Science of the Total Environment 733 (2020) 139208

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Science of the Total Environment

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Planning for the wetland restoration potential based on the viability of the seed bank and the land-use change trajectory in the Sanjiang Plain of

Sixue Shi a,b,YuChanga, Guodong Wang c,ZhenLib,d, Yuanman Hu a,MiaoLiua,⁎, Yuehui Li a, Binglun Li a,b, Min Zong a,b,WentaoHuange a CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China b College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China c Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, China d Research Center for Eco-Environmental Science, Chinese Academy of Sciences, Beijing 100085, China e College of Land and Environment, Shenyang Agricultural University, Shenyang, 110866, China

HIGHLIGHTS GRAPHICAL ABSTRACT

• Potential wetland restoration areas were identified by the seed bank viabil- ity and land use model. • The annual wetland degradation trajec- tory was determined for 1986–2016 using of CLUE-S model. • The restoration potential of farmland back to wetland decreases as the time since agricultural development in- creases. • The percent of wetland area to water- shed area (PWW) could act as an indica- tor of watershed ecological sustainability.

article info abstract

Article history: The Sanjiang Plain has the largest marsh wetland area in China. Since the 1950s its size has declined due to land Received 6 December 2019 development, between 1986 and 2016 nearly 6072 km2 (57.5% of the area) was lost due to farm land expansion. Received in revised form 1 May 2020 Since the “Wetland for Grain” project in 2003, efforts have been made to improve marsh area for animal habitat Accepted 2 May 2020 and ecological protection. A key management concern is prioritizing areas for wetland restoration in scientific Available online 7 May 2020 planning and polices making. In this study, the natural wetland restoration potentials were evaluated based on Editor: Jan Vymazal land-use change trajectory, seed bank viability and watershed sustainability and restorability. The annual land use maps from 1986 to 2016 were reconstructed using CLUE-S model with land use maps in 1995, 2000, 2005, Keywords: 2010 and 2016, which were interpreted from Landsat TM/ETM images. Seed bank viability was determined by Wetland restoration field sampling in wetland and farm land with different reclamation years and germination in lab. Sub- Seed bank catchment was chosen as sustainability analysis unit, which was quantified by the impacts of wetland on peak Watershed sustainability flow reduction. The watershed restorability was performed with the factors of wetland degradation degree, CLUE-S seed bank viability, and the percentage of wetland to watershed area (PWW) with different restoration years. PCA The results indicated that reclaimed wetland with a time since last development (TLD) of b15 years had a higher

⁎ Corresponding author. E-mail addresses: [email protected] (S. Shi), [email protected] (Y. Chang), [email protected] (G. Wang), [email protected] (Z. Li), [email protected] (Y. Hu), [email protected] (M. Liu), [email protected] (Y. Li), [email protected] (B. Li), [email protected] (M. Zong), [email protected] (W. Huang).

https://doi.org/10.1016/j.scitotenv.2020.139208 0048-9697/© 2020 Elsevier B.V. All rights reserved. 2 S. Shi et al. / Science of the Total Environment 733 (2020) 139208

recovery potential and accounted for 39.2% of the lost wetland. Seventeen sub-catchments with a total area of 2177 km2 of farmland could be planned for restoration, which could support more than half of the sub- catchments in the study area. Priority areas were identified for short-, mid- and long-term restoration planning. The results can support the scientific planning demands of various restoration goals in the study area, and provide a new method for wetland restoration. © 2020 Elsevier B.V. All rights reserved.

