2.4 Distribution modeling of P. villosa
In order to predict the impact that Quaternary climatic oscillation
might have on the geographic distribution change of P. villosa ,
we employed an ecological niche modelling (ENM) approach to evaluate the
potential distribution of P. villosa at the Last Inter-Glacial
(LIG, ~ 120,000 - 140,000 years before present), the
Last Glacial Maximum (LGM, ~ 21,000 years before
present),
the present and future times (2050s and 2070s), respectively. In
addition to the distribution records of our field surveys, we also
collected GPS data from the Chinese Virtual Herbarium (CVH, http:
//www.cvh.ac.cn), Global Biodiversity Information Facility (http: //www.
gbif.org), China National Specimen Information Infrastructure
(http://www.nsii.org.cn) and Specimen Resources Sharing Platform for
Education (http://mnh.scu.edu.cn/main.aspx) for P. villosa . In
total, after removing duplicate and ambiguous records, we used 155
localities to generate spatial distribution models for P. villosa(Table S2). To improve abilities in
establishing
high-resolution predictions and identifying the critical factors
influencing the species’ distribution, we obtained 19 bioclimatic
variables and three geographic factors, such as altitude, slope and
aspect, at 2.5 arc-min resolution from WorldClim database (Hijmans,
Cameron , Parra, Jones, & Jarvis, 2005, www.worldclim.org). The future
climate data involved in two emission scenarios of representative
concentration pathways (RCP8.5 and RCP2.6) with the CCSM4 model (Van et
al., 2011). We excluded highly correlated variables according to
Spearman’s correlation test (Peterson & Nakazawa, 2008). Specifically,
we selected the variables with a relative contribution score ≥ 0.8 or a
correlation of < 0.75 compared to other variables. Based on
the outcome of Spearman’s, we retained the eleven variables with the
lowest correlations to build a maximum entropy model for the habitat ofP. villosa using. Subsequently, we generated this model
representing the potential distribution of P. villosa in
environmental space in MaxEnt 3.3.3k (Phillips, Anderson, & Schapire,
2006; Phillips & Dudik, 2008). Within MaxEnt, we performed modeling
with 75% of localities randomly selected for training and 25% selected
for testing 500 times independently to ensure reliable results, and we
evaluated model performance using the area under the curve (AUC) of
receiver operating characteristic (ROC). The value of AUC ranges between
0 (randomness) and 1 (exact match), and the value above 0.9 indicated
good performance of the model (Swets, 1988). Additionally,
we
projected the predicted geographic ranges of species based on the ENMs
using ArcGIS 10.2. In particular, we divided suitable habitat into four
classes:
highly suitable habitat (0.5 ≤ P ≤ 1.0), moderately suitable
habitat (0.3 ≤ P < 0.5), poorly suitable habitat (0.1 ≤P < 0.3) and unsuitable habitat (0.0 ≤ P< 0.1).
In
order to measure the niche similarity between populations occurring in
groups, we calculated Schoener’s D (Schoener, 1968) and
standardized Hellinger distance (calculated as I ) in ENMTools 1.3
(Warren, Glor, & Turelli, 2008, 2010). We obtained the null
distribution of niche models in the identity test based on 1000 pseudo
replicates generated by random sampling from the data points pooled for
each pair of cluster. We determined measures of niche similarity
(D and I ) by comparing with null distributions drawn from
pooled occurrences retaining original cluster size, and we drew
histograms of frequency distributions using R 2.13
(http://www.r-project.org/).