Environmental gradients shape the structure and carbon-related potential metabolisms of the prokaryotic communities in deltaic wetlands

Environmental gradients shape the structure and carbon-related potential metabolisms of the prokaryotic communities in deltaic wetlands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental gradients shape the structure and carbon-related potential metabolisms of the prokaryotic communities in deltaic wetlands Javier Miralles-Lorenzo, Antonio Picazo, Carlos Rochera, Daniel Morant, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7998527/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mediterranean deltaic wetlands play an important role in the carbon cycle due, in part, to the metabolic capacities of their prokaryotic communities. Nonetheless, these wetlands are very diverse and show different environmental characteristics. This work surveyed the structure and carbon-related metabolisms of the prokaryotic communities inhabiting three representatives of the wetlands from the Ebro River Delta, one of the biggest deltas in the Mediterranean. These wetlands are embedded in a strong salinity gradient and experience different levels of eutrophication. These factors were expected to influence the structure and potential carbon-related metabolisms of the prokaryotic communities. The most saline wetlands shared somewhat similar prokaryotic communities, which differed from those of the freshwater wetland. Water communities were also affected by the trophic status. Actual rates and potential (inferred) photosynthesis showed a linear relationship though this was not found between actual and potential respiration. The potential for methanogenic activity was kept along the salinity gradient, but methane production was controlled by increased salinity favoring instead dissimilatory sulphate reduction in the most saline wetlands at the expense of methanogenesis. Further, the abundance (and potential activity) of aquatic bacteria related to methane consumption modulated the final methane emissions of the studied deltaic wetlands. The water co-occurrence networks showed more complexity than those of the sediment networks, which is related to the higher environmental fluctuations in water, while sediment communities were more resilient in a more stable environment. Our results show the influence of the environmental drivers on the complex prokaryotic interactions that determine the carbon fluxes in deltaic wetlands. Deltaic wetlands trophic status microbial carbon-related metabolisms GHG methane emissions prokaryotic metabolic interactions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Wetlands play a major role in the global biogeochemical cycles, being actively involved in the main steps of carbon metabolism, as they can be a source of carbon-greenhouse gases (C-GHG) such as carbon dioxide (CO 2 ) and methane (CH 4 ) but they also behave as carbon sinks, showing strong carbon fixation capacities [ 1 – 7 ]. The biological communities that inhabit wetlands show metabolisms associated with atmospheric CO 2 fixation, such as photosynthesis, and others associated with C-GHGs emission, such as aerobic respiration, sulphate-reduction, and methanogenesis [ 2 – 5 ]. The behaviour of the different Mediterranean wetlands as net carbon sinks or emitters depends on the balance between the metabolisms that fix carbon and those releasing it. Different environmental drivers, such as salinity, seasonality, temperature, and the conservation status of wetlands, influence the rates of these metabolic processes [ 3 – 5 ], and the restoration of ecosystem processes can return these rates to natural levels [ 7 ]. Information on the dynamics of these metabolisms in Mediterranean wetlands is limited, especially considering the high regional diversity of wetland types [ 8 – 9 ], but highly relevant to understand the role of Mediterranean wetlands in the carbon cycle under the ongoing climate change scenario. River deltas are a unique and relevant type of ecosystem, being the transition zone between freshwater and seawater. In deltaic environments different types of wetlands appear, such as salt marshes, estuaries, brackish lagoons, or flooded forests [ 10 ]. Microbial communities that inhabit these wetlands play a strong role in their functioning, powering the main biogeochemical cycles and supplying nutrients for both plants and animals [ 11 ]. Consequently, the factors affecting the microbial communities of these ecosystems, particularly the groups of prokaryotes that participate in the different stages of the carbon cycle, have a significant impact on the carbon balance of the wetlands and their possible behaviour as net carbon sinks or emitters. The emissions of carbon-GHG in wetlands result from a balance between the metabolisms that participate in their production and consumption. At the microbial level, heterotrophic microorganisms produce the majority of CO 2 . In the presence of oxygen, aerobic respiration predominates over the other types of respiration and is responsible for the remineralization of most of the organic matter; therefore, the role of this metabolism in the carbon cycle is extremely significant [ 12 ]. Regarding CO 2 fixation, this is primarily carried out by light-dependent metabolisms that, among prokaryotes, can only be performed by specific groups. Among them, cyanobacteria stand out for their abundance and activity, being capable of performing oxygenic photosynthesis, although CO 2 fixation can also be performed, to a lesser extent, by other minoritarian microbial metabolisms [ 13 – 17 ]. On the other hand, methane emissions also result from a complex balance between the methane production by methanogenic archaea and methane consumption by aerobic prokaryotes, particularly methanotrophic bacteria, or by anaerobic prokaryotes, such as ANME archaea [ 18 – 19 ]. Methanogenic archaea, which are strict anaerobes, function as terminal decomposers in anaerobic environments [ 20 ] and couple the production of methane (methanogenesis) through different metabolic pathways to the obtention of energy. There are three major methanogenic pathways [ 21 ]: (1) methylotrophic methanogenesis, which may or may not rely on hydrogen and uses methylated substrates such as methylamines; (2) hydrogenotrophic methanogenesis, which is based on the reduction of CO 2 by hydrogen; and (3) aceticlastic methanogenesis, whose main substrate is acetate. All methanogens share an enzyme known as mcr (methyl-coenzyme M reductase). This enzyme catalyses the final step of methanogenesis, which is the release of methane, and is widely used as a gene marker to study the diversity and transcriptional activity of methanogens [ 20 – 22 ]. Nonetheless, the relative importance of the three methanogenic pathways changes across ecosystems. Most methanogenic pathways are inhibited by competition for metabolic substrates or electron donors between methanogenic archaea and sulphate-reducing bacteria (SRB), which are better competitors than methanogenic archaea in high-salinity (sulphate-rich) environments. Therefore, in saline ecosystems, methylotrophic methanogenesis predominates over other methanogenic pathways because it is the only type that does not compete with SRB for substrates [ 23 ]. Methanotrophic bacteria, on the other hand, can use the methane produced by methanogens as a carbon and energy source [ 24 ]. These microorganisms can consume methane at high rates [ 25 ], so their role in controlling methane emissions in wetlands may be significant. One of the main environmental features in deltaic wetlands is the salinity gradient, which strongly affects the structuration of the microbial communities that inhabit them [ 26 – 27 ]. Nonetheless, other environmental factors also exert a strong influence in the deltaic microbial communities. For example, trophic status can determine the structure of prokaryotic communities in coastal systems [ 28 ], and temperature and temporal variation also affect the microbial populations [ 29 – 30 ]. The effect of the environmental factors on the structure and, by inference, the functioning, of the prokaryotic communities, can be assessed through the sequencing of the 16S rRNA gene, a widespread procedure in microbial ecology which has led to a profound understanding of prokaryotic communities and the factors that govern their structure and activity [ 31 ]. Different bioinformatic tools, derived from the sequencing of this gene, have been developed to decipher their metabolic potential or to delve deeper into the interactions between microorganisms. One example is PICRUSt2 [ 32 ], which can infer metabolic functions through the analysis of the prokaryotic taxa that form the microbial communities, providing a general idea about their potential metabolisms. Also, the interactions between community members are important to understand the roles played by the different prokaryotic groups in the community. One way to assess these interactions is by constructing co-occurrence networks, which detect significant co-presence or mutual exclusion between the different prokaryotic members of a community [ 33 ]. Our work focuses on the drivers that influence the structure and the potential carbon-related metabolisms of the prokaryotic communities that inhabit the water and sediment of three different types of wetlands in the Ebro River Delta. We hypothesize that the high diversity of wetlands coexisting in the same deltaic system should be reflected in the diversity, structure and function of the prokaryotic communities that inhabit the water and sediment of the different wetland types. These wetlands were embedded in a strong salinity gradient and showed different trophic status, which affected the structure of the prokaryotic communities and modulated the actual and the predicted relevance of the different prokaryotic metabolisms involved in the C-GHG emissions. Our results show the importance of combining molecular procedures and in situ metabolic measurements for a broader understanding of the role of prokaryotic communities in the carbon fluxes of deltaic wetlands. Methods Study sites Three representative sites of the three main wetlands of the Ebro River Delta (Catalonia, Spain), which in turn are representative of the main Mediterranean coastal wetland types, were selected as pilot sites for the study. These wetlands were sampled during the winter, spring, and summer of the 2016-17 hydrological cycle. The location and main characteristics of the studied wetlands were described by Morant et al. [ 4 ]. Briefly, and following a decreasing salinity gradient, Alfacs marshes (ALFA) are microtidal salt marshes partially separated from the Mediterranean Sea by a narrow sandy beach. They are a mosaic of permanent and temporary flooded areas covered by meadows of Salicornia spp. Encanyissada (ENCA) is a brackish coastal wetland whose depth is controlled by a network of artificial channels that indirectly connect it to the sea and to the Ebro River. Hydrological inputs are highly variable, and this wetland has strong conductivity variations, but normally presents brackish waters. On the other hand, Filtre Biològic (FBIO) is a freshwater wetland recovered on 43 ha. plot previously occupied by rice fields. Currently, this wetland acts as a biological filter reducing, by natural processes, the nutrient concentrations coming from surrounding rice fields. Water inputs and outputs in FBIO are also regulated, maintaining a regular water depth. Sampling and physical and chemical analysis For each wetland and sampling time, all the samples were obtained at the same point. The water samples were collected in sterile bottles, and the water for DNA extraction was kept cold until arrival at the laboratory and then filtered through 0.22 µm pore size polycarbonate membranes filters (Nucleopore, Whatman), which were then kept at -80ºC until DNA extraction. For dissolved nutrient analysis, the water was filtered with Whatman GF/F glass microfiber filters, and the filtered water was kept frozen until further analysis. Sediment samples from a depth of 5–10 cm were obtained in triplicate at each sampling point and were placed in sterile plastic pots, which were kept cold until they were processed in the laboratory within the following 24 h. Each triplicate was homogenized by mixing with a metal rod. Samples for DNA extraction were placed in sterile 1.5 mL Eppendorf tubes which were maintained at -80ºC until DNA extraction. For DNA extraction, about 300 mg of sediment per sample were used. Water conductivity, dissolved oxygen (DO) and temperature were measured in situ, using a multiparameter probe WTW Multi 3410 logger. The specific probes used were WTW Tetracon® 925 IDS for conductivity and WTW FDO® 925 IDS for dissolved oxygen and temperature. A salinity correction was automatically applied (if necessary) to DO measurements. The pH was measured with a Crison Basic-20 pH-meter. Maximum depth was obtained using a limnimeter at the deepest point of the lakes. The analysis of dissolved inorganic nutrients and the other water environmental variables was carried out mostly following the standard methods [ 34 ] (see Suppl Mat. for further details). Chl- a concentration was determined by HPLC [ 35 ]. For the sediment physical-chemical variables, the different measurements were carried out in triplicate. Sediment conductivity and pH were measured by 1/5 dilution of the sediment with distilled water [ 36 ]. Conductivity was measured with a WTW LF-191 conductivity meter, while pH was obtained with a Crison Basic-20 pH meter. The proxy of organic matter content (LOI) and the carbonate content of the dried sediments were, respectively, obtained by the loss on ignition method [ 37 ]. For the studied wetlands, plankton and benthos metabolic rates were determined following Morant et al. [ 4 ], and methane emissions were determined using the procedure described in Camacho et al. [ 3 ] (see Suppl. Mat. for further information). DNA extraction, sequencing, and taxonomic assignment DNA extraction of water filters and sediment samples was performed with the EZNA soil DNA isolation kit (Omega Bio-Tek, Inc., Norcorss, GA, United States) following the supplier’s instructions. The sequencing of the V4 region of the 16S rRNA gene was carried out with the primers 515f/806r [ 38 ] in the Illumina MiSeq System (2x250 bp) according to the facilities and protocols of the RTSF-MSU (Michigan State University, USA). For further information, see Suppl. Mat. The raw sequence data of this study was deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA595160. Raw sequences were processed using the UPARSE pipeline [ 39 ]. After the merging of read pairs, sequences were filtered by a maximum expected error of 0.5, and chimeric reads were removed by the UCHIME algorithm. The filtered sequences were clustered in Zero-radius Operational Taxonomic Units (ZOTUs), which are sequences at 100% identity. Taxonomic assignment was performed with SINA aligner v.1.2.11 with the SILVA 138.1 reference database. ZOTUs which had low alignment scores (< 90%) were filtered, and sequences classified as mitochondria and chloroplasts were removed. The resulting ZOTU table consisted of 3260 ZOTUs for 18 samples. Rarefactions were performed separately for the water and sediment samples. To avoid the loss of less abundant ZOTUs, rarefactions were repeated 100 times [ 40 ] and then unified into two average ZOTU tables, with a minimum threshold of 7723 reads/sample for water and 12177 reads/sample for sediment. Both Silva 138.1 and RDP 11.5 were used for taxonomic assignment, using the RDP classifier 2.13 tool [ 41 ]. Metabolic potential of the prokaryotic communities The bioinformatic tool PICRUSt2 (phylogenetic investigation of communities by reconstruction of unobserved states) [ 32 ] was used to describe the potential metabolisms that could be performed by the prokaryotic communities from the water and sediment. To do this, we used the raw ZOTU table prior to the filtering and rarefaction, as PICRUSt2 has its own normalization procedure. A selection of the inferred genes that participate in the carbon-related metabolisms measured in the field was performed. The inferred genes for carbon-related metabolisms were the psbA gene for photosynthesis [ 42 ], the coxA gene for aerobic respiration [ 43 ], the mcrA gene for methanogenesis [ 43 – 44 ] and the pmoA gene for aerobic methanotrophy [ 44 ]. For the sulphur-related metabolism, the selected gene was dsrB for dissimilatory sulphate reduction [ 45 ]. In addition, the inferred presence or absence of psbA , pmoA , dsrB and mcrA marker genes in each of the ZOTUs was also determined. Prokaryotic co-occurrence networks For the water and sediment, two different co-occurrence networks were constructed based on the rarefied ZOTU tables through CoNet software [ 33 ] with Silva 138.1 taxonomic assignment. In the networks, each ZOTU was assigned a specific level for each of the factors “wetland” (three levels: ALFA, ENCA and FBIO) and “season” (three levels: winter, spring and summer) if its abundance in any of these specific levels was greater than 70% of its total reads. The ZOTUs that did not reach a minimum abundance of 70% of its total reads in any level of the factors were divided into two classes. On the one hand, if within the wetland factor, a ZOTU had reads at the three levels of that factor, though less than an abundance of 70% of its total reads in any specific level of the factor, it was considered as Cosmopolitan. If the same was true for the season factor, having a ZOTU reads in all the three seasons but with an abundance lower than 70% of its total reads in any specific level of the season factor, it was considered to be present throughout the year (Core). On the other hand, if a ZOTU had reads only at one or two of the levels of wetland or season factors, but not in all the three levels, it was considered as not cosmopolitan/not core (NC). Only ZOTUs with a minimum abundance of 20 reads for water and 50 for sediment were considered for constructing the networks. Co-occurrence and co-exclusion relationships between network nodes were derived with different metrics: Pearson correlation and Bray-Curtis dissimilarity for the water, and Pearson correlation, Mutual information and Bray-Curtis dissimilarity for the sediment. Environmental data were included in the analysis. Up to 1000 top and bottom edges were considered. The significance of the edges was assessed through a combination of permutation and bootstrap distributions generated with 100 iterations and enabling renormalization to avoid the compositionality bias