What drives the vegetation dynamics in the Hengduan Mountain region, southwest China: Climate change or human activity?

Ecological Indicators 112 (2020) 106013

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What drives the vegetation dynamics in the Hengduan Mountain region, southwest China: Climate change or human activity?

T

⁎

Le Yina,c, Erfu Daib,c, , Du Zhenga,c, Yahui Wangb,c, Liang Mab,c, Miao Tongb,c a Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b 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 c University of Chinese Academy of Sciences, Beijing 100049, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Vegetation dynamics NPP Climate change Human activity Separation Attribution

Quantitatively identifying the relative contributions of climate change and human activity to net primary productivity (NPP) is critical for understanding vegetation dynamics and maintaining regional carbon balances. This study focuses on the driving mechanisms of NPP changes and proposes a research framework for evaluating the relative impacts of climate change and human activity. Based on the Thornthwaite Memorial and CASA models, this study first determined the relative contributions of climate change and human activity to actual net primary productivity (ANPP) changes in the Hengduan Mountain region, and then analyzed the response of ANPP to major climate factors at the pixel scale. We found that the contribution of human activities (66.11%) to ANPP change was about twice that of climate change (33.89%) in the Hengduan Mountain region. The ANPP in the north and south region was mainly affected by temperature and precipitation respectively, while although implementing ecological restoration projects had been important for improving vegetation conditions across the Hengduan Mountain region, several human activities, such as overgrazing, sloping cropland reclamation, and urban expansion, were the main reasons for ANPP’s decrease, especially in dry-hot valley areas. There was a significant gradient difference between in how climate change and human activities influence the ANPP, human activity impacted ANPP more in high relief areas along the horizontal gradient, while climate change impacts on ANPP first decreased then increased with rising elevation along the vertical gradient. This study provides a theoretical basis and methodological reference for quantitatively evaluating ecosystems.

1. Introduction

There is now general consensus that climate change and human activity are the main driving forces of vegetation dynamics, and many researchers have relied on correlation (Su and Fu, 2012), regression (Del Grosso et al., 2008), principal component (Caputo et al., 2016), and sensitivity (Sanchez-Canales et al., 2015) analyses to investigate how NPP responses to specific factors on different temporal and spatial scales, ranging respectively from interannual to millennium and regional to global (DeFries et al., 1999; Zhang et al., 2016; Zhou et al., 2003). However, the relative effects on NPP from climate change and human activity are still unclear. Although several studies have focused on these issues and tried to discriminate among the impacts of climate change and human activity (Fu et al., 2013; Li et al., 2016b), many uncertainties and obstacles have yet to be unresolved. For example, distinguishing between climate and human-induced changes in vegetation across different spatial scales remains a notable challenge (Li

Net primary production (NPP) quantifies the accumulated carbon resulting from vegetation photosynthesis and is the basis for the material circulation and energy flow in an ecosystem (Potter et al., 2012). NPP not only provides an indication of ecosystem and ecological balance, but is also a key factor used for evaluating carbon sinks and ecological regulation behavior (Fu et al., 2013; Gao et al., 2009). Climate change and human activities are currently the predominant drivers of changes in NPP (Li et al., 2016b). Global climate change significantly affects ecosystem productivity by changing the rates of plant photosynthesis, respiration, and soil organic carbon decomposition (Melillo et al., 1993; Zhao et al., 2010). Human activity, such as land use change, directly changes the structure, processes and function of an ecosystem (Imhoff et al., 2004; Pei et al., 2013).

⁎ Corresponding author at: 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 E-mail address: [email protected] (E. Dai).

https://doi.org/10.1016/j.ecolind.2019.106013 Received 1 August 2019; Received in revised form 24 October 2019; Accepted 12 December 2019 1470-160X/ © 2019 Published by Elsevier Ltd.

