Acta Oecologica 44 (2012) 46e57
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Acta Oecologica
journal homepage: www.elsevier.com/locate/actoec
Original article
Variation of ecosystem services and human activities: A case study
in the Yanhe Watershed of China
Chang-hong Su a, Bo-Jie Fu a, *, Chan-Sheng He b, Yi-He Lü a
a
b
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P. O. Box 2871, Beijing 100085, PR China
Department of Geography, Western Michigan University, Kalamazoo, MI 49008-5053, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 29 December 2010
Accepted 25 November 2011
Available online 22 February 2012
The concept of ‘ecosystem service’ provides cohesive views on mechanisms by which nature contributes
to human well-being. Fast social and economic development calls for research on interactions between
human and natural systems. We took the Yanhe Watershed as our study area, and valued the variation of
ecosystem services and human activities of 2000 and 2008. Five ecosystem services were selected i.e. net
primary production (NPP), carbon sequestration and oxygen production (CSOP), water conservation, soil
conservation, and grain production. Human activity was represented by a composite human activity
index (HAI) that integrates human population density, farmland ratio, influence of residential sites and
road network. Analysis results of the five ecosystem services and human activity (HAI) are as follows:
(i) NPP, CSOP, water conservation, and soil conservation increased from 2000 to 2008, while grain
production declined. HAI decreased from 2000 to 2008. Spatially, NPP, CSOP, and water conservation in
2000 and 2008 roughly demonstrated a pattern of decline from south to north, while grain production
shows an endocentric increasing spatial pattern. Soil conservation showed a spatial pattern of high in the
south and low in the north in 2000 and a different pattern of high in the west and low in the east in 2008
respectively. HAI is proportional to the administrative level and economic development. Variation of
NPP/CSOP between 2000 and 2008 show an increasing spatial pattern from northwest to southeast. In
contrast, the variation of soil conservation shows an increasing pattern from southeast to northwest.
Variation of water conservation shows a fanning out decreasing pattern. Variation of grain production
doesn’t show conspicuous spatial pattern. (ii) Variation of water conservation and of soil conservation is
significantly positively correlated at 0.01 level. Both variations of water conservation and soil conservation are negatively correlated with variation of HAI at 0.01 level. Variations of NPP/CSOP are negatively
correlated with variations of soil conservation and grain production at 0.05 level. (iii) Strong tradeoffs
exist between regulation services and provision service, while synergies exist within regulation services.
Driving effect of human activities on ecosystem services and tradeoffs and synergies among ecosystem
service are also discussed.
Ó 2011 Elsevier Masson SAS. All rights reserved.
Keywords:
Ecosystem services
Human activity index
Yanhe watershed
Correlation
Tradeoff
Synergy
1. Introduction
Human activities bring about profound influence on ecosystem
and impair its ability in providing ecosystem services (Kochtcheeva
and Singh, 2000). Human interferences with the Earth’s system are
so profound that the recent era has been dubbed as “anthropocene”
(Rockström et al., 2009). In a regional scale, human activity affected
ecosystem services by altering land use pattern, biogeochemical and
hydrological cycles (Foley et al., 2005; Lambin et al., 2003, 2001; MA,
2005a; Turner II. et al., 2003; Vitousek et al., 1997). Fast social and
* Corresponding author. Tel./fax: þ86 10 62923557.
E-mail address: [email protected] (B.-J. Fu).
1146-609X/$ e see front matter Ó 2011 Elsevier Masson SAS. All rights reserved.
doi:10.1016/j.actao.2011.11.006
economic development, characterized by industrialization, urbanization, population growth, and expansion of human activity exacerbates the conflicts between high requirements for natural
resources and the limited and irreversible land reserves, which, in
turn, hinders the social and economic development in the long run.
Disentangling the interrelations between ecosystem services and
human activity is essential in probing the underlying driving
mechanisms of ecological degradation, providing the scientific
bases for ecological decision making, and lowering the uncertainties
of ecological policies. Human activity and ecosystem services
research take the lead in coupling human and nature research.
Spatial and temporal variation of ecosystem services and human
activity is important in that: (1) a static ‘snapshot’ can not fully
depict an actually complex, dynamic, and non-linear interrelated
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
systems of ecology and human society (Costanza et al., 1997); (2)
spatial analysis is fundamental to ecosystem service valuation as
the production of biophysical functions and the social determinants
of services depend upon the landscape context (Boyd and Wainger,
2002; Bockstael, 1996). Unfortunately, the bulk of current
ecosystem service valuation methods mainly were centered on
proxy categorical information and coarse assessments through the
use of aggregated coefficients chiefly based on land use types,
which ignore the complex, multi-scale dynamics of ecosystem
service, and are insufficient to conduct scenario analyses or inform
land use planning decisions. Ecological process based valuation
method backed up by models integration is conducive to disclosing
the underlying mechanism of ecosystem services variation, which
is one of the purposes we want to test in this article.
