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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

At least 1,387 records · Page 77Linked to original sources

USGS2026

Enhancements to the USGS Landsat Level 2 surface temperature and emissivity product for Collection 3 reprocessing

The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations.

Remote Sensing of Environment↗

USGS2026

Hydrologic variability drives environmental and geospatial relationships in Smallmouth Bass (Micropterus dolomieu) distribution

Hydrologic variation is a primary driver of stream ecosystems. Changing hydrology can lead to assemblage shifts and alterations in suitable habitat for freshwater species. As climate change is predicted to alter flow patterns in addition to increasing water temperatures, insight into relationships between species occupancy, hydrology, and temperature is critical for understanding current and future distributions. We examined how hydrologic variability, temperature, and other environmental variables interact to influence Micropterus dolomieu (Smallmouth Bass) occurrence. We used Spatial Stream Network models, allowing for the incorporation of spatial autocorrelation along streams' unique dendritic network, to examine Smallmouth Bass occupancy across a range of hydrologic variation in the Ozark-Ouachita Interior Highlands, USA. Hydrologic variation was the main driver of Smallmouth Bass occurrence, with occurrence more likely in groundwater streams with low hydrologic variation and high flow permanence. For groundwater streams, occurrence was positively associated with summer stream temperature and negatively associated with annual stream temperature. As variation increased, more variables showed significant relationships with occurrence. Distance metrics were important for all models, however as hydrologic disturbance increased, flow connected distance played a lesser role and stream distance played a greater role. Hydrologic variability was the overarching determinant of Smallmouth Bass occurrence and strongly influenced the predictive importance of environmental variables and geospatial relationships. Greater hydrologic variability resulted in stronger statistical relationships between occurrence and environmental variables and an increased importance of system connectivity. As climate change alters hydrologic processes and streams become more variable, understanding and accounting for these shifting relationships is essential.

Arkansas, Kansas, Missouri, Oklahoma↗

USGS2026

Continuous measurements reveal wind and temperature affect orphan well methane emissions on the Kevin-Sunburst Dome, Montana

Fifteen leaking orphan wells on the Kevin-Sunburst Dome in northern Montana had emission rates that were affected by surface winds and diurnal temperature swings based on continuous monitoring data. Some wells showed correlating spikes in emissions when temperatures changed or wind speed increased while others demonstrated independent flow behavior despite being drilled into the same reservoir and located only a few hundred meters apart. Time-weighted mean methane emission rates ranged from non-detectable levels up to 2.7 kg/h in their as-discovered conditions, with leaking wells averaging 211 g/h. Emissions were measured continuously for up to 452 h per well during monitoring, revealing that leak rates can fluctuate by an order of magnitude within hours. Fluctuations in emission rates often synchronized between wells with overlapping emission measurement intervals, suggesting weather conditions, such as temperature and wind, affect emission rates (up to a factor of 4) with the most relevant factor being the effect of wind on wells with open holes. Additionally, this study presents the first methane emissions measured from an orphan well in two distinct conditions: as initially discovered (closed leaking valve, 2.7 kg/h) and again under unrestricted flow conditions (open valve, 11.8 kg/h), illustrating the maximum unobstructed leak rate and quantifying the constraints restricted leaking wells can have on emissions compared to open holes.

Montana↗

USGS2026

Hybrid particulate matter generated by lava-ignited wildfires at the Litli-Hrutur 2023 eruption, Iceland