1. Introduction Several approaches to develop plans for restoring Sanjiang Plain have been proposed, such as determining the minimum and optimal ecolog- Wetlands play a significant role in ecosystem services, such as nutri- ical water requirements of typical wetlands in Sanjiang Plain (J.-W. Liu ent cycling and climate change mitigation, due to their rich biological et al., 2017), and predicting the spatiotemporal distribution of wetlands resources (Jensen et al., 1993; Wang et al., 2006a; Zhou et al., 2009). under future climate changes (Chen et al., 2018). These study cases in- Over the past century, wetland loss and degradation has occurred all dicated a general wetland restoration trend considering geographic or over the world, and this trend is aggravated by anthropogenic effects social factors. However, the annual trajectory of wetland degradation and climatic change (Balmford et al., 2002). The degradation or loss of and the most suitable location for wetland restoration still needs to be wetlands results in decreased biodiversity, reduced water storage, in- explored, which can provide powerful scientific support for planning creased flooding potential, increased soil erosion (Finlayson et al., and policy making. 1999; Han et al., 2012), and increased pollution of water bodies due to Therefore, the objectives of this study are to: (1) elucidate the an- agricultural activities (Downing et al., 1999). Wetlands are carbon nual change trajectory of wetlands in Sanjiang Plain, China; (2) evaluate sinks and mitigators of climatic change (Chmura et al., 2003; Duarte the viability of soil seed banks with various TLDs in the wetlands of et al., 2013; Erwin, 2009), therefore wetland restoration can be a valu- Sanjiang Plain; and (3) develop a wetland restoration plan based on able management tool locally and worldwide. land-use change trajectory and soil seed banks. With the serious problems caused by wetland degradation (Bedford, 1999), more efforts have been made to restore wetland (Martinez- 2. Materials and methods Martinez et al., 2014; Richardson et al., 2011; Yang et al., 2010), such as the Wetland Reserve Program and the Conservation Reserve 2.1. Study area Program-Wetlands Initiative made by the USA Department of Agricul- ture (USDA) (Yepsen et al., 2014) and the Red Line for Wetland Conser- The Sanjiang Plain (43°49′55″-48°27′40″N, 129°11′20″-135°05′ vation in China (G.-d. Wang et al., 2015a). In wetland restoration 26″), located in the northeast of Heilongjiang Province, with a total processing, locations are primarily identified where restoration with area of 108, 900 km2, is the largest concentrated area of freshwater wet- highly success. The methods include the use of multisource remote lands in China (Fig. 1a). It is an alluvial plain formed by the River, sensing data (Wang et al., 2008), image segmentation (Kalpana, the , and the River. The region experiences a tem- 2018), geospatial modeling (Li et al., 2012), and many integrated perate humid to sub-humid continental monsoon climate, with annual modeling frameworks (Sohl et al., 2016; Sohl and Claggett, 2013). But precipitation of 500–650 mm. The coldest temperature is approxi- the identification on a long-term annual scale for wetland restoration mately −21 °C in January, and the warmest temperature is 21–22 °C potential areas was rare. Wetland restoration then may focus on water in July. The frost-free period is 120–140 days. The main vegetation requirements (Voldseth et al., 2007), soil nutrients (Gao et al., 2010; types are meadow and marsh dominated by Calamagrostis angustifolia Hemes et al., 2019), and species biodiversity (Bedford, 1999) to sustain and Carex lasiocarpa. The vegetation coverage is generally between ecosystem or human needs. In contrast, another important aspect of 70% and 90%. The cultivated land accounts for 10.5%. wetland restoration, seed bank viability, has been evaluated infre- quently in current wetland restoration planning. The soil seed bank is 2.2. Datasets an important component and constraint of ecological restoration, par- ticularly if the species remain viable in the soil for long periods of time Multi-spectral remote sensing images can be used to detect the land (Metsoja et al., 2014). The importance can be even greater, because use changes (Chen and Wang, 2010), Landsat 5 and Landsat 8 remote other sources of seeds, e.g. storage facilities of wild seeds, can provide sensing images with cloud cover b5% were derived from May to October only a small percent of seeds needed to restore a given area (Merritt in 2010 and 2016 (https://earthexplorer.usgs.gov/). The images, with a and Dixon, 2011). It is much more effective to begin a wetland restora- resolution of 30 m, were visually interpreted in ERDAS and ArcGIS. tion with a natural soil seed bank. The viability of this soil seed bank is Seven land-use types, dry farmland, paddy field, wetland, forestland, affected by the time since the last development (TLD). Restored wetland construction land, water area, and grassland, were classified. Land-use seed banks are often dominated by mudflat and shallow annuals while and land-cover data in 1995, 2000, and 2005 were from the Northeast perennials were missing in shallow and deep areas compared to those Institute of Geography and Agroecology, Chinese Academy of Sciences. in the original wetlands (Beas et al., 2013). Seed banks of highly de- A total of 216 sites were chosen for validation as determined by histor- graded fields can support the formation of a new vegetation community ical images and talking with local landowners in June and September of over time and under suitable environmental conditions (Stroh et al., 2017. The classification accuracies were 88.6%, 84.2%, 86.4%, 88.6%, and 2012). 91.2% for 1995, 2000, 2005, 2010 and 2016, respectively. The digital el- Since 1950, 80% of wetlands in the Sanjiang Plain have been lost, and evation model (DEM) data with 30 m resolution was derived from the most of these changes are caused by human activities ( et al., 2017). Earth Explorer website of USGS (United States Geological Survey). The wetland area is still declining, but the rate has been significantly re- duced since 2010 (He et al., 2017) because of the implementation of the 2.3. Analysis “Wetland for Grain” project in 2003. Heilongjiang Province decided to restore 420 km2 of these floodplains from 2017 to 2020 (Xing-Bo The wetland restoration potential area was identified by three steps: et al., 2016). Therefore, as a good candidate for developing recovery first, quantifying the change trajectory of wetland (the purple modules strategies, this region urgently needs to develop wetland restoration. in Fig. 2), with annual land use maps for the previous years, that S. Shi et al. / Science of the Total Environment 733 (2020) 139208 3

