2. Methods
2.1 Study area
The Gyirong Valley (28°16′-29° 00′ N, 84°56′-85°24′ E) is located in the
southern part of the Tibetan Plateau of China, and border with northern
part of Nepal (Fig. 1). The valley is expanded over 90 km, and spans an
elevational range from 1,840 to 7,341 m above sea level (a.s.l.). Due to
the influence of the Indian Ocean monsoon, the valley contain steep
environmental gradients and distinct elevational vegetation zones which
can be divided into evergreen broadleaf forest (1,800-2,500 m a.s.l.),
coniferous and broadleaf mixed forest (2,500-3,300 m a.s.l.), subalpine
coniferous forest (3,300-3,900 m a.s.l.), alpine bush and coryphilum
(3,900-4,700 m a.s.l.) and alpine tundra with sparse herbs (4,700-5,400
m a.s.l.). Above 5,400 m a.s.l. there is the scree and nival zone where
very few creatures can survive.
Our study was conducted along an elevation gradient from Resuo village
at 1,800 m a.s.l. to Mt. Kongtanglamu and Mt. Mala at 5,400 m a.s.l.. We
divided the gradient into 12 elevational bands of 300-m. Elevation lower
than 1800 m a.s.l. and high than 5400 m a.s.l. were excluded from the
study due to the geopolitical restrictions and scree and nival zone
where very few creatures can survive.
2.2 Species sampling
Field study was carried out by 96 sampling plots along the 12
elevational bands from 1800 to 5400 m a.s.l. in July and August 2018.
For each band, 8 sampling plots were placed depending upon the most
common physiognomic vegetation and the topographic accessibility. In
each plot, the vascular plant inventories were conducted exhaustively
(2-4 hours by 5 persons) with a quadrat of 400 m2following Fang et al. (2009). Species that could not be identified in
the field were taken to the Museum of Beijing Forestry University for
identification.
2.3 Species grouping
To assess the influence of life form and biogeographical affinities on
range size variation, we classified the vascular plants into four
groups: (1) woody species, including trees and shrubs, (2) herbaceous
species, including herbs and climber, (3) temperate species, whose
distribution centers located in north temperate regions, and (4)
tropical species, whose distribution centers located in pantropic
regions. Life form and biogeographical affinities were determined
referring to Flora of China (www.efloras.org), Flora of Pan-Himalayas
(www.flph.org), Flora Xizangica (Wu, 1983) and Floristics of Seed Plants
from China (Wu et al., 2010).
2.4 Species range size
For each species, elevational range size was estimated as the difference
between maximum and minimum elevational band that it occurred. Following
Steven’s method (Steven, 1992), we average species range size for all
groups of vascular plants within each elevational band.
2.5 Environmental variables
Ten environmental variables which were divide into six groups were used
to examine the role of mean climate condition, climate variability,
historical climate change, disturbance, competition and the mid-domain
effect on the elevational variation in vascular plants range size.
The mean climate condition variables include annual temperature (MAT)
and mean annual precipitation (MAP). The climate variability variables
include temperature seasonality (TS) and annual temperature range
(MATR). MAT, MAP, TS, and MATR were derived from six mini weather
stations establishing along the Gyirong Valley from 2016 to 2018 (2,457,
2,792, 3,368, 3,740, 4,140, and 5,230 m a.s.l., Fig. 1). We averaged
three years’ data of the four variables for each station and
extrapolated them for the entire study area using Kriging interpolation
in a GIS environment (Hu et al., 2018), and then used the average of 8
grid cells corresponding to the sampling plots at each elevational band.
The variables of historical climate change included change in mean
annual temperature (TC) and precipitation (PC) between present and the
Last Glacial Maximum (LGM, about 22,000 years ago). The annual
temperature and precipitation of the LGM were derived from the average
of three Global Climate Models (GCMs), namely, CCSM4, MIROC‐ESM, and
MPI‐ESM‐P, which were obtained from the WorldClim dataset
(www.worldclim.org).
Variables related disturbance factors included the population (POP) and
area of anthropogenic land use (AALU). The POP data was provided by the
authority of Mount Qomolangma National Nature Reserve. The AALU was
calculated as the area of artificial surfaces and cultivated land (Zhang
et al., 2013) extracted from the GlobeLand30 land cover data
(http://www.globallandcover.com).
Since the direct measurement of competition is difficult to achieve, we
used the interpolated species richness as an indirect reflection
(Stevens, 1996). The MDE were tested using the predicted average range
size under geometric constraints, which were computed by randomizing the
empirical species range size within the bounded domain using 1000 Monte
Carlo simulations in the modules Mid-Domain Null (MaCain, 2004; Luo et
al., 2011).
2.6 Statistical analysis
For each elevational band, we calculated the species accumulation curve
to assess the sampling adequacy. Sampling in a band was considered
adequate when the species accumulation curve reaches a plateau (Magurran
and McGill, 2011). Additionally, we computed non-parametric estimated
species richness (Chao2 and Jackknife2) for each band, and then
performed regression of the observed species richness against the
non-parametric estimated species richness (Colwell & Coddington, 1994;
Rowe, 2009).
Linear regressions were calculated to assess the relationship between
elevation and range size for all groups of vascular plants. Rapoport’s
rule is supported where the relationship is positive
(Moreno et al., 2008).
Relationships between species range size and elevation and each
environmental variable were assessed using ordinary least squares (OLS)
models. Simultaneous autoregressive (SAR) model was also performed in
supplement to take account for the spatial autocorrelation in variables.
All variables were standardized (mean = 0 and standard deviation = 1) to
make the regression coefficients comparable in OLS and SAR.
Random Forests model was used to explore the relative importance of each
environmental variable in explaining the range size variations for each
species group. We chose the Random Forests model because it does not
require strict assumptions in data and can better handle
multicollinearity and non-linear relationships which perplexed most
traditional methods like GLMs (Breiman, 2001; Feng et al., 2016). We run
the Random Forests model 1000 times, and assessed the relative
importance of each environmental variable based on the average of the
percentage increase in mean squared error (%IncMSE) from the models.
The %IncMSE was calculated by repeated permutation of each regression
variable, which represents the increase in prediction error caused by
each individual variable.
Species accumulation curve and estimated species richness were
calculated by EstimateS 9.10 (Colwell, 2013). linear regression, OLS,
SAR, and the Random Forest models as well as the bivariate model were
conducted in R 3.5.1 using vegan, spdep, and randomForest package.