Submitted 10 April 2019Accepted 13 August 2019Published 12 September 2019

Corresponding authorChi Zhang zcmsxjbaccn

Academic editorRobert Costanza

Additional Information andDeclarations can be found onpage 15

DOI 107717peerj7665

Copyright2019 Li et al

Distributed underCreative Commons CC-BY 40

OPEN ACCESS

Variations in ecosystem service value inresponse to land useland cover changesin Central Asia from 1995ndash2035Jiangyue Li12 Hongxing Chen12 Chi Zhang13 and Tao Pan12

1 State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of Sciences Urumqi China

2University of Chinese Academy of Sciences Beijing China3Research Center for Ecology and Environment of Central Asia Chinese Academy of Sciences UrumqiChina Urumqi China

ABSTRACTAcute farmland expansion and rapid urbanization in Central Asia have accelerated landuseland cover changes which have substantial effects on ecosystem services Howeverthe spatiotemporal variations in ecosystem service values (ESVs) in Central Asia are notwell understood Here based on land use products with 300-m resolution for the years1995 2005 and 2015 and transfer methodology we predicted land use and land cover(LULC) for 2025 and 2035 using CA-Markov assessed changes in ESVs in response toLULC dynamics and explored the elasticity of the response of ESV to LULC changesWe found significant expansions of cropland (+2210) and urban areas (+32240)and shrinking of water bodies (minus3843) and bare land (minus942) during 1995ndash2035The combined value of ecosystem services of water bodies cropland and grasslandaccounted for over 90 of the total ESVs Our study showed that cropland ecosystemservices value increased by 9345 billion US$ from 1995 to 2035 which was mainlycaused by the expansion of cropland area However the area of water bodies decreasedsharply during 1995ndash2035 causing a loss of 6438 billion US$ Biodiversity foodproduction and water regulation were major ecosystem service functions accountingfor 8052 of the total ESVs Our results demonstrated that effective land-use policiesshould be made to control farmland expansion and protect water bodies grassland andforestland for more sustainable ecosystem services

Subjects Ecosystem Science Natural Resource Management Environmental Impacts FoodWater and Energy Nexus Spatial and Geographic Information ScienceKeywords Ecosystem services Value coefficient LULC change Central Asia

INTRODUCTIONEcosystem services (ES) refer to the direct and indirect benefits that people obtainfrom ecosystems (Costanza et al 1997) including provisioning services (food and rawmaterial) regulating services (water regulation climate regulation and gas regulation)supporting services (soil formation waste treatment and biodiversity) and cultural services(recreation cultural and tourism) (Hassan Scholes amp Ash 2005) Quantifying the benefitsobtained from ecosystems can be achieved through evaluation of ecosystem services values(ESV) in monetary units (Costanza et al 2014 Metzger Rounsevell amp Acosta 2006) The

How to cite this article Li J Chen H Zhang C Pan T 2019 Variations in ecosystem service value in response to land useland coverchanges in Central Asia from 1995ndash2035 PeerJ 7e7665 httpdoiorg107717peerj7665

measurement of ESV in monetary units is an important step to improve incentives andobtain expenditures needed for their conservation and sustainable use (eg systems ofPayments for Ecological Services) (Farley amp Costanza 2010) In addition these valuescan help policy makers make optimal decisions on the rational allocation of resourcesand provide useful information for understanding user interests and the relative value ofcurrent ES (Farley 2008)

For the evaluation of ESV inmonetary units fourmethodswere found inTalberth (2015)including stated preference method revealed preference method cost-based method andbenefit transfer method Among the various methods of regional or global ES assessmentbenefit transfer method (BTM) has been widely used because of its feasibility and simplicity(Costanza et al 1997 Costanza et al 2014 DeGroot et al 2012) This method suggeststhat the value of each ecosystem service functions uses specific value for a particularland cover obtaining from single or multiple case studies (DeGroot et al 2012) Mostnotably the global biosphere was first classified into 16 sub-ecosystems and 17 ecosystemservice functions and the value of each ES was evaluated by BTM (Costanza et al 1997)However some researchers have severely criticized their findings (eg Serafy 1998Wilsonamp Howarth 2002) due to the limitations and uncertainties in their local use (Kindu et al2016 Li et al 2007) Recently the evaluation has been updated based on more than 300case studies worldwide (Costanza et al 2014 DeGroot et al 2012) Costanza et al (2014)claimed that the models and basic data they use in their evaluation can be applied at variousscales to evaluate dynamic changes of ESV

Land use and land cover (LULC) changes alter structures and functions of ecosystemsand influences the supply of ecosystem services (Hu Liu amp Min 2008 Kreuter et al 2001Yirsaw et al 2017) Excessive utilization of land resources may lead to severe degradationor loss of local or regional ES (Collin amp Melloul 2001) Recent research has shown thatcropland conversion urbanization and deforestation have led to the loss of carbonsequestration reduction in biodiversity decline in water quality and land degradationand resulting in a significant decline in ESV (Maitre et al 2007 Polasky et al 2011)Numerous studies have evaluated the impact of LULC dynamics on ES worldwide byutilizing the valuation coefficients of Costanza et al (1997) and Costanza et al (2014) Forexample Arowolo et al (2018) studied landuse change and its impacts on ESV in NigeriaYi et al (2017) assessed the effects of land-use change on ES in the San Antonio River BasinTexas These studies have provided valuable references for land-use policy makers

Situated in the center of the Eurasian continent Central Asia dryland has experiencedcomplex LULC changes since the collapse of the Soviet Union in the early 1990s (Behnkeamp Mortimore 2016 Hamidov Helming amp Balla 2016) These changes have had profoundeffects on the fragile ecological environment inCentral Asia For example animal husbandryheavily relies on the grassland resources in the study area as a major food supply (Han etal 2016Huang Luo amp Han 2018) The vast pastures of Central Asia constitute the largestcontinuous grazing area in the world (Gintzburger et al 2003) Furthermore grasslandsprovide other important ecosystem services such as carbon sequestration climate and gasregulation soil and water conservation and biodiversity (Eichelmann et al 2016 Grace2010 Huang Luo amp Lv 2017) In recent years water-stressed grassland ecosystems have

Li et al (2019) PeerJ DOI 107717peerj7665 222

been frequently disturbed by human activities (eg grazing and reclamation) and climatechange which has led to the decline of grassland ecosystem service quality (Chen et al2018 Han et al 2018 Hobbs amp Norton 1996 Tanentzap amp Coomes 2012) Additionallythe Syr Darya and Amu Darya Rivers are essential sources of water used for agriculturein the study area (Kulmatov et al 2017) In the Amu Darya River Basin in the KhorezmProvince of Uzbekistan and the Fergana Valley the efficiency of the irrigation and drainagesystems from agricultural fields is extremely low (Awan et al 2016 Karimov et al 2014)Excessive irrigation not only leads to the waste of large amounts of water resources in theAmu Darya Delta but also results in fertilizer loss soil salinization and salt storms (Devkotaet al 2015) Because of irrational land use and improper management there is serioussoil erosion desertification and extensive land degradation in some basins Therefore itis urgent to evaluate the impacts of human disturbances on ES in Central Asia Such astudy is important for ecological monitoring sustainability management and disturbanceregulation in this fragile dryland ecosystem Except for a few qualitative estimates on ESchanges in response to LULC changes (Chen Bai amp Li 2013 Chi et al 2016) to date therehave been no quantitative assessments of ESV in Central Asia

Therefore the aim of this study is as follows (1) to estimate and project the LULCchanges in Central Asia during the period 1995ndash2035 (2) to evaluate changes in ESVin response to LULC changes and (3) to explore the elasticity of the response of ESVto LULC changes by 50 adjustment of value coefficients Our findings could providepolicy makers with important references for ecological environmental protection and thesustainable development of Central Asia

MATERIALS amp METHODSStudy areaOur study area covered five Central Asian countries (from 3508primeN 5525primeN to 4628primeE8729primeE) including Tajikistan Kyrgyzstan Turkmenistan Uzbekistan and Kazakhstan(Fig 1) With a land area of 4 million km2 Central Asia extends from Russia in the northto Afghanistan in the south and from the Caspian Sea in the west to western borders ofChina in the east (Ozturk et al 2017) The terrain gradually changes from the westernCaspian lowlands to the Altai Mountains Tianshan Mountains and Pamirs (Beurs et al2015) Because this area is located far from the ocean in the hinterland of the Eurasiancontinent it has a distinctive continental arid and semiarid climate with low precipitationand intensive evaporation (Lioubimtseva amp Henebry 2009) In recent decades potentialevapotranspiration has increased in Central Asia especially in the Aral Sea region andwestern Kazakhstan with an annual increase of 742 mmyear (Jiang et al 2019) CentralAsia has a heterogeneous landscape including diverse land-cover types such as temperatedeserts (eg Kyzylkum Karakum Desert) forests lakes (eg the Aral Sea Balkhash Lake)and vast grasslands Major transboundary rivers such as the Syr Darya the Amu Daryathe Irtysh River and the Ili River are critical water sources for regional ecosystems andagriculture (Zhou et al 2019) With the large-scale development of irrigated agriculture inthe Aral Sea Basin in the early 1960s the irrigated cultivated land area increased by 60

Li et al (2019) PeerJ DOI 107717peerj7665 322

Figure 1 Map showing the location of Central AsiaFull-size DOI 107717peerj7665fig-1

and the planted cotton area doubled between the 1960s and the 1990s Now irrigated landaccounts for almost half of the total cultivated land area (Suo et al 2009)

Data collection and land use classificationMultiscale satellite observations were used to characterize LULC change (Song 2018)In a preliminary study we compared the multiple LULC datasets for the studyarea including the GLC2000 (Corresponding amp Belward 2005) the GlobCover 2009(Arino et al 2008) MODIS land cover (Friedl et al 2010) and the newly releasedannual European Space Agency Climate Change Initiative land cover maps (CCI-LC)(httpmapselieuclacbeCCIviewer) (Radoux et al 2013) The comparison showed thatthe CCI-LC has the highest spatial resolution and has better accuracy in the study area(Hartley et al 2017 Wei et al 2015) This product was developed using the GlobCoverunsupervised classification chain and combined a variety of earth observation productsbased on ESArsquos GlobCover products Unlikemany remote sensing products based on single-sensor methods this dataset is generated using multiple sensors such as the AdvancedVery High Resolution Radiometer (AVHRR) System Probatoire drsquoObservation de laTerre Vegetation (SPOT-VGT) and PROBA-V To validate the dataDefourny et al (2016)compared the CCI LC product for the 2010 with the certain and homogeneous pointsof the GlobCover 2009 with a total accuracy of 732 (Georgievski amp Hagemann 2019)More high-accuracy land classifications were found water bodies (92ndash96) irrigatedcropland (89ndash92) rainfed irrigated cropland (89ndash83) bare land (89ndash88) andurban areas (88ndash86) (Defourny et al 2016) However natural vegetation and sparsevegetation have low user accuracy values but all errors relate to confusion between theseand other LULC classes which very much limits the impact of the low values Details aboutthe CCI-LC dataset including its accuracy and the confusion matrix can be found in thestudy by Defourny et al (2016) To match Costanzarsquos biomes plant functional types were

