BIOGEOCHEMICAL PROCESSES IN ANTARCTIC AQUATIC ENVIRONMENTS: LINKAGES AND LIMITATIONS by Trista Juliana Vick-Majors A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Ecology and Environmental Sciences MONTANA STATE UNIVERSITY Bozeman, Montana January 2016 ©COPYRIGHT by Trista Juliana Vick-Majors 2016 All Rights Reserved ii ACKNOWLEDGEMENTS I would like to thank my advisor Dr. John Priscu, for giving me the opportunity to be involved in so many amazing projects, for opening the door to the Antarctic (I didn’t even know it had one), and for giving me the freedom and support to explore my ideas. I am especially grateful to my committee members, John Dore, Eric Boyd, Anne Camper, and Dan Miller for their support and guidance. Special thanks to John Dore for seeing me through both of my graduate degrees, and Eric Boyd for always having an open door. The work in this dissertation would not have been possible without collaborations with many excellent scientists on the McMurdo LTER, the WISSARD Project, and beyond. Amanda Achberger and Alex Michaud – “we’re still in this!” Thank you for your camaraderie and talking science with me. My research and fieldwork would not have been possible without Brent Christner and Mark Skidmore, and I am grateful for their support. Special thanks to Susan Kelly, for being a wonderful friend and advocate, and giving me the opportunity to share my science with the public. Pamela Santibáñez, thank you for your constant support and sharing your wonderful mind with me. My friends and family have seen me though a long road to get here, and I am grateful to all of them. Thank you, Pa, for teaching me to love nature, and Mom, for teaching me to love learning. To my wonderful husband, Shelby – you are my rock. I can’t thank you enough. And to Aunt Jayme, for being there when we needed you. My research was funded by the National Science Foundation Office of Polar Programs (grants to John C. Priscu), grants from the Montana Institute on Ecosystems, and an American Association of University Women Fellowship. iii TABLE OF CONTENTS 1. INTRODUCTION ...........................................................................................................1 Structure of the Dissertation .............................................................................................1 Microbial Ecology in Antarctic Aquatic Environments ...................................................2 McMurdo Dry Valley Lakes ....................................................................................6 Life Under Ice Shelves.............................................................................................7 Subglacial Aquatic Environments ............................................................................8 Significance of My Research ..........................................................................................10 Hypotheses and Objectives .............................................................................................12 References .......................................................................................................................15 2. MODULAR COMMUNITY STRUCTURE SUGGESTS METABOLIC PLASTICITY DURING THE TRANSITION TO POLAR NIGHT IN ICE-COVERED ANTARCTIC LAKES .......................................20 Contribution of Authors and Co-Authors ......................................................................20 Manuscript Information Page ........................................................................................21 Introduction ....................................................................................................................22 Methods .........................................................................................................................23 Sample Collection ..................................................................................................23 Sequencing .............................................................................................................24 Sequence Processing ..............................................................................................24 Taxonomy Assignment ..........................................................................................24 Diversity Calculations ............................................................................................24 Network Analysis...................................................................................................24 Statistics .................................................................................................................24 Results and Discussion ...................................................................................................24 Seasonal Variation in Microbial Communities ......................................................24 Co-occurance Patterns and the Molecular Ecological Network ............................28 Conclusions .....................................................................................................................30 Conflict of Interest ..........................................................................................................30 Acknowledgements .........................................................................................................30 References .......................................................................................................................30 Supplemental Methods....................................................................................................34 Supplemental Discussion of Significant Modules .................................................36 References ..............................................................................................................39 iv TABLE OF CONTENTS – CONTINUED 3. PARTITIONING OF INORGANIC CARBON-FIXATION IN PERMANENTLY ICE-COVERED ANTARCTIC LAKES ........................................70 Contribution of Authors and Co-Authors ......................................................................70 Manuscript Information Page ........................................................................................71 Acknowledgements .......................................................................................................78 Supplemental Methods ..................................................................................................79 References .....................................................................................................................81 4. A MICROBIOLOGICALLY CLEAN STRATEGY FOR ACCESS TO THE WHILLANS ICE STREAM SUBGLACIAL ENVIRONMENT ..................84 Contribution of Authors and Co-Authors ......................................................................84 Manuscript Information Page ........................................................................................86 Introduction ....................................................................................................................87 Methods..........................................................................................................................89 Borehole Filtration and Germicidal Treatment System .........................................89 Dye Test .................................................................................................................89 Bead Removal Experiment ....................................................................................89 Silt Removal Experiment .......................................................................................90 Bacterial Culture Removal and Viability Test .......................................................90 Lakewater Bacterial Removal and Viability ..........................................................90 Lakewater Pasteurization Test ...............................................................................90 Surface-Based Cleaning Experiments ....................................................................91 Results ...........................................................................................................................91 Dye Test Results ....................................................................................................91 Bead Removal Experiment ....................................................................................92 Silt Removal Experiment .......................................................................................92 UV Exposure and Cell Viability ............................................................................93 Lakewater Bacterial Removal and Viability ..........................................................93 Lakewater Pasteurization Test ...............................................................................94 Surface Cleaning Experiments ...............................................................................94 Discussion .....................................................................................................................94 Acknowledgements .......................................................................................................96 References .....................................................................................................................96 v TABLE OF CONTENTS – CONTINUED 5. BIOGEOCHEMISTRY AND MICROBIAL DIVERSITY IN THE MARINE CAVITY BENEATH THE MCMURDO ICE SHELF, ANTARCTICA .................................................................98 Contribution of Authors and Co-Authors ......................................................................98 Manuscript Information Page ......................................................................................100 Abstract .......................................................................................................................101 Introduction .................................................................................................................102 Methods .......................................................................................................................106 Site Description ....................................................................................................106 Physical and Chemical Parameters ......................................................................107 Biological Parameters ..........................................................................................111 Results .........................................................................................................................117 Water Column Structure ......................................................................................117 Inorganic Chemistry.............................................................................................118 Microbiological Characteristics ...........................................................................119 Bacterial and Archaeal Diversity and Community Structure ