www.sciencemag.org/content/352/6292/1455/suppl/DC1

Supplementary Materials for Improvements in ecosystem services from investments in natural capital

Zhiyun Ouyang,* Hua Zheng, Yi Xiao, Stephen Polasky, Jianguo Liu, Weihua Xu, Qiao Wang, Lu Zhang, Yang Xiao, Enming Rao, Ling Jiang, Fei Lu, Xiaoke Wang, Guangbin

Yang, Shihan Gong, Bingfang Wu, Yuan Zeng, Wu Yang, Gretchen C. Daily*

*Corresponding author. Email: zyouyang@rcees.ac.cn (Z.O.), gdaily@stanford.edu (G.C.D.)

Published 17 June 2016, Science 352, 1455 (2016) DOI: 10.1126/science.aaf2295

This PDF file includes

Materials and Methods Figs. S1 to S5 Tables S1 to S5 References

2

Materials and Methods

China Ecosystem Assessment Framework

China’s first national ecosystem assessment, spanning 2000-2010, was organized by

Ministry of Environmental Protection and Chinese Academy of Sciences and conducted

by >3,000 scientists in 2012-2014. The China Ecosystem Assessment (CEA) covered the

total area of mainland China (31 provinces, autonomous regions and municipalities directly

under the Central Government) and was organized at three scales relevant to policy and

management: national, provincial, and regional (the latter focused on major river basins,

protected areas, urban regions, coastal zones, ecological conservation and restoration

program areas, etc.). We developed an assessment framework to reveal current status and

trends in: (i) ecosystem pattern; (ii) ecosystem quality; (iii) ecosystem services; and (iv)

ecological problems (Fig. S1).

We compiled data from multiple sources to quantify ecosystem pattern, quality,

services and problems, including: 20,355 multi-source satellite images; biophysical data

(DEM, soils, hydrology, meteorology, etc.); 114,500 field surveys; repeated 10-day

datasets of ecological parameters; historical records of biodiversity; and special

assessments from several departments of China (e.g., surveys of rock desertification, soil

erosion, and grassland desertification) (Fig. S1).

In this article and Supplementary Information, we focus on (iii), and do not report on

other aspects of the CEA.

China Ecosystem Assessment Procedures

(i) Establishment of ecosystem classification system. We established an ecosystem

classification system in China, which is different from traditional land use and land cover

classification and is more suitable for ecosystem assessment. The new ecosystem

classification system includes 8 level I classes, 22 level II classes and 42 level III classes

(Table S1).

(ii) Acquisition and treatment of remote sensing data. We used 20,355 multi-source

satellite images (including HJ-1CCD, Landsat TM, MODIS) in the CEA for ecosystem

classification and derivation of ecological parameters. We also used high-resolution (<10

m) remote sensing data to acquire ecosystem inputs into ecosystem service models.

(iii) Interpretation of remote sensing images. We classified land cover and use, and

ecosystem type, in eight regions: Northeast, North China, East China, South China, central

China, Southwest, Northwest and Xinjiang. The 8 level I classes and 22 level II classes for

national ecosystem were acquired in 2000 and 2010 (Table S1).

(iv) Derivation of ecological parameters. We extracted the following ecological

parameters for the CEA: vegetation coverage, leaf-area index, net primary productivity,

biomass, surface temperature, etc. Repeated 10-day datasets of ecological parameters were

developed over the full 2000-2010 assessment period.

(v) Ground survey of ecosystems. We collected data at 114,500 new field survey points

distributed across the country (including ground validation of ecosystem types, vegetation

cover, leaf-area index, net primary productivity and biomass). Data from 5,333 quadrats

and 39 long-term ecosystem monitoring stations (CERNweb, http://www.cern.ac.cn.) were

3

used to build a space and earth synergy inversion model for quantitative extraction of the

main vegetation parameters: vegetation coverage, leaf-area index, net primary

productivity, and biomass.

(vi) Validation of ecosystem classification accuracy. A total of 31,675 further

independent ground survey samples (beyond those in (v)) were used for accuracy

evaluation. The average accuracy of ecosystem classification was >86%.

(vii) Collection and collation of other data. In addition to remote sensing images and

ground survey data, we also collected meteorological data, historical records of

biodiversity; special assessments from several government agencies (surveys of rock

desertification, soil erosion, grassland desertification, and of nature reserves), demographic

and socioeconomic statistics data, and environmental monitoring data (water quality,

hydrology, etc.) (Table S2). We compiled these in a shared data platform for use by the

scientists conducting the CEA.

(viii) Ecosystem assessment. The CEA focused on ecosystem pattern, ecosystem

quality (for forest, shrubland, and grassland), ecosystem services (focused in this first

assessment on the 7 major services that could be characterized most rigorously across the

entire country at different scales, including: food production; carbon sequestration; soil

retention; sand storm prevention; water retention; flood mitigation; and provision of habitat

for biodiversity), and ecological problems (including land degradation; coastal

degradation; natural habitat degradation; river degradation; and lake degradation). The

indicator system for the CEA was showed in Table S3.

Ecosystem Service Assessment Methods

(i) Food production.

A. Estimation of supply. Food production refers to the food produced by terrestrial

and freshwater ecosystems. Quantification of food production services used different

categories of production data at the county level in China. The categories were food crops,

beans, potatoes, oil crops, vegetables, fruits, nuts, freshwater products, meat, etc. To

represent the production of various types of products comparably and consistently, we used

a food-supply calories method that converted product yield (metric tons) into heat value

(kcal) (Fig. S2A), calculated as follows:

𝐸𝑆 = ∑ 𝐸𝑖𝑛𝑖=1 = ∑ (100 × 𝑀𝑖 × 𝐸𝑃𝑖 × 𝐴𝑖)𝑛

𝑖=1

where Esis the total food supply of one county in calories (kcal), i is the production

category numbered from 1 to n, Ei is the food supply in calories by category，Mi is the

product yield (t) per category i in one county，EPi is the edible percentage of the product

by category，and Ai is the calories per 100g of the edible parts of the product by category.

The calories per 100g of the edible parts of every product were obtained from the China

Food Composition Table (28).

B. Translation from supply to importance. We assumed that food supplied from a

given location benefits all of China equally, so that we estimated importance simply as

equal to supply.

4

(ii) Carbon sequestration and storage

A. Estimation of supply. Carbon sequestration refers to carbon sequestered by

terrestrial ecosystems and thereby slowing down the current rate of increase of atmospheric

CO2 (29), while storage refers to the carbon remaining in terrestrial ecosystems, possibly

over the long term (30, 31). Carbon storage represents not only the result of carbon

sequestration (31), but also indicates the importance of restoration or avoidance of

deforestation (32). We examined the dynamics of biomass carbon storage in China's forest,

grassland, and wetland ecosystems, and estimated the average annual carbon sequestration

of China's terrestrial ecosystems (Fig. S2B). The biomass carbon storage of different types

of ecosystem (BCSin) was obtained with the following formula:

BCSin = ∑ 𝐵𝐶𝐷𝑖𝑗𝑚𝑛𝑗=1 × 𝐴𝑅𝑖 × 10−6

where BCDijn is the biomass carbon density of ecosystem i in pixel j in year m.

Ecosystem i could be forest and shrubland or grassland, and year m could be 2000 or 2010.

The unit of BCDijm is t C/km2. ARi is the area of each pixel, which is 0.0625 km2 according

to the RS-based biomass data. The BCDijm is derived with the following formula:

BCDijm = BijmCCi

Where Bijm (in t/km2) is the biomass density of ecosystem i in pixel j in year m, the

data is from The Institute of Remote Sensing and Digital Earth, Chinese Academy of

Sciences. CCi is the carbon content in the biomass of ecosystem i, which is 0.5 for forest

and wetland, and 0.45 for grassland (33, 34).

Based on estimation of total BCS of different ecosystems in the years 2000 and 2010,

the average annual carbon sink of China's terrestrial ecosystems was estimated with the

following formula:

ACS= (ΣBCSi2010 -ΣBCSi2000)/10

where ACS (in TgC/yr) is the average annual carbon sink of China's terrestrial

ecosystems from the year 2000 to 2010.

B. Translation from supply to importance. We assumed that carbon sequestration

from a given location benefits all of China equally, so that we estimated importance simply

as equal to supply.

(iii) Soil retention.

A. Estimation of supply. Soil retention refers to the soil retained by the ecosystems

within a certain period (one year for this study). Soil retention was calculated using the

Universal Soil Loss Equation (USLE) (35) and InVEST model (7, 8) (Fig. S2C), and the

model can be expressed as:

1SC R K LS C

where SC represents the soil retention capacity (t ha−1 a−1), R is the rainfall erosivity

factor (MJ mm ha−1 h−1 a−1), K is the soil erodibility factor (t ha h ha−1 MJ−1 mm−1), LS is

the topographic factor, and C is the vegetation cover factor.

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Rainfall erosivity reflects the potential for raindrops and runoff to induce soil erosion

(36). In this study, we adopted the Daily Rainfall Erosivity Model (37), for which only

conventional rainfall data (daily precipitation) is needed.

Soil erodibility reflects the sensitivity of soil particles to erosive forces and it is an

internal factor affecting soil erosion that is closely related to soil attributes (36). The

Erosion/Productivity Impact Calculator (EPIC) was employed to calculate K using the soil

clay, silt, sand, and organic carbon content (38, 39).

The topographic factor reflects the effects of terrain (slope length and gradient) on

soil erosion (40). We integrated the relevant research on gentle slopes and steep slopes,

and performed calculations using different slope segments (41-43).

The vegetation cover factor describes the effect of vegetation on soil erosion, and is

related to vegetation structure and cover. Values were assigned to the vegetation cover

factor after referring to domestic and foreign literature (44-46), where different ecosystem

types and vegetation coverage were considered (43).

B. Translation from supply to importance.

We estimated the importance of each location as supply weighted by the number of

downstream beneficiaries of soil retention. We used a 3km x 3km resolution DEM and a

population raster as input datasets into the flow length tool in ArcGIS 10.0 to calculate the

accumulative population along the flow path of each cell from downstream to upstream

within China (Fig. S3). The affected area is the downstream watershed area that receives

soil retention services from cells upstream; this approach aligns with the definition of

serviceshed (32).

Then, the biophysical magnitude of ES produced by each cell was multiplied by the

total population benefiting to quantify the importance of each grid cell, considering both

services production and number of beneficiaries.

𝐵𝑖 = 𝐸𝑖𝑃𝑖

where Bi indicated the ES importance in cell i; 𝐸𝑖 is the biophysical ES produced by

cell i; 𝑃𝑖 is the total population who received the ES flow produced by cell i.

(iv) Sand storm prevention.

A. Estimation of supply.

Sand storm prevention refers to the sand retained in an ecosystem within a certain

period (one year for this study). We used the Revised Wind Erosion Equation (RWEQ)

model (47) to estimate the sand storm prevention service (Fig. S2D).

The RWEQ combines empirical and process modeling and has been extensively tested

under broad field conditions. To simulate sand / soil loss at a regional scale over varying

vegetation cover and patterns, we rewrote the RWEQ into the dynamic modelling language

of PC Raster (48). PC Raster is an environmental modelling language embedded in a

Geographical Information System, providing spatial and temporal functions that can be

used to construct regional models.

The RWEQ model estimates sand / soil loss at a specific point (SL; kg m-2) as a

function of several factors: weather (WF); soil erodibility (EF); soil crust (SCF); surface

6

roughness (K’); and vegetation cover (C), which permit estimation of the maximum

transport capacity by wind (Qmax) as follows:

where Qmax (kg m-1) is the maximum transport capacity, S (m) is the critical field

length, Z (m) is the distance from the upwind edge of the field, SL (kg m-2) is the soil loss

caused by wind erosion.

Weather Factor (WF) represents the influence of climate condition on wind erosion,

and WF is partitioned according to the preponderance and positive parallel ratio values

from the weather file (49, 50). WF is determined by dividing the total wind value for each

period by 500 and multiplying by the number of days in the period (51, 52).

Soil Erodible Factor (EF). The erodible fraction is that fraction of the surface 25mm

of sand / soil that is smaller than 0.84mm in diameter as determined by a standard compact

rotary sieve (53). From a soil sieving data base, the highest value for EF during a year for

each site was correlated with basic soil physical and chemical properties (54).

Soil Crusting Factor (SCF). When raindrops impact the soil surface, there is a

redistribution of soil particles and a formation of surface crust. The resulting soil surface

can be extremely hard or very fragile and may decrease or increase wind erosion potential

(55). The SCF equation was developed using laboratory wind tunnel tests on resistance of

soil aggregates and crusts to windblown sand (56).

Vegetation Factor (C). The vegetation quantity on the ground surface has a significant

impact on sand / soil erosion by wind. To quantify the effect of vegetation, the fraction of

the ground surface covered with non-erodible plant material (flat residues), the plant

silhouette from standing plant residues (standing residues), and growing crop canopies

(crop canopy) are used in RWEQ (57).

Surface Roughness Factor (K'). Original RWEQ was designed to calculated wind

erosion loss in a field scale. Tillage operations modify the soil surface roughness and flatten

and bury crop residues (58). When scaled up to a region, we replaced soil ridge roughness

with roughness caused by topography, and was calculated by the Smith-Carson equation.

B. Translation from supply to importance. We calculated the importance of each

location as supply weighted by the number of downwind beneficiaries of sand storm

prevention. Based on the 3km x 3km resolution population raster, we estimated the affected

population within downwind grids (Fig. S4), where the wind erosion distance is 1300

kilometers (59) and the prevailing winds are northwest in winter and spring according to

meteorological observation in China.

max 109.8 'Q WF EF SCF K C

2( / )

2

2 z s

L MAX

zS Q e

S

0.3711150.71 ( ' )S WF EF SCF K C

7

(v) Water retention.

A. Estimation of supply. Water retention refers to the water retained in ecosystems

within a certain period (one year for this study). We estimate water retention by using the

following model, which was revised from the InVEST model (7, 8) (Fig. S2E).

i

j

i

iii AETRPTQ 1

)(

Where TQ is total water retention, Pi is precipitation, Ri is storm runoff, ETi is

evapotranspiration, and Ai is the area of the ecosystem as defined by land cover.

Precipitation. We acquired rainfall from 726 meteorological stations nationwide from

1961 to 2010 and got the national precipitation map by using the GIS spatial interpolation

method.

Evapotranspiration. We acquired evapotranspiration data from the Chinese Academy

of Sciences.

Runoff. Runoff coefficient values were estimated from >300 publications on surface

runoff across a range of land ecosystems.

R = P × α

where R is the runoff, P is the precipitation, is the runoff coefficient (Table S4).

