chiang_simulation-of-ecosystem-service-responses-to-multiple-disturbances-from-an-earthquake-and-several-typhoons_2014.pdf...

Landscape and Urban Planning 122 (2014) 41 55

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Landscape and Urban Planning

jou rn al hom ep age: www.elsev ier .com/ l

Research Paper

Simulation of ecosystem service responses to mufrom an earthquake and several typhoons

Li-Chi Chianga, Yu-Pin Linb,, Tao Huangc, Dirk S. Schmellerd,e, Peter H. Verburgf,Yen-Lan Liug, Tzung-Su Dingh

a Department ob Department oc Department od Department oe Universit de f Institute for Eg College of Huh School of Fore

h i g h l

Multiple d An earthqu Climate va Landscape Identicati

a r t i c l

Article history:Received 2 MaReceived in reAccepted 28 OAvailable onlin

Keywords:Ecosystem serLandscape chaPhysical distur

CorresponE-mail add

[email protected]@tmu

0169-2046/$ http://dx.doi.of Civil and Disaster Prevention Engineering, National United University, Taiwanf Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwanf Bioenvironmental Systems Engineering, National Taiwan University, Taiwanf Conservation Biology, Helmholtz-Center of Environmental Research UFZ, GermanyToulouse, UPS, INPT, EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement), Francenvironmental Studies, VU University Amsterdam, The Netherlandsmanities and Social Science, Taipei Medicine University, Taiwanst and Resources Conservation, National Taiwan University, Taiwan

i g h t s

isturbances can cumulatively impact ecosystem functioning.ake had the greatest impact on the ecosystem.riation had a stronger impact on water yield and soil conservation.

change had a stronger impact on water purication.on of the sensitive areas enhances an ecosystem management plan.

e i n f o

y 2012vised form 18 October 2013ctober 2013e 5 December 2013

vicesngebance

a b s t r a c t

Ongoing environmental disturbances (e.g., climate variation and anthropogenic activities) alter anecosystem gradually over time. Sudden large disturbances (e.g., typhoons and earthquakes) can havea signicant and immediate impact on landscapes and ecosystem services. This study explored howprecipitation variation (PV) and land use/land cover (LULC) changes caused by multiple disturbances cancumulatively impact ecosystem functioning in the Chenyulan watershed in central Taiwan. We simulatedfour ecosystem services (water yield production, water purication, soil conservation, carbon storage)and biodiversity using the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model toanalyze the spatiotemporal changes and obtain information regarding changes in the ecosystem. Ourresults indicate that the Chi-Chi earthquake had the greatest impact on the ecosystem. Specically, theecosystem was altered by the earthquake and could no longer absorb disturbances of a similar magnitudeas before the earthquake. By differentiating the impacts of the PV and LULC changes on ecosystem ser-vices and biodiversity, we observe that the PV had a stronger impact on water yield and soil conservation,whereas the LULC change had a stronger impact on water purication. Our results also suggest that acomprehensive ecosystem management plan should consider the cumulative and hierarchical contextof disturbance regimes to prevent reductions in ecological variability and ecosystem resilience, partic-ularly in areas that are more sensitive to large disturbances. In this way, ecosystem resilience may bemaintained at a level sufcient to preserve ecosystem functioning and ecosystem services in the eventof unexpected large-scale environmental disturbances.

2013 Elsevier B.V. All rights reserved.

ding author. Tel.: +886 2 3366 3467; fax: +866 2 2368 6980.resses: [email protected] (L.-C. Chiang), [email protected],cu.edu.tw (Y.-P. Lin), [email protected] (T. Huang),@ufz.de (D.S. Schmeller), [email protected] (P.H. Verburg),.edu.tw (Y.-L. Liu), [email protected] (T.-S. Ding).

1. Introduction

Ecosystem services are dened as the manners in whichecosystems benet humans, e.g., water supply, water regulation,soil retention, soil accumulation, and carbon storage. A completelist can be found in de Groot, Wilsonb, and Boumans (2002)and Mace, Norris, and Fitter (2012). Despite its importance, this

see front matter 2013 Elsevier B.V. All rights reserved.rg/10.1016/j.landurbplan.2013.10.007ocate / landurbplan

ltiple disturbances

42 L.-C. Chiang et al. / Landscape and Urban Planning 122 (2014) 41 55

natural capital is inadequately understood, seldom monitored, andprone to frequent rapid degradation and depletion by multiplenatural and anthropogenic disturbances (Fraterrigo & Rusak, 2008;Tallis et al., 2011). Degradation and depletion particularly occurin urbanizerapid conveTurner (201short to m(centuries)

Several sical disturbecosystem 2004; SinclLikens, and(e.g., wildlogical procWondzell, 2bon seques& Deng, 20oods) can 2008; Wilsobehavior (Fture of sh

Exactly ecosystem saddition, thmakes it difand manag2009). A prtems respo1998; Turnether. Such rto maintain(Cote & Darexternal dismay be forand structua disturbantolerated p(Folke et aof non-equiare essentiaincreased usingle or sptive and hiin mismana(Mori, 2011and accurattial distribumaking effeBraat, Hein,et al., 2005after a majmeans of idgia (Linden

Taiwan sEuro-AsianearthquakeLee, & Liu, trigger lanslopes and 2008). Morexperiencesa tremendorain dependcenter of a t

Anth ropo genic

Sudden disturbance

P

E

Sudden disturbance

Gradual disturbance

Climate variaon

Gradual disturbance

terizmpe

of eter thpopung, dppinthe svicese, sehis stl Taiwces. tem ecip(LULC

of a are ies. T

chanLC cosyss thnentvereuenttem omm

teria

udy a

Che, and encompasses an area of 449 km2 (Fig. 2). This typicalain drainage watershed has a mean altitude of 1540 m, meanf 32, and relief intensity of 585 m/km. The dominant litholo-

the metamorphic terrain are slates and meta-sandstones al., 2004; Lin, Liu, et al., 2006). The average annual pre-ion ranges from 2000 to 4000 mm; and approximately 80%

annual rain falls during the Southwest Monsoon seasono October). Particularly during the typhoon season (July tober), short yet intensive periods of rain often trigger land-

causing signicant denudation in the mountainous regionsChen, 2005).

