Alpine Sparsely Vegetated Areas in the Eastern Qilian Mountains Shrank with Climate Warming in the Past 30 Years

Article

Progress in Physical Geography 1–16 ª The Author(s) 2018 Alpine sparsely vegetated areas Reprints and permission: sagepub.co.uk/journalsPermissions.nav in the eastern Qilian Mountains DOI: 10.1177/0309133318765196 shrank with climate warming in journals.sagepub.com/home/ppg the past 30 years

Biao Zeng Lanzhou University, Gansu Sheng, China Fuguang Zhang Lanzhou University, Gansu Sheng, China Taibao Yang Lanzhou University, Gansu Sheng, China Jiaguo Qi Michigan State University, Michigan, USA Mihretab G. Ghebrezgabher Lanzhou University, Gansu Sheng, China

Abstract Alpine sparsely vegetated areas (ASVAs) in mountains are sensitive to climate change and rarely studied. In this study, we focused on the response of ASVA distribution to climate change in the eastern Qilian Mountains (EQLM) from the 1990s to the 2010s. The ASVA distribution ranges in the EQLM during the past three decades were obtained from the Thematic Mapper remote sensing digital images by using the threshold of normalized difference vegetation index (NDVI) and artificial visual interpretation. Results indicated that the ASVA shrank gradually in the EQLM and lost its area by approximately 11.4% from the 1990s to the 2010s. The shrunken ASVA with markedly more area than the expanded one was mainly located at altitudes from 3700 m to 4300 m, which were comparatively lower than the average altitude of the ASVA distribution ranges. This condition led to the low ASVA boundaries in the EQLM moving upwards at a significant velocity of 22 m/decade at the regional scale. This vertical zonal process was modulated by topography-induced differences in local hydrothermal conditions. Thus, the ASVA shrank mainly in its lower parts with mild and sunny slopes. Annual maximum NDVI in the transition zone increased significantly and showed a stronger positive correlation with significantly increasing temperature than insignificant precipitation variations during 1990–2015. The ASVA shrinkage and up-shifting of its boundary were attributed to climate warming, which facilitated the upper part of alpine meadow in the EQLM by releasing the low temperature limitation on vegetation growth.

Corresponding author: Fuguang Zhang, Institute of Glaciology and Ecogeography, College of Earth and Environmental Sciences, Lanzhou university, 222 Tianshui S Rd, Chengguan Qu, Lanzhou Shi, Gansu Sheng 730000, China. Email: [email protected] 2 Progress in Physical Geography XX(X)

Keywords Alpine sparsely vegetated area, climate change, topography, remote sensing, eastern Qilian Mountains

I Introduction related vegetation belt shifts under climate change at a regional scale (Mu et al., 2013; Zhou Change in vegetation cover is a main research et al., 2014). topic in the international scientific community The alpine sparsely vegetated area (ASVA) is (IPCC, 2013). Many studies have found that aregionofrareorsparsevegetationthatis climate change has caused significant changes in vegetation distribution and growth from the located above the alpine meadow zone within global scale through local ones. Cold areas are the vegetation spectrum along altitudinal gradi- usually highly sensitive to climate change ents. The ASVA, including landscape type of (Aerts et al., 2006; Chen et al., 2012; Qin subnival belts and snow-ice zone, exhibits great et al., 2013). Their vegetation shows not only contrast with the surrounding areas in vegeta- changes in growth and canopy coverage, but tion status, surface radiation characteristics, also significant changes in distribution range hydrology, and soil (Aerts et al., 2006; Wang under the current changing climate. Thus, cli- et al., 2011; Ma, 2013). The ASVA situated in mate warming is an important driving force such high altitudes and cold areas is highly sen- that leads to a vegetation boundary shift to sitive to climate change (Aerts et al., 2006; high latitudes and altitudes, which are charac- Wang et al., 2007; Zeng and Yang, 2009; Xu, terized by cold climates (Chen et al., 2012; 2010; John Briks, 2013), and it is less impacted IPCC, 2013). These types of local shifts can by direct human activities because of scarce result in observable vegetation belt changes in population (Chen and Han, 2010; Yuan, zonal and vertical dimensions at a regional 2015). In the transition zone between alpine scale (Erschbamer et al., 2009; Jia, 2010; Sun meadow and the ASVA, alpine meadows invade and Cheng, 2014). upward into the ASVA or retreat downward Alpine systems of high-altitude gradient and under climate variations, thereby resulting in cold climate are ideal places to observe vegeta- shrinkage/expansion of the ASVA. These con- tion dynamics and belt shifts under climate ditions allow the possibility to observe alpine change. Many studies have paid attention to the ecosystem changes under a pure natural process. interactions between vegetation and climate on This study focused on the distribution the Tibetan Plateau in the past decades (Levy, changes of the ASVA in the eastern Qilian 2008; Yu and Xu, 2009). However, given the Mountains (EQLM), which is one of the main constraints of data and workload, such studies distributing regions of the ASVA on the Tibe- were mainly based on coarse-resolution remote tan Plateau. The ASVA changes in the EQLM sensing monitoring at a large scale or simula- from the 1990s to the 2010s and implications tions by a regional climate model. These works to climate change under topographical modu- failed to capture details of vegetation dynamics lation were investigated with high temporal influenced by local terrain and soil factors, and spatial resolution data. This study might which may significantly affect vegetation distri- provide an important background of natural bution in the mountain system (Hwang et al., forces, which is essential for further studies 2011; Jahan and Gan, 2011). Moreover, few on alpine ecosystem dynamics under complex studies on the Plateau have focused on the quan- forces mixing climate variations and human titative invasion of the alpine meadow and its interruptions. Zeng et al. 3