Fig. 1. Location (a), land use distribution in 2016 (b), and (c) seed bank sampling point in the Nongjiang watershed of Sanjiang Plain in Heilongjiang Province, China. The white areas in (b) and (c) are the land-use types other than wetland and farmed land.

estimated annual land use change from wetland to farmland and other The land-cover maps from 1986 to 2015 were backward simulated land use types and the TLD. Second, determining the vitality of soil seed by the CLUE-S model using the 2016 land-use map as an input layer. banks by field sampling and germination in lab (yellow modules). Third, The land-use maps in 1995, 2000, 2005, and 2010 were used to validate planning for the wetland natural restoration potential, including the di- the results from the CLUE-S simulation. The CLUE-S model allocates de- vision of sub-catchments and the determination of restoration years mands endogenously to either changes in land cover type or land-use with sub-catchment sustainability and restorability (the red parts in intensity, depending on the driving forces, which include the location- Fig. 2). specific conditions (e.g., distance to wetland, river, road, and residen- tial), the elevation and the slope of the region in our study. We input the original seven land use types of 2016 into the CLUE-S model and 2.3.1. Determining the change trajectory of wetlands then reclassified the results into three types: farmland (including dry farmland and paddy field), wetland, and others (including forestland, 2.3.1.1. Land-use model. Land-use changes have been previously ana- construction land, water body, and grassland).The historical land use lyzed in the Sanjiang Plain (Kong and Yu, 2012; Wang et al., 2011; from 1986 to 2015 was simulated with the CLUE-S model in five differ- Zhang et al., 2003) using remote sensing imagery data and topographic ent cities: Hegang, Jiamusi, Jixi, Shuangyashan, and Qitaihe (including maps. These analyses focused only on certain periods of time and lacked Qitaihe city and Yilan county), with an improved spatial resolution of long-term continuous annual wetland change information. Land-use approximately 200 m. models could support the evaluations of change analysis over longer pe- For testing the spatial consistency between the simulated maps pro- riods. One particular land use model showing promise is the conversion duced by the CLUE-S model and the interpreted land-use data (1995, of land use and its effect at small regional extent (CLUE-S) model, which 2000, 2005, and 2010), the kappa index (0–1) was calculated in the assumes that land system changes are determined by the regional de- MCK (Map Comparison Kit) software (Visser and de Nijs, 2006), and mands, and the distribution pattern of land use is a response to the dy- the result is substantially consistent when the kappa index is between namic balance among the land demands, natural environment and 0.6 and 0.8 (Foody, 2002; Rodriguez-Galiano et al., 2012). The regres- socio-economy (Liu et al., 2009b). At present, the CLUE-S model has sion results of the driving factors were tested with the receiver operat- been verified (Liu et al., 2009a), improved (Jiansheng et al., 2012; ing characteristic (ROC) method (embedded in the CLUE-S model). Miao et al., 2014) and applied worldwide (Liu et al., 2011; Z. Liu et al., When the area under the ROC Curve (AUC) is above 0.7, the result indi- 2017; Zhang et al., 2013). cates that the driving forces explain land-use changes rather well. 4 S. Shi et al. / Science of the Total Environment 733 (2020) 139208

