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Journal of Resources and Ecology >

2019 , Vol. 10 >Issue 5: 472 - 480

DOI: https://doi.org/10.5814/j.issn.1674-764X.2019.05.002

Resources and Ecology in the Qinghai-Tibet Plateau

Driving Mechanism of Gross Primary Production Changes and Implications for Grassland Management on the Tibetan Plateau

  • SUN Wei 1 ,
  • LI Meng 1, 2 ,
  • WANG Junhao 1, 2 ,
  • FU Gang , 1, *
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  • 1.Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, 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
* FU Gang, E-mail:

Received date: 2019-04-15

  Accepted date: 2019-06-20

  Online published: 2019-10-11

Supported by

National Natural Science Foundation of China(31600432)

National Key Research Projects of China(2017YFA0604801)

National Key Research Projects of China(2016YFC0502005)

Bingwei Outstanding Young Talents Program of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences(2018RC202)

Tibet Science and Technology Major Projects of the Pratacultural Industry(XZ201901NA03)

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Copyright reserved © 2019
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Abstract

The contribution of climatic change and anthropogenic activities to vegetation productivity are not fully understood. In this study, we determined potential climate-driven gross primary production (GPPp) using a process-based terrestrial ecosystem model, and actual gross primary production (GPPa) using MODIS Approach in alpine grasslands on the Tibetan Plateau from 2000 to 2015. The GPPa was influenced by both climatic change and anthropogenic activities. Gross primary production caused by anthropogenic activities (GPPh) was calculated as the difference between GPPp and GPPa. Approximately 75.63% and 24.37% of the area percentages of GPPa showed increasing and decreasing trends, respectively. Climatic change and anthropogenic activities were dominant factors responsible for approximately 42.90% and 32.72% of the increasing area percentage of GPPa, respectively. In contrast, climatic change and anthropogenic activities were responsible for approximately 16.88% and 7.49% of the decreasing area percentages of GPPa, respectively. The absolute values of the change trends of GPPp and GPPh of meadows were greater than those of steppes. The GPPp change values were greater than those of GPPh at all elevations, whereas both GPPp and GPPh showed decreasing trends when elevations were greater than or equal to 5000 m, 4600 m and 4800 m in meadows, steppes and all grasslands, respectively. Climatic change had stronger effects on the GPPa changes when elevations were lower than 5000 m, 4600 m and 4800 m in meadows, steppes and all grasslands, respectively. In contrast, anthropogenic activities had stronger effects on the GPPa changes when elevations were greater than or equal to 5000 m, 4600 m and 4800 m in meadows, steppes and all grasslands, respectively. Therefore, the causes of actual gross primary production changes varied with elevations, regions and grassland types, and grassland classification management should be considered on the Tibetan Plateau.

Key words： alpine regions; elevation; grassland classification management; grassland types

Cite this article

SUN Wei , LI Meng , WANG Junhao , FU Gang . Driving Mechanism of Gross Primary Production Changes and Implications for Grassland Management on the Tibetan Plateau[J]. Journal of Resources and Ecology, 2019 , 10(5) : 472 -480 . DOI: 10.5814/j.issn.1674-764X.2019.05.002

