%) species recorded
range points fall outside their mapped distributions. Filtered
Minimum-convex polygons performed better than expert range maps in
reproducing modeled diversity patterns. Main conclusions Expert range maps
showed high bias at administrative borders in all taxa, but this was
highest at the transition from tropical to subtropical regions. Methods
used were inconsistent across space, time and taxa, and ranges mapped did
not match species distribution data. Alternate approaches can better
reconstruct patterns of distribution than expert maps, and data driven
approaches are needed to provide reliable alternatives to better
understand species distributions.