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Journal of Geographical Sciences >

2017 , Vol. 27 >Issue 6: 681 - 696

DOI: https://doi.org/10.1007/s11442-017-1400-x

Orginal Article

Mapping the hotspots and coldspots of ecosystem services in conservation priority setting

  • LI Yingjie , 1, * ,
  • ZHANG Liwei , 1 ,
  • YAN Junping 1 ,
  • WANG Pengtao 1 ,
  • HU Ningke 1 ,
  • CHENG Wei 2 ,
  • FU Bojie 2
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  • 1. Department of Geography, Tourism and Environment College of Shaanxi Normal University, Xi'an 710119, China
  • 2. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, CAS, Beijing 100085, China

Author: Li Yingjie (1990-), specialized in environmental change and ecosystem services. E-mail:

*Corresponding author: Zhang Liwei (1985-), Assistant Professor, specialized in landscape ecology and ecosystem services. E-mail:

Received date: 2016-03-15

  Accepted date: 2016-07-29

  Online published: 2017-06-10

Supported by

National Natural Science Foundation of China, No.41601182

National Social Science Foundation of China, No.14AZD094

National Key Research and Development Plan of China, No.2016YFC0501601

China Postdoctoral Science Foundation, No.2016M592743

Fundamental Research Funds for the Central Universities, No.GK201603078

Key Project of the Ministry of Education of China, No.15JJD790022

Copyright

Journal of Geographical Sciences, All Rights Reserved
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Abstract

Spatial-explicitly mapping of the hotspots and coldspots is a vital link in the priority setting for ecosystem services (ES) conservation. However, little research has identified and tested the compactness and efficiency of their ES hotspots and coldspots, which may weaken the effectiveness of ecological conservation. In this study, based on the RUSLE model and Getis-Ord Gi* statistics, we quantified the variation of annual soil conservation services (SC) and identified the statistically significant hotspots and coldspots in Shaanxi Province of China from 2000 to 2013. The results indicate that, 1) areas with high SC presented a significantly increasing trend as well, while areas with low SC only changed slightly; 2) SC hotspots and coldspots showed an obvious spatial differentiation—the hotspots were mainly spatially aggregated in southern Shaanxi, while the coldspots were mainly distributed in the Guanzhong Basin and Sand-windy Plateau; and 3) the identified hotspots had the highest capacity of providing SC, with 29.6% of the total area providing 59.7% of the total service. In contrast, the coldspots occupied 46.3% of the total area, but only provided 17.2% of the total SC. In addition to conserving single ES, the Getis-Ord Gi* statistics method can also help identify multi-functional priority areas for conserving multiple ES and biodiversity.

Key words： ecosystem services mapping; soil conservation; spatial clustering; Getis-Ord Gi* statistics; Shaanxi Province

Cite this article

LI Yingjie , ZHANG Liwei , YAN Junping , WANG Pengtao , HU Ningke , CHENG Wei , FU Bojie . Mapping the hotspots and coldspots of ecosystem services in conservation priority setting[J]. Journal of Geographical Sciences, 2017 , 27(6) : 681 -696 . DOI: 10.1007/s11442-017-1400-x

1 Introduction

Ecosystem services (ES) are regarded as an effective communication tool to bridge the knowledge between science, policy making, and practice. Work in this field has gained increasing attention in recent years (Trabucchi et al., 2014; Guerra et al., 2016). Generally, ES are grouped into supporting, provisioning, regulating, and cultural services (MA, 2005; Adhikari and Hartemink, 2016). Soil conservation service (SC) is a critical regulating service supplied by terrestrial ecosystems to prevent soil erosion. It is well known that soil loss is one of the most severe and widespread environmental problems in China, especially in the Loess Plateau (Fu et al., 2011). The soil deterioration caused by soil loss brings a series of negative impacts on the fragile ecosystem, threatening the sustainability of the food security and social economy in the region (Fu et al., 2005; Fu et al., 2011; Guerra et al., 2016), which have drawn much attention from the stakeholders who suffered the dilemma (Fu et al., 2015). The Chinese central and local governments launched a series of soil and water conservation measures to alleviate this situation, and the Grain-for-Green Program (GfG) is one of the most ambitious and widespread actions to restore vegetation. These kinds of giant projects usually need an enormous investment of manpower and material resources, which may add a great burden to our national economy and consequently restrict the sustainability of the projects. Thus, spatial-explicitly assessing soil conservation is of great importance to convey effective messages to the stakeholders and facilitate targeted decision making. ES mapping, especially the identification of ES hotspots is a primary node bringing ES into the process of ecological conservation assessment.
ES hotspots are defined as regions with high service-diversity, high biophysical or monetary value of services, or high capability of supplying services; the opposite features are defined as coldspots (Li, 2014; Schröterand Remme, 2016). Here, we focus on “hotspot” defined as areas with a high biophysical value of a single service. Identifying hotspots and coldspots can offer a reference for scientifically defining conservation boundary and setting conservation priority area when allocating limited resources in the process of ecosystem management (Reyers et al., 2009; Zhang and Fu, 2014). A range of relevant research has been performed by using several priority-setting approaches (Trabucchi et al., 2013; Zhang et al., 2014), which can be classified into two types. The first one is by defining a certain threshold to determine hotspots and coldspots. For example, Wu et al. (2013) defined the top 10% of the grid cell value as the hotspots of that ES. Similarly, Gimona and van der Horst (2007) counted the grid cells values above or below the median value of all grid cells as hotspots. These maneuverable practices can map priority areas and provide references for systematic ecological conservation planning. However, threshold or quantiles-based method usually ignore the landscape connectivity between or within the identified hotspots, which can lead to undesirable and severe landscape fragmentation. Implementing conservation projects in fragmentized patches can be thorny and costly (Mitchell et al., 2015). Thus, spatial clustering methods are needed to prevent identifying fragmentary hotspots. Fortunately, another type of method is based on spatial aggregation/clustering analysis, among which, Kernel density estimation (KDE), a frequently used hotspot analysis method can reveal where point or line features are concentrated (Alessa et al., 2008). However, the KDE method only takes the location information into the identification but does not put the features’ attribute values into the results. Therefore, KDE is only useful for the aggregation of scattered, location-based data (such as the survey data of multiple ES), but is unfit for spatially uniform grid data. In addition, geostatistical analyses, such as Moran’s I, Getis-Ord Gi* statistics can also be used to identify hotspots, but in practice, the Getis-Ord Gi* statistics (or Gi* statistics, for short) was proved to be superior to the alternatives (Braithwaite and Li, 2007). The greatest advantage of the Gi* statistics is that it takes the value of all neighboring features into consideration and reports hotspots and coldspots with different levels of statistical significance. The output hotspots can present better continuous surface, which is an expression of landscape connectivity. Hotspot analysis using the Gi* statistics has been widely applied in crime analysis, epidemiology, traffic accidents, economic geography, demographics and similar parameters (Alessa et al., 2008; ESRI, 2013; Barro et al., 2015); in recent years, it is commonly seen in biodiversity study (O'Farrell et al., 2011; Di Minin et al., 2013). However, seldom has it been used in the identification of ES conservation priority areas. Although a few “hotspots mapping” can be found in relevant studies by using kernel density estimation or defining a certain threshold (Guerra et al., 2016; Li, 2014), these methods are not sufficient from the perspective of statistical significance (Mitchell, 2005), thus are less effective when compared to the Gi* statistics.
In this study, we use Shaanxi Province as a case study. Based on the RUSLE model and the newly introduced hotspot analysis method (i.e., Gi* statistics), the objectives of this study are to 1) map and assess the spatio-temporal variations of SC in Shaanxi from 2000 to 2013; 2) identify the hotspots and coldspots of SC and evaluate their capacity of supplying SC; and 3) discuss the driving factors that led to changes in SC. The results and method may contribute to conservation planning as well as support the policy making associated with sustainable land-use planning and ecosystem management.

2 Materials and methodology

2.1 Study area

Shaanxi Province (105°29°-110°15°E, 31°42°-39°35°N) is located in northwest China, with an area of 205.8 thousand km2 and population of about 37.75 million by the end of 2013 (Figure 1). Characterized by a mainland monsoon climate, the annual precipitation decreases from south to north. The annual precipitation in the Hanjiang River Basin is about 1000 mm, reduces to 800 mm in the Qinling Mountain zone, and is only 400 mm in the Sand-windy Plateau zone. The major soil types in Shaanxi include loessial soil, (yellow) brown soil, cinnamon soil and aeolian sandy soil (Feng, 2013). Terrains of Shaanxi are high in the north and south, and low the middle part of the Guanzhong Basin. The Yellow River Basin and the Yangtze River Basin account for 62.6% and 35.4% of Shaanxi’s area, respectively, and approximately 40% of Shaanxi is on the Loess Plateau, where the inappropriate land use and degraded vegetation have made it the most severe soil loss region in China (Jiang et al., 2015; Su et al., 2012).Under the background of climate change and rapid economic development in recent decades, the ecosystem degradation in Shaanxi has prompted great concern about the conservation of biodiversity and ES (Jia et al., 2014). During the past decades, many ameliorative actions, like the Three-North Shelter Forest Program (TNSFP) and the Grain-for-Green Program (GfG), have made great contributions to vegetation restoration and soil loss control in China. However, this kind of program at the physical regionalization scale or watershed scale may invite poor accountability for regional management (Zhang and Song, 2003; Wang et al., 2010b). Thus we conducted the research at an administrative regional scale, hoping to facilitate the policy making of ecosystem restoration.
Figure 1 Location of meteorological stations and geographical division in Shaanxi Province, China

