Simulation of ecosystem service responses to multiple disturbances from an earthquake and several typhoons

Landscape and Urban Planning 122 (2014) 41–55

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Research Paper

Simulation of ecosystem service responses to multiple disturbances from an earthquake and several typhoons Li-Chi Chiang a , Yu-Pin Lin b,∗ , Tao Huang c , Dirk S. Schmeller d,e , Peter H. Verburg f , Yen-Lan Liu g , Tzung-Su Ding h a

Department of Civil and Disaster Prevention Engineering, National United University, Taiwan Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan c Department of Bioenvironmental Systems Engineering, National Taiwan University, Taiwan d Department of Conservation Biology, Helmholtz-Center of Environmental Research – UFZ, Germany e Université de Toulouse, UPS, INPT, EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement), France f Institute for Environmental Studies, VU University Amsterdam, The Netherlands g College of Humanities and Social Science, Taipei Medicine University, Taiwan h School of Forest and Resources Conservation, National Taiwan University, Taiwan b

h i g h l i g h t s • • • • •

Multiple disturbances can cumulatively impact ecosystem functioning. An earthquake had the greatest impact on the ecosystem. Climate variation had a stronger impact on water yield and soil conservation. Landscape change had a stronger impact on water purification. Identification of the sensitive areas enhances an ecosystem management plan.

a r t i c l e

i n f o

Article history: Received 2 May 2012 Received in revised form 18 October 2013 Accepted 28 October 2013 Available online 5 December 2013 Keywords: Ecosystem services Landscape change Physical disturbance

a b s t r a c t Ongoing environmental disturbances (e.g., climate variation and anthropogenic activities) alter an ecosystem gradually over time. Sudden large disturbances (e.g., typhoons and earthquakes) can have a significant and immediate impact on landscapes and ecosystem services. This study explored how precipitation variation (PV) and land use/land cover (LULC) changes caused by multiple disturbances can cumulatively impact ecosystem functioning in the Chenyulan watershed in central Taiwan. We simulated four ecosystem services (water yield production, water purification, soil conservation, carbon storage) and biodiversity using the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model to analyze the spatiotemporal changes and obtain information regarding changes in the ecosystem. Our results indicate that the Chi-Chi earthquake had the greatest impact on the ecosystem. Specifically, the ecosystem was altered by the earthquake and could no longer absorb disturbances of a similar magnitude as before the earthquake. By differentiating the impacts of the PV and LULC changes on ecosystem services 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 purification. Our results also suggest that a comprehensive ecosystem management plan should consider the cumulative and hierarchical context of disturbance regimes to prevent reductions in ecological variability and ecosystem resilience, particularly in areas that are more sensitive to large disturbances. In this way, ecosystem resilience may be maintained at a level sufficient to preserve ecosystem functioning and ecosystem services in the event of unexpected large-scale environmental disturbances. © 2013 Elsevier B.V. All rights reserved.

1. Introduction ∗ Corresponding author. Tel.: +886 2 3366 3467; fax: +866 2 2368 6980. E-mail addresses: [email protected] (L.-C. Chiang), [email protected], [email protected] (Y.-P. Lin), [email protected] (T. Huang), [email protected] (D.S. Schmeller), [email protected] (P.H. Verburg), [email protected] (Y.-L. Liu), [email protected] (T.-S. Ding). 0169-2046/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.landurbplan.2013.10.007

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

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natural capital is inadequately understood, seldom monitored, and prone to frequent rapid degradation and depletion by multiple natural and anthropogenic disturbances (Fraterrigo & Rusak, 2008; Tallis et al., 2011). Degradation and depletion particularly occur in urbanized and naturally disturbed areas, including areas of rapid conversion of natural habitat to human-dominated land use. Turner (2010) posited that changing disturbance regimes in the short to medium term (years to decades) and in the long term (centuries) alter landscapes and ecosystem services. Several studies have demonstrated the way in which large physical disturbances influence the structure and functioning of an ecosystem (Lin, Chu, Wang, Yu, & Wang, 2009; Millward & Kraft, 2004; Sinclair & Byrom, 2006; Turner & Dale, 1998). Lindenmayer, Likens, and Franklin (2010) found that large natural disturbances (e.g., wildfires, typhoons, and earthquakes) influence critical ecological processes, including sediment flows (Nakamura, Swanson, & Wondzell, 2000), biogeochemical cycles (Houlton et al., 2003), carbon sequestration (Running, 2008), and hydrology (Hong, Chu, Lin, & Deng, 2010). Other large disturbances (e.g., tropical storms and floods) can alter stream habitats (Chuang, Shieh, Liu, Lin, & Liang, 2008; Wilson, Graham, Pratchett, Jones, & Polunin, 2006), stream behavior (Fitzsimons & Nishimoto, 1995) and the community structure of fish (Power, Matthews, & Stewart, 1985). Exactly how disturbances influence the spatial variation of ecosystem services across landscapes has seldom been analyzed. In addition, the interaction of various disturbance factors (Mori, 2011) makes it difficult to collect information essential to inform land use and management decisions (Balmford et al., 2002; Nelson et al., 2009). A previous disturbance can significantly affect an ecosystem’s response to a new disturbance (Paine, Tegner, & Johnson, 1998; Turner, 2010), possibly altering the ecosystem resilience further. Such resilience is characterized by the ability of an ecosystem to maintain its structure, function, and feedback after a disturbance (Cote & Darling, 2010; Spieles, 2010; Walker & Salt, 2006). When external disturbances exceed the ecosystem resilience, the latter may be forced to change to a new state with different functions and structures (Thrush et al., 2009). In a degraded ecosystem, even a disturbance with a lower magnitude than those the ecosystem tolerated previously might cause an unexpected sudden change (Folke et al., 2004; Holling, 1973). As fundamental components of non-equilibrating ecosystems, hierarchical disturbance regimes are essential to ecosystem management (Mori, 2011). Despite an increased understanding of the spatiotemporal variations of a single or specific landscape, the failure to recognize the cumulative and hierarchical context of disturbance regimes may result in mismanagement, eventually reducing the ecosystem resilience (Mori, 2011). Managing and maintaining ecosystem components and accurate information of their function with respect to the spatial distribution of ecosystem functions and services are essential to making effective land management decisions (de Groot, Alkemade, Braat, Hein, & Willemen, 2010; Egoh et al., 2007, 2008; van Jaarsveld et al., 2005). Accurate information can be collected immediately after a major natural disturbance, making it the most effective means of identifying the locations and functional roles of key refugia (Lindenmayer et al., 2010). Taiwan sits on the Philippine plate near the boundary with the Euro-Asian plate, which explains why plate convergence generates earthquakes that have disastrous effects on the island (Lin, Liu, Lee, & Liu, 2006; Lin, Lin, Deng, & Chen, 2008). Earthquakes may trigger landslides, during which sediments are removed from slopes and transported by fluvial action (Keefer, 1994; Lin et al., 2008). Moreover, Taiwan is located in a sub-tropical region and experiences an average of 3–4 typhoons per year, which deposit a tremendous amount of rain from July to October. The amount of rain depends on the size, speed and intensity of the rain-producing center of a typhoon (Jan & Chen, 2005). These natural disturbances

