Higher methanotroph abundance and bottom-water methane in ponds with floating photovoltaic arrays

Higher methanotroph abundance and bottom-water methane in ponds with floating photovoltaic arrays | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Higher methanotroph abundance and bottom-water methane in ponds with floating photovoltaic arrays Nicholas E. Ray , Sophia Aredas , Steven M. Grodsky , Ash Canino , Simone J. Cardoso , Meredith A. Holgerson , Meredith Theus , View ORCID Profile Marian L. Schmidt doi: https://doi.org/10.1101/2025.07.24.666521 Nicholas E. Ray 1 School of Marine Science & Policy, University of Delaware , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: nickray{at}udel.edu Sophia Aredas 2 Department of Microbiology, Cornell University , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steven M. Grodsky 3 U.S. Geological Survey, New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources and the Environment, Cornell University , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ash Canino 4 Department of Natural Resources and Environment, Cornell University , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simone J. Cardoso 5 Department of Zoology, Institute of Biological Sciences, Universidade Federal de Juiz de Fora , Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Meredith A. Holgerson 6 Department of Ecology and Evolutionary Biology, Cornell University , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Meredith Theus 6 Department of Ecology and Evolutionary Biology, Cornell University , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marian L. Schmidt 2 Department of Microbiology, Cornell University , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marian L. Schmidt Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Floating photovoltaic (FPV) arrays alter the methane (CH 4 ) cycling dynamics of waterbodies on which they are deployed. Here, we investigated dissolved CH 4 dynamics and associated CH 4 cycling microbial communities (methanogens and methanotrophs) in the second year of FPV deployment (70% aerial coverage) in experimental ponds. We found that bottom-water CH 4 concentrations were twice as high in ponds with FPV compared to those without, while surface water CH 4 concentrations were orders of magnitude lower than bottom-waters, but did not differ between treatments. There was no change in the relative abundances of putative sediment methanogens or methanotrophs, but FPV restructured methanogen communities. FPV promoted late-summer methanotroph blooms in the water column, with abundances surpassing 1,000,000 cells mL -1 . We conclude that prolonged periods of CH 4 production in low oxygen FPV ponds favored blooms of methanotrophs, that may mitigate diffusive CH 4 emissions to the atmosphere by consuming dissolved CH 4 . Scientific Significance Statement Producing energy using floating photovoltaic (FPV) powerplants offers an opportunity to produce renewable energy, spare land, and reduce evaporation from ponds, lakes, and reservoirs. However, FPV deployment in these ecosystems is associated with colder temperatures, less oxygen availability, and changes in carbon cycling processes. Experimental evidence demonstrates an increase in concentrations of methane – a potent greenhouse gas – in ponds following FPV deployment. In this study, we investigate how microbial communities associated with aquatic methane cycling differ between ponds with and without FPV. We show that FPV deployment increases bottom-water methane concentrations and triggers dense blooms of methane-oxidizing bacteria that create a methane biofilter. These results provide the first evidence that microbial communities respond strongly to engineered shading and may help buffer greenhouse gas emissions in solar-covered waters. Introduction Methane (CH 4 ) emissions from aquatic ecosystems have increased because of human activity (Jackson et al. 2024). Floating photovoltaic arrays (FPV), first deployed in 2007, are a rapidly expanding renewable energy technology shown to alter aquatic CH 4 dynamics and emissions ( Cazzaniga and Rosa-Clot 2021 ; Ray et al. 2024 ). By 2023, over 643 FPV power plants had been installed on lakes, ponds, and reservoirs worldwide ( Nobre et al. 2024 ; Ramanan et al. 2024 ). There is substantial interest and potential in expanding FPV use on reservoirs and lakes to meet demands for renewable energy (Jin et al. 2023; Woolway et al. 