Lava flows from the Litli-Hrútur 2023 eruption ignited the largest moss wildfires since modern record keeping in Iceland began. Volcanically ignited wildfires present a cascading and more complex hazard than standalone volcanic eruptions and are likely to become more frequent globally due to climate change. Both volcanic eruptions and wildfire events generate well-characterized air pollution hazards through the emission of gas and particulate matter, but the physicochemical consequences of mixing between end-member emission types during compound events remain poorly understood. In this study, we collected samples of end-member volcanic, wildfire, and mixed plume particulate matter during the Litli-Hrútur 2023 eruption and wildfires. Geochemical and morphological analysis showed that wildfire smoke and lava flow outgassing have distinctive chemical signatures and PM size distributions, but that when mixing occurs between them, either directly at the lava-moss burning interface or during downwind transport, it can result in the formation of hybrid PM. This hybrid PM may be formed through mechanical interactions via well-established processes such as agglomeration and particle scavenging, although interactions are unique in the context of a compound volcanic-wildfire event as they occur between emissions from two different sources (e.g. scavenging of smaller volcanic particles the by larger partially combusted moss particles). We demonstrate that the formation of hybrid PM via these mechanisms may result in altered physicochemical characteristics and suggest that this may have consequences for depositional processes and atmospheric and environmental transport pathways of key species, when compared to stand-alone volcanic eruptions.

Litli-Hrutur volcano↗

USGS2026

Legacy of the fumigant 1,2-dibromo-3-chloropropane (DBCP) in California groundwater

The fumigant pesticide 1,2-dibromo-3-chloropropane (DBCP) was widely used in California agriculture during the 1960s and 1970s before being banned in 1979. Despite this ban, DBCP continues to contaminate groundwater due to its persistence and mobility. This study evaluates the distribution, historical trends, and projected persistence of DBCP in California using data from over 13,000 public supply wells and additional domestic, irrigation, and observation wells (1980-2022). Since 2010, DBCP has been detected in 9% of public supply wells statewide, with higher frequencies in the San Joaquin Valley (21%) and upper Santa Ana River watershed (13%), where DBCP use was most prevalent. Approximately 70% of wells had decreasing concentration trends, whereas increases were more common in deeper wells, indicating downward vertical migration of the DBCP front. Groundwater age estimates show that recharge timing aligns with the 1960s–1970s loading period, enabling reconstruction of peak inputs and providing a basis for age based modeling. To estimate future persistence, we applied a one dimensional advection–dispersion model that simulates long term declines in peak concentrations based on groundwater age, historical loading, and a 38 year degradation half life. Model projections suggest that concentrations above the maximum contaminant level may persist in a declining number of wells until approximately 2080 (range: 2048–2109), with longer persistence in the San Joaquin Valley. The simplified modeling framework, based on age distributions typical of wells capturing peak concentrations, can provide practical regional scale assessment of non-point source contaminants where long-term monitoring exists. This study highlights how the legacy of DBCP contamination will likely affect California's groundwater resources throughout the 21st century.

California↗

USGS2026

Multi-temporal mapping and analyses of post-wildfire surface soil moisture modeled from Landsat Thermal-IR and Sentinel-1 SAR

Wildfires alter surface soil moisture by consuming vegetation and exposing the soil to increased solar radiation, increased precipitation throughfall, and reduced infiltration from soil hydrophobicity. This can affect the water balance within the watershed and, in turn, influence post-fire nutrient cycling and ecosystem succession. Modeling surface soil moisture over large areas using satellite remote sensing can aid monitoring of soil moisture patterns and dynamics. We trained a random forest regression model using an in situ soil moisture network and examined pre- and post-fire soil moisture dynamics for three wildfires that burned in 2016 and 2017 in coastal California, U.S.A. Model inputs included Sentinel-1A synthetic aperture radar (SAR) and Landsat thermal infrared imagery (TIR), which produced a high resolution (30 m) soil moisture map time series with 18–20 estimates across 2 years. An unbiased root mean square error of 0.065 and an r 2 of 0.624 indicates a good fit between the model and reference data. We found a significant interaction between treatment (wildfire or unburned) and period (before or after wildfire) by examining the modeled surface soil moisture in high-severity burned areas. Before-after control-impact metrics showed that modeled surface soil moisture was lower after each fire within wildfire areas compared to unburned controls, and overall changes in soil moisture were greater in the wildfire perimeters than unburned areas. These results demonstrate the potential of Sentinel-1 SAR and Landsat TIR data for the development of high-resolution soil moisture time series products to support monitoring and assessment of post-fire soil hydrologic conditions.