B. Translation from supply to importance. We calculate the importance of each

location as supply weighted by the number of downstream beneficiaries of water retention.

In future CEAs, we may account for beneficiaries through major water redistribution

engineering projects. We used a 3km x 3km resolution DEM and a population raster as

input datasets into the flow length tool in ArcGIS 10.0 to accumulative population along

the flow path of each cell from downstream to upstream within China. Then, the

biophysical supply of each ES was multiplied by this total population to quantify the

importance of each grid cell, considering both service production and number of

beneficiaries. The method of beneficiary identification is similar to that for soil retention.

𝐵𝑖 = 𝐸𝑖𝑃𝑖

where 𝐵𝑖 indicated the ES importance in cell i; 𝐸𝑖 is the biophysical ES production by

cell i; 𝑃𝑖 is the total population who received the ES flow produced by cell i.

(vi) Flood mitigation.

A. Estimation of supply. Flood mitigation refers to the volume of wetlands (e.g.,

lakes, reservoirs, swamps) that can mitigate flooding. Wetlands can regulate stream flows

and mitigate flooding by storing water temporarily. Available storage capacity, flood

control storage capacity, and surface stagnation of water were used as indicators of flood

mitigation for lakes, reservoirs, and swamps, respectively (Fig. S2F).

For lakes, a flood mitigation model was constructed based on the relationship between

available storage capacity and lake area (83, 84), since the latter is closely related to water

regulating capacity and is much easier to acquire. In addition, five models were

constructed, one for each lake zone, considering their different hydrological conditions.

For reservoirs, the flood mitigation model was constructed based on the relationship

8

between flood control storage capacity and total storage capacity (85), which is available

for most reservoirs in China. For wetlands, the surface stagnation of water was calculated

based on its area and average maximum depth in flood period, where 1 meter is used (86).

For lakes:

Ln(Cr)=1.128Ln(A)+4.924 (for lakes in Eastern Plain)

Ln(Cr)=0.680 Ln(A)+5.653 (for lakes in Inner Mongolia-Xinjiang Plateau)

Ln(Cr)=0.927 Ln(A)+4.904 (for lakes in Yunnan-Guizhou Plateau)

Ln(Cr)=0.678 Ln(A)+6.636 (for lakes in Tibetan Plateau)

Ln(Cr)=0.866 Ln(A)+5.808 (for lakes in Northeast China Plain and Mountain)

where Cr is the available storage capacity (104 m3), A is the lake area (km2).

For reservoirs:

Cr = 0.35 × Ct

where Cr is the flood control storage capacity (104 m3), Ct is the total storage capacity

(104 m3).

For swamps:

Cr = A × D

where Cr is the surface stagnation of water (104 m3), A is the swamp’s area (km2), D

is the average maximum depth of stagnation (cm).

B. Translation from supply to importance. We calculated the importance of each

location as supply weighted by the number of downstream beneficiaries of flood

mitigation. We used a 3km x 3km resolution DEM and a population raster as input datasets

into the flow length tool in ArcGIS 10.0 to calculate accumulative population along the

flow path of each cell from downstream to upstream within China. Then, the magnitude of

ES supply was multiplied by the total benefiting population to quantify the importance of

each grid cell, considering both service production and number of beneficiaries. The

method of beneficiary identification is similar to that for soil retention.

𝐵𝑖 = 𝐸𝑖𝑃𝑖

where 𝐵𝑖 indicated the ES importance in cell i; 𝐸𝑖 is the magnitude of ES produced by

cell i; 𝑃𝑖 is the total population who benefited from the ES flow produced by cell i.

(vii) Provision of habitat for biodiversity.

A. Estimation of supply.

Provision of habitat for biodiversity refers to the total habitat area of endemic,

endangered, and nationally protected species within each county (Fig. S2G). First, a total

of 2820 species were selected as indicators of biodiversity (64 amphibians, 150 reptiles,

273 birds, 182 mammals, and 2151 plants). Species were required to meet at least one of

the following selection criteria: 1) indigenous to China and endangered; 2) found mainly

in China and endangered; 3) indigenous to China and under threat according to the IUCN

Red List of Threatened Species (87) and the First Volume of the Red Data List of Chinese

Species (88); and 4) other species that have been listed in level I in the Law of the People’s

Republic of China on the Protection of Wildlife or Plants (89). We assigned different

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protection targets (60%, 40%, or 30%) to species with different levels of rarity according

to the three grades specified above for the following site selection (90).

Second, we developed a habitat mapping process using a simplified conceptual model

and applied at the county level (91, 92).

𝑝𝐻𝑖 = 𝐶𝑖𝐼𝐻

where 𝑝𝐻𝑖 indicates whether polygon i is a potential habitat or not. 𝐶𝑖 is the record of

historical distributions of indicator species in county i; 𝐼 is the overlap of suitable elevation,

slope and aspect for the species; and H is the suitable habitat for the species;

MARXAN (93), a site selection software program, was used to measure the

biodiversity conservation value for each analytical unit by an irreplaceability index. The

irreplaceability index refers to the frequency by which a unit was selected in a single best

conservation solution, targeting particular land parcels (in 100 runs in this study).

Therefore, units with higher irreplaceability also have a higher conservation value. Species

distributions were modeled using data included in the species location datasets

(supplemented by some pre-existing field studies), including 1) China Animal Scientific

Database (94); 2) Scientific Database of China Plant Species (95); 3) Chinese Biodiversity

Information System (96); and 4) Chinese Species Information System (97). The major

types of vegetation and counties of species appearance were determined from these

databases that were used in biodiversity richness mapping.

B. Translation from supply to importance. We assumed that habit supplied from a

given location for biodiversity benefits all of China equally, so that we estimate importance

simply as equal to supply of habitat.

(viii) Index of ecosystem services.

To quantify the relative importance of each grid cell for sustaining national and

regional ecological security, we created an integrated index. First we ranked the importance

of each grid cell for the provision of a single service, service by service (apart from food

production, which is helpful to hold separately for policy applications). For example, to

identify critical areas for soil retention, we classified all grid cells into one of four levels

of importance: vital, important, moderate, and general. The classification steps were as

follows, for the national level (and the same conducted region by region): (i) calculate the

soil retention of each grid cell; (ii) sort all grid cells by soil retention capacity in descending

order, and then calculate the cumulative proportion of soil retention across grid cells; (iii)

assign “very high” to the grids with cumulative proportion between 0~50%, “high” to the

grids with cumulative proportion between 50%~75%, “medium” to the grids with

cumulative proportion between 75%~90%, and “Normal” to the grids with cumulative

proportion between 90%~100%.

Thereafter, the importance of each service – including carbon sequestration, soil

retention, sand storm prevention, water retention, flood mitigation, and provision of habitat

for biodiversity – was synthesized into the integrated index of ES importance. We applied

the Maximum Value method, whereby the index value equals the highest importance value

of any service in each grid cell. Thus, a cell will be scored “important” if the cell is

“important” for any single service, in accordance with the irreplaceability of each

ecosystem service (Fig. S2H).

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Impact Factors of Ecosystem Service Changes

Changes in the provision of ecosystem services from 2000 to 2010 are the result of

natural capital investment policies, changes in biophysical factors, and socioeconomic

development (Table S5). Here, we considered the three major national conservation

policies (i.e., the Natural Forest Conversation Program (NFCP), the Sloping Land

Conversion Program targeting forest restoration (SLCP_F), the Sloping Land Conversion

Program targeting grassland restoration (SLCP_G)), together with a broad set of

biophysical and socioeconomic variables (see descriptive statistics in Table S5).

(i) Natural capital investment policies. We selected three major national policies

operating over 2000-2010 (i.e., Natural Forest Conservation Program (NFCP), Sloping

Land Conversion Program targeting forest restoration (SLCP_F) and Sloping Land

Conversion Program targeting grassland restoration (SLCP_G)).

(ii) Biophysical variables. The initial level of ecosystem services, together with

changes in land use and ecosystem quality, are the main biophysical factors influencing

ecosystem service changes. We selected 14 variables (carbon sequestration in 2000, soil

retention in 2000, sand storm prevention in 2000, water retention in 2000, above-ground

forest biomass per unit area in 2000, above-ground grass biomass per unit area in 2000,

forest area ratio in 2000, change in forest area ratio, shrub area ratio in 2000, change in

shrub area ratio, grassland area ratio in 2000, change in grassland area ratio, cropland area

ratio in 2000 and change in cropland area ratio) to analyze the impacts on ecosystem service

change, including the initial level of four ecosystem services; above-ground biomass of

forest and grassland and their changes over 2000-2010; and the area ratio, and changes

therein (over 2000-2010) of the main land-use types (forest, shrub, grassland and cropland).

(iii) Socioeconomic variables. We selected total human population, urban human

population and livestock inventories, which can reflect the human activity influences on

land-use changes or grassland degradation, to analyze the impacts on ecosystem service

changes.

We performed correlation analysis and multiple general linear model (GLM)

regression to identify the relationships between four types of ecosystem services and the

above impact factors. All statistical analyses were conducted using the software Stata

(version 13.0; StataCorp, College Station, Texas, USA).

From 2000 to 2010, the number of counties that experienced ecosystem service

increase was much greater than the number undergoing ecosystem service reduction (Table

S5). This is seen in the aggregate increase of four ecosystem services (Fig. 1A). Overall,

our results imply that China’s national conservation policies (SLCP_F, SLCP_G and

NFCP) and initial ecosystem service levels in 2000 significantly contributed to the increase

of those ecosystem services. Many other control variables are also significantly associated

with the increase of each the following ecosystem services (Table 1):

(i) Carbon sequestration. Shrub area ratio and its change show significantly positive

associations (P<0.001 and P<0.05, respectively), which may result from the application the

SLCP_F policy. The reason why the coefficients of grassland area ratio and the changes of

grassland area ratio and livestock inventories are significantly negative (P<0.001, P<0.01

and P<0.001, respectively) may be the low carbon sequestration rate of grassland. In

addition, most forest and human population is concentrated in eastern China. The similar

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patterns may lead to significantly positive coefficients for changes in human population

density (Table 1).

(ii) Soil retention: The coefficient of above-ground forest biomass is negatively

significant (P<0.001), which may result from limited space for soil retention increase in

natural forests. Change in cropland area ratio shows significantly negative association

(P<0.01). The application of SLCP policy may play a key role in reducing sloping cropland,

increasing both forest area and the soil retention service. The coefficients of human

population density and livestock inventories are significantly negative (P<0.01 and

P<0.001, respectively), which may reflect the negative impacts of human activities on the

soil retention service. The significantly positive association of change in human population

density (P<0.01) is reflected in similar patterns of population increase and forest restoration

in eastern China, which also may imply that Chinese population are less directly dependent

on forests and causing less disturbance over 2000-2010, since implementation of new

conservation policies (e.g., NFCP, SLCP).

(iii) Sand storm prevention. The coefficient of above-ground grassland biomass is

significantly negative (P<0.01), which may result from limited space for sand storm

prevention service increase in high quality grassland. The significantly negative

association of change in cropland area ratio (P<0.05) shows that agricultural development

decreased the sand storm prevention service (Table 1).

(iv) Water retention: Forest area ratio has significantly negative association (P<0.001)

because forest has higher evapotranspiration and lower water retention services than other

ecosystems. The significantly positive associations of changes in forest area ratio, shrub

area ratio, grassland area ratio (P<0.001, P<0.001 and P<0.001, respectively) maybe

mainly result from the application of SLCP policy and the decrease of surface runoff. The

significantly positive association of population density (P<0.01) can reflect the impacts of

human activities on water retention services. Change in human population density and

urban population ratio, and in livestock inventories, show significantly negative

associations (P<0.01, P<0.001, and P<0.05), which mainly reflect the influences of human

activities on water retention services. Rao et al. (98) also report that urbanization benefits

ecosystem service conservation due to the decrease of human pressure on natural

ecosystems (Table 1).

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Fig. S1.

Technical framework of the China Ecosystem Assessment

Ecosystem pattern

and changesEcosystem quality

and changes

Ecosystem services

and changes

Status , change trends and driving forces

Ecological problems

and changes

Multiple-resolution and multiple-

source remote sensing monitoring Monitoring data

Meteorological, hydrological, environmental data etc.

Land use and cover types Derivation of ecological parameters Ecosystem modelsTo

p-d

ow

nB

ott

om

-up

Demographic

data

Field

surveysGeological

data

Biophysical

dataSocial

data

Statistical reporting

to the government

Ec

olo

gic

al p

rote

cti

on

po

lic

yd

es

ign

an

d im

pro

ve

me

nt

Ecosystem assessment models

13

Fig. S2

Spatial pattern of ecosystem service provision in China in 2010 (not weighted by number of people affected; see Fig. 2, main text). A.

Food production (108 kcal km-2, 0-146); B. Carbon sequestration (t km-2, 0-2952); C. Soil retention (104 t km-2, 0-133); D. Sand

storm prevention (102 t km-2, 0-684); E. Water retention (104 t km-2, 0-216); F. Flood mitigation (106 m3, 0-843); G. Provision of

habitat for biodiversity (total species richness of endemic, endangered, and nationally protected species) (0-380 species county-1); H.

Index of relative importance of regulating services.

14

Fig. S3

Calculation of accumulative population

15

Fig. S4

The downwind beneficiaries for each pixel of sand storm prevention services

1300 km

13

00

km

16

Fig. S5

Important areas for ecosystem services. I - Great Khingan; II - Changbai Mountains; III -

Hunshandake Sandy Area; IV - Tianshan Mountains; V - Loess Plateau; VI –

Sanjiangyuan Area; VII - Qinling-Daba Mountains; VIII - Hengduan Mountains; IX -

Adjacent Mountains of Zhejiang Province and Fujian Province; X - Nanling Mountains.

17

Table S1.

Ecosystem classification system based on remote sensing data for the CEA

I Code II

Class II Code

II

Class

III

Code

III

Class

1 Forest

11 Broad-leaved forest 111 Evergreen broad-leaved forest

112 Deciduous broad-leaved forest

12 Coniferous forest 121 Evergreen coniferous forest

122 Deciduous coniferous forest

13 Mixed broadleaf-

conifer forest 131 Mixed broadleaf-conifer forest

14 Sparse forest 141 Sparse forest

2 Shrub

21 Broad-leaved shrub 211 Evergreen broad-leaved shrub

212 Deciduous broad-leaved shrub

22 Coniferous shrub 221 Evergreen coniferous shrub

23 Sparse shrub 231 Sparse shrub

3 Grassland

31 Meadow 311 Meadow

32 Steppe 321 Steppe

33 Grass 331 Grass

34 Sparse grassland 341 Sparse grassland

4 Wetland

41 Wetland

411 Forest wetland

412 Shrub wetland

413 Grass wetland

42 Lake 421 Lake

422 Reservoir (human-made)

43 River 431 River

432 Canal (human-made)

5 Cropland

51 Farmland 511 Paddy land

512 Dryland

52 Orchard 521 Tree orchard

522 Shrub orchard

6 Urban

61 Residential land 611 Residential land

62 Urban green land

621 Tree green land

622 Shrub green land

623 Grass green land

63

Industrial, mining

& transportation

land

631 Industrial land

632 Transportation land

633 Mining land

7 Desert 71 Desert

711 Desert

712 Bare rock desert

713 Bare soil desert

714 Saline and alkaline land desert

18

I Code II

Class II Code

II

Class

III

Code

III

Class

8 Others

81 Glacier and

perennial snowfield 811

Glacier and perennial

snowfield

82 Bareland

821 Mosses/lichens

822 Bare rock

823 Bare soil

824 Saline and alkaline land

825 Sand

19

Table S2.

Principal data sources

Data Resolution Source

Vegetation zoning map 1:6,000,000 Chinese Academy of Sciences

Precipitation, 0.05 degree

Chinese National Metrological Information

Center/ China Meteorological Administration

(NMIC/CMA)

Temperature 0.05 degree

Chinese National Metrological Information

Center/ China Meteorological Administration

(NMIC/CMA)

Evapotranspiration 90m Chinese Academy of Sciences

Average annual

rainfall erosivity

(1981–2010)

- Beijing Normal University

DEM 90 m the Shuttle Radar Topography Mission

(SRTM)

Soil map and attribute

data 1:1,000,000 the second National Soil Survey of China

Vegetation zoning map 1:6,000,000 Chinese Academy of Sciences

Aboveground biomass 250m Chinese Academy of Sciences

Vegetation coverage 250m Chinese Academy of Sciences

Ecosystem types

(2010) 90 m, TM

The Institute of Remote Sensing Applications

of the Chinese Academy of Sciences

Socioeconomic data counties Statistical Yearbook

20

Table S3.

Indicator system for the CEA

Survey

content

Assessment

content

Assessment indicators

Ecosystem

pattern

Composition and

distribution

Ecosystem composition

Ecosystem distribution

Transformation

characteristics

Transformation direction of ecosystem

Transformation intensity of ecosystem

Natural

ecosystem

quality

Forest quality Relative biomass density index

Shrub quality Relative biomass density index

Grassland quality Coverage

Ecosystem

services

Food production

Carbon

sequestration

Kcal amount

Carbon sequestration amount

Soil retention Soil retention amount

Sand storm

prevention

Water retention

Flood mitigation

Sand fixation amount

Water amount

Flood mitigation amount

Provision of habitat

for biodiversity Natural habitat area of indicator species

Ecological

problems

Land degradation Intensity of soil erosion

Degree of soil desertification

Degree of rocky desertification

Coastal degradation Proportion of natural coastline

Habitat degradation Proportion of natural habitat

River degradation Length / proportion of dried-up river

Lake degradation Lake area change and eutrophication

Driving

factors

Natural factors Temperature and precipitation

Extent and severity of forest fire

Extent and severity of earthquake

Anthropogenic

factors

Socioeconomic activity intensity

(population density, economic density,

urbanization rate)

Development activity intensity (water

resource, mineral resources)

Agricultural activity intensity (grazing

intensity, fertilizer application)

21

Table S4.

The mean runoff coefficient value of natural ecosystems

Ecosystem types Mean coefficient

value（%） References

Forest type

Evergreen broad-leaved forest 2.67 (60-63)

Deciduous broadleaved forest 1.33 (64-65)

Evergreen coniferous forest 3.02 (61, 66-67)

Deciduous coniferous forest 0.88 (68)

Mixed broadleaf-conifer

forest 2.29 (63, 69)

Sparse forest 19.20 (70)

Shrub type

Evergreen broadleaf shrubs 4.26 (63, 71)

Deciduous broad-leaved shrub 4.17 (72)

Evergreen coniferous shrub 4.17 (72)

Sparse shrub 19.2 (70)

Grassland type

Meadow 8.20 (73-76)

Steppe 4.78 (73, 77-81)

Grass 9.37 (82)

Sparse grassland 18.27 (76)

Wetland Wetland 0 -

22

Table S5.

Descriptive statistics of variables used in regression models

Category Variable N Mean SD

Ecosystem service

Increase in carbon sequestration (103

ton km-2) 2416 0.256 0.256

Increase in soil retention (103 ton

km-2) 2220 0.404 0.979

Increase in sand storm prevention

(103 ton km-2) 186 0.979 1.329

Increase in water retention (103 ton

km-2) 1566 4.919 9.546

Reduction in carbon sequestration

(103 ton km-2) 362 -0.013 0.024

Reduction in soil retention (103 ton

km-2) 606 -0.107 0.341

Reduction in sand storm prevention

(103 ton km-2) 144 -0.493 0.752

Reduction in water retention (103 ton

km-2) 1229 -5.516 13.548

Policy

Sloping Cropland to Forest Program

(1: yes; 0: no) 2852 0.734 0.442

Sloping Cropland to Grassland

Program (1: yes; 0: no) 330 0.442 0.497

Natural Forest Protection Program

(1: yes; 0: no) 2852 0.297 0.457

Biophysical

variables

Carbon sequestration in 2000 (103

ton km-2) 2852 0.864 1.012

Soil retention in 2000 (103 ton km-2) 2852 40.104 50.577

Sand storm prevention in 2000 (103

ton km-2) 330 2.529 2.676

Water retention in 2000 (103 ton km-

2) 2852 195.268 227.479

Above-ground forest biomass per

unit area in 2000 (103 ton km-2) 2852 1.425 1.844

Above-ground grass biomass per unit

area in 2000 (103 ton km-2) 2852 0.090 0.167

Forest area ratio in 2000 (unitless) 2852 0.258 0.272

23

Category Variable N Mean SD

Change in forest area ratio (unitless) 2852 0.006 0.019

Shrub area ratio in 2000 (unitless) 2852 0.079 0.105

Change in shrub area ratio (unitless) 2852 -0.002 0.016

Grassland area ratio in 2000

(unitless) 2852 0.114 0.196

Change in grassland area ratio

(unitless) 2852 -0.002 0.016

Cropland area ratio in 2000 (unitless) 2852 0.397 0.265

Change in cropland area ratio

(unitless) 2852 -0.026 0.047

Socioeconomic

variables

Human population density in 2000

(103 individual km-2) 1971 0.288 0.275

Change in human population density

(103 individual km-2) 1971 0.019 0.039

Urban human population ratio in

2000 (unitless) 1494 0.188 0.145

Change in urban population ratio

(unitless) 1490 0.007 0.137

Livestock inventories (sheep unit

km-2) 1840 198.370 235.709

Change in livestock inventories

(sheep unit km-2) 1810 -17.177 160.721

Notes: Unit of analysis is the county. The numbers here are based on all counties in

mainland China with available data. The total number of observations may differ from

one regression model to another due to the inclusion of different conservation policies

with varied targeted areas and missing values in some indicators. Livestock inventories

include cattle and sheep; one cattle equals to five sheep (33).