Fig. 2. Research methods and analytical framework

2.3.1.2. The analysis of determining the land use change trajectory of wet- into wetland and non-wetland categories. The average seed density lands. A land use transfer matrix was used to detect the wetland change and richness of each species were calculated. trajectory of CLUE-S model simulations in Sanjiang Plain. The TLD of the wetlands is time between the starting year when wetlands were devel- 2.3.3. Planning for the wetland natural restoration potential oped into farmland and the year 2016, no other land use types changed during this period. It was performed in GIS using the tools of Extract, 2.3.3.1. Evaluation unit. Planning wetland restoration in the Sanjiang Mosaic, Zonal Statistics, and Raster Calculator. Plain is difficult because land and water uses differ greatly among re- gions. The watershed is the unit used to analyze wetlands because of 2.3.2. Determining the vitality of soil seed banks the hydrogeomorphic characteristics of wetlands and river (Comín Soil samples from twenty-three sites were collected by shovel from et al., 2014). To present the spatial differences in a watershed, we soybean fields (farmed wetland group) and the adjacent natural wet- used the sub-catchment as our evaluation unit. Twenty-two sub- lands, which are sedge meadows (wetland group for reference) that catchments were defined by the Soil and Water Assessment Tool are still intact along the Nongjiang River (Fig. 1(c)). At each site, five (SWAT) according to the Digital Elevation Model (DEM) and main rivers quadrats (25 × 25 cm) at least 10 m apart from each other were chosen maps, which were from the National Earth System Science Data Sharing randomly, and soil samples within a depth of 10 cm were brought back Infrastructure, National Science & Technology Infrastructure of China to the lab in plastic bags for a six-month germination experiment in the (http://www.geodata.cn). greenhouse. A total of 230 soil samples were collected (23 sites × 2 groups ×5 samples/site). The soil samples were sieved with water and 2.3.3.2. Sub-catchment attributes for the evaluation. Wetlands act like a pushed through the screen to remove debris and roots. After this step, sponge when flooding conditions occur (Acreman and Holden, 2013). each soil sample was mixed evenly then divided into five subsamples Edwin Martinez-Martinez used SWAT to evaluate the effects of wetland to produce five replicates. Each soil sample was evenly spread over area (2%, 4%, 10%, and 20%) on peak flow reduction at the watershed 2 cm in a pot (25 × 25 cm), which was previously filled with washed scale. And found that sub-catchments in the 2% scenario had negligible vermiculite to a depth of 8 cm. To keep the soil moist, the pots were effects on stream flow, 4% and 10% proved to be better than 2%, while watered by filling the bottom dishes. Newly emerged seedlings were wetland with 20% resulted in the greatest reduction in peak flow identified, counted, and removed from plots. Species were classified (Martinez-Martinez et al., 2014). The percentages of wetland area to S. Shi et al. / Science of the Total Environment 733 (2020) 139208 5 watershed area (PWW) could act as an indicator of watershed ecologi- The PWWs of restoration within five,ten,andfifteen years are all cal sustainability (Qu et al., 2019), ensuring the environmental and eco- based on the simulation map. logical conditions sufficiently support the ecosystem ecological in terms To calculate the wetland area after restoration, the PWWi was de- of watershed value (Yu et al., 2014). Previous studies (Acreman and fined as the PWW after restoration within i years, WARi was named Holden, 2013; Brody et al., 2007; Delaney, 1995) have also shown that for wetland area after restoration within i years, and the RWAp-q pre- if watersheds comprised 5–10% wetlands, the flood peaks may be re- sents the reclaimed wetland area between yearp and yearq, which al- duced by 50% compared to watersheds without wetlands, but small ways be farmland in the remaining years until 2016, A and B wetland losses could have a significant effect on increased flood flows. presented the wetland in 2016 and 1986. C represented the sub- When the ratio is N10%, the wetlands appear sufficient to moderate a catchment area in 2016. Some of the wetland area after restoration for- watershed's annual hydroperiod. Therefore, we divided the sub- mula was calculated as follow: catchments into unsustainable, sustainable with no reducing, sustain- WAR0 =A able, and abundant with strong sustainability categories with the WAR1 =A+RWA2015–2016 PWW b5%, 5% ~ 10%, 10% ~ 15%, and N15% respectively. WAR2 =WAR1 +RWA2014–2015 The criteria for evaluating wetland restoration included the per- WAR5 =WAR4 +RWA2011–2012 centage of water body area to each sub-catchment area, the degree WAR10 =WAR9 +RWA2006–2007 of wetlands lost calculated as the percentage of lost wetland area WAR15 =WAR14 +RWA2001–2002 to each sub-catchment area, the vitality of the seed bank, and the WAR30 =B PWWs with different restoration scenarios. The time span of restora- PWW0 =WAR0/C tion from farmland was determined by the results of the germination PWW1 =WAR1/C experiment, and the PWWs with complete restoration and no resto- PWW2 =WAR2/C ration were calculated by the simulation map in 1986 and 2016 PWW5 =WAR5/C (same to the interpretation map in 2016), and the short-, middle- PWW10 =WAR10/C and long-term restoration scenarios were referred to restore farm- PWW15 =WAR15/C land with a TLD for 5, 10, and 15 years to wetlands, respectively. PWW30 =WAR30/C

Fig. 3. The land-use maps interpreted from remote sensing images (a-e, 1995, 2000, 2002, 2010, and 2016). 6 S. Shi et al. / Science of the Total Environment 733 (2020) 139208

Table 1 Table 2 The area of wetlands transferred to other land-use types in different periods (km2). The weight of seed bank vitality at different farming years.

