DOI: 10.5061/dryad.jm63xsjb0
title: Niche differentiation in the bank vole: Maxent input and output data

description: 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.
description: <strong>Environmental data</strong><br> 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). <br> <strong>Environmental Niche
 Modelling</strong><br> The ENM was performed using the maximum
 entropy approach implemented in MaxEnt, v.3.4.1 (Phillips, Dudík,
 & Schapire, 2004).<br> 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).<br> 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).<br> Our
 training area encompasses the bank vole distribution range and surrounding
 areas, covering Europe, western Siberia, the Anatolian Peninsula and the
 Caucasus. <br> 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.