Li et al (2019) PeerJ DOI 107717peerj7665 422

classified into seven major LULC types including cropland forestland grassland wetlandurban areas bare land and water bodies (Table S1)

Model projection of future LULC changeFuture LULC change from 2015 to 2035 was projected by combining a GeographicInformation System (GIS) with a CA-Markov model which is a robust approach forsimulation of the spatialmdashtemporal change patterns of LULC (eg Fu Wang amp Yang2018 Hartley et al 2017 Muller amp Middleton 1994) The CA-Markov model mergescellular automata (CA) andMarkov chain (Ji et al 2009) TheMarkov chain is constructedbased on the probability of change matrices for LUCC from t to t+1

s(t+1)= s(t )timesA

where s(t+1) is the state probability of any time and s(t ) is the initial state probability A isthe transition probability matrix and the formula is as follows

A=

A11 A12 A1n

A21 A22 A2n

An1 An2 Ann

where Aij is the sum of areas from the ith land cover category to the jth category from theinitial period to forecast periods and n is the number of LULC categoriesWe accomplishedthis process by using the MARKOV module in the IDRISI a software that integrates GISand image processing functions

The CA model is used to predict the spacendashtime dynamic change pattern using atransition map of the LULC (Yang Zheng amp Lv 2012) and the model can be defined asfollows (Wang Zheng amp Zang 2012)

S(t t+1)= f(S(t )N

)where S is a set of cellular states N is the cellular field t and t+1 represent different timeperiods and f is the local transition rule of the cell

To ensure the reliability of the simulation results we used the kappa index to test theconsistency level of the simulated and observed land cover maps (Mitsova Shuster amp Wang2011)

kappa=p0minuspc1minuspc

where kappa is the index of simulation accuracy pc is the expected simulation accuracy ina random state and p0 is the actual simulation accuracy

By incorporating the advantages of these two methods the CAndashMarkov model is able toaccomplish a better simulation of LULC changes both in quantity and space (Yang et al2014) In this study the transition probability matrix was performed for the time periodbetween 1995 and 2005 to predict the LULC map of 2015 which would be used to verifymodel accuracy Then using the 2015 classified map as the LULC baseline and the 2005and 2015 maps for the transition probability matrix we predicted the 2025 and 2035 LULCmaps with the CA-Markov model

Li et al (2019) PeerJ DOI 107717peerj7665 522

Table 1 The value coefficient of ecosystem services on seven LULC categories in Central Asia (US$ha 1 yr 1)

Service type Sub-type Cropland Forestland Grassland Wetland Urban Bare land Waterbodies

Provisioning Food production 2323 299 1192 614 0 0 106Raw material 219 181 54 539 0 0 0

Regulating Gas regulation 0 0 9 0 0 0 0Climate regulation 411 152 40 3474 905 0 0Water regulation 400 191 63 6014 16 0 9322

Supporting Soil-formationand retention

639 107 46 4320 0 0 0

Waste-treatment 397 120 75 3015 0 0 918Biodiversity 1096 1097 2494 3502 0 0 0

Culture Recreationcultural and tourism

82 990 193 4203 5740 0 2166

Total ecosystem value 5567 3137 4166 25681 6661 0 12512

Assessment of ecosystem service valuesIn this study ESV were estimated based on benefit transfer method (BTM) proposed byCostanza et al (1997) Nine ecosystem service functions generated from Xie et al (2008)derived from the 17 ecosystem services listed by Costanza et al (1997) were selected in thisassessment (Sannigrahi et al 2018) Cultivated land temperate forest grasslandshrublandswampsfloodplains urban areas and deserttundraicebare-rocks in Central Asia werematched to cropland forests grassland wetland urban and bare land in Costanza et alrsquos(2014) model respectively (Table S1) The biomes that we used as proxies for the 7 LULCcategories are not perfectly matched to those of Costanza et alrsquos (1997) ESV model in somecases (Table S1) but they are closely related (Kreuter et al 2001) The equivalent valuecoefficient of each ES updated by Costanza et al (2014) was used to calculate ecosystemservices values (Table 1) The formula is as follows

ESVk =6f AktimesVCkf

ESVf =6kAktimesVCkf

ESV =6f 6kAktimesVCkf

where ESVk refers to the ecosystem service value of LULC type lsquokrsquo Ak represents thearea (ha) for LULC category lsquokrsquo and VC fk is the value coefficient (US $hayear) offunction f for the LULC type lsquokrsquo (Kreuter et al 2001) ESVf is the ecosystem service valueof service function f and ESV is the total ecosystem service value We use the followingformula to evaluate changes in ESV

ESVcr =ESVt2minusESVt1

ESVt1times100

In this expression ESVcr refers to the change rate of ESV from the initial year to thefinal year and ESVt1 and ESVt2 represent the total ESV at the start and end of the studyperiod respectively

