Ji Tan

Eric Lichtfouse

Min Luo
email: luomin@fzu.edu.cn

Yuxiu Liu

Fengfeng Tan

Changwei Zhang

Xin Chen

Jiafang Huang

Leilei Xiao
email: llxiao@yic.ac.cn

Aquaculture drastically increases methane production by favoring acetoclastic rather than hydrogenotrophic methanogenesis in shrimp pond sediments

Keywords:

Emissions of methane (CH 4 ), a major greenhouse gas, should be cut by at least 30% by 2030 according to the last Conference of the Parties, CoP26. Aquaculture pond is a major CH 4 emitter, yet the microbial mechanisms ruling methanogenesis by degradation of organic matter in sediments remain unclear. In particular, the respective roles of hydrogenotrophic and acetoclastic methanogenesis, and the impact of aquaculture farming practices are unknown. We studied methanogenesis in the surface sediments from a freshwater and an oligohaline pond before, during, and after shrimp farming. Hydrogenotrophic and acetoclastic contributions were distinguished by acetoclastic inhibition with methylfluoride (CH 3 F), and by 13 C-analysis of CO 2 and CH 4 . We also monitored the methanogenic community structure, dissolved organic carbon (DOC) levels, carbon to nitrogen (C/N) ratios, and humification indices derived from Fourier transform infrared spectroscopy. The results reveal that aquaculture farming practices increased methanogenesis rates, and these increases were explained by higher levels of DOC and lower C/N ratios during farming. Of the total methane produced, 51%-78% was by hydrogenotrophic methanogenesis. However, the total methane contribution from acetoclastic methanogenesis increased from approximately 22% before farming to approximately 45% during and after farming, with a decreasing isotope fractionation factor α c and an increasing relative abundance of Methanosaeta acetoclastic methanogen. All humification indices decreased during and after farming compared to before farming due to the input of polysaccharide-rich aquafeed. The close relationship between the humification indices and methanogenesis pathways indicates that the changes in sediment substrate quality drove the variation in the methanogenesis pathways. Increases in salinity decreased the methanogenesis rates but did not change the methanogenesis pathways. Overall, our findings reveal that aquaculture farming practices increase methanogenesis rates and favor acetoclastic over hydrogenotrophic methanogenesis, and that adjusting shrimp diets, increasing salinity, and removing residual aquafeed could reduce methanogenesis.

Introduction

Methane, a major greenhouse gas, has a sustained global warming potential of 45 times higher than CO 2 over 100 years [START_REF] Neubauer | Moving beyond global warming potentials to quantify the climatic role of ecosystems[END_REF]. Methane concentrations increased by approximately 156% from 1750 to 2019, with levels of CH 4 reaching a value of 1866.3 ± least 30% by 2030 according to the last Conference of the Parties, CoP26 (European Union, 2021). Aquatic ecosystems (including lakes, reservoirs, and rivers) contributed 32%-58% of earth's total natural CH 4 emissions (Saunois et al., 2020). Small but numerous aquaculture ponds are essential components of aquatic ecosystems [START_REF] Naylor | A 20-year retrospective review of global aquaculture[END_REF]. Coastal aquaculture ponds account for 50% of the global aquaculture ponds and are expected to increase 1.3-fold from 2010 to 2050 [START_REF] Waite | Improving productivity and environmental performance of aquaculture[END_REF]. Although coastal aquaculture ponds have been recently recognized as major anthropogenic sources of CH 4 [START_REF] Cui | China's coastal wetlands: understanding environmental changes and human impacts for management and conservation[END_REF][START_REF] Song | Anthropogenic effects on fluxes of ecosystem respiration and methane in the Yellow River estuary, China[END_REF][START_REF] Yang | Fluxes of carbon dioxide and methane across the water-atmosphere interface of aquaculture shrimp ponds in two subtropical estuaries: the effect of temperature, substrate, salinity and nitrate[END_REF][START_REF] Yang | Ebullition was a major pathway of methane emissions from the aquaculture ponds in Southeast China[END_REF][START_REF] Yuan | Rapid growth in greenhouse gas emissions from the adoption of industrial-scale aquaculture[END_REF], the underlying mechanisms of methanogenesis are unclear.

Methane is produced at the terminal step of organic matter degradation (Conrad et al., 2010b). Organic matter is fermented to acetate, H 2 , and CO 2 , which are converted to CH 4 by methanogenic archaea (Conrad et al., 2010b;Xiao et al., 2020a). Acetoclastic and hydrogenotrophic are the two main pathways of methanogenesis in lowsalinity aquatic systems [START_REF] Conrad | Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal[END_REF]:

-Acetoclastic methanogenesis: CH 3 COOH → CH 4 + CO 2 .

-Hydrogenotrophic methanogenesis: 4H 2 + CO 2 → CH 4 + H 2 O. Indeed, methanogens have preference for either H 2 or acetate substrates [START_REF] Conrad | Characterization of methanogenic archaea and stable isotope fractionation during methane production in the profundal sediment of an oligotrophic Lake (lake Stechlin, Germany)[END_REF][START_REF] Etminan | Radiative forcing of carbon dioxide, methane, and nitrous oxide: a significant revision of the methane radiative forcing[END_REF]. For instance, Methanoregula, Methanobacterium, Methanospirillum, Methanolinea, and Methanogenium prefer H 2 , whereas Methanosaeta prefer acetate [START_REF] Liu | Metabolic, phylogenetic, and ecological diversity of the methanogenic archaea[END_REF][START_REF] Sakai | Methanocella paludicola gen. nov., sp. nov., a methane-producing archaeon, the first isolate of the lineage 'Rice cluster I', and proposal of the new archaeal order Methanocellales ord. nov[END_REF]. Some methanogens, for example, Methanosarcina, are mixotrophs, which can use both H 2 and acetate substrates [START_REF] Penger | Stable carbon isotope fractionation by methylotrophic methanogenic archaea[END_REF]. Nevertheless, two strategies allow us to experimentally distinguish the relative importance of two pathways: the addition of methylfluoride (CH 3 F) to inhibit acetoclastic methanogenesis, and measuring 13 C isotope ratios of CO 2 and CH 4 [START_REF] Conrad | Acetate turnover and methanogenic pathways in Amazonian lake sediments[END_REF]Xiao et al., 2020b). The isotope fractionation is valid because hydrogenotrophic methanogens produces lighter methane with the δ 13 CH 4 ranging from -110‰ to -60‰, whereas acetoclastic methanogenesis produces heavier methane with the δ 13 CH 4 ranging from -70‰ to -30‰ [START_REF] Conrad | Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal[END_REF][START_REF] Hodgkins | Changes in peat chemistry associated with permafrost thaw increase greenhouse gas production[END_REF].

Methanogenesis is theoretically governed by stoichiometric equivalence, i.e., 67% of the CH 4 is produced by acetoclastic methanogenesis, whereas 33% comes from the reduction of CO 2 with H 2 [START_REF] Conrad | Contribution of hydrogen to methane production and control of hydrogen concentrations in methanogenic soils and sediments[END_REF]. Nonetheless, in sediments of lakes [START_REF] Mandic-Mulec | Methanogenesis pathways in a stratified eutrophic alpine lake (Lake Bled, Slovenia)[END_REF], rivers [START_REF] Mach | Methane production potentials, pathways, and communities of methanogens in vertical sediment profiles of river Sitka[END_REF][START_REF] Qin | Multiple stable isotopic signatures corroborate the predominance of acetoclastic methanogenesis during CH4 formation in agricultural river networks[END_REF], coves [START_REF] King | Metabolism of acetate,methanol,and methylated amines in intertidal sediments of Lowes Cove, Maine[END_REF], paddies [START_REF] Ji | Change of the pathway of methane production with progressing anoxic incubation of paddy soil[END_REF], peatlands [START_REF] Hines | Uncoupling of acetate degradation from methane formation in Alaskan wetlands: connections to vegetation distribution[END_REF][START_REF] Kotsyurbenko | Acetoclastic and hydrogenotrophic methane production and methanogenic populations in an acidic west-Siberian peat bog[END_REF][START_REF] Krohn | CH 4 and CO 2 production below two contrasting peatland micro-relief forms: an inhibitor and δ 13 C study[END_REF], and wetlands [START_REF] Li | A new insight into the strategy for methane production affected by conductive carbon cloth in wetland soil: beneficial to acetoclastic methanogenesis instead of CO 2 reduction[END_REF][START_REF] Xiao | Biochar promotes methane production at high acetate concentrations in anaerobic soils[END_REF], the contribution of acetoclastic methanogenesis to the total methanogenesis varied from 0.5% to 99%. The contribution of each methanogenesis pathway depends on the pH [START_REF] Ye | pH controls over anaerobic carbon mineralization, the efficiency of methane production, and methanogenic pathways in peatlands across an ombrotrophic-minerotrophic gradient[END_REF], temperature [START_REF] Chen | Dissecting methanogenesis for temperature-phased anaerobic digestion: impact of temperature on community structure, correlation, and fate of methanogens[END_REF], and more importantly, organic matter quantity and quality of the sediments [START_REF] Corbett | The relative importance of methanogenesis in the decomposition of organic matter in northern peatlands[END_REF][START_REF] Liu | The impact of dissolved organic carbon on the spatial variability of methanogenic archaea communities in natural wetland ecosystems across China[END_REF][START_REF] Hofmann | Abundance and potential metabolic activity of methanogens in well-aerated forest and grassland soils of an alpine region[END_REF].

Farming practices could significantly alter methanogenesis rates in aquaculture pond systems. Indeed, large temporal variations in CH 4 emission flux across farming periods have been reported in various aquaculture pond systems [START_REF] Ma | A comparison of methane and nitrous oxide emissions from inland mixed-fish and crab aquaculture ponds[END_REF][START_REF] Song | Anthropogenic effects on fluxes of ecosystem respiration and methane in the Yellow River estuary, China[END_REF][START_REF] Tong | Annual CO 2 and CH 4 fluxes in coastal earthen ponds with Litopenaeus vannamei in southeastern China[END_REF][START_REF] Yang | Large Fine-Scale Spatiotemporal Variations of CH 4 Diffusive Fluxes From Shrimp Aquaculture Ponds Affected by Organic Matter Supply and Aeration in Southeast China[END_REF]. Yet, little is known about how farming practices affect methanogenesis pathways in aquaculture pond systems. Nevertheless, several studies have reported that farming practices can modify the organic matter quality and quantity of sediments (Ali et al., 2021;[START_REF] Mulat | Changing feeding regimes to demonstrate flexible biogas production: effects on process performance, microbial community structure, and methanogenesis pathways[END_REF][START_REF] Pusceddu | Trophic status of earthen ponds used for semi-intensive shrimp (Litopenaeus stylirostris, Stimpson, 1874) farming in New Caledonia (Pacific Ocean)[END_REF][START_REF] Sabu | Influence of environmental variables on methane related microbial activities in a tropical biosecured zero-exchange shrimp culture system[END_REF][START_REF] Wu | Anaerobic co-digestion of waste activated sludge and fish waste: methane production performance and mechanism analysis[END_REF]. For instance, in grass carp (Ctenopharyngodon idella) aquaculture ponds, carp feeding increased sediment dissolved organic carbon (DOC) concentrations by 2-5 times [START_REF] Chen | Variations in CO 2 fluxes from grass carp Ctenopharyngodon idella aquaculture polyculture ponds[END_REF]. Furthermore, in a Labeo victorianus pond, the total nitrogen contents from the pre-farming to post-farming periods increased from 1.58 to 1.84 mg g -1 [START_REF] Magondu | Effect of C/N ratio levels and stocking density of Labeo victorianus on pond environmental quality using maize flour as a carbon source[END_REF]. Additionally, in a shrimp (Litopenaeus stylirostris) pond in New Caledonia, from the initial stage to the last stage of shrimp farming, the protein content increased from 3.8 to 5.9 mg g -1 , while the lipid content increased from 1.2 to 3.0 mg g -1 [START_REF] Pusceddu | Trophic status of earthen ponds used for semi-intensive shrimp (Litopenaeus stylirostris, Stimpson, 1874) farming in New Caledonia (Pacific Ocean)[END_REF]. Such changes in the substrate quality and quantity of the sediments could affect the accumulation of H 2 , CO 2 , and acetate and thus, change the methanogenesis pathway [START_REF] Berberich | Spatial variability of sediment methane production and methanogen communities within a eutrophic reservoir: importance of organic matter source and quantity[END_REF][START_REF] Holmes | CO 2 and CH 4 isotope compositions and production pathways in a tropical peatland[END_REF]. Consequently, it is reasonable to expect that methanogenesis rates and pathways differ across aquaculture stages, although there is currently a paucity of actual field data to demonstrate this hypothesis.

Salinity is another important environmental driver affecting the rates of methanogenesis. In numerous coastal wetland studies, methanogenesis was shown to decrease with salinity (Luo et al., 2019a;[START_REF] Neubauer | Saltwater intrusion into tidal freshwater marshes alters the biogeochemical processing of organic carbon[END_REF][START_REF] Chambers | Effect of salinity-altering pulsing events on soil organic carbon loss along an intertidal wetland gradient: a laboratory experiment[END_REF], and similar results were also reported in aquaculture pond systems. For instance, a prior study suggested that the CH 4 emission fluxes in the ponds with a salinity of 1.9‰-3.4‰ were approximately 10 times higher than those of the ponds with a salinity of 6.1‰-15.1‰ [START_REF] Yang | Fluxes of carbon dioxide and methane across the water-atmosphere interface of aquaculture shrimp ponds in two subtropical estuaries: the effect of temperature, substrate, salinity and nitrate[END_REF]. In algae-shellfish mariculture ponds, net CH 4 exchange was also found to decrease with increasing salinity [START_REF] Zhang | Carbon dioxide uptake overrides methane emission at the air-water interface of algae-shellfish mariculture ponds: evidence from eddy covariance observations[END_REF]. Furthermore, studies have reported that methanogenic community composition varies significantly with changing salinity since different methanogen taxa have different salinity tolerances (Chen et al., 2020;[START_REF] Liu | Salinity drives archaeal distribution patterns in high altitude lake sediments on the Tibetan plateau[END_REF]. For instance, Methanomethylovorans only can live in sediments with salinity below 1.7‰ [START_REF] Bräuer | Methanoregula boonei gen. nov., sp. nov., an acidiphilic methanogen isolated from an acidic peat bog[END_REF], while Methanomassiliicoccus and Methanospirillum can tolerate salinities of 10‰-15‰ [START_REF] Dridi | Methanomassiliicoccus luminyensis gen. Nov., sp. nov., a methanogenic archaeon isolated from human faeces[END_REF]. A study in the lake sediments of the Tibetan Plateau has even suggested that the distribution patterns of hydrogenotrophic and acetotrophic methanogens are related to changes in salinity [START_REF] Liu | Salinity drives archaeal distribution patterns in high altitude lake sediments on the Tibetan plateau[END_REF]. Therefore, variation in salinity between ponds may affect methanogenesis pathways in coastal aquaculture pond systems. A more comprehensive understanding of the responses of methanogenesis pathways to salinity may help us to better understand methanogenesis mechanisms in aquaculture systems.

To bridge these knowledge gaps, we investigated methanogenesis before, during, and after farming, in a freshwater and an oligohaline pond in the Min River Estuary, East China Sea. To isolate the effects of aquaculture farming practices and salinity, the selected ponds were similar in terms of size, sediment texture, bulk density, and adopted the consistent aquiculture management practices for all three aquaculture farming stages (Table 1 and Fig. 1). We monitored the methanogenesis and distinguished the contribution of acetoclastic versus hydrogenotrophic methanogenesis by CH 3 F addition, measuring δ 13 CO 2 and δ 13 CH 4 , and investigating the methanogenic community structure. The specific goal of this study was to: (i) clarify how aquaculture farming practices affect rates and pathways of methanogenesis across three aquaculture farming stages; and (ii) identify whether methanogenesis metabolism differs between freshwater and oligohaline ponds. We hypothesized that (i) aquaculture farming practices increase the rates of methanogenesis and contribute to acetoclastic methanogenesis because of the introduction of polysaccharide-rich aquafeeds; (ii) oligohaline ponds have lower methanogenesis rates than freshwater ponds because of the salt stress. .3 ppb in 2019.3 ppb in (IPCC, 2021)). Emissions of CH 4 should be cut by at

Materials and methods

Location and sampling strategy

The study was conducted in monoculture ponds of white shrimp, Litopenaeus vannamei, in the Min River Estuary, southeast China (26

• 00 ′ N-26 • 05 ′ N, 119 • 30 ′ E-119 • 41 ′ E).
White shrimp accounted for 53% of total global crustacean production in 2016, and is one of the most important commercial species in the local coastal aquaculture industry (FAO, 2018). The climate in the study area is subtropical marine monsoon with an annual average temperature of 19.6 • C and an annual average precipitation of 1350 mm (www.weather.com.cn). We selected a freshwater pond, with salinity of 0.3‰-0.4‰, and an oligohaline pond, with salinity of 3.3‰-3.8‰, along the Min River Estuary (Fig. 1a). L. vannamei is a euryhaline shrimp species that could grow well in both ponds. The ponds were similar in size, maximum water depth, sediment texture, and bulk density (Table 1). Both ponds were converted from Cyperus malaccensis wetlands 7-9 years ago.

Aquaculture farming management was consistent for the two ponds during the experimental period from April to December 2018 (Fig. 1b). Specifically, during the pre-farming stage (April 3-May 4, 2018), the residual sludge from the previous harvest was removed mechanically and water from nearby estuaries was introduced into the ponds. There was no water exchange during the farming and post-farming periods. Also, the maximum water depth of the two ponds ranged from 1.51 ± 0.20 m to 1.58 ± 0.17 m (Table 1). During the farming stage (May 5-November 17, 2018), the shrimp larvae were released into the ponds on May 5. The aquafeed was commercial aquatic feed pellets (including crude protein 31.1%, crude fat 6.8%, moisture 8.7%, ash 5.3%, and fiber 1.1%) and was added to the ponds twice a day. The feeding rates were 10-15, 40-50, and 30-40 kg ha -1 d -1 during the growth periods of the larvae, juveniles, and adults, respectively. On average, a total of ~3.0 t of aquafeed was added to each pond during the farming period. Then, the shrimps were harvested from November 12 to November 17, 2018. During the post-farming stage (November 18-December 20, 2018), the aquaculture wastewater remained in the ponds, and no more artificial disturbance occurred.

Sediment and porewater collection

In 2018, sediment sampling was conducted at the pre-farming stage (April 25), farming stage (July 21), and post-farming stage (December 11) in both shrimp ponds (Fig. 1b). During sediment sampling, PVC tubes (length: 10 cm; inner diameter: 10 cm) were randomly inserted the headspace pressure was measured using a pressure gauge (SSI technologies). Then, 0.5 mL of the headspace gas was injected into 20-mL airtight gas sampling bags that were pre-evacuated and filled with 10 mL helium gas, for the CO 2 and CH 4 analysis. After the gas was sampled, the bottles were backfilled with 0.5-mL N 2 to re-establish normal atmospheric pressure. The concentrations of the CO 2 and CH 4 were determined using a GC-2010 Shimadzu gas chromatography equipped with a packed Porapak Q column and a flame ionization detector. Prior to the chromatography, the CO 2 was converted to CH 4 by reduction with H 2 in a nickel reactor at 350 • C. The CO 2 and CH 4 detection limits were 10 ppm and 0.3 ppm, respectively.

Isotope analysis

After 60 days of incubation, 15 mL of the headspace gas was sampled then overpressure injected into 12 mL pre-evacuated exetainer sample vials for the δ 13 C isotope analysis. The δ 13 C isotope analysis was performed using a GasBench-PreCon trace gas system coupled to a Delta V Plus IRMS from Thermo Scientific. Gas samples (0.5 mL) were used to determine the isotopic composition of the released CO 2 . Analyses of the δ 13 CH 4 used a Precon-Gas Bench-Isotope Ratio Mass Spectrometer (Precon-GB-IRMS) with gas samples (10 mL) injected into the Precon with a helium carrier gas. The 13 C/ 12 C isotope ratios were expressed as δ 13 C relative to the international standards of Vienna Pee Dee Belemnite (V-PDB), where δ 13 C = (R sample / R standard -1) × 1000, and R sample and R standard are the 13 C/ 12 C ratios in the sample and the PDB standard, respectively. Since the δ 13 CH 4 also depends on δ 13 CO 2 , the isotope fractionation factor, α c : (δ 13 CO 2 + 10 3 ) / (δ 13 CH 4 + 10 3 ) is calculated to distinguish the contributions of CH 4 and CO 2 [START_REF] Whiticar | Carbon and hydrogen isotope systematics of bacterial formation and oxidation of methane[END_REF].

Genetic analysis

DNA was extracted from 0.25 g of each sediment sample using the MOBIO Laboratories PowerSoil DNA Isolation Kit. Real-time quantitative polymerase chain reaction (qPCR) assays were used to assess the abundance of methanogenic groups using the specific primers mlasmod-F (5 ′ -GGYGGTGTMGGDTTCACMCARTA-3 ′ ) and mcrA-rev-R (5 ′ -CGTTCATBGCGTAGTTVG GRTAGT-3 ′ ) [START_REF] Steinberg | Phylogenetic comparison of the methanogenic communities from an acidic, oligotrophic fen and an anaerobic digester treating municipal wastewater sludge[END_REF]. The qPCR assays of the standards and samples were performed using the Thermo Fisher Scientific Applied Biosystems 7500 Real-Time PCR System. The reaction efficiencies for mcrA gene groups were 91.3%, with an r 2 above 0.99. The results were reported as the number of gene copies per gram of dry sediment.

High-throughput amplicon sequencing was performed using an Miseq PE300 sequencing platform (Illumina Inc., CA, USA). For amplicon sequencing, the mcrA gene fragments were amplified using the primer pair mlas-mod-F and mcrA-rev-R [START_REF] Steinberg | Phylogenetic comparison of the methanogenic communities from an acidic, oligotrophic fen and an anaerobic digester treating municipal wastewater sludge[END_REF]. The qualified reads were separated using the sample-specific barcode sequences and trimmed using Illumina Analysis Pipeline version 2.6. The datasets were analyzed using QIIME1.8.0 (http://qiime.org/; [START_REF] Caporaso | Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms[END_REF]. A total of 470,017 high-quality Illumina sequence reads were obtained for the mcrA genes. Rarefaction analysis was used to rarefy the operational taxonomic unit (OTU) tables to count to 26,444 reads for mcrA gene sequences. This was the lowest sequencing depth obtained from a sample, and was therefore used as the threshold for rarefaction calculations [START_REF] Gotelli | Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness[END_REF]. The sequences were assigned to OTUs by referencing with a subset of the SILVA 128 database for mcrA genes.

Data analysis

All analyses were conducted using R software (version 4.0.3; [START_REF] Core | The R project for statistical computing[END_REF]. We applied modified Gompertz models to fit the CO 2 and CH 4 accumulation curves (Zwietering et al., 1990): into the pond sediments. Each pond had three biological replicates within one stage. The sampling depth was 0-5 cm as this layer was the most impacted by aquaculture farming practices [START_REF] Suárez-Abelenda | The effect of nutrient-rich effluents from shrimp farming on mangrove soil carbon storage and geochemistry under semiarid climate conditions in northern Brazil[END_REF][START_REF] Tho | Physico-chemical characteristics of the improved extensive shrimp farming system in the Mekong Delta of Vietnam[END_REF]. Sediments were bubbled using nitrogen (N 2 ) and closed with silicone gaskets on both ends before being stored on ice and being transferred to the laboratory. Sediment samples were pooled, homogenized, and sieved (2 mm mesh size) in an anaerobic glove bag from AtmosBag, Sigma-Aldrich. Sediment subsamples for the molecular genetic analyses were immediately snap frozen in liquid nitrogen and stored at -80 • C. The porewater samples for determining sulfate (SO 4 2-), DOC, ammonia (NH 4

+

), and nitrate (NO - 3 ) concentrations were extracted by centrifuging the sediment subsamples for 6 min at 4000 rpm and were filtered through 0.45-μm pore size membrane filters. Sediment and porewater subsamples were stored at 4 • C and the analyses were completed within 7 days.

Sediment and porewater properties analyses

Sediment total organic carbon (TOC) and total nitrogen (TN) were measured using a CN-elemental analyzer with detection limits of 0.02 mg g -1 (Vario MAX CN, Germany). Prior to performing the TOC measurements, sediments were leached with 10% HCl to remove inorganic carbon, washed with distilled water at pH 7, then dried at room temperature [START_REF] Hou | Anaerobic ammonium oxidation (anammox) bacterial diversity, abundance, and activity in marsh sediments of the Yangtze estuary[END_REF]. The C/N ratio refers to the TOC versus the TN. Sediment temperature was measured by using a Thermo Fisher Scientific Temp 10 Series thermometer, with a detection accuracy of 0.1 • C. Sediment pH was measured using a Thermo Fisher Scientific pH 450 pH meter, with a detection accuracy of 0.1. Porewater salinity was measured using a salinity meter (SALT 6+, Eutech instruments, Thermo Fisher Scientific), with a detection accuracy of 0.1‰. The DOC concentrations were analyzed using a TOC-VCPH analyzer from Shimadzu, with a detection limit of 2 μmol L -1 . Porewater SO -concentrations were analyzed in an ICS-2000 Dionex ion chromatograph (Thermo Fisher Scientific) with a detection limit of 2 μmol L -1 . Porewater NH 4 + and NO - 3 concentrations were analyzed by the sodium hypobromite and copper-cadmium reduction method, respectively [START_REF] Zhang | Spatial and seasonal variations of nutrients in sediment profiles and their sediment-water fluxes in the Pearl River estuary, Southern China[END_REF].

Porewater NH 4

+ and NO - 3 concentrations were determined using a San++ continuous flow analyzer (Skalar Analytical) with detection limits of 2 μmol L -1 and 1 μmol L -1 , respectively.

Fourier-transform infrared spectroscopy

The relative abundance of the different sediment C compound classes in the sediment powders were determined by attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) using a Nicolet iS10 instrument (Thermo Fisher Scientific). The spectra were acquired by averaging 32 scans at 4-cm -1 resolution over 800-3500 cm -1 . The spectra were corrected for the ATR to allow for differences in the depth of the beam penetration at different wavelengths, and then baseline was corrected using the instrument software. The humification indices were calculated using the ratio of the absorbance of aliphatics, aromatic esters, lignin and aromatics with the polysaccharides following the methodology described by [START_REF] Beer | Geochemical controls on anaerobic organic matter decomposition in a northern peatland[END_REF].

Incubation experiments

The incubation procedure followed the methods of [START_REF] Conrad | Characterization of methanogenic archaea and stable isotope fractionation during methane production in the profundal sediment of an oligotrophic Lake (lake Stechlin, Germany)[END_REF]. Approximately 20 g sediment subsamples were slurried and deoxygenated with in-situ filtered pondwater (1:2, w/w) in a nitrogen atmosphere, transferred into 175 mL sterile serum bottles, flushed with N 2 , closed with butyl rubber stoppers, and then incubated at room temperature (20-23 • C) in the dark. Methyl fluoride (CH 3 F) was added to the headspace of half of the bottles at 2% (vol/vol) of the bottle volume to inhibit acetoclastic methanogenesis. Gas samples were collected at 1-7 days intervals, depending on the CO 2 and CH 4 production rates. Before sampling, the bottles were vigorous shaken, and

