Terrain gradient variations in ecosystem services of different vegetation types in mountainous regions: Vegetation resource conservation and sustainable development

Terrain gradient variations in ecosystem services of different vegetation types in mountainous regions: Vegetation resource conservation and sustainable development

Forest Ecology and Management 482 (2021) 118856 Contents lists available at ScienceDirect Forest Ecology and Management journal homepage: www.elsevi...

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Forest Ecology and Management 482 (2021) 118856

Contents lists available at ScienceDirect

Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Terrain gradient variations in ecosystem services of different vegetation types in mountainous regions: Vegetation resource conservation and sustainable development Shuai Ma a, 1, Yong-Peng Qiao b, 1, Liang-Jie Wang a, *, Jin-Chi Zhang a a

Co-Innovation Center of Sustainable Forestry in Southern China, Jiangsu Provincial Key Lab of Soil Erosion and Ecological Restoration, Nanjing Forestry University, Nanjing 210037, China School of Computer Science and Engineering, Northeastern University, Shenyang 110006, China

b

A R T I C L E I N F O

A B S T R A C T

Keywords: Vegetation types Ecosystem services Terrain gradient Priority conservation areas Protection measures

Natural vegetation plays a vital role in ecosystem services, while anthropogenic land-use change causes extensive damage to natural vegetation, decreasing ecosystem services, and impacting human well-being. Therefore, it is of great significance to establish protected areas and implement vegetation protection measures. Ecosystem services depend on the vegetation type and terrain, but this topic has not been considered in detail in policy imple­ mentations. In this study, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was used to evaluate water yield, carbon storage, soil conservation, and water purification in the Chuan-Dian ecological shelter. Statistical methods were applied to analyze the differences in ecosystem services of different vegetation types and determine spatial variations in ecosystem services along terrain (altitude, slope, landform relief, and terrain niche) gradients. The results showed that ecosystem services differed for different vegetation types, and natural forests provided higher ecosystem services than other vegetation types, except for water yield. The rational allocation of forests, shrubs, and grasses and the implementation of organic agriculture and agroforestry systems are important measures to ensure sustainable economic and ecological development. We used the ordered weighted average method to select priority conservation areas based on ecosystem services. The priority conservation areas can play an important role in vegetation management in mountainous regions. This study provides new perspectives for vegetation protection and sustainable development in large-scale mountain areas.

1. Introduction Terrestrial ecosystems provide a variety of ecosystem services for human society, creating and maintaining environmental conditions and providing resources for human survival and development (Costanza et al., 1997; Daily, 1997). As an important part of terrestrial ecosystems, vegetation plays an irreplaceable role in maintaining biodiversity, car­ bon storage, and water conservation (Onaindia et al., 2013; Pan et al., 2011; Strand et al., 2018). In addition, increased attention has focused on the role of vegetation in dealing with climate change and improving the ecological environment (Knoke et al., 2014; Perez-Giron et al., 2020; Woodbury et al., 2007). Vegetation ecosystem services contribute to human well-being, and protection should be strengthened to improve

vegetation ecosystem services (Alamgir et al., 2016; Felipe-Lucia et al., 2018). However, deforestation and degradation of vegetation have led to a decline in ecosystem services (Dai et al., 2018). Since 2000, China has implemented a large-scale Natural Forest Protection Program (NFPP), whose purpose is to protect natural vegetation for sustainable development (Ouyang et al., 2016; Wen and Theau, 2020). With the development and utilization of resources and the continuous imple­ mentation of ecological restoration projects, a large amount of natural vegetation has been gradually replaced by plantations or urban land­ scapes (Chen et al., 2019; Wang and Huang, 2020). Numerous studies have shown that vegetation contributes the most to ecosystem services, but the ecosystem services of different vegetation types are rarely considered (Liu et al., 2019b; Rimal et al., 2019). Therefore, a scientific

* Corresponding author. E-mail addresses: [email protected] (S. Ma), [email protected] (L.-J. Wang). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.foreco.2020.118856 Received 14 September 2020; Received in revised form 29 November 2020; Accepted 6 December 2020 Available online 13 December 2020 0378-1127/© 2020 Elsevier B.V. All rights reserved.

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understanding of the relationship between ecosystem services and vegetation types is a prerequisite for realizing multi-objective and sus­ tainable management of ecosystems. Mountainous areas account for approximately 70% of China’s land area, and approximately 56% of China’s population is dependent on mountain resources (Wang and Dai, 2020). Mountain areas are domi­ nated by natural vegetation, which provides a variety of high-quality ecosystem services for human beings. To our knowledge, few scholars, such as Fang et al. (2020) and Yu et al. (2020), have focused on the assessment of mountain ecosystem services based on different vegeta­ tion types. Thus, the study of mountain ecosystem services of different vegetation types has great practical significance. In mountain areas, a gradient effect of the environmental impact on vegetation cover, precipitation and temperature has been observed (Liu et al., 2019a; Wang et al., 2018). Affected by the terrain, the vegetation exhibits clear gradient changes, which affects the differentiation of ecological structure and spatial patterns in mountainous areas, leading to changes in landscape patterns and ecosystem services (Briner et al., 2013; Sanders and Rahbek, 2012). Also, climatic conditions such as precipitation, temperature, and potential evapotranspiration affect the distribution characteristics of vegetation (Fensholt et al., 2012; Zhang et al., 2020). However, the effects of the terrain gradient on the char­ acteristics of ecosystem services in mountain areas remain unclear, and in-depth research is lacking. An understanding of the impact of terrain factors on ecosystem services in mountain areas is of great significance to improving our knowledge of ecosystem services, rationally regulating human activities, adjusting measures according to local conditions, and developing effective ecosystem management. However, China’s com­ plex and diverse terrain and large mountainous areas complicate vege­ tation protection (Wang and Dai, 2020). The lack of accurate information on ecosystem services for vegetation types in different ter­ rains poses considerable challenges to vegetation management (Branco et al., 2019; Liu et al., 2019d). Therefore, a comprehensive analysis of ecosystem services of vegetation types in different locations is required in current research on vegetation ecosystem services and sustainable management. The imbalance between ecological protection and economic devel­ opment results in an impaired ability of the ecosystem to provide ser­ vices and resources for human well-being in mountain areas (Franke et al., 2019; Liu et al., 2019d). Moreover, the degradation of ecosystems in mountainous areas with diverse vegetation and complex terrain is a significant environmental problem. A large number of studies have shown that combining ecosystem services with conservation, manage­ ment, and related policies of protected areas can bring economic bene­ fits to people and protect the ecological environment (Chang et al., 2019; Liu et al., 2017; Yu et al., 2020). However, there are few studies on the selection of priority protection areas in mountainous areas. The se­ lection of priority conservation areas based on ecosystem services can provide references for the optimization of the design of spatial patterns of mountain areas and government planning. The Chuan-Dian ecological shelter has considerable terrain undula­ tion and abundant vegetation resources, and the vegetation shows sig­ nificant vertical changes (Fu et al., 2017). The ecological environment in this area not only affects the economic and social development within the region but also plays an important role in water regulation and stabilization of the current climate pattern in the middle and lower reaches of the Yangtze River and even the whole county (Fu et al., 2017). Due to people’s insufficient understanding of the importance of vege­ tation resources, a large number of exploitative mining and extensive management methods have been adopted for vegetation resources, leading to a sharp decrease in the area of primary forests, a decline in forest quality, and the weakening of various services provided by vegetation ecosystems, thus threatening the regional ecological envi­ ronment and biodiversity (Fu et al., 2017). Therefore, on the basis of understanding the spatial differences in ecosystem services for different vegetation types and terrain gradients, the selection of priority

