Short-term effect of a temperature gradient on the activity of methanogenic archaea and associated methane fluxes in different types of Mediterranean wetlands

Short-term effect of a temperature gradient on the activity of methanogenic archaea and associated methane fluxes in different types of Mediterranean 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 Short-term effect of a temperature gradient on the activity of methanogenic archaea and associated methane fluxes in different types of Mediterranean wetlands Javier Miralles-Lorenzo, Antonio Picazo, Carlos Rochera, Daniel Morant, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8490616/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Mediterranean wetlands play an important role in carbon cycling, partly due to the metabolic activity of the prokaryotic communities inhabiting these ecosystems. Among the different metabolisms involved, the methanogenesis carried out by methanogenic archaea is of great interest in the current context of global warming, as methane is a powerful greenhouse gas. Mediterranean wetlands, however, are very heterogeneous, and both the structure of prokaryotic communities and their metabolic activities vary greatly and are shaped by different environmental factors. This study analysed the short-term effect of different temperatures on methane fluxes and the expression of genes related to methanogenesis in the sediment of different Mediterranean wetlands. Results The increase in incubation temperatures caused the rise of methane emissions and the overexpression of the mcrA gene, which is involved in the final stage of methanogenesis, though this pattern was demonstrated for non-saline waters in low water renewal sites, whereas saline water system did not display these responses. In addition, a significant relation was observed between bioinformatically inferred methanogenesis and the amount of mcrA gene transcripts also for non-saline waters in low water renewal sites. Furthermore, data suggested that rising temperatures may influence competition for different metabolic substrates between methanogenic archaea and sulphate-reducing bacteria, thus determining the methane fluxes in Mediterranean wetlands. Conclusions Our results allowed us to infer the effects of a general increase in temperatures on methane emissions and the activity of methanogenic archaea in Mediterranean wetlands. Overall, these results may be helpful in improving the management of these ecosystems considering current and future climate change scenarios. Temperature GHG methane emissions methanogenic archaea gene expression Mediterranean wetlands. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 BACKGROUND Wetlands are terrestrial areas where water covers the ground, either above or below the surface, where the soil is saturated with water. These types of ecosystems largely contribute to the dynamics of carbon greenhouse gases (C-GHG), such as carbon dioxide (CO 2 ) and methane (CH 4 ), and thus the metabolisms involved are highly active [ 1 – 3 ]. However, little information is available on the processes that control the carbon metabolism in Mediterranean wetlands, which are highly diverse and have different characteristics [ 4 , 5 ]. Furthermore, the behaviour of different types of Mediterranean wetlands as net carbon sinks or emitters depends on various factors, such as salinity, seasonal variation, temperature, and the conservation status of the system [ 2 , 3 , 6 , 7 ]. Indeed, Mediterranean wetlands improve their C-GHG mitigation capacity after restoration [ 8 ]. In turn, the increase in temperature associated with climate change [ 9 , 10 ] has been linked to increases in methane emissions, which significantly alter the carbon balance and enhance their activity as GHG emitters [ 2 , 7 ]. Prokaryotic communities in wetlands play a key role in the functioning of these ecosystems, as they are involved in the main biogeochemical cycles [ 11 ]. Therefore, the factors affecting these communities, especially those involved in the production or consumption of methane, are of great interest to better understand the dynamics of this greenhouse gas. Methane emissions depend on the balance between its production by methanogenic archaea and its consumption by other prokaryotes, both aerobic (methanotrophic bacteria) and anaerobic (anaerobic methane-oxidizing archaea, ANME) [ 12 , 13 ]. Methanogens are strict anaerobes and act as terminal decomposers in anaerobic environments [ 14 ], where they couple methane production (i.e., methanogenesis) to energy acquisition through three main pathways [ 15 ]: (1) hydrogenotrophic methanogenesis, where CO 2 is reduced using hydrogen; (2) aceticlastic methanogenesis, which uses acetate and (3) methylotrophic methanogenesis, which uses methylated substrates such as methylamines and may or may not require hydrogen. A common enzyme in the three pathways is the methyl-coenzyme M reductase (mcr), which is involved in the final step of methanogenesis. This enzyme is made up of different subunits, and the gene encoding the alpha subunit ( mcrA ) has been used to study both the diversity of methanogenic archaea and their methanogenic activity [ 14 – 16 ]. The three methanogenic pathways do not, however, have the same relative importance in all ecosystems. Most pathways are not effective at high salinities [ 17 ], as they are affected by competition for metabolic substrates or electron donors between methanogenic archaea and sulphate-reducing bacteria (SRB), which are better competitors than the former under conditions of high salinity linked to high sulphate concentrations. In such saline ecosystems, like salt marshes and inland saline lakes, methylotrophic methanogens prevail over other methanogenic groups because they do not compete with SRB [ 18 ]. Due to the wide variety of prokaryotes and the impossibility of obtaining axenic cultures of all of them, molecular tools are extremely useful for studying the composition and activity of prokaryotic communities. Thus, one of the most widely used procedures for studying these communities is the sequencing and subsequent analysis of the 16S rRNA gene. In addition, various bioinformatic tools have been developed that, based on the sequences of this gene, allow for a deeper understanding of the structure and metabolisms of prokaryotic communities. One of these tools is PICRUSt2 [ 19 ], which infers the metabolic potential of communities based on the relative abundance and taxonomic classification of their different members [ 19 – 20 ]. However, the results of studying prokaryotic communities through DNA analysis, which provide information on the bulk community, can be very different from the results obtained through RNA analysis, which is more closely related to the potentially active community [ 21 ]. Therefore, the direct study of prokaryotic gene expression in the environment provides interesting results at the ecological level, since changes in such expression are strongly influenced by environmental factors [ 22 ]. Thus, changes in environmental conditions, such as an increase in temperature, can be reflected in the gene expression patterns of the different microorganisms that make up the community under study. Our working hypothesis is that warming will result in an increase of methane emissions, and that the magnitude of this increase will depend on the limnological properties of the wetlands studied. We thus investigated the short-term effect of a temperature gradient on methane emissions using archaeal 16S rRNA and mcrA gene transcripts as proxies for archaeal growth and methanogenic activity, respectively. The experiment was carried out with sediments from wetlands of the four major categories of Mediterranean wetland systems to obtain an overall picture of how methane emissions will respond to warming in such diverse ecosystems. METHODS Study sites For this study, 16 shallow lakes and wetlands representative of the main categories of Spanish Mediterranean wetlands and shallow lakes were selected: inland saline, coastal, volcanic, and inland non-saline (Table 1 ). For these experiments, these wetlands were sampled once during a specific period of the 2017-18 hydrological cycle, though the characteristics shown in Table 1 were obtained from various samplings performed between 2015 and 2025. Inland saline shallow lakes occur in endorheic basins, where water loss through evaporation exceeds water inflow. This leads to salt accumulation, resulting in high salinity in both water and sediment and high sulfate concentrations in water compared to lakes with low salinity (Table 1 ). In addition, most of these lakes are temporary, drying up during the summer. Within this category, there was a marked salinity gradient between the lakes, which also differed in their trophic status [ 23 ]. Also, one of them, Caballo Alba, is a soda lake, rich in sodium bicarbonate but with much lower salinity [ 7 , 23 ]. Coastal wetlands vary greatly in terms of structure, genesis, and dynamics. In some of them, the influence of the sea is very high, especially in wetlands that are directly connected. The wetlands selected for this study showed a marked gradient of salinity in both water and sediment, as well as in trophic status, with the saltiest wetlands having the highest concentrations of sulfate in the water column (Table 1 ). The studied volcanic wetlands are characterized by their location in ancient volcanic areas. They are shallow, most of the water comes from rain and surface runoff thus allowing low water renewal, and none of them are permanent. The selected wetlands have similar conductivities but display a marked trophic gradient (Table 1 ). Inland non-saline wetlands can be permanent (with high water flux and high water renewal accordingly to its karstic origin) or temporary (with low water flux, thus low water renewal, as fed by rain water). They are generally characterized by low mineralization and low conductivity values, both in water and sediment, as well as low concentrations of sulphate in the water column. However, for the studied wetlands, the amount of organic matter in the sediment was generally higher than in most of other types of wetlands studied (Table 1 ). Table 1 Main features of the Mediterranean wetlands studied. Within each category, the wetlands have been arranged in ascending order of their water salinity. The results shown (average and standard deviation) have been obtained during different sampling campaigns conducted between 2015 and 2025. Chl- a : Chlorophyll- a . Cond: conductivity. LOI: organic matter. Water Sediment Classification Wetland Code UTM X UTM Y Chl- a µg/L SD Cond mS·cm − 1 SD Sulphate g·L − 1 SD Cond mS·cm − 1 SD LOI % SD Inland saline lakes Caballo Alba ALBA 365239.35 4567208.48 0.60 0.83 3.7 1.5 - - 7.3 2.8 8.4 4.9 Zorrilla ZORR 244572.03 4084038.72 3.83 4.49 35.2 51.8 19.1 19.2 19.6 18.4 8.2 4.1 Chiprana CHIP 736010.54 4569469.29 3.32 3.32 87.0 50.7 45.6 19.5 63.2 18.0 24.3 5.8 Coastal Bullent BULL 754655.95 4310253.48 1.13 1.98 10.1 7.4 0.3 0.1 3.5 2.1 0.6 0.1 Senillar SENI 250423.86 4286037.75 4.13 3.60 10.6 2.6 0.4 0.1 7.3 1.4 4.7 1.6 Encanyissada ENCA 304310.15 4502849.96 22.53 20.76 31.3 21.2 2.4 0.9 36.2 16.8 26.5 9.8 Cabo de Gata GATA 569325.90 4068853.21 0.70 0.33 59.6 11.5 4.1 0.2 39.9 12.3 6.3 2.0 Volcanic Carrizosa CARR 392060.51 4299939.45 1.90 1.73 0.4 0.2 0.1 0.1 0.8 1.0 15.6 6.1 Caracuel CARA 407142.75 4298564.04 6.01 6.16 1.7 0.6 - - 1.1 1.0 12.2 3.8 Fuentillejo FUEN 408704.36 4310553.43 19.78 33.46 3.1 2.6 0.4 - 5.3 2.7 6.1 1.4 Inland non-saline Beleña BELE 478586.83 4526128.41 0.04 0.01 0.1 0.0 < 0.1 - 0.8 0.8 18.7 9.6 Pradales PRAD 355930.93 4729773.56 7.47 8.72 0.3 0.4 < 0.1 < 0.1 0.4 0.2 16.3 7.8 La Mueda MUED 637741.00 4714014.12 1.44 1.03 1.1 0.4 0.2 0.1 2.7 2.4 10.7 5.9 Baldoví BALD 731453.42 4347765.67 6.30 5.30 2.9 0.2 0.2 < 0.1 2.4 0.8 18.2 4.4 Alcaparrosa ALCA 249561.48 4104039.18 1.02 1.06 3.4 0.8 3.4 0.2 22.8 28.9 11.2 5.9 El Burgo de Ebro BURG 686065.69 4605782.49 - - - - - - 9.3 2.4 11.4 4.0 Sampling, incubation and physical and chemical analyses All analyses of the environmental variables compiled in Table 1 were carried out by standardised methods [ 24 ]. Sediment cores were collected from each of the studied wetlands using methacrylate tubes, following the procedure described in [ 2 ]. Upon arrival at the laboratory, these cores were purged with air to remove methane and placed in climate chambers at different experimental temperatures for a specified period for acclimatization. After acclimatization, the cores were hermetically sealed and incubated at 4ºC, 14ºC and 25ºC in the climate chambers. At each temperature, six replicates were incubated between two and five days, according to previous measurements [ 6 , 7 ]. Once the incubation finished, three cores out of six were randomly chosen. A portion of surface sediment (up to 5 cm deep) was extracted from each of these three cores, which was homogenized with a metal rod previously sterilized by flaming with ethanol, and immediately frozen using liquid nitrogen and then stored at − 80°C until extraction of nucleic acids. The process of collecting and freezing the samples was carried out in less than a minute to maintain the gene expression profile of the prokaryotic community, as the half-life of RNA is very short. Immediately after collecting the sediment sample, the core was shaken to release the gas retained, and the concentration of methane was measured using an Aeroqual Gas A200 CH 4 methane meter calibrated by gas chromatography [ 2 ]. The methane concentration measurements were converted into carbon emission rates per unit of time using the ideal gas law equation [ 3 , 6 ]. Extraction of nucleic acids, sequencing and processing Of the three frozen sediment replicates for each temperature, two were chosen randomly from which total nucleic acids were extracted using the RNeasy PowerSoil Total RNA Kit, which was coupled with the RNeasy PowerSoil DNA Elution Kit. In this way, both DNA and RNA were extracted from the same sediment sample (approx. 