M t = M 0 ⋅exp { -exp [ R max ⋅e M o (λ -t) + 1 ] } (1)
where M t (μg g -1 ) denotes the cumulative methanogenesis per gram of dry sediment; M 0 (μg g -1 ) denotes the maximum methanogenesis per gram of dry sediment by the end of incubation; t (d) denotes the incubation time; R max (μg g -1 d -1 ) denotes the maximum methanogenesis rates per gram of dry sediment; λ (d) denotes the time lag. The fitting results were determined by r 2 with a significant level of p < 0.05. Model fitting used the 'nls' function. The rates of CO 2 production, CH 4 production, and hydrogenotrophic methanogenesis were equal to the R max that was derived from the accumulation curves of CO 2 , CH 4 , and CH 4 with an inhibitor, respectively. The rates of acetoclastic methanogenesis were calculated using the difference between CH 4 production rates and hydrogenotrophic methanogenesis rates. The contribution of the CH 4 production rates to the total sum of the production rates of CH 4 and CO 2 (i.e., f CH4 ) was calculated as follows:

f CH4 = CH 4 production rates/(CH 4 + CO 2 ) production rates (2)
The contribution of acetoclastic methanogenesis to the total CH 4 production rates (i.e., f acetoclastic ) was calculated as follows:

f acetoclastic = Acetoclastic methanogenesis rates/CH 4 production rates

(3) Two-way analysis of variance (ANOVA) was used to explore the primary and interactive effects of farming practices and the salinity on the sediment and porewater properties, CH 4 and CO 2 production rates, hydrogenotrophic and acetoclastic methanogenesis rates, f CH4 , f aceto- clastic , and the abundance of mcrA genes at a significance level of p < 0.05. Prior to performing ANOVA, all datasets were analyzed to identify whether they met the assumptions of homogeneity (the Brown and Forsythe test) and normality (the Shapiro-Wilk test) and data was log 10or box-cox transformed if the transformation substantially improved the distribution. The ANOVA was calculated using the 'aov' function. Multiple comparisons across the three aquaculture farming stages were calculated using one-way ANOVAs followed by a Tukey's HSD post hoc test. Pairwise comparisons between the freshwater and oligohaline ponds were performed using the paired-sample t-tests. Pearson correlations between the methanogenesis rates and environmental variables were calculated using the 'cor' function with significance at p < 0.05. Linear regression models were used to examine the relationships between the humification indices and the f acetoclastic using the 'lm' function with significance at p < 0.05. The beta diversity of the methanogenic community composition was visualized using Nonmetric Multidimensional scaling analyses (NMDS) with a weighted Bray-Curtis distance. Dissimilarity tests were performed by two-way PerMANOVA (permutational multivariate analysis of variance) based on the Bray-Curtis distance. The NMDS and PerMANOVA were calculated using the 'vegan' package.

A hypothesized structural equation model was built (Fig. S2) to access the impact of aquaculture farming practices and salinity on rates and pathways of methanogenesis using 'lavaan' package [START_REF] Rosseel | Lavaan: an R package for structural equation modeling[END_REF]. After the initial model was evaluated, we went through a process of adding/removing pathways and variables. The goodness of fit was assessed based on a chi-squared (χ 2 ) test, the Comparative Fit Index, and the Akaike Information Criterion values.

Results

Sediment and porewater properties

Both the freshwater and oligohaline ponds possessed a higher sediment temperature (of approximately 30 • C) in the farming stage compared with the pre-farming stage (approximately 24 • C) and the post-farming stage (approximately 18 • C) (p < 0.001; Table 2). The sediment pH was 7.6-8.1 and did not significantly vary between the two ponds or with the farming stages (p > 0.05; Table 2). The freshwater pond samples had lower porewater salinity and sulfate concentrations than the oligohaline pond samples (p < 0.001; Table 2). The porewater salinity and sulfate concentrations were similar in all three farming stages in each pond (p > 0.05; Table 2). The TOC contents (range: 21.1-24.5 mg g -1 ) were similar between the ponds and aquaculture farming stages (p > 0.05; Table 2). The C/N ratios (range: 8.8-11.3) and the DOC concentrations (range: 8.8-19.9 mmol L -1 ) did not vary between the ponds (p > 0.05; Table 2). However, lower C/N ratios and higher DOC concentrations occurred in the farming and post-farming stages compared with the pre-farming stage (p < 0.01; Table 2). The porewater NH 4 + concentrations ranged from 44.7 to 201.9 μmol L -1 (Table 2), while the porewater NO 3 -concentrations ranged from 1.0 to 2.8 μmol L -1 (Table 2). The porewater NH 4

+ and NO 3 -concentrations were higher in the farming and post-farming stages compared with the pre-farming stage (p < 0.001; Table 2). Different uppercase letters indicate significant differences across all three farming stages. Asterisks denote significant difference between the ponds within a single stage (*p < 0.05; **p < 0.01; ***p < 0.001). Bold letters denote significant differences at p < 0.05. < 0.001; Table 3). The humification indices were not significantly different between the freshwater and oligohaline ponds (p > 0.05; Table 3).

Production of CH 4 and CO 2 during sediment incubation

Incubating the pond sediments induced a rapid increase in the cumulative CH 4 and CO 2 concentrations during the first 16 days, then plateaued until day 60 (Fig. 2). The addition of CH 3 F (an inhibitor of acetoclastic methanogenesis) clearly inhibited the CH 4 production in both the freshwater and oligohaline sediments, while the CO 2 production was not significantly modified (Fig. 2). The CH 4 and CO 2 production curves fitted the modified Gompertz models with r 2 coefficients above 0.85 (p < 0.001) (Fig. 2 and Table S1).

Methanogenesis rates in the sediments ranged from 0.11 to 0.54 μg g -1 d -1 , and the CO 2 production rates ranged from 6.2 to 29.5 μg g -1 d -1 . Across the three aquaculture farming stages, the CH 4 and CO 2 production rates had higher levels in the farming and post-farming stages, when compared with the pre-farming stage (p < 0.001; Fig. 3a andb). The f CH4 was comparable in all three aquaculture farming stages (p > 0.05; Fig. 3c). The freshwater pond had relatively higher CH 4 production rates but lower CO 2 production rates compared with the oligohaline pond (p < 0.01; Fig. 3a andb), resulting in higher values of f CH4 in the freshwater pond than the oligohaline pond (p < 0.001; Fig. 3c).

The hydrogenotrophic methanogenesis rates ranged from 0.08 to 0.29 μg g -1 d -1 , and the acetoclastic methanogenesis rates ranged from 0.02 to 0.26 μg g -1 d -1 (Fig. 3d ande). Both the hydrogenotrophic and acetoclastic methanogenesis rates were higher in the farming and postfarming stages, compared with the rates in the pre-farming stage (p < 0.01; Fig. 3d ande). Moreover, the f acetoclastic were higher at the farming stages and post-farming stages than the pre-farming stages (p < 0.001; Fig. 3f). Both the hydrogenotrophic and acetoclastic methanogenesis rates were higher in the freshwater pond than the oligohaline pond (p < 0.05; Fig. 3d ande). However, the f acetoclastic did not vary between the freshwater and oligohaline ponds (p > 0.05; Fig. 3f).

Isotope analysis of CH 4 and CO 2

In the pond sediments, the δ 13 CH 4 ranged from -87‰ to -72‰, whereas the δ 13 CO 2 ranged from -21‰ to -18‰ (Fig. 4a andb). The δ 13 CH 4 and δ 13 CO 2 were not significantly different between the oligohaline and freshwater ponds (p > 0.05; Fig. 4a andb). The δ 13 CH 4 were lower prior to farming, compared with the farming and post-farming stages (p < 0.001; Fig. 4a). The isotopic fractionation factors (α c ) were lower in the farming and post-farming stages (1.055-1.062), compared with pre-farming stage (p < 0.001; 1.070-1.074) (Fig. 4c).

Methanogenic community abundance and composition

The abundance of mcrA gene in the sediments ranged from 9.92 × 10 6 to 12.03 × 10 6 copies g -1 and did not vary significantly between the farming stages and salinity levels (p > 0.05; Fig. 5a). The methanogenic community clearly varied in the pre-farming, farming, and post-farming stages, according to the NMDS and two-way PerMANOVA analyses (Fig. 5b). The major methanogenic genera were Methanoregula (~21%), Methanosaeta (~16%), Methanobacterium (~8%), Methanospirillum (~6%), Methanosarcina (~6%), Methanolinea (~3%), and Methanococcoides (~2%), Methanogenium (~2%), Methanosphaera (~2%), and Methanocella (~1%) (Fig. 5c). Other methanogenic genera accounted for less than ~33% of the mcrA gene. The relative abundance of Methanosaeta was much higher during and after farming than before farming (p < 0.05; Fig. 5c).

Relationships between the methanogenesis, organic substrate, humification indices, and methanogenic community composition

The methanogenesis rates increased with DOC concentrations (r = 0.77; p < 0.001; n = 18), decreased with the C/N ratios (r = -0.69; p < 0.01; n = 18). The mcrA gene abundance were not correlated with the methanogenesis rates (p > 0.05; n = 18). Changes in the humification indices were significantly associated with the f acetoclastic (p < 0.001; Figs. 6a to 6d).

The structural equation model suggested that aquaculture farming practices were related to the changes in the methanogenic community composition, while the latter were significantly linked to changes in the methanogenesis pathways (Fig. 7). Aquaculture farming practices also contributed to the changes in the methanogenesis rates (Fig. 7). Salinity was not related to the changes in the methanogenic community composition, whereas negatively related to changes in methanogenesis rates (Fig. 7).

Discussion

We analyzed the microbial production of methane and the underlying mechanisms in the sediments of a freshwater and an oligohaline pond before, during, and after shrimp farming. We focused on the effect of farming practices on methanogenesis and the respective contribution * FTIR peaks used to generate the humification indices: wavenumbers of lignin or aromatics (1630 cm -1 ), aromatic esters (1740 cm -1 ), and aliphatics (2920 cm -1 and 2850 cm -1 ) with respect to polysaccharides (1000 cm -1 ). References for the wavenumbers are Artz et al. (2008) and Parikh et al. (2014). Different uppercase letters indicate significant differences across all three farming stages. Bold letters denote significant differences at p < 0.05.

Chemical composition of organic matter

The main compounds in the sediment organic matter in ponds are polysaccharides, lignin, and aromatics (aromatic esters and aliphatics) (Fig. S1). All samples possessed similar compounds, but we observed changes in their relative abundances across three farming stages. The humification indices decreased from 0.04 to 0.13 before shrimp farming commenced to 0.01-0.05 during farming and 0.02-0.07 after farming (p of acetoclastic and hydrogenotrophic methanogenesis.