1 Introduction

Terrestrial ecosystems are suffering from the dual pressures of the ongoing climatic change and anthropogenic activities (Haberl, 1997; Field, 2001; Luo et al., 2013; Yang et al., 2016; Zhang et al., 2016). Both climatic change and human activities have significantly influenced the carbon cycling, ecosystem structure and function in terrestrial ecosystems (Haberl, 1997; Klein et al., 2004; Dorji et al., 2013). Quantifying the relative contributions of climatic change and anthropogenic activities to the changes of terrestrial ecosystems is a challenge for either quantifying global carbon cycling or scientific management of ecosystems under global change, considering that it is actually difficult to separate the influences of these two factors (Wessels et al., 2007). Increasing numbers of studies have analyzed the impacts of climatic change and/or anthropogenic activities on terrestrial ecosystems at various spatial and temporal scales (Xu et al., 2010; Xu et al., 2011; Zhang et al., 2011; Wang et al., 2012; Zhou et al., 2013; Chen et al., 2014; Zhou et al., 2015; Jahelnabi et al., 2016; Li et al., 2016; Yang et al., 2016; Yang et al., 2017). Several studies have attributed the changes of terrestrial ecosystems to anthropogenic activities (Liu and Diamond, 2005; Yang et al., 2005). In contrast, other studies have indicated that climatic change is the main factor driving the changes of terrestrial ecosystems (Ravi et al., 2010). Therefore, it remains unclear which of these two is the predominant factor driving the changes of terrestrial ecosystems.
Alpine regions are more sensitive to climatic change and human activities than other regions throughout the world, and the Tibetan Plateau is an important and typical alpine region (Hinzman et al., 2005; Piao et al., 2006; You et al., 2010). The Tibetan Plateau plays a prominent role in the evolution of the Asian monsoon system and it is a promotor of climatic change (Yao et al., 1991; Li et al., 2016). Grasslands account for nearly 25% of the global land surface and they are important components of global terrestrial ecosystems (Yang and Piao, 2006; Yang et al., 2009; Chu et al., 2013; Yang et al., 2016). The Tibetan Plateau is mainly covered by grasslands (Ni, 2002; Chen et al., 2014; Shen et al., 2014), which are a vital component of the global alpine ecosystems (Xu et al., 2007). Alpine grasslands are important pastures and livestock grazing is one of the main land use types in alpine grasslands, where grazing forms the important basis of local livelihoods on the Tibetan Plateau (Fu et al., 2012b; Wu et al., 2013). However, human activities (e.g. overgrazing) have resulted in damage to the natural vegetation and grassland degradation on the Tibetan Plateau (Li et al., 2016). On the other hand, various ecological protection measures, such as forbidding grazing and controlling the livestock numbers, have been implemented since 2003 to restore degraded grasslands throughout the Tibetan Plateau (Chen et al., 2014; Li et al., 2016). Some recent studies have indicated that climatic conditions (especially precipitation) can play a role in restoring degraded alpine grasslands that is more important than forbidding grazing, and that primary production under fencing conditions can be lower than that under grazing conditions in some regions on the Tibetan Plateau (Yan and Lu, 2015; Zhang et al., 2015). This argues the viewpoint that fencing is effective and economical in restoring degraded grasslands (Yu et al., 2016). Therefore, it is necessary to separate the effects of human activities on the alpine grasslands from climatic change on the Tibetan Plateau.
To date, only a few studies have quantified the contributions of climatic change and human activities to the changes of alpine grasslands on the Tibetan Plateau (Chen et al., 2014; Huang et al., 2016; Li et al., 2016; Wang et al., 2016; Feng et al., 2017a; Feng et al., 2017b). Some of these previous studies have compared the relationships between ecosystem production and both precipitation and temperature under either grazed or ungrazed conditions (Wang et al., 2013; Wu et al., 2014), whereas they could not directly obtain the relative contributions of climatic change and human activities to the changes of alpine grasslands. On the other hand, growing numbers of studies have adopted the difference method, in which the primary production reduction by human activities is the difference between the potential climate-driven primary production and the actual primary production (Chen et al., 2014). Although the difference method may ignore compensatory effects, it can directly provide the relative contributions of climatic change and human activities to the changes of alpine grasslands. Moreover, all these previous studies have focused on net primary production (NPP) rather than gross primary production (GPP). The GPP, as an important component of carbon cycling, is related to plant growth, ecosystem respiration and net ecosystem production (Welker et al., 2004; Oberbauer et al., 2007; Yang et al., 2009; Fu et al., 2012a). The quantification of GPP is a challenge in quantifying global carbon cycling (Turner et al., 2003; Zhao et al., 2005). Generally, both NPP and GPP are obtained through models rather than direct measurements at regional and global scales. In most cases, NPP modelling is directly based on GPP modelling, which in turn may increase uncertainties and affect the accuracy. Moreover, GPP may have advantages over NPP in representing grassland ecosystems, because unlike GPP, NPP excludes part of the vegetation characteristics (i.e. vegetation autotrophic respiration). In other words, GPP includes more ecosystem information than NPP. Therefore, in this study, we quantified the contributions of climatic change and human activities to the changes of GPP based on the difference method; and analyzed the relationships between GPP changes and elevation in alpine grasslands on the Tibetan Plateau.