300 mg). The RNA was digested with DNAse to remove residual DNA using the TURBO DNA-free kit and retrotranscribed to DNA (cDNA) using the SuperScript III RT kit and random sequence hexamer primers. Subsequently, the V4 region of the 16S rRNA gene from both DNA and cDNA was sequenced using the Illumina MiSeq platform (2x250 bp) as previously described in [ 25 ]. Briefly, for each sample, libraries of the V4 region were obtained using the 515f/806r primer pair. The libraries were then normalised, quantified, loaded onto an Illumina MiSeq v2 flow cell and sequenced in 2x250 bp format, obtaining FastQ format sequences for each sample, which were deposited in the NCBI Sequence Read Archive under BioProject ID PRJNA1397488. The raw sequences were processed using the UPARSE pipeline [ 26 ]. After merging the read pairs (2x250 bp), the sequences were filtered with a maximum expected error of 0.5 and chimeras were removed using the UCHIME algorithm. These filtered sequences were grouped into ZOTUs (zero-radius Operational Taxonomic Units), which correspond to sequences with 100% identity. The resulting ZOTU table, containing 41,722 ZOTUs, was used for downstream bioinformatics analyses (see below). Metabolic potential of prokaryotic communities The PICRUSt2 bioinformatics tool (phylogenetic investigation of communities by reconstruction of unobserved states) [ 19 ] was used to determine the potential metabolic functions of the sediment prokaryotic communities in both the DNA (bulk community) and the cDNA extracts (active community). For this purpose, the ZOTU table was used without filtering or rarefaction, as PICRUSt2 has its own normalization system. Afterward, inferred genes involved in methanogenesis were selected. The mcrA gene was used as a marker for methanogenesis [ 27 – 28 ] and the dsrB gene was used as a marker for dissimilatory sulphate reduction [ 29 ]. The results of the dsrB gene inference were normalized by recA gene copies, obtained in the PICRUSt2 analysis itself, since prokaryotes only have one copy of this gene per genome [ 30 ]. Quantification of gene expression in methanogenic archaea Quantitative PCR (qPCR) was performed on cDNA extracts using specific primers (Supplementary Table 1) for the 16S rRNA gene of methanogenic archaea and for the gene encoding the alpha subunit of the mcr enzyme ( mcrA ), which is involved in the final step of methanogenesis. Both primer pairs provide a good overview of the different groups of methanogens [ 31 ], with a coverage from 26.7% to 100% of the sequences of the main classes of methanogenic archaea. The standards for the qPCR standard curve were made from genomic DNA extracted from a pure culture of Methanobacterium sp. for the 16S rRNA gene of methanogens and from Methanosaeta sp. for the mcrA gene. The absolute copies per µl of the methanogen 16S rRNA gene and the mcrA gene were quantified by qPCR from the extracted cDNA. In each sample, qPCRs were performed in duplicate. In addition, triplicate amplifications of water samples without genetic material were performed in each qPCR run to verify the absence of contamination. The reactive mixtures for qPCR were prepared by mixing each pair of primers (10 mM) with a hot start master mix (Roche LightCycler 480) and molecular biology-grade water to a final volume of 30 µl. For the 16S rRNA gene of methanogens, the qPCR cycle consisted of a 3-minute cycle at a temperature of 95°C, followed by 40 cycles of 30 seconds at a temperature of 95°C and 30 seconds at a temperature of 62°C. For the mcrA gene, the qPCR cycle consisted of a 3-minute cycle at a temperature of 94°C, followed by 40 cycles of 40 seconds at a temperature of 94°C and 30 seconds at a temperature of 57°C. The specificity of the reactions was verified by analysing the dissociation curve and by subsequent electrophoresis of the qPCR products, where it was verified that the band appearing on the agarose gel showed the appropriate molecular weight. After quantification, the number of 16S rRNA gene transcripts of methanogens and mcrA gene transcripts per gram of dry weight of sediment was calculated. Statistical analyses Statistical analyses and figures were done using JMP 16.2 and SigmaPlot 15.0 software. Methane production and the number of transcripts in each wetland determined by qPCR were represented using box plots. The regressions between temperature and methane emissions and between temperature and the number of mcrA and 16S rRNA gene transcripts were carried out through a single 2 parameters exponential growth model. The remaining regressions were determined with a linear model. RESULTS The wetlands studied showed significant differences in both the salinity (conductivity) of their waters and sediments, as well as in (the percentage of) organic matter content in the sediment (Table 1 ). In terms of sediment organic matter, there was considerable variation within all wetland categories. With respect to salinity, the highest conductivities were observed in inland saline lakes and in some coastal wetlands. In the inland saline lakes, a salinity gradient was observed, with the soda Lake Caballo Alba showing the lowest conductivity and amounts of organic matter in the sediments, while Laguna Salada de Chiprana showed the highest conductivity and organic matter sediment content. A well-marked salinity gradient was also observed in the coastal wetlands, with Bullent showing the lowest conductivity and, in turn, a very low amount of organic matter in sediment, while the highest conductivities were observed in Encanyissada and Salinas del Cabo de Gata. The volcanic wetlands showed low conductivity, and Laguna de Fuentillejo showed the lowest values of organic matter in sediment. As for the inland non-saline wetlands, all volcanic wetlands had high levels of organic matter in the sediment, higher than 10% (dry weight). The low flux Laguna de Beleña, Balsa la Mueda and Laguna de Pradales showed the lowest conductivities among inland non-saline waters, while the higher flux systems as Laguna de la Alcaparrosa and Galacho de El Burgo de Ebro (oxbow lake) showed the highest conductivities. Methane fluxes in relation to temperature The influence of warming on methane fluxes showed different patterns for each wetland category and for each individual wetland (Fig. 1 ). In general, the highest methane fluxes and exponential enhancement by temperature were observed in high water renewal non-saline inland and in volcanic wetlands, all of these usually those with the lower salinity, while coastal wetlands and inland saline lakes, those of higher salinities, had the lowest methane fluxes. However, within each wetland category, there was considerable variability in methane fluxes, especially in inland non-saline wetlands. Specifically, Laguna de Beleña, Laguna de Pradales and Balsa de la Mueda, with much lower water renewal, showed the highest methane fluxes, but very low methane fluxes were measured in the high water renewal sites, such as Ullal de Baldoví, Laguna de la Alcaparrosa and Galacho de El Burgo de Ebro. On the other hand, in inland saline lakes and coastal wetlands, lower methane fluxes were observed as sediment conductivity increased. Regarding the relationship between methane fluxes and temperature, all the wetland categories showed a high coefficient (R = 0.35–0.91.6), but only a significant positive regression between increasing temperature and methane fluxes was observed in the soda lake (R = 0.916, p < 0.05), in volcanic (R = 0.619, p < 0.01) and in high flux inland non-saline (R = 0.863, p < 0.0001) wetlands. Influence of temperature and wetland category on gene expression The concentration of methanogens 16S rRNA gene transcripts were above the detection limit of the qPCR assay in all the wetlands studied, with considerable variability within each category (Fig. 2 ). The highest concentration of 16S rRNA gene transcripts was detected in low water renewal inland non-saline wetlands, more specifically in Laguna de Beleña. However, low values were detected in high water renewal non-saline wetlands, particularly in Laguna de la Alcaparrosa and El Burgo de Ebro. In volcanic wetlands, the highest number of transcripts was found for Laguna de Carrizosa, while Laguna de Caracuel and Laguna de Fuentillejo showed similar values. In coastal wetlands, the amount of methanogen 16S rRNA gene transcripts decreased as sediment conductivity increased, with the lowest values measured in Salinas del Cabo de Gata and Bullent, even if the latter did not display high conductivity, it contained very low amounts of organic matter in the sediment. In the inland saline lakes, with low methanogen 16S rRNA transcripts, a negative relationship was also observed between conductivity and the concentration of methanogen 16S rRNA gene transcripts, with the highest values measured in Laguna de Zorrilla and the lowest in Laguna Salada de Chiprana, which is a hypersaline lake. On the other hand, no significant regression was observed between the increase in temperature and the concentration of 16S rRNA gene transcripts at the global level. When considered by wetland category (Fig. 2 B), only volcanic wetlands showed a significant positive regression (R: 0.6, p < 0.05). On the other hand, the increase in temperature was strongly related to the amount of mcrA gene transcripts (Fig. 3 ). The variation in the concentration of transcripts depended on the wetland category, and, in turn, showed specific patterns in each wetland. In each wetland category, we measured a great variability in the concentration of mcrA transcripts. The highest concentrations were observed in low water renewal inland non-saline and in volcanic wetlands, while coastal wetlands and inland saline lakes showed the lowest concentrations, which were below the detection limit in most of them (Fig. 3 A). In inland non-saline wetlands, the highest concentration of mcrA gene transcripts were observed in the low water renewal Laguna de Beleña, Laguna de Pradales and Balsa de la Mueda, while very low transcript copies were measured in the high water renewal inland non-saline sites Ullal de Baldoví, Laguna de la Alcaparrosa and El Galacho de El Burgo de Ebro. In volcanic wetlands, also with very low water renewal, transcript quantities were high, with the lowest values observed in Laguna de Fuentillejo. In coastal wetlands, mcrA gene transcripts were only detected in El Senillar de Moraira, which is a brackish water spring, while in the rest of the coastal wetlands with higher salinity (Encanyissada and Salinas del Cabo de Gata) or with very low amounts of organic matter in sediment (Bullent), no mcrA transcripts were detected. In inland saline lakes there was also no detection of mcrA gene transcripts, so no variations with temperature could be tested. The only soda lake Caballo Alba showed higher levels of mcrA gene transcripts similar to some of the low water flux inland non saline wetlands. When considering the concentration of transcripts per wetland category and incubation temperature (Fig. 3 B), we observed an increase of mcrA expression with temperature only for non-saline wetlands, but with a specific pattern for each wetland category. In low water renewal inland non-saline wetlands, the most pronounced increase in transcript concentration occurred when jumping from 4°C to 14–25°C, whereas for the volcanic wetlands this jump was observed between 14°C and 25°C. In the soda lake Caballo Alba the increase was consistently exponential. When all the wetlands were considered (Fig. 4 A), a significant positive regression was observed between the incubation temperature and the concentration of mcrA transcripts (R = 0.35, p < 0.001). However, when this relationship was broken down into the different wetland categories (Fig. 4 B), in Senillar de Moraira, the only coastal wetland where mcrA gene transcripts could be detected, the variations were non-significant, though there was a very low amounts of transcripts detected. Both the soda lake Caballo Alba (R = 0.936, p < 0.01) and the volcanic wetlands (R = 0.636, p < 0.05) showed a significant positive regression. Methane fluxes and gene expression The relationship between methane fluxes and the concentration of transcripts of mcrA and methanogens 16S rRNA genes showed different patterns when comparing all wetlands together and when separating them by wetland category (Figs. 5 and 6 ). Overall, methane fluxes showed a significant positive linear regression with the concentration of methanogens 16S rRNA gene transcripts (R = 0.55, p < 0.0001) and mcrA gene transcripts (R = 0.542, p < 0.0001) (Figs. 5 A and 6 A). When separating by wetland category (Fig. 5 B), a significant linear regression for 16S rRNA transcripts and methane fluxes was only observed in low water renewal (high methane flux) inland non-saline wetlands (R: 0.50, p < 0.0001), in high water renewal (low methane flux) inland non-saline wetlands (R: 0.526, p < 0.0001), as well as in volcanic wetlands (R: 0.928, p < 0.0001). In turn, when applied to the mcrA transcripts (Fig. 6 B), all categories showed a high and significant linear regression except for coastal and high water renewal inland non saline wetlands (low methane flux). On the other hand, the amounts of transcripts of the mcrA gene and 16S rRNA gene of methanogenic archaea were related, but the pattern of relationship between both amounts was influenced by the increase in temperature (Fig. 7 , Supplementary Fig. 1). The concentrations of transcripts of the mcrA and methanogen 16S rRNA genes showed a significant regression (R: 0.85, p < 0.001) (Fig. 7 A). However, most of samples incubated at 25°C showed a higher concentration of mcrA transcripts than that estimated from the concentration of 16S rRNA gene transcripts. By observing the ratio between the concentration of both gene transcripts in each wetland (Suppl. Figure 1A-K), we can infer that the expression of mcrA gene increased per unit of 16S rRNA gene as temperature increased. Furthermore, this ratio showed the highest values in volcanic wetlands, where at 25 ºC the amount of mcrA gene transcripts was almost half that of methanogen 16S rRNA gene transcripts, and also in low water renewal inland non-saline wetlands which in turn had higher methane fluxes (Laguna de Beleña, Laguna de Pradales and Balsa de la Mueda). At 25 ºC, this ratio was also high in the soda lake Caballo Alba. L’Ullal de Baldoví, la Laguna de Alcaparrosa and El Galacho de El Burgo de Ebro, the high water renewal inland non-saline wetlands, also had a low ratio of mcrA /methanogen 16S rRNA transcripts, which hardly changed with temperature, along with El Senillar de Moraira, which also had low methane fluxes. The pattern of increase in this ratio with temperature differed according to the wetland category. Thus, in low water renewal inland non-saline wetlands with higher methane fluxes and in the soda lake, the increase in the ratio with temperature was progressive, while in the volcanic wetlands the ratio did not change between 4–14ºC but sharply increased at 25ºC. mcrA gene expression and metabolic potential The relationship between the concentration of mcrA gene transcripts and the gene counts of this gene inferred by PICRUSt2, and the relationship between methane fluxes and the gene counts of the mcrA gene, differed depending on whether the inference was made from DNA or cDNA datasets, and also varied with temperature and wetland category (Fig. 8 ). No significant regression was observed between the concentration of mcrA gene transcripts measured by qPCR and the gene counts inferred by PICRUSt2 from DNA (Fig. 8 A), but instead significant regression appeared when using the gene counts inferred from cDNA (R = 0.66, p < 0.01, Fig. 8 B). Furthermore, the latter relation was statistically significant in inland non-saline wetlands (R = 0.74, p < 0.01), volcanic wetlands (R = 0.86, p < 0.01) and in the soda lake (R = 0.67, p < 0.01). Nonetheless, in El Senillar de Moraira (the only coastal wetland with mcrA transcripts detected by qPCR, albeit in very low quantities) no relation was observed (Fig. 8 -C). On the other hand, the regression between the concentration of mcrA gene transcripts detected by qPCR and the gene counts inferred from cDNA was positive and significant at all incubation temperatures (minimum R value = 0.65, p < 0.01) (Fig. 8 D). In addition, both mcrA gene counts inferred from DNA (R = 0.46, p < 0.05) and those inferred from cDNA (R = 0.76, p < 0.01) showed a significant positive regression with methane fluxes (Fig. 8 E-F), but the regression based on gene counts from cDNA showed a higher R. Therefore, the inference of gene counts using PICRUSt2 from both DNA and cDNA yielded reliable results. On the other hand, no statistically significant regression was observed between the mcrA gene transcripts measured using qPCR and the gene counts of the dsrB gene (marker of dissimilatory sulphate reduction) obtained from DNA datasets using PICRUSt2 (Fig. 7 B). However, regarding the relationship between cDNA-gene counts of the mcrA gene and DNA-gene counts of the dsrB gene, it was observed that, in general, this ratio increased with temperature (Supplementary Fig. 2A-K). Since the PICRUSt2 data for the dsrB gene was normalised by copies of the recA gene (single copy gened per cell), we observed that the number of gene counts of the mcrA gene compared to the number of sulphate-reducing bacteria cells increased with temperature, which means that an increase in temperature favoured methanogenic archaea over sulphate-reducing bacteria. The inland non-saline wetlands showed great variability in the values of this ratio, with the highest values recorded in the low water renewal sites Laguna de Beleña, Laguna de Pradales and Balsa de la Mueda. In the high water renewal sites Laguna de Alcaparrosa and Ullal de Baldoví, the ratio did not increase with temperature, while in Galacho de El Burgo de Ebro it only increased at 25ºC. In the volcanic wetlands and in the soda lake Caballo Alba, this ratio also increased, while in El Senillar de Moraira (the only coastal wetland with mcrA transcripts detected by qPCR), which showed very low methane fluxes, no relationship with temperature was found. DISCUSSION This study examined the short-term effect of variations in temperature on methane fluxes