Land-use typea 1986–1995 1995–2000 2000-2010 2010–2016 Farming years 0–56–10 11–15 16–21 22–30 Weight 0.9 0.6 0.4 0.3 0.1 Grassland 0 24 2 0 Forest land 91 0 76 0 Paddy field 71 2214 1608 385 Dry farmland 486 700 377 37 (Fig. 3 and Table 1). Wetland loss mainly occurred along rivers in the Wetland 9832 6897 4859 4451 central and northeast parts of the Sanjiang Plain. The average annual a There is no transfer from wetland to water area and construction land during each rate of decrease in wetland area was 2.8%, with the lowest value of period. 0.7% during 1986–1995 and the highest value of 6.0% during 1995–2000. Major transitions occurred from 1995–2000 to 2000–2010, with areas of 2937 km2 and 2064 km2, respectively. The 2.3.3.3. Evaluation method. Principal component analysis (PCA) (Bro and wetland was mainly converted to farmland (including paddy field and Smilde, 2014) was chosen to estimate wetland restorability specifically, dry farmland), with an area of 58,787 km2 during 1986–2016, which the sub-catchments were used as samples, and their attributes men- represented 96.8% of the lost wetland area. From 1986 to 1995, tioned above were used as variables. The clusters of samples and vari- 6497 km2 of wetland was lost, of which 74.9% was converted to dry ables were divided according to their distribution characteristics in farmland. In the other three periods, an average of 81.5% of the wetland different principal component spaces. The wetland restoration potential area was converted to paddy field. was then explained based on the attributes (variables) of each sub- catchment (samples). The variables and samples in different principal 3.2. The vitality of the soil seed bank component spaces were calculated and mapped with the R platform (Maindonald, 2011). In 1954, wetlands covered N72% in Nongjiang watershed, since then, The basic recovery years were determined based on the PWW, and wetlands have been developed and b12% remain (C. Wang et al., 2015). then the efficient recovery area and years were determined according This change is a microcosm of the change in wetlands throughout the to the sub-catchment cluster and its characteristics. To ensure the sub- Sanjiang Plain (Liu et al., 2018). The soybean fields were converted catchment sustainability is stronger than that in 2016, if the sub- from wetlands over various periods of time (1–50 years). As the domi- catchment is determined to be unsustainable (PWW b 5%) before and nant species of sedge meadows, the seed density of Carex spp. (sedge) after recovery, wetland restoration should be carried out urgently; and Calamagrostis angustifolia (grass) decreased significantly with TLD that is, the cultivated land should be restored to wetland within (Fig. 4a). Carex spp. was visible only in soils cultivated b5yearswitha 15 years. For a sub-catchment reaching the sustainable level after recov- small number of seeds, while Calamagrostis angustifolia decreased ery, the year of restoration is when the sub-catchment can be at its steadily in soils cultivated for 1 to 15 years and were absent in soil cul- highest level. Comparing the recovery year of the PWW and the PCA re- tivated for N15 years. The seed density of non-wetland species was low sults, the maximum year was chosen as the restoration year, and the in soybean fields farmed for any length of time. goal of restoring the largest area with maximum efficiency was used We proposed a conceptual model (Fig. 4b) based on the relation- in each sub-catchment. ship between seed density and the time since the last exploitation. The natural restoration potential can be categorized according to 3. Results the cultivation time: high (0–5 years), medium (6–15 years) and low (N16 years). During the high restoration potential period with 3.1. The wetland change trajectory in Sanjiang Plain b5 years of cultivation, the majority of the wetland species seeds, in- cluding those of the dominant sedge (Carex spp.) and grass Most of the AUC values were above 0.7, especially with the high (Calamagrostis angustifolia), were still viable in soil, even though values of farmland and wetland, which indicated the driving factors the germination of sedges was relatively lower than grass and were capable of simulating wetland change with the CLUE-S model. other wetland species. In the medium restoration potential period The kappa indices of the simulated maps and the interpreted land use (6–15 years), although certain critical components of vegetation maps in 1995, 2000, 2005, and 2010 were all between 0.6 and 0.8, indi- were not retained in the seed banks, grasses and other wetland spe- cating that the CLUE-S model had a high accuracy over the 30 year cies (e.g., forbs, shrubs) existed with lower values than those in the simulation. natural sedge meadows, and the seed banks could still construct a The wetland area in the Sanjiang Plain decreased greatly (57.5%) in new community of novel wetland vegetation assemblages. In fields the past 30 years, from 10,481 km2 in 1986 to 4451 km2 in 2016 cultivated for N15 years (low restoration potential), most of the

