Diverse and divergent influences of phenology on herbaceous aboveground biomass across the Tibetan Plateau alpine grasslands

Ecological Indicators 121 (2021) 107036

Contents lists available at ScienceDirect

Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Diverse and divergent influences of phenology on herbaceous aboveground biomass across the Tibetan Plateau alpine grasslands Peixian Li a, b, Wenquan Zhu a, b, *, Zhiying Xie a, b a

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

b

A R T I C L E I N F O

A B S T R A C T

Keywords: Phenology Biomass Influence mechanism Grass Sedge Tibetan Plateau

Indirect influences of phenology on productivity (e.g., phenology-functional traits-productivity) could exist due to close associations between phenology and plant functional traits, which may further result in the divergent responses of vegetation biomass to phenology among plant functional groups (PFGs). Here, we introduced functional traits (i.e., plant height and growth rate) to analyse the influences of phenology on aboveground biomass (AGB) of two symbiotic PFGs based on 19-year in-situ observational data for plant phenology at 7 sites across the Tibetan Plateau alpine grasslands. The vegetative growth phase (VGP) and reproductive growth phase (RGP) were calculated according to the observed green-up date, first flowering date and first fruit-ripening date. The commonality analysis and structural equation modelling were applied to explore the paths and degrees of phenological influence on AGB. The results showed that the phenological influences on herbaceous AGB included both direct (“phenology-AGB”) and indirect paths (“phenology-functional traits-AGB”) and differed between two PFGs. Phenological metrics, especially VGP, were the key factors driving the adjustment (path coefficients |w| = 0.19 – 0.67), although maximum plant height (Hmax) contributed to the most to the AGB (|w| = 0.78). Specif­ ically, the grasses had two indirect phenological influence paths on AGB, while the sedges had multiple direct and indirect influence paths. Comparing the differences in phenology and functional traits between two PFGs, our study implied that grasses may tend to preferentially adjust vegetative (stem) growth via phenology, while sedges may tend to preferentially adjust root and reproductive growth via phenology, which could be relevant to the differences in the synthesis, accumulation, allocation, and decomposition of organic matter in diverse organs and the growth and reproduction strategies, competition and symbiosis of different PFGs.

1. Introduction

between phenology and plant functional traits (e.g., maximum height, growth rate, stem tissue mass density, specific leaf area, leaf dry matter content, leaf thickness and so on) (“phenology-functional traits”) were also observed (Sun and Frelich, 2011; Huang et al., 2018; Liu et al., 2019; McKown et al., 2013), which suggests that indirect effects of phenology on productivity (“phenology-functional traits-productivity”) may exist. Currently, however, the specific influence paths and influence degrees of “phenology-functional traits-productivity” for plants remain still unclear, especially for herbaceous plants. This study focuses on the alpine grasslands (including alpine meadow and steppe) across the Tibetan Plateau, which are the widely distributed vegetation types of this region (>60% of the area) and are

Globally, shifts of plant phenology have been observed at multiple scales from the species to community and have influenced ecosystem productivity (Cleland et al., 2007; Richardson et al., 2010; Xia et al., 2015; Piao et al., 2007). As a result, ecosystem carbon fixation and ecological responses (e.g., species competition) could be changed (Pri­ mack and Gallinat, 2017; Piao et al., 2019). At present, the direct effects of plant phenology on productivity (“phenology-productivity”) have been reported from forest to grassland ecosystem in North America and Europe (Keenan et al., 2014; Xie et al., 2019; Wu et al., 2013; Dragoni et al., 2011). Besides, close associations or interactive influences

Abbreviations: GUD, green-up date; FFD, first flowering date; FRD, first fruit-ripening date; VGP, vegetative growth phase; RGP, reproductive growth phase; Hmax, maximum plant height; msGR, maximum standardized growth rate; AGB, aboveground biomass. * Corresponding authors at: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. E-mail address: [email protected] (W. Zhu). https://doi.org/10.1016/j.ecolind.2020.107036 Received 30 March 2020; Received in revised form 13 September 2020; Accepted 2 October 2020 Available online 23 October 2020 1470-160X/© 2020 Elsevier Ltd. All rights reserved.

P. Li et al.

Ecological Indicators 121 (2021) 107036

more sensitive and vulnerable to climate change than many other areas (Chen et al., 2015; Wu et al., 2010; Yang et al., 2009; Fig. S1). In recent years, the influence of shifting phenology on plant productivity (carbon fixation) across Tibetan Plateau alpine grasslands has also been inves­ tigated, which mainly focused on the direct influences of green-up date, leaf senescence date and growing season length on ecosystem produc­ tion (Wang et al., 2017; Zhu et al., 2017; Zheng et al., 2020). However, on the one hand, significant changes of key plants functional traits (e.g., plant height) in Arctic and alpine biome have already occurred under climate warming (Bjorkman et al, 2018; Zhu et al, 2020), so temporal change of plant traits in response to warming should be explicitly considered (Zhu et al, 2020). Nevertheless, few studies combined the functional traits to explore these phenological indirect effects on Tibetan Plateau alpine grassland productivity. On the other hand, over the past three decades, primary production of alpine grasslands on the Tibetan Plateau remained stabilized via shifting plant functional group compo­ sition, that is, increased deeper-root grasses abundance at the expense of biomass of sedges and forbs (Liu et al., 2018). It has also been reported that changes in growth patterns (including earlier phenology, faster growth and shortening growth period) of grasses and forbs stabilized the alpine grassland biomass (Wang et al., 2020). Therefore, it is necessary to explore the possible influence paths and influence degrees of phenology on biomass among different plant functional groups, which could enhance our understanding for herbaceous carbon fixation and survival strategies. Based on the observational data for phenological stages (i.e., vege­ tative and reproductive growth phases) and plant functional traits (i.e., plant height and growth rate), our study aims to explore the paths and degrees of phenological influence on biomass across the Tibetan Plateau alpine grasslands and specifically test the following two hypotheses: (i) in addition to the direct influence of herbaceous phenology on plant biomass, there are indirect paths of “phenology-functional traitsbiomass”; and (ii) paths and degrees of phenological influence on biomass are divergent among plant functional groups (e.g., grass and sedge).