and gene expression related to methanogenesis in the sediment of 16 representative wetlands and shallow lakes of different types of Mediterranean wetlands. The increase in temperature increased the gene expression of the mcrA gene, which encodes for a key enzyme involved in the last step of methanogenesis, and this increase was associated with an increment of methane fluxes. Moreover, higher temperatures favoured the activity of methanogens over sulphate-reducing bacteria (SRB). Previous studies have shown how the natural ecological characteristics of Mediterranean wetlands determine the structure and potential metabolism of their prokaryotic communities [ 23 , 32 ]. We here demonstrated that low water renewal inland non-saline wetlands were also those with the highest mcrA gene expression (4.92×10 3 – 5.87×10 7 transcripts·g.d.w. –1 ), due to the large amount of organic matter in the sediment, low salinity ( i.e. , low sulphate concentrations) and hydrological stability ( i.e. , low sediment disturbance thus favouring lower redox conditions needed for methanogenesis), all of them favouring methanogenesis over sulphate reduction. Regarding high water renewal inland non-saline wetlands, Galacho de El Burgo de Ebro, despite having 11.4% of organic matter in the sediment, exposure to more oxidised waters or even of the sediment to the air during dry periods, explains the low methane fluxes and transcripts. The occasionally dry sediment with high conductivity (9.3 mS·cm –1 ) also explains the low methanogenic activity and the low concentrations of mcrA transcripts measured (0-1.8×10 5 transcripts·g.d.w. –1 ). Ullal de Baldoví (18.2% organic matter and 2.4 mS·cm –1 in conductivity) and Laguna de Alcaparrosa (11.2% organic matter and 22.8 mS·cm –1 in conductivity) also had high water renewal rates which, as argued above, explain the low methanogenic activity (< 6.23×10 4 mcrA transcripts g.d.w. –1 ) [ 18 ]. In turn, the high salinities ( i.e. , high sulphate concentrations) in coastal wetlands such as Encanyissada (36.2 mS·cm –1 ) and Salinas del Cabo de Gata (39.9 mS·cm –1 ) also explain the low methanogenic activity measured ( mcrA transcripts below the limit of detection). A special case among coastal wetlands is Bullent, where the low amount of organic matter in the sediment (0.6%) can be the main explanation for the low activity of methanogens ( mcrA transcripts < LoD). Although the increase in the concentration of mcrA gene transcripts with temperature was quite common to most types of Mediterranean wetlands analysed, each category followed a characteristic response pattern. As determined in previous studies, most Mediterranean wetland types have specific methanogen communities [ 23 , 32 ], which can be differentiated one from another either by the relative abundance of methanogens or by the taxonomy and functional behaviour of the prevalent taxa. The abundance of different methanogen families can vary with temperature, with some taxa dominating at low temperatures and others at higher temperatures [ 33 ]. In addition, an increase in temperature may promote a change in the dominant methanogenic pathway [ 33 ]. In most of the wetlands studied, the family Methanomassiliicoccaceae was the most abundant [ 23 , 32 ]. The increase in temperature favoured a greater activity of rare methanogenic taxa such as Methanosarcinaceae and Methanobacteriaceae , which showed the most considerable changes in abundance between the different wetland types [ 32 ]. A particular case is that of inland saline lakes, where the concentrations of mcrA gene transcripts were below the detection limit. Furthermore, no mcrA gene transcripts could be detected in coastal wetlands with higher salinities, such as Encanyissada or Salinas de Cabo de Gata. In both inland saline lakes and deltaic wetlands, the increase in salinity was associated with low methane fluxes [ 2 – 3 , 7 ]. According to these results, we conclude that the low methane fluxes measured in these wetlands were due to their low methanogenic activity rather than a high methane consumption by methanotrophy. In saline environments, methylotrophic methanogenesis is the predominant pathway [ 34 ] due to the presence of methylamines [ 18 ]. In the wetlands studied, especially in inland saline lakes and some coastal wetlands, the major groups of methanogens identified belonged to methylotrophic taxa, while non-methylotrophic methanogens were less abundant [ 23 , 32 ]. However, the presence of methylamines in inland saline lakes may be low [ 23 ], so methane production could largely depend on acetoclastic and hydrogenotrophic methanogenesis, which is outcompeted by sulphate reduction under high salinities [ 17 ]. In fact, in sediments from other saline environments with high rates of dissimilatory sulphate-reduction, hydrogenotrophic methanogenesis was found to be the most relevant, but it occurred mainly in the innermost layers of sediment where sulphate reduction rates were lower than at the surface, where they reached their maximum [ 35 ]. Accordingly, the possible occurrence of niches in the sediment with a low sulphate-reduction rates that would allow a low activity of aceticlastic and/or hydrogenotrophic methanogenesis could not be excluded, though the low expression of the mcrA gene (under the limit of detection) in the saline wetlands studied seems to support the lack of significant methanogenic activity in highly saline systems. On the other hand, in saline wetlands transcripts of the methanogen 16S rRNA gene were detected, sometimes in quantities comparable to those found in less saline wetlands. This suggests that in saline wetlands, methanogenic archaea, although present, were incapable of effectively performing methanogenesis and must obtain energy either through other metabolisms that do not involve methanogenesis or maybe they maintain symbiotic relationships with other microorganisms without producing methane. In fact, methanogens have been described that can alternate between methanogenesis and the reduction of iron compounds and even rely solely on the reduction of iron compounds without producing methane [ 36 ]. In addition, methanogens synthesize photoactive molecules and have genes related to carotenoid production, so it might be that methanogenic archaea produce chromophores that could be coupled to transmembrane ion pumping (which could be used to synthesize ATP) or to photon-mediated redox reactions [ 37 ]. Therefore, the variety of metabolisms that can be carried out by methanogenic archaea in saline environments may be greater than expected, thus not relying solely on methanogenesis, which would explain their presence despite no transcripts of gene neither methane fluxes were registered. The positive correlation between methane fluxes and the concentration of mcrA and 16S rRNA transcripts indicated that the increase in methane fluxes with temperature could be due to: i) an increase in the abundance of methanogens within the days of incubation (2 to 5 days), ii) an increase in the expression of the mcrA gene, or iii) both. However, the lack of correlation between the concentration of methanogen 16S rRNA gene transcripts with temperature and the increase in the mcrA/methanogen 16S rRNA ratio with temperature indicated that the measured increase in methane fluxes with temperature was due to an overexpression of mcrA genes. The objective of this study was to evaluate the rapid response of methanogenic archaea to an increase in temperature, so the incubation time of the sediment samples at different temperatures was short (less than 1 week), making it difficult to achieve a significant increase in the abundance of methanogenic archaea. In this regard, experiments with artificial ponds analysing the effect of a long-term temperature increase (11 years) did observe an increase in the abundance of methanogens, which in turn was related to higher methane production [ 38 ]. Focusing on the relationship between the amount of mcrA gene transcripts determined by qPCR and the gene counts estimated using PICRUSt2, we have determined that the gene counts inferred using cDNA were more similar to the actual activity of methanogens and the methane fluxes measured than the gene counts inferred using DNA datasets. This divergence between the two approaches may be explained by the fact that inference from cDNA datasets mostly considered the active fraction of the community, whereas using the DNA libraries took into account the bulk community regardless they are active or not Regarding the competitive relationship between methanogenic archaea and SRB in saline environments, SRB are better competitors than methanogenic archaea that perform aceticlastic methanogenesis or methanogenesis based on CO 2 reduction [ 18 ]. However, our results indicate that increases in temperature might partially reverse this competitive relationship, favouring the potential activity of methanogenic archaea over that of SRB. In saline environments, the increase in temperature indicated by climate change predictions [ 9 – 10 ] may decrease the inhibition of methanogenesis by SRB and result in a significant increase in methane fluxes from these wetlands, where they are normally low [ 2 – 3 , 7 ]. Furthermore, considering that in the Mediterranean saline ecosystems studied, the amount of organic matter in the sediment can be high (Table 1 ), at high temperatures methanogenic archaea will compete better than SRB for the metabolic substrates found in this organic matter, which may increase methane fluxes from these saline ecosystems [ 2 ]. On the other hand, our results suggest that in low water renewal inland non-saline wetlands, an increase in temperature would further favour methanogenic archaea, leading to an increase in methane fluxes that would significantly affect the carbon balance of this kind of ecosystem. CONCLUSIONS This study estimated the short-term effect of a temperature increase on methane fluxes and gene expression in methanogenic archaea present in the sediment of 16 lagoons representative of various categories of Mediterranean wetlands. An increase in methane fluxes was observed as the temperature rose, which coincided to an increment in the gene expression of methanogenic archaea. In addition, a significant relationship was found between the metabolic inference of active microorganisms and gene expression analysed by qPCR, and it has been observed that increasing temperatures favoured the potential activity of methanogenic archaea over that of SRB. These results provide insights to infer the effect that climate change may have on methane fluxes and the activity of methanogens in Mediterranean wetlands, and may help to design management measures for these wetlands from a climate perspective. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This work was supported by projects CLIMAWET-CONS (PID2019-104742RB-I00) and CLIMAWET (CGL2015-69557-R), funded by the Agencia Estatal de Investigación of the Spanish government, and by the project ECCAEL (PROMETEO CIPROM-2023-031), funded by the Generalitat Valenciana, both granted to Antonio Camacho (PI), as well as by other funds raised by the PI from research contracts with companies. 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. Author Contribution J.M.L.: sampling, conceptualization, data curation, formal analysis, investigation, methodology, writing – original draft, writing – review and editing. A.P.: sampling, formal analysis, software, supervision, validation. C.R.: sampling, methodology, supervision, validation. D.M.: sampling, methodology, supervision, validation. A.S.M.: methodology. C.M.B.: conceptualization, investigation, methodology, supervision, validation, writing – review and editing. A.C.: conceptualization, formal analysis, investigation, methodology, funding acquisition, project administration, supervision, validation, writing – review and editing. All authors read and approved the final manuscript. Acknowledgements Not applicable. Data Availability Raw sequencing data are available in the NCBI Sequence Read Archive (SRA) under the BioProject accession ID PRJNA1397488.Accession link:https://dataview.ncbi.nlm.nih.gov/object/PRJNA1397488?reviewer=aq9psn97mhpnnd3hkjn7ntau99 References Sica YV, Quintana RD, Radeloff VC, Gavier-Pizarro GI. Wetland loss due to land use change in the Lower Paraná River Delta, Argentina. Sci Total Environ. 2016;568:967–78. Camacho A, Picazo A, Rochera C, Santamans AC, Morant D, Miralles-Lorenzo J, Castillo-Escriva A. Methane emissions in Spanish saline lakes: Current rates, temperature and salinity responses, and evolution under different climate change scenarios. Water. 2017;9:659. Morant D, Picazo A, Rochera C, Santamans AC, Miralles-Lorenzo J, Camacho-Santamans A, et al. Carbon metabolic rates and GHG emissions in different wetland types of the Ebro Delta. PLoS ONE. 2020;15:4. Britton RH, Crivelli AJ. Wetlands of southern Europe and North Africa: mediterranean wetlands. Wetlands of the world: Inventory, ecology and management Volume I: Africa, Australia, Canada and Greenland, Mediterranean, Mexico, Papua New Guinea, South Asia, Tropical South America, United States. Springer Netherlands; 1993. pp. 129–94. Morant D, Camacho-Santamans A, Hidalgo R, Camacho A. Transdisciplinary approach to the characterisation and current status of Spanish karstic lakes on gypsum. Environ Earth Sci. 2024;83:457. Morant D, Picazo A, Rochera C, Santamans AC, Miralles-Lorenzo J, Camacho A. Influence of the conservation status on carbon balances of semiarid coastal Mediterranean wetlands. Inland Waters. 2020;10:453–67. Morant D, Rochera C, Picazo A, Miralles-Lorenzo J, Camacho-Santamans A, Camacho A. Ecological status and type of alteration determine the C-balance and climate change mitigation capacity of Mediterranean inland saline shallow lakes. Sci Rep. 2024;14:29065. Camacho-Santamans A, Morant D, Rochera C, Picazo A, Camacho A. Towards an enhancement of the climate change mitigation capacity of inland saline shallow lakes through hydrological regime and vegetation management: a modelling approach. Water Int. 2024;50:1–28. [IPCC] Intergovernmental Panel on Climate Change. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva (CH): IPCC; 2014. [MedECC] Mediterranean Experts on Climate and Environmental Change. Climate and Environmental Change in the Mediterranean Basin – Current situation and Risks for the Future. First Mediterranean Assessment Report Union for the Mediterranean, Plan Bleu. Marseille (FR): UNEP/MAP; 2020. Andreote FD, Jiménez DJ, Chaves D, Dias ACF, Luvizotto DM, Dini-Andreote F, et al. The microbiome of Brazilian mangrove sediments as revealed by metagenomics. PLoS ONE. 2012;7:6. Bridgham SD, Cadillo-Quiroz H, Keller JK, Zhuang Q. Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales. Glob Chang Biol. 2013;19:1325–46. Timmers PH, Welte CU, Koehorst JJ, Plugge CM, Jetten MS, Stams AJ. Reverse methanogenesis and respiration in methanotrophic archaea. Archaea. 2017. doi.org/10.1155/2017/1654237 . Watanabe T, Kimura M, Asakawa S. Distinct members of a stable methanogenic archaeal community transcribe mcrA genes under flooded and drained conditions in Japanese paddy field soil. Soil Biol Biochem. 2009;41:276–85. Evans PN, Boyd JA, Leu AO, Woodcroft BJ, Parks DH, Hugenholtz P, Tyson GW. An evolving view of methane metabolism in the Archaea. Nat Rev Microbiol. 2019;17:219–32. Wilkins D, Lu XY, Shen Z, Chen J, Lee PK. Pyrosequencing of mcrA and archaeal 16S rRNA genes reveals diversity and substrate preferences of methanogen communities in anaerobic digesters. Appl Envirn Microbiol. 2015;81:604–13. Oren A. Thermodynamic limits to microbial life at high salt concentrations. Environ Microbiol. 2011;13:1908–23. Sorokin DY, McGenety T. Methanogens and Methanogenesis in Hypersaline Environments. In: Stams AJM, Sousa DZ, editors. Biogenesis of Hydrocarbons. Handbook of Hydrocarbon and Lipid Microbiology. Springer; 2019. pp. 1–24. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–88. Sang S, Zhang X, Dai H, Hu BX, Ou H, Sun L. Diversity and predictive metabolic pathways of the prokaryotic microbial community along a groundwater salinity gradient of the Pearl River Delta, China. Sci Rep. 2018;8:1–11. Liu R, Wang Z, Wang L, Li Z, Fang J, Wei X, et al. Bulk and active sediment prokaryotic communities in the Mariana and Mussau Trenches. Front Microbiol. 