Driving forces of changes in China’s wetland area from the first (1999–2001) to second (2009–2011) National Inventory of Wetland Resources

Journal Pre-proof Driving forces of changes in China's wetland area from the first (1999–2001) to second (2009–2011) National Inventory of Wetland Resources Hualin Bian, Wei Li, Youzhi Li, Bo Ren, Yandong Niu, Zhangquan Zeng PII:

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Global Ecology and Conservation

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Please cite this article as: Bian, H., Li, W., Li, Y., Ren, B., Niu, Y., Zeng, Z., Driving forces of changes in China's wetland area from the first (1999–2001) to second (2009–2011) National Inventory of Wetland Resources, Global Ecology and Conservation (2020), doi: https://doi.org/10.1016/j.gecco.2019.e00867. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

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Driving forces of changes in China’s wetland area from the First (1999‒2001) to

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Second (2009‒2011) National Inventory of Wetland Resources

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Hualin Biana†, Wei Lib†, Youzhi Lia*, Bo Rena, Yandong Niuc, Zhangquan Zengc

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a

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China

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b

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c

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†Co-first authors

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*Corresponding author. E-mail: [email protected]

College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128,

Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China

Hunan Academy of Forestry, Changsha 410004, China

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1

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Abstract: Based on the wetland areas recorded in the First National Inventory of Wetland

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Resources (FNIWR; 1999‒2001) and the Second National Inventory of Wetland Resources

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(SNIWR; 2009‒2011), as well as regional environmental parameters including meteorological

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conditions and land-use structure, the driving forces of changes in China’s wetland area from the

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FNIWR to the SNIWR were investigated. The total area of China’s wetlands larger than 1 km2

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decreased from 384.8 × 103 km2 in the FNIWR to 350.8 × 103 km2 in the SNIWR. Natural wetland

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areas, including marshes, rivers, lakes, and coastal wetlands, decreased by 33.8 × 103 km2 over the

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study period, accounting for 99.4% of the decrease in the wetland area. Regions with decreased

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marsh and reservoir wetland areas were located mainly in Central and Eastern China, and those with

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increased river and lake wetland areas were located in Western China. There were no significant

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regression coefficients between the percentages of change in marsh and lake wetland areas and

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environmental parameters. However, changes in river wetland areas were positively regressed with

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changes in forest areas, mean annual temperature, and extreme minimum temperature, and changes

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in reservoir wetland areas were positively regressed with changes in city construction areas and

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mean annual temperature. Therefore, it seems that increased forest area and regional temperature

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alleviated the degradation of river wetland areas, while increased city construction areas and

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regional temperature alleviated the degradation of reservoir areas.

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Keywords: Marsh; River; Lake; Reservoir; Forest land; Regional temperature

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1. Introduction

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Wetlands are estimated to cover a total area of 12.8 million square kilometers (km2) globally,

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comprising 8.5% of the Earth’s land surface (Finlayson et al., 1999). Not only do wetlands provide

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habitats for highly endangered wildlife, they also provide important functions, such as food

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production, climate change mitigation, and nutrient cycling (Mitsch and Gosselink, 1986; Li et al.,

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2018). For these reasons, wetlands are described as a “storage area of natural genes” and the “kidney

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of nature” (Mitsch and Gosselink, 1986; Zhou et al., 2009). Currently, the Ramsar Convention on

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Wetlands involves 170 contracting parties and a total of 2335 Wetlands of International Importance

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(Ramsar sites) (Brazil, 2018; DPRK, 2018). These Wetlands of International Importance cover over

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2.0 million km2 of wetlands and associated habitats (Bolivia, 2013).