2024 ), but the ecological and biogeochemical tradeoffs – particularly effects on CH 4 cycling – must be evaluated before broader deployment (Almeida et al. 2022; Gallaher et al. 2025 ). FPV deployment is associated with lower water temperatures ( Dörenkämper et al. 2021 ; Andini et al. 2022 ), reduced oxygen availability (Wang et al. 2022; Ray et al. 2024 ), and changes in stratification ( Ilgen et al. 2023 ). These factors influence CH 4 dynamics in small, shallow systems where FPV has most commonly been deployed ( Bastviken et al. 2004 ; Aben et al. 2017; Ray and Holgerson 2023 ; Nobre et al. 2024 ). Indeed, in the first year of FPV deployment, experimental ponds with FPV showed twice the dissolved CH 4 concentrations and CH 4 ebullition compared to control ponds without FPV, but diffusive CH 4 emissions were lower ( Ray et al. 2024 ). Determining how FPV affects methanogens (CH 4 producers) and methanotrophs (CH 4 consumers) is essential for understanding CH 4 dynamics in ecosystems with FPV. Methanogenic archaea and methanotrophic archaea and bacteria jointly regulate CH 4 dynamics, yet it remains unresolved how FPV-induced changes in temperature, light, oxygen, and primary production influences their abundance, composition, and activity. Evidence from non-FPV settings suggests changes in these limnological conditions can re-structure CH4-cycling communities and alter metabolic rates ( Wang et al. 2021 ; Bertolet et al. 2022 ). Clarifying microbial responses to FPV is critical for determining whether this technology amplifies or dampens CH 4 emissions and, more broadly, for understanding how pond microbial communities respond to reduced temperature, oxygen, and light. Here, we investigate how FPV affects CH 4 dynamics in temperate experimental ponds during the second year of FPV deployment, with a focus on identifying microbial and biogeochemical mechanisms underlying dissolved CH 4 concentrations. We pair observations of temperature, dissolved oxygen, and dissolved CH 4 with measurements of methanogen and methanotroph community structure and CH 4 process rates during an intensive mid-summer sampling campaign in ponds with and without FPV. To capture microbial mechanisms relevant to dissolve CH 4 concentrations, we focus on well-characterized methanotrophs and integrate community data with functional rate measurements to capture both composition and activity. Methods Location and Sampling Scheme In 2023, FPV arrays (70% aerial coverage) were deployed on three ponds at the Cornell Experimental Ponds Facility in Ithaca, New York, USA (42.5049 N, 76.4666 W). Observational sampling of six ponds starting in 2022 prior to FPV deployment in 2023, providing three replicate control and three replicate FPV ponds ( Ray et al. 2024 ). Each pond is a 30 x 30 m inverted, truncated pyramid, 1.75 m deep, unlined, and with sediment accumulation since construction in 1958-1959. Rooted, submerged macrophytes dominate primary production. We returned in summer 2024 to continue measurements of dissolved CH 4 (surface and bottom), temperature, and dissolved oxygen (4 depths; 0.1, 0.75, 1, and 1.25 m from the water surface). We sampled methanogens and methanotrophs four times in 2024 from surface water, bottom water, and sediments. On July 11, 2024, we measured diffusive CH 4 exchange and net CH 4 production or consumption in surface waters, bottom waters, and sediments using bottle incubations ( Supplemental Methods ). For each date, we calculated density gradients of pond water columns using temperature profile data to compare mixing and stratification dynamics between ponds with and without FPV ( Supplemental Methods ). Observational CH 4 Concentration Measurements We collected samples to determine dissolved CH 4 concentrations in the surface and bottom water of each pond six times between June 20 and September 5. Samples were collected in triplicate from the pond center using a headspace equilibration approach, with surface samples collected by hand at 10 cm depth and bottom water collected using a Van-Dorn bottle ∼10 cm from the sediment-water interface ( Ray et al. 2024 ). Samples were stored in 12 mL pre-evacuated borosilicate exetainer vials until analysis using a gas chromatograph equipped with a flame ionization detector (Shimadzu GC-2014). Microbial Sample Collection, Preservation, and Analysis Water samples for microbial analysis were collected as for CH 4 measurements and sequentially filtered