ways in which large-scale disturbances impact the ecosys-ere assessed in this study by collecting SPOT satellite imagesafter each disturbance. The SPOT images were those withd and naturally disturbed areas, including areas ofrsion of natural habitat to human-dominated land use.0) posited that changing disturbance regimes in the

edium term (years to decades) and in the long termalter landscapes and ecosystem services.tudies have demonstrated the way in which large phys-ances inuence the structure and functioning of an(Lin, Chu, Wang, Yu, & Wang, 2009; Millward & Kraft,air & Byrom, 2006; Turner & Dale, 1998). Lindenmayer,

Franklin (2010) found that large natural disturbancesres, typhoons, and earthquakes) inuence critical eco-esses, including sediment ows (Nakamura, Swanson, &000), biogeochemical cycles (Houlton et al., 2003), car-tration (Running, 2008), and hydrology (Hong, Chu, Lin,10). Other large disturbances (e.g., tropical storms andalter stream habitats (Chuang, Shieh, Liu, Lin, & Liang,n, Graham, Pratchett, Jones, & Polunin, 2006), stream

itzsimons & Nishimoto, 1995) and the community struc-(Power, Matthews, & Stewart, 1985).how disturbances inuence the spatial variation ofervices across landscapes has seldom been analyzed. Ine interaction of various disturbance factors (Mori, 2011)cult to collect information essential to inform land useement decisions (Balmford et al., 2002; Nelson et al.,evious disturbance can signicantly affect an ecosys-nse to a new disturbance (Paine, Tegner, & Johnson,r, 2010), possibly altering the ecosystem resilience fur-esilience is characterized by the ability of an ecosystem

its structure, function, and feedback after a disturbanceling, 2010; Spieles, 2010; Walker & Salt, 2006). Whenturbances exceed the ecosystem resilience, the latterced to change to a new state with different functionsres (Thrush et al., 2009). In a degraded ecosystem, evence with a lower magnitude than those the ecosystemreviously might cause an unexpected sudden changel., 2004; Holling, 1973). As fundamental componentslibrating ecosystems, hierarchical disturbance regimesl to ecosystem management (Mori, 2011). Despite annderstanding of the spatiotemporal variations of aecic landscape, the failure to recognize the cumula-erarchical context of disturbance regimes may resultgement, eventually reducing the ecosystem resilience). Managing and maintaining ecosystem componentse information of their function with respect to the spa-tion of ecosystem functions and services are essential toctive land management decisions (de Groot, Alkemade,

& Willemen, 2010; Egoh et al., 2007, 2008; van Jaarsveld). Accurate information can be collected immediatelyor natural disturbance, making it the most effectiveentifying the locations and functional roles of key refu-mayer et al., 2010).its on the Philippine plate near the boundary with the

plate, which explains why plate convergence generatess that have disastrous effects on the island (Lin, Liu,2006; Lin, Lin, Deng, & Chen, 2008). Earthquakes maydslides, during which sediments are removed fromtransported by uvial action (Keefer, 1994; Lin et al.,eover, Taiwan is located in a sub-tropical region and

an average of 34 typhoons per year, which depositus amount of rain from July to October. The amount ofs on the size, speed and intensity of the rain-producingyphoon (Jan & Chen, 2005). These natural disturbances

characand tequencymay alto the plannithe maaffect the serby larg

In tcentraturbanecosysThe prcover abilitydriversactivitof theand LUand ecto areacompohow sesubseqecosyswe rec

2. Ma

2.1. St

TheTaiwanmountslope ogies in(Lin etcipitatof the(May tSeptemslides,(Jan &

Thetem wtaken Earthqua ke acvity

recipitaon variaon

(PV)

Land use/land cover (LULC)

change

cosystem se rvice (ES) provisioning (i. e. water yield producon , water pu ri caon , soil

conse rvaon, carbon st orage) & Biodiversity

Ecosyst em funconing

Typhoons

Fig. 1. Research approach and objectives of this study.

e the structure, function and dynamics of the tropicalrate forest ecosystems in Taiwan. Owing to the fre-arthquakes and typhoons in Taiwan, their frequencye islands ecosystems and the services that they providelation. Correspondingly, organizations involved in landisaster management and restoration heavily prioritizeg and assessment of the ways that landscape changespatiotemporal dynamics of ecosystem functions and

they provide, particularly large-scale changes inducedquential, physical disturbances.udy, we quantied the ecosystem services in a region ofan that is frequently affected by multiple physical dis-

This study focused primarily on identifying changes inservice that occur after multiple disturbances (Fig. 1).itation variation (PV) and changes in land use/land) were considered to be key drivers determining the

n ecosystem to provide ecosystem services (ES). Bothaffected by natural disturbances and/or anthropogenicherefore, this study provided quantitative estimatesges in ecosystem services caused by cumulative PVhanges. In addition, hotspots of high habitat qualitytem service concentration were identied, in additionat are sensitive to disturbances. The latter are criticals in ecosystem management. This study also evaluated

natural disturbances impact ecosystem resilience and,ly, ecosystem functioning and spatial distribution ofservices. Furthermore, based on the results of this study,end areas of future research in land management.

ls and methods

rea

nyulan watershed is located in Nantou County, central

L.-C. Chiang et al. / Landscape and Urban Planning 122 (2014) 41 55 43

Fig. 2. Locatio(right).

Source of local

Table 1Typhoons duri

Typhoon

Herb Xiangsane Toraji Midulle Aere Matsa

zero cloud Research Cesied usingOctober 31and (2) No2004, and NXiangsane, cation of theexceeded 8Wu, Chiang

2.2. Large-s

Several lto 2005 (Figdamage cauduced by anto the wind2005). Mostation, partn of Taiwan (left-top), Typhoon Toraji (left-bottom), and location of the Chenyulan wate

magnitude: Central Weather Bureau. Available at http://www.cwb.gov.tw/V7/earthquake

ng 19962005.

