Exploring alternatives
Trimming of ERMs by landcover and elevation is regularly promoted as a
means to trim ERMs, but it is unknown if simple elevation and landcover
trimming correct biases effectively. We tested diversity patterns
generated via original ERMs versus those from analysis of bat point data
with and without trimming and with published models (Hughes 2017). Point
data were clipped for Eurasia and minimum convex polygons (MCPs) created
in ArcMap for species with at least five points. Filters were created
for each species based on elevation and landcover, both using IUCN
assessment data exclusively and that based on extracting environmental
data from points, and these were then paired with associated
environmental data to clip species range on a per species basis.
We used point data to extract elevation from 1km-resolution dem, with
min, max, mean and standard deviation per species from summary
statistics. Species exclusively <1000m=lowland,
1000-2000m=mid, >2000m=high, between these ranges ranked
accordingly: lowland, low-mid, low-high, mid, high. DEMs were reclassed
to corresponding elevation bands. IUCN assessment listings of
elevational preference were recorded. A “integrated” status was
determined based on comparing the point-based with IUCN-based
assessments (when species were assessed in IUCN and had sufficient point
data): where only one assessment was given it was retained, where the
two agreed it was retained, and where they differed we used the
point-based data given higher precision and transparency.
For habitat intactness, we collated IUCN assessments and data extracted
from point data. For IUCN assessments we used keywords to assay
disturbance tolerance. Habitat listings which referenced roosting in
buildings, houses, tunnels were assigned as generalists. Species listed
in cultivated areas, paddies, plantations, agriculture were assigned as
semi-intact and those listing forest and no other “disturbed” habitats
assigned as intact. For point data we classified population layers to
under 50 people per kilometer as intact, 51-100 as semi-intact and over
100 per km as generalist. From point data species with over 50% of
localities in the generalist category were listed as generalists, and
species with at least 75% of records in the under 50 people were
classed as intact. The IUCN and point generated categories were then
compared, where the two categories differed we selected the “final”
classification based on further searches or actual experience with the
species listed.
For richness mapping, we joined the elevation field based on species
names, split into five elevation categories, each of which was then
clipped by a polygon layer of the appropriate elevation bands and
merged. This was repeated for the MCP layer and ERM layers. The ERM
layer was run twice, for the “integrated” assessment data using the
“integrated” category, and once for IUCN elevation assessments. These
were then merged to form three species elevation trimmed species
collations (one MCP, two ERM). Layers were then joined to intactness
categories, and split into three categories prior to trimming with the
appropriate intactness filter (intact, semi-intact, generalist). These
were then merged before using the count overlap toolbox to count the
number of species overlapping in any given area. This enabled comparison
of trimmed and untrimmed layers to a previously published Maxent layer
(Hughes 2017) to assess how useful these alternate approaches are.