2.2 Datasets and methodology

2.2.1 Data sources
The monthly meteorological data (precipitation and temperature) of 45 stations (Figure 1) are retrieved from the website of National Meteorological Information Center. Topographical parameters (i.e. slope, aspect, and elevation) are derived from STRM (Shuttle Radar Topography Mission) DEM data. The soil properties data come from Harmonized World Soil Database (version 1.2). The NDVI (Normalized Difference Vegetation Index) data are acquired from NASA’s Earth Observing System. The Land Use and Land Cover (LULC) maps are derived and interpreted from the Landsat Thematic Mapper (TM) data, and we control the accuracy at about 92% by field reconnaissance and Google Earth verification. We resampled all the parameters (listed in Table 1) into 250 m resolution before inputting them into the model simulation.
Table 1 The datasets sources
Data Type Resolution Time period Sources
Meteorological data Point — 2000-2013 http://cdc.cma.gov.cn/
Soil properties Raster 1 km 2000 http://webarchive.iiasa.ac.at/
DEM Raster 90 m 2004 http://srtm.csi.cgiar.org/
LULC Polygon 30 m 2000, 2013 http://www.landcover.org/data/
MODIS NDVI Raster 250 m 2000-2013 http://ladsweb.nascom.nasa.gov/data/
2.2.2 Method for mapping SC
The staple soil conservation (SC) assessment methods are mainly based on empirical soil erosion models, i.e., the RUSLE (Revised Universal Soil Loss Equation) model (Renard et al., 1997; Wischmeier and Smith, 1965; Rao et al., 2014), by which soil conservation can be quantified by the difference between potential soil loss and actual soil loss (Li, 2014; Guerra et al., 2014; Baró et al., 2015).
SC = Ap - Ar = R × K × L × S - R × K × L × S × C × P (1)
where SC is the annual amount of soil conservation (t·hm-2·yr-1); Ap presents the annual potential soil erosion without ES supplied (here, vegetation cover), and Ar is annual actual soil loss; other parameters are estimated as follows:
1) R: rainfall erosivity factor (MJ·mm·hm-2·h-1·yr-1) is calculated by using the empirical formula developed by Wischmeier and Smith (1978); the Pi and P are the monthly and annual precipitation (mm) respectively.
$R=\sum\limits_{i=1}^{12}{1.735\times {{10}^{\left( 1.5\lg \frac{{{P}_{i}}^{2}}{P}-0.8188 \right)}}}$ (2)
2) K: soil erodibility factor (t·ha·h·ha-1·MJ-1·mm-1) describes the vulnerability of the soil to raindrop detachment and runoff wash. The calculation is based on the EPIC (Erosion- Productivity Impact Calculator) equation formulated by Sharpley and Williams (1990); the SAN, SIL, CLA and C are the percentage (%) of sand, silt, clay and organic matter in soil, and SNI= 1 - SAN/100 (Zhang et al., 2014)
$\begin{align} & K=\left\{ 0.2+0.3\exp \left[ -0.0256SAN\frac{1-SIL}{100} \right] \right\}{{\left( \frac{SIL}{CLA+SIL} \right)}^{0.3}}\times \\ & \ \ \ \ \ \left[ 1.0-\frac{0.25C}{C+\exp (3.72-2.95C)} \right]\left[ 1.0-\frac{0.7SNI}{SNI+\exp (-5.51+22.9SNI)} \right]\times 0.1317 \\ \end{align}$ (3)
3) L: the slope length factor is calculated using formula defined by McCool et al. (1987); S stands for slope factor, and m is a dimensionless constant depending on the percent slope (θ).
$L\text{=}{{\left( \frac{\lambda }{22.13} \right)}^{m}}\left\{ \begin{align} & m=0.5\ \ \ \theta \ge 9 \\ & m=0.4\ \ \ 9>\theta \ge 3 \\ & m=0.3\ \ \ 3>\theta \ge 1 \\ & m=0.2\ \ \ 1>\theta \\ \end{align} \right.$ (4)
$S\text{=}\left\{ \begin{align} & 10.8\text{sin}\theta +0.03\ \ \ \theta <9 \\ & 16.8\sin \theta -0.50\ \ \ 9\le \theta \ge 18 \\ & 21.91\sin \theta -0.96\ \ \theta >18 \\ \end{align} \right.$ (5)
4) C: crop and management factor; C is estimated by using Cai et al. (2000) model. The f parameter refers to vegetation coverage, which is computed by using NDVI data (Fu et al., 2011).
$C\text{=}\left\{ \begin{align} & \ \ \ \ \ \ \ \ \ \ \ 1\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ f=0 \\ & 0.6508-0.3436\lg f\ \ \ \ \ \ 0<f\le 78.3 \\ & \ \ \ \ \ \ \ \ \ \ 0\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ f>78.3 \\ \end{align} \right.$ (6)
5) P refers to conservation practice factor, which is estimated according to the method applied in Loess Plateau (Lufafa et al., 2003; Fu et al., 2011). α is the percentile slope gradient and is calculated from DEM.
P = 0.2 +0.03α(7)
2.2.3 Gi* statistics-based hotspots and coldspots analysis
In this paper, the Gi* statistics was used to identify hotspots and coldspots of soil conservation service (SC). As a tool integrated in ArcGIS 10.2, this approach takes each raster pixel within the context of neighboring features into the calculation and outputs a new feature class with z-score, p-value and confidence level. Features with high z-score and small p-value indicate statistically significant hotspots, and features with low negative z-score and small p-value demonstrate statistically significant coldspots. The magnitude of the absolute value of the z-score explains the intensity of the clustering (Getis and Ord, 1992; Mitchell, 2005). This approach can help identify hotspots and coldspots with different significant levels, so based on which stakeholders can set corresponding priorities according to the actual requirements. The principle of this method is shown as follows:
$G_{i}^{*}\text{=}\frac{\sum\limits_{j=\text{1}}^{n}{{{w}_{ij}}{{x}_{j}}-\overline{X}\sum\limits_{j=\text{1}}^{n}{{{w}_{ij}}}}}{S\sqrt{\frac{{{\left[ n\sum\limits_{j=\text{1}}^{\text{n}}{w_{ij}^{2}-\left( \sum\limits_{j=\text{1}}^{n}{{{w}_{ij}}} \right)} \right]}^{2}}}{n-1}}}$ (8)
where the$G_{i}^{*}$ is a z-score of patch i. xj is the attribute value for patch j; wij is the spatial weight between patch i and patch j, if the distance from a neighbor j to the feature i is within the distance, wij = 1; otherwise wij = 0; n is the total number of grid cells and
$\overline{X}=\frac{\sum\limits_{j=1}^{n}{{{x}_{j}}}}{n}$, $S=\sqrt{\frac{\sum\limits_{j=\text{1}}^{n}{x_{j}^{2}}}{n-1}-{{(\bar{X})}^{2}}}$ (9)
Identifying and mapping the hotspots and coldspots can visualize priority areas spatial-explicitly, which is helpful for targeted policy making. Crossman and Bryan (2009) have demonstrated that the conservation benefits could increase by 25% by using hotspots analysis over a random approach.
2.2.4 Other methods
Linear trend analysis has been widely used in analyzing the vegetarian cover change and climate change for a continuous period (Lu et al., 2015; Deng et al., 2013). In this paper, we adopted linear regression to analyze the changing trend of SC from 2000 to 2013. Also, Kriging spatial interpolation was conducted using the geostatistical analysis module in ArcGIS 10.2 (ESRI, 2013), through which we can adjust the parameters and construct the ideal interpolation model.

3 Results

3.1 Spatio-temporal variations of SC

The spatial patterns of SC show that the amount of SC increased approximately from north to south (Figure 2a). Precisely, divided by Qinling Mountains, the SC in the Guanzhong Basin and Sand-windy Plateau zone was low, while the Qinling-Daba Mountains zone showed high SC.
Figure 2 Spatial patterns of SC (a), change rate of SC (b) and the significant (P<0.05) change areas (c) from 2000 to 2013. The change rate at cell level was calculated by using the least square method (LSM).
The total SC in Shaanxi experienced a significant increase at a rate of 0.47 t·hm-2·a-1 (P<0.01) from 2000 to 2013. It increased from 5.43×108 t in 2000 to 14.07×108 t in 2013, which generally synchronized with the rainfall. Figures 2b and 2c show the change rate and the significance of changing trend, respectively. The significant increase of SC (P<0.05) was intensively aggregated in the west of Qinling-Daba Mountains and was sporadically distributed in the north of the Loess Plateau zone. Besides, areas with high SC presented a significant increase while areas with low SC changed slightly.
Since the Grain-for-Green Program (GfG) was launched in 1999, the vegetation restoration has improved a lot. Accordingly, the soil loss control has made great progress in the Loess Plateau, especially in the middle of the Loess Plateau zone. SC’s spatial distribution pattern may be closely related to local topography and human activities. Northern Shaanxi is extensively covered by gully and desert, where the structure of loess is relatively loose and the loose loess tends to be eroded by rainfall and the wind. Also, in the arid and semiarid regions, the heterogeneous seasonal precipitation and recurring storms often lead to fragile land cover and low capacity of conserving water and soil. While the Guanzhong Basin is characterized by a low slope and dense population, extensive and intensive human activities have severely altered the land cover; thereby produced a profound influence on the capability of water and soil conversation. However, the Qinling-Daba Mountains zone with slope gradient above 25% in southern Shaanxi provided the most SC. This hilly region accounts for nearly 42.76% of Shaanxi Province, and the population is concentrated in the Hanjiang River Basin. Thus human activities have less impact on the land cover in this region (Zhang et al., 2010). Furthermore, abundant rainfall there can ensure the exuberant vegetation, thereby facilitates the soil conservation.

3.2 Hotspots and coldspots of SC

3.2.1 Identifying and mapping hotspots and coldspots
The statistically significant hotspots and coldspots with different confidence levels are shown in Figure 3. Generally, P<0.05 (i.e., 95% confidence level) is defined as statistically significant (Bryan et al., 2010). Thus, we focused on hotspots and coldspots with above 95% confidence in this study.
Figure 3 Hotspots and coldspots with different confidence levels (The double star (**) and single star (*) superscript indicate hotspots or coldspots are significant at 99% and 95% level respectively)
The statistically significant hotspots (hotspots**, for short) of SC accounting for 29.6% of the area of Shaanxi provided 59.7% of the total SC, while the coldspots with above 95% confidence (coldspots**, for short) accounting for 46.3% of the area provided only 17.2% of the total SC (Table 2). The results indicated that the hotspots have the highest capacity of conserving soil. Areas with hotspots**could supply mean amount of 508.08 t·km-2·a-1 soil conservation service, which is six times as many as the coldspots**. This high effectiveness of hotspots means implementing conservation project in these areas will be more cost-efficient. This knowledge can facilitate workable policy making and targeted action taking (Guerra et al., 2016).
Table 2 Statistics on the hotspots and coldspots of soil conservation service in Shaanxi Province, China
Annual SC per unit area (t·hm-2·a-1) Area percentage (%) Annual SC percentage (%)
Coldspots** 80.85 41.40 13.61
Coldspots* 175.99 4.95 3.55
Coldspots 190.68 2.41 1.87
Not Significant 239.34 20.63 20.08
Hotspots 296.79 1.02 1.24
Hotspots* 309.95 1.86 2.34
Hotspots** 508.08 27.73 57.31

Note: The double star (**) and a single star (*) superscript indicate hotspots or coldspots are significant at 99% and 95% level, respectively.

Moreover, the spatial patterns of hotspots and coldspots can guide targeted priority policy making. Hotspots**of SC were mainly scattered in southern Shaanxi (i.e., the QDM zone), while coldspots** were mainly distributed in the Guanzhong Basin and Sand-windy Plateau zone (Figure 3). The results indicated that the supply of soil conservation service was mainly centered in the south of Shaanxi and these hotspots should be well protected in case of being disrupted. However, in the north of Shaanxi, there were few SC hotspots. Encouragingly, the LOP zone, where characterized by the most serious soil loss in the last century, has not seen coldspots in recent 14 years, which also illustrated the positive effects of vegetation restoration projects.
3.2.2 LULC types in hotspots and coldspots
Vegetation cover plays a vital role in ameliorating the degradation of ES (Reyers et al., 2009). It can enhance the trapping of rainwater and reduce the kinetic energy of rainfall, thus mitigate the erosion. By regulating the spatial configuration of LULC, we can enhance the conservation of water and soil (Fu et al., 2015; Lorencova et al., 2013). For the hotspots identified, 78.9% of which were covered by woodland and grassland, while there were only 43.6% of the coldspots covered by vegetation (Figure 4). Also, a total proportion of 45.8% of the coldspots were found in farmland. Thus, the hotspots usually have high vegetation cover.
Figure 4 LULC types in hotspots and coldspots. The pie charts indicate the percentage of each LULC from the total area of hotspots or coldspots.
Effective land-use policies can optimize spatial configuration of LULC, thereby influence the provisioning of ES (Fu et al., 2015). The SC capacity under different vegetation states reflects the great importance of vegetation for ecosystems, so the hotspots with a high fraction of vegetation cover should be treated as priority reserve where laws and regulations are needed to be reinforced but not too many funds should be invested. As for alleviating coldspots, both policy and funds are required. Regarding the Loess Plateau region, the vegetation restoration and construction like GfG and TNSPF projects should give priority to local or native species to ensure survival rate and long-term ecological effects. For the Guanzhong Basin, where covered by extensive farmland and residential land, more efforts should be placed on irrigation and water conservancy projects, so as to reduce water and soil loss.