24

References and Notes 1. Q. Ye, M. H. Glantz, The 1998 Yangtze floods: The use of short-term forecasts in the context

of seasonal to interannual water resource management. Mitig. Adapt. Strategies Glob. Change 10, 159–182 (2005). doi:10.1007/s11027005-7838-7

2. J. Liu, S. Li, Z. Ouyang, C. Tam, X. Chen, Ecological and socioeconomic effects of China’s policies for ecosystem services. Proc. Natl. Acad. Sci. U.S.A. 105, 9477–9482 (2008). Medline doi:10.1073/pnas.0706436105

3. P. Zhang, G. Shao, G. Zhao, D. C. Le Master, G. R. Parker, J. B. Dunning Jr., Q. Li, China’s forest policy for the 21st century. Science 288, 2135–2136 (2000). Medline doi:10.1126/science.288.5474.2135

4. J. Liu, Z. Ouyang, W. Yang, W. Xu, S. Li, in Encyclopedia of Biodiversity, S. A. Levin, Ed. (Academic Press, Waltham, MA, ed. 2, 2013), pp. 372–384.

5. M. N. Tuanmu, A. Viña, W. Yang, X. Chen, A. M. Shortridge, J. Liu, Effects of payments for ecosystem services on wildlife habitat recovery. Conserv. Biol. n/a (2016). 10.1111/cobi.12669 Medline doi:10.1111/cobi.12669

6. Materials and methods are available as supplementary materials on Science Online.

7. R. Sharp et al., InVEST +VERSION+ User’s Guide (The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund, 2015).

8. P. Kareiva, H. Tallis, T. H. Ricketts, G. C. Daily, S. Polasky, Eds., Natural Capital: Theory and Practice of Mapping Ecosystem Services (Oxford Univ Press, New York, 2011).

9. I. J. Bateman, A. R. Harwood, G. M. Mace, R. T. Watson, D. J. Abson, B. Andrews, A. Binner, A. Crowe, B. H. Day, S. Dugdale, C. Fezzi, J. Foden, D. Hadley, R. Haines-Young, M. Hulme, A. Kontoleon, A. A. Lovett, P. Munday, U. Pascual, J. Paterson, G. Perino, A. Sen, G. Siriwardena, D. van Soest, M. Termansen, Bringing ecosystem services into economic decision-making: Land use in the United Kingdom. Science 341, 45–50 (2013). Medline doi:10.1126/science.1234379

10. J. J. Lawler, D. J. Lewis, E. Nelson, A. J. Plantinga, S. Polasky, J. C. Withey, D. P. Helmers, S. Martinuzzi, D. Pennington, V. C. Radeloff, Projected land-use change impacts on ecosystem services in the United States. Proc. Natl. Acad. Sci. U.S.A. 111, 7492–7497 (2014). Medline doi:10.1073/pnas.1405557111

11. S. Hatfield-Dodds, H. Schandl, P. D. Adams, T. M. Baynes, T. S. Brinsmead, B. A. Bryan, F. H. Chiew, P. W. Graham, M. Grundy, T. Harwood, R. McCallum, R. McCrea, L. E. McKellar, D. Newth, M. Nolan, I. Prosser, A. Wonhas, Australia is ‘free to choose’ economic growth and falling environmental pressures. Nature 527, 49–53 (2015). Medline doi:10.1038/nature16065

12. Millennium Ecosystem Assessment, Ecosystems and Human Well-Being: Synthesis (Island Press, Washington, DC, 2005).

25

13. UKNEA (UK National Ecosystem Assessment), The UK National Ecosystem Assessment Technical Report [United Nations Environment Programme (UNEP)–World Conservation Monitoring Centre, Cambridge, 2011).

14. UNEP, Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Decision IPBES-2/4: Conceptual framework for the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services: Report of the second session of the plenary of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (UNEP, 2014).

15. WAVES (Wealth Accounting and the Valuation of Ecosystem Services), WAVES Annual Report 2015 (World Bank, Washington, DC, 2015).

16. P. R. Ehrlich, P. M. Kareiva, G. C. Daily, Securing natural capital and expanding equity to rescale civilization. Nature 486, 68–73 (2012). Medline doi:10.1038/nature11157

17. Ministry of Environmental Protection of China and Chinese Academy of Sciences, National Ecosystem Service Zoning in China (Ministry of Environmental Protection and CAS, Beijing, 2015).

18. Ministry of Environmental Protection, National Ecological Protection Redlining (Ministry of Environmental Protection, Beijing, 2015).

19. China Council for International Cooperation on Environment and Development, Report on Institutional Innovation of Ecological Protection Redlining (CCICED, Beijing, 2014).

20. Ministry of Transport of China, National Road Development Planning (2014–2030) (MTC, Beijing, 2013).

21. A. Viña, W. McConnell, H. B. Yang, Z. C. Xu, J. G. Liu, Effects of conservation policy on China's forest recovery. Sci. Adv. 2, e1500965 (2016). doi:10.1126/sciadv.1500965

22. B. Fu, Blue skies for China. Science 321, 611 (2008). Medline doi:10.1126/science.1162213

23. J. G. Liu, P. H. Raven, China’s environmental challenges and implications for the world. Crit. Rev. Environ. Sci. Technol. 40, 823–851 (2010). doi:10.1080/10643389.2010.502645

24. G. N. Bratman, J. P. Hamilton, G. C. Daily, The impacts of nature experience on human cognitive function and mental health. Ann. N. Y. Acad. Sci. 1249, 118–136 (2012). Medline doi:10.1111/j.1749-6632.2011.06400.x

25. S. R. Carpenter, H. A. Mooney, J. Agard, D. Capistrano, R. S. Defries, S. Díaz, T. Dietz, A. K. Duraiappah, A. Oteng-Yeboah, H. M. Pereira, C. Perrings, W. V. Reid, J. Sarukhan, R. J. Scholes, A. Whyte, Science for managing ecosystem services: Beyond the Millennium Ecosystem Assessment. Proc. Natl. Acad. Sci. U.S.A. 106, 1305–1312 (2009). Medline doi:10.1073/pnas.0808772106

26. C. Folke, S. R. Carpenter, B. Walker, M. Scheffer, Resilience thinking: Integrating resilience, adaptability and transformability. Ecol. Soc. 15, 20 (2010).

27. National Development and Reform Commission of China, Opinions on Accelerating the Construction of Ecological Civilization (NDRCC, Beijing, 2013).

26

28. Y. X. Yang, G. Y. Wang, X. C. Pan, China Food Composition (Peking Univ. Medical Press, Beijing, 2009).

29. S. Piao, J. Fang, P. Ciais, P. Peylin, Y. Huang, S. Sitch, T. Wang, The carbon balance of terrestrial ecosystems in China. Nature 458, 1009–1013 (2009). Medline doi:10.1038/nature07944

30. S. L. Lewis, G. Lopez-Gonzalez, B. Sonké, K. Affum-Baffoe, T. R. Baker, L. O. Ojo, O. L. Phillips, J. M. Reitsma, L. White, J. A. Comiskey, M.-N. Djuikouo K, C. E. N. Ewango, T. R. Feldpausch, A. C. Hamilton, M. Gloor, T. Hart, A. Hladik, J. Lloyd, J. C. Lovett, J.-R. Makana, Y. Malhi, F. M. Mbago, H. J. Ndangalasi, J. Peacock, K. S.-H. Peh, D. Sheil, T. Sunderland, M. D. Swaine, J. Taplin, D. Taylor, S. C. Thomas, R. Votere, H. Wöll, Increasing carbon storage in intact African tropical forests. Nature 457, 1003–1006 (2009). Medline doi:10.1038/nature07771

31. H. Keith, B. G. Mackey, D. B. Lindenmayer, Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests. Proc. Natl. Acad. Sci. U.S.A. 106, 11635–11640 (2009). Medline doi:10.1073/pnas.0901970106

32. L. Mandle, H. Tallis, L. Sotomayor, A. Vogl, Tracking ecosystem service redistribution from road development and mitigation in the Peruvian Amazon. Front. Ecol. Environ 13, 309–315 (2015). doi:10.1890/140337

33. P. Q. Chen, X. K. Wang, L. M. Wang, Carbon Budget and Its Sink Promotion of Terrestrial Ecosystem in China (Science Press, Beijing, 2008).

