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Article

Driving Climatic Factors at Critical Plant Developmental Stages for Qinghai–Tibet Plateau Alpine Grassland Productivity

by
Dechao Zhai
1,2,
Xizhang Gao
1,3,
Baolin Li
1,2,3,*,
Yecheng Yuan
1,
Yuhao Jiang
4,
Yan Liu
1,2,
Ying Li
1,2,
Rui Li
1,2,
Wei Liu
1,2 and
Jie Xu
1,2
1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Jiangsu Center of Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(7), 1564; https://doi.org/10.3390/rs14071564
Submission received: 28 February 2022 / Revised: 21 March 2022 / Accepted: 22 March 2022 / Published: 24 March 2022
(This article belongs to the Special Issue Ecosystem Changes in Tibetan and Other Alpine Regions from Earth Observation)
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Abstract

:
Determining the driving climatic factors at critical periods and potential legacy effects is crucial for grassland productivity predictions on the Qinghai–Tibet Plateau (QTP). However, studies with limited and ex situ ground samples from highly heterogeneous alpine meadows brought great uncertainties. This study determined the key climatic factors at critical plant developmental stages and the impact of previous plant growth status for interannual aboveground net primary productivity (ANPP) variations in different QTP grassland types. We hypothesize that the impact of climatic factors on grassland productivity varies in different periods and different vegetation types, while its legacy effects are not great. Pixel-based partial least squares regression was used to associate interannual ANPP with precipitation and air temperature at different developmental stages and prior-year ANPP from 2000 to 2019 using remote sensing techniques. Results indicated different findings from previous studies. Precipitation at the reproductive stage (July–August) was the most prominent controlling factor for ANPP which was also significantly affected by precipitation and temperature at the withering (September–October) and dormant stage (November–February), respectively. The influence of precipitation was more significant in alpine meadows than in alpine steppes, while the differentiated responses to climatic factors were attributed to differences in water consumption at different developmental stages induced by leaf area changes, bud sprouting, growth, and protection from frost damage. The prior-year ANPP showed a non-significant impact on ANPP of current year, except for alpine steppes, and this impact was much less than that of current-year climatic factors, which may be attributed to the reduced annual ANPP variations related to the inter-annual carbon circulation of alpine perennial herbaceous plants and diverse root/shoot ratios in different vegetation types. These findings can assist in improving the interannual ANPP predictions on the QTP under global climate change.