Albeit important, ecosystem services research are confronted by
various difficulties. Firstly, ecosystem service is an overarching
term embodied by its broad definition ‘conditions and processes
that sustain and fulfill human life (Daily, 1997)’. Millennium
Ecosystem Assessment (MA, 2005b) divided the ecosystem services
into provisioning, regulating, supporting, and cultural services,
which were critiqued as merely a ‘heuristic’ rather than a practical
tool (Fisher et al., 2008). Careless selection of ecosystem services
without distinguishing the medium ecological process and
endpoint services give rise of risk of double counting (Fu et al.,
2011). Complex interactions existed between ecosystem services
due to there common drivers or intrinsic links of ecosystem
services per se (Bennett et al., 2009). To some extent, policy making
is a process of balancing the tradeoff between different ecosystem
services (Foley et al., 2005; Pereira et al., 2005; Rodríguez et al.,
2005; van Jaarsveld et al., 2005). Secondly, the voluminous
ecosystem services call for an array of assessing methods. These
methods include material flow analysis (Bouman et al., 2000;
Finnveden and Moberg, 2005), emergy analysis (Voora and Thrift,
2010), and economic valuation (Costanza et al., 1997), within
which the economic valuation arouse the most attentions, which
can be broadly categorized into stated preference methods (e.g.,
contingent valuation methods) and revealed preference methods
(e.g., travel cost methods). Due to its inherent weakness of
economically theoretic uncertainties and human bias, the
economic valuation is gradually dwarfed by the process based
valuation methods which can better reflect the underlying mechanism of ecosystem services and effectively reduce the uncertainties. Notwithstanding the latter was also faced by challenges of
model improvement and parametrical calibration.
Compared with ecosystem service, human activity research has
lagged far behind. Lack of intact natural ecosystem impedes
obtaining the absolute magnitude of human impact. The dominating uni-indicator valuation methods which take individual
factor, e.g., population (Wang et al., 2001; Zheng et al., 1993), agriculture (Elhatip et al., 2003), and coal mining (Lee and Bukaveckas,
2002) to indicate the human impact can not sufficiently reflect the
panorama of human activity as it simplified the de facto overarching
human activity with de facto single index. The United Nations
Development Program (UNDP, 1990) provided an ambitious
framework of human development index (HDI) by amalgamating
life expectancy, education, and per-capita gross domestic product
(GDP). Since then, researches have made various attempts in
assessing human activities based on the HDI framework proposed
by UNDP (Wen, 1998; Li et al., 2004; Zhang and Wang, 2004). The
major challenge of integrated human activity valuation lies in
selecting, formulating, and quantifying multiple indicators. Low
availability of data and low compatibility of different data from
various sources further worsen the already tough situation.
Exploring the interrelations between human activity and
ecosystem service requires expertise and techniques of both socio-
47
economic and physical sciences. The major challenge lies in scale
match between ecology and society (Cumming et al., 2006; Hong
et al., 2009; Turner, 2000), low compatibility between the
biophysical and social economic data sources; and the intrinsic
complexities of the human nature coupling system, e.g., threshold,
nonlinearity, resilience, adaptability etc. (Kochtcheeva and Singh,
2000). Four questions embrace the whole process of ecosystem
services and human activity valuation: (1) What are the status quos
of ecosystem services and human activity? (Liu et al., 2007); (2)
What are the appropriate forecasts of ecosystem services and
human activities over the medium and long term periods? (Cohen,
2004; Euliss Jr et al., 2010; Butler and Oluoch-Kosura, 2006); (3)
What are the significance and implications of the current land use
policies? (Daily et al., 2009; Nelson et al., 2006) and (4) How do we
handle the uncertainties of the ecological conservation policy?
(Bradshaw and Borchers, 2000). The key to these questions lies in
the effective valuation methods and high accurate time-series data.
Backed up by spatial analysis software, e.g, ArcGis, and ecological
research network establishment, the technique of spatiotemporal
variation of ecosystem services and human impact were greatly
improved, which plays significant roles in enacting ecological
conservation policy with full consideration of spatial heterogeneity.
Being highly regarded as the cradle of Chinese civilization, the
Loess Plateau is also famous its notorious soil erosion. Thick mantle
of loess, dry climate, and complex topography cause extremes of
droughts, floods and soil erosion in this region (Fu, 1989). Inappropriate land use and degraded vegetation are two of the major
causes of ecological degradation in the Loess Plateau (Fu, 1989; Fu
and Gulinck, 1994; Jiang, 1997). Fast population growth, longterm clearance of natural forests, rapid expansion of built-up
areas and cultivated lands, fast development of oil exploration
further degraded local ecological conditions (Peng and Yu, 1995; Shi
and Shao, 2000). As a typical gully and ridge region in the Loess
Plateau, the Yanhe Watershed was also seriously plagued by soil
erosion. About 4540 t/km2 of soil was lost from the Yanhe Watershed each year, causing enormous sedimentation and increasing
flood risks in the downstream area of the Yellow River (Stolte et al.,
2003). Under such background, our research takes the Yanhe
Watershed as the study area to: (1) explore the variation of
ecosystem services and human activities of 2000e2008; (2) probe
the correlations between human activities and ecosystem services;
and (3) analyze the interrelations within the major ecosystem
services.
2. Methods
2.1. Study area
Located in the central Loess Plateau (Fig. 1), the Yanhe Watershed (36 210 e37190 N，108 380 e110 290 E) covers an area of
7725 km2. Characterized by a typical warm, temperate continental
monsoonal climate, the Yanhe Watershed has a multi-year mean
temperature ranging from 8.8 to 10.2 C. Within the mean annual
precipitation of 495 mm, over 65% falls from June to September.