2 Materials and methods

2.1 Terrestrial Ecosystem Model (TEM) and climate-driven potential GPP (GPPp)

The TEM, a process-based ecosystem model, is widely used to model carbon and nitrogen dynamics of plants and soils in terrestrial ecosystems (Raich et al., 1991; Melillo et al., 1993; Tian et al., 1998; Zhuang et al., 2010; Feng et al., 2017a). The TEM is only driven by climatic factors and it is unaffected by anthropogenic activities (Chen et al., 2014), thus it is used to simulate potential climate-driven GPP (GPPp). The TEM GPP is calculated as follows:
$GPPp\text{ }={{C}_{\max }}\times \frac{PAR}{PAR+{{k}_{i}}}\times \frac{{{C}_{i}}}{{{C}_{i}}+{{k}_{c}}}\times TEMP\times KLEAF$
where Cmax is the maximum rate of carbon assimilation under optimal environmental conditions and changes with vegetation types (alpine meadow: 946.9 g C m-2; alpine steppe: 617.9 g C m-2; alpine desert steppe: 251.2 g C m-2); PAR is photosynthetically active radiation; ki is the irradiance at which carbon assimilation proceeds at one-half its maximum rate (314 J cm-2 d-1); Ci is the concentration of CO2 inside leaves; kc is the internal CO2 concentration at which assimilation proceeds at one-half its maximum rate (200 uL L-1); TEMP indicates the effect of air temperature and KLEAF indicates the effect of vegetation phenology.

2.2 Moderate Resolution Imaging Spectroradiometer (MODIS) GPP

Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 GPP data (MOD17A2H) from 2000 to 2015 were treated as actual GPP (GPPa) in this study. The original spatial resolution was 0.50 km × 0.50 km and the original MODIS GPP data were resampled into 1 km × 1 km in the current study. The MODIS Collection 6 GPP data have been validated in alpine grasslands on the Tibetan Plateau by previous studies (Fu et al., 2017b; Niu et al., 2017). Simulated GPPa explained 83% of the variation of observed GPPa (Fig. 1).

Fig. 1 Relationship between observed and simulated GPPa

Note: GPPa: actual gross primary production

The MODIS GPP algorithm is based on a light use efficiency (ε) algorithm and a detailed description can be found in previous studies (Fu et al., 2017b; Niu et al., 2017). Briefly, MODIS GPP is calculated as follows:
$\begin{matrix} & GPPa\text{ }=\text{ }APAR\text{ }\times \varepsilon \text{ }=\text{(}FPAR\times PAR)\times \\ & \ \ \ \ \ \ \ \ \ \ \ \ \ ({{\varepsilon }_{\max }}\times {{T}_{scalar}}\text{ }\times {{W}_{scalar}}) \\ \end{matrix}$
where APAR is the photosynthetically active radiation absorbed by the vegetation canopy; FPAR is the fractional photosynthetically active radiation; PAR is the photosynthetically active radiation; εmax is maximum light use efficiency; Tscalar and Wscalar are temperature and water attenuation scalars of εmax, respectively; Tscalar is derived from daily minimum air temperature; and Wscalar is derived from daytime average vapor pressure deficit.

2.3 GPP caused by anthropogenic activities (GPPh)

The GPPp is only affected by climatic change, and the GPPa is influenced by both climatic change and human activities. Therefore, the GPPh, is only affected by human activities and can be calculated as follows:
$GPPh\text{ }=\text{ }GPPp\text{ }-GPPa$

2.4 Climatic data

Air temperature and precipitation from 136 meteorological stations in the Tibetan Plateau and surrounding areas were selected and interpolated into raster data layers with a spatial resolution of 1 km × 1 km using the ANUSPLIN 4.2. The interpolated monthly mean air temperature (R2 = 0.99, linear slope = 1.01) and total precipitation (R2 = 0.78, linear slope = 0.99) agreed well with measured monthly mean air temperature and total precipitation, respectively (Fu et al., 2017a).

