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Genome structure of DhNV
The circular genome of DhNV is 119,754 bp and contains 106 hypothetical ORFs, with 56 on the positive strand and 50 on the negative strand (Fig. 1). Fifty-nine of the ORFs met our comparative e-value threshold of  More

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Study site
The study was conducted in the Tone river basin, central Japan (Fig. 1). The Tone river is Japan’s second-longest river, running through the entire Kanto plain in central Japan. The Tone river basin is covered mainly by rice paddies and also contains arable fields other than rice, seminatural grasslands, coppice forests, farm villages, and urban areas29. The Tone river basin is located in the Kanto plain which is the largest plain field in Japan (approximately 170,000 km2), including large floodplains. Thus, this area have a variety of both terrain conditions and agricultural modernization works.
Figure 1

Location of the Tone river basin and monitoring sites.

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Plants community data
The Institute for Agro-Environmental Sciences, NARO, Japan conducted the program for monitoring biodiversity, including birds29 and plants34, in each of the thirty-two 1-km2 grids in the Tone river basin in 2002. In this program, the Tone river basin was initially divided into one hundred 1-km square grids (hereafter, 1-km grid), and each square was classified into one of four major land use types in the region: (1) midstream paddy; (2) downstream lowland paddy; (3) plateau and valley-bottom paddy; and (4) urban fringe29. Then, eight grids were selected randomly as study sites from each land use type, making a total of 32 grids (Fig. 1). The grids were more than 5 km apart (Fig. 1), so they were spatially independent of each other. In this study we used the plant monitoring records from the program. In the plant monitoring program there were three terms—2002, 2007, and 2012—of vegetation survey based on the Braun–Blanquet approach in each 1 km grid 35. In each survey, approximately 20 quadrats measuring 1 m2 were placed randomly in each 1-km grid in each survey term and the coverage ratios of all plant species in four hierarchies—(1) tall tree, (2) semi-tall tree, (3) shrub, and (4) grasses—were recorded. In this study, we used only the grasses class without abundance and the presence or absence of species records in the grasses class. We pooled all the species records within each 1-km grid for analysis. All plant monitoring data are available as Open Data (CC BY 4.0) at github space own by Dr. N. Iwasaki who was the member of this monitoring program (https://github.com/wata909/RuLIS_monitoring, accessed at 25, May 2020).
Dividing wetland plants and non-wetland plants
To test our hypothesis, we needed to divide the plants that typically grow in wetlands (hereafter, wetland plants) and those that typically grown in non-wetlands (hereafter, non-wetland plants) to evaluate the habitat quality of paddy fields as wetland. To this end, we used a published checklist of wetland plants in Japan (Shutoh et al. 2019; https://wetlands.info/tools/plantsdb/wetlandplants-checklist/, accessed at 25, May 2020). This checklist defined 8,358 Japanese vascular plants as wetland and aquatic plants according to their habitat requirements and the “wetland” definition of the Ramsar Convention (Ramsar Convention Secretariat 2016, https://www.ramsar.org/sites/default/files/documents/library/manual6-2013-e.pdf, accessed at 25, May 2020). We used this checklist to identify the wetland plant species in the monitoring records.
Land use, terrain condition, and human activity
A digitized land use map for paddy fields in 2009 that relatively matched the plant monitoring terms (2002, 2007, and 2012) was prepared from the National Land Numerical Information (National Land Information Division, MLIT of Japan: https://nlftp.mlit.go.jp/ksj-e/index.html, accessed at 25, May 2020). These map data were developed using both topographic maps and satellite imaging data, with the land use labeled on the basis of nationwide land use classifications, including paddy fields, at approximately 100-m grid resolution (National Land Information Division, MLIT of Japan: https://nlftp.mlit.go.jp/ksj-e/index.html, accessed at 25, May 2020).
A FAV, which was ascertained by accumulating the weights of all cells that flowed into each downslope cell, was used to define the concave areas (ESRI, https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/how-flow-accumulation-works.htm, accessed at 25, May 2020); lower elevations and valley areas had a higher FAV because they could potentially store more water, whereas higher ridge areas had low FAVs (Fig. 2). We used FAV to define the wetland potential, as this value could reflect the water accumulation from upper areas to lower areas, which strongly relates to the natural process of wetland formation36. We considered that terrain variable could reflect the geographical conditions of paddy field namely potentially wetland habitat for their intact ecosystem. We considered high FAV areas to have high potential of wetland habitat for their intact ecosystem. We calculated FAV value on a whole for mainland Japan; therefore, that range could cover the entire basin which overlapped with our target areas. The FAV was calculated using ArcGIS 10.5 with Spatial analyst (ESRI, Redlands, CA, USA) using a 50-m digital elevation model from the Japanese Map Centre (https://www.jmc.or.jp/, accessed at 25, May 2020). The FAV and paddy field maps were overlaid, and the total FAVs for paddy fields in each 1-km grid were calculated to determine the potentiality of the paddy fields in the 1-km grid being wetland. If a paddy field had an extremely high FAV within the basin which included the paddy field, that paddy field could have been a wetland because that area could store a large amount of water naturally.
Figure 2

Conceptual image of the flow accumulation value to indicate the potential of wetland.