Li et al (2019) PeerJ DOI 107717peerj7665 622

Elasticity for the response of ESV to LULC changeThe biomes that we used as proxies for the 7 LULC categories are not perfectly matched toCostanza et alrsquos (1997) ESV model in some cases (Table S1) which results in uncertaintiesin the assessment of the ESV Thus we used sensitivity analysis to evaluate the changesin ESV in response to 50 adjustments of the ESV coefficients for each LULC type(Kindu et al 2016) The standard economic concept of elasticity was used to calculate thecoefficient of sensitivity (CS) using the following formula (Kreuter et al 2001)

CS=(ESVjminusESVi)ESVi

(ESVjkminusESVik)ESVik

where ESV is the estimated total value of ecosystem services VC is the value coefficientand lsquoirsquo lsquojrsquo and lsquokrsquo represent the initial adjusted values and LULC categories respectivelyIf CS gt1 then the estimated ESV is elastic with respect to that coefficient if CSle1 theestimated ESV is inelastic Thus when CS lt1 even if the accuracy of VC values used asproxy biomes is low the results of estimation of ESV are credible (Kreuter et al 2001)

RESULTSAnalysis of LULC dynamicsCombining the GIS technology with the CA-Markov model we used the LULC base mapfrom 2005 and transition probabilities from 1995 to 2005 to simulate the LULC for 2015The kappa statistic of 093 indicates that there is good consistency with the actual value ofthe LULC types and the predicted results for the base year Then the future LULC in 2025was predicted with the CA-Markov model in IDRISI using the LULC base map from 2015and the transition probabilities from 2005 to 2015 Following the above process the futureLULC in 2035 was predicted

Figure 2 presents the patterns of spatial distribution of LULC in Central Asia from1995 to 2035 and Table 2 shows the magnitude of changes for the same periods In 1995grassland occupied approximately 5139 of the study area followed by bare land andcroplandwhich occupied 2390and 1898of the study area respectively (Fig 3)During1995ndash2015 cropland and urban area increased substantially The cropland expanded at arate of 076 per annum increasing 121956 times 104 ha by the year 2015 (Table 2) Rapidurbanization also caused the proportion of urban build-up to increase from 2757times104 hain 1995 to 6021 times 104 ha in 2005 and then to 8919 times 104 ha in 2015 with an averagegrowth rate of 1064 per year (Table 2) Cropland and urban areas were expected tocontinue to increase in the 2025 and 2035 periods However the coverage of bare landmainly concentrated in the Kyzylkum Desert and Karakum Desert decreased from 2390to 2295 during 1995ndash2015 and was projected to further decrease to 2165 by 2035

Wetlands and water bodies only account for 3 of the total study area Wetlandsincreased from 12114times104 ha in 1995 to 12525times104 ha in 2015 and 128 41times104 ha in2035 (Table 2) mainly near Balkhash Lake (Fig 2) The area of water bodies decreased29259 times104 ha from 1995 to 2015 and is expected to further decrease 22190times104 haby 2035 Among water bodies in Central Asia the Aral Sea has shrunk most dramatically(Fig 2)

Li et al (2019) PeerJ DOI 107717peerj7665 722

Figure 2 Spatial distribution of LULC in Central Asia (A) 1995 (B) 2005 (C) 2015 (D) 2025 (E) 2035Full-size DOI 107717peerj7665fig-2

Table 2 Area changes of LULC in Central Asia from 1995 to 2035

LULC Cropland Forestland Grassland Wetland Urban Bare land Water bodies Total

1995 759503 80090 2056052 12114 2757 956326 134206 40010482005 859177 80105 1981087 12169 6021 944980 117508 40010482015 881459 80048 1994814 12525 8919 918336 104948 40010482025 905158 79949 2007726 12525 9082 892689 93917 4001048

area (104 ha)

2035 927375 79842 2020347 12841 11647 866238 82758 40010481995ndash2015 1606 minus005 minus298 339 22345 minus397 minus2180 ndash2015ndash2035 521 minus026 128 252 3059 minus567 minus021 ndashchanges ()

1995ndash2035 2210 minus031 minus174 600 32240 minus942 minus3834 ndash

Changes in total ecosystem services valuesAccording to our estimation the total ESV in Central Asia was approximately 150531billion US$ in 1995 (Table 3) Grassland had the highest contribution of 5690 followedby cropland and water bodies (2809 and 1115 respectively) (Fig 3) Due to LULCchange the regional ESV increased 568 billion US$ during 1995ndash2005 mainly due tothe increased ESV in cropland and urban build-up which overcompensated the ESV lossin grassland and water bodies The regional ESV further increased 523 billion US$ from2005ndash2015 Overall the ESV in Central Asia increased 1091 billion US$ during 1995ndash2015It is noteworthy that the proportion of water bodies decreased sharply by 2180 from1995ndash2015 causing a loss of 3661 billion US$ These trends will continue to occur in 2025and 2035 (Table 3)

Li et al (2019) PeerJ DOI 107717peerj7665 822

0

20

40

60

80

100

1995 2005 2015 2025 2035

Per

cen

tage

of

lan

d a

rea

(a)

Cropland Forestland Grassland Wetland Urban Bare landWater bodies

0

20

40

60

80

100

1995 2005 2015 2025 2035

Per

cen

tage

of

ES

V

(b)