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region. Due to the limited radiation stations in the study area, we calculated total solar radiation from sunshine hours using an empirical formula (Glover and McCulloch, 1958). In this study, ANUSPLIN software was used to interpolate meteorological data with a resolution of 1000 m.

et al., 2012). In China, the Hengduan Mountain region functions as an important carbon sink (Liu et al., 2012). However, because of significant anthropogenic disturbances, such as deforestation, overgrazing, and cropland reclamation (Fan et al., 2012; Shang et al., 2018), soil erosion and rocky desertification in the mountainous region has become more severe since the 1990s (Jiang et al., 2003; Rao et al., 2016). Moreover, vegetation destruction has resulted in a sharp decline in biodiversity, frequent natural disasters, and serious ecological degradation (Tang et al., 2006; Zhang et al., 2007). Since the late 1980s, the Chinese government has launched a variety of initiatives, namely the Yangtze River Shelter Forest Project, Natural Forest Protection Project, and Grain for Green Program (Liu et al., 2004; Zhou et al., 2009) to restore degraded ecosystems (Cao, 2011; Lu et al., 2018). Continued support for restoration requires distinguishing between the impacts of climate change and human activity on NPP, and is not just of theoretical significance, but it also can provide valuable guidance for ecosystem management. However, the complex terrain characterizing the Hengduan Mountain region has created distinct climate differences (Li et al., 2010), making it extraordinarily difficult to evaluate the impacts of climate change and human activity. This study uses NPP as an indicator to characterize vegetation conditions and proposes an integrated quantitative attribution framework (based on separation and attribution) to explore NPP’s response to specific influencing factors. We addressed the following key issues: (i) exploring the spatial distribution and temporal dynamic characteristics of ANPP in the Hengduan Mountain region, (ii) distinguishing the relative effects of climate change and human activity on ANPP, (iii) identifying the response of ANPP to major climatic factors in the Hengduan Mountain region, (iv) analysing the gradient differences of climate change and human activities’ contribution to ANPP changes.

2.3. Calculation of NPP PNPP predicts an ideal condition without interference from human activities, one that is only determined by climate conditions. The ANPP is calculated under the combined impact of climate change and human activities. Therefore, the difference between PNPP and ANPP values can be used to represent the influence of human activity on ANPP, namely HNPP, expressed as follows: (1)

PNPP − HNPP = ANPP

Numerous models have been developed to calculate NPP, including the climate productivity (Thornthwaite Memorial model, Miami model), process (CENTURY, TEM), and light use efficiency (CASA, VPM). In this study, we used the Thornthwaite Memorial and CASA models to calculate PNPP and ANPP. The Thornthwaite Memorial model was built on the relationship between evaporation (ET), temperature, precipitation, and vegetation, and was later modified and described by Lieth (1975); it uses the follows formulas:

PNPP = 3000[1 − e−0.0009695(v − 20) ] v=

(2)

1.05N 1 + (1.05N / L)2

(3) (4)

L = 300 + 25t + 0.05t3

where t is the average annual temperature (°C), L is the annual maximum evapotranspiration (mm), and v is the average annual actual evapotranspiration (mm). The net primary productivity for vegetation in the CASA model is determined by the photosynthetically active radiation (APAR) and the conversion rate of light energy absorbed by vegetation, which was proposed by Potter et al. (1993); the formula is as follows:

2. Materials and methods 2.1. Study area The Hengduan Mountain region is located between 24°29′–33°43′N and 97°10′–104°25′E, and is the general name for the north–south trending mountains west of Sichuan and Yunnan Province and the eastern part of the Tibet Autonomous Region (Fig. 1). The area spans subtropical and plateau temperate zones with annual average precipitation ranging from 500 to 1000 mm and annual mean temperature from 5 °C to 13 °C (Li et al., 2010). The elevation of the Hengduan Mountain region is 421–6233 m, with an average elevation above 3000 m. The large difference in precipitation and temperature conditions between different regions contribute to a variety of vegetation types, including shrub, coniferous forest, and meadows.

(5)

ANPP (x, t) = APAR(x, t) × ε (x, t)

where x is spatial location (the pixel number) and t is time. APAR(x , t ) (MJ m−2 mon-1) represents the photosynthetically active radiation absorbed by pixel x in time t and ε (x , t ) represents the actual light use efficiency (g C MJ−1) of pixel x in time t . APAR(x , t ) and ε (x , t ) in the equation are calculated as follows: (6)

APAR(x, t) = SOL(x, t) × FPAR(x, t) × 0.5 −2

whereSOL (x , t ) is the total solar radiation (M Jm ) of pixel x in timet and FPAR(x , t ) is the fraction of the photosynthetically active radiation absorbed by vegetation. FPAR(x , t ) is determined by NDVI and 0.5 represents the proportion of total solar radiation available for vegetation.