Gradual disturbance Climate variaon

Sudden disturbance

Sudden disturbance

Typhoons

Earthquake

Precipitaon variaon (PV)

Gradual disturbance Anthropogenic acvity

Land use/land cover (LULC) change

Ecosystem funconing Ecosystem service (ES) provisioning (i.e. water yield producon, water purificaon, soil conservaon, carbon storage) & Biodiversity Fig. 1. Research approach and objectives of this study.

characterize the structure, function and dynamics of the tropical and temperate forest ecosystems in Taiwan. Owing to the frequency of earthquakes and typhoons in Taiwan, their frequency may alter the island’s ecosystems and the services that they provide to the population. Correspondingly, organizations involved in land planning, disaster management and restoration heavily prioritize the mapping and assessment of the ways that landscape changes affect the spatiotemporal dynamics of ecosystem functions and the services they provide, particularly large-scale changes induced by large, sequential, physical disturbances. In this study, we quantified the ecosystem services in a region of central Taiwan that is frequently affected by multiple physical disturbances. This study focused primarily on identifying changes in ecosystem service that occur after multiple disturbances (Fig. 1). The precipitation variation (PV) and changes in land use/land cover (LULC) were considered to be key drivers determining the ability of an ecosystem to provide ecosystem services (ES). Both drivers are affected by natural disturbances and/or anthropogenic activities. Therefore, this study provided quantitative estimates of the changes in ecosystem services caused by cumulative PV and LULC changes. In addition, hotspots of high habitat quality and ecosystem service concentration were identified, in addition to areas that are sensitive to disturbances. The latter are critical components in ecosystem management. This study also evaluated how severe natural disturbances impact ecosystem resilience and, subsequently, ecosystem functioning and spatial distribution of ecosystem services. Furthermore, based on the results of this study, we recommend areas of future research in land management. 2. Materials and methods 2.1. Study area The Chenyulan watershed is located in Nantou County, central Taiwan, and encompasses an area of 449 km2 (Fig. 2). This typical mountain drainage watershed has a mean altitude of 1540 m, mean slope of 32◦ , and relief intensity of 585 m/km. The dominant lithologies in the metamorphic terrain are slates and meta-sandstones (Lin et al., 2004; Lin, Liu, et al., 2006). The average annual precipitation ranges from 2000 to 4000 mm; and approximately 80% of the annual rain falls during the Southwest Monsoon season (May to October). Particularly during the typhoon season (July to September), short yet intensive periods of rain often trigger landslides, causing significant denudation in the mountainous regions (Jan & Chen, 2005). The ways in which large-scale disturbances impact the ecosystem were assessed in this study by collecting SPOT satellite images taken after each disturbance. The SPOT images were those with

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Fig. 2. Location of Taiwan (left-top), Typhoon Toraji (left-bottom), and location of the Chenyulan watershed, local magnitude of the Chi-Chi earthquake and typhoon paths (right). Source of local magnitude: Central Weather Bureau. Available at http://www.cwb.gov.tw/V7/earthquake/damage eq.htm.

Table 1 Typhoons during 1996–2005. Typhoon

Period

Strength

Radius (km)

Max. wind speed (m/s)

Herb Xiangsane Toraji Midulle Aere Matsa

07/29–08/01, 1996 10/30–11/01, 2000 07/28–07/31, 2001 06/28–07/03, 2004 08/23–08/26, 2004 08/03–08/06, 2005