Graphical Abstract

1. Introduction

Global climate change is altering existing climatic conditions, including dramatic changes in temperature and precipitation affecting terrestrial ecosystem processes [1,2,3]. Alpine grasslands, one of the most vulnerable ecosystems, are extremely sensitive to climate change and have complex feedback processes with the environment [4,5]. Alterations in alpine grassland productivity driven by climate change affect forage resource sustainability, pastoral livelihood profitability and the global carbon balance [6]. However, spatiotemporal variations in alpine grassland productivity and its interactions with global climate change are not well understood [6,7]. Accordingly, clarifying the sensitive climatic factors affecting grasslands and the corresponding periods can assist in predicting the growth responses of alpine plants under global climate change, leading to more rational and effective pasture management recommendations for decision-makers [4,8].
The current understanding of the relationship between climatic factors and grassland productivity based on field observation is derived from temporal models using long-term datasets from one location, or spatial models using datasets that describe spatial patterns across a climatic gradient [9,10]. These two approaches seem to provide substantially different predictions of ecological responses to future environmental changes [9,11,12]. Spatial models reflect the chronic ecological responses of plants related to species composition and their corresponding traits to long-term changes in climatic conditions [9,13]. Temporal models predict changes in plant growth within communities with altered climatic factors and indicate relatively short-term ecological responses to climatic variations related to plant density, leaf size, and stem thickness per plant [9,10]. Compared to spatial models, temporal models can better predict relatively short-term forage yield variations for livestock production. Therefore, exploring the responses of alpine grassland aboveground net primary productivity (ANPP) to climatic variables using a temporal model is necessary.
Alpine grasslands on the Qinghai–Tibet Plateau (QTP), known as the global “third pole” [14], are extremely sensitive to climate change [15]. Under the dual pressures of global climate change and rapid population increase, alpine grasslands of the QTP have been severely degraded, greatly affecting the animal husbandry industry and herder’s livelihoods [16]. To predict grassland productivity and arrange pastoral production, many studies on vegetation productivity–climate relationships from a temporal perspective have been conducted based on current-year climatic factors [15,17,18,19] and possible potential lagged or legacy effects [20,21].
The impact of the current-year climatic factors on grassland productivity on the QTP has been studied from annual and growing season [17,18,19] to seasonal and monthly scales [15,22,23]. Plant resource requirements vary in different vegetation types and plant developmental stages owing to different life activities [24,25,26]. Thus, ANPP variations may be more relevant to climatic factors during periods pertinent to the specific phenology or life history of plants in an ecosystem [27,28]. More studies at seasonal and monthly scales have demonstrated that temperature was the dominant climatic factor for alpine meadows, shrubs in spring, summer, and fall (Table S1) [19,22,29]. For alpine steppes, vegetation growth was affected by both precipitation and temperature, but climatic factors were more prominent in summer than in spring and fall (Table S1) [15,19,29]. However, the seasonal time division is inconsistent with that of the developmental stage.
Recently, a developmental stage-based study of ANPP and climatic factors in alpine meadows was conducted by Li et al. [4] with long-term in situ observations. However, limited vegetation types and ex situ ground samples under the high heterogeneity of grasslands may induce great uncertainties in determining the responses of interannual ANPP variations to climatic changes. Satellite-derived vegetation indices may be ideal surrogates for field-harvested biomass/ANPP in environmentally harsh or large areas [30,31]. Therefore, it is necessary to determine the vegetation productivity–climate relationship of different vegetation types at the developmental stage scale based on remote sensing images on the QTP. In this study, we hypothesized that the impact of climatic factors on plant growth varies at different developmental stages and different vegetation types have diverse responses (Hypothesis 1).
Prior-year or earlier climatic conditions can significantly affect plant growth (lagged or legacy effects) and are necessary for the prediction of grassland ANPP [32,33,34]. However, relevant studies on the QTP have focused on the impact of relatively short-term (several months) legacy effects within the current year, and a 0–2-month lag has been detected in the vegetation productivity–climate relationship [20,21,35]. Therefore, confirming the impact of long-term (at least 1 year) legacy effects on grassland ANPP is also significant for the QTP.
The currently proposed legacy effects can be divided into two categories: biotic and abiotic legacies [36]. Abiotic legacies indicate that prior-year climatic conditions may influence current-year ANPP through soil moisture carryover [37,38,39,40], or by modifying soil nutrient availability [41,42,43]. Studies showed that precipitation can contribute the legacy impact on plants if their rooting depth allows them to access the stored water from earlier wet years [44]. Precipitation in the previous year may also influence the inorganic nitrogen pool by affecting the microbial mineralization, immobilization, and nitrogen leaching, further promoting or suppressing vegetation growth [41]. Biotic legacies can be mediated by changes in individual plants or shifts in community structure [45,46,47]. Such individual plant changes include tiller [48], stolon [49], and meristem [25] dynamics. Variations in tiller and stolon density regulate the vegetation productivity [48,50]. However, long-lasting (at least 1 year) abiotic legacies hardly exist on the QTP because the soil wetting duration usually lasts less than 2 days even after heavy rain on the QTP [51]. In addition, soil inorganic nitrogen availability is affected by rainfall events and only lasts for a short phase (less than 3 months) [52].
Accordingly, the long-lasting legacy in plant growth responses to climate change on the QTP, if any, is more likely to be a biotic legacy. Biotic legacies generally shape the current-year ANPP by affecting the plant growth status of previous year through tiller, stolon, and meristem dynamics [25,48,50]. However, the inter-annual carbon circulation of plants may be more pronounced in the prior-year climatic legacy effect on the current-year ANPP variations under harsh environments. Plant interannual carbon allocation is a widespread strategy that allows perennial herbaceous plants to seasonally accumulate non-structural carbohydrates (NSCs) in underground organs before the onset of cold or dry periods, and to transfer NSCs to aboveground organs the following year, serving as a vital resource for life activities in the following year [53,54,55]. This strategy can guarantee that plants complete their life circle in harsh environments and is particularly significant for the development of alpine plants [56], and it may also reduce the interannual ANPP fluctuation in alpine grasslands and decrease the impact of prior-year legacy from climatic factors. In addition, the root/shoot (R/S) ratios of different grassland types varied enormously with different vegetation types [57,58,59,60,61]. Species with higher R/S ratios have developed root systems to store more NSCs in winter to alleviate the effects of legacies. Consequently, we also hypothesized that alpine grasslands may not indicate a great prior-year legacy impact, and vegetation types dominated by species with higher R/S ratios have lower legacy effects and vice versa (Hypothesis 2). In this study, we tested the abovementioned two hypotheses and answered the following two questions: (i) What are the main climatic factors and critical developmental stages driving ANPP for different vegetation types in the QTP grassland ecosystem; (ii) Is ANPP of different vegetation types on the QTP affected by the antecedent plant growth status, and if it is, how much are the impacts?

2. Materials and Methods

2.1. Study Area

The study area is located in the Three-River Headwaters (TRH) region (31°39′–37°17′N, 89°45′–102°23′E), referring to the source area of the Yangtze, Yellow, and Lancang Rivers. It is also called the “Chinese Water Tower”, with a total area of approximately 3.95 × 105 km2 (Figure 1). This region is dominated by mountain and canyon landforms, and the overall terrain gradually rises from southeast to northwest, with altitudes ranging from 3200 to 4700 m [62]. The annual precipitation ranges from 262 to 723 mm, and the annual average temperature ranges from −5.6 to 3.8 °C, from the northwest to southeast TRH region, with climatic zones changing from semiarid (dominant climatic zone) to humid subtropical [63,64]. The vegetation types in the TRH region are primarily grasslands (approximately 68%), including alpine meadows and steppes which predominately consist of alpine steppes, except a small area of temperate steppes. Alpine meadows are the most widely distributed and largest natural grassland in the TRH region, accounting for approximately 50% of the total grassland area. They can be divided into typical alpine meadow and alpine swamp meadow, which are dominated by Kobresia, with some mixed forbs and grasses.

