This is the accepted version of the article: Wang, Miaoying; Wang, Chun; Lan, Xingfu; [et al.]. «Coupled steel slag and biochar amendment correlated with higher methanotrophic abundance and lower CH4 emission in subtropical paddies». Environmental Geochemistry Health, Vol. 42, issue 2 (Feb. 2020), p. 483–497. DOI 10.1007/s10653-019-00378-4 This version is avaible at https://ddd.uab.cat/record/218325 under the terms of the license 1 Coupled steel slag and biochar amendment correlated with higher 2 methanotrophic abundance and lower CH4 emission in 3 subtropical paddies 4 5 Miaoying Wanga, Chun Wangb, Xingfu Lana, Abbas Ali Abidc, Xuping Xua*, Ankit 6 Singlad, Jordi Sardanse,f*, Joan Llusiàe,f, Josep Peñuelase,f, Weiqi Wangb* 7 a 8 College of Life Science, Fujian Normal University, Fuzhou 350108, China b 9 Key Laboratory of Humid Sub-tropical Eco-geographical Process of Ministry 10 ofEducation, Fujian Normal University, Fuzhou 350007, China c 11 Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Key 12 Laboratory of Environment Remediation and Ecological Health, Ministry of Education, 13 Zhejiang University, Hangzhou, 310058, P.R China d 14 Regional Centre of Organic Farming, Ministry of Agriculture and Farmers Welfare, 15 Bhubaneswar, Odisha 751-021, India e 16 CSIC, Global Ecology Unit, CREAF-CSIC-UAB, Bellaterra 08193, Catalonia, Spain f 17 CREAF, Cerdanyola del Vallès 08193, Catalonia, Spain. 18  These authors contributed equally to this work. 19 * Corresponding author. E-mail: j.sardans@creaf.uab.cat; xuping@fjnu.edu.cn; 20 wangweiqi15@ 163.com 21 1 22 Abstract Aerobic methanotrophs in paddies serve as methane (CH4) filters and thereby 23 reduce CH4 emissions. Amending soil with waste products can mitigate CH4 emissions 24 in crops, but little is known about the impacts of amendments with steel slag and 25 biochar on the populations and activities of aerobic methanotrophs in rice cropland. We 26 used Real-time quantitative PCR detecting system (qPCR) and high-throughput 27 sequencing to determine the effects of slag and biochar amendments on CH4 emission, 28 abundance and community structure of methanotrophs, and the relationships between 29 soil properties and the abundance and community composition of methanotrophs during 30 the rice growing season in both early and late paddies. Soil salinity and pH were 31 significantly higher for an amendment with both slag andbiochar than the control in 32 both the early and late paddies, and pH was significantly higher for a slag amendment 33 in the late paddy. Cumulative CH4 emission was lower for the slag and slag+biochar 34 amendments than the control in early paddy by - 34.1%. Methanotrophic abundance 35 was three- and six-fold higher for the slag + biochar amendment than the control in the 36 early and late paddies (p<0.05), respectively. The abundance of different groups of 37 methanotrophs varied among the treatments. The relative abundance of Methylosarcina 38 was higher for the slag amendment than the control, and the relative abundance of 39 Methylomonas was lower for biochar, and slag +biochar amendments than the control. 40 The relative abundance of Methylocystis was higher for the slag and slag+biochar 41 amendments than the control in the early paddy, and the relative abundance of 42 Methylocystis was higher for the slag, biochar, and slag +biochar amendments in the 43 late paddy. Univariate and multivariate analyses indicated that the higher abundance of 2 44 methanotrophic bacteria for the slag and slag+biochar amendments was correlated with 45 soil pH, salinity, soil organic carbon (SOC), and C:N ratio, and the relative abundances 46 of Methylocystis, Methylomonas, and Methylosarcina were associated with the 47 effective mitigation of CH4 emission in the paddies. A discriminant general analysis 48 indicated that the total population of methanotrophs was larger for the slag+biochar 49 amendment than the control and that this effect was only weakly correlated with 50 changes in the soil properties, demonstrating that this effect on the size and species 51 composition of methanotrophic soil populations was mostly associated with a direct 52 effect of the slag+biochar amendment. 53 54 Keywords Slag · biochar · greenhouse gases · methanotrophs · paddies 55 56 57 3 58 Introduction 59 Methane is the second most important greenhouse gas after carbon dioxide, 60 contributing approximately 18% of anthropogenic radiative forcing (Bridgham et al. 61 2013). Even small changes of CH4 concentrations in the atmosphere contribute 62 substantially to global warming, because the global-warming potential of CH4 is 25- 63 fold higher than that of CO2 (Bridgham et al. 2013; Lee et al. 2014). CH4 is an important 64 greenhouse gas due to its geophysical properties, such as its atmospheric residence time 65 of 12.4 years and instantaneous forcing of 1.37×105 W m-2 ppb-1 (IPCC 2014). Rice 66 paddy fields, which are cultivated worldwide on 155 million ha, contribute about 5-19% 67 of the annual atmospheric CH4 emissions and are considered the most important 68 anthropogenic source of CH4 (Ma et al. 2010). 