California↗

USGS2026

GST-1: A high-resolution global sediment thickness model

Global Sediment Thickness 1 (GST-1) is a high-resolution sedimentary thickness model calculated on a 0.125° x 0.125° grid. It modifies the sediment thickness of the 1° x 1° Earth Crustal Model 1 (ECM1) by means of 3D inversions of free air gravity anomalies. GST-1 is calculated by performing structural inversions on high-density contrasts across two crustal boundaries: the sediment – basement interface and the crystalline crust – upper mantle interface. The inversions are calculated in each of ten overlapping 3D models that span the globe. These ten models are merged to obtain the GST-1 global model, providing an eight-fold increase in lateral spatial resolution in comparison with ECM1 and CRUST 1.0. Our sediment thickness model exploits the nearly continuous sampling of gravity data when compared to the irregular, sparse sampling of seismic refraction data. Sediment thickness values in GST-1 are in excellent agreement with independently derived cross sections from well-studied sedimentary basins, and within expected resolution limits of seismic refraction data. GST-1 offers a robust, high resolution global model of sedimentary thickness to support studies of sedimentary basins.

Tectonophysics↗

USGS2026

Comparative assessment of STIC sensors, streamflow and rain gauges for quantifying river connectivity in intermittent systems

In intermittent stream systems, including those occurring in Texas, USA, the severity of low-flow conditions, duration of seasonal disconnection, and frequency of no-flow events have been amplified by drought. Documentation of these no-flow events is necessary to evaluate ecosystem health. However, many intermittent reaches remain un-gauged given that perennial river sec-tions are often prioritized for gauge placement. Our objectives were to 1) document stream flow using Stream Temperature, Intermittency, and Conductivity (STIC) loggers to determine the frequency and duration of no-flow events in intermittent tributaries of the Colorado River, Texas and 2) compare logger data to publicly available data from streamflow discharge and precipitation gauge networks to understand differences among these data types for drying event characterization. We use these comparisons to summarize benefits and limitations of the application of in-stream data loggers. STIC loggers were deployed at 19 sites, one in each pool and riffle habitat of a stream reach. STIC loggers recorded a measurement of relative conductance every six hours from June 2022 to March 2024, which was used to determine the presence or absence of flow connectivity in a reach. No-flow duration among intermittent reaches varied between 37 and 270 days across tributaries during an ongoing drought in the study area. Overall, logger data was more precise than discharge data for characterizing no-flow events or precipitation data when documenting presence of water in the stream channel due to runoff. Lack of discharge gauges in intermittent tributaries left large sections of stream reaches undocumented and resulted in mischaracterization of flow patterns. Drought severity across the tributaries did not follow longitudinal patterns that would be expected by the climatic precipitation gradient of the study area. More research is needed to determine if factors such as population size affect severity. Likewise, precipitation data did not correlate well with logger water presence data, lacking consideration for groundwater recharge, soil hydrophobicity, and surface compaction. This study shows that to monitor no-flow events, detailed spatial datasets are necessary and that STIC loggers are useful tools that provide data to fill spatial information gaps and facilitate more accurate flow characterization and water presence data in intermittent systems.

