Responses of landscape structure to the ecological restoration programs in the farming-pastoral ecotone of Northern China

Journal Pre-proof Responses of landscape structure to the ecological restoration programs in the farming-pastoral ecotone of Northern China

Dong Liu, Jiquan Chen, Zutao Ouyang PII:

S0048-9697(19)36307-7

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136311

Reference:

STOTEN 136311

To appear in:

Science of the Total Environment

Received date:

26 August 2019

Revised date:

17 December 2019

Accepted date:

22 December 2019

Please cite this article as: D. Liu, J. Chen and Z. Ouyang, Responses of landscape structure to the ecological restoration programs in the farming-pastoral ecotone of Northern China, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.136311

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© 2019 Published by Elsevier.

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Responses of landscape structure to the ecological restoration programs in the farming-pastoral ecotone of Northern China Dong Liu a*, Jiquan Chenb,c, Zutao Ouyangd a

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College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China b Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI 48823, USA c Center for Global Changes and Earth Observation, Michigan State University, East Lansing, MI 48823, USA d Department of Earth System Science, Stanford University, Stanford, CA 94305, USA

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*Corresponding author:

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Dong Liu

Sciences, Beijing 100049, China

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19A, Yuquan Road, College of Resources and Environment, University of Chinese Academy of

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E-mail address:[email protected]

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Abstract: Ecological restoration programs (ERPs) have been conducted in China since 2000 to

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improve ecological conditions, particularly in the farming-pastoral ecotone of Northern China. Few have studied the effects of ERPs on landscape structure. Taking West Liaohe River Basin (WLRB) as a case study, we explored how landscape dynamics were altered before and after ERPs from 1990 through 2015 by using multi-temporal Landsat TM images. We analyzed the effects of ERPs on landscape structure by exploring the relationships between landscape features and land cover change (LCC). The results indicate that dramatic changes in land cover and landscape structure occurred before and after ERPs implementation. During 2000-2015 1

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woodlands increased with a sharper annual growth, grasslands reclamation slowed down and was restricted, whereas more croplands were converted to grasslands and woodlands. ERPs decreased landscape fragmentation and increased landscape diversity, due mostly to the portion and spatial configures of croplands, grasslands and woodlands. Landscape fragmentation was significantly correlated with mean patch size of grasslands (r = -0.677, p < 0.0001) and woodlands (r = -0.515,

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p < 0.0001), as well as patch number ratio of croplands to the sum of grasslands and woodlands

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(r = -0.414, p < 0.01). Additionally, landscape diversity had a significant negative correlation

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with the areal ratio of grasslands (r = -0.345, p < 0.001). Our findings indicate that the LCCs

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were in agreement with ERPs‟ key goals. The changes in landscape structure in WLRB, however,

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were not expected from the ERPs design. Given the importance of landscape structure in human

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vulnerability to environment, it seemed that EPRs from the central government should not only regulate specific land use but also focus on the health and sustainability of the landscapes.

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Explicit function-based local landscape management should be taken into account for the future

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through better design and implementations of ERPs. Keywords: Land cover change; Landscape structure; Land use policy; Ecological restoration programs; Farming-pastoral ecotone of Northern China; West Liaohe River Basin 1. Introduction Coupled with rapid population and economic growth, China is experiencing widespread environmental consequences from overutilization of natural resources, particularly in ecologically vulnerable farming-pastoral ecotones -- defined as a mosaic of transitional zones 2

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between the traditional farming and pastoral lands (Li et al., 2018). Here, deforestation, overgrazing, and land conversions have been widely reported as major practices during the last century (Wang et al., 2013). To mitigate environmental damage, the Chinese government has implemented a series of conservation policies since 2000 to restore ecosystems and to promote sustainable development of the farming-pastoral ecotones. Launched in 2000, the nationwide

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Natural Forest Conservation Program (NFCP) called for expanding forest cover through

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reducing deforestation and promoting plantations (Guo and Zhang, 2009; Zhang et al., 2000).

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The Grain for Green Project (GGP), aimed at “Returning Farmlands to Forest and Grassland,” is

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another major effort to enhance ecosystem service (Liu et al., 2013). This project also debuted in

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2000, with the purpose of replacing croplands with forests to combat soil erosion on deep slopes

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(The University of Nottingham, 2010; Wang et al., 2007; Zhou et al., 2012). The Beijing-Tianjin Sandstorm Source Treatment Project (BTSST) was initiated in 2001, aiming at reducing the

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sandstorms that affected the capital city through vegetation restoration (Li and Zhang, 2004). The

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objectives of these Ecological Restoration Programs (ERPs) are to increase vegetation cove, reduce water loss and soil erosion, and prevent sandstorms by converting croplands on steep slopes to forests and/or grasslands (Cao et al., 2009; Wang et al., 2017a). The design and implementation of these ERPs sought to convert areas that were not suitable for crops to grasslands or woodlands by providing food and financial compensation to farmers. These large-scale ERPs resulted in inevitable regional land cover change (LCC) and raised many environmental concerns (Wang et al., 2017b). Several studies have examined the 3

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effectiveness of ERPs in different parts of China by focusing on changes in vegetation (Zhao et al., 2017), LCC (Restrepo et al., 2017; Yin et al., 2018), driving forces for the changes (Su et al., 2011; Wang et al., 2010), and ecological consequences (Jia et al., 2014; Wang et al., 2017a; Wang et al., 2017b). However, literature on the consequences of the ERPs since 2000 on landscape structure remains is rare. Landscape structure is an important attribute of regional

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environment change, and it is the foundation for developing sound ecological restoration and

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management practices. More importantly, governance in China is organized hierarchically, with

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national policies planned by the central government (e.g. EPRs referred here) and applied

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without consideration of regional or local social, economic, or ecological settings (i.e., not

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contextualized). As a result, the consequences vary extensively and substantively across the

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nation. Sound assessment of these effects on local landscapes, including changes in landscape structure, would be necessary to provide scientific bases for future policies and actions.