and thus mitigate the establishment of spurious correlations between nodes. The final edge significance was obtained by fusing the p-value of the edges of each metric with Brown's method, and with a multiple testing correction using the Benjamini-Hochberg method. The final network was then visualized with the yfiles organic layout from Cytoscape network visualizing software [ 46 ]. In these general networks the clustering coefficient was determined, which gives a measure of the complexity of the network [ 47 ], and also the modularity (with the MCL algorithm), which reports the degree to which a network is divided into different subnetworks or modules, which are made up of members that have more relationships with each other than with the members of the other modules [ 48 ]. Also, in these networks, for the genes psbA , pmoA and dsrB , the total degree was defined as the sum of the degrees (number of connections) of the individual nodes that presented each gene. The topological roles of the individual nodes that formed the networks were determined according to the simplified classification of Olesen et al. [ 49 ] (see Suppl Mat. for further details). Statistical analyses Statistical analyses were performed with Primer 7 and R software. Principal Coordinates Analyses (PCOs) were carried out with the Euclidean distance matrix obtained from the normalized environmental variables. For the water and sediments, the rarefied ZOTU tables were standardized and square root transformed prior to obtaining their Bray-Curtis similarity matrices. A heatmap analysis (gplots R package) was performed for water and sediment prokaryotic communities based on the Bray-Curtis matrices. Only ZOTUs with an abundance in any sampling time greater than 0.5% for the water and greater than 0.3% for the sediment were considered. For the water and sediment, the statistical differences between the prokaryotic communities of the different wetlands were tested with a PERMANOVA analysis (999 permutations) [ 50 ] based on the Bray-Curtis similarity matrices. These matrices were also employed to carry out a distance-based Redundancy Analysis (dbRDA) [ 51 ] to observe the effect of the environmental variables on the ordination of the water and sediment prokaryotic communities. Further than environmental variables, in the water matrix, the data on plankton GPP and respiration rates were also included. Similarly, in the sediment matrix, the data on benthos GPP and respiration rates were also included. The values of these rates, along with those of the methane emissions rates, are those given in Morant et al. [ 4 ]. Results Environmental characteristics of the deltaic wetlands The values of the most relevant environmental variables for water and sediment in the studied wetlands are given in Table 1 , and the rest of the values for the other environmental variables are given in the Supplementary Tables 1–2. Table 1 Average values (± SD) of the most relevant environmental variables of water and sediment of the studied wetlands. Sediment values of organic matter (LOI) are expressed as percentage of dry weight (% d.w.). ALFA (Alfacs), ENCA (Encanyissada), FBIO (Filtre Biològic). ALFA ENCA FBIO Water Conductivity (mS·cm − 1 ) 53.4 ± 16.3 32.1 ± 20.1 2.1 ± 0.3 Chlorophyll- a (mg m − 3 ) 4.6 ± 2.9 33.4 ± 27.5 28.5 ± 6.8 Sulphate (g·L − 1 ) 3.4 ± 0.5 2.4 ± 0.9 0.1 ± 0.1 Sediment Conductivity (mS·cm − 1 ) 45.8 ± 15.4 36.2 ± 16.8 1.7 ± 0.7 LOI (% d.w.) 34.2 ± 25.0 28.1 ± 10.9 3.9 ± 0.7 The studied wetlands were located along a marked salinity gradient. In the water, the highest average conductivity values were found in Alfacs (53.4 mS·cm − 1 ) followed by Encanyissada (32.1 mS·cm − 1 ) which, however, showed a large variation in conductivity values throughout the different sampling dates, while Filtre Biològic had the lowest values (2.1 mS·cm − 1 ) that were quite stable. Encanyissada and Filtre Biològic showed a higher trophic status than Alfacs, a microtidal and more oligotrophic area fed mainly with marine waters, with higher average Chl- a concentrations for the former of 33.4 and 28.5 µg·L − 1 respectively. In parallel with the conductivity, the average sulphate concentrations were higher in Alfacs and Encanyissada (3.4 and 2.4 g·L − 1 respectively) and very low in Filtre Biològic (0.1 g·L − 1 ). The conductivity pattern was the same in the sediments as in the water, with Alfacs having the highest average values (45.8 mS·cm − 1 ) and Filtre Biològic the lowest (1.7 mS·cm − 1 ), while the highest average values of organic matter in the sediments were also observed in Alfacs (34.2%) due to the accumulation of rests of halophytic plants, and the lowest in Filtre Biològic (3.9%). Regarding the ordination of the water samples with respect to the values of environmental variables (Fig. 1 -A), salinity was the most important factor in separating the wetlands, as Alfacs and Encanyissada were influenced by high conductivity but also by high sulphate concentrations, unlike Filtre Biològic. In addition, seasonality was also a relevant factor since, in each wetland, the summer sampling was separated from winter and spring samplings and related to higher temperatures. In the sediment (Fig. 1 -B), Alfacs and Encanyissada overlapped in the ordination but were separated from Filtre Biològic, as they presented higher values of conductivity and organic matter, while the values of these variables in Filtre Biològic were low. In turn, seasonality was also relevant in the sediment, since in each wetland the spring and summer samplings were separated from the winter ones. Structure and seasonal dynamics of the prokaryotic communities Water and sediment prokaryotic communities showed different structure patterns of their prokaryotic communities (Figs. 2 and 3 ). In the water (Fig. 2 -A), 203 cosmopolitan ZOTUs were present in all the wetlands. Alfacs showed the highest number of specific ZOTUs, followed by Filtre Biològic and Encanyissada. Filtre Biològic showed little relationship with the more saline wetlands, as it shared very few ZOTUs with Alfacs and Encanyissada, while the latter had 487 ZOTUs in common. On the other hand, 575 ZOTUs appeared in all seasons. Winter was the season with the most specific ZOTUs, followed by summer and spring. In turn, the winter and spring samples were more closely related to each other than to the summer sample, as both shared more ZOTUs (399) than each of them with the summer sample. In the sediment (Fig. 2 -B), 253 cosmopolitan ZOTUs appeared in all the studied wetlands. Compared to the water, the effect of the salinity gradient was more evident, as Filtre Biològic shared very few ZOTUs (33) with Alfacs, the saltiest wetland, while Alfacs and Encanyissada shared 845 ZOTUs. Moreover, the number of ZOTUs that were present in the sediments in all the seasons was higher than in the water, with 1831 ZOTUs, showing higher temporal resilience, and summer was the season that shared the fewest ZOTUs with the others, especially with winter, to which it had just 90 ZOTUs in common. On the other hand, taxonomic classification of the amplicons of the gene coding for 16S rRNA resulted in 43 phyla for the water and 54 for the sediment (Fig. 3 ). In the water (Fig. 3 -A), the most abundant phylum was Cyanobacteria, the Cyanobiaceae family being present along the entire salinity gradient but presenting the highest relative abundances during the warmer seasons in the wetlands with the highest trophic status. This family was responsible for 25.1% of the normalised reads in summer in Encanyissada and 46.1% of the reads during spring in Filtre Biològic. The other most abundant phyla were Proteobacteria, mostly represented by the family Rhodobacteraceae , which was present along the entire salinity gradient, and Actinobacteriota, due to the high abundance of the family Microbacteriaceae in Encanyissada during the winter (34.5% of the normalised reads). In contrast, in the sediment (Fig. 3 -B) there were no taxa with a high relative abundance. The most represented phyla were Desulfobacterota, with the Desulfosarcinaceae family being more abundant in the more saline wetlands, and Chloroflexi, with the Anaerolineaceae family presenting the highest abundance in Encanyissada and Filtre Biològic (from 2.7 to 10.6% of the normalised reads), which were the wetlands with intermediate or low conductivities. Regarding the statistical differences between the prokaryotic communities of the different wetlands, the PERMANOVA analysis showed that in the water there were significant differences (p < 0.05) between the communities of Filtre Biològic and Encanyissada-Alfacs, but not between those of Encanyissada and Alfacs. On the other hand, in the sediment there were significant differences (p < 0.05) between the communities of all the studied wetlands. As for the relationship between the different wetlands based on their communities (Figs. 4 and 5 ), both in water and sediments of the more saline wetlands, Alfacs and Encanyissada, were part of a saline wetland cluster separated from the other cluster, formed only by Filtre Biològic, the freshwater wetland. In the water (Fig. 4 ), the different seasonal samplings of each wetland showed large differences in the composition of the prokaryotic community. In Filtre Biològic, the spring and summer samplings were grouped together and differed from the winter sampling, more evidently for the summer sample, as they presented a higher relative abundance of cyanobacterial ZOTUs classified as Cyanobium , while in the winter sampling the ZOTUs belonging to the Rhodobacteraceae and Alcaligenaceae families (both in phylum Proteobacteria) had a higher abundance. On the other hand, the different samples of Alfacs and Encanyissada overlapped and did not form a group specific to each wetland. This overlap between the communities of the two wetlands occurred because their summer samples formed a cluster due to the high abundance of a cyanobacterial ZOTU classified as Synechococcus . In addition, the Encanyissada samplings and the Alfacs summer sampling were characterised by the high relative abundance of actinobacterial ZOTUs classified as Microbacteriaceae and PeM15, while in the rest of the Alfacs samplings periods the most abundant ZOTUs were from the families Cyanobiaceae (phylum Cyanobacteria), Rhodobacteraceae (phylum Proteobacteria) and the NS3a marine group (phylum Bacteroidota). In contrast to the water, in the sediment (Fig. 5 ) the different samplings of each wetland were more stable over time and did not show large seasonal changes. This led to a good differentiation of the wetland according to their prokaryotic sediment communities, as there was no overlap between Alfacs and Encanyissada. In Filtre Biològic, the ZOTUs with the highest abundance belonged to the genus of proteobacteria Acinetobacter and to the family Anaerolineaceae (phylum Chloroflexi). On the other hand, the sulphate-reducing bacteria families (belonging to the phylum Desulfobacterota) were found in the more saline wetlands and not in FBIO. One of the most abundant ZOTUs in Encanyissada belonged to the archaeal family Nitrosopumilaceae (phylum Crenarchaeota), while in Alfacs the most abundant ZOTUs belonged to the family Cyanobiaceae (phylum Cyanobacteria) and to the genus Sulfurovum (phylum Campilobacterota). Methane-related prokaryotic taxa Regarding the distribution of methanogenic archaea and bacteria related to methane consumption in the studied sites (Fig. 6 ), different patterns related to the salinity gradient were observed. Sediment methanogenic archaea (Fig. 6 -A) showed the highest relative abundance in Alfacs, while Encanyissada and Filtre Biològic displayed similar levels. The family Methanomasiliicoccaceae , which performs methylotrophic methanogenesis, was the most abundant family in all the wetlands. Alfacs and Encanyissada showed a very similar community of methanogens, with the families Methanoregulaceae (capable of hydrogenotrophic methanogenesis) and Methanosarcinaceae (which can perform all the types of methanogenesis) being the most important non-majority families in both sites. In contrast, the families Methanopyraceae (capable of hydrogenotrophic methanogenesis) and Methanobacteriaceae (capable of methylotrophic or hydrogenotrophic methanogenesis) were only found in Filtre Biològic. As for bacteria related to methane consumption (Fig. 6 B-C), different patterns were found between the water and sediment. In the water (Fig. 6 -B), Filtre Biològic showed the highest relative abundance, followed by Alfacs and Encanyissada, the latter being the wetland with the lowest relative abundance of bacteria related to methane consumption in its water and the highest methane emission. Alfacs and Encanyissada showed similar communities dominated by the Methylococcaceae family, while Filtre Biològic, apart from presenting families that also exist in the wetlands with higher salinity, showed a high relative abundance of the Methylocystaceae family, which was only present in this wetland. In the sediment (Fig. 6 -C), Alfacs and Encanyissada, the latter being the wetland with the highest relative abundance of bacteria related to methane consumption, showed similar communities dominated by the Methylococcaceae family, while in Filtre Biològic this family was also the most abundant, followed by the Beijerinckiaceae family. In situ carbon-related metabolisms and molecular inference The relationship between the rates of carbon-related metabolisms measured in situ and the molecular inference of the gene counts of their respective marker genes showed common patterns, but also differences between the water and sediment (Figs. 7 and 8 ). In both the water and sediment there was a significant correlation between gross primary production (GPP) rates and psbA inferred gene counts, but no relationship was found between aerobic respiration rates and coxA inferred gene counts (Figs. 7 and 8 , A-B). In the water, in the wetlands with a higher trophic status, a large increase in the ratio of GPP and respiration rates to the ratio of inferred gene counts of the respective marker genes was observed during the warmer months (Fig. 7 -C), resulting in a linear yet non-statistically significant correlation between both ratios (Fig. 7 -D). The correlation between these ratios also appeared for the sediments, though in this case it reached statistically significance, with the ratio for the inferred genes psbA/coxA decreasing along the salinity gradient (Fig. 8 C-D). On the other hand, the waters of Filtre Biològic showed the highest number of pmoA inferred gene counts, followed by Alfacs and Encanyissada, the latter showing the lowest number of pmoA inferred gene counts and the highest methane emission (Fig. 7 -E). However, no significant correlation was found between the ratio of the inferred gene counts of the dsrB and mcrA genes with conductivity, nor between the inferred gene counts of the pmoA and dsrB genes with conductivity or organic matter (Fig. 7 F-G-H) in water. In the sediment there was no correlation between methane emissions and the inferred gene counts of the mcrA gene (Fig. 8 -E), but instead a significant positive correlation was observed between the ratio of the inferred gene counts of the dsrB and mcrA genes with conductivity (Fig. 8 -F). In addition, for the sediments, the inferred gene counts of the dsrB gene increased significantly with both conductivity and organic matter, while the inferred gene counts of the mcrA gene remained constant regardless of conductivity or the amount of organic matter present (Fig. 8 G-H). Prokaryotic co-occurrence networks An analysis of the interactions of members of the water and sediment prokaryotic communities resulted in a water community network consisting of 340 nodes and 1259 edges (Supplementary Fig. 1) and a sediment community network consisting of 458 nodes and 1910 edges (Supplementary Fig. 2). The water network showed a clustering coefficient (reporting network complexity) of 0.357 and a modularity (reporting network compartmentalisation) of 0.827, while the sediment network showed a clustering coefficient of 0.315 and a modularity of 0.693, suggesting a higher complexity and compartmentalisation of the water network compared to the sediment network. Both networks showed a very low number of cosmopolitan ZOTUs present in all wetlands, 17 in the water network, which mainly belonged to families Cyanobiaceae , Beijerinckiaceae , Desulfobaccaceae and Clostridiaceae , and 12 in the sediment network, mainly corresponding to the families Anaerolineaceae , Thermoanaerobaculaceae and Desulfosarcinaceae . Therefore, the communities that formed the networks in the different wetlands were highly differentiated from each other. Concerning water, in Alfacs the ZOTUs with the highest degree belonged to families Flavobacteriaceae and Rhodobacteraceae , in Encanyissada to the families Chthoniobacteraceae and Rhodothermaceae , and in Filtre Biològic to the families Cyanobiaceae and Chthoniobacteraceae . With respect to the sediment, in Alfacs the ZOTUs with the highest degree belonged to families Desulfobulbaceae and Thermoanaerobaculaceae , in Encanyissada to the families Nitrosopumilaceae and Thermoanaerobaculaceae and in Filtre Biològic to the families Sphingomonadaceae and Gemmatimonadaceae . In addition, the two networks showed ZOTUs present only in specific seasons and ZOTUs present during all the seasons (Supplementary Fig. 3), the latter being much more abundant in the sediment network than in the water network. With respect to water, the ZOTUs with the highest degree belonged to the families Woeseiaceae and Sandaracinaceae in winter, to the families Flavobacteriaceae and Rhodobacteraceae in spring, and to the families Cyanobiaceae and Beijerinckiaceae during summer. Regarding the sediment, the ZOTUs with the highest degree belonged to the families Flavobacteriaceae and Sulfurovaceae during winter, to the families Sulfurovaceae and Sulfurimonadaceae in spring and to the families Nitrosopumilaceae and Anaerolineaceae in summer. Also, in the water network, ZOTUs specific to Alfacs and present throughout the year in this wetland were negatively related to Chl- a (mainly belonging to families Rhodobacteraceae and Cyanobiaceae ) while ZOTUs specific to Filtre Biològic, and also present in all the seasons (mainly belonging to families Desulfobaccaceae and Chromatiaceae ), showed a negative relationship with conductivity. In the sediment network, ZOTUs specific to Filtre Biològic, and present in all the sampling times, were also negatively related to conductivity and organic matter. These ZOTUs belonged mainly to families Sphingomonadaceae , Gemmatimonadaceae and Nitrososphaeraceae . With regard to the abundance and total degree of ZOTUs with inferred genes related to photosynthesis (with psbA gene), dissimilatory sulphate reduction ( dsrB gene) and aerobic methane oxidation ( pmoA gene) (Fig. 9 ), in the water network (Fig. 9 A-B), we observed that the vast majority of ZOTUs with psbA gene were wetland-specific, whereas few ZOTUs were present in all the wetlands (cosmopolitan), which belonged to family Cyanobiaceae . In Alfacs and Filtre Biològic, ZOTUs with the psbA gene were present in all seasons (core), but the vast majority of ZOTUs with this gene were seasonal and more abundant in the warmer seasons in Encanyissada and Filtre Biològic, the wetlands with a higher trophic status and higher GPP rates. On the other hand, the total degree of the inferred genes was defined as the sum of the degrees (number of connections) of the individual nodes that had these genes. Thus, the total degree of ZOTUs with the psbA gene was also higher in the warmer seasons in Encanyissada and Filtre Biològic. In contrast, ZOTUs with the pmoA gene were only found in Filtre Biològic, the least saline wetland, and mostly in ZOTUs present during all the seasons. However, the highest total degree of ZOTUs with this gene was observed in summer. With respect to the sediment (Fig. 9 C-D), ZOTUs with the psbA gene were only found in Alfacs, and the majority were ZOTUs present throughout the year, although ZOTUs with this gene were also observed in summer, their total degree being comparable to that of ZOTUs present in all seasons. On the other hand, some ZOTUs with the dsrB gene were found in all the wetlands (cosmopolitan), belonging mainly to family Desulfosarcinaceae . Also, there were ZOTUs with the dsrB gene appearing in all seasons (core). However, the abundance of these ZOTUs increased along the salinity gradient, with Filtre Biològic, the freshwater wetland, showing the lowest number of ZOTUs with the dsrB gene, and Alfacs being the one with the highest number of ZOTUs with this inferred gene. Moreover, the highest total degrees of ZOTUs with the dsrB gene were observed in Alfacs, especially in summer-specific ZOTUs. The ZOTUs with the pmoA gene in the sediment are not shown in the figure because they correspond to ammonium-oxidising archaea, which actually do not have this gene but rather the amoA gene. PICRUSt2 is not able to differentiate between these two genes due to their strong evolutionary similarity, and can wrongly assigns the pmoA gene to ammonium-oxidising archaea. Regarding the topological roles of nodes in the water and sediment co-occurrence networks (Supplementary Fig. 4), in both networks most nodes were classified as peripheral, and only module hubs were found in the sediment network, which were assigned to the family Sulfurovaceae and the phylum Gemmatimonadota (assignment to the last taxonomic rank defined). Peripheral nodes showed very different relative abundances, with some being highly abundant, belonging to families Rhodobacteraceae and Cyanobiaceae in water and families Moraxellaceae and Hydrogenophilaceae in the sediment, while others showed very low abundance. Nodes classified as module hubs also showed low relative abundances. In both networks, the nodes that presented genes related to carbon metabolisms were all peripheral, showing the taxonomic/metabolic diversity of the carbon metabolisms. In the water, the most abundant node possessed the psbA gene, and was assigned to the genus Synechococcus . In the sediment, the most abundant node, with a gene related to sulphur metabolism ( dsrB gene), belonged to the family Hydrogenophilaceae . Environmental gradients and ordination of the prokaryotic communities The salinity gradient was shown to be the most relevant factor in the ordination of the water and sediment communities in the wetlands (Fig. 10 ), although aquatic communities were also affected by seasonality. In the water (Fig. 10 -A), the Alfacs and Encanyissada communities were influenced by the high conductivity and high sulphate levels of these wetlands, and showed some overlap, while the Filtre Biològic communities were determined by low conductivity and sulphate concentrations, and they were clearly differentiated from the more saline wetlands. In all the wetlands, seasonality separated winter samples from spring and, especially, from summer samples, which were affected by higher temperatures and higher Chl- a concentrations. Further, higher plankton GPP rates were also observed in the wetlands with higher trophic status. On the other hand, the Alfacs communities showed higher abundances of members of the family Cyanobiaceae and halotolerant groups such as the NS3a marine group, while in Encanyissada communities of the genus Synechococcus , the family Saprospiraceae and the taxon PeM15 stood out. In addition, Encanyissada showed the highest methane emissions. On the other hand, in the Filtre Biològic communities, the genus Cyanobium , the family Rhodobacteraceae and the taxon GKS98 freshwater group were more abundant. In the sediments (Fig. 10 -B), the Alfacs and Encanyissada communities were also determined by high conductivity and organic matter levels, but unlike in the water, these wetlands were clearly differentiated from each other. Moreover, seasonality was not a relevant factor in the structuration of the sediment communities, since the samples from the different wetlands were very close to each other in the ordination, except for Encanyissada, where the summer sample was separated from the others. In Alfacs, the Desulfobacteraceae , and the Cyanobiaceae (benthic cyanobacteria forming biofilms in the sediment surface) families stood out and the highest benthos GPP rates were observed. In Encanyissada, the Nitrosopumilaceae (ammonia-oxidizing archaea) and Nitrosomonadaceae (ammonia-oxidizing bacteria) families and also the Desulfobacteraceae family were the most relevant, and this wetland also showed the highest methane emission rates. In contrast, the Filtre Biològic communities were affected by low conductivities and low levels of organic matter, with the Nitrososphaeraceae family and the genus Thiobacillus standing out. Discussion This work has studied the main factors affecting the structure and carbon-related metabolisms of water- and sediment-dwelling prokaryotic communities in three wetlands representative of the main Mediterranean deltaic wetland types, taking as examples those of the Ebro River Delta. The three wetlands are distributed along a salinity gradient and presented different trophic status, both factors determining the specific communities of each wetland and the relationships between the different groups of prokaryotes involved in the main carbon-related metabolisms. The prokaryotic communities in the water and sediment showed both similar though, somewhat, divergent patterns. As in other ecosystems framed by a salinity gradient [ 52 – 53 ], salinity was a very important factor in structuring this community. Thus, Alfacs and Encanyissada, the most saline wetlands, were more closely related to each other and shared more ZOTUs compared to Filtre Biològic, the wetland with less saline water. In contrast, the effect of seasonality was different in the water and sediment. The higher number of ZOTUs present repeatedly during all seasons in the sediment indicates that this matrix is more stable than water [ 54 ] and that sediment can buffer, more effectively than water, the seasonal changes in environmental variables affecting the communities, displaying a greater resilience to the temporal environmental changes. The greater stability of the sediment allows the presence of a community with little seasonal change, but also favours good differentiation between the communities of the different wetlands, which are determined by factors unaltered by seasonality. In contrast, the water communities are much more affected by seasonal changes and by the differences in the environmental drivers [ 55 ], which can be observed especially in Encanyissada. Human control of water flow can influence aquatic microbial communities [ 56 ]. The flow of water in Encanyissada is regulated by a network of irrigation channels, which causes large variations in the salinity of the water that end up producing an overlap between the prokaryotic communities of this wetland and those of Alfacs, which is highly influenced by seawater as it is connected to the sea following a microtidal pattern. In turn, the difference in the stability of the water and sediment is also reflected in the structuring patterns of their communities. In the water, large fluctuations in environmental variables create transient conditions that benefit opportunistic taxa that can respond quickly and end up being very abundant for a short period of time [ 57 ]. This is the case of some cyanobacteria, which can be classified as opportunistic taxa [ 58 ]. In the wetlands with a higher trophic status, especially in Filtre Biològic, which acts as a filter receiving the nutrient-rich water from the surrounding rice fields, the higher concentration of nutrients leads to the dominance of cyanobacteria in its aquatic communities, as it happens in Encanyissada during the warmer months. In contrast, in the sediment the temporal fluctuations of the environmental variables are lower, allowing the development of stable communities without strong dominance of specific taxa. As observed in other wetlands located along a strong salinity gradient of saline inland lakes [ 59 ], the Rhodobacteraceae family was abundant in the water along the entire salinity gradient. This family shows more than 300 species with different physiologies [ 60 – 61 ], which allows its members to thrive and be abundant in environments with such different salinities. In turn, salinity has been described as a highly relevant factor in the distribution of cyanobacteria [ 62 ], which could explain the differential distribution of cyanobacteria observed in the deltaic wetlands, with the genus Cyanobium being more representative in Filtre Biològic and the genus Synechococcus in the more saline wetlands. In the sediment, the sulphate-reducing bacteria (SRB) family Desulfosarcinaceae (phylum Desulfobacterota) was more abundant in the more saline wetlands, as it has been observed in other cases, where SRB were more active and abundant in highly saline ecosystems [ 63 ]. In contrast, the bacterial family Anaerolineaceae (phylum Chloroflexi) was more abundant in wetlands with intermediate to low conductivities. Members of the Anaerolineaceae family can establish syntrophic relationships with methanogenic archaea [ 64 – 65 ], and therefore a higher relative abundance of members of this family in the sediment of certain wetlands may be indicative of higher methanogenic activity of sediment methanogenic archaea. Methane emissions in the studied wetlands are higher in those with moderate or low conductivity (Encanyissada and Filtre Biològic) [ 4 ], indicating that the higher abundance of the Anaerolineaceae family in the sediment of these wetlands could be linked to higher methane production by methanogenic archaea. On the other hand, in the sediment of Encanyissada, there is a high relevance of Nitrosopumilaceae and Nitrosomonadaceae families. These ammonia-oxidizing prokaryotes can produce N 2 O, a very important greenhouse gas [ 66 ], and their metabolic activity may be relevant in the climate change mitigating potential of the wetlands, though we were unable to correlate with actual rates of N 2 O as these were not measured. Regarding the groups of prokaryotes involved in methane production and consumption, previous work in the same study sites showed that the total abundance of methanogens was highest in Alfacs, followed by Encanyissada and Filtre Biològic [ 4 ]. Furthermore, the differences in families of methanogens that have been observed among the more saline wetlands (Alfacs and Encanyissada), which show a similar methanogenic community, and the less saline wetland (Filtre Biològic), with a much more different community, may be associated with salinity, as this factor is one of the most important in the structuring of methanogenic archaeal communities [ 67 ]. On the other hand, the Methanomassiliicoccaceae family, which is the most abundant family of methanogens in the studied wetlands, belongs to the order Methanomassiliicoccales, which currently has only one isolated representative in pure culture, Methanomassiliicococcus luminyensis . This only produces methane from the reduction of methanol or methylamines with hydrogen [ 68 – 69 ]. The hydrogen dependence shown by this methanogen to generate methane contrasts with the high abundance of this family in Alfacs, the most saline wetland, where hydrogen-dependent methanogenic pathways will be restricted by the competitive activity of SRB, which is higher in saline environments [ 23 ]. However, because there is only one representative of this family in pure culture, it is likely that as more members of this family are cultured, other members with less hydrogen-dependent methanogenic pathways could be discovered, such as those capable of methylotrophic methanogenesis without hydrogen involvement, which is the most relevant methanogenic pathway in saline ecosystems because it is not outcompeted by SRB [ 23 ]. Therefore, in Alfacs and Encanyissada, a priori , most of the methane production should fall back on the minority groups of methanogens, especially the family Methanosarcinaceae , which can perform methanogenic pathways which do not depend on hydrogen, thus avoiding the competition with SRB. Nonetheless, the low methane emissions in Alfacs suggest that in the studied saline wetlands, despite the competition with SRB, most of the methane production is carried out by members of Methanomassiliicoccaceae family, which perform hydrogen-dependent methylotrophic methanogenesis, and that the minoritarian groups of methanogens with types of methanogenesis that do not compete with SRB are not very active, because if they were, the methane emissions in the studied saline wetlands would presumably be higher. On the other hand, in Filtre Biològic, where sulphate levels are low and the competition with SRB is weaker, all the methanogenic pathways, including the hydrogen-dependent, can be performed. Concerning bacteria related to methane consumption, the structure of their communities shows the same pattern as for methanogenic archaea, where the effect of salinity, one of the environmental factors that more controls the distribution of these microorganisms [ 44 , 70 ], is observed in the higher similarity of the communities of bacteria related to methane consumption between the most saline wetlands (Alfacs and Encanyissada), with Filtre Biològic being the wetland with the most different community. Regarding the relationship between the rates of gross primary production (GPP) and respiration with the gene counts inferred from the respective marker genes, when compared to inland saline lakes located along a strong salinity gradient [ 59 ], it can be observed that in both inland saline lakes and deltaic wetlands there is a good correlation between GPP rates and gene counts of the psbA gene (marker of oxygenic photosynthesis). However, in contrast to inland saline lakes, in deltaic wetlands there is no good correlation between respiration and gene counts of the coxA gene (marker of aerobic respiration). This indicates that the relationships between the actual and potential metabolism may vary between different types of Mediterranean wetlands. In the water, the correlation between the ratio of GPP and respiration and the ratio of psbA and coxA genes is not significant, because in the wetlands with a higher trophic status during the warmer months, their GPP rates show a high increase. However, in the sediment this correlation is significant. This demonstrates the deep effect of anthropogenic alterations on the metabolisms of aquatic communities [ 3 – 4 ], while sediment communities are less affected by such disturbances, more particularly when these are transient, such as the high supply of nutrients received by the Ebro Delta wetlands partly feed by rice fields runoff. Regarding methane emissions, these result from a complex balance between methane production and consumption. As explained before, in saline environments, where sulphate concentrations are high, SRB outcompete methanogenic archaea whose methanogenic pathways are hydrogen-dependent, because SRB have a higher affinity for the metabolic substrates needed by these archaea, and because dissimilatory sulphate reduction in general yields more energy than methanogenesis [ 23 , 71 ]. The results of our work show that in deltaic wetlands methane production is strongly regulated by salinity, since competition between SRB and methanogenic archaea favours one group of microorganisms or another depending on salinity levels. Thus, in Alfacs and Encanyissada SRB are favoured by high sulphate concentrations coming from seawater (which are linked to high salinity), so methane production would be very low, while in Filtre Biològic the opposite happens. Also, these lower methane emissions in the saline wetlands may indicate that the methane production in these wetlands falls back mainly in the members of Methanomassiliicoccaceae family, which produce methane from the reduction of methanol or methylamines with hydrogen [ 68 – 69 ], and that are outcompeted by SRB. Therefore, the methane emissions in Alfacs and Encanyissada are low. Nonetheless, despite that they are outcompeted by SRB, which have greater affinity of hydrogen, the members of Methanomassiliicoccaceae family are probably so abundant in the studied saline wetlands, especially in Alfacs, because these wetlands show a high percentage of organic matter in sediment, which may provide a constant supply of methylamines or methanol, as these compounds can come from the decomposition of plant-derived material [ 72 ], thus partially reducing the competence with SRB and explaining the methane emissions recorded, even if these are modest. On the other hand, in Filtre Biològic the low salinity levels (and thus low sulphate levels) allow for higher methane production by Methanomassiliicoccaceae family. However, this did not result into higher methane emissions in this wetland, as the results of our work show the important role of aquatic bacteria related to methane consumption in the final balance of methane emissions. Thus, the higher methane production in Filtre Biològic would allow a high relative abundance and high potential methanotrophy of the bacteria related to methane