to August. 2.1.1. Phenological phases and plant functional groups The green-up date (GUD), first flowering date (FFD) and first fruitripening date (FRD) were used to represent the vegetative growth phase (VGP) and reproductive growth phase (RGP). All the dates of GUD, FFD and FRD were converted to the day of year (DOY) (Zhu et al., 2018). Specifically, the VGP is defined as the difference of days between GUD and FFD, and the RGP is between FFD and FRD (Table 1). Two PFGs (i.e., grass and sedge) from the herbaceous plants at the seven sites showed the distinct frequency distribution patterns of the two phenophases (vegetative and reproductive growth phases) (Fig. 1). The distinct characteristics of the two PFGs were shown in Table 2. 2.1.2. Structural and physiological parameters Plant height is a primary characteristic related to species survival and competition (Westoby, 1998; Sun and Frelich, 2011). Maximum plant height (Hmax) is closely related to the ecosystem biomass and carbon storage of herbaceous plants (Sun and Frelich, 2011; Gaudet and Keddy, 1988). Many studies have found that plant height (or Hmax) of perennial herbs was positively correlated with FFD (Bolmgren and Cowan, 2008; Sun and Frelich, 2011; Du and Qi, 2010), which means that the time of transition from vegetative growth to reproductive growth (i.e., FFD) could also affect the annual biomass accumulation through influencing Hmax. Maximum standardized growth rate (msGR) of plant was the standardized growth rate when plant height (H) reaches 50% Hmax, which represented species’ relative growth rate regardless of interspecific differences in plant height and the ability of completing lifecycle (Huang et al., 2018), and was controlled by both genetic and environmental factors (Grime and Hunt, 1975). A high msGR is usually associated with a high potential productivity. The msGR is also related to other plant traits that affect plant adaptability (Grime and Hunt, 1975). Therefore, our study used the height growth pattern (i.e., plant functional traits), including Hmax and msGR, to represent the structural parameters of herbaceous plants. We used a logistic function to fit plant height to characterize the plant height growth trajectory for each species (Huang et al., 2018):

2. Data and methods 2.1. In-situ observational data

H=

The in-situ observational data were acquired from the phenological observation network established by the China Meteorological Admin­ istration. The data included plant phenology, plant height and above­ ground biomass of 11 herbaceous species (see Table S1 for details) at seven sites across the northeastern Tibetan Plateau alpine grasslands (Fig. S1, Table S1). These data were collected from 1994 to 2012, but some years were excluded due to missing data. Plant phenology was observed once every two days in the growing season. The plant height of herbaceous plants from germination to maturity was recorded once every 10 days from April to September. Herbaceous aboveground biomass was sampled and measured at the end of each month from May

Hmax 1 + e− b×(T−

(1)

a)

where the measured plant height (H) is a function of the observed time (T), Hmax is the fitted maximum plant height, a is the DOY when reaching 50% Hmax (T_msGR), and b is the maximum standardized growth rate (msGR) (Huang et al., 2018). The aboveground biomass (AGB) was adopted as a physiological parameter in this study. The AGB data selected in autumn were close to and lagging behind the FRD as possible.

Table 1 Definition of phenological metrics. Phenological events

Phenological phases

Phenological metrics

Abbreviation

Definition

Unit

References

Green-up date

GUD

DOY

First flowering date

FFD

the date when 50% of individual herbaceous plants display green leaves that grow up to one centimeter in spring the date when a few flowers are fully blooming

First fruit-ripening date Vegetative growth phase Reproductive growth phase

FRD

the date when a few fruits are fully ripening

DOY

CMA (1993); Chen et al. (2015) CMA (1993); Zhu et al. (2018) CMA (1993)

VGP

FFD − GUD

days

this study

RGP

FRD − FFD

days

this study

DOY is the day of year. CMA is the China Meteorological Administration. 2

DOY

P. Li et al.

Ecological Indicators 121 (2021) 107036

Fig. 1. Frequency of phenophases for all herbaceous plants at the seven sites of the alpine grasslands on Tibetan Plateau.

framework that describes the interdependence of a series of related variables represented by a fitted covariance structure in a single network and detects whether a specific organization exists among complex multivariable structures (Grace, 2006; Lefcheck, 2016). SEM can assess the direct and indirect effects of multiple independent variables on the dependent variable (Grace, 2006). Therefore, we set the VGP as the driving variable and tested the direct and indirect effects of phenophases (VGP and RGP) and height growth patterns (Hmax and msGR) on AGB. Path analysis was completed using SPSS Amos version 22.0 (IBM, USA), and the maximum likelihood method was used for parameter estima­ tion. The evaluation indicators of the overall fitting effect for SEM can be found in Table S2. Before performing CA and SEM, some measured variables (inde­ pendent variables) were subjected to a natural logarithm transformation to meet the assumption of normal distribution of the model as much as possible (Table S3), and all measured variables were subjected to Z-score normalization (van der Sande et al., 2017; Ali et al., 2019; Gustafsson and Norkko, 2019). CA is an exploratory analysis method (exploratory tool) that analyses the first-order effect and higher-order effect of various factors on AGB (we considered two cases: only phenology and phenology + height growth pattern). Then, we determined the main influence factors and constructed a conceptual model (relationship paths) of “phenology-functional traits-biomass” based on the segmen­ tation variance (or effect size) and the chronological principle. We used SEM (confirmatory tool) to test and adjust the relationship paths ac­ cording to mathematical evaluation indicators and to support or reject the structural relationship (Koebsch et al., 2020). At each simulation, the optimal SEM result was selected in terms of the most similar paths to the conceptual model.

Table 2 Phenological differences between grasses and sedges PFGs. Family (sample size)

Vegetative growth phase (days)

Reproductive growth phase (days)

Grasses (197) Sedges (55)

>60 ≤31

<60 >60

2.2. Analytical methods The commonality analysis (CA) and structural equation modelling (SEM) were applied to explore the paths and degrees of phenological influence on biomass across the Tibetan Plateau alpine grasslands. 2.2.1. Commonality analysis Commonality analysis (CA) is a method of variance segmentation that uses multiple linear regression to decompose the explanatory variance of the outcome variable into a subset of the explanatory vari­ ance of the predictors (Mood, 1969; Seibold and McPhee, 1979; RayMukherjee et al., 2014). CA can divide the total effects of the outcome variable into the following effect types: (i) first-order effects (unique effects), which refers to the independent variance of a single predictor to the outcome variable, indicating that the predictor has a direct causal relationship to the outcome variable (e.g., Hmax → AGB); and (ii) secondorder or higher-order effects (common effects), which are expressed by the shared variance of the outcome variable among two or more inde­ pendent variables, indicating that there are indirect moderating effects. For instance, the second-order effect size of VGP and Hmax for grasses is x % (|x| > 1), and the direction of the path is determined by the chro­ nological principle; thus, the conceptual model can be constructed as VGP → Hmax → AGB. The chronological principle is the sequence of happening for the influences among plant phenology, functional traits and biomass; that is, the events that occur earlier affect the events that occur later. The threshold of the effect size is set to ±1%. Negative commonality may occur in the presence of inhibitory effects or negative correlations between predictors (Ray-Mukherjee et al., 2014). Thus, we used the absolute value of the effect size to build the relationship paths. The R package yhat was used to implement CA (Nimon et al., 2013).

