Shifts in composition and function of bacterial communities reveal the effect of small barriers on nitrous oxide and methane accumulation in fragmented rivers

Rivers are often blocked by barriers to form different habitats, but it is not clear whether this change will affect the accumulation of N2O and CH4 in rivers. Here, low barriers (less than 2 m, LB) increased N2O concentration by 1.13 times and CH4 decreased by 0.118 times, while high barriers (higher than 2 m, less than 5 m high, HB) increased N2O concentration by 1.19 times and CH4 by 2.76 times. Co-occurrence network analysis indicated LB and HB can promote the enrichment of Cyanobium and Chloroflexi, further limiting complete denitrification and increasing N2O accumulation. The LB promotes methanotrophs (Methylocystis, Methylophilus, and Methylotenera) to compete with denitrifiers (Pseudomonas) in water, and reduce CH4 accumulation. While the HB can promote the methanotrophs to compete with nitrifiers (Nitrosospira) in sediment, thus reducing the consumption of CH4. LB and HB reduce river velocity, increase water depth, and reduce dissolved oxygen (DO), leading to enrichment of nirS-type denitrifiers and the increase of N2O concentration in water. Moreover, the HB reduces DO concentration and pmoA gene abundance in water, which can increase the accumulation of CH4. In light of the changes in the microbial community and variation in N2O and CH4 accumulation, the impact of fragmented rivers on global greenhouse gas emissions merits further study.


Introduction
Natural rivers are typically characterized by their free-flowing, and they are often fragmented by barriers to free flow (Grill et al., 2019). Artificial interception (barriers) causes fragmentation and blockage of urban rivers (Grill et al., 2019), resulting in the formation of different habitats of rivers (Carpenter et al., 2011). There are more than 1 million barriers cause of rivers fragmentation (Belletti et al., 2020). Barriers higher than 15 m are rare, 68% had less than 2 m height and 91% had OPEN ACCESS EDITED BY less than 5 m height (Belletti et al., 2020), leading to a major fragmentation caused by small barriers (Jones et al., 2019). However, the environmental effects of small barriers on rivers are not clear, especially whether it affects the accumulation of greenhouse gases in rivers.
There is growing evidence that urban river networks may be hot spots for greenhouse gas (N 2 O, CH 4 ) emissions (Zhang W. et al., 2021). Inland waters are significant emitters of N 2 O (Gongqin et al., 2021), and the total estimated N 2 O from rivers is approximately 1.05 Tg N-N 2 O Yr −1 (global total emissions are 17.7 Tg N-N 2 O Yr −1 ; Frostegard et al., 2022). CH 4 is generally considered to be a more important greenhouse gas than N 2 O (Stein, 2020;Neubauer, 2021). An increasing number of studies have shown that river interception significantly damages river continuity and flood pulsation (Wang et al., 2010), changed the fluxes and ecological dynamics of water and nutrients (Deemer et al., 2016) and affected the migration and transformation process of nitrogen and carbon.
The production of N 2 O is strongly affected by microorganisms and mainly involves the oxidation and reduction of active nitrogen (ammonia, nitrate, and nitrite; Babbin and Ward, 2013). N 2 O in rivers is thought to be formed in the bottom sediment, where a variety of microbes produce a large amount of N 2 O (Beaulieu et al., 2011). Incomplete denitrification is the main cause of N 2 O production in rivers (Clough et al., 2007;Babbin and Ward, 2013;Lansdown et al., 2015). Variation in river hydrological conditions affects undercurrent exchange, which in turn affects river water quality (such as the reactive nitrogen load) and ultimately N 2 O production (Marzadri et al., 2014). Given the complexity of N 2 O generation, N 2 O accumulation in different habitats of fragmented rivers is still uncertain, especially the effects on the structure and function of N 2 O accumulation-related microbial communities.
Biomethane sinks are composed mainly of methanophile microorganisms that use methane monooxygenase to oxidize CH 4 to methanol using oxygen (Kallistova et al., 2017), which is then oxidized to formaldehyde, formic acid and carbon dioxide by a series of enzymes. It's estimated that microbial oxidation can remove about 5% of CH 4 emissions into the atmosphere each year (Stein, 2020). Current studies suggest that O 2 is the factor that strongly affects microbial metabolism and CH 4 emissions in river ecosystems (Kallistova et al., 2017). The aggregation of multiple microbial processes in river aerobic/anoxic zones suggests that these zones are highly dynamic in controlling CH 4 fluxes and may be the most important region for CH 4 mitigation. The riverine barriers slow the flow of fragmented rivers and increase the depth of water, which may lead to a decrease in the concentration of dissolved oxygen (DO) in the water. Changes in the DO concentration of fragmented rivers may lead to changes in microbial activity related to CH 4 oxidation and thus affect CH 4 accumulation in the rivers. Therefore, it is not clear if the effect of barriers over DO may lead to changes in CH 4 oxidation, thus affecting CH 4 accumulation.
In this study, Liangtan river (China) was studied as an example of fragmented river, where four habitats were delimited according to the height of the barriers between them. High-throughput sequencing was used to analyze the water and sediment microbial communities to explore how the barriers changed the microbial network structure in the four habitats. Quantitative PCR was used to analyze the changes in gene abundance of functional enzymes, further revealing the metabolic process of N 2 O and CH 4 accumulation in different habitats of fragmented rivers.

