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.