3. Results 3.1. Phenological, structural and physiological characteristics The mean GUD of grasses and sedges occurred on DOY 112 and 118, respectively. The mean FRD of grasses and sedges occurred on DOY 232 and 220, respectively. The mean FFD of grasses and sedges occurred on DOY 196 and 137, respectively, which indicated that the mean VGP of grasses was about 65 days longer than that of sedges, and the mean RGP of grasses was about 47 days shorter than that of sedges (Fig. 2). Grasses had higher Hmax and AGB, and the median (mean) values

2.2.2. Structural equation modelling Structural equation modelling (SEM) constructs a statistical 3

P. Li et al.

Ecological Indicators 121 (2021) 107036

Fig. 2. The differences in phenological metrics, height growth pattern and biomass between grasses and sedges across the Tibetan Plateau alpine grasslands. GUD, green-up date; FFD, first flowering date; FRD, first fruit-ripening date; VGP, vegetative growth phase; RGP, reproductive growth phase; Hmax, maximum plant height; msGR, maximum standardized growth rate; AGB, aboveground biomass. The cross and bold centre line indicate the mean and median value, respectively; box edges denote upper and lower quartiles; whiskers represent the maximum and minimum value estimates within 1.5 × the interquartile range; outliers are beyond the whiskers.

were 36 cm (44 cm) and 16 g/m2 (70 g/m2), respectively (Fig. 2). In contrast, the sedges were shorter and had lower AGB, and the median (mean) Hmax and AGB were only 8 cm (8 cm) and 5 g/m2 (7 g/m2), respectively. Sedges had a lower maximum standardized growth rate (msGR) than grasses, and their average values were 0.051 cm/day and

0.060 cm/day, respectively. In addition, the Hmax and AGB of grasses had larger quantiles and value ranges (maximum – minimum) than those of sedges, indicating a greater variation within grasses.

Fig. 3. Commonality analysis results and the conceptual model diagram of grasses based on phenophases. 4

P. Li et al.

Ecological Indicators 121 (2021) 107036

3.2. Influence factors and paths

and degrees of phenological influence on AGB for the two PFGs (Fig. 5). For the grasses, we found two phenological indirect influence paths (Fig. 5). The VGP firstly positively affected the Hmax, and then, the Hmax positively affected the AGB (i.e., VGP + → Hmax + → AGB); the VGP firstly negatively affected RGP, and then RGP positively affected the Hmax, finally, the Hmax positively affected the AGB (i.e., VGP − → RGP + → Hmax + → AGB). For the sedges, however, there were multiple in­ fluence paths of the phenology on AGB: (i) direct paths: the VGP can directly negatively affect the AGB (i.e., VGP − → AGB), or the VGP first negatively affected the RGP, and then, the RGP negatively affected the AGB; (ii) indirect paths: the VGP negatively affected the msGR, and then the msGR positively affected the AGB (i.e., VGP − → msGR + → AGB); or the VGP negatively affected the AGB by negatively adjusting the msGR and RGP in sequence (i.e., VGP − → msGR − → RGP − → AGB). In addition, it was not significant that the VGP indirectly adjusted AGB through Hmax (p > 0.05), while Hmax affected AGB directly (p < 0.001).

When only the phenological metrics were considered, the VGP of the grasses and sedges played a major role in the AGB, with the first-order effects reaching 100% and 93%, respectively. However, the total vari­ ance (R2) was just 17% and 8% (Fig. S2 and S3). If height growth patterns (Hmax and msGR) were added, the overall variance in the commonality analysis (CA) increased to 63% and 72% for grasses and sedges, respectively. Among the effects, the first-order effects of Hmax for grasses and sedges were 42% and 73%, respec­ tively, implying that Hmax was a major influence factor on the AGB, while the first-order effects of phenology for the two PFGs were small (Figs. 3 and 4). Moreover, the total effects of the grasses were 146%, indicating collinearity among factors. Therefore, it is necessary to distinguish the second-order and higher-order effects of the influence factors. Furthermore, the second-order and higher-order effects of the grasses were 55%, and the phenology-related effects were 25%. How­ ever, for the sedges, the second-order and higher-order effects were only 10%, but the interaction among phenology and plant height patterns was more complex and scattered than that of the grasses (Figs. 3 and 4), implying there were multiple influence paths of the phenology on AGB. The conceptual models for the influence paths of the grasses and sedges were then established based on the chronological principle results (Fig. S4) and CA results (Figs. 3 and 4).

4. Discussion 4.1. Multiple paths of phenological influence on aboveground biomass Multiple influence paths of phenology on herbaceous AGB existed across the Tibetan Plateau alpine grasslands. For the grasses, two indi­ rect influence paths were found (i.e., “phenology-functional traitsAGB”), while for the sedges, the phenological influence paths included two direct paths (i.e. “phenology-AGB“, such as “VGP − → AGB” and “VGP − → RGP − → AGB”) and two indirect paths (i.e., “phenologyfunctional traits-biomass”, such as “VGP − → msGR + → AGB” and “VGP − → msGR − → RGP − → AGB”). Based on the plant growth function, we suggest that phenology can affect the morphological structures and physiological functions through adjusting the time (Fig. S5 and S6) and duration (Fig. 5) of the growth

3.3. Paths and degrees of phenological influence on aboveground biomass The conceptual models constructed based on CA results passed the mathematical test of SEM successfully after adjustments (p > 0.05, CFI ≥ 0.95, GFI ≥ 0.90, AGFI ≥ 0.90, RMSEA ≤ 0.05, Table S2). The total variance of the four variables was 62% and 71% for grasses and sedges, respectively. There were obvious differences in the paths

Fig. 4. Commonality analysis results and the conceptual model diagram of sedges based on phenophases. 5

P. Li et al.

Ecological Indicators 121 (2021) 107036

Fig. 5. Paths and degrees of phenological influence on aboveground biomass for grasses and sedges w is standardized regression weight (path coefficient), r2 is squared multiple correlation coefficient, and p indicates the significance level of w. The thickness of the path reflects the size of w, grey and solid lines represent negative values of w, black and solid lines represent positive values of w, and dotted lines indicate p > 0.05. VGP, vegetative growth phase; RGP, reproductive growth phase; Hmax, maximum plant height; msGR, maximum standardized growth rate; AGB, aboveground biomass; and ln() indicates natural logarithmic operation.