Full size image

The proportional area of field consolidation as current human activity was calculated for each grid square using digital polygon data on the shape of farmland, as derived from aerial imagery collected in 2001 by the Ministry of Agriculture, Forestry, and Fisheries (MAFF), Japan. We obtained data on land leveling in agricultural areas from MAFF (https://www.maff.go.jp/j/tokei/porigon/, accessed at 25, May 2020) and used these data as an index of consolidated farmland because land leveling is one of the important components of agricultural consolidation in Japan4,23. Generally, agricultural consolidation in Japan involves land leveling, which integrates small, patchy farmland areas. Each polygon was assigned a status of “leveled” or “not leveled” according to its current status. Using ArcGIS, we calculated the ratio of consolidation for paddy fields in each 1-km grid that had survey sites.
Statistical analysis
We performed the two types of analysis used in this study with the statistical package R version 3.5.2 (R development core Team, https://www.r-project.org/, accessed at 17, Feb. 2020). First, we tested the species number in each 1-km grid using GLM with Poisson distributions (log link) and a Wald test37. The response variables were total species number, number of wetland plants, and number of non-wetland plants in each 1-km grid in each survey term. Explanatory variables were the log-transformed FAV values for the paddy fields and consolidation ratio of the paddy field within the 1-km grid. The aim of this analysis was to assess the effects on species diversity of both the original environmental condition of and current human activities in the paddy fields. Prior to the GLM analysis, all explanatory variables were tested for multicollinearity by calculating the variance inflation factors (VIFs)38; no significant multicollinearity was found (VIF  More

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Field investigation
This study was performed between from May 2017 and October 2019 at Xinsheng Aquaculture Professional Cooperative (121° 0′ 56″ N, 30° 58′ 17″ E) in Qingpu District, Shanghai, Eastern China. This region has a subtropical monsoon climate with a mean monthly air temperature of 17.6 ± 2.3 °C and mean monthly precipitation of 126.9 ± 24.6 mm.
Each RC paddy (667 m2) had a rice-growing area (80% of the total area), aquaculture area (10%) and ridge area (10%; Fig. 4A). In the aquaculture area, a 1.2 m deep ditch was dug to provide a more comfortable habitat for the crayfish and eels. The ridge had a height of 40 cm, and it was covered with a high-density polyethylene film to prevent the aquatic animals from escaping. Every May, rice (Oryza sativa L., Qing-Xiang-Ruan-Geng) seedlings were transplanted from a nursery into the paddies at a planting density of 20 × 20 cm (one seedling on each hill). Moreover, the juvenile crayfish weighing 1.5 ± 0.3 g were released into the paddies according to the standard of 45,000 juveniles per hectare, and the crayfish were allowed to self-propagate inside the rice paddies. A total of nine RC paddies were divided into three groups according to the rearing density of the eels: control group (C), low-density group (LD) and high-density group (HD) with rearing densities of 0, 6000 and 12,000 ind. ha−1, respectively. The LD and HD groups were supplemented with juvenile eels at a density of 2000 and 4000 ind. ha−1 in June 2018 and 2019. The average weights of juvenile eels in 2017, 2018 and 2019 were 21.4 ± 1.8, 24.1 ± 0.9 and 26.8 ± 1.1 g, respectively. All juvenile crayfish and eels were purchased from Shanghai Xiangsheng Aquaculture Cooperative. In the aquaculture area, floating plants, such as duckweed (Lemna minor L.) and foxtail (Myriophyllum spicatum L.), covered one-third of the water surface. The soil contained 20.6–23.7 g kg−1 of organic matter, 0.7–1.2 g kg−1 of total N and 0.31–0.37 g kg−1 of total P.
Figure 4

Photographs of the paddies in the field investigation (A) and plots in the mesocosm experiment (B).

