Specialization predicted from environmental niche models
To link abiotic environmental variation with host specialization (Q3),
we identified areas with optimal conditions for specialization using
environmental niche models (ENMs). ENMs are a widely used tool for
characterizing suitable habitat for a species based on correlation
between environment and observed occurrences of an organism (Elithet al. 2010). An implicit assumption is that organisms should
have higher fitness in locations with higher habitat suitability
predicted by ENMs (Nagaraju et al. 2013; Wittmann et al.2016).
We constructed ENMs based on all parasite occurrences (all-occurrence
model) and subsets of occurrences associated with a particular host.
ENMs were built using MaxEnt (Phillips et al. 2006), a machine
learning based method for modeling distributions based on presence-only
data. Models were built using ENMeval (Muscarella et al.2014) to test a variety of tuning parameters. Eight environmental
variables were considered, including several associated with
differential parasitism on host species (soil clay content; Wilson-Jones
1955; Mohamed et al. 2001), host exudation and production of
hormones required for parasite germination (soil nitrogen and
phosphorus; Yoneyama et al. 2013), or parasitism severity (annual
rainfall; Wilson-Jones 1955). Bioclimatic and topographic variables
(annual rainfall, mean temperature of the wettest quarter,
isothermality, potential evapotranspiration, and topographic wetness
index) were obtained from CHELSA (Karger et al. 2017) and ENVIREM
(Title & Bemmels 2018) datasets. Soil variables (clay content,
nitrogen, and phosphorus) were obtained from SoilGRIDs250m or
AfSoilGrids250m (Hengl et al. 2015, 2017). For AfSoilGrids and
SoilGrids250m products, we down-sampled to 1-km resolution using
bilinear interpolation with the ‘resample’ function of the rasterpackage (Hijmans 2020). To characterize the background of the study, we
randomly sampled 10,000 points from within a 500 km radius of all
occurrences; the same background points were used to build each of the
host-specific models and the all-occurrence model. We previously found
that habitat suitability values from an ENM of S. hermonthicawere associated with a large effect resistance allele in sorghum
landraces, supporting both the accuracy of the model and some degree of
local host adaptation to S. hermonthica (Bellis et al.2020).
Here, we hypothesized that ENM contrasts, which we define as the
difference in habitat suitability between two different ENMs, might also
predict variation in performance on different hosts. We created ENM
contrasts by subtracting the logistic-transformed ENM output for
parasite occurrence on a focal host species from the all host
species-occurrence model for each grid cell. Higher potential for
specialization on a given host is indicated by more negative values in
ENM contrasts (lower bound of -1) and lower potential for specialization
is indicated by more positive values (upper bound of 1). Values close to
zero can be interpreted as locations where generalist parasites are
likely. We calculated the average value within 50 km of S.
hermonthica locations from empirical studies as a measure of predicted
specialization by ENMs, but similar values were observed at a range of
distances (Fig. S2).
We investigated the factors most important for predicting overall
occurrence in each model using permutation importance. However,
environmental factors most important for determining habitat suitability
may differ across space; to identify variables that most influence model
prediction at each location, we created limiting factor maps (Elithet al. 2010) with rmaxent (Baumgartner & Wilson nd),
fitting models based on the optimal regularization multipliers and
feature classes parameters determined by ENMeval (millet:
betamultiplier = 3.5, noproduct, nothreshold; maize: betamultiplier =
0.5, noproduct, nothreshold, nohinge; sorghum: betamultiplier = 3.5,
nothreshold). After masking to a 200 km radius of any known S.
hermonthica occurrence, niche overlap statistics were calculated inENMTools version 0.2 (Warren et al. 2010), in
environmental space with the ‘env.overlap’ function or in geographic
space with the ‘calc.niche.overlap’ function (Warren et al.2019).