and development of plant organs, which can be modelled with below equation: G = F(r, α, T or ΔT)

redistribution of aboveground biomass to belowground occurs in the middle and late reproductive growth phase of sedges, which explains the negative influence of RGP on the AGB of the sedges in this study (see details in Section 4.3.1). In addition, for the sedges, the indirect path of phenological influence (“VGP − →msGR − → RGP − → AGB”) (Fig. 5) could also serve as a preferential root growth via phenology analo­ gously. Additionally, sedge species were characterized as having high root-shoot biomass ratios relative to those of grass species (Lin et al., 2015; Song et al., 2020), which indicated that more relative carbon shifts from shoots to ground for sedges than those for grasses. The preferential reproductive growth via phenology for sedges adjusted by phenology is illustrated in Section 4.2. In the multiple influence paths of the two PFGs, Hmax was a major factor affecting the herbaceous AGB (the absolute value of the path coefficient |w| was about 0.78). The powerful positive influence of Hmax (canopy height) on plant biomass has also been found in aquatic plants (Gustafsson and Norkko, 2019), salt marsh plants (Minden and Kleyer, 2015), and woody plants (Ali et al., 2019), which were due to the ad­ vantages of high plant height for light access and substantial carbon storage (Finegan et al., 2015; Gustafsson and Norkko, 2019). In addi­ tion, |w| of each influence path of phenophases for AGB was about 0.19–0.67 under the framework of biotic factors, indicating that phenological metrics (especially the VGP) were the key factors driving and participating in the adjustment of AGB. Under the framework of abiotic factors, It was also reported that phenological influences on peatland gross ecosystem productivity (GEP) accounted for 84%, 10%, and 61% during the green-up period, peak period and senescence period, showing phenology was a key mediating (or dominant) variable that affected GEP in peatlands (Koebsch et al., 2020).

(2)

where G is the biomass of plant organs during the growth or senescence phase; r is the rate of growth or senescence, α is the trait coefficient related to specific species and organs, T is the date of phenological event such as green-up date, first flowering date or first fruit-ripening date, etc. ΔT is the phenophase (e.g., VGP = first flowering date − green-up date). F, the logistic model or other comprehensive functions (Kauf­ mann, 1981; Lindh et al., 2016). Therefore, the differences in the syn­ thesis, accumulation, allocation and decomposition of organic matter in different plants organs can occur through multiple paths of phenological influence on biomass under external stimulus and endogenous controls. The grasses have a taller stem (Hmax) and higher AGB with a longer VGP (average 84 days, Fig. 2). The delay from vegetative growth to reproductive growth can prolong the activity of the vegetative meristem (Demura and Ye, 2010). Besides, the VGP of grasses positively affected Hmax to achieve aboveground biomass accumulation (p < 0.001, Fig. 5). Therefore, we propose that the grasses may tend to preferentially adjust vegetative (stem) growth via phenology (termed as “preferential stem growth via phenology”). Interestingly, the RGP of grasses can positively affect AGB by positively affecting Hmax, which demonstrates that above mechanism also exists throughout the growing season. In contrast, sedges have a shorter stem (Hmax) and lower AGB with a shorter VGP (average 19 days, Fig. 2), indicating that sedges maintain a short growth period after germination and then bloom after meeting basic nutritional conditions. Further, the phenology (VGP and RGP) negatively affected AGB (p < 0.05, Fig. 5). Therefore, the sedges may tend to preferentially adjust root growth and reproductive growth via phenology (termed as “preferential root growth and reproductive growth via phenology”). Similarly, Ma et al. (2010) has also reported that sedges in alpine meadows on the Tibetan Plateau had higher root fraction, especially fine roots, compared with forbs and grasses. In addition, Li et al. (1996) observed that the leaf and stem growth of Kobresia pygmaea (sedge) seedlings was less than the growth of the root fraction and length during a 4-year growth period on the Qinghai-Tibetan Plateau, which suggested that sedge species tended to grow roots preferentially. Wu et al. (2010) has also monitored photosynthetic carbon flux shifts from roots to soils of dominant sedge species such as Kobresia humilis and Kobresia pygmaea on the Tibetan Plateau using in situ 13C pulse labelling on July 29 (equivalent to the late RGP for sedges in our study) and found that 58.7% of the assimilated 13C was transferred from the shoot to the ground; among the assimilated 13C, 30.9% was transferred to the living roots, 3.4% was transferred to the dead roots, 17.2% was lost in the respiration of the roots, and 7.3% was retained in the soil. Therefore, the

4.2. Divergence of phenological influences on aboveground biomass among different plant functional groups Our studies found that the influence paths and degrees of phenology on aboveground biomass varied between two plant functional groups (i. e., grass and sedge), which can be attributed to the effects from resource allocation mechanisms among organs and the plant survival strategies. Specifically, the VGP of the grasses indirectly adjusted the AGB by positively affecting the Hmax; the VGP of sedges directly negatively adjusted the AGB, but had no significant effect on Hmax. Therefore, for the sedges, the longer is the VGP before blossoming, the more unfav­ ourable for the accumulation of AGB. This is opposite to the conclusions drawn from previous studies (Piao et al., 2007; Wang et al., 2017). This phenomenon can be explained by the AGB accumulation timing for different PFGs. To further explain the mechanisms, we calculated the day numbers between T_Hmax (DOY of reaching Hmax) and FFD 6