Such concentrated rainfall pattern is apt of forming stormlit with
strong energy of runoff. The Yanhe watershed is covered by thick
mantle of loess, an erosion-prone fine silt soil. The multi-year
mean annual runoff in the watershed is 2.89 108 m3, with
runoff and sediment transportation of 36,425 m3/km2$a and
7.80 104 t/km2$a respectively. The study area has a very rugged
topography: over 90% of the territory is composed by gullied and
ridges. There are 35 townships in four counties within the study
area (Fig. 1). Land uses in this area consist of slope farmland,
terrace farmland, orchard, shrubbery land, woodland, traffic land,
residential land and sparse grassland. During the period from
48
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
Fig. 1. Location of study area.
2000 to 2008, there were 8477 ha and 165,800 ha of farmland
converted to forest and grassland respectively (Su et al., 2011). For
long history, farming is the pivotal means of livelihood partially
reflected by the high ratio of agricultural population (over 82% in
2008). With the implementation of industrial readjustment and
agricultural diversification, large numbers of farmers immigrated
to urban areas which impose high pressures on city infrastructure
facilities.
2.2. Indicators selection
Selecting the right indicators for ecosystem services is a vexed
problem given the diverse land use types of the study area. MA
(2005b) broadly divides ecosystems services into provisioning,
regulating, supporting, and cultural services. This classification
system is more a heuristic tool than a practical one (Fisher et al.,
2008). As the system analysis argued that largely unexplored
correlations between indicators could reduce the marginal information of new indicator (Ronchi et al., 2002), more indicators may
give rise to confusing messages. The selection of ecosystem
services should best reflect the local ecological constraints. The
major ecological problems of the Yanhe Watershed are vegetation
degradation, water loss, and soil erosion. Under the adoption of
Grain for Green (GfG) project, large areas of farmland were converted to non-farm area, which strongly affected the grain
production. In such context, we selected net primary production
(NPP), carbon sequestration and oxygen production (CSOP), water
conservation, soil conservation, and grain production as indices for
ecosystem services. As a dominating driving force for social and
economic development, population is a must for valuation of
human activity index (HAI). In the Yanhe Watershed the increasing
population leads to expanding demand for farm produce and
natural resources. Rapid economic development drives urban
sprawl rapidly. Under such context, human population, farmland
ratio, influence of residential settlement, and influence of road
networks are necessary factors composing the human activity
index. The reason we integrate the four indicators into a comprehensive human activity index rather than taking each component
independently lies in: 1) uni-indicator valuation method are
fraught of bias and uncertainty which can not reflect the panorama of human activity impact; 2) the peculiar traits of some
indicator make it difficult to analyze its relations with spatial
explicit ecosystem services, e.g., the line data of road network and
point data of residential site.
2.3. Quantification of ecosystem services
2.3.1. NPP
NPP was calculated by the process-based Carnegie-Ames-Stanford Approach (CASA) model based on the principle that plant
productivity is correlated with the amount of photosynthetically
active radiation absorbed or intercepted by green foliage (Monteith
and Moss, 1977; Potter et al., 1993). There are three equations that
depict the general mechanism of CASA model:
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
49
X
NPP ¼
½APARðtÞ εðtÞ
(1)
Soil conservation can be calculated by subtracting equation (5)
from equation (6).
APARðx; tÞ ¼ PARðx; tÞ FPARðx; tÞ
(2)
Ac ¼ Ap Ar ¼ R K L S ð1 C PÞ
ε ¼ ε* T1 T2 W
(3)
Where: PAR is the total incident photosynthetically active radiation
(0.4e0.7 mm)(MJ/m2). FPAR is the fraction of PAR absorbed by
vegetation canopy, which is decided by vegetation type and
coverage; APAR is canopy-absorbed incident solar radiation integrated over a time periods;
ε and ε*refer to actual light use efficiency (g/mJ) and maximum
light use efficiency respectively, T1 and T2 refer to temperature
stress coefficients based on monthly mean temperature；W refers
to water stress coefficient calculated from evapotransporation (ET)
by the models of ETWatch (Wu et al., 2008)
2.3.2. CSOP
According to the photosynthesis equation: 6CO2 þ 6H2 O/6O2
þC6 H12 O6 , the ratio of organic matter produced by photosynthesis,
carbon sequestered by photosynthesis, and oxygen released by
photosynthesis is 1:1.47:1.07. The volume of CSOP can be calculated
from NPP production based on this ratio.
2.3.3. Water conservation
Vegetation conserves water through the process of rainfall
interception, evapotranspiration, sorption and storage (Li et al.,
2006; Ren et al., 2003). Generally, water conservation by vegetation can be calculated by summation of canopy retention, litter
absorption, and soil storage.
(7)
Where: Ac is the amount of soil conserved; Ap is the potential soil
erosion; Ar is the actual soil erosion.
The rainfall erosivity factor (R) was calculated with Wischmeier
empirical methods (Wischmeier and Smith, 1978):
S and L can be calculated by the following methods (Zhou and
Liu, 2006):
S ¼
L ¼
0:6
sinq
0:0896
l
(8a)
m
22:13
(8b)
Where q is percentage grade, l is the horizontal length of the slope;
both q and l were extracted from the DEM of the Yanhe watershed.
m is the coefficient for slope degree whose value was taken from
Wischmeier et al. (1971).
At large spatial scale, support practices, e.g. terracing or contour
tillage can not be identified. We calculated the support practice P
with the empirical methods of Wiener equation P ¼ 0:2 þ 0:3 q
(Lufafa et al., 2003), where q is percentage grade.