Cropland Forestland Grassland Wetland Urban Bare landWater bodies

Figure 3 (A) The percentage of land use area and (B) the percentage of ecosystem service value ofdifferent land use types

Full-size DOI 107717peerj7665fig-3

Table 3 Ecosystem service value of Central Asia from 1995 to 2035

LULC ESV (billion US$) Changes ()

1995 2005 2015 2025 2035 1995ndash2015 2015ndash2035 1995ndash2035

Cropland 42279 47827 49068 50387 51624 1606 521 2210Forestland 2512 2513 2511 2508 2505 minus005 minus026 minus031Grassland 85654 82531 83102 83640 84166 minus298 128 minus174Wetland 3111 3125 3217 3247 3298 339 252 600Urban 184 401 594 645 776 22341 3059 32233Bare land 000 000 000 000 000 000 000 000Water bodies 16792 14702 13131 11751 10354 minus2180 minus2114 minus3834Total 150531 151099 151623 152178 152722 073 073 146

We further analyzed the ESV for administrative units in 1995 (Fig S1) The highest ESVwas found in the Karaganda state (17409 billion US$) followed by East Kazakhstan (13169billion US$) Aktobe (12109 billion US$) and Almaty (11670 billion US$) states The ESVfor these administrative units weremainly contributed by grassland in Karaganda (8613)East Kazakhstan (67148) and Aktobe (8639) Andijon had the lowest ESV of 217billion US$ 9373 of which was contributed by cropland In addition we calculatedthe ESV change rates during 1995ndash2015 2005ndash2015 2015ndash2025 and 2025ndash2035 for theadministrative units (Fig 4) From 1995ndash2005 the ESV of Karakalpakistan Uzbekistandeclined 3108 with a total loss of 1736 billion US$ (Fig 4A) The ESV in Kyzylorda alsodecreased substantially which was mainly caused by shrinking of water bodies In contrastthe ESV in Pavlodar and Karaganda increased substantially mainly due to the increasedcropland ESV During 2005ndash2015 the ESV in Karakalpakistan Kyzylorda andManghystaufurther decreased (Fig 4B) with the highest ESV loss found in Karakalpakistan (minus2477)followed by Kyzylorda (minus883) and Manghystau (minus105) In contrast Karaganda hadthe highest increase rate of ESV (572) followed by Aktobe (141) and Almaty (125)

Li et al (2019) PeerJ DOI 107717peerj7665 922

Figure 4 Ecosystem service value change rate () from 1995 to 2005 (A) 2005 to 2015 (B) 2015 to2025 (C) and 2025 to 2035 (D) The state names of andashr can be found in Table S2

Full-size DOI 107717peerj7665fig-4

It is worth noting that the trend of decreasing ESVwill continue to occur in Karakalpakistanand Kyzylorda from 2015 to 2035 (Figs 4C and 4D)

Changes in values of ecosystem service functionsTable 4 shows the changes in individual ecosystem functions (ESV f ) The most importantESV f in Central Asia were biodiversity food production and water regulation whichcontributed to 4044 2830 and 1178 of the total ESV in 1995 respectively 40032947 and 1021 of the total ESV in 2015 and 4051 3014 and 893 of the totalESV in 2035 Most of the ESV f decreased during 1995ndash2015 except for food productionraw materials climate regulation soil formation and waste treatment which increased by487 792 1211 1201 and 291 respectively (Fig 5) It is noteworthy that theESV of water regulation declined more rapidly than other ecosystem services (minus1270)followed by gas regulation (minus300) cultural and tourism (minus314) and biodiversity(minus029) However most of the ESVf were projected to increase from 2015ndash2035 (Fig 5)Only the ESV of water regulation and cultural servicetourism were expected to decreasein the future

Ecosystem sensitivity analysisIn the observed (1995ndash2015) and projected (2025ndash2035) study periods CS for grasslandwas the highest (055) due to the high service value coefficient and large grassland area(Table 5) Meanwhile CS for cropland increased from 028 in 1995 to 032 in 2015 andto 034 in 2035 Compared with grassland and cropland the CS (002) for forestland wasrelatively stable The CS for water bodies decreased from 011 in 1995 to 009 in 2015 and to007 in 2035 In this study all CS were far less than lsquolsquo1rsquorsquo indicating that the total estimatedecosystem values are inelastic with respect to the ecosystem value coefficients

Li et al (2019) PeerJ DOI 107717peerj7665 1022

-22

-17

-12

-7

-2

3

8

13

18

Foo

d P

rod

uct

ion

Raw

Mat

eria

l

Gas

Reg

ula

tion

Cli

mat

e R

egu

lati

on

Wat

er R

egula

tio

n

Soil

Fo

rmat

ionan

d R

eten

tion

Was

te T

reat

men

t

Bio

div

ersi

ty

Rec

reat

ion

C

ult

ura

l an

d T

ou

rism

Ch

ang

es (

)

1995-2015 2015-2035 1995-2035

Figure 5 Change rate of ecosystem service function in Central Asia from 1995 to 2035Full-size DOI 107717peerj7665fig-5

Table 4 Estimated values for different ecosystem functions in Central Asia in 1995ndash2035

Service type Sub-type Ecosystem service value (billion US$)

1995 2005 2015 2025 2035

Provisioning Food production 42608 44012 44682 45374 46031Raw material 2985 3164 3222 3280 3337

Regulating Gas regulation 187 180 181 182 183Climate regulation 4509 4920 5056 5159 5290Water regulation 17736 16534 15483 14558 13633