Period Strength Radius (km) Max. windspeed (m/s)

07/2908/01, 1996 Strong 350 5310/3011/01, 2000 Medium 250 3807/2807/31, 2001 Medium 250 3806/2807/03, 2004 Medium 250 4508/2308/26, 2004 Medium 200 3808/0308/06, 2005 Medium 250 40

cover obtained from the Space and Remote Sensingnter. In addition, the watershed land cover was clas-

images from (1) November 8, 1996, March 6, 1999, and, 1999 (i.e., before and after the Chi-Chi earthquake);vember 27, 2000, September 21, 2001, November 19,ovember 11, 2005 (i.e., before and after Typhoons Herb,Toraji, Midulle, Aere and Matsa) (Fig. 3). During classi-

nal SPOT images, all of the accuracy and kappa values2% and 0.77, respectively (Hong et al., 2010; Lin, Chang,, & Lin, 2006).

cale natural disturbance events

arge disturbances impacted central Taiwan from 1996. 2; Table 1; CWB-TDB, 2013; TTFRI-DBAR, 2000). Thesed by Typhoon Herb was more severe than that pro-y other typhoon over the previous four decades, owing

speed and the radius of this typhoon (Jan & Chen,t of the damage was due to extremely high precipi-icularly in central Taiwan (Chiang, 1996; Yu & Tuan,

1996), whicing two daisland in 20rainfall ove(Cheng, HuTyphoons XrespectivelyChen, 2005Aere, causeToraji).

On Septmoment mgered by thThe epicenChenyulan quake causChelungpu altered theter. Numerduring high

Forest ctivated lanthe watersseveral typdisturbed ttyphoons oous, as shodecreased fzones decliland under (Fig. 3).rshed, local magnitude of the Chi-Chi earthquake and typhoon paths

/damage eq.htm.

h received approximately 30% of its annual rainfall dur-

ys (Jan & Chen, 2005). Typhoon Xiangsane struck the00. In 2001, Typhoon Toraji delivered the most intenser a short period, with a return period of 300 yearsang, Wu, Yeh, & Chang, 2005). The heavy rain duringiangsane and Toraji triggered 100 and 192 debris ows,, compared to 52 debris ows caused by Herb (Jan &

). In 2004, two medium-strength typhoons, Midulle andd a similar high number of debris ows (Xiangsne and

ember 21, 1999, the Chi-Chi earthquake (7.3 on theagnitude scale, with a focal depth of 8.0 km) was trig-e reactivation of the Chelungpu fault in central Taiwan.ter was located at 23.87N and 120.75E, near thewatershed in southern Nantou County. This earth-

ed surface ruptures along 100 km of the north-trendingfault and triggered 10,000 landslides, which seriously

landscape of the region, particularly near the epicen-ous extension cracks, which accelerated the landslides-rainfall events, also developed on the hillsides.overs over 75% of the Chenyulan watershed, and cul-d and grassland cover approximately 10% and 5% ofhed, respectively (Table 2). Between 1996 and 2005,hoons and the massive Chi-Chi earthquake of 1999he watershed, resulting in large landslide areas. Thef 1996, 1999, 2001 and 2005 were particularly seri-wn in Fig. 3 and Table 2. Since 1996, the forested arearom 351.2 km2 to 332.6 km2, and the area of riparianned from 6.3 km2 to 2.3 km2. Meanwhile, the area ofcultivation increased by 46%, from 43.5 km2 to 63.9 km2


Fig. 3. Land use/land cover (LULC) distribution in the Chenyulan watershed during 19962005.

Table 2Land use/land cover (LULC) distribution for various events (km2).

1996/11/8 March 99 October 99 2000/11/27 2001/11/20 2004/11/19 2005/11/11

Riparian 6.3 6.5 5.2 2.3 2.2 3.1 2.3Grass 23.1 19.6 16.6 22.1 27.6 26.9 20.9Built-up land 2.0 2.0 2.2 2.4 2.8 3.1 3.4Cultivated land 43.5 47.5 48.8 51.7 54.5 61.6 63.9River sand 8.9 8.7 10.0 12.8 13.0 12.1 12.9Landslide 13.5 6.8 15.7 9.8 14.5 8.1 13.2Forest 351.7 358.0 350.7 348.0 334.6 334.2 332.6


2.3. Estimating and mapping ecosystem services

The 2.1 beta version of the software program Integrated Valua-tion of Ecosystem Services and Tradeoffs (InVEST) was developed bythe Natural Capital Project (Tallis et al., 2011). InVEST consists of asuite of models that use land use/land cover patterns to estimatethe levels and economic values of ecosystem services, biodiver-sity conservation, and market value of commodities provided by alandscape (Nelson et al., 2009). Owing to its focus on the valuationand visualization of ecosystem services across landscapes, InVESTis widely used in evaluating ecosystem services at the pixel level(Goldstein et al., 2012; Nelson et al., 2010). For example, in addi-tion to analyzing two scenarios of global changes in urban landand cropland at the pixel level, Nelson et al. (2010) measured howthese changes impacted the ecosystem services and biodiversity.Goldstein et al. (2012) subsequently evaluated the environmentaland nancial implications of planning scenarios that encompasscontrasting land-use combinations, including biofuel feedstocks,food crops, forestry, livestock, and residential development. Byusing the InVEST model, we examined the changes in the bio-physical forlandscape pwater puriconservatiodiversity wthe pixel antions reprethe simulatfactors for piration anthe sum ormany largeapproximatdata resolutto allow foSutton, & Su

Based oInVEST, theservice of aamount of This model in landscapaddition, thference betthat underwpartition ofBudyko curof the Budwater cont

factor that represents the amount and distribution of seasonalrainfall (Tallis et al., 2011). The potential ET was the product of thereference ET and the plant ET coefcient. Details of the latter canbe found in Allen, Pereira, Raes, and Smith (1998). Based on the soiltexture, the plant-available water content was estimated using thesoilplantairwater (SPAW) computer model (Saxton & Willey,2006; Saxton, 2006). The InVEST model was used to estimate theavailable water content (AWC) as the difference between the eldcapacity and wilting point over the minimum soil depth and rootdepth (Tallis et al., 2011). In addition, the plant-available watercontent was calculated based on the soil depth. Changes in the landuse/land cover do not affect the AWC. Notably, the precipitationprofoundly impacts the simulation of water yield, and oodingevents can degrade the ecosystem services. As was assumed in thestudy, areas with a higher water yield than the specied thresholdwere identied as hotspots that provide an important ecosystemservice in terms of water provisioning.