The cover-management factor C is relevant to vegetation type,
pattern of rainfall, coarseness of land surface, rotation method, and
soil water content. Soil erodibility K is determined by soil type. The
value of C and K were refereed from research results of Fu et al.
(2005).
Qt ¼ Qc þ Ql þ Qs
(4a)
Q c ¼ ε Py A
(4b)
Ql ¼ L A
(4c)
2.3.5. Grain production
Grain production was calculated by dividing grain yield of each
township by its territory to illustrate per-unit provision service, and
then was input to the attribute table of vectorized township image
for spatialiazation.
Qs ¼ P A
(4d)
2.4. Quantification of human activities by HAI index
Where: Qt refers to the total amount of water conserved; Qc refers
to water intercepted by vegetation canopy; Ql refers to water
absorption by litter; Qs refers to water stored by soil under vegetation; ε refers to interception ratio of the canopy; Py stands for
annual precipitation; A refers to the area of each vegetation type;
L refers to water retention ratio by litter per unit area of vegetation;
P refers to the maximum storage capacity of soil under vegetation.
The values of ε, L, and P were taken from the researches of same
locality (Jiao et al., 2002).
2.3.4. Soil conservation
RUSLE (Revised Universal Soil Loss Equation), an empirical
model upgraded from the original USLE (Universal Soil Loss Equation), was used in estimating soil conservation (Renard et al., 1997).
A ¼ RK LSCP
(5)
where: A refers to estimated average soil loss; R refers to rainfallrunoff erosivity factor; K refers to soil erodibility of soil; L refers
to slope length factor; S refers to slope steepness factor; C refers to
cover-management factor; P refers to support practice factor;
If there are no vegetation coverage and practice factors, i.e. C and
P are assigned the value of 1, then the calculated soil erosion will be
the potential soil erosion (Ap):
Ap ¼ R K L S
(6)
The HAI index was calculated by integrating the factors of
human population, farmland ratio, road influence, and residential
influence. We calculated the weights of these four factors through
Analytic Hierarchy Process (AHP) (Saaty, 1990) and empirical
methods as: human population 0.3, farmland 0.3, road influence
0.2, and residential influence 0.2. The equation for HAI was as
follows:
HAI ¼ P 0:3 þ C 0:3 þ R 0:2 þ S 0:2
(9)
Where, P, C, R, and S stand for the standardized human population,
farmland ratio, road network influence, and residential influence
respectively. Within these four components, residential site and
road network are difficult to quantify. By reference to the State Basic
Geographical Data Coding System (Hu et al., 2007), we assigned the
values of influences to residential site and road network (Table 1
and Table 2).
2.5. Spatialization and correlation analysis of ecosystem services
and HAI
The values of NPP, CSOP, soil conservation, water conservation,
and HAI were calculated by ArcGIS. Township boundary was firstly
vectorized by the application of ArcMap. Statistic and meteorological data were first added to the attribute table of vectorized
township map and then further converted into raster format.
50
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
3. Results
Table 1
Influence of residential Land (after Hu et al., 2007).
Residential level
Baotab
Ansai
Yanchang
Zhidanc
a
b
c
Cityb
Town
Township
Village
County
Town
Township
Village
County
Town
Township
Village
e
Town
Township
Village
2000
2008
24917a
14950
9965
4982
19845
10961
9923
4961
19657
14342
9828
4914
e
12975
9983
4991
25000a
15000
10000
5000
20000
15000
10000
5000
20000
15000
10000
5000
e
15000
10000
5000
The value is dimensionless.
Baota district is the only seat for Yanan City.
County seat of Zhidan is out of Yanhe Watershed.
Categorized influence of residential site and road network were
assigned to the attribute table of township, then spatialized by
method of IDW (inverse distance weight) and PD (Path distance)
respectively. Finally, values of ecosystem services and HAI were
allotted to townships by the ArcGIS module of zonal statistics.
We calculated the variation of ecosystem services and HAI
(difference between 2000 and 2008), and tested the correlations
between variations of each individual ecosystem service and HAI
using SPSS11.5.
2.6. Data sources
MODIS images of 1-km resolution (for NPP and CSOP) were
downloaded from internet (http://ladsweb.nascom.nasa.gov/data/
search.html). Data of Land uses (for water conservation and soil
conservation) were extracted from the interpretation of Landsat TM
image (2000) and Cibers image (2008). Slope angle/length (for soil
conservation) was obtained from 1:50,000 digital elevation map
(DEM) of the Yanhe watershed. Data of soil types (for soil conservation) were obtained from the 1:50,000 soil map. Precipitation
(for soil conservation and water conservation) and temperature
(for NPP and CSOP) were obtained from the Meteorological Bureau
of Yan’an City. Grain production was extracted from statistical
materials (ASB, 2000, 2008; BSB, 2000, 2008; YSB, 2000, 2008; ZSB,
2000, 2008).
HAI is composed of population density, farmland ratio, residential influence, and road influence. Township administrative
maps and residential sites were obtained from local civil affairs
departments (ACAB, 2003; BCAB, 2001; YCAB, 2004; ZCAB, 2004).
Data of population density were obtained from statistic materials
(ASB, 2000, 2008; BSB, 2000, 2008; YSB, 2000, 2008; ZSB, 2000,
2008). Road network maps were extracted from atlases of Shaanx
Province (Ouyang et al., 2008; Qian et al., 2000).
Table 2
Influence of road network (after Hu et al., 2007).
