Statistical tests of different scales in the Nanling Mountains
In addition to the phylogeography of the 5 target species, we examined
the occurrence records of all other bird species in the Nanling region
to determine the long-term effects of the mountains on their
distribution patterns. Bird occurrence in the Nanling region was
determined from records downloaded from the CBRC and the eBird database.
The data included species’ name, latitude and longitude and number of
observations (Supporting information). Duration and distance of the
observation were omitted in order to reconcile the formats of 2
datasets. Comparisons focused on the differences among three geographic
subunits: north of the Nanling Mountains, south of the mountains, and
the mountains themselves (Figure 1, Supporting information). The Nanling
region as a whole, and consequently its birds, was delimited manually by
including administrative counties that overlapped with the mountains
(Figure 1), while the north-end was decides so as to form comparable
size of area (North Nanling 177534 km2, Nanling itself
162695 km2 and south Nanling 175820
km2). The Nanling in the middle is rugged with low
mountains, while in the north, there are plains with surrounding
mountains. The south Nanling region comprises mountains, waters and
coastlines. Before testing distribution patterns, we pruned wetland- and
coastal birds based on the habitat data from AVONET (Tobias et al.
2022). The original datasets were based on observation events, which
would lead to bias introduced by observers (Strimas-Mackey et al. 2020).
We transformed the dataset by thinning the records omitting the time
variance. For the 2 datasets from different databases, species were
reduced to one record per 15 km radius. In the end, 27402 occurrence
records for 446 species were used (Supporting information).
Two statistical tests from different taxonomic level were applied to
examine differences in birds among the three geographic subunits. We
first performed chi-square tests on the number of species in each family
between each region pairs to locate significant differences in family
composition. For this we used chisq.test in R core functions.
Second, we performed one-tailed Wilcoxon’s rank tests in both directions
number of each bird species records for each family to determine which
family distributes differently among three geographic subunits. All
tests were done using the wilcox.test in R core functions. To
avoid abnormal estimates from very rare occurrence records, we omitted
families with less than 10 occurrence records from the results.