2.2. Data sources and processing Data were collected and inputted to the Thornthwaite Memorial and CASA models to calculate potential NPP (PNPP) and actual NPP (ANPP) in the Hengduan Mountain region. These data include land cover, normalized-difference vegetation index (NDVI), and meteorological data. The land cover data were provided by the Cold and Arid Regions Sciences Data Center (http://westdc.westgis.ac.cn), including 10 types: coniferous forest, mixed forest, broadleaf forest, shrubs, grass, meadows, swamp, alpine vegetation, cultivated vegetation, and water bodies (glacier snow). We downloaded the 16-day with 500-meter spatial resolution NDVI data (MOD13A1) from the National Aeronautics and Space Administration (https://ladsweb. nascom.nasa.gov), and synthesized monthly NDVI data using the Maximum Value Composites (MVC) method. Meteorological data were obtained from the National Meteorological Information Center (http://data.cma.cn/), and include monthly average temperature and total precipitation from 75 meteorological stations in and around the Hengduan Mountain

ε (x, t) = Tε1 (x, t) × Tε 2 (x, t) × Wε (x, t) × εmax

(7)

where Tε1 (x , t ) and Tε2 (x , t ) are temperature stress coefficients, which reflect the reduction of light-use efficiency caused by temperature factor. Wε (x , t ) is the moisture stress coefficient, which indicates the reduction of light-use efficiency caused by moisture factor. εmax is the maximum light-use efficiency under ideal conditions. 2.4. Trend analysis This study used linear regression to determine the rates of NPP change at 1000 m in the Hengduan Mountain region from 2000 to 2015. The rate of NPP change is calculated this way: 2

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Fig. 1. Location of the Hengduan Mountain region with DEM and vegetation types in the study area. n

SNPP =

n

n

variables.

n × ∑i = 1 i × NPPi − ∑i = 1 i ∑i = 1 NPPi n×

n ∑i = 1 i 2

−

n ( ∑i = 1

i)2

(8) 2.6.2. Partial correlation analysis In a multi-factor-affected geographic system, the partial correlation coefficient can be used to measure the relationship between two elements while holding other factors constant. In this study, partial correlation was used to analyze the relationship between ANPP and temperature and between ANPP and precipitation.

when SNPP > 0, NPP increases and vice versa. 2.5. The relative impacts of climate change and human activity on ANPP A positive SPNPP or negative SHNPP trend indicates that climate change or human activity is beneficial to vegetation growth. Conversely, a negative SPNPP or positive SHNPP trend indicates that climate change or human activity promotes vegetation degradation. In this study, we defined six scenarios to determine the relative roles of climate change and human activity, for which we calculated the relative contribution of these two factors using SPNPP and SHNPP (Li et al., 2016b).

rxy, z =

t= 2.6.1. Multiple correlation analysis Multiple correlation analysis was used to characterize the correlation between the dependent variable and multiple independent variables. This study calculates calculated the multiple correlation coefficient between ANPP, temperature, and precipitation. 2 2 1 − (1 − rxy )(1 − rxz ,y)

R2x , yz 1 − R x2, yz

×

n−k−1 k

(11)

rxy, z 2 1 − rxy ,z

n−m−1 (12)

where n is the number of samples; m is the number of independent variables. 3. Results

(9) 3.1. ANPP spatial distribution and changing trends

where Rx , yz represents the multiple correlation coefficient of the dependent variable x and the independent variable y , z ; rxz, y is the partial correlation coefficient of variables x and z for a fixed variable y , and: rxy is the correlation coefficient between variables x and y . In this study, the F-test was used to test the significance of multiple correlation; the formula is as follows:

F=

2 2 (1 − rxz )(1 − ryz )

where, rxy, z is the partial correlation coefficient of variables x and y when holdingz factors constant. rxy , rxz , and ryz represent the correlation coefficients of variables x and y , x and z , and y and z , respectively. In this study, the t-test is used to test significance of partial correlation; the formula is as follows:

2.6. Correlation analysis

Rx , yz =

rxy − rxz − ryz

The average ANPP of the Hengduan Mountain region from 2000 to 2015 was 475.30 g C/(m2·yr), which generally agrees with the findings of Chen et al. (2017) and Wang et al. (2017), respectively. Generally, ANPP was high in the south and low in the north, and the high-value areas were mainly distributed in the low altitude areas with appropriate temperature and precipitation conditions, such as the Nu, Jinsha, Yalong, and Dadu River valleys (Fig. 2a). The ANPP showed an increasing trend from 2000 to 2015 over an area of 335,015 km2, accounting for 74.42% of the total study area. This area was mainly distributed in the

(10)

where n is the number of samples and k is the number of independent 3

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Fig. 2. (a) Multi-year average ANPP (2000–2015); (b) Changes in the ANPP (2000–2015).