Strong Medium Medium Medium Medium Medium

350 250 250 250 200 250

53 38 38 45 38 40

zero cloud cover obtained from the Space and Remote Sensing Research Center. In addition, the watershed land cover was classified using images from (1) November 8, 1996, March 6, 1999, and October 31, 1999 (i.e., before and after the Chi-Chi earthquake); and (2) November 27, 2000, September 21, 2001, November 19, 2004, and November 11, 2005 (i.e., before and after Typhoons Herb, Xiangsane, Toraji, Midulle, Aere and Matsa) (Fig. 3). During classification of the final SPOT images, all of the accuracy and kappa values exceeded 82% and 0.77, respectively (Hong et al., 2010; Lin, Chang, Wu, Chiang, & Lin, 2006). 2.2. Large-scale natural disturbance events Several large disturbances impacted central Taiwan from 1996 to 2005 (Fig. 2; Table 1; CWB-TDB, 2013; TTFRI-DBAR, 2000). The damage caused by Typhoon Herb was more severe than that produced by any other typhoon over the previous four decades, owing to the wind speed and the radius of this typhoon (Jan & Chen, 2005). Most of the damage was due to extremely high precipitation, particularly in central Taiwan (Chiang, 1996; Yu & Tuan,

1996), which received approximately 30% of its annual rainfall during two days (Jan & Chen, 2005). Typhoon Xiangsane struck the island in 2000. In 2001, Typhoon Toraji delivered the most intense rainfall over a short period, with a return period of 300 years (Cheng, Huang, Wu, Yeh, & Chang, 2005). The heavy rain during Typhoons Xiangsane and Toraji triggered 100 and 192 debris flows, respectively, compared to 52 debris flows caused by Herb (Jan & Chen, 2005). In 2004, two medium-strength typhoons, Midulle and Aere, caused a similar high number of debris flows (Xiangsne and Toraji). On September 21, 1999, the Chi-Chi earthquake (7.3 on the moment magnitude scale, with a focal depth of 8.0 km) was triggered by the reactivation of the Chelungpu fault in central Taiwan. The epicenter was located at 23.87◦ N and 120.75◦ E, near the Chenyulan watershed in southern Nantou County. This earthquake caused surface ruptures along 100 km of the north-trending Chelungpu fault and triggered 10,000 landslides, which seriously altered the landscape of the region, particularly near the epicenter. Numerous extension cracks, which accelerated the landslides during high-rainfall events, also developed on the hillsides. Forest covers over 75% of the Chenyulan watershed, and cultivated land and grassland cover approximately 10% and 5% of the watershed, respectively (Table 2). Between 1996 and 2005, several typhoons and the massive Chi-Chi earthquake of 1999 disturbed the watershed, resulting in large landslide areas. The typhoons of 1996, 1999, 2001 and 2005 were particularly serious, as shown in Fig. 3 and Table 2. Since 1996, the forested area decreased from 351.2 km2 to 332.6 km2 , and the area of riparian zones declined from 6.3 km2 to 2.3 km2 . Meanwhile, the area of land under cultivation increased by 46%, from 43.5 km2 to 63.9 km2 (Fig. 3).

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Fig. 3. Land use/land cover (LULC) distribution in the Chenyulan watershed during 1996–2005.

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

Riparian Grass Built-up land Cultivated land River sand Landslide Forest

1996/11/8

March 99

October 99

2000/11/27

2001/11/20

2004/11/19

2005/11/11

6.3 23.1 2.0 43.5 8.9 13.5 351.7

6.5 19.6 2.0 47.5 8.7 6.8 358.0

5.2 16.6 2.2 48.8 10.0 15.7 350.7

2.3 22.1 2.4 51.7 12.8 9.8 348.0

2.2 27.6 2.8 54.5 13.0 14.5 334.6

3.1 26.9 3.1 61.6 12.1 8.1 334.2

2.3 20.9 3.4 63.9 12.9 13.2 332.6

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2.3. Estimating and mapping ecosystem services The 2.1 beta version of the software program Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) was developed by the Natural Capital Project (Tallis et al., 2011). InVEST consists of a suite of models that use land use/land cover patterns to estimate the levels and economic values of ecosystem services, biodiversity conservation, and market value of commodities provided by a landscape (Nelson et al., 2009). Owing to its focus on the valuation and visualization of ecosystem services across landscapes, InVEST is widely used in evaluating ecosystem services at the pixel level (Goldstein et al., 2012; Nelson et al., 2010). For example, in addition to analyzing two scenarios of global changes in urban land and cropland at the pixel level, Nelson et al. (2010) measured how these changes impacted the ecosystem services and biodiversity. Goldstein et al. (2012) subsequently evaluated the environmental and financial implications of planning scenarios that encompass contrasting land-use combinations, including biofuel feedstocks, food crops, forestry, livestock, and residential development. By using the InVEST model, we examined the changes in the biophysical forms of ecosystem services affected by changes in the landscape pattern caused by disturbances. Water yield production, water purification (nitrogen (N) and phosphorus (P) retention), soil conservation, carbon storage and habitat quality in terms of the biodiversity were also simulated. The output from the model at both the pixel and watershed level was evaluated. Pixel-level calculations represented the heterogeneity of the key driving factors in the simulation of ecosystem services. For example, the key driving factors for the water yields were the precipitation, evapotranspiration and soil type. Calculations at the watershed level were the sum or average of the pixel-level calculations. Compared to many large-scale assessments conducted at spatial resolutions of approximately 1 km2 (e.g., Maes, Paracchini, & Zulian, 2011), the data resolution in this study was 20 m × 20 m, which was sufficient to allow for an accurate simulation of the ecosystem (Konarska, Sutton, & Sutton, 2002). Based on the Reservoir Hydropower Production model of InVEST, the water yields were calculated as the provisioning service of an ecosystem. This model was used to calculate the amount of water contributed by various parts of the landscape. This model thus provided further insight into the way that changes in landscape patterns impact the annual surface water yield. In addition, the water yield of a given pixel was calculated as the difference between the precipitation and the fraction of precipitation that underwent evapotranspiration. The evapotranspiration (ET) partition of the annual water balance was an approximation of the Budyko curve (Zhang, Dawes, & Walker, 2001), which is a function of the Budyko dryness index (Budyko, 1974), plant available water content, average annual precipitation, and a seasonality