conservation areas and the proposal of sustainable vegetation manage­ ment can achieve a balance between ecosystem services and human welfare. In this study, we selected the Chuan-Dian ecological shelter as the study area and evaluated the ecosystem services. The objectives were to: (1) quantify the ecosystem services of different vegetation types; (2) analyze the variations in the terrain gradient of ecosystem services of different vegetation types; (3) define priority conservation areas based on ecosystem services; (4) propose ecosystem management suggestions in different regions based on different vegetation features. The results can provide theoretical guidance and decision support for sustainable management and ecological security in mountain regions. 2. Materials and methods 2.1. Study area The Chuan-Dian ecological shelter (24◦ 40′ - 34◦ 55′ N; 98◦ 40′ 108◦ 20′ E) is located in northwestern Yunnan Province, central Sichuan Province, and southwest Shanxi Province, China, with an area of 338, 109 km2 (Fig. 1) The Chuan-Dian ecological shelter has significant relief fluctuation, and the elevation ranges from 210 m to 7143 m. The climate is dominated by a plateau mountain temperate zone and cold temperate monsoon climate, with low average temperatures, high precipitation, and well-defined dry and wet seasons. Strongly controlled by terrain, the climate, soil, and vegetation of the Chuan-Dian ecological shelter show significant vertical changes along the altitude gradient. The Chuan-Dian ecological shelter is a typical agro-pastoral ecotone. Areas of farmland are common in the warm temperate mountain valleys below 2000–3000 m. Forested areas are primarily found in the mountain valleys with an altitude of 2000–4000 m in areas connected to the plateau. Animal grazing occurs primarily in the high mountains and plains above altitudes of 3000–3500 m. Due to its unique geographical location, the Chuan-Dian ecological shelter is an important water con­ servation area in the middle and lower reaches of the Yangtze River and an ecological barrier to the eastern farming areas. The area has an important strategic position in China’s economic development and ecological security. 2.2. Framework of this study The framework of the study comprised four stages (Fig. 2): (1) Data preparation, including Digital Elevation Model (DEM) data, climate data, land use/land cover (LULC) data, and other biophysical data; (2) Ecosystem services assessment, using Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to evaluate water yield, carbon storage, soil conservation, and water purification; (3) Results and analysis, using analysis of variance (ANOVA) to quantify differences in ecosystem services between regions with different terrains, cluster analysis to highlight similarities and differences in ecosystem services between vegetation types, and ordered weighted average (OWA) to select priority conservation areas; (4) Develop effective and targeted vegetation protection schemes based on the results. 2.3. Data resources The LULC data of 2015 with a resolution of 90 m were obtained from the ChinaCover dataset (Wu et al., 2014; Wu et al., 2015). The LULC types were divided into six classes and 26 sub-classes (Table 1). Climate data were acquired from the Data Center for Resources and Environ­ mental Sciences of the Chinese Academy of Sciences (http://www.resdc. cn). The digital elevation model (DEM) with a resolution of 90 m was obtained from the Geospatial Cloud Platform (http://www.gscloud.cn). The 1:1000,000 soil type map came from the National Tibetan Plateau Data Center (http://westdc.westgis.ac.cn). 2

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Fig. 1. Geographical overview and spatial distribution of digital elevation model (DEM) of the Chuan-Dian ecological shelter in southwest China.

2.4. Quantification of various ecosystem services

AETxj is the actual evapotranspiration of the pixel x with land use type j, and Px is the annual precipitation. The biophysical table and related parameter calculation method are derived from different studies (Fang et al., 2020; Lu et al., 2013; Sun et al., 2005; Sun, 2017).

Compared with other models, the InVEST model is characterized by refinement, quantification, and spatialization, is being updated and improved continuously, and is more mature (Sharp et al., 2020). It has been widely used in evaluating natural capital and ecosystem services and achieved good results in the United States (Wang et al., 2017b), the United Kingdom (Redhead et al., 2016), and China (Hu et al., 2019; Ouyang et al., 2016; Zhang et al., 2017). In addition, in consideration of the relevant policies, the characteristics of the ecological environment of the study area, and data accessibility, we used the InVEST model to evaluate the water yield, carbon storage, soil conservation and water purification.

2.4.2. Carbon storage In the InVEST model, the carbon module for calculating the carbon storage of the terrestrial ecosystem assumes that the total carbon density of each LULC type corresponds to the total carbon density of terrestrial ecosystems in this region, which consists of above-ground carbon den­ sity, below-ground carbon density, soil organic carbon density and dead organic matter carbon density (Arkema et al., 2015; Hu et al., 2019; Rimal et al., 2019). The formula is as follows: Ctotal = Cabove + Cbelow + Cdead + Csoil

2.4.1. Water yield The water yield of ecosystems is relevant to human life and affects human well-being in many ways. The water yield module of the InVEST model calculates the water yield based on water balance principle (Bai et al., 2019; Redhead et al., 2016; Wen et al., 2019). The formula is as follows: Yxj = (1 −

AETxj )⋅Px Px

(2)

where Ctotal , Cabove , Cbelow , Cdead , and Csoil are the carbon storage, the above-ground carbon density, below-ground carbon density, soil organic carbon density, and dead organic matter carbon pools, respec­ tively. The biophysical table is derived from different studies (Fang et al., 2020; Sun, 2017).

(1)

2.4.3. Soil conservation The Sediment Delivery Ratio (SDR) module of the InVEST model is based on the universal soil loss equation (Bai et al., 2020; Liu et al.,

where Yxj is the average water yield of pixel x for the land use type j, 3

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Fig. 2. Framework of this study.

Agriculture Organization (FAO) and the study of Fu et al. (2017). L and S are calculated by the InVEST model. C and P are obtained from different studies (Fu et al., 2017; Sun, 2017; Zhang, 2014).

Table1 LULC Classification. 1

2

LULC type

LULC code

LULC type

Forest lands

101 102 103 104 105 106 107 108 109 110 111 112 113 114 201 202 203 204 205 206 207 301 302 4 5 6

Evergreen broadleaf forest Deciduous broadleaf forest Evergreen needleleaf forest Deciduous needleleaf forest Broadleaf and needleleaf mixed forest Evergreen broadleaf shrubland Deciduous broadleaf shrubland Evergreen needleleaf shrubland Sparse forest Sparse shrubland Tree orchard Shrub orchard Tree garden Shrub garden Temperate steppe Alpine steppe Temperate meadow Alpine meadow Tussock Sparse grassland Lawn Paddy field Dry farmland Wetlands Built-up lands Other lands

Grasslands

3

Croplands

4 5 6

Wetlands Built-up lands Other lands

2.4.4. Water purification The Nutrient Delivery Ratio (NDR) module of the InVEST model calculates water purification by estimating the amount of nutrients exported due to runoff (Pham et al., 2019; Sun et al., 2020). The higher the nutrient export, the stronger the water purification is. The formula is as follows:

where ALVx is the load value adjusted by pixel x; HSSx is the hydro­ logical sensitivity score value of pixel x; and polx is the output coefficient of pixel x. The biophysical table is derived from different studies (Fan, 2017; Fang et al., 2020; Liu et al., 2019c). 2.5. Statistical analysis The terrain is an important factor affecting the distribution and structure of ecosystem services. This study selected altitude, slope, landform relief, and terrain niche to analyze the terrain gradient changes of ecosystem services. We used spatial analysis tools in ArcGIS to determine the differences in ecosystem services for topographically related vegetation types. We divided the study area into five regions

2019d). The formula is as follows: A = R × K × L × S × (1 − C × P)

(4)

ALVx = HSSx ⋅polx

Table 2 Classification of altitude, slope, landform relief, and terrain niche gradients.

(3)

where A is the soil conservation capacity, R is the precipitation erosivity, K is the soil erodibility, L is the slope length factor, S is the slope gradient factor, C is the vegetation management factor, and P is the practice factor. The R and K are calculated using the methods of the Food and 4

rank

altitude

slope

landform relief

terrain niche

1 2 3 4 5

200–900 m 900–1700 m 1700–2400 m 2400–3300 m 3300–7200 m

0–8.54◦ 8.54◦ -16.48◦ 16.48◦ –23.50◦ 23.50◦ -31.12◦ 31.12◦ -77.81◦

0–293 m 293–516 m 516–699 m 699–932 m 932–2562 m

0.11–0.83 0.83–1.23 1.23–1.50 1.50–1.78 1.78–2.79

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(each about 20% of the total area) according to altitude, slope, landform relief, and terrain niche (Table 2). Random samples of terrain gradients and vegetation types were then selected for ANOVA and cluster analysis.

The higher the trade-off, the more evenly weighted the ecosystem ser­ vices are. By setting multiple risk values, we can obtain more scenarios but also increase the calculation time and difficulty. Since the risk value of losing the ecosystem services is between 0 and 1, each scenario was assigned an interval of 0.1 from 0 to 1 to balance the risk. We calculated the three mathematical formulas (Eqs. (6) and (7)) in Python to obtain the tradeoffs and weights for different scenarios. Finally, we chose the maximum tradeoff of the same risk to establish scenarios with different weight combinations and set 11 scenarios based on 11 identified risks (Table 3). Very few studies have investigated the definition scope of priority conservation area in China. We identified regions with the top values of ecosystem services of 20%as priority conservation area by referring to existing studies (Qin et al., 2019; Zhang et al., 2015) to protect ecosystem services in the study area efficiently. By comparing the pro­ tection efficiency of ecosystem services in different scenarios, the highest protection efficiency can be ensured for multiple ecosystem services. The area corresponding to this scenario is the priority conser­ vation area. The calculation formula of conservation efficiency is as follows:

2.5.1. Analysis of variance ANOVA was used to examine the statistically significant differences in ecosystem services between regions with different terrains. We used ArcGIS software to select 10,000 points randomly in each terrain regions to extract ecosystem service information. This information can provide technical support for comparing differences in ecosystem services in different terrain gradient regions. The information of these points was imported into SPSS to conduct ANOVA. 2.5.2. Cluster analysis Different vegetation types were sampled, and the main vegetation types of the ecosystem services were clustered by cluster analysis, which highlighted similarities and differences in ecosystem services between 23 vegetation types. The random point tool in ArcGIS was used to select 1000 points randomly in each vegetation type, for a total of 2300 random points. The efficiency of each ecosystem service in each vege­ tation type was calculated for cluster analysis. The clustering standard was constantly revised during the analysis. The specific clustering pro­ cess described by Fang et al. (2020) was used.

E=

An integration of the OWA method with GIS has been proven to balance multiple conflicting objectives in the decision-making process, providing a method for handling the trade-offs between multiple ecosystem services when identifying priority conservation areas (Qin et al., 2019; Yu et al., 2020; Zhang et al., 2015). The calculation formula is as follows: n ∑

ωx Sxj , (ωx ∈ [0, 1]and

x

n ∑

3. Results 3.1. Vegetation types in different terrain gradients The distribution of vegetation types differed for different terrain gradients (Fig. S1). Three vegetation types comprised the majority of the study area. Evergreen needleleaf forest occupied the largest proportion at altitudes of 1700–7200 m (28.16%-50.72%), a slope of 8.54◦ -77.81◦ (28.30%-36.43%), landform relief of 293–2562 m (27.93%-41.43%), and a terrain niche of 0.83–2.79 (23.82%-40.85%), respectively. This vegetation type accounted for a relatively high proportion at the alti­ tudes of 200–1700 m (17.66%-18.76%), a slope of 0–8.54◦ (12.43%), landform relief of 0–293 m (13.52%), and a terrain niche of 0.11–0.83 (12.91%). Deciduous broadleaf shrubland comprised the majority of the area at altitudes of 900–1700 m (19.23%) and was also found at alti­ tudes of 1700–2400 m (11.40%) and 3300–7200 m (10.93%-12.45%), a slope of 8.54◦ -77.81◦ (10.36%-18.28%), landform relief of 293–2562 m (11.58%-14.71%), and a terrain niche of 0.83–2.79 (10.55%-15.16%). Dry farmland occupied the largest area at altitudes of 200–900 m (33.73%), a slope of 0–8.54◦ (27.85%), landform relief of 0–293 m (31.44%), and a terrain niche of 0.11–0.83 (33.04%). This vegetation was also found at the altitudes of 900–2400 m (11.17%-14.00%), a slope of 8.54◦ –23.50◦ (10.82%-17.99%), landform relief of 293–516 m

ωx = 1, foriandj = 1, 2, 3, ..., n

x

(5)

where axj is the attribute value of position × in the normalized grid j. A min–max normalization is used, and the results are in the range of 0 to 1. Sxj is the new layer obtained by ranking the raster values of axj of the five ecosystem services from large to small after normalization. ωx is the new ordered weight of the new data set Sxj . The risks and trade-offs are different for different ordered weights, and the relationship is as follows: risk = (n − 1)−

1

n ∑

(n − x)ωx (0⩽risk⩽1)

(6)

x

trade − off = 1 -

√̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ )2 √ n ( √ ∑ 1 √n ω x n √ x n-1

(0⩽trade − off⩽1)

(8)

where E is the conservation efficiency of the specific ecosystem services of the priority conservation area; ESc is the average value of specific ecosystem services in the priority conservation area; and ES0 is the average value of specific ecosystem services in the study area.

2.6. Selection of the optimal conservation area

OWA(axj ) =

ESc ES0

(7)

where n is the total number of raster layers of the ecosystem services, ωx is the weight of raster layer × ,risk refers to the decision-maker’s risk avoidance probability, and trade-off refers to the degree of trade-off for different risk coefficients, representing the equilibrium degree of the distribution of each ecosystem service under different risk conditions. The lower the expected risk, the higher the weight given to the low ecosystem services is to reduce the risk probability. If policymakers choose low risk (risk-aversion), they give high weight to low ecosystem services. If they choose a higher risk (risktaking), they assign a high weight to high ecosystem services. If the decision-makers want the maximum trade-off, they assign the same weight to each ecosystem service (Qin et al., 2019; Zhang et al., 2015). If decision-makers assign the highest or lowest ecosystem service a maximum weight value of 1, they obtain the lowest trade-off value of 0.

Table 3 The risks, trade-offs, and weights under scenario 1–11.

5

scenarios

risk

trade-off

w1

w2

w3

w4

1 2 3 4 5 6 7 8 9 10 11

0 0.1 0.2 0.3 0.4 0.5 0.4 0.3 0.2 0.1 0

0 0.37 0.57 0.71 0.86 1 0.86 0.71 0.57 0.37 0

0 0.01 0.05 0.10 0.17 0.25 0.35 0.46 0.60 0.76 1

0 0.04 0.11 0.17 0.21 0.25 0.27 0.28 0.25 0.18 0

0 0.18 0.25 0.28 0.27 0.25 0.21 0.17 0.11 0.04 0

1 0.76 0.60 0.46 0.35 0.25 0.17 0.10 0.05 0.01 0

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(15.96%), and a terrain niche of 0.83–1.23 (18.34%). In general, the area of evergreen needleleaf forest showed a trend of initial increase followed by a decrease along the altitude gradient, reaching its peak at altitudes of 2400–3300 m, and there was an increasing trend with increasing slope, landform relief, and terrain niche gradient. In addition to the fluctuation with the altitude gradient, the area of deciduous broadleaf shrubland first increased and then decreased with the other terrain gradient, reaching its peak at altitudes of 900–1700 m, a slope of 23.50◦ -31.12◦ , landform relief of 516–699 m, and a terrain niche of 1.23–1.50. Dry farmland decreased sharply with the terrain gradient along all gradients and was rarely found at a high altitude, high land­ form relief, high terrain niche, and on steep slopes. The terrain differences were also evident in other vegetation types. Evergreen broadleaf forest occupied areas at the altitudes of 1700–3300 m (10.52%-14.96%), and was observed in the terrain niche of 1.23–1.50 (10.02%). Deciduous broadleaf forest occurred at altitudes of 900–1700 m (15.77%), and was less common in the terrain niche of 0.83–1.23 (10.78%). Evergreen broadleaf shrubland occurred at altitudes of 1700–2400 m (10.62%). Alpine steppe occupied a large area at altitudes of 3300–7200 m (21.23%) and a terrain niche of 1.78–2.79 (18.83%). Paddy fields were distributed at altitudes of 200–900 m (23.55%), a slope of 0–8.54◦ (24.31%), landform relief of 0–293 m (25.88%), and a terrain niche of 0.11–0.83 (26.39%). In general, the areas of evergreen broadleaf forest and deciduous broadleaf forest first increased and then decreased with the terrain gradient. Evergreen broadleaf forest reached its peak at altitudes of 1700–2400 m, a slope of 16.48◦ –23.50◦ , landform relief of 699–932 m, and a terrain niche of 1.23–1.50. Deciduous broadleaf forest reached its peak at altitudes of 900–1700 m, a slope of 16.48◦ –23.50◦ , landform relief of 516–699 m, and a terrain niche of 0.83–1.23. Evergreen broadleaf shrubland increased along the slope and landform relief gradient, and the area first increased and then decreased along the altitude and terrain niche gradient, reaching its peak at the altitudes of 1700–2400 m, and a terrain niche of 1.50–1.78. The alpine steppe area showed an increasing trend, whereas paddy fields showed the opposite trend along all gradients.

3.2. Ecosystem services in different terrain zones The assessment of water yield, carbon storage, soil conservation, and nitrogen export is shown in Fig. 3. As described in Section 2.5.1, ANOVA was used to determine the differences in the average ecosystem service levels (df = 49,995, p < 0.05), which reflected the differences in ecosystem services along the terrain gradients (Fig. 4). In the altitude gradient, the nitrogen export decreased with increasing altitude. The changes in the water yield, carbon storage, and soil conservation fluctuated. Water yield decreased with the altitude, and then increased in the high-altitude area. Carbon storage and soil conservation showed opposite trends. These fluctuations occurred at altitudes of 2400–3300 m, and carbon storage and soil conservation increased at altitudes of 200–2400 m and decreased at altitudes of 3300–7200 m. In the slope gradient, carbon storage and soil conservation increased with increasing slope. The water yield in the three high slope zones was similar (332.71 mm at 16.48◦ –23.50◦ , 317.95 mm at 23.50◦ -31.12◦ , and 321.81 mm at 31.12◦ -77.81◦ ), and was significantly lower (p < 0.05) than in the 0–8.54◦ (406.03 mm) zone and 8.54◦ -16.48◦ (358.77 mm). Nitrogen export exhibited a downward trend along the slope gradient. In the landform relief gradient, carbon storage and soil conservation increased with an increase in the landform relief. The range of carbon storage was large in the landform relief of 0–516 m, and the range of soil conservation was large in the landform relief of 699–2562 m. The water yield and nitrogen export decreased with an increase in the landform relief. There were few changes in the water yield and nitrogen export in the landform relief of 293–2562 m. In the terrain niche gradient, the water yield was significantly higher (p < 0.05) in the terrain niche of 0.11–0.83 (426.58 mm) and 0.83–1.23 (375.55 mm) and lower in the terrain niche of 1.50–1.78 (285.56 mm) than in other terrain niche gradients, whereas it was similar in the terrain niche of 1.23–1.50 (322.42 mm) and 1.78–2.79 (327.69 mm). Carbon storage was significantly lower (p < 0.05) in the terrain niche of 0.11–0.83 (21.44 t/ha) and 0.83–1.23 (40.93 t/ha) than in other terrain niche gradients and was highest in the terrain niche of 1.50–1.78 (49.14 t/ha). In contrast, it was similar in the other two zones (45.74 t/ha and 44.64 t/ha in the terrain niche of 1.23–1.50 and 1.78–2.79,

Fig. 3. Spatial distribution of altitude, slope, landform relief, terrain niche, water yield, carbon storage, soil conservation, and nitrogen export in the Chuan-Dian ecological shelter in Southwest China. 6

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Fig. 4. Differences in water yield, carbon storage, soil conservation, and nitrogen export along the terrain gradients in the Chuan-Dian ecological shelter in Southwest China.: 1–5 represents the rank of the altitude, slope, landform relief, and terrain niche gradients, and see Table 2 for details of terrain gradient.

respectively). Soil conservation increased with an increase in the terrain niche, whereas nitrogen export showed the opposite trend. The changes in the soil conservation and nitrogen export were significant in the terrain niche of 0.11–1.23. In general, carbon storage at low terrain levels was not as good as at high terrain levels, and the opposite was observed for water yield and nitrogen export. Nitrogen export was higher in regions with lower terrain levels, indicating a high nutrient supply that is conducive to economic cultivation, but excessive nitrogen export will put consider­ able pressure on water purification. Ecosystem services in medium terrain zones were relatively balanced, with high carbon storage and soil conservation and medium water yield and nitrogen export.

than the average of all vegetation types, whereas that of evergreen forests and mixed forest was higher than the average of all vegetation types. ‘Natural forests’ occurred primarily in higher terrain gradients, but there were few ‘natural forest’ areas in the first terrain gradient zone. ‘Natural shrublands’ comprised four types of natural shrublands and shrub garden. Their carbon storage and nitrogen export were signifi­ cantly lower (p < 0.05) than that of other vegetation types, whereas their water yield was higher than the average of all vegetation types. In addition, their soil conservation levels were higher than the average of all vegetation types, except for the shrub garden. ‘Natural shrublands’ were distributed in all terrain gradient zones, and there were few in the first terrain gradient zone. ‘Plantations’ comprised tree orchard, shrub orchard, and tree gar­ den. Their water yield and carbon storage were higher than the average of all vegetation types, whereas their soil conservation and nitrogen export were lower than the average of all vegetation types. ‘Plantations’ occurred mostly in lower terrain gradient zones. ‘Natural grasslands’ comprised six types of natural grassland and lawn. Their carbon storage and nitrogen export were significantly lower (p < 0.05) than those of other vegetation types, except for cultivated land. In addition, their soil conservation levels were higher than the average of all vegetation types, except for alpine meadow and lawn, whereas their water yield was lower than the average of all vegetation types, except for sparse grassland. ‘Natural grasslands’ were distributed in all terrains, and there were few in the first terrain level. ‘Natural

3.3. Vegetation clustering and distribution As described in Section 2.5.2, the 23 types of vegetation were further clustered into 5 new major vegetation types following the method by Fang et al. (2020) (Fig. 5 and S2) based on the similarities and differ­ ences determined from the ecosystem service assessment. The vegeta­ tion types with relatively small areas were also clustered into the main types. The 5 main vegetation types were natural forests, natural shrub­ lands, plantations, natural grasslands and cultivated land. ‘Natural forests’ comprised six types of natural forests. Their carbon storage levels were significantly higher (p < 0.05), whereas their ni­ trogen export and water yield were lower than those of other vegetation types. Soil conservation in deciduous forests and sparse forest was lower 7

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Fig. 5. Ecosystem service efficiencies of 5 new major vegetation types in the Chuan-Dian ecological shelter in Southwest China: WY, water yield; CS, carbon storage; SC, soil conservation; NE, nitrogen export.

grassland’ areas were most common at higher altitudes (3300–7200 m). ‘Cultivated land’ comprised paddy field and dry farmland. Their water yield and nitrogen export were above average, and the nitrogen export was significantly higher (p < 0.05) than that of other types, whereas their carbon storage and soil conservation were below the average of all vegetation types. ‘Cultivated land’ was most common in the first terrain gradient level. We further analyzed the distribution characteristics of the five major vegetation types in the terrain gradient (Fig. S3). In general, ‘natural forests’ increased with increasing slope and landform relief, and a considerable change (28.98%) occurred at altitudes of 2400–7200. The increases in the area of the ‘natural forests’ in the terrain niche of 0.11–1.23 were large (26.75%). Areas of ‘natural shrublands’ first increased and then decreased with an increasing level of the terrain gradient, except for altitude, reaching their peak at altitudes of 900–1700 m, a slope of 23.50◦ -31.12◦ , landform relief of 516–699 m, and a terrain niche of 1.23–1.50. The areas of ‘natural shrublands’ fluctuated with increasing altitude. The change in the area of ‘natural shrublands’ was substantial (5.28%) at the altitudes of 200–1700 m. The area of ‘plantations’ decreased with increasing slope and landform relief and first increased and then decreased with increasing altitude and terrain niche, reaching their peak at altitudes of 900–1700 m and a terrain niche of 0.83–1.23. ‘Natural grasslands’ areas were more frequent in high terrain than in low terrain, whereas ‘cultivated land’ was more frequent in low terrain than in high terrain.

changing the ranking weight. We multiplied the four ecosystem services by the weights to obtain 11 scenarios. The lower the risk value of the scenarios, the lower the risk of losing ecosystem services is. At this time, decision-makers tend to assign high weights to low ecosystem services. In scenarios 1–5, decision makers tend to favor low risk (risk-aversion) and give high weight to low ecosystem services, whereas in scenarios 7–11, the opposite is true. With the increase in the risk, the trade-off tended to increase first and then decrease. In Scenario 6, decisionmakers assign equal weight to each ecosystem service, and the tradeoff is the greatest. From scenario 1 to 11, policy-makers focus increasing on high ecosystem services. Due to the changes in risks and trade-offs in different scenarios, the spatial location of the identified priority conservation area varies in the study area. The locations of the priority conservation areas in each scenario are shown in Fig. S4. 4. Discussion 4.1. Different ecosystem services in different vegetation types and terrain gradients The altitudes of 200–900 m, a slope of 0–8.54◦ , landform relief of 0–293 m, and a terrain niche of 0.11–0.83 had the highest nitrogen export and water yield and the lowest soil conservation and carbon storage. This result was likely caused by relatively little natural vege­ tation in these areas and the fact that cultivated land accounted for the largest proportion in the first level of the gradient. Cultivated areas had high nitrogen export (5.1 times the average level, which is similar to the results of Fang et al. (2020)), which adversely affected water sources due to the extensive use of pesticides, fertilizers, and insecticides. In contrast, large forest areas purify the water (Doody et al., 2016; Wang