P. Li et al.

Ecological Indicators 121 (2021) 107036

(hereafter “T_Hmax − FFD”) (Fig. S7). The “T_Hmax − FFD” for the sedges were significantly positively correlated with the AGB (p < 0.05), while no significant relationship was for the grasses (Fig. S8), which is in good agreement with the findings of Sola and Ehrl´ en (2007) and Jia et al. (2011) that flowering time will change the cost-effective balance of vegetative phenology by changing the optimal time of vegetative growth. The early-flowering sedges tend to increase the AGB via extending “T_Hmax − FFD”, so the optimal time for vegetative growth is after blossoming (i.e., preferential reproductive growth via phenology), which is opposite to that of the late-flowering grasses. In addition, this mechanism (strategy) can also partially explain why the msGR of the early-flowering sedges being slower than that of the later-flowering grasses. The herbaceous plant species that bloom earlier are commonly believed to grow faster than the species that bloom later (Sun and Frelich, 2011). The harsh physical environment of the Tibetan Plateau gives her­ baceous perennials a more limited growing season window. The earlyflowering sedges resort to a long RGP to ensure sufficient development and maturity of seeds (Schmitt, 1983) because plant flowering onset time is generally negatively related to fruit size (Primack, 1985), seed quality (Mazer, 1990; Vile et al., 2006; Bolmgren and Cowan, 2008) and (Jia et al., 2011). In contrast, late-flowering grasses rely on a longer VGP and faster msGR to achieve vegetative stem (Hmax) growth (Fig. 2) and further make full use of light and space resources (Falster and Westoby, 2003) through greater heights and biomass than those of the earlyflowering sedges. Both VGP and RGP can positively affect Hmax and then significantly increase AGB (w = 0.784, p < 0.001), which also implied that the grasses were more inclined to implement competitive strategies as noted in Grime CSR theory (competition, stress and ruderal) (Grime, 1974) compared with the sedges. Previous report has also indicated that the herbaceous biomass accounted for 63% of the varia­ tion in species’ ability to compete, while plant height, canopy diameter, canopy size and leaf shape accounted for most of the remaining varia­ tion (Gaudet and Keddy, 1988). Therefore, our study implies that the grasses have stronger above-ground community competitiveness in comparison with the sedges. Certainly, some prerequisites, such as suf­ ficient soil N availability and soil water content, are necessary in nondegraded grasslands (Guo et al., 2017; Lin et al., 2015; Shen et al., 2019; Song et al., 2020). The divergences in phenology, structure and physiology between grasses and sedges in the alpine grasslands result from their different adaptive strategies to the natural environment. The paths and degrees of phenological influence on aboveground biomass revealed the organ adjustment mechanisms (internal) and survival strategies (external) for the two herbaceous functional groups. Therefore, plant phenology is not only the timeline of niche differentiation (Sun and Frelich, 2011; Huang et al., 2018), but also a biological “behaviour” that promotes plant growth and reproduction, competition and symbiosis. Base on above survival strategies, we found that the influences of phenology on biomass for the same plant functional group might be varied under different natural habitats (e.g., grasses in Alpine Grass, Carex Steppe and grasses in Temperate Needlegrass Arid Steppe, Fig. S9 and 10). How­ ever, the influence of grass species difference cannot be eliminated completely. Actually, the paths among plant traits are likely to be affected by species and diversity of species since other mechanisms affecting biomass may be also exist, such as the niche complementarity effect (Tilman et al., 2001).

4.3.1. Influences of phenology on belowground biomass The global root-shoot biomass ratio is over 4.0 for temperate grass­ lands (Mokany et al., 2006), and it is about 5.8 for the alpine grasslands based on the data from 141 sites across the Tibetan Plateau (Yang et al., 2009). With increasing altitudes, perennial herbs allocate more biomass to belowground storage organs to tolerate high-altitude environmental stresses and to facilitate germination and regeneration in the following year. For example, the biomass of herbaceous plant stems and flowers for Kobresia meadow on the Tibetan Plateau decreased by 45% and 41%, respectively from the subalpine (3700 m) sites to the alpine (4300 m) and subnival (5000 m) sites, while the biomass of fine roots increased by 86% and 102% (Ma et al., 2010). Our study further found that the RGP of sedges negatively and significantly adjusted the AGB (w = − 0.194, p < 0.05) on the Tibetan Plateau, suggesting that the carbon in the AGB of the sedges was redistributed to the root during the RGP and used for growth in the coming year. Nevertheless, the effect of VGP on below­ ground biomass still needs more data to assess. On the one hand, the growth of plant roots depends on the carbon fixation of aboveground organs, and thus belowground biomass can be affected by aboveground phenology to some extent (Abramoff and Finzi, 2015). It was reported that the redistribution of biomass for salt marsh plants (Spartina alter­ niflora) from aboveground to belowground was related to flowering phenology, and aboveground growth began to stop during flowering (similar to the grasses but different from the sedges in our study), while belowground allocation increased after flowering (Crosby et al., 2015), which is consistent with our conclusion that the aboveground biomass of the sedges was partially transported to the root during the RGP, but more evidence is still needed for the below-ground biomass. On the other hand, the response of root phenology to climate change may be very different from that of aboveground phenology (Blume-Werry et al., 2016), which may lead to divergences in phenological adjustment be­ tween aboveground and belowground biomass. For example, the aboveground net primary productivity (ANPP) on the Tibetan Plateau alpine meadow has not changed with warming and drying in recent decades, but the composition of plant communities has changed towards more deep-rooted species (Liu et al., 2018). Therefore, it is particularly important to consider belowground biomass for a comprehensive study of phenological influences on biomass, especially in alpine regions (Piao et al., 2019). 4.3.2. Combined effects of biotic and abiotic factors on biomass Our study found that the phenology and plant functional traits (height growth pattern) can explain about 60–70% of the AGB varia­ tions. This means that the remaining 30–40% unaccounted variations are resulted from other influencing factors, such as traits associated with resource acquisition (e.g., leaf traits) or abiotic factors. Variations in gross primary productivity (GPP) are driven by a wide range of biotic and abiotic factors, which work mainly through the changes in phenology and physiological processes (Xia et al., 2015). Abiotic factors, trait values of dominant species, and functional trait diversity may be the best combination to explain the differences in community above­ ground biomass in natural ecosystems (Schumacher and Roscher, 2009). According to a recent report, warming and drying led to an increase in productivity for deep-rooted grasses and forbs and no change or reduction in productivity for shallow-rooted sedges from long-term monitoring (32 years) across Tibetan Plateau alpine meadows (Liu et al., 2018). Therefore, the establishment of a structural equation modelling of “environmental factors-phenology-biomass (productivity)” will be valuable to further explore the influence mechanisms of phenology on biomass (productivity). For example, phenology has been proved to be a key regulator of seasonal variation in GEP across Europe peatlands considering the impact of temperature and radiation (Koebsch et al., 2020). Nevertheless, considering the influence of other abiotic factors such as water availability can help promote the model perfor­ mance (Koebsch et al., 2020). Accordingly, the selection of essential environmental parameters, phenological parameters, plant functional

4.3. Further consideration of belowground biomass and abiotic factors This study just analysed the influences of phenology on aboveground biomass, but phenology can affect both the aboveground and below­ ground biomass (Blume-Werry et al., 2016), which ulteriorly involves phenology to adjust the allocation of the two parts. Additionally, the joint influences of biotic and abiotic factors on biomass need to be considered. 7

P. Li et al.

Ecological Indicators 121 (2021) 107036

traits, and the response divergences of plant functional groups needs to be considered.

