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2.8 | Species distribution modelling (SDM)
We generated SDMs for four time periods: the present time, the
Mid-Holocene (5–7.5 kya), the LGM (about 21 kya) and the last interglacial
(LIG; about 120–140 kya) to investigate the possible influence
of climatic changes on the distribution of H. annectans, using MaXent
v.3.3.3e (Phillips, Anderson, & Schapire, 2006). We obtained the occurrence
data of H. annectans used to build the SDMs in this study
from four sources: samples used in this study, two literature records
(Liao & Lu, 2010; Shen, 1996), the Global Biodiversity Information
Network GBIF (http://www.gbif.org), and VertNet (http://vertn
et.org/; Table S3). First, we removed data with no detailed locality
information, imprecise GPS coordinates, obviously erroneous location
(i.e., located in water bodies), as well as duplicate data. We only
retained data at a resolution higher than 5 km, which corresponds to
the resolution of the climatic data of each grid cell with a size of 2.5
arc min (approximately 5 km). This resulted in a total 119 occurrence
data points (Table S3). They were further assigned to main lineages
(viz. lineage E [n = 23], C [n = 26], N1 [n = 15], N2 [n = 12], and W
[n = 43]) as inferred by the phylogeographic analyses (see Results).
We searched for the best combinations of feature classes (determining
the shape of the response curves) and regularization multipliers
(determining the penalty for adding parameters in the model)
by evaluating model scores based on the Akaike information criterion
(AICc). We used the ENMeval package (Muscarella et al., 2014)
to identify the best model with the “ENMevaluate” function in R.
The model feature types used were “L”, “H”, “LQ”, “LQH”, “LQHP”,
“LQHTP” (where: L = linear, Q = quadratic, H = hinge, P = product
and T = threshold) and regularization (RM) values (0, 0.5, 1, 1.5, 2,
2.5, 3, 3.5, 4). For a proper evaluation, we calibrated SDMs using a
random subset of 75% of the sampling sites; the remaining 25% were
reserved to test the validity of the models. We used 19 bioclimatic
layers as environmental predictors at 30-arcsec (~1 km) resolution,
which we downloaded from the WorldClim database (http://www.
world clim.org/; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). To
avoid multicollinearity, we selected BIO (bioclimatic variables) parameters
using PCA. The results showed that the variance of the
climate in the study area could be explained by four principal components
(PCs) that captured 90% of the variance in the data. Thus,
we selected one representative BIO parameter per PC (BIO1 = annual
mean temperature, BIO2 = mean diurnal range, BIO12 = annual
precipitation and BIO14 = precipitation of driest month, Table S4)
to create the SDMs. The three general circulation models (GCMs)
used to generate Mid-Holocene and LGM climate scenarios were
the CCSM4, MIROC-ESM (Watanabe et al., 2011) and MPI-ESM-P
models available from the WorldClim database (http://www.world
clim.org/). Only one GCM of the LIG period was available. We used
ARCGIS v.10.3 to manipulate and visualize the spatial environmental
data and model output.
We employed the Mobility-oriented parity (MOP; Owens
et al., 2013) analysis to assess if the study areas had similar environmental
conditions currently, during the LGM and during the LIG,
and if extrapolation risks exist. We used the 10% as a subsampling
percentage for study area in the current climate. We performed
the analysis using the KUENM package (Cobos, Peterson, Barve, &
Osorio-Olvera, 2019) in R.