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factor that represents the amount and distribution of seasonal rainfall (Tallis et al., 2011). The potential ET was the product of the reference ET and the plant ET coefficient. Details of the latter can be found in Allen, Pereira, Raes, and Smith (1998). Based on the soil texture, the plant-available water content was estimated using the soil–plant–air–water (SPAW) computer model (Saxton & Willey, 2006; Saxton, 2006). The InVEST model was used to estimate the available water content (AWC) as the difference between the field capacity and wilting point over the minimum soil depth and root depth (Tallis et al., 2011). In addition, the plant-available water content was calculated based on the soil depth. Changes in the land use/land cover do not affect the AWC. Notably, the precipitation profoundly impacts the simulation of water yield, and flooding events can degrade the ecosystem services. As was assumed in the study, areas with a higher water yield than the specified threshold were identified as hotspots that provide an important ecosystem service in terms of water provisioning. Using the water purification nutrient retention model of InVEST (Tallis et al., 2011), we calculated the amounts of the nutrients N and P retained in each pixel of the watershed. Moreover, a hydrologic sensitivity score (HSS) was calculated using the simulated annual average runoff of each pixel derived from the reservoir hydropower production module (Tallis et al., 2011). The HSS accounted for differences between the field measurements and model conditions and was used to adjust the pollutant exports of a given pixel. By using the digital elevation model (DEM), the InVEST model routed water down the flow paths and allowed the downstream pixels to retain pollutant loads, based on the land cover type and efficiency of the land cover’s pollutant retention (Table 3). In this study, the annual pollutant loads reported by Huang (2001) were used; these loads were based on the retention efficiency of the module’s default parameters (Tallis et al., 2011). The pollutant load not retained by a pixel was continuously transported as additional load to the next (downstream) pixel. The model then aggregated the loads retained by each pixel and loads exported from each pixel to represent the loads retained and exported at the watershed level, respectively. Based on the sediment retention model, the average annual soil loss of a pixel and a pixel’s ability to retain sediment were also calculated in this study. In addition, the potential soil loss based on geomorphologic and climatic conditions was calculated by using the universal soil loss equation (USLE), which is a function of the rainfall erosivity, soil erodibility, slope length, crop/vegetation and management, and support practice factors (Wischmeier & Smith, 1978). The first three factors determined the potential soil erosion in the pixels without vegetation. The last two factors in the USLE largely determined the amount of soil erosion that can be prevented by the existing vegetation cover and/or support practices (Table 3). Vegetation can trap sediment that has eroded from upstream pixels. The scheme used to simulate the sediment retention and export

Table 3 Parameters for simulation of nutrient retention, soil conservation and carbon storage in the InVEST model. Ecosystem service

Nutrient retention

Soil conservation

Carbon storage

Parameters

N load (kg/ha) N retention efficiency (%) P load (kg/ha) P retention efficiency (%) USLE C USLE P TSS retention efficiency (%) C above (Mg/ha) C below (Mg/ha) C soil (Mg/ha) C dead (Mg/ha)

Land use/land cover (LULC) classes Riparian

Grass

Built-up land

Cultivated land

River sand

Landslide

Forest

1 50 0.1 50 0.01 1 40 1 1 0 0

1 50 0.1 50 0.01 1 40 1 1 10 0

3.5 0 0.5 0 0.01 1 5 0 0 0 0

16 5 0.5 5 0.1 1 30 3 2 10 0

3.5 0 0.5 0 1 1 5 0 0 0 0

3.5 10 0.5 10 1 1 5 1 1 10 0

1.6 80 0.25 80 0.01 1 60 200 130 130 65

Note: C above = carbon in aboveground biomass; C below = carbon in belowground biomass; C dead = carbon in dead organic matter; C soil = carbon in soil.

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of each pixel was similar to the nutrient retention model and was used to estimate the sediment trapping efficiency (Table 3). The USLE crop/vegetation and management factor (C factor) and the USLE support practice factor (P factor) were determined using the InVEST database. Based on the land use map of Tallis et al. (2011), we estimated the amount of carbon stored in a land pixel by aggregating the amount of carbon stored in the biomass above and below the ground and in soil and dead organic matter. The amount of carbon stored in various carbon pools was obtained from the IPCC global database (IPCC, 2006; Table 3). The forested areas contain the largest carbon pools of any of the land use/land cover types (Table 3), which explains why changes in those areas may be expected to reflect the change in the total amount of carbon stored in the watershed. The InVEST biodiversity model evaluates habitat quality as a proxy for biodiversity. In particular, the biodiversity was estimated by analyzing how threats impact the habitat. In this study, cultivated lands, highways, built-up areas and landslides were viewed as threats, and riparian zones, forested areas, and grassland were viewed as habitats. The model estimates the impact of threats to habitats based on the relative impact of each threat, the relative sensitivity of each habitat type to each threat, and the distance between the habitat and threat (Tallis et al., 2011; Table 4). The impact of a threat diminished with the distance between the habitat and threat. The relationship between the distance-decay rate of a threat and the maximum effective distance of the threat can be modeled in a linear or an exponential manner. In this study, an exponential rate of decrease was selected. The level of protection from disturbances (e.g., social and physical boundaries) may be an additional landscape factor that can mitigate the way that threats impact habitats. In addition to considering this threat by accounting for the accessibility to the sources of degradation, we assumed that each pixel had complete accessibility. First, the InVEST model calculated the degradation score of each pixel. The model then converted this score to a habitat quality value between 0 and 1, with 1 as the best possible habitat quality, i.e., a high level of biodiversity. Spatiotemporal hotspots of ecosystem services were determined based on the methods of Egoh et al. (2008) and Bai, Zhuang, Ouyang, Zheng, and Jiang (2011). Hotspot areas were those that provided a large amount of a single service and those that provided a large number of various services. Single-service hotspots were those areas with the highest 20% of a provision value (level) among the pixels for each service (Bai et al., 2011). In this study, ecosystem services–habitat quality (ES-HQ) richness hotspots were defined as the areas of overlap of at least three ES hotspots. The threshold baseline of a hotspot was defined using the data from 1996. Hotspots of ecosystem services were identified using the data from subsequent years. Based on the hotspots of habitat quality and five ecosystem services (i.e., water yield, N, P and sediment retention, and carbon storage), we developed a map of ES-HQ richness by superimposing the six hotspot maps. In the hotspot map, a range of 0–6 represented the number of hotspots of an ecosystem service or the habitat quality that a location can provide. A high value implied more ecosystem services or a better habitat quality provided by the location. In addition, a value of 0 implied that the provision of all considered ecosystem services and habitat quality was lower than the threshold value. The changes in ES-HQ richness over time were evaluated based on the weighted average richness, which was calculated using the equation

