Projecting richness from inventories
Where insufficient data exists for species specific modelling,
projecting richness from inventories may be the only possibility. This
requires a lower resolution than many other approaches (as inventories
must be drawn from an area) and can also not reflect biogeographic
differences, meaning that where island biogeography is important it
cannot account for that. Projecting richness using this approach relies
on site-based inventories of species present. To do this we first used
the same dataset as above, we created a 10km2 fishnet
as a polygon grid. The fishnet was trimmed to continental boundaries
using the same clip as previously used. This was then imported along
with point data into QGIS 3.26.3 and the sampling point tool used to
intersect grids with point data, with the FID of each grid used in an
additional column as a unique ID for the grid. We then reimported this
into ArcMap 10.8 and used the summary statistics tool to calculate the
number of points and species per grid cell. This was then reconnected to
the original grid using joins and relates, and cells with at least 30
unique records were selected as inventories, the latitudes and
longitudes of the centroids added, and this data exported to a CSV. To
better reflect appropriate data from adequately inventoried but
potentially less diverse sites we then added the publically available
data from the Darkcide database (Tanalgo et al 2022) once we removed any
listings with only a single species present (which may reflect species
specific inventories, for example a number of sites only listedOtomops species). The final dataset had 417 inventories for the
region which were then used for modelling.
This data could then be modelled for richness. This has the advantage
that we can use the same variables as used for species specific
modelling, including not requiring the use of a landcover map as a
filter and thus enabling more nuance. Variables selected are noted in
supplemental data, these were chosen to reflect climate parameters, and
continuous metrics of habitat structure. Variables included actual
evapotranspiration, annual mean potential evapotranspiration, aridity,
two metrics of distance to bedrock (bdticm, bedroom from ISRIC world
soil grids, as well as Estimated soil organic carbon stock as a measure
of fertility), continental moisture index, continentality, embergers
pluviothermic quotient, growing degree days 5, potential
evapotranspiration of the driest quarter (resources during the most
limiting time of year) potential evapotranspiration seasonality,
thermicity (from Envirem) and bio 3,4,5,6,12,13,14 and 15 from Worldclim
as well as vegetation canopy height.
Therefore for both forms of modelling (richness modelling based on
inventories and species modelling) we used canopy height of all
vegetation, a range of soil variables, and variables representative of
climate, moisture and seasonality. Richness inventories were then
grouped into classes (i.e. under 5 species, 5-10 species, etc) and
modelled as individual classes using Maxent, all outcomes had an AUC of
over 0.9. Within Maxent we modelled each richness level, ran three
replicates (and used an average) and used default parameters. The
average was then reclassified in ArcMap 10.8 using the 10 percentile
cloglog threshold as a minimum bound of suitability, and then using an
equal division between the threshold and the maximum value of 1 to
reflect the maximum and minimum values of the richness level, with areas
“unsuitable” given a value of 0. The mosaic to new raster tool was
then used with the selection set to “maximum” to give the maximum
number of species any given area was suitable for based on model
outcomes.