Habitat trimmed maps
We needed to filter out habitat. This step is key, and likely will
introduce errors, as whilst widely advocated, most landuse maps
misrepresent actual landcover by delineating landcover types in a fairly
arbitrary way. This will over-emphasize the importance of more sampled
habitats, and may miss undersampled and smaller habitats; thus the
choice of this layer is key. Here we used the IUCN ecosystem typology
(published in 2022) because it provides overlapping classes of
ecologically relevant landcover and thus provides the most nuanced
mechanism to map landcover for regions where accurate national maps of
landuse may not be available. National maps are generally better
calibrated for a given region than global maps, which must necessarily
simplify land-cover categories, and may not be sufficiently tested at a
local level. Furthermore, the IUCN ecosystem typology is recognised by
the UN System of Environmental Economic Accounting (SEEA) and is
therefore already a global standard. An alternative here if the region
of interest is predominantly forested is to use tree density or height
as the delimiter for habitats, however, in a region with diverse
habitats, more nuance may be needed given that trimming in this way
cannot account for climate differences across the recorded maximum
bounds of species distribution. Notably urban areas were not included as
biases in sample collection to more populated areas can skew results
(Hughes et al., 2021a).
After removing duplicate species points we then extracted the landcover
types based on the typology under all locality points. We then used the
summary statistics tool to calculate the total number of points per
species in each landcover category as well as overall, then used this to
calculate the percentage of locality points for each species within each
category. We then filtered out habitat with at least 10% of locality
points within each landcover category which fell within the polygon for
the species. To do this we masked all habitat types suitable for the
species with their individual MCP then mosaiced all parts of the species
range together using the mosaic to new raster tool, all suitable
habitats were given a value of one. Because of errors in the mosaic to
new raster tool if applied to stack richness for large numbers of
species we then mosaiced all species range maps onto a mask of the
African region, then used the raster calculator to sum richness for
groups of 40 species, numbered these sequentially, then added the
numbered MCP filtered outputs to sum overall MCP filtered richness.
In addition we used the same filtered habitat requirements and repeated
the process using the IUCN polygons rather than the MCPs. This has the
advantage of better representing overall species range (MCPs will
necessarily not include the edges of species ranges as they can only
delimit the maximum bounds of recorded range), but we know from former
work IUCN maps include pervasive biases and also artificially
under-estimate many parts of species ranges (Hughes et al., 2021, Li et
al., 2020). Furthermore, IUCN maps are not available for most plants and
invertebrates, as well as being notably incomplete in some small bodied
vertebrate taxa. Filtered IUCN maps were then stacked and richness
calculated in the same way as MCP filtered richness using the raster
calculator.