69 CH4 emission from paddies are governed primarily by two microbial processes, 70 CH4 production (by methanogens) and CH4 oxidation (by methanotrophs) (Wang et al. 71 2014; Nguyen et al. 2015). Methanogens are a group of strict anaerobic microorganisms 72 that produce CH4 using CO2 or acetate as the final electron acceptor and hydrogen as 73 an electron donor and are phylogenetically affiliated with the phylum Euryarchaeota of 74 the domain Archaea (Woese et al. 1990). Methanotrophs, though, use CH4 as the main 75 carbon source and can metabolize both aerobically and anaerobically. Aerobic 76 methanotrophs belong to the Proteobacteria and Verrucomicrobia taxa. The former can 77 be broadly divided into two physiological and phylogenetic groups: type I 78 (Gammaproteobacteria) and type II (Alphaproteobacteria) methanotrophs (Sharp et 79 al.2014). 4 80 Some practices of agricultural management (e.g. water management and addition 81 of straw compost) have been recommended for reducing CH4 emissions from paddies 82 (Pandey et al 2014; Wang et al. 2014; Nguyen et al. 2015). Biochar amendment may 83 reduce CH4 emissions mostly by inhibiting methanogenic activity or increasing CH4 84 oxidation associated with an increase in soil aeration (Liu et al. 2011; Dong et al. 2013; 85 Han et al. 2016). Increasing the abundance of methanotrophs or decreasing the 86 mcrA/pmoA ratio can also reduce CH4 emissions (Feng et al. 2012; Han et al. 2016). 87 The application of biochar may therefore decrease CH4 emissions, and the structure of 88 biochar can provide a suitable environment for bacterial CH4 oxidation, but little 89 attention has been paid to the influence of biochar amendments on methanotrophic 90 diversity in paddies. 91 Steel slag is a waste product from the pyro-metallurgical processing of various ores. 92 Interest in finding uses for slag has been steadily increasing, because large volumes, on 93 the order of hundreds of millions of tonnes, of this waste are produced annually 94 worldwide (Piatak et al. 2015). Steel slag contains high concentrations of electron 95 acceptors, such as active and free oxides of iron, and can effectively lower CH 4 96 emissions from temperate paddies (Ali et al. 2008a, 2009). The steel slag should 97 increase the soil redox status, and thus we should expect effects on methane production 98 and oxidation processes (Wang et al. 2018). The addition of steel slag and/or biochar to 99 soil could influence CH4 emissions by affecting the physicochemical properties of the 100 soil and thus the microorganisms that emit and metabolize CH4 (Ali et al. 2008b; Wang 101 et al. 2015). However, these effects remain largely unknown 5 102 By using molecular approaches such as polymerase chain reactions (PCRs) 103 targeting methane monooxygenase (pMMO) genes (Lau et al. 2013; Lima et al., 2014; 104 Lüke et al. 2014; Yun et al. 2015) we aimed to : (1) determine the abundance and 105 community structures of methanotrophs and the relationships between soil properties 106 and the abundance and community composition of methanotrophs during the growing 107 season in both early and late paddies, and (2) analyze the relationship between 108 methanotrophs and CH4 emission. The results from this study may provide insights into 109 the effects of waste amendments on soil methanotrophic communities and the 110 subsequent effects on CH4 emissions. In-depth knowledge of these relationships should 111 be particularly important for providing a theoretical and practical basis to control CH4 112 emissions in paddies. 113 114 Material and Methods 115 Study site and experimental design 116 The field experiment was carried out in the Wufeng Agronomy field of the Fujian 117 Academy of Agricultural Sciences, Fujian, southeastern China (Fig. 1). We studied the 118 effect of the application of steel slag, biochar, and slag + biochar on CH4 emissions and 119 methanotrophs during the early paddy season (16 April to 16 July, Hesheng 10 cultivar) 120 and the late paddy season (25 July to 6 November, Qinxiangyou 212 cultivar) in 2015. 121 Air temperature and humidity during the study period are shown in Fig. S1. Our study 122 site has a “Warm and humid climate” according with Köppen (1936) classification. The 123 physicochemical properties in the top 15 cm were shown in Wang et al. (2014, 2015). 6 124 The management of the paddies (including plowing, water management, and 125 fertilization) was typical for subtropical paddies in China (Zhang et al. 2013; Wang et 126 al. 2015), more detail was shown in the Wang et al. (2015). 127 The experimental field plots were laid out in a randomized block design, with 128 triplicate plots (10 m2) for each of the four treatments (including a control). The 129 experiment tested the following treatments in a completely randomized block design: 130 1) control; 2) steel slag; 3) biochar; and 4) slag + biochar. We applied 8 Mg ha-1 of both 131 steel slag and biochar. The selected steel slag was provided in granular form by Fujian 132 Jinxing iron company and was composed mainly of CaO (34.9%), SiO2 (40.7%), and 133 Fe2O3 (4.8%). This represents C (56.6%), N (1.4%), P (1.0%) and K (1.8%) on dry 134 weight. Rice straw was heated at 600 °C to produce biochar. The chemical compositions 135 of both amendments are presented in Table S1. All control and amended plots received 136 the same amount of water and same mineral fertilizer and urea. More detail seen in 137 Wang et al. (2015). 