2.5 Statistical Analysis

We analyzed the relationships between spatially averaged GPPp and the main climatic variables (i.e. precipitation and air temperature) across the grassland regions of the Tibetan Plateau. The regression coefficients between GPP and the time series (i.e. 2000-2015) of linear trends were used to obtain the change trends of GPP during 2000-2015 for each pixel as follows:
$slope=\frac{n\times \sum\limits_{i=1}^{n}{(i\times {{y}_{i}})-\sum\limits_{i=1}^{n}{i\times \sum\limits_{i=1}^{n}{{{y}_{i}}}}}}{n\times \sum\limits_{i=1}^{n}{{{i}^{2}}-{{\left( \sum\limits_{i=1}^{n}{i} \right)}^{2}}}}$
where i is the time series from 2000 to 2015 and yi is the value of the variables concerned in the ith year.
Sp, Sa and Sh indicate the change trends of GPPp, GPPa and GPPh, respectively. The related descriptions of the causes of GPPa changes are listed in Table 1.
Table 1 Causes of changes in annual actual gross primary production (GPPa) during 2000-2015 in alpine grasslands on the Tibetan Plateau
Slope comparison Cause of GPPa change
Sa > 0 and abs(Sp) > abs(Sh) GPPa increase mainly due to climatic change (ICC)
Sa > 0 and abs(Sp) < abs(Sh) GPPa increase mainly due to human activities (IHA)
Sa < 0 and abs(Sp) > abs(Sh) GPPa decrease mainly due to climatic change (DCC)
Sa < 0 and abs(Sp) < abs(Sh) GPPa decrease mainly due to human activities (DHA)

Note: Sp, Sa and Sh indicate the change trends of GPPp, GPPa and GPPh, respectively. abs is the absolute value of the indicated variables; GPPp is climate-driven potential gross primary production; and GPPh is human- induced gross primary production.

According to Table 1, the grassland regions on the Tibetan Plateau were classified into four regions (i.e. the ICC, DCC, IHA and DHA regions). For each region, we first calculated spatially averaged GPPa, GPPp and GPPh, and then calculated the slopes of spatially averaged GPPa, GPPp and GPPh according to equation 4 in meadows, steppes and meadows and steppes combined. We also calculated the spatially averaged annual precipitation, air temperature and their slopes according to equation 4 for the ICC and DCC regions. Then, we compared the sensitivities of GPPp to drought and increased precipitation.
High elevation is one of the most distinguishing features of the Tibetan Plateau (Zhang et al., 2000) and the sensitivities of grassland ecosystems to global change may change with elevation on this Plateau (Fu et al., 2019; Fu et al., 2014; Yu et al., 2019). Therefore, we determined whether climatic change or human activities was more important in affecting GPPa at 14 elevation ranges [3000 m (<3100 m), 3200 m (3100-3300 m), 3400 m (3300-3500 m), 3600 m (3500-3700 m), 3800 m (3700-3900 m), 4000 m (3900- 4100 m), 4200 m (4100-4300 m), 4400 m (4300-4500 m), 4600 m (4500-4700 m), 4800 m (4700-4900 m), 5000 m (4900-5100 m), 5200 m (5100-5300 m), 5400 m (5300- 5500 m) and 5600 m (>5500 m)] in this study. For each elevation range, we first calculated spatially averaged GPPa, GPPp and GPPh, and then calculated the slopes of the spatially averaged GPPa, GPPp and GPPh according to equation 4 in meadows, steppes and meadows and steppes combined.

3 Results

3.1 Changes of GPPa, GPPp and GPPh

Spatially averaged GPPa, GPPp and GPPh showed great interannual variations (Fig. 2). Approximately 24.37% of alpine grassland areas showed decreasing trends, whereas approximately 75.63% of alpine grassland areas showed increasing trends of GPPa (Fig. 3a). Approximately 46.25% of alpine grassland regions demonstrated decreasing trends of GPPp, most of which were distributed in Tibet and the border between Tibet and Sichuan Province (Fig. 3b). Approximately 53.75% of alpine grassland regions indicated increasing trends of GPPp, most of which were distributed in Qinghai Province (Fig. 3b). Approximately 53.86% of alpine grassland areas showed decreasing trends of GPPh, most of which were in Tibet and the border between Tibet and Sichuan Province (Fig. 3c). Approximately 46.14% of alpine grassland areas showed increasing trends of GPPh, most of which were distributed in Qinghai Province (Fig. 3c).