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Due to economic development and human population growth, large wetland areas have been

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degraded or lost. In the Choke Mountain range (with an area of 17,443 km2) in the Upper Blue Nile

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basin (a key headwater region of the Nile River), 607 km2 of seasonal wetland and 22.4 km2 of open

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water were lost between 1986 and 2005 (Teferi et al., 2010). Satellite images from 14 deltas

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(Danube, Ganges-Brahmaputra, Indus, Mahanadi, Mangoky, McKenzie, Mississippi, Niger, Nile,

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Shatt el Arab, Volga, Yellow, Yukon, and Zambezi) indicated that a total of 15,845 km2 of wetlands

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were lost between 1985 and 2000 at a mean rate of 95 km2/yr (Coleman et al., 2008). The global

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extent of wetlands is estimated to have declined by between 64‒71% in the 20th century and

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wetland losses and degradation continue worldwide (Gardner et al., 2015). Over the past several

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decades, China has lost large areas of wetlands. In the Yellow River Delta, the total area of natural

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wetlands decreased from 2565.6 km2 in 1986 to 1574.5 km2 in 2008 (Wang et al., 2012). Decreasing

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water levels caused by extensive human activities, global warming, and decreasing precipitation has

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also transformed large areas of marshland in China’s Sanjiang Plain into meadows (Zhou et al.,

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2009), and 65% of marshes in that area were lost between 1975 and 2004 (Zhang et al., 2010). The

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loss of wetlands globally is currently a priority issue in the political agendas of many countries and,

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therefore, estimates of changes in the extent of these ecosystems have been made to provide relevant

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information for the establishment of guidelines for the conservation and sustainable use of wetlands

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(Zhou et al., 2009; Sica et al., 2016; Serran et al., 2018).

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Many processes can lead to wetland loss: temperature increases, changes in precipitation, 3

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draining, farming, aquaculture, urbanization, infrastructure development, and also mining, intensive

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agriculture, and intensive cattle raising (Zhao et al., 2005; Coleman et al., 2008; Gong et al., 2010;

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Davidson, 2014). The hydrological regime is considered the dominant environmental factor

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controlling and maintaining the ecological integrity of wetland ecosystems (Deane et al., 2017). In

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the Sanjiang Plain, China, water shortage is a key reason for wetland degradation due to the

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extensive human activities, a locally warming climate, and the decreasing precipitation (Zhou et al.,

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2009). Until 1999, sea-level rise caused by melting glaciers (due to climate change) has resulted in

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losses of up to 22% of the world’s coastal wetlands (Nicholls et al., 1999). Human activity is another

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factor contributing to the degradation of wetland ecosystems in China. Extensive land reclamation

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due to the rapidly growing human population resulted in the water surface area of the Dongting

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Lake (once the largest freshwater lake in China) decreasing by 49.2% from 1930 to 1998 (Zhao et al.,

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2005). According to the National Wetland Inventory of 1981‒1982, approximately 40% of the 233

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wetlands in the United States have been destroyed as a result of human activities (Holland et al.,

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1995).

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In China, national and local governments have attempted to control wetland degradation and

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protect wetland resources. China currently has 57 Ramsar sites, and the National Forestry

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Administration (NFA, currently called the National Forestry and Grassland Administration)

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conducted the First National Inventory of Wetland Resources (FNIWR) in 1999‒2001 and the

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Second National Inventory of Wetland Resources (SNIWR) in 2009‒2011. According to the data

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collected by both inventories and in terms of regional environments (e.g., meteorological conditions

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and land-use structure), the objective of this study was to clarify the changes in China’s wetland area

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between the FNIWR and SNIWR, as well as the links between these changes and regional

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environments. This will contribute to revealing the driving forces behind wetland area changes in

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China over the period from 1999‒2001 to 2009‒2011 and provide a theoretical basis for wetland

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protection.

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2. Materials and Methods

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2.1 Data sources

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The NFA compiled the FNIWR in 1999‒2001 and the SNIWR in 2009‒2011 and identified the

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basic status of all wetlands in China larger than 1 km2 and 0.08 km2, respectively. In this study, data 4

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on the five types of wetland areas (river, lake, marsh, coastal, and reservoir wetlands) were collected

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from NFA (2015). Data regarding regional environmental parameters (i.e., amount of water required

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by people [AWR]; amount of wastewater discharge [AWD]; area of city construction [ACC]; area of

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forest [AF]; area of agricultural crops [AAC]; length of highways [LH]) from the periods of the

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FNIWR and SNIWR were collected from provincial statistical yearbooks (e.g., Anhui Statistical

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Yearbook, Fujian Statistical Yearbook, Hunan Statistical Yearbook). Meteorological data (i.e., mean

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annual precipitation [MAP]; maximum daily precipitation [MDP]; mean annual temperature [MAT];

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extreme minimum temperature [EMIT]; extreme maximum temperature [EMAT]) from the periods

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of both inventories were collected from the National Meteorological Information Center

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(http://data.cma.cn/).