138 139 Measurement of CH4 flux 140 Static closed chambers were used to measure CH4 emissions during the rice growing 141 season (Datta et al. 2013). More detail seen the Wang et al. (2015). We deployed three 142 replicate chambers in each treatment. A wooden boardwalk was built for accessing the 143 plots to minimize disturbance of the soil during gas sampling. Gas flux was measured 144 weekly in all chambers. The sampling CH4 concentrations in the headspace air samples 145 were determined by gas chromatography (GC) using a stainless steel Porapak Q column 7 146 (2 m long, 4 mm outside diameter, 80/100 mesh) (Shimadzu GC-2010, Kyoto, Japan). 147 More detail of the gas sampling and concentrations determination were shown in Wang 148 et al. (2015). 149 150 Measurement of soil properties 151 Soil samples were collected from the 0-15 cm layer in triplicate for each treatment using 152 a soil sampler during the elongation stage (Wang et al. 2014; Wang et al. 2018). We 153 only collected one time in the elongation period, i.e. at mid period of rice growth, when 154 the water and fertilizer management was also at the average level between the beginning 155 of the rice transplantation and the rice ripening period. This sampling time was the 156 sampling time also used in previous studies (Wang et al. 2018). The samples were 157 immediately stored in sterile bags in ice coolers and transported to the laboratory. 158 Subsamples were then immediately processed for DNA extraction. The remaining soil 159 was stored at 4 °C until the analysis of physical and chemical properties. 160 Soil properties, such as pH was measured with a pH/temperature meter (IQ 161 Scientific Instruments, Carlsbad, USA), and salinity was measured using a 2265FS EC 162 meter (Spectrum Technologies Inc., Paxinos, USA). Soil water content was measured 163 by weighing the soil before and after drying at 105 °C to a constant weight (Barton et 164 al. 2013). Soil organic carbon (SOC) and TN concentrations were determined using a 165 Vario Max Elemental Analyzer (Elementar Scientific Instruments, Hanau, Germany). 166 167 DNA extraction and PCR amplification 168 When we determined the amount of DNA we use three repeats per plot. However, when 8 169 we determined the microbe structure we only used the mixed into 0.5-g composite 170 samples per plot, thus following the method applied in several previous studies (Wang 171 et al. 2018). Total genomic DNA was extracted from these samples using an E.Z.N.A TM 172 Soil DNA Kit (Omega, USA). The DNA quality and concentration were assessed by 173 1× TAE agarose gel (1%) electrophoresis and spectrophotometric analysis using a 174 NanoDrop 1000 spectrophotometer (Thermo Scientific Technologies Inc., Waltham, 175 USA). 176 The pmoA genes of methanotrophs were amplified by PCR using the primer pair 177 A189F (5′-GGN GAC TGG GAC TTC TGG-3′) and mb661R (5′-CCG GMG CAA 178 CGT CYT TAC C-3′)(May et al. 2018). Amplification was performed in a final volume 179 of 25 μL containing 2.5 μL of 10× PCR buffer, 2.5 μL of 2.5 mM dNTPs, 0.25 μL of 180 Taq polymerase (5 U μL-1) (Takara, Japan), 0.5 μL of each primer (final concentration 181 0.3 μM), and 20 ng of extracted DNA. The PCR program had an initial denaturation at 182 94 °C for 3 min followed by 30 cycles of denaturation at 94 °C for 45 s, annealing at 183 55 °C for 1 min, and extension at 72 °C for 90 s, with a final extension at 72 °C for 10 184 min. Amplified PCR products were purified with a PCR clean-up kit (Sangon Inc., 185 Shanghai, China) and stored at -20 °C for further analysis. 186 MiSeq Sequence processing and analysis 187 All PCR products were sequenced by Novogene Corporation, Beijing, China. 188 Index sequences were trimmed, aligned to the SILVA database (Quast et al. 2013), 189 screened, and filtered by the mothur pipeline (Schloss et al. 2009). The sequences were 190 taxonomically classified using the training set, version 9, of the Ribosomal Database 9 191 Project (Cole et al. 2009), followed by the removal of non-archaeal/bacterial sequences 192 based on the taxonomic classification. Diversity indices and operational taxonomic 193 units (OTUs) at 97% identity with pmoA were estimated using mothur 194 (https://www.mothur.org/). The pmoA sequences have been deposited in the GenBank 195 database (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA445632/) under accession 196 number SRR6901783. Data can be obtained from the Biosample database 197 (https://www.ncbi.nlm.nih.gov/biosample), accession number SAMN 08794436. 198 199 Quantitative analysis of the methanotrophs by real-time PCR 200 Methanotrophic abundance was determined by qPCR using pmoA-targeted primers 201 A189F/mb661R in triplicate 20-μL reaction mixtures containing SYBR green Master 202 Mix (Sangon Inc., Shanghai, China). The reaction mixture contained 1× Master Mix 203 (Sangon Inc., Shanghai, China), 200 nM of each primer, ten-fold diluted DNA 204 extractions, and double-distilled H2O to a final volume of 20 μL. Real-time quantitative 205 PCR detecting system (qPCR) was carried out with the protocol for target groups as: 206 denaturation at 95 °C for 3 min followed by 45 cycles of denaturation at 95 °C for 15 207 s, annealing at 57 °C for 20 s, and plate reading at 83 °C. Standard curves were obtained 208 with serial dilutions of plasmid DNA containing the target genes. The data were 209 analyzed using LightCycler 480 Software Setup (Roche Inc., Shanghai, China). 