3.4. Weights, risks and protection locations in different scenarios OWA simulates the decision result for different decision-makers’ preferences by ranking the importance of the factor attributes and 8

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et al., 2019). Excessive nitrogen export results in water pollution, affecting the ecological security and water quality of the region. Studies have shown that natural forests provided higher carbon storage and soil conservation than cultivated land (Fang et al., 2020; Li et al., 2008; Zhang et al., 2007). Therefore, the first level of the terrain gradient is an important area for ecosystem protection and restoration in the ChuanDian ecological shelter, and its ecological protection needs to be strengthened. The carbon storage and soil conservation at altitudes of 2400–3300 m, a terrain niche of 1.50–1.78, a slope of 31.12◦ -77.91◦ , and landform relief of 932–2563 m were higher than those of the other terrain gradient zones; however, their water yield was lower. This was likely caused by 80% natural vegetation, with natural forests accounting for >50%, natural shrublands for about 20%, and natural grasslands for nearly 10%. Carbon storage and soil conservation were higher at alti­ tudes of 200–3300 m and lower at altitudes of 3300–7200 m, and they increased with increasing slope. Similarly, the results for carbon storage and soil conservation in the Qinling and Daba mountains and two shelters and three belts were consistent with our results (Wang et al. 2020a; Yu et al., 2020). However, Fang et al. (2020) showed that carbon storage and soil conservation increased with increasing altitude. Forests are denser in steeper areas and at higher altitudes, but beyond a certain altitude, forests are less common and grasses occupy large portions of the landscape (An et al., 2018; Feng et al., 2020). In addition, the maximum altitude of the study area is 7200 m, and carbon storage of forests is higher than that of grasses, leading to the change in carbon storage with increasing altitude (Liang et al., 2021; Tang et al., 2020). Studies have shown that the water yield capacity of natural forests is low (Fang et al., 2020; Wen et al., 2019). Plantations and cultivated land with low carbon storage and high nitrogen export decreased with an increase in the level of the terrain gradient and accounted for a small proportion in these regions. As a result, the water yield and nitrogen export were relatively low. The combination of forests, shrubs, grasses, and broadleaf and needleleaf mixed forest proved advantageous and could be used as a model for the ecological protection of other regions (Canedoli et al., 2020; Zheng et al., 2019). Ecosystem services at altitudes of 900–2400 m, a slope of 8.54◦ 16.48◦ , landform relief of 293–699 m, and a terrain niche of 0.83–1.50 were close to the average of all vegetation types. The carbon storage and soil conservation in the third terrain gradient zones were higher than in the second terrain gradient zone, but the opposite was observed for the water yield and nitrogen export. This result was attributed to the high proportion of natural forests that increased with the gradient. The nat­ ural grassland with low water yield also increased with the gradient, and the cultivated land with high nitrogen export and low soil conservation decreased with the gradient, resulting in a decrease in the nitrogen export and an increase in soil conservation (Wang et al., 2019; Zhang et al., 2007). Agroforestry can ensure a sustainable supply of multiple ecosystem services. Thus, ecological protection measures in the second terrain gradient zones need to be implemented to improve soil conser­ vation and water purification.

presence of multiple grids with the same ecosystem service value; thus, these scenarios were not considered in the selection of the priority conservation areas. The conservation efficiency of each ecosystem ser­ vice in each scenario was calculated using Eq. (8). The conservation efficiency of soil conservation and carbon storage was higher in the ecosystem services provided by priority conservation areas. The distri­ bution of priority conservation areas was similar for all scenarios, and the largest areas were located in the south, middle, and north of the Chuan-Dian ecological shelter. The main reason was that the natural forests of the priority conservation areas accounted for 77.12%, among which evergreen needleleaf forest accounted for 55.83%, and evergreen broadleaf forest accounted for 16.79%. Natural forests have a strong carbon storage ability and prevent soil and water loss (Liu et al., 2019d; Luo et al., 2019). The soil conservation service of natural forests plays an important role in the ecological protection of mountain areas and can promote the sustainable development of an ecological economy (Rao et al., 2014; Wang et al., 2020b). The Chuan-Dian ecological shelter is located in the upper reaches of the Yangtze River in the plateau mountain temperate and cold temperate monsoon climate, with annual average precipitation between 200 and 1900 mm (Fu et al., 2017). However, the conservation efficiency of water yield in the priority conservation areas is low because of the wide distribution of natural forests. Vegetation growth consumes large amounts of water, which exacerbates the regional water pressure and leads to an imbalance in the water resource utilization efficiency (Wen and Theau, 2020). It is vitally important for ecological and sustainable development to understand the effects of vegetation increase on water consumption (Cao et al., 2011; Wang et al., 2020a). The conservation efficiency represents the level of protection of priority conservation areas. In this study, the conservation efficiency was classified into low (<1), medium (1–2), and high (>2) grades. A comparison of the conservation efficiency of the ecosystem services with the average conservation efficiency in each scenario indicated that pri­ ority conservation areas corresponding to scenario 2 are the optimal choice. Table 4 list the priority conservation areas that were optimal for soil conservation. The soil conservation efficiency is higher in the central region of the Chuan-Dian ecological shelter and lower in the south and north (Fig. 6). In addition, the conservation efficiency regarding carbon storage in the priority conservation areas was better, with large areas exceeding the average and small areas located in the middle below the average. The conservation efficiency for water purification and water yield in the priority conservation areas was general. Although the water yield in the priority conservation areas was higher than in the entire region, there were still large areas with low conservation efficiency. We calculated the proportion of priority conservation areas in each terrain gradient zone to determine the gradient effect on the priority conservation areas (Table 5). Significant differences were observed. Most priority conservation areas were located at medium altitude, on high slopes, in high landform relief, and low terrain niches. In general, Table 4 The conservation efficiencies of water yield, carbon storage, soil conservation, and water purification for different scenarios in the Chuan-Dian ecological shelter in Southwest China.