Supporting Soil formationand retention 6398 7002 7166 7323 7484Waste treatment 6249 6437 6431 6433 6438Biodiversity 60901 60126 60724 61305 61873

Culture Recreation cultural and tourism 8959 8725 8678 8492 8452Total 150532 151099 151623 152178 152723

Table 5 Percentage change in estimated total ESV and coefficient of sensitivity

Change of value coefficient 1995 2005 2015 2025 2035

CS CS CS CS CS

Cropland VCplusmn 50 1404 028 1583 032 1618 032 1656 033 1690 034Forestland VCplusmn 50 083 002 083 002 083 002 082 002 082 002Grassland VCplusmn 50 2845 057 2731 055 2740 055 2749 055 2756 055Wetland VCplusmn 50 103 002 142 003 179 004 106 002 108 002Urban VCplusmn 50 006 000 013 000 020 000 020 000 025 001Bare land VCplusmn 50 000 000 000 000 000 000 000 000 000 000Waterbodies VCplusmn 50 558 011 487 010 433 009 386 008 339 007

Li et al (2019) PeerJ DOI 107717peerj7665 1122

DISCUSSIONImpact of LULC change on ecosystem services in Central AsiaGlobally patterns of LULC change are themselves changing rapidly due to the accelerationof processes such as population growth expansion of urban areas and agriculturalintensification (Lambin amp Geist 2006) which could affect regional ecosystem services (HuLiu amp Min 2008 Nahuelhual et al 2014) these effects are particularly prominent in theCentral Asia region TheUnitedNations statistics show thatCentral Asiarsquos urban populationincreased by 3797 between 1995 and 2015 from 2401million to 3313million which ledto the overexploitation of water and land resources to meet peoplersquos needs for water foodand energy (Granit et al 2012) We found significant expansions of cropland (+2210)and urban areas (+32240) and shrinking of water bodies (minus3843) and bare land(minus942) during 1995ndash2035 (Table 2) Correspondingly cropland ecosystem servicesvalue increased by 9345 billion US$ from 1995 to 2035 which was mainly caused by theexpansion of cropland area (Table 3) However the area of water bodies decreased sharplyduring 1995ndash2035 causing a loss of 6438 billion US$ (Table 3)

Cropland changes may seem economically profitable but a large increase in agriculturalland can result in the loss of natural ecosystem services More specifically the impactof complex human agricultural activities on the ecosystem services of water quantityand quality is considerably overlooked by current agricultural management models thatmaximize food production andminimize other ecosystem services (Coupe Barlow amp Capel2012) We found that the expansion of agricultural land has largely led to an increase inthe service functions of food production (+3223 billion US$) raw materials (+3223billion US$) soil formation (+1086 billion US$) and biodiversity (+972 billion US$)during 1995ndash2035 in Central Asia (Table 4) However these findings are consistent withthe results of numerous studies around the world showing that agriculture and urbanexpansion have a negative impact on the provision of other important ES such as waterregulation (minus4103 billion US$) (Schroumlter et al 2005) gas regulation (minus004 billion US$)(Wang et al 2006) and recreation cultural and tourism (minus2191 billion US$) (Nahuelhualet al 2014) Especially in Amu River Basin irrigation water consumption of crops such aswheat cotton andmaize increased by at least 60 from 1962 to 2002 leading to a significantreduction in the amount of water flowing into the Aral Sea (McDermid amp Winter 2017)Now the Aral Sea has shrunk to approximately 110 of its original area (Ablekim et al2017) Our results show that the ESV in Karakalpakistan Uzbekistan decreased by morethan 50 during 1995ndash2035 (Fig 4) which was mainly caused by the shrinkage of the AralSea area Therefore wider cultivation of crops with lower water consumption and higheradded value would reduce the shrinkage of the Aral Sea and thereby enhance ecosystemservices

In addition LULC changes are interrelated with other global processes such asclimate change and land degradation (Kerteacutesz Nagy amp Balaacutezs 2019) which directlyor indirectly affect local ecosystem services (Hossu et al 2019 Qiao et al 2019) Forexample approximately 23 of farmland in the Amu Lowland has been degradeddue to inappropriate land use activities (Djanibekov et al 2018) resulting in the loss of

Li et al (2019) PeerJ DOI 107717peerj7665 1222

biodiversity and the decline of carbon storage capacity which will further affect agriculturalproduction disturbance regulation and climate regulation (Sutton et al 2016) Namelydriven by common factors (eg land use changes and land degradation etc) synergiesand trade-offs exist between system services between ESs over time (Cord et al 2017)

Limitations and areas of future researchRemote sensing images are the most important data source for research in ES and LULC isthe most widely used variable for assessment in ESV (Song 2018) However the limitationsof global land cover data arise from the product generation process including satellite sensorcharacteristics (spectral temporal and spatial resolutions) definition and classificationmethods of land cover Because of error propagation in the process of quantifying landcover data (Sexton et al 2015) LULC change may be considerably underestimated oroverestimated (Table 2) Such uncertainties are inevitably introduced in the analysis ofspatial change patterns when the biomes used as proxies match LULC types (Kreuter et al2001) In this study shrubs and sparse vegetation are used as grasslands (Table S1) whichobviously exaggerates the area of grasslands and leads to an overestimation of ESV (Table3) Therefore to address these limitations higher spatial resolution remote sensing dataand more precise LULC classification will be used to further accurately assess ecosystemservices in Central Asia