Site description and sampling
The Liangtan River (29°26′-29°52′ N, 106°18′-106°24′ E) is a firstclass tributary of the lower right bank of the Jialing River ( Figure 1A). In a preliminary investigation, we found more than 15 barriers that led to fragmentation of the river. According to the barrier height, the Liangtan River can be divided into four habitats: pond (P) and stream (S) for low barriers (less than 2 m high, LB) and lake (L) and river (R) for high barriers (between 2 m and 5 m, HB; Figure 1; Fuller et al., 2015;Belletti et al., 2020).
Samples were collected from the four habitats of the Liangtan River (December 2020 and August 2021). Sediment samples were obtained at a depth of 10 cm below the interface sediment: water, whereas water samples were obtained from a total of 20 sampling sites in the river channels. Sediments were immediately placed in sterile bags and sealed, placed on ice bags, shipped back to the laboratory within 12 h, and stored in a −80°C freezer. The water samples were divided into two parts. The first part was supplemented with HgCl 2 (20 μg/mL) for subsequent determination of water quality indicators. The other part was returned to the laboratory within 12 h and filtered through a 0.22-μm glass fiber membrane to remove trapped microorganisms for subsequent highthroughput sequencing. Characteristics of the 20 sites studied (5 sites per habitat) in the Liangtan River are shown in Supplementary Table S1.

Chemical analysis of water quality parameters
Electrical conductivity (EC), water temperature (T), and DO were determined by a YSI ® ProODO DO meter, and the pH was determined by a YSI ® 63 pH meter. Total organic carbon (TOC) was determined by a Shimazu ® TOC-VWP analyzer (Shimadzu ® , Japan). Chlorophyll a (Chl a) was extracted by ethanol and determined by spectrophotometry (Ortega et al., 2019). Total nitrogen (TN) was measured by the potassium persulfate oxidation method (Zhang L. Y. et al., 2021). Total phosphorus (TP) was extracted by HClO 4 -H 2 SO 4 and measured by the molybdenum blue method. Nitrite nitrogen (NO 2 − -N) was determined by N-(1-)-ethylenediamine spectrophotometry. Nitrate nitrogen (NO 3 − -N) was measured by potassium peroxodisulfate solution spectrophotometric methods. Ammonium nitrogen (NH 4 + -N) was measured by spectrophotometry with a Nessler reagent (Ministry of Environmental Protection, 2012).