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2.2 Data analysis

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Due to the differing threshold areas of the wetlands selected for inclusion in the FNIWR and

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SNIWR, wetlands larger than 1 km2 were selected to compare the differences in wetland area

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between the inventories. Percentages of the change in the areas of wetlands (marsh, river, lake, and

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reservoir) from the FNIWR to the SNIWR were determined as the ratio of the difference in wetland

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area between these inventories to the original area from the FNIWR. Taking river wetlands as an

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example, PR = [(ARS - ARF) / (ARF)] × 100%, where PR is the percentage of change in river wetland

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(water surface and floodplains of the rivers) area; ARF is the river wetland area in the FNIWR, and;

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ARS is the river wetland area in the SNIWR. Similarly, the percentage change of environmental

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parameters was determined as the ratio of the difference in environmental parameters between the

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FNIWR and SNIWR to that of the FNIWR. Environmental parameters during the FNIWR and

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SNIWR were calculated as the mean of specific parameters from 1999, 2000, and 2001, and from

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2009, 2010, and 2011, respectively.

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Due to the incomplete wetland areas in some regions (province, city, or autonomous region), 24

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regions with comparable wetland areas in the FNIWR and the SNIWR were selected for this study

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(Figure 1). Linear regression analyses of the percentages of wetland area change, and of

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environmental parameters from the FNIWR to the SNIWR, were performed considering each

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region as a data-point at a 0.05 significance level using SPSS v.17.0 (SPSS Inc., Chicago, IL, USA).

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Additionally, redundancy analyses were conducted to analyze relationships between percentages of 5

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wetland area change and environmental parameters using CANOCO 4.5.

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3. Results

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3.1 Areas of wetlands in the FNIWR and SNIWR

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In the FNIWR, the total area of China’s wetlands larger than 1 km2 was 384.8 × 103 km2, whereas,

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in the SNIWR, the total area of wetlands larger than 0.08 km2 was 497.7 × 103 km2 (Figure 2).

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However, the total area of China’s wetlands larger than 1 km2 decreased from 384.8 × 103 km2 in the

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FNIWR to 350.8 × 103 km2 in the SNIWR. The combined area of natural marsh, river, lake, and

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coastal wetlands decreased from 362.0 × 103 km2 in the FNIWR to 328.2 × 103 km2 in the SNIWR.

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However, the area of reservoir wetlands decreased by only 200 km2 from the FNIWR to the

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SNIWR.

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3.2 Changes in wetland areas and environmental parameters

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The percentage of the total selected regions with decreases in wetland area from the FNIWR to

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the SNIWR were 29.2%, 41.2%, 69.6%, and 69.6% for reservoir, marsh, river, and lake wetlands,

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respectively (Table 1). For instance, the area of river wetlands increased in Chongqing City, Fujian

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Province, Heilongjiang Province, Qinghai Province, Shanxi Province, Tibet Autonomous Region,

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and Xinjiang Uygur Autonomous Region, and decreased in the remaining 16 regions from the

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FNIWR to the SNIWR.

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Among the investigated environmental parameters, AWR, AWD, AF, ACC, AAC, LH, MAP,

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MDP, MAT, and EMAT increased from the FNIWR to the SNIWR, whereas EMIT decreased over

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the same period (Table 1). For instance, the EMIT decreased in Anhui Province, Chongqing City,

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Hainan Province, Hubei Province, Henan Province, Hunan Province, Jilin Province, Jiangxi

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Province, Liaoning Province, Ningxia Hui Autonomous Region, Sichuan Province, Shandong

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Province, Shanxi Province, Tibet Autonomous Region, Yunnan Province, Zhejiang Province and

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increased in the remaining 8 regions from the FNIWR to the SNIWR.