4 Discussion

4.1 Driving factors of the SC change

Climate and land use change have been demonstrated to the two main factors that influence the spatio-temporal variation of soil conservation (Su et al. 2012, Lorencova et al. 2013). In Shaanxi, characterized by fragile underlying surface, intensive human-environment interactions play an increasingly important role in shaping the hydrological processes and sediment export.
4.1.1 LULC change
LULC transition can affect major eco-hydrological processes, including energy exchange, water cycling, soil loss and biogeochemical cycles (Felipe Lucia et al., 2014), which directly and indirectly influence the provision of ES (MA, 2005; Fu et al., 2015). Due to the joint efforts of GfG and TNSPF, the land cover in northern Shaanxi has changed noticeably (Su and Fu, 2013). Woodland and grassland increased by 1625.33 km2 and 929.64 km2 from 2000 to 2013 respectively, and the farmland was reduced by 3780.90 km2. Meanwhile, the mean annual SC in each LULC type showed an increasing trend from 2000 to 2013 (Figure 5a). Figure 5b shows the areas and spatial distribution of that other land types transferred to woodland and grassland. The transition was mainly scattered on the middle Loess Plateau zone, which was in accordance with the significant increase of SC in this area. Moreover, we tested the SC supplying capacities of each LULC by applying the Zonal Statistics (ESRI, 2013). The results showed that woodland held the highest capacity of supplying soil conservation service, followed by grassland. In contrast, the capacity of residential land and desert were very low. Therefore, we can conclude that LULC is a key driving force of SC change, which was consistent with Fu et al.’s study on the Loess Plateau (Fu et al., 2011).
Figure 5 Comparison of LULC change between 2000 and 2013: (a) the area (AR) of LULC change and the mean SC provided by each LULC type; (b) the distribution of other types of LULC transferred to woodland and grassland
4.1.2 Climate change
Though the vegetation restoration projects were widely carried out in China, except the pronounced LULC change in northern Shaanxi, no evident change appeared in the south (Figure 5b). However, the SC in southern Shaanxi did present a significant increasing trend (Figure 2). Therefore, other driving forces, for example, climate change may contribute to the variation of SC. Taking the Yanhe River basin as an example, it was the earliest and fastest region to implement the Grain-for-Green Program (GfG), and the LULC has changed dramatically. Meanwhile, the annual precipitation showed an increasing trend in recent 14 years, which also promoted the growth of vegetation in this arid area (Yapp et al., 2010; Fu et al., 2011). Thus, the strengthened soil conservation service in this area was the synthetical effects of LULC and climate change. Though the amount of precipitation has been illustrated to be the main driving factor of soil erosion (Fu et al., 2011), the precipitation intensity, precipitation frequency and precipitation-concentration-degree and precipitation-concentration-period (Li et al., 2016) may also influence the rainfall erosivity (Wei et al., 2009; López-Tarazón et al., 2010). Thus, the detailed influence mechanism awaits to be further studied.
Particularly, we compared the spatial patterns of SC, precipitation and temperature, and found that the spatial configuration of SC (Figure 2) was similar to precipitation’s (Figure 6): both of them showed high values as well as increasing trends in QDM and LOP. Therefore, apart from the vegetation restoration, climate change might also contribute to the increase of SC in the LOP. Further, the temperature in LOP seemingly tended to decrease in recent 14 years, which may help reduce the vegetational evaporation loss to avoid drought. In this semi-arid and arid area, water is a key factor that constrains the growth of vegetation, and bare or sparsely vegetated ground is prone to be eroded by rainfall. As for southern Shaanxi, especially the west of QDM, both precipitation and temperature showed an evident increasing trend, which was in accordance with the variation of SC. This region is well covered by forest, and little land-cover transfer occurred in the process of vegetation restoration projects. So the significant increase of precipitation may help enhance the SC in the south of Shaanxi.
Figure 6 Spatial patterns of mean annual precipitation (PPT) (a), temperature (Tem) (c) and their change rates (b and d) from 2000 to 2013. The change rate at grid cell level was calculated by using the least square method (LSM).
In conclusion, LULC change under the policy of Grain-for-Green in northern Shaanxi was an incontrovertible driving force of SC increase. Meanwhile, the increasing precipitation in recent years also contributed to this improvement.

4.2 Why using the Getis-Ord Gi* statistics for hotspots analysis?

Though several spatial clustering analyses (such as KDE, Local Moran’s I) can help identify hotspots, the alternatives are all insufficient compared with the Gi* statistics. Kernel density estimation (KDE) is efficient for the scattered, location-based data (such as random investigation data of multiple ES as seen in Figure 7a), but for spatially uniform grid data, it loses efficiency (see Figure 7b). Besides, KDE can only show the location of the clusters, but cannot tell whether the clusters are significant or not. Local Moran’s I is more suitable for finding statistically significant clusters of high (or low) values and the outlier (ESRI, 2013), such as figuring out the sharp boundaries between rich and poor in a certain region, or the location of unexpectedly high rates of disease across the study area (ESRI, 2013). This statistic method underlines the level of events of each individual feature of a neighborhood, instead of the combined level of events for the neighborhood as a whole (Braithwaite and Li, 2007), so it is less sensitive to spatial weights among features. Furthermore, unlike epidemic or crime in social science (Eck et al., 2005; Braithwaite and Li, 2007; Ahmad et al., 2015), research objects in natural science (for example, SC in this text) are generally homogeneous and have continuous surface and good connectivity. Thus, we do not focus on outlier but on good landscape connectivity for cost-efficient management.
Figure 7 Comparison of three hotspot analysis methods: KDE (1), Local Moran’s I (2), Getis-Ord Gi* statistics (3)

Note: When input features are spatially scattered (see Figure 7a), the KDE can only identify spatial cluster, but it mixes the cluster of high values (i.e., hotspots) and the cluster of low values (coldspots); that is, the KDE can neither tell what the cluster is nor whether it is significant. Fortunately, the Local Moran’s I and Getis-Ord Gi* statistics can both make it. The difference is that the Local Moran’s I is more efficient at identifying the outliers (see Figure 7a-(2) above), while the Gi* statistics is even better at identifying statistically significant hotspots and coldspots with different confidence levels (see Figure 7a-(3)). When input features are spatially uniform grid data (see Figure 7b), the KDE becomes inefficient, while the Local Moran’s I and Gi* statistics work well in this case, and the Gi* statistics especially holds its unique superiority.

The Gi* statistics is one of the spatial clustering methods, which works by calculating the local sum for a feature and its neighbors and then comparing the preliminary result proportionally to the sum of all features. When the calculated local sum is diametrically different from the expected one, and that difference beyond a random chance, then a statistically significant z-score (i.e., Gi) outputs (ESRI, 2013). Therefore, the Gi* statistics is a more robust method for identifying hotspots and coldspots.

4.3 Implications of the hotspots and coldspots mapping for conserving ES

While ES mapping can provide guidance for conservation policy making, we propose that hotspots and coldspots analysis should be integrated into the priority area setting for systematic conservation. Prioritization sites with ES hotspots are considered to be comprehensive, compact and cost-effective (Schröter and Remme, 2016). In reality, the conservation budgets are usually not sufficient to conserve all sites. To achieve cost-effectiveness, the hotspots must be compact and with low edge-to-area ratio. Hotspots identified based on quantiles and threshold method are quite fragmented and isolated (Schröter and Remme, 2016), which are in poor quality for reserve networks. But the Gi* statistics-based hotspot analysis method, one of the spatial-clustering quantitative method, is especially efficient for assessing and identifying ES hotspots and coldspots with good spatial connectivity. Thereby, this method is more beneficial for practical and cost-effective ES conservation management (Guerra et al., 2014; Moilanen et al., 2014).
ES hotspots maps can be used as visual and vivid tools to initiate communications with stakeholders about management planning (Maes et al., 2013). Clarifying the hotspots and coldspots sites helps set priorities for maintaining essential ES when financial resources are limited (de Groot et al., 2010; Newburn et al., 2005; Crossman and Bryan, 2009). ES hotspots should get the priorities of being reserved and avoid being damaged; as for ES coldspots, targeted measures should be taken to ameliorate the severe status quo by analyzing and sorting out local drivers of ES conflicts and degeneration (Jiang et al., 2013). In this text, the SC hotspots mainly occurred in areas with high vegetation cover, while SC coldspots mostly appeared in areas covered by farmland (Figure 4). With this knowledge, we suggest that the Grain for Green Program should continue to be implemented in the coldspots areas.
For different purposes of ES conservation, there are different definitions for ES hotspot. As for Shaanxi Provence, soil loss is one of the most serious environmental problems in this area, so we focus on a single service and identify the SC hotspots and coldspots to set priorities for conservation. But our ecosystems are often complex, and one landscape usually holds several functions and services. Thus, multiple ES should be considered at a time to conserve multi-functional hotspots. The Gi* statistics-based hotspots analysis is also efficient for this case (Schröter and Remme, 2016).

5 Conclusions

To achieve efficient ecosystem conservation, we need to find out the optimal scheme for resource allocation. ES hotspots and coldspots mapping provides a pathway for conservation priority setting. By integrating several spatial datasets and models, this case study examined the spatio-temporal variation of the soil conservation service for Shaanxi Province, and further mapped the ES hotspots and coldspots based on Getis-Ord Gi* statistics method.
The results showed that the annual SC in Shaanxi experienced an evident increasing tendency from 2000 to 2013 as a whole, but the changes in SC and its drivers were spatially heterogeneous. We found that the increase of SC in northern Shaanxi was mainly due to LULC change, while in the south, the increase was mainly affected by precipitation.
Furthermore, our study pointed out that Gi* statistics has the potential to guide conservation priority setting, since this method can help identify ES hotspots and coldspots with high landscape connectivity and compactness. Hotspots identified using the method have a much higher capacity of supplying ES compared with the non-hotspots. This means protecting less area (i.e., hotspots) can benefit more service. Thus, this study offered a cost-efficient and spatially-explicit framework for ES conservation priority setting. Stakeholders can also integrate this method into their framework for identifying and conserving multi-functional hotspots of ES or biodiversity to support targeted ecosystem policy making.

The authors have declared that no competing interests exist.

References

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[1]
Adhikari K, Hartemink A E, 2016. Linking soils to ecosystem services: A global review.Geoderma, 262: 101-111.Soil plays a crucial role in ecosystem functioning. In the 1990s ecosystem services (ES) research focused on developing the concept and framework and only a few studies linked soil properties to ecosystem services. This study reviews the literature on the relationship between soils and ecosystem services and aims to contribute to the scientific understanding on soil and ecosystem services and their interrelations. Most studies have focused on provisioning and regulating ES relating to soil physico-chemical properties. Cultural services had only a few studies, and supporting services were mostly related to soil physico-chemical and biological properties. The number of ES papers increased rapidly after 2000 and in the past 5 years, regulating services such as carbon sequestration, climate and gas regulations, were commonly studied. Once the concept was established in the 1990s, studies focusing on the assessment, valuation, and payments of services became more prominent. Most soil-ES research is published in Geoderma . Soil scientists seems to be hesitant to use the term cosystem services even if their research is devoted to linking soils to ecosystem services. We suggest that future ES research should focus on exploring soil functional diversity of soil biota and the spatial aspects of soil properties to lower level ecosystem services (e.g., water purification, gene pool, and climate regulation). Soil scientists should engage professionals from other disciplines to further promote the contribution of soils to ecosystem services delivery and human well-being. ES soil studies could be used in local and national policy development and program on natural resource use and management.

DOI

[2]
Ahmad S, Aziz N, Butt Aet al., 2015. Spatio-temporal surveillance of water based infectious disease (malaria) in Rawalpindi, Pakistan using geostatistical modeling techniques.Environmental Monitoring and Assessment, 187(9): 1-15.In recent times, surface water resource in the Western Region of Ghana has been found to be inadequate in supply and polluted by various anthropogenic activities. As a result of these problems, the demand for groundwater by the human populations in the peri-urban communities for domestic, municipal and irrigation purposes has increased without prior knowledge of its water quality. Water samples were collected from 14 public hand-dug wells during the rainy season in 2013 and investigated for total coliforms, Escherichia coli, mercury (Hg), arsenic (As), cadmium (Cd) and physicochemical parameters. Multivariate statistical analysis of the dataset and a linear stoichiometric plot of major ions were applied to group the water samples and to identify the main factors and sources of contamination. Hierarchal cluster analysis revealed four clusters from the hydrochemical variables (R-mode) and three clusters in the case of water samples (Q-mode) after z score standardization. Principal component analysis after a varimax rotation of the dataset indicated that the four factors extracted explained 93.3 % of the total variance, which highlighted salinity, toxic elements and hardness pollution as the dominant factors affecting groundwater quality. Cation exchange, mineral dissolution and silicate weathering influenced groundwater quality. The ranking order of major ions was Na(+)65>65Ca(2+)65>65K(+)65>65Mg(2+) and Cl(-)65>65SO4 (2-)65>65HCO3 (-). Based on piper plot and the hydrogeology of the study area, sodium chloride (86 %), sodium hydrogen carbonate and sodium carbonate (14 %) water types were identified. Although E. coli were absent in the water samples, 36 % of the wells contained total coliforms (Enterobacter species) which exceeded the WHO guidelines limit of zero colony-forming unit (CFU)/100 mL of drinking water. With the exception of Hg, the concentration of As and Cd in 79 and 43 % of the water samples exceeded the WHO guideline limits of 10 and 3 μg/L for drinking water, respectively. Reported values in some areas in Nigeria, Malaysia and USA indicated that the maximum concentration of Cd was low and As was high in this study. Health risk assessment of Cd, As and Hg based on average daily dose, hazard quotient and cancer risk was determined. In conclusion, multiple natural processes and anthropogenic activities from non-point sources contributed significantly to groundwater salinization, hardness, toxic element and microbiological contamination of the study area. The outcome of this study can be used as a baseline data to prioritize areas for future sustainable development of public wells.