Fig. 3. Trends in (a) PNPP and (b) HNPP.

southeastern Ganzi Tibetan Autonomous Prefecture, western Liangshan Yi Autonomous Prefecture. The decrease of ANPP mainly ioccurred in dry-hot valley areas (Fig. 2b).

3.2. Contributions of climate change and human activity to ANPP 3.2.1. Trends in PNPP and HNPP To discriminate the impacts of climate change and human activity, this study analyzed trends in PNPP and HNPP from 2000 to 2015 based on the Matlab platform. PNPP trends indicate that climate change was conducive to vegetation growth in most of the northern Hengduan 4

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Table 1 Defined scenarios. Scenario

Relative role

Contribution Climate (%)

Human (%)

SANPP > 0

SPNPP >

0,SHNPP < 0

Both

100×|SPNPP | |SPNPP |+| SHNPP |

100×|SHNPP | |SPNPP |+| SHNPP |

0,SHNPP < 0

SANPP < 0

SPNPP < SPNPP > SPNPP < SPNPP > SPNPP <

Human activity Climate change Climate change Human activity Both

0 100 100 0

100 0 0 100

100×|SPNPP | |SPNPP |+| SHNPP |

100×|SHNPP | |SPNPP |+| SHNPP |

0,SHNPP > 0 0,SHNPP < 0 0,SHNPP > 0 0,SHNPP > 0

Fig. 4. Spatial distribution of (a) driving factors and (b) contributions to ANPP.

Only in a small part of the north and south did climate change have greater impacts on ANPP than human activity (Fig. 4b). Generally, climate change contributed 33.89% to ANPP changes, while human activity contributed 66.11% to ANPP changes.

Mountain region, accounting for 57.51% of the total area. In contrast, the remaining 42.49% of the total area was not conducive to vegetation growth, being mainly distributed in the southwestern Hengduan Mountain region (Fig. 3a). Trends in HNPP indicate that human activity was beneficial to vegetation growth in most areas of the Hengduan mountain region (SHNPP < 0), accounting for 72.64% of the total area. Vegetation degradation areas caused by human activity were mainly distributed in dry-hot valley areas (SHNPP > 0 ), accounting for 27.36% of the total area (Fig. 3b).

3.3. Relationship between ANPP and climatic factors 3.3.1. Multiple correlation between ANPP and major climatic factors Temperature and precipitation are the main climatic factors affecting ANPP (Del Grosso et al., 2008; Li et al., 2016b). To better understand the relationship between ANPP and climatic factors, we used multiple linear regression to explore the response of ANPP to temperature and precipitation. The spatial distribution of those areas with high multiple correlation coefficients that also passed the significance test was generally consistent with the general area characterized by high climate contributions, as shown in Fig. 5.

3.2.2. The relative impacts of climate change and human activity According to the classification criteria provided in Table 1 and the trends in PNPP and HNPP, this study divided the study area according to the dominant influencing factor and assessed the relative contribution of climate change and human activity to ANPP changes. These results showed that ANPP was affected by both climate change and human activity in most areas (44.63%) of the Hengduan Mountain region. The area dominated solely by human activity accounted for 39.06%, while the area dominated solely by climate change accounted for just 16.31% (Fig. 4a). The impact of human activity on ANPP in most parts of the Hengduan Mountain region was greater than that of climate change.

3.3.2. Partial correlation between ANPP and major climate factors According to the results of partial correlation analysis between ANPP, temperature, and precipitation (Figs. 6 and 7), the ANPP in the northern Hengduan Mountain region was positively correlated with temperature, while in the southern region the ANPP was negatively correlated with temperature. The regions with significant positive 5

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Fig. 5. (a) Multiple correlation coefficient and (b) significance level between ANPP, temperature, and precipitation.

Fig. 6. Partial correlation coefficient (a) and significance level (b) between ANPP and temperature.

the classification criteria (Chen et al., 2017; Wang et al., 2014) (Table 2). Fig. 8 shows the spatial distribution of dominant climatic factors affecting ANPP in the Hengduan Mountain region from 2000 to 2015. These climate factors significantly affected ANPP in 17.27% of the total area (p < 0.1). The area dominated by temperature was relatively widely distributed, and accounted for 42.44% of the total significant

correlation between ANPP and precipitation were generally distributed in the northwest and southeast, while most of other regions had negative correlations.

