2 | Materials and
Methods
2.1 | Study site and experimental
design
Our study site is located at the field research station of Beijing
Research & Development Center for Grasses and Environment (40°10’ 45”
N, 116°26’13” E) on Xiaotang Mountain in Beijing, China. The mean
annual temperature is 11.8 oC (2000-2018). The mean
annual precipitation is 526 mm (2000-2018), with >80% of
the precipitation falling in the growing season (June-September). Soil
organic carbon of top soil (0-10 cm) is around 1%, and the average pH
value is 7.47. The study area is dominated by perennial plants,
including E. nutans and T. mongolicum .
Our experimental plots were established within an area of 8×12 m in
2019, using a randomized block design with nitrogen (N) addition and
plant diversity gradient as main treatment factors. Each block contained
twelve treatments, crossing two levels of N addition (no nitrogen
addition and add 60 kg N ha-1 yr-1as urea) and six levels of plant diversity (0, 1, 2, 4, 6, and 8). Each
diversity level had four plant assemblage types, except for 0 and 8
(Supplementary Table 1). Each treatment had four replicates, resulting
in 144 polyvinylchlorid bottom sealed pipes (Supplementary Fig. 1b).
Each pipe is 30 cm in diameter and 50 cm in height, three holes were
drilled into the bottom to provide drainage, filled with uniformly mixed
soil and sand (soil-sand ratio is 3:1), and then buried into soil
(Supplementary Fig. 2). Soil inside and outside of pipe was 5 cm lower
than its upper edge. According to the germination rate of each species
(Supplementary Table 1), seeds was sown at the depth of 1 cm in each
plot after fully watered in March, and maintained 120 seedlings in each
plot. The eight species we selected were Poa annua , Carex
breviculmis , Medicago sativa , Astragalus adsurgens Pall ,Dianthus barbatus , Penstemon campanulatus ,Chrysanthemum maximum , and Allium schoenoprasum , the
assemblage types at each diversity level were showed in the
Supplementary Table 1. The amount of N addition is twice the background
deposition in Beijing (Yu et al., 2019), and urea was applied by
spraying on 1 May.
2.2 | Phenology monitoring
To track flowering phenology of M. sativa , phenology was
monitored every 3–4 days during the growing season from May to
September in 2019. Three individuals for each plot were randomly
selected, marked, and monitored for the growing season. The first and
last date a flower was observed for each of the marked individuals was
recorded as the first and last flowering day, the periods between the
first and last flowering day was recorded as the flowering duration.
Flower number was counted for each of the marked individuals. Flowering
phenology events and flower numbers were averaged for three individuals
of each plot.
2.3 | Functional traits and abiotic factors
measurements
Light acquisition traits (plant height and relative height, leaf mass
and area, leaf length and width, and specific leaf area) and nutrient
acquisition traits (Leaf carbon (C) and nitrogen (N) content, leaf C/N
ratio, biomass and abundance, relative biomass and abundance) are
closely related to plant phenology (Lavorel & Grigulis, 2012; Grigulis
et al., 2013). Consequently, we determined these traits to explore the
mechanisms underlying regulating the response of flowering phenology to
experimental N addition and plant diversity gradients. Before the
measurements, we investigated the abundance and height of each species
in the plot. M. sativa is the predominant species (relative
abundance >40% in each pine) and 6 healthy mature
individuals were selected to measure the species-level traits in each
plot. The functional traits were quantified using standard methods
proposed by Pérez-Harguindeguy et al. (2013). The specific leaf area was
calculated as the ratio of leaf area to its dry weight. To measure leaf
area, length, width, and maximum width, spread leaves were scanned and
analyzed Li-Cor 310 (Li-Cor Inc. USA), and then leaves were oven-dried
to a constant weight. Finally, the oven-dried leaf samples were ground
to determine leaf carbon and nitrogen with an elemental analyzer (PE
2400 II, USA). To measure the biomass of community and individual
species, the aboveground part of each plot was clipped in early
September (the peak of growing season). Plants clipped from each plot
were pooled together, sorted to species, and then oven-dried to a
constant weight.
Soil temperature and moisture at the depth of 10 cm were measured every
week from April to October with W. E. T sensor kit (Delta-T Devices Ltd,
UK). Three soil cores were collected in each plot in early September at
the depth of 10 cm, and then mixed together into one sample. Available
soil N (Ammonium (NH4+) and nitrate
(NO3-)) contents in the extracts were
determined colorimetrically by automated segmented flow analysis (Bran +
Luebbe AAIII, Germany)
2.4 | Statistical
analyses
We analyzed experimental data with the following three steps. First, we
scaled the species-level height to the community level by calculating
the mean of the abundance distributions (Equation 1, Gross et al.,
2009):
\(\text{Mean}_{j}=\sum_{i}^{n}{p_{i}T_{i}}\) (1)
where \(p_{i}\) and \(T_{i}\) are the relative abundance and the plant
height of the species j , respectively, and n is the number
of species.
Second, we examined how N addition and plant diversity affected
environmental factors, the functional traits and flowering events ofM. sativa . We applied linear mixed effects models using “lme4”
function (package “NLME”, Pinheiro et al ., 2007) to test the
effects of N addition and plant diversity loss on soil temperature and
moisture separately in 2019. We set treatments as fixed effects, block
and time as a random effect in each model to account for variation among
repeated measurements of temperature or moisture. Linear mixed effects
models were also used to examine the effect of N addition and plant
diversity loss on flowering phenology (first and last flowering day,
flowering duration, and flower numbers) and functional traits (leaf and
community traits). Treatment was treated as fixed effects, and block was
treated as a random effect to account for variation within block.
Third, partial correlation was conducted to evaluate the relationships
between the flowering events and the various factors (Chen et al.,
2019). For example, after controlling N addition and plant diversity
levels, we examined the relationships of the flowering events with light
acquisition traits, nutrient acquisition traits, and abiotic factors.
Variation partitioning analysis that partitioned the variance shared by
all factors was then used to quantify the unique contribution of each
group of factors (Chen et al., 2019). Structural equation modelling
analysis was employed to evaluate the hypothesized underlying factors
that influence flowering phenology (Wang & Tang, 2019b) using the
package ‘piecewise-SEM ’ in R software (Shipley, 2000). The model
was assessed by Fish C statistic, Akaike information criterion
(AIC), and P values.
All statistical analyses and graphs were prepared in R 3.2.2 (R Core
Team, 2018). Differences were considered to be statistically significant
at P ≤ 0.05.