Identifying key landscape pattern indices influencing the ecological security of inland river basin: The middle and lower reaches of Shule River Basin as an example

Science of the Total Environment 674 (2019) 424–438

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Identifying key landscape pattern indices influencing the ecological security of inland river basin: The middle and lower reaches of Shule River Basin as an example Libang Ma ⁎, Jie Bo, Xiaoyang Li, Fang Fang, Wenjuan Cheng College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Landscape structure and function change would seriously threaten regional and global ecological security. • The temporal-spatial variation of ecological security and landscape pattern was significant in the Shule River. • The variation of ecological security was U-shaped and periodic, but the variation period was gradually shortened. • There was temporal variation of the relationship between ecological security and landscape pattern.

a r t i c l e

i n f o

Article history: Received 16 November 2018 Received in revised form 5 April 2019 Accepted 8 April 2019 Available online 11 April 2019 Editor: Albert Santasusagna Riu Keywords: Ecological security PSR model Landscape pattern Factor identification The middle and lower reaches of Shule River basin

⁎ Corresponding author. E-mail address: [email protected] (L. Ma).

https://doi.org/10.1016/j.scitotenv.2019.04.107 0048-9697/© 2019 Elsevier B.V. All rights reserved.

a b s t r a c t Landscape pattern evolution leads to changes of landscape spatial structure, which are intuitively reflected in the changes of ecosystem structure and composition and finally affect ecological security. In this paper, we assessed the spatiotemporal variation of the ecological security and landscape pattern of the middle and lower reaches of Shule River Basin in 1987–2015. Further, we analyzed the correlation between the ecological security and landscape pattern of the study region and correlation coefficients were calculated. On this basis, the key landscape pattern indices influencing the ecological security of the study region were identified. This may provide useful information for ecological regulation and design. The results show that: (1) From 1987 to 2015, the ecological security in the middle and lower reaches of the Shule River Basin was of medium or low level, showing periodic “U” shaped fluctuations, and the fluctuation period was gradually shortened. In addition, there was an overall spatial pattern of “high ecological security in the west, middle and south and low ecological security in the east”. (2) The landscape pattern showed clear stage characteristics. The complexity of landscape pattern increased from 1987 to 1996 and decreased after 1996. (3) Landscape size, shape, quantity, type and spatial configuration had important impacts on ecological security and showed significant temporal variation. In a period when the influence of human activities was weak, ecological security was mainly related to landscape area indices. With increase in human activities, landscape shape, fragmentation and connectivity changed greatly, which led to changes in the structure and composition of ecosystem, thus finally affecting ecological security. © 2019 Elsevier B.V. All rights reserved.

L. Ma et al. / Science of the Total Environment 674 (2019) 424–438

1. Introduction Since the 20th century, the worldwide industrialization and urbanization have effectively promoted global socioeconomic development. However, the extensive development pattern, high-intensity land use and fast land use changes have put great pressure on the environment (Li et al., 2010; Naveh, 1994). The increasingly unreasonable exploitation and utilization of natural resources leads to excessive depletion of resources and increases the vulnerability of the environment (Naveh, 1994). The resultant ecological crisis and disasters can directly affect regional landscape pattern and sustainable development (Hodson and Marvin, 2009; Li et al., 2010), as well as economic competition and national security in the context of globalization (Hodson and Marvin, 2009). As dramatic global environmental changes are putting more and more pressure on the survival and development of humans, ecological security has received extensive attention from governments and become a hot topic in global environmental research as well as a new theme for sustainable socioeconomic development (Steffen et al., 2015; Wu, 2014). Ecological security is a complex issue that involves many aspects. It can be understood in both a broad and narrow sense. The former is represented by the definition put forward by the International Institute of Applied Systems Analysis (Ma et al., 2018): Ecological security refers to a state in which necessary resources for human survival and development, social order and human adaptability to environmental changes are not threatened (Ma et al., 2018). It includes natural, economic and social aspects. In a narrow sense, ecological security refers to the security of natural and semi-natural ecosystems, i.e., the overall integrity and health of ecosystems (Shi et al., 2018; Xiao et al., 2002). In other words, it is a state in which the ecological environment of a region is not threatened and can provide ecological guarantee for the safe and sustainable development of the whole eco-economic system. To maintain regional ecological security, the key is to strengthen the management of regional ecological environment. Ecological security assessment enables to determine the integrity and sustainability of ecosystems under various risks (Xu et al., 2016), and provide a basis for ecological regulation, management and decision-making (Liu et al., 2018, Wellington and Rashid, 2010), thus it is an important part of ecological security research (Xiao et al., 2002). In the 1980s, the World Commission on Environment and Development (WECD) and the International Institute of Applied Systems Analysis (IIASA) formally proposed ecological security issues and monitoring systems. In 2005, the United Nations Millennium Ecosystem Assessment pointed out that the degradation of ecosystem services would seriously affect the human living environment and threaten regional and global ecological security (Millenium Ecosystem Assessment, 2005). According to the Millennium Development Goals of the United Nations, the International Organization for Ecological Security Cooperation (IESCO), initiated by China, was established in 2006. In 2012, the Intergovernmental Science Policy Platform for Biodiversity and Ecosystem Services (IPBES) was formally established under the auspices of United Nations Environment Programme (UNEP), aiming to serve policy decision-making needs through scientific assessment. All these indicate that the international community attaches great importance to ecosystem services, ecological security and sustainable development of human society. Global research on ecological security is divided into macro and micro scales. The macro-scale research mainly focuses on various concepts and their relationship with national security, military strategy, sustainable development and globalization (Helman and Ratner, 1992–1993; Mathews, 1989; Rogers, 1997) and there is also policy research on ecological security (Pickard et al., 2015). The micro-scale research centers on risk sources and strategies. For example, there are assessments of ecological risks, biodiversity and ecosystem services under industrial and agricultural pollution (Brand and Vadrot, 2013; Faggiano et al., 2010; Gonzálezpleiter et al., 2013; Hayes and Landis, 2004; Moraes et al., 2002), and research on the coupling of ecosystem