4.2. Conservation efficiency in different scenarios Priority conservation areas were identified by the OWA method. Few studies investigated the definition scope of priority conservation areas in China. We identified regions with the top values of ecosystem services of 20% as priority conservation areas by referring to existing studies (Qin et al., 2019; Zhang et al., 2015) to protect ecosystem services in the study area efficiently. From scenario 1 to 11, the risk increased, and the trade-off increased first and then decreased. The greater the value of risk gained in scenario, the higher the risk of losing ecosystem services was. From Scenario 1 to 5, low-value ecosystem services had high weights. In Scenario 6, each ecosystem service had the same weight. From Scenario 7 to 11, high-value ecosystem services had high weights. Scenario 1 and scenario 11 were too small and too large, respectively, due to the

Scenario

2 3 4 5 6 7 8 9 10

9

Conservation efficiency Water yield

Carbon storage

Soil conservation

Water purification

1.21 1.09 1.04 1.01 0.96 0.92 0.89 0.88 0.78

1.56 1.75 1.79 1.81 1.83 1.84 1.85 1.86 1.86

2.64 2.07 1.79 1.65 1.54 1.48 1.43 1.45 1.29

1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05

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Fig. 6. Conservation efficiency of the priority conservation areas in the Chuan-Dian ecological shelter in Southwest China. Table 5 The proportion of priority conservation areas in altitude, slope, landform relief, and terrain niche gradients in the Chuan-Dian ecological shelter in Southwest China. Altitude

Proportion

Slope

Proportion

Landform relief

proportion

Terrain niche

Proportion

200–900 m 900–1700 m 1700–2400 m 2400–3300 m 3300–7200 m

11.66% 28.67% 25.71% 22.62% 11.34%

0–8.54◦ 8.54◦ -16.48◦ 16.48◦ –23.50◦ 23.50–31.12◦ 31.12◦ -77.81◦

9.39% 21.07% 22.29% 22.72% 25.53%

0–293 m 293–516 m 516–699 m 699–932 m 932–2562 m

7.17% 20.12% 20.93% 23.45% 16.48%

0.11–0.83 0.83–1.23 1.23–1.50 1.50–1.78 1.78–2.79

9.36% 25.72% 24.04% 22.36% 18.51%

relatively few priority conservation areas were located in the first zone of the terrain gradient zone, and some were located in the middle zones of the terrain gradient. The selection of the priority conservation areas can protect the ecological environment of mountainous regions, and specific protection measures can be developed to ensure sustainable development of the ecological environment of mountainous regions.

throughout the country, and stabilizing the climate pattern (Fu et al., 2017). The national key program of 2018, the “Research on Restoration and Projection of Typical Fragile Ecosystems”, suggested that the ecosystem service pattern of “two shelters and three belts” should be utilized to delineate and enhance regional ecosystem services and ensure national ecological security. One of the important tenets of the 2018 National Key Research and Development Program was to identify critical regions for the national ecological shelter zone. The selection of conservation priorities can provide a reference for the adjustment of the national ecological shelter zone and the improvement of ecosystem services. Studies have shown that the “Natural Forest Protection Program” and the “Grain for Green Program” have improved the ecological envi­ ronment in the Chuan-Dian ecological shelter (Fu et al., 2017; Wang

4.3. Vegetation protection measures The Chuan-Dian ecological shelter is not only a national ecological shelter zone, but also an important region of significant ecosystem protection and restoration projects in China. The Chuan-Dian ecological shelter plays an important role in ensuring the ecological security of the lower reaches of the Yangtze River, regulating the water balance 10

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et al., 2020b). However, these policies are based on large-scale consid­ erations and ignore the different environmental suitabilities of different vegetation types (Miao et al., 2018). Fu et al. (2017) pointed out that afforestation and deforestation coexist in the Chuan-Dian ecological shelter due to the difficulty in implementing detailed policies, and deforestation exceeds the afforestation. Therefore, we propose the following measures to promote the conservation and sustainable development of vegetation resources according to different vegetation characteristics in different terrain regions on the basis of setting priority conservation areas in the Chuan-Dian ecological shelter: The important task at altitudes of 200–900 m, a slope of 0–8.54◦ , landform relief of 0–293 m, and a terrain niche of 0.11–0.83 is to control the use of agricultural fertilizers to reduce nitrogen export. Agriculture is required for the survival and development of local residents; thus, it is unrealistic to prohibit the use of agricultural fertilizers (Smajgl et al., 2015). Commercial planting of forests is a viable means to guide eco­ nomic development and ecological conservation (Stone, 2008). The mixed farming model can effectively protect vegetation diversity, biodiversity, and farmers’ livelihoods (Kragt and Robertson, 2014; van der Horst et al., 2014). In addition, soil and water pollution can be reduced by applying organic fertilizer, reducing the use of chemical fertilizers and pesticides, establishing farmland shelterbelt, creating high-standard farmland, and developing ecological agriculture and organic agriculture (Chabert and Sarthou, 2020; Sandhu et al., 2010). Moreover, payments for ecosystem services to farmers can significantly mitigate pollution from cultivated land, improve water quality, and curb ecosystem degradation (Kaczan et al., 2013; Song et al., 2018). Ever­ green broadleaf forest, deciduous broadleaf forest, and evergreen nee­ dleleaf forest should be protected. Urban greening and ecological engineering are also worthy of attention. The ecosystem services in the middle terrain gradient zones are relatively balanced, and natural forests should be protected in these areas. First, priority conservation areas need to be established, the scope of priority conservation areas should be monitored to prevent destruc­ tion, and the management and conservation of forests in priority con­ servation areas should be strengthened. Ecological compensation is conducive to improving the overall service efficiency by maintaining and supervising forest resources (Chang et al., 2020; Sheng et al., 2017). Agroforestry systems should be promoted because they not only improve the utilization of land resources in the Chuan-Dian ecological shelter, but also provide better ecosystem services than a single vegetation type (Torralba et al., 2016; Varah et al., 2020). In addition, agroforestry systems maintain a stable internal microclimate, control soil erosion, improve water quality, increase economic income, and promote sus­ tainable development of agriculture and forestry (Kay et al., 2019; Zheng et al., 2019). The government can set up demonstration areas for agroforestry ecosystems, promote them in other regions, and give sub­ sidies to farmers who implement agroforestry systems. Finally, ecosystem supervision and management should be strengthened to drive the development of the local economy. The water yield in the high terrain gradient zones was relatively low. In combination with the climate background and site conditions, sci­ entific management programs or engineering measures should be implemented to increase water storage capacity and monitor the impact of forest structure changes on water yield. It should be noted that some cultivated land occurred in high-slope (>25◦ ) areas. The “Sloping Land Conservation Program” needs to be implemented to ensure tree planting. The characteristics of the shrub ecosystem services indicated that a change in the proportion of trees, shrubs, and grasses led to an increase in the water yield and soil conservation level. This finding also emphasizes the importance of different vegetation structures, such as trees, shrub, and grasses. Multiple high ecosystem services can be ob­ tained simultaneously by adjusting the proportion of trees, shrubs, and grasses. In addition, the development of mixed forests can improve carbon storage, soil conservation, and nitrogen export. Natural grass­ land protection and rational grazing should be strengthened to reduce

