Global karst vegetation regime and its response to climate change and human activities

Global karst vegetation regime and its response to climate change and human activities

Ecological Indicators 113 (2020) 106208 Contents lists available at ScienceDirect Ecological Indicators journal homepage: www.elsevier.com/locate/ec...

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Ecological Indicators 113 (2020) 106208

Contents lists available at ScienceDirect

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

Global karst vegetation regime and its response to climate change and human activities

T



Sen Zhaoa,b,c, Paulo Pereirad, Xiuqin Wua,b,c, , Jinxing Zhoua,b,c, Jianhua Caoe,f, Weixin Zhanga,b,c a

School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China Key Laboratory of Soil and Water Conservation of State Forestry Administration, Beijing Forestry University, Beijing 100083, China c Jianshui Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China d Environment Management Laboratory, Mykolas Romeris University, Ateities g. 20, LT-08303 Vilnius, Lithuania e Institute of Karst Geology, Chinese Academy of Geological Sciences, Key Laboratory of Karst Dynamics, Ministry of Land and Resources and Science and Technology Department of Guangxi, Guilin 541004, China f International Research Center on Karst under the Auspices of UNESCO, Guilin 541004, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Climate change First-order difference Human footprint Inter-annual variability Karst vegetation NDVI

The vegetation in karst regions (KR) is crucial to maintain fragile local ecosystems. Therefore, it is critical to understand the factors that affect their vitality. The objective of this work is to study global KR vegetation dynamics between 1986 and 2015 and the natural and anthropogenic factors that affect it. The results showed a significantly (p < 0.05) positive greening trend (greening and browning trends were estimated to be 31.90% and 14.29%, respectively). The recuperation of KR vegetation was mainly observed at high latitudes and equatorial regions, where there is less human influence. Nevertheless, the growth of vegetation in some middle and low latitudes areas was a consequence of the human intervention. Vegetation degradation in KR was especially observed at middle latitudes due to the harsh environment and human impact. No significant correlation was observed between karst vegetation stability, the variation of annual average temperature and precipitation accumulated. Globally, karst vegetation dynamics depend more on precipitation than in temperature. In some areas, vegetation can recover gradually, even under extreme conditions and without human intervention. In areas with high population density, vegetation recuperation is mainly as a consequence of restoring programs.

1. Introduction Karst is a geological formation created by chemical (dissolution of limestone, dolomite and gypsum) and physical processes (water erosion, and disaggregation), and occupies more than 10 percent of the terrestrial surface (Ford and Williams, 2007; LeGrand, 1973). In these environments, the rate of the soil formation is low, and the permeability is high due to the presence of large fractures (Dai et al., 2017; Fu et al., 2016). Karst regions (KR) are vulnerable areas to land degradation as a consequence of the reduced vegetation cover (Ford and Williams, 2007; LeGrand, 1973). This vulnerability to degradation has an essential impact on the populations that live in these areas, especially in developing countries (Yan and Cai, 2015). The singular characteristics of KR increase their susceptibility to human impacts, especially in a socio-economic context where the demand for resources is growing, and the effects of land degradation are evident (e.g., desertification). These

areas are extremely vulnerable to meteorological and human-related disasters (LeGrand, 1973; Wang et al., 2004; Yue et al., 2012). Vegetation dynamics is an essential indicator of global climate change (Arneth et al., 2010; Falkowski, 2000). NDVI (Normalized Difference Vegetation Index) is an indicator widely used to measure vegetation vitality (Keenan and Riley, 2018; Zhu et al., 2016). Previous works have been focused on vegetation dynamics in KR of southwest China, using NDVI. They found that greening is increasing fast (Chen et al., 2019; Qu et al., 2018; Tong et al., 2018; Yang et al., 2017; Yue et al., 2012). Other works observed a decreasing trend in vegetation cover in rural areas located in KR (Huang et al., 2017; Keenan and Riley, 2018; Pan et al., 2018). Despite the importance of regional studies, it is crucial to have a global assessment of KR vegetation trends. Several works used temperature and precipitation (or hydrothermal conditions) to assess vegetation dynamic at the global level (Chen et al., 2019; Liu et al., 2018a; Wu et al., 2015). Frequently, regression analysis



Corresponding author at: School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China. E-mail addresses: [email protected] (S. Zhao), [email protected] (P. Pereira), [email protected] (X. Wu), [email protected] (J. Zhou), [email protected] (J. Cao), [email protected] (W. Zhang). https://doi.org/10.1016/j.ecolind.2020.106208 Received 31 July 2019; Received in revised form 23 January 2020; Accepted 8 February 2020 1470-160X/ © 2020 Elsevier Ltd. All rights reserved.

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Derived from Toward Locally Relevant Global Hydrological Simulations (GLOH2O) (http:// www.gloh2o.org/koppen/)

Derived from Dryad Digital Repository (http://www.ciesin.org/)

New global maps of the Köppen-Geiger climate classification Climate Type

0.0083°*0.0083°

Derived from the Climate Research Unit, version TS 4.01 (Harris and Jones, 2017). 1986 to 2015

1993 and 2009 2018 0.083°*0.083° Human Footprint (HFP)

In this study, we aggregated the semi-monthly GIMMS NDVI3g data of each studied year by averaging data of each grid cell. The aggregated

0.5°*0. 5°, 15 days Annual mean temperature

2.2. NDVI3g

0.5°*0. 5°, 15 days

Monthly pixel data resampled into a spatial resolution of 0.05° and temporal resolution of one month. Monthly pixel data resampled into a spatial resolution of 0.05° and temporal resolution of one month. Human pressures on the environment (Venter et al., 2016). Annual precipitation

Table 1 Datasets used in this study.

The datasets (NDVI, annual precipitation, annual mean temperature, human footprint (HFP), and climate types) used in this work are shown in Table 1 and Fig. S1. Previous works used these variables in global climate and vegetation studies (e.g., Tong et al., 2018). Global KR data were collected from the Karst Scientific Data Center. We divided the global KR into climate zones according to the modified Köppen-Geiger version (Beck et al., 2018).

NDVI3g

Manufactured by the School of Geography, Geology, and Environmental Science (SGGES). Index from 0 to 1. Global KR

2. Materials and methods 2.1. Datasets

1986 to 2015

Derived from the Global Inventory Monitoring and Modeling Studies (GIMMS) (3g. v1 available at https://ecocast.arc.nasa.gov/data/pub/gimms/) Derived from the Climate Research Unit, version TS 4.01(Harris and Jones, 2017). 0.05°*0.05°, 15 day

1986 to 2015

Karst Scientific Data Center (http://www.karstdata.cn)

Using Description Data

Resolution

Period

Origin

is usually applied to evaluate their impact (e.g., Liu et al., 2018d; Sun et al., 2015) and detected relations with NDVI. Although the interaction effects can be assessed using the least square method, some shortcomings were observed (Jong et al., 2013; Wu et al., 2015). Residual analysis is another method used to distinguish the changes in land degradation, vegetation, and climate factors (e.g., temperature and precipitation). However, these analyses only consider the value difference (Burrell et al., 2017). The usage of the value difference in time series analysis may result in unstable data. This method does not eliminate external interferences (e.g., the values affected by natural disasters or seasons) and may produce spurious correlations. In KR, the relationship between precipitation and NDVI is complicated, since vegetation dynamics do not depend exclusively from the precipitation, but also local factors such as human impact and surface water loss or accumulation (Ford and Williams, 2007). Therefore, it is essential to identify the relationship between differences between NDVI and climate factors, which is instead of on their absolute values. Due to the spatial heterogeneity of ecosystems, vegetation responses to climate vary considerably and have heterogeneous spatial patterns and time-lag effects (Wu et al., 2015). Therefore, to make a consistent analysis, it is fundamental to consider the impacts of time-lag effects. The increasing anthropogenic effects have negative impacts on vegetation dynamics and distribution (Venter et al., 2016a). At the global scale, several works were carried out about the relationship between human activities and vegetation in different climatic zones and geographical areas (Liu et al., 2019; Bryan et al., 2018; Knauer et al., 2017; Sun et al., 2015; Evans and Geerken, 2004). However, in some KR areas of the southwest China, the effect of human activities in vegetation is not entirely understood (Jiang et al., 2014; Lang et al., 2018; Liao et al., 2018; Tong et al., 2017; Wang et al., 2015). KR biophysical characteristics such as low productivity, slow vegetation recover, and the intense human impact in an environment with limited resources, increase the vulnerability of these areas to degradation (Liu et al., 2018a; Liu et al., 2018b; Zhang et al., 2015) substantially. Therefore, studying the spatiotemporal changes of vegetation caused by human activities is crucial to understand the degree of anthropogenic impact. Global karst vegetation is related to natural and human factors (Yan and Cai, 2015). Natural conditions in KR make the limiting factors for vegetation diversity (Gutiérrez et al., 2014). The studies mentioned were focused on the spatial distribution of long-term greening or browning trends. However, little is known about these trends in KR and how natural processes and human activities can affect it. Our study provides the first comprehensive analysis of the spatiotemporal evolution of global KR vegetation. The aim of this work is to study the vegetation dynamics in global KR from 1986 to 2015 and detect natural and human activities impact. The specific objectives are to assess: (1) the changes of vegetation in global karst areas; (2) the relation between climatic and anthropogenic factors in karst vegetation dynamic; (3) the coupling relationship between humans and vegetation in KR.

Shape

S. Zhao, et al.

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the coefficient that describes the sensitivity of VQP to VQVI and β is the coefficient that describes the sensitivity of VQT to VQVI. The positive and negative values of α and β represent the response of VQVI to VQP and VQT, respectively. MLR provides an effective method of summarizing the factors affecting vegetation dynamics. This can be applied in further research to fit multiple variables and obtain a temporal model (Liu et al., 2018c).

data was further processed into an annual sequence per gird (mean value) from 1986 to 2015. Annual NDVI3g pixel values lower than 0.1 were excluded to show the global karst vegetation distribution accurately during the growing season. 2.3. HFP classification HFP is an indicator created from infrastructure, land cover, and human access into natural areas data. It represents the spatiotemporal impacts of human pressures on the environment (Venter et al., 2016a, 2016b). We calculated the HFP spatiotemporal changes in global KR by subtracting the corresponding grid in ArcGIS 10.2. The regions were graded as follows: (a) change in HFP < 0 (decreasing); (b) change in HFP = 0 (stable); (c) 0 < change in HFP < 1 (slow increasing); (d) 1 < change in HFP < 5 (steady increasing); (e) 5 < change in HFP (sharp increasing). In region b, the pixels with 0 value in HFP between 1993 and 2009 were removed. These regions represent the non-disturbing or the natural regions (f), and the others were the areas disturbed.

2.5. Partial correlation and correlation analysis To further differentiate the impacts on VQVI and avoid the inclusion of the mutual relation to VQP and VQT, a partial correlation analysis was carried out. Based on the first-order difference sequences in annual VQVI, VQP and VQT, the partial correlation coefficients of VQVI, VQP and VQT were calculated in each pixel to discriminate the specific level, using Eq. (6): n

rxy =

2.4. NDVI3g and climate factors analysis

NDVI3gj − NDVI3gi ⎞ ⎟ j−i ⎠

(1)

where δ is the calculated trend value, and NDVI3gt is the NDVI3g value at time t (i ≤ t ≤ j). In order to avoid inaccurate results in multiple linear regression analyses, in this paper, we applied the variation of NDVI, temperature, and precipitation to build three sequences. These three sequences (firstorder) are from the latter value minus the prior one. This can express the fluctuation in more detail. The coefficients of determination obtained from the regression analysis are more dependent on using the first-order difference sequence than in itself. The first-order difference sequence can efficiently express the connection since it is obtained from the original datasets (Hall and Van Keilegom, 2003). NDVI3g varies with the precipitation and temperature inter-annually. However, the interaction between the variable quantity of climate factors (VQC) and of NDVI3g (VQVI) is ignored by the multiple linear regression (MLR). The first-order positive difference (FOPD) datasets in pixel-by-pixel NDVI3g was obtained from Eq. (2), using the annual mean value of each grid cell. This will represent the fluctuation of NDVI3g value in the described range. It can also describe the stability of NDVI3g and illustrate the external environmental response. The variable quantity of annual total precipitation (VQP) and annual mean temperature (VQT) can also be introduced into the FOPD datasets. This was calculated using Eqs. (3) and (4), respectively. The VQT and VQP also express the fluctuation of total annual temperature and precipitation. In order to identify highly sensitive regions, the MLR (Eq. (5)) was constructed to quantify the sensitivity of VQVI (to the stability degree of VQC) by considering VQP and VQT in 1986–2015. The Equations are the following:

VQVIt = NDVI3gt − NDVI3gt − 1

(2)

VQPt = Pt − Pt − 1

(3)

VQTt = Tt − Tt − 1

(4)

VQVIt = ε0 + αVQPt + βVQTt + ε

(5)

n

n

∑i = 1 (x i − x¯)2 ∑i = 1 (yi − y¯)2

(6)

where xy is the factor requiring the calculation of partial correlation coefficient; rxy is the partial correlation coefficient. We used MATLAB R2018a and ArcGIS 10.2 to calculate and map the spatial pattern of rxy. Significant differences were considered at p < 0.05. According to the p level difference, different sensitivity levels were used (Extremely sensitive, p < 0.01; Very sensitive, p < 0.05; Sensitive, p < 0.1; Not sensitive, 0.1 < p < 1). To detect trends among the karst vegetation dynamics in different climatic regions, KR and the global during 1986–2015, a Pearson correlation analysis (p < 0.05 was considered significant) was applied using the Origin 2018 and R software 3.5.3 (https://www.r-project.org/).

The non-parametric Theil-Median (TS) (Sen, 1968) and Contextual Mann-Kendall tests (CMK) (Neeti and Eastman, 2011) were applied to mean NDVI3g KR pixels during the period of 1985–2016 to calculate the trend. This method is robust and is computed according to the formula shown in Eq. (1)

δ = mean ⎜⎛ ⎝

∑i = 1 (x i − x¯)(yi − y¯)

3. Results 3.1. Spatiotemporal patterns of global karst vegetation 3.1.1. Spatial NDVI3g value in global KR The results showed a low average NDVI3g in central Asia and northern Africa (< 0.3). In the equator, the average was high (> 0.7), while in the Southern Hemisphere, it was moderate (0.3 – 0.7) (Fig. 1a). Karst vegetation showed a heterogeneous spatial distribution. KR vegetation was distributed in areas with low (Qinghai-Tibet plateau, central Asia, northern Africa, and northern North America) and high (southwest China, Southeast Asia, Europe, and United States) NDVI3g. In the Northern Hemisphere, the pattern of karst vegetation was different between the east and the west. In the Eastern part, followed a radiating pattern from sparse to dense towards the center. In the Western region, we identified a reduction from north to south. In the northwest half of the globe, KR NDVI3g increases from high to low latitude. 3.1.2. Vegetation dynamics in KR TS and CMK test results showed that at the global level, KR vegetation increased between 1986 and 2015 (31.90%; p < 0.05). The GIMMS NDVI3g results highlighted that the areas where this increase was more evident were southwest China, Russia (Eastern European plain east of the Ural Mountains and north-central Siberia), central Asia (India, Afghanistan, Pakistan), Mediterranean countries (Georgia, Turkey, France, Spain, Morocco, Algeria, Libya, Egypt, Lebanon, Syria), the southern area of Hudson Bay in northeast Canada and most of the African KR. On the contrary, a significant decreasing trend (browning) was observed in less than 15% (p < 0.05) of global KR. The distribution of these areas was scattered in Kazakhstan, Uzbekistan, Turkmenistan, Ukraine, central and western Canada, and southern Ontario (Fig. 1.b, c) (Table S2).

where the subscript t represents the corresponding values of parameters at the time t. ε0 is the intercept for the groups; ε are the residuals; α is 3

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Fig. 1. (a) Average NDVI3g during 1986–2015 in each global karst region. (b) Spatial NDVI3g trends during the period 1986–2015 in global KR. The annual pixel values represent the trend of changes in vegetation dynamics. Colors ranging from brown to green express the value of velocity from browning to greening. (c) Spatial NDVI3g significance during the period 1986–2015 in global karst regions. The pixel values represent the significance of vegetation dynamics.

areas where the temperature has a substantial impact on vegetation are positioned in central Asia, northeast Russia, the Arabian Peninsula, the southern United States, northern Canada, central Europe, and the eastern Mediterranean. Most of the vegetated areas in KR were not vulnerable to climate factors. However, precipitation was more critical to vegetation greenness than temperature (lower-left corner of Fig. 2a and b). This was mainly observed in Ethiopia, southern Africa, and southern Australia.

3.2. Variation sensitivity between climate and vegetation 3.2.1. Sensitivity of VQP and VQT to VQVI in KR Precipitation and temperature affected significantly (p < 0.05) the vegetation dynamics in KR, accounting for 19.82% (Fig. 2b) and 10.76% (Fig. 2a), of the global KR, respectively. The areas where precipitation plays a decisive role in vegetation sensitivity are located in central Asia, the equatorial and southern part of Africa. In contrast, the

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Fig. 2. Spatial sensitivity of VQT, VQP, and VQC to VQVI from 1986 to 2015 in global karst regions. a–b: partial correlations between VQVI and (a) VQT, (b) VQP. c: square of multiple correlation (i.e., coefficient of determination, donated R2) between VQVI and other climate factors (temperature and precipitation). Non-vegetated areas were removed. In the inferior left corner of each figure, it is shown the histogram of the partial correlations (a–b; left side) or R2 (c; left side). The X-axis is the pvalue or R2; the Y-axis is the frequency (log10) of the pixel count. Subfigures (a) ~ (b) have the same legend, which is placed below the figure.

(northern Russia, central Asia, south-central America, equatorial regions, southern Africa, and karst regions of Australia).

3.2.2. Integral climate factors variation sensitivity to VQVI in KR Vegetation and climate variables did not have the same spatial pattern. The relationship between the variation in climate and vegetation showed a decrease from weak to strong (lower-left corner of the Fig. 2c). Climate factors had little or no influence on 58.24% of KR vegetation. Only in 7.45% of areas, it was observed a r2 higher than 0.3

3.3. The vegetation trend between global and different KR We observed that all the KR are mainly located in Arid and Cold 5

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Fig. 3. Pearson correlation between the annual NDVI3g of KR from 1986 to 2015. The histograms above the scatterplots show the KR NDVI3g distribution of the global and the climate regions studied.

regions, accounting for 33.84% and 33.13% of KR, respectively (Fig. S2, Table S1). The Pearson coefficients (Figs. 3 and 4) showed that the trend of change in global karst vegetation was similar to the trend in global vegetation (r = 0.69, p < 0.05). This also can be identified in the different five karst climate regions. The vegetation in cold KR contributed more to the global vegetation dynamics (r = 0.86, p < 0.05), while karst vegetation in arid areas (r = 0.62, p < 0.05) contributed less. Arid karst vegetation had a reduced response to the global and local vegetation change (r was normally non-significant, p > 0.05).

or decreased about 7: 3. The highest NDVI3g fluctuation was identified in the region b, as a consequence of the human pressure. Here, the NDVI3g value increased (22.96%) or decreased (12.61%) importantly. In region c, the increase of NDVI3g was 11.57%, while the decrease was 4.68%. In this region c, more than 70% of the vegetation showed an increasing trend. Region d had the lowest change of all, only 5.12% (3.58% NDVI3g increase and 1.54% decrease) of all significant NDVI3g changes in KR. On the contrary, in the region f where almost no HFP occurred, accounts for 25.12% (18.62% increased, and 6.50% declined) of the significant NDVI3g fluctuation (Fig. 7).