High-throughput sequencing analysis
Total DNA was extracted from 0.5-g sediment samples using a Rapid Soil DNA Isolation Kit. The concentration of the extracted DNA was then quantified using an ND-2000C spectrophotometer (Nanodrop, Thermo Scientific, United States), and the extracted DNA was used for high-throughput sequencing. The V3-V4 hypervariable region of the 16S rRNA gene was amplified with 338F/806R universal primers (Supplementary Table S2), and each sample was identified. Specific PCR amplification conditions of the 16S rRNA gene were based on those described in previous studies (Junfeng et al., 2015). The resulting PCR products were then sequenced on an Illumina HiSeq 2000 platform. Data processing and statistical analysis were carried out (see the attachment for specific methods

Measurement of N 2 O and CH 4 concentrations in water
The concentrations of N 2 O and CH 4 in water were measured using the static headspace method. In this study, the traditional method (Donis et al., 2017) was improved. Firstly, a syringe is used to extract 200 mL water sample from the water sampler. The sample should be extracted slowly during the collection process to avoid bubbles. Extract 100 mL nitrogen from an air bag containing high purity nitrogen (99.999% purity) (500 mL air bag) to form an air chamber above the syringe (the same syringe used to collect water sample). Hold the syringe and shake it up and down for 3 min to achieve a balance between the gas-liquid phase. Push the gas in the syringe into the prepared vacuum air bag (300 mL air bag) for preservation. Record the temperature of the water after the shake. The preserved headspace samples were returned to the laboratory and the concentration of N 2 O and CH 4 was determined by gas chromatography.

Quantitative PCR
16S rRNA and the abundance of functional genes of denitrifying bacteria were detected by qPCR using the Majorbio Cloud Platform (Shanghai). The functional genes were mainly divided into nitrite reductase (nir), NO reductase (nor), N 2 O reductase (nos), and particulate methane monooxygenase (pmo). Three repeats of qPCR were performed on each sample using an ABI 7500 sequence detection system with the SYBR method (Applied Biosystems, Canada). Primer sequences are listed in Supplementary Table S2. qPCR-specific tests were performed using a melting profile and gel electrophoresis to reduce the possibility of overestimating gene abundances. In addition, each qPCR reaction consisted of negative control, in which no DNA template was added. The abundance of functional genes was converted to the number of copies of functional genes per gram of dry sediment or per milliliter of water. The efficiency was assumed to be 100% for DNA extracted from sediment or water samples (Zheng et al., 2019).

Statistical analyses
One-way ANOVA and Tukey's post hoc tests were used to evaluate the differences in microbial diversity and abundance, structure, and environmental factors among habitat types. The above statistical analyses were conducted using PASW Statistics 18 software (IBM SPSS Inc., Chicago, United States). The molecular ecological networks of habitats S, P, R, and L were constructed to analyze the microbial community structure. Linear discriminant analysis (LDA) was used to estimate biomarkers of the four habitats based on analysis software (LEfSe). Based on Origin, a heatmap was drawn to analyze the microbial abundance related to the production of N 2 O and CH 4 in the four habitats. To further elucidate the direct and indirect effects of environmental factors and microbial communities on N 2 O concentrations and CH 4 concentrations, we conducted path analyses using the maximum likelihood estimation According to the height of artificial barriers above the river (lower than 2 m or lower than 5 m), river channels are divided into four habitats. For LB (barriers less than 2 m high), the matrix habitat formed is called 'pond' (P), while the patch habitat is called 'stream'(S). For HB (barriers less than 5 m high), matrix habitat formed by barriers is called 'lake' (L), while patch habitat is called 'river' (R). The abundant biomarkers in water (A) and sediment (B) obtained for four habitats. All detected taxa, with a relative abundance of >0.5% in at least one sample, were assigned to phyla (outermost), classes, orders, families, and genera (innermost) and were used to determine the taxa or clades most likely to explain differences between the four habitats.
method. Gephi version 0.8.2 was used for network visualization and modular analysis. According to Banerjee et al. (2018), keystone species were identified using the following thresholds: genera with high mean degree (>30) and low betweenness centrality (<700) in the network. Path coefficients, R 2 , direct and indirect effects, and model fit parameters were calculated using R studio.