Using the water purication nutrient retention model of InVEST(Tallis et al., 2011), we calculated the amounts of the nutrients N andP retained in each pixel of the watershed. Moreover, a hydrologic

vity e runtiones beas ushe ddownpolluland

pollere

eters wasstreah pixetained on

a pix in trphoiversl eroseme

The pixel

deteexisttion

sch

Table 3Parameters for ST mo

Ecosystem s

land

Nutrient ret

Soil conserv

Carbon stora

Note: C above ad = cams of ecosystem services affected by changes in theattern caused by disturbances. Water yield production,cation (nitrogen (N) and phosphorus (P) retention), soiln, carbon storage and habitat quality in terms of the bio-ere also simulated. The output from the model at bothd watershed level was evaluated. Pixel-level calcula-

sented the heterogeneity of the key driving factors inion of ecosystem services. For example, the key drivingthe water yields were the precipitation, evapotrans-d soil type. Calculations at the watershed level were

average of the pixel-level calculations. Compared to-scale assessments conducted at spatial resolutions ofely 1 km2 (e.g., Maes, Paracchini, & Zulian, 2011), theion in this study was 20 m 20 m, which was sufcientr an accurate simulation of the ecosystem (Konarska,tton, 2002).n the Reservoir Hydropower Production model of

water yields were calculated as the provisioningn ecosystem. This model was used to calculate thewater contributed by various parts of the landscape.thus provided further insight into the way that changese patterns impact the annual surface water yield. Ine water yield of a given pixel was calculated as the dif-ween the precipitation and the fraction of precipitationent evapotranspiration. The evapotranspiration (ET)

the annual water balance was an approximation of theve (Zhang, Dawes, & Walker, 2001), which is a functionyko dryness index (Budyko, 1974), plant availableent, average annual precipitation, and a seasonality

sensitiaveragproducferencand wusing twater retain of the annualloads wparama pixel(downby eacloads r

Basloss ofculatedgeomothe unrainfalmanag1978).in the largelyby the Vegetaels. The

simulation of nutrient retention, soil conservation and carbon storage in the InVE

ervice Parameters Land use/land cover (LULC) classes

Riparian Grass Built-up

ention N load (kg/ha) 1 1 3.5 N retention efciency (%) 50 50 0 P load (kg/ha) 0.1 0.1 0.5 P retention efciency (%) 50 50 0

ation USLE C 0.01 0.01 0.01 USLE P 1 1 1 TSS retention efciency (%) 40 40 5

ge C above (Mg/ha) 1 1 0 C below (Mg/ha) 1 1 0 C soil (Mg/ha) 0 10 0 C dead (Mg/ha) 0 0 0

= carbon in aboveground biomass; C below = carbon in belowground biomass; C descore (HSS) was calculated using the simulated annualoff of each pixel derived from the reservoir hydropower

module (Tallis et al., 2011). The HSS accounted for dif-tween the eld measurements and model conditionsed to adjust the pollutant exports of a given pixel. Byigital elevation model (DEM), the InVEST model routed

the ow paths and allowed the downstream pixels totant loads, based on the land cover type and efciencycovers pollutant retention (Table 3). In this study, theutant loads reported by Huang (2001) were used; thesebased on the retention efciency of the modules default

(Tallis et al., 2011). The pollutant load not retained by continuously transported as additional load to the nextm) pixel. The model then aggregated the loads retainedel and loads exported from each pixel to represent theed and exported at the watershed level, respectively.

the sediment retention model, the average annual soilel and a pixels ability to retain sediment were also cal-his study. In addition, the potential soil loss based onlogic and climatic conditions was calculated by usingal soil loss equation (USLE), which is a function of theivity, soil erodibility, slope length, crop/vegetation andnt, and support practice factors (Wischmeier & Smith,rst three factors determined the potential soil erosions without vegetation. The last two factors in the USLErmined the amount of soil erosion that can be preventeding vegetation cover and/or support practices (Table 3).can trap sediment that has eroded from upstream pix-eme used to simulate the sediment retention and export

del.

Cultivated land River sand Landslide Forest

16 3.5 3.5 1.65 0 10 800.5 0.5 0.5 0.255 0 10 800.1 1 1 0.011 1 1 1

30 5 5 603 0 1 2002 0 1 130

10 0 10 1300 0 0 65

rbon in dead organic matter; C soil = carbon in soil.


of each pixel was similar to the nutrient retention model and wasused to estimate the sediment trapping efciency (Table 3). TheUSLE crop/vegetation and management factor (C factor) and theUSLE support practice factor (P factor) were determined using theInVEST data

Based onthe amounamount of ground andbon stored global datathe largest (Table 3), wexpected toin the wate

The InVproxy for biby analyzinvated landsas threats, aviewed as hhabitats basensitivity between thimpact of atat and throf a threat be modeledexponentiafrom disturadditional limpact habing for the athat each pcalculated tverted this as the best

Spatiotemined baseOuyang, Zhprovided a la large numthose areasthe pixels foserviceshaas the areaold baselinHotspots ofsubsequentecosystem and carbonsuperimpos06 represethe habitat more ecosythe locationall considerthan the thrwere evaluacalculated u

R =7

i=1R7i=

where R is richness of

Table 4Parameters used for simulation of habitat quality in the InVEST model.

Max. distanceof impact to

Relative impactto threats

Sensitivity ofhabitat to threats

ated laay p lan

lide

olask

LULurbad arangeed toontral disr 19s idecumu

actiV) duphooing tcolo

the c ecoe/lanbasel

basewer cd byd LUe ecoasel

itatioater e ande ba

richne the

ecos of Pd co

hnese 19

of Lferenion a

ults

tima

changes in the hotspot areas of each ecosystem servicee level of habitat quality simulated under the correspond-ate and baseline climate conditions displayed remarkable

nces between 1996 and 2005 (Fig. 4). During these years, thee annual precipitation increased from 2694 mm to 4072 mm,ng in greater water yields. Specically, the annual waterncreased from 886 million m3 in 1996 to 1.496 billion m3