2.2. Data Collection and Preprocessing

In this study, the Terra Moderate-Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI, MOD13A1) was used for indicating ANPP between 2000 and 2019, with a spatial resolution of 500 m and a temporal resolution of 16 days. The dataset was confirmed to be of high quality and suitable for expressing vegetation dynamics [30,65]. The NDVI dataset was obtained from Google Earth Engine (http://earthengine.google.com, accessed on 20 March 2021) [66]. Monthly NDVI values were calculated using the maximum value composites method, which further eliminated errors caused by clouds, atmosphere, sun angle, and other interferences [67]. NDVI cannot indicate vegetation status accurately under low vegetation coverage with low greenness due to the impact of soil background [68]. Thus, we excluded pixels with low NDVI value for analysis to reduce uncertainties. We selected 0.1 as threshold of low NDVI according to the suggestion from Piao et al. [69,70].
The meteorological dataset included the monthly average temperature and monthly total precipitation from January 1982 to December 2019 at a spatial resolution of 1 km, and was downloaded from the National Earth System Science Data Center (https://gre.geodata.cn/, accessed on 20 March 2021). These meteorological data were downscaled in China using the Delta spatial downscaling scheme and based on the global 0.5° climate dataset published by the Climatic Research Unit and the global high-resolution climate dataset published by WorldClim [71]. The resulting dataset was verified to be credible by 496 independent meteorological observation stations [71,72].
Vegetation types and their distributions were obtained from a 1:1,000,000 digitalized vegetation map of China [73]. The alpine grasslands in the TRH region were extracted from the vegetation map, including the typical alpine meadow (TAM), alpine swamp meadow (ASM), alpine steppes (AST), and alpine shrubs (ASH). Other vegetation types were not considered because of their small proportions. Finally, we obtained an approximate distribution of the five vegetation types in the study area (Figure 1).

2.3. Methods

2.3.1. Developmental Stages of Grasslands on QTP

In addition to the annual and growing season precipitation/temperature, this study also concentrated on climatic factors at plant developmental stages. Considering the plant germination time and months that do not influence the estimated annual ANPP, the developmental stages of alpine grassland were determined based on a plant-centric year instead of the calendar year. The measured annual ANPP of grasslands on the QTP is commonly determined by approximating the maximum aboveground biomass during the growing season [74,75]. In a TRH ecosystem with a spring–summer growing season, the maximum biomass typically occurs in mid–late August [76,77,78]. Most herbaceous species on the QTP begin the germination after the maximum biomass is reached, and then enter the growth stage after experiencing winter dormancy [62,79,80]. Precipitation or temperature that occurs after the maximum biomass is reached should not influence the estimated annual ANPP. Therefore, this study took a plant-centric year from September to August instead of January to December of the calendar year (Figure 2).
We defined the non-growing and growing seasons as November to April and May to October, respectively [4]. The non-growing season was sub-divided into two periods: the dormant stage (S0, from November to February) and the pre-growing season stage (S1, two months from the end of S0 to the end of the non-growing season). The growing season was divided into three equal stages, following the method by Robinson et al. [27], each of which lasted for two months: the early-growing season, also called the vegetative stage (S2); the mid-growing season, also known as the reproductive stage (S3); and the late-growing season, also known as the withering stage (S4). Considering the plant germination after the maximum biomass occurred from mid–late August, we used a previous withering stage (S4p) instead of S4 to fill the plant-centric year (Figure 2).

2.3.2. Indices of Input Variables

Grassland biomass is commonly obtained through field harvesting, and annual ANPP can be estimated using aboveground biomass (AGB). However, intense field sampling and sample processing [81,82] incur high costs for large spatial areas [83], and it is particularly difficult to collect samples from the QTP under harsh natural conditions. In addition, studies conducted with limited and ex situ samples in highly heterogeneous grassland may induce great uncertainties in determining the responses of ANPP to climatic changes. Thus, vegetation indices, indicating a close relationship with AGB or annual ANPP, have commonly been used as ideal surrogates of AGB or ANPP from field-harvested biomass in environmentally harsh or large areas [30,31,84]. Accordingly, we used NDVI at the peak of the growing season as a proxy for the net photosynthetic activity to determine the impacts of climatic changes on grassland annual ANPP, which was considered as a suitable approximation for these ecosystems [74,75].
The total precipitation and mean air temperature during a study period, including annual, growing season, and developmental stage scales were used as the climatic factor indices. The annual period was from September to August, and the growing season was from May to October. The developmental stages used in this study (S4p–S3), as discussed in Section 2.3.1, were from September to October, November to February, March to April, May to June, and July to August, respectively (Figure 2). The precipitation and temperature in the five developmental stages are abbreviated to P4p–P3 and T4p–T3, respectively.
To study the effects of the prior-year plant growth status on that of the current year, the maximum NDVI of the previous year (Np), representing the ANPP of that year, was added as an input variable. The long-lasting legacy in the responses of plant growth to climate change on the QTP is more likely to be a biotic legacy. However, prior-year vegetation growth status may be affected by precipitation, temperature, and radiation, etc. from the previous year, and it may also influence the bud growth and ability to resist freezing damage, thereby affecting grassland productivity next year. Thus, linking the prior-year climatic factors to the current-year vegetation productivity introduces large uncertainties owing to complex impact paths (prior-year climatic factors → prior-year plant growth status → current year grassland ANPP). The antecedent vegetation growth status is easy to obtain with the advancement of remote sensing technology. Consequently, directly considering the prior-year plant growth status as an impact factor instead of the antecedent climatic factors to detect the ANPP drivers may be a better option [85,86].

2.3.3. Impact of Driving Factors on ANPP

The impact of driving factors on ANPP was evaluated by partial least squares (PLS) regression, which integrates the strengths of multiple linear regression, principal component analysis and canonical correlation analysis [87]. Unlike many other regression methods, PLS can be used effectively when the number of observations is close to or lower than that of the independent variables; these variables are also highly correlated [88,89].
Standardized model coefficients (SMC) and variable importance in projection (VIP) are the two main outputs in PLS regression [90]. SMC values represent the direction and strength of each variable impact in PLS regression [88]. VIP values indicate the importance of independent variables that explain the variations of dependent variables based on the weighted sum of squares of PLS loadings [88,90]. For interpretation purposes, only predictors with a VIP of more than 0.8 are considered important [91]. Centering and scaling of the dependent and independent variables were also conducted to allow comparisons between different variables [90].
The independent variables included the total precipitation, mean temperature at five developmental stages, and the prior year maximum NDVI; the dependent variable was the current year maximum NDVI. The correct number of components for the PLS model was selected based on the root mean squared error (RMSE) of the PLS model [87]. In the PLS regression, variables with a VIP > 0.8 and large absolute SMC values represent the climatic factors in relevant phases or Np significantly influencing vegetation ANPP. Positive SMC values indicate that increasing prior-year NDVI or current-year climatic factors in the relevant period should promote ANPP, whereas negative SMC values imply negative effects on grassland productivity [88].
To determine the impact of driving factors on the ANPP of a grassland type or the entire area, SMC and VIP values in each pixel were calculated with PLS regression and then we averaged all pixels within a given extent. Predictors with an average VIP of more than 0.8 were considered important.

3. Results

3.1. Impact of Current-Year Climatic Factors

Grassland ANPP was significantly affected by precipitation at the annual and growing season scales, but not in response to air temperature. The SMC values of the growing season and annual precipitation in the whole region were 0.316 and 0.313, clearly higher than the 0.105 (VIP = 0.726) and 0.079 (VIP = 0.696) of the temperature in the growing season and plant-centric year, respectively (Figure 3). The VIP values of the growing season/annual precipitation were >0.8, whereas those of temperature were <0.8 for the entire area and four vegetation types (Figure 4). This indicated that precipitation, rather than temperature, was the main driving factor of ANPP variations on the QTP.
ANPP variations showed divergent responses to climatic factors at different developmental stages. The SMC value of P3 was 0.284 (VIP = 1.441) in the whole region, 6.03 times the average coefficient for all the impact factors. For different vegetation types, the SMC value was 6.77, 7.98, 4.33, and 6.38 times its average coefficient of all impact factors, from TAM, ASM, AST, to ASH, respectively (Figure 5). P4p and T0 also affected ANPP significantly, but were much less than P3. The SMC value of P4p was 0.087 (VIP = 0.836) in the entire area, and 0.070 (VIP = 0.808), 0.086 (VIP = 0.811), 0.124 (VIP = 0.893), and 0.054 (VIP = 0.806), respectively, for TAM, ASM, AST, and ASH; the SMC value of T0 was 0.054 (VIP = 0.837) in the entire area, and 0.065 (VIP = 0.851), 0.039 (VIP = 0.861), 0.067 (VIP = 0.837), and 0.066 (VIP = 0.816), respectively, for TAM, ASM, AST, and ASH (Figure 5). In other developmental stages, ANPP did not show a consistently pronounced response to climatic factors for different vegetation types. This indicated that the critical climatic factors for alpine grassland were P3, P4p, and T0, with the corresponding key developmental stages of S3, S4p, and S0, respectively; P3 was the most prominent controlling factor.
ANPP variations showed differentiated responses to the dominant climatic factor for different vegetation types. ANPP variations for ASM exerted the strongest response to growing season/annual precipitation with SMC of 0.397/0.388, followed by TAM and ASH, with an SMC of 0.341/0.342 and 0.311/0.314, respectively, and AST with an SMC of 0.259/0.238 (Figure 3). The impact of P3 was also more pronounced in alpine meadows (TAM and ASM) than in alpine steppes, and this impact in ASM was higher than in TAM. This indicated that the influence of precipitation on interannual ANPP was more significant in alpine meadows than in alpine steppes on the QTP.

3.2. Impact of Prior-Year Plant Growth Status

The effect of prior-year ANPP on current-year ANPP was not significant. The VIP value of Np at the growing season and annual scales in the entire area was 0.736 and 0.740, respectively, all <0.8 (Figure 4), indicating a non-significant effect on ANPP variations. The VIP value of Np at the developmental stage scale was 0.822, while the corresponding SMC value was 0.017, only 6% of P3 (Figure 5). This implied that the prior-year legacy impact on current-year ANPP was limited in alpine grasslands on the QTP.
The effect of prior-year ANPP on current-year ANPP varied with vegetation types. The SMC of Np was 0.133 (VIP = 0.850), 0.129 (VIP = 0.852), and 0.079 (VIP = 0.944) for the AST at annual, growing season, and developmental stage scales, respectively (Figure 3 and Figure 5). In the other three vegetation types, SMCs of Np ranged from −0.02 to 0.02, and VIPs were <0.8, with no significant effects on grassland ANPP variations (Figure 4 and Figure 6). This implied that the effect of antecedent plant growth status on ANPP was non-significant, except for alpine steppes.