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Here we designed and conducted an empirical investigation by using the West Liaohe River

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Basin (WLRB) as our study landscape. WLRB is located in a typical farming-pastoral ecotone in Northern China. Land use regulations under ERPs were the primary policy for future land use (Chen et al., 2015c; Hill et al., 2014; Miao et al., 2013; Renwick et al., 2013). Prior to the ERPs, grasslands and hilly slopes in WLRB were intensively cultivated due to population growth and coupled production expansion, resulting in a decrease in grasslands. To achieve the objectives of ERPs, grassland restoration has been widely practiced across the basin. It is noticeable that, due to a large number of land contracts in China, the distribution of croplands was uneven, and they 4

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often were scattered across the basin, which to some extent resulted in irregular spatial mosaics of croplands and grasslands/woodlands before and after ERPs implementation. Thus, we expect that land use policy directly impacted landscape structure and dynamics in terms of land use direction and intensity. We hypothesize that the magnitude and spatial changes in croplands, grasslands and woodlands and their spatial relationships induced by ERPs would be the primary

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reasons for landscape changes across the basin, including landscape composition, patch

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configuration, patch classes, and the overall mosaic. To test this hypothesis, we analyzed the

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changes in landscape structure prior to and post implementation of ERPs during 1990-2015. The

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goal of the study was to evaluate and compare the dynamics of the landscape structure in

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different periods during the ERP process and quantify how ERPs affected landscape structure

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changes. Lessons learned from this exercise should help us to revise current management and decision-making for the future.

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Three periods were identified: 1990-2000, 2000-2005, and 2005-2015. They represent the

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period before, during and after ERPs, respectively. We challenged ourselves with several intriguing questions on the effectiveness of the ERPs at landscape scale: 1) To what degree did land cover type and composition shift after the implementation of the ERPs in 2000? 2) What other landscape features, such as patch size/density and fragmentation, were altered? 3) Were these changes in landscape structure expected from the ERPs design? and 4) What might be the other options of the ERPs for the long-term sustainability of the landscape?

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Journal Pre-proof 2. Materials and methods 2.1 Study area The farming-pastoral ecotone in Northern China is a mosaic farming and pastoral lands with diverse land use types and landscape structure (Wang et al., 2018). It is located between the sub-humid region and the arid/semi-arid region (Shi et al., 2018). Most of these land areas are

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considered fragile, owing to their sensitivity to climate change and human disturbance (Liu et al.,

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2018). WLRB (41°05′-45°13′N, 116°32′-124°30′E) lies on the northeastern margin of the

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farming-pastoral ecotone in Northern China, covering 23 counties in 137×103 km2 in Inner

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Mongolia Autonomous Region, Hebei, Liaoning and Jilin provinces (Fig. 1). The main

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tributaries in the basin are Laoha River, XarMoron River, Jiaolai River and Xinkai River. Surrounded by mountains to the north, west and south, and neighboring the Songliao Plain to the

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east, the region is normally divided into the higher southwestern region and the lower

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northeastern region. The basin has a typical semi-humid and semi-arid climate, with a mean

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annual precipitation of 300-550 mm, for which 70% falls in June, July and August. The average annual evapotranspiration and temperature are 1800-2000 mm and 5-6.5 oC, respectively (Feng et al., 2014). In 2010 the gross domestic production (GDP) was ~264 billion RMB (40.6 billion USD). The total population was 10.5 million, including 8.1 million rural people (82.7%). WLRB is also an important and high-quality maize production region (Long et al., 2010). In recent decades, the ecosystems of the basin have seriously deteriorated, due to both the rapid climate change and intensified human activities, as is evidenced by the large loss of natural

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vegetation, increased soil erosion, etc. WLRB is considered as one of the major target regions by several ERPs (e.g., NFCP, GGP and BTSST). 2.2 Data sources 2.2.1 Image data

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Landsat TM/ETM images were used to detect LCC in WLRB. Landsat 4-5 Thematic Mapper

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(TM) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) images

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at 30 m×30 m spatial resolution were obtained for 1990, 2000, 2005 and 2015 from the United States Geological Survey (https://earthexplorer.usgs.gov/). Altogether, twelve images were

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needed to cover the entire basin. Images were collected for June-September when the differences

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among land cover types were the highest. The cloud-free images were preferred; cloud cover of

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<10% were used instead when they are not available.

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2.2.2 Reference data for image classification

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We used multiple data sources as the reference datasets for developing training classification algorithms. The training data for 2000 were obtained from random sampling of the GlobeLand30 -- a fine-scale global land cover map with a spatial resolution of 30 m generated by National Geomatics Center of China (NGCC) (Chen et al., 2015b). This has been widely used at national and regional levels in different countries with high accuracy (Arsanjani 2018; Arsanjani et al., 2016; Brovelli et al., 2015; Yu et al., 2018). This product, however, includes maps only for 2000 and 2010. Training data for 1990 and 2005 were obtained from historical imagery in Google Earth Pro 7

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(GEP) (Google Inc., 2015). GEP is a software displaying satellite imagery, maps, photographs and terrain on Earth from past to present. It has been broadly used as a platform for collecting high-resolution geo-referenced information on land covers (Boardman 2016; Chen et al., 2015a; Liang et al., 2018; Lu et al., 2015). Although historical images were available for most areas of WLRB in 1990 and 2005, some parts of the basin were not covered by the images. In this case,

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we used historical images of 1991/1992 and 2006/2007. Random points were generated for

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interpreting land covers; we discarded the points that were under the clouds.