3.3.3. Identifying climatic factors on ANPP changes on a spatial scale To identify regionally dominant climate factors affecting the changes in ANPP, we used previously published research to formulate 6

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Fig. 7. Partial correlation coefficient (a) and significance level (b) between ANPP and precipitation.

Table 2 Criteria for classifying dominant climate factors. Driving factors

Criteria

Strong driving of temperature and precipitation

[T + P]+

FC < F0.1,tT < t0.05, tP < t0.05

Weak driving of temperature and precipitation Temperature Precipitation

[T + P]-

FC < F0.1,tT ≥ t0.05, tP ≥ t0.05

[T] [P]

FC < F0.1,tT < t0.05, tP ≥ t0.05 FC < F0.1,tT ≥ t0.05, tP < t0.05

Note: FC, tT, and tP represent the significant test statistic of multiple correlation between NPP and major climatic factors, partial correlation between NPP and temperature, and partial correlation between NPP and precipitation, respectively.

area; while 25.23% of the total significant area was dominated by precipitation, mainly distributed in the north and southeast regions. The area dominated by both temperature and precipitation accounted for 32.33% of the total significant area, mainly distributed in the north and southwest.

4. Discussion 4.1. Impact of climatic factors on ANPP Temperature and precipitation were identified as the main climate factors affecting ANPP distribution and changing trends (Liu et al., 2015; Liu et al., 2014; Rizzo and Wiken, 1992). From 2000 to 2015, most of the Hengduan Mountain region experienced a warming and drying trend; for only a small region in the northeast did the climate undergo a warming and wetting trend. Based on partial correlation results, we identified the dominant factors affecting ANPP in different regions. In the northern Hengduan Mountain region, when temperature rose and precipitation remained constant, ANPP increased; conversely, when precipitation increased and temperature remained constant, ANPP only increased in dry-hot

Fig. 8. The spatial distribution of climate factors significantly affecting ANPP in the Hengduan Mountain region from 2000 to 2015.

7

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Fig. 9. Trends in temperature (T) and precipitation (P) in the Hengduan Mountain region from 2000 to 2015.

Fig. 10. Trends in (a) temperature (°C) and (b) precipitation (mm) in the Hengduan Mountain region from 2000 to 2015.

the main driving force for the evolution of the ecological environment in the Hengduan Mountain region. These activities include ecological restoration projects, overgrazing, sloping cropland reclamation, forest harvesting, urbanization, and human-induced natural disasters (Ou et al., 2016). The implementation of the Yangtze River Shelter Forest Project (initiated in 1989), Natural Forest Protection Program (initiated in 1998), and Grain for Green Project (initiated in 2000) have significantly improved vegetation conditions in the Hengduan Mountain region (Qu et al., 2018). Land use change has been recognized as direct human influence on the ecological environment (Padilla et al., 2010; Yang et al., 2014). From 2000 to 2015, the main land use change in the Hengduan Mountain region was the conversion from grassland to woodland and cropland and unused land to grassland (Fig. 11), which

valley areas, indicating that temperature was the dominant factor. In the southern Hengduan Mountain region, when temperature rose and precipitation remained constant, ANPP showed a decreasing trend; when temperature remained constant, the trend in ANPP was consistent with the trend in precipitation, indicating that precipitation was the dominant factor affecting ANPP changes in this area (Figs. 6, 7, 9, 10). 4.2. Impact of human activities on ANPP Human activities are the purposeful transformation or ecological construction of nature, including productive activities and lifestyles (Boumans et al., 2015; Machlis et al., 1997). In recent decades, with the increase in the range and intensity of human activities and the diversity of such interference, anthropogenic disturbance has gradually become 8