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functions and processes, ecological protection and restoration, natural and socio-economic systems (Dong et al., 2016; Liu et al., 2015; Motesharrei et al., 2016). There are also few studies mainly focusing on regional-scale ecological risk and ecological health assessment (Costanza et al., 1992; Serveiss, 2002). The frequently used models include Pressure-State-Response (PSR), Driving Force-State-ExposureEffect-Action (DFSEEA), ECCO, etc. (Baudot and Moomaw, 1999; Costanza et al., 1992). Ecological security research started relatively late in China. In the late 1990s, some Chinese scholars began to pay attention to the issue of ecological security. Following the research abroad, the related research in China gradually developed from conceptual discussion and theoretical study in an early stage to the use of GIS models, mathematical statistics (Chen et al., 2018; Peng et al., 2018) and evaluation models developed by international researchers (Liu et al., 2018) to comprehensively evaluate the ecological security pattern (Yu et al., 2009), status (Zhang et al., 2014), quality (Wang et al., 2017) and ecological carrying capacity (Zhu et al., 2017) of multiple evaluation objects and typical regions. Especially, the ecological security pattern has become a research hotspot in the management of ecological security. Related research mainly centers on the identification and construction of ecological security pattern, such as dividing spatial ecological security into different levels according to case-based ecological evaluation (Dong et al., 2016; Meng et al., 2011), using spatial superposition, objective optimization and “source-corridor” to construct ecological security pattern (Meng et al., 2012; Zagas et al., 2011) as well as analyzing the function, service and relationship of the pattern (Wang et al., 2016). However, it is still necessary to conduct in-depth study on the formation mechanism, evolution law, impact mechanism, security early warning and regulation of the pattern. Rapid urbanization process has become the most remarkable feature of human society since the 20th century. In this process, with the rapid transformation of land development and use patterns, the fragile environment has even deteriorated (Zhao et al., 2015), directly affecting regional landscape pattern, sustainable development (Xiao et al., 2017) and ecological security in the context of globalization (Fu et al., 2009). Landscape pattern and process study provides important information for ecological protection and management. It can reveal the spatial distribution and concentration characteristics of landscape factors. Therefore, ecological security study should not only consider landscape pattern and process, but also consider the impact of landscape factors that can reflect landscape structure and function on ecological security. In addition, the impact of landscape pattern on ecological security varies greatly with regions and spatial scales. Identifying the key landscape pattern indices is an important basis for ecological protection and planning. However, at present, global researchers mainly analyze regional ecological security from the perspective of landscape structure (Ji et al., 2013; Shi et al., 2013), and seldom discuss the impact of landscape pattern on ecological security from the perspective of landscape indices. Therefore, it is of great significance to identify the key landscape pattern indices affecting ecological security. Since the 1980s, encouraged by national policies such as “Agricultural Construction and Immigrant Settlement Project in Two West Regions” and “Integrated Project of Agricultural Irrigation and Immigrant Settlement in Shule River Basin”, 143.9 thousand people in China have migrated to the middle and lower reaches of Shule River Basin (Chang and Zhang, 2014). This has changed the landscape structure and function of this region and greatly affected its biodiversity, ecological quality and ecological carrying capacity, thus threatening ecological security. In this paper, in view of the current environmental degradation in the Shule River Basin, landscape indices were selected and used to quantitatively characterize the landscape pattern of the middle and lower reaches of Shule River Basin. Besides, a comprehensive ecological security index obtained through ecological security assessment was used to quantitatively characterize the status of ecological environment in the study region. Taking the whole study region, irrigation area and town

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as geographical units, respectively, we explored the key landscape pattern indices that influence ecological security at different spatial scales. The response of ecological security to landscape pattern was revealed. The results provide not only a scientific basis for improving ecological security in the oasis of Hexi Corridor region, but also recommendations for ecosystem protection, restoration and reconstruction, as well as ecological planning and design.

reservoir and Yulin reservoir are built around Shule River and its branches, and they provide water for agricultural irrigation in three major irrigation areas including Changma irrigation area, Shuangta irrigation area and Huahai irrigation area. In this paper, Yumen City and the south part of Guazhou County (94.81–98.25°E; 39.63 N–41.47°N) at the middle and lower reaches of Shule River Basin was taken as the study region. This region is adjacent to Jinta County at its east and Dunhuang City at its west, with an area of 27,160.2 km2 (Fig. 1).

2. Overview of study region 2.2. Social economy 2.1. Natural geography Shule River is located between 92°11′E and 98°30′E and between 38°0′N and 42°48′N, at the westernmost of Hexi Corridor, a transition zone between Qinghai-Tibet Plateau and Alashan Plateau of Inner Mongolia. It is one of the three major inland rivers in Hexi Corridor and occupies an area of 100,000 km2. Shule River Basin has a temperate arid climate, with sufficient and strong solar radiation. The annual average temperature is 7–9 °C. The annual average rainfall is b60 mm, but the evaporation amount reaches 1500–3000 mm (Jiuquan City Local Records Compilation Committee, 2008). Changma Valley and Shuangta Reservoir divide Shule River into upstream portion, midstream portion and downstream portion. Specifically, the upstream portion is between the source of the river and Changma Valley, the midstream portion is between Changma Valley and Shuangta Reservoir and the downstream portion is the portion at the downstream of Shuangta Reservoir. The midstream and downstream regions are characterized by a flat terrain. Oasis and desert coexist in these regions. Since Han Dynasty, there has been agricultural activities and water conservancy construction in the middle and lower reaches of Shule River Basin. Irrigated agriculture is relatively well developed in Yumen City, Guazhou County and Dunhuang City. The branches of Shule River include Yulin River, Shiyou River, etc. Four reservoirs including Changma reservoir, Shuangta reservoir, Chijin

In 2015, the total population was 267,800, the GDP was 17.747 billion Yuan and rural per capita income was 13,351 Yuan in Yumen City and Guazhou County. Compared with other regions in Gansu Province, this region develops relatively well. This region includes 23 towns in 2015. In 1983 and 1996, “Agricultural Construction and Immigrant Settlement Project in Two West Regions” and “Integrated Project of Agricultural Irrigation and Immigrant Settlement in Shule River Basin” were implemented in Shule River Basin, respectively. After 1997, Chinese government implemented a series of policies emphasizing regional development and environmental protection, such as “Reconstruction of Hexi Corridor”, “Western Development”, “Three-North Shelterbelt Project”, “Grain for Green Project”, etc., which directly or indirectly influenced landscape pattern in Shule River Basin. 3. Data sources and research methods 3.1. Data sources The data used in this paper came from four sources: (1) Remote sensing data. Landsat5 TM remote sensing images for 1987, 1990, 1996, 2000, 2007, 2010 and 2015 were obtained from United States Geological Survey (USGS) website and Institute of Remote Sensing

Fig. 1. Survey map of the study region.

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and Digital Earth (RADI), Chinese Academy of Sciences. The path/row of the images are 135/32, 136/31, 137/31 and 137/32, respectively. A total of 35 images were obtained for the seven years. The images were taken in July or August when the vegetation coverage rate is the highest. The average cloud cover of the images was no higher than 10%, thus the quality of the data can meet the requirements. (2) Basic maps. Topographic map for Guazhou County and Yumen City (1:250,000) and vector administrative boundary (1:250 000) were obtained from Gansu Province Surveying and Mapping Bureau. Land use survey data (1: 1,000,000) in 1998 and 2008 were obtained from Gansu Province Land and Resources Department. Land use change data (vector format) in 2015 were also obtained from Gansu Province Land and Resources Department. (3) Social economy statistics. Statistics about population, social economy and so on in 1987–2015 were obtained from Yumen Statistics Bureau and Guazhou Statistics Bureau. The basic data of the Project of Immigrant Development in Shule River Basin were obtained from Gansu Province Shule River Construction Administration Bureau (Project Completion Report of the World Bank Loan Project for Hexi Corridor (Shule River) in Gansu Province of China). (4) Field survey data. From 2007 to 2015, we carried out field survey for seven times in the study region and interviewed the staff in local related departments, such as agriculture, forestry, water conservancy, nature reserves, etc. On this basis, we developed a good understanding of land use changes in the study region. 3.2. Research methods 3.2.1. Ecological security assessment model (1) Pressure-State-Response (PSR) framework

PSR model was proposed by Canadian statisticians David J. Rapport et al (1979) (David and Henry, 1980). It is a frequently used model for assessing ecological health in environmental quality research. In fact, it has been widely used in various fields including sustainable utilization of resources, ecological safety assessment and environmental impact assessment (Wang et al., 2017; Zhang et al., 2012). Compared with other models, the PSR model often gives a clearer causal relationship (Whitall et al., 2007; Wolfslehner and Vacik, 2008), emphasizing the interaction between human activities and the ecological environment. As human activities cause a certain level of pressure (P) on the environment, the state (S) of the environment will change and in response (R) to these changes, humans will take some measures to improve environmental quality and prevent environmental degradation (Wang et al., 2017). However, the PSR model has some limitations. First, during model construction, the selection of indices and the determination of weights are relatively dependent on researchers' personal experience and are thus subjective. Second, in large-scale spatial research, natural endowments and development levels may vary spatially and it remains difficult for a static evaluation system to effectively match the actual situation of the study area (Adriaanse, 1993; Kelly, 1998). There are three reasons why this model was used in this paper. First, the PSR model is very systematic and has high operability (Whitall et al., 2007). It can reveal clear causal relationships in ecological and environmental systems. Second, after comprehensively considering the actual situation of the study region, this study is able to select an appropriate index system. Third, the study region (Guazhou County and Yumen City) is small, thus the spatial variation of natural endowments and development levels is insignificant. The two areas can then be evaluated using the same index system to avoid incomparable evaluation results caused by different index systems. (2) Calculating comprehensive ecological security index