DOI PMID

[3]
Alessa L, Kliskey A, Brown G, 2008. Social-ecological hotspots mapping: A spatial approach for identifying coupled social-ecological space.Landscape and Urban Planning, 85(1): 27-39.This paper advances the concept of a coupled social–ecological system (SES), where human and biophysical systems are closely linked, to examine and explain variations in landscape values perceived by people in their region. In this paper, we describe an approach that allows the mapping of SES by linking survey research with geographic information systems (GIS) to provide spatial representations of social and ecological system convergence. Using survey data that measured landscape values from multiple communities on the Kenai Peninsula, Alaska, we identify geographical areas where both human-perceived and physically measured ecological values overlap and are referred to as social–ecological “hotspots”. Community landscape values, collected as point data, were used to generate point density maps to produce hotspot surfaces for each value. These value surfaces were spatially cross-correlated with other communities’ value surfaces and with an ecological map layer (net primary productivity) to demonstrate social–ecological mapping. Moderate spatial cross-correlation coefficients were found between most landscape values by community with 18 hotspot surfaces pairings exhibiting strong positive spatial cross correlations. Moderately significant, positive linear relationships were found between perceived biological values and net primary productivity for three of six communities. The exploratory spatial analysis presented in this paper is a first step in identifying and describing the presence of SES in a regional context. We conclude the paper by discussing the potential managerial and ecological implications of coupled social–ecological systems including system resilience and vulnerability, and the limitations of the approach that need to be considered.

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[4]
Baró F, Haase D, Gómez-Baggethun Eet al., 2015. Mismatches between ecosystem services supply and demand in urban areas: A quantitative assessment in five European cities.Ecological Indicators, 55: 146-158.Assessing mismatches between ecosystem service (ES) supply and demand can provide relevant insights for enhancing human well-being in urban areas. This paper provides a novel methodological approach to assess regulating ES mismatches on the basis of environmental quality standards and policy goals. Environmental quality standards (EQS) indicate the relationship between environmental quality and human well-being. Thus, they can be used as a common minimum threshold value to determine whether the difference between ES supply and demand is problematic for human well-being. The methodological approach includes three main steps: (1) selection of EQS, (2) definition and quantification of ES supply and demand indicators, and (3) identification and assessment of ES mismatches on the basis of EQS considering certain additional criteria. While ES supply indicators estimate the flow of an ES actually used or delivered, ES demand indicators express the amount of regulation needed in relation to the standard. The approach is applied to a case study consisting of five European cities: Barcelona, Berlin, Stockholm, Rotterdam and Salzburg, considering three regulating ES which are relevant in urban areas: air purification, global climate regulation and urban temperature regulation. The results show that levels of ES supply and demand are highly heterogeneous across the five studied cities and across the EQS considered. The assessment shows that ES supply contributes very moderately in relation to the compliance with the EQS in most part of the identified mismatches. Therefore, this research suggests that regulating ES supplied by urban green infrastructure are expected to play only a minor or complementary role to other urban policies intended to abate air pollution and greenhouse gas emissions at the city scale. The approach has revealed to be appropriate for the regulating ES air purification and global climate regulation, for which well-established standards or targets are available at the city level. Yet, its applicability to the ES urban temperature regulation has proved more problematic due to scale and user dependent constraints.

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[5]
Barro A S, Kracalik I T, Malania Let al., 2015. Identifying hotspots of human anthrax transmission using three local clustering techniques.Applied Geography, 60: 29-36.This study compared three local cluster detection methods to identify local hotspots of human cutaneous anthrax (HCA) transmission in the country of Georgia where cases have been steadily increasing since the dissolution of the Soviet Union. Recent reports have indicated that the disease has reached historical levels in 2012 highlighting the need for better informed policy recommendations and targeted control measures. The purpose of this paper was to identify spatial clusters of HCA to aid in the implementation of targeted public health interventions. At the same time, we compared the utility of different statistical tests in identifying hotspots. We used the Getis-Ord ( G i 65 ( d ) ) mathContainer Loading Mathjax , a multidirectional optimal ecotope-based algorithm (AMOEBA) – a cluster morphology statistic, and the spatial scan statistic in SaTScan64. Data on HCA cases from 2000 to 2012 at the community level were aggregated to an 8×8km grid surface and population data from the Global Rural and Urban Mapping Project (GRUMP) were used to calculate local incidence. In general, there was agreement between tests in the locations of HCA hotspots. Significant local clusters of high HCA incidence were identified in the southern, eastern and western regions of Georgia. The G i 65 ( d ) mathContainer Loading Mathjax and spatial scan statistics appeared more sensitive but less specific than the AMOEBA algorithm. The scan statistic identified larger geographic areas as hotspots of transmission. In general, the spatial scan statistic and G i 65 ( d ) mathContainer Loading Mathjax performed well for spatial clusters with lower incidence rates, whereas AMOEBA was well suited for defining local spatial clusters of higher HCA incidence. In resource constrained areas, efficient allocation of public health interventions is crucial. Our findings identified hotspots of HCA that can be used to target public health interventions such as livestock vaccination and training on proper outbreak management. This paper illustrates the benefits of evaluating statistical approaches for defining disease hotspots and highlights differences in these clustering approaches applicable beyond public health studies.

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[6]
Braithwaite A, Li Q, 2007. Transnational terrorism hot spots: Identification and impact evaluation.Conflict Management and Peace Science, 24(4): 281-296.To combat transnational terrorism, it is important to understand its geography. The extant literature on the geography of terrorism, however, is small and focuses on the distribution and diffusion of terrorism among aggregate regions such as Europe and the Middle East. In this analysis, we study transnational terrorism hot spots at the country level. We employ local spatial statistics to identify terrorism hot spot neighborhoods and countries that are located within. We also assess empirically the impact of these hot spots on future patterns of terrorist incidents. We find that countries with significant experiences with terrorism are often located within these hot spots, but that not all countries within the hot spots have experienced large numbers of terrorist incidents. We also find in a pooled time-series analysis of 112 countries from 1975 to 1997 that when a country is located within a hot spot neighborhood, a large increase in the number of terrorist attacks is likely to occur in the next period. This effect is robust under alternative definitions of geographic proximity and across the two most popular measures of local hot spots of data he G* i statistic and the Local Moran's I. These findings have important implications for the continuing fight against transnational terrorism.

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[7]
Bryan B A, Raymond C M, Crossman N Det al., 2010. Targeting the management of ecosystem services based on social values: Where, what, and how?Landscape and Urban Planning, 97(2): 111-122.Whilst biophysical and economic values are often included in spatial planning for conservation and environmental management, social values are rarely considered. This study demonstrates a method for targeting the management of ecosystem services based on social values within the South Australian Murray-Darling Basin region, Australia. A total of 56 community representatives were interviewed and their values for ecosystem services were elicited and mapped. Spatial indicators of abundance, diversity, rarity, and risk were adapted from ecological science and applied to the mapped social values for ecosystem services. Those areas with the highest social value abundance, diversity, rarity, and risk scores were defined as priority areas for the management of ecosystem services. Four hotspots were located in overlapping areas of high priority for multiple spatial indicators. The ecosystem services contributing to high abundance, diversity, rarity, and risk were identified for management in these focal areas. Community suggestions for managing specific ecosystem services in focal areas were collated and synthesized. The results of this study enable the targeting of management of ecosystem service values in the landscape by identifying where high priority management areas are, specifying what services should be managed, and summarizing how they should be managed. This information can complement biophysical and economic information in systematic landscape planning studies.

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[8]
Burkhard B, Kroll F, Müller Fet al., 2009. Landscapes’ capacities to provide ecosystem services: A concept for land-cover based assessments.Landscape Online, 15(1): 22.Landscapes differ in their capacities to provide ecosystem goods and services, which are the benefits humans obtainfrom nature. Structures and functions of ecosystems needed to sustain the provision of ecosystem services are alteredby various human activities. In this paper, a concept for the assessment of multiple ecosystem services is proposedas a basis for discussion and further development of a respective evaluation instrument. Using quantitative andqualitative assessment data in combination with land cover and land use information originated from remote sensingand GIS, impacts of human activities can be evaluated. The results reveal typical patterns of different ecosystems apacities to provide ecosystem services. The proposed approach thus delivers useful integrative information forenvironmental management and landscape planning, aiming at a sustainable use of services provided by nature. Theresearch concept and methodological framework presented here for discussion have initially been applied in differentcase studies and shall be developed further to provide a useful tool for the quantification and spatial modelling ofmultiple ecosystem services in different landscapes. An exemplary application of the approach dealing with foodprovision in the Halle-Leipzig region in Germany is presented. It shows typical patterns of ecosystem service distributionaround urban areas. As the approach is new and still rather general, there is great potential for improvement,especially with regard to a data-based quantification of the numerous hypotheses, which were formulated as base forthe assessment. Moreover, the integration of more detailed landscape information on different scales will be neededin future in order to take the heterogeneous distribution of landscape properties and values into account. Therefore,the purpose of this paper is to foster critical discussions on the methodological development presented here.

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[9]
Cai Chongfa, Ding Sshuwen, Shi Zhihuaet al., 2000. Study of applying USLE and geographical information system IDRISI to predict soil erosion in small watershed.Journal of Soil and Water Conservation, 14(2): 19-24. (in Chinese)Based on the field survey in the typical small watershed, the method of geographical data base building was studied. Runoff and soil loss amount from experimental plots were used to generate K, C and P factors of USLE. By the support of IDRISI geographical information system and integration of the GIS and USLE, the soil erosion amount of the small watershed was predicted. Results showed that the serious eroded area (sediment is higher than 8 000 t/km 2) is 20%, but contributes 80% sediments of all watershed, while no or slightly eroded area (sediment is lower than 1 000 t/km 2) is 67%, only contributes 3% sediments.

[10]
Crossman N D, Bryan B A, 2009. Identifying cost-effective hotspots for restoring natural capital and enhancing landscape multifunctionality.Ecological Economics, 68(3): 654-668.Much effort is expended toward planning for conservation, natural resource management and sustainable land use in agricultural landscapes. Although often not explicitly stated, the aims of these efforts are often to restore natural capital for the provision of ecosystem services and stimulate multifunctionality in landscapes. However, the scarcity of resources for, and the potential economic impact of, ameliorative actions that restore natural capital necessitates the identification of cost-effective geographic priorities, or hotspots , which provide multiple ecosystem goods and services. This requires the integrated spatial modelling of multiple environmental and economic processes accompanied by clear goals and performance indicators. Identification of hotspots provides guidance for highly targeted land use change that cost-effectively adds to the stocks of natural capital in a landscape. Additionally, the multifunctionality of the landscape can be increased through the provision of multiple ecosystem goods and services. This paper begins by examining data requirements for identifying geographic hotspots for land use change. This study integrates traditionally disparate landscape-scale biophysical and economic data and models. The elements of natural capital considered here are species and ecosystems, soil and water resources, and the atmosphere. It is demonstrated that locating ameliorative actions towards hotspots will be more cost-effective at restoring natural capital and stimulating landscape multifunctionality than a random targeting approach. We calculate these efficiencies using a small set of indicators for assessing aspects of multifunctionality. The focus of this study is the agricultural landscapes of the Lower Murray region of south-eastern Australia.

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[11]
de Groot R S, Alkemade R, Braat Let al., 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making.Ecological Complexity, 7(3): 260-272.Despite the growing body of literature on ecosystem services, still many challenges remain to structurally integrate ecosystem services in landscape planning, management and design. This paper therefore aims to provide an overview of the challenges involved in applying ecosystem service assessment and valuation to environmental management and discuss some solutions to come to a comprehensive and practical framework.First the issue of defining and classifying ecosystem services is discussed followed by approaches to quantify and value ecosystem services. The main part of the paper is focussed on the question how to analyze trade-offs involved in land cover and land use change, including spatial analysis and dynamic modelling tools. Issues of scale are addressed, as well as the question how to determine the total economic value of different management states.Finally, developments and challenges regarding the inclusion of ecosystem services in integrative landscape planning and decision-making tools are discussed.It is concluded that the ecosystem service approach and ecosystem service valuation efforts have changed the terms of discussion on nature conservation, natural resource management, and other areas of public policy. It is now widely recognized that nature conservation and conservation management strategies do not necessarily pose a trade-off between the “environment” and “development”. Investments in conservation, restoration and sustainable ecosystem use are increasingly seen as a “win-win situation” which generates substantial ecological, social and economic benefits.