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vegetation growth (Liu et al., 2010; Zhao et al., 2015). The less-developed production and lifestyles, e.g., fuelwood as the main energy source, in mountain areas has aggravated forest harvest, which is a primary cause of vegetation degradation (Wang et al., 2001). In addition, urban expansion, mining, and natural disasters were likely responsible for the decline in regional ANPP (Fig. 12). The declines in ANPP in Dali City, Gucheng District, and Xichang City were mainly due to an accelerating urban expansion (Peng et al., 2017; Xu et al., 2015). Vegetation degradation in Panzhihua City was mainly caused by the acceleration of urbanization and expansion of mining areas (Chen et al., 2018). For Wenchuan County, frequent natural disasters have caused devastating damage to the vegetation there (Li et al., 2016a; Ye et al., 2012). 4.3. The relative effects of climate change and human activities on ANPP across spatial scales Spatial heterogeneity refers to the heterogeneity and complexity of ecological process and pattern across a spatial distribution (Pickett and Cadenasso, 1995). The significant differences in temperature and precipitation conditions and anthropogenic disturbance in mountainous areas has created obvious spatial heterogeneities in vegetation dynamics. This study analyzed the relative effects of climate change and human activities on vegetation dynamics in different regions across horizontal and vertical gradients.

Fig. 11. Conversion area (km2) of each land use type during 1990–2015.

played a decisive role in promoting ANPP (Qu et al., 2018; Tao et al., 2016; Zhang et al., 2009). Although ecological restoration projects have promoted ANPP’s increase in most areas of the Hengduan Mountain region, anthropogenic disturbance has created regional ecological degradation in some dry-hot valley areas, especially in the Jinsha River, Anning River (the largest tributary of the Yalong River), and Dadu River valleys. In dry-hot valley areas, overgrazing, sloping cropland reclamation, and forest harvest were the major human factors contributing to ANPP reduction. The number of large livestock (cattle and sheep) rose 183%, 170%, 119%, 97%, 77%, 51%, and 40% in Binchuan County, Huidong County, Jianchuan County, Dongchuan District, Miyi County, and Ningnan County during the 2000–2010 period (http://www.ecosystem. csdb.cn/index.jsp). Affected by population growth, intensified human activity, an extensive production mode, and sloping cropland reclamation has resulted in strong soil erosion, which is unfavorable for

4.3.1. Horizontal gradient differentiation Here, we used geomorphic regions to analyze the differences in the relative effects of climate change and human activities on ANPP changes across horizontal gradients. According to land surface relief, the Hengduan Mountain region can be divided into seven geomorphic types: plain (< 30 m), terrace (30–70 m), hill (70–200 m), low relief mountain (200–500 m), medium relief mountain (500–1000 m), high relief mountain (1000–2500 m), and extreme high relief mountain (> 2500 m), among which medium and high relief mountain accounted for about 70% of the total area.

Fig. 12. The main areas of human-induced NPP decreases in the Hengduan Mountain region. 9

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0.006

60%

200

60%

180

55% 0.005 50% 45%

0.004

160 140

50%

40%

120 40% 0.003

100

30%

35% 80 30%

0.002

25% 0.001 20%

60 40

20%

10%

20

15%

0.000

n ai

pl

e ac

rr

te

l il

n ai

t

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w lo

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h ig

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h ig

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0%

mo

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n ai

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Climate contribution(%) NDVI trends Area(103km2) Proportion(%)

Fig. 13. Contribution of climate change to ANPP in different geomorphological areas compared to the NDVI trends. 110% 20000

80% 70%

10000

60% 50%

5000

40%

Climate contribution(%)

90% 15000

Area(km2)

increased (Fig. 14). Due to significant evaporation in dry-hot valley areas, increases in temperature have not facilitated increases in ANPP, which has resulted in changing vegetation at high altitude to adapt to ongoing climate change (Li et al., 2011; Liang et al., 2018; Ma and Zeng, 2005). With increases in altitude, the impact of human activities decreased, and ANPP changes were mainly affected by climate change. The fluctuation in climate change contributions to ANPP at different altitudes may be related to differences in sensitivity to climate change between vegetation types (Dai et al., 2018; Seddon et al., 2016).

100%

4.4. Uncertainty

30% 0

Admittedly, there are complex interactions between climate change and human activities (Findell et al., 2017; Ojima et al., 1994). Since this study regards climate change and human activities as two independent driving forces, it ignores their interactions, which could have influenced the accuracy of the results separating the impacts of climate change and human activities on ANPP. There were also some uncertainties associated with NPP assessment made in our study. When calculating PNPP, the Thornthwaite Memorial model only considers the effects of temperature and precipitation, ignoring other climatic factors, such as solar radiation and sunshine hours, which may have affected the accuracy of PNPP calculation. Given the few studies on maximum light-use efficiency (εmax ) of vegetation in the Hengduan Mountain region of China, the εmax parameter used in the CASA model to calculate ANPP was based on values of previous studies (Potter et al., 1993; Zhu et al., 2006). Further, climate data (such as temperature, precipitation, and solar radiation) used in this study were obtained via spatial interpolation, which represents another potential source of uncertainty. Additionally, the resolution of all data used in this study is 1 km × 1 km, resulting in a loss of finer-scale details for some minor changes, which was another source of uncertainty. Given the above uncertainties, when using different methods to analyze the driving mechanism of NPP, the spatial distribution of the influcing factors may consequently differ. However, in studies addressing large-scale driving mechanisms of NPP, it is more important to understand its spatial distribution and changing trend. With the improved resolution of land use, climate, and soil data, coupled with the continuous improvement