2020;11:1521. Gionchetta G, Oliva F, Romaní AM, Bañeras L. Hydrological variations shape diversity and functional responses of streambed microbes. Sci Total Environ. 2020;714:136838. Miralles-Lorenzo J, Picazo A, Rochera C, Morant D, Casamayor EO, Menéndez‐Serra M, Camacho A. Environmental gradients and conservation status determine the structure and carbon‐related metabolic potential of the prokaryotic communities of Mediterranean inland saline shallow lakes. Ecol Evol. 2025. doi.org/10.1002/ece3.71286 . APHA Standard Methods for Water and Wastewater Examination. 18th ed. American Public Health Association. Washington (DC);2005. Picazo A, Rochera C, Villaescusa JA, Miralles-Lorenzo J, Velázquez D, Quesada A, Camacho A. Bacterioplankton community composition along environmental gradients in lakes from Byers peninsula (Maritime Antarctica) as determined by next-generation sequencing. Front Microbiol. 2018;10:908. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–98. Kessler AJ, Chen YJ, Waite DW, Hutchinson T, Koh S, Popa ME, et al. Bacterial fermentation and respiration processes are uncoupled in anoxic permeable sediments. Nat Microbiol. 2019;4:1014–23. Chen S, Wang P, Liu H, Xie W, Wan XS, Kao SJ, et al. Population dynamics of methanogens and methanotrophs along the salinity gradient in Pearl River Estuary: implications for methane metabolism. Appl Microbiol Biotechnol. 2020;104:1331–46. Roy A, Sar P, Sarkar J, Dutta A, Sarkar P, Gupta A, et al. Petroleum hydrocarbon rich oil refinery sludge of North-East India harbours anaerobic, fermentative, sulfate-reducing, syntrophic and methanogenic microbial populations. BMC Microbiol. 2018;18:1–22. Florindo C, Ferreira R, Borges V, Spellerberg B, Gomes JP, Borrego MJ. Selection of reference genes for real-time expression studies in Streptococcus agalactiae. J Microbiol Methods. 2012;90:220–7. Compte-Port S, Borrego CM, Moussard H, Jeanbille M, Restrepo‐Ortiz CX, de Diego A, et al. Metal contaminations impact archaeal community composition, abundance and function in remote alpine lakes. Environ Microbiol. 2018;20:2422–37. Miralles-Lorenzo J, Picazo A, Rochera C, Morant D, Camacho A. Environmental gradients shape the structure and carbon-related potential metabolisms of the prokaryotic communities in deltaic wetlands. Submitted; 2025. Fu L, Song T, Lu Y. Snapshot of methanogen sensitivity to temperature in Zoige wetland from Tibetan plateau. Front Microbiol. 2015;6:131. Zhou J, Theroux SM, Bueno de Mesquita CP, Hartman WH, Tian Y, Tringe SG. Microbial drivers of methane emissions from unrestored industrial salt ponds. ISME J. 2022;16:284–95. Saxton MA, Samarkin VA, Madigan MT, Bowles MW, Sattley WM, Schutte CA, Joye SB. Sulfate reduction and methanogenesis in the hypersaline deep waters and sediments of a perennially ice-covered lake. Limnol Oceanogr. 2021;66:1804–18. Sivan O, Shusta SS, Valentine DL. Methanogens rapidly transition from methane production to iron reduction. Geobiology. 2016;14:190–203. Buan NR. Methanogens: pushing the boundaries of biology. Emerg Top Life Sci. 2018;2:629–46. Zhu Y, Purdy KJ, Eyice Ö, Shen L, Harpenslager SF, Yvon-Durocher G, et al. Disproportionate increase in freshwater methane emissions induced by experimental warming. Nat Clim Chang. 2020;10:685–90. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 10 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 08 Jan, 2026 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8490616","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599371456,"identity":"53b5a70b-7a3d-4044-9393-6df2b0e8cb03","order_by":0,"name":"Javier Miralles-Lorenzo","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Miralles-Lorenzo","suffix":""},{"id":599371457,"identity":"d8b656a7-6cd0-4a28-97df-5db32c9cab2f","order_by":1,"name":"Antonio Picazo","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Picazo","suffix":""},{"id":599371458,"identity":"6996af91-6fc5-4421-a820-70f67e84d9eb","order_by":2,"name":"Carlos Rochera","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Rochera","suffix":""},{"id":599371459,"identity":"4f9fae9f-32b3-441e-920b-229e6bd81510","order_by":3,"name":"Daniel Morant","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Morant","suffix":""},{"id":599371460,"identity":"235a6b48-c952-4229-bf5d-fa396aa812e4","order_by":4,"name":"Alexandre Sánchez-Melsió","email":"","orcid":"","institution":"Catalan Institute for Water Research","correspondingAuthor":false,"prefix":"","firstName":"Alexandre","middleName":"","lastName":"Sánchez-Melsió","suffix":""},{"id":599371461,"identity":"9a76bb21-a7d2-462e-8939-1bb21b5a286d","order_by":5,"name":"Carles M Borrego","email":"","orcid":"","institution":"Catalan Institute for Water Research","correspondingAuthor":false,"prefix":"","firstName":"Carles","middleName":"M","lastName":"Borrego","suffix":""},{"id":599371464,"identity":"a47b1639-fb08-4705-9c17-134a5114b035","order_by":6,"name":"Antonio Camacho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYNACAyjN2MAgR4RyZlQtxkRqgQGglsQGQhr4Z/cffFxQYMPA33468cPPHTbpG24kMD78gUeLxJ3DzMYzDNIYJM7kbpbsPZOWC9TCbMyDz5obyWzSPAaHgd7J3SDN2HY4d8OZA2zS+HTIw7Xwv938G6gl3eDMAfaf+BxmANcikbsNZEuCwfEGNgZ8DjO8kWxszGOQxiNx4+02y962NMOZxxubpfFpkbuR+PAxzx8bOf7+3M03frbZyPMdZj74EZ/DYADZWGDsjIJRMApGwSigDAAAIIlJuLO6taoAAAAASUVORK5CYII=","orcid":"","institution":"University of Valencia","correspondingAuthor":true,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Camacho","suffix":""}],"badges":[],"createdAt":"2025-12-31 14:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8490616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8490616/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103940387,"identity":"8dc2d2b6-fb29-4f6e-bcf4-c966acd2c37b","added_by":"auto","created_at":"2026-03-04 19:03:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":213546,"visible":true,"origin":"","legend":"\u003cp\u003eMethane fluxes in each wetland´s assays and their relationship with temperature. A) Box plots showing methane fluxes from each wetland measured during the experiment. Wetlands have been grouped into six wetland categories: soda lakes, inland saline lakes, coastal, volcanic and inland non-saline (divided in wetlands with high or low water renewal). Inland saline lakes and coastal wetlands have been ordered from left to right according to an ascending salinity gradient. The dotted line represents the mean and the solid line the median. B) Exponential regressions between methane emissions and temperature increase in each wetland category. *: \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05. **: \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.01. ****: \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/f35d36749c4e1e5699ef7706.png"},{"id":103940395,"identity":"d4dcc0cf-631d-4f91-9736-f22f787dc327","added_by":"auto","created_at":"2026-03-04 19:03:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":217904,"visible":true,"origin":"","legend":"\u003cp\u003eA) Box plots showing the concentration of methanogen’s \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts in each wetland. Wetlands have been grouped into six wetland categories: soda lakes, inland saline lakes, coastal, volcanic and inland non-saline (divided in wetlands with high or low water renewal). The dotted line represents the mean and the solid line the median. B) Exponential regressions between temperature increase and the concentration of \u003cem\u003e16S rRNA\u003c/em\u003egene transcripts (16S MTG). *: \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05. g.d.w.: grams of dry weight.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/5575491fa494aab9283ce7bc.png"},{"id":104401914,"identity":"68ab618f-d972-48ef-85b5-94a31999cc8a","added_by":"auto","created_at":"2026-03-11 12:13:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143808,"visible":true,"origin":"","legend":"\u003cp\u003eA) Box plots showing the concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts in each wetland. B) Concentrations of \u003cem\u003emcrA\u003c/em\u003e gene transcripts for each wetland category (except for inland saline lakes, which had no transcripts) and temperature. In A and B, the dotted line represents the mean, and the solid line represents the median. Inland non-saline wetlands are divided in wetlands with high or low water flux).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/32311e53d869bcbb851641cd.png"},{"id":103940390,"identity":"f40bae74-50fb-4945-9a38-7f8f2697533b","added_by":"auto","created_at":"2026-03-04 19:03:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":175580,"visible":true,"origin":"","legend":"\u003cp\u003eA) Overall exponential regressions between the increase in the incubation temperature and the concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts. B) Same than in A but for each wetland category (except for inland saline lakes, which had no transcripts). *: \u003cem\u003ep\u003c/em\u003e-value\u0026lt;0.05. **: \u003cem\u003ep\u003c/em\u003e-value\u0026lt;0.01. ***: \u003cem\u003ep\u003c/em\u003e-value\u0026lt;0.001. g.d.w.: grams of dry weight.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/b9aa1a5b849e8eb56cbc96d5.png"},{"id":103940388,"identity":"6138f566-9435-4860-b3aa-502dccc918d7","added_by":"auto","created_at":"2026-03-04 19:03:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57138,"visible":true,"origin":"","legend":"\u003cp\u003eA) Linear regression between methane fluxes and the concentration of \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts in all lagoons. Only samples in which transcripts of both the \u003cem\u003emcrA\u003c/em\u003egene and the \u003cem\u003e16S rRNA\u003c/em\u003e gene were detected were included in the analysis. B) Same as in A but for each wetland category (except for inland saline lakes, which had no transcripts). The dotted line represents the 95% confidence interval. ****: \u003cem\u003ep\u003c/em\u003e-value\u0026lt;0.0001. g.d.w.: grams of dry weight.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/b81a148e8fc56d9da39f491c.png"},{"id":103940394,"identity":"6858e1d4-1f94-4ed5-8e92-7a36b6407b7b","added_by":"auto","created_at":"2026-03-04 19:03:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":202853,"visible":true,"origin":"","legend":"\u003cp\u003eA) Linear regression between methane fluxes and the concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts in all wetlands. B) Same as in A but for each wetland category (except for inland saline lakes, which had no transcripts). The dotted line represents the 95% confidence interval. *: \u003cem\u003ep\u003c/em\u003e-value\u0026lt;0.05. **: \u003cem\u003ep\u003c/em\u003e-value\u0026lt;0.01. ***: \u003cem\u003ep\u003c/em\u003e-value\u0026lt;0.001. ****: \u003cem\u003ep\u003c/em\u003e-value\u0026lt;0.0001. g.d.w.: grams of dry weight.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/d24cb56f8612011ffc9c5054.png"},{"id":103940389,"identity":"85e8aa3a-3895-409d-9321-4ce3141acb89","added_by":"auto","created_at":"2026-03-04 19:03:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":174348,"visible":true,"origin":"","legend":"\u003cp\u003eA) Linear regression between the amounts of \u003cem\u003emcrA\u003c/em\u003e and methanogen \u003cem\u003e16S rRNA \u003c/em\u003egene transcripts. B) Linear regression between \u003cem\u003emcrA\u003c/em\u003e transcripts and \u003cem\u003edsrB\u003c/em\u003egene counts inferred from DNA sequencing datasets (see Material and Methods for details). ***: \u003cem\u003ep\u003c/em\u003e-value\u0026lt;0.001. g.d.w.: grams of dry weight.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/8cea2088aa001fac5c076464.png"},{"id":104401797,"identity":"56206b5c-95b7-4ddc-9727-d1f04bb61652","added_by":"auto","created_at":"2026-03-11 12:13:34","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":262400,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between the number of \u003cem\u003emcrA\u003c/em\u003e gene transcripts quantified by qPCR and the gene counts inferred using PICRUSt2. Only samples in which \u003cem\u003emcrA\u003c/em\u003e gene transcripts were detected by qPCR were included in the statistical analysis. A) Linear regression between the concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts and the gene counts inferred from DNA. B) Same as in A but using gene counts inferred from cDNA. C) Linear regression between the concentration of \u003cem\u003emcrA\u003c/em\u003egene transcripts and the gene counts inferred from cDNA by wetland category. D) Linear regression between the concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts and the gene counts inferred from cDNA by temperature. E) Linear regression between methane fluxes and the gene counts of the \u003cem\u003emcrA\u003c/em\u003e gene inferred from DNA. F) Linear regression between methane fluxes and the gene counts of the \u003cem\u003emcrA\u003c/em\u003egene inferred from cDNA. *: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. **: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. g.d.w.: grams of dry weight.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/6ccc1869a3b04ee12a242063.png"},{"id":104408480,"identity":"e62e5403-752d-458b-a674-0939c2dc8bb2","added_by":"auto","created_at":"2026-03-11 12:42:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1702046,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/44c422dc-1b5a-42ae-8a31-a3a817025e53.pdf"},{"id":104402454,"identity":"7776aaf4-2688-4e3b-b8af-16d1d732955c","added_by":"auto","created_at":"2026-03-11 12:15:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":423640,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8490616/v1/d1715193795883711105c141.