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[12]
Deng Shaofu, Yang Taibao, Zeng Biaoet al., 2013. Vegetation cover variation in the Qilian Mountains and its response to climate change in 2000-2011.Journal of Mountain Science, 10(6): 1050-1062.An understanding of variations in vegetation cover in response to climate change is critical for predicting and managing future terrestrial ecosystem dynamics. Because scientists anticipate that mountain ecosystems will be more sensitive to future climate change compared to others, our objectives were to investigate the impacts of climate change on variation in vegetation cover in the Qilian Mountains(QLM), China, between 2000 and 2011. To accomplish this, we used linear regression techniques on 250-m MODIS Normalized Difference Vegetation Index(NDVI) datasets and meteorological records to determine spatiotemporal variability in vegetation cover and climatic factors(i.e. temperature and precipitation). Our results showed that temperatures and precipitation have increased in this region during our study period. In addition, we found that growing season mean NDVI was mainly distributed in the vertical zone from 2,700 m to 3,600 m in elevation. In the study region, we observed significant positive and negative trends in vegetation cover in 26.71% and2.27% of the vegetated areas. Correlation analyses indicated that rising precipitation from May to August was responsible for increased vegetation cover in areas with positive trends in growing season mean NDVI. However, there was no similar significant correlation between growing season mean NDVI and precipitation in regions where vegetation cover declined throughout our study period. Using spatialstatistics, we found that vegetation cover frequently declined in areas within the 2,500 3,100 m vertical zone, where it has steep slope, and is on the sunny side of mountains. Here, the positive influences of increasing precipitation could not offset the drier conditions that occurred through warming trends. In contrast, in higher elevation zones(3,900 4,500 m)on the shaded side of the mountains, rising temperatures and increasing precipitation improved conditions for vegetation growth. Increased precipitation also facilitated vegetation growth in areas experiencing warming trends at lower elevations(2,000 2,400 m) and on lower slopes where water was more easily conserved. We suggest that spatial differences in variation in vegetation as the result of climate change depend on local moisture and thermal conditions, which are mainly controlled by topography(e.g. elevation, aspect, and slope), and other factors, such as local hydrology.

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[13]
Di Minin E, Hunter L T, Balme G Aet al., 2013. Creating larger and better connected protected areas enhances the persistence of big game species in the maputaland-pondoland-albany biodiversity hotspot.PloS One, 8(8): e71788.The ideal conservation planning approach would enable decision-makers to use population viability analysis to assess the effects of management strategies and threats on all species at the landscape level. However, the lack of high-quality data derived from long-term studies, and uncertainty in model parameters and/or structure, often limit the use of population models to only a few species of conservation concern. We used spatially explicit metapopulation models in conjunction with multi-criteria decision analysis to assess how species-specific threats and management interventions would affect the persistence of African wild dog, black rhino, cheetah, elephant, leopard and lion, under six reserve scenarios, thereby providing the basis for deciding on a best course of conservation action in the South African province of KwaZulu-Natal, which forms the central component of the Maputaland-Pondoland-Albany biodiversity hotspot. Overall, the results suggest that current strategies of managing populations within individual, small, fenced reserves are unlikely to enhance metapopulation persistence should catastrophic events affect populations in the future. Creating larger and better-connected protected areas would ensure that threats can be better mitigated in the future for both African wild dog and leopard, which can disperse naturally, and black rhino, cheetah, elephant, and lion, which are constrained by electric fences but can be managed using translocation. The importance of both size and connectivity should inform endangered megafauna conservation and management, especially in the context of restoration efforts in increasingly human-dominated landscapes.

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[14]
Eck J, Chainey S, Cameron J et al., 2005. Mapping Crime: Understanding Hot Spots. USA: National Institute of Justice. Available online at .

[15]
ESRI, 2013. ArcGISDesktop: Release 10.2. Redmond, CA: Esri Inc.

[16]
Felipe Lucia M, Comín F A, Bennett E M, 2014. Interactions among ecosystem services across land uses in a floodplain agroecosystem.Ecology and Society, 19(1): 20.Managing human-dominated landscapes such as agroecosystems is one of the main challenges facing society today. Decisions about land-use management in agroecosystems involve spatial and temporal trade-offs. The key scales at which these trades-offs occur are poorly understood for most systems, and quantitative assessments of the services provided by agroecosystems under different combinations of land uses are rare. To fill these knowledge gaps, we measured 12 ecosystem services (ES), including climate regulation, gas regulation, soil stability, nutrient regulation, habitat quality, raw material production, food production, fishing, sports, recreation, education, and social relationships, in seven common land-use types at three spatial scales, i.e., patch, municipality, and landscape, in a riparian floodplain in Spain. We identified the provision of each ES in each land-use type either by direct measurement or from public databases. We analyzed the interactions, i.e., trade-offs and synergies, among ES across land uses and spatial scales and estimated ES provision in several land-use change scenarios. Our results illustrated that each land-use type provides unique bundles of ES and that the spatial scale at which measurements were taken affected the mixture of services. For instance, a land-use type with low provision of services per hectare but with an extensive area can supply more services to the overall landscape than a land-use type supplying higher values of services per hectare but with a smaller extent. Hence, riparian forest supplied the most service of any land-use type at the patch scale, but dry cereal croplands provided the most services across the municipality and landscape because of their large area. We found that most ES should be managed primarily at the patch scale, but food production, fishing, and social relationships were more relevant to manage at the municipality scale. There was great variability in ES interactions across scales with different causes of trade-offs at each scale. We identified more significant synergies among ES than trade-offs. Trade-offs were originated because some services were mutually incompatible within a given land use, whereas the provision of others depended on land-management decisions within a land-use type. Thus, we propose a classification of ES interactions that incorporates societal values as drivers of management decisions along with biophysical factors as likely causes of ES trade-offs and conclude with practical suggestions to reduce trade-offs and to enhance the supply of multiple ES to society.

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[17]
Feng Lei, 2013. Study on the soil and water conservation function regionalization of Shaanxi Province [D]. Beijing: Beijing Forestry University. (in Chinese)

[18]
Fu B, Liu Y, Lü Yet al., 2011. Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China.Ecological Complexity, 8(4): 284-293.Soil erosion in terrestrial ecosystems, as an important global environmental problem, significantly impacts on environmental quality and social economy. By protecting soil from wind and water erosion, terrestrial ecosystems supply human beings with soil erosion control service, one of the fundamental ecosystem services that ensure human welfare. The Loess Plateau was one of the regions in the world that suffered from severe soil erosion. In the past decades, restoration projects were implemented to improve soil erosion control in the region. The Grain-to-Green project, converting slope croplands into forest or grasslands, launched in 1999 was the most massive one. It is needed to assess the change of soil erosion control service brought about by the project. This study evaluated the land cover changes from 2000 to 2008 by satellite image interpretation. Universal Soil Loss Equation (USLE) was employed for the soil erosion control assessment for the same period with localized parameters. Soil retention calculated as potential soil erosion (erosion without vegetation cover) minus actual soil erosion was applied as indicator for soil erosion control service. The results indicate that ecosystem soil erosion control service has been improved from 2000 to 2008 as a result of vegetation restoration. Average soil retention rate (the ratio of soil retention to potential soil loss in percentage) was up to 63.3% during 2000–2008. Soil loss rate in 34% of the entire plateau decreased, 48% unchanged and 18% slightly increased. Areas suffering from intense erosion shrank and light erosion areas expanded. Zones with slope gradient of 8°–35° were the main contribution area of soil loss. On average, these zones produced 82% of the total soil loss with 45.5% of the total area in the Loess Plateau. Correspondingly, soil erosion control capacity was significantly improved in these zones. Soil loss rate decreased from 500002t02km02yr to 360002t02km02yr, 690002t02km02yr to 470002t02km02yr, and 850002t02km02yr to 550002t02km02yr in the zones with slope gradient of 8°–15°, 15°–25°, and 25°–35° respectively. However, the mean soil erosion rate in areas with slope gradient over 8° was still larger than 360002t02km02yr, which is far beyond the tolerable erosion rate of 100002t02km02yr. Thus, soil erosion is still one of the top environmental problems that need more ecological restoration efforts.Highlights? The soil erosion control service of regional ecosystems was evaluated based on land cover change and USLE modeling. ? Results indicated that soil erosion control service has been greatly enhanced through vegetation restoration in the Loess Plateau region. ? The spatiotemporal variations of the soil erosion control service were determined, which may be crucial for regional ecological restoration.

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[19]
Fu B, Zhang L Xu Zet al., 2015. Ecosystem services in changing land use.Journal of Soils and Sediments, 15(4): 833-843.Ongoing population growth and economic development place increasing demands on the supply of services produced in and by ecosystems. The resulting degradation compromises their ability to continue sup

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[20]
Fu B, Zhao W, Chen Let al., 2005. Assessment of soil erosion at large watershed scale using RUSLE and GIS: A case study in the Loess Plateau of China. Land Degradation & Development, 16(1): 73-85.Soil erosion is a serious problem in the Loess Plateau of China, and assessment of soil erosion at large watershed scale is urgently need. This study used RUSLE and GIS to assess soil loss in the Yanhe watershed. All factors used in the RUSLE were calculated for the watershed using local data. RUSLE-factor maps were made. The mean values of the R-factor, K-factor, LS-factor, C-factor and P-factor were 970 209 MJ km(-2) h(-1) a(-1), 0.0195 Mg h MJ(-1) mm(-1), 10.27, 0.33359 and 0.2135 respectively. The mean value of the annual average soil loss was found to be 14 458 Mg km(-2) per year, and the soil loss rate in most areas was between 5000 and 20 000 Mg km(-2) per year. There is more erosion in the centre and southeast than in the northwest of Yanhe watershed. Because of the limitations of the RUSLE and spatial heterogeneity, more work should be done on the RUSLE-factor accuracy, scale effects, etc. Furthermore, it is necessary to apply some physical models in the future, to identify the transport and deposition processes of sediment at a large scale. Copyright (c) 2005 John Wiley S Sons, Ltd.

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[21]
Getis A, Ord J K, 1992. The analysis of spatial association by use of distance statistics.Geographical Analysis, 24(3): 189-206.Introduced in this paper is a family of statistics, G , that can be used as a measure of spatial association in a number of circumstances. The basic statistic is derived, its properties are identified, and its advantages explained. Several of the G statistics make it possible to evaluate the spatial association of a variable within a specified distance of a single point. A comparison is made between a general G statistic and Moran's I for similar hypothetical and empirical conditions. The empirical work includes studies of sudden infant death syndrome by county in North Carolina and dwelling unit prices in metropolitan San Diego by zip-code districts. Results indicate that G statistics should be used in conjunction with I in order to identify characteristics of patterns not revealed by the I statistic alone and, specifically, the G i and G i * statistics enable us to detect local “pockets” of dependence that may not show up when using global statistics.

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[22]
Gimona A, van der Horst D, 2007. Mapping hotspots of multiple landscape functions: A case study on farmland afforestation in Scotland.Landscape Ecology, 22(8): 1255-1264.Many conservation and restoration efforts in developed countries are increasingly based on the premise of recognising and stimulating more ‘multi-functionality’ in agricultural landscapes. Public policy making is often a pragmatic process that involves efforts to negotiate trade-offs between the potentially conflicting demands of various stakeholders. Conservationists’ efforts to influence policy making, can therefore benefit from any tool that will help them to identify other socio-economic functions or values that coincide with good ecological conservation options. Various types of socio-economic objectives have in recent years been mapped across landscapes and so there are now important opportunities to explore the spatial heterogeneity of these diverse functions across the wider landscape in search of potential spatial synergies, i.e. ‘multiple win locations’ or multifunctional ‘hotspots’.This paper explores the potential occurrence of such synergies within the agricultural landscape of northeast Scotland and evaluates an existing woodland planting policy using and combining three different policy objectives. Our results show that there are indeed broad areas of the studied landscape where multiple objectives (biodiversity, visual amenity and on-site recreation potential) could be achieved simultaneously (hotspots), and that the case study which we evaluate (the Farm Woodland Premium Scheme) could be much better spatially targeted with regards to each individual objective as well as with regards to these hotspots of multifunctionality.