Effect of sediment organic substrate on methanogenesis

Shrimp aquaculture induced a sharp stimulation in the methanogenesis rates in the sediments at both the farming and post-farming stages compared to the pre-farming stage (Fig. 3a, supporting Hypothesis I). This finding is consistent with previous studies reporting that CH 4 emission fluxes raised during farming in aquaculture ponds [START_REF] Ma | A comparison of methane and nitrous oxide emissions from inland mixed-fish and crab aquaculture ponds[END_REF][START_REF] Song | Anthropogenic effects on fluxes of ecosystem respiration and methane in the Yellow River estuary, China[END_REF][START_REF] Yang | Fluxes of carbon dioxide and methane across the water-atmosphere interface of aquaculture shrimp ponds in two subtropical estuaries: the effect of temperature, substrate, salinity and nitrate[END_REF]. The raised methanogenesis rates may be due to changes in the quantity and quality of the organic substrate.

The hypothesis that methanogenesis rates is affected by organic substrate quantity, is validated by our results showing that the DOC concentrations increased by an average of 82% during and after farming (Table 2), and were correlated with methanogenesis rates (r = 0.77, p < 0.001). The high aquafeed loads are typically hydrolyzed to DOC, which is a suitable substrate for methanogens [START_REF] Olsen | Perspectives of nutrient emission from fish aquaculture in coastal waters: literature review with evaluated state of knowledge[END_REF][START_REF] Suhr | Anaerobic digestion of solid waste in RAS: effect of reactor type on the biochemical acidogenic potential (BAP) and assessment of the biochemical methane potential (BMP) by a batch assay[END_REF]. Moreover, the production of shrimp excreta is also likely to enrich the DOC in low molecular weight, easily assimilable organic compounds [START_REF] Queiroz | Hidden contribution of shrimp farming effluents to greenhouse gas emissions from mangrove soils[END_REF][START_REF] Wang | Deciphering dissolved organic matter from freshwater aquaculture ponds in eastern China based on optical and molecular signatures[END_REF]. Previous research reported similar correlations of methanogenesis rates and DOC concentrations in pristine ponds and paddies [START_REF] Bertora | Dissolved organic carbon cycling, methane emissions and related microbial populations in temperate rice paddies with contrasting straw and water management[END_REF][START_REF] Holgerson | Drivers of carbon dioxide and methane supersaturation in small, temporary ponds[END_REF].

The effect of the quality of the organic substrate on methanogenesis rates was demonstrated by a high C/N ratio before farming (10.6-11.3), which decreased during and after farming (8.9-9.6), suggesting an input of easily assimilable protein-like nutrients [START_REF] Panigrahi | Carbon:Nitrogen (C:N) ratio level variation influences microbial community of the system and growth as well as immunity of shrimp (Litopenaeus vannamei) in biofloc based culture system[END_REF]. A C/ N ratio higher than 10.0 is typical of recalcitrant compounds that are not available for benthic microbial metabolism [START_REF] Baldi | Biochemical and microbial features of shallow marine sediments along the Terra Nova Bay (Ross Sea, Antarctica)[END_REF][START_REF] Wei | Paddy soils have a much higher microbial biomass content than upland soils: a review of the origin, mechanisms, and drivers[END_REF]. Decreasing C/N ratios raise the N availability for methanogens and thus accelerate methanogenesis rates [START_REF] Hou | Continuous milk vetch amendment in rice-fallow rotation improves soil fertility and maintains rice yield without increasing CH 4 emissions: evidence from a long-term experiment[END_REF][START_REF] Kim | Combined effects of carbon, nitrogen and phosphorus on CH 4 production and denitrification in wetland sediments[END_REF]. Overall, aquaculture farming enhanced methanogenesis rates at the farming and post-farming stages relative to the pre-farming stages by changing the quantity and quality of the organic substrates for the methanogens.

Contribution of acetoclastic and hydrogenotrophic methanogenesis

Acetoclastic and hydrogenotrophic methanogenesis coexisted in the sediments before, during, and after shrimp farming, with a predominance of the hydrogenotrophic pathway by 51%-78% (Figs. 3d to 3f). The rates of the acetoclastic pathway increased by approximately 5.3 times in the sediments at both the farming and post-farming stages compared to the pre-farming stage (Fig. 3e, supporting Hypothesis I). Besides, the contribution of the acetoclastic pathway increased from approximately 22% before farming to approximately 45% during and after farming (Fig. 3f). Therefore, aquaculture farming practices favor acetoclastic rather than hydrogenotrophic methanogenesis. The rise of acetoclastic methanogenesis was supported by a decrease in the isotopic fractionation α c from before farming (1.070-1.074) to during and after farming (1.055-1.062) (Fig. 4c). These values and their corresponding pathways agreed with previous literature [START_REF] Holmes | Effect of nutrient enrichment on δ 13 CH 4 and the methane production pathway in the Florida Everglades[END_REF][START_REF] Hornibrook | Evolution of stable carbon isotope compositions for methane and carbon dioxide in freshwater wetlands and other anaerobic environments[END_REF]. We also observed substantial changes in the composition of the methanogenic community during and after farming (Fig. 5b). In particular, the relative abundance of the acetoclastic methanogen, Methanosaeta increased from approximately 9% before farming to approximately 21% during and after farming, confirming the rise of acetoclastic methanogenesis (Fig. 5c).

The shift in the contributions of the different methanogenesis pathways are presumably due to changes in the chemical composition of sediment organic substrate. This is supported by the high association between methanogenesis pathways and the humification indices derived from FTIR (Figs. 6a to 6d). Before farming, the sediments contain relatively stable and recalcitrant organic matter (such as lignin, aromatics, and aliphatics), which cause high humification indices (Table 3). The presence of lignin, aromatics, and aliphatics in sediments agrees with the historical conversion of these vegetated tidal wetlands to ponds as these wetlands are typically dominated by sedge species [START_REF] Mitsch | Wetlands[END_REF]. When buried, the plant debris is partially decomposed, leaving lignin, aliphatics, and other complex compounds [START_REF] Berberich | Spatial variability of sediment methane production and methanogen communities within a eutrophic reservoir: importance of organic matter source and quantity[END_REF][START_REF] Gleixner | Soil organic matter dynamics: a biological perspective derived from the use of compound-specific isotopes studies[END_REF][START_REF] Lichtfouse | A novel model of humin[END_REF][START_REF] Martínez-García | Organic management and cover crop species steer soil microbial community structure and functionality along with soil organic matter properties[END_REF]. These recalcitrant compounds can partly be oxidized to produce H 2 (Conrad et al., 2010a;[START_REF] Keller | Climate change effects on carbon and nitrogen mineralization in peatlands through changes in soil quality[END_REF], thus favoring hydrogenotrophic methanogenesis.

During and after farming, the amounts of aquafeed were added into the ponds. Indeed, 30% of aquafeed is not captured by shrimps, and settles in the sediment [START_REF] Soto | Feed intake improvement, gut microbiota modulation and pathogens control by using Bacillus species in shrimp aquaculture[END_REF]. The aquafeed was mainly composed of peanut and bean dregs, which are rich in polysaccharides [START_REF] Dawood | Beneficial roles of feed additives as immunostimulants in aquaculture: a review[END_REF]. Consequently, the humification indices decreased during and after farming due to the high relative abundance of polysaccharides (Table 3). [START_REF] Rawoof | Sequential production of hydrogen and methane by anaerobic digestion of organic wastes: a review[END_REF] stated that polysaccharide hydrolysis produces glucose, fructose, and xylose, which are further biodegraded into acetate, thus stimulating acetoclastic methanogens. The stimulation Fig. 3. CH 4 and CO 2 production rates (a and b), f CH4 (c), hydrogenotrophic and acetoclastic methanogenesis rates (d and e), and f acetoclastic (f) across three aquaculture farming stages in the sediments of a freshwater and an oligohaline shrimp pond. All data are the mean ± standard deviation (n = 3 biological replicates). Different uppercase letters indicate significant differences across the three aquaculture farming stages. Asterisks denote significant differences between the ponds within one stage (*p < 0.05; **p < 0.01; ***p < 0.001). f CH4 : the contribution of CH 4 production rates to the sum production rates of CH 4 plus CO 2 . f acetoclastic : the contribution of acetoclastic methanogenesis to the CH 4 production rates.

of acetoclastic methanogenesis by labile C has also been suggested in peat [START_REF] Keller | Pathways of anaerobic carbon cycling across an of community structure and abundance of methanogens in soils along a freshwater-brackish water gradient in subtropical estuarine marshes[END_REF]. Our previous study also showed acetoclastic methanogenesis was feasible when there were high concentrations of polysaccharides in anaerobic soils [START_REF] Xiao | Biochar promotes methane production at high acetate concentrations in anaerobic soils[END_REF]. Overall, aquaculture farming favored acetoclastic over hydrogenotrophic methanogenesis, because of the input of polysacchariderich aquafeeds.

Effect of salinity on methanogenesis

The methanogenesis rates were lower in the oligohaline pond than the freshwater pond (Fig. 3a). Salinity is known to control the methanogenic community by increasing ionic toxicity and altering the osmotic potential [START_REF] Luo | Anaerobic organic carbon mineralization in tidal wetlands along a low-level salinity gradient of a subtropical estuary: rates, pathways, and controls[END_REF][START_REF] Tully | The invisible flood: the chemistry, ecology, and social implications of coastal saltwater intrusion[END_REF]. Nonetheless, the abundance of the mcrA gene was not significantly different between the oligohaline and freshwater ponds (Fig. 5a). The results are probably due to the major methanogenic genera, e.g., Methanoregula, Methanosaeta, Methanobacterium, Methanospirillum, and Methanosarcina are all frequently encountered methanogenic taxa in estuaries where salinity is high (ranging from 5‰ to 20‰) [START_REF] Chen | Population dynamics of methanogens and methanotrophs along the salinity gradient in Pearl River estuary: implications for methane metabolism[END_REF][START_REF] Liu | Salinity drives archaeal distribution patterns in high altitude lake sediments on the Tibetan plateau[END_REF][START_REF] Tong | Changes Ji Tan 1,2,3,4 , Eric Lichtfouse 5[END_REF]. Therefore, salt stress is probably not the cause of the lower methanogenesis rates in the oligohaline pond relative to the freshwater pond (against Hypothesis II).