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3.3 Environmental parameters driving changes in wetland areas

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Linear regression analyses showed no significant regression coefficients between changes in the

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areas of marsh and lake wetlands and any of the environmental parameters (Table 2). Positive

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regression coefficients exist between changes in the areas of river wetlands and forest area, mean

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annual temperature, and extreme minimum temperature. Changes in the area of reservoir wetlands 6

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exhibited positive regression coefficients with changes in the city construction area and mean

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annual temperature.

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Redundancy analyses showed that the first two axes account for 77.8% and 13.3% of changes in

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wetland area in relation to environmental parameters (Table 3, Figure 3). The first axis is positively

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correlated with changes in forest area and negatively correlated with mean annual precipitation. The

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second axis is positively correlated with changes in mean annual temperature. Changes in reservoir

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wetland area were correlated with changes in forest area, and changes in lake wetland area were

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correlated with changes in mean annual precipitation.

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4. Discussion

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4.1 Changes in areas of wetlands from the FNIWR to SNIWR

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This study showed that China’s wetland area decreased rapidly from the FNIWR (1999‒2001) to

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the SNIWR (2009‒2011). The total area of natural (i.e., marsh, river, lake, and coastal) wetlands

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decreased by 33.8 × 103 km2 over the period, which accounts for 99.4% of the decrease in the total

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wetland area. In the Yellow River Delta, the river wetland area decreased from 241 km2 in 1977 to

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90 km2 in 2004 (Wang et al., 2012). In the Dongting Lake wetlands, the water surface area decreased

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from 4955 km2 in 1930 to 2518 km2 in 1998 (Zhao et al., 2005). In the Honghe National Nature

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Reserve in Sanjiang Plain, Northeast China, there was a 10% reduction in the marsh area from 1998

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to 2005; with this degradation rate, the remaining area is likely to disappear within the next 30 years

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(Zhou et al., 2009). Unlike natural wetlands, artificial reservoir wetlands decreased by only 200 km2

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from the FNIWR to the SNIWR. Gong et al. (2010) found that the total wetland area in China

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decreased from 355.2 × 103 km2 in 1990 to 304.8 × 103 km2 in 2000, whereas the area of artificial

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wetlands increased from 22.5 × 103 km2 to 34.9 × 103 km2. Compared to the 304.8 × 103 km2 area of

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China’s wetlands in 2000 (Gong et al., 2010), an area of 384.8 × 103 km2 was reported in the

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FNIWR (1999‒2001). The difference in wetland areas in the same period may be that the former

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wetland area was calculated from remote sensing observations and the latter wetland area was

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obtained from remote sensing and field investigation, and those wetlands with an area larger than 1

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km2 were included in the FNIWR. These data indicate that large areas of natural wetlands have been

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lost in the 20 years during 1990‒2010.

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4.2 Driving forces of changes in China’s wetland area from the FNIWR to SNIWR 7

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There were no significant correlations between changes in marsh and lake wetland areas and

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environmental parameters. However, there was a positive correlation between changes in the river

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wetland area and the forest area. Compared to the forest areas in the FNIWR, 95.8% of the regions

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had a larger forest area in the SNIWR. For instance, in Shanghai City, the forest area in the FNIWR

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was more than three times larger than that in the SNIWR. In 1978, the Chinese government

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launched multiple forest conservation programs, including the Three North Shelterbelt

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Development, the Nature Forest Conservation Program, the Grain to Green, and Return Farmland to

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Forest Programs, to control soil and water erosion (Zhang et al., 2016). For example, the forest land

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and construction land in Zhangjiakou City, located in a farming-pastoral ecotone in Northern China,

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increased from 9147 km2 and 1000 km2 in 1989, respectively, to 12303 km2 and 1623 km2 in 2015,

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respectively (Liu et al., 2017). These conservation and reforestation programs have been

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implemented mainly along rivers and shores, and have effectively decreased river wetland

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degradation (Collins and Montgomery, 2002). China currently has the largest plantation area

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globally, accounting for approximately 36% of the total forested area in China (NFA, 2014).

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Redundancy analyses showed a positive correlation between changes in river wetland and forest

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area (Table 3, Figure 3). Combined with the positive regression between changes in river wetland

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and forest area in regression analyses (Table 2), it was suggested that increased forest land may

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decrease the degradation of river wetlands. Positive regression coefficients between changes in river

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wetland areas and the mean annual temperature, as well as the extreme minimum temperature,

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indicate that increased mean annual temperature and extreme minimum temperatures increased the

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river wetland area. In this study, 58.3% of the regions had higher mean annual temperature (P =

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0.190, t-tests) and 33.3% of the regions had a higher extreme minimum temperature (P = 0.007,

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t-tests) in the FNIWR compared to those in the SNIWR. For instance, in the Qinghai Province, the

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mean annual temperature in the FNIWR was more than 1.5 times higher than that in SNIWR. As a

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consequence of climate change, temperature and precipitation in China have increased sharply in

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the past 50 years (Chen et al., 2015; Tian et al., 2015; Zhou et al., 2016). For example, the mean

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maximum temperature in Central and Northern China increased by 0.13 ºC and 0.37 ºC, respectively,

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since the 1960s (Zhou et al., 2016). With increasing temperature, melting glaciers, frozen soil, and

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multiyear snow in Western China (including the Tibet-Qinghai Plateau and Xinjiang) formed large 8

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river wetlands and contributed to an increase in its area (Ye et al., 2008; Gong et al., 2010; Niu et al.,

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2012). Therefore, the primary driving forces behind changes in the river wetland area appear to be

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forest area and regional temperature.

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In addition to the positive correlation between changes in reservoir wetland area and mean annual

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temperature in the redundancy analysis, the positive regression coefficient between changes in

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reservoir and city construction areas and mean annual temperature in the regression analysis

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indicate that city construction and regional temperature appear to be the primary driving forces

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behind changes in the reservoir wetland area. Our results indicated that 95.8% of the regions had a

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larger area of city construction in FNIWR compared to the area in SNIWR. The increase of

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reservoirs is more likely to be due to the construction of new areas to accumulate water in such a

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way that there is greater water availability for an increasing human population (Han et al., 2017).

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However, as one of the main driving forces for wetland area changes (Zhou et al. 2009; Gong et al.,

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2010), agricultural land did not show significant regression or correlation with any wetland type in

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this study. A previous study by Zhang et al. (2010) indicated that increasing agricultural land (paddy

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fields) resulted in the loss of 67% of wetlands in the Sangjiang Plain, China, from 1975 to 2004. Niu

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et al. (2012) noted that due to extensive agricultural development, almost no natural wetlands

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remain in the plains of Eastern China, with particularly considerable losses since the early 1980s.

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4.3 Wetland protection and restoration in China

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Since the 1990s, China has implemented a series of wetlands protection and restoration programs

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(Gong et al., 2010; Cheng et al., 2018). The Chinese government developed the National Wetland

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Conservation Action Plan in 2000 and approved the 2002–2030 National Wetland Conservation

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Engineering Program (NWCEP) in 2003 (Wang et al. 2012). The NWCEP had a set of ambitious

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goals including establishing 713 wetland reserves–with more than 90% of natural wetlands

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effectively protected by 2030, and restoring 1.4 × 109 ha of natural wetlands. Subsequently, the

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Chinese State Council approved the 2005–2010 NWCEP Implementation Plan and the 2010–2015

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NWCEP Implementation Plan. For example, during the period 2010–2015, the five key construction

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projects included wetland protection, wetland restoration, wetland sustainable use, wetland

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monitoring capacity improvement, and wetland ecological benefits compensation. The investment

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of these programs during 2010–2015 exceeded 1.3 × 1011 RMB (1.8 × 1010 USD). Up to 2015, 49 9

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Wetlands of International Importance, 600 nature reserves, and over 1000 wetland parks (including

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705 national wetland parks) were established. More than 2.4 × 108 ha of wetlands have been

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protected and the percentage of the protected wetland area to the total wetland area in China reached

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44.6%. By 2020, the percentage of protected wetland area will reach over 50% after the 2015–2020

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NWCEP Implementation Plan. This will provide useful experience and successful cases for

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international wetland conservation and restoration, and promote the development of global wetland

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conservation.