Figure 1. Study area.

II Materials and methods significantly under recent warming (Liu et al., 2.1 Study area 2012; Wang et al., 2012; Yao et al., 2013). Although some vegetation was destroyed in low The EQLM, situated in the northeast Tibetan altitudes, the transition zones between the Plateau, contains the Lenglongling, Wushaol- ASVA and alpine meadow are less influenced ing, and Dabanshan areas (Figure 1). It ranges by direct human activities (Chen and Han, 2010; in longitudes 100 40’ E–102 52’ E and lati- Yuan, 2015). tudes 36 44’ N–38 13’ N. The length and width of the EQLM are 225 and 160 km, respectively. Our study only inspected the lands of 2.2 Datasets elevations above 2900 m in the EQLM to focus 2.2.1 Remote sensing images. The Thematic Map- on the dynamics between the ASVA and alpine per (TM) and the Operational Land Imager meadow. (OLI) remote sensing data, which had a spatial The EQLM possesses a long slope of large resolution of 30 m with a vertical projection, area and huge elevation differences in the south were used in this study. In general, a few hun- and a relatively steeper one in the north. The dred meters in the vertical direction in the mountain topography results in great diversity mountain area indicate several kilometers in the in local climate (especially in air temperature horizontal direction. Thus, the distribution and and precipitation), which generates a typical dynamics of the ASVA can be investigated alpine and semi-arid mountain ecosystem in the using the TM remote sensing data. EQLM (Sha et al., 2016). Vegetation-related The Landsat TM and OLI data were obtained environment of the EQLM has changed from the United States Geological Survey 4 Progress in Physical Geography XX(X)

Table 1. Correlation analysis of GIMMS NDVI and MODIS NDVI in 2000–2006. Year 2000 2001 2002 2003 2004 2005 2006 Correlation coefficient 0.962** 0.958** 0.973** 0.957** 0.901** 0.928** 0.895**

Note: ** indicates significance at p < 0.01 levels.

(http://glovis.usgs.gov/) covering the time span used to reduce cloud noise, atmospheric condi- of 1990–2010. The images in 1995, 2005, and tions, and sensor geometries (Eklundh and Jo¨ns- 2015 were adopted to interpret the status of the son, 2010). Given that the NDVI time series was AVSA in the 1990s, 2000s, and 2010s, respec- applied to explore vegetation changes and its tively. The image data used in this process were dynamic trend, we only adopted pixels with a selected under two critical conditions: (a) within 26-year monthly average NDVI greater than the summer seasons (July and August), when 0.1. The values beyond double standard devia- snow cover exerts a slight influence on images tion of the corresponding monthly mean (com- and vegetation growth reaches its maximum; monly noise data) were abandoned and replaced and (b) cloud cover is lower than 10%. After by the monthly mean. This procedure guaran- the radiometric and geometric corrections, a ter- tees that the subsequent analysis will not be rain correction was performed for each image affected by non-vegetation-related variations using the ground control points. (Myneni et al., 1997). Finally, the annual NDVI A successive normalized difference vegeta- maximum images were derived from the tion index (NDVI) series was used to observe monthly NDVI time series by the maximum vegetation changes in the transition zone value composite method to represent the best between the ASVA and alpine meadow. This vegetation growth status in a year. NDVI series was composed of a 17-year (1990–2006) Advanced Very High-Resolution 2.2.2 Climate data. Climate data from 30 meteor- Radiometer NDVI series compiled by the Glo- ological stations near the EQLM were adopted bal Inventory Mapping and Monitoring Study to interpolate the distributed monthly climate (GIMMS) group at NASA/Goddard Space series of the study area because few meteorolo- Flight Center (https://www.nasa.gov/centers/ gical stations were in situ in the high mountains. goddard) and a 16-year (2000–2015) Moderate Monthly terrestrial climate data were obtained Resolution Imaging Spectrometer (MODIS) from the China Meteorological Data Sharing NDVI (MOD13Q1) dataset derived from the Service System (http://cdc.cma.gov.cn) from Land Processes Distributed Active Archive 1990 to 2015. Annual mean temperature and Center (https://lpdaac.usgs.gov). The consis- annual total precipitation were interpolated tency of GIMMS NDVI and MODIS NDVI was using ordinary kriging with varying local mean checked on the basis of the overlapping 7a data methods (Guan et al., 2009). The locally vary- from 2000 to 2006 (Table 1). The result showed ing means were evaluated using multivariate a correlation coefficient above 0.8, which indi- linear regression, which considered the latitude, cates good consistency between the two time- longitude, and elevation of the stations and the series data. residuals between the locally varying means The GIMMS images were resampled into (Goovaerts, 2000; Guan et al., 2009; Szentimrey 250 m pixels to match the MODIS ones after et al., 2011). The root mean square error was being converted into Albers Conical Equal Area applied to examine the accuracy of the interpo- projection. Savitzky–Golay function fitting was lated climatic surfaces (Chang, 2010). The Zeng et al. 5

EQLM parts were clipped from the interpolated ASVA because its annual mean temperature images and used as the basic climatic data. reaches above 0 C, which is generally taken as the lower boundary of alpine meadow. This 2.2.3 Digital elevation model. In this study, the study focused on the ASVA influenced by the ASTER GDEM V2 data at a spatial resolution upper boundary of alpine meadow. Thus, a cri- of 30 m were obtained from National Aeronau- terion, elevation above 2900, was applied to the tics and Space Administration (http://gdem.ers resulting distribution maps based on the DEM dac.jspacesystems.or.jp/). The ASTER GDEM image of the study area. V2 data, as a correction for GDEM, possess Artificial visual modification was also exe- high definition and precision in horizontal and cuted to verify obscure boundaries, correct vertical directions that can fully meet the obvious flaws, and remove the direct impact research needs. of human activities by masking the mines and quarries, constructed roads, and all other artificial objects from the study area. Therefore, the 2.3 Methods changes forced purely by natural processes 2.3.1 ASVA interpretation. The ASVA and alpine could be presented by the interpreted results. meadow present great differences in vegetation By these means, the ASVA distribution cover and show a distinguished boundary in the images were separately retrieved for three peri- remote sensing images. Thus, a composite ods: the 1990s, 2000s, and 2010s. method by NDVI screening and visual interpretation was utilized to determine the ASVA 2.3.2 Accuracy evaluation of interpreted results. boundaries. In particular, the NDVI value was The ASVA interpretation under artificial inter- calculated for each Landsat TM image after data vening may produce an offset error at a pixel pre-processing mentioned in Section 2.1. The level. In general, the error is insignificant if it is ASVA distribution was preliminarily inter- less than half pixel size or 5%, which is accep- preted by a set of NDVI thresholds, below table (Bolch et al., 2010). Thus, buffer analysis which it was believed to be the ASVA because was adopted to estimate the results of the per- of its sparse vegetation. The NDVI thresholds formed artificial visual interpretation by creat- were determined on the basis of the correspon- ing a 7.5 m buffer and then checking the errors dence between NDVI images and the GPS field (Granshaw and Fountain, 2006). This analysis survey data. The NDVI values were read from was performed for each final extraction result of NDVI images at the locations of the ASVA the three periods. The interpretation errors were boundaries measured by GPS in situ or deter- 2.74%, 2.73%, and 2.78% for the 1990s, 2000s, mined by visual interpretation in some typical and 2010s, respectively, which are generally sections. The values were averaged after remov- acceptable. ing the outliers beyond double standard devia- tions. The results were utilized as the thresholds 2.3.3 Detection and analyses of ASVA distribution in which a preliminary ASVA distribution was change. The ASVA distribution changes in the extracted separately for each image. An inter- EQLM were retrieved by comparing the distri- section was then processed for the three remote bution ranges among the 1990s, 2000s, and sensing images in a same year to obtain a con- 2010s. The ASVA dynamics were classified servative result of the ASVA distribution for a into three types: the stable (no change), shrun- certain decadal period. ken, and expanded areas. The stable area indi- A sparsely vegetated area below 2900 m in cates no change, the shrunken area indicates the EQLM is considered impossible to be the invasion by alpine meadow and established 6 Progress in Physical Geography XX(X) vegetation, and the expanded area indicates III Results vegetation clearance in the originally vege- 3.1 Changes in the ASVA distribution tated areas. The area changes of the three types of 3.1.1 Changes at the regional scale. The ASVA dynamics were analyzed at the regional and distribution in the EQLM generally shrank from local scales in different topographic condi- the 1990s to the 2010s but was fragmentarily tions. Specifically, the ASVA dynamics of the expanded in some local sections (Figure 2). The EQLMwereinspectedby23elevationbelts shrinkage rate was rapid from the 1990s to that were divided from the total elevation range the 2000s and slightly slow from the 2000s to (2900–5232 m) at an interval of 100 m based the 2010s; consequently, the shrunken area in the first period was greater than that of the on DEM. The results were compared between second one (Figure 3 and Table 2). Meanwhile, elevation belts. Similar analyses were con- the expansion of the ASVA was localized and ducted by slope and aspect at 2 and 45 of low in area and changing ratio (Figure 3); how- intervals, respectively. ever, it slightly increased during the 2000s to the 2010s compared with that in the 1990s–2000s, 2.3.4 Linear trend and correlation analyses. The especially at altitudes between 3500 and 3700 m linear trends in the time series of NDVImax and (Figure 3(c)). climatic factors during the study period were The lower boundary of the ASVA shifted extracted using the linear regression function upwards to higher altitudes at a rate of approx- defined as imately 22 m/decade during the study period (Figure 4 and Table 3). Yi ¼ A þ BXi þ e ð1Þ where Yi is the ith measurement of the Y time 3.1.2 Changes in different elevation belts. As series (the temperature/precipitation value in shown in Figure 3, the ASVA was mainly the ith year), Xi is the ith measurement of the located at the altitudes above 3700 m, with a independent variable (time), A is the intercept, B peak of area within 4000–4300 m. Above 4300 is the slope of the linear regression, and E is an m, the ASVA area of elevation belt declined with unexplained random error. For the regression the increase in altitude purely due to terrain char- coefficients, the slopes were estimated using acteristics; that is, less surface area of mountain linear least squares (Rawlings et al., 1998). The peaks. The mountains above 4300 m were dom- 2 coefficient of determination (R ) and probabil- inated by the stable ASVA with no vegetation ity value (p) were calculated and used to analyze since the 1990s (Figure 3(c)), which probably the fitness of linear regression (Rawlings et al., meant a strong growth limitation of alpine vege- 1998). We adopted the results at the signifi- tation. Zhang et al. (2012) and Zhang (2015) cance level of p < 0.005. emphasized that vegetation activities are always Correlations between NDVImax variations weak and non-existent at high altitudes with and single climate factor changes were also strong temperature limitation and steep slopes analyzed by the function above at the inter- where water availability in soils is very low for annual time scale. The partial and complex vegetation establishment. correlation coefficients between NDVImax and The ASVA distribution mainly changed at multiple climatic factors (annual mean tem- the lower part of its distribution range, namely, perature and annual total precipitation) were altitudes from 3700 m to 4300 m. Although the calculated by Statistical Package for Social stable ASVA was still dominantly situated in Science software. the area, the ASVA dynamics, the expanded and Zeng et al. 7

Figure 2. Distribution range of the ASVA in the EQLM in (a) the 1990s, (b) the 2000s, and (c) the 2010s, respectively; and (d) the sample of changes in the Wushaoling area in the past 30 years. shrunken areas, showed significant percentages Below 3700 m, the ASVA distribution and exhibited an area peak at around 4000 m became very sparse, and its boundary area was (Figure 3(a) and (b)). Notably, the area peak reduced significantly (Figures 3 and 4). The of the ASVA dynamics appeared at the bottom shrunken ASVA took a dominant percentage of the peak range of the ASVA distribution. compared with the stable area particularly in the These findings indicated that the ASVA distri- 1990s–2000s. During the 2000s–2010s, the bution changes in terms of absolute area were shrinkage slowed down and more ASVA concentrated at the boundary of its main distri- became stable. Meanwhile, the expanded bution range. In addition, the shrunken area of ASVA increased at these lower altitudes during the ASVA was far larger than the expanded area this period but still slightly contributed to the during the two separate periods and subse- ASVA boundary dynamics because of its small quently in the entire period. This phenomenon absolute area. resulted in the shrinkage of the ASVA at the In general, the ASVA shrinkage ratio gradu- regional scale mentioned above. ally increased with the decrease in elevation. 8 Progress in Physical Geography XX(X)

Figure 3. Changes in the distribution of the ASVA at different elevations: changes in the stable, shrunken, and expanded areas at different elevations of the ASVA in (a) the 1990s–2000s and (b) 2000s–2010s, respectively; and (c) change ratio of the stable, shrunken, and expanded areas at different elevations of the ASVA in the 1990s–2000s and 2000s–2010s. Note: Changes in area ratio of the stable, shrunken, and expanded areas at different altitudes are calculated by the corresponding variation between two interdecadal years divided by the ASVA area of the base years. In the second image of (c), the absolute area of the ASVA is very small after the previous period’s atrophy, which is less than 3500 m. The change in its proportion cannot reflect the zonality law.

This result may imply that the lower part of dynamics within different slope intervals. The alpine area with a warm climate background results in Figure 5 indicated that the ASVA possesses high possibility to be released from distribution changes were mainly located in cold temperature limitation of vegetation the areas with a slope below 12 in the past growth. 30 years, and ASVA shrinkage generally decreased with the increase in slope. 3.1.3 Changes in local slopes and aspects. The The total amount of the shrunken ASVA was effects of slope on the ASVA distribution slightly greater in the 1990s–2000s than in the changes were inspected by comparing 2000s–2010s in each slope interval (Figure 5(b)). Zeng et al. 9

Table 2. Area change in the ASVA in the EQLM in the 1990s–2010s. Years Total area (km 2) Shrinking area (km 2) Rate of shrinking area Annual reduction (km 2) 1990s 1895.95 119.38 6.3% 11.94 2000s 1776.57 91.24 5.1% 9.12 2010s 1685.33 – – – Sum – 210.62 11.4% –

Figure 4. Variations in the lower boundary of the ASVA in the EQLM in the past 30 years. Note: The points represent the ASVA with sporadic distribution in the transitional zone between the ASVA and alpine meadows. The elevation of the lower boundary in the transitional zone was extracted in the 1990s, 2000s, and 2010s. Weighted average of area was used to obtain the average elevation of the lower boundary of the ASVA. Polynomial fitting was used to fit the distribution of its boundary and obtain its distribution trend.

Table 3. Average elevation of the lower boundary By contrast, the total amount of the expanded of the ASVA in the EQLM. ASVA was greater in the 2000s–2010s than in the 1990s–2000s at different slopes. The chang- Average elevation Upward moving Years (meters) (meters) ing ratios showed similar results (Figure 5(a) and (b)). 1990s 3914.45 – Meanwhile, the ASVA dynamics appeared þ48.48 nonhomogeneous in different aspects (Figure 2000s 3952.93 6). The shrunken ASVAs were concentrated in þ16.18 the sunny aspects (SE, S, SW, and W) with a 2010s 3964.46 Sum – þ64.66 warm condition, whereas the expanded ASVAs showed no significant preference (Figure 6(c)). 10 Progress in Physical Geography XX(X)

Figure 5. Changes in the distribution of the ASVA at different slopes: area changes and its ratio in the stable, shrunken, and expanded areas at different slopes of the ASVA in (a) the 1990s–2000s and (b) 2000s–2010s, respectively.

3.2 NDVI trend in the transition zone and its consistent with the upward-shifting trend of the correlations with climatic variations ASVA distribution boundary (Figure 7). At the same time, the annual mean tempera- A vertical 50 m buffer zone to the ASVA bound- ture in the EQLM increased by approximately ary (an elevation belt of 50 m interval below the 3.9 C since 1990, with a significant warming boundary) was made as the transition zone rate of 1.3 C per decade (p < 0.05; Figure 8 and between alpine meadows and the ASVA. The Table 4). This warming trend was significant in 26-year NDVImax sequence of the transition the local growing seasons (generally from May zone was obtained from the pre-processed to October) of vegetation. However, the NDVI image data mentioned in the introduc- changes in annual precipitation showed no sig- tion. Linear regression was applied to examine nificant trend during the study period and only the trend of the inter-annual variations of fluctuated in the range of 300–600 mm in the NDVImax in the transition zone. time series (Table 4 and Figure 8). NDVImax of the transition zone between Correlation analyses of NDVI, temperature, alpine meadows and the ASVA increased sig- and precipitation in the transition zone were nificantly during the study period, which was carried out by single-factor correlation, partial Zeng et al. 11