5. The lowest annual precipitation was 2414 mm in 1999.base. the land use map of Tallis et al. (2011), we estimated

t of carbon stored in a land pixel by aggregating thecarbon stored in the biomass above and below the

in soil and dead organic matter. The amount of car-in various carbon pools was obtained from the IPCCbase (IPCC, 2006; Table 3). The forested areas containcarbon pools of any of the land use/land cover typeshich explains why changes in those areas may be

reect the change in the total amount of carbon storedrshed.EST biodiversity model evaluates habitat quality as aodiversity. In particular, the biodiversity was estimatedg how threats impact the habitat. In this study, culti-, highways, built-up areas and landslides were viewednd riparian zones, forested areas, and grassland wereabitats. The model estimates the impact of threats to

sed on the relative impact of each threat, the relativeof each habitat type to each threat, and the distancee habitat and threat (Tallis et al., 2011; Table 4). The

threat diminished with the distance between the habi-eat. The relationship between the distance-decay rateand the maximum effective distance of the threat can

in a linear or an exponential manner. In this study, anl rate of decrease was selected. The level of protectionbances (e.g., social and physical boundaries) may be anandscape factor that can mitigate the way that threatsitats. In addition to considering this threat by account-ccessibility to the sources of degradation, we assumed

ixel had complete accessibility. First, the InVEST modelhe degradation score of each pixel. The model then con-score to a habitat quality value between 0 and 1, with 1possible habitat quality, i.e., a high level of biodiversity.mporal hotspots of ecosystem services were deter-d on the methods of Egoh et al. (2008) and Bai, Zhuang,eng, and Jiang (2011). Hotspot areas were those thatarge amount of a single service and those that providedber of various services. Single-service hotspots were

with the highest 20% of a provision value (level) amongr each service (Bai et al., 2011). In this study, ecosystembitat quality (ES-HQ) richness hotspots were deneds of overlap of at least three ES hotspots. The thresh-e of a hotspot was dened using the data from 1996.

ecosystem services were identied using the data from years. Based on the hotspots of habitat quality and veservices (i.e., water yield, N, P and sediment retention,

storage), we developed a map of ES-HQ richness bying the six hotspot maps. In the hotspot map, a range ofnted the number of hotspots of an ecosystem service orquality that a location can provide. A high value impliedstem services or a better habitat quality provided by. In addition, a value of 0 implied that the provision ofed ecosystem services and habitat quality was lowereshold value. The changes in ES-HQ richness over timeted based on the weighted average richness, which wassing the equation

i Ni1Ni

the weighted average ES-HQ richness, Ri is the ES-HQland use i, and Ni is the number of pixels of land use i.

CultivHighwBuilt-uLands

Source: P

Theral distforesteuse chassumis in cnaturaOctobechangeas the humantion (Pand ty

Owity in ewhichdiverseland usas the a validand locoverePV aning th1996-bprecip(i.e., wstoragwith thES-HQindicaton theimpactuse/lanHQ ricwith thimpactthe difcondit

3. Res

3.1. Esquality

Theand thing climdiffereaveragresultiyield iin 200habitats

Riparian Grass Forest

nds 4 0.8 0.6 0.6 0.72 0.7 0.7 0.7 0.8

d 5 1 0.5 0.5 0.81 1 1 1 1

y, Nelson, Pennington, and Johnson (2011).

C changes represented the combined impact of natu-nces and human activities (Fig. 3). In the mountainousea, the inuence of anthropogenic activities on land

was relatively low. In addition, human activities were have a negligible impact on the habitat quality, whichst with the enormous impact of the sudden, intenseturbances during the 8-month period from March to99 and the 4-year period from 2001 to 2004. The LULCntied after the seven major disturbances were viewedlative impact of the earthquake and typhoons and not

vity. Moreover, the spatiotemporal precipitation varia-ring the study period was caused by climate variationns.o the close correlation between the temporal variabil-gical phenomena and climatic variability, the extent tolimate may affect disturbance regimes and the resultingsystem responses must be examined (Mori, 2011). Thed cover and annual precipitation in 1996 were selectedine in this study. The year 1996 is assumed to representline time, owing to fewer environmental disturbancesumulative impacts than those of the subsequent years

this study. We also differentiated between the wayLC changes impacted ecosystem services by model-system services for each year separately against theine and the corresponding climatic conditions. Becausen affected only the water-related ecosystem servicesyield, water purication and soil conservation), carbon

habitat quality were not included in the simulationsseline climate data (precipitation). The changes in theess in any year after 1996 compared with the baseline

cumulative combined impact of PV and LULC changesystem in that year. Therefore, the cumulative individualV on an ecosystem was calculated under the same landver conditions of the difference between simulated ES-s with the corresponding annual precipitation and that96 precipitation. In addition, the cumulative individualULC changes on the ecosystem was calculated based once between simulated ES-HQ richness under any LULCnd that under the LULC conditions of 1996.

ting and mapping ecosystem services and habitat


Although thin 2005, thever, the inthe expansin 2005. Mincreased bDuring that89,216,700 (Figs. 4 and

The trenyield. The Nand the hoFig. 4. Hotspot (%) for each ecosystem service and habitat q

e hotspot area increased from 19.6% in 1996 to 98.2%e change before 2001 was comparatively small. How-creasing annual precipitation after 2001 resulted inion of the hotspot area from 30.8% in 2001 to 98.2%oreover, the precipitation and simulated water yieldy 51% and 69%, respectively, between 1996 and 2005.

period, the water yield increased from 88,096,300 m3 tom3, and the hotspot area increased from 18.9% to 21.4%

5).d in nitrogen retention was similar to that of the water

retention increased from 119,428 kg to 140,397 kg,tspot area increased from 15.8% to 17.3% between

1996 and 2retention inand the exover, the athan in 19thermore, t2005.

Under ttion hotspo(Fig. 4). Hothe hotspotods: 1996 uality during 19962005.

005. During the same period, the annual rate of P the watershed ranged from 10,865 kg to 11,304 kg,port of P ranged from 1312 kg to 1959 kg. More-

nnual rate of P retention was higher in October 199996 and higher in 2005 than in 2004 (Fig. 4). Fur-he hotspot area remained stable between 2000 and

he baseline climatic conditions, the sediment reten-t area changed only slightly between 1996 and 2005wever, under the corresponding climatic conditions,

area increased signicantly during the following peri-to March 1999, 2000 to 2001 and 2004 to 2005.


Fig. 5. Sp

Notably, thduring theThis differein 2001. Laowing to areas obserimpact of a(Fig. 4).