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Training data for 2015 were obtained from both field surveys and GEP. To assure

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classification accuracy we carried out a field survey and selected more than 500 random samples

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across WLRB in July of 2016. Land cover type, longitude, latitude and altitude were recorded at

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each location. Additionally, we used historical imagery in 2015 from GEP as auxiliary information for training data. Jeffries-Matusita (JM) pair-wise class distances was carried out to

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evaluate class separability (Richards and Jia, 2006). The ArcGIS 10.2 software (ESRI, 2015) was

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used to generate random samples of training points from the reference datasets for 1990, 2000, 2005 and 2015. Approximately 500 training points were used for each year to interpret the images.

2.2.3 Reference data for validation The geo-tagged high-resolution photographs on GEP and field survey samples were used for collecting independent datasets for validation. We collected geo-tagged field photos for the basin and interpreted land cover type to validate image classifications for 2000 and 2005. Due to a lack 8

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of field photos for 1990, we used a subset of reference data collected in 2000 that were located in areas where land cover remained unchanged from 1990 to 2000 to validate the classification of 1990. For 2015, 10% ground samples from field surveys were randomly selected for validation. 3. Data analysis

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3.1 Image classification

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Our image classification was conducted using a supervised classification algorithm on the ENVI

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platform (Exelis Visual Information Solutions, 2015). All Landsat TM images were

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geometrically rectified by selecting ground control points and projected to a Krasovsky_1940_Albers coordinate system based on the 1:100,000 DEM. The spectral bands

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were stacked and saved as a multiband image in TIFF format. To reduce the effects of clouds,

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cloud and cloud-shadow removal were performed. The Landsat imagines for each date were

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mosaicked. We applied the Maximum Likelihood Classification (MLC) trained by reference data

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for classification. Under the assumption of normal distribution of the data, MLC considers variance and covariance of land cover class signature files to assign unknown pixels to a specific land cover type (Lillesand et al., 2014). Post-classification was conducted to improve classification, include filtering, smooth class boundaries, and remove small isolated regions. The images were classified into six land cover types following Liu et al. (2010): cultivated land, woodland, grassland, water body, built-up land, and unused land (Table S1). Land cover maps were overlaid and intersected under ArcGIS software (ESRI, 2015) to examine the land cover change. Cross-tabulation analyses were performed to identify land cover changes between 9

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different periods, i.e., 1990-2000, 2000-2005 and 2005-2015. An accuracy assessment was performed for classified images. The overall accuracy (Foody, 2002), Kappa coefficient of agreement (Foody, 2002), user‟s accuracy and producer‟s accuracy (Foody, 2002; Lillesand et al., 2014) were calculated from confusion matrices to assess the classification accuracy through comparing image classification with the reference dataset.

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3.2 Landscape analysis

Several landscape metrics reflecting fragmentation and heterogeneity were calculated, including

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composition metrics (number of patches - NP, Shannon's diversity index - SHDI) and the

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configuration metrics (mean patch size - MPS, edge density - ED, mean shape index -

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SHAPE_MN, mean perimeter-area ratio - MPAR) (Cushman et al., 2008). The algorisms and

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their basic statistical properties were listed in Supplemental Information (Table S2). NP, ED, and MPS were used to evaluate landscape fragmentation, with higher values of NP and ED and lower

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values of MPS representing a more fragmented landscape. SHAPE_MN and MPAR reflect

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landscape regularity and complexity, with higher values indicating more complex shape. SHDI reflects landscape heterogeneity, expressing the diversity and distribution (regular or irregular) of patch types in an area. A high SHDI means the number of different patch types increased and/or the proportional distribution of area among patch types became more equitable. Using land cover dataset we classified, except for SHDI only at landscape level, FRAGSTATS software 3.3 (McGarigal et al., 2012) was used to calculate these metrics at both class and landscape level for 1990-2015. 10

Journal Pre-proof 3.3 Measures of LCC and landscape pattern dynamics response To assess the land cover and landscape changes due to ERPs, we estimated the relative change (RC) and the annual change rate (ACR) of areas by land cover type and landscape metrics: RC 

CAij Ai

(1)

CAij

(2)

N ij

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ACR 

100%

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where CAij is the area for a specific land cover type converted from year i to j, or the change for a

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specific metric from year i to j; Ai is the area of land cover i, or the value of a landscape metric in

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year i; and Nij is the time interval from year i to j.

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To relate the changes to the policies, a Pearson correlation test was used to analyze the relationship between the landscape structures and the change of each land class by county.

al., 2008): N p 1 Nc

(3)

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LFI 

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Landscape fragmentation index (LFI) of each county was based on following equation (Zhao et

where Np is the total number of patch types, and Nc is the ratio of the total area to the minimum patch area in a county. A LFI value of 0 or 1 indicates a low to high fragmentation, respectively, for a county.

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4.1 ERP-induced LCC of WLRB The producer‟s and user's accuracies of individual classes were >75%. The overall accuracies were 86.9%, 85.1%, 84.3% and 87.8%, and the Kappa coefficients were 0.78, 0.80, 0.79 and 0.83 for 1990, 2000, 2005 and 2015, respectively (Table S3). The spatial distribution of land

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cover in 1990, 2000, 2005, and 2015 are presented in Fig. 2. During 1990-2015, grassland was

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the most dominant cover in WLRB, followed by cultivated land, woodland and unused land (Fig.

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2 and 3). Bodies of water and built-up land were about 4% of the basin (i.e., 4.1%, 4.0%, 4.0%

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and 4.2% for 1990, 2000, 2005 and 2015, respectively). Because they are not correlated with the

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ERPs, they were excluded from further analysis.