4. Discussion

4.1. Comparison with Previous Studies

4.1.1. Comparison with Results of Previous Studies on QTP

More studies had indicated that temperature was the dominant climatic factor for vegetation growth on the QTP (Table S1) [17,92,93,94]. However, our results showed that the responses of ANPP variations to precipitation rather than temperature were more pronounced whether in the entire area or in different vegetation types (Figure 3 and Figure 4), which supported the findings of a small number of studies [18,20,95,96]. These results were consistent with the findings of previous controlled experiments on the QTP [74,97,98,99,100]. A global meta-analysis also indicated that cold ecosystems were more responsive to precipitation variations than other ecosystems [74,101].
Our results also showed that the S3 stage precipitation (P3) was the main controlling factor, and the S4p stage precipitation (P4p) and the S0 stage temperature (T0) also significantly affected vegetation productivity, which was inconsistent with the findings of Li et al. [4], in which the annual ANPP variations were determined by the onset and end of the growing season temperature (T1 and T4p). However, the limited and ex situ ground samples under the high heterogeneity of grasslands may induce uncertainties in uncovering the vegetation–climate relationship and the ground samples in this study were only collected in alpine meadows. Our results also showed a diverse response of inter-annual ANPP to climatic factors in different vegetation types. The influence of P3 was more significant in alpine meadows (TAM and ASM) than in alpine steppes, and its influence in ASM was higher than in TAM (Figure 5 and Figure 6).
Research on the legacy effects of climate on grassland productivity in the QTP ecosystem has been scant to date [102]. This study showed that the impact of prior year plant growth status on ANPP was non-significant except for the alpine steppes, even though the significant impact of the alpine steppes was much less than that of current-year climatic factors. This difference in the legacy effect between alpine meadows and steppes is consistent with reports from Li et al. [102] on the QTP along an elevation gradient.

4.1.2. Possible Reasons for Discrepancies from Other Studies

First, the differences in the surrogates were used as vegetation productivity. In this study, the annual maximum NDVI was used as a surrogate of annual ANPP [4,17,70]. However, other studies often used the mean or cumulative value of NDVI/biomass within one period (month, season, growing season, or year) as a surrogate of vegetation productivity, but these indices should be an approximation of the average biomass state over the study period [15,17,18]. Therefore, these studies may not reflect the relationships between annual vegetation ANPP and interannual climatic factors during different periods.
Second, different study phases were chosen. Studies have shown inter-decadal variation in the responses of vegetation productivity to interannual variations in precipitation or temperature, and such responses are strongly affected by the interactions between these two climatic factors [19]. Zhang et al. [94] suggested that changes in climatic conditions before and after 2000 had directly resulted in the restricting factors of plant growth being converted from temperature and radiation to precipitation in the TRH region.
Third, differences in the NDVI products were identified. There are two NDVI products that were mainly used in the remote sensing-based research on the vegetation–climate relationship. One is MODIS NDVI, and the other is the advanced very high-resolution radiometer (AVHRR) NDVI. However, discrepancies have been found between these two NDVI datasets in some arid and Arctic areas with sparse herbaceous and shrub covers [103]. Recent research has revealed an opposite trend of vegetation greenness on the QTP since the beginning of 21st century based on the two NDVI products [104]. This was probably because the AVHRR sensor suffered from some well-known shortcomings, such as orbit drift in the satellite overpass time and post-launch degradation in sensor calibrations [105]. Problems with AVHRR satellite imagery with coarse spatial resolution have been reported in many previous studies on the QTP [7,106], which failed to reveal the detailed growth curves of alpine grasslands.

4.2. Low-Temperature Acclimation in the Alpine Ecosystem May Introduce a Pronounced Impact of Precipitation on ANPP Rather than Temperature

Lower temperatures are not the limiting factor in the interannual variations of alpine grassland productivity because of physiological thermal acclimation in alpine ecosystems [56,107,108]. Thermal acclimation to low temperatures primarily results from changes in the photosynthetic apparatus and is associated with electron transport in the thylakoid membrane, particularly photosystem II [109,110]. The optimum temperature for photosynthesis correlates with active quantum flux density (QFD) so that the optimum is at low/high temperatures when QFD is low/high [56]. Thus, the photosynthetic temperature response curve is so wide [111,112] that photosynthesis can operate at 95% of the maximum rate over a range of 8 °C [56]. At the same time, re-adjustment from the optimum temperatures to the prevailing temperatures is very fast (only a few days) [113,114]. Therefore, changes in vegetation ANPP did not demonstrate a significant correlation with annual or growing season air temperatures (Figure 3).
Our temporal model-based results indicate that alpine plant growth is significantly affected by precipitation (Figure 3). Effective control of alpine plants to potential moisture shortages can be conducted through the reduction of leaf area (or increase in root production) per plant and/or the reduction of coverage per unit of land area in the short term, and replacement of species from less to more water-condition-adapted ones in the long term [56]. In addition, mineral nutrients are highly correlated with soil water content which mainly comes from precipitation. Lower soil water content may reduce nutrient supply for alpine plants by limiting nutrient cycling and microbial activity [56,115]. Temporal models of vegetation productivity–climate relationships depict a relatively short-term impact of water conditions on plant growth [9]. Thus, to reduce the physiological water restriction, the adaptive community responses of alpine plants to potential moisture shortage will operate via low ground cover and high R/S ratios. Consequently, the responses of alpine plant productivity to precipitation were more significant than those to temperature (Figure 3).