A more probable explanation is the sulfate reduction in the oligohaline pond yields a stronger Gibbs free energy compared to methanogenesis, and therefore inhibits methanogen activities [START_REF] Herbert | A global perspective on wetland salinization: ecological consequences of a growing threat to freshwater wetlands[END_REF]. This assumption is supported by the sulfate concentrations, these are approximately 10-times higher in the oligohaline pond compared to the freshwater pond (Table 2). Moreover, sulfate-reducing bacteria can oxidize CH 4 to CO 2 under anaerobic conditions [START_REF] Rotaru | A new diet for methane oxidizers[END_REF][START_REF] Wilms | Methane and sulfate profiles within the subsurface of a tidal flat are reflected by the distribution of sulfate-reducing bacteria and methanogenic archaea[END_REF], which could also explain the lower methanogenesis rates in the oligohaline pond.

While both hydrogenotrophic and acetoclastic pathways displayed lower methanogenesis rates in the oligohaline when compared to the freshwater pond (Fig. 3d), their respective contributions were similar (Fig. 3f). Similarly, the isotopic fractionation factor α c and the methanogenic community composition did not differ between the two ponds (Figs. 4c and5b). These findings suggest salinity did not alter the partitioning of the methanogenesis pathways. A previous study suggested that salinity may change methanogenesis pathways due to the different relative salt-tolerances of the hydrogenotrophic and acetoclastic methanogens [START_REF] Liu | Salinity drives archaeal distribution patterns in high altitude lake sediments on the Tibetan plateau[END_REF]. However, the dominated hydrogenotrophic and acetoclastic methanogens were both tolerant to lowsalinity as discussed before. Therefore, salinity differences between the ponds did not lead to different partitioning of methanogenesis pathways in the current study.

Implication for how aquaculture management can reduce methane emission in the future

First, we revealed the rise of acetoclastic versus hydrogenotrophic methanogenesis in shrimp farming. This rise is explained by the input of aquafeeds increasing the proportions of labile, microbially assimilable organic compounds, such as polysaccharides. Using this knowledge, we suggest changing shrimp diets, similarly to cow farming to better control and possibly reduce methane emissions [START_REF] Hassanat | Replacing alfalfa silage with corn silage in dairy cow diets: effects on enteric methane production, ruminal fermentation, digestion, N balance, and milk production[END_REF][START_REF] Marette | Economic benefits from promoting linseed in the diet of dairy cows for reducing methane emissions and improving milk quality[END_REF]. For instance, the addition of oilseeds (camelina and safflower) may not influence the growth of aquaculture products; however, these lipid-rich feeds have a toxic effect on the activities of archaea, which would further reduce methanogen abundance [START_REF] Wang | Contribution of ruminal fungi, archaea, protozoa, and bacteria to the methane suppression caused by oilseed supplemented diets[END_REF].

Second, our results reported that the lower rates of methanogenesis in the oligohaline pond are because of the inhibition of sulfate reduction and oxidation of CH 4 to CO 2 by sulfate in anaerobic conditions. Based on this conclusion, we suggested that freshwater shrimp aquaculture ponds would benefit from moderate salt levels. The addition of moderate salt (salinity = 2.5‰) into ponds had been applied in carp freshwater aquaculture systems in India, which was found to stimulate the growth of the fish and greatly suppress CH 4 emissions [START_REF] Naskar | Effect of different organic manures and salinity levels on greenhouse gas emission and growth of common carp in aquaculture systems[END_REF]. L. vannamei is a euryhaline shrimp species, which could grow well in ponds with salinity of 0‰ to 30‰ [START_REF] Jannathulla | Growth and carcass mineralisation of Pacific white leg shrimp Penaeus vannamei Boone 1931 in response to water salinity[END_REF]. Therefore, the addition of salt in the freshwater ponds would not influence the growth of the shrimps; however, it would greatly decrease the CH 4 emissions.

Third, we have found that methanogenesis maintained high-levels even after farming. This was because the amounts of aquafeed residues deposited on the pond sediments. Therefore, an effective way to reduce methanogenesis in aquaculture ponds was to clean aquafeed [START_REF] Whiticar | Carbon and hydrogen isotope systematics of bacterial formation and oxidation of methane[END_REF]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) residues [START_REF] Chik | Microalgae and its premises towards sustainable energy development[END_REF]. Once the farming activities are completed, the pond water should be drained and the aquafeed residues should be removed immediately to reduce the potential of methanogenesis.

Conclusions

This study reveals shrimp farming increases the acetoclastic methanogenesis when compared to hydrogenotrophic methanogenesis in a pond. This is due to the input of polysaccharide-rich aquafeed and shrimp excreta increasing the levels of fresh organic matter in the pond sediments. The methanogenesis rate is lower in the oligohaline pond than freshwater pond. Salinity did not change the relative composition of the methanogenic community or modify the respective contribution of the acetoclastic and hydrogenotrophic methanogenesis. In the future, methanogenesis in shrimp ponds could be reduced by changing shrimp diets, increasing salinity, and removing residual aquafeeds. Table S1 CO2 and CH4 production parameters (means ± standard deviation; n = 3) based on the modified Gompertz model (Zwietering et al., 1990)

  3

Fig. 1 .

 1 Fig. 1. (a) Location of the freshwater and the oligohaline ponds in the Min River Estuary, southeast China. (b) Aquaculture farming and sampling schedules across the aquaculture farming stages in 2018.

Fig. 2 .

 2 Fig. 2. Cumulative CH 4 and CO 2 production during incubation across three aquaculture farming stages in the sediments of a freshwater and an oligohaline shrimp pond. All data are mean ± standard deviation (n = 3 biological replicates). The lines represent the modified Gompertz model curves.

Fig. 4 .

 4 Fig. 4. δ 13 CH 4 and δ 13 CO 2 values (mean ± standard deviation; n = 3 biological replicates) of the sediment at the completion of a 60 d-incubation across three aquaculture farming stages of a freshwater and an oligohaline shrimp pond. Different uppercase letters indicate significant differences across the three aquaculture farming stages. Asterisks denote significant difference between ponds within a single stage (*p < 0.05; **p < 0.01; ***p < 0.001). The red line in panel c provides the separation between the relative importance of hydrogenotrophic (above) and acetoclastic (below) methanogenesis[START_REF] Whiticar | Carbon and hydrogen isotope systematics of bacterial formation and oxidation of methane[END_REF]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6 .

 6 Fig. 6. Relationships between humification indices and the f acetoclastic . Humification indices are derived from Fourier-transform infrared spectroscopy. f acetoclastic : the contribution of acetoclastic methanogenesis the CH 4 production rates.

Fig. 7 .

 7 Fig. 7. A structural equation modelling accessing the impact of aquaculture farming practices and salinity on rates and pathways of methanogenesis. Numbers on the arrows indicate the coefficients and the arrow width indicates their significance. Figures next to the variables indicate their explained variance (r 2 ). DOC: dissolved organic carbon; HM: hydrogenotrophic methanogenesis; AM: acetoclastic methanogenesis.

Figure S1

 S1 Figure S1 Fourier transform infrared (FTIR) spectroscopy within the region of 800 to 3500 cm -1 of the sediments of the freshwater and oligohaline ponds in the Min River Estuary, southeast China. References for wavenumbers according to Artz et al. (2007) and Parikh et al. (2014).

Figure S2

 S2 Figure S2Hypothesized path model structure to evaluate the effects of aquaculture farming practices and salinity on rates and pathways of methanogenesis. DOC: dissolved organic carbon; HM: hydrogenotrophic methanogenesis; AM: acetoclastic methanogenesis.

Table 1

 1 Location, pond size, sediment texture, bulk density, and maximum water depth (means ± standard deviation, n = 3 biological replicates) of the freshwater and oligohaline ponds in the Min River Estuary, southeast China.

	Ponds
	Freshwater	Oligohaline

Table 2

 2 Sediment and porewater properties (means ± standard deviation, n = 3 biological replicates) across three aquaculture farming stages of the freshwater and oligohaline shrimp ponds in the Min River Estuary, southeast China.