References Abramoff, R.Z., Finzi, A.C., 2015. Are above- and below-ground phenology in sync? New Phytol. 205, 1054–1061. https://doi.org/10.1111/nph.13111. Ali, A., Lin, S.L., He, J.K., Kong, F.M., Yu, J.H., Jiang, H.S., 2019. Big-sized trees overrule remaining trees’ attributes and species richness as determinants of aboveground biomass in tropical forests. Glob. Change Biol. 25, 2810–2824. https://doi.org/ 10.1111/gcb.14707. Bjorkman, A.D., Myers-Smith, I.H., Elmendorf, S.C., et al., 2018. Plant functional trait change across a warming tundra biome. Nature 562, 57–62. https://doi.org/ 10.1038/s41586-018-0563-7. Blume-Werry, G., Wilson, S.D., Kreyling, J., Milbau, A., 2016. The hidden season: growing season is 50% longer below than above ground along an arctic elevation gradient. New Phytol. 209, 978–986. https://doi.org/10.1111/nph.13655. Bolmgren, K., Cowan, P.D., 2008. Time size tradeoffs: a phylogenetic comparative study of flowering time, plant height and seed mass in a north-temperate flora. Oikos 117, 424–429. https://doi.org/10.1111/j.2007.0030-1299.16142.x. Chen, X., An, S., Inouye, D.W., Schwartz, M.D., 2015. Temperature and snowfall trigger alpine vegetation green-up on the world’s roof. Glob. Change Biol. 21, 3635–3646. https://doi.org/10.1111/gcb.12954. China Meteorological Administration, 1993. Observation Criterion of Agricultural Meteorology. China Meteorological Press, Beijing, pp. 131–145. Cleland, E.E., Chuine, I., Menzel, A., Mooney, H.A., Schwartz, M.D., 2007. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365. https://doi. org/10.1016/j.tree.2007.04.003. Crosby, S.C., Ivens-Duran, M., Bertness, M.D., Davey, E., Deegan, L.A., Leslie, H.M., 2015. Flowering and biomass allocation in U.S. Atlantic coast Spartina alterniflora. Am. J. Bot. 102, 669–676. https://doi.org/10.3732/ajb.1400534. Demura, T., Ye, Z.H., 2010. Regulation of plant biomass production. Curr. Opin. Plant Biol. 13, 298–303. https://doi.org/10.1016/j.pbi.2010.03.002. Dragoni, D., Schmid, H.P., Wayson, C.A., Potter, H., Grimmond, C.S.B., Randolph, J.C., 2011. Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA. Glob. Change Biol. 17, 886–897. https://doi.org/10.1111/j.1365-2486.2010.02281.x. Du, G., Qi, W., 2010. Trade-offs between flowering time, plant height, and seed size within and across 11 communities of a QingHai-Tibetan flora. Plant Ecol. 209, 321–333. https://doi.org/10.1007/s11258-010-9763-4. Falster, D.S., Westoby, M., 2003. Plant height and evolutionary games. Trends Ecol. Evol. 18, 337–343. https://doi.org/10.1016/S0169-5347(03)00061-2. Finegan, B., Pe˜ na-Claros, M., de Oliveira, A., Ascarrunz, N., Bret-Harte, M.S., Carre˜ noRocabado, G., Casanoves, F., Díaz, S., Eguiguren Velepucha, P., Fernandez, F., Licona, J.C., Lorenzo, L., Salgado Negret, B., Vaz, M., Poorter, L., 2015. Does functional trait diversity predict above-ground biomass and productivity of tropical forests ? Testing three alternative hypotheses. J. Ecol. 103, 191–201. https://doi. org/10.1111/1365-2745.12346. Gaudet, C.L., Keddy, P.A., 1998. A comparative approach to predicting competitive ability from plant traits. Nature 334, 242–243. https://doi.org/10.1038/334242a0. Grace, J.B., 2006. Structural Equation Modeling and Natural Systems. Cambridge University Press, New York, pp. 7–76. Grime, J.P., 1974. Vegetation classification by reference to strategies. Nature 250, 26–31. https://doi.org/10.1038/250026a0. Grime, J.P., Hunt, R., 1975. Relative growth-rate: its range and adaptive significance in a local flora. J. Ecol. 63, 393. https://doi.org/10.2307/2258728. Guo, Y., Liu, L.P., Zheng, L.L., Yu, F.H., Song, M.H., Zhang, X.Z., 2017. Long-term grazing affects relationships between nitrogen form uptake and biomass of alpine meadow plants. Plant Ecol. 218, 1035–1045. https://doi.org/10.1007/s11258-017-0746-6. Gustafsson, C., Norkko, A., 2019. Quantifying the importance of functional traits for primary production in aquatic plant communities. J. Ecol. 107, 154–166. https:// doi.org/10.1111/1365-2745.13011. Huang, L., Koubek, T., Weiser, M., Herben, T., 2018. Environmental drivers and phylogenetic constraints of growth phenologies across a large set of herbaceous species. J. Ecol. 106, 1621–1633. https://doi.org/10.1111/1365-2745.12927. Jia, P., Bayaerta, T., Li, X., Du, G., 2011. Relationships between flowering phenology and functional traits in eastern Tibet alpine meadow. Arctic Antarct. Alp. Res. 43, 585–592. https://doi.org/10.1657/1938-4246-43.4.585. Kaufmann, K.W., 1981. Fitting and using growth curves. Oecologia 49, 293–294. https:// doi.org/10.1007/BF00347588. Keenan, T.F., Gray, J., Friedl, M.A., Toomey, M., Bohrer, G., Hollinger, D.Y., Munger, J. W., O’Keefe, J., Schmid, H.P., Wing, I.S., Yang, B., Richardson, A.D., 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Chang. 4, 598–604. https://doi.org/10.1038/nclimate2253. Koebsch, F., Sonnentag, O., J¨ arveoja, J., Peltoniemi, M., Alekseychik, P., Aurela, M., Arslan, A.N., Dinsmore, K., Gianelle, D., Helfter, C., Jackowicz-Korczynski, M., Korrensalo, A., Leith, F., Linkosalmi, M., Lohila, A., Lund, M., Maddison, M., Mammarella, I., Mander, Ü., Minkkinen, K., Pickard, A., Pullens, J.W.M., Tuittila, E. S., Nilsson, M.B., Peichl, M., 2020. Refining the role of phenology in regulating gross ecosystem productivity across European peatlands. Glob. Change Biol. 26, 876–887. https://doi.org/10.1111/gcb.14905. Lefcheck, J.S., 2016. piecewiseSEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579. https://doi. org/10.1111/2041-210X.12512. Li, X., Li, F., Huang, B., Qiao, Y., Sun, H., Sun, B., 1996. Seedling development and biomass aeeumulation of Kobresia on Qinghai-Tibetan Plateau. Acta Pratac. Sin. 5, 48–54 (in Chinese).