210 211 Statistical analysis 212 The sequencing data were processed as described by Caporaso et al. (2010) and Wang 10 213 et al. (2017). A one-way analysis of variance (ANOVA) was conducted to test the 214 differences in soil physicochemical properties and methanotrophic abundances among 215 the treatments in both crop seasons. All statistical analyses used SPSS Statistics 17.0 216 (IBM SPSS Inc., Chicago, USA). 217 We also performed multivariate statistical analyses. We used a principal 218 component analysis (PCA) to determine the overall differences of soil salinity, pH, 219 water content, TN content, C:N ratio, SOC content, bulk density, CH4 emission, and 220 methanotrophic gene abundance among treatments in the early and in late paddies. We 221 conducted one-way ANOVAs with Bonferroni post hoc tests of the scores of the first 222 PC axis to determine differences among the treatments. We then used a general 223 discriminant analysis (GDA) to determine the overall differences of soil salinity, pH, 224 water content, TN content, C:N ratio, SOC content, bulk density, CH4 emission, and 225 methanotrophic gene abundance among treatments using the combined data from the 226 two paddy seasons. These analyses also assessed the component of the variance due to 227 the paddy season (early and late) as an independent categorical variable. Discriminant 228 analyses consist of a supervised statistical algorithm that derives an optimal separation 229 between groups established a priori by maximizing between-group variance while 230 minimizing within-group variance (Raamsdonk et al. 2001). GDA is thus an appropriate 231 tool for identifying the variables most responsible for the differences among groups 232 while controlling the component of the variance due to other categorical variables, the 233 two paddy seasons (early and late) in this study. The GDAs were performed using 234 Statistica 8.0 (StatSoft, Inc., Tulsa, USA). 11 235 236 Results 237 Soil physicochemical properties in the paddies 238 The soil physicochemical properties for the treatments and paddy fields are shown in 239 Table 1. Soil salinity and pH were significantly higher (p<0.05) in the slag + biochar 240 treatment than the control in both the early and late paddies, and pH was significantly 241 higher (p<0.05) in the slag treatment than the control in the late paddy. 242 243 Cumulative CH4 emission in paddies 244 Total cumulative CH4 emission varied among the treatments in both the early and late 245 paddies (Fig. 2). Cumulative CH4 emission was lower in the slag and slag + biochar 246 amendments than the control in both the early and late paddies, by 10.2 and 34.1% and 247 14.9 and 33.5%, respectively. Cumulative CH4 emission for the biochar treatment, 248 however, was 7.6% higher in the early paddy field but 43.7% lower in late paddy 249 relative to the control. 250 251 Methanotrophic abundance in the paddies 252 Methanotrophic pmoA copy number for both the early and late paddies were shown in 253 Fig. 3. Methanotrophic pmoA abundance in the control, slag, biochar, and slag + biochar 254 treatments in the early paddies were 2.84×105, 1.35×105, 2.50×105 and 1.44×106 g-1 dry 255 soil, respectively. The pmoA copy number was 52.5 and 12.1% lower in the slag and 256 biochar treatments, respectively, but about three-fold higher in the slag + biochar 12 257 treatment, than the control. The copy number was significantly higher (p<0.05) in the 258 slag + biochar treatment than the control. The pmoA copy numbers in the late paddy 259 field were 1.32×105, 5.63×104, 1.91×105, and 9.88×105 g-1 dry soil in the control, slag, 260 biochar, and slag + biochar treatments, respectively. The copy number was 57.6% lower 261 in the slag treatment but 43.5% and six-fold higher in the biochar and slag + biochar 262 treatments, respectively, than the control. The pmoA copy number was significantly 263 higher (p<0.05) in the slag + biochar treatment than the control in both the early and 264 late paddies. The copy number was also significantly higher (p<0.05) in the early than 265 the late paddy for the control and slag treatments. 266 267 Relationships between methanotrophic abundance and environmental factors 268 The relationships between the abundance of methanotrophs and environmental factors 269 in both the early and late paddies were shown in Table 3. The pmoA copy numbers in 270 both the early and late paddies were strongly positively correlated with soil salinity, pH 271 and, SOC content (Table 3). But CH4 emission was not strongly correlated the soil 272 parameters, indicating that CH4 emissions were not directly affected by the 273 physicochemical parameters(Table 3). 