Fig. 2 Interannual variations of GPPa, GPPp and GPPh

Note: GPPa: actual gross primary production (GPP); GPPp: climate- driven potential GPP; GPPh: human-induced GPP; Sa: the anomaly of spatially averaged GPPa; Sp: the anomaly of spatially averaged GPPp; Sh: the anomaly of spatially averaged GPPh

Fig. 3 The change trends of (a) GPPa, (b) GPPp and (c) GPPh

Note: GPPa: actual gross primary production (GPP); GPPp: climate-driven potential GPP; GPPh: human-induced GPP; Sa: the anomaly of spatially averaged GPPa; Sp: the anomaly of spatially averaged GPPp; Sh: the anomaly of spatially averaged GPPh.

3.2 Causes of GPPa change

Climatic change was responsible for a 16.88% decline in GPPa, most of which was distributed in Tibet and the border between Tibet and Sichuan Province (Fig. 4). Human activities were responsible for a 7.49% decrease in GPPa, most of which was distributed in the borders among Tibet, Qinghai Province and Sichuan Province (Fig. 4). Climatic change was responsible for a 42.90% increase in GPPa, most of which was distributed in Qinghai Province and Northern Sichuan Province (Fig. 4). Human activities were responsible for a 32.72% increase in GPPa, most of which was distributed in Tibet and Southern Sichuan Province (Fig. 4).

Fig. 4 Spatial distribution of different drivers of alpine grassland GPP change

Note: GPP: gross primary production; DCC: actual gross primary production (GPPa) decrease due to climatic change; DHA: GPPa decrease due to human activities; ICC: GPPa increase due to climatic change; IHA: GPPa increase due to human activities.

The change trends of GPPa, GPPp and GPPh in meadows and steppes combined are illustrated in Fig. 5. For the DCC regions, the GPPh showed a decreasing trend (Fig. 5a), indicating that the GPP influenced by human activities was reduced and more GPP may remain in alpine grassland ecosystems (Fig. 5a). However, the GPPp showed a greater decreasing trend, which in turn resulted in the decline in GPPa for the DCC regions. For the DHA regions, the GPPp showed an increasing trend due to favorable climatic conditions (Fig. 5b). However, the consumption of GPP by human activity increased considering that GPPh also showed an increasing trend (Fig. 5b). The GPP impacted by human activities was greater than the increased GPP caused by climatic change (Fig. 5b), which in turn caused the decline in GPPa for the DHA regions. For the ICC regions, although the consumption of GPP by human activities showed an increasing trend, the favorable climatic conditions resulted in a greater increasing rate of GPPp (Fig. 5c), which in turn caused the increase in GPPa. For the IHA regions, climatic changes resulted in the decline in GPPp, but the consumption of GPP by human activities showed a higher decreasing rate (Fig. 5d), which in turn resulted in the increase in GPPa.

Fig. 5 Interannual variation of GPPa, GPPp and GPPh in (a) DCC, (b) DHA, (c) ICC and (d) IHA regions for meadows and steppes.

Note: GPPa: actual gross primary production (GPP); GPPp: climate-driven potential GPP; GPPh: human-induced GPP; Sa: the anomaly of spatially averaged GPPa; Sp: the anomaly of spatially averaged GPPp; Sh: the anomaly of spatially averaged GPPh; DCC: actual gross primary production (GPPa) decrease due to climatic change; DHA: GPPa decrease due to human activities; ICC: GPPa increase due to climatic change; IHA: GPPa increase due to human activities.

