Niche differentiation in the bank vole: Maxent input and output data - Escalante, Marco; Horníková, Michaela; Marková, Silvia; Kotlik, Petr (Dryad, 2021)
Species-level environmental niche modelling has been crucial in efforts to
understand how species respond to climate variation and change. However,
species often exhibit local adaptation and intraspecific niche differences
that may be important to consider in predicting responses to climate.
Here, we explore if phylogeographic lineages of the bank vole originating
from different glacial refugia (Carpathian, Western, Eastern and Southern)
show niche differentiation, which would suggest a role for local
adaptation in biogeography of this widespread Eurasian small mammal. We
first model the environmental requirements for the bank vole using
species-wide occurrences (210 filtered records) and then model each
lineage separately to examine niche overlap and test for niche
differentiation in geographical and environmental space. We then use the
models to estimate past [Last Glacial Maximum (LGM) and mid-Holocene]
habitat suitability to compare to previously hypothesized glacial refugia
for this species. Environmental niches are statistically significantly
different from each other for all pairs of lineages in geographical as
well as environmental space and these differences cannot be explained by
habitat availability within their respective ranges. Together with the
inability of most of the lineages to correctly predict the distributions
of other lineages, these result support intraspecific ecological
differentiation in the bank vole. Model projections of habitat suitability
during the LGM support glacial survival of the bank vole in the
Mediterranean region as well as in central and western Europe. Niche
differences between lineages and the resulting spatial segregation of
habitat suitability suggest ecological differentiation has played a role
in determining the present phylogeographic patterns in the bank vole. Our
study illustrates that models pooling lineages within a species may
obscure the potential for different response to climate change among
populations.
Environmental data Raster layers representing 19 bioclimatic variables
were downloaded from the WorldClim (www.worldclim.com) v.1.4 dataset
(Hijmans, Cameron, Parra, Jones, & Jarvis, 2005), at 30 arc-second
resolution, and were clipped to the boundaries of the study
area.  To account for climate modelling uncertainties (Schorr,
Holstein, Pearman, Guisan, & Kadereit, 2012), two General
Circulation Models (GCM) of past climate, CCSM4 (Gent et al., 2011) and
MIROC-ESM (Watanabe et al., 2011), were used for the LGM (about 22 kyr
ago) and mid-Holocene (about 6 kyr ago). Environmental Niche Modelling The
ENM was performed using the maximum entropy approach implemented in
MaxEnt, v.3.4.1 (Phillips, Dudík, & Schapire, 2004). Two different
sets of predictor variables were used to ascertain the robustness of the
results (Araújo et al., 2019). Both sets were selected based on a
reiterative jackknife procedure of model construction and stepwise removal
of the least contributing variables (Zeng, Low, & Yeo, 2016), but
each used a different data set and metric (i.e. training gain or test
gain) to measure the contribution of the variables to the model. After
removing the uninformative variables by the jackknife procedure, the final
set of variables was produced in each case by removing one variable from
each pair of correlated variables, based on a correlation matrix of the
climate layers (cut-off of r ˂ 0.8; Merow, Smith, Silander, Merow,
& Silander, 2013). The first set of predictors (Set 1) was
selected based on the training gain using the species-level occurrences as
the training data set and it contained Mean Diurnal Range (BIO 2),
Temperature Annual Range (BIO 7), Mean Temperature of Wettest Quarter (BIO
8), Mean Temperature of Driest Quarter (BIO 9), Precipitation Seasonality
(BIO 15), Precipitation of Wettest Quarter (BIO 16), Precipitation of
Driest Quarter (BIO 17) and Precipitation of Warmest Quarter (BIO 18). The
second set (Set 2) was selected using Western Siberia as an independent
test area. The variables included in Set 2 based on the test gain were
Isothermality (BIO 3), Mean Temperature of Warmest Quarter (BIO 10),
Precipitation of Wettest Quarter (BIO 16) and Precipitation of Warmest
Quarter (BIO 18). Niche models were built independently with Set 1 and Set
2. A total of 50 replicates of each model were generated by the
subsampling method in MaxEnt, which randomly selected 25% of the
occurrence points reserved as test data (Phillips et al., 2004).
Subsequently, the estimates are based on the overall mean of the
replicates. To minimize the possible effect of inadequate representation
of the environmental background (Guevara, Gerstner, Kass, &
Anderson, 2018), 1,000,000 background points were used. Default values
were used for the other parameters, as recommended when comparing models
at different evolutionary levels (i.e. species and intraspecific lineages)
and with different sampling efforts (Merow et al., 2013; Phillips
& Dudík, 2008). Our training area encompasses the bank
vole distribution range and surrounding areas, covering Europe, western
Siberia, the Anatolian Peninsula and the Caucasus.  The models
were projected to the CCSM4 and MIROC-ESM bioclimatic layers for the LGM
and mid-Holocene. The logistic format and ascii type were used to generate
the raster output.
Keywords: FOS: Biological sciences