Nine ecosystem service functions proposed by Xie et al (2008) were selected to calculatethe total ESV in Central Asia (Table 1) which could mask the value of other key ecosystemservice functions For example sand fixation is an important function provided by desertecosystems in arid and semi-arid areas which has a close relationship with ecologicalbenefits (Li amp Xu 2019 Musa Deming amp Cunyang 2014 Taylor et al 2017) Hao et al(2013) found that the ESV of sand fixation in the Ulan Buh desert ecosystem was 068billion US$ in 2009 However there is no comprehensive assessment of ecosystem servicefunctions for dryland ecosystem services in this study To address this limitations Schildet al (2018) have identified and compiled a comprehensive database to estimate the ESVin arid areas However their findings indicate that there are still important limitationsin the valuation of ESV in arid region In further research we will critically evaluate andfurther improve monetary valuation techniques to promote sustainable land managementin Central Asia

Our approach for estimating the ecosystem services value by using simple benefitstransfer methods has certain limitations (Schmidt Manceur amp Seppelt 2016) For examplewe assumed homogeneity of ESV in the entire LULC category and generalized the unitvalues of one area as the average unit value of all areas As Costanza et al (2014) estimatedfarmland ecosystem services provided $ 5567ha in 2011 (Table 1) Although the averageunit value provides a common value coefficient for all nations of the world to comparethe relative differences different types of farmland (eg rainfed cropland in northernKazakhstan and irrigate cropland in Amu River Delta) have different crops plantingstructures and provide different ecosystem services and functions (Zheng Zhang amp Cao2018) In addition the costs of ecosystem services (eg soil salinization the loss of genetic

Li et al (2019) PeerJ DOI 107717peerj7665 1322

resources and water eutrophication) were ignored in assessing the value of ecosystemservices which might exaggerate the natural ecosystem services value (Cao et al 2018)As Cao et al (2018) revealed the value of ecosystem services decreased by 5266 in 2014when the costs of water resources investments in ecological protection land rent andmanagement were considered to estimate the net farmland ecosystem services value inChina In Central Asia especially in Uzbekistan irrigated agriculture remains the basis ofthe Uzbek economy consuming more than 90 of the water resources and 22840 kghaof fertilizers (FAO 2015) and employing 33 of the labor force (UZSTAT 2010) All ofthese factors suggest that farmland ecosystems have paid massive costs while providing alarge number of services However because we did not incorporate these negative factorsinto the evaluation system the ESV of farmland was overestimated (Table 3) Thereforedevelopment of a framework that can assess the net value of ecosystem services is urgentlyneeded to make reasonable arrangements for resource allocation

We determined that the CS for the ESV was quite low (Table 5) when the valuecoefficients were adjusted by 50 to estimate the effects However we only focused onthe directional change in ESV but ignored the magnitude of the ecosystem services valuesat specific points in time It is worth noting that the interaction of some ES changeddramatically with different levels of economic development For example ESV of urbanareas was overestimated (Table 3) in this study masking the loss of essential ES providedby other LULC types Yi et al (2017) also believed that the value coefficient of urbanbuilt-up was assigned Costanza et al (2014) seems unreasonable We suggest that thevalue coefficients should be adjusted by collecting satellite or radar imagery and productsobtained through efficient processing (eg soil moisture content vegetation carbon stocksand chlorophyll content) to accurately reflect local ecosystem services of Central Asia infurther studies

CONCLUSIONSOur study showed that the increase in cropland ecosystem services value was approximately6789 billion US$ from 1995 to 2015 which was caused by the increasing areas (1606)of cropland However the area of water bodies decreased sharply by 3834 during 1995ndash2035 causing a loss of 6438 billion US$ It is important to note that cropland changesmay seem economically profitable (eg food and raw material) but a large increase inagricultural land can result in the loss of natural ecosystem services (eg water regulationand climate regulation) Meanwhile unsustainable agricultural practices and overgrazingpractices are the major drivers of land degradation and desertification in the region Asa result many ecosystem services are disappearing In the future we will make a preciseassessment of ecosystem services in Central Asia by combining remote sensing observationsand other technical means to understand the interaction of ecosystem services and managethe water-climate-food nexus to maximize its benefits

Li et al (2019) PeerJ DOI 107717peerj7665 1422

ACKNOWLEDGEMENTSWe thank the reviewers for their constructive comments that greatly helped us to improvethe quality of this manuscript Thanks are due to Hongxing Chen Tao Pan Jiaqi Lu QianDing HongTao Xu Tao Yu Hua Shao Yun Li Haoyang Du Xia Fang Xiaolong Jing

ADDITIONAL INFORMATION AND DECLARATIONS

FundingThis study was supported by the Strategic Priority Research Program of the ChineseAcademy of Sciences (Grant No XDA2006030201) and the State Key Laboratory of Desertand Oasis Ecology (Y471163) Chi Zhang is supported by the Taishan Scholars Programof Shandong China (Grant No ts201712071) The funders had no role in study designdata collection and analysis decision to publish or preparation of the manuscript

Grant DisclosuresThe following grant information was disclosed by the authorsStrategic Priority Research Program of the Chinese Academy of Sciences XDA2006030201State Key Laboratory of Desert and Oasis Ecology Y471163Taishan Scholars Program of Shandong China ts201712071