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-term effect of a temperature gradient on the activity of methanogenic archaea and associated methane fluxes in different types of Mediterranean wetlands","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eWetlands are terrestrial areas where water covers the ground, either above or below the surface, where the soil is saturated with water. These types of ecosystems largely contribute to the dynamics of carbon greenhouse gases (C-GHG), such as carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) and methane (CH\u003csub\u003e4\u003c/sub\u003e), and thus the metabolisms involved are highly active [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, little information is available on the processes that control the carbon metabolism in Mediterranean wetlands, which are highly diverse and have different characteristics [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, the behaviour of different types of Mediterranean wetlands as net carbon sinks or emitters depends on various factors, such as salinity, seasonal variation, temperature, and the conservation status of the system [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Indeed, Mediterranean wetlands improve their C-GHG mitigation capacity after restoration [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In turn, the increase in temperature associated with climate change [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] has been linked to increases in methane emissions, which significantly alter the carbon balance and enhance their activity as GHG emitters [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProkaryotic communities in wetlands play a key role in the functioning of these ecosystems, as they are involved in the main biogeochemical cycles [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, the factors affecting these communities, especially those involved in the production or consumption of methane, are of great interest to better understand the dynamics of this greenhouse gas. Methane emissions depend on the balance between its production by methanogenic archaea and its consumption by other prokaryotes, both aerobic (methanotrophic bacteria) and anaerobic (anaerobic methane-oxidizing archaea, ANME) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Methanogens are strict anaerobes and act as terminal decomposers in anaerobic environments [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], where they couple methane production (i.e., methanogenesis) to energy acquisition through three main pathways [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]: (1) hydrogenotrophic methanogenesis, where CO\u003csub\u003e2\u003c/sub\u003e is reduced using hydrogen; (2) aceticlastic methanogenesis, which uses acetate and (3) methylotrophic methanogenesis, which uses methylated substrates such as methylamines and may or may not require hydrogen. A common enzyme in the three pathways is the methyl-coenzyme M reductase (mcr), which is involved in the final step of methanogenesis. This enzyme is made up of different subunits, and the gene encoding the alpha subunit (\u003cem\u003emcrA\u003c/em\u003e) has been used to study both the diversity of methanogenic archaea and their methanogenic activity [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The three methanogenic pathways do not, however, have the same relative importance in all ecosystems. Most pathways are not effective at high salinities [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], as they are affected by competition for metabolic substrates or electron donors between methanogenic archaea and sulphate-reducing bacteria (SRB), which are better competitors than the former under conditions of high salinity linked to high sulphate concentrations. In such saline ecosystems, like salt marshes and inland saline lakes, methylotrophic methanogens prevail over other methanogenic groups because they do not compete with SRB [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to the wide variety of prokaryotes and the impossibility of obtaining axenic cultures of all of them, molecular tools are extremely useful for studying the composition and activity of prokaryotic communities. Thus, one of the most widely used procedures for studying these communities is the sequencing and subsequent analysis of the 16S rRNA gene. In addition, various bioinformatic tools have been developed that, based on the sequences of this gene, allow for a deeper understanding of the structure and metabolisms of prokaryotic communities. One of these tools is PICRUSt2 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which infers the metabolic potential of communities based on the relative abundance and taxonomic classification of their different members [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the results of studying prokaryotic communities through DNA analysis, which provide information on the bulk community, can be very different from the results obtained through RNA analysis, which is more closely related to the potentially active community [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, the direct study of prokaryotic gene expression in the environment provides interesting results at the ecological level, since changes in such expression are strongly influenced by environmental factors [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Thus, changes in environmental conditions, such as an increase in temperature, can be reflected in the gene expression patterns of the different microorganisms that make up the community under study.\u003c/p\u003e \u003cp\u003eOur working hypothesis is that warming will result in an increase of methane emissions, and that the magnitude of this increase will depend on the limnological properties of the wetlands studied. We thus investigated the short-term effect of a temperature gradient on methane emissions using archaeal \u003cem\u003e16S rRNA\u003c/em\u003e and \u003cem\u003emcrA\u003c/em\u003e gene transcripts as proxies for archaeal growth and methanogenic activity, respectively. The experiment was carried out with sediments from wetlands of the four major categories of Mediterranean wetland systems to obtain an overall picture of how methane emissions will respond to warming in such diverse ecosystems.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sites\u003c/h2\u003e \u003cp\u003eFor this study, 16 shallow lakes and wetlands representative of the main categories of Spanish Mediterranean wetlands and shallow lakes were selected: inland saline, coastal, volcanic, and inland non-saline (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For these experiments, these wetlands were sampled once during a specific period of the 2017-18 hydrological cycle, though the characteristics shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were obtained from various samplings performed between 2015 and 2025.\u003c/p\u003e \u003cp\u003eInland saline shallow lakes occur in endorheic basins, where water loss through evaporation exceeds water inflow. This leads to salt accumulation, resulting in high salinity in both water and sediment and high sulfate concentrations in water compared to lakes with low salinity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, most of these lakes are temporary, drying up during the summer. Within this category, there was a marked salinity gradient between the lakes, which also differed in their trophic status [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Also, one of them, Caballo Alba, is a soda lake, rich in sodium bicarbonate but with much lower salinity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCoastal wetlands vary greatly in terms of structure, genesis, and dynamics. In some of them, the influence of the sea is very high, especially in wetlands that are directly connected. The wetlands selected for this study showed a marked gradient of salinity in both water and sediment, as well as in trophic status, with the saltiest wetlands having the highest concentrations of sulfate in the water column (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe studied volcanic wetlands are characterized by their location in ancient volcanic areas. They are shallow, most of the water comes from rain and surface runoff thus allowing low water renewal, and none of them are permanent. The selected wetlands have similar conductivities but display a marked trophic gradient (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInland non-saline wetlands can be permanent (with high water flux and high water renewal accordingly to its karstic origin) or temporary (with low water flux, thus low water renewal, as fed by rain water). They are generally characterized by low mineralization and low conductivity values, both in water and sediment, as well as low concentrations of sulphate in the water column. However, for the studied wetlands, the amount of organic matter in the sediment was generally higher than in most of other types of wetlands studied (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eMain features of the Mediterranean wetlands studied. Within each category, the wetlands have been arranged in ascending order of their water salinity. The results shown (average and standard deviation) have been obtained during different sampling campaigns conducted between 2015 and 2025. Chl-\u003cem\u003ea\u003c/em\u003e: Chlorophyll-\u003cem\u003ea\u003c/em\u003e. Cond: conductivity. LOI: organic matter.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e \u003cp\u003eSediment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUTM X\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUTM Y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChl-\u003cem\u003ea\u003c/em\u003e \u0026micro;g/L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCond mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSulphate g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCond mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eLOI %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSD\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\u003eInland saline lakes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaballo Alba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eALBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e365239.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4567208.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZorrilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZORR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e244572.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4084038.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChiprana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e736010.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4569469.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e63.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCoastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBullent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBULL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e754655.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4310253.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenillar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSENI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250423.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4286037.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEncanyissada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e304310.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4502849.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCabo de Gata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e569325.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4068853.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVolcanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarrizosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCARR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e392060.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4299939.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaracuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCARA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e407142.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4298564.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuentillejo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFUEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e408704.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4310553.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eInland non-saline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBele\u0026ntilde;a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBELE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e478586.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4526128.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePradales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePRAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e355930.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4729773.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLa Mueda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMUED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e637741.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4714014.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaldov\u0026iacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBALD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e731453.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4347765.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcaparrosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eALCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249561.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4104039.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEl Burgo de Ebro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBURG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e686065.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4605782.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling, incubation and physical and chemical analyses\u003c/h3\u003e\n\u003cp\u003eAll analyses of the environmental variables compiled in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were carried out by standardised methods [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSediment cores were collected from each of the studied wetlands using methacrylate tubes, following the procedure described in [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Upon arrival at the laboratory, these cores were purged with air to remove methane and placed in climate chambers at different experimental temperatures for a specified period for acclimatization. After acclimatization, the cores were hermetically sealed and incubated at 4\u0026ordm;C, 14\u0026ordm;C and 25\u0026ordm;C in the climate chambers. At each temperature, six replicates were incubated between two and five days, according to previous measurements [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Once the incubation finished, three cores out of six were randomly chosen. A portion of surface sediment (up to 5 cm deep) was extracted from each of these three cores, which was homogenized with a metal rod previously sterilized by flaming with ethanol, and immediately frozen using liquid nitrogen and then stored at \u0026minus;\u0026thinsp;80\u0026deg;C until extraction of nucleic acids. The process of collecting and freezing the samples was carried out in less than a minute to maintain the gene expression profile of the prokaryotic community, as the half-life of RNA is very short. Immediately after collecting the sediment sample, the core was shaken to release the gas retained, and the concentration of methane was measured using an Aeroqual Gas A200 CH\u003csub\u003e4\u003c/sub\u003e methane meter calibrated by gas chromatography [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The methane concentration measurements were converted into carbon emission rates per unit of time using the ideal gas law equation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eExtraction of nucleic acids, sequencing and processing\u003c/h3\u003e\n\u003cp\u003eOf the three frozen sediment replicates for each temperature, two were chosen randomly from which total nucleic acids were extracted using the RNeasy PowerSoil Total RNA Kit, which was coupled with the RNeasy PowerSoil DNA Elution Kit. In this way, both DNA and RNA were extracted from the same sediment sample (approx. 300 mg). The RNA was digested with DNAse to remove residual DNA using the TURBO DNA-free kit and retrotranscribed to DNA (cDNA) using the SuperScript III RT kit and random sequence hexamer primers. Subsequently, the V4 region of the \u003cem\u003e16S rRNA\u003c/em\u003e gene from both DNA and cDNA was sequenced using the Illumina MiSeq platform (2x250 bp) as previously described in [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Briefly, for each sample, libraries of the V4 region were obtained using the 515f/806r primer pair. The libraries were then normalised, quantified, loaded onto an Illumina MiSeq v2 flow cell and sequenced in 2x250 bp format, obtaining FastQ format sequences for each sample, which were deposited in the NCBI Sequence Read Archive under BioProject ID PRJNA1397488.\u003c/p\u003e \u003cp\u003eThe raw sequences were processed using the UPARSE pipeline [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. After merging the read pairs (2x250 bp), the sequences were filtered with a maximum expected error of 0.5 and chimeras were removed using the UCHIME algorithm. These filtered sequences were grouped into ZOTUs (zero-radius Operational Taxonomic Units), which correspond to sequences with 100% identity. The resulting ZOTU table, containing 41,722 ZOTUs, was used for downstream bioinformatics analyses (see below).