34. J. Fang, Y. Yang, W. Ma, A. Mohammat, H. Shen, Ecosystem carbon stocks and their changes in China’s grasslands. Sci. China Life Sci. 53, 757–765 (2010). Medline doi:10.1007/s11427-010-4029-x

35. W. H. Wischmeier, D. D. Smith, Predicting Rainfall Erosion Losses: A Guide to Conservation Planning (U.S. Department of Agriculture, Washington, DC, 1978).

36. W. Wang, J. Jiao, Quantitative evaluation on factors influencing soil erosion in China. Bull. Soil Water Conserv. 16, 1–20 (1996).

37. S. Q. Yin, W. B. Zhang, Y. Xie, S. H. Liu, F. Liu, Spatial distribution of rainfall erosivity in China based on high-density station network. Soil Water Conserv. China 10, 45–51 (2013).

38. J. R. Williams, K. G. Renard, P. T. Dyke, EPIC - A new method for assessing erosions effect on soil productivity. J. Soil Water Conserv. 38, 381–383 (1983).

39. K. L. Zhang, A. P. Shu, X. L. Xu, Q. K. Yang, B. Yu, Soil erodibility and its estimation for agricultural soils in China. J. Arid Environ. 72, 1002–1011 (2008). doi:10.1016/j.jaridenv.2007.11.018

40. R. Van Remortel, M. Hamilton, R. Hickey, Estimating the LS-factor for RUSLE through iterative slope length processing of digital elevation data within ArcInfo grid. Cartography 30, 27–35 (2001). doi:10.1080/00690805.2001.9714133

41. D. K. McCool, G. O. George, M. Freckleton, C. L. Douglas, R. I. Papendick, Topographic effect on erosion from cropland in the northwestern wheat region. Trans. Am. Soc. Agric. Engin. 36, 1067–1071 (1993). doi:10.13031/2013.28435

27

42. B. Y. Liu, M. A. Nearing, L. M. Risse; Slope gradient effects on soil loss for steep slopes. Trans. Am. Soc. Agric. Engin. 37, 1835–1840 (1994). doi:10.13031/2013.28273

43. E. M. Rao, Z. Ouyang, X. Yu, Y. Xiao, Spatial patterns and impacts of soil conservation service in China. Geomorphology 207, 64–70 (2014). doi:10.1016/j.geomorph.2013.10.027

44. H. J. Carter, D. L. Eslinger, Nonpoint Source Pollution and Erosion Comparison Tool (N-SPECT) Technical Guide (National Oceanic and Atmospheric Administration Coastal Services Center, Charleston, SC, 2004).

45. B. Liu, S. Liu, S. Zheng, Soil conservation and coefficient of soil conservation of crops. Res. Soil Water Conserv. 6, 32–36 (1999).

46. H. B. Wei, R. Li, Q. K. Yang, Research advances of vegetation effect on soil and water conservation in China. Acta Phytoecol. Sin. 26, 489–496 (2002).

47. D. W. Fryrear, A. Saleh, J. D. Bilbro, H. M. Schomberg, J. E. Stout, T. M. Zobeck, Revised Wind Erosion Equation (RWEQ) (Tech. Bull. 1, Wind Erosion and Water Conservation Research Unit, USDA-ARS, Southern Plains Area Cropping Systems Research Laboratory, Lubbock, TX, 1998).

48. D. Karssenberg, K. De Jong, Dynamic environmental modelling in GIS: 2 modelling error propagation. Int. J. Geogr. Inf. Sci. 19, 623–637 (2005). doi:10.1080/13658810500104799

49. E. L. Skidmore, J. Tatarko, Stochastic wind simulation for erosion modeling. Trans. Am. Soc. Agric. Engin. 33, 1893–1899 (1990). doi:10.13031/2013.31555

50. E. L. Skidmore, J. Tatarko, L. E. Wagner, in Proceedings of a Workshop on Climate and Weather Research, C. W. Richardson, V. A. Ferreira, P. C. Doraiswamy, Eds., Denver, CO, 17 to 19 July 1995 (U.S. Department of Agriculture, Washington, DC, 1996).

51. R. A. Bagnold, The Physics of Blown Sand and Desert Dunes (Methuen, London, 1943)

52. A. W. Zingg, Some characteristics of aeolian sand movement by saltation process. Editions Centre National Recherche Scientifique 7, 197–208 (1953).

53. W. S. Chepil, A compact rotary sieve and the importance of dry sieving in physical soil analysis. Soil Sci. Soc. Am. Proc. 26, 4–6 (1962). doi:10.2136/sssaj1962.03615995002600010002x

54. D. W. Fryear, C. A. Krammes, D. L. Williamson, T. M. Zobeck, Computing the wind erodible fraction of soils. J. Soil Water Conserv. 49, 183–188 (1994).

55. T. M. Zobeck, Abrasion of crusted soils: Influence of abrader flux and soil properties. Soil Sci. Soc. Am. J. 55, 1091–1097 (1991). doi:10.2136/sssaj1991.03615995005500040033x

56. L. J. Hagen, E. L. Skidmore, A. Saleh, Wind erosion: Prediction of aggregate abrasion coefficients. Trans. Am. Soc. Agric. Engin. 35, 1847–1850 (1992). doi:10.13031/2013.28805

57. J. D. Bilbro, D. W. Fryear, Wind erosion losses as related to plant silhouette and soil cover. Agron. J. 86, 550–553 (1994). doi:10.2134/agronj1994.00021962008600030017x

28

58. R. G. Nelson, L. E. Wagner, K. Stueve, in Proc. ASAE Winter Meeting, Chicago, IL, 14 to 17 December 1993, Paper 932539 (1993).

59. X. Yang, Y. S. Liu, C. Z. Li, Y. L. Song, H. P. Zhu, X. D. Jin, Rare earth elements of Aeolian deposits in Northern China and their implications for determining the provenance of dust storms in Beijing. Geomorphology 87, 365–377 (2007). doi:10.1016/j.geomorph.2006.10.004

60. G. Y. Zhou, Q. B. Zeng, J. Huang, in Chinese Forest Ecosystem Long-term Research. Science and Technology Department of Forestry Ministry, Ed. (Northeast Forestry University Press, Haerbin, China, 1994), pp. 439–447.

61. L. Pan, W. P. Tang, Z. L. Huang, Y. H. Shi, D. J. Ma, H. X. Cui, Study on surface runoff of typical vegetations in granite region of Three Gorges of Yangtze River in rainstorm. Hubei For. Sci. Technol. 161, 1–4 (2010).

62. X. S. Wen, B. H. He, H. J. Zhang, F. He, C. Y. Miao, An analysis of the surface runoff characteristic and runoff yield of different woodlands in the Three Gorges Reservoir Area. J. Southwestern Univ. 29, 74–80 (2007).