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[23]
Guerra C A, Maes J, Geijzendorffer Iet al., 2016. An assessment of soil erosion prevention by vegetation in Mediterranean Europe: Current trends of ecosystem service provision.Ecological Indicators, 60: 213-222.The concept of ecosystem services has received increased attention in recent years, and is seen as a useful construct for the development of policy relevant indicators and communication for science, policy and practice. Soil erosion is one of the main environmental problems for European Mediterranean agro-forestry systems, making soil erosion prevention a key ecosystem service to monitor and assess. Here, we present a spatially and temporally explicit assessment of the provision of soil erosion prevention by vegetation in Mediterranean Europe between 2001 and 2013, including maps of vulnerable areas. We follow a recently described conceptual framework for the mapping and assessment of regulating ecosystem services to calculate eight process-based indicators, and an ecosystem service provision profile. Results show a relative increase in the effectiveness of provision of soil erosion prevention in Mediterranean Europe between 2001 and 2013. This increase is particularly noticeable between 2009 and 2013, but it does not represent a general trend across the whole Mediterranean region. Two regional examples describe contrasting trends and illustrate the need for regional assessments and policy targets. Our results demonstrate the strength of having a coherent and complementary set of indicators for regulating services to inform policy and land management decisions.

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[24]
Guerra C A, Pinto-Correia T, Metzger M J, 2014. Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy.Ecosystems, 17(5): 878-889.Current spatially explicit approaches to map and assess ecosystem services are often grounded on unreliable proxy data based on land use/cover to derive ecosystem service indicators. These approaches fail to make a distinction between the actual service provision and the underlying ecosystem capacity to provide the service. We present an integrative conceptual framework to estimate the provision of soil erosion prevention by combining the structural impact of soil erosion and the social cological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision . It also shows that although the average actual ecosystem service provision in the region is sufficient to mitigate the existing structural impact, vulnerable areas can be identified where significant soil losses are not mitigated at present. This becomes more significant when comparing different land management intensities. Considering these results, we argue that the general assumption that there is an almost direct relation between the capacity for ecosystem service provision of a given area and the actual ecosystem service provision is wrong. We also discuss how the framework presented here could be used to support land management and policy, and how it can be adapted for other regulating services.

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[25]
Jacobs S, Burkhard B, Van Daele Tet al., 2015. ‘The Matrix Reloaded’: A review of expert knowledge use for mapping ecosystem services.Ecological Modelling, 295: 21-30.Additionally, we argue that an extended matrix model could provide more than only scientifically sound and politically legitimate results. It could serve as a tool to improve cooperation between natural and social sciences, experts, stakeholders and decision makers: collaborative development of the matrix model contributes to transdisciplinary ecosystem service research aimed at effective implementation and action.

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[26]
Jia X, Fu B, Feng Xet al., 2014. The tradeoff and synergy between ecosystem services in the Grain-for-Green areas in Northern Shaanxi, China.Ecological Indicators, 43: 103-113.As an important part of the strategy of Western development, the Grain-for-Green Program (GFGP) was initiated to protect the environment and mitigate disasters. Ecosystem services and their dynamics are considered emerging features of ecological quality and the change in direction by many scholars and practitioners. Extending from ecosystem services (ESs) modeling, we propose a simple and feasible framework for quantitatively assessing the benefits and equilibrium of the consequences of the GFGP. Our starting evaluation shows that ESs has changed dramatically in the GFGP area. By fitting pair-wise ESs spatial concordances at the grid-cell level, we have revealed the tradeoffs between provisioning and regulating services and the synergies between the regulating services. The analysis of the variability of the relationship between ESs on different land cover types clearly identifies the vegetation that has produced exceptionally strong ESs. Our findings suggest that quantifying the interactions between ESs may improve the ecosystem-based management practices and support policy-making to address the challenges of the sustainable use of natural resources. The framework designed for regional-scale analysis can help in clearly understanding the interrelations of ESs and make natural resources related decisions more effective and efficient, although this framework still needs to move beyond these fundamental and illustrative analyses to more fully explain the synergies and tradeoffs.

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[27]
Jiang M, Bullock J M, Hooftman D A Pet al., 2013. Mapping ecosystem service and biodiversity changes over 70 years in a rural English county.Journal of Applied Ecology, 50(4): 841-850.1. Biodiversity and ecosystem services continue to be compromised by land-use change, which is often focussed on enhancing agricultural production. Assessment of losses would be aided by analyses of temporal changes in the extent and spatial pattern of services and biodiversity. To date, no studies have mapped long-term changes in ecosystem services using historical maps. 2. We mapped changes between the 1930s - before the considerable intensification of land use in the UK starting in the 1940s - and 2000 in climate change amelioration services (carbon storage), provisioning services (agriculture and forestry) and plant species richness (biodiversity) for Dorset, a rural English county. 3. We combined land-use maps (1-ha resolution) with multiple proxies of service delivery for the 10 different Broad Habitats in the region. Biodiversity was mapped using plant survey data from the two time periods. We used bootstrapping to include uncertainty due to the different proxies and Gini coefficients to quantify statistical changes in spatial pattern. 4. Overall, we found significant increases in agricultural provisioning and large losses in biodiversity over the period, which reflect widespread conversion and intensification of land use. We found no change in Dorset's carbon store, because carbon lost through land-use intensification was balanced by increases in woodland over the 20th century. 5. The carbon storage and the delivery of provisioning services both became more unequally distributed, indicating a change from relatively homogeneous delivery of services to concentration into hotspots. The maps from the year 2000 showed spatial dissociation of hotspots for carbon, provisioning and biodiversity, which suggests that, compared to the 1930s, modern, intensive land use creates conflicts in delivery of multiple services and biodiversity. 6. Synthesis and applications. Detailed maps of historical changes in location-specific service delivery and biodiversity provide valuable information for land-use planning, highlight trade-offs and help to identify drivers. Furthermore, historical maps provide an important baseline to indicate the suitability and potential success of suggested actions, such as habitat restoration, and their relevance to traditional land use. Various frameworks could be informed by our approach, including the ecosystem service aims of the EU biodiversity strategy and the newly created UK Nature Improvement Areas.

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[28]
Jiang R, Xie J, He Het al., 2015. Use of four drought indices for evaluating drought characteristics under climate change in Shaanxi, China: 1951-2012.Natural Hazards, 75(3): 2885-2903.Drought severity was simulated with four drought indices to examine the impacts of climate change on drought conditions in Shaanxi province over the period 1951 to 2012. The drought metrics analyzed w

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[29]
López-Tarazón J, Batalla R, Vericat Det al., 2010. Rainfall, runoff and sediment transport relations in a mesoscale mountainous catchment: The River Isábena (Ebro basin).Catena, 82(1): 23-34.This paper examines the relations between rainfall, runoff and suspended sediment transport in the Is bena basin during a and flood sediment loads varying from 27 to 54,000 t per hydrological event. Most sediment load was concentrated in spring when competent floods occur frequently. Pearson correlation matrix and backward stepwise multiple regression indicate that the hydrological response of the catchment is strongly correlated with total precipitation, event duration, and rainfall of the previous days. Very low correlation was observed with rainfall intensity. The relation between rainfall and sediment transport followed the same trend. Sediment variables (e.g., total load and SSC) were significantly correlated with variables such as total rainfall and rainfall over the previous days, although the significance level was lower in comparison with the runoff related variables. There was again no correlation between sediment variables and rainfall intensity. On-going research in the area suggests that, apart from rainfall, factors such as sediment availability in the badlands and accumulation of sediment in the channels influences the river's sedimentary response. The non-linear hydrosedimentary response is reflected in the wide range of runoff coefficients and sediment loads that have been observed in response to similar amounts of precipitation.

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[30]
Li Shuangcheng, 2014. The Geography of Ecosystem Services. The Geography of Ecosystem Services. Beijing: Science Press, 75-79. (in Chinese)

[31]
Li Yingjie, Yan Junping, Liu Yonglin, 2016. Research on the relationship between dryness/wetness and precipitation heterogeneity in north and south of the Qinling Mountains.Arid Zone Research, 33(3): 619-627. (in Chinese)

[32]
Lorencova E, Frelichova J, Nelson Eet al., 2013. Past and future impacts of land use and climate change on agricultural ecosystem services in the Czech Republic.Land Use Policy, 33: 183-194.Climatic and land use change are amongst the greatest global environmental pressures resulting from anthropogenic activities. Both significantly influence the provision of crucial ecosystem services, such as carbon sequestration, water flow regulation, and food and fibre production, at a variety of scales. The aim of this study is to provide spatially explicit information at a national level on climate and land use change impacts in order to assess changes in the provision of ecosystem services. This work provides a qualitative and quantitative analysis of the impacts on selected ecosystem services (carbon sequestration, food production and soil erosion) in the agricultural sector of the Czech Republic. This assessment shows that, historical land use trends and land use under projected climate scenarios display some shared spatial patterns. Specifically, these factors both lead to a significant decrease of arable land in the border fringes of the Czech Republic, which is to some extent replaced by grasslands, in turn affecting the provision of ecosystem services. Moreover, this assessment contributes to a useful method for integrating spatially explicit land use and climate change analysis that can be applied to other sectors or transition countries elsewhere.

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[33]
Lü Y, Zhang L, Feng Xet al., 2015. Recent ecological transitions in China: Greening, browning, and influential factors.Scientific Reports, 5: 8732.

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[34]
Lufafa A, Tenywa M, Isabirye Met al., 2003. Prediction of soil erosion in a Lake Victoria basin catchment using a GIS-based Universal Soil Loss model.Agricultural Systems, 76(3): 883-894.Soil erosion patterns in watersheds are patchy, heterogeneous and therefore difficult to assess. The problem can be overcome by using predictive models. However, wide spread soil erosion model-factor parameterization and quantification is difficult due to the costs, labor and time involved. The objective of this study was to evaluate different methods of USLE input parameter derivation and to predict soil loss within a microcatchment of the Lake Victoria basin (LVB). The highest soil loss was predicted for annual cropland use (93 t ha 611year 611), followed by rangeland (52 t ha 611year 611), banana–coffee (47 t ha 611year 611), banana (32 t ha 611year 611) and forest and papyrus swamp. In the terrain units, soil loss was highest within the back slopes (48 t ha 611year 611) followed by the summits (42 t ha 611year 611) and valleys (0 t ha 611year 611). For the soil units, soil loss was highest in the Chromic Luvisols (52 t ha 611year 611) followed by Petroferric Luvisols (37 t ha 611year 611), Mollic Gleysols (5 t ha 611year 611) Dystric Planasols (0 t ha 611year 611) in large part because soil classification often correspond to various slope position.

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[35]
MA [Millenium Ecosystem Assessment], 2005. Ecosystems and Human Well-Being: Current State and Trends. Washington, DC Island Press.

[36]
Maes J, Teller A, Erhard Met al., 2013. Mapping and assessment of ecosystems and their services: An analytical framework for ecosystem assessmentsunder action 5 of the EU biodiversity strategy to 2020. Luxembourg: Publications Office of the European Union.The aim of this report is to illustrate by means of a series of case studies the implementation of mapping and assessment of forest ecosystem services in different contexts and geographical levels. Methodological aspects, data issues, approaches, limitations, gaps and further steps for improvement are analysed for providing good practices and decision making guidance. The EU initiative on Mapping and Assessment of Ecosystems and their Services (MAES), with the support of all Member States, contributes to improve the knowledge on ecosytem services. MAES is one of the building-block initiatives supporting the EU Biodiversity Strategy to 2000.