Results
3.1. Differences in diversity and community composition of water and sediment bacteria in four habitats Samples were taken from five randomly selected points in each habitat ( Figures 1A-C). Through principal component cluster analysis of physical and chemical indexes of four habitats, it was found that the four habitats could be distinguished well, which indicated that it was appropriate to divide the different habitats of Liangtan River (Figure1D). The abundance and composition at the phylum level of microbial communities from water and sediment was analyzed for each habitat (S, P, R, and L; Supplementary Figure S1). A total of 14,481,930 high-quality bacterial 16S rRNA gene sequences and 30,863 operational taxonomic units (OTUs) were obtained by high-throughput sequencing.

Water and sediment bacteria of four habitats exhibited contrasting ecological network patterns
The topological structures of the bacterial communities at the OTU level are shown in Figure 3. Regarding the sediment samples, the percentage of Chloroflexi nodes increased in P compared with that in S, while the node ratio in Proteobacteria and Actinobacteriota decreased. Similarly, the percentage of Chloroflexi nodes increased in P compared with that in S, while the node ratio in Proteobacteria and Actinobacteriota decreased.
Then we further analyzed the variation of ecological network parameters in the four habitats. The two networks were defined according to the same threshold, and the corresponding statistical results are shown in Supplementary Table S3. In water, the modularity of the S bacterial network (1.734) was lower than that of the P bacterial network (3.147). In S, the resulting networks consisted of 334 nodes linked by 2,809 edges, which consisted of 1,816 positive correlations and 993 negative correlations. In P, the resulting networks consisted of 287 nodes linked by 2,105 edges, which consisted of 1,210 positive correlations and 895 negative correlations. Compared with R, the modularity of L was lower. In R, the resulting networks consisted of 320 nodes linked by 2,812 edges, which consisted of 1,759 positive correlations and 1,042 negative correlations. In L, the resulting networks consisted of 300 nodes linked by 2,816 edges, which consisted of 1,764 positive correlations and 1,052 negative correlations. The presence of barriers will reduce the number of nodes, and positive or negative correlations in the microbial network, leading to the estrangement of microbial connections in the water.
In sediments, the modularity of S was higher than that of P, and that of L was higher than that of R. In S, the network consisted of 370 nodes and 3,756 edges. The network showed 2,293 positive correlations, which was higher than the negative correlations (1,463 green lines). In P, the network consisted of 388 nodes and 4,789 edges. The network showed 2,967 positive correlations and 1,822 negative correlations. In R, the network consisted of 409 nodes and 4,128 edges. The network showed 2,730 positive correlations and 1,428 negative correlations. In L, the network consisted of 373 nodes and 4,733 edges. The network showed 2,735 positive correlations and 1,999 negative correlations. This suggested that the presence of obstacles may lead to closer connections of microbial networks in sediments.

Effects of microbial composition in four habitats on CH 4 and N 2 O accumulation
In water, the abundances of anammox bacteria and nitrifiers were low, and denitrifiers (Pseudomonas) and methanotrophs (Methylocystis) were more abundant ( Figure 4A). The denitrifying bacterial abundance of P was higher than that of S, and that of L was higher than that of R. The methanotrophs abundance of S was higher than that of P, and that of L was higher than that of R. In the sediment, the abundances of anammox bacteria and denitrifiers were low, the nitrifiers (Nitrosospira) and methanotrophs (Methylophilus, Methylocystis, Methylocystis) were more abundant ( Figure 4B). The nitrifying bacterial abundance of S was higher than that of P, and that of R was higher than that of L. The methanotrophs abundance of S was higher than that of P, and that of L was higher than that of R.
The concentrations of dissolved N 2 O in S, P, R, and L were 0.014-0.029, 0.014-0.037, 0.019-0.078, and 0.012-0.11 μmol/L, respectively ( Figure 5A). Compared with S, the concentration of N 2 O in P showed an increasing trend. The concentration of N 2 O in L was higher than that Heatmap of anammox bacteria, nitrifiers, denitrifiers and methanotrophs (average abundance in all samples) of water and sediment in four habitats.