Road rank
Influence
High- grade expressway
National trunk highway
Rail way
Provincial trunk highway
County and township road
Cart road
Earth road
12000a
10000
10000
8000
5000
3000
2000
a
The value is dimensionless.
3.1. Spatiotemporal patterns of ecosystem service and HAI
3.1.1. NPP and CSOP
In 2000, NPP/CSOP demonstrated a gradient increasing
tendency from northwest and southeast to the southwest in 2000,
with the highest NPP/CSOP of 9.18t/ha/23.32 t/ha in Liulin and
lowest of 3.21 t/ha/8.16t/ha in Liandaowan respectively (Fig. 2).
2008 saw a big increase of NPP/CSOP across the whole area (Figs. 2
and 3). The spatial pattern of NPP/CSOP in 2008 showed a trivial
alteration from those of 2000, i.e. townships with high NPP/CSOP
shifted more eastward. NPP/CSOP of 2008 assumed a tendency of
high NPP/CSOP in the south and low in the northwest (Fig. 2) with
the highest NPP/CSOP of 15.58t/ha/39.58t/ha in Liulin and the
lowest of 6.58 t/ha/16.45 t/ha in Liandaowan respectively. The
increment of NPP/CSOP from 2000 to 2008 assumed a conspicuous
spatial pattern of gradient increasing from the northwest to the
southeastern (Fig. 2), with the highest increment of 8.80 t/ha/
22.35 t/ha in An’gou and the lowest of 3.26 t/ha/8.29 t/ha in
Liandaowan.
3.1.2. Water conservation
Water conservation in 2000 assumes a rough increasing tendency
from the northwest to southern fringe (Fig. 2) with the highest of
720.58 t/ha in Zhengzhuang and lowest of 247.67 t/ha in Zhengwudong. In 2008, water conservation was greatly increased from that in
2000 (Figs. 2 and 3), with the highest value of 952.28 t/ha in
Chuankou and lowest of 502.44 t/ha in Zhangqu. The spatial pattern
of water conservation in 2008 is relatively obscure with some highwater-conservation townships located more northward (Fig. 2). The
increment of water conservation demonstrates a decreasing fanning
out spatial pattern from the northeast to the surrounding area with
the highest increment of 391.50 t/ha in Fengzhuang and lowest of
62.58 t/ha in An’gou (Fig. 2).
3.1.3. Soil conservation
Compared with water conservation, soil conservations in both
2000 and 2008 show somewhat obscure spatial pattern. In 2000,
soil conservation is roughly high in the south and low in the north
(Fig. 2) with the highest of 84.89 t/ha in Qiaogou and the lowest of
47.66 t/ha in Yuanlongsi. In 2008, soil conservation was greatly
increased, especially in the northwestern part (Figs. 2 and 3). The
spatial pattern in 2008 is quite different from that of 2000, roughly
demonstrated a decreasing tendency from west to east (Fig. 2) with
highest of 120.34 t/ha in Qiaogou and lowest of 65.21 t/ha in Guoqi.
The increment of soil conservation shows a strong gradient
increasing pattern from the southeastern to the northwestern with
highest increment of 47.59 t/ha in Pingqiao and lowest of 8.22 t/ha
in Angou.
3.1.4. Grain production
Grain production in 2000 demonstrated an endocentric
increasing tendency (Fig. 2), with highest grain production in Liqu
(62.74 kg/ha) and lowest in Zhangjiatan (14.42 kg/ha). The pattern
dimmed a little in 2008 which roughly shows a decreasing gradient
in the order of middle, northwest, and southeast, with the highest
in Liqu (52.08 kg/ha) and the lowest in Heijiapu (3.44 kg/ha).
Temporally, the bulk part of the townships (26 out of 35) in the
Yanhe watershed experienced decreasing tendency in grain
production (Figs. 2 and 3) with Gan’guyi decreased the most
(24.45 kg/ha). There are 9 townships showing a slight increase of
grain production with a small margin of 0.17e8.43 kg/ha. Overall,
grain production decreased by 17.25% from 27.59 kg/ha in 2000 to
22.83 kg/ha in 2008.
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
51
Fig. 2. Spatial variation of ecosystem services. Grain production was calculated through dividing the grain yield by the territory area of the township. The variation of ecosystem
services was obtained through subtraction of ecosystem service images of 2008 by those of 2000. NPP, net primary production; CSOP, carbon sequestration and oxygen production.
52
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
Fig. 3. Temporal variation of ecosystem service and HAI for the year of 2000e2008. Grain production was obtained through dividing the grain yield by the territory area of the
township. NPP, net primary production; CSOP, carbon sequestration and oxygen production.
3.1.5. HAI
HAI values of 2000 demonstrate a gradient decreasing pattern
from the urban area, through the surrounding area, to the remote
area, proportional to the joint effect of administrative level and
economic development (Fig. 4), with the HAI of 0.59 in an urban
area of Qiaogou and lowest of 0.177 in the remotest northwestern
township of Zhangqu. In 2008, except Qiaogou, almost all the
township underwent a sharp decrease in HAI (Figs. 3 and 4). The
highest HAI in 2008 was 0.52 in Qiaogou and lowest of 0.065 in
Wangyao. The decrease assumed a distinct spatial pattern of
fanning out from the northeast (Fig. 4).
between variation of grain production and those of NPP/CSOP
(at 0.05 level) and between the variation of soil conservation and
those of NPP/CSOP (at 0.05 level). We don’t count in correlation
between NPP and CSOP because CSOP was derived from NPP.