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[37]
McCool D K, Brown L C, Foster G Ret al., 1987. Revised slope steepness factor for the Universal Soil Loss Equation.Transactions of the ASAE, 30(5): 1387-1396.ABSTRACT A reanalysis of historical and recent data from both natural and simulated rainfall soil erosion plots has resulted in new slope steepness relationships for the Universal Soil Loss Equation. For long slopes on which both interrill and rill erosion occur, the relationships consist of two linear segments with a breakpoint at 9% slope. These relationships predict less erosion than current relationships on slopes steeper than 9% and slopes flatter than about 1%. A separate equation is proposed for the slope effect on short slopes where only interrill erosion is present. For conditions where surface flow over thaw-weakened soil dominates the erosion process, two relationships with a breakpoint at 9% slope are presented.

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[38]
Mitchell A, 2005. The ESRI Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics. Redlands. CA: Esri Press.

[39]
Mitchell M G, Suarez-Castro A F, Martinez-Harms Met al., 2015. Reframing landscape fragmentation’s effects on ecosystem services.Trends in Ecology & Evolution, 30(4): 190-198.Landscape structure and fragmentation have important effects on ecosystem services, with a common assumption being that fragmentation reduces service provision. This is based on fragmentation's expected effects on ecosystem service supply, but ignores how fragmentation influences the flow of services to people. Here we develop a new conceptual framework that explicitly considers the links between landscape fragmentation, the supply of services, and the flow of services to people. We argue that fragmentation's effects on ecosystem service flow can be positive or negative, and use our framework to construct testable hypotheses about the effects of fragmentation on final ecosystem service provision. Empirical efforts to apply and test this framework are critical to improving landscape management for multiple ecosystem services.

DOI PMID

[40]
Moilanen Aet al., 2014. Zonation spatial conservation planning framework and software v.4.0, User Manual. Biodiversity Conservation Informatics Group, Department of Biosciences, University of Helsinki, Finland. (Date of access: 01/03/2016.

[41]
Newburn D, Reed S, Berck Pet al., 2005. Economics and landuse change in prioritizing private land conservation.Conservation Biology, 19(5): 1411-1420.Abstract Abstract: Incentive-based strategies such as conservation easements and short-term management agreements are popular tools for conserving biodiversity on private lands. Billions of dollars are spent by government and private conservation organizations to support land conservation. Although much of conservation biology focuses on reserve design, these methods are often ineffective at optimizing the protection of biological benefits for conservation programs. Our review of the recent literature on protected-area planning identifies some of the reasons why. We analyzed the site-selection process according to three important components: biological benefits, land costs, and likelihood of land-use change. We compared our benefit-loss-cost targeting approach with more conventional strategies that omit or inadequately address either land costs or likelihood of land-use change. Our proposed strategy aims to minimize the expected loss in biological benefit due to future land-use conversion while considering the full or partial costs of land acquisition. The implicit positive correlation between the likelihood of land-use conversion and cost of land protection means high-vulnerability sites with suitable land quality are typically more expensive than low-vulnerability sites with poor land quality. Therefore, land-use change and land costs need to be addressed jointly to improve spatial targeting strategies for land conservation. This approach can be extended effectively to land trusts and other institutions implementing conservation programs.

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[42]
O'Farrell P J, De Lange W J, Le Maitre D Cet al., 2011. The possibilities and pitfalls presented by a pragmatic approach to ecosystem service valuation in an arid biodiversity hotspot.Journal of Arid Environments, 75(6): 612-623.Arid regions are home to unique fauna, flora, and vulnerable human populations, and present a challenge for sustainable land-use management. We undertook an assessment and valuation of three key services, grazing, tourism and water supply in the arid Succulent Karoo biome in western South Africa - a globally recognised biodiversity hotspot. We were looking for ways and values that could be used to promote conservation in this region through the adoption of sustainable land-use practices which have human welfare benefits. Our study adopted a variety of methods in valuing these services in developing ranges of values for these services. At the biome level, total annual values ranged from $ 19–114 million for grazing, $ 2–$ 20 million for tourism, and $ 300–3120 million for water. These values are generally low compared with values derived for other biomes and regions and do not adequately reflect known dependence and the importance of ecosystem services to the residents of this biome. The ecosystems here provide small but critical benefits enabling communities to sustain themselves and small changes in service levels can have major welfare effects. Highlighting these sensitivities will require finding more appropriate ways to link ecological and social factors.

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[43]
Rao E, Ouyang Z, Yu Xet al., 2014. Spatial patterns and impacts of soil conservation service in China.Geomorphology, 207: 64-70.61USLE can be used to assess soil conservation service in China with high accuracy.61Ecosystems in the south-east performed better than the north-west in erosion control.61Rainfall and terrain dominated the spatial pattern of soil conservation service.61Population growth did not affect soil conservation service directly.61Land reclamation could impair soil conservation service and intensify soil erosion.

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[44]
Renard K G, Foster G, Weesies Get al., 1997. Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Department of Agriculture Washington, DC.The book provides guidelines for the selection of the best control methods for farms, ranches and other erosion-prone areas throughout USA. The prediction of soil loss founded on the Universal Soil Loss Equation (USLE) is revised using information available on monthly precipitation and temperature, front-free period, annual rain erosivity, below ground biomass, canopy cover and height at 15 days intervals, and soil cover disturbances associated with farming operations. The information is available on CITY, CROP and OPERATION databases.

[45]
Reyers B, O'Farrell P J, Cowling R M et al., 2009. Ecosystem services, land-cover change, and stakeholders: Finding a sustainable foothold for a semiarid biodiversity hotspot. Ecology and Society, 14(1): 38. URL: .

[46]
Schröter M, Remme R P, 2016. Spatial prioritisation for conserving ecosystem services: Comparing hotspots with heuristic optimisation.Landscape Ecology, 31(2): 431-450.Abstract CONTEXT: The variation in spatial distribution between ecosystem services can be high. Hence, there is a need to spatially identify important sites for conservation planning. The term 'ecosystem service hotspot' has often been used for this purpose, but definitions of this term are ambiguous. OBJECTIVES: We review and classify methods to spatially delineate hotspots. We test how spatial configuration of hotspots for a set of ecosystem services differs depending on the applied method. We compare the outcomes to a heuristic site prioritisation approach (Marxan). METHODS: The four tested hotspot methods are top richest cells, spatial clustering, intensity, and richness. In a conservation scenario we set a target of conserving 10 % of the quantity of five regulating and cultural services for the forest area of Telemark county, Norway. RESULTS: Spatial configuration of selected areas as retrieved by the four hotspots and Marxan differed considerably. Pairwise comparisons were at the lower end of the scale of the Kappa statistic (0.11-0.27). The outcomes also differed considerably in mean target achievement, cost-effectiveness in terms of land-area needed per unit target achievement and compactness in terms of edge-to-area ratio. CONCLUSIONS: An ecosystem service hotspot can refer to either areas containing high values of one service or areas with multiple services. Differences in spatial configuration among hotspot methods can lead to uncertainties for decision-making. This also has consequences for analysing the spatial co-occurrence of hotspots of multiple services and of services and biodiversity.

DOI PMID

[47]
Sharpley A N, Williams J R, 1990. EPIC-erosion/productivity impact calculator: 1. Model documentation.Technical Bulletin-United States Department of Agriculture, 1768: 235.

[48]
Su C, Fu B, 2013. Evolution of ecosystem services in the Chinese Loess Plateau under climatic and land use changes.Global and Planetary Change, 101: 119-128.Due to the lengthy historic land use by humans and the climate change characterized by warming and drying, the Loess Plateau has been plagued by ecosystem degradation for a long time. A series of ecological conservation projects launched since the 1970s altered the land use pattern greatly, and exerted a profound influence on the ecosystem services. Based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and Carnegie-Ames-Stanford Approach (CASA) models, we assessed the historical fluctuation of sediment control, water yield, and net primary production (NPP) in the Loess Plateau. The results showed that sediment retention was greatly consolidated indicated by the sharp decrease of sediment export. Water yield decreased at first and increased later. Both sediment export and water yield showed an increasing 'spatial homogenization' tendency during the period. NPP was steady between 1990 and 2000, and then increased greatly after 2000. Ecosystem services are interlinked closely and complexly. Correlation analyses indicated a positive relationship between the difference in sediment export and water yield (r(2)=0.776**) from 1975 to 2008, with negative correlations between the difference of NPP and water yield (r(2)=-0.422**)/sediment export (r(2)=-0.240*) from 1990 to 2008. This, to some extent, implies that there are tradeoffs between the services of water yield and sediment control/NPP, and there is synergy between sediment control and NPP. Climatic and land use changes are the major drivers on ecosystem services fluctuation. Correlation analyses showed that the decrease of precipitation significantly hindered water yield (r(2)=0.980**) and sediment export (r(2)=0.791**). The increase of temperature exerted a slight negative influence on water yield (r(2)=-0350**). A spatial concordance existing between the 'cropland to grass/woodland' area and the high sediment control (r(2)=0313**)/NPP (r(2)=0.488**) area indicated that the land use change from cropland to woodland/grassland significantly consolidated sediment control and NPP production. The observed spatio-temporal variation of ecosystem services and their correlations provide an operable criterion for land use management policies. (C) 2013 Elsevier B.V. All rights reserved.

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[49]
Su C, Fu B J, He Cet al., 2012. Variation of ecosystem services and human activities: A case study in the Yanhe Watershed of China.Acta Oecologica, 44: 46-57.The concept of cosystem service provides cohesive views on mechanisms by which nature contributes to human well-being. Fast social and economic development calls for research on interactions between human and natural systems. We took the Yanhe Watershed as our study area, and valued the variation of ecosystem services and human activities of 2000 and 2008. Five ecosystem services were selected i.e. net primary production (NPP), carbon sequestration and oxygen production (CSOP), water conservation, soil conservation, and grain production. Human activity was represented by a composite human activity index (HAI) that integrates human population density, farmland ratio, influence of residential sites and road network. Analysis results of the five ecosystem services and human activity (HAI) are as follows: (i)NPP, CSOP, water conservation, and soil conservation increased from 2000 to 2008, while grain production declined. HAI decreased from 2000 to 2008. Spatially, NPP, CSOP, and water conservation in 2000 and 2008 roughly demonstrated a pattern of decline from south to north, while grain production shows an endocentric increasing spatial pattern. Soil conservation showed a spatial pattern of high in the south and low in the north in 2000 and a different pattern of high in the west and low in the east in 2008 respectively. HAI is proportional to the administrative level and economic development. Variation of NPP/CSOP between 2000 and 2008 show an increasing spatial pattern from northwest to southeast. In contrast, the variation of soil conservation shows an increasing pattern from southeast to northwest. Variation of water conservation shows a fanning out decreasing pattern. Variation of grain production doesn show conspicuous spatial pattern. (ii) Variation of water conservation and of soil conservation is significantly positively correlated at 0.01 level. Both variations of water conservation and soil conservation are negatively correlated with variation of HAI at 0.01 level. Variations of NPP/CSOP are negatively correlated with variations of soil conservation and grain production at 0.05 level. (iii) Strong tradeoffs exist between regulation services and provision service, while synergies exist within regulation services. Driving effect of human activities on ecosystem services and tradeoffs and synergies among ecosystem service are also discussed.

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[50]
Trabucchi M, Comin F A, O'Farrell P J, 2013. Hierarchical priority setting for restoration in a watershed in NE Spain, based on assessments of soil erosion and ecosystem services.Regional Environmental Change, 13(4): 911-926.Maintaining and enhancing ecosystem services through the restoration of degraded ecosystems have become an important biodiversity conservation strategy. Deciding where to restore ecosystems for the attainment of multiple services is a key issue for future planning, management, and human well-being. Most restoration projects usually entail a small number of actions in a local area and do not consider the potential benefits of planning restoration at broad regional scales. We developed a hierarchical priority setting approach to evaluate the performance of restoration measures in a semiarid basin in NE Spain (the Mart n River Basin, 2,112 km 2 ). Our analysis utilized a combination of erosion (a key driver of degradation in this Mediterranean region) and six spatially explicit ecosystem services data layers (five of these maps plotted surrogates for soil retention and accumulation, water supply and regulation, and carbon storage, and one plotted a cultural service, ecotourism). Hierarchical maps were generated using a geographic information system that combined areas important for providing a bundle of ecosystem services, as state variables, with erosion maps, as the disturbance or regulatory variable. This was performed for multiple scales, thereby identifying the most adequate scale of analysis and establishing a spatial hierarchy of restoration actions based on the combination of the evaluation of erosion rates and the provision of ecosystem services. Our approach provides managers with a straightforward method for determining the spatial distribution of values for a set of ecosystem services in relation to ecological degradation thresholds and for allocating efforts and resources for restoration projects in complex landscapes.