20% 0

1000

2000

3000

4000

5000

6000

7000

Elevation(m) Fig. 14. Vertical differences in climate change contributions to ANPP.

In contrast to the common understanding that human activities are generally weaker in areas with large relief, this study found that the contribution of climate change to ANPP decreased gradually with increasing relief (Fig. 13). In other words, human-induced NPP increases were concentrated in high relief areas, as corroborated by the NDVI trends. Forest reservation, artificial afforestation, and sloping lands conversion have improved vegetation conditions in the Hengduan Mountain region (http://www.forestry.gov.cn/portal/main/s/72/ content-94998.html), and ecological restoration projects have been implemented in high relief areas to reduce soil erosion. In contrast, human activities such as grazing, harvesting, and urban expansion were mainly concentrated in low relief areas, which are not conducive to vegetation growth. Therefore, the contribution of human activities to NPP changes in high relief areas has been greater than in low relief areas. 4.3.2. Vertical gradient differentiation Along the vertical gradient, the contribution of climate change to ANPP in the Hengduan Mountain region first decreased and then 10

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Appendix A. Supplementary data

of models and better calibration of parameters, the uncertainty of NPPdriving mechanism research will be gradually reduced.

Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.106013.

5. Conclusions

References

Based on the Thornthwaite Memorial and CASA models, this study quantitatively distinguished the relative contributions of climate change and human activity to ANPP changes, and analyzed the response of ANPP to climatic factors on pixel scale in the Hengduan Mountain region. We summarize our main conclusions as follows:

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(1) Generally, the distribution of ANPP in the Hengduan Mountain region decreased from south to north. From 2000 to 2015, ANPP in > 70% of the regions showed an increasing trend, while in < 30% of the region it showed a decreasing trend, especially in dry-hot valley areas. (2) Climate change and human activity were the primary driving forces of ANPP changes in the Hengduan Mountain region. In more than half of the region (55.80%), ANPP was dominated by both climate change and human activity, whereas parts dominated solely by human activities and climate change accounted for 26.81% and 17.04% of the total area, respectively. During the 2000–2015 study period, the contribution of human activities to ANPP’s change was about twice that of climate change in the Hengduan Mountain region, with climate change accounting for 33.89% and human activity accounting for 66.11%, respectively. (3) Over the study period, climate change had various impacts on ANPP changes in the Hengduan Mountain region. Low temperature was the main limiting factor for ANPP’s increase in the north, so a rising temperature was beneficial to increase ANPP. By contrast, in the south, ANPP’s change was mainly affected by precipitation, and reduced precipitation was not conducive to increasing the ANPP there. Notably, rising temperatures were disadvantageous to vegetation growth in dry-hot valley areas. (4) Similarly, human activities showed variable impacts on ANPP changes in the Hengduan Mountain region. Although implementing ecological restoration projects has significantly improved regional vegetation conditions, certain human activities, such as overgrazing, sloping cropland reclamation, and forest harvest, has resulted in vegetation degradation, especially in the dry-hot valley areas. Additionally, ANPP has been severely affected by accelerated urbanization, mining, and natural disasters in some areas of the region. (5) There is a significant gradient difference in how climate change and human activities influence the ANPP. Along the horizontal gradient, human activity had a greater impact on ANPP in high relief areas than in low relief areas. Along the vertical gradient, ANPP changes in low altitude valleys and high altitude areas were mainly driven by climate change. 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. Acknowledgments This research was funded by the National Natural Science Foundation of China [41571098, 41530749], the National Basic Research Program of China (973Program) [2015CB452702], the Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19040304], the Key Programs of the Chinese Academy of Sciences [ZDRW-ZS-2016-6-4-4], and the National Key R&D Program of China [2017YFC1502903, 2018YFC1508805]. 11

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