\u003c/p\u003e\n\u003ch3\u003eMetabolic potential of prokaryotic communities\u003c/h3\u003e\n\u003cp\u003eThe PICRUSt2 bioinformatics tool (phylogenetic investigation of communities by reconstruction of unobserved states) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was used to determine the potential metabolic functions of the sediment prokaryotic communities in both the DNA (bulk community) and the cDNA extracts (active community). For this purpose, the ZOTU table was used without filtering or rarefaction, as PICRUSt2 has its own normalization system. Afterward, inferred genes involved in methanogenesis were selected. The \u003cem\u003emcrA\u003c/em\u003e gene was used as a marker for methanogenesis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and the \u003cem\u003edsrB\u003c/em\u003e gene was used as a marker for dissimilatory sulphate reduction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The results of the \u003cem\u003edsrB\u003c/em\u003e gene inference were normalized by \u003cem\u003erecA\u003c/em\u003e gene copies, obtained in the PICRUSt2 analysis itself, since prokaryotes only have one copy of this gene per genome [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eQuantification of gene expression in methanogenic archaea\u003c/h3\u003e\n\u003cp\u003eQuantitative PCR (qPCR) was performed on cDNA extracts using specific primers (Supplementary Table\u0026nbsp;1) for the \u003cem\u003e16S rRNA\u003c/em\u003e gene of methanogenic archaea and for the gene encoding the alpha subunit of the mcr enzyme (\u003cem\u003emcrA\u003c/em\u003e), which is involved in the final step of methanogenesis. Both primer pairs provide a good overview of the different groups of methanogens [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], with a coverage from 26.7% to 100% of the sequences of the main classes of methanogenic archaea.\u003c/p\u003e \u003cp\u003eThe standards for the qPCR standard curve were made from genomic DNA extracted from a pure culture of \u003cem\u003eMethanobacterium\u003c/em\u003e sp. for the \u003cem\u003e16S rRNA\u003c/em\u003e gene of methanogens and from \u003cem\u003eMethanosaeta\u003c/em\u003e sp. for the \u003cem\u003emcrA\u003c/em\u003e gene. The absolute copies per \u0026micro;l of the methanogen \u003cem\u003e16S rRNA\u003c/em\u003e gene and the \u003cem\u003emcrA\u003c/em\u003e gene were quantified by qPCR from the extracted cDNA. In each sample, qPCRs were performed in duplicate. In addition, triplicate amplifications of water samples without genetic material were performed in each qPCR run to verify the absence of contamination. The reactive mixtures for qPCR were prepared by mixing each pair of primers (10 mM) with a hot start master mix (Roche LightCycler 480) and molecular biology-grade water to a final volume of 30 \u0026micro;l. For the \u003cem\u003e16S rRNA gene\u003c/em\u003e of methanogens, the qPCR cycle consisted of a 3-minute cycle at a temperature of 95\u0026deg;C, followed by 40 cycles of 30 seconds at a temperature of 95\u0026deg;C and 30 seconds at a temperature of 62\u0026deg;C. For the \u003cem\u003emcrA\u003c/em\u003e gene, the qPCR cycle consisted of a 3-minute cycle at a temperature of 94\u0026deg;C, followed by 40 cycles of 40 seconds at a temperature of 94\u0026deg;C and 30 seconds at a temperature of 57\u0026deg;C. The specificity of the reactions was verified by analysing the dissociation curve and by subsequent electrophoresis of the qPCR products, where it was verified that the band appearing on the agarose gel showed the appropriate molecular weight. After quantification, the number of \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts of methanogens and \u003cem\u003emcrA\u003c/em\u003e gene transcripts per gram of dry weight of sediment was calculated.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses and figures were done using JMP 16.2 and SigmaPlot 15.0 software. Methane production and the number of transcripts in each wetland determined by qPCR were represented using box plots. The regressions between temperature and methane emissions and between temperature and the number of \u003cem\u003emcrA\u003c/em\u003e and \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts were carried out through a single 2 parameters exponential growth model. The remaining regressions were determined with a linear model.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe wetlands studied showed significant differences in both the salinity (conductivity) of their waters and sediments, as well as in (the percentage of) organic matter content in the sediment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In terms of sediment organic matter, there was considerable variation within all wetland categories. With respect to salinity, the highest conductivities were observed in inland saline lakes and in some coastal wetlands. In the inland saline lakes, a salinity gradient was observed, with the soda Lake Caballo Alba showing the lowest conductivity and amounts of organic matter in the sediments, while Laguna Salada de Chiprana showed the highest conductivity and organic matter sediment content. A well-marked salinity gradient was also observed in the coastal wetlands, with Bullent showing the lowest conductivity and, in turn, a very low amount of organic matter in sediment, while the highest conductivities were observed in Encanyissada and Salinas del Cabo de Gata. The volcanic wetlands showed low conductivity, and Laguna de Fuentillejo showed the lowest values of organic matter in sediment. As for the inland non-saline wetlands, all volcanic wetlands had high levels of organic matter in the sediment, higher than 10% (dry weight). The low flux Laguna de Bele\u0026ntilde;a, Balsa la Mueda and Laguna de Pradales showed the lowest conductivities among inland non-saline waters, while the higher flux systems as Laguna de la Alcaparrosa and Galacho de El Burgo de Ebro (oxbow lake) showed the highest conductivities.\u003c/p\u003e\n\u003ch3\u003eMethane fluxes in relation to temperature\u003c/h3\u003e\n\u003cp\u003eThe influence of warming on methane fluxes showed different patterns for each wetland category and for each individual wetland (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn general, the highest methane fluxes and exponential enhancement by temperature were observed in high water renewal non-saline inland and in volcanic wetlands, all of these usually those with the lower salinity, while coastal wetlands and inland saline lakes, those of higher salinities, had the lowest methane fluxes. However, within each wetland category, there was considerable variability in methane fluxes, especially in inland non-saline wetlands. Specifically, Laguna de Bele\u0026ntilde;a, Laguna de Pradales and Balsa de la Mueda, with much lower water renewal, showed the highest methane fluxes, but very low methane fluxes were measured in the high water renewal sites, such as Ullal de Baldov\u0026iacute;, Laguna de la Alcaparrosa and Galacho de El Burgo de Ebro. On the other hand, in inland saline lakes and coastal wetlands, lower methane fluxes were observed as sediment conductivity increased. Regarding the relationship between methane fluxes and temperature, all the wetland categories showed a high coefficient (R\u0026thinsp;=\u0026thinsp;0.35\u0026ndash;0.91.6), but only a significant positive regression between increasing temperature and methane fluxes was observed in the soda lake (R\u0026thinsp;=\u0026thinsp;0.916, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), in volcanic (R\u0026thinsp;=\u0026thinsp;0.619, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and in high flux inland non-saline (R\u0026thinsp;=\u0026thinsp;0.863, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) wetlands.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of temperature and wetland category on gene expression\u003c/h2\u003e \u003cp\u003eThe concentration of methanogens \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts were above the detection limit of the qPCR assay in all the wetlands studied, with considerable variability within each category (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe highest concentration of \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts was detected in low water renewal inland non-saline wetlands, more specifically in Laguna de Bele\u0026ntilde;a. However, low values were detected in high water renewal non-saline wetlands, particularly in Laguna de la Alcaparrosa and El Burgo de Ebro. In volcanic wetlands, the highest number of transcripts was found for Laguna de Carrizosa, while Laguna de Caracuel and Laguna de Fuentillejo showed similar values. In coastal wetlands, the amount of methanogen \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts decreased as sediment conductivity increased, with the lowest values measured in Salinas del Cabo de Gata and Bullent, even if the latter did not display high conductivity, it contained very low amounts of organic matter in the sediment. In the inland saline lakes, with low methanogen \u003cem\u003e16S rRNA\u003c/em\u003e transcripts, a negative relationship was also observed between conductivity and the concentration of methanogen \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts, with the highest values measured in Laguna de Zorrilla and the lowest in Laguna Salada de Chiprana, which is a hypersaline lake. On the other hand, no significant regression was observed between the increase in temperature and the concentration of \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts at the global level. When considered by wetland category (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), only volcanic wetlands showed a significant positive regression (R: 0.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eOn the other hand, the increase in temperature was strongly related to the amount of \u003cem\u003emcrA\u003c/em\u003e gene transcripts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The variation in the concentration of transcripts depended on the wetland category, and, in turn, showed specific patterns in each wetland. In each wetland category, we measured a great variability in the concentration of \u003cem\u003emcrA\u003c/em\u003e transcripts. The highest concentrations were observed in low water renewal inland non-saline and in volcanic wetlands, while coastal wetlands and inland saline lakes showed the lowest concentrations, which were below the detection limit in most of them (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn inland non-saline wetlands, the highest concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts were observed in the low water renewal Laguna de Bele\u0026ntilde;a, Laguna de Pradales and Balsa de la Mueda, while very low transcript copies were measured in the high water renewal inland non-saline sites Ullal de Baldov\u0026iacute;, Laguna de la Alcaparrosa and El Galacho de El Burgo de Ebro. In volcanic wetlands, also with very low water renewal, transcript quantities were high, with the lowest values observed in Laguna de Fuentillejo. In coastal wetlands, \u003cem\u003emcrA\u003c/em\u003e gene transcripts were only detected in El Senillar de Moraira, which is a brackish water spring, while in the rest of the coastal wetlands with higher salinity (Encanyissada and Salinas del Cabo de Gata) or with very low amounts of organic matter in sediment (Bullent), no \u003cem\u003emcrA\u003c/em\u003e transcripts were detected. In inland saline lakes there was also no detection of \u003cem\u003emcrA\u003c/em\u003e gene transcripts, so no variations with temperature could be tested. The only soda lake Caballo Alba showed higher levels of \u003cem\u003emcrA\u003c/em\u003e gene transcripts similar to some of the low water flux inland non saline wetlands. When considering the concentration of transcripts per wetland category and incubation temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), we observed an increase of \u003cem\u003emcrA\u003c/em\u003e expression with temperature only for non-saline wetlands, but with a specific pattern for each wetland category. In low water renewal inland non-saline wetlands, the most pronounced increase in transcript concentration occurred when jumping from 4\u0026deg;C to 14\u0026ndash;25\u0026deg;C, whereas for the volcanic wetlands this jump was observed between 14\u0026deg;C and 25\u0026deg;C. In the soda lake Caballo Alba the increase was consistently exponential.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen all the wetlands were considered (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), a significant positive regression was observed between the incubation temperature and the concentration of \u003cem\u003emcrA\u003c/em\u003e transcripts (R\u0026thinsp;=\u0026thinsp;0.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, when this relationship was broken down into the different wetland categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), in Senillar de Moraira, the only coastal wetland where \u003cem\u003emcrA\u003c/em\u003e gene transcripts could be detected, the variations were non-significant, though there was a very low amounts of transcripts detected. Both the soda lake Caballo Alba (R\u0026thinsp;=\u0026thinsp;0.936, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the volcanic wetlands (R\u0026thinsp;=\u0026thinsp;0.636, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) showed a significant positive regression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMethane fluxes and gene expression\u003c/h2\u003e \u003cp\u003eThe relationship between methane fluxes and the concentration of transcripts of \u003cem\u003emcrA\u003c/em\u003e and methanogens \u003cem\u003e16S rRNA\u003c/em\u003e genes showed different patterns when comparing all wetlands together and when separating them by wetland category (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, methane fluxes showed a significant positive linear regression with the concentration of methanogens \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts (R\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and \u003cem\u003emcrA\u003c/em\u003e gene transcripts (R\u0026thinsp;=\u0026thinsp;0.542, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). When separating by wetland category (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), a significant linear regression for \u003cem\u003e16S rRNA\u003c/em\u003e transcripts and methane fluxes was only observed in low water renewal (high methane flux) inland non-saline wetlands (R: 0.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), in high water renewal (low methane flux) inland non-saline wetlands (R: 0.526, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), as well as in volcanic wetlands (R: 0.928, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In turn, when applied to the \u003cem\u003emcrA\u003c/em\u003e transcripts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), all categories showed a high and significant linear regression except for coastal and high water renewal inland non saline wetlands (low methane flux).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, the amounts of transcripts of the mcrA gene and 16S rRNA gene of methanogenic archaea were related, but the pattern of relationship between both amounts was influenced by the increase in temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThe concentrations of transcripts of the \u003cem\u003emcrA\u003c/em\u003e and methanogen \u003cem\u003e16S rRNA\u003c/em\u003e genes showed a significant regression (R: 0.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). However, most of samples incubated at 25\u0026deg;C showed a higher concentration of \u003cem\u003emcrA\u003c/em\u003e transcripts than that estimated from the concentration of \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts. By observing the ratio between the concentration of both gene transcripts in each wetland (Suppl. Figure\u0026nbsp;1A-K), we can infer that the expression of \u003cem\u003emcrA\u003c/em\u003e gene increased per unit of \u003cem\u003e16S rRNA\u003c/em\u003e gene as temperature increased. Furthermore, this ratio showed the highest values in volcanic wetlands, where at 25 \u0026ordm;C the amount of \u003cem\u003emcrA\u003c/em\u003e gene transcripts was almost half that of methanogen \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts, and also in low water renewal inland non-saline wetlands which in turn had higher methane fluxes (Laguna de Bele\u0026ntilde;a, Laguna de Pradales and Balsa de la Mueda). At 25 \u0026ordm;C, this ratio was also high in the soda lake Caballo Alba. L\u0026rsquo;Ullal de Baldov\u0026iacute;, la Laguna de Alcaparrosa and El Galacho de El Burgo de Ebro, the high water renewal inland non-saline wetlands, also had a low ratio of \u003cem\u003emcrA\u003c/em\u003e/methanogen \u003cem\u003e16S rRNA\u003c/em\u003e transcripts, which hardly changed with temperature, along with El Senillar de Moraira, which also had low methane fluxes. The pattern of increase in this ratio with temperature differed according to the wetland category. Thus, in low water renewal inland non-saline wetlands with higher methane fluxes and in the soda lake, the increase in the ratio with temperature was progressive, while in the volcanic wetlands the ratio did not change between 4\u0026ndash;14\u0026ordm;C but sharply increased at 25\u0026ordm;C.