7 R=

Ri × Ni

i=1  7

i=1

Ni

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

Table 4 Parameters used for simulation of habitat quality in the InVEST model. Max. distance of impact to habitats

Relative impact to threats

Sensitivity of habitat to threats

Riparian Grass Forest Cultivated lands Highway Built-up land Landslide

4 2 5 1

0.8 0.7 1 1

0.6 0.7 0.5 1

0.6 0.7 0.5 1

0.7 0.8 0.8 1

Source: Polasky, Nelson, Pennington, and Johnson (2011).

The LULC changes represented the combined impact of natural disturbances and human activities (Fig. 3). In the mountainous forested area, the influence of anthropogenic activities on land use change was relatively low. In addition, human activities were assumed to have a negligible impact on the habitat quality, which is in contrast with the enormous impact of the sudden, intense natural disturbances during the 8-month period from March to October 1999 and the 4-year period from 2001 to 2004. The LULC changes identified after the seven major disturbances were viewed as the cumulative impact of the earthquake and typhoons and not human activity. Moreover, the spatiotemporal precipitation variation (PV) during the study period was caused by climate variation and typhoons. Owing to the close correlation between the temporal variability in ecological phenomena and climatic variability, the extent to which the climate may affect disturbance regimes and the resulting diverse ecosystem responses must be examined (Mori, 2011). The land use/land cover and annual precipitation in 1996 were selected as the baseline in this study. The year 1996 is assumed to represent a valid baseline time, owing to fewer environmental disturbances and lower cumulative impacts than those of the subsequent years covered by this study. We also differentiated between the way PV and LULC changes impacted ecosystem services by modeling the ecosystem services for each year separately against the 1996-baseline and the corresponding climatic conditions. Because precipitation affected only the water-related ecosystem services (i.e., water yield, water purification and soil conservation), carbon storage and habitat quality were not included in the simulations with the baseline climate data (precipitation). The changes in the ES-HQ richness in any year after 1996 compared with the baseline indicate the cumulative combined impact of PV and LULC changes on the ecosystem in that year. Therefore, the cumulative individual impact of PV on an ecosystem was calculated under the same land use/land cover conditions of the difference between simulated ESHQ richness with the corresponding annual precipitation and that with the 1996 precipitation. In addition, the cumulative individual impact of LULC changes on the ecosystem was calculated based on the difference between simulated ES-HQ richness under any LULC condition and that under the LULC conditions of 1996. 3. Results 3.1. Estimating and mapping ecosystem services and habitat quality The changes in the hotspot areas of each ecosystem service and the level of habitat quality simulated under the corresponding climate and baseline climate conditions displayed remarkable differences between 1996 and 2005 (Fig. 4). During these years, the average annual precipitation increased from 2694 mm to 4072 mm, resulting in greater water yields. Specifically, the annual water yield increased from 886 million m3 in 1996 to 1.496 billion m3 in 2005. The lowest annual precipitation was 2414 mm in 1999.

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Fig. 4. Hotspot (%) for each ecosystem service and habitat quality during 1996–2005.

Although the hotspot area increased from 19.6% in 1996 to 98.2% in 2005, the change before 2001 was comparatively small. However, the increasing annual precipitation after 2001 resulted in the expansion of the hotspot area from 30.8% in 2001 to 98.2% in 2005. Moreover, the precipitation and simulated water yield increased by 51% and 69%, respectively, between 1996 and 2005. During that period, the water yield increased from 88,096,300 m3 to 89,216,700 m3 , and the hotspot area increased from 18.9% to 21.4% (Figs. 4 and 5). The trend in nitrogen retention was similar to that of the water yield. The N retention increased from 119,428 kg to 140,397 kg, and the hotspot area increased from 15.8% to 17.3% between

1996 and 2005. During the same period, the annual rate of P retention in the watershed ranged from 10,865 kg to 11,304 kg, and the export of P ranged from 1312 kg to 1959 kg. Moreover, the annual rate of P retention was higher in October 1999 than in 1996 and higher in 2005 than in 2004 (Fig. 4). Furthermore, the hotspot area remained stable between 2000 and 2005. Under the baseline climatic conditions, the sediment retention hotspot area changed only slightly between 1996 and 2005 (Fig. 4). However, under the corresponding climatic conditions, the hotspot area increased significantly during the following periods: 1996 to March 1999, 2000 to 2001 and 2004 to 2005.