through 200 µm and 20 µm NYTEX mesh into 2 L Nalgene bottles and kept on ice until return to the lab the same day. Surface sediments (top 2 cm) were collected using a PVC pole corer and stored in WhirlPak bags in the cooler. Water was filtered onto 0.22 µm polyethersulfone filters using a peristaltic pump (190-2020 mL per sample). Sediments were homogenized using a stainless-steel coffee grinder (three 1 second pulses per sample). Filters (halved) and homogenized sediment were stored in 2 mL cryovials at −80 °C for until microbial DNA extraction, sequencing, and bioinformatics ( Supplemental Methods ). Samples for flow cytometry were fixed with 1 μL 25% glutaraldehyde, diluted 15–20×, stained with SYBR Green I, and run in triplicate on an Attune NxT ( Supplemental Methods ) Diffusive CH 4 Flux Measurements and Estimation of k600 We measured diffusive fluxes at the center and edge of each pond using floating chambers (18.93 L volume; 0.071 m 2 cross sectional area) attached to a portable analyzer that determines CH 4 concentrations in air using off-axis integrated cavity output spectroscopy (GLA132-GGA, ABB Measurement and Analytics; Ray et al. 2024 ). At the same time, we collected headspace samples for determination of dissolved CH 4 concentrations. Using diffusive CH 4 flux measurements and dissolved CH 4 concentrations, we calculated gas transfer velocities (i.e., k 600 values as: Where S c and n are the Schmidt number for CH 4 and the Schmidt number exponent, respectively. Statistical Comparisons All statistical analyses were performed using R statistical software and figures were made using the ggplot2 package ( Wickham 2016 ). We considered the results of statistical tests to be significant when p < 0.05. We fit linear mixed-effects models (fixed: treatment, day of year; random: pond) to compare environmental and microbial metrics in ponds with FPV compared to those without FPV (i.e., control) ponds using lme4 ( Bates et al. 2015 ; Table S2). Rates of water column CH 4 production or consumption in surface and bottom waters were compared using a mixed effects model of light or dark bottle and FPV presence with pond as a random effect (Table S3). Contrasts between treatments were made using least square means tests via emmeans ( Lenth 2018 ) (Table S4). Water temperature and oxygen were depth-averaged by pond and day. Diffusive CH₄ fluxes, k600, and potential sediment CH₄ production were compared using two-tailed t-tests (Tables S5&6). Bray–Curtis dissimilarities were visualized with principal coordinates analysis (PCoA) and tested using PERMANOVA (adonis2), β-dispersion (betadisper), Mantel tests, and Procrustes analysis in vegan (Oksanen et al. 2018). Water column ASVs were analyzed as absolute abundances by scaling relative sequence abundances to total microbial cell counts (cells mL -1 ), whereas sediment ASVs were analyzed as relative abundances following rarefaction. Differentially abundant ASVs were identified with ANCOMBC-II (prevalence ≥5%), testing for a time-averaged FPV effect without temporal interactions and retaining ASVs with a FDR-adjusted q 0.5 (Lin and Peddada 2024). To complement these tests, we additionally applied a seasonally explicit, time-resolved ASV-level screening to visualize FPV treatment separation at individual sampling dates (Supplemental Methods) Results FPV Ponds had Higher CH 4 Concentrations at Depth Dissolved CH 4 concentrations were relatively stable over time ( Fig. 1A & 1C). Surface CH 4 did not differ between ponds with FPV (1.79 ± 4.01 µmol CH 4 L -1 ) and control ponds without FPV (2.07 ± 3.19 µmol CH 4 L -1 ; p = 0.208; Fig. 1B ). However, bottom water CH 4 was two orders of magnitude higher than in surface waters, and more than twice as high in ponds with (306.2 ± 419.0 µmol CH 4 L -1 ) than control ponds (129.7 ± 45.60 µmol CH 4 L -1 ; p = 0.003; Fig. 1D ). Download figure Open in new tab Fig. 1: Dissolved methane concentrations (A-D) and dissolved oxygen concentrations (E-H) in the surface (A, B, E, F) and bottom water (C, D, G, H) of ponds with and without floating photovoltaic (FPV) arrays in summer 2024. In (A, C, E, G) points indicate the mean CH 4 concentration from pond centers on a given date by treatment group and error bars indicate standard deviation. Colored lines connect the means. In panels (B, D, F, H) each point on the boxplot indicates a measured CH 4 concentration in an individual pond based on whether FPV is present (FPV) or not (Open) and p-values indicate the result of a least-squares mean test of the treatment effect of the mixed model (Table S3). FPV Ponds were Colder and Less Oxygenated Ponds with FPV were almost 4 °C colder across the water column (18.87 ± 1.69 °C; mean ± SD) than control ponds (22.78 ± 2.29 °C) during summer (p 0.29 kg m -3 m -1 ; Holgerson et al 2022 ), but we found no evidence that FPV changed pond mixing and stratification patterns, as mean density gradients in FPV ponds (0.76 ± 0.63 kg m -3 m -1 ) were similar to control ponds (0.62 ± 0.42 kg m -3 m -1 ; p = 0.180). While all ponds were generally undersaturated in DO, surface waters in control ponds were occasionally oversaturated, unlike FPV ponds, which remained undersaturated throughout the sampling period (Fig. S2; Table S7). Mean DO in FPV ponds (2.70 ± 0.98 mg L 1 ) was half that of control ponds (5.42 ± 2.20 mg L -1 ; p < 0.001; Fig. 1 E-H & Fig. S3). FPV Ponds had Higher Water Column Methanotroph Abundance Water column methanotroph abundance showed increasing divergence between ponds with and without FPV as the season progressed. In surface waters, methanotroph abundance was 3.7 times higher in FPV ponds (4.4 ± 3.7 x 10 5 cells mL -1 ) than ponds without FPV on average (1.2 ± 1.0 x 10 5 cells mL -1 ; p = 0.021; Fig. 2A-B ). Bottom waters displayed similar trends, with 2.8. times more methanotrophs in FPV ponds ( Fig. 2E-H ). Abundance of methanogens in the water column was orders of magnitude lower than methanotrophs and there were no differences between treatments ( Fig. 2C & D). Methanogens averaged ∼20% of the sediment community and were more abundant than methanotrophs (∼6%), but neither group differed between treatments ( Fig. 2I-L ). Download figure Open in new tab Fig. 2: Total abundance (A-H) of methanotrophs and methanogens in surface waters and bottom waters and relative abundance (I-L) of methanotrophs and methanogens in sediments of ponds with FPV (“FPV”) and those without (“Open”) in summer 2024. Information as for Fig. 1 . P-values reflect least-square means test result of the mixed model (Table S3). Download figure Open in new tab Fig. 3: Bray–Curtis PCoA of combined methanogen and methanotroph communities in ponds with FPV (“FPV”) and ponds without FPV (“Open”). (A) Dissimilarity among water column samples was calculated using absolute cell abundances. (B) Sediment samples were rarefied to 20,826 reads to calculate dissimilarity based on relative abundance; replicates are shown separately. Shapes denote ponds. Methanotroph Blooms and Seasonal Functional Redundancy in Sediment Methanogens in FPV Ponds FPV altered methanotroph and methanogen community composition in both water and sediments ( Fig. 4 & S4; Table S8 & S9), producing stronger group separation than pond identity across all comparisons (Table S8 & S9). Community composition varied with day of year (DOY), with significant FPV x DOY and pond x DOY interactions in both habitats. Spatial structuring by FPV presence and pond identity was more pronounced in sediments than in the water column. Download figure Open in new tab Fig. 4: Methanotroph ASVs identified as differentially abundant using time-averaged ANCOM-BC2 analyses are shown as absolute abundance (cells mL⁻¹) across the sampling season for ponds with FPV (“FPV”) and ponds without FPV (“Open”). (A) ASVs exhibiting bloom-like increases under FPV infrastructure and (B) ASVs enriched in ponds without FPV installed. Taxonomic assignments are shown at the lowest resolved rank. For clarity, two low-abundance ASVs enriched in ponds with FPV (ASV_2028, ASV_346) and one ASV in ponds without FPV with minimal separation (ASV_1019) were omitted. Water column community structure was tightly coupled with methanotroph composition rather than methanogens, indicated by strong correlations between water column community structure and methanotroph composition, supported by Mantel (r = 0.996, p < 0.001) and Procrustes (correlation = 0.999, m12 = 0.002, p < 0.001) analyses. At the class level, FPV restructuring of water-column methanotrophs was driven by increased gammaproteobacterial (Type I) abundance under FPV and higher alphaproteobacterial (Type II) abundance in ponds without FPV, whereas sediment methanotroph classes showed limited treatment separation and FPV-associated effects were most evident in Methanosarcinia (Fig. S4). FPV altered seasonal trajectories in sediment methanogen communities, but