		Ponds	Stages			Factor	Two-way ANOVA
			Pre-farming	Farming	Post-farming		Stage	Pond	Interaction
	Sediment temperature	Freshwater	23.6 ± 0.2B	30.4 ± 0.3A	18.2 ± 0.2C	F	4628.839	4.598	3.322
	( • C)	Oligohaline	24.2 ± 0.2B	30.5 ± 0.2A	18.3 ± 0.3C	P	<0.001	>0.05	>0.05
	pH	Freshwater Oligohaline	8.0 ± 0.3A 8.1 ± 0.4A	7.6 ± 0.1A 7.8 ± 0.2A	7.9 ± 0.2A 7.9 ± 0.2A	F P	2.803 >0.05	0.115 >0.05	0.234 >0.05
	Salinity	Freshwater	0.4 ± 0.1A**	0.3 ± 0.0A**	0.3 ± 0.0A**	F	1.696	907.837	2.130
	(‰) SO 4 2-(mmol L -1 )	Oligohaline Freshwater Oligohaline	3.3 ± 0.3A 0.04 ± 0.01A** 0.42 ± 0.05A	3.5 ± 0.2A 0.03 ± 0.00A* 0.38 ± 0.06A	3.8 ± 0.3A 0.03 ± 0.01A** 0.41 ± 0.06A	P F P	0.225 0.542 0.595	<0.001 336.03 <0.001	0.162 0.308 0.740
	TOC (mg g -1 )	Freshwater Oligohaline	22.0 ± 2.0A 21.1 ± 2.5A	22.7 ± 1.5A 24.5 ± 3.0A	23.8 ± 2.6A 23.1 ± 1.8A	F P	1.514 >0.05	0.004 >0.05	0.646 >0.05
	C/N ratio	Freshwater Oligohaline	11.3 ± 1.2A 10.6 ± 1.0A	9.0 ± 0.4B 8.8 ± 0.9B	9.9 ± 0.4B 9.4 ± 0.5B	F P	11.129 0.002	1.456 >0.05	0.176 >0.05
	DOC (mmol L -1 ) NH 4 + (μmol L -1 ) NO 3 -(μmol L -1 )	Freshwater Oligohaline Freshwater Oligohaline Freshwater Oligohaline	8.8 ± 0.6C 9.7 ± 1.3C 44.7 ± 9.2B 47.9 ± 10.9B 1.0 ± 0.2C 1.2 ± 0.2C	17.8 ± 2.2A 19.9 ± 1.4A 141.0 ± 16.5A 150.2 ± 17.4A 1.5 ± 0.3B 1.8 ± 0.4B	14.0 ± 1.6B 15.6 ± 2.0B 173.3 ± 12.6A 201.9 ± 12.2A 2.6 ± 0.2A 2.8 ± 0.4A	F P F P F P	54.820 <0.001 174.876 <0.001 48.180 <0.001	4.016 >0.05 4.676 >0.05 2.597 >0.05	0.192 >0.05 1.452 >0.05 0.050 >0.05

Table 3

 3 Humification indices derived from Fourier-transform infrared spectroscopy (FTIR; means ± standard deviation, n = 3 biological replicates) across three aquaculture farming stages of the freshwater and oligohaline shrimp ponds in the Min River Estuary, southeast China.

	FTIR peaks used for humification index*	Ponds	Stages			Factor	Two-way ANOVA
			Pre-farming	Farming	Post-farming		Stage	Pond	Interaction
	1630/1000	Freshwater Oligohaline	0.11 ± 0.01A 0.11 ± 0.03A	0.04 ± 0.01B 0.05 ± 0.02B	0.07 ± 0.01B 0.06 ± 0.02B	F P	19.658 <0.001	0.044 >0.05	0.436 >0.05
	1740/1000	Freshwater Oligohaline	0.04 ± 0.01A 0.05 ± 0.02A	0.01 ± 0.00B 0.01 ± 0.01B	0.02 ± 0.01B 0.02 ± 0.01B	F P	15.755 <0.001	0.226 >0.05	0.204 >0.05
	2850/1000	Freshwater Oligohaline	0.10 ± 0.03A 0.08 ± 0.03A	0.02 ± 0.02B 0.03 ± 0.02B	0.04 ± 0.00B 0.05 ± 0.02B	F P	13.407 <0.001	0.005 >0.05	0.652 >0.05
	2920/1000	Freshwater Oligohaline	0.13 ± 0.03A 0.12 ± 0.04A	0.02 ± 0.02B 0.04 ± 0.03B	0.06 ± 0.00B 0.06 ± 0.04B	F P	15.439 <0.001	0.107 >0.05	0.356 >0.05

  of the freshwater and oligohaline ponds in the Min River Estuary, southeast China. ): the cumulative methane production per gram of dry sediment; Rmax (µg•g -1 •d -1 ): the maximum methane production

	Stages	Ponds	Incubation	M(t) (µg•g -1 )	λ (d)	Rmax (µg•g -1 •d -1 )	RMSE (µg•g -1 )	p	R 2
	CH4 production
	Pre-farming	Freshwater Oligohaline	Control With CH3F Control With CH3F	2.78±0.12 2.13±0.01 1.98±0.31 1.56±0.29	0.00±0.00 0.00±0.00 0.04±0.05 1.68±0.80	0.16±0.02 0.13±0.01 0.11±0.01 0.08±0.01	0.23±0.02 0.22±0.03 0.23±0.05 0.24±0.07	<0.001 <0.001 <0.001 <0.001	0.95 0.93 0.98 0.91
	Farming	Freshwater Oligohaline	Control With CH3F Control With CH3F	13.58±3.10 7.66±3.07 10.26±0.35 5.47±0.82	0.04±0.06 0.01±0.06 1.68±0.80 0.20±0.34	0.54±0.06 0.29±0.04 0.39±0.06 0.39±0.03	1.07±0.07 0.71±0.22 1.06±0.13 0.58±0.17	<0.001 <0.001 <0.001 <0.001	0.94 0.91 0.98 0.97
	Post-farming	Freshwater Oligohaline	Control With CH3F Control With CH3F	12.71±1.83 8.22±0.60 9.02±1.09 4.66±0.78	0.07±0.07 0.00±0.00 1.83±1.62 1.63±1.55	0.44±0.03 0.25±0.03 0.30±0.03 0.17±0.02	0.74±0.13 1.08±0.33 0.66±0.49 0.72±0.21	<0.001 <0.001 <0.001 <0.001	0.96 0.94 0.98 0.94
	CO2 production
	Pre-farming	Freshwater Oligohaline	Control With CH3F Control With CH3F	125.8±9.7 125.7±11.4 150.0±16.8 164.0±19.7	0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00	6.2±0.5 9.3±0.9 8.2±1.1 7.3±1.2	108.4±8.2 110.1±9.9 120.3±18.4 128.1±14.2	<0.001 <0.001 <0.001 <0.001	0.85 0.89 0.87 0.86
	Farming	Freshwater Oligohaline	Control With CH3F Control With CH3F	525.3±104.4 507.1±119.6 570.4±144.5 600.7±92.9	0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00	22.6±2.5 25.8±2.6 29.5±3.9 29.0±8.5	430.8±72.2 422.6±90.4 474.0±111.0 496.7±88.0	<0.001 <0.001 <0.001 <0.001	0.91 0.92 0.92 0.90
	Post-farming	Freshwater Oligohaline	Control With CH3F Control With CH3F	391.3±58.8 410.5±43.7 366.8±53.0 357.7±17.6	0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00	15.2±1.7 16.2±3.7 17.8±1.0 17.6±2.5	314.1±44.1 325.1±43.8 305.4±38.0 293.7±23.3	<0.001 <0.001 <0.001 <0.001	0.87 0.92 0.98 0.95
	Mt (µg•g -1

rates per gram of dry sediment; λ (d): time lag; RMSE (µg•g -1 ): root mean square error.

Acknowledgments

This work was financially supported by grants from the National Science Foundation of China (No. 32071598; 42077025; 41671088), the Natural Science Foundation of Fujian Province (No. 2020J01503), the

Data availability

Data deposited in the figshare, https://figshare.com/s/ a081915a95654a76efd0. Raw mcrA gene sequence reads are also available on NCBI Short Read Archive (SRA) under the BioProject number PRJNA532292.

Author contributions Luo and Xiao designed the experiment. Luo and Tan co-wrote the manuscript with substantial contributions from Lichtfouse. Tan, Zhang, Chen, Liu and Huang did the scientific analysis. All authors conducted the research and analyzed data. All authors edited the manuscript.

Data availability statement

Data deposited in the figshare, https://figshare.com/s/a081915a956 54a76efd0. Raw mcrA gene sequence reads for each sample are also available on NCBI Short Read Archive (SRA) under the BioProject number PRJNA532292.

CRediT authorship contribution statement

Ji Tan: Investigation, Writingoriginal draft. Eric Lichtfouse:

Declaration of Competing Interest

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

Appendix A. Supplementary data

Supplementary data to this article can be found below and online at https://doi. org/10.1016/j.aquaculture.2022.738999.

Supplementary material

Aquaculture drastically increases methane production by favoring acetoclastic rather than hydrogenotrophic methanogenesis in shrimp pond sediments


Joanna Kedra

Pascal Claudepierre

René-Marc Flipo

Marco Garrido-Cumbrera

Françoise Alliot-Launois

Emilie Desfleurs

Laurent Grange

Laure Gossec

Impact of axial spondylarthritis on quality of life: results from the European Map of Axial Spondyloarthritis (EMAS) study in France

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The impact of axSpA on patients' quality of life has been well assessed in terms of body structures and functions, but much less in terms of social interactions and activities, work, and fears related to social interactions or activities [START_REF] Sieper | Axial spondyloarthritis[END_REF].

A better understanding of patients' daily difficulties is a key issue in rheumatology, as it could positively affect patients' well-being and the management of their disease [START_REF] Santiago | Invalidation in Patients with Rheumatic Diseases: Clinical and Psychological Framework[END_REF]. In this context, a large patients' survey, the European Map of Axial Spondyloarthritis (EMAS) study, was performed to explore patients' perceptions of axSpA [START_REF] Garrido-Cumbrera | The European Map of Axial Spondyloarthritis: Capturing the Patient Perspective-an Analysis of 2846 Patients Across 13 Countries[END_REF][START_REF] Garrido-Cumbrera | Atlas of axial spondyloarthritis in Spain 2017: Study design and population[END_REF][START_REF] Garrido-Cumbrera | Gender differences in patient journey to diagnosis and disease outcomes: results from the European Map of Axial Spondyloarthritis (EMAS)[END_REF]. The aim of the present study was to assess the impact of axSpA on quality of life, and particularly on social interactions, social activities, working issues and fears, using data from the EMAS French participants.