3.4. The relationship between human activity and vegetation in KR

4. Discussion

The results showed relative stability in HFP in KR (Fig. 5). 49.37% of KR had a stable HFP, while an increase was observed in 38.87% of the studied area. This increase was identified, especially in southwestern China, central Asia, the Mediterranean coast, and Africa. A decrease was observed in 11.77% of the KR area, especially in southwest China, western Russia, and Eastern Europe (Fig. 5). HFP changed less in the western hemisphere compared to the eastern hemisphere. We also observed that the natural regions (e.g., Tibet Plateau, north of Canada, South of Hudson Bay, and the Central Sahara) in KR cover 14.59% (Fig. 6). In order to explore the interaction of HFP in vegetated KR, some areas were chosen where it was observed a significant NDVI3g change (p < 0.05, from 1986 to 2015) (Table S2). Overall, there was a significant increase in NDVI3g in 69.16% of the analyzed area. In region a, we identified a decline of HFP with the ratio of NDVI3g either increased

In the previous studies, the distribution of vegetation in KR around the world did not follow a specific pattern, and this was attributed to the different rock properties (Ford and Williams, 2007; LeGrand, 1973). That affects groundwater flow, large voids, high flow velocities and rate (Bakalowicz, 2005). Naturally, climate conditions and vegetation were also different. The human pressures in KR also affected vegetation dynamics (Lang et al., 2018; Tong et al., 2018). The results at different latitudes showed that natural and human impacts on vegetation are not clear (Defriez and Reuman, 2017). It is challenging to have a consistent explanation for the complexity of the processes that affect KR vegetation on a global scale. However, in the regions, some patterns can be identified. In the high latitudes of the northern hemisphere (nearly above the 50° N), our study showed that the NDVI3g in KR was reduced because of the low temperatures. Vegetation dynamics in this KR was different across the regions. A high NDVI3g was observed in Eurasia, 6

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Fig. 4. Relationships between different pairs of annual NDVI3g in various KR from 1986 to 2015. Different colors indicate the degree of correlation. * p < 0.05 and **p < 0.01.

acclimation mechanisms (Huang et al., 2017). Vegetation productivity at latitudes higher than 50° N is limited to the short growing-season period and maximum photosynthetic capacity (Xia et al., 2015). Growing-season maximum photosynthetic capacity is an indicator of vegetation cover. Also, we observed that in high northern latitudes, there is no common trend in vegetation productivity (Huang et al., 2017). This was attributed to the fact that the proportion of KR over high northern latitudes is low as a consequence of the adverse climate conditions and the reduced human disturbance (Ford and Williams, 2007). In these regions, karst vegetation is very peculiar. Due to the short growing season period and poor soil properties (e.g., water

while a low value was identified in North America. Nevertheless, both increased in the studied period. In the sparsely populated KR, vegetation growth was evident. In the northern part of KR in North America, the vegetation cover increased. In lofty mountains of Russia, it was identified as a high vegetation cover as well. NDVI3g increasing was attributed to global warming, which is responsible for snowmelt and vegetation colonization of ice-free areas (Pettorelli et al., 2005; Zhu et al., 2016). The vegetation productivity over high northern latitudes did not follow the same pattern of the temperature. This mismatch was attributed to the effects of resource availability and vegetation

Fig. 5. Spatial HFP change in KR from 1993 to 2009. The pixel values represent the HFP difference. 7

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Fig. 6. Natural KR regions between 1993 and 2009.

driver of global KR vegetation pattern. These results are consistent with previous works (Dai et al., 2017; Fu et al., 2016; Liu et al., 2018c). Despite this, there is an increase in global temperature, and this mainly observed in the Northern hemisphere, where most of the KR are distributed (Bronselaer et al., 2018; Keenan and Riley, 2018; Zeng et al., 2017; Zhu et al., 2016). In northern KR, the temperature increase in low stability areas affects vegetation dynamics. The coupled effect vegetation growth and temperature in boreal regions are weakening. Nevertheless, the vegetation is still greening (Guo et al., 2018). This shows that temperature does not have a substantial importance in vegetation dynamics, as we identified in our work. On a regional scale, we found out that the climatic conditions ruling vegetation in karst areas are different. For arid and cold KR, though the change in vegetation trend was similar to the observed on a global level, the degree of change was different. In our study, KR vegetation cover changed with abundant precipitation, except in arid KR. The relationship of vegetation trend in arid KR with the others KR was not significant. The land use of this region is mainly desert and steppe (Table S1). Here, vegetation is scarce, and precipitation is low and irregular. Therefore, it is difficult to identify a clear vegetation tendency.