Many methods have been proposed for assessing ecological security, such as comprehensive index method, matter element model method,

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simulated nerve method and ecological footprint method (Jogo and Hassan, 2010; Kelble et al., 2013; Muradyan and Asmaryan, 2015; Ruževičius, 2011). Comprehensive index method is the most widely used one in ecological security assessment. Simply speaking, it is a linear weighting method, which is comprehensive, integrated and easy to implement. The difficulty of this method lies in establishing a standard index system to characterize ecological security. In order to overcome this difficulty, a scientific and reasonable ecological security evaluation index system was constructed in this paper. The comprehensive ecological security index of each system is calculated by weighted summation method:

Ei ¼

n X

W j  Zij

ð1Þ

j¼1

where Ei is the comprehensive ecological security index of ith system, Zij is the standardized value of jth ecological security index for ith system, Wj is the weight of jth ecological security index, and n is the total number of indices. The greater the value of Ei, the higher the ecological security. The smaller the value of Ei, the lower the ecological security. On the basis of literatures (Gao et al., 2006; Zhang et al., 2014) and in combination with the calculation results of this paper, criteria were developed for classifying the ecological security of the study region into five levels: level I (poor status, Ei ≤ 0.3), level II (relatively poor status, 0.3 b Ei ≤ 0.4), level III (fair status, 0.4 b Ei ≤ 0.5), level IV (relatively good status, 0.5 b Ei ≤ 0.6), and level V (good status, 0.6 b Ei ≤ 1). Since the study region is relatively small, the spatial variations of natural environment, immigrant development policies and other aspects are insignificant. In order to better reflect the spatial variation of the ecological security in the study region, the classification criteria used here were slightly different from those used for related research at a larger scale. (3) Constructing ecological security assessment index system

Constructing a comprehensive and effective index system is the key to accurately and quantitatively assessing the ecological security of the study region. At present, however, the academic circles have not yet reached an agreement on a universal index system for ecological security assessment. Following the principles of scientificity, purposefulness and practicability, the multi-level PSR evaluation index system, which includes target level, item level, factor level and index level, has been unanimously recognized by scholars (Belousova, 2000; Wellington and Rashid, 2010). The index level is the most basic level in the system and consists of measurable indices. The index system involves economic, social and ecological aspects (Belousova, 2000; Muradyan and Asmaryan, 2015; Spiegel et al., 2001; Wellington and Rashid, 2010). The selection of eco-environmental stress indices mostly takes into account the direct or indirect burden of human or natural factors, such as population, agricultural production input and economic level, on the environment (Muradyan and Asmaryan, 2015; Zuo, 2004). Ecoenvironmental state indices can reflect the state of environmental quality, natural resources and ecosystems, as well as the changes of resources and environment over time (Ren and Liu, 2013; Zhu et al., 2014). Response indices describe the degree of response of human society to environmental change and the relevant measures taken (Ren and Liu, 2013; Zhu et al., 2014; Zuo, 2004). River basin is a complex ecosystem that consists of multi-attribute, multi-level subsystems. It mainly includes production environment, living environment and ecological environment. On the basis of PSR model, assessment index system developed in the literatures (Belousova, 2000; Zhu et al., 2014) and the characteristics of natural environment and social economy in the study region, we constructed an assessment index system for ecological security from pressure, state

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Table 1 Ecological security assessment index system and index weight. Target layer

Project layer

Index layer (unit)

Weight

Positive or negative

Ecological security index

Pressure index (0.375)

Population density (people/km2) Built-up area (ha) The amount of fertilizers used (t) The amount of plastic films used (t) Total mechanical power (kw) The amount of farm animals Per capita cultivated land area (hm2) Per capita water resources (m3) The proportion of immigrants in total population (%) GDP growth rate (%) Natural population growth rate (%) Investment in immigrant project (10,000 Yuan) Normalized difference vegetation index (NDVI) Natural vegetation coverage (%) Cultivated land area (hm2) Total water resources (100 million m3) Annual average rainfall (mm) Desertification area (hm2) Wetland area (hm2) Overall level of economic development The area ratio of food to cash crops Grain yield (kg) Rural per capita income (Yuan) Afforestation area in corresponding year (hm2) Desert management area in corresponding year (hm2) Annual increase in cultivated land area (hm2) Per capita GDP (10,000 Yuan) Fixed asset investment (10,000 Yuan) Urbanization rate (%)

0.0365 0.0330 0.0334 0.0219 0.0256 0.0274 0.0465 0.0319 0.0415 0.0218 0.0241 0.0318 0.0290 0.0382 0.0334 0.0267 0.0314 0.0212 0.0312 0.0314 0.0629 0.0311 0.0199 0.0534 0.0403 0.0430 0.0400 0.0659 0.0254

− − − − − − + + − − − + + + + + + − + + + − − + + + + + −

State index (0.243)

Response index (0.382)

and response aspects. It consists of 3 second-level indices and 29 thirdlevel indices (Table 1). The ecological pressure index of the study region reflects the pressure caused by human activities on the environment. It mainly involves four aspects: population growth, rapid urbanization, agricultural modernization and immigrant development. With population growth, more resources are used and the pressure on the ecological environment increases correspondingly. The rapid urbanization in the study region leads to the conversion of natural lands to construction uses and the change of ecological landscape into urban landscape, thus the stability of landscape is declining. Agricultural modernization promotes the continuous improvement of agricultural production conditions but at the same time brings about environmental pollution due to the use of chemical fertilizers and plastic films. Immigrant development projects result in population growth, large-scale reclamation of grasslands, woodlands and other lands, and transformation of natural oasis into artificial oasis, thus the stability of the ecosystem is deteriorating. In order to reflect the pressure caused by the above four aspects on the ecological environment, 12 indices were selected (Table 1). The ecological state index mainly reflects the natural conditions, land uses and social economy of the region, and also reflects the integrity and stability of the ecological structure. Here, eight indices were selected (Table 1). The response index mainly considers the response of human society to environmental changes. Here, nine indices were selected (Table 1). (4) Calculating index weight

During the ecological security assessment of the study region, the initial data were standardized to eliminate the influences of dimension and numerical size on results. If the greater the value of an index, the more conducive it is to system development, then the index is a positive index and Eq. (2) will be used for its standardization. If the smaller the value of an index, the more conducive it is to system development, then the index is a negative index and Eq. (3) will be used for its standardization. The equations are as follows:

Positive index: Z ij ¼

  C ij − min C j     max C j − min C j

ð2Þ

Negative index:   max C j −C ij     Z ij ¼ max C j − min C j

ð3Þ

where Cij and Zij are the initial and standardized values of jth index; max {Cj} and min{Cj} are the maximum and minimum values of jth index. In addition, in order to reduce the influences of subjective factors on assessment results, coefficient of variation was used to assign weight to each index. The procedures are as follows: δj ¼

Dj

ð4Þ

Zj

δj W j ¼ Pn j¼1

δj

ð5Þ

where δj, Dj, Z j and Wj are the coefficient of variation, mean square error, mean and weight of jth index, respectively (Table 1). 3.2.2. Selection of landscape pattern indices According to the current land use status in the study region, six land types (grassland, woodland, urban and rural construction land, wetland, cultivated land, and vegetation-free area) were identified. Since the area of each land plot in the study region was no larger than 1 hm2, the land use data space was resampled to 30 m × 30 m grids. Fourteen landscape pattern indices were selected from six aspects including landscape area, landscape density, landscape shape, landscape proximity, landscape dispersion and landscape diversity (Table S1). The values of these landscape pattern indices were calculated in Fragstats 4.2 to quantitatively present the spatiotemporal characteristics of landscape pattern in the study region.