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5. Conclusion

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Our results indicated that the total area of wetlands decreased during the period from 1999‒2010.

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The decreased areas in natural wetland areas (including marshes, rivers, lakes, and coastal wetlands)

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accounted for 99.4% of the decrease in the total wetland area. Regions with decreased marsh and

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reservoir wetland areas were mainly located in Central and Eastern China, and those with increased

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river and lake wetland areas were located in Western China. This means that wetland protection and

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restoration actions may not be completely effective for natural wetlands. Additionally, it seems that

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increased forest area and regional temperature decreased the degradation of river wetland areas,

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while increased city construction areas and regional temperature decreased the degradation of

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reservoir areas. Therefore, wetland management must focus on the complex impacts of climate and

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humans on wetlands, and effective protection approaches, such as forestation or reforestation along

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rivers and lakes and reservoir construction with city expansion, may achieve long-term conservation

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goals.

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Acknowledgments

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This study was financially supported by the Biodiversity Survey and Assessment Project of the

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Ministry of Ecology and Environment, China (2019HJ2096001006) and Biosafety and Genetic

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Resources Management Project of National Forestry and Grassland Administration

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(KJZXSA2018011).

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14

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Table 1 Percentages (%) of change in wetland areas and environmental parameters from the First to the Second National Inventory of Wetland Resources in 24 regions