‘Spider diagram’ is a simple yet powerful approach in portraying
the tradeoffs and synergies between ecosystem services (DeFries
et al., 2004). We divided each individual ecosystem service of 2008
by that of 2000 to show their variations from 2000 to 2008. A ‘spider
diagram’ was drawn based on the quotients which show a tradeoff
between regulation and supporting services vs. provisioning service
and various synergies within certain regulation services (Fig. 5).
3.2. Correlations and tradeoff analysis
4. Discussion
By application of SPSS 11.5, we analyzed the correlations
between variations of each individual ecosystem services and that
of HAI by township (Table 3). As to the interrelation between
ecosystem services and HAI, there are significant negative correlation between variation of water conservation and that of HAI
(at 0.01 level) and between variation of soil conservation and that
of HAI (at 0.01 level). Within ecosystem services, there is significant
positive correlation between variation of soil conservation and that
of water conservation at 0.01 level. There are negative correlations
4.1. Driving forces of ecosystem service changes
Ecosystem service change is caused by combinations of drivers
that work over time, over level of organization, and that happen
intermittently (such as droughts, wars, and economic crises) (MA,
2005a). Driving forces are natural or human factors that directly or
indirectly cause a change in an ecosystem (MA, 2005a). The major
direct driving forces include climate change, plant nutrient use,
land conversion, invasive species, and diseases, whereas the major
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
53
Fig. 4. Spatial variation of HAI. HAI is dimensionless.
indirect driving forces include population, income, technological
development, and changes in human behavior. The direct drivers
influence ecosystem processes unequivocally, while the indirect
drivers operate more diffusely, often by altering the direct drivers
(MA, 2005a). Generally, the indirect driving forces are induced by
humans and can be considered as anthropogenic drivers, whereas
the direct forces are more centered on physical factors. Driving
forces of ecosystem services are not independent; rather they
interact across spatial, temporal, and organization scales, though,
sometimes, these interactions are seemingly farfetched. For
example, changes in export prices of cash crops in South Africa can
Table 3
Correlations of ecosystem services and HAI.
NPP
CSOP
SOIL
WATER
GRAIN
HAI
trigger land use change on the local level; removal of national
credits and subsidies can make some farmers more vulnerable to
environmental changes while others profit from easier access to
markets are less vulnerable to climate change (Leichenko and
O’Brien, 2002).
NPP
GP
NPP
CSOP
SOIL
WATER
GRAIN
HAI
1
1**
0.600**
0.004
0.423*
0.070
1**
1
0.600**
0.004
0.423*
0.070
0.600*
0.600*
1
0.561**
0.106
0.489**
0.004
0.004
0.561**
1
0.212
0.957**
0.423*
0.423*
0.106
0.212
1
0.185
0.070
0.070
0.489**
0.957**
0.185
1
NPP: Net primary production; CSOP: Carbon sequestration and oxygen production;
SOIL: Soil conservation; WATER: Water conservation; GRAIN: Grain production;
HAI: Human activity index.
**Correlation is significant at the 0.01 level (2-tailed).
*Correlation is significant at the 0.05 level (2-tailed).
CSOP
2000
2008
SC
WC
Fig. 5. Diagram illustrating the trade and synergy between ecosystem services. Individual ecosystem service of 2008 was divided by that of 2000 to show their temporal
variation.
54
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
The anthropogenic drivers of ecosystem services can be both
positive and negative. Historically, due to low awareness ecological
conservation, undue human activities caused forest devastation,
soil erosion, water loss, biodiversity loss, habitat fragmentation
(Kochtcheeva and Singh, 2000; Lotze et al., 2005; Rechkemmer and
von Falkenhayn, 2009; WRI, 1998). Thanks to the arousal of
ecological awareness, the Chinese central government enacted
series of ecological conservation policy with ‘Grain for Green
Project (GfG)’ in 1999 as the most massive one. As one of the first
and certainly the most ambitious “payment for ecosystem services”
project in China, GfG project planned spending 40 billion dollars to
convert 147 million hectares of croplands and 173 million hectares
of barren mountains and wastelands into forestlands and grasslands in 25 provinces from 1999 to 2010 (Zhao et al., 2010). Under
the GfG projects farmers were subsided by 100 kg of grain
(140 RMB U since 2004) and 20 RMB U for every mu (1/15 ha) of
re-vegetated cropland (for a period of 8 years for ecological forest, 5
years for cash forest, and 2 years for grass). In addition, farmers
were also paid 50 RMB U once to cover the expenses on purchasing
seedling and afforestation per mu (State Council of PRC, 2002). The
GfG project rigidly stipulated that farmland exceeding 25 degrees
of slope should be completely converted to forest or grassland and
farmland between 15 and 25 degree of slope is also suggested to be
converted to forest or grass if deemed ecologically significant. Over
a decade passed since the inception of the GfG project, the Yanhe
watershed underwent a tremendous change in land use pattern
characterized by shrinking farmland and recovering vegetation
(mainly grasses, shrubs, and trees) (Cao et al., 2009; Uchida et al.,
2005; Xu et al., 2006). The analysis of satellite images of the
Yanhe watershed shows that farmland decreased from
292,900 hm2 in 2000 to 118,500 hm2 in 2008, while the vegetation
area (forest and grass) increased from 389,000 hm2 to 563,200 hm2
(Su et al., 2011). Various researches showed that replacing farmland
or pasture to forest is helpful to consolidate ecosystem services
such as conserving soil and water (Feng et al., 2010). Our results
strongly echoed this by the increasing of NPP, CSOP, and soil and
water conservation in the Yanhe Watershed during the period of
2000e2008.
