10.5061/DRYAD.JM63XSJB0
Escalante, Marco
0000-0003-4461-2124
Academy of Sciences of the Czech Republic
Horníková, Michaela
Czech Academy of Sciences
Marková, Silvia
Czech Academy of Sciences
Kotlik, Petr
Czech Academy of Sciences
Niche differentiation in the bank vole: Maxent input and output data
Dryad
dataset
2021
FOS: Biological sciences
Czech Science Foundation
https://ror.org/01pv73b02
16-03248S
Czech Science Foundation
https://ror.org/01pv73b02
20-11058S
Ministry of Education, Youth and Sports of the Czech Republic*
EXCELLENCE CZ.02.1.01/0.0/0.0/15_003/0000460 OP RDE
Ministry of Education, Youth and Sports of the Czech Republic
EXCELLENCE CZ.02.1.01/0.0/0.0/15_003/0000460 OP RDE
2022-05-07T00:00:00Z
2022-05-07T00:00:00Z
en
7500892994 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.