Fig. 4. The relationships between the time since last exploitation and (a) the seed density of the dominant species and (b) the natural restoration potential of sedge meadows. S. Shi et al. / Science of the Total Environment 733 (2020) 139208 7 wetland species were missing, and the non-wetland species 3.3. Planning for the wetland natural restoration potential (e.g., Chenopodium glaucum) dominated the seed bank, which made those fields difficult to restore via natural recolonization. This infor- 3.3.1. The development time map of wetlands mation could be used to determine which farmed wetlands might be Based on the CLUE-S simulation results, a spatial distribution map targeted for restoration. (Fig. 5) was constructed for the TLD. A large area of wetlands has To perform PCA, the weight of seed bank vitality was quantified been cultivated in the past 20 years. The extensive wetland develop- based on different cultivation times (Table 2). The regional seed bank vi- ment occurred 16 to 21 years ago in the northeastern half of the tality equals the weighted average of the seed bank vitality multiplied Sanjiang Plain. Wetlands areas that were cultivated for N22 years by the farmed wetland area in the same duration (Eq. (1)). were small and were scattered throughout the study area. The sites in which development occurred 1–5years,6–10 years, and 11–15 years ago were mainly distributed in the central and north- Y ¼ 0:9 S0–5 þ 0:6 S6–10 þ 0:4 S11–15 þ 0:3 S16–21 þ 0:1 eastern Sanjiang Plain, though a small number were in the north- ð Þ S22–30 1 west, southwest, and southeast.

Fig. 5. The time since last development (TLD) inferred from the CLUE-S simulations from 1986 to 2016. 8 S. Shi et al. / Science of the Total Environment 733 (2020) 139208

According to the seed bank viability, the places with high restoration According to the PWW value with restoration years increasing, the potential were farmed for b5 years and were concentrated in the north- sub-catchment was divided into three parts colored in pink, yellow east and the middle of the Sanjiang Plain, with an area of 558 km2.The and gray in Fig. 6. In the pink area, the PWW increases with the recovery area with medium restoration potential (6–10 and 11–15 years) was years. In the yellow and gray parts, the PWW did not change as the re- near the area with high potential, with a total area of 1824 km2. The covery years increased, specifically, the difference is yellow in the sus- area with low restoration potential was 3531 km2. Considering the hy- tainable level and gray in the unsustainable level. All the sub- drology, we divided the low potential restoration area into two parts. catchments in the gray part were in an unsustainable state with differ- The lower restoration potential area was the largest area among the ent restoration scenarios, these sub-catchments must be restored for fif- field with different durations of farming, with a total area of teen years to recover. The sub-catchments in yellow are at a sustainable 3002 km2. The areas with the lowest potential of wetland restoration level, they do not need to be restored but development should stop to were farmed for N22 years. maintain this level. In addition, sub-catchment 4, after recovery for ten years, and sub-catchments 6, 12, and 16, after recovery for fifteen 3.3.2. Wetland potential restoration area years, could achieve the highest sustainability level in 1986 before de- The potential restoration areas were strongly dependent on location velopment (complete restoration). If catchment 7 is restored for five with different trajectories depending on sub-catchments. The sub- years, it could reach a sustainable level with no decreasing state (Fig. 6). catchment sustainable development levels with different restoration The first two principal components (PC1 and PC2) explain 82.1% of scenarios (no, short-, middle-, long-term and complete scenarios re- the variance in the sub-catchment attributes, with PC1 and PC2 ac- ferred to restore farmland with a TLD for 0, 5, 10, 15 and 30 years to wet- counting for 63.0% and 19.1%, respectively were less disturbed and wet- lands, respectively) were visualized in a radar graph (Fig. 6). Because land reduction were lower than others. In the short-term and middle- fields cultivated for N15 years will have low restoration potential, the re- term restoration groups, the degree of wetland loss was not significant. covery period was set to b15 years except for the complete restoration. The short-term restoration sub-catchments had more waterbody area The PWW was divided into three rings in the graph. The innermost cir- and less wetland loss than the middle-term restoration sub- cle, with a PWW value between 0 and 5% (rank 1, unsustainable sub- catchments. In the long-term restoration group, the sub-catchments 4, catchment), indicated the wetland area is too small to sustain the sub- 6, 8, and 20 with large areas of wetland loss and a shorter TLD could catchment ecological balance, need restoration as more as they can. be determined as the key recovery area (Fig. 7, Table 3). The middle ring, between 5%–10% (rank 2, sustainable sub-catchment with no reducing), represented the sub-catchments is sustainable as 3.3.3. Wetland restoration planning guidance long as the number and size of wetland does not decrease. The sub- By combining the sub-catchment sustainability and restorability, the catchments with sustainability in rank 3 (10% ~ 15%) and strong sustain- recovery years of each sub-catchment were determined (Table 4). The ability in rank 4 (N15%) were represented by the outermost ring. sub-catchments 0, 10, 17, 18, and 21 distributed near the Ussuri River