not methanotroph communities (Table S9). For both methanogens and methanotrophs, community composition was structured primarily by pond identity, with additional contributions from FPV and DOY, and for methanogens, a significant FPV x DOY interaction indicating FPV-specific seasonal shifts (Fig. S5A & B; Table S9). FPV-associated shifts in water column methanotrophs were driven by a small number of Type 1 (hereafter gammaproteobacterial) methanotrophic ASVs in the family Methylomonadaceae ( Fig. 4 ). Four gammaproteobacterial methanotrophic ASVs within the order Methylococcales were 3-12x enriched in FPV ponds compared to controls. The strongest enrichment was observed for ASV_32 ( Methyloparacoccus , family Methylococcaceae), which increased almost 12x under FPV. Additional enriched ASVs belonged to the family Methylomonadaceae, including ASV_141 ( Methylobacter_C ), ASV_13 (genus unresolved), and ASV_119 and ASV_44 ( Methylomonas alba ), which each increased ∼3-4x ( Fig. 4A ). Collectively, these taxa reached abundances exceeding 300,000 cells mL -1 in FPV ponds. Six gammaproteobacterial methanotroph ASVs within the family Methylococcaceae declined under FPV (∼3x lower abundance), including multiple ASVs classified as Methyloterricola oryzae ( Fig. 4B ). These taxa occurred at much lower abundances and were more abundant in the bottom water ( Fig. 4 ), remaining one to two orders of magnitude less abundant than FPV enriched methanotrophs ( Fig. 4 ). Unlike methanotrophs, water-column methanogen ASVs were sparse but three ASVs had modest enrichment in ponds without FPV (Fig. S6). In sediments, time-averaged analyses detected several ASVs enriched in ponds without FPV, while few were enriched in FPV ponds (Fig. S7). However, sediment methanogen communities exhibited a significant FPV x DOY interaction (Table S9), indicating seasonal dynamics not captured by time-averaged analyses. Accordingly, a seasonally explicit (time-resolved) approach revealed FPV-associated divergent trajectories spanning hydrogenotrophic, acetoclastic, and methylotrophic methanogens, indicating cross-genus functional redundancy in FPV responses across 18 ASVs ( Fig. 5 ). In contrast, seasonally explicit FPV responses among sediment methanotrophs were limited to only eight ASVs spanning multiple methanotrophic types (Fig. S8), consistent with more limited functional redundancy in methane oxidation. Time-averaged patterns were dominated by functionally diverse ASVs enriched in ponds without FPV, whereas enrichment in ponds with FPV was limited to a small, constrained set of hydrogenotrophic methanogens and a single anaerobic methanotroph (Fig. S7). Download figure Open in new tab Fig. 5: Relative abundance (%) of sediment methanogen ASVs over time in ponds with FPV (“FPV”) and ponds without FPV (“Open”). ASVs were identified using a seasonally explicit (time-resolved) screening approach targeting abundant taxa (mean relative abundance > 0.05%) and quantifying FPV–Open separation at individual sampling dates using median abundances; ASVs were retained when peak FPV enrichment was supported across multiple dates and/or FPV ponds (Supplemental Methods). No Apparent Differences in CH 4 Cycling Processes We found no differences in diffusive water-air CH 4 exchange or gas transfer velocities between ponds with and without FPV or between the center and edge of ponds (Table S5&10). On average, all ponds emitted CH 4 to the atmosphere at a rate of 404.1 ± 896.6 µmol CH 4 m -2 h -1 . We also found no difference in rates of water column CH 4 net-production or consumption between ponds with and without FPV (Table S3). Sediments from both ponds with and without FPV produced CH 4 , but we found no difference in potential CH 4 production rates by sediments in ponds with FPV (5.43 ± 4.81 ppm CH 4 g dry sediment -1 d -1 ) compared to those without (14.13 ± 10.92 ppm CH 4 g dry sediment -1 d -1 ; p = 0.303; Table S6). Discussion Following FPV deployment, bottom waters sustained elevated dissolved CH 4 concentrations for a second year along with enhanced water column methanotroph abundances ( Fig. 1D ; Fig. 2 ) Surface water CH 4 – and diffusive CH 4 emissions – were similar between ponds with and without FPV. Together, these patterns suggest FPV-induced changes in redox conditions promote CH 4 accumulation in bottom-waters, while microbially mediated CH 4 oxidation dynamically constrains diffusive CH₄ fluxes to the atmosphere, positioning