consumption in the water of this wetland, so that most of the methane produced by methanogens could be consumed by these bacteria, resulting in low methane emissions despite being a wetland with low saline water. However, in Encanyissada, although the potential methane production is lower due to its higher salinity, the low relative abundance and low methanotrophic potential of the aquatic bacteria related to methane consumption present in this wetland meant that the methane produced was barely consumed and could escape to the atmosphere. In contrast, in Alfacs, the synergy between low potential methanogenesis and the consumption by aquatic bacteria related to methane consumption of the small amount of methane that could be generated can explain the pattern of low methane emissions observed in this wetland. Therefore, this strong interdependence between different metabolic processes also explains the lack of correlation between methane emissions and the mcrA gene counts of the sediment of the studied wetlands. The overview of the interaction between environmental variables, potential metabolisms and prokaryotic communities shows different patterns between the water and sediment. Communities in the water are affected by a combination of salinity, trophic status and seasonality, while those in the sediment are mostly influenced by salinity but are much more stable as seasonal changes are much less evident in the sediment matrix. The lower capacity of the water to buffer environmental changes compared to the more resilient sediment, generates aquatic co-occurrence networks with many temporary ZOTUs and very few ZOTUs present throughout all the seasons. This may explain the greater complexity and compartmentalization of the co-occurrence network in the water, as high values of modularity are related to higher habitat heterogeneity [ 49 ], because the low stability of the water generates a heterogeneous environment that favours the presence of purely temporary communities of prokaryotes that respond to transient environmental conditions. In the sediment, however, temporal changes in environmental variables are less intense, thus generating more stable communities over time. On the other hand, in the water co-occurrence network of deltaic wetlands, the highest abundance of ZOTUs with the psbA gene, and therefore with photosynthetic capacity, is found in the wetlands with higher trophic status during the warmer months, which recorded higher water GPP rates [ 4 ], but in the sediment this gene is found only in Alfacs where microbial mats are common. This suggests a greater capacity to photosynthesize in the benthos of the deltaic wetlands with a low trophic status, explaining the higher GPP rates described in the benthos of Alfacs compared to the benthos of Encanyissada and Filtre Biològic [ 4 ]. ZOTUs with the dsrB gene, and therefore with the capacity to carry out dissimilatory sulphate reduction, were found mainly in the sediment of the most saline wetlands (Alfacs and Encanyissada), since their higher sulphate concentrations allow for greater SRB activity to the detriment of methanogenesis, while ZOTUs with the pmoA gene, i.e. related with the capacity to carry out aerobic methane oxidation, were only found in the water of Filtre Biològic, the wetland with the highest relative abundance of aquatic bacteria related to methane consumption, which in turn had low methane emissions. In conclusion, the results of our work show the influence of salinity and trophic status on the structure and carbon-related metabolisms of prokaryotic communities in the water and sediment of three wetlands representative of the main deltaic wetland types worldwide. Salinity determines the community organization and also methanogenesis in the sediment. Trophic status also influences the community structure and is linked to higher GPP rates. The complexity of the interactions between the structure and function of prokaryotic communities in deltaic wetlands underlines the importance of studies that combine in situ measurements of the rates of the main carbon-related metabolisms with molecular approaches to better understand the processes that determine carbon fluxes in this type of ecosystem. Statements and Declarations Funding This work was supported by the project CLIMAWET-CONS (PID2019-104742RB-I00) a, funded by the Agencia Estatal de Investigación of the Spanish government, and the project PROMETEO CIPROM-2023-031 funded by Generalitat Valenciana, both granted to Antonio Camacho. Javier Miralles-Lorenzo and Daniel Morant held an FPU Predoctoral Scholarship by the Spanish Ministry of Science, Innovation and Universities under grants FPU15/03930 and FPU16/01444. Competing interests The authors declare no competing interests. Acknowledgements We thank Raquel González for her help in the performance of the statistical analyses, as well as the Agencia Estatal de Investigación of the Spanish government for its financial support under contract PID2019-104742RB-I00, and to the Generalitat Valenciana for the funding of the project ECCAEL (PROMETEO CIPROM-2023-031), both granted to Antonio Camacho. Author contribution Javier Miralles-Lorenzo: conceptualization, data curation, formal analysis, investigation, methodology, software, writing – original draft, writing – review and editing. Antonio Picazo: conceptualization, data curation, formal analysis, investigation, methodology, software, supervision, validation, writing– review and editing. Carlos Rochera: data curation, investigation, methodology, supervision, validation. Daniel Morant: investigation, methodology. 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Narrowe AB, Borton MA, Hoyt DW, Smith GJ, Daly RA, Angle JC et al (2019) Uncovering the diversity and activity of methylotrophic methanogens in freshwater wetland soils. Msystems. https://doi.org/10.1128/mSystems.00320-19 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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07:16:46","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":752032,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/403b6db0f83141815978a9ea.png"},{"id":95934309,"identity":"7f71f34e-8902-4b32-95db-ea17f56a264e","added_by":"auto","created_at":"2025-11-14 15:10:20","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":202822,"visible":true,"origin":"","legend":"","description":"","filename":"882e42616ba249d48461a082d4f59d241structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/152b906cfb6f0ff6084d18d2.xml"},{"id":95934310,"identity":"4207f025-2218-46f3-9f69-33f49762f7a5","added_by":"auto","created_at":"2025-11-14 15:10:20","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217209,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/45b3620abec2b6d58a82d488.html"},{"id":95934273,"identity":"e6940ca1-3fde-4a38-8fd9-7262f798cb70","added_by":"auto","created_at":"2025-11-14 15:10:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77508,"visible":true,"origin":"","legend":"\u003cp\u003ePCO ordinations of the water samples (A) and sediment samples (B) according to the main environmental variables. Sites: ALFA (Alfacs), ENCA (Encanyissada), FBIO (Filtre Biològic). Season: Winter (▼), Spring (●), Summer (▲). Environmental variables: Chla (Chlorophyll-\u003cem\u003ea\u003c/em\u003e), Alk (Alkalinity), LOI (organic matter), NH\u003csub\u003e4\u003c/sub\u003e (ammonia), Oxy (Oxygen), Temp (Temperature), SRP (Soluble reactive orthophosphate), TSS (Total suspended solids), Cond (Conductivity), NO\u003csub\u003e3\u003c/sub\u003e (Nitrate), SO\u003csub\u003e4\u003c/sub\u003e (Sulphate), Depth (maximum depth), pH, and Carbonate.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/78c5c7866a52c54bd8decf21.png"},{"id":95934279,"identity":"49dc5c78-2dcf-4347-ae96-c7dc00d6c5a7","added_by":"auto","created_at":"2025-11-14 15:10:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":302271,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagrams showing the distribution of ZOTUs in water (A) and sediment (B) for factors wetland and season. ALFA: Alfacs. ENCA: Encanyissada. FBIO: Filtre Biològic.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/f4ef1a6d827b4b21f94fa502.png"},{"id":95934274,"identity":"38790b8b-57fd-412e-afc2-ec37f0563482","added_by":"auto","created_at":"2025-11-14 15:10:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97608,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of prokaryotic families, sorted by Phyla, in the water (A) and sediment (B) of the studied wetlands. Taxonomic assignment was obtained with SINA aligner v.1.2.11 with SILVA 138.1 reference database.. The families classified as minoritarian had a maximum relative abundance between 1-5%. Families grouped in others did not have a relative abundance higher than 1% in any sampling time. ALFA: Alfacs. ENCA: Encanyissada. FBIO: Filtre Biològic. 1: Winter. 2: Spring. 3: Summer.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/fc4e6f9dacc6dc3a2e776874.png"},{"id":95934275,"identity":"6059b0d0-f9c7-44bb-ab82-77e3778aa4c3","added_by":"auto","created_at":"2025-11-14 15:10:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157991,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the relationship of the water samples based on their prokaryotic community composition. Only ZOTUs with a total relative abundance higher than 0.5% were considered. ALFA: Alfacs. ENCA: Encanyissada. FBIO: Filtre Biològic. 1: Winter. 2: Spring. 3: Summer. ZOTUs of the genus \u003cem\u003eCyanobium\u003c/em\u003e are indicated with a light green circle and those of the genus \u003cem\u003eSynechococcus\u003c/em\u003e with a dark green circle.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/c87e5a683523d334b5eb96bc.png"},{"id":96243644,"identity":"578067dc-1a05-47e1-96bb-3a38ef8d702e","added_by":"auto","created_at":"2025-11-19 07:16:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119952,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the relationship of the sediment samples based on their prokaryotic community composition. Only ZOTUs with a total relative abundance higher than 0.3% were considered. ALFA: Alfacs. ENCA: Encanyissada. FBIO: Filtre Biològic. 1: Winter. 2: Spring. 3: Summer. ZOTUs of the phylum Desulfobacterota are indicated with a yellow circle and those of the family \u003cem\u003eAnaerolineaceae\u003c/em\u003e with a brown circle.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/944fd269456aecdac63a3f73.png"},{"id":95934276,"identity":"6296c75a-cdf1-41bb-8cc3-37e4a63aa268","added_by":"auto","created_at":"2025-11-14 15:10:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76874,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the average abundance of methane-related microorganisms in the studied wetlands. A: Relative abundance of methanogenic archaeal families in the sediment. Brown circle: hydrogenotrophic methanogenesis. Yellow circle: aceticlastic methanogenesis. Green circle: hydrogenotrophic methanogenesis. B: Relative abundance of methanotrophic bacteria families in water. The average methane emission (red triangle) of each wetland is also shown. C: Relative abundance of methanotrophic bacteria families in the sediment. In B and C, the taxonomy of the microorganisms at genus level has been indicated when the genus could be assigned. ALFA: Alfacs. ENCA: Encanyissada. FBIO: Filtre Biològic.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/f3a52fbbf6be14c5d87a0e46.png"},{"id":96243915,"identity":"5bd158b6-8021-4a07-9fc7-30aa7d0b3e8c","added_by":"auto","created_at":"2025-11-19 07:17:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":273171,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of metabolic rates with the results obtained with PICRUSt2 in water. In the regressions, the symbols representing the Alfacs samples are in red, those of Encanyissada in blue and those of Filtre Biològic in green. A: regression between gross primary production (GPP) rates and the inferred \u003cem\u003epsbA\u003c/em\u003e gene counts. B: regression between respiration rates and the inferred \u003cem\u003ecoxA\u003c/em\u003e gene counts. C: comparison of the ratios of GPP and respiration rates to the ratios of the inferred gene counts of the \u003cem\u003epsbA\u003c/em\u003e and \u003cem\u003ecoxA\u003c/em\u003e genes. D: regression between the ratios of the GPP and respiration rates with the inferred \u003cem\u003epsbA\u003c/em\u003eand \u003cem\u003ecoxA\u003c/em\u003e gene counts. E: Comparison between the average inferred gene counts of the \u003cem\u003epmoA\u003c/em\u003e gene and the average methane emission of the wetlands. F: regression between conductivity and the ratio of inferred \u003cem\u003edsrB\u003c/em\u003e(red line) and \u003cem\u003emcrA\u003c/em\u003e (blue line) gene counts. G: regression between organic matter (LOI) and the inferred \u003cem\u003edsrB\u003c/em\u003e (red line) and \u003cem\u003epmoA\u003c/em\u003e (blue line) gene counts. H: regression between conductivity and the inferred \u003cem\u003edsrB\u003c/em\u003eand \u003cem\u003epmoA\u003c/em\u003e gene counts. **: p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/11790e4e44cb9ccd3696788b.png"},{"id":96243674,"identity":"0f253f78-ec76-4de0-a3da-135f8b17a8e7","added_by":"auto","created_at":"2025-11-19 07:16:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":281724,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of metabolic rates with the results obtained with PICRUSt2 in sediment. In the regressions, the symbols representing the Alfacs samples are in red, those of Encanyissada in blue and those of Filtre Biològic in green. A: regression between gross primary production (GPP) rates and the inferred \u003cem\u003epsbA\u003c/em\u003e gene counts. B: regression between respiration rates and the inferred \u003cem\u003ecoxA \u003c/em\u003egene counts. C: comparison of the average ratio of GPP and respiration rates to the average ratio of inferred \u003cem\u003epsbA\u003c/em\u003e and \u003cem\u003ecoxA\u003c/em\u003e gene counts. D: regression between the ratios of the GPP and respiration rates with the ratio of inferred \u003cem\u003epsbA\u003c/em\u003eand \u003cem\u003ecoxA\u003c/em\u003e gene counts. E: regression between methane emission rates and the inferred \u003cem\u003emcrA\u003c/em\u003e gene counts. F: regression between conductivity and the ratio of the inferred \u003cem\u003edsrB\u003c/em\u003e and \u003cem\u003emcrA\u003c/em\u003e gene counts. G: regression between organic matter (LOI) and \u003cem\u003edsrB \u003c/em\u003e(red line) and \u003cem\u003emcrA\u003c/em\u003e (blue line) inferred gene counts. H: regression between conductivity and \u003cem\u003edsrB \u003c/em\u003e(red line) and \u003cem\u003emcrA\u003c/em\u003e (blue line) inferred gene counts. *: p \u0026lt; 0.05. **: p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/1def8d85381168ccb7938f7c.png"},{"id":96243448,"identity":"9e145c64-3293-408f-a4cf-809439d69b58","added_by":"auto","created_at":"2025-11-19 07:16:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":69811,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution, in each wetland and season, of the number and total degree of ZOTUs that presented genes related with carbon metabolisms in the water (A-B) and sediment (C-D) co-occurrence networks. In the X-axis: Cosmo: ZOTUs present in all wetlands (cosmopolitan). C: within each wetland, ZOTUs that were present in all seasons (core). ALFA: Alfacs. ENCA: Encanyissada. FBIO: Filtre Biològic.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/9a3077c7a2b1ea08c72ef08f.png"},{"id":95934288,"identity":"a2836f93-f632-489a-8c2c-2167124af442","added_by":"auto","created_at":"2025-11-14 15:10:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":119762,"visible":true,"origin":"","legend":"\u003cp\u003edbRDA ordination of the prokaryotic communities of the water (A) and sediment (B) based on the environmental variables. The symbols representing the Alfacs samples are in red, those of Encanyissada in blue and those of Filtre Biològic in green. The ellipses that highlight the samples of each wetland have the same colour code. The sampling season is indicated next to the name of the wetland and also by the shape of the symbol that represents it: Winter (▼), Spring (●), Summer (▲). The small light blue dots represent the ZOTUs considered in the dbRDA, but only the taxonomic assignment (up to the last level with assignment) of the ZOTUs with the highest discrimination capacity (dark blue squares) is shown. Environmental variables: Chla (Chlorophyll-\u003cem\u003ea\u003c/em\u003e), Alk (Alkalinity), LOI (Organic Matter), NH4 (Ammonium), Oxy (Oxygen), Temp (Temperature), SRP (Soluble Reactive Orthophosphate), TSS (Total Suspended Solids), Cond (Conductivity), NO\u003csub\u003e3\u003c/sub\u003e (Nitrate), SO\u003csub\u003e4\u003c/sub\u003e (Sulphate), Depth (Maximum Depth), pH and Carbonate. Metabolic processes rates: GPP (gross primary production), RESP (aerobic respiration), CH\u003csub\u003e4\u003c/sub\u003e (methane emissions).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/c9a20ec11e10fdae67098dff.png"},{"id":104418151,"identity":"92c1511a-e413-4476-8baf-e683b7e66467","added_by":"auto","created_at":"2026-03-11 13:22:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2076974,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/db8b06e6-0fff-4f91-9d3e-a8301465952a.pdf"},{"id":95934289,"identity":"12b28806-94ef-42a4-9374-25fed8694485","added_by":"auto","created_at":"2025-11-14 15:10:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6600904,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7998527/v1/681fe8724a32bf0a22eddb43.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental gradients shape the structure and carbon-related potential metabolisms of the prokaryotic communities in deltaic wetlands","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWetlands play a major role in the global biogeochemical cycles, being actively involved in the main steps of carbon metabolism, as they can be a source of carbon-greenhouse gases (C-GHG) such as carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) and methane (CH\u003csub\u003e4\u003c/sub\u003e) but they also behave as carbon sinks, showing strong carbon fixation capacities [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The biological communities that inhabit wetlands show metabolisms associated with atmospheric CO\u003csub\u003e2\u003c/sub\u003e fixation, such as photosynthesis, and others associated with C-GHGs emission, such as aerobic respiration, sulphate-reduction, and methanogenesis [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The behaviour of the different Mediterranean wetlands as net carbon sinks or emitters depends on the balance between the metabolisms that fix carbon and those releasing it. Different environmental drivers, such as salinity, seasonality, temperature, and the conservation status of wetlands, influence the rates of these metabolic processes [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and the restoration of ecosystem processes can return these rates to natural levels [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Information on the dynamics of these metabolisms in Mediterranean wetlands is limited, especially considering the high regional diversity of wetland types [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], but highly relevant to understand the role of Mediterranean wetlands in the carbon cycle under the ongoing climate change scenario.\u003c/p\u003e\u003cp\u003eRiver deltas are a unique and relevant type of ecosystem, being the transition zone between freshwater and seawater. In deltaic environments different types of wetlands appear, such as salt marshes, estuaries, brackish lagoons, or flooded forests [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Microbial communities that inhabit these wetlands play a strong role in their functioning, powering the main biogeochemical cycles and supplying nutrients for both plants and animals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Consequently, the factors affecting the microbial communities of these ecosystems, particularly the groups of prokaryotes that participate in the different stages of the carbon cycle, have a significant impact on the carbon balance of the wetlands and their possible behaviour as net carbon sinks or emitters.\u003c/p\u003e\u003cp\u003eThe emissions of carbon-GHG in wetlands result from a balance between the metabolisms that participate in their production and consumption. At the microbial level, heterotrophic microorganisms produce the majority of CO\u003csub\u003e2\u003c/sub\u003e. In the presence of oxygen, aerobic respiration predominates over the other types of respiration and is responsible for the remineralization of most of the organic matter; therefore, the role of this metabolism in the carbon cycle is extremely significant [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Regarding CO\u003csub\u003e2\u003c/sub\u003e fixation, this is primarily carried out by light-dependent metabolisms that, among prokaryotes, can only be performed by specific groups. Among them, cyanobacteria stand out for their abundance and activity, being capable of performing oxygenic photosynthesis, although CO\u003csub\u003e2\u003c/sub\u003e fixation can also be performed, to a lesser extent, by other minoritarian microbial metabolisms [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOn the other hand, methane emissions also result from a complex balance between the methane production by methanogenic archaea and methane consumption by aerobic prokaryotes, particularly methanotrophic bacteria, or by anaerobic prokaryotes, such as ANME archaea [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Methanogenic archaea, which are strict anaerobes, function as terminal decomposers in anaerobic environments [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and couple the production of methane (methanogenesis) through different metabolic pathways to the obtention of energy. There are three major methanogenic pathways [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]: (1) methylotrophic methanogenesis, which may or may not rely on hydrogen and uses methylated substrates such as methylamines; (2) hydrogenotrophic methanogenesis, which is based on the reduction of CO\u003csub\u003e2\u003c/sub\u003e by hydrogen; and (3) aceticlastic methanogenesis, whose main substrate is acetate. All methanogens share an enzyme known as mcr (methyl-coenzyme M reductase). This enzyme catalyses the final step of methanogenesis, which is the release of methane, and is widely used as a gene marker to study the diversity and transcriptional activity of methanogens [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nonetheless, the relative importance of the three methanogenic pathways changes across ecosystems. Most methanogenic pathways are inhibited by competition for metabolic substrates or electron donors between methanogenic archaea and sulphate-reducing bacteria (SRB), which are better competitors than methanogenic archaea in high-salinity (sulphate-rich) environments. Therefore, in saline ecosystems, methylotrophic methanogenesis predominates over other methanogenic pathways because it is the only type that does not compete with SRB for substrates [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Methanotrophic bacteria, on the other hand, can use the methane produced by methanogens as a carbon and energy source [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These microorganisms can consume methane at high rates [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], so their role in controlling methane emissions in wetlands may be significant.\u003c/p\u003e\u003cp\u003eOne of the main environmental features in deltaic wetlands is the salinity gradient, which strongly affects the structuration of the microbial communities that inhabit them [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Nonetheless, other environmental factors also exert a strong influence in the deltaic microbial communities. For example, trophic status can determine the structure of prokaryotic communities in coastal systems [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and temperature and temporal variation also affect the microbial populations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The effect of the environmental factors on the structure and, by inference, the functioning, of the prokaryotic communities, can be assessed through the sequencing of the 16S rRNA gene, a widespread procedure in microbial ecology which has led to a profound understanding of prokaryotic communities and the factors that govern their structure and activity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Different bioinformatic tools, derived from the sequencing of this gene, have been developed to decipher their metabolic potential or to delve deeper into the interactions between microorganisms. One example is PICRUSt2 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which can infer metabolic functions through the analysis of the prokaryotic taxa that form the microbial communities, providing a general idea about their potential metabolisms. Also, the interactions between community members are important to understand the roles played by the different prokaryotic groups in the community. One way to assess these interactions is by constructing co-occurrence networks, which detect significant co-presence or mutual exclusion between the different prokaryotic members of a community [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur work focuses on the drivers that influence the structure and the potential carbon-related metabolisms of the prokaryotic communities that inhabit the water and sediment of three different types of wetlands in the Ebro River Delta. We hypothesize that the high diversity of wetlands coexisting in the same deltaic system should be reflected in the diversity, structure and function of the prokaryotic communities that inhabit the water and sediment of the different wetland types. These wetlands were embedded in a strong salinity gradient and showed different trophic status, which affected the structure of the prokaryotic communities and modulated the actual and the predicted relevance of the different prokaryotic metabolisms involved in the C-GHG emissions. Our results show the importance of combining molecular procedures and \u003cem\u003ein situ\u003c/em\u003e metabolic measurements for a broader understanding of the role of prokaryotic communities in the carbon fluxes of deltaic wetlands.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy sites\u003c/h2\u003e\u003cp\u003eThree representative sites of the three main wetlands of the Ebro River Delta (Catalonia, Spain), which in turn are representative of the main Mediterranean coastal wetland types, were selected as pilot sites for the study. These wetlands were sampled during the winter, spring, and summer of the 2016-17 hydrological cycle. The location and main characteristics of the studied wetlands were described by Morant et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Briefly, and following a decreasing salinity gradient, Alfacs marshes (ALFA) are microtidal salt marshes partially separated from the Mediterranean Sea by a narrow sandy beach. They are a mosaic of permanent and temporary flooded areas covered by meadows of \u003cem\u003eSalicornia\u003c/em\u003e spp. Encanyissada (ENCA) is a brackish coastal wetland whose depth is controlled by a network of artificial channels that indirectly connect it to the sea and to the Ebro River. Hydrological inputs are highly variable, and this wetland has strong conductivity variations, but normally presents brackish waters. On the other hand, Filtre Biol\u0026ograve;gic (FBIO) is a freshwater wetland recovered on 43 ha. plot previously occupied by rice fields. Currently, this wetland acts as a biological filter reducing, by natural processes, the nutrient concentrations coming from surrounding rice fields. Water inputs and outputs in FBIO are also regulated, maintaining a regular water depth.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling and physical and chemical analysis\u003c/h3\u003e\n\u003cp\u003eFor each wetland and sampling time, all the samples were obtained at the same point. The water samples were collected in sterile bottles, and the water for DNA extraction was kept cold until arrival at the laboratory and then filtered through 0.22 \u0026micro;m pore size polycarbonate membranes filters (Nucleopore, Whatman), which were then kept at -80\u0026ordm;C until DNA extraction. For dissolved nutrient analysis, the water was filtered with Whatman GF/F glass microfiber filters, and the filtered water was kept frozen until further analysis. Sediment samples from a depth of 5\u0026ndash;10 cm were obtained in triplicate at each sampling point and were placed in sterile plastic pots, which were kept cold until they were processed in the laboratory within the following 24 h. Each triplicate was homogenized by mixing with a metal rod. Samples for DNA extraction were placed in sterile 1.5 mL Eppendorf tubes which were maintained at -80\u0026ordm;C until DNA extraction. For DNA extraction, about 300 mg of sediment per sample were used.\u003c/p\u003e\u003cp\u003eWater conductivity, dissolved oxygen (DO) and temperature were measured in situ, using a multiparameter probe WTW Multi 3410 logger. The specific probes used were WTW Tetracon\u0026reg; 925 IDS for conductivity and WTW FDO\u0026reg; 925 IDS for dissolved oxygen and temperature. A salinity correction was automatically applied (if necessary) to DO measurements. The pH was measured with a Crison Basic-20 pH-meter. Maximum depth was obtained using a limnimeter at the deepest point of the lakes. The analysis of dissolved inorganic nutrients and the other water environmental variables was carried out mostly following the standard methods [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (see Suppl Mat. for further details). Chl-\u003cem\u003ea\u003c/em\u003e concentration was determined by HPLC [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the sediment physical-chemical variables, the different measurements were carried out in triplicate. Sediment conductivity and pH were measured by 1/5 dilution of the sediment with distilled water [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Conductivity was measured with a WTW LF-191 conductivity meter, while pH was obtained with a Crison Basic-20 pH meter. The proxy of organic matter content (LOI) and the carbonate content of the dried sediments were, respectively, obtained by the loss on ignition method [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the studied wetlands, plankton and benthos metabolic rates were determined following Morant et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and methane emissions were determined using the procedure described in Camacho et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] (see Suppl. Mat. for further information).\u003c/p\u003e\n\u003ch3\u003eDNA extraction, sequencing, and taxonomic assignment\u003c/h3\u003e\n\u003cp\u003eDNA extraction of water filters and sediment samples was performed with the EZNA soil DNA isolation kit (Omega Bio-Tek, Inc., Norcorss, GA, United States) following the supplier\u0026rsquo;s instructions. The sequencing of the V4 region of the 16S rRNA gene was carried out with the primers 515f/806r [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] in the Illumina MiSeq System (2x250 bp) according to the facilities and protocols of the RTSF-MSU (Michigan State University, USA). For further information, see Suppl. Mat. The raw sequence data of this study was deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA595160.\u003c/p\u003e\u003cp\u003eRaw sequences were processed using the UPARSE pipeline [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. After the merging of read pairs, sequences were filtered by a maximum expected error of 0.5, and chimeric reads were removed by the UCHIME algorithm. The filtered sequences were clustered in Zero-radius Operational Taxonomic Units (ZOTUs), which are sequences at 100% identity. Taxonomic assignment was performed with SINA aligner v.1.2.11 with the SILVA 138.1 reference database. ZOTUs which had low alignment scores (\u0026lt;\u0026thinsp;90%) were filtered, and sequences classified as mitochondria and chloroplasts were removed. The resulting ZOTU table consisted of 3260 ZOTUs for 18 samples. Rarefactions were performed separately for the water and sediment samples. To avoid the loss of less abundant ZOTUs, rarefactions were repeated 100 times [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and then unified into two average ZOTU tables, with a minimum threshold of 7723 reads/sample for water and 12177 reads/sample for sediment. Both Silva 138.1 and RDP 11.5 were used for taxonomic assignment, using the RDP classifier 2.13 tool [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMetabolic potential of the prokaryotic communities\u003c/h3\u003e\n\u003cp\u003eThe bioinformatic tool PICRUSt2 (phylogenetic investigation of communities by reconstruction of unobserved states) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was used to describe the potential metabolisms that could be performed by the prokaryotic communities from the water and sediment. To do this, we used the raw ZOTU table prior to the filtering and rarefaction, as PICRUSt2 has its own normalization procedure. A selection of the inferred genes that participate in the carbon-related metabolisms measured in the field was performed. The inferred genes for carbon-related metabolisms were the \u003cem\u003epsbA\u003c/em\u003e gene for photosynthesis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], the \u003cem\u003ecoxA\u003c/em\u003e gene for aerobic respiration [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], the \u003cem\u003emcrA\u003c/em\u003e gene for methanogenesis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and the \u003cem\u003epmoA\u003c/em\u003e gene for aerobic methanotrophy [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. For the sulphur-related metabolism, the selected gene was \u003cem\u003edsrB\u003c/em\u003e for dissimilatory sulphate reduction [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In addition, the inferred presence or absence of \u003cem\u003epsbA\u003c/em\u003e, \u003cem\u003epmoA\u003c/em\u003e, \u003cem\u003edsrB\u003c/em\u003e and \u003cem\u003emcrA\u003c/em\u003e marker genes in each of the ZOTUs was also determined.\u003c/p\u003e\n\u003ch3\u003eProkaryotic co-occurrence networks\u003c/h3\u003e\n\u003cp\u003eFor the water and sediment, two different co-occurrence networks were constructed based on the rarefied ZOTU tables through CoNet software [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] with Silva 138.1 taxonomic assignment.\u003c/p\u003e\u003cp\u003eIn the networks, each ZOTU was assigned a specific level for each of the factors \u0026ldquo;wetland\u0026rdquo; (three levels: ALFA, ENCA and FBIO) and \u0026ldquo;season\u0026rdquo; (three levels: winter, spring and summer) if its abundance in any of these specific levels was greater than 70% of its total reads. The ZOTUs that did not reach a minimum abundance of 70% of its total reads in any level of the factors were divided into two classes. On the one hand, if within the wetland factor, a ZOTU had reads at the three levels of that factor, though less than an abundance of 70% of its total reads in any specific level of the factor, it was considered as Cosmopolitan. If the same was true for the season factor, having a ZOTU reads in all the three seasons but with an abundance lower than 70% of its total reads in any specific level of the season factor, it was considered to be present throughout the year (Core). On the other hand, if a ZOTU had reads only at one or two of the levels of wetland or season factors, but not in all the three levels, it was considered as not cosmopolitan/not core (NC). Only ZOTUs with a minimum abundance of 20 reads for water and 50 for sediment were considered for constructing the networks. Co-occurrence and co-exclusion relationships between network nodes were derived with different metrics: Pearson correlation and Bray-Curtis dissimilarity for the water, and Pearson correlation, Mutual information and Bray-Curtis dissimilarity for the sediment. Environmental data were included in the analysis. Up to 1000 top and bottom edges were considered. The significance of the edges was assessed through a combination of permutation and bootstrap distributions generated with 100 iterations and enabling renormalization to avoid the compositionality bias and thus mitigate the establishment of spurious correlations between nodes. The final edge significance was obtained by fusing the p-value of the edges of each metric with Brown's method, and with a multiple testing correction using the Benjamini-Hochberg method. The final network was then visualized with the yfiles organic layout from Cytoscape network visualizing software [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In these general networks the clustering coefficient was determined, which gives a measure of the complexity of the network [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and also the modularity (with the MCL algorithm), which reports the degree to which a network is divided into different subnetworks or modules, which are made up of members that have more relationships with each other than with the members of the other modules [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Also, in these networks, for the genes \u003cem\u003epsbA\u003c/em\u003e, \u003cem\u003epmoA\u003c/em\u003e and \u003cem\u003edsrB\u003c/em\u003e, the total degree was defined as the sum of the degrees (number of connections) of the individual nodes that presented each gene. The topological roles of the individual nodes that formed the networks were determined according to the simplified classification of Olesen et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] (see Suppl Mat. for further details).