Texas↗

USGS2026

The global proliferation of aquatic, benthic Microcoleus : Taxonomy, distribution, toxin production, ecology, and future directions

There have been sporadic reports of aquatic, benthic Microcoleus proliferations in freshwater rivers, lakes, and reservoirs for four decades, with reports increasing in frequency over the last twenty years, suggesting a possible rise in their global distribution, frequency, and intensity. Microcoleus can produce anatoxins which are neurotoxic, and ingestion of toxic mats has caused hundreds of dog fatalities and raised serious human and ecological health concerns. This review synthesizes and evaluates current knowledge on Microcoleus distribution, taxonomy, toxin production, toxicity, ecology, environmental drivers, and biotic interactions. Toxin-producing Microcoleus have been reported in at least 18 countries, though many regions have not conducted toxin testing, suggesting a broader but under-reported distribution. Proliferations occur across diverse habitats, including cobble-bedded streams, large sandy rivers, reservoirs, and lakes. Microcoleus proliferations also occur on macrophytes, both in lakes and rivers. Genomic analyses currently classify anatoxin-producing Microcoleus into distinct species, with all known anatoxin-producers isolated from freshwater ecosystems. Anatoxin concentrations vary widely over space and time, within and among waterbodies. While studies on environmental drivers remain limited, research in cobble-bedded rivers suggests that moderate enrichment of dissolved inorganic nitrogen and low dissolved reactive phosphorus concentrations in the water column promote proliferation. Metagenomic approaches have revealed unique nutrient acquisition and storage strategies used by Microcoleus . Key knowledge gaps remain around the environmental and ecological triggers of proliferation, toxin production, genomic diversity and microbial interactions. Addressing these gaps through coordinated, global studies using robust datasets and consistent methods is critical to improve prediction, monitoring, and mitigation of this increasingly widespread public and ecological health threat.

Water Research↗

USGS2026

Fifty years of riverine harmful algal bloom modeling: A global synthesis of approaches, challenges, and opportunities

This systematic literature review critically examines 162 articles on harmful algal bloom (HAB) modeling in riverine systems to uncover persistent gaps, redefine critical challenges, and propose trackable opportunities to advance future modeling efforts. Articles largely focused on site-specific applications (93%) across more than 80 rivers worldwide. Most modeled systems were large, eutrophic rivers with flow modifications or obstructions. Geographic clustering of modeled systems was pronounced, with South Korea accounting for 26% of articles, followed by Europe (25%), United States (21%), and China (12%). Modeling approaches were led by process-based models (59%), though use of data-driven models (37%) increased over time, reflecting advances in computing and monitoring technology. Modeling endpoints varied widely across the articles with many focused on gross measures of algal abundance and fewer representing more refined endpoints like algal toxins or community composition. Furthermore, inconsistent units and taxonomic resolution hindered comparability between models. Datasets used for model development and calibration typically spanned 5 years, with weekly to monthly sampling at 1–10 sites, though durations and site counts were positively skewed. Quantitative metrics of model skill were often absent and included a diverse set of metrics when reported. Across all models, nutrients, light availability, streamflow, algal physiological processes, and water temperature emerged as key predictors, though algal processes were rarely incorporated in data-driven models. Scenario analyses primarily were conducted with process-based models and addressed flow management, whereas forecasting applications were less common and typically used data-driven models. After almost 50 years of riverine HAB modeling, persistent challenges include underrepresentation of benthic habitats, neglect of side-channel and backwater influences, insufficient documentation of river features, and weak linkages between modeled endpoints and potential harms. Addressing these gaps through reporting of contextual information, models from other aquatic settings, benchmark datasets, and community-driven tools could advance riverine HAB modeling towards increased transferability and ultimately operational forecasts.

Water Research↗

USGS2026

Phylogenomic analyses reveal introgression and cryptic speciation in the globally distributed, vector-transmitted pathogen Plasmodium relictum