4.2. Human activity and ecosystem services
Human influence ecosystem services through alternating land
use change and biogeochemical and hydrological cycles (Metzger
et al., 2006). The raw classification into direct and indirect drivers
by MA (2005a) can not explain the underlying mechanisms.
Researchers on human and nature coupling system (CHANS)
refined the human driving factors of ecosystem services as various
‘stressors’ (Liu et al., 2007; Stevenson, 2011), in which human
activities can be either positive or negative in generating the
‘stressors’. Population growth, industry, agriculture, and urban
development generate stressors, whereas environment friendly
scientific and technological renovation, and ecosystem restoration
intends to reduce stressors. Ecosystem services also feedback to
human activities in different manners. Supporting service serves to
prop the ecosystem services per se; regulating services act as
switches controlling the human generating ecological stressors;
provisioning service directly underpins human activities, and the
cultural service plays inevitable roles in for human well being,
which indirectly affected human activities (Fig. 6).
Comparing the difference of ecosystem services or HAI from
2000 to 2008 is more meaningful than comparing their status quo,
as can effectively offset the inherent vested endowment difference.
In our study, correlation analysis demonstrated that there exist
significantly negative correlations between variation of HAI and
that of soil and water conservation services, which eloquently
Human
activities
Stressor
Soil/water loss;
Deforestation
Contaminants
Habitat alteration
……
Ecosystem
service
Supporting
service
Regulating service
Provisioning service
Human well-being
Cultural service
Fig. 6. Relations between human activities and ecosystem services (Modified from
Stevenson (2011)).
demonstrated that the decreasing HAI strongly drove the betterment of certain ecosystem services.
Human activity and ecosystem services coevolved synchronically and simultaneously. At a small scale, this co-evolution was
drastic and well tuned to the ecological policy. The role of ecological
policy in guiding human activity lies in; (i) balance the social and
ecological rhythms; (ii) address the feedback mechanisms of
CHANS, damper the vicious circles and amplify the virtuous circle,
and (iii) establish early warning systems to signal impending
disasters. In addition to ecological policy, social responsibility and
public ethics also need to pay important as most ecosystem services
are ‘public goods’
With the advancement of industrial readjustment movement
under the background of GfG project, farmers in the Yanhe
Watershed were gradually accustomed to the non farming mode of
production and living style. Agricultural production was gradually
replaced by non-farm production, which greatly lowered the
pressure on ecosystem in the Yanhe Watershed. These tendencies
were well reflected by the evident decrease of HAI from 2000 to
2008. In addition, this variation of HAI has distinct spatial pattern.
Boasting of a priori advantages of geographical endowment i.e.,
relatively vast flat areas for enterprises and vicinity to urban area,
farmers in the northeastern part of the Yanhe Watershed have
a higher willingness to convert their farmland to non-farm. Human
activity in northeastern decreased more abruptly than other area
(Fig. 4). The variation of human activity caused profound influence
on ecosystem services fluctuation. Except for grain production, the
other 4 ecosystem services decreased with conspicuous, albeit
diverse, spatial pattern (Fig. 2). The grain production decreased
with a dim spatial pattern (Fig. 2) possibly due to the localitydependent of farmers’ willingness to forgo their farmland and the
possible malpractice of local township government in implementing ecological policy.
The major challenges in human activity and ecosystem
services research lies in the human nature scale mismatch and
gaming between different stakeholders. Ecosystem services are
linked closely with the spatial configuration of land use types,
whereas human activities are tinted by administration or institution characteristics. Different stakeholders require ecosystem
services at diverse scales, e.g., individual or households are more
concerned with small-scale services with explicit property right
whereas the governments are more concerned with larger-scale
public services. Handling the requirement conflicts of various
stakeholders and harmonizing the low human nature compatibility are and will be the kernel of human activity and ecosystem
services research.
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
4.3. Tradeoff between ecosystem services
Interactions occur when multiple ecosystem services responding to common drivers or links within ecosystem services (Bennett
et al., 2009). The intricately linked ecosystem causes multiple
synergy and tradeoff between ecosystem services (Power, 2010; Fu
et al., 2011). Action to enhance some ecosystem services, mainly
provision service always lead to declines in others, i.e., regulating,
supporting, and cultural services (Bennett and Balvanera, 2007;
Carpenter et al., 2009; Raudsepp-Hearne et al., 2010; Rodriguez
et al., 2006). More specifically, providing additional food will lead to
further expansion of agricultural land, and this in turn will lead to
the loss of natural forest and grassland, as well as the loss of
ecosystem services associated with this land (genetic resources,
wood production, habitat for fauna and flora). Excessive water use
will deteriorate the ecosystem services provided by clean freshwater systems (drinking quality, genetic resources, fish production,
habitat for aquatic and riparian flora and fauna). Our research
demonstrates that tradeoffs existing between provision service
(grain production) and regulating services (NPP, CSOP, water
conservation, and soil conservation) (Fig. 5).
Tradeoffs of ecosystem services depend on spatial scale,
temporal scale, or reversibility degree (Carpenter et al., 2009;
Rodriguez et al., 2006). Balancing the tradeoffs of different
ecosystem services is a major task of ecosystem management. A
case in point was the Wangyao Reservoir in Yanhe Watershed.