274 275 Analysis of alpha and beta diversity of pmoA in the paddies 276 The microbial communities in the paddies were analyzed based on pmoA gene 277 sequences using high-throughput sequencing. The number of sequences, coverage, 278 number of OTUs, and ecological indices are summarized in Table 2. More than 98% 13 279 coverage was obtained for all samples, with the number of OTUs ranging from 612 to 280 2537. The heatmap in Fig. 4 shows the abundance cluster of the top 50 ranking species 281 of methanotrophs. The number of unclassified bacterial genera in the paddies ranged 282 from 1.63 to 62.97%. Most methanotrophic species in the paddies belonged to 283 Methylocystis, Methylogaea, and Methylococcus, representing 17.26-79.35% of the 284 total. 285 286 Methanotrophic community composition in the paddies 287 High-throughput sequencing of pmoA was used to investigate changes in the 288 composition of the methanotrophic communities in both the early and late paddies (Fig. 289 5). Approximately 24.40-53.36% of the Proteobacteria sequencing reads for both the 290 early and late paddies were classified as type I methanotrophs, including 291 Methylosarcina, Methylogaea, Methylomonas, Methylococcus, Methylomicrobium, 292 Methylobacter, Methylocaldum, Methylovulum, and Methylomarinum, and 293 approximately 12.63-63.00% of the sequencing reads were classified as type II 294 methanotrophs, including Methylocystis and Methylosinus. Methylocystis (12.26- 295 59.63%), Methylosarcina (6.52-22.69%), Methylogaea (3.24-22.27%), and 296 Methylomonas (2.48-18.09%) were generally dominant in both the early and late 297 paddies. 298 299 PCA and GDA of CH4 emission, methanotrophs, and physicochemical parameters 300 The PCA (both in early and in late paddy) found that the amendments containing slag 14 301 (alone or particularly with biochar) were associated with larger methanotrophic 302 populations and with higher soil pH, salinity, and TN and SOC contents and lower CH4 303 emissions (Fig.6, 7). The GDA supported these results. We must, however, stress that 304 all treatments were significantly separated (Table S2) and that overall soil traits, CH4 305 emissions, and methanotrophic abundance differed the most in the slag + biochar 306 treatment (Fig. 8). The GDA found that methanotrophic abundance and soil pH to some 307 extent were most responsible for these differences among the treatments (Table S3). 308 309 Discussion 310 Understanding the ecology of methanotrophs and their abundance and taxonomic 311 composition under different waste amendments is crucial for controlling CH4 emissions 312 in paddies. Our results provided clear evidence that amendments with slag and slag + 313 biochar decreased CH4 emissions in both the early and late paddies, an effect correlated 314 with the increase in the methanotrophic abundance, specially for the relative 315 abundances of Methylocystis, Methylomonas, and Methylosarcina that were associated 316 with the effective mitigation of CH4 emission in the paddies. The various treatments, 317 however, were associated with different soil properties. The effect of the treatments was 318 especially important in changing methanotrophic abundance and community 319 composition, evidence that the change of methanotrophic abundance and community 320 composition was due mainly to the components of the treatments, mainly slag. 321 322 Effects of slag amendment on the abundance and community structure of 15 323 methanotrophs 324 The application of slag inhibited total CH4 emissions from the paddy fields, consistent 325 with previous reports by Singla et al. (2015) and Wang et al. (2015). The slag 326 amendments in our study slightly increased soil salinity, because slag usually contains 327 a specific suite of salt components (K, Ca, Mg, and Fe) that are important for soil 328 fertility (Ali et al. 2008b). Slag contains large amounts of chemically reactive iron oxide, 329 which increases soil salinity (Ali et al. 2008b). Our results, though, also strongly 330 suggest that the slag amendments increased soil pH, as also reported by Lee et al. (2012), 331 due to the calcium and iron contents in the slag (Susilawati et al. 2015). Methanotrophic 332 abundance, however, was not higher in the slag amendments than the control, perhaps 333 because slag contains iron that can reduce CH4 emissions. High levels of electron 334 acceptors suppress CH4 emission (Susilawati et al. 2015). The higher soil salinity and 335 pH due to amendment with slag may not decrease CH4 emissions by increasing the 336 abundance of methanotrophs, but by changing the community composition or structure 337 or the abundance of methanogens. 338 Slag amendment increased the relative abundance of Methylosarcina compared 339 with the control (Fig. 5). Methylosarcina is a type I methanotroph dominant in 340 paddies(Lee et al. 2014). The growth of Methylosarcina requires a pH of 5.0-9.0 (Wise 341 et al., 2001), so slag amendments with near-neutral pHs (6.90-6.99) may increase the 342 relative abundance of Methylosarcina by increasing soil pH. Furthermore, iron is an 343 important element for methanotrophs (Mohanty et al., 2014), because methane 344 monooxygenase, a di-iron protein complex, uses iron as a transition metal (co-factor) 16 345 at the active site (Guallar et al. 2002), so slag containing iron oxide may inhibit CH4 346 emissions by increasing soil pH and thereby increasing the relative abundance of 347 Methylosarcina. The iron in slag fertilizers may also act as an electron acceptor, 348 suppressing CH4 emission by decreasing methanogenic activity (Jackel et al. 2000). 