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analyses\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed with Primer 7 and R software. Principal Coordinates Analyses (PCOs) were carried out with the Euclidean distance matrix obtained from the normalized environmental variables. For the water and sediments, the rarefied ZOTU tables were standardized and square root transformed prior to obtaining their Bray-Curtis similarity matrices. A heatmap analysis (gplots R package) was performed for water and sediment prokaryotic communities based on the Bray-Curtis matrices. Only ZOTUs with an abundance in any sampling time greater than 0.5% for the water and greater than 0.3% for the sediment were considered. For the water and sediment, the statistical differences between the prokaryotic communities of the different wetlands were tested with a PERMANOVA analysis (999 permutations) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] based on the Bray-Curtis similarity matrices. These matrices were also employed to carry out a distance-based Redundancy Analysis (dbRDA) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] to observe the effect of the environmental variables on the ordination of the water and sediment prokaryotic communities. Further than environmental variables, in the water matrix, the data on plankton GPP and respiration rates were also included. Similarly, in the sediment matrix, the data on benthos GPP and respiration rates were also included. The values of these rates, along with those of the methane emissions rates, are those given in Morant et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental characteristics of the deltaic wetlands\u003c/h2\u003e\u003cp\u003eThe values of the most relevant environmental variables for water and sediment in the studied wetlands are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and the rest of the values for the other environmental variables are given in the Supplementary Tables\u0026nbsp;1\u0026ndash;2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage values (\u0026plusmn;\u0026thinsp;SD) of the most relevant environmental variables of water and sediment of the studied wetlands. Sediment values of organic matter (LOI) are expressed as percentage of dry weight (% d.w.). ALFA (Alfacs), ENCA (Encanyissada), FBIO (Filtre Biol\u0026ograve;gic).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eALFA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eENCA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFBIO\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eWater\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConductivity (mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChlorophyll-\u003cem\u003ea\u003c/em\u003e (mg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSulphate (g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSediment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConductivity (mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e45.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOI (% d.w.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe studied wetlands were located along a marked salinity gradient. In the water, the highest average conductivity values were found in Alfacs (53.4 mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) followed by Encanyissada (32.1 mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) which, however, showed a large variation in conductivity values throughout the different sampling dates, while Filtre Biol\u0026ograve;gic had the lowest values (2.1 mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) that were quite stable. Encanyissada and Filtre Biol\u0026ograve;gic showed a higher trophic status than Alfacs, a microtidal and more oligotrophic area fed mainly with marine waters, with higher average Chl-\u003cem\u003ea\u003c/em\u003e concentrations for the former of 33.4 and 28.5 \u0026micro;g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively. In parallel with the conductivity, the average sulphate concentrations were higher in Alfacs and Encanyissada (3.4 and 2.4 g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively) and very low in Filtre Biol\u0026ograve;gic (0.1 g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The conductivity pattern was the same in the sediments as in the water, with Alfacs having the highest average values (45.8 mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and Filtre Biol\u0026ograve;gic the lowest (1.7 mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), while the highest average values of organic matter in the sediments were also observed in Alfacs (34.2%) due to the accumulation of rests of halophytic plants, and the lowest in Filtre Biol\u0026ograve;gic (3.9%).\u003c/p\u003e\u003cp\u003eRegarding the ordination of the water samples with respect to the values of environmental variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-A), salinity was the most important factor in separating the wetlands, as Alfacs and Encanyissada were influenced by high conductivity but also by high sulphate concentrations, unlike Filtre Biol\u0026ograve;gic. In addition, seasonality was also a relevant factor since, in each wetland, the summer sampling was separated from winter and spring samplings and related to higher temperatures. In the sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-B), Alfacs and Encanyissada overlapped in the ordination but were separated from Filtre Biol\u0026ograve;gic, as they presented higher values of conductivity and organic matter, while the values of these variables in Filtre Biol\u0026ograve;gic were low. In turn, seasonality was also relevant in the sediment, since in each wetland the spring and summer samplings were separated from the winter ones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStructure and seasonal dynamics of the prokaryotic communities\u003c/h2\u003e\u003cp\u003eWater and sediment prokaryotic communities showed different structure patterns of their prokaryotic communities (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the water (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A), 203 cosmopolitan ZOTUs were present in all the wetlands. Alfacs showed the highest number of specific ZOTUs, followed by Filtre Biol\u0026ograve;gic and Encanyissada. Filtre Biol\u0026ograve;gic showed little relationship with the more saline wetlands, as it shared very few ZOTUs with Alfacs and Encanyissada, while the latter had 487 ZOTUs in common. On the other hand, 575 ZOTUs appeared in all seasons. Winter was the season with the most specific ZOTUs, followed by summer and spring. In turn, the winter and spring samples were more closely related to each other than to the summer sample, as both shared more ZOTUs (399) than each of them with the summer sample. In the sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B), 253 cosmopolitan ZOTUs appeared in all the studied wetlands. Compared to the water, the effect of the salinity gradient was more evident, as Filtre Biol\u0026ograve;gic shared very few ZOTUs (33) with Alfacs, the saltiest wetland, while Alfacs and Encanyissada shared 845 ZOTUs. Moreover, the number of ZOTUs that were present in the sediments in all the seasons was higher than in the water, with 1831 ZOTUs, showing higher temporal resilience, and summer was the season that shared the fewest ZOTUs with the others, especially with winter, to which it had just 90 ZOTUs in common.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOn the other hand, taxonomic classification of the amplicons of the gene coding for 16S rRNA resulted in 43 phyla for the water and 54 for the sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the water (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A), the most abundant phylum was Cyanobacteria, the \u003cem\u003eCyanobiaceae\u003c/em\u003e family being present along the entire salinity gradient but presenting the highest relative abundances during the warmer seasons in the wetlands with the highest trophic status. This family was responsible for 25.1% of the normalised reads in summer in Encanyissada and 46.1% of the reads during spring in Filtre Biol\u0026ograve;gic. The other most abundant phyla were Proteobacteria, mostly represented by the family \u003cem\u003eRhodobacteraceae\u003c/em\u003e, which was present along the entire salinity gradient, and Actinobacteriota, due to the high abundance of the family \u003cem\u003eMicrobacteriaceae\u003c/em\u003e in Encanyissada during the winter (34.5% of the normalised reads). In contrast, in the sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B) there were no taxa with a high relative abundance. The most represented phyla were Desulfobacterota, with the \u003cem\u003eDesulfosarcinaceae\u003c/em\u003e family being more abundant in the more saline wetlands, and Chloroflexi, with the \u003cem\u003eAnaerolineaceae\u003c/em\u003e family presenting the highest abundance in Encanyissada and Filtre Biol\u0026ograve;gic (from 2.7 to 10.6% of the normalised reads), which were the wetlands with intermediate or low conductivities.\u003c/p\u003e\u003cp\u003eRegarding the statistical differences between the prokaryotic communities of the different wetlands, the PERMANOVA analysis showed that in the water there were significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the communities of Filtre Biol\u0026ograve;gic and Encanyissada-Alfacs, but not between those of Encanyissada and Alfacs. On the other hand, in the sediment there were significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the communities of all the studied wetlands.\u003c/p\u003e\u003cp\u003eAs for the relationship between the different wetlands based on their communities (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), both in water and sediments of the more saline wetlands, Alfacs and Encanyissada, were part of a saline wetland cluster separated from the other cluster, formed only by Filtre Biol\u0026ograve;gic, the freshwater wetland.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the water (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the different seasonal samplings of each wetland showed large differences in the composition of the prokaryotic community. In Filtre Biol\u0026ograve;gic, the spring and summer samplings were grouped together and differed from the winter sampling, more evidently for the summer sample, as they presented a higher relative abundance of cyanobacterial ZOTUs classified as \u003cem\u003eCyanobium\u003c/em\u003e, while in the winter sampling the ZOTUs belonging to the \u003cem\u003eRhodobacteraceae\u003c/em\u003e and \u003cem\u003eAlcaligenaceae\u003c/em\u003e families (both in phylum Proteobacteria) had a higher abundance. On the other hand, the different samples of Alfacs and Encanyissada overlapped and did not form a group specific to each wetland. This overlap between the communities of the two wetlands occurred because their summer samples formed a cluster due to the high abundance of a cyanobacterial ZOTU classified as \u003cem\u003eSynechococcus\u003c/em\u003e. In addition, the Encanyissada samplings and the Alfacs summer sampling were characterised by the high relative abundance of actinobacterial ZOTUs classified as \u003cem\u003eMicrobacteriaceae\u003c/em\u003e and PeM15, while in the rest of the Alfacs samplings periods the most abundant ZOTUs were from the families \u003cem\u003eCyanobiaceae\u003c/em\u003e (phylum Cyanobacteria), \u003cem\u003eRhodobacteraceae\u003c/em\u003e (phylum Proteobacteria) and the \u003cem\u003eNS3a marine group\u003c/em\u003e (phylum Bacteroidota).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn contrast to the water, in the sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) the different samplings of each wetland were more stable over time and did not show large seasonal changes. This led to a good differentiation of the wetland according to their prokaryotic sediment communities, as there was no overlap between Alfacs and Encanyissada. In Filtre Biol\u0026ograve;gic, the ZOTUs with the highest abundance belonged to the genus of proteobacteria \u003cem\u003eAcinetobacter\u003c/em\u003e and to the family \u003cem\u003eAnaerolineaceae\u003c/em\u003e (phylum Chloroflexi). On the other hand, the sulphate-reducing bacteria families (belonging to the phylum Desulfobacterota) were found in the more saline wetlands and not in FBIO. One of the most abundant ZOTUs in Encanyissada belonged to the archaeal family \u003cem\u003eNitrosopumilaceae\u003c/em\u003e (phylum Crenarchaeota), while in Alfacs the most abundant ZOTUs belonged to the family \u003cem\u003eCyanobiaceae\u003c/em\u003e (phylum Cyanobacteria) and to the genus \u003cem\u003eSulfurovum\u003c/em\u003e (phylum Campilobacterota).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMethane-related prokaryotic taxa\u003c/h2\u003e\u003cp\u003eRegarding the distribution of methanogenic archaea and bacteria related to methane consumption in the studied sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), different patterns related to the salinity gradient were observed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSediment methanogenic archaea (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-A) showed the highest relative abundance in Alfacs, while Encanyissada and Filtre Biol\u0026ograve;gic displayed similar levels. The family \u003cem\u003eMethanomasiliicoccaceae\u003c/em\u003e, which performs methylotrophic methanogenesis, was the most abundant family in all the wetlands. Alfacs and Encanyissada showed a very similar community of methanogens, with the families \u003cem\u003eMethanoregulaceae\u003c/em\u003e (capable of hydrogenotrophic methanogenesis) and \u003cem\u003eMethanosarcinaceae\u003c/em\u003e (which can perform all the types of methanogenesis) being the most important non-majority families in both sites. In contrast, the families \u003cem\u003eMethanopyraceae\u003c/em\u003e (capable of hydrogenotrophic methanogenesis) and \u003cem\u003eMethanobacteriaceae\u003c/em\u003e (capable of methylotrophic or hydrogenotrophic methanogenesis) were only found in Filtre Biol\u0026ograve;gic. As for bacteria related to methane consumption (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C), different patterns were found between the water and sediment. In the water (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-B), Filtre Biol\u0026ograve;gic showed the highest relative abundance, followed by Alfacs and Encanyissada, the latter being the wetland with the lowest relative abundance of bacteria related to methane consumption in its water and the highest methane emission. Alfacs and Encanyissada showed similar communities dominated by the \u003cem\u003eMethylococcaceae\u003c/em\u003e family, while Filtre Biol\u0026ograve;gic, apart from presenting families that also exist in the wetlands with higher salinity, showed a high relative abundance of the \u003cem\u003eMethylocystaceae\u003c/em\u003e family, which was only present in this wetland. In the sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-C), Alfacs and Encanyissada, the latter being the wetland with the highest relative abundance of bacteria related to methane consumption, showed similar communities dominated by the \u003cem\u003eMethylococcaceae\u003c/em\u003e family, while in Filtre Biol\u0026ograve;gic this family was also the most abundant, followed by the \u003cem\u003eBeijerinckiaceae\u003c/em\u003e family.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn situ\u003c/b\u003e \u003cb\u003ecarbon-related metabolisms and molecular inference\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe relationship between the rates of carbon-related metabolisms measured \u003cem\u003ein situ\u003c/em\u003e and the molecular inference of the gene counts of their respective marker genes showed common patterns, but also differences between the water and sediment (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn both the water and sediment there was a significant correlation between gross primary production (GPP) rates and \u003cem\u003epsbA\u003c/em\u003e inferred gene counts, but no relationship was found between aerobic respiration rates and \u003cem\u003ecoxA\u003c/em\u003e inferred gene counts (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, A-B). In the water, in the wetlands with a higher trophic status, a large increase in the ratio of GPP and respiration rates to the ratio of inferred gene counts of the respective marker genes was observed during the warmer months (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e-C), resulting in a linear yet non-statistically significant correlation between both ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e-D). The correlation between these ratios also appeared for the sediments, though in this case it reached statistically significance, with the ratio for the inferred genes psbA/coxA decreasing along the salinity gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-D).