The Sa, Sp and Sh of meadows and steppes are shown in Table 2. Both Sp and Sh of meadows were lower than those of steppes, and all of them were lower than zero for the DCC and IHA regions. Thus, the negative effects of climatic changes on the gross primary production of meadows were stronger than those of the GPP of steppes, and the decreasing magnitudes of GPPh of meadows were greater than those of steppes for the DCC and IHA regions. However, both Sp and Sh of meadows were greater than those of the steppes, and all of them were greater than zero for the ICC and DHA regions. Thus, climatic changes had more beneficial effects on gross primary production of meadows than that of steppes, whereas the increasing magnitudes of GPPh of meadows were greater than those of steppes for the ICC and DHA regions. The Sa of meadows was greater than that of steppes for the IHA region, and the Sa of meadows was lower than that of steppes for the DCC, DHA and ICC regions.
Table 2 The Sa, Sp and Sh in DCC, DHA, ICC and IHA regions in meadows and steppes.
Steppes Meadows
DCC DHA ICC IHA DCC DHA ICC IHA
Sa -0.44 -0.55 1.89 0.67 -0.60 -0.80 1.68 1.20
Sp -2.49 1.62 3.47 -1.69 -3.02 4.31 4.97 -2.18
Sh -2.05 2.16 1.58 -2.36 -2.42 5.11 3.28 -3.38

Note: GPPa: actual gross primary production (GPP); GPPp: climate- driven potential GPP; GPPh: human-induced GPP; Sa: the anomaly of spatially averaged GPPa; Sp: the anomaly of spatially averaged GPPp; Sh: the anomaly of spatially averaged GPPh; DCC: actual gross primary production (GPPa) decrease due to climatic change; DHA: GPPa decrease due to human activities; ICC: GPPa increase due to climatic change; IHA: GPPa increase due to human activities.

3.3 Effects of elevation on GPPa, GPPp and GPPh

Generally, both Sp and Sh showed unimodal curves with increasing elevation, whereas Sa decreased with increasing elevation (Fig. 6). The absolute values of Sp were greater than those of Sh when elevation was lower than 4800 m, whereas the absolute values of Sp were lower than those of Sh when elevation was greater than or equal to 4800 m across meadows and steppes (Fig. 6a). Both Sp and Sh were lower than zero when elevation was greater than or equal to 4800 m, indicating that climatic change was not conducive to gross primary production and that the negative effects of human activities on gross primary production were dampened during 2000-2015 when elevation was greater than or equal to 4800 m across meadows and steppes (Fig. 6a). The absolute values of Sp were greater than those of Sh when elevation was lower than 5000 m, whereas the absolute values of Sp were lower than those of Sh when elevation was greater than or equal to 5000 m in meadows (Fig. 6b). Both Sp and Sh were lower than zero when elevation was greater than or equal to 5000 m, indicating that climatic change was not conducive to gross primary production and that the negative effects of human activities on gross primary production were dampened during 2000-2015 when elevation was greater than or equal to 5000 m in meadows (Fig. 6b). The absolute values of Sp were greater than those of Sh when elevation was lower than 4600 m, whereas the absolute values of Sp were lower than those of Sh when elevation was greater than or equal to 4600 m in steppes (Fig. 6c). Both Sp and Sh were lower than zero when elevation was greater than or equal to 4600 m, indicating that climatic change was not conducive to gross primary production and that the negative effects of human activities on gross primary production were dampened during 2000-2015 when elevation was greater than or equal to 4600 m in steppes (Fig. 6c).

Fig. 6 The change trends of actual Sa, Sp and Sh in (a) meadows and steppes, (b) meadows and (c) steppes.

Note: Sa: the anomaly of spatially averaged actual gross primary production; Sp: the anomaly of spatially averaged climate-driven potential gross primary production; Sh: the anomaly of spatially averaged human-induced gross primary production.