FIGURE 5
The concentration of N 2 O (A) and CH 4 (B) in four habitats.
in R. LB and HB can increase the N 2 O concentration in rivers by 1.13 and 1.29 times, respectively. The concentrations of dissolved CH 4 in S, P, R, and L were 0.30-4.18, 0.04-0.67, 0.03-2.4, and 0.03-34.0 μmol/L, respectively ( Figure 5B). The CH 4 concentration in S was higher than that in P, and that in L was higher than that in R, it can be inferred that that LB reduces CH 4 accumulation and HB increase it. LB reduced the CH 4 concentration by 0.118 times, while HB increased it by 2.76 times. Next, we conducted path analyses to further elucidate the direct and indirect effects of environmental factors and microbial communities on

Relative contribution of genes (nirS, nirK, nosZ, and pmoA) in four habitats to N 2 O and CH 4 accumulation
As can be seen from the above analysis, the LB and HB promote the enrichment of denitrifiers in the P and L habitat. To further verify our hypothesis, the abundances of functional enzyme genes of denitrifiers of water were analyzed in four habitats. The abundances of nirS, nirK, and nosZ in water and sediment were counted (Supplementary Tables S6-S9). To estimate the abundance contribution of different genes in the denitrification pathway to N 2 O accumulation, the nisS/nirK and (nisS + nirK)/nosZ copy number ratios were analyzed Frontiers in Microbiology 07 frontiersin.org (Figure 7). The nirS/nirK of P and L was higher than that of S and R, suggesting that the LB and HB promote the enrichment of nirS-type denitrifiers, and then affect the accumulation of N 2 O. In sediments (nirS + nirK)/nosZ copy number ratios were decreased in P and L compared with S and R, suggesting that the LB and HB promoted the nosZ enrichment in sediments. Then, the abundances of pmoA in water and sediment were counted (Supplementary Tables S6-S9). To estimate the contribution of functional genes to the abundance of CH 4 accumulation in CH 4 oxidation, we analyzed the pmoA/16S rRNA copy number ratio in water and sediment of four habitats (Figure 7). pmoA is a key enzyme gene for CH 4 oxidation, which reduce CH 4 accumulation (Lieberman and Rosenzweig, 2004;Vrieze and Verstraete, 2016). In water, the pmoA/16S rRNA copy number ratio of R was the highest, while in sediment, the pmoA/16S rRNA copy number ratio of P was the highest. These results further suggest that the presence of small barriers affects CH 4 accumulation by altering the activities of enzymes involved in methane oxidation.