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3.2.3. Pearson's correlation analysis In SPSS20.0 software, Pearson correlation coefficient method was used for analysis of correlation between ecological security and landscape pattern of the study region. Two significance levels (0.05 and 0.01) were set and two-tailed test was performed. On this basis, the key landscape pattern indices that can influence the ecological security of the study region in different periods can be identified. C ðY; X i Þ ¼  maxfjC 1 ðY; X i Þj; jC 2 ðY; X i Þjg

ð6Þ

C 2 ðY; X i Þ ¼  maxfjC 1 ð ln Y; X i Þj; jC 1 ðY; ln X i Þj; jC 1 ð ln Y; ln X i Þjg

ð7Þ

where Y is ecological security index; Xi is the ith landscape pattern index; C1(Y,Xi) is the Pearson correlation coefficient between Y and Xi; C2(Y,Xi) is the Pearson correlation coefficient that has the maximum absolute value among Pearson correlation coefficients between lnY and Xi, between Y and lnXi and between lnY and lnXi; C(Y,Xi) is the Pearson correlation coefficient that has the greater absolute value between C1(Y,Xi) and C2(Y,Xi). The signs (positive or negative) of C(Y,Xi) and C2(Y,Xi) are consistent with those of the original values. 4. Results 4.1. Temporal-spatial variation of ecological security in the study region in 1987–2015 4.1.1. Temporal variation of ecological security in the study region The ecological security index and three second-level indices of the study region in 1987–2015 were calculated (Fig. 2). The ecological security index varied significantly in the study period. The variation was Ushaped and variation cycle gradually shortened. Note that the ecological security index remained smaller than 0.5, thus the ecological security of the study region was between level II and level III. 1987–2002 corresponded to the first U-shaped variation period of ecological security index, which was thus 15 years long. The ecological security index reached a minimum value of 0.3855 in 1995 and increased to 0.4626 in 2002. 2002–2010 corresponded to the second U-shaped variation period, which lasted for 8 years. The ecological security index decreased to 0.3916 from 2002 to 2008 and then increased to a maximum value of 0.4905 in 2010. 2010–2015 corresponded to the third U-shaped variation period, which lasted for only 5 years. In this period, the value of ecological security index was relatively large (N0.4), indicating level-III ecological security. In sum, from 1987 to 2015, there was an overall rising trend in ecological security, suggesting that the ecological security of the study region was gradually under control and the ecological environment was improved. Among the three second-level indices, pressure index fluctuated and decreased with time, whereas state index and response index fluctuated and increased with time (Fig. 2). Pressure index was 0.2341 in 1987 and decreased to 0.1250 in 2015, with annual average decrease of 0.004.

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State index and response index were 0.1080 and 0.1037, respectively, in 1987, and increased to 0.1527 and 0.1903, respectively, in 2015, with annual average increases of 0.0017 and 0.0030, respectively. Among the three indices, response index increased the most rapidly, with annual average increase rate of 3.7%. Above results reveal that the development and protection policies implemented since 1987 have great effects on the ecological environment in the middle and lower reaches of Shule River Basin. The simultaneous implementation of environmental protection and development projects help to slow down the declining trend of ecological security and maintain it within a certain level. The conclusion is consistent with that of Pan and Liu showing that the ecological risk in the north of Shule River basin is higher than that in the south and the ecological security level has improved over the past 30 years (Pan and Liu, 2016) and that of Gong et al. revealing that the eco-environmental quality in the middle-lower reaches of Shule River Basin has been significantly improved (Gong et al., 2017). 4.1.2. Spatial variation of ecological security in the study region Taking towns as geographical units, we calculated the ecological security index, pressure index, state index and response index of each town in the study region in 1987, 1996, 2008 and 2015. Then, Arcgis10.2 was used to obtain spatial distribution of index values (Figs. 3–6). In 1987, the ecological security of all towns, except Guazhou town (level I), were of level II or level III (Fig. 3). The towns with level-II and level-III ecological security accounted for 26.67% and 66.67% of all towns in the study region. There existed a spatial pattern of “high ecological security in the middle west and low ecological security in the southeast”. Overall, the ecological security was of low level. In 1996, the spatial pattern of ecological security in the study region changed significantly (Fig. 3). The ecological security in the middle south decreased. The towns with level-II ecological security accounted for the highest proportion, reaching 50%, followed by towns with level-III ecological security, accounting for 43.75%. Changma Town at the south of the study region had the lowest ecological security, with an ecological security index of 0.286. There existed a spatial pattern of “high ecological security in the east and west and low ecological security in the middle”. Overall, the ecological security decreased compared with that in 1987. In 2008, the ecological security of the study region improved. Owning to immigrant and development policies, the number of towns increased from 16 in 1996 to 23 in 2008. The ecological security of most towns was of level II or level III. These two kinds of towns each accounted for 47.83% of all towns in the study region. All newly constructed towns, except Guangzhi town (level I), Shuangta town (level III) and Xiaojinwan town (level IV), were characterized by level-II ecological security. There were concentrated distribution of towns with level-II and level-III ecological security and sparse distribution of towns with level-I and levelIV ecological security. Overall, the ecological security increased a little compared with that in 1996. In 2015, the ecological security of the study region further improved. Except Huahai town (level II) and

Fig. 2. Temporal variation of the ecological security index and three second-level indices of the study region in 1987–2015.

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Fig. 3. Spatial distribution of ecological security index in the study region in 1987, 1996, 2008 and 2015.

Lianghu town (level II), all towns had an ecological security index N0.40. The number of towns with level-III ecological security reached 14, accounting for 60.87% of all towns. The number of towns with level-IV ecological security reached 6, accounting for 26.07% of all towns. Qidun town was the only town with level-V ecological security and its ecological security index reached 0.608. There existed a spatial pattern of “high ecological security in the west, middle and south and low ecological security in the east”.

The spatial variation of pressure index in the study region was significant but gradually reduced with time (Fig. 4). In 1987, except Changma town and Qingquan town, all towns in the study region had a pressure index N0.2, accounting for 86.67% of all towns. There existed difference in pressure index between the west and east. Overall, the environmental pressure in the study region was of low level. From 1987 to 1996, the pressure indices of 93.75% of towns increased. The difference in pressure index between the west and east enlarged. Specifically, the

Fig. 4. Spatial distribution of pressure index in the study region in 1987, 1996, 2008 and 2015.

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Fig. 5. Spatial distribution of state index in the study region in 1987, 1996, 2008 and 2015.

pressure indices of the towns decreased in the middle, increased in the east, and decreased in the west. Overall, the environmental pressure in the study region increased. In 2008, the pressure index of the middle east was relatively small, while that of the west was relatively large. Except Lianghu town, Shuangta town and Xihu town (0.17–0.20), all towns had a pressure index smaller than 0.17. Especially, 14 towns had a pressure index smaller than 0.14. In 2015, the pressure indices of all towns tended to be close to each other. Specifically, 18 towns

had a pressure index smaller than 0.14, accounting for 78.26% of all towns. Only three towns had a pressure index N0.17. The spatial difference in state index in the study region reduced from 1987 to 2015 (Fig. 5). In 1987 and 1996, there was large spatial difference in state index in the study region, which was characterized by crisscross distribution of various ecological security states. Overall, in 1987, there existed a spatial pattern “high state index in the west and east and low state index in the middle”. In 1996, the spatial pattern

Fig. 6. Spatial distribution of response index in the study region in 1987, 1996, 2008 and 2015.