\u003c/p\u003e \u003cp\u003e \u003cb\u003emcrA\u003c/b\u003e \u003cb\u003egene expression and metabolic potential\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe relationship between the concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts and the gene counts of this gene inferred by PICRUSt2, and the relationship between methane fluxes and the gene counts of the \u003cem\u003emcrA\u003c/em\u003e gene, differed depending on whether the inference was made from DNA or cDNA datasets, and also varied with temperature and wetland category (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). No significant regression was observed between the concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts measured by qPCR and the gene counts inferred by PICRUSt2 from DNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), but instead significant regression appeared when using the gene counts inferred from cDNA (R\u0026thinsp;=\u0026thinsp;0.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Furthermore, the latter relation was statistically significant in inland non-saline wetlands (R\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), volcanic wetlands (R\u0026thinsp;=\u0026thinsp;0.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and in the soda lake (R\u0026thinsp;=\u0026thinsp;0.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Nonetheless, in El Senillar de Moraira (the only coastal wetland with mcrA transcripts detected by qPCR, albeit in very low quantities) no relation was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-C). On the other hand, the regression between the concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts detected by qPCR and the gene counts inferred from cDNA was positive and significant at all incubation temperatures (minimum R value\u0026thinsp;=\u0026thinsp;0.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). In addition, both \u003cem\u003emcrA\u003c/em\u003e gene counts inferred from DNA (R\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and those inferred from cDNA (R\u0026thinsp;=\u0026thinsp;0.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) showed a significant positive regression with methane fluxes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE-F), but the regression based on gene counts from cDNA showed a higher R. Therefore, the inference of gene counts using PICRUSt2 from both DNA and cDNA yielded reliable results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, no statistically significant regression was observed between the \u003cem\u003emcrA\u003c/em\u003e gene transcripts measured using qPCR and the gene counts of the \u003cem\u003edsrB\u003c/em\u003e gene (marker of dissimilatory sulphate reduction) obtained from DNA datasets using PICRUSt2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). However, regarding the relationship between cDNA-gene counts of the mcrA gene and DNA-gene counts of the dsrB gene, it was observed that, in general, this ratio increased with temperature (Supplementary Fig.\u0026nbsp;2A-K). Since the PICRUSt2 data for the \u003cem\u003edsrB\u003c/em\u003e gene was normalised by copies of the \u003cem\u003erecA\u003c/em\u003e gene (single copy gened per cell), we observed that the number of gene counts of the mcrA gene compared to the number of sulphate-reducing bacteria cells increased with temperature, which means that an increase in temperature favoured methanogenic archaea over sulphate-reducing bacteria. The inland non-saline wetlands showed great variability in the values of this ratio, with the highest values recorded in the low water renewal sites Laguna de Bele\u0026ntilde;a, Laguna de Pradales and Balsa de la Mueda. In the high water renewal sites Laguna de Alcaparrosa and Ullal de Baldov\u0026iacute;, the ratio did not increase with temperature, while in Galacho de El Burgo de Ebro it only increased at 25\u0026ordm;C. In the volcanic wetlands and in the soda lake Caballo Alba, this ratio also increased, while in El Senillar de Moraira (the only coastal wetland with mcrA transcripts detected by qPCR), which showed very low methane fluxes, no relationship with temperature was found.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study examined the short-term effect of variations in temperature on methane fluxes and gene expression related to methanogenesis in the sediment of 16 representative wetlands and shallow lakes of different types of Mediterranean wetlands. The increase in temperature increased the gene expression of the \u003cem\u003emcrA\u003c/em\u003e gene, which encodes for a key enzyme involved in the last step of methanogenesis, and this increase was associated with an increment of methane fluxes. Moreover, higher temperatures favoured the activity of methanogens over sulphate-reducing bacteria (SRB).\u003c/p\u003e \u003cp\u003ePrevious studies have shown how the natural ecological characteristics of Mediterranean wetlands determine the structure and potential metabolism of their prokaryotic communities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We here demonstrated that low water renewal inland non-saline wetlands were also those with the highest \u003cem\u003emcrA\u003c/em\u003e gene expression (4.92\u0026times;10\u003csup\u003e3\u003c/sup\u003e \u0026ndash; 5.87\u0026times;10\u003csup\u003e7\u003c/sup\u003e transcripts\u0026middot;g.d.w.\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), due to the large amount of organic matter in the sediment, low salinity (\u003cem\u003ei.e.\u003c/em\u003e, low sulphate concentrations) and hydrological stability (\u003cem\u003ei.e.\u003c/em\u003e, low sediment disturbance thus favouring lower redox conditions needed for methanogenesis), all of them favouring methanogenesis over sulphate reduction. Regarding high water renewal inland non-saline wetlands, Galacho de El Burgo de Ebro, despite having 11.4% of organic matter in the sediment, exposure to more oxidised waters or even of the sediment to the air during dry periods, explains the low methane fluxes and transcripts. The occasionally dry sediment with high conductivity (9.3 mS\u0026middot;cm\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) also explains the low methanogenic activity and the low concentrations of \u003cem\u003emcrA\u003c/em\u003e transcripts measured (0-1.8\u0026times;10\u003csup\u003e5\u003c/sup\u003e transcripts\u0026middot;g.d.w.\u003csup\u003e\u0026ndash;1\u003c/sup\u003e). Ullal de Baldov\u0026iacute; (18.2% organic matter and 2.4 mS\u0026middot;cm\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in conductivity) and Laguna de Alcaparrosa (11.2% organic matter and 22.8 mS\u0026middot;cm\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in conductivity) also had high water renewal rates which, as argued above, explain the low methanogenic activity (\u0026lt;\u0026thinsp;6.23\u0026times;10\u003csup\u003e4\u003c/sup\u003e \u003cem\u003emcrA\u003c/em\u003e transcripts g.d.w.\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In turn, the high salinities (\u003cem\u003ei.e.\u003c/em\u003e, high sulphate concentrations) in coastal wetlands such as Encanyissada (36.2 mS\u0026middot;cm\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) and Salinas del Cabo de Gata (39.9 mS\u0026middot;cm\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) also explain the low methanogenic activity measured (\u003cem\u003emcrA\u003c/em\u003e transcripts below the limit of detection). A special case among coastal wetlands is Bullent, where the low amount of organic matter in the sediment (0.6%) can be the main explanation for the low activity of methanogens (\u003cem\u003emcrA\u003c/em\u003e transcripts\u0026thinsp;\u0026lt;\u0026thinsp;LoD).\u003c/p\u003e \u003cp\u003eAlthough the increase in the concentration of \u003cem\u003emcrA\u003c/em\u003e gene transcripts with temperature was quite common to most types of Mediterranean wetlands analysed, each category followed a characteristic response pattern. As determined in previous studies, most Mediterranean wetland types have specific methanogen communities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which can be differentiated one from another either by the relative abundance of methanogens or by the taxonomy and functional behaviour of the prevalent taxa. The abundance of different methanogen families can vary with temperature, with some taxa dominating at low temperatures and others at higher temperatures [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, an increase in temperature may promote a change in the dominant methanogenic pathway [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In most of the wetlands studied, the family \u003cem\u003eMethanomassiliicoccaceae\u003c/em\u003e was the most abundant [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The increase in temperature favoured a greater activity of rare methanogenic taxa such as \u003cem\u003eMethanosarcinaceae\u003c/em\u003e and \u003cem\u003eMethanobacteriaceae\u003c/em\u003e, which showed the most considerable changes in abundance between the different wetland types [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA particular case is that of inland saline lakes, where the concentrations of \u003cem\u003emcrA\u003c/em\u003e gene transcripts were below the detection limit. Furthermore, no \u003cem\u003emcrA\u003c/em\u003e gene transcripts could be detected in coastal wetlands with higher salinities, such as Encanyissada or Salinas de Cabo de Gata. In both inland saline lakes and deltaic wetlands, the increase in salinity was associated with low methane fluxes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. According to these results, we conclude that the low methane fluxes measured in these wetlands were due to their low methanogenic activity rather than a high methane consumption by methanotrophy.\u003c/p\u003e \u003cp\u003eIn saline environments, methylotrophic methanogenesis is the predominant pathway [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] due to the presence of methylamines [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In the wetlands studied, especially in inland saline lakes and some coastal wetlands, the major groups of methanogens identified belonged to methylotrophic taxa, while non-methylotrophic methanogens were less abundant [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, the presence of methylamines in inland saline lakes may be low [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], so methane production could largely depend on acetoclastic and hydrogenotrophic methanogenesis, which is outcompeted by sulphate reduction under high salinities [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In fact, in sediments from other saline environments with high rates of dissimilatory sulphate-reduction, hydrogenotrophic methanogenesis was found to be the most relevant, but it occurred mainly in the innermost layers of sediment where sulphate reduction rates were lower than at the surface, where they reached their maximum [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Accordingly, the possible occurrence of niches in the sediment with a low sulphate-reduction rates that would allow a low activity of aceticlastic and/or hydrogenotrophic methanogenesis could not be excluded, though the low expression of the \u003cem\u003emcrA\u003c/em\u003e gene (under the limit of detection) in the saline wetlands studied seems to support the lack of significant methanogenic activity in highly saline systems.\u003c/p\u003e \u003cp\u003eOn the other hand, in saline wetlands transcripts of the methanogen 16S rRNA gene were detected, sometimes in quantities comparable to those found in less saline wetlands. This suggests that in saline wetlands, methanogenic archaea, although present, were incapable of effectively performing methanogenesis and must obtain energy either through other metabolisms that do not involve methanogenesis or maybe they maintain symbiotic relationships with other microorganisms without producing methane. In fact, methanogens have been described that can alternate between methanogenesis and the reduction of iron compounds and even rely solely on the reduction of iron compounds without producing methane [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, methanogens synthesize photoactive molecules and have genes related to carotenoid production, so it might be that methanogenic archaea produce chromophores that could be coupled to transmembrane ion pumping (which could be used to synthesize ATP) or to photon-mediated redox reactions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, the variety of metabolisms that can be carried out by methanogenic archaea in saline environments may be greater than expected, thus not relying solely on methanogenesis, which would explain their presence despite no transcripts of gene neither methane fluxes were registered.\u003c/p\u003e \u003cp\u003eThe positive correlation between methane fluxes and the concentration of \u003cem\u003emcrA\u003c/em\u003e and \u003cem\u003e16S rRNA\u003c/em\u003e transcripts indicated that the increase in methane fluxes with temperature could be due to: i) an increase in the abundance of methanogens within the days of incubation (2 to 5 days), ii) an increase in the expression of the \u003cem\u003emcrA\u003c/em\u003e gene, or iii) both. However, the lack of correlation between the concentration of methanogen \u003cem\u003e16S rRNA\u003c/em\u003e gene transcripts with temperature and the increase in the mcrA/methanogen 16S rRNA ratio with temperature indicated that the measured increase in methane fluxes with temperature was due to an overexpression of \u003cem\u003emcrA\u003c/em\u003e genes. The objective of this study was to evaluate the rapid response of methanogenic archaea to an increase in temperature, so the incubation time of the sediment samples at different temperatures was short (less than 1 week), making it difficult to achieve a significant increase in the abundance of methanogenic archaea. In this regard, experiments with artificial ponds analysing the effect of a long-term temperature increase (11 years) did observe an increase in the abundance of methanogens, which in turn was related to higher methane production [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFocusing on the relationship between the amount of \u003cem\u003emcrA\u003c/em\u003e gene transcripts determined by qPCR and the gene counts estimated using PICRUSt2, we have determined that the gene counts inferred using cDNA were more similar to the actual activity of methanogens and the methane fluxes measured than the gene counts inferred using DNA datasets. This divergence between the two approaches may be explained by the fact that inference from cDNA datasets mostly considered the active fraction of the community, whereas using the DNA libraries took into account the bulk community regardless they are active or not\u003c/p\u003e \u003cp\u003eRegarding the competitive relationship between methanogenic archaea and SRB in saline environments, SRB are better competitors than methanogenic archaea that perform aceticlastic methanogenesis or methanogenesis based on CO\u003csub\u003e2\u003c/sub\u003e reduction [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, our results indicate that increases in temperature might partially reverse this competitive relationship, favouring the potential activity of methanogenic archaea over that of SRB. In saline environments, the increase in temperature indicated by climate change predictions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] may decrease the inhibition of methanogenesis by SRB and result in a significant increase in methane fluxes from these wetlands, where they are normally low [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, considering that in the Mediterranean saline ecosystems studied, the amount of organic matter in the sediment can be high (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), at high temperatures methanogenic archaea will compete better than SRB for the metabolic substrates found in this organic matter, which may increase methane fluxes from these saline ecosystems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. On the other hand, our results suggest that in low water renewal inland non-saline wetlands, an increase in temperature would further favour methanogenic archaea, leading to an increase in methane fluxes that would significantly affect the carbon balance of this kind of ecosystem.