63. S. L. Qi, H. J. Zhang, F. He, J. H. Cheng, Effect of the vegetation types on runoff generation on slope land in Simian Mountain of Chongqing. Sci. Soil Water Conserv. 4, 33–38 (2006).

64. Q. S. Ren, C. L. Zhang, in Chinese Forest Ecosystem Long-term Research, Science and Technology Department of Forestry Ministry, Ed. (Northeast Forestry University Press, Haerbin, China, 1994), pp. 439–447.

65. J. W. Zhu, J. D. Shi, The hydrological effect of broad-leaved Korean Pine forest in Hsingan Mountains of north-eastern China. Journal of North-Eastern Forestry Institute 4, 37–44 (1982).

66. Q. F. Ji, X. Q. Zhang, K. L. Zhang, Y. Yang, G. X. Yang, Z. K. Gu, Runoff and sediment characteristics of slope land in karst areas of Guizhou Province. Res. Soil Water Conserv. 19, 2–5 (2012).

67. X. Z. Lv, thesis, Beijing Forestry University (2013).

68. W. B. Duan, S. C. Liu, Analysis on runoff and sediment yields of water conservation forests in Lianhua Lake reservoir area. J. Soil Water Conserv. 20, 12–15 (2006).

69. X. M. Zhang, Z. F. Sun, X. P. Zhang, Analysis on the function of different stand affecting runoff and sediment from rainstorm in gullied loess hill of Jinxi. Sci. Soil Water Conserv. 1, 37–42 (2003).

70. Z. Q. Liu, K. Q. Wang, Y. M. Li, Study on runoff and sediment production on slopes treated by different measures in dry-hot valley in Yunnan. Soil Water Conserv. Sci. Technol. Shanxi 4, 14–18 (2009).

71. Y. K. Shen, Y. J. Wang, N. Qi, X. M. Yang, Y. M. Li, C. Cheng, Effect of different vegetation types on runoff generation of slope land in Jinyun Mountains of Chongqing City. Bull. Soil Water Conserv. 29, 80–84 (2009).

29

72. X. X. Xu, X. M. Mu, D. S. Jiang, Study of rainfall redistribution on slope in loess hilly region. Res. Soil Water Conserv. 9, 249–253 (2002).

73. H. Y. He, K. J. Che, A preliminary study of water and soil erosion conditions in Sidalong forest region in the Qilian mountain areas. J. Soil Water Conserv. 6, 48–56 (1992).

74. Q. B. Chen, Y. K. Cun, Z. Q. Liu, Study on runoff and sediment production on slope land of western plateau of Yunnan Province. Research of Soil and Water Conservation 12, 71–73 (2005).

75. X. Zhou, Y. H. Zhao, H. J. Zhang, Study on the characteristics of runoff and sediment production on slope land of plateau wetland Napa Lake. Yunnan Nong Ye Da Xue Xue Bao 26, 81–85 (2011).

76. Y. S. Li, G. X. Wang, Y. P. Shen, Impacts of land coverage on runoff production and sediment yield in the headwaters of the Yangtze River, China. J. Glaciol. Geocryol. 27, 869–875 (2005).

77. H. Y. Zhao, J. W. Zhu, W. H. Wang, Study on the runoff in forestbelt and grassland. J. Soil Water Conserv. 8, 56–61 (1994).

78. S. Li et al., Study on characteristics of runoff and nutrition loss between different vegetation land in typical karst rock desertification zone. J. Soil Water Conserv. 23, 1–6 (2009).

79. G. T. Meng, X. J. Fang, G. X. Li, Study on soil and water conservation capacity of three perennial forage grasses under artificial simulated rainfall. Res. Soil Water Conserv. 17, 49–53 (2010).

80. C. Q. Zuo, L. Ma, Study on soil and water conservation effect under different tillages for orchards on red soil slopeland. J. Soil Water Conserv. 18, 12–15 (2004).

81. D. B. Cheng, C. Q. Zuo, C. F. Cai, Studying the characters and factors of water and soil losses over different ground covers under one rainfall event. Acta Pratacult. Sinica 16, 84–89 (2007).

82. C. W. Wu, L. X. Wang, Analysis on the plot experiment of the benefit of soil and water conservation protecting forest of water resources. J. Nanchang Inst. Technol. 1, 45–49 (1995).

83. S. M. Wang, H. S. Dou, Records for Chinese Lakes (Science Press, Beijng, 1998).

84. E. M. Rao, Y. Xiao, Z. Y. Ouyang, B. Jiang, D. H. Yan, Status and dynamics of China’s lake water regulation. Acta Ecol. Sin. 34, 6225–6231 (2014).

85. E. M. Rao, Y. Xiao, Z. Y. Ouyang, Assessment of flood regulation service of lakes and reservoirs in China. J. Nat. Resour. 29, 1356–1365 (2014).

86. T. Q. Zhao, Z. Y. Ouyang, X. K. Wang, H. Miao, Y. C. Wei, Ecosystem services and their valuation of terrestrial surface water system in China. J. Nat. Resour. 18, 443–452 (2003).

87. International Union for Conservation of Nature, The IUCN Red List of Threatened Species (IUCN, Gland, Switzerland, 2014); http://www.iucnredlist.org/.

88. S. Wang, Y. Xie, First Volume of the Red Data List of Chinese Species (Higher Education Press, Beijing, 2004).

30

89. State Forestry Administration of China, Lists of Plants under Special State Protection (State Forestry Administration of China, Beijing, 2010).

90. L. Zhang, W. H. Xu, Z. Y. Ouyang, C. Q. Zhu, Determination of priority nature conservation areas and human disturbances in the Yangtze River Basin, China. J. Nat. Conserv. 22, 326–336 (2014). doi:10.1016/j.jnc.2014.02.007

91. J. Liu, M. Linderman, Z. Ouyang, L. An, J. Yang, H. Zhang, Ecological degradation in protected areas: The case of Wolong Nature Reserve for giant pandas. Science 292, 98–101 (2001). Medline doi:10.1126/science.1058104

92. W. Xu, X. Wang, Z. Ouyang, J. Zhang, Z. Li, Y. Xiao, H. Zheng, Conservation of giant panda habitat in South Minshan, China, after the May 2008 earthquake. Front. Ecol. Environ 7, 353–358 (2009). doi:10.1890/080192

93. E. T. Game, H. S. Grantham, Marxan User Manual: For Marxan version 1.8.10 (University of Queensland, St. Lucia, Queensland, Australia, and Pacific Marine Analysis and Research Association, Vancouver, British Columbia, Canada, 2008).

94. CAS (Chinese Academy of Sciences), China Animal Scientific Database (2011); http://www.zoology.csdb.cn/.

95. CAS (Chinese Academy of Sciences), Scientific Database of China Plant Species (2011); http://db.kib.ac.cn/eflora/Default.aspx.

96. CAS (Chinese Academy of Sciences), Chinese Biodiversity Information (2005); http://cbis.brim.ac.cn/.

97. WCSC (Wildlife Conservation Society China), China Species Information System (2001); http://monkey.ioz.ac.cn/bwg-cciced/english/cesis/csispage.htm.

98. E. M. Rao, Y. Xiao, Z. Y. Ouyang, H. Zheng, Changes in ecosystem service of soil conservation between 2000 and 2010 and its driving factors in southwestern China. Chin. Geogr. Sci. 26, 165–173 (2016). doi:10.1007/s11769-015-0759-9