\u003c/p\u003e\u003cp\u003eOn the other hand, the waters of Filtre Biol\u0026ograve;gic showed the highest number of \u003cem\u003epmoA\u003c/em\u003e inferred gene counts, followed by Alfacs and Encanyissada, the latter showing the lowest number of \u003cem\u003epmoA\u003c/em\u003e inferred gene counts and the highest methane emission (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e-E). However, no significant correlation was found between the ratio of the inferred gene counts of the \u003cem\u003edsrB\u003c/em\u003e and \u003cem\u003emcrA\u003c/em\u003e genes with conductivity, nor between the inferred gene counts of the \u003cem\u003epmoA\u003c/em\u003e and \u003cem\u003edsrB\u003c/em\u003e genes with conductivity or organic matter (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF-G-H) in water. In the sediment there was no correlation between methane emissions and the inferred gene counts of the \u003cem\u003emcrA\u003c/em\u003e gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-E), but instead a significant positive correlation was observed between the ratio of the inferred gene counts of the \u003cem\u003edsrB\u003c/em\u003e and \u003cem\u003emcrA\u003c/em\u003e genes with conductivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-F). In addition, for the sediments, the inferred gene counts of the \u003cem\u003edsrB\u003c/em\u003e gene increased significantly with both conductivity and organic matter, while the inferred gene counts of the \u003cem\u003emcrA\u003c/em\u003e gene remained constant regardless of conductivity or the amount of organic matter present (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-H).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eProkaryotic co-occurrence networks\u003c/h2\u003e\u003cp\u003eAn analysis of the interactions of members of the water and sediment prokaryotic communities resulted in a water community network consisting of 340 nodes and 1259 edges (Supplementary Fig.\u0026nbsp;1) and a sediment community network consisting of 458 nodes and 1910 edges (Supplementary Fig.\u0026nbsp;2). The water network showed a clustering coefficient (reporting network complexity) of 0.357 and a modularity (reporting network compartmentalisation) of 0.827, while the sediment network showed a clustering coefficient of 0.315 and a modularity of 0.693, suggesting a higher complexity and compartmentalisation of the water network compared to the sediment network.\u003c/p\u003e\u003cp\u003eBoth networks showed a very low number of cosmopolitan ZOTUs present in all wetlands, 17 in the water network, which mainly belonged to families \u003cem\u003eCyanobiaceae\u003c/em\u003e, \u003cem\u003eBeijerinckiaceae\u003c/em\u003e, \u003cem\u003eDesulfobaccaceae\u003c/em\u003e and \u003cem\u003eClostridiaceae\u003c/em\u003e, and 12 in the sediment network, mainly corresponding to the families \u003cem\u003eAnaerolineaceae\u003c/em\u003e, \u003cem\u003eThermoanaerobaculaceae\u003c/em\u003e and \u003cem\u003eDesulfosarcinaceae\u003c/em\u003e. Therefore, the communities that formed the networks in the different wetlands were highly differentiated from each other. Concerning water, in Alfacs the ZOTUs with the highest degree belonged to families \u003cem\u003eFlavobacteriaceae\u003c/em\u003e and \u003cem\u003eRhodobacteraceae\u003c/em\u003e, in Encanyissada to the families \u003cem\u003eChthoniobacteraceae\u003c/em\u003e and \u003cem\u003eRhodothermaceae\u003c/em\u003e, and in Filtre Biol\u0026ograve;gic to the families \u003cem\u003eCyanobiaceae\u003c/em\u003e and \u003cem\u003eChthoniobacteraceae\u003c/em\u003e. With respect to the sediment, in Alfacs the ZOTUs with the highest degree belonged to families \u003cem\u003eDesulfobulbaceae\u003c/em\u003e and \u003cem\u003eThermoanaerobaculaceae\u003c/em\u003e, in Encanyissada to the families \u003cem\u003eNitrosopumilaceae\u003c/em\u003e and \u003cem\u003eThermoanaerobaculaceae\u003c/em\u003e and in Filtre Biol\u0026ograve;gic to the families \u003cem\u003eSphingomonadaceae\u003c/em\u003e and \u003cem\u003eGemmatimonadaceae\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn addition, the two networks showed ZOTUs present only in specific seasons and ZOTUs present during all the seasons (Supplementary Fig.\u0026nbsp;3), the latter being much more abundant in the sediment network than in the water network. With respect to water, the ZOTUs with the highest degree belonged to the families \u003cem\u003eWoeseiaceae\u003c/em\u003e and \u003cem\u003eSandaracinaceae\u003c/em\u003e in winter, to the families \u003cem\u003eFlavobacteriaceae\u003c/em\u003e and \u003cem\u003eRhodobacteraceae\u003c/em\u003e in spring, and to the families \u003cem\u003eCyanobiaceae\u003c/em\u003e and \u003cem\u003eBeijerinckiaceae\u003c/em\u003e during summer. Regarding the sediment, the ZOTUs with the highest degree belonged to the families \u003cem\u003eFlavobacteriaceae\u003c/em\u003e and \u003cem\u003eSulfurovaceae\u003c/em\u003e during winter, to the families \u003cem\u003eSulfurovaceae\u003c/em\u003e and \u003cem\u003eSulfurimonadaceae\u003c/em\u003e in spring and to the families \u003cem\u003eNitrosopumilaceae\u003c/em\u003e and \u003cem\u003eAnaerolineaceae\u003c/em\u003e in summer. Also, in the water network, ZOTUs specific to Alfacs and present throughout the year in this wetland were negatively related to Chl-\u003cem\u003ea\u003c/em\u003e (mainly belonging to families \u003cem\u003eRhodobacteraceae\u003c/em\u003e and \u003cem\u003eCyanobiaceae\u003c/em\u003e) while ZOTUs specific to Filtre Biol\u0026ograve;gic, and also present in all the seasons (mainly belonging to families \u003cem\u003eDesulfobaccaceae\u003c/em\u003e and \u003cem\u003eChromatiaceae\u003c/em\u003e), showed a negative relationship with conductivity. In the sediment network, ZOTUs specific to Filtre Biol\u0026ograve;gic, and present in all the sampling times, were also negatively related to conductivity and organic matter. These ZOTUs belonged mainly to families \u003cem\u003eSphingomonadaceae\u003c/em\u003e, \u003cem\u003eGemmatimonadaceae\u003c/em\u003e and \u003cem\u003eNitrososphaeraceae\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWith regard to the abundance and total degree of ZOTUs with inferred genes related to photosynthesis (with \u003cem\u003epsbA\u003c/em\u003e gene), dissimilatory sulphate reduction (\u003cem\u003edsrB\u003c/em\u003e gene) and aerobic methane oxidation (\u003cem\u003epmoA\u003c/em\u003e gene) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), in the water network (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-B), we observed that the vast majority of ZOTUs with \u003cem\u003epsbA\u003c/em\u003e gene were wetland-specific, whereas few ZOTUs were present in all the wetlands (cosmopolitan), which belonged to family \u003cem\u003eCyanobiaceae\u003c/em\u003e. In Alfacs and Filtre Biol\u0026ograve;gic, ZOTUs with the \u003cem\u003epsbA\u003c/em\u003e gene were present in all seasons (core), but the vast majority of ZOTUs with this gene were seasonal and more abundant in the warmer seasons in Encanyissada and Filtre Biol\u0026ograve;gic, the wetlands with a higher trophic status and higher GPP rates. On the other hand, the total degree of the inferred genes was defined as the sum of the degrees (number of connections) of the individual nodes that had these genes. Thus, the total degree of ZOTUs with the \u003cem\u003epsbA\u003c/em\u003e gene was also higher in the warmer seasons in Encanyissada and Filtre Biol\u0026ograve;gic. In contrast, ZOTUs with the \u003cem\u003epmoA\u003c/em\u003e gene were only found in Filtre Biol\u0026ograve;gic, the least saline wetland, and mostly in ZOTUs present during all the seasons. However, the highest total degree of ZOTUs with this gene was observed in summer.\u003c/p\u003e\u003cp\u003eWith respect to the sediment (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC-D), ZOTUs with the \u003cem\u003epsbA\u003c/em\u003e gene were only found in Alfacs, and the majority were ZOTUs present throughout the year, although ZOTUs with this gene were also observed in summer, their total degree being comparable to that of ZOTUs present in all seasons. On the other hand, some ZOTUs with the \u003cem\u003edsrB\u003c/em\u003e gene were found in all the wetlands (cosmopolitan), belonging mainly to family \u003cem\u003eDesulfosarcinaceae\u003c/em\u003e. Also, there were ZOTUs with the \u003cem\u003edsrB\u003c/em\u003e gene appearing in all seasons (core). However, the abundance of these ZOTUs increased along the salinity gradient, with Filtre Biol\u0026ograve;gic, the freshwater wetland, showing the lowest number of ZOTUs with the \u003cem\u003edsrB\u003c/em\u003e gene, and Alfacs being the one with the highest number of ZOTUs with this inferred gene. Moreover, the highest total degrees of ZOTUs with the \u003cem\u003edsrB\u003c/em\u003e gene were observed in Alfacs, especially in summer-specific ZOTUs. The ZOTUs with the \u003cem\u003epmoA\u003c/em\u003e gene in the sediment are not shown in the figure because they correspond to ammonium-oxidising archaea, which actually do not have this gene but rather the \u003cem\u003eamoA\u003c/em\u003e gene. PICRUSt2 is not able to differentiate between these two genes due to their strong evolutionary similarity, and can wrongly assigns the \u003cem\u003epmoA\u003c/em\u003e gene to ammonium-oxidising archaea.\u003c/p\u003e\u003cp\u003eRegarding the topological roles of nodes in the water and sediment co-occurrence networks (Supplementary Fig.\u0026nbsp;4), in both networks most nodes were classified as peripheral, and only module hubs were found in the sediment network, which were assigned to the family \u003cem\u003eSulfurovaceae\u003c/em\u003e and the phylum Gemmatimonadota (assignment to the last taxonomic rank defined). Peripheral nodes showed very different relative abundances, with some being highly abundant, belonging to families \u003cem\u003eRhodobacteraceae\u003c/em\u003e and \u003cem\u003eCyanobiaceae\u003c/em\u003e in water and families \u003cem\u003eMoraxellaceae\u003c/em\u003e and \u003cem\u003eHydrogenophilaceae\u003c/em\u003e in the sediment, while others showed very low abundance. Nodes classified as module hubs also showed low relative abundances. In both networks, the nodes that presented genes related to carbon metabolisms were all peripheral, showing the taxonomic/metabolic diversity of the carbon metabolisms. In the water, the most abundant node possessed the \u003cem\u003epsbA\u003c/em\u003e gene, and was assigned to the genus \u003cem\u003eSynechococcus\u003c/em\u003e. In the sediment, the most abundant node, with a gene related to sulphur metabolism (\u003cem\u003edsrB\u003c/em\u003e gene), belonged to the family \u003cem\u003eHydrogenophilaceae\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental gradients and ordination of the prokaryotic communities\u003c/h2\u003e\u003cp\u003eThe salinity gradient was shown to be the most relevant factor in the ordination of the water and sediment communities in the wetlands (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), although aquatic communities were also affected by seasonality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the water (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-A), the Alfacs and Encanyissada communities were influenced by the high conductivity and high sulphate levels of these wetlands, and showed some overlap, while the Filtre Biol\u0026ograve;gic communities were determined by low conductivity and sulphate concentrations, and they were clearly differentiated from the more saline wetlands. In all the wetlands, seasonality separated winter samples from spring and, especially, from summer samples, which were affected by higher temperatures and higher Chl-\u003cem\u003ea\u003c/em\u003e concentrations. Further, higher plankton GPP rates were also observed in the wetlands with higher trophic status. On the other hand, the Alfacs communities showed higher abundances of members of the family \u003cem\u003eCyanobiaceae\u003c/em\u003e and halotolerant groups such as the NS3a marine group, while in Encanyissada communities of the genus \u003cem\u003eSynechococcus\u003c/em\u003e, the family \u003cem\u003eSaprospiraceae\u003c/em\u003e and the taxon PeM15 stood out. In addition, Encanyissada showed the highest methane emissions. On the other hand, in the Filtre Biol\u0026ograve;gic communities, the genus \u003cem\u003eCyanobium\u003c/em\u003e, the family \u003cem\u003eRhodobacteraceae\u003c/em\u003e and the taxon GKS98 freshwater group were more abundant.\u003c/p\u003e\u003cp\u003eIn the sediments (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-B), the Alfacs and Encanyissada communities were also determined by high conductivity and organic matter levels, but unlike in the water, these wetlands were clearly differentiated from each other. Moreover, seasonality was not a relevant factor in the structuration of the sediment communities, since the samples from the different wetlands were very close to each other in the ordination, except for Encanyissada, where the summer sample was separated from the others. In Alfacs, the \u003cem\u003eDesulfobacteraceae\u003c/em\u003e, and the \u003cem\u003eCyanobiaceae\u003c/em\u003e (benthic cyanobacteria forming biofilms in the sediment surface) families stood out and the highest benthos GPP rates were observed. In Encanyissada, the \u003cem\u003eNitrosopumilaceae\u003c/em\u003e (ammonia-oxidizing archaea) and \u003cem\u003eNitrosomonadaceae\u003c/em\u003e (ammonia-oxidizing bacteria) families and also the \u003cem\u003eDesulfobacteraceae\u003c/em\u003e family were the most relevant, and this wetland also showed the highest methane emission rates. In contrast, the Filtre Biol\u0026ograve;gic communities were affected by low conductivities and low levels of organic matter, with the \u003cem\u003eNitrososphaeraceae\u003c/em\u003e family and the genus \u003cem\u003eThiobacillus\u003c/em\u003e standing out.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis work has studied the main factors affecting the structure and carbon-related metabolisms of water- and sediment-dwelling prokaryotic communities in three wetlands representative of the main Mediterranean deltaic wetland types, taking as examples those of the Ebro River Delta. The three wetlands are distributed along a salinity gradient and presented different trophic status, both factors determining the specific communities of each wetland and the relationships between the different groups of prokaryotes involved in the main carbon-related metabolisms.\u003c/p\u003e\u003cp\u003eThe prokaryotic communities in the water and sediment showed both similar though, somewhat, divergent patterns. As in other ecosystems framed by a salinity gradient [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], salinity was a very important factor in structuring this community. Thus, Alfacs and Encanyissada, the most saline wetlands, were more closely related to each other and shared more ZOTUs compared to Filtre Biol\u0026ograve;gic, the wetland with less saline water. In contrast, the effect of seasonality was different in the water and sediment. The higher number of ZOTUs present repeatedly during all seasons in the sediment indicates that this matrix is more stable than water [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and that sediment can buffer, more effectively than water, the seasonal changes in environmental variables affecting the communities, displaying a greater resilience to the temporal environmental changes. The greater stability of the sediment allows the presence of a community with little seasonal change, but also favours good differentiation between the communities of the different wetlands, which are determined by factors unaltered by seasonality. In contrast, the water communities are much more affected by seasonal changes and by the differences in the environmental drivers [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], which can be observed especially in Encanyissada. Human control of water flow can influence aquatic microbial communities [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The flow of water in Encanyissada is regulated by a network of irrigation channels, which causes large variations in the salinity of the water that end up producing an overlap between the prokaryotic communities of this wetland and those of Alfacs, which is highly influenced by seawater as it is connected to the sea following a microtidal pattern. In turn, the difference in the stability of the water and sediment is also reflected in the structuring patterns of their communities. In the water, large fluctuations in environmental variables create transient conditions that benefit opportunistic taxa that can respond quickly and end up being very abundant for a short period of time [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This is the case of some cyanobacteria, which can be classified as opportunistic taxa [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In the wetlands with a higher trophic status, especially in Filtre Biol\u0026ograve;gic, which acts as a filter receiving the nutrient-rich water from the surrounding rice fields, the higher concentration of nutrients leads to the dominance of cyanobacteria in its aquatic communities, as it happens in Encanyissada during the warmer months. In contrast, in the sediment the temporal fluctuations of the environmental variables are lower, allowing the development of stable communities without strong dominance of specific taxa.\u003c/p\u003e\u003cp\u003eAs observed in other wetlands located along a strong salinity gradient of saline inland lakes [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], the \u003cem\u003eRhodobacteraceae\u003c/em\u003e family was abundant in the water along the entire salinity gradient. This family shows more than 300 species with different physiologies [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], which allows its members to thrive and be abundant in environments with such different salinities. In turn, salinity has been described as a highly relevant factor in the distribution of cyanobacteria [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], which could explain the differential distribution of cyanobacteria observed in the deltaic wetlands, with the genus \u003cem\u003eCyanobium\u003c/em\u003e being more representative in Filtre Biol\u0026ograve;gic and the genus \u003cem\u003eSynechococcus\u003c/em\u003e in the more saline wetlands. In the sediment, the sulphate-reducing bacteria (SRB) family \u003cem\u003eDesulfosarcinaceae\u003c/em\u003e (phylum Desulfobacterota) was more abundant in the more saline wetlands, as it has been observed in other cases, where SRB were more active and abundant in highly saline ecosystems [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. In contrast, the bacterial family \u003cem\u003eAnaerolineaceae\u003c/em\u003e (phylum Chloroflexi) was more abundant in wetlands with intermediate to low conductivities. Members of the \u003cem\u003eAnaerolineaceae\u003c/em\u003e family can establish syntrophic relationships with methanogenic archaea [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and therefore a higher relative abundance of members of this family in the sediment of certain wetlands may be indicative of higher methanogenic activity of sediment methanogenic archaea. Methane emissions in the studied wetlands are higher in those with moderate or low conductivity (Encanyissada and Filtre Biol\u0026ograve;gic) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], indicating that the higher abundance of the \u003cem\u003eAnaerolineaceae\u003c/em\u003e family in the sediment of these wetlands could be linked to higher methane production by methanogenic archaea. On the other hand, in the sediment of Encanyissada, there is a high relevance of \u003cem\u003eNitrosopumilaceae\u003c/em\u003e and \u003cem\u003eNitrosomonadaceae\u003c/em\u003e families. These ammonia-oxidizing prokaryotes can produce N\u003csub\u003e2\u003c/sub\u003eO, a very important greenhouse gas [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], and their metabolic activity may be relevant in the climate change mitigating potential of the wetlands, though we were unable to correlate with actual rates of N\u003csub\u003e2\u003c/sub\u003eO as these were not measured.