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changed to “high state index in the middle and east and low state index in the west”. In 2008, the spatial pattern changed to “high state index in west and east and low state index in the middle”. The state indices of the towns in west and east were in the range of 0.12–0.14. In the middle, the state indices of all towns, except Hedong town, were smaller than 0.12, and the number of such towns accounted for 86.96% of all towns. In 2015, the spatial pattern further changed to “low state index in the west and high state index in the east”. The state indices of all towns were N0.12 and the state indices of 47.83% of towns were in the range of 0.14–0.16. Overall, the ecological security state was improved. The spatial variation of response state in the study region was significant (Fig. 6). In 1987 and 1996, the response index was relatively small in the study region. More than 80% of towns had a response index smaller than 0.12. Notably, in 1987, the response index decreased from the east to the west. From 1987 to 1996, the response indices of Suoyangcheng town and Xihu town at the west of the study region changed from level I to level II, while that of Nancha town changed from level II to level I. In 1996, there was spatial pattern of “low response index in the east and high response index in the west” in the study region. From 1996 to 2008, the spatial pattern of response index changed greatly. Overall, the response index increased, from level II to level III. There gradually appeared a spatial pattern of “high response index in the west and east and low response index in the middle”. From 2008 to 2015, the spatial pattern changed to “low response index in the west and east and high response index in the middle”. 4.2. Temporal-spatial variation of landscape pattern in the study region in 1987–2015 4.2.1. Temporal variation of landscape pattern in the study region We calculated 14 landscape pattern indices for the study region in 1987–2015. Due to limited space, this paper only listed landscape pattern indices at four time points (Table 2). In terms of landscape fragmentation, MPS of the study region increased from 1987 to 1996 and then decreased from 1996 to 2015; PD and ED of the study region decreased from 1987 to 1996 and then increased from 1996 to 2015. These results reveal that the study region was characterized by low degree of landscape fragmentation before 1996, while after 1996 the landscape fragmentation degree gradually increased. LPI was high in 1987 and 1996 (vegetation-free area accounting for N80% of total area). After 1996, LPI decreased a little. In terms of landscape shape, the LSI and AWMSI of the study region increased from 16.62 and 8.43 in 1987 to 16.84 and 8.55 in 1996, respectively. This indicates that the landscape shape in 1996 was more complex and more irregular than that in 1987. Also, the mutual interference between landscapes increased and the complexity of spatial pattern increased. After 1996, the LSI and AWMSI of the study region decreased, indicating that the complexity and irregularity of the

Table 2 The landscape pattern indices of the study region in 1987–2015. Landscape pattern index

1987

1996

2008

2015

TA LPI MPS PD ED LSI AWMSI MPI MNN CONTAG SPLIT AI SHDI SHEI

3,938,729.49 87.57 1831.12 0.05 3.14 16.62 8.43 75,492.66 585.44 86.69 1.30 99.54 0.45 0.25

3,802,930.56 87.58 1902.42 0.05 3.03 16.84 8.55 39,469.60 640.37 86.31 1.30 99.55 0.46 0.26

3,787,118.10 86.97 1783.01 0.06 3.04 15.87 8.07 26,535.90 641.88 85.04 1.32 99.55 0.51 0.28

3,923,452.80 86.92 1723.84 0.06 3.05 15.14 7.96 47,070.51 608.79 84.81 1.32 99.55 0.51 0.29

landscape shape decreased. In terms of proximity, before 2008, MPI and MNN changed oppositely. This suggests that the patches of the same type became more dispersed and landscape connectivity decreased. After 2008, the distance between patches of the same type shortened and there was an increasing trend of concentrated distribution of these patches. CONTAG decreased from 1987 to 2015, indicating that landscape connectivity decreased in general. In terms of diversity, the SHDI and SHEI of the study region were relatively low and increased slowly. Specifically, SHDI increased from 0.45 in 1987 to 0.51 to 2008 and then remained stable. This reveals that the distribution of various types of patches in the study region was uniform and the uniformity increased slowly. The proportion of vegetation-free area in the study region remained higher than 80%. SHEI was relatively low and reached only 0.29 in 2015, suggesting high landscape advantage. Pan et al. studied landscape pattern change in the middle and lower reaches of Shule River and also concluded that the landscape density increased, the largest patch index decreased after an initial increase, the weighted area index increased, and the landscape shape became irregular (Pan et al., 2012; Pan and Hu, 2014). In addition, they found that the nearest distances between patches and the separation among different types of patches decreased, and the landscape diversity and fragmentation increased. Their conclusions are consistent with the conclusions of this study. 4.2.2. Spatial variation of landscape pattern in the study region Taking towns as geographical units, we calculated the landscape pattern indices of each town in 1987, 1996, 2008 and 2015. Then, Arcgis10.2 was used to obtain the spatial distribution of the landscape pattern indices in the study region (Table 3). There was obvious spatial variation of landscape fragmentation in the study region. The degree of landscape fragmentation in the middle was higher than that in the west and east. In 1987 and 1996, the west and east were characterized by high LPI. In 2008, medium and low LPIs were dominant in the study region, especially the middle and west. In 2015, there appeared a spatial pattern of “high LPI in the west and east and low LPI in the middle”. At the four time points, the spatial distributions of MPS, PD and ED were consistent. High and low MPS values were distributed in Suoyangcheng town and Changma irrigation area, respectively. The west and east of the study region were characterized by medium and high MPS values. The study region was mainly characterized by low PD and ED values. There was a spatial pattern of “low PD and ED in the west and east and high PD and ED in the middle”. At the four time points, the spatial patterns of LSI and AWMSI were relatively complex. Especially, the landscape patches in the east had more complex shapes than those in the middle and west. In 1987, high LSI values were mainly distributed in Liuhe town and Xiaxihao town in the middle. In 1996, LSI values tended to decrease. In 2008, there appeared a spatial pattern of “lowest LSI in the west, medium LSI in the east and highest LSI in the middle”. From 1987 to 2015, AWMSI decreased. More than 80% of towns had an AWMSI smaller than 4.0. There was a spatial pattern of “high AWMSI in the east and low AWMSI in the middle west”. At the four time points, the degree of landscape fragmentation and dispersion was higher in the west than in the east. High MPI values only appeared in 1987. From 1987 to 1996, low MPI values spread from the middle to the west and east. The MNN values of the east part of Shuangta irrigation area and the south part of Changma irrigation area increased from 1987 to 1996. In 2008, there appeared a spatial pattern of “high MPS and MNN in the west and low MPS and MNN in the east”. In 2015, the spatial pattern of MPS and MNN was opposite to that in 2008. At the four time points, the spatial distribution of CONTAG remained consistent, presenting a pattern of “high values in the west and east and low values in the middle”. The spatial pattern of SPLIT was relatively complex and presented as “high values in the middle and low values

L. Ma et al. / Science of the Total Environment 674 (2019) 424–438 Table 3 Spatial variation of landscape pattern indices in the study region in 1987–2015. Index LPI