water column methanotrophs as a potentially critical biofilter. Specifically, we hypothesize that prolonged low-oxygen conditions in FPV ponds facilitate longer periods of CH 4 accumulation in bottom waters overlying sediments. In response to this accumulation, water column methanotrophs reached bloom-like densities in late summer, exceeding 1,000,000 cells mL -1 and reaching abundances higher in ponds with FPV than those without ( Fig 2A ). The coexistence of a persistently large CH 4 pool and low, but not inhibitory, oxygen concentrations created conditions that selectively favored gammaproteobacterial methanotrophs later in the season, supporting elevated CH 4 oxidation throughout the water column. By oxidizing CH 4 , methanotrophs serve as a fundamental biological sink that likely buffers diffusive CH 4 emissions. Beyond CH 4 availability, FPV-driven hypoxia restructured microbial composition in a lineage-and time-dependent manner. At the ASV level, closely related gammaproteobacterial methanotrophs within the order Methylococcales exhibited divergent, family-level responses to FPV across the season, with Methylomonadaceae ASVs strongly enriched and Methylococcaceae ASVs declining in FPV conditions ( Fig 4 & S5). Although both families represent gammaproteobacterial (Type I) methanotrophs, Methylomonadaceae taxa like Methylomonas alba demonstrate a “rapid response capability,” with the potential to increase abundance by two orders of magnitude in days under oxygen limited conditions ( van Grinsven et al., 2020 ). This physiological capacity is consistent with use of the ribulose monophosphate (RuMP) pathway for carbon assimilation, which is more energy efficient than the serine pathway used by alphaproteobacterial (Type II) methanotrophs and supports rapid growth under high CH₄ availability ( Knief, 2015 ). The resulting dominance is further supported by metabolic flexibility, including the genomic potential for fermentation and denitrification, which likely facilitates their enigmatic persistence in anoxic zones (Reis et al., 2024). Conversely, Methylococcaceae like Methyloterricola oryzae appear less responsive to transient hypoxia, as they are often found in stable, well oxygenated surface waters (Reis et al. 2020). Thus, FPV does not uniformly enhance gammaproteobacterial methanotrophs, but instead selectively favors specific Methylomonadaceae lineages with life-history traits suited to rapidly exploiting FPV-induced redox shifts. A similar FPV x DOY signal emerged within sediment methanogen communities. Although relative abundance did not differ by FPV presence ( Fig 2K-L ), a pattern commonly observed in temperate lake sediments ( Bertolet et al., 2019 ; Lyautey et al., 2021 ), ASV-level time series revealed clear, treatment-specific seasonal trajectories across many of the most abundant methanogens ( Fig 5 ). These shifts in peak timing and magnitude likely reflect the response of specific ASVs to changes in the supply of methanogenic precursors, such as acetate or hydrogen, produced by syntrophic bacteria during the decomposition of settling organic matter ( Bertolet et al., 2019 , 2022 ). This compositional divergence suggests a high degree of cross-genus functional redundancy, with 18 methanogen ASVs spanning all three metabolic pathways responding to FPV ( Fig. 5 ). Such redundancy implies that methanogenesis is robust to FPV-induced perturbation of community composition as many lineages can exploit a fluctuating pool of metabolic byproducts ( Rissanen et al., 2021 ). In contrast, sediment methanotroph responses were more constrained (8 ASVs; Fig. S8), indicating lower functional redundancy (Reis et al., 2019, 2024). This divergence suggests that FPVs act as a deterministic environmental filter that restructures the timing and internal organization of sediment methane cycling without altering overall abundances ( Fig. 2 ). In late summer, methanotrophs surpassed 1,000,000 cells mL -1 in FPV surface and bottom waters, a density rarely reported in any aquatic systems where previous concentrations peak within 100,000 cells mL -1 ( Milucka et al., 2015 ; Reis et al., 2019). These extreme bloom magnitudes far exceed variation expected from differences in 16S rRNA operon copy number, indicating true population-level expansion rather than methodological inflation, even relative to CARD-FISH–based estimates. Blooms may result from the metabolic advantage of gammaproteobacterial