retention capacity), the conditions for photosynthesis and vegetation productivity are limited (Huang et al., 2019). Therefore, karst substrate can also be one that the reasons for the reduced vegetation cover in high latitudes of the northern hemisphere. Though there was no apparent correlation in vegetation cover and climate factors in KR, the impacts caused by these drivers cannot be ignored. The variability between vegetation stability and environmental factors such as temperature and precipitation is due to the inconsistency in long-term climatic conditions and the weak capacity of species adaptation (Huang et al., 2017). Considering the time-lag effects of global vegetation responses to climate change Wu et al. (2015) found that the vegetation is not highly sensitive to changes in temperature and precipitation. We observed spatial heterogeneity in the relationship between vegetation vs. temperature and vegetation vs. precipitation. In the Northern hemisphere, the vegetation depends more on the temperature, while in the Southern hemisphere, it is more influenced by precipitation. In total, precipitation has a higher impact on vegetation greening in KR. On a global scale, the fluctuation of precipitation is likely to be a pivotal aspect of vegetation change in KR. Water distribution is more important than temperature and therefore is the major

Fig. 7. Sunrise pattern in HFP and NDVI3g Trend in KR between 1993 and 2009. Color blocks in the central ring represent different HPF areas. For each color block of the inner ring, the outer ring has two parts, green with “+” and brown with “-”, which represent the increase and decrease of NDVI3g. The letters in the inner ring represent the HPF regions, while the values in the outer ring represent the % of NDVI3g increase. The graphic was done using Echarts (https://www.echartsjs.com/).

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5. Conclusion

This factor makes the regional trend in arid KR different from global (Schwalm et al., 2017). Under dry climate conditions, insufficient water limits vegetation cover, reducing plant photosynthesis (Stocker et al., 2019). Cold KR had the highest relationship with the global KR in vegetation trends. The low temperatures at high latitudes and cold regions masked the role of karst in vegetation. Low temperatures reduce soil weathering and vegetation growth rates. This has implications for plant physiology (Ford and Williams, 2007; Huang et al., 2017). With rapid global warming (Feng and Zou, 2019), the temperature may become a relevant factor in the greening trend of these regions (Keenan and Riley, 2018). The areas with improved vegetation were especially located in regions with high population pressure and relatively weak vegetation status, such as Southwest China and India (Chen et al., 2019). Over time, several measures were adopted in some parts of the world to reduce the impact of human activities on land degradation and land abandonment as a consequence of rural exodus. This may have contributed to an increase in the area occupied by vegetation. In several areas of the world, efforts have been carried out in Mediterranean countries like Slovenia (Breg Valjavec et al., 2018; Ravbar and Šebela, 2015), Spain (Pardo-Igúzquiza et al., 2012), Turkey (Ramos et al., 2018) and France (Chen et al., 2017), to restore degraded areas and protect wildland areas. Similar measures were conducted in India (Mane et al., 2019) and southwest China, the most densely populated KR. Both increased and decreased HFP occur in Southwest China. Recently, with the implementation of the environmental programs in China, some measures (e.g., derelict lands, relocation, and eco-compensation) were taken to protect the environment, which resulted in rural exodus. This shows the impact of political decisions on karst vegetation restoration. Therefore, land abandonment and restoration programs played an essential role in vegetation development (Tong et al., 2018). Despite conservation efforts, human activities are the most important cause of vegetation degradation in KR. Due to a lack of resources, local governments cannot achieve the balance between human wellbeing and the protection of the environment (Stefan and Paul, 2008; UNCCD, 2017). In most of the areas are located at the north of latitude 60° north, in the high-altitude hinterland of the Qinghai-Tibet plateau and in the middle of the Sahara Desert (Fig. 6), the adverse natural conditions limit human occupation (Li et al., 2018; Luo et al., 2018). In these areas, KR vegetation increased, and this is a shred of evidence that the plant can recover naturally. Reducing land degradation by 2030 is one of the United Nations Sustainability Development Goals (UN-SDGs) (UNCCD, 2017). KR is prone to soil erosion; therefore, natural vegetation recuperation or restored artificially will have an essential role in soil conservation (Liu et al., 2016). In degraded lands, water erosion reduces the capacity of vegetation importantly to grow. As noted above, KR vegetation is more sensitive to precipitation. Therefore, water and soil conservation in KR is the key to prevent land degradation. UNDP will continue to engage in global efforts to halt and reverse this process. Target 15.3 (Land Degradation Neutrality) is a vital instrument to support countries on sustainable land management and restoration (UNDP, 2017). Based on our analysis, vegetation restoration plans need to be implemented according to climatic conditions and species characteristics, especially in areas densely populated. In a context of global change, karst ecosystems assume high importance as a consequence of their vulnerability. This study did not consider global karst formation processes considering different lithology. Since this was a preliminary study of global karst vegetation, phenology has yet to be addressed. Overall, this work cannot explain all the problems of KR at the global and regional level, but also provides preliminary insights for balancing human activities in a fragile ecosystem. This study lays a foundation for the next steps towards developing and protecting the fragile karst ecosystems and provides a deep understanding of the interaction among vegetation, humans in a global climate change context.