MPS

PD

ED

LSI

AW MSI

MPI

MNN

CON TAG

SPLIT

AI

SHDI

SHEI

Landscape pattern index change chart from 1987 to 2015

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in the west and east”. In 1996, the spatial difference in SPLIT reduced, but it increased after 1996. The AI values were generally high in the study region, especially in 1996. The high values were mainly distributed in the southeast and west parts of the study region and highvalue towns accounted for 50% of all towns. After 1996, the AI values of the east were decreased. The spatial patterns of SHDI and SHEI were consistent and the two indices tended to increase, indicating increasing uniformity of landscape distribution. 4.3. Identifying key landscape pattern indices influencing ecological security in 1987–2015 Pearson correlation coefficient method was used for analyzing correlation between ecological security (ecological security index, pressure index, state index and response index) and landscape pattern (14 landscape pattern indices). Two significance levels (0.05 and 0.01) were set and two-tailed test was performed. On this basis, we identified the key landscape pattern indices that influence ecological security as well as pressure, state and response (Table 4). 4.3.1. Identifying key landscape pattern indices influencing ecological security Table 4 shows that there was significant temporal variation of relationship between ecological security and landscape pattern. In 1987, ecological security index was closely correlated with TA and MPS, with correlation coefficients of −0.617 and −0.620 (P b 0.05), respectively. This means that the landscape area characteristics had significant negative influence on ecological security. The main reason is that the vegetation-free area accounted for N80% of total area. Its “advantage”

was too prominent and not conducive to the improvement of ecological security. In 1996, ecological security index was closely correlated with MPS, but the degree of correlation decreased compared with that in 1987, with correlation coefficient of −0.515 (P b 0.05). In addition, the coefficient of correlation between ecological security index and MNN was as high as −0.533 (P b 0.05). As can be seen, the large vegetation-free area remained an important factor influencing ecological security. At the same time, the connectivity of landscapes of the same type also had negative influence on ecological security. The longer the distance between landscapes of the same type, the poorer the stability of ecological environment and the lower the ecological security. In 2008, ecological security was closely correlated with landscape shape characteristics instead of landscape area characteristics. The coefficients of correlation between ecological security index and LSI as well as AWMSI reached 0.476 and 0.452 (P b 0.05), respectively. This indicates that with increase in land-use intensity, the landscape shapes became more complex and irregular, which was conducive to the improvement of ecological security. In 2015, the correlation between ecological security and landscape pattern became even stronger. Specifically, ecological security index was closely correlated with LPI, PD, ED, SPLIT and AI (P b 0.01), with correlation coefficients of −0.551, 0.560, 0.557, 0.558 and −0.55, respectively. Besides, ecological security index was closely correlated with CONTAGE, SHDI and SHEI (P b 0.05), with correlation coefficients of −0.524, 0.52 and 0.512, respectively. With increase in human activities, landscape shape, fragmentation and connectivity changed significantly. High fragmentation degree, diverse landscape types and complex landscape shapes were conducive to the improvement of ecological security.

Table 4 Analysis results of correlation between ecological security and landscape pattern. Landscape pattern index

Ecological security index 1987

TA MPS LPI PD ED LSI AWMSI MPI MNN CONTAG SPLIT AI SHDI SHEI Landscape pattern index

TA MPS LPI PD ED LSI AWMSI MPI MNN CONTAG SPLIT AI SHDI SHEI a b

a

−0.617 −0.620a −0.039 0.364 0.344 −0.079 −0.355 −0.236 −0.455 −0.184 0.115 −0.339 0.144 0.166

Pressure index

1996

2008

2015

1987

1996

2008

2015

−0.077 −0.515a −0.249 0.472 0.477 0.37 0.017 0.168 −0.533a −0.296 0.302 −0.479 0.273 0.277

0.294 0.077 −0.14 0.041 0.041 0.476a 0.452a 0.157 −0.253 −0.08 −0.037 −0.06 0.126 0.087

−0.344 −0.325 −0.551b 0.560b 0.557b −0.108 −0.33 −0.348 −0.308 −0.524a 0.558b −0.550b 0.520a 0.512a

−0.113 −0.271 −0.057 0.030 −0.002 −0.149 −0.376 −0.175 −0.369 0.169 0.020 0.003 −0.169 −0.181

0.146 −0.130 0.051 0.003 0.037 0.152 0.194 0.405 −0.057 0.125 0.033 −0.044 −0.152 −0.140

0.024 −0.003 0.126 −0.207 −0.184 0.149 0.177 0.289 0.053 0.129 −0.249 0.173 −0.089 −0.118

−0.308 −0.239 −0.332 0.487a 0.397 −0.402 −0.460a −0.432a −0.067 −0.290 0.300 −0.382 0.286 0.273

State index

Response index

1987

1996

2008

2015

1987

1996

2008

2015

−0.360 −0.201 −0.086 0.393 0.443 0.133 0.043 −0.170 0.165 −0.511 0.245 −0.437 0.489 0.509

−0.325 −0.684b −0.497 0.718b 0.736b 0.510a −0.061 −0.066 −0.698b −0.594a 0.514a −0.737b 0.588a 0.577a

0.482a 0.264 0.160 −0.026 −0.118 0.613b 0.484a 0.063 −0.414a 0.181 −0.146 0.083 −0.126 −0.181

0.199 0.063 −0.050 −0.078 −0.012 0.159 0.034 0.056 −0.059 −0.171 0.161 0.018 0.160 0.188

−0.726b −0.633a 0.108 0.296 0.254 −0.054 −0.153 −0.014 −0.510 −0.128 −0.057 −0.252 0.066 0.111

0.289 0.266 0.329 −0.275 −0.351 −0.320 −0.105 0.018 0.139 0.303 −0.342 0.357 −0.318 −0.297

0.081 −0.072 −0.462 0.289 0.335 0.144 0.173 −0.089 −0.144 −0.387 0.297 −0.326 0.377 0.386

−0.349 −0.318 −0.532b 0.461a 0.510a 0.134 −0.102 −0.167 −0.396 −0.471a 0.519a −0.516a 0.473a 0.459a

Means significance under 95% confidence level. Means significance under 99% confidence level.

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4.3.2. Identifying key landscape pattern indices influencing ecological pressure, state and response In 1987, response index was closely correlated with TA and MPS, with correlation coefficients of −0.726 (P b 0.01) and −0.633 (P b 0.05), respectively. Note that ecological security index was also closely correlated with TA and MPS. It was thus inferred that by influencing the response of human society to environmental changes, TA and MPS was able to influence ecological security. In 1996, ecological state index was closely correlated with 10 (out of 14) landscape pattern indices. Specifically, ecological state index was negatively correlated with CONTAG (P b 0.05) and positively correlated with LSI, SPLIT, SHDI and SHEI (P b 0.05). In addition, ecological state index was negatively correlated with MPS, MNN and AI (P b 0.01) and positively correlated with PD and ED (P b 0.01). These results show that landscape pattern had great influence on ecological state. The changes in landscape density, shape, proximity and dispersion degree can lead to changes in ecological state. The increases in PD, ED, LSI, SPLIT, SHDI and SHEI were conducive to the improvement of ecological state. In 2008, the situation was similar to that in 1996. The ecological state index was positively correlated with LSI (P b 0.01), TA and AWMSI (P b 0.05), and negatively correlated with MNN (P b 0.05). These results reveal that landscape shape and area were important factors influencing ecological state. Irregular complex landscape shape and strong mutual interference between landscapes were conducive to the improvement of ecological state. In 2015, ecological pressure index was closely correlated with PD, AWMSI and MPI. Specifically, pressure index had negative correlation with AWMSI and MPI. Response index was closely correlated with LPI, PD, ED, CONTAG, SPLIT, AI, SHDI and SHEI. Specifically, response index had negative correlation with LPI, CONTAG and AI. These results indicate that with increase in fragmentation degree, ecological pressure increased. With increase in human activities' influence on ecosystem and landscape diversity, the response of human society to environmental changes increased. 5. Discussion 5.1. Assessment method and classification criteria Ecological security assessment based on PSR model can intuitively reflect the ecological security status of a region (Wang et al., 2008). However, the key ecological processes that have important influence on ecological security are often not fully explained. Landscape pattern refers to the spatial configuration of landscapes with different sizes and shapes and considered as the “carrier” of ecological process, in which various driving forces play their roles (Su and Fu, 2012). Landscape pattern and ecological process interact with each other. This interaction drives the dynamics of landscape evolution and enables the landscape to show certain characteristics and functions. Here, assessment index systems were constructed for ecological security and landscape pattern. We then analyzed the spatiotemporal variation in the ecological security and landscape pattern of the middle and lower reaches of Shule River Basin in 1987–2015. Such analysis is a core of landscape ecology and also a trend of research. Studying the influence of landscape pattern on ecological security can help reveal the correlation between landscape pattern evolution and ecological process and provide insights into the regulation mechanism of ecological security in the study region. However, ecological security analysis is a very complex problem since it involves numerous factors and requires the construction of scientific assessment index system. The criteria for classifying ecological security in the study region were in accordance with those in the literatures, but further discussion on these criteria is necessary. In addition, the selection of landscape pattern indices was based on literatures and our own understanding of landscape pattern. Therefore, a certain degree of subjectivity existed in the selection