Fig. 6. The percentages of wetland area to watershed area (PWW) in different periods. The number outside the outermost circle is the sub-watershed number. The PWW increases from 0 to 15% in 5% intervals from the center to the edge. Colored points in the outer circle represent the PWW beyond 15% in the corresponding periods. The colors of dots indicate the PWW with different periods: complete restoration from farmland is in orange, no restoration in red, and restoration for 5, 10, and 15 years are blue, green and purple, respectively. In the pink area, the PWW increases with the recovery years. In the yellow (sub-catchments in sustainable level) and gray (sub-catchments in unsustainable level) the PWW did not change as the recovery years increased. S. Shi et al. / Science of the Total Environment 733 (2020) 139208 9

-0.2 0.0 0.2 0.4 Table 4 Type 1 The recovery years by PWW and PCA of each sub-catchment. 0 Type 2 2 0.5 Type 3 Sub-catchment Restoration Restoration Restoration years 21 Wetland 2016yr% Type 4 number years years by 10 Type 5 by PWW only by PCA only both PWW and PCA 17 Short -term restoration% Waterbody% Component loadings 8 0, 10,17, 18, 21 0 0 0 25 22 Middle-term restoration%

) 23 16 15 0 10 10 13 18 %

8 0 0.0 20 0 15 15 2 Long-term restoration% 0 11 . 20 9 12 8 0 15 15 24 1 ( 3 15 7 7 5 10 10

2 Wetland 1986yr%

C 4101515 P 2, 11, 13, 16, 22, 23, 15 0 15 14 4 -2 -0.5 25 The vitality of seed bank 3, 12, 24 15 5 15 14 15 10 15 Wetland degradation degree 6 6151515

-4 -1.0 -202468 was largely driven by the need to meet the food requirement and agri- PC1 (63.01%) cultural policies at that time (Liu et al., 2015). After decades of develop- ment, the Sanjiang Plain has become one of the most important Fig. 7. The sub-catchments and their attributes plotted in two principal component spaces. commodity grain bases and has the largest agricultural land area in Wetland 1986 yr% refers to the percentage of wetland area in 1986 to the sub-catchment China. area, and the same is true for the next variables with the % symbol. The three restoration From 1986 to 1995, the contradiction between farmland and wet- scenarios, i.e., short-, middle- and long-term, refer to restoring farmland with TLD values land was not prominent. The demand for industrial development was of 5, 10, and 15 years to wetlands, respectively, the same is shown in Fig. 8. greater than the demand for food production. From 1995 to 2000, with the enaction of the farmland protection policy and the preforma- did not need recovery because their wetland losses were not significant tion of the market economy, farmland had sudden growth, and wetland and the PWW values were relatively high in 2016. Sub-catchment 7 falls was heavily cultivated without awareness of its ecological value. From into the middle-term restoration category, and the remaining sub- 1998 to present, Heilongjiang Province has completely prohibited wet- catchments are in the long-term restoration category because of the land development and has begun to return farmlands to wetlands. In low PWW values or high potential for wetland restoration. The main fact, the government and people have begun to consciously protect restoration sub-catchments are 4, 6, 8, 12 and 20, which account for the wetlands (Wang et al., 2012). However, during 2000–2010, the wet- 77.7% of the entire restoration area (Fig. 8). PWW level after restoration land area was still decreasing, although the rate of decline was 50% of some sub-catchments have changed, more than half of the sub- slower than that observed in the previous period (Man et al., 2017; catchments reach the sustainable level (Figs. 6 and 8). The high poten- Mao et al., 2018). Until 2010–2016, the wetland area did not decrease tial area in each sub-catchment was determined with three restoration any more. The government paid more attention to wetland conserva- scenarios. The short-term restoration in red is the highest restoration tion, and they identified the red line for wetland conservation in 2014 potential area because of the highest seed bank vitality. If the wetland (G.-d. Wang et al., 2015). In the red-line area, wetlands will be strictly managers do not want to restore large areas at once, they could restore conserved. the short-term restoration area first, but all these areas should be re- stored from farmland to wetland gradually. 4.2. The vitality of the soil seed bank

