Detecting isolation-by-distance versus barrier
To identify IBD patterns among populations, we analyzed the correlation
between pairwise genetic distances and geographic locations of all
populations using BARRIER 2.0 (Manni et al. 2004). BARRIER applies a
geometric approach and produces the length and width of barriers. We
also applied Mantel tests to determine concordance and statistical
robustness between geographic and genetic distances. Mantel tests
compared geographic distance matrices (produced according to the
multiple dispersal scenarios described above) and the N-distance matrix.
Mantel tests were performed on the 5 species with 4 scenarios of
dispersal, respectively, while the BARRIER software accepts coordinate
as input. We also calculated AIC for linear correlation models of
genetic distance and geographic distance under different scenarios with
the AIC function in R core functions (R Core Team 2020). Values
were formatted into a ΔAIC matrix to infer the best scenario for IBD
pattern.
As noted by Wright (1943), an IBD pattern would be disrupted by
non-random dispersal in reaction to specific environments. This
observation led ultimately to the development of the idea of
isolation-by-environment (IBE; Nosil et al. 2005). To test for IBE, we
selected 20 environmental variables—including 19 bioclimatic variables
from the public database CHELSA (Karger et al.
2017, Karger et al. 2018; Supporting
information) and the GMTED2020 (Danielson and Gesch 2011) elevational
dataset mentioned above—that are expected to be correlated with
factors consequential to bird habitat. A negative correlation or no
correlation between the climatic variables and genetic distance,
therefore, would dispute IBE. To avoid overfitting in the environmental
analyses, we first remove highly correlated variables. We randomly
sampled 10000 grids to characterize each candidate variable and
performed PCA analysis of their correlation with one another (Supporting
information). Seven of the variables—Bio04 (temperature seasonality),
Bio07 (annual range of air temperature), Bio12 (annual precipitation),
Bio13 (precipitation of the wettest month), Bio16 (mean monthly
precipitation of the wettest quarter), Bio18 (mean monthly precipitation
of the warmest quarter), and elevation, were the least related to one
another. Using these 7 variables, we tested for IBE by applying Mantel
tests and linear regression on variable differences versus N-distances
among genetic sample points using the mantel function in thevegan package (Oksanen et al.
2019) and lm function in R core functions (R Core Team 2020). A
significant correlation would support IBE among populations.
To examine the effects of recent climate changes on the distribution of
the 5 species, we performed environmental niche modeling (ENM), using
the same 7 environmental variables and MAXENT 3.4.4 (Phillips et al.
2006) with default parameters. Variable values from the last glacial
maximum (LGM, ~21,000 years before present), the final
stages of the LGM (BAW, ~14,600 years ago at the maximum
of the Bølling-Allerød warming; Karger et al. 2018), and present day
(CHELSA; Karger et al. 2017) were used. The elevation above sea level
and land contours for modeling and mapping were from
Lambeck et al. (2014) (96 meters
lower than present for the BAW), and Siddall et al. (2003) (125 meters
lower than present for the LGM). Ten replicates were run, and
cross-validation was used to test the model. To build the models, record
data from the China Bird Report Center (CBRC, http://www.birdreport.cn/,
retrieved on 2 February 2020) and eBird database (Sullivan et al. 2009;
retrieved in May 2021) for the 5 species were used. Point occurrence
records were reduced to one record per 15 km radius. Records with longer
observation and higher count were selected during the thinning process.
The final collection consisted of 3931 records in total (1480 forI. mcclellandii, 312 for A. hueti, 1079 for L.
lutea , 663 for P. pectoralis and 397 for S. torqueola ).
For graphic illustration of the distribution of the species over time,
we collected the logistic output raster. We also prepared a raster map
of summed logistic output of all 5 species for an overview.