4.3. Possible Mechanisms for ANPP Responses to Climatic Factors at the Critical Developmental Stage

4.3.1. More Water Consumption from the Largest Plant Leaf Area May Lead to the Most Pronounced Impacts of S3 Stage Precipitation

The vegetation–precipitation relationship is discussed in Section 4.2. The short-term regulation of alpine plants to moisture is achieved by changing the leaf area or coverage (e.g., LAI) and R/S ratios (morphology). Leaf area index (LAI), defined as “the one-sided green leaf area per unit ground surface area”, is an important indicator reflecting plant productivity. Alpine plants in the reproductive period (S3 stage) had the largest LAI, and the absolute growth rate of dominant species was approximately 2 times that of plants in the vegetative period (S2 stage) on the QTP [62]. Plants with higher LAI or coverage consume more soil moisture and are more sensitive to precipitation changes, which explains why alpine plant productivity is highly responsive to P3.
Periodic moisture shortages commonly occurs in alpine plants in a semi-arid climate [56], and they also appear on the QTP, which is characterized as a semi-arid or arid region. Thus, vegetation types dominated with species with a higher leaf area are more likely to be affected by water availability because these plants consume more soil moisture, as mentioned above. The peak LAI in the S3 stage varied among different vegetation types within the study area [116,117,118]. According to observations, LAI in ASM, TAM, and AST is approximately 2.5~4.0 [118,119], 1~2.5 [118,119], and 0.2~1.9 [117,118,119] m2m−2, respectively. Therefore, the influence of P3 was more significant in alpine meadows (TAM and ASM) than in alpine steppes, and its influence in ASM was higher than that in TAM.

4.3.2. Better Water Availability Promotes Bud Bank Size and Status at the S4 Stage Leading to a Higher ANPP

Changes in soil water availability can influence grassland productivity by altering the number of active buds in bud banks [120]. Many studies have indicated that plant population regeneration and maintenance are principally regulated by belowground bud bank dynamics and vegetative reproduction in perennial herbaceous plants [121,122,123]. Rainfall or drought events that occur during the peak periods of bud production and outgrowth significantly affect bud bank size [24,25] since bud primordium production and its differentiation and development are particularly sensitive to water stress [124].
Overwintering buds are common in most alpine plants on the Tibetan Plateau. Alpine meadows and steppes are the two main vegetation types on the QTP. Kobresia, the dominant species of alpine meadows, is a rhizomatous perennial sedge that is widely distributed in the eastern region of the QTP. Overwintering buds of a typical tiller of Kobresia (Kobresia humilis, K. pygmaea, and K. tibetica) generally emerge near September [62,79,80,125]. Stipa purpurea, one of the dominant species in alpine steppes, is a perennial bunchgrass species. Given the lack of relevant materials on the growth cycle of S. purpurea, we used two perennial bunchgrasses, Leymus chinensis [120] and Agropyron desertorum [124], as references, which are widely distributed in the Eastern Eurasian steppes and the Great Basin, USA, respectively. The two bunchgrasses grew overwintering buds in the fall. Based on the behaviors of the two bunchgrasses, we postulate that overwintering buds of S. purpurea may also shoot out in fall. Consequently, water availability in the S4 stage greatly influenced sprout and bud bank sizes, which subsequently affected vegetation growth (Figure 5).

4.3.3. Higher Temperature at the S0 Stage Benefits Overwintering Buds Facilitating Plant Growth Next Year

For perennial herbaceous plants, the belowground meristem population related with rhizomes and other perennial organs (bud bank) plays an essential role in local plant structure, population persistence, and dynamics [122,126]. Overwintering buds (or meristem in general) commonly exist in most alpine plants on the QTP, but they are more easily damaged by low temperatures during winter period (e.g., frost damage) [127,128,129]. Although buds of perennial herbaceous plants can be protected via insulation from snow cover, fewer and infrequent snowfalls lead to very thin snow cover on the QTP (winter snowfalls at 55 stations from 1971 to 2011 were <30 mm [130]), which is much lower than that in the high-latitude and Arctic regions of Europe and North America, where the low temperature environment is similar to that of the QTP [131,132]. Additionally, strong winds in winter will further hamper the formation of extensive snow cover [133]. Therefore, warmer winter can protect overwintering buds from freezing damage and promote the bud survival rate, which explains why vegetation growth exerts a significant positive response to T0 (Figure 5).