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process and using the selected landscape pattern indices might not necessarily enable to obtain responsive relationship between ecological security and landscape pattern indices. In sum, further research should focus on improving the accuracy of ecological security classification criteria or constructing new ecological security assessment index system. Besides, a more scientific method should be proposed for studying ecological security and landscape factors influencing it. Notably, this paper used Pearson correlation analysis to evaluate the relationship between ecological security and landscape pattern. On this basis, the key landscape pattern indices affecting ecological security were determined. There are mainly two considerations. First, landscape pattern indices can be used to quantitatively reflect the structure and spatial distribution characteristics of landscapes, on which basis Pearson correlation analysis can be used to reveal the relationship between landscape pattern and ecological security. Second, landscape ecology can be applied to the evaluation of ecological security (Lee et al., 1999; Heggem et al., 2000). Landscape quality is closely related to ecological security. Landscape pattern refers to the spatial distribution and concentration characteristics of landscape factors (Fu et al., 2002) and is an important component of landscape quality. The change of landscape pattern will cause the change of ecological security indices such as per capita resource occupancy, agricultural input and output, land use process, etc. Pearson correlation analysis enables to quantify the impact degree. In other words, it enables to reveal the impact of landscape pattern on ecological security. 5.2. Relationship between human activities and landscape pattern Human activities can directly or indirectly influence the environment, thus leading to changes of landscape types and finally affecting energy transformation in ecosystems. Human disturbance is considered to be the major driving force for increase in landscape heterogeneity on a small temporal-spatial scale. The mechanism of human disturbance in affecting landscape evolution and ecological functions (e.g. mass exchange and energy transformation in ecosystems) varies with the intensity of the disturbance. Medium-level disturbance is found conducive to the improvement of biodiversity (Feng et al., 2018). With increasing influence of human activities on the environment, landscape pattern and its evolution become more controlled by human disturbance. Finally, landscape diversity can decrease and the degree of landscape fragmentation will increase (Chen and Fu, 1996), which are consistent with the results of this paper. Population growth and human disturbance are important reasons of landscape fragmentation and ecological degradation (Liu et al., 2001). Shule River Basin is an oasis-desert ecosystem in China's arid region. Water resource shortage and low vegetation coverage are the major factors impeding the sustainable development of this region. With increasing ecological pressure caused by population growth and rapid economic development, this ecosystem becomes especially vulnerable. From 1987 to 2015, population in the middle and lower reaches of Shule River Basin increased rapidly. Socioeconomic activities and especially human activities promoted by important policies have led to significant land use changes (Ma et al., 2018). As a result, landscape structure and function have become more complex. Since 1987, many immigrant development projects have been carried out in Shule River Basin (Chang and Zhang, 2014). Among them, “Integrated Project of Agricultural Irrigation and Immigrant Settlement in Shule River Basin” that was launched in 1996 and lasted for 10 years has the most important influence on Shule River Basin. From 1996 to 2006, about 1.884 billion Yuan were invested in the project, 62,000 people immigrated to Shule River Basin, the area afforested reached 4218.95 hm2, and cultivated land area increased by 14,212 hm2. The human activities promoted by such policies led to the conversion of vegetation-free area into cultivated lands and large-scale afforestation. The implementation of largescale hydraulic engineering projects resulted in rapid decrease in the area of natural ecosystems. From 1987 to 2015, the areas of semi

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artificial land (farming land) and artificial land (urban and rural construction land) increased consistently with increase in oasis area. However, the area of semi natural land (woodland, grassland and wetland) decreased annually. The area of semi artificial land increased the most rapidly, by 977.96 km2 from 1987 to 2015, with an annual average increase of 33.72 km2. Note that the area of semi artificial land increased especially rapidly from 1996 to 2007, by 593.61 km2 (annual average increase of 53.96 km2), accounting for 60.7% of total increase from 1987 to 2015. The area of artificial land increased relatively rapidly after 2007, by 46.40 km2 (annual average increase of 5.80 km2), accounting for 51.69% of total increase from 1987 to 2015 (Ma et al., 2018; Qi et al., 2014). By conducting hydraulic engineering projects (reservoirs and artificial canals), humans have changed the way in which lands are used. Artificial and semi artificial lands gradually replace natural and semi natural lands, which affects water circulation and disturbs the balance of hydrological ecosystem in the oasis. In other words, these human activities affect the role of water, as an ecological factor, in the development of ecological environment and further affect the evolution of landscape pattern in the study region. This indicates that human activities have increased in the study region, which has thus gradually changed from a natural ecological environment to an artificial ecological environment (Peng et al., 2003). In addition, almost all surface water has been directed to farming areas for agricultural production. As a result, the role of water in the development of regional landscape pattern is becoming more and more influenced by human activities. The natural distribution of water resources is changed, thus the regional landscape pattern relying on water circulation also changes. This situation is getting worse and spreading to other regions (Ma et al., 2018). 5.3. Relationship between landscape pattern and ecological security Landscape pattern evolution has important influence on regional ecological environment. Landscape pattern evolution means changes of landscape spatial structure and is caused by land use changes (Quigley et al., 2001). Landscape change is the most intuitive indicator of land use or land cover change. The interaction between land use change and landscape pattern change is a focus of environmental change research (Feng et al., 2010). Landscape pattern and its changes reflect the comprehensive influence of natural and human factors or ecological process on ecosystem at a certain scale (Xie, 2008). Especially in landscape with high-intensity human activities, ecological processes occurring due to different land use types and land use intensities have regional and cumulative characteristics, which are intuitively reflected in the structure and composition of ecosystem, thus affecting ecological security (Fu et al., 2009). With population growth and rapid socioeconomic development since 1960s, the exploitation and development of inland river basin in arid regions of China has reached an unprecedented high intensity. The three major inland rivers including Shule River, Shiyang River and Heihe River in Hexi Corridor of China have all been exploited. Notably, many hydraulic engineering projects were carried out. For example, five reservoirs including Changma, Shuangta, Chijinxia, Danghe and Yulinhe have been constructed around Shule River Basin. Ten reservoirs have been constructed around the outlets of the eight branches of Shiyang River. Ninety-eight reservoirs have been constructed around Heihe river. In 1987, the land development and utilization was relatively slow in the Shule River Basin. Among the three major irrigation areas, Changma irrigation area in the middle was characterized by a relatively high land-use intensity, while the land-use intensities of the other two irrigation areas were low and close to each other. In 1996, the spatial pattern of land-use intensity in all irrigation areas, except Changma irrigation area, remained almost unchanged. From 1996 to 2007, the landuse intensity in the study region increased and this trend continued after 2007 (Ma et al., 2018). Population growth, hydraulic engineering projects and large-scale reclamation caused changes in water circulation and further changes in landscape fragmentation, shape,