The impiment retenatial distribution of water yield hotspot (primary hotspot under baseline climate conditi

e sediment retention rate in 2001 was the highest simulation period, being 74% higher than in 1996.nce was due to the higher rainfall erosivity index (R)rger hotspot areas were observed in October 1999,a higher number of landslides. The larger hotspotved in 2001 and 2005 were caused by the combined

larger number of landslides and greater precipitation

act of the climate on the spatial distribution of the sed-tion hotspots was observed between March 1999 and

2005 (Fig. 6conditions In 2001, ththe entire wsponding centire watearea also in

Between351.7 km2 t18.6 to 17.6on; secondary hotspot under corresponding climate condition).

). The sediment hotspot area under the 2000 climaticwas very close to the area under the 1996 conditions.e hotspot of sediment retention expanded to 84.2% ofatershed. Therefore, the hotspot area under the corre-

limatic condition increased to 56.1% and 54.7% of thershed in March and October 1999, respectively. Thiscreased from 43.5% in 2001 to 84.2% in 2005.

1996 and 2005, the forested area decreased fromo 332.6 km2, the amount of stored carbon declined from

million tons, and the hotspot area decreased from 78.6%


Fig. 6. Spatial

to 74.1%. Thulation periecosystem sis the majoin the foresgradual decin the habiOwing to thportion of ththat could hMarch 1999 distribution of sediment retention hotspot (primary hotspot under baseline climate cond

e change in the carbon storage capacity during the sim-od was generally smaller than the changes in the otherervices. This difference is due to the fact that the forestr land cover in the watershed; in addition, the changeted area was relatively small (78.374%). However, therease in the forested area after 1996 led to a declinetat quality score from 0.71 in 1996 to 0.61 in 2005.e increasing number of landslides in the southwesterne watershed in October 1999, 2001 and 2005, the areasave provided ecosystem services were smaller than in, 2000 and 2004, respectively. Therefore, despite the

decrease in1996, the ch2004 (Fig. 4

3.2. Impact

Compardition reveaand a 4.46in 2005. Thcipitation. Cition; secondary hotspot under corresponding climate condition).

the number of hotspot (high number of ESs) areas sinceanges were more pronounced in March 1999, 2000 and).

of precipitation variation (PV) on ecosystem services

ing the water yield with the 1996 baseline climate con-led a 6.513.4% decrease in the water yield before 20017.8% increase after 2001, with the highest water yieldis water yield trend reects the changes in the pre-ompared to 1996, the annual precipitation decreased


by 6.310.4Unlike the eyield, the clchanges ran0 and 0.3% f

When thN retentiontion betweethe correspthe variatioFig. 7. Land use/land cover (LULC), precipitation variation (PV) and combine

% before 2001 and increased by 7.351.2% after 2001.xtent to which the PV signicantly impacted the waterimate only slightly impacted the N and P retention. Theged between 0.1 and 0.6% for N retention and betweenor P retention (Fig. 7).e baseline climatic conditions were used to assess the, the trend of hotspot areas in terms of the N reten-n 1996 and 2005 was observed to be similar to that ofonding climatic conditions. This nding suggests thatns in the climate only slightly impacted the N retention

(Fig. 4). Theof the wateimpact of Psignicantlranging fromarily affecimportant fof the PV imwas similarretention ad impacts on ES during March 19992005.

hotspot area of P retention accounted for 13.614.4%rshed (Fig. 4). Notably, our results suggest only a slightV on the N and P retention (Fig. 7). Meanwhile, the PVy impacted the sediment retention, with retention ratesm 5.5% in 2000 to 76.3% in 2001 (Fig. 7). The PV was pri-ted by the rainfall erosivity index, which was the mostactor in determining the sediment retention. The trendpact on sediment retention from March 1999 to 2005

to that of PV impact on the sediment export. Both thend export ranged between 5.5 and 76.3%.


3.3. Impactservices and

The LULchanges in of the nutri1.9 to 17.7%tivated landthat the cugreater N rethe N retenthe N retenimpacted thThe N and from 89.4%LULC explaFig. 8. Distribution of ES-HQ richness in 1996 and difference in ES-HQ richnes

of land use/land cover (LULC) changes on ecosystem habitat quality

C changes impacted the water yield less than did thethe PV yet had a greater impact than the PV in termsent retention and export. The N retention increased by, owing to the LULC increase and the increase in cul-

(Table 2). This increase was largely due to the factltivated land contained more N (Table 3), resulting intention. The increasing impact of the LULC changes ontion between 1996 and 2005 was similar to the trend intion hotspots (Figs. 4 and 7). The LULC changes slightlye P retention, which ranged between 2.1 and 1.8%.P retention rates declined from 87.8% to 84.3%, and

to 85.8%, respectively, during the period 19962005.ined nutrient export but less nutrient retention.

Similarly, tits retentiomore than to 98.3% ofcapacity to the climatecating thatThe cumulaamount of s(Fig. 7).

3.4. Identirichness

Based onhabitat quas due to land use/land cover (LULC) change.

he LULC trend explained the sediment export but notn. In addition, the LULC impacted the sediment exportthe sediment retention. The watershed retained 96.7

the total sediment losses, which was greater than itsretain the nutrient losses of 82.288.5%. The impact of

and LULC on the sediment export was similar, indi- they both play a major role in the sediment export.tive impact of the LULC and climatic change on theediment exported was twice as high in 2001 as in 1996

cation of potential protection areas based on ES-HQ

the hotspot areas of ve ecosystem services and thelity under various climatic conditions, we calculated


Table 5Distribution of ES-HQ richness and weighted-average ES-HQ richness under corresponding and baseline climate conditions.

ES-HQ richness 1996 March 99 October 99 2000 2001 2004 2005

(a) Under corresponding climate condition0 1 2 3 4 5 6 Average E

(b) Under ba0 1 2 3 4 5 6 Average E

the ES-HQ rtem protectES-HQ richgradually ished provid(Table 5(a)to the basecates that and the Chimpacted thservices.