349 350 Effects of biochar amendment on CH4 emission associated with changes of the 351 abundance and community structure of methanotrophs 352 The biochar amendment decreased the total CH4 emission, but without increasing 353 methanotrophic abundance, during the entire study period compared to the control, 354 highly consistent with previous studies (Castaldi et al. 2011; Zhang et al. 2012). Biochar 355 application can reduce CH4 emissions (Feng et al. 2012; Wang et al. 2012) and has 356 increased rice yield up to 28% (Zhang et al. 2010, 2012; Wang et al. 2012) by increasing 357 methanotrophic abundance, which would decrease CH4 emission (Zhang et al. 2010). 358 The impact of biochar on CH4 emission may be due to the biochar components and the 359 influence of biochar on soil physicochemical properties. The SOC content and the C:N 360 ratio were higher in the biochar and slag + biochar treatments than the control. The 361 highly aromatic chemical structure of biochar provides an organic carbon with higher 362 biochemical activity and thermal stability, allowing the biochar to be preserved in the 363 environment for a long time. The addition of biochar to soil may therefore improve soil 364 stability. The reduced CH4 emissions from paddies with added biochar are likely due to 365 a lack of substrate (CO2) availability (Liu et al. 2011; Chang et al. 2012). Kim et al. 366 (2017) reported that biochar inhibited methanogenesis by increasing soil aeration and 17 367 oxygen availability. The slightly higher pHs due to the alkaline properties of biochar, 368 though, may contribute to the inhibition of CH4 emissions by changing methanotrophic 369 community structure. The abundance of Methylomonas was lower in the slag, biochar, 370 and slag + biochar treatments than the control. The growth of Methylomonas requires a 371 pH of 5.0-9.0 and is optimal at pHs of 6.5-7.0 (Lu et al. 2016). Amendments with slag, 372 biochar, or slag + biochar with higher pHs (6.46-7.41) may thus not be suitable for the 373 growth and proliferation of this group. Members of the genus Methylomonas require 374 higher oxygen and CH4 concentrations for CH4 oxidation than other methanotrophs 375 (Reim et al. 2012). A reduction in the relative abundance of Methylomonas by altering 376 soil pH may thus be a mechanism to suppress CH4 emission under waste amendments. 377 Wang et al. (2012), however, found that CH4 emission in paddy fields increased 378 significantly when amended with biochar. The addition of biochar in flooded paddies 379 increases the substrate supply and creates a favorable environment for methanogenic 380 activity (Kögel-Knabner et al. 2010; Lehmann et al. 2011). The labile components of 381 biochar can decompose and become the predominant source of substrates for 382 methanogens, particularly in the early stages of the rice growing season (Knoblauch et 383 al. 2008). The effects of biochar amendment on CH4 emission are thus inconsistent 384 among studies, and the underlying mechanisms may vary with soil type, agricultural 385 management, and origin of the biochar (Lehmann et al. 2011). 386 387 Effects of the slag + biochar amendment on CH4 emission associated with changes of 388 methanotrophic abundance and community structure 18 389 Our study demonstrated that the three amended treatments lowered total CH4 emissions 390 to different extents relative to the control treatment. Slag + biochar was the best 391 amendment for suppressing total CH4 emissions in early paddy field. Methanotrophic 392 abundance was significantly higher in the slag + biochar treatment than the control, 393 biochar, and slag treatments, likely because biochar can improve soil permeability and 394 the granular texture of slag can further enhance the ability of soil to supply oxygen. 395 Slag and biochar contain K, Ca, Mg, and Fe, which strongly increase CH4 oxidation, 396 which would increase the abundance of methanotrophs in the slag + biochar treatment. 397 CH4 emission was inversely correlated with the abundance of methanotrophs, so the 398 increase in methanotrophic abundance would likely decrease in CH4 emissions under 399 the slag + biochar treatment. The relative abundance of Methylocystis in the early paddy 400 was higher in the slag and slag + biochar treatments than the control, especially in the 401 slag + biochar treatment where the abundance was about 183% higher. The three 402 amended treatments increased the relative abundance of methanotrophs in the late 403 paddy. Methylocystis can form resting cells, surviving on multi-carbon compounds and 404 using CH4 at both high and low concentrations (Ho et al. 2013). Some type II 405 methanotrophs (Methylocella, Methylocapsa, and Methylocystis) have recently been 406 characterized as facultative methanotrophs able to conserve energy for growth on multi- 407 carbon compounds such as acetates, pyruvate, succinate, malate, and ethanol (Esson et 408 al. 