Small barriers affect N 2 O and CH 4 accumulation
LB and HB lead to lower flow rates and increased water depth in fragmented rivers, which change environmental variables and affect  (Supplementary Tables S4, S5), which promotes the accumulation of N 2 O in P and L. Both LB and HB reduce river velocity, leading to an increase in the water bed contact area per unit of water volume, and an increase in the diffusion efficiency of NO 3 − -N at the river-sediment interface (Mulholland et al., 2008). This is consistent with previous work showing that nitrate concentrations strongly affect the production of N 2 O (Meyer et al., 2008). With few exceptions, the nitrate concentration in sediment and surface water is positively correlated with the production of N 2 O (Quick et al., 2019). The positive correlation between N 2 O and NO 3 − -N might stem from incomplete denitrification (Beaulieu et al., 2011;Abed et al., 2013;Jung et al., 2019). LB and HB promoted the enrichment of denitrifiers in the water of P and L respectively, which may lead to an increase in incomplete denitrification under unit nitrogen load (Mulholland et al., 2008).
Moreover, in most rivers, the overlying water above the surface of the sediment is anoxic and carries dissolved oxygen that continuously seeps into the sediment (Xia et al., 2018). The depth of dissolved oxygen penetration into sediment is affected by sediment roughness, porosity, and connectivity (Xia et al., 2018). LB and HB lead to increased water depth and decreased DO concentration in the river (Supplementary Figure S3), which is conducive to incomplete denitrification.
In addition, the higher abundance of nitrifiers (Nitrosospira) in the lotic habitats (S and R) suggest that the LB and HB would inhibit the growth of nitrifiers. The main reason is that the barriers reduce the DO in the river, thus inhibiting the growth of nitrifiers (aerobic bacteria). At present, there are still many uncertainties about the relationship between nitrifiers and N 2 O accumulation (Beaulieu et al., 2010). According to our results, the decrease of nitrifiers promotes N 2 O accumulation to a certain extent in river surface sediments.
According to the CH 4 concentration in four habitats, the CH 4 concentration in P was lower than in S. The main reason was that the abundance of methanotrophs in P was higher than that in S, resulting in a large amount of CH 4 consumption. It can be seen that the LB reduces CH 4 accumulation. Compared with R, the CH 4 concentration in L was higher. The potential reason was that the concentration of DO in L was reduced, which was conducive to the generation of CH 4 under anaerobic conditions. It can be seen that the HB increases the accumulation of CH 4 , these results were consistent with other literature reports (He et al., 2021).

Microbial network and functions of water and sediment in the four habitats
Denitrifiers and methanotrophs were abundant in the four habitats and significantly affected N 2 O production and CH 4 oxidation, respectively. Hence, a co-occurrence network analysis was conducted to identify the cooperative and competitive relationships among denitrifiers, methanotrophs, and other microorganisms. In networks of P, Proteobacteria was still the first dominant phylum, and the second dominant phylum was Cyanobacteria. Cyanobium_PCC-6307 is a genus with high content of Cyanobacteria phylum. It has been reported that the abundance of denitrifiers has a competitive relationship with non-diazotrophic Cyanobium sp. (Song et al., 2022), so it is further speculated that LB increases the abundance of Cyanobium, further limits complete denitrification and increases N 2 O accumulation. Interestingly, denitrifiers (Pseudomonas) and methanotrophs (Methylocystis, Methylophilus, Methylotenera), were dominant nodes in water. Previous studies have also reported low N 2 O and high CH 4 fluxes in rivers, which they suggest are due to competition (Deemer et al., 2016). The potential cause is the production of a copper chelator (methanobactin) by methanotrophs during the CH 4 cycle (Spirito et al., 2016), which has been shown to effectively compete for copper from denitrifiers, thereby increasing N 2 O production while reducing CH 4 emissions (Chang et al., 2018). Similarly, the availability of copper is particularly important for nosZ expression and activity, since copper is required for the active site of the enzyme (Gaimster et al., 2018). nosZ will compete with methanotrophs for copper ions, thus reducing N 2 O production and increasing CH 4 accumulation. This is consistent with our results that the ratio of nosZ and concentration of CH 4 in S and L is higher than that of P and R (Supplementary Figure S4).
In sediments, the Chloroflexi was the dominant bacteria in P and L habitats, it was speculated that the accumulation of Chloroflexi may promote the accumulation of N 2 O. It has been reported that bacteria attached to Chloroflexi may be highly active protein degradation, breaking down the extracellular peptides bound to the extracellular polymer matrix and simultaneously breathing nitrate to produce nitrite (Lawson et al., 2017). The increase of nitrite can promote denitrification (Quick et al., 2019), and thus increase the accumulation of N 2 O, which further proves that the barriers can increase the accumulation of N 2 O. Unlike in water, nitrifiers (Nitrosospira) and methanotrophs (Methylocystis, Methylophilus, Methylotenera), were dominant nodes in sediments. It has been reported that nitrifiers can be inhibited by methanotrophs due to competition between nitrifiers and methanotrophs for available oxygen and inorganic nitrogen (Megmw and Knowles, 1987). As LB and HB reduce DO concentration, nitrifiers in P and L were more severely inhibited compared with S and R, resulting in the stronger activity of methanotrophs. On the other hand, ammoxidation microorganisms may consume CH 4 , thereby reducing the amount of CH 4 oxidized by methanotrophs (Bodelier and Laanbroek, 2004), and thus competing with methanotrophs for CH 4 . Therefore, the decrease of CH 4 content in habitats P and R was also related to the nitrifiers.