connectivity and diversity, which then affected the ecological environment (Cheng and Zhao, 2008). Finally, environmental degradation might occur. With the rapid development of economy, more and more water resources were consumed and water that should participate in various ecological and environmental processes were also consumed by human society. As a result, ecological water shortage and environmental degradation appeared as two environmental problems. Moreover, a vicious circle may be formed. The ecological carrying capacity of the river basin continuously decreased (Yue et al., 2011) due to the above-mentioned environmental problems. However, the demands of humans for the ecological carrying capacity of the river basin increased rapidly (Yue et al., 2006). Consequently, ecological carrying capacity demand was greater than its supply and the river basin was in a state of ecological insecurity (Yue et al., 2012). Such a state can not only endanger the living environment of humans and aggravate poverty, but also impede the sustainable development of the ecology, society and economy of the inland river basin in arid regions. In addition, land-use intensity varied with time and the difference in land-use intensity could lead to difference in landscape pattern (Ma et al., 2018) as well as ecological pressure, state and response. Therefore, ecological security presented cyclical fluctuations in the study region.

6. Conclusions In this paper, PSR model and landscape index system were constructed and used to analyze the spatiotemporal variation of ecological security and landscape pattern in the middle and lower reaches of Shule River Basin in 1987–2015. We also quantitatively identified the key landscape pattern indices that influence the ecological security in the study region. One contribution of this work is the spatiotemporal analysis of the ecological security and landscape pattern in the middle and lower reaches of Shule River Basin and visualization of the analysis results. From a temporal perspective, the ecological security index of the study region remained smaller than 0.5 in 1987–2015, varying between level II and level III. The variation was U-shaped and periodic, but the variation period was gradually shortened. From a spatial perspective, the spatial pattern of “high ecological security in the middle west and low ecological security in the east” in 1987 changed to “high ecological security in the west and east and low ecological security in the middle” in 1996 and then to “high ecological security in the southwest and middle and low ecological security in the east” in 2015. Landscape pattern also varied greatly with time. From 1987 to 1996, landscape fragmentation degree was relatively low, landscape shapes tended to be complex and irregular, mutual interference between landscapes increased, and the spatial pattern became more complex. From 1987 to 2008, landscapes of the same type tended to be more disperse, landscape connectivity decreased and the uniformity of landscape distribution increased. In other periods, the trends of changes in landscape pattern were opposite to those mentioned above. The second contribution of this work is the in-depth analysis of the influence of ecological processes on ecological security and identification of key landscape pattern indices influencing ecological security. Landscape size, shape, number, type and spatial configuration all have important influence on ecological security and were found to vary with time. In 1987, ecological security was closely correlated with landscape area indices (TA and MPS). In 1996, ecological security was closely correlated with MPS and MNN. In 2008, ecological security was correlated with LSI and AWMSI. Landscape shapes tended to be more complex and irregular. In 2015, ecological security was closely correlated with more landscape pattern indices (8 out of 14). With increase in human activities, landscape shape, fragmentation degree and connectivity all changed. High degree of landscape fragmentation, diverse landscape types and complex landscape shapes were conducive to the improvement of ecological security.

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Human activities can directly or indirectly influence the ecological environment, thus leading to changes of landscape types and finally affecting energy transformation in ecosystems. Landscape pattern evolution is closely related to the intensity of human activities. With increasing influence of human activities on the ecological environment, landscape pattern and its evolution become more controlled by human activities, which will be reflected in the changes of ecosystem structure and composition, finally affecting ecological security. Acknowledgments Research grants from the National Natural Science Foundation of China (Grant number 41661105). The authors would like to thank the anonymous reviewers for their helpful and constructive feedback. Author contributions Libang Ma and Jie Bo designed the study and processed the data., Xiaoyang Li, Fang Fang and Wenjuan Cheng gave comments on the manuscript. All authors Contributed to the results, related discussions and manuscript writing. Conflicts of interest The authors declare no conflict of interest. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.04.107. References Adriaanse, A., 1993. Environmental Policy Performance Indicators. A Study on the Development of Indicators for Environmental Policy in the Netherlands. Sdu Uitgeverij Koninginneg racht, The Hague. Baudot, B.S., Moomaw, W.R., 1999. People and their planet: searching for balance. Peop. Their Plan. Sear. Bal. 284–298. Belousova, A.P., 2000. A concept of forming a structure of ecological indicators and indexes for regions sustainable development. Environ. Geol. 39, 1 227–1 236. Brand, U., Vadrot, A., 2013. Epistemic selectivities and the valorization of nature: the cases of the Nagoya protocol and the intergovernmental science-policy platform for biodiversity and ecosystem services(IPBES). Law Environ. Dev. J. 9, 202–220. Chang, G.Y., Zhang, W.X., 2014. Ecological civilization-based rethinking of large-scale immigration and land development along Shule River. J. Lanzhou Univ.: Natural Sciences. 50, 405–409 (in Chinese). Chen, L.X., Fu, B.J., 1996. Analysis of impact of human activity on and scape structure in Yellow River delta-a case study of Dongying region. Acta Ecol. Sin. 16, 337–344 (in Chinese). Chen, Y.L., Xie, B.G., Zhong, D., Wu, L.Q., Zhang, A.M., 2018. Predictive simulation of ecological space based on a particle swarm optimization-markov composite model: a case study for Chang-Zhu-Tan urban agglomerations. Acta Ecol. Sin. 38, 55–64 (in Chinese). Cheng, G.D., Zhao, C.Y., 2008. An integrated study of ecological and hydrological processes in the inland river basin of the arid regions, China. Adv. Earth. Sci. 23, 1005–1012 (in Chinese). Costanza, R., Norton, B.G., Haskell, B.D., 1992. Ecosystem health: new goals for environmental management. Ecosyst. Heal. N. Goals Environ. Manage. 234–246. David, J.R., Henry, A.R., 1980. An Ecological Approach to Environmental Information. Ambio 9 (1), 22–27. Dong, S.K., Kassam, K.S., Tourrand, J.F., Boone, R.B., 2016. Building resilience of HumanNatural Systems of Pastoralism in the Developing World: Interdisciplinary Perspectives. Springer, New York. Faggiano, L., Zwart, D.D., Garcíaberthou, E., Lek, S., Gevrey, M., 2010. Patterning ecological risk of pesticide contamination at the river basin scale. Sci. Total Environ. 408, 2319–2326. Feng, Y.X., Luo, G.P., Zhou, D.C., Han, Q.F., Lu, L., Xu, W.Q., Zhu, L., Yin, C.Y., Dai, L., Li, Y.Z., 2010. Effects of land use change on landscape pattern of a typical arid watershed in the recent 50 years: a case study on Manas river watershed in Xinjiang. Acta Ecol. Sin. 30, 4 295–4 305 (in Chinese). Feng, Y., Yang, Q., Tong, X., Chen, L., 2018. Evaluating land ecological security and examining its relationships with driving factors using gis and generalized additive model. Sci. Total Environ. 633, 1469–1479. Fu, B.J., Chen, L.D., MA, K.M., 2002. Principle and Application of Landscape Ecology. Science Press, Beijing, pp. 188–192 (in Chinese).

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