EMAS was conducted in 13 European countries in 2017-2018 (4); the findings for French patients are presented here. Participants had to be aged≥18 years, to self-report a diagnosis of axSpA (either radiographic or non-radiographic), and to report having seen a healthcare professional for axSpA in the 12 months prior to participation.

The impact of axSpA was evaluated by an online questionnaire developed by an international scientific committee. Four categories of impact were assessed: social interactions (5 items: with spouse, family, friends and colleagues, and sexual intercourse frequency), frequency of leisure activities and social life (4 items: restaurants, cultural outings, travels, sports), working issues (current sick leave), and fears for the future. Sociodemographic and disease-related data, as well as selfreported comorbidities (anxiety, depression, overweight/obesity, fibromyalgia), were also collected.

Impact of axSpA on quality of life was assessed qualitatively: worse or much worse social interactions for at least one of the five items, less or much less frequent social activities for at least one of the four items, current sick leave, and at least a positive response to a fear-related question. The key outcome here was major impact, defined as having an impact on 2 or more aspects of quality of life (among social interactions, social activities, sick leave or fears). Statistics were descriptive, without imputation of missing data. Univariable and multivariable logistic regression models were built in order to determine the factors associated with major impact. All variables with p-value < 0.20 in univariable analysis were included in the multivariable model [START_REF] Bursac | Purposeful selection of variables in logistic regression[END_REF].

Data from 638 patients were analyzed: mean age 41.5±11.1 years, 77.9% women, mean disease duration 6.9±8.2 years. Disease was active (mean BASDAI: 5.9±1.7), and 19.1% participants were on biologics at the time of the survey.

Participants frequently reported anxiety (N=312, 50.4%), being overweight/obese (N=237, 38.2%) or depression (N=199, 32.8%).

Overall, 615 (96.4%) patients reported any of the four impacts among social interactions, social life, sick leave and fears (Figure 1). A major impact on quality of life (i.e., ≥ 2 categories impacted) was reported by 575 participants (90.1%).

A total of 505 participants (79.4%) reported a negative impact of axSpA on social interactions with partners, family, friends or coworkers, particularly in terms of frequency of sexual intercourse. 548 (86.2%) participants engaged in fewer leisure activities due to their axSpA and more specifically going to restaurants or physical activity in nearly 80% of cases. More than half of participants were employed at the time of the survey, while 75 (12.0%) and 87 (14.0%) participants were respectively on temporary or permanent sick leave. Finally, 585 (91.7%) participants reported fears for the future, the main fear reported by the surveyed participants being alteration of daily activities (N=453, 71.0%).

Factors associated with major impact on quality of life in univariable and confirmed in multivariable analysis were: being unemployed (odd ratio [OR]=3. 1).

This study brings to light that more than 90% of patients with axSpA in France describe a major impact of axSpA on their social and working life, in a context of fears for the future. Factors associated with an increased risk of major impact were not related to the activity of the disease, except receiving biologics; however, this association was probably secondary to an indication bias, as these treatments are prescribed in the most severe/active axSpA, which are more likely to affect patients' quality of life [START_REF] Fragoulis | Treatment strategies in axial spondyloarthritis: what, when and how?[END_REF]. Factors which were significantly associated with major impact were rather related to patients' background, experience and identity, which is concordant with previous studies [START_REF] Kreis | Relationship between optimism and quality of life in patients with two chronic rheumatic diseases: axial spondyloarthritis and chronic low back pain: a cross sectional study of 288 patients[END_REF][START_REF] Rusman | Gender Differences in Axial Spondyloarthritis: Women Are Not So Lucky[END_REF][START_REF] Putrik | Inequities in access to biologic and synthetic DMARDs across 46 European countries[END_REF]. Indeed, sociodemographic characteristics influence patients'

understanding and perception of their disease, and ultimately influence their experience and quality of life.

These findings underline the need of a multidisciplinary approach to enable a more global and efficient management of axSpA, and ultimately improve patient's quality of life.

Figure 1 :

 1 Figure 1: Venn diagram for the 4 domains of quality of life

1

  Impact of axial spondylarthritis on quality of life: results from the European Map of Axial

	committee Sandoz, Key indexing terms: Spondylitis, Ankylosing; Quality of life; Patient Reported Outcome Paris, France.
	Spondyloarthritis (EMAS) study in France HP.Sorbonne & Pitié-Salpêtrière hospital, AP-Measures; Surveys and Questionnaires TITLE PAGE	Sanofi; from Novartis Amgen, Pharma consulting fees: AbbVie,
	Short running head: axial spondyloarthritis and QoL Full title: Impact of axial spondylarthritis on quality of life: results from the European Map of Axial Spondyloarthritis (EMAS) study in France Marco Garrido-Cumbrera MD, PhD Health & Territory Research (HTR), Universidad de mcumbrera@us.es --Has Université, Rheumatology BMS, Biogen, Manuscript details: received department, Paris, Celgene, honoraria for Janssen, participatin France. Galapagos, Gilead, Word count: 792/800
	Authors: Joanna Kedra, Pascal Claudepierre, René-Marc Flipo, Marco Garrido-Cumbrera, Françoise Alliot-Launois , Emilie Desfleurs, Laurent Grange, Laure Gossec Name Statu s Affiliation Email adress ORCI D numbe r Joanna Kedra MD, MS c Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et jkedra.pro@gmail.com 0000-e (AFLAR), 3183 Antirhumatismal 3535-Launois Lutte 0003-None Françoise Alliot-Association Française de francoisealliotlaunois@gmail.com None UCB. interest Pharma Aventis, Disclosures of Sevilla, Spain -g in EMAS Lilly, Novartis, Tables/figures: 2/2 scientific Pfizer, committee from Sanofi-Novartis Samsung Bioepis, References: 10/10
		de Santé Publique, Paris, France
	Emilie Desfleurs	Paris, France. PhD Medical Affairs, & HP.Sorbonne Malmaison, hospital, AP-Pharma, Rueil-Pitié-Salpêtrière Novartis	om emilie.desfleurs@novartis.c	Novartis employee
		Université, France
	Rheumatology Rheumatology department, Paris, department, France. Rheumatology department, Henri Mondor hospital, AP-HP, Université Paris Est Créteil, EA 7379 -EpiDermE, Créteil, France Grenoble Alpes University Hospital, Hôpital Sud, Échirolles, France & Association Française de Lutte Antirhumatismal Rheumatology department, Roger Salengro University e (AFLAR), study, no ethics committee approval was required in France. EMAS global results have Laurent MD, LGrange@chu-grenoble.fr None Grange PhD Pascal Claudepierr e MD, PhD pascal.claudepierre@aphp.f r Has received honoraria for participatin g in EMAS scientific committee from Novartis Pharma René-Marc Flipo MD, PhD renemarc.flipo@chru-lille.fr Has Paris, France received honoraria for Laure Gossec MD, INSERM, Institut 4528-Amgen, PhD Sorbonne Université, laure.gossec@aphp.fr 0000-0002-research grants: already published.
		hospital, Lille Pierre Louis	310X	participatin Galapagos,
		University, Lille, France d'Epidémiologie et de Santé Publique,		Janssen, g in EMAS scientific Lilly, Pfizer,

Source(s) of support in the form of grants or industrial support: This study was funded by Novartis Pharma. Corresponding author: Joanna Kedra, MD, MSc, Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, and Pitié-Salpêtrière hospital, AP-HP.Sorbonne Université, Rheumatology department, 47-83, boulevard de l'Hôpital -75013 Paris France Email: jkedra.pro@gmail.com Tel : +33 1 42 17 84 21 Fax : +33 1 42 17 79 59

Ethics and consent: All patients agreed to their participation through informed opt-in consent. In this study, no clinical trial was conducted. As it was not an interventional

Table 1 .

 1 Factors associated with major impact on quality of life (i.e. having at least two domains impacted by axSpA)

	Variable	Mean (SD)	Mean (SD)	p-value for	p-value for	OR (95% CI) for
		or N(%) in	or N(%) in	univariable	multivariable	multivariable
		population	population	analysis	analysis	analysis
		with ≥ 2	with < 2
		impacts	impacts
	Age	41.5 (±10.9)	42.2 (±12.9)	0.62	N/A	N/A
	Gender	461 (80.2)	36 (57.1)	<0.001	0.002	2.6 (1.4, 4.8)
	(Female)
	Educational	295 (51.3)	19 (30.2)	0.002	0.029	2.0 (1.1, 3.3)
	level (high
	school or less)
	Married/living in	370 (64.3)	42 (66.7)	0.72	N/A	N/A
	couple (Yes)
	Employment	268 (48.7)	13 (21.0)	<0.001	0.001	3.3 (1.7, 10.0)
	status
	(Unemployed)
	Smoking (Yes)	188 (32.7)	19 (30.2)	0.68	N/A	N/A
	Symptoms	14.3 (±10.5)	13.5 (±9.3)	0.57	N/A	N/A
	duration
	bDMARD (Yes)	366 (64.6)	22 (36.7)	<0.001	<0.001	3.0 (1.6, 5.3)
	Comorbidities (0	1.4 (±1.2)	0.7 (±1.1)	<0.001	0.001	1.6 (1.2, 2.2)
	to 4, odds ratio
	for one
	additional
	comorbidity)

Acknowledgements:

The authors acknowledge the EMAS working group. The EMAS project is a collaboration led by the Health & Territory Research group of the University of Seville, the Ankylosing Spondylitis International Federation, and a steering committee composed of patient representatives and internationally recognized rheumatologists, psychologists, and researchers specialized in axSpA.

Novartis funded this study and participated in the study design, research, analysis, data collection, interpretation of data, reviewing, and approval of the publication. All authors had access to relevant data and participated in the drafting, review, and approval of this publication.