2.2 Ecological niche modeling
We used ecological niche modeling (ENM; Soberon & Peterson 2005) to
characterize the spatial distribution of suitable conditions forC. chuniana and locate potential distributional areas in
conjunction with historical biological inferences. We based the analysis
on high-resolution paleoclimate data inferred for the Last Interglacial
(LIG, 0.14 ~ 0.12 Ma), Last Glacial Maximum (LGM, ≈ 0.02
Ma), Middle Holocene (MH, ≈ 0.006 Ma), and current. Bioclimatic
variables were downloaded from the WorldClim database
(http://worldclim.org/download;
Fick & Hijmans, 2017) for the four different stages with 2.5-minute
spatial resolution. The LIG, LGM, and MH data were obtained from
circulation model simulation of the Community Climate System Model
(CCSM) (Collins et al., 2006), which provides downscaled high-resolution
estimates of the climate parameters (Hijmans, Cameron, Parra, Jones, &
Jarvis, 2005). We used the maximum entropy modeling method with Maxent
v3.3.2 (Phillips, Anderson, & Schapire, 2006). Herbarium specimen
records of C. chuniana from nine herbaria (A, IBEC, IBK, IBSC,
KUN, LBG, NMNH, PE and SCFI) as well as our sample collection locations
were used to determine the locations of populations considered to occur
at present. The analysis pipelines and parameter settings, including the
occurrence points, current/past bioclimatic variables as well as the
convergence threshold and maximum number of iterations were all as in
Dai et al. (2011) and Gong et al. (2016). Model accuracy was assessed by
evaluating the area under the curve (AUC) of the receiver-operating
characteristic (ROC) plot (Phillips et al. , 2006), where scores
higher than 0.70 were considered to show good model performance
(Fielding & Bell, 1997). This approach is thus conservative,
identifying the minimum predicted area possible while maintaining zero
omission error in the training dataset (Pearson, Raxworthy, Nakamura, &
Peterson, 2007). The most influential climate factors were also
compared, including precipitation and temperature in each month or on
average.