Figure 6. Changes in the distribution of the ASVA at different aspects: changes in the stable, shrunken, and expanded areas at different aspects of the ASVA in (a) the 1990s–2000s and (b) 2000s–2010s, respectively; and change ratio of the stable, shrunken, and expanded areas at different aspects of the ASVA in (c) the 1990s–2000s and (d) 2000s–2010s, respectively. correlation, and complex correlation, respec- increase in annual mean temperature and annual tively (Table 5). A significant positive correla- total precipitation, with the former being more tion was detected between annual NDVImax and effective than the latter. annual mean temperature and annual total precipitation in the transition zone. The complex correlation coefficient was 0.86. The pos- 3.3 Impacts of climate change on the ASVA itive correlation coefficient between annual dynamics NDVImax and annual mean temperature was The ASVA boundary is the upper boundary of greater than that between annual NDVImax and the alpine meadow belt. Thus, changes in the annual total precipitation in the partial correla- ASVA distribution were mainly controlled by tion analysis. A similar result was found for the alpine meadow upward invasion and retreat. single-factor correlation coefficient. The regres- The influences of precipitation and air tempera- sion formula in Table 5 indicated that vegeta- ture changes on vegetation growth are more tion in the transition zone was facilitated by the direct and significant than other climate factors 12 Progress in Physical Geography XX(X)

Figure 7. Variations of NDVImax in the transition zone for 1990–2015.

Figure 8. Variations in the annual mean temperature and annual total precipitation measured in the EQLM for 1990–2015.

(Lin, 2010; Jin et al., 2016). They affect growth maintaining vegetation growth (Shen et al., and distribution of alpine plants through photo- 2015; Yan et al., 2011). synthesis, respiration, and soil organic carbon The upper part of the alpine meadow in the decomposition. Specifically, the growth rate of EQLM was facilitated during the study period vegetation is seriously limited when tempera- by significant climate warming that released ture is lower than its minimum need for low temperature limitation on vegetation Zeng et al. 13

Table 4. p values of air temperature (T) and precipitation (P) trends for annual and monthly durations during 1990–2015. Annual Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. T <0.001 0.279 0.033 0.041 0.015 0.003 <0.001 0.003 0.042 0.045 <0.001 0.310 0.711 P 0.237 -0.896 -0.844 -0.042 -0.555 -0.990 0.808 -0.297 0.594 0.113 0.401 0.035 0.758

Note: Positive values represent increasing trends, whereas negative values indicate decreasing trends.

Table 5. Correlation between climatic variables and vegetation NDVI in the transition zone. Correlation Partial correla- analysis tion analysis T P T/P P/T Regression analysis

** ** ** ** 2 ** NDVImax 0.848 0.806 0.518 0.292 NDVImax ¼ 0.002641*T þ 0.00022*P þ 0.155768 (R ¼ 0.74 ) Note: Partial correlation analysis (T/P) is related to temperature and NDVI under rainfall stationary conditions. Similarly, partial correlation analysis (P/T) is related to precipitation and NDVI under temperature fixation. ** indicates significance at p < 0.01 levels. growth. This deduction could be drawn from the phenomenon may be due to the topology- ASVA distribution changes and the NDVI induced differences in hydrothermal conditions. increasing trend in the transition zone. Climate Liu et al. (2013) suggested that an area with a warming rather than variations in precipitation steep slope commonly possesses poor soil was more emphasized because of its sensitivity foundation and water availability, which are in correlation analyses and no significant trend important for vegetation establishment. Thus, in precipitation (Tables 4 and 5). This result was areas with steep slopes remain stable as non- consistent with the findings of Zeng and Yang vegetated in the alpine regions (Figure 5). (2009), who emphasized that vegetation growth Meanwhile, alpine areas with low altitude and in a high-altitude area is mainly limited by tem- sunny aspect usually possess a warm climate perature, and the increase in temperature has background and high possibility for released- improved the vegetation in these areas in recent from-cold temperature limitation of vegetation decades. growth under the same warming event. On the Under this facilitation process, the alpine contrary, the expansion may result from vege- meadow in the EQLM invaded into the ASVA tation deteriorations induced by warming- under climate warming and resulted in ASVA reduced moisture availability on the slopes of shrinkage and up-shifting of its boundary at the less fine soil particles to contain water or by regional scale during the past 30 years. local detritus cover from frost weathering. This However, the terrain modulated this zonal condition was significant at the altitudes process in the vertical vegetation spectrum. As (below 4000 m) that experienced a release of shown in Figure 3, the ASVA did not shift temperature limitation during the former upwards in some elevation belts. Instead, the period (Figure 4). shrunken ASVAs were detected in many eleva- Notably, the shrinkage slowed down in the tion belts, and the shrunken and expanded areas 2000s–2010s than in the 1990s–2000s. How- coexisted in the same elevation belt. This ever, no significant differences were found in 14 Progress in Physical Geography XX(X)