2016). The slag + biochar treatment in our study also decreased the relative 409 abundance of Methylomonas. Bacterial diversity may be affected by SOC and N 410 contents (Chan et al. 2006). We also found that the community composition of 19 411 methanotrophs was significantly correlated with SOC content and the C:N ratio (Fig. 412 6), suggesting that the combination of slag and biochar, with its high SOC content, may 413 provide a rich substrate for CH4 synthesis, which would further change methanotrophic 414 community structure. Methylocystis has a low minimum threshold concentration 415 (Michaelis constant, Km) for CH4 oxidation (Lee et al. 2014). A lower Km is associated 416 with a higher affinity of enzymes and substrates and stronger CH4 oxidation. Increasing 417 the abundance of CH4-oxidizing bacteria would likely have a critical impact on CH4 418 reduction (Kima et al. 2017). Increasing the relative abundance of Methylocystis may 419 therefore reduce CH4 emission. In conclusion, the slag + biochar treatment likely 420 increased the oxygen content of the soil, which would increase the oxidation of CH4 by 421 methanotrophs, leading to lower CH4 emissions. 422 423 Conclusions and final remarks 424 Our results indicated that soil amendment with both slag and biochar significantly 425 increased soil salinity and pH in both the early and late paddies and that amendment 426 with slag significantly increased pH in the late paddy. The slag and slag + biochar 427 treatments decreased the cumulative CH4 emission compared to the control in both the 428 early and late paddies, by 10.2 and 34.1% and 14.9 and 33.5%, respectively. The 429 abundance of methanotrophs in the slag + biochar treatment significantly increased 430 methanotrophic abundance about three- and six-fold in the early and late paddies 431 (p<0.05), respectively. The slag treatment increased the relative abundance of 432 Methylosarcina, and biochar, slag + biochar treatments decreased the relative 20 433 abundance of Methylomonas, relative to the control. The slag and slag + biochar 434 treatments increased the relative abundance of Methylocystis in the early paddy, and all 435 three amended treatments increased the relative abundance of Methylocystis in the late 436 paddy, relative to the control. The application of both slag and biochar provided the best 437 overall results, increasing soil pH, salinity, SOC content, and the C:N ratio associated 438 with methanotrophic abundance and the relative abundances of Methylocystis, 439 Methylomonas, and Methylosarcina, which may effectively mitigate CH4 emissions in 440 paddies. 441 442 Acknowledgements 443 The authors would like to thank Wanli An and Guixiang Chen for their assistance with 444 field sampling. Funding was provided by the National Science Foundation of China 445 (41571287, 31000209), Research Project of Public Institute of Fujian Province 446 (2018R1034-1), Outstanding Young Research Talents in Higher Education of Fujian 447 Province (2017), European Research Council Synergy grant ERC-SyG-2013-610028 448 IMBALANCE-P, Spanish Government grant CGL2016-79835-P, and Catalan 449 Government grant SGR 2017-1005. 450 451 References 452 Ali, M.A., Lee, C.H. & Kim, P.J. (2008a). 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Agriculture Ecosystems and Environment, 173, 37-45. 30 641 Tables 642 Table 1 Physicochemical properties for the amended and control plots in the early and late paddy fields Bulk density Water content Soil organic carbon Total Nitrogen Paddy field Treatment Salinity (dS·m-1) pH C/N (g·cm-3) (%) (mg·g-1) (mg·g-1) Control 0.20±0.03 b 6.33±0.06 b 1.01±0.03 a 59.51±1.60 a 16.96±0.25 a 2.02±0.04 a 8.42±0.08 a Slag 0.22±0.03 b 6.99±0.23 ab 1.03±0.01 a 57.92±0.14 a 17.45±0.56 a 2.05±0.07 a 8.51±0.01 a Early Biochar 0.20±0.02 b 6.54±0.08 ab 1.06±0.04 a 56.03±1.95 a 17.91±1.09 a 2.01±0.01 a 8.91±0.58 a Slag + biochar 0.57±0.04 a 7.41±0.46 a 1.08±0.02 a 54.69±1.30 a 22.72±5.40 a 2.08±0.09 a 10.76±2.05 a Control 0.23±0.03 b 6.08±0.22 b 1.05±0.05 a 51.34±2.64 a 15.70±1.24 a 1.93±0.14 a 8.13±0.05 a Slag 0.31±0.04 b 6.90±0.03 a 1.04±0.04 a 52.78±3.05 a 16.14±0.55 a 1.97±0.06 a 8.18±0.07 a Late Biochar 0.30±0.02 b 6.46±0.04 ab 1.11±0.01 a 48.40±0.68 a 20.94±3.47 a 2.11±0.08 a 9.84±1.21 a Slag + biochar 0.47±0.05 a 7.21±0.39 a 1.08±0.02 a 50.52±1.06 a 21.61±3.78 a 2.12±0.06 a 10.12±1.50 a 643 Different letters within a column indicate significant differences among the treatments at p<0.05. 644 645 31 646 Table 2 Methanotrophic diversity for the treatments in the growth stage for the early and late paddy fields Alpha diversity Paddy field Treatment Sequences OTUs Chao1 ACE Shannon Coverage (%) Control 43 989 1929 2328 2396.45 5.08 98.82 Slag 50 324 2098 2500 2650.87 5.31 98.86 Early Biochar 84 218 2433 2692 2802.28 5.62 99.44 Slag + biochar 18 683 612 940 1244.10 3.52 98.57 Control 42 203 2020 2337 2435.33 5.09 98.82 Slag 26 199 1336 1741 1734.17 5.04 98.47 Late Biochar 31 884 1762 2153 2245.01 5.17 98.39 Slag + biochar 35 717 2537 2811 2971.21 5.36 98.29 647 648 649 650 32 651 Table 3. Correlations between methanotrophic abundance and environmental factors (soil salinity, pH, SOC content, TN content, 652 bulk density, water content, and CH4 flux) CH4 flux pmoA Salinity pH SOC content TN content Bulk density Water content CH4 flux 1 -0.36 -0.15 -0.41 -0.45 -0.53 0.04 -0.62 pmoA -0.36 1 0.90** 0.71* 0.83* 0.61 0.42 -0.11 Salinity -0.16 0.90** 1 0.79* 0.82* 0.56 0.55 -0.35 pH -0.41 0.71* 0.79* 1 0.64 0.57 0.24 0.02 SOM -0.45 0.83* 0.82* 0.64 1 0.88** 0.76* -0.35 TN -0.53 0.61 0.56 0.57 0.88** 1 0.60 -0.22 Bulk density 0.04 0.42 0.55 0.24 0.76* 0.60 1 -0.80* Water content -0.62 -0.11 -0.35 0.02 -0.35 -0.22 -0.80* 1 653 * and ** indicate significant correlations at the 0.05 and 0.01 levels (2-tailed), respectively. 