Materials and Methods
This study was conducted in the town of Xihai, Haiyan County (100°57′N,
36°56′E, 3100 m ASL), Qinghai Province, China, at the eastern edge of
Qinghai Lake (Fig. S1). Mean annual temperature is 1.4°C, mean annual
precipitation is 330–370 mm, and mean annual potential evaporation is
approximately 1,400 mm. The typical vegetation is alpine meadow,
dominated by sedges and grasses. The soil type is loam, and the vertical
profile is 0–30 cm for clay loam and 30–50 cm for silty loam.
Four key species in the alpine meadow on the QTP, i.e., Stipa
purpurea (grass), Kobresia humilis (sedge), Artemisia
scoparia (non-legume forb), and Astragalus laxmannii (legume)
have been monitored from 1997 to 2017 (with one observation for each of
the four species in 2000; for 2006, data for Astragalus laxmanniiwere missing) (Figs. S1 to S5). A randomly selected area of 100 × 100-m
was equally divided into four 50 × 50-m blocks; each block was divided
into four 25 × 25-m cells and marked permanently, and each cell was
further separated into three sections. The first section was used for
monitoring phenological time periods (e.g., green-up, flowering,
fruiting, and withering), using 10 individuals of each species in each
cell. Plant phenology was recorded every 2 days after the green-up
period. The second section was used for monitoring plant height, using
10 individuals of each species in each cell. Plant height was recorded
every 10 days after the green-up period. The last section was used for
monitoring plant biomass, for which each plant in a randomly selected 1
× 1-m quadrant was harvested after the withering period, and the
selected cell was rotated clockwise every 4 years in each block (Fig.
S1). After harvesting, each plant was oven-dried at 65°C to constant
weight. Soil moisture at a depth of 0–30 cm was determined via
oven-drying of soil samples at 105℃ to constant weight every 10 days
from March to the end of September. The climatic data were obtained from
the Haiyan meteorological station (nearby the study site), recording
daily average temperature, daily maximum temperature, daily minimum
temperature, daily average soil temperature (0–30 cm), daily
precipitation, daily relative humidity, and daily sunshine length
(note: data of daily sunshine
length were missing in the second half of 2017).
We defined the date when the phenomenon occurred (Julian day) in more
than 50% of the marked plants as the time of the phenological period
(e.g., green-up, flowering, fruiting, and withering) and the duration
(number of days) from flowering to fruiting as the length of the
generative growth period. We used the duration (number of days) from
green-up to withering minus the length of the generative growth period
as the length of the vegetative growth period.
To describe the growth dynamics across growing seasons with the height
measured every 10 days (Figs. S2 to S5), we used a three-parameter
logistic function (Paine et al. 2012). We also calculated the
annual peak height, the timing of maximum growth (Julian day), and the
intrinsic growth rate. To determine the start and end growth for each
species, we consulted previous studies (Richardson et al. 2013)
and set the time to reach 21% of the maximum height as the time for the
start of the rapid growth phase (Julian day) and to reach 79% of the
maximum height as the time for the end of the rapid growth phase (Julian
day). We defined the duration (number of days) from the beginning of
rapid growth to the end of rapid growth as the length of the rapid
growth phase.
To determine phenology dynamics, growth patterns, and annual biomass in
different
plants, we used variance analysis (Table S1) and employed a linear model
to show the trends at the start of the rapid growth phase, the timing of
maximum growth, the end of the rapid growth phase, green-up time,
flowering time, fruiting time, withering time, annual peak height,
intrinsic rate of plant growth, and biomass of key plant species (Figs.
1 to 3). We used linear regression to explore the relationships among
plant biomass, intrinsic growth rate, length of the rapid growth phase,
length of the vegetative growth period, and length of the generative
growth period over time (Table S2).
To identify optimal length
thresholds (i.e., how many days) and the factors determining the growth
and phenological events which are significantly associated with plant
biomass, we also used linear models to evaluate the effects of average
temperature, maximum temperature, minimum temperature, average soil
temperature, precipitation, relative humidity, soil moisture, and
sunshine length on plant growth (start of the rapid growth phase, the
timing of maximum growth, and the end of the rapid growth phase) and
phenological events (green-up, flowering, fruiting, and withering)
during the growing season. To determine which days affected the key
growth and phenological events, we compared the fit of each factor to
each growth and phenological event with a step length of 2 to 60 (30
models in total), in which precipitation and sunshine length were the
sums of multi-day values. However, other factors (average temperature,
maximum temperature, minimum temperature, average soil temperature,
relative humidity, and soil moisture) were represented by the average of
multi-day values, and we used the measured values to indicate the value
of 5 days before and after the measurement since the soil moisture was
measured once every 10 days. There was collinearity between average and
maximum temperature (Pearson´s correlation coefficient = 0.85), average
temperature and minimum temperature (Pearson´s correlation coefficient =
0.85), average temperature and average soil temperature (Pearson´s
correlation coefficient = 0.90), as well as maximum temperature and
average soil temperature (Pearson´s correlation coefficient = 0.86)
(Fig. S6). Three parallel models were constructed to remove
collinearity, i.e., average temperature + precipitation + relative
humidity+ soil moisture + sunshine length; maximum temperature+ minimum
temperature + precipitation + relative humidity+ soil moisture +
sunshine length; minimum temperature + soil temperature+ precipitation +
relative humidity+ soil moisture + sunshine length, resulting in a total
of 90 models (30 * 3 parallel models). Finally, we produced candidate
models based on a cut-off of ΔAIC < 5 and subsequently
selected the model with the shortest number of days as the best model
among the candidate models. We calculated the average of the factors in
multiple parallel models to select the best model, i.e., the multiple
parallel models within the same day (Tables S3 and S4).
The ‘stats’ and ‘deSolve’ packages were employed for the logistic
function, the ‘multcomp’ and ‘agricolae’ packages to perform variance
analysis, and the ‘basicTrendline’ package for the linear model. We used
the ‘PerformanceAnalytics’ package for correlation analysis and the
‘stats’ package for multiple regression analysis in R v. 3.5.1 (R
Development Core Team).