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We found dramatic changes in land cover in WLRB before and after ERPs implementation. From 1990 to 2000 (i.e., prior to implementation), the largest relative change was a 13.7%

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increase in cultivated land -- which was equivalent to 458.7 km2/yr. On the other hand, woodland

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experienced the smallest change, with an annual growth rate of 2.2% (38.3 km2/yr) (Fig. 4a and 4b). Land conversion mainly involved the transition from grassland to cultivated land, accounting for 60.1% of the total transformation, with 81.2% of cultivated land converted from grassland (Table S4). We focused on the spatial changes of the three targeted cover types under ERPs within the basin: cultivated land, grassland and woodland (Fig. 5). The conversion from grassland to cultivated land was mainly found in the lower reaches of WLRB and southeastern parts of the basin during 1990-2000 (Fig. 5a). From 2000 to 2005 (i.e., immediately after 12

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implementation of ERPs), we observed a relatively small number of patches by cover type but a high rate of change. During 2000-2005, the relative (absolute) change rate was 6.4% (2444.2 km2), 1.2% (207.8 km2), -4.3% (2544.1 km2), and -1.4% (203.8 km2) for cultivated land, woodland, grassland, and unused land, respectively. Other than unused land, all land cover types experienced high rates of annual change, with the highest rate for grassland (-508.8 km2/yr)

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(Table S4, Fig. 4b). The conversion of grassland to cultivated land remained high but decreased

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to 26.9% during this period. The gain in cultivated land from grassland decreased to 72.2%

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(Table S4), which was mainly found in the middle and upper reaches of the basin (Fig. 5b). It is

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worth noting that the area of cultivated land converted into grassland and woodland increased to

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1103.9 km2 and 291.7 km2, respectively (Fig. 4c). The conversion of cultivated land into

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woodland was primarily concentrated in the mountainous areas with higher elevations (Fig. 5b). From 2005 to 2015 land cover change showed a different trend. Woodland drastically

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increased, with a relative change of 40.2% (Fig. 4a). The proportion of grassland converted to

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cultivated land continued to decline (15.8% of the total area conversion) (Table S4). The area of cultivated land was almost unchanged, rising only by 3.8 km2, indicating that cultivated land expansion was significantly weakened during this period. The annual change rate for woodland increased to 721.0 km2/yr (Fig. 4b), most of it converted from cultivated land and grassland over this 10-year period. About 81.2% of new woodland had been grassland, and 16.7% had been cultivated land (Fig. 4d). In addition, the area of cultivated land converted to grassland and woodland was 5565.7 km2 and 2402.6 km2, respectively (Fig. 4c), which was significantly higher 13

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than between 2000 and 2005. As shown in Fig. 5c, most of the conversion of cultivated land to woodland occurred in the western and southern mountainous areas of WLRB with high elevation (>800 m) and steep terrain (slope >15º). The plain in the middle and lower reaches of the basin and the gentle-slope land with elevation <500m and a slope of 2-6° had larger areas of cultivated land transferred to grassland, accounting for 83.5% of the total converted area from cultivated

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land to grassland.

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In sum, after the implementation of ERPs (2000-2015), the relative change in cultivated land

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growth of WLRB decreased from 13.7% to 6.4%, while woodland sharply increased with an

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annual growth rate of 494.5 km2/yr, which was much higher than that in 1990-2000 (38.8 km2/yr)

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(Fig. 4a and 4b). In addition, the annual change rate from cultivated land to grassland and

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woodland also increased from 38.3 km2/yr to 624.3 km2/yr (Fig. 4c). The conversion of cultivated land to woodland occurred mostly in the mountainous areas with high elevation and

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slope, while the transformation of cultivated land to grassland was widely distributed in the

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plains and mountainous regions with low elevation and gentle slopes (Fig. 5b and 5c). 4.2 ERP-induced landscape structure changes of WRLB The most apparent changes in landscape structure of WRLB before and after ERPs implementation were patch size: replacement of many, smaller patches by a few large dominant ones, especially for woodland and grassland (Fig. 6a). This was reflected by the significant increase in MPS of woodland and grassland from 111.9 ha and 258.4 ha in 2000 to 261.9 ha and 557.0 ha, respectively, during 2005-2015 (i.e., an increase of 134.0% and 115.6%). During 14

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1990-2000, MPS of cultivated land, woodland, grassland and unused land decreased, but these all increased after 2000, with a greater annual change rate in 2005-2015 than in 2000-2005 (Fig. 6e). The overall landscape appeared less fragmented after ERPs. At landscape level, WLRB landscape prior to EPRs experienced an increase in fragmentation, which is evidenced by an

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increase in NP, ED, SHAPE_MN and MPAR, and a decrease in MPS (Table 1). MPS in WLRB

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decreased from 187.2 ha to 182.9 ha from 1990 to 2000, with an annual change rate of -0.4 ha/yr.

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A dramatic decrease in landscape fragmentation occurred after EPRs (2000-2015), as indicated

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by decreases in NP, ED, SHAPE_MN and MPAR, and an increase in MPS (Fig. 6a-6e). More

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importantly, the changes in NP, MPS, ED, SHAPE_MN and MPAR slowed down during

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2005-2015.

Similar results were also found at the class level. Prior to 2000 all classes, including

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cultivated land, woodland, grassland and unused land, were fragmented, as evidenced by

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increases in NP, SHAPE_MN, MPAR and ED, and a decrease in MPS (Fig. 6a-6e). After 2000 these changes shifted toward homogenization, with greater values of annual relative change rate. Cultivated land, woodland, and unused land experienced a faster progress in homogenization during 2005-2015. However, the annual change rate of grassland had lower value for NP (-2.9%/yr) and SHAPE_MN (2.0%/yr) in 2005-2015 than those of 2000-2005 (-4.7%/yr, 8.0%/yr and -4.8%, respectively). Landscape diversity also increased significantly. During 1990-2000, SHDI varied between 15

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1.38 and 1.37, but increased to 1.39 and 1.45 in 2005 and 2015, respectively, resulting in more patch types with equal weight during 2000-2015 than during 1990-2000. Additionally, a gradually faster increase in landscape diversity over the study area as ERPs progressed was shown by trends in SHDI. From 2000 to 2005 the annual change rate of SHDI was 0.002; it rose to 0.006 during 2005-2015, respectively (Table 1).