654 33 Figure legends Fig. 1 Location of the study area and sampling sites (▲) in Fujian province, southeastern China Fig. 2 Cumulative methane emissions for the control and treatments in paddies. Different letters above the bars indicate significant differences among the treatments at p<0.05 Fig. 3 Quantification of methanotrophic pmoA copy number for the control and treatments in the early and late paddies. Different letters above the bars indicate significant differences among the treatments at p<0.05 Fig. 4 Heatmap of methanotrophs for the treatments in the early and late paddies. E- SB: slag + biochar amendment in early paddy; E-B: biochar amendment in early paddy; L-S: slag amendment in late paddy; L-B: biochar amendment in late paddy; E-S: slag amendment in early paddy; L-SB: slag + biochar amendment in late paddy; E-C: control in early paddy; L-C: control in late paddy Fig. 5 Relative abundances of methanotrophs in the treatments determined by high- throughput sequencing of the pmoA gene for the early (A) and late (B) paddies Fig. 6 PCA analysis of physicochemical parameters in the treatments in the early paddy field. SOC, soil organic carbon concentration; Water (%), soil water content; [N], soil total nitrogen concentration; GA, methanotrophic gene abundance; C:N, carbon:nitrogen ratio Fig. 7 PCA of physicochemical parameters in the treatments in the late paddy field. 34 SOM, soil organic carbon concentration; Water (%), soil water content; [N], soil total nitrogen concentration; GA, methanotrophic gene abundance; C:N, carbon:nitrogen ratio Fig. 8 GDA of physicochemical parameters as independent continuous variables, waste amendment as a categorical dependent variable, and crop season (early and late) as a dependent controlling continuous variable. SOC, soil organic carbon concentration; Water, soil water content; [N], soil total nitrogen concentration; GA, methanotrophic gene abundance; C:N, carbon:nitrogen ratio 35 Fig. 1. 36 Early paddy 20 Late paddy a a 18 16 a Cumulative emission (gm ) -2 14 a 12 10 ab 8 a ab 6 b 4 2 0 Control Slag Biochar Slag+biochar Fig. 2. 37 1.4x10 6 Early paddy Late paddy a 6 1.2x10 pmoA copy number (g soil ) -1 a 6 1.0x10 5 b 3x10 b b 5 2x10 b b 5 1x10 c 0 Control Slag Biochar Steel slag+biochar Fig. 3. 38 Unclassified Uncultured_Methylocystis_sp. Uncultured_Methylosarcina_sp. Methylogaea_oryzae Uncultured_Methylomonas_sp. Methylocystis_sp._SD5 Methylosarcina_lacus Uncultured_Methylococcus_sp. Uncultured_Methylobacter_sp. Methylomicrobium_Kenyense Uncultured_Methylomicrobium_sp. Methylocystis_parvus Methylosinus_sp._LW2 Methylocystis_sp._KS30 Methylomonas_sp._ML04 Methylocystis_sp._KS7 Methylocystis_sp._5FB2 Methylocystis_sp._M212 Methylomonas_paludios Methylocystis_sp._KS9 Methylocystis_sp._KS3 Methylosinus_trichosporium Methylocystis_sp._L6 Methylosinus_sp._ML19 Methylocystis_sp._SS2C Methylocystis_echinoldes Methylomonas_sp._ML64 Methylomicrobium_album Methylocystis_sp._IMET_10484 Methylomicrobium_buryatense Methylocystis_sp._m231 Methylobacter_sp._LW1 Methylocystis_sp._56 Uncultured_Methylocaldum_sp. Methylocystis_sp._KS12 Methylocystis_sp._SH31p Methylomonas_koyamae Methylocystis_sp._42_22 Methylomonas_methanica Methylomonas_sp._LW21 Methylobacter_sp._LW12 Methylocystis_sp._O14c Methylovulum_miyakonense Methylomarinum_vadi uncultured_Methylosinus_sp. Methylocystis_sp._SM16 Methylococcus_capsulatus Methylocystis_sp._B16 Methylocystis_sp._12.2 Others E-SB E-B L-S L-B E-S L-SB E-C L-C Fig. 4. 39 Methylomarinum Methylovulum (A) (B) Methylocaldum Methylobacter 100 Methylomicrobium Methylococcus Methylomonas Methylogaea Methylosarcina 80 Methylosinus Methylocystis Unclassified Relative abundance (%) 60 40 20 0 Control Slag Biochar Slag+biochar Control Slag Biochar Slag+biochar Fig. 5 40 Control Biochar Early paddy 2 Steel slag Slag + 1 biochar PC3 (10.7%) 0 -1 -2 -3 -6 -4 -2 0 2 4 PC1 (52.7%) 1.0 0.5 PC3 (10.7%) pH CH4 emissions [N] 0.0 SOC C:N Water (%) Salinity GA -0.5 Bulk density -1.0 -1.0 -0.5 0.0 0.5 1.0 PC1 (52.7%) Fig. 6 41 Control Biochar Late paddy Steel slag 2 Slag + biochar PC2 (20.7%) 0 -2 -4 -2 0 2 PC1 (45.6%) 1.0 Water (%) 0.5 [N] Salinity PC2 (20.7%) GA SOC CH4 emissions pH C:N 0.0 -0.5 Bulk density -1.0 -1.0 -0.5 0.0 0.5 1.0 PC1 (45.6%) Fig. 7 42 Control Biochar 3 Steel slag Root 2 (2%) canonical scores 2 Slag + biochar 1 0 -1 -2 -3 -10 -5 0 5 10 15 20 Root 1 (96.8%) canonical scores 0.6 pH 0.4 Root 2 (2%) loaded Salinity 0.2 Water (%) [N] 0 SOC C:N CO2 emissions -0.2 Gene abundance -0.4 Bulk density -0.6 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 Root 1 (96.8%) loaded Fig. 8 43