Accumulation patterns of N 2 O and CH 4 in fragmented rivers
In order to further analyze the accumulation patterns of N 2 O and CH 4 in fragmented rivers, the potential metabolic pathways of N 2 O and CH 4 in different habitats were further analyzed. Compared with microbial functional groups, the metabolism between functional enzyme genes can better reveal the coupling relationship of element metabolic processes in environmental media. First, the potential N 2 O metabolism pathways of microorganisms in four habitats were analyzed. The analysis of the metabolic processes of nitrogenous microorganisms in four habitats revealed that the content of enzymes ([EC:1.7.7.2]) involved in nitrate transformation processes was higher in P compared with S in water (Figure 8), and this promoted the generation of N 2 O. This further proves that LB promotes the generation of N 2 O. The expression of enzyme genes involved in the dissimilatory nitrate reduction to ammonium (DNRA) process (Li et al., 2019) Li et al., 2019) involved in CH 4 oxidation processes was higher in P compared with S in sediment (Figure 8), and directly reduced the generation of CH 4 . It further shows that LB inhibits CH 4 accumulation by promoting CH 4 oxidation. The enzymes (EC:1.14.18.3; Zhang et al., 2020) involved in CH 4 oxidation processes were higher in R compared with L in water and sediment, and directly reduced the accumulation of CH 4 . It further shows that HB increases CH 4 accumulation by inhibiting CH 4 oxidation. Some studies have speculated that aerobic CH 4 oxidation coupled to denitrification process and anaerobic nitrite-dependent CH 4 oxidation processes in river sediments (Zhang et al., 2020). It was speculated that the barriers may affect these two processes and thus the accumulation of N 2 O and CH 4 .
In summary, we preliminarily sorted out the accumulation patterns of N 2 O and CH 4 in fragmented rivers (Figure 9). It was found that N 2 O accumulated continuously with the increase of barriers height, and the potential reasons were related to enrichment of Cyanobium, Chloroflexi, and nirS-type denitrifiers, as well as the increase of NO 3 − -N concentration and  Accumulation patterns of N 2 O and CH 4 in fragmented rivers. The water concentration of N 2 O increases with the height of barriers. The barriers (less than 2 m) inhibit CH 4 accumulation, while barriers (higher than 2 m, less than 5 m high) promote CH 4 accumulation. The darker the arrow, the higher the solute concentration.
decrease of DO concentration in water (Figure 9). The reason for the decrease of CH 4 concentration caused by the LB may be related to the compete with Pseudomonas in water. The HB can lead to the accumulation of CH 4 , which was largely related to the methanotrophs to compete with Nitrosospira in sediment and the decrease of DO concentration.

Conclusion
In this study, the small barriers lead to the formation of different habitats, and the microbial structure and network also change. Both LB and HB reduce river velocity, leading to an increase in the water bed contact area per unit of water volume, and enrichment of nirS-type denitrifiers in water, promoting the accumulation of N 2 O. The LB and HB lead to the increase of water depth, which reduces the DO concentration, relieves the inhibition of oxygen on nitrate reductase activity, and promotes the accumulation of N 2 O.
In addition, the LB leads to an increase in the methanotrophs abundance in water, and an increase in the abundance of the pmoA gene in sediments, which reduces the accumulation of CH 4 . Moreover, the HB reduces DO concentration and pmoA gene abundance in water, which can increase the accumulation of CH 4 .

Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.