5. Conclusion In this study, the paths and degrees of phenological influence on herbaceous aboveground biomass (AGB) between two plant functional groups (PFGs) across the Tibetan Plateau alpine grasslands were ana­ lysed based on 19-year in-site observational data. Both direct (“phenology-AGB”) and indirect paths (“phenology-functional traitsAGB”) were found for the influences of phenology on herbaceous AGB and differed between two PFGs, which were related to herbaceous organ adjustments and survival strategies. Hmax contributed to the most to the AGB accumulation. Notably, phenological metrics (especially VGP) were the key factors driving the adjustment. Comparing with the dif­ ferences in phenology and functional traits between the two PFGs, our results implied that grasses may tend to preferentially adjust vegetative (stem) growth via phenology, while sedges may tend to preferentially adjust root and reproductive growth via phenology. These findings can not only enhance our understanding on the re­ lationships among herbaceous phenology, functional traits and physi­ ology (biomass), but also infer that the other influence mechanisms on the herbaceous biomass due to the abiotic factors (e.g., meteorological drivers, soil moisture, etc.) and the allocation of aboveground and belowground biomass may exist. The habitat type, species and diversity of species have not been adequately considered because of limited samples (e.g., only 55 samples of sedges) in this study. Therefore, future research should consider above factors to explore the influence mech­ anism of phenology on biomass. Most previous studies focused on the direct and single influence path of “phenology-biomass”, but our study revealed diverse and divergent influence paths of phenology on herba­ ceous AGB at the level of PFGs. Introducing functional traits and plant functional groups are expected to provide valuable points for a deeper understanding of plant carbon fixation and ecological responses under climate warming. CRediT authorship contribution statement Peixian Li: Conceptualization, Methodology, Formal analysis, Writing - original draft, Writing - review & editing, Visualization. Wenquan Zhu: Conceptualization, Methodology, Formal analysis, Writing - original draft, Writing - review & editing. Zhiying Xie: Methodology. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We thank the China Meteorological Administration that provided the in-situ observational data for this study. We are grateful to Professor Chen Guangsheng from Zhejiang A&F University for revising the lan­ guage of the paper. This work was supported by the National Natural Science Foundation of China (Grant No. 41771047) and the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (Grant No. 2019QZKK0606). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.ecolind.2020.107036. 8

P. Li et al.

Ecological Indicators 121 (2021) 107036 Sola, A.J., Ehrl´ en, J., 2007. Vegetative phenology constrains the onset of flowering in the perennial herb Lathyrus vernus. J. Ecol. 95, 208–216. https://doi.org/10.1111/ j.1365-2745.2006.01191.x. Song, M.H., Zhu, J.F., Li, Y.K., Zhou, H.K., Xu, X.L., Cao, G.M., Lin, L., Ouyang, H., 2020. Shifts in functional compositions predict desired multifunctionality along fragmentation intensities in an alpine grassland. Ecol. Indic. 112 https://doi.org/ 10.1016/j.ecolind.2020.106095. Sun, S., Frelich, L.E., 2011. Flowering phenology and height growth pattern are associated with maximum plant height, relative growth rate and stem tissue mass density in herbaceous grassland species. J. Ecol. 99, 991–1000. https://doi.org/ 10.1111/j.1365-2745.2011.01830.x. Tilman, D., Reich, P.B., Knops, J., Wedin, D., Mielke, T., Lehman, C., 2001. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845. https:// doi.org/10.1126/science.1060391. van der Sande, M.T., Pe˜ na-Claros, M., Ascarrunz, N., Arets, E.J.M.M., Licona, J.C., Toledo, M., Poorter, L., 2017. Abiotic and biotic drivers of biomass change in a Neotropical forest. J. Ecol. 105, 1223–1234. https://doi.org/10.1111/13652745.12756. Vile, D., Shipley, B., Garnier, E., 2006. A structural equation model to integrate changes in functional strategies during old-field succession. Ecology 87, 504–517. https:// doi.org/10.1890/05-0822. Wang, H., Liu, H., Cao, G., Ma, Z., Li, Y., Zhang, F., Zhao, Xia, Zhao, Xinquan, Jiang, L., Sanders, N.J., Classen, A.T., He, J.S., 2020. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecol. Lett. 701–710. https://doi.org/10.1111/ele.13474. Wang, S., Zhang, B., Yang, Q., Chen, G., Yang, B., Lu, L., Shen, M., Peng, Y., 2017. Responses of net primary productivity to phenological dynamics in the Tibetan Plateau, China. Agric. For. Meteorol. 232, 235–246. https://doi.org/10.1016/j. agrformet.2016.08.020. Westoby, M., 1998. A leaf-height-seed (LHS) plant ecology strategy scheme. Plant Soil 199, 213–227. https://doi.org/10.1023/A:1004327224729. Wu, C., Chen, J.M., Black, T.A., Price, D.T., Kurz, W.A., Desai, A.R., Gonsamo, A., Jassal, R.S., Gough, C.M., Bohrer, G., Dragoni, D., Herbst, M., Gielen, B., Berninger, F., Vesala, T., Mammarella, I., Pilegaard, K., Blanken, P.D., 2013. Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn. Glob. Ecol. Biogeogr. 22, 994–1006. https://doi. org/10.1111/geb.12044. Wu, Y., Tan, H., Deng, Y., Wu, J., Xu, X., Wang, Y., Tang, Y., Higashi, T., Cui, X., 2010. Partitioning pattern of carbon flux in a Kobresia grassland on the Qinghai-Tibetan Plateau revealed by field 13C pulse-labeling. Glob. Chang. Biol. 16, 2322–2333. https://doi.org/10.1111/j.1365-2486.2009.02069.x. Xia, J., Niu, S., Ciais, P., Janssens, I.A., Chen, J., Ammann, C., Arain, A., Blanken, P.D., Cescatti, A., Bonal, D., Buchmann, N., Curtis, P.S., Chen, S., Dong, J., Flanagan, L.B., Frankenberg, C., Georgiadis, T., Gough, C.M., Hui, D., Kiely, G., Li, J., Lund, M., Magliulo, V., Marcolla, B., Merbold, L., Montagnani, L., Moors, E.J., Olesen, J.E., Piao, S., Raschi, A., Roupsard, O., Suyker, A.E., Urbaniak, M., Vaccari, F.P., Varlagin, A., Vesala, T., Wilkinson, M., Weng, E., Wohlfahrt, G., Yan, L., Luo, Y., 2015. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl. Acad. Sci. U. S. A. 112, 2788–2793. https://doi.org/10.1073/ pnas.1413090112. Xie, Z., Zhu, W., Qiao, K., Zhan, P., Li, P., 2019. Seasonal differences in relationships between changes in spring phenology and dynamics of carbon cycle in grasslands. Ecosphere 10, 1–18. https://doi.org/10.1002/ecs2.2733. Yang, Y., Fang, J., Ji, C., Han, W., 2009. Above- and belowground biomass allocation in Tibetan grasslands. J. Veg. Sci. 20, 177–184. https://doi.org/10.1111/j.16541103.2009.05566.x. Zheng, Z., Zhu, W., Zhang, Y., 2020. Direct and lagged effects of spring phenology on net primary productivity in the alpine grasslands on the Tibetan Plateau. Remote Sens. 12, 1–14. https://doi.org/10.3390/rs12071223. Zhu, J., Zhang, Y., Jiang, L., 2017. Experimental warming drives a seasonal shift of ecosystem carbon exchange in Tibetan alpine meadow. Agric. For. Meteorol. 233, 242–249. https://doi.org/10.1016/j.agrformet.2016.12.005. Zhu, W., Zheng, Z., Jiang, N., Zhang, D., 2018. A comparative analysis of the spatiotemporal variation in the phenologies of two herbaceous species and associated climatic driving factors on the Tibetan Plateau. Agric. For. Meteorol. 248, 177–184. https://doi.org/10.1016/j.agrformet.2017.09.021. Zhu, J., Zhang, Y., Yang, X., Chen, N., Li, S., Wang, P., Jiang, L., 2020. Warming alters plant phylogenetic and functional community structure. J. Ecol. 1–10 https://doi. org/10.1111/1365-2745.13448.

