Taxon-specific analyses
Data exploration exposed taxon- and regional-specific biases requiring
additional analysis. In these cases, the causes of biases were assessed
by comparing range boundary density maps to high-resolution imagery and
administrative maps via the ArcGis server. These included relationships
between 1) amphibians with county borders in the US and 2) dragonflies
and river basins globally. In these cases, species boundary density maps
were used as a basis to identify potential biases which were then
explored empirically using appropriate methods.
For amphibians, counties were digitized using https://gadm.org/
with 2.5km buffers. Species boundary density maps for amphibians were
reclassified showing where species range boundaries existed with other
areas reclassified “no data.” Percentages of combined species boundary
areas falling within county buffers vs areas without were calculated.
For Odonata, many species were mapped to river basin borders. We used
river basins of levels 6-8 in the river hierarchy
(https://hydrosheds.org). Two datasets existed for Odonata, the IUCN
Odonata specialist group spatial dataset
(https://www.iucnredlist.org/resources/spatial-data-download), and
a larger dataset available via the RedList website
(https://www.iucnredlist.org/resources/grid/spatial-data)
containing an additional 1000 polygons relative to the previous file (as
of September 2019), predominately in Latin America. We examine both, as
either may be used for contemporary analyses on Odonata.
For reptiles, two grids resolutions were visible when mapping species
range boundary density (1, 0.5 degrees). Gridding in range delineation
was examined by developing 1-, 0.5-degree fishnet grids globally. Grids
were then dragged into alignment with the noted reptile range boundary
grids in central Africa; if grids are not a genuine artifact of
digitization, this would not be possible, or it would be inconsistent in
different regions. Alignment between the digitized fishnet grid and
range boundaries was reconfirmed in Central Asia and South America.
Grids were then clipped to landareas and merged with national boundaries
into a combined shapefile. Species range boundary density was quantified
and layers reclassified for areas with >3 species
boundaries overlapping, then intersected with both grid-sizes to
quantify percentages of boundary hotspots overlapping with grids or
borders.