353

in China

354 355 356 357

Wetlands Environmental parameter Marsh River Lake Reservoir AWR AWD AF ACC AAC LH MAP MDP MAT EMIT EMAT AH -3.1 -7.1 7.4 39.8 24.5 7.5 39.6 0.5 235.7 17.5 7.5 0.5 -27.6 2.1 CQ 48.0 -5.2 841.2 28.1 -5.5 10.8 95.9 -6.4 285.3 -17.4 -11.5 5.6 -22.3 9.7 FJ 98.7 -99.0 122.9 9.5 23.6 2.1 68.7 -18.7 78.2 10.0 -4.4 -0.6 103.1 2.5 GZAR 31.7 3.7 42.9 11.8 30.4 -5.8 92.4 13.4 18.9 2.4 25.9 0.8 HAN -39.0 -99.4 -24.5 -4.2 11.0 10.3 39.6 -8.0 22.0 2.9 4.7 0.5 -14.8 3.4 HB -46.9 -11.2 -13.5 1.3 18.7 16.4 10.9 42.1 5.5 256.5 9.8 -1.5 -0.4 -13.9 2.2 HEN -87.5 -36.3 -5.6 16.7 11.9 43.1 10.6 46.7 8.5 280.3 -12.7 -7.6 1.4 -14.9 2.3 HLJ 11.5 50.2 -18.0 2.1 25.3 3.7 8.9 22.6 30.3 202.2 11.6 4.1 -2.5 1.2 5.2 HUN 245.0 -62.1 -1.7 -59.3 0.5 7.2 6.9 34.3 2.7 274.7 14.0 36.6 3.2 -24.1 3.5 JL 35.1 -61.0 -41.6 -47.2 21.0 31.6 6.3 46.5 15.0 156.8 40.2 56.6 -1.6 -3.6 1.0 JS -84.2 -61.2 -17.6 -62.0 5.1 19.2 78.9 50.3 -4.1 159.1 -10.6 -20.9 -2.8 8.3 3.5 JX -77.5 -1.3 -15.6 30.5 17.8 33.8 2.4 49.3 -3.4 278.7 29.0 36.7 2.7 -10.0 4.7 LN -2.7 -15.0 -71.2 -2.3 10.3 11.7 10.3 34.2 12.5 122.9 99.7 69.8 -6.7 -2.9 -8.2 IMAR 11.2 -34.9 5.2 60.0 6.1 76.1 -0.1 75.2 18.4 134.6 25.2 -10.8 -2.9 2.0 2.2 NHAR -6.0 -12.3 286.6 -2.3 71.0 56.1 30.9 22.8 121.4 22.8 26.3 -0.5 -7.6 -6.0 QH 20.6 609.3 15.6 39.3 2.0 58.2 45.3 -10.3 -1.2 232.9 17.2 37.0 63.1 0.9 0.1 SC 237.6 -33.8 63.4 13.8 9.5 5.9 2.7 27.7 -1.4 192.8 0.2 13.6 9.2 -1.1 4.6 SD 1246.6 -33.6 -62.1 23.5 3.5 65.3 16.4 44.7 -3.0 225.2 18.3 8.8 -1.3 -0.7 2.1 SH 0.7 -24.7 1882.6 6.9 28.4 243.6 -22.9 102.7 -15.2 24.4 0.0 68.6 4.5 SX -45.5 -28.3 -2.7 -9.3 10.4 52.6 14.6 20.5 -8.1 235.1 23.0 56.8 3.5 -2.5 0.7 TAR -43.3 342.6 13.7 580.0 25.8 -15.0 7.6 15.3 4.0 158.8 -4.2 11.4 34.7 -9.5 8.2 XUAR 352.6 428.0 8.7 18.9 7.6 39.4 80.7 46.6 40.3 89.0 49.7 49.5 1.2 8.6 -0.4 YN 383.8 -22.6 15.6 679.7 0.4 17.5 3.1 107.2 11.3 27.9 -7.7 9.6 6.8 -150.5 7.4 ZJ 136.8 -27.0 32.6 -2.5 -2.3 40.3 0.9 42.8 -30.1 162.5 21.8 -4.7 -0.4 -9.1 6.0 Regions: AH, Anhui Province; CQ, Chongqing City; FJ, Fujian Province; GZAR, Guangxi Zhuang Autonomous Region; HAN, Hainan Province; HB, Hubei Province; HEN, Henan Province; HLJ, Heilongjiang Province; HUN, Hunan Province; JL, Jilin Province; JS, Jiangsu Province; JX, Jiangxi Province; LN, Liaoning Province; IMAR, Inner Mongolia Autonomous Region; NHAR, Ningxia Hui Autonomous Region; QH, Qinghai Province; SC, Sichuan Province; SD, Shandong Province; SH, Shanghai City; SX, Shanxi Province; TAR, Tibet Autonomous Region; XUAR, Xinjiang Uygur Autonomous Region; YN, Yunnan Province; ZJ, Zhejiang Province. Environmental parameters: AWR, amount of water requirement by people; AWD, amount of wastewater Province

15

358 359

discharge; AF, area of forest; ACC, area of city construction; AAC, area of agricultural crops; LH, length of highway; MAP, mean annual precipitation; MDP, maximum daily precipitation; MAT, mean annual temperature; EMIT, extreme minimum temperature; EMAT, extreme maximum temperature. – refers to missing values.

16

360

361 362 363

Table 2 Results from linear regression analyses of percentages of change in wetland areas and environmental parameters Environmental parameter