Overall, the karst vegetation regime had the same tendency as global vegetation. KR vegetation recovery was mainly observed at high latitudes and equatorial regions, where human presence is reduced. However, human interventions can result in the growth of vegetation in some areas located at middle and low latitudes. Vegetation deterioration in KR in the middle latitudes (e.g., Kazakhstan, Uzbekistan, Turkmenistan, Ukraine, central and western Canada, and southern Ontario) resulted from the interaction of harsh environment and human impact. No significant correlation was observed between karst vegetation stability and natural factors. However, precipitation is the most influential factor for vegetation distribution. Precipitation has a stronger influence on karst vegetation dynamics compared to temperature. Vegetation recovers steadily, even in extreme situations, with no human interference. In areas densely occupied by humans, vegetation recuperation is mainly because of human intervention in the landscape, primarily through environmental restoring programs focused on reverse land degradation. CRediT authorship contribution statement Sen Zhao: Conceptualization, Methodology, Software, Writing original draft, Visualization, Writing - review & editing, Formal analysis. Paulo Pereira: Conceptualization, Visualization, Writing - review & editing. Xiuqin Wu: Conceptualization, Software, Visualization, Writing - review & editing, Project administration, Data curation. Jinxing Zhou: Supervision, Project administration. Jianhua Cao: Supervision, Project administration. Weixin Zhang: Software, Visualization, Formal analysis. 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 work was supported by funding from the National Key Research and Development Program of China (2016YFC0502506) and the National Natural Science Foundation of China (41671080). We are grateful to our colleagues for their insightful comments on our earlier version of this manuscript. We gratefully acknowledge the Beijing Municipal Education Commission for their financial support through Innovative Transdisciplinary Program “Ecological Restoration Engineering”. We would also like to thank experts who offered their knowledge and time commitment for this research. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2020.106208. References Arneth, A., Harrison, S.P., Zaehle, S., Tsigaridis, K., Menon, S., Bartlein, P.J., Feichter, J., Korhola, A., Kulmala, M., O’Donnell, D., Schurgers, G., Sorvari, S., Vesala, T., 2010. Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci. 3, 525. Bakalowicz, M., 2005. Karst groundwater: A challenge for new resources. Hydrogeol. J. 13, 148–160. https://doi.org/10.1007/s10040-004-0402-9. Beck, H.E., Zimmermann, N.E., McVicar, T.R., Vergopolan, N., Berg, A., Wood, E.F., 2018. Present and future köppen-geiger climate classification maps at 1-km resolution. Sci. Data 5, 1–12. https://doi.org/10.1038/sdata.2018.214. Breg Valjavec, M., Zorn, M., Čarni, A., 2018. Bioindication of human-induced soil degradation in enclosed karst depressions (dolines) using Ellenberg indicator values (Classical Karst, Slovenia). Sci. Total Environ. 640–641, 117–126. https://doi.org/10.

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