Located at the upper reach of Yanhe River, Wangyao Reservoir is the
major source of drinking water for Yan’an City. To safeguard the
water quality, areas within certain distance from the reservoir was
prohibited from farm production (YEPB, 2002), which formed
tradeoff between drinking water and farming production. In addition, the local people in the history reclaimed large forest and
grassland for agriculture production, and compromised the
ecological soundness for later generation, this is tradeoff across
times. As opposite of tradeoff, synergies is a by-products of tradeoff.
In our research, positive correlation between variation of water
conservation and that of soil conservation imply there are synergies
between them. Tradeoffs and synergies can be managed by either
reducing their associated costs or enhance landscape multifunctionality respectively (Bennett and Balvanera, 2007; Sachs
and Reid, 2006). In the case of the Yanhe Watershed, GfG project
is effective in enhancing the soil and water conservations
simultaneously.
Ecosystem service tradeoff should be stressed as they are
strongly shaped by social and ecological factors, especially the
direct human activities (Raudsepp-Hearne et al., 2010). As human
societies continue to transform ecosystems to obtain greater
provision of specific services, the scope of tradeoffs will be even
larger (Foley et al., 2005; Rodriguez et al., 2006).
Managing tradeoff of ecosystem services depends on the causes
to tradeoff. Tradeoff caused by direct interactions of ecosystem
services can be magnified, reduced, or removed by managing the
process that creates the interactions. For tradeoff that is caused by
spatial incompatibilities, it is necessary to understand tradeoffs
associated with different organizations of social-ecological systems
and inform the public all the options, so that a prudent policy can
be enacted (Raudsepp-Hearne et al., 2010). By analyzing the spatial
configurations of ecosystem services, Raudsepp-Hearne et al.
(2010) proposed a framework of ‘bundle’ in analyzing multiple
ecosystem services. Bennett et al. (2009) proposed a typology
based on the role of drivers underlying ecosystem services and
demonstrated that the tradeoffs and synergies can be creatively
manipulated in ecological management. Tradeoff and synergy are
inseparable, sometimes they are inter-convertible. A case in point is
the organic farming advocated in the Yanhe Watershed, which
55
successfully solved the tradeoff of grain production and water
quality conservation. In addition, Yanhe Watershed is famous for
production of apple, jujube, and walnut. As cash forests in the GfG
project, these fruit trees provide the dual service of fruits production and soil erosion control. This can be viewed as a wise use of
synergy of provisioning services and regulating services.
People prefer provisioning service over other services mainly
due to that provisioning ecosystem service is more tangible and
identifiable than the cultural, regulating, and supporting service
(Rodriguez et al., 2006). The possible outcome of overwhelming
emphasis on provision service over regulating/supporting service is
environmental degradation. This is the case for most developing
counties. The period of 1950s in China saw the inception of ‘great
leap forward’ movement motivated by irrational enthusiasm under
which large areas of vegetation were logged down for the great
cause of ‘communal iron/steel smelting’. In the 1960s, destroying
forest for farming production was encouraged under the guideline
of ‘take grain production as the key link’. Both actions neglected the
importance of regulating and supporting ecosystem services due
low environmental conservation awareness. The consequence is
that a spate of ecological problems poured in with soil and water
losses as its core. The Yanhe watershed is a typical case of such
tragedy. Regulating/supporting service is important since they are
critical for producing provisioning/cultural services, impairing the
former is bound to harm the resilience of social-ecological systems
(Rodriguez et al., 2006). The key to the GfG project in the Yanhe
Watershed lies in how to balance the tradeoff (game) between
short-term small-scale agricultural production by farmers and
long-term large-scale sustainability of human well-being of the
public.
5. Conclusions
In order to explore the co-evolution of ecosystem services and
its anthropogenic driving forces, we valuated NPP, CSOP, water
conservation, soil conservation, and grain production, with an
integrated human activity index in the Yanhe Watershed during the
GfG project (2000 and 2008). The conclusions are as follows:
(i) The regulating/supporting ecosystem services of NPP, CSOP,
watershed conservation and soil conservation increased
during the period of 2000e2008. While the provisioning
service of grain production decreased. HAI also decreased from
2000 to 2008.
(ii) Variations of both soil conservation and water conservation
are negatively correlated to variation of HAI. Within the
ecosystem services, variation of water conservation and that of
soil conservation are positively correlated, while variation of
grain production is negatively correlated with NPP/CSOP.
Negative correlation also exists between soil conservation and
NPP/CSOP.
(iii) Tradeoff exists between regulating/supporting services and
production service, while synergies exist within regulation
services.
Complex interrelations existed between human activities and
ecosystem services. The sole quantitative comparison between
ecosystem services and an integrated human activity surely can not
portrait the whole underlying mechanisms. Future directions of
this research lie in: 1) exploring on more accurate and operable
valuation scheme for human activity, 2) develop model sets for
valuating ecosystem services catering to specific localities, and 3)
establish a paradigm in quantifying the relations between human
activity and ecosystem services.
56
C.-h. Su et al. / Acta Oecologica 44 (2012) 46e57
Acknowledgments
The research was supported by the National Basic Research
Program of China (No. 2009CB421104), National Natural Science
Foundation of China (No. 40621061) and Chinese Academy of
Sciences (No: KZCX2-YW-T13 and GJHZ0948).
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