Establishing species limits is challenging, particularly for pathogens of wildlife. These pathogens can be difficult to sample and culture, and their genome sequencing must often be conducted in the presence of high levels of host DNA. Plasmodium relictum is a mosquito-vectored avian malaria pathogen that is a globally distributed host generalist, comprised of several genetic lineages. We used sequence capture data from 52 P. relictum infections originating from multiple continents to generate a genomic dataset of the pathogen. With this data, we established a robust phylogeny and determined species limits among P. relictum lineages. We generated phylogenomic trees by maximum likelihood and Bayesian methods with multi-species coalescent models and confirmed robustness of the topology by varying the amount of missing data in the analyses. Our results suggest the existence of two cryptic species among the infections we analyzed and provide evidence of genetic introgression between these species. One of the cryptic species, GRW4, devastated the endemic and immunologically naïve avifauna of Hawaii after its introduction to the islands ca. 100 years ago, and so was tested for positive selection in the GRW4 Hawaiian clade. Although we hypothesized it would be released from host selective pressures, we did not find evidence of positive selection in the Hawaiian GRW4 clade, and we discuss possible explanations. Overall, our results underscore the importance of genomic analyses for resolving pathogen species limits and understanding pathogen evolution.

Molecular Phylogenetics and Evolution↗

USGS2026

How to accelerate advances in ecological forecasting

Ecological forecasting offers critical insights for managing natural resources and safeguarding public well-being. Despite growing demand for these forecasts, progress is hindered by fragmented systems, redundant workflows, and limited interoperability. Drawing lessons from weather forecasting and recent successes like the NEON Ecological Forecasting Challenge, shared cyberinfrastructure is important for advancing ecological prediction. By adopting common standards, open-source tools, and scalable architectures, and fostering transdisciplinary collaboration, the ecological forecasting community can overcome technical and institutional barriers. Such investments could accelerate scientific understanding, improve forecast reliability, and empower decisionmakers to anticipate environmental change and respond effectively.

Eos, American Geophysical Union↗

USGS2026

A hybrid approach for revealing headwater hydrology

Coordinated work to compile existing data and apply models pairing physical understanding with machine learning could substantially improve streamflow predictions for little-known headwater basins.

Eos, American Geophysical Union↗

USGS2026

Socio-ecological impacts of the 2025 Los Angeles urban fires on communities, neighborhoods, and homes

Human settlements are increasingly being impacted by urban fires initiated by wildfires. Metrics such as area burned and number of structures destroyed are important, but research often overlooks the socio-ecological complexity of urban fires. We study the impacts of the 2025 Los Angeles fires on two communities at the neighborhood and residential parcel scales. Geospatial analyses and econometric modeling explore the relationships between urban morphology, socio-demographic factors, and home destruction. Here we show that socio-ecological characteristics and scale are key in parsing the dynamics of urban fires. Also, new socio-demographic populations are being affected and urban morphology metrics are more important than vegetation cover. Despite parallels with 19 th and early 20 th century urban conflagrations, understanding these re-emerging urban fires requires transdisciplinary approaches and unique metrics. Investigating the socio-ecological scales and dynamics of urban fires provides a valuable next step towards understanding and adapting to the risk associated with these disasters.

California↗

USGS2026

Per- and polyfluoroalkyl substances (PFAS) and other contaminants of concern in tribal waters of Montana

We assessed potential exposures to a broad suite of contaminants (inorganic, organic and microbial) in culturally important surface waters from three watersheds in a northern plains Native American community (Apsáalooke [Crow Tribe of Montana]) in south-central Montana, United States, with water insecurity concerns. Inorganic (37), organic (435) and microbial (3) constituents were assessed in 12 surface water sites from the Pryor Creek ( n = 2), Bighorn River ( n = 2) and Little Bighorn River ( n = 8) valleys. Twenty-six organics, 33 inorganics and Escherichia coli were detected. Despite relatively low concentrations in surface waters within the Crow Reservation, mixture toxicity indicated prevalent chronic ecological effects and human-health secondary contact (recreation) effects at multiple sites. Further, to address Tribal concerns over the prevalence and corresponding risks of per- and polyfluoroalkyl substances (PFAS), we sampled water, sediment, biofilms and fish at a limited number of locations in the Little Bighorn River. Results indicated that PFAS were prevalent in fish tissues, including whole blood and filets, and to a lesser extent in biofilms, despite few detections in water and sediment samples. This is the first attempt to document environmental PFAS contamination within the reservation and the potential human-health concerns for the general population from consumption of recreational/subsistence fish. Overall, this effort provided preliminary information on the contaminant mixtures present and their potential health implications, which can support the protection of community health and culturally meaningful resources across the Crow Reservation.

