Data from: New regionally modelled soil layers improve prediction of vegetation type relative to that based on global soil models

Aim: High-resolution spatial soil data are crucial to species distribution
modelling for fundamental research and conservation planning. Recent
globally-modelled soil layers (e.g. SoilGrids) have transformed
distribution modelling, but may fail to represent regional soil
characteristics accurately. We hypothesize that in the Cape biodiversity
hotspot of South Africa, the use of global soil layers has led to
underestimation of the importance of edaphic factors as determinants of
species’ and vegetation distributions. We present a series of new,
regionally-modelled layers to address this deficiency. Location: Greater
Cape Floristic Region (GCFR, South Africa) Methods: We georeferenced
edaphic characteristics from literature and other sources and used boosted
regression trees (BRT) to associate edaphic characteristics with
spatially-explicit topographic, climatic, soil texture and biotic
variables. Multinomial BRTs were used to predict mapped vegetation types
from the collated edaphic and other data. Results: BRTs reliably predicted
pH (92% of variance), Na (87%), K (85%), electrical conductivity (81%) and
P (73%), but were less accurate for total N (55%) and total C (61%), for
which data were sparser. Soil clay and pH values differed markedly in
range and in spatial variation from those in SoilGrids. Using our new
edaphic layers, we were able to accurately predict spatial distributions
of vegetation types within the GCFR (multi-class AUC = 0.96). The
multinomial BRT predicted vegetation less well when based on SoilGrids
data alone (AUC = 0.84). Main conclusions: The more faithful
representation of soil properties in our model is attributable both to its
use of ca. 10-fold more samples, and to its regional focus. Our model of
edaphic characteristics captures important edaphic variability that is
vital for understanding plant and consequently faunal distributions, with
wide-ranging conservation implications. Ongoing development of global
syntheses of soil data requires more samples, especially in areas with
high spatial heterogeneity and extreme edaphic conditions.