the warming trend and NDVImax increasing meadow growth and facilitated vegetation trend between the two time periods (Figures 7 establishment in the alpine areas of the EQLM. and 8). In other words, climate warming and facilitation on vegetation growth continued, Acknowledgements but ASVA shrinkage slowed down. This phe- The authors would like to thank the anonymous nomenon should be attributed to mountain reviewers for their constructive comments, which topography instead of changes in the shrinking improved the quality of this study. process. ASVA shrinkage similarly occurred Declaration of Conflicting Interests during the latter period, which could be con- firmed by the enhanced shrinkage within ele- The authors declared no potential conflicts of interest with respect to the research, authorship, and/or pub- vation belts from 4000 m to 4500 m (Figure 3). lication of this article. ASVA shrinkage slowed down at lower altitudes that already experienced a release of tem- Funding perature limitation during the former period. The author(s) disclosed receipt of the following However, the shrinkage was significantly financial support for the research, authorship, and/ enhanced at higher altitudes of newly released or publication of this article: This work was sup- temperature limitation. Higher altitudes of a ported by NSFC project No.40901056. mountain generally come with steep slopes and less surface area within a given elevation inter- References val, which are unfavorable for the establish- Aerts R, Cornelissen JHC and Dorrepaal E (2006) Plant ment of alpine meadow. performance in a warmer world: General responses of plants from cold, northern biomes and the importance of winter and spring events. Plant Ecology 182(2): 65–77. IV Conclusions Bolch T, Yao T, Kang S, et al. (2010) A glacier inventory In this study, the ASVA distribution changes in for the western Nyainqentanglha Range and the Nam the EQLM during the past 30 years and NDVI Co Basin, Tibet, and glacier changes 1976–2009. trend in the transition zone between the ASVA Cryosphere 4(3): 419–433. and alpine meadow were investigated by remote Chang K (2010) Introduction to Geographic Information Systems. New York: McGraw-Hill. sensing. The ASVA in the EQLM shrank by Chen J, Yang YA and Sun H (2012) Advances in the nearly 11.4% because of alpine meadow inva- studies of responses of alpine plants to global warming. sion under recent climate warming, whereas the Chinese Journal of Applied and Environmental Biology ASVA expanded in local sections. As a conse- 17(3): 435–446. quence, the ASVA average boundary shifted Chen RS and Han TT (2010) Hydrology, ecology and upwards to higher altitudes at a rate of approx- climate significance and its research progress of the imately 22 m/decade during the study period. alpine cold desert. Progress in Earth Science 25(3): This vertical zonal process was modulated by 255–263. topography-induced differences in local hydro- Eklundh L and Jo¨nsson P (2010). Timesat 3.0 Software thermal conditions, which resulted in ASVA Manual. Lund: Lund University. Erschbamer B, Kiebacher T, Mallaun M, et al. (2009) shrinkage mostly in the lower parts with mild Short-term signals of climate change along an altitu- and sunny slopes. Correlation analyses and dinal gradient in the south alps. Plant Ecology 202(1): synthesized assessment showed that the ASVA 79–89. shrinkage and up-shifting of its boundary were Goovaerts P (2000) Geostatistical approaches for incor- attributed to climate warming, which released porating elevation into the spatial interpolation of the low temperature limitation on alpine rainfall. Journal of Hydrology 228(1/2): 113–129. Zeng et al. 15