methanotrophs under CH 4 -rich, low-O₂ conditions, as observed in freshwater lakes where gammaproteobacterial methanotrophs proliferate under hypoxia and anoxia (Reis et al. 2020; Reis et al. 2024). Even under apparently anoxic conditions, aerobic methanotrophy may be sustained by cryptic O₂ cycling, in which oxygen produced by photosynthetic algae or cyanobacteria is rapidly consumed by methanotrophs ( Milucka et al., 2015 ). Although light availability was not measured, shading from FPVs may further enhance methanotrophic dominance by suppressing photoinhibition or reducing phytoplankton competition ( Murase & Sugimoto 2005 ; Thottathil et al. 2018 ). Our results also demonstrate that the effect of FPV on pond biogeochemistry and CH 4 cycling varies over time. Immediately following FPV deployment in 2023, FPV ponds had lower oxygen concentrations and increased surface and bottom water CH 4 concentrations ( Ray et al. 2024 ). By contrast, in the second year (2024), surface CH 4 concentrations no longer differed between treatments, and methanotroph abundance was substantially higher in FPV surface waters. These patterns suggest a time-lagged ecological response, where sustained hypoxia promotes bottom-water CH₄ accumulation, followed by methanotroph enrichment that buffers CH₄ flux near the surface. Quantifying how CH₄ ebullition responds to FPV deployment across seasons and years remains a key open question for understanding total emission dynamics. Identifying strategies for low emission energy production is critical for combating climate change. As FPV expansion accelerates, understanding how this technology interacts with aquatic CH 4 dynamics is important for ensuring sustainability. Our study provides mechanistic evidence that FPV deployment can restructure microbial communities in ways that influence carbon cycling, offering a foundation for evaluating the long-term ecosystem-level impacts of continued FPV proliferation across aquatic systems. Acknowledgements This work was supported by an Atkinson Academic Venture Fund to SMG, MAH, MLS, awards 441993/2023-0 and 200781/2024-3 from National Council for Scientific and Technological Development (CNPq) to SJC, and startup funds from the University of Delaware made available to NER. We thank Benj Sterrett for helping to maintain access to the ponds, Autumn Newman, Augustus Pendleton, Sophia Richter and Kailyn Hanke for assistance with field and lab work, and Jera Jansen, Caitlin Davis, Mônica Antunes-Ulyssea, Sheena Dwyer-McNulty, Trifosa Simamora, Tim Boycott, and Dave Grodsky for helping construct the floating solar arrays. Funder Information Declared Atkinson Academic Venture Fund National Council for Scientific and Technological Development (CNPq) , 441993/2023-0 , 200781/2024-3 University of Delaware , Start Up Funds Cornell University , Start Up Funds Footnotes Data and Code Availability: All raw and processed data for this project are publicly available. The code used for statistical comparisons, generating figures, and processing microbial community data are available on GitHub at https://github.com/MarschmiLab/Ray_LO_Letters_FPV_Methane . Biogeochemical data and summaries of methane cycling microbe abundances is available for download via the Figshare Repository ( https://doi.org/10.6084/m9.figshare.29614025.v1 ). The raw, compressed 16S rRNA gene sequencing fastq files are available in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1417770 ( https://www.ncbi.nlm.nih.gov/bioproject/1417770 ). All flow cytometry data are available for download from Zenodo ( https://zenodo.org/records/18462017 ). Overall improvements to manuscript, especially in mechanistic lineage-specific (ASV-level) shifts in methanogen and methanotroph microbial responses to floating solar. https://github.com/MarschmiLab/Ray_LO_Letters_FPV_Methane https://doi.org/10.6084/m9.figshare.29614025.v1 https://doi.org/10.5281/zenodo.16333749 https://www.ncbi.nlm.nih.gov/bioproject/1417770 ) https://zenodo.org/records/18462017 References Aben , R. C. H. and others. 2017 . Cross continental increase in methane ebullition under climate change . Nat Commun 8 : 1682 . doi: 10.1038/s41467-017-01535-y OpenUrl CrossRef PubMed Almeida , R. and others. 2022 . Floating solar power: evaluate trade-offs . Nature 606 : 246 – 249 . OpenUrl PubMed ↵ Andini , S. , N. Suwartha , E. A. Setiawan , and S. Ma’arif . 2022 . 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