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ERP-induced landscape changes mainly were evident in the changes in three land cover

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types: cultivated land, grassland and woodland (Fig. 7). Landscape fragmentation increased, with

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a decrease of MPS in grassland (r = -0.677, p < 0.0001) and woodland (r = -0.515, p < 0.0001)

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(Fig. 7a and 7b). The Pearson correlation coefficient (r) suggested that grassland MPS had a

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greater influence on landscape fragmentation than woodland MPS. During 1990-2000, landscape

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fragmentation of WLRB increased as MPS of grassland (265.9 ha to 258.4 ha) and woodland (118.9 ha to 111.9 ha) decreased. After 2000, landscape fragmentation began to decrease, as

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showed by an increase of MPS during 2005-2015 (Fig. 6e). Moreover, we found that landscape

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fragmentation was negatively correlated with the patch number ratio of cultivated land to the sum of grassland and woodland (Rc/gw) (r = -0.414, p < 0.01) (Fig. 7c). The Rc/gw decreased from 0.36 to 0.32 from 1990 to 2000, and rose to 0.41 in 2015. With the Rc/gw changed, the landscape fragmentation increased prior to ERPs (1990-2000) and decreased following ERPs (2000-2015). Landscape diversity was negatively correlated with the area ratio of grassland (r = -0.345, p < 0.001) (Fig. 7d). In sum, landscapes of WLRB since 1990 showed significant decreases in the extent of grassland and increases in cultivated land. Grasslands dominated the basin throughout 16

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the entire study period, but their areal proportion dramatically declined from 46.7% to 35.6% between 1990 and 2015 (Fig. 3). Other land cover types increased gradually with the reduction in grassland, especially cultivated land and woodland. The significant increase in landscape diversity can be attributed to the conversion of grassland to other land use types. These changes also led to more equal distribution among land cover types, resulting in an increase in landscape

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diversity in the basin.

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5. Discussion

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5.1 Land cover change

The land cover changes from this study are similar to previous studies conducted in other regions

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in western China, where ecological restoration policies also were adopted (Dulamsuren et al.,

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2008; Fan et al., 2015; Liu et al., 2014b; Wang et al., 2010; Zhao et al., 2010; Zhao et al., 2013).

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Within the WLRB, the area of cultivated land and woodland increased, and that of grassland

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decreased, between 1990 and 2015. Similar findings were reported in other studies. Dulamsuren et al. (2008) indicated the expansion of cultivated land and the shrinking of grassland were almost inevitable during the process of GGP in China. Liu et al. (2014b) showed that cultivated land increased mainly derived from the conversion of forest and grassland in the 1990s in Northern China. Wang et al. (2010) and Zhao et al. (2013) studied land cover change LCC in the Yellow River Basin and the Tarim River Basin, respectively. Both reported an increase in cropland and decreased grassland in their study areas. Both biophysical processes and human activities drive land cover changes, but 17

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anthropogenic factors are widely recognized to be more responsible (Chen et al., 2015c; Foley et al., 2005; Meyfroidt et al., 2013; Pekin, 2016). Increasing food demand, driven by a growing population, was the main cause of cultivated land increase in WLRB. WLRB is located in the golden maize belt in northeastern China -- an important commodity grain-producing base for the nation. Food security possesses a higher priority than ecological restoration, which might be a

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major factor influencing the change in cultivated land area over the past twenty-five years.

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Prior to 2000 large tracts of grassland were converted to cultivated land, leading to a rapid

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increase in cultivated land that directly influenced grain production in the basin (Fig. S1a).

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During the initial implementation of ERPs, inclinations toward ecological restoration and grain

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production appeared vague. When cropland shrank rapidly, the government limited cropland

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retirement programs to assure food production, which led to expansion of agriculture production to meet food needs (Yin et al., 2018). This explains why the area of cultivated land remained

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unchanged during 2005-2015. Additionally, many cultivated lands were retired for tree

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plantations in the beginning of GGP implementation within WLRB. However, these plantations were often ecologically inappropriate for some sites. For example, trees such as poplars that are not native to the area were planted in areas that likely are more suitable for grasses or shrubs (Chen et al. 2018). The annual water loss through evapotranspiration (ET) for poplar in Northern China is ~590 mm (Xiao et al., 2013; Zhou et al., 2014), and the annual average precipitation in the WLRB is 300-550 mm. This obvious water deficit between ET and precipitation is elevated by the large amounts of soil/ground water consumed by poplar plantations. When there is no 18

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groundwater to subsidize the water deficit and/or irrigation, poplars will die. Such afforestation has proved unsustainable (Chen et al., 2018), yet it has been widely practiced across Inner Mongolia (Jiang et al., 2006). Consequently, maintenance of cultivated land seems to be preferred by some landowners (Fig. S1b and S1c) -- another reason for the continued increase in cultivated land.

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ERPs also aimed to stabilize soil and improve hydrological functions in the region by

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converting croplands on steep slopes to forests and grasslands. During 2000-2015 the proportion

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of cultivated land returning to forest on steeper slopes increased from 9.5% to 57.7%, reflecting

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the intention to reduce soil erosion in mountain areas. Trees planted at high elevations on

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mountains would likely survive (Fig. S2a) during 2000-2015, but those planted on the flood

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plains likely would not (Fig. S2b). In this study we found that woodlands performed better on steeper terrains than on the plains (Fig. S2a and S2b). This demonstrates that restoring woodland

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is more suitable in mountainous areas, where their demand for water can be met. This was part of

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the ERPs‟ goal for advocating forest restoration. In general, the land cover changes observed in WLRB are in agreement with the key environmental goals of ERPs, including converting cropland on steep slopes to forest and slowing and restricting grassland reclamation (see Section 4.1). These changes in land cover due to ERPs are supported by socioeconomic evidence, such as farmer attitudes (Cao et al., 2009). The willingness of farmers in Inner Mongolia to participate in ERPs was confirmed by a large number of household surveys (Song et al., 2014; Yan and Le, 19

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2010). Through ERPs, farmers can be compensated for their losses in farming lands. Subsidies for 1500 kg grain production per hectare per year were provided during the program and eight years after the programs end. Each farmer received 300 RMB (US$ 42.86) per year to transform one hectare of farmland into grassland or woodland. Farmers appeared satisfied with the food and financial compensation. Although about a fifth of farmers felt that their livelihoods had been

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affected, about 64% of the farmers surveyed supported the programs (Cao et al., 2009). Based on

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the National Statistics of Mongolia, we found that rural labors within WRLB increased by 5.15

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million, from 25.8% to 31.1% of the total population during 2000-2015. This is despite the large

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number of farmers who moved to cities in the last decade (Chen et al., 2015c), suggesting that

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5.2 Landscape structure changes

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the rural population stayed steady or increased.