4.4. Interannual Carbon Circulation May Decrease the ANPP Responses to Antecedent Vegetation Growth Status

Our results illustrated that the antecedent vegetation growth status had non-significant impacts on the current year’s ANPP, except for alpine steppe plants (Figure 3 and Figure 5). This may be related to the interannual carbon circulation for perennial plants in the alpine ecosystem. Plants accumulate NSCs seasonally in underground organs before the onset of cold or dry periods and transfer them to aboveground organs the following year, serving as a vital resource for life activities next year [53,54,55]. This type of carbon allocation strategy ensures that plants grow and reproduce securely even in years with extreme climatic conditions, and this is particularly critical for the development of alpine vegetation in harsh environments [56]. Therefore, this strategy will reduce annual ANPP fluctuations, leading to a low legacy impact of prior-year plant growth status.
The interannual carbon circulation of perennial herbs is commonly observed on the QTP. Many reports have demonstrated that the underground biomass of plants in the alpine meadow on the QTP presented an N-shaped change from May to October during the growing season, and seasonal valley and peak values were found in July–August and September–October, respectively [134,135,136]. This is because most plants in alpine meadows on the QTP enter the reproductive period from July to August, particularly with flowering and fruiting, and consume plentiful NSCs stored underground in the previous fall [62,136]. Thus, plants can grow and reproduce safely even under extreme climatic conditions, ensuring a stabilized ANPP.
Compared with sedges (the dominant species in alpine meadows), grasses (the dominant species in alpine steppes) have relatively lower R/S ratios on the QTP [57,58,59,60,61]. R/S ratios can be interpreted as manifesting the differential photosynthate investment between the above- and belowground organs in plants [137]. Consequently, ANPP fluctuations were magnified in alpine steppe plants with lower R/S ratios because of relatively fewer stored NSCs in underground organs each year, resulting in a higher correlation with antecedent vegetation growth status. Although ANPP variations in alpine steppes are affected by the antecedent plant growth status, this impact is significantly weaker than that of precipitation because of the generally high R/S ratios of alpine plants (Figure 3 and Figure 5).

4.5. Research Implications and Uncertainties

This study demonstrated that alpine plants were more sensitive to precipitation (Figure 3) and exerted divergent responses to climatic factors during different developmental stages (Figure 5), which differed from the antecedent understanding based on remote sensing images and in situ observations on the QTP. This study also showed that the antecedent plant growth status has no significant impact on ANPP variations, except for AST (Figure 3 and Figure 5). These results shed further light on the mechanisms underlying the short-term responses of alpine ecosystems to climate variation and legacy effects. Given the divergent responses of different-stage climatic factors and the divergent impacts of antecedent plant growth status for different vegetation types, short-term grassland productivity can be predicted more accurately, which can help predict forage yield and be used as guidelines for alpine grassland management on the QTP.
This study also involved uncertainties in its results. First, the satellite-based NDVI was taken as a proxy for ANPP and we did not use the observed ANPP directly. This may introduce uncertainties. NDVI may also have some limitations in identifying the ANPP of plants in ASM with open water because water bodies can affect the reflection albedo of vegetation from the mixed pixels of the used images [138]. Second, although we aimed to reveal the impact of climatic factors on vegetation ANPP on the QTP, anthropogenic activities, such as grazing, can also influence grassland ANPP. However, complete separation of these factors remains challenging [18,93,139]. Third, the poor research foundation of plant physiology and ecology on the QTP brought additional uncertainties in ascertaining the responsive mechanisms of ANPP to climatic change. In this study, we only presented the possible responsive mechanisms of ANPP to climatic change, and more fieldwork and investigation should be conducted to verify and improve the understanding of these mechanisms.

5. Conclusions

We detected the responses of alpine grassland ANPP to the current-year climatic variables at the developmental stage scale and the prior-year plant growth status using a temporal model on the QTP. The results indicated that precipitation, rather than temperature, was the main driving factor for all vegetation types. The reproductive-stage (July–August) precipitation was the most prominent controlling factor, and the withering stage (September–October) precipitation and dormant stage temperature (November–February) also significantly affected grassland productivity. For different vegetation types, the influence of precipitation was more significant in alpine meadows (TAM and ASM) than in alpine steppes, and the influence in ASM was higher than in TAM. This study also showed that the impact of prior-year plant growth status on ANPP was non-significant, except for the alpine steppes, and even the conspicuous impact from the alpine steppes was much less than that of current-year climatic factors. Given the divergent responses of climatic factors at different developmental stages and the divergent impacts of antecedent plant growth status for different vegetation types, more accurate predictions of grassland productivity and forage yield can be provided on the QTP.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs14071564/s1, Table S1: Previous studies on the dominant climatic factors of vegetation productivity variations at different time scales on the Qinghai–Tibet Plateau [15,17,18,19,20,22,23,29,70,92,93,94,95,96,140,141,142,143,144,145,146].