4. Discussion The natural restoration from farmland to wetland requires acquire- ment of the soil seed banks vitality (Baldwin et al., 2010). Our germina- 4.1. The change trajectory of wetlands in the Sanjiang Plain tion experiment showed that the seed density of the Carex spp. (sedge) and Calamagrostis angustifolia (grass) decreased significantly within The wetlands of the Sanjiang Plain changed greatly over the last 15 years of the TLD. The results are consistent with previous studies 30 years. The decrease in wetland area occurred mainly in the northern (Li et al., 2002; Middleton, 2003) and have important implications for part of the Plain from 1995 to 2000 and 2000 to 2010, accounting for the natural restoration of cultivated wetlands. If the wetland has been N80% of the total area of wetland reduction in our study period. The wet- cultivated for N15 years, it is difficult to restore it to its original wetland. land was mainly converted to dry farmland in the early stage This threshold may help policy makers focus on the wetlands that have (1986–1995) and paddy fields in the later stages (1995–2000, been developed within 15 years when planning wetland natural resto- 2000–2010, and 2010–2016) (Shi et al., 2016). The change trend of wet- ration. However, in our study, we did not perform germination experi- land areas in our study was consistent with that reported in other stud- ments in paddy soil. A recent study showed that annuals could ies (Kaishan et al., 2008; Wang et al., 2006b). The reduction of wetlands germinate in dewatered mudflats 14 years later (Baskin et al., 2019). We infer that our results could be applicable in the restoration of paddy fields to wetlands. Table 3 The component loadings of variables in principle components. 4.3. Planning for the wetland natural restoration potential Variables PC1 PC2 Wetland 1986 yr% 0.43 −0.17 Successful planning for restoring reclaimed fields to wetlands re- Wetland 2016 yr% 0.39 0.35 quires knowing where and when to perform restoration practices. Qu Waterbody% −0.10 0.17 Wetland degradation degree 0.11 −0.69 et al. (2018) designed a restoration plan according to restorability anal- The vitality of seed bank 0.29 −0.51 ysis based on natural conditions, anthropogenic impacts and regional Short restoration % 0.42 0.24 sustainable development at the county level. However, the recovery po- Middle restoration % 0.43 0.12 tential area in our study is only one tenth of his/her, which proves that Long restoration % 0.44 0.05 our study has accurately identified the high potential recovery areas. In 10 S. Shi et al. / Science of the Total Environment 733 (2020) 139208 addition, his/her planning used administrative boundaries other than large areas at once, these areas could be restored gradually. The shorter natural watershed boundaries. As the restoration potential areas are the TLD, the greater the recovery potential. Our planning could meet the strongly location-dependent in watersheds, it is more appropriate to demands of various restoration goals. focus on areas within ecological boundaries rather than those defined by humans, such as administrative boundaries (Brody et al., 2007). In 4.4. Limitations our study, we used sub-catchments as the evaluation units and identi- fied the potential areas suitable for natural restoration based on water- Wetland restoration is a complex and systematic process that must shed sustainability and sub-catchment attributes analysis in PCA. consider many factors (Zedler, 2000), such as the hydrological connec- Seventeen sub-catchments with a total area of 2177 km2 were planned tivity (Wu et al., 2017; Yao et al., 2014); trade-offs between water re- for restoration, which could guarantee the sustainable development in sources and growing farmland water requirements (Zhou et al., 2006; more than half of the sub-catchments in the Sanjiang Plain. Zou et al., 2018); soil physical, nutrient and moisture conditions (Song We further categorized the planning into short-, mid- and long-term et al., 2012; Wang et al., 2009); biodiversity and the attribute by birds categories according to the TLD (Fig. 8). Those fields with a TLD less than (Qu et al., 2019; Reynolds and Cumming, 2016); microbial environment and equal to 5 years were planned for short-term restoration, and have (Xu et al., 2017). In this paper, we considered only the PWW (wetland the highest restoration potential. Those of 5–10 years for mid-term res- area to watershed area) as a proxy for watershed ecological sustainabil- toration and 10–15 years for long-term restoration also have high resto- ity, soil seed bank vitality, and time since the last development. In addi- ration potential, but if the wetland managers do not want to restore tion, we did not classify different wetland types (e.g., river, lake, forest

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