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[51]
Trabucchi M, O'Farrell P J, Notivol Eet al., 2014. Mapping ecological processes and ecosystem services for prioritizing restoration efforts in a semi-arid Mediterranean river basin.Environmental Management, 53(6): 1132-1145.Semi-arid Mediterranean regions are highly susceptible to desertification processes which can reduce the benefits that people obtain from healthy ecosystems and thus threaten human wellbeing. The European Union Biodiversity Strategy to 2020 recognizes the need to incorporate ecosystem services into land-use management, conservation, and restoration actions. The inclusion of ecosystem services into restoration actions and plans is an emerging area of research, and there are few documented approaches and guidelines on how to undertake such an exercise. This paper responds to this need, and we demonstrate an approach for identifying both key ecosystem services provisioning areas and the spatial relationship between ecological processes and services. A degraded semi-arid Mediterranean river basin in north east Spain was used as a case study area. We show that the quantification and mapping of services are the first step required for both optimizing and targeting of specific local areas for restoration. Additionally, we provide guidelines for restoration planning at a watershed scale; establishing priorities for improving the delivery of ecosystem services at this scale; and prioritizing the sub-watersheds for restoration based on their potential for delivering a combination of key ecosystem services for the entire basin.

DOI PMID

[52]
Wang Xiaofeng, Chang Junjie, Yu Zhengjunet al., 2010a. Research on water disrict of soil erosion based on RUSLE: Take the South-North Diversion Middle Route Project in Shaanxi for example. Journal of Northwest University (Natural Science Edition), 40(3): 545-549. (in Chinese)Aim To study the soil erosion in Shaanxi watershed of Middle Route Project of the South-to-North Diversion,provide an important basis for water resource zones of ecological protection,soil and water conservation. Methods The factors in the Revised Universal Soil Loss Equation ( RUSLE) were quantitatively analyzed by using the data of remote sensing image interpretation,digital elevation model ( DEM) ,soil,rainfall and so on based on GIS technology,and achieved soil erosion estimates in Shaanxi watershed of Middle Route Project of the South-to-North Diversion. Results By calculating available,micro-water areas accounting for 12. 87% ,mild erosion area accounting for 30. 4% ,moderate erosion area accounting for 30. 66% ,the intensity erosion area accounting for 26. 07% in Shaanxi watershed of Middle Route Project of the South-to-North Diversion. Conclusion In the SouthNorth Diversion water district most of the region belongs to the heavily eroded region,only a few areas to micro-degree erosion. Soil and water conservation is a difficult task.

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[53]
Wang X M, Zhang C X, Hasi Eet al., 2010b. Has the Three Norths Forest Shelterbelt Program solved the desertification and dust storm problems in arid and semiarid China?Journal of Arid Environments, 74(1): 13-22.From the late 1970s to the present, a large-scale afforestation program called the hree Norths Forest Shelterbelt program was carried out to combat desertification and control dust storms in China. However, few detailed and systemic assessments have evaluated its success despite the huge investment in the program, its long-term, the extensive area covered by the program, and the importance of combating desertification and controlling dust storms. Although numerous Chinese researchers and government officials have claimed that the afforestation has successfully combated desertification and controlled dust storms, there is surprisingly little unassailable evidence to support their claims. Using basic data on afforestation, desertification, and dust storms, we assessed the effects of this afforestation on combating desertification and controlling dust storms. Although the large-scale afforestation program may have had some beneficial effects on reducing dust storms and controlling desertification in China, the results of our analysis suggest that the importance of this project seems to have been overstated. Thus, future research must seek stronger and more direct evidence for the causal relationships that we have proposed as possible explanations for the observed trends, and the true significance of the Three Norths program should be reassessed.

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[54]
Wei W, Chen L, Fu B, 2009. Effects of rainfall change on water erosion processes in terrestrial ecosystems: A review.Progress in Physical Geography, 33(3): 307-318.Water erosion is the most destructive erosion type worldwide, causing serious land degradation and environmental deterioration. Against a background of climate change and accelerated human activities, changes in natural rainfall regimes have taken place and will be expected to become more pronounced in future decades. Long-term shifts may challenge the existing cultivation systems worldwide and eventually alter the spatiotemporal patterns of land use and topography. Meanwhile, specific features of soil crusting/sealing, plant litter and its decomposition, and antecedent soil moisture content (ASMC) will accompany rainfall variability. All these changes will increase pressures on soil erosion and hydrological processes, making accurate erosion prediction and control more difficult. An improved knowledge and understanding of this issue, therefore, is essential for dealing with the forthcoming challenges regarding soil and water conservation practices. In this paper, the characteristics of changes in natural rainfall, its role on terrestrial ecosystems, the challenges, and its effect on surface water erosion dynamics are elaborated and discussed. The major priorities for future research are also highlighted, and it is hoped that this will promote a better understanding of water erosion processes and related hydrological issues.

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[55]
Wischmeier W, Smith D, 1965. Rainfall-erosion losses from cropland east of the Rocky Mountains, guide for selection of practices for soil and water conservation.Agriculture Handbook, 282-295.

[56]
Wu J, Feng Z, Gao Yet al., 2013. Hotspot and relationship identification in multiple landscape services: A case study on an area with intensive human activities.Ecological Indicators, 29: 529-537.The identification of the relationships between different landscape services is important in social-ecological complex systems, especially in areas with intensive human activities. In this paper, Beijing and its peripheral regions are taken as a case study to calculate and map the intensities of five classic landscape services including material production, carbon storage, soil retention, habitat conservation, and population support based on grid maps. Overlap and correlation analyses were used to identify multiple service hotspots and the relationships between landscape services. The results show that (1) landscape services have spatial heterogeneity: high-intensity area of material production and population support is on the southeast plains of this region and high-intensity area of soil retention and habitat conservation is on the northwest; (2) approximately 96.03% of the region can provide at least one type of landscape services, whereas approximately three-quarters of the area provide multiple services, with the multiple service hotspots surrounding Beijing and Tianjin; (3) correlations exist between all pairs of landscape services, but strong correlations (correlation coefficient >0.5 or <-0.5) exist between four pairs, which are soil retention and habitat conservation (0.672), soil retention and population support (-0.613), habitat conservation and population support (-0.540), and material production and population support (0.529); (4) the services can be divided into two trade-off service bundles: the "natural" bundle, which contains carbon storage, soil retention, and habitat conservation, and the "artificial" bundle, which contains material production and population support. Only 4.19% of the area in this region contains these service bundles simultaneously. Finally, an improved understanding of the relationships between services was illustrated, and the importance of such services was highlighted for decision-makers and stakeholders. (C) 2013 Elsevier Ltd. All rights reserved.

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[57]
Yapp G, Walker J, Thackway R, 2010. Linking vegetation type and condition to ecosystem goods and services.Ecological Complexity, 7(3): 292-301.Our focus here is on how vegetation management can be used to manipulate the balance of ecosystem services at a landscape scale. Across a landscape, vegetation can be maintained or restored or modified or removed and replaced to meet the changing needs of society, giving mosaics of vegetation types and ondition classes that can range from intact native ecosystems to highly modified systems. These various classes will produce different levels and types of ecosystem services and the challenge for natural resource management programs and land management decisions is to be able to consider the complex nature of trade-offs between a wide range of ecosystem services. We use vegetation types and their condition classes as a first approximation or surrogate to define and map the underlying ecosystems in terms of their regulating, supporting, provisioning and cultural services. In using vegetation as a surrogate, we believe it is important to describe natural or modified (e.g. agronomic) vegetation classes in terms of structure which in turn is related to ecosystem function (rooting depth, nutrient recycling, carbon capture, water use, etc.). This approach enables changes in vegetation as a result of land use to be coupled with changes to surface and groundwater resources and other physical and chemical properties of soils. For Australian ecosystems an existing structural classification based on height and cover of all vegetation layers is suggested as the appropriate functional vegetation classification. This classification can be used with a framework for mapping and manipulating vegetation condition classes. These classes are based on the degree of modification to pre-existing vegetation and, in the case of biodiversity, this is the original vegetation. A landscape approach enables a user to visualise and evaluate the trade-offs between economic and environmental objectives at a spatial scale at which the delivery of ecosystem services can meaningfully be influenced and reported. Such trade-offs can be defined using a simple scoring system or, if the ecological and socio-economic data exist in sufficient detail, using process-based models. Existing Australian databases contain information that can be aggregated at the landscape and water catchment scales. The available spatial information includes socio-economic data, terrain, vegetation type and cover, soils and their hydrological properties, groundwater quantity and surface water flows. Our approach supports use of this information to design vegetation management interventions for delivery of an appropriate mix of ecosystem services across landscapes with diverse land uses.

DOI

[58]
Zhang Liwei, Fu Bojie, 2014. The progress in ecosystem services mapping: A review.Acta Ecologica Sinica, 34(2): 316-325. (in Chinese)Ecosystem services mapping has been becoming one of the forefronts in the field of ecosystem services researches. Ecosystems deliver bunches of vital services for human society,such as food,water provision and water purification,carbon sequestration,soil protection,and entertainment. The sustainable capability of ecosystems to provide these services is influenced by changes of biophysical condition( e. g. The changes in land use and land cover, biodiversity, atmospheric composition and climate) and human society( e. g. The changes of socio-economic characteristics,demand and consumptive patterns of human beings),which alter the correlation of demand and supply in ecosystem services through impacting the composition,structure,and processes of ecosystems.Ecosystem services mapping is a process that assesses the component,spatial distribution and mutual relationships of ecosystem services in specific spatial-temporal scales by using multiple mapping methods and multi-sources data. These series of spatially explicit maps not only reveal the quantitative characteristics of the current regional ecosystem service,but also exposit the potential changes caused by different environmental scenarios. These visualized mapping result can facilitate decision makers to integrate ecosystem services into environmental protection planning and implementing,and assist them to weigh the pros and cons of the possible environmental variable scenarios and the consequences of likely decisions,in addition,to make an informed decision which benefits the sustainable developmental of natural-human systems. In the past few years,the widespread use of GIS and availability of the high spatial and temporal resolution RS data sets have prominently promoted the capability of ecosystem services mapping. After reviewing the researches published recently,we identified and summarized that ecosystem services mapping is primarily focusing on:( 1) the mapping ofecosystem services supply which refers to the capacity of an given area to provide a specific bundle of ecosystem services within a particular time scale;( 2) the mapping of ecosystem services demand which is the sum of all ecosystem services consumed by people in a particular area and specific time period;( 3) and the mapping of the ecosystem services trade-offs and synergies,the former refers to the increment of one ecosystem service which is at the cost of the other ecosystem service,the latter means the synchronous variations among many different ecosystem services. Although there are lots of practical cases,ecosystem services mapping is still at its early stage. The core of ecosystem services mapping is a process about how to meet the needs of policy makers by using appropriate mapping methods,however,cautions must be mentioned in the broad use of mapping methods or models because they are scale-dependent and context-specific,and the mapping results need to be validated and verified against the observational data.

DOI

[59]
Zhang L, Fu B, Lü Yet al., 2015. Balancing multiple ecosystem services in conservation priority setting.Landscape Ecology, 30(3): 535-546.Conservation priority setting is the critical process of allocating the limited resources available for nature conservation and; safeguarding the sustainability of biodiversity and ecosystem services

DOI

[60]
Zhang Lixiao, Song Yuqin, 2003. Efficiency of the Three-North Forest Shelterbelt Program.Acta Scicentiarum Naturalum Universitis Pekinesis, 39(4): 594-600. (in Chinese)As one of the four biggest forest shelterbelt programs in the wolrld, the Three-North Forest Shelterbelt Program didn achieve due defending purpos e . As for the reasons, there are conflictions with Chinese climate pattern and po pulation pressure, ecological principles and market rules since the very beginni ng of this progrma. New situation of Chinese desertification demands transformat ion of the Three-North Forest Shelterbelt Program,which should solve the conf li ctions firstly. And several suggestions,such as satisfying water demand of affo restation, peristing in diversification route and market direction and establish ing ecological compensation mechanism, were put forward at last.

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