Author Contributions

Conceptualization, D.Z., B.L. and X.G.; formal analysis, D.Z. and B.L.; methodology, D.Z., B.L., Y.Y., W.L. and J.X.; investigation, Y.L. (Yan Liu), Y.L. (Ying Li), R.L. and W.L.; resources, X.G., B.L. and Y.Y.; data curation, Y.J.; writing—original draft preparation, D.Z.; writing—review and editing, D.Z. and B.L.; visualization, D.Z., W.L. and J.X.; supervision, X.G.; project administration, B.L.; funding acquisition, B.L. and X.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (grant no. U2243206) and the National Key Research and Development Plan of China (grant no. 2016YFC0500205). This research was sponsored by the Ministry of Science and Technology of China.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study area, including the Three-River Headwaters region: (a) location in relation to China, and (b) vegetation types.
Figure 1. Study area, including the Three-River Headwaters region: (a) location in relation to China, and (b) vegetation types.
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Figure 2. Plant-centric year was defined starting from September to the end of August, and the major growing season was from early May to October. The dotted line represents the complete developmental cycle divided into five stages, including the dormant (S0) and pre- (S1) to late- (S4) growing season stages [27]. However, we used the S4p instead of S4 for a complete plant-centric year because the developmental process of most herbaceous species begins with germination after the maximum biomass occurring in mid–late August.
Figure 2. Plant-centric year was defined starting from September to the end of August, and the major growing season was from early May to October. The dotted line represents the complete developmental cycle divided into five stages, including the dormant (S0) and pre- (S1) to late- (S4) growing season stages [27]. However, we used the S4p instead of S4 for a complete plant-centric year because the developmental process of most herbaceous species begins with germination after the maximum biomass occurring in mid–late August.
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Figure 3. Responses of aboveground net primary productivity (ANPP) to antecedent plant growth status and climatic factors for different vegetation types and the entire area at the growing season and annual scales. ‘TAM’, ‘ASM’, ‘AST’, ‘ASH’, and ‘ALL’ represents typical alpine meadow, alpine swamp meadow, alpine steppes, alpine shrubs, and the entire area; ‘*’ represents average VIP > 0.8; error bar represents the standard deviation of SMCs for the entire area or a vegetation type.
Figure 3. Responses of aboveground net primary productivity (ANPP) to antecedent plant growth status and climatic factors for different vegetation types and the entire area at the growing season and annual scales. ‘TAM’, ‘ASM’, ‘AST’, ‘ASH’, and ‘ALL’ represents typical alpine meadow, alpine swamp meadow, alpine steppes, alpine shrubs, and the entire area; ‘*’ represents average VIP > 0.8; error bar represents the standard deviation of SMCs for the entire area or a vegetation type.
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Figure 4. Average VIP values of PLS regression in the responses of ANPP to antecedent plant growth status and climatic factors for different vegetation types and the entire area at the growing season and annual scales. The error bar represents the standard deviation of VIPs for the entire area or a vegetation type. ‘TAM’ to ‘ALL’ were the same as that in Figure 3.
Figure 4. Average VIP values of PLS regression in the responses of ANPP to antecedent plant growth status and climatic factors for different vegetation types and the entire area at the growing season and annual scales. The error bar represents the standard deviation of VIPs for the entire area or a vegetation type. ‘TAM’ to ‘ALL’ were the same as that in Figure 3.
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Figure 5. Responses of ANPP to antecedent plant growth status and climatic factors for different vegetation types and the entire area at the developmental stage scale. ‘*’ represents average VIP > 0.8, while the error bar represents the standard deviation of SMCs for the entire area or a vegetation type. S4p to S3 are five plant developmental stages in a plant-centric year, representing the withering, dormant, pre-growing season, vegetative, and reproductive stage, respectively. ‘Np’ indicates previous-year annual ANPP.
Figure 5. Responses of ANPP to antecedent plant growth status and climatic factors for different vegetation types and the entire area at the developmental stage scale. ‘*’ represents average VIP > 0.8, while the error bar represents the standard deviation of SMCs for the entire area or a vegetation type. S4p to S3 are five plant developmental stages in a plant-centric year, representing the withering, dormant, pre-growing season, vegetative, and reproductive stage, respectively. ‘Np’ indicates previous-year annual ANPP.
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Figure 6. Average VIP values of PLS regression in the responses of ANPP to antecedent plant growth status and climatic factors for different vegetation types and the entire area at the developmental stage scale. The error bar represents the standard deviation of VIPs for the entire area or a vegetation type. S4p to S3 and ‘Np’ were the same as that in Figure 5.
Figure 6. Average VIP values of PLS regression in the responses of ANPP to antecedent plant growth status and climatic factors for different vegetation types and the entire area at the developmental stage scale. The error bar represents the standard deviation of VIPs for the entire area or a vegetation type. S4p to S3 and ‘Np’ were the same as that in Figure 5.
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Zhai, D.; Gao, X.; Li, B.; Yuan, Y.; Jiang, Y.; Liu, Y.; Li, Y.; Li, R.; Liu, W.; Xu, J. Driving Climatic Factors at Critical Plant Developmental Stages for Qinghai–Tibet Plateau Alpine Grassland Productivity. Remote Sens. 2022, 14, 1564. https://doi.org/10.3390/rs14071564

AMA Style

Zhai D, Gao X, Li B, Yuan Y, Jiang Y, Liu Y, Li Y, Li R, Liu W, Xu J. Driving Climatic Factors at Critical Plant Developmental Stages for Qinghai–Tibet Plateau Alpine Grassland Productivity. Remote Sensing. 2022; 14(7):1564. https://doi.org/10.3390/rs14071564

Chicago/Turabian Style

Zhai, Dechao, Xizhang Gao, Baolin Li, Yecheng Yuan, Yuhao Jiang, Yan Liu, Ying Li, Rui Li, Wei Liu, and Jie Xu. 2022. "Driving Climatic Factors at Critical Plant Developmental Stages for Qinghai–Tibet Plateau Alpine Grassland Productivity" Remote Sensing 14, no. 7: 1564. https://doi.org/10.3390/rs14071564

APA Style

Zhai, D., Gao, X., Li, B., Yuan, Y., Jiang, Y., Liu, Y., Li, Y., Li, R., Liu, W., & Xu, J. (2022). Driving Climatic Factors at Critical Plant Developmental Stages for Qinghai–Tibet Plateau Alpine Grassland Productivity. Remote Sensing, 14(7), 1564. https://doi.org/10.3390/rs14071564

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