Lin, L., Li, Y.K., Xu, X.L., Zhang, F.W., Du, Y.G., Liu, S.L., Guo, X.W., Cao, G.M., 2015. Predicting parameters of degradation succession processes of Tibetan Kobresia grasslands. Solid Earth 6, 1237–1246. https://doi.org/10.5194/se-6-1237-2015. Lindh, M., Johansson, J., Bolmgren, K., Lundstr¨ om, N.L.P., Br¨ annstr¨ om, Å., Jonz´en, N., 2016. Constrained growth flips the direction of optimal phenological responses among annual plants. New Phytol. 209, 1591–1599. https://doi.org/10.1111/ nph.13706. Liu, H., Mi, Z., Lin, L., Wang, Y., Zhang, Z., Zhang, F., Wang, H., Liu, L., Zhu, B., Cao, G., Zhao, X., Sanders, N.J., Classen, A.T., Reich, P.B., He, J.S., 2018. Shifting plant species composition in response to climate change stabilizes grassland primary production. Proc. Natl. Acad. Sci. U. S. A. 115, 4051–4056. https://doi.org/10.1073/ pnas.1700299114. Liu, Z., Jiang, F., Li, F., Jin, G., 2019. Coordination of intra and inter-species leaf traits according to leaf phenology and plant age for three temperate broadleaf species with different shade tolerances. For. Ecol. Manage. 434, 63–75. https://doi.org/10.1016/ j.foreco.2018.12.008. Ma, W.L., Shi, P.L., Li, W.H., He, Y.T., Zhang, X.Z., Shen, Z.X., Chai, S.Y., 2010. Changes in individual plant traits and biomass allocation in alpine meadow with elevation variation on the Qinghai-Tibetan Plateau. Sci. China-Life Sci. 53, 1142–1151. https://doi.org/10.1007/s11427-010-4054-9. Mazer, S.J., 1990. Seed mass of Indiana Dune genera and families: taxonomic and ecological correlates. Evol. Ecol. 4, 326–357. https://doi.org/10.1007/BF02270931. McKown, A.D., Guy, R.D., Azam, M.S., Drewes, E.C., Quamme, L.K., 2013. Seasonality and phenology alter functional leaf traits. Oecologia 172, 653–665. https://doi.org/ 10.1007/s00442-012-2531-5. Minden, V., Kleyer, M., 2015. Ecosystem multifunctionality of coastal marshes is determined by key plant traits. J. Veg. Sci. 26, 651–662. https://doi.org/10.1111/ jvs.12276. Mokany, K., Raison, R.J., Prokushkin, A.S., 2006. Critical analysis of root: Shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96. https://doi.org/10.1111/j.13652486.2005.001043.x. Mood, A.M., 1969. Macro-analysis of the American educational system author. Oper. Res. 17, 770–784. https://doi.org/10.1287/opre.17.5.770. Nimon, K., Oswald, F., Roberts, J., Nimon, M., MBESS, S., 2013. Package “yhat” – Interpreting Regression Effects, 1–25. Piao, S., Friedlingstein, P., Ciais, P., Viovy, N., Demarty, J., 2007. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycle 21, 1–11. https://doi.org/10.1029/ 2006GB002888. Piao, S., Liu, Q., Chen, A., Janssens, I.A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., Zhu, X., 2019. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940. https://doi.org/10.1111/gcb.14619. Primack, R.B., 1985. Patterns of flowering phenology in communities, populations, individuals, and single flowers. In: White, J. (Ed.), The population structure of vegetation. Springer, Dordrecht, pp. 571–593. Primack, R.B., Gallinat, A.S., 2017. Insights into grass phenology from herbarium specimens. New Phytol. 213, 1567–1568. https://doi.org/10.1111/nph.14439. Ray-Mukherjee, J., Nimon, K., Mukherjee, S., Morris, D.W., Slotow, R., Hamer, M., 2014. Using commonality analysis in multiple regressions: a tool to decompose regression effects in the face of multicollinearity. Methods Ecol. Evol. 5, 320–328. https://doi. org/10.1111/2041-210X.12166. Richardson, A.D., Black, T.A., Ciais, P., Delbart, N., Friedl, M.A., Gobron, N., Hollinger, D.Y., Kutsch, W.L., Longdoz, B., Luyssaert, S., Migliavacca, M., Montagnani, L., Munger, J.W., Moors, E., Piao, S., Rebmann, C., Reichstein, M., Saigusa, N., Tomelleri, E., Vargas, R., Varlagin, A., 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B-Biol. Sci. 365, 3227–3246. https://doi.org/10.1098/rstb.2010.0102. Schmitt, J., 1983. Individual flowering phenology, plant size, and reproductive success in Linanthus androsaceus, a California annual. Oecologia 59, 135–140. https://doi.org/ 10.1007/BF00388084. Schumacher, J., Roscher, C., 2009. Differential effects of functional traits on aboveground biomass in semi-natural grasslands. Oikos 118, 1659–1668. https:// doi.org/10.1111/j.1600-0706.2009.17711.x. Seibold, D.R., McPhee, R.D., 1979. Commonality analysis: a method for decomposing explained variance in multiple regression analyses. Hum. Commun. Res. 5, 355–365. https://doi.org/10.1111/j.1468-2958.1979.tb00649.x. Shen, H., Dong, S., Li, S., Xiao, J., Han, Y., Yang, M., Zhang, J., Gao, X., Xu, Y., Li, Y., Zhi, Y., Liu, S., Dong, Q., Zhou, H., Yeomans, J.C., 2019. Effects of simulated N deposition on photosynthesis and productivity of key plants from different functional groups of alpine meadow on Qinghai-Tibetan plateau. Environ. Pollut. 251, 731–737. https://doi.org/10.1016/j.envpol.2019.05.045.

9