Competing InterestsThe authors declare there are no competing interests

Author Contributionsbull Jiangyue Li conceived and designed the experiments performed the experimentsanalyzed the data contributed reagentsmaterialsanalysis tools prepared figures andortables authored or reviewed drafts of the paper approved the final draftbull Hongxing Chen performed the experiments contributed reagentsmaterialsanalysistools prepared figures andor tables approved the final draftbull Chi Zhang conceived and designed the experiments authored or reviewed drafts of thepaper approved the final draftbull Tao Pan analyzed the data authored or reviewed drafts of the paper approved the finaldraft

Data AvailabilityThe following information was supplied regarding data availability

The raw data are available in the Supplemental Files including the value coefficientproposed by Constanta et al (2014) and the transfer matrix generated by CA-MarkovModel The raw transfer matrix data from IDRISI 170 is available in Table S3ndashS5 LULCcategorical data is available in Table S1

Supplemental InformationSupplemental information for this article can be found online at httpdxdoiorg107717peerj7665supplemental-information

Li et al (2019) PeerJ DOI 107717peerj7665 1522

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Beurs KMD Henebry GM Owsley BC Sokolik I 2015 Using multiple remote sensingperspectives to identify and attribute land surface dynamics in Central Asia 2001ndash2013 Remote Sensing of Environment 17048ndash61 DOI 101016jrse201508018

Cao S Yu Z Zhang J Feng F Xu D Mu X 2018 Costndashbenefit analysis of ecosystemservices in China Ecological Engineering 125143ndash148DOI 101016jecoleng201810022

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Chen X Bai J Li XY 2013 Changes in land useland cover and ecosystem services inCentral Asia during 1990ndash2009 Current Opinion in Environmental Sustainability5116ndash127 DOI 101016jcosust201212005

Chi Z Lu D Xi C Zhang Y Maisupova B Ye T 2016 The spatiotemporal patterns ofvegetation coverage and biomass of the temperate deserts in Central Asia and theirrelationships with climate controls Remote Sensing of Environment 175271ndash281DOI 101016jrse201601002

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DeGroot R Brander L Van der Ploeg S Costanza R Bernard F Braat L ChristieM Crossman N Ghermandi A Hein L 2012 Global estimates of the value ofecosystems and their services in monetary units Ecosystem Services 150ndash61DOI 101016jecoser201207005

Devkota M Martius C Gupta RK Devkota KP Mcdonald AJ Lamers JPA 2015Managing soil salinity with permanent bed planting in irrigated productionsystems in Central Asia Agriculture Ecosystems amp Environment 20290ndash97DOI 101016jagee201412006

Djanibekov U Van Assche K Boezeman D Villamor GB Djanibekov N 2018 Acoevolutionary perspective on the adoption of sustainable land use practices thecase of afforestation on degraded croplands in Uzbekistan Journal of Rural Studies591ndash9 DOI 101016jjrurstud201801007

Eichelmann EWagnerriddle CWarland J Deen B Voroney P 2016 Evapotranspi-ration water use efficiency and energy partitioning of a mature switchgrass standAgricultural amp Forest Meteorology 217108ndash119 DOI 101016jagrformet201511008

FAO 2015 The state of food and agriculture Rome Food and Agriculture Organizationof the United Nations

Farley J 2008 The role of prices in conserving critical natural capital In ConservationBiology 22

Farley J Costanza R 2010 Payments for ecosystem services from local to globalEcological Economics 692060ndash2068 DOI 101016jecolecon201006010

Friedl MA Sulla-Menashe D Tan B Schneider A Ramankutty N Sibley A HuangX 2010MODIS collection 5 global land cover algorithm refinements andcharacterization of new datasets Remote Sensing of Environment 114168ndash182DOI 101016jrse200908016

Fu XWang X Yang YJ 2018 Deriving suitability factors for CA-Markov land use sim-ulation model based on local historical data Journal of Environmental Management20610ndash19 DOI 101016jjenvman201710012

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Gintzburger G Toderich KN Mardonov BK MahmudovMM 2003 Rangelands of thearid and semi-arid zones in Uzbekistan

Grace J 2010 Understanding and managing the global carbon cycle Journal of Ecology92(2)189ndash202 DOI 101111j0022-0477200400874x

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Hamidov A Helming K Balla D 2016 Impact of agricultural land use in Central Asia areview Agronomy for Sustainable Development 361ndash23DOI 101007s13593-015-0343-9

HanQ Li C Zhao C Zhang Y Li S 2018 Grazing decreased water use efficiency inCentral Asia from 1979 to 2011 Ecological Modelling 38872ndash79

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Hao Y Ding G Liu F Xin Z Xu J Zhang J Zhao Y 2013 Primary assessment on thewind-breaking and sand-fixing function of the vegetation and its value in Ulan Buhdesert ecosystem Journal of Arid Land Resources and Environment 2741ndash46 (inChinese) DOI 101016jscitotenv201812152

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Hossu CA Iojă I-C Onose DA Niţă MR Popa A-M Talabă O Inostroza L 2019Ecosystem services appreciation of urban lakes in Romania Synergies and trade-offsbetween multiple users Ecosystem Services 3771ndash89DOI 101016jecoser2019100937

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Jiang L Bao A Jiapaer G Guo H Zheng G Gafforov K Kurban A DeMaeyer P2019Monitoring land sensitivity to desertification in Central Asia convergence ordivergence Science of the Total Environment 658669ndash683DOI 101016jscitotenv201812152

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