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Fig. 5. Spatial distribution of water yield hotspot (primary hotspot under baseline climate condition; secondary hotspot under corresponding climate condition).

Notably, the sediment retention rate in 2001 was the highest during the simulation period, being 74% higher than in 1996. This difference was due to the higher rainfall erosivity index (R) in 2001. Larger hotspot areas were observed in October 1999, owing to a higher number of landslides. The larger hotspot areas observed in 2001 and 2005 were caused by the combined impact of a larger number of landslides and greater precipitation (Fig. 4). The impact of the climate on the spatial distribution of the sediment retention hotspots was observed between March 1999 and

2005 (Fig. 6). The sediment hotspot area under the 2000 climatic conditions was very close to the area under the 1996 conditions. In 2001, the hotspot of sediment retention expanded to 84.2% of the entire watershed. Therefore, the hotspot area under the corresponding climatic condition increased to 56.1% and 54.7% of the entire watershed in March and October 1999, respectively. This area also increased from 43.5% in 2001 to 84.2% in 2005. Between 1996 and 2005, the forested area decreased from 351.7 km2 to 332.6 km2 , the amount of stored carbon declined from 18.6 to 17.6 million tons, and the hotspot area decreased from 78.6%

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Fig. 6. Spatial distribution of sediment retention hotspot (primary hotspot under baseline climate condition; secondary hotspot under corresponding climate condition).

to 74.1%. The change in the carbon storage capacity during the simulation period was generally smaller than the changes in the other ecosystem services. This difference is due to the fact that the forest is the major land cover in the watershed; in addition, the change in the forested area was relatively small (78.3–74%). However, the gradual decrease in the forested area after 1996 led to a decline in the habitat quality score from 0.71 in 1996 to 0.61 in 2005. Owing to the increasing number of landslides in the southwestern portion of the watershed in October 1999, 2001 and 2005, the areas that could have provided ecosystem services were smaller than in March 1999, 2000 and 2004, respectively. Therefore, despite the

decrease in the number of hotspot (high number of ESs) areas since 1996, the changes were more pronounced in March 1999, 2000 and 2004 (Fig. 4). 3.2. Impact of precipitation variation (PV) on ecosystem services Comparing the water yield with the 1996 baseline climate condition revealed a 6.5–13.4% decrease in the water yield before 2001 and a 4.4–67.8% increase after 2001, with the highest water yield in 2005. This water yield trend reflects the changes in the precipitation. Compared to 1996, the annual precipitation decreased

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Fig. 7. Land use/land cover (LULC), precipitation variation (PV) and combined impacts on ES during March 1999–2005.

by 6.3–10.4% before 2001 and increased by 7.3–51.2% after 2001. Unlike the extent to which the PV significantly impacted the water yield, the climate only slightly impacted the N and P retention. The changes ranged between −0.1 and 0.6% for N retention and between 0 and 0.3% for P retention (Fig. 7). When the baseline climatic conditions were used to assess the N retention, the trend of hotspot areas in terms of the N retention between 1996 and 2005 was observed to be similar to that of the corresponding climatic conditions. This finding suggests that the variations in the climate only slightly impacted the N retention

(Fig. 4). The hotspot area of P retention accounted for 13.6–14.4% of the watershed (Fig. 4). Notably, our results suggest only a slight impact of PV on the N and P retention (Fig. 7). Meanwhile, the PV significantly impacted the sediment retention, with retention rates ranging from 5.5% in 2000 to 76.3% in 2001 (Fig. 7). The PV was primarily affected by the rainfall erosivity index, which was the most important factor in determining the sediment retention. The trend of the PV impact on sediment retention from March 1999 to 2005 was similar to that of PV impact on the sediment export. Both the retention and export ranged between 5.5 and 76.3%.

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Fig. 8. Distribution of ES-HQ richness in 1996 and difference in ES-HQ richness due to land use/land cover (LULC) change.

3.3. Impact of land use/land cover (LULC) changes on ecosystem services and habitat quality The LULC changes impacted the water yield less than did the changes in the PV yet had a greater impact than the PV in terms of the nutrient retention and export. The N retention increased by 1.9 to 17.7%, owing to the LULC increase and the increase in cultivated land (Table 2). This increase was largely due to the fact that the cultivated land contained more N (Table 3), resulting in greater N retention. The increasing impact of the LULC changes on the N retention between 1996 and 2005 was similar to the trend in the N retention hotspots (Figs. 4 and 7). The LULC changes slightly impacted the P retention, which ranged between −2.1 and 1.8%. The N and P retention rates declined from 87.8% to 84.3%, and from 89.4% to 85.8%, respectively, during the period 1996–2005. LULC explained nutrient export but less nutrient retention.

Similarly, the LULC trend explained the sediment export but not its retention. In addition, the LULC impacted the sediment export more than the sediment retention. The watershed retained 96.7 to 98.3% of the total sediment losses, which was greater than its capacity to retain the nutrient losses of 82.2–88.5%. The impact of the climate and LULC on the sediment export was similar, indicating that they both play a major role in the sediment export. The cumulative impact of the LULC and climatic change on the amount of sediment exported was twice as high in 2001 as in 1996 (Fig. 7). 3.4. Identification of potential protection areas based on ES-HQ richness Based on the hotspot areas of five ecosystem services and the habitat quality under various climatic conditions, we calculated

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

March 99

October 99

2000

2001

2004

2005

(a) Under corresponding climate condition 2926 0 1 18750 2 12307 7520 3 2230 4 603 5 29 6 Average ES-HQ richness 1.8

1996

3300 12373 15227 9985 2495 1013 9 2.0

3475 12848 15348 9145 2615 967 4 1.9

3873 17475 11738 8188 2377 688 63 1.8

525 6672 18161 13311 4157 1466 111 2.4

2308 10831 15029 11360 3450 1300 120 2.2

100 6051 13107 13859 8231 2257 772 2.8

(b) Under baseline climate condition 0 1 2 3 4 5 6 Average ES-HQ richness

2783 18566 12678 7611 2158 554 27 1.8

3053 19254 12044 7249 2206 547 24 1.7

3132 19043 12357 7236 2096 487 26 1.7

3607 19127 12414 6727 2034 452 16 1.7

3651 18768 12729 6744 2049 425 11 1.7

3861 18545 12582 6826 2110 437 16 1.7

2926 18750 12307 7520 2230 603 29 1.8

the ES-HQ richness to identify areas that may benefit from ecosystem protection (Table 5). According to these results, the weighted ES-HQ richness under the corresponding climatic conditions gradually increased from 1.8 to 2.8, indicating that the watershed provided more ES-HQ hotspots following large disturbances (Table 5(a)). However, the ES-HQ richness decreased compared to the baseline climatic condition (Table 5(b)). This finding indicates that the changes in land cover induced by the typhoons and the Chi-Chi earthquake during the study period negatively impacted the ecosystem’s functions and the provision of ecosystem services. We also identified how and where the degradation occurred due to the LULC changes. Based on the results shown in Table 5(b), the area-weighted ES-HQ richness was similar to the baseline climate condition for all years; in addition, the locations of various ES-HQ richness values that an area can provide were similar. Therefore, only the ES-HQ richness distribution for 1996 was presented (Fig. 8). Those areas containing more than three hotspot types were areas warranting additional protection. The ES-HQ richness appeared to be relatively stable (white colored) over time in most of the areas of the watershed. The differences in the ES-HQ richness in any sequential year was between 7 and 12% (green and red colors denote decreased and increased ES-HQ richness), and these changes occurred primarily in grassland, cultivated land and forested areas (Figs. 8 and 9). In the areas that are sensitive to large disturbances, converting grassland and cultivated land to forest may increase the ES-HQ richness of such areas. However, the ESHQ richness could decrease when an area of forest is converted to grassland or cultivated land or is forcibly removed with landslide movement. Under the baseline climatic conditions, the ecosystem displayed only slight fluctuations in the ES-HQ richness. In contrast, under the corresponding climatic conditions, the ES-HQ richness increased significantly after 2000 due to typhoons of similar magnitudes and spatial characteristics (Tables 1 and 5(a)). 4. Discussions 4.1. Impacts of disturbances on ecosystem services Given the forecasts of increased disturbances (both in number and frequency) related to climate change (e.g., Emanuel, 2005; Lindenmayer et al., 2010; Westerling, Hidalgo, Cayan, & Swetnam, 2006), the capacity to initiate rapid, post-disturbance studies of ecosystems should be improved. This study involved an evaluation of how changes in the PV and LULC, as caused by multiple

disturbances, impacted ecosystem functioning. Based on our results, the PV impacted the water yield and soil conservation more than did the LULC changes, whereas the latter impacted the nutrient retention more. With regard to sediment retention, the hotspot areas were larger in October 1999 (i.e., after the Chi-Chi earthquake) than in March 1999. This difference was due to the fact that the upstream landslides generated larger volumes of sediment than did the downstream forested areas, which thus retaining more sediment. However, sediment retention at the watershed outlet was low in October 1999, indicating that the disturbance caused by the earthquake degraded the ecosystem in terms of sediment conservation. When the impacts of the typhoons and the earthquake were combined, the impact on sediment conservation due to landscape changes was concealed by the impact of greater precipitation, which increased the total amount of sediment. Therefore, the amounts of sediment retention at the watershed outlet in 2001 and 2005 were greater than those in 2000 and 2004, respectively. The increases in N and P losses were more stable after October 1999 than the variation in precipitation and water yields. This difference was likely due to the fact that the ChiChi earthquake more significantly impacted the landscape than the typhoons that followed. Although climatic variations significantly impacted the water yield and sediment retention, the LULC changes more significantly impacted the other ecosystem services. According to our results, the cumulative impacts of the disturbances were not everywhere obvious across the entire landscape. The ecosystem services were impacted by climatic variations and anthropogenic changes to land use/land cover in the area (Swift et al., 1998). The average annual precipitation significantly impacted the water yield. Our comparison of the nutrient and sediment retention rates revealed that the earthquake significantly impacted ecosystem services. Before the Chi-Chi earthquake, typhoons only slightly impacted the N and P retention rates. However, after the earthquake, typhoons with various paths and magnitudes affected the landscape patterns in various ways (also see Lin, Chang, et al., 2006). The N and P retention rates declined more in 2001 than in 2000 because Typhoon Toraji (2001) affected the landscape patterns and variations more than did Typhoon Xangsane (2000) (Lin, Chang, et al., 2006). The increase in the retention rates after the earthquake demonstrates the cumulative impact of the earthquake and typhoons. In addition, the magnitudes and paths of the typhoons and the land use/land cover affected the cumulative impacts of the disturbances (Lin, Chang, et al., 2006).

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Fig. 9. Difference (%) in ES-HQ richness grouped by different types of land use/land cover (LULC) change between 2000 and 2001 (notes: 1, riparian; 2, grass; 3, built-up land; 4, cultivated land; 5, river sand; 6, landslide; 7, forest).

4.2. Impacts on ecosystem resilience The state of the ecosystem in the study area was represented by the ES-HQ richness, which was an indication of the number of ecosystem services operating at high levels in an area (Table 5). While open and heterogeneous in many regions, ecosystems are usually in a non-equilibrium state (Phillips, 2004; Wallington, Hobbs, & Moore, 2005). Thus, disturbances, particularly infrequent large disturbances, profoundly impact a dynamic non-equilibrium system (Moore et al., 2009; Phillips, 2004). Such landscapes never achieve a steady state (Romme & Despain, 1989; Turner & Romme, 1994). Although conservation managers may often seek a dynamic equilibrium in ecosystems, most terrestrial vegetation systems are dynamic because climatic instability prevents establishment of an equilibrium state (Mori, 2011). Under the baseline climatic condition, the state of the ecosystem fluctuated only slightly in terms of its ES-HQ richness (Table 5(b)). In contrast, under the corresponding climatic conditions, the ES-HQ richness increased significantly after 2000 following typhoons of certain magnitudes and spatial characteristics (Tables 1 and 5(a)). Before the earthquake, the ES-HQ richness was lower yet relatively stable. However, the Chi-Chi earthquake resulted in a more complex ecosystem with a higher diversity and greater spatial variations. Our results confirm the results of Lin, Chang, et al. (2006), who found that the typhoons and earthquake increased the complexity of the land cover in terms of a more scattered landscape pattern after 2000. The changes in the ES-HQ richness indicate that the earthquake and various other disturbances may have reduced the ecosystem’s resilience. Consequently, the ecosystem cannot tolerate disturbances of such magnitudes, whereas previously it may have been able to maintain the former equilibrium before the severe disturbances occurred (Folke et al., 2004; Holling, 1973). Notably, the subsequent typhoons impacted the ecosystem less than did the earthquake. 4.3. Land use management and strategies The analysis of a range of spatial indicators can help to identify areas that provide abundant or rare value types of ecosystem services, a diverse set of value types, or areas where community values are at risk (Raymond et al., 2009). For example, the Joint Research Centre (JRC) of the European Commission suggested that nitrogen retention (%) be used as an indicator of water quality regulation services; in addition, the capacity of an ecosystem’s functioning in climate regulation should be assessed using its carbon storage capacity (Maes et al., 2011; Nelson et al., 2009). Moreover, Maes, Paracchini, Zulian, Dunbar, and Alkemade (2012) mapped 9 ecosystem services by using 10 spatial indicators. Value-specific

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management can then be applied to such areas. Based on our analysis, we believe that rich hotspot areas and areas that are sensitive to disturbances require additional protection to maintain ecosystem services in the future. The results of our analysis indicate that investment should be directed toward forested lands in the western and southern parts of the watershed, where Mount Ali and Jade Mountain National Parks are located. We recommend that management should focus on enhancing water and biologic assets to produce a range of ecosystem services. Given that those areas were the most resilient to the multiple environmental disturbances experienced recently, their resilience should be enhanced by restoring the key structural, compositional, and functional characteristics of the vegetation systems, thereby mitigating ecosystem degradation induced by disturbances. In addition to the areas that provide high levels of ecosystem services, the areas that displayed sensitivity to the natural disturbances (e.g., forested land) should receive greater attention. Forest restoration in natural reserve areas should avoid, or at least reduce human disturbances to, possibly altering the natural cycle of forest succession (Huang, 1999; Xi, Chen, & Chu, 2012). Protection can be provided by developing a new infrastructure to prevent future disasters (Yu & Chen, 2009). Because most of the forests in Taiwan are located at high elevations, the Taiwan Forestry Bureau (TFB) suggested that slopes should be stabilized by restoring slope land; in addition, a re-planting plan should be the primary strategy for post-disaster rejuvenation (TFB, 2009). Although the identified areas focus on the protection of the five ecosystem services of concern and habitat quality, this evaluation method provides an alternative way to place into operation the concept of ecosystem services for sustainable ecosystem management. Additional evaluation of prospective services is thus warranted in land use planning related to additional ecosystem services (e.g., cultural services).

5. Conclusions Given the forecasts of increased disturbances (both in number and frequency) related to climate change (e.g., Emanuel, 2005; Lindenmayer et al., 2010; Westerling et al., 2006), the ability to initiate rapid, post-disturbance studies of ecosystems should be improved. In this study, we evaluated the manner in which precipitation variation (PV) and land use/land cover (LULC) changes induced by multiple disturbances affected the functioning of an ecosystem. Based on our results, although the PV impacted the water yield and soil conservation than did changes in the LULC, the latter impacted the nutrient retention more. Protection and appropriate management of selected areas can accelerate the recovery of ecosystem services and biodiversity and reduce the brunt of environmental disturbances (Cote & Darling, 2010). Without a strategic selection of such protected areas, the species in those areas will most likely be limited to weedy and disturbance-tolerant general species that may not preserve ecosystem services and functions (Cote & Darling, 2010). Given the dynamic and non-equilibrium nature of the ecosystem, the changes in ES-HQ richness indicate the non-equilibrium states of the ecosystem due to the degradation of ecosystem resilience, particularly after the Chi-Chi earthquake. To facilitate future ecosystem management, we recommended using the ES-HQ richness as an index to identify areas that provide at least three ecosystem services and/or habitat quality and areas that are sensitive to large physical disturbances. Closely examining the spatial relationships between scientifically assessed ecosystem services and local priorities can identify hotspots of value alignment and conflict. Although the identified areas warranting protection were intended to comprise only the five ecosystem services of concern and habitat quality, the evaluation method presented in this study provides an alternative

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