human disturbances, reduce the vulnerability of the ecological envi­ ronment, and improve the ability to resist risks. 4.4. Limitations and expectations In this study, altitude, slope, landform relief, and terrain niche were selected to investigate the changes in ecosystem services along a terrain gradient. However, ecosystem services are also influenced by many factors and additional environmental issues should be considered, such as climate change, north–south aspect, time dynamics, and ecological engineering (Wang et al., 2017a; Wang and Dai, 2020; Wang et al., 2018). Many studies have shown that land use, accessibility, and man­ agement intensity have important impacts on ecosystem services; these factors were not considered in this study (Peng et al., 2019; Shirmo­ hammadi et al., 2020; Zheng et al., 2016). Therefore, future studies should comprehensively analyze the drivers of variations in ecosystem services, considering climate factors, land use policies, accessibility, and management intensity. In addition, due to the lack of field or survey data, we used spatial distribution data from other relevant studies to perform model calibration. Therefore, this model has several un­ certainties, but overall, the results were consistent. Moreover, biodi­ versity is an important ecosystem service in the Chuan-Dian ecological shelter, but we did not discuss it due to the lack of available data. The innovation of this study is the combination of remote sensing technology and spatial statistics to quantify ecosystem services of vegetation types. Most studies do not consider the impact of vegetation types and terrain on ecosystem services when assessing ecosystem ser­ vices, and the lack of such information makes it difficult to formulate detailed natural vegetation protection schemes and implement effective vegetation protection measures (Mao et al., 2019; Peng et al., 2019). Our research can provide decision-makers with information on the suit­ ability of different vegetation types for different environments and the variations in ecosystem services provided by different vegetation types when formulating vegetation protection and restoration policies. Ecosystem service trade-offs/synergies have the characteristics of scale dependence and spatial differences, which is the key to achieve sustainable development and has become the focus of ecosystem man­ agers (Bai et al., 2020; Qiao et al., 2019; Zheng et al., 2019). However, most studies on mountain ecosystem services ignored the impact of terrain, which led to the complex spatial heterogeneity of ecosystem services tradeoffs/synergies (Briner et al., 2013; Liu et al., 2019a; Yu et al., 2020). The relationship between ecosystem service trade-offs/ synergies and terrain gradients was not explored in this study. Future research can further explore the trade-offs and synergies of ecosystem services at different spatial scales, thus contributing to the development of sub-regional and subtype vegetation resource management schemes and providing theoretical guidance and decision support for sustainable vegetation management. Priority conservation areas are regions that are selected based on ecosystem service trade-offs and have a variety of high ecosystem ser­ vices (Qin et al., 2019; Yu et al., 2020; Zhang et al., 2015). However, many conservation areas were designed by the state, often ignoring the areas of high conservation values. Thus, the selection of priority con­ servation areas should be incorporated into national reserve planning. Many scholars believe that ecosystem service trade-offs can be reduced or even offset by management decisions, thus forming a “win–win” sit­ uation (Howe et al., 2014; Johnson et al., 2014; Peng et al., 2019; Zheng et al., 2016). Management activities that improve ecosystem services above average or reduce or eliminate ecosystem service trade-offs can be considered to optimize conservation priorities (Peng et al., 2019; Zheng et al., 2016). Thus, ecosystem services can be combined with land use decisions to set different scientific priority conservation scenarios in the future. Moreover, future studies should simulate and compare ecosystem services for future land use patterns under different scenarios to provide decision support for future ecological protection and formulate sustainable development policies. 11

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5. Conclusions

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In this study, we assessed the ecosystem services of different vege­ tation types and the changes in ecosystem services along a terrain gradient. We focused on the four aspects of altitude, slope, landform relief, and terrain niche, which were not given sufficient attention in policy implementation. It was found that the ecosystem services differed significantly for different vegetation types, and a mixture of vegetation types can guarantee the sustainable development of multiple ecosystem services. The terrain is an important factor affecting ecosystem services; thus, different geographical distributions were observed for different ecosystem services in the gradient. We proposed ecological management measures based on the characteristics of the vegetation types and ecosystem services in different terrain zones. In low terrain areas, we proposed to reduce the use of fertilizer, develop high-standard culti­ vated land, and vigorously develop organic agriculture. In intermediate terrain areas, natural forests should be protected, and mixed forests and agroforestry systems should be promoted. In high terrain areas, the natural grassland needs to be protected, a reasonable mixture of forests, shrubs, and grasses should be chosen, and restoration of forestland and grassland should be a priority. We investigated 11 scenarios of the pri­ ority conservation areas in the Chuan-Dian ecological shelter using the OWA method. Scenario 2 was the optimal scenario, and the conservation efficiency of the water yield, carbon storage, soil conservation, and water purification were 1.21, 1.56, 2.64, and 1.05, respectively. This study provides an important reference for the protection, management, and sustainable development of vegetation in mountainous regions. CRediT authorship contribution statement Shuai Ma: Conceptualization, Software, Investigation, Writing original draft, Writing - review & editing. Yong-Peng Qiao: Software, Investigation, Writing - original draft. Liang-Jie Wang: Conceptuali­ zation, Supervision, Resources, Writing - original draft, Writing - review & editing. Jin-Chi Zhang: Resources. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment This study was supported by the National Key Research and Devel­ opment Program of China (2018YFC0507304) and the National Natural Science Foundation of China (41601209). We thank the anonymous reviewers and academic editor for the invaluable suggestions. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi. org/10.1016/j.foreco.2020.118856. References Alamgir, M., Turton, S.M., Macgregor, C.J., Pert, P.L., 2016. Ecosystem services capacity across heterogeneous forest types: understanding the interactions and suggesting pathways for sustaining multiple ecosystem services. Sci. Total Environ. 566, 584–595. An, S., Zhu, X.L., Shen, M.G., Wang, Y.F., Cao, R.Y., Chen, X.H., Yang, W., Chen, J., Tang, Y.H., 2018. Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000–2016 on the Tibetan Plateau. Glob. Change Biol. 24, 5411–5425. Arkema, K.K., Verutes, G.M., Wood, S.A., Clarke-Samuels, C., Rosado, S., Canto, M., Rosenthal, A., Ruckelshaus, M., Guannel, G., Toft, J., Faries, J., Silver, J.M., Griffin, R., Guerry, A.D., 2015. Embedding ecosystem services in coastal planning leads to better outcomes for people and nature. Proc. Natl. Acad. Sci. USA 112, 7390–7395.

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