Montana↗

USGS2026

Multireservoir allocation framework considering societal and ecological needs in a time-frequency domain

Existing reservoir management frameworks traditionally consider historical (predam) flow conditions to deliver environmental flows. Such frameworks may not be feasible because current demand and/or climate could be different from predam conditions. Hence, we developed a multireservoir framework that explicitly considers both human water demands and environmental flow requirements to minimize deviations under current hydroclimatic conditions and demand patterns. The multireservoir framework, Generalized Reservoir Analyses using Probabilistic Streamflow (GRAPS), was modified and implemented to solve the problem of minimizing the flow deviations using feasible sequential quadratic programming for three reservoirs in the Chattahoochee River Basin, Southeastern United States, which is known for its imperiled native biodiversity and productive estuarine ecosystem. Our results show that downstream reservoirs in the cascade system are less influenced by upstream reservoirs’ regulation because the downstream reservoirs receive a significant amount of natural flows. By comparing the average wavelet power spectrum at different periodicities between natural flows and downstream releases, we found that the current release policy and modified releases resulted in highly altered flows under shorter periodicities (e.g., less than 2 months) but synchronized flow variance between natural flow and downstream releases at longer periodicities (e.g., greater than 3 years). This framework of linking the multireservoir allocation model through the time–frequency analysis using wavelet power spectrum could not only advance sustainable water management policies to meet water for human and environmental needs but can also add additional value in meeting the downstream environmental demand at desired periodicities.

Alabama, Florida, Georgia↗

USGS2026

Fish introductions related to strong and diversifying effects on zooplankton assemblages in high-elevation mountain lakes

Freshwater environments are threatened by multiple anthropogenic stressors. High-elevation mountain lakes are particularly vulnerable to introduced nonnative fish and nutrient deposition because they were historically fishless and typically oligotrophic. To understand the potential effects of fish introduction and nutrient levels on high-elevation lake ecosystems, we assessed differences in zooplankton size, biomass, and density in 76 alpine and subalpine lakes in the Wind River Range, Wyoming, USA, and related those differences to the presence of introduced trout and to food quality (seston nutrient content) and quantity (chlorophyll a concentration). Trout presence, and to a lesser extent trout species, were the strongest predictors of zooplankton composition. Fishless lakes were dominated by low densities of copepods and other large-bodied taxa, and lakes with introduced trout were dominated by high densities of cladocerans, rotifers, and other small-bodied taxa. These assemblage differences are likely because trout reduce or eliminate all large zooplankton taxa by size-selective predation, including predation on Hesperodiaptomus shoshone (S. A. Forbes, 1893), a keystone species that effectively controls populations of rotifers and small crustacean zooplankton taxa. In contrast, the quantity and quality of seston was not associated with zooplankton assemblages. Zooplankton composition in lakes with Rocky Mountain Cutthroat Trout Oncorhynchus virginalis (Girard, 1856) or Golden Trout Oncorhynchus aguabonita (Jordan, 1892) was highly variable, but in lakes with primarily Brook Trout Salvelinus fontinalis (Mitchill, 1814), zooplankton composition was consistently distinct from that in fishless lakes, suggesting that Brook Trout introductions altered the zooplankton assemblage to a greater extent than Cutthroat or Golden trout did. These results contribute to global evidence that predatory fish introductions fundamentally restructure alpine lake food webs. The slow or incomplete recovery of native zooplankton assemblages following fish removal suggests long-term ecological legacies of fish introductions and highlights the importance of understanding factors that promote resilience in high-elevation lake ecosystems.

Wyoming↗

USGS2026

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

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 .

BioRxiv↗