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study estimated the short-term effect of a temperature increase on methane fluxes and gene expression in methanogenic archaea present in the sediment of 16 lagoons representative of various categories of Mediterranean wetlands. An increase in methane fluxes was observed as the temperature rose, which coincided to an increment in the gene expression of methanogenic archaea. In addition, a significant relationship was found between the metabolic inference of active microorganisms and gene expression analysed by qPCR, and it has been observed that increasing temperatures favoured the potential activity of methanogenic archaea over that of SRB. These results provide insights to infer the effect that climate change may have on methane fluxes and the activity of methanogens in Mediterranean wetlands, and may help to design management measures for these wetlands from a climate perspective.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by projects CLIMAWET-CONS (PID2019-104742RB-I00) and CLIMAWET (CGL2015-69557-R), funded by the Agencia Estatal de Investigaci\u0026oacute;n of the Spanish government, and by the project ECCAEL (PROMETEO CIPROM-2023-031), funded by the Generalitat Valenciana, both granted to Antonio Camacho (PI), as well as by other funds raised by the PI from research contracts with companies. 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\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.M.L.: sampling, conceptualization, data curation, formal analysis, investigation, methodology, writing \u0026ndash; original draft, writing \u0026ndash; review and editing. A.P.: sampling, formal analysis, software, supervision, validation. C.R.: sampling, methodology, supervision, validation. D.M.: sampling, methodology, supervision, validation. A.S.M.: methodology. C.M.B.: conceptualization, investigation, methodology, supervision, validation, writing \u0026ndash; review and editing. A.C.: conceptualization, formal analysis, investigation, methodology, funding acquisition, project administration, supervision, validation, writing \u0026ndash; review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eRaw sequencing data are available in the NCBI Sequence Read Archive (SRA) under the BioProject accession ID PRJNA1397488.Accession link:https://dataview.ncbi.nlm.nih.gov/object/PRJNA1397488?reviewer=aq9psn97mhpnnd3hkjn7ntau99\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSica YV, Quintana RD, Radeloff VC, Gavier-Pizarro GI. Wetland loss due to land use change in the Lower Paran\u0026aacute; River Delta, Argentina. Sci Total Environ. 2016;568:967\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamacho A, Picazo A, Rochera C, Santamans AC, Morant D, Miralles-Lorenzo J, Castillo-Escriva A. Methane emissions in Spanish saline lakes: Current rates, temperature and salinity responses, and evolution under different climate change scenarios. Water. 2017;9:659.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorant D, Picazo A, Rochera C, Santamans AC, Miralles-Lorenzo J, Camacho-Santamans A, et al. Carbon metabolic rates and GHG emissions in different wetland types of the Ebro Delta. PLoS ONE. 2020;15:4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBritton RH, Crivelli AJ. Wetlands of southern Europe and North Africa: mediterranean wetlands. Wetlands of the world: Inventory, ecology and management Volume I: Africa, Australia, Canada and Greenland, Mediterranean, Mexico, Papua New Guinea, South Asia, Tropical South America, United States. Springer Netherlands; 1993. pp. 129\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorant D, Camacho-Santamans A, Hidalgo R, Camacho A. Transdisciplinary approach to the characterisation and current status of Spanish karstic lakes on gypsum. Environ Earth Sci. 2024;83:457.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorant D, Picazo A, Rochera C, Santamans AC, Miralles-Lorenzo J, Camacho A. Influence of the conservation status on carbon balances of semiarid coastal Mediterranean wetlands. Inland Waters. 2020;10:453\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorant D, Rochera C, Picazo A, Miralles-Lorenzo J, Camacho-Santamans A, Camacho A. Ecological status and type of alteration determine the C-balance and climate change mitigation capacity of Mediterranean inland saline shallow lakes. Sci Rep. 2024;14:29065.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamacho-Santamans A, Morant D, Rochera C, Picazo A, Camacho A. Towards an enhancement of the climate change mitigation capacity of inland saline shallow lakes through hydrological regime and vegetation management: a modelling approach. Water Int. 2024;50:1\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e[IPCC] Intergovernmental Panel on Climate Change. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva (CH): IPCC; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e[MedECC] Mediterranean Experts on Climate and Environmental Change. Climate and Environmental Change in the Mediterranean Basin \u0026ndash; Current situation and Risks for the Future. First Mediterranean Assessment Report Union for the Mediterranean, Plan Bleu. Marseille (FR): UNEP/MAP; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreote FD, Jim\u0026eacute;nez DJ, Chaves D, Dias ACF, Luvizotto DM, Dini-Andreote F, et al. The microbiome of Brazilian mangrove sediments as revealed by metagenomics. PLoS ONE. 2012;7:6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBridgham SD, Cadillo-Quiroz H, Keller JK, Zhuang Q. Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales. Glob Chang Biol. 2013;19:1325\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimmers PH, Welte CU, Koehorst JJ, Plugge CM, Jetten MS, Stams AJ. Reverse methanogenesis and respiration in methanotrophic archaea. Archaea. 2017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1155/2017/1654237\u003c/span\u003e\u003cspan address=\"10.1155/2017/1654237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe T, Kimura M, Asakawa S. Distinct members of a stable methanogenic archaeal community transcribe mcrA genes under flooded and drained conditions in Japanese paddy field soil. Soil Biol Biochem. 2009;41:276\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans PN, Boyd JA, Leu AO, Woodcroft BJ, Parks DH, Hugenholtz P, Tyson GW. An evolving view of methane metabolism in the Archaea. Nat Rev Microbiol. 2019;17:219\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkins D, Lu XY, Shen Z, Chen J, Lee PK. Pyrosequencing of mcrA and archaeal 16S rRNA genes reveals diversity and substrate preferences of methanogen communities in anaerobic digesters. Appl Envirn Microbiol. 2015;81:604\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOren A. Thermodynamic limits to microbial life at high salt concentrations. Environ Microbiol. 2011;13:1908\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSorokin DY, McGenety T. Methanogens and Methanogenesis in Hypersaline Environments. In: Stams AJM, Sousa DZ, editors. Biogenesis of Hydrocarbons. Handbook of Hydrocarbon and Lipid Microbiology. Springer; 2019. pp. 1\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDouglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSang S, Zhang X, Dai H, Hu BX, Ou H, Sun L. Diversity and predictive metabolic pathways of the prokaryotic microbial community along a groundwater salinity gradient of the Pearl River Delta, China. Sci Rep. 2018;8:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu R, Wang Z, Wang L, Li Z, Fang J, Wei X, et al. Bulk and active sediment prokaryotic communities in the Mariana and Mussau Trenches. Front Microbiol. 2020;11:1521.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGionchetta G, Oliva F, Roman\u0026iacute; AM, Ba\u0026ntilde;eras L. Hydrological variations shape diversity and functional responses of streambed microbes. Sci Total Environ. 2020;714:136838.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiralles-Lorenzo J, Picazo A, Rochera C, Morant D, Casamayor EO, Men\u0026eacute;ndez‐Serra M, Camacho A. Environmental gradients and conservation status determine the structure and carbon‐related metabolic potential of the prokaryotic communities of Mediterranean inland saline shallow lakes. Ecol Evol. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1002/ece3.71286\u003c/span\u003e\u003cspan address=\"10.1002/ece3.71286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAPHA Standard Methods for Water and Wastewater Examination. 18th ed. American Public Health Association. Washington (DC);2005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicazo A, Rochera C, Villaescusa JA, Miralles-Lorenzo J, Vel\u0026aacute;zquez D, Quesada A, Camacho A. Bacterioplankton community composition along environmental gradients in lakes from Byers peninsula (Maritime Antarctica) as determined by next-generation sequencing. Front Microbiol. 2018;10:908.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessler AJ, Chen YJ, Waite DW, Hutchinson T, Koh S, Popa ME, et al. Bacterial fermentation and respiration processes are uncoupled in anoxic permeable sediments. Nat Microbiol. 2019;4:1014\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Wang P, Liu H, Xie W, Wan XS, Kao SJ, et al. Population dynamics of methanogens and methanotrophs along the salinity gradient in Pearl River Estuary: implications for methane metabolism. Appl Microbiol Biotechnol. 2020;104:1331\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy A, Sar P, Sarkar J, Dutta A, Sarkar P, Gupta A, et al. Petroleum hydrocarbon rich oil refinery sludge of North-East India harbours anaerobic, fermentative, sulfate-reducing, syntrophic and methanogenic microbial populations. BMC Microbiol. 2018;18:1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlorindo C, Ferreira R, Borges V, Spellerberg B, Gomes JP, Borrego MJ. Selection of reference genes for real-time expression studies in Streptococcus agalactiae. J Microbiol Methods. 2012;90:220\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCompte-Port S, Borrego CM, Moussard H, Jeanbille M, Restrepo‐Ortiz CX, de Diego A, et al. Metal contaminations impact archaeal community composition, abundance and function in remote alpine lakes. Environ Microbiol. 2018;20:2422\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiralles-Lorenzo J, Picazo A, Rochera C, Morant D, Camacho A. Environmental gradients shape the structure and carbon-related potential metabolisms of the prokaryotic communities in deltaic wetlands. Submitted; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu L, Song T, Lu Y. Snapshot of methanogen sensitivity to temperature in Zoige wetland from Tibetan plateau. Front Microbiol. 2015;6:131.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Theroux SM, Bueno de Mesquita CP, Hartman WH, Tian Y, Tringe SG. Microbial drivers of methane emissions from unrestored industrial salt ponds. ISME J. 2022;16:284\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaxton MA, Samarkin VA, Madigan MT, Bowles MW, Sattley WM, Schutte CA, Joye SB. Sulfate reduction and methanogenesis in the hypersaline deep waters and sediments of a perennially ice-covered lake. Limnol Oceanogr. 2021;66:1804\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSivan O, Shusta SS, Valentine DL. Methanogens rapidly transition from methane production to iron reduction. Geobiology. 2016;14:190\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuan NR. Methanogens: pushing the boundaries of biology. Emerg Top Life Sci. 2018;2:629\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Y, Purdy KJ, Eyice \u0026Ouml;, Shen L, Harpenslager SF, Yvon-Durocher G, et al. Disproportionate increase in freshwater methane emissions induced by experimental warming. Nat Clim Chang. 2020;10:685\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Temperature, GHG, methane emissions, methanogenic archaea, gene expression, Mediterranean wetlands.","lastPublishedDoi":"10.21203/rs.3.rs-8490616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8490616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMediterranean wetlands play an important role in carbon cycling, partly due to the metabolic activity of the prokaryotic communities inhabiting these ecosystems. Among the different metabolisms involved, the methanogenesis carried out by methanogenic archaea is of great interest in the current context of global warming, as methane is a powerful greenhouse gas. Mediterranean wetlands, however, are very heterogeneous, and both the structure of prokaryotic communities and their metabolic activities vary greatly and are shaped by different environmental factors. This study analysed the short-term effect of different temperatures on methane fluxes and the expression of genes related to methanogenesis in the sediment of different Mediterranean wetlands.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe increase in incubation temperatures caused the rise of methane emissions and the overexpression of the \u003cem\u003emcrA\u003c/em\u003e gene, which is involved in the final stage of methanogenesis, though this pattern was demonstrated for non-saline waters in low water renewal sites, whereas saline water system did not display these responses. In addition, a significant relation was observed between bioinformatically inferred methanogenesis and the amount of \u003cem\u003emcrA\u003c/em\u003e gene transcripts also for non-saline waters in low water renewal sites. Furthermore, data suggested that rising temperatures may influence competition for different metabolic substrates between methanogenic archaea and sulphate-reducing bacteria, thus determining the methane fluxes in Mediterranean wetlands.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results allowed us to infer the effects of a general increase in temperatures on methane emissions and the activity of methanogenic archaea in Mediterranean wetlands. Overall, these results may be helpful in improving the management of these ecosystems considering current and future climate change scenarios.\u003c/p\u003e","manuscriptTitle":"Short-term effect of a temperature gradient on the activity of methanogenic archaea and associated methane fluxes in different types of Mediterranean wetlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 19:02:55","doi":"10.21203/rs.3.rs-8490616/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-20T14:47:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198745437864096590730185967158574026961","date":"2026-03-02T13:59:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T19:01:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-10T11:16:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T18:06:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Microbiome","date":"2026-01-08T13:10:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0aee34cf-c44f-45d5-8ec6-29935383f9c9","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T19:02:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 19:02:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8490616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8490616","identity":"rs-8490616","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}