Granshaw FD and Fountain AG (2006) Glacier change Ma Y (2013) Study on the change of the Tibetan Plateau (1958–1998) in the North Cascades National Park climate system and the mechanism of its impact on Complex, Washington, USA. Journal of Glaciology eastern Asia. In: AGU Fall Meeting AGU Fall Meeting 52(52): 251–256. Abstracts. Washington, DC: American Geophysical Guan H, Wilson JL and Makhnin O (2009) Geostatistical Union (Vol. 16). mapping of mountain precipitation incorporating Mu S, Yang H, Li J, et al. (2013) Spatio-temporal autosearched effects of terrain and climatic character- dynamics of vegetation coverage and its relationship istics. Journal of Hydrometeorology 6(6): 1018. with climate factors in inner Mongolia, China. Journal Hwang T, Song C, Vose JM, et al. (2011) Topography- of Geographical Sciences 23(2): 231–246. mediated controls on local vegetation phenology esti- Myneni RB, Keeling CD, Tucker CJ, et al. (1997) mated from MODIS vegetation index. Landscape Increased plant growth in the northern high latitudes Ecology 26(4): 541–556. from 1981 to 1991. Nature 386(6626): 698–702. IPCC (2013) Summary for policymakers. In: Stocker, T.F., Qin JH, Zhang WX, Sun H, et al. (2013) Temperature D. Qin, G.-K. Plattner, M. et al. (eds) Climate Change sensitivity of enzyme activity in subalpine forest soil in 2013: The physical science basis. Contribution of Southwest China. Acta Pedologica Sinica 50(6): Working Group I to the Fifth Assessment Report of the 1241–1245. Intergovernmental Panel on Climate Change.Cam- Rawlings JO, Pantula SG and Dickey DA (1988) Applied bridge, UK, New York, NY: Cambridge University Regression Analysis. New York: Springer-Verleg. Press. Sha W, Dong S, Liu S, et al. (2016) The natural pro- Jahan N and Gan TY (2011) Modelling the vegetation– tection of spatial pattern of plant community bio- climate relationship in a boreal mixed wood forest of mass and species diversity and the influencing Alberta using normalized difference and enhanced factors of Altun Mountain. Journal of Ecology 35 vegetation indices. International Journal of Remote (2): 330–337. Sensing 32(2): 313–335. Shen X, Liu B, Li G, et al. (2015) Impacts of grassland Jia QY (2010) Research advances in effects of climate types and vegetation cover changes on surface air change on the vegetation zone. Journal of Anhui temperature in the regions of temperate grassland of Agricultural Sciences. China. Theoretical and Applied Climatology 126(1/2): Jin X, Liu J, Wang S, et al. (2016) Vegetation dynamics 141–150. and their response to groundwater and climate variables Sun J and Cheng GW (2014) Review on the study of the in Qaidam Basin, China. International Journal of vertical belts of mountains. Journal of Ecological Remote Sensing 37(3): 710–728. Environment 09: 1544–1550. John Briks (2013) Alpine plants and recent climate Szentimrey T, Bihari Z, Lakatos M, et al. (2011) Mathe- change. Climatica July 12. Available at: http://clima matical and methodological questions concerning the tica.org.uk/alpine-plants-and-recent-climate-change spatial interpolation of climate elements. Idojaras (accessed March 15, 2017). 115(1): 1–11. Levy JK (2008) Spatiotemporal vegetation cover varia- Wang G, Bai W, Li N, et al. (2011) Climate changes and its tions in the Qinghai–Tibet Plateau under global climate impact on the tundra ecosystem in Qinghai–Tibet Pla- change. Science Bulletin 53(6): 915–922. teau, China. Climatic Change 106(3): 463–482. Lin L (2010) Temporal and spatial response of vegetation Wang G, Wang Y, Li Y, et al. (2007) Influences of alpine NDVI to temperature and precipitation in eastern ecosystem responses to climatic change on soil prop- China. Journal of Geographical Sciences 20(20): erties on the Qinghai–Tibet Plateau, China. Catena 163–176. 70(3): 506–514. Liu J, Liu XL and Hou LM (2012) Changes and ecological Wang Y, Jia W, Liu C, et al. (2012) Ecological envi- vulnerability of landscape pattern in eastern Qilian ronment change in north slope of Qilian Mountains. Mountains. Arid Land Geography 35(5): 795–805. Chinese Forestry Science and Technology (3): Liu MX and Wang G (2013) Responses of plant commu- 42–43. nity diversity and soil factors to slope aspect in alpine Xu GP (2010) Response and Adaptation of Plants and meadow. Chinese Journal of Ecology 32(2): 259–265. Plant Communities to Grazing in Alpine Meadow. 16 Progress in Physical Geography XX(X)

China: Northwest Institute of Plateau Biology, Chinese Zeng B and Yang TB (2009) Impacts of climate warming Academy of Sciences. on vegetation in Qaidam area from 1990 to 2003. Yan YH, Qi-Fei YI, and Huang ZL (2011) The ecological Environmental Monitoring and Assessment 144(1/3): response of fern diversity to vegetation succession in 403–417. Gudoushan Nature Reserve, Guangdong. Chinese Zhang H, Qian Y, Wu Z, et al. (2012) Vegetation– Biodiversity 19(1): 41–33. environment relationships between the northern Yao JQ, Yang Q, Chen YN, et al. (2013) Climate change in slope of Karlik Mountain and Naomaohu Basin, arid areas of northwest China in the past 50 years and its East Tianshan Mountains. Chinese Geographical effects on the local ecological environment. Chinese Science 22(3): 288–301. Journal of Ecology 32(5): 1283–1291. Zhang WX (2015) Effects of topographic factors on plant Yu HY and Xu JC (2009) Effects of climate change growth in mountainous areas. National Geographic (3): on vegetations on Qinghai–Tibet Plateau: A review. 76–77. Chinese Journal of Ecology 28(4): 747–754. Zhou W, Gang C, Li J, et al. (2014) Spatial-temporal Yuan YP (2015) Response of radial growth of Qinghai dynamics of grassland coverage and its response to spruce at different altitudes in Qilian to climate. Doc- climate change in China during 1982–2010. Acta toral dissertation, Lanzhou University, China. Geographica Sinica 69(1): 15–30.