We alsodue to the Lthe area-wemate condiES-HQ richnfore, only th(Fig. 8). Thwere areas appeared toof the areasness in anyred colors dthese changforested aredisturbancemay increaHQ richnesgrassland omovement.displayed ounder the cincreased snitudes and

4. Discussi

4.1. Impact

Given thber and freqLindenmay2006), the ecosystemstion of how

ance, theid thentioere

thae uphan dsedimwas

by tconsuake

landitatioe am1 antivelctob

Thisrthquhooimpas m

ordin werosys2926 3300 3475 18750 12373 12848 12307 15227 15348 7520 9985 9145 2230 2495 2615 603 1013 967 29 9 4

S-HQ richness 1.8 2.0 1.9

seline climate condition2926 2783 3053 18750 18566 19254 12307 12678 12044 7520 7611 7249 2230 2158 2206 603 554 547 29 27 24

S-HQ richness 1.8 1.8 1.7

ichness to identify areas that may benet from ecosys-ion (Table 5). According to these results, the weightedness under the corresponding climatic conditionsncreased from 1.8 to 2.8, indicating that the water-ed more ES-HQ hotspots following large disturbances). However, the ES-HQ richness decreased comparedline climatic condition (Table 5(b)). This nding indi-the changes in land cover induced by the typhoonsi-Chi earthquake during the study period negativelye ecosystems functions and the provision of ecosystem

identied how and where the degradation occurredULC changes. Based on the results shown in Table 5(b),ighted ES-HQ richness was similar to the baseline cli-tion for all years; in addition, the locations of variousess values that an area can provide were similar. There-e ES-HQ richness distribution for 1996 was presentedose areas containing more than three hotspot typeswarranting additional protection. The ES-HQ richness

be relatively stable (white colored) over time in most of the watershed. The differences in the ES-HQ rich-

sequential year was between 7 and 12% (green andenote decreased and increased ES-HQ richness), andes occurred primarily in grassland, cultivated land andas (Figs. 8 and 9). In the areas that are sensitive to larges, converting grassland and cultivated land to forestse the ES-HQ richness of such areas. However, the ES-s could decrease when an area of forest is converted to

disturbresultsthan dent retareas wquake)that thment tmore outlet causediment earthqdue toprecipfore, thin 200respecafter Oyields.Chi eathe typcantly changevices.

AccbancesThe ecr cultivated land or is forcibly removed with landslide Under the baseline climatic conditions, the ecosystemnly slight uctuations in the ES-HQ richness. In contrast,orresponding climatic conditions, the ES-HQ richnessignicantly after 2000 due to typhoons of similar mag-

spatial characteristics (Tables 1 and 5(a)).

ons

s of disturbances on ecosystem services

e forecasts of increased disturbances (both in num-uency) related to climate change (e.g., Emanuel, 2005;

er et al., 2010; Westerling, Hidalgo, Cayan, & Swetnam,capacity to initiate rapid, post-disturbance studies of

should be improved. This study involved an evalua- changes in the PV and LULC, as caused by multiple

and anthro(Swift et alimpacted tsediment rcantly impatyphoons oHowever, amagnitudessee Lin, Chamore in 200the landscaXangsane (retention raimpact of nitudes andaffected thet al., 2006)3873 525 2308 10017475 6672 10831 605111738 18161 15029 131078188 13311 11360 138592377 4157 3450 8231688 1466 1300 225763 111 120 7721.8 2.4 2.2 2.8

3132 3607 3651 386119043 19127 18768 1854512357 12414 12729 125827236 6727 6744 68262096 2034 2049 2110487 452 425 43726 16 11 161.7 1.7 1.7 1.7

s, impacted ecosystem functioning. Based on our PV impacted the water yield and soil conservation moree LULC changes, whereas the latter impacted the nutri-n more. With regard to sediment retention, the hotspot

larger in October 1999 (i.e., after the Chi-Chi earth-n in March 1999. This difference was due to the factstream landslides generated larger volumes of sedi-id the downstream forested areas, which thus retainingent. However, sediment retention at the watershedlow in October 1999, indicating that the disturbancehe earthquake degraded the ecosystem in terms of sed-ervation. When the impacts of the typhoons and the

were combined, the impact on sediment conservationscape changes was concealed by the impact of greatern, which increased the total amount of sediment. There-ounts of sediment retention at the watershed outlet

d 2005 were greater than those in 2000 and 2004,y. The increases in N and P losses were more stableer 1999 than the variation in precipitation and water

difference was likely due to the fact that the Chi-ake more signicantly impacted the landscape than

ns that followed. Although climatic variations signi-cted the water yield and sediment retention, the LULCore signicantly impacted the other ecosystem ser-

g to our results, the cumulative impacts of the distur-e not everywhere obvious across the entire landscape.tem services were impacted by climatic variations

pogenic changes to land use/land cover in the area., 1998). The average annual precipitation signicantlyhe water yield. Our comparison of the nutrient andetention rates revealed that the earthquake signi-cted ecosystem services. Before the Chi-Chi earthquake,nly slightly impacted the N and P retention rates.fter the earthquake, typhoons with various paths and

affected the landscape patterns in various ways (alsong, et al., 2006). The N and P retention rates declined1 than in 2000 because Typhoon Toraji (2001) affectedpe patterns and variations more than did Typhoon2000) (Lin, Chang, et al., 2006). The increase in thetes after the earthquake demonstrates the cumulative

the earthquake and typhoons. In addition, the mag- paths of the typhoons and the land use/land cover

e cumulative impacts of the disturbances (Lin, Chang,.


Fig. 9. Differecover (LULC) cland; 4, cultiva

4.2. Impact

The statby the ES-Hecosystem While openusually in Hobbs, & Mlarge distursystem (Moachieve a st1994). Althoequilibriumare dynamiof an equilicondition, tterms of itcorrespondsignicantlyand spatialquake, the Ethe Chi-Chia higher divrm the retyphoons acover in terThe changeand variousresilience. bances of suable to maibances occusubsequentearthquake

4.3. Land u

The analareas that pvices, a diveare at risk (Centre (JRCretention (%services; inin climate age capacitMaes, Parac9 ecosystem

management can then be applied to such areas. Based on our anal-ysis, we believe that rich hotspot areas and areas that are sensitiveto disturbances require additional protection to maintain ecosys-tem services in the future. The results of our analysis indicate

vestmn ande Managto prere

exporingtics oatione higvity

grea avog the& Chfraste mowaned b

be th Althe ection he coemeted s (e.

clus

en thd freqmay

raped.