Full size image

Only basal fertilizer was used for rice cultivation, and it contained 587 kg ha−1 of urea (46.4% N), 625 kg ha−1 of superphosphate and 150 kg ha−1 of potassium chloride. Every day, 500 g of commercial fish diet (5.83% N) was applied, and no pesticides or herbicides were used in the paddies.
In late August, the mature crayfish and eels were collected using ground cages to measure the aquatic product yields. The immature crayfish and eels were returned to the paddy fields during the collection. After the rice was harvested, the rice grains were air-dried and weighed to estimate the rice yield. The N content of the rice grains and aquatic animals was determined using the semi-micro Kjeldahl method33. Before testing, rice grains, crayfish and eels were weighted, dried at 65 °C and ground. Then, all the samples were digested with concentrated sulphuric acid (H2SO4) and hydrogen peroxide.
Water samples were collected every month during the co-culture period. Three duplicate 500 mL water samples were collected from 0 to 10 cm below the surface in the aquaculture area; the three subsamples were combined to obtain one sample per paddy. In the laboratory, the total N content of the water was analysed using UV spectrophotometry after digestion by alkaline potassium persulfate oxidation.
Soil samples were collected after the rice-planting period. In each paddy, three samples were collected from a rice-planting area of 0.25 m × 0.25 m × 0.10 m. All the soil samples were air-dried, ground, passed through a 0.15 mm sieve and digested with K2SO4–CuSO4–Se solution. Then, the semi-micro Kjeldahl method was used to test the total N content of the soil.
The N2O flux rate was measured using the static chamber method34. The size of the chamber was 1.0 m × 1.0 m × 1.0 m. The N2O samples were collected every half month between 8:30 and 10:30 AM from June to October. In each paddy, four gas samples were collected using 40 mL vacuum tubes at 10 min intervals (0, 10, 20 and 30 min after chamber closure). All samples were analysed with gas chromatography (GC 2010; Shimadzu, Kyoto, Japan). The N2O flux rate was calculated using the following equation:

$$F = rho times h times left[ {{273}/left( {{273} + T} right)} right] times {text{d}}C{text{/d}}t$$
(1)

where F is the N2O flux rate (μg N m−2 h−1); ρ, density of N2O at the standard state (μg m−3); h, height of the chamber (m); T, average temperature in the chamber during gas collection and dC/dt, concentration variation rate of N2O.
The ammonia volatilization flux was measured with a continuous airflow enclosure method35. The NH3 flux was measured every half month from 09:00 to 11:00 AM during the rice-planting period. NH3 was absorbed using boric acid, and 0.01 M H2SO4 was used to titrate the solution to determine the rate of NH3 volatilization. The ammonia volatilization flux was calculated using the following equation:

$$F = 14 times V times C times A^{ – 1} times t^{ – 1}$$
(2)

where F denotes the ammonia volatilization flux (mg N m−2 h−1); V, volume of H2SO4 titrated (L); C, concentration of H2SO4 (mol L−1); A, area of the chamber base (m2) and t, continuous measurement time.
In this study, all the data were shown as mean ± standard error of the mean (SEM) values. One-way ANOVA and Tukey’s test (SPSS V.16.0) were used to compare the differences of the yields and total N content among the three groups and three investigated years.
Mesocosm experiment
Between May and October 2019, the mesocosm experiment was conducted at Shanghai Academy of Agricultural Sciences. Each mesocosm consisted of an experimental plot (1.2 m × 1.2 m × 0.6 m) covered with a high-density polyethylene film (Fig. 4B). In each experiment plot, 30 kg of soil from Xinsheng Aquaculture Professional Cooperative was used to construct a rice-planting platform and an aquaculture ditch (40 cm in depth). The platform area was about three-fourth of the cross-sectional area of the plot.
A total of six mesocosms were constructed: three experimental plots (RCE) and three control plots (RC). In each plot, the rice seedlings were planted in hills (one seedling per hill) within rows in May, with 20 cm between rows and 20 cm between hills in the same row for the experimental and control plots. The fertilizers used in each plot contained 84.5 g of urea (N content, 46.8%; 15N abundance, 10.15%), 90 g of superphosphate and 15 g of potassium chloride. The duckweed was planted in the aquaculture area, and it covered 30% of the aquaculture zone. Mudsnails (Cipangopaludina cathayensis, 500 g) were added to each plot. After a month, 12 crayfish were cultured in each simulated paddy, and two eels were reared in each experiment plot. The proportion of crayfish and eels was set according to that in the LD group of field investigation. The crayfish feed was supplied once every day, and the daily allowance was about 3% of the estimated crayfish weight in each mesocosm. The rice and aquatic products were harvested in October.
Rice, crayfish and eel samples were collected to measure the total N content and 15N abundance. The total N content of the soil and organism samples were measured using the semi-micro Kjeldahl method after digestion with concentrated H2SO4 and hydrogen peroxide. The 15N abundance was measured in all samples by using the MAT-271 isotope mass spectrometer (Finnigan MAT, California). The accumulation of N in rice, crayfish and eels from N fertilizer was calculated using the following equations:

$${text{Percentage of accumulated N from fertilizer NDFF}});( % ) = {text{A}}% ;{text{E of the organism sample/A}}% ;{text{E of the fertilizer sample}} times 100$$
(3)

$${text{Amount of accumulated N from fertilizer}} = {text{organism N accumulation amount}} times {text{NDFF}}$$
(4)

$${text{N use efficiency NUE}});(% ) = {text{amount of N accumulated by the organism accumulated from N fertilizer/total N content of the fertilizer}} times 100$$
(5)

where A% E is the difference between the 15N abundance of the samples or 15N-labelled fertilizers and natural abundance of 15N.
The independent-samples t-test was used to determine the differences in total N, N use efficiency and percentage of N derived from fertilizer between RCE and RC at 95% confidence level by using SPSS 16.0 (P value  More

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