4 Discussion

Our findings implied that precipitation had a stronger effect on the interannual variation of spatially averaged GPPp than did air temperature (R2 = 0.01 for temperature; R2 = 0.58 for precipitation). This finding was consistent with many previous studies conducted in grasslands on the Tibetan Plateau (Fu et al., 2018; Huang et al., 2016; Piao et al., 2012; Shi et al., 2014; Wang et al., 2013; Wu et al., 2014) and other regions (Niu et al., 2008; Sharp et al., 2013). For example, air humidity had a greater influence on gross primary production than did soil/air temperatures in three alpine meadows of the Northern Tibetan Plateau (Fu and Shen, 2016). Precipitation had a stronger effect on potential net primary production than did air temperature in grasslands on the Tibetan Plateau (Chen et al., 2014; Feng et al., 2017b).
A 2.90 mm yr-1 decline in precipitation caused a 2.91 g C m-2 yr-1 decline in GPPp in the DCC region, whereas a 5.24 mm yr-1 increase in precipitation caused a 4.62 g C m-2 yr-1 increase in GPPp in the ICC region. These findings implied that the sensitivity of GPPp to decreased precipitation (1.00 g C m-2 mm-1) was greater than the sensitivity to increased precipitation (0.88 g C m-2 mm-1). Therefore, drought may have a greater effect on gross primary production than increased precipitation in grasslands on the Tibetan Plateau.
Our findings implied that the causes of GPPa changes varied with elevations and regions (Figs. 4, 6). This finding was similar to those of several previous studies which were performed in alpine grasslands on the Tibetan Plateau (Chen et al., 2014; Huang et al., 2016; Li et al., 2016; Feng et al., 2017b). Under the national policy of the Grazing Withdrawal Program, ecological compensation and fencing degrading grasslands has been implemented since 2003 on the Tibetan Plateau, and livestock number has shown a decreasing trend since 2000 (Chen et al., 2014). These ecological protective measures that were implemented can benefit the restoration of degraded grasslands (Huang et al., 2016; Wang et al., 2016; Yu et al., 2016). However, the negative effect of climatic change on gross primary production was greater than the positive effects of these ecological protective measures on gross primary production, which in turn resulted in the decline in GPPa for the DCC regions. These results implied that either these ecological protective measures may have little impact on the restoration of degraded grasslands during the past dozen years (Yu et al., 2016), or that a longer time can be needed when the positive effects of these ecological measures on gross primary production was greater than the negative effect of climatic change in the DCC regions (Yan and Lu, 2015).
In the DHA regions, the rate of increase of gross primary production impacted by human activities exceed that of climate-driven potential gross primary production, which in turn resulted in the decline in GPPa. These findings implied that it may be necessary to reduce the amount of gross primary production that is consumed by human activities in order to restore degraded grasslands in the DHA regions. In contrast, the increased rate of gross primary production consumed by human activities was not greater than that of climate-driven potential gross primary production in the ICC regions when the elevation was lower than a specific level (i.e. 4800 m for meadows and steppes; 5000 m for meadows; 4600 m for steppes). That is, it may suitably increase the amount of gross primary production that is consumed by human activities in the ICC regions and/or when the elevation was lower than a specific threshold. The rate of decrease of gross primary production consumed by human activities was greater than that of climate-driven potential gross primary production in the IHA regions and/or when the elevation was greater than or equal to a specific threshold. These results implied that it may be suitable to further increase ecological protection measures to restore more rapidly degraded grasslands in the IHA regions and/or when the elevation is greater than or equal to a specific level. Moreover, the GPPp of meadows may have stronger responses to climatic changes than those of steppes. The magnitudes of change of GPPh in meadows were different from those in steppes. Therefore, grassland management measures may vary with elevations, regions and grassland types, indicating that grassland classification management should be strengthened (Yu et al., 2016).

5 Conclusions

In this study, we analyzed the causes of actual gross primary production changes and change trends of actual gross primary production along an elevation gradient during 2000- 2015 in alpine grasslands on the Tibetan Plateau. We found that the causes of actual gross primary production changes varied by regions, elevations and grassland types. Climatic change and human activities accounted for 59.78% and 40.22% of the area percentage of change of actual gross primary production, respectively. Climatic change had stronger effects on the change of actual gross primary production when the elevation was lower than 4800 m, whereas human activities had stronger effects on the change of actual gross primary production when the elevation was greater than or equal to 4800 m across meadows and steppes. Therefore, different regions, elevations and grassland types should adopt different grassland management measures.

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