\u003c/p\u003e\u003cp\u003eRegarding the groups of prokaryotes involved in methane production and consumption, previous work in the same study sites showed that the total abundance of methanogens was highest in Alfacs, followed by Encanyissada and Filtre Biol\u0026ograve;gic [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, the differences in families of methanogens that have been observed among the more saline wetlands (Alfacs and Encanyissada), which show a similar methanogenic community, and the less saline wetland (Filtre Biol\u0026ograve;gic), with a much more different community, may be associated with salinity, as this factor is one of the most important in the structuring of methanogenic archaeal communities [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. On the other hand, the \u003cem\u003eMethanomassiliicoccaceae\u003c/em\u003e family, which is the most abundant family of methanogens in the studied wetlands, belongs to the order Methanomassiliicoccales, which currently has only one isolated representative in pure culture, \u003cem\u003eMethanomassiliicococcus luminyensis\u003c/em\u003e. This only produces methane from the reduction of methanol or methylamines with hydrogen [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The hydrogen dependence shown by this methanogen to generate methane contrasts with the high abundance of this family in Alfacs, the most saline wetland, where hydrogen-dependent methanogenic pathways will be restricted by the competitive activity of SRB, which is higher in saline environments [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, because there is only one representative of this family in pure culture, it is likely that as more members of this family are cultured, other members with less hydrogen-dependent methanogenic pathways could be discovered, such as those capable of methylotrophic methanogenesis without hydrogen involvement, which is the most relevant methanogenic pathway in saline ecosystems because it is not outcompeted by SRB [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, in Alfacs and Encanyissada, \u003cem\u003ea priori\u003c/em\u003e, most of the methane production should fall back on the minority groups of methanogens, especially the family \u003cem\u003eMethanosarcinaceae\u003c/em\u003e, which can perform methanogenic pathways which do not depend on hydrogen, thus avoiding the competition with SRB. Nonetheless, the low methane emissions in Alfacs suggest that in the studied saline wetlands, despite the competition with SRB, most of the methane production is carried out by members of \u003cem\u003eMethanomassiliicoccaceae\u003c/em\u003e family, which perform hydrogen-dependent methylotrophic methanogenesis, and that the minoritarian groups of methanogens with types of methanogenesis that do not compete with SRB are not very active, because if they were, the methane emissions in the studied saline wetlands would presumably be higher. On the other hand, in Filtre Biol\u0026ograve;gic, where sulphate levels are low and the competition with SRB is weaker, all the methanogenic pathways, including the hydrogen-dependent, can be performed. Concerning bacteria related to methane consumption, the structure of their communities shows the same pattern as for methanogenic archaea, where the effect of salinity, one of the environmental factors that more controls the distribution of these microorganisms [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], is observed in the higher similarity of the communities of bacteria related to methane consumption between the most saline wetlands (Alfacs and Encanyissada), with Filtre Biol\u0026ograve;gic being the wetland with the most different community.\u003c/p\u003e\u003cp\u003eRegarding the relationship between the rates of gross primary production (GPP) and respiration with the gene counts inferred from the respective marker genes, when compared to inland saline lakes located along a strong salinity gradient [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], it can be observed that in both inland saline lakes and deltaic wetlands there is a good correlation between GPP rates and gene counts of the \u003cem\u003epsbA\u003c/em\u003e gene (marker of oxygenic photosynthesis). However, in contrast to inland saline lakes, in deltaic wetlands there is no good correlation between respiration and gene counts of the \u003cem\u003ecoxA\u003c/em\u003e gene (marker of aerobic respiration). This indicates that the relationships between the actual and potential metabolism may vary between different types of Mediterranean wetlands. In the water, the correlation between the ratio of GPP and respiration and the ratio of \u003cem\u003epsbA\u003c/em\u003e and \u003cem\u003ecoxA\u003c/em\u003e genes is not significant, because in the wetlands with a higher trophic status during the warmer months, their GPP rates show a high increase. However, in the sediment this correlation is significant. This demonstrates the deep effect of anthropogenic alterations on the metabolisms of aquatic communities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], while sediment communities are less affected by such disturbances, more particularly when these are transient, such as the high supply of nutrients received by the Ebro Delta wetlands partly feed by rice fields runoff.\u003c/p\u003e\u003cp\u003eRegarding methane emissions, these result from a complex balance between methane production and consumption. As explained before, in saline environments, where sulphate concentrations are high, SRB outcompete methanogenic archaea whose methanogenic pathways are hydrogen-dependent, because SRB have a higher affinity for the metabolic substrates needed by these archaea, and because dissimilatory sulphate reduction in general yields more energy than methanogenesis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. The results of our work show that in deltaic wetlands methane production is strongly regulated by salinity, since competition between SRB and methanogenic archaea favours one group of microorganisms or another depending on salinity levels. Thus, in Alfacs and Encanyissada SRB are favoured by high sulphate concentrations coming from seawater (which are linked to high salinity), so methane production would be very low, while in Filtre Biol\u0026ograve;gic the opposite happens. Also, these lower methane emissions in the saline wetlands may indicate that the methane production in these wetlands falls back mainly in the members of \u003cem\u003eMethanomassiliicoccaceae\u003c/em\u003e family, which produce methane from the reduction of methanol or methylamines with hydrogen [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], and that are outcompeted by SRB. Therefore, the methane emissions in Alfacs and Encanyissada are low. Nonetheless, despite that they are outcompeted by SRB, which have greater affinity of hydrogen, the members of \u003cem\u003eMethanomassiliicoccaceae\u003c/em\u003e family are probably so abundant in the studied saline wetlands, especially in Alfacs, because these wetlands show a high percentage of organic matter in sediment, which may provide a constant supply of methylamines or methanol, as these compounds can come from the decomposition of plant-derived material [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], thus partially reducing the competence with SRB and explaining the methane emissions recorded, even if these are modest. On the other hand, in Filtre Biol\u0026ograve;gic the low salinity levels (and thus low sulphate levels) allow for higher methane production by \u003cem\u003eMethanomassiliicoccaceae\u003c/em\u003e family. However, this did not result into higher methane emissions in this wetland, as the results of our work show the important role of aquatic bacteria related to methane consumption in the final balance of methane emissions. Thus, the higher methane production in Filtre Biol\u0026ograve;gic would allow a high relative abundance and high potential methanotrophy of the bacteria related to methane consumption in the water of this wetland, so that most of the methane produced by methanogens could be consumed by these bacteria, resulting in low methane emissions despite being a wetland with low saline water. However, in Encanyissada, although the potential methane production is lower due to its higher salinity, the low relative abundance and low methanotrophic potential of the aquatic bacteria related to methane consumption present in this wetland meant that the methane produced was barely consumed and could escape to the atmosphere. In contrast, in Alfacs, the synergy between low potential methanogenesis and the consumption by aquatic bacteria related to methane consumption of the small amount of methane that could be generated can explain the pattern of low methane emissions observed in this wetland. Therefore, this strong interdependence between different metabolic processes also explains the lack of correlation between methane emissions and the \u003cem\u003emcrA\u003c/em\u003e gene counts of the sediment of the studied wetlands.\u003c/p\u003e\u003cp\u003eThe overview of the interaction between environmental variables, potential metabolisms and prokaryotic communities shows different patterns between the water and sediment. Communities in the water are affected by a combination of salinity, trophic status and seasonality, while those in the sediment are mostly influenced by salinity but are much more stable as seasonal changes are much less evident in the sediment matrix. The lower capacity of the water to buffer environmental changes compared to the more resilient sediment, generates aquatic co-occurrence networks with many temporary ZOTUs and very few ZOTUs present throughout all the seasons. This may explain the greater complexity and compartmentalization of the co-occurrence network in the water, as high values of modularity are related to higher habitat heterogeneity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], because the low stability of the water generates a heterogeneous environment that favours the presence of purely temporary communities of prokaryotes that respond to transient environmental conditions. In the sediment, however, temporal changes in environmental variables are less intense, thus generating more stable communities over time. On the other hand, in the water co-occurrence network of deltaic wetlands, the highest abundance of ZOTUs with the \u003cem\u003epsbA\u003c/em\u003e gene, and therefore with photosynthetic capacity, is found in the wetlands with higher trophic status during the warmer months, which recorded higher water GPP rates [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], but in the sediment this gene is found only in Alfacs where microbial mats are common. This suggests a greater capacity to photosynthesize in the benthos of the deltaic wetlands with a low trophic status, explaining the higher GPP rates described in the benthos of Alfacs compared to the benthos of Encanyissada and Filtre Biol\u0026ograve;gic [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. ZOTUs with the \u003cem\u003edsrB\u003c/em\u003e gene, and therefore with the capacity to carry out dissimilatory sulphate reduction, were found mainly in the sediment of the most saline wetlands (Alfacs and Encanyissada), since their higher sulphate concentrations allow for greater SRB activity to the detriment of methanogenesis, while ZOTUs with the \u003cem\u003epmoA\u003c/em\u003e gene, i.e. related with the capacity to carry out aerobic methane oxidation, were only found in the water of Filtre Biol\u0026ograve;gic, the wetland with the highest relative abundance of aquatic bacteria related to methane consumption, which in turn had low methane emissions.\u003c/p\u003e\u003cp\u003eIn conclusion, the results of our work show the influence of salinity and trophic status on the structure and carbon-related metabolisms of prokaryotic communities in the water and sediment of three wetlands representative of the main deltaic wetland types worldwide. Salinity determines the community organization and also methanogenesis in the sediment. Trophic status also influences the community structure and is linked to higher GPP rates. The complexity of the interactions between the structure and function of prokaryotic communities in deltaic wetlands underlines the importance of studies that combine \u003cem\u003ein situ\u003c/em\u003e measurements of the rates of the main carbon-related metabolisms with molecular approaches to better understand the processes that determine carbon fluxes in this type of ecosystem.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the project CLIMAWET-CONS (PID2019-104742RB-I00) a, funded by the Agencia Estatal de Investigación of the Spanish government, and the project PROMETEO CIPROM-2023-031 funded by Generalitat Valenciana, both granted to Antonio Camacho. Javier Miralles-Lorenzo and Daniel Morant held an FPU Predoctoral Scholarship by the Spanish Ministry of Science, Innovation and Universities under grants FPU15/03930 and FPU16/01444.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Raquel González for her help in the performance of the statistical analyses, as well as the Agencia Estatal de Investigación of the Spanish government for its financial support under contract PID2019-104742RB-I00, and to the Generalitat Valenciana for the funding of the project ECCAEL (PROMETEO CIPROM-2023-031), both granted to Antonio Camacho.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJavier Miralles-Lorenzo: conceptualization, data curation, formal analysis, investigation, methodology, software, writing – original draft, writing – review and editing. Antonio Picazo: conceptualization, data curation, formal analysis, investigation, methodology, software, supervision, validation, writing– review and editing. Carlos Rochera: data curation, investigation, methodology, supervision, validation. Daniel Morant: investigation, methodology. Antonio Camacho: conceptualization, funding acquisition, investigation, methodology, project administration, supervision, validation, writing – review and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdekola O, Mitchell G (2011) The Niger Delta wetlands: threats to ecosystem services, their importance to dependent communities and possible management measures. Int J Biodivers Sci Ecosyst Serv Manag. https://doi.org/10.1080/21513732.2011.603138\u003c/li\u003e\n \u003cli\u003eSica YV, Quintana RD, Radeloff VC, Gavier-Pizarro GI (2016) Wetland loss due to land use change in the Lower Paran\u0026aacute; River Delta, Argentina. 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Msystems. https://doi.org/10.1128/mSystems.00320-19\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deltaic wetlands, trophic status, microbial carbon-related metabolisms, GHG, methane emissions, prokaryotic metabolic interactions","lastPublishedDoi":"10.21203/rs.3.rs-7998527/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7998527/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMediterranean deltaic wetlands play an important role in the carbon cycle due, in part, to the metabolic capacities of their prokaryotic communities. Nonetheless, these wetlands are very diverse and show different environmental characteristics. This work surveyed the structure and carbon-related metabolisms of the prokaryotic communities inhabiting three representatives of the wetlands from the Ebro River Delta, one of the biggest deltas in the Mediterranean. These wetlands are embedded in a strong salinity gradient and experience different levels of eutrophication. These factors were expected to influence the structure and potential carbon-related metabolisms of the prokaryotic communities. The most saline wetlands shared somewhat similar prokaryotic communities, which differed from those of the freshwater wetland. Water communities were also affected by the trophic status. Actual rates and potential (inferred) photosynthesis showed a linear relationship though this was not found between actual and potential respiration. The potential for methanogenic activity was kept along the salinity gradient, but methane production was controlled by increased salinity favoring instead dissimilatory sulphate reduction in the most saline wetlands at the expense of methanogenesis. Further, the abundance (and potential activity) of aquatic bacteria related to methane consumption modulated the final methane emissions of the studied deltaic wetlands. The water co-occurrence networks showed more complexity than those of the sediment networks, which is related to the higher environmental fluctuations in water, while sediment communities were more resilient in a more stable environment. Our results show the influence of the environmental drivers on the complex prokaryotic interactions that determine the carbon fluxes in deltaic wetlands.\u003c/p\u003e","manuscriptTitle":"Environmental gradients shape the structure and carbon-related potential metabolisms of the prokaryotic communities in deltaic wetlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 15:10:15","doi":"10.21203/rs.3.rs-7998527/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0aee34cf-c44f-45d5-8ec6-29935383f9c9","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T13:17:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 15:10:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7998527","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7998527","identity":"rs-7998527","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}