We hypothesized that the implementation of ecological restoration had directly impacted

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landscape structure. In this study we found a reverse trend in changes in landscape structure

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before and after EPRs implementation. Landscape fragmentation was eased with the increase in structure diversity in WLRB. A similar conclusion regarding ERPs effects was also reported in other areas under ERPs. For example, landscape aggregation and connection at both class and landscape level were increased in Shaanxi province during 2000-2010 (Chen et al., 2015a). On the Manas River watershed in the northwest China a significantly higher landscape diversity was also found during 1999-2008 (Feng et al, 2011). Landscape diversity is widely believed to have a positive ecological significance (Hansen 20

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and DiCastri, 2012; Deng et al., 2017b). For example, landscape diversity has been shown to enrich biodiversity and enhance the competition between pests and their natural enemies (Deng et al., 2017a; Letourneau et al., 2013; Shackelford et al., 2013; Veres et al., 2013). Deng et al. (2017b) reported that maintaining a certain amount of landscape diversity is necessary for maintenance of crop production. Here modern agricultural intensification tends to simplify

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landscape structure -- i.e., lowering landscape diversity decrease (Landis, 2017). As the

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proportion of crop area in a landscape increases, cropland is more likely to be directly adjacent to

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other crops than to more diverse non-crop habitats. WLRB has been dominated by maize (Feng

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et al., 2014). In some regions of WLRB where monocrop dominated the landscape, and thereby

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landscape diversity was lost, an increase in landscape diversity through an increase in

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semi-natural vegetation (e.g. woodland) will likely promote biological controls of pests and hence crop production (Veres et al., 2013). Grain production during 2000-2015 increased from

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4.1 Mt to 12.7 Mt in WLRB, but there appeared to be a non-linear relationship between

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landscape diversity and crop production (Deng et al., 2017b), likely due to other management efforts, such as fertilization and irrigation. Substantially more research, such as field surveys and other comprehensive studies, is needed to assess how landscape diversity may have benefited crop production. We found that there was a parabolic relationship between landscape diversity and landscape fragmentation (Fig. S3). With the increase of SHDI, the LFI increased and reached the maximum value, and then decreased. In this study, the equation can be expressed as LFI = -4.6300 SHDI2 + 21

Journal Pre-proof 10.4648 SHDI – 5.273 (R2 = 0.5522, p < 0.001). When SHDI was < 1.13 (the threshold of their relationship), landscape fragmentation was coupled with an increase in landscape diversity. On the contrary, landscape fragmentation decreased as landscape diversity increased when SHDI was > 1.13. It represented a different relationship between them during the different periods. In this study, the values of SHDI in WLRB were all > 1.13 in 1990, 2000, 2005 and 2015,

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suggesting that landscape diversity is negatively associated with landscape fragmentation,

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landscape fragmentation with the changes of SHDI.

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leading to an increasing trend during 1990-2000 and a decreasing trend during 2000-2015 for

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5.3 Implications for future ecological restoration programs

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Landscape fragmentation is an important attribute of land pattern and has potential implications

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for land protection and management (Lam et al., 2018). ERPs aimed to increase natural vegetation cover by converting cropland on steep slopes to forests and grasslands. In our study,

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we found that grassland and woodland played an important role in landscape fragmentation.

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They function better than cropland in reducing water surface runoff and controlling soil erosion (Jia et al., 2014). From this perspective, actions under the ERPs should be continued to connect isolated patches of both woodlands and grassland. Fortunately, the central government announced plans to increase the extent of GGP in 2015. Woodlands and grasslands are expected to continue to increase in coverage and connectivity. Enhancing ecosystem services also has become the new focus of future ERPs. Although implementation of ERPs has been shown to improve many ecosystem services, others have 22

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displayed negative consequences (Bennett et al., 2009; Egoh et al., 2008). For example, increases in woodland could reduce soil erosion (Jia et al., 2014) and increase soil organic carbon (Deng et al., 2014; Lu et al., 2018), but they also can decrease the water yield that is critical to arid and semi-arid regions (Cao et al., 2010). Within the WLRB, soil erosion has been more serious in western and southern mountainous areas because of the steep slopes. Through

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this study we found that woodland converted from cultivated land at high-elevation mountains

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and steep terrain appeared to survive. These trees can enhance soil fixation by increasing soil

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shear strength (Frydman and Operstein, 2000) and provide structural support or reduce pore

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water pressure through roots system (Gray and Sotir, 1996), suggesting that the ecosystem

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service of soil conservation will be enhanced (Jia et al., 2014). Therefore, the soil conservation

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capacity is high in these mountain areas of WLRB (Fig. 5b and 5c). Grasslands also can play important roles in soil water conservation. Han et al. (2005) reported that, compared with the

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natural meadow soil of 0-50 cm depth, the water storage capacity of soil under crops at 1 m

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depth decreased by 9% in spring, 13.2% in summer and 10.2% in autumn. The soil water storage will increase accordingly with croplands converted to grasslands. Here in the WLRB, conversions of cropland to grassland were made in the plain of the lower reaches, where the soil water storage was relatively high (Fig. 5b and 5c). While reducing soil erosion, the increase in vegetation cover would elevate water loss through evapotranspiration and thus decrease water yield for WLRB, revealing another tradeoff among ecosystem services. Although the western and southern mountainous areas with high elevation and steep terrains had high levels of 23

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ecosystem service for soil conservation, there was a water yield reduction potential. Since water is the most limiting natural resource in WLRB, this influence on the hydrological balance must be taken into account in afforestation. Unfortunately, we did not quantify the changes in ecological functions and service in WLRB due to the lack of the ground data. Nevertheless, some studies have reported the rough carbon sequestration benefits of China‟s GGP at national scale

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(Deng et al., 2014, 2017a; Liu et al., 2014a), though not on WLRB. A major effort is needed to

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collect field data for major ecosystem services throughout the basin.