ion vd bytem.yieldter imtectioate t

the Withs in tancence (%) in ES-HQ richness grouped by different types of land use/landhange between 2000 and 2001 (notes: 1, riparian; 2, grass; 3, built-upted land; 5, river sand; 6, landslide; 7, forest).

s on ecosystem resilience

e of the ecosystem in the study area was representedQ richness, which was an indication of the number ofservices operating at high levels in an area (Table 5).

and heterogeneous in many regions, ecosystems area non-equilibrium state (Phillips, 2004; Wallington,oore, 2005). Thus, disturbances, particularly infrequentbances, profoundly impact a dynamic non-equilibriumore et al., 2009; Phillips, 2004). Such landscapes neveready state (Romme & Despain, 1989; Turner & Romme,ugh conservation managers may often seek a dynamic

in ecosystems, most terrestrial vegetation systemsc because climatic instability prevents establishmentbrium state (Mori, 2011). Under the baseline climatiche state of the ecosystem uctuated only slightly ins ES-HQ richness (Table 5(b)). In contrast, under theing climatic conditions, the ES-HQ richness increased

after 2000 following typhoons of certain magnitudes characteristics (Tables 1 and 5(a)). Before the earth-S-HQ richness was lower yet relatively stable. However,

earthquake resulted in a more complex ecosystem withersity and greater spatial variations. Our results con-

sults of Lin, Chang, et al. (2006), who found that thend earthquake increased the complexity of the landms of a more scattered landscape pattern after 2000.s in the ES-HQ richness indicate that the earthquake

other disturbances may have reduced the ecosystemsConsequently, the ecosystem cannot tolerate distur-ch magnitudes, whereas previously it may have been

ntain the former equilibrium before the severe distur-rred (Folke et al., 2004; Holling, 1973). Notably, the

typhoons impacted the ecosystem less than did the.

that inwesterand Jathat massets areas wbancesby restacterisdegradprovidsensitireceiveshouldalterinChen, new inBecausthe Taistabilizshould2009).the vevaluaation tmanagwarranservice

5. Con

Givber anLindeninitiateimprovcipitatinduceecosyswater the lat

Proaccelerreduce2010).speciedisturbse management and strategies

ysis of a range of spatial indicators can help to identifyrovide abundant or rare value types of ecosystem ser-rse set of value types, or areas where community valuesRaymond et al., 2009). For example, the Joint Research) of the European Commission suggested that nitrogen) be used as an indicator of water quality regulation

addition, the capacity of an ecosystems functioningregulation should be assessed using its carbon stor-y (Maes et al., 2011; Nelson et al., 2009). Moreover,chini, Zulian, Dunbar, and Alkemade (2012) mapped

services by using 10 spatial indicators. Value-specic

tem servicdynamic anin ES-HQ rictem due toafter the Chagement, wto identify and/or habidisturbancescienticallidentify hoidentied aonly the vevaluation ent should be directed toward forested lands in thed southern parts of the watershed, where Mount Aliountain National Parks are located. We recommendement should focus on enhancing water and biologicoduce a range of ecosystem services. Given that thosethe most resilient to the multiple environmental distur-erienced recently, their resilience should be enhanced

the key structural, compositional, and functional char-f the vegetation systems, thereby mitigating ecosystem

induced by disturbances. In addition to the areas thath levels of ecosystem services, the areas that displayedto the natural disturbances (e.g., forested land) shouldter attention. Forest restoration in natural reserve areasid, or at least reduce human disturbances to, possibly

natural cycle of forest succession (Huang, 1999; Xi,u, 2012). Protection can be provided by developing aructure to prevent future disasters (Yu & Chen, 2009).st of the forests in Taiwan are located at high elevations,

Forestry Bureau (TFB) suggested that slopes should bey restoring slope land; in addition, a re-planting plane primary strategy for post-disaster rejuvenation (TFB,ough the identied areas focus on the protection ofosystem services of concern and habitat quality, thismethod provides an alternative way to place into oper-ncept of ecosystem services for sustainable ecosystemnt. Additional evaluation of prospective services is thusin land use planning related to additional ecosystemg., cultural services).

ions

e forecasts of increased disturbances (both in num-uency) related to climate change (e.g., Emanuel, 2005;

er et al., 2010; Westerling et al., 2006), the ability toid, post-disturbance studies of ecosystems should beIn this study, we evaluated the manner in which pre-ariation (PV) and land use/land cover (LULC) changes

multiple disturbances affected the functioning of an Based on our results, although the PV impacted the

and soil conservation than did changes in the LULC,pacted the nutrient retention more.n and appropriate management of selected areas canhe recovery of ecosystem services and biodiversity andbrunt of environmental disturbances (Cote & Darling,out a strategic selection of such protected areas, thehose areas will most likely be limited to weedy and-tolerant general species that may not preserve ecosys-es and functions (Cote & Darling, 2010). Given thed non-equilibrium nature of the ecosystem, the changeshness indicate the non-equilibrium states of the ecosys-

the degradation of ecosystem resilience, particularlyi-Chi earthquake. To facilitate future ecosystem man-e recommended using the ES-HQ richness as an indexareas that provide at least three ecosystem servicestat quality and areas that are sensitive to large physicals. Closely examining the spatial relationships betweeny assessed ecosystem services and local priorities cantspots of value alignment and conict. Although thereas warranting protection were intended to comprisee ecosystem services of concern and habitat quality, themethod presented in this study provides an alternative


means of taking into account the concept of ecosystem services forsustainable ecosystem management.

Acknowledgments

The authcil of the Rthis researcand 101-29under the 7this researc

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Simulation of ecosystem service responses to multiple disturbances from an earthquake and several typhoons1 Introduction2 Materials and methods2.1 Study area2.2 Large-scale natural disturbance events2.3 Estimating and mapping ecosystem services

3 Results3.1 Estimating and mapping ecosystem services and habitat quality3.2 Impact of precipitation variation (PV) on ecosystem services3.3 Impact of land use/land cover (LULC) changes on ecosystem services and habitat quality3.4 Identification of potential protection areas based on ES-HQ richness

4 Discussions4.1 Impacts of disturbances on ecosystem services4.2 Impacts on ecosystem resilience4.3 Land use management and strategies

5 ConclusionsAcknowledgmentsReferences

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