Marsh

River

Lake

Reservoir

Unstandardized coefficients

t-Statistic

Unstandardized coefficients

t-Statistic

Unstandardized coefficients

t-Statistic

Unstandardized coefficients

t-Statistic

Constant

371.008

0.323

-135.029

-1.638

-61.360

-1.347

-117.146

-0.565

AWR

-17.831

-0.772

1.138

0.549

-0.312

-0.272

4.522

0.823

AWD

4.599

0.576

0.181

0.205

0.289

0.593

-1.925

-0.831

AF

-0.158

-0.021

2.239

2.590*

0.299

0.626

-0.171

-0.075

ACC

-7.794

-0.493

0.757

0.763

-0.367

-0.670

7.227

2.775*

AAC

5.969

0.530

2.534

1.806

0.489

0.631

0.339

0.091

LH

-0.248

-0.081

-0.233

-0.852

0.152

1.005

-0.371

-0.537

MAP

2.280

0.167

2.896

2.045

0.427

0.545

-3.217

-0.880

MDP

0.129

0.014

-0.299

-0.254

-0.215

-0.331

1.307

0.424

MAT

-8.489

-0.593

10.422

7.066***

0.378

0.464

9.203

2.371*

EMIT

-3.049

-0.323

1.163

2.278*

-0.373

-1.324

-0.735

-0.545

EMAT R2 P

41.857 0.298 0.989

0.580

15.368 0.915 0.001

1.853

5.889 0.488 0.593

1.285

-13.275 0.679 0.114

-0.603

* P < 0.05; ** P < 0.01; *** P < 0.001. AWR, amount of water required by people; AWD, amount of wastewater discharge; AF: area of forest; ACC: area of city construction: AAC: area of agricultural crops; LH: length of highway; MAP: mean annual precipitation; MDP: maximum daily precipitation; MAT: mean annual temperature; EMIT: extreme minimum temperature; EMAT: extreme maximum temperature.

17

364

Table 3 Redundancy analysis on correlations between percentages of change in wetland areas and

365

environmental parameters Environments factors

Axis 1

Axis 2

AWR

0.0664

0.1533

AWD

-0.2011

-0.0477

AF

0.7173***

0.0198

ACC

-0.0288

-0.3787

AAC

-0.2702

0.2120

LH

-0.2047

0.0271

MAP

-0.4303*

0.1176

MDP

-0.0782

0.1910

MAT

0.0934

0.7230***

EMIT

0.0881

0.1818

EMAT

0.3410

-0.1009

Eigenvalues

0.508

0.087

Wetland area change rate–environmental factors change rate correlations

0.891

0.873

Cumulative percentage variance of change in wetland area

50.8%

59.5%

77.8%

91.1%

Cumulative percentage variance of change in wetland area–change in environmental parameters 366 367 368 369

* P < 0.05; ** P < 0.01; *** P < 0.001. AWR, amount of water required by people; AWD, amount of wastewater discharge; AF, area of forest; ACC, area of city construction; AAC, area of agricultural crops; LH, length of highway; MAP, mean annual precipitation; MDP, maximum daily precipitation; MAT, mean annual temperature; EMIT, extreme minimum temperature; EMAT, extreme maximum temperature.

370

18

371 372

Figure 1 24 administrative regions (province, city, or autonomous region) selected in this study.

373

AH, Anhui Province; CQ, Chongqing City; FJ, Fujian Province; GZAR, Guangxi Zhuang

374

Autonomous Region; HAN, Hainan Province; HB, Hubei Province; HEN, Henan Province; HLJ,

375

Heilongjiang Province; HUN, Hunan Province; JL, Jilin Province; JS, Jiangsu Province; JX,

376

Jiangxi Province; LN, Liaoning Province; IMAR, Inner Mongolia Autonomous Region; NHAR,

377

Ningxia Hui Autonomous Region; QH, Qinghai Province; SC, Sichuan Province; SD, Shandong

378

Province; SH, Shanghai City; SX, Shanxi Province; TAR, Tibet Autonomous Region; XUAR,

379

Xinjiang Uygur Autonomous Region; YN, Yunnan Province; ZJ, Zhejiang Province.

380

19

600

400

3

2

Area (10 km )

FNIWR SNIWR

200

0

381

Marsh

River

Lake

Coastal

Reservoir

Total

382

Figure 2 Area of different wetland types in the First (FNIWR, > 1 km2) and Second National

383

Inventory of Wetland Resources (SNIWR, > 0.08 km2).

384

20

1.0

River

MAT

AAC MAP

MDP AWR EMIT Lake

LH

AF Reservoir

AWD EMAT Marsh

ACC -0.6 -0.6

1.0

385 386

Figure 3 Redundancy analysis results for correlations between percentages of change in wetland

387

areas and environmental parameters; AWR, amount of water required by people; AWD, amount of

388

wastewater discharge; AF, area of forest; ACC, area of city construction; AAC, area of agricultural

389

crops; LH, length of highway; MAP, mean annual precipitation; MDP, maximum daily

390

precipitation; MAT, mean annual temperature; EMIT, extreme minimum temperature; EMAT,

391

extreme maximum temperature

21

There are no conflicts of interest to declare.