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China remains a top-down centralized governance. An advantage of this system is that it

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allows rapid wide implementation of policies such as GGP across the nation. An obvious pitfall

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is that in such a “one shoe fits all” approach, region-specific plans often are not considered.

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Landscape function of grassland and woodland on reducing water surface runoff and controlling soil erosion were the core objectives of EPRs. Yet guidelines for returning farmland to

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forest/grassland were not provided with spatial details. In this study, we found that ERPs greatly

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changed land cover and landscape structure. Unfortunately, these policies usually were implemented at individual farms without considering the consequences at regional or landscape level. Based on ERPs' objectives (Table 2), they focus on combating desertification, increasing agricultural productivity, and promoting the regional economic development, with no discussion of landscape structure and its socio-ecological consequences. This prevents us from scaling up from individual sites to the landscape toward sustainability of the basin (Lamb et al., 2005). Given the importance of landscape structure in human vulnerability to environment, it seems that 24

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nationwide ecological restoration policies should not only regulate individual land use types but also focus on a healthy and sustainability of the landscapes. Here we suggest that the impact on the landscape be considered within national and regional restoration design. Moreover, we recommend that explicit function-based landscape management be included in the process of ERPs implementation, which could improve traditional management and maximize the

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ecosystem service benefits of ERPs, depending on local ecological, geographic and land-use

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context.

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Natural system (NS) and human system (HS) are interdependent, with complex interactions

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(Chen et al., 2015c). Understanding the interaction between NS and HS requires considering the

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elements from both socioeconomic and climatic perspectives. Focusing on the role of land use

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policy, we found that ERPs play an important role in regional landscape structure and dynamic. It may contribute to better understanding the relationship between human actions and the

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environmental change. For example, LCC between different land types can influence physical

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and chemical properties of soil (Mukumbuta et al., 2019). In our study, regional landscape structure is changing under the ERPs, accompanying with the transformation between grassland and cropland, which is one of the land cover changes caused by human activities. We can anticipate that soil property, and even regional soil nutrient balance and cycling, one of the environmental process, could be affected due to the land use policy implementation. In sum, the data and analysis support our hypothesis that land use policy has an impact on landscape via changing land use direction and intensity. The inter-relationship among cropland, 25

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grassland and woodland influence the landscape structure in WLRB. However, this study has its limitations. The research was conducted for one region to show the effectiveness of EPRs at landscape scale. Although we concluded that landscape fragmentation decreased and landscape diversity increased in WLRB after the implementation of EPRs, different regions may not show similar effects due to unique land covers and management. Therefore, comparison studies across

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implementation areas under among regions are recommended.

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6. Conclusions

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We explored the effects of implementing ERPs on landscape structure in WLRB in a

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farming-pastoral ecotone of Northern China. Dramatic land cover change occurred before and

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after ERPs implementation. After ERPs (2000-2015), woodland increased with a high annual

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growth, grassland reclamation appeared restricted, and more cultivated land was converted to grassland and woodland. The conversion of cultivated land to woodland primarily occurred in

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the mountainous areas with high elevation (>800 m) and steep terrain (slope >15º), while the

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transformation of cultivated land to grassland was found in the plains and mountainous areas with elevation <500 m and a slope of 2-6°. Many smaller patches were replaced by a few large dominant ones, especially for woodland and grassland. ERPs also induced a decreasing trend in landscape fragmentation and an increasing trend in landscape diversity. Landscape fragmentation was negatively correlated with MPS of grassland and woodland, and patch number ratio of cultivated land to the sum of grassland and woodland. Landscape diversity had a negative correlation with the areal ratio of grassland. Our findings shed light on how ERPs modify 26

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regional changes in land cover and landscape structure. Unfortunately, these changes were not included in the ERPs design. We recommend function-based, local landscape management for future ERPs and implementation. Conflict of interest

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All authors declare no actual or potential conflict of financial or other interests.

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Acknowledgements

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This work was supported by the National Natural Science Foundation of China (Grant No.

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41671525; Grant No. 41101553), the Natural Science Foundation of Beijing Municipality (Grant

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No. 8152030), NASA‟s Land-Cover and Land-Use Change (LCLUC) Program (Grant No. NNH18ZDA001N), and the National Key Research and Development Program of China (Grant

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No. 2016YFC0503500). We thank the three anonymous reviewers provided valuable reviews for

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improving the quality of earlier manuscripts. We also thank Kristine Blakeslee for editing the

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Yin, H., Pflugmacher, D., Li, A., Li, Z.G., Hostert, P., 2018. Land use and land cover change in Inner Mongolia-understanding the effects of China's re-vegetation. Remote Sens. Environ. 204, 918-930.

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Yu, Q.Y., Hu, Q., Vliet, J., Verburg, P.H., Wu, W.B., 2018. GlobeLand30 shows little cropland area loss but greater fragmentation in China. Int. J. Appl. Earth Obs. Geoinf. 66, 37-45.

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Zhang, P.C., Shao, G.F., Zhao, G., Master, D.C.L., Parker, G.R., Dunning Jr., J.B., Li, Q.L., 2000. China‟s Forest Policy for the 21st Century. Science 288, 2135-2136.

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Zhao, A.Z., Zhang, A.B., Lu C.Y., Wang, D.L., Wang, H.F., Liu, H.X., 2017. Spatiotemporal variation of vegetation coverage before and after implementation of Grain for Green Program in Loess Plateau, China. Ecol. Eng. 104, 13-22. Zhao, R.F, Chen, Y.N, Shi, P.J., Zhang, L.H., Pan, J.H., Zhao, H.L., 2013. Land use and land cover change and driving mechanism in the arid inland river basin: a case study of Tarim River, Xinjiang, China. Environ. Earth Sci. 68, 591-604. Zhao, X., Lu X., Dai, J., 2010. Impact assessment of the “Grain for Green Project” and discussion on the development models in the mountain-gorge regions. Front. Earth Sci. China 4, 105-116. Zhao, Y., Liu, Y.S., Deng, X.Z., 2008. A landscape approach to quantifying land cover changes in Yulin, Northwest China. Environ Monit Assess. 138, 139-147. Zhou, D.C, Zhao, S.Q., Zhu, C., 2012. The Grain for Green Project induced land cover change in the Loess Plateau: A case study with Ansai County, Shanxi Province, China. Ecol. Indic. 23, 88-94. Zhou, J., Zhang, Z., Sun, G., Fang, X., Zha, T., Chen, J., Noormets, A., Guo, J., McNulty, S. 2014. Water-use 32

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efficiency of a poplar plantation in Northern China J. For. Res. 19 483-92.

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Figures

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Fig. 1 The digital elevation model (DEM) of the West Liaohe River Basin (WLRB) in eastern Inner Mongolia, China. The west reach of Liaohe River dominates the landscape with elevation of 88 - 2054 m.

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Fig. 2 Spatial distribution of land cover in 1990, 2000, 2005, and 2015 within the West Liaohe River Basin (WLRB)

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Fig. 3 Landscape composition (%) of WLRB in 1990, 2000, 2005, and 2015

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Fig. 4 Relative change (a), the annual change rate (b) of land cover types, the area converted from cultivated lands to the other land cover classes (c), and the area converted from the other land cover types to woodlands (d) within the WLRB during 1990-2015. CL denotes cultivated lands, WL woodlands, GL grasslands, UL unused lands, and OL other lands (including built-up land and water bodies).

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Fig. 5 Land cover changes of cultivated lands, grasslands and woodlands in WLRB during 1990-2000 (a), 2000-2005 (b) and 2005-2015 (c)

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Fig. 6 Changes in major landscape metrics within WLRB from 1990 to 2015 at class level and the whole landscape for: (a) number of patches (NP), (b) mean shape index (SHAPE_MN), (c) mean perimeter-area ratio (MPAR), (d) edge density (ED), and (e) mean patch size (MPS).

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Fig. 7 The relationship between mean patch size (MPS) and landscape fragmentation index (LFI): (a) grassland, (b) woodland, (c) the patch number ratio of cultivated land to the sum of grassland and woodland (Rc/gw). The relationship between the area ratio of grassland and landscape diversity (Shannon‟s index) (d) from 1990 to 2015. r is the Pearson correlation coefficient. Each spot corresponded to each county in WLRB.

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Table 1 Changes of landscape metrics of WLRB at landscape level from 1990 to 2015 Year

MPS

ED

SHAPE_MN

MPAR

SHDI

1990

72089

187.2

50.0

3.7

19396.8

1.38

2000

73782

182.9

50.5

3.8

18997.8

1.37

2005

57857

233.3

40.8

2.3

285.1

1.39

2015

46135

292.5

33.8

2.2

204.0

1.45

0.2

-0.2

0.1

0.4

-0.2

-0.1

169.3

-0.4

0.1

-39.9

-0.001

-21.6

27.5

-19.2

-40.6

-98.5

1.3

-3185.0

10.1

-0.31

-3742.5

0.002

-20.3

25.4

-17.2

-4.0

-28.4

4.5

-0.7

-0.01

-8.1

0.006

Annual

change

rate Relative change 2000-2005

(%) Annual

change

rate Relative change (%) 2005-2015

Annual rate

change

-1172.2

0.01

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(%)

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1990-2000

-1.9

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Relative change

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Statistic type

of

NP

(Period)

5.9

Notes: NP: number of patches, MPS: mean patch size, ED: edge density, SHAPE_MN: mean shape index, MPAR: mean

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perimeter-area ratio, SHDI: Shannon's diversity index

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Table 2 The objectives and measures of ecological restoration programs referred in this paper Program Name

Objectives

Measures 



Beijing and Tianjin

and its surrounding areas 

Improving livelihoods of local people



Expanding living space of local residents

Grain for Green Project

Natural Forest

forests

or

grasslands 

Afforestation



Grazing

of

etc. 

Returning

protect and improve the regional environment

or grassland

Modifying rural industrial structure



Increasing local farmers‟ income



Afforestation



Grazing exclusion, etc.

Preventing and controlling drought, wind and sand hazards and soil erosion, and improving regional environment



Afforestation



Increasing agricultural productivity



Mountain closure



Increasing farmers‟ income

and



Promoting regional economic development

prohibition, etc.



Developing modern forestry and promoting ecological civilization

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Conservation Program

to

farmland to forest





farmlands

Preventing and controlling soil erosion and sandstorm hazards to

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(GGP)

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

of

abandonment,

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(BTSST)

Promoting the economic development of implementation areas

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Treatment Project



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Sandstorm Source

(NFCP)

Combating desertification and improving the environment of Beijing

Conversion

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grazing

Journal Pre-proof Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Graphical Abstract

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· Ecological restoration programs (ERPs) affected landscape composition and structure · ERPs decreased landscape fragmentation and increased landscape diversity · Landscape fragmentation was correlated with mean patch size of grassland and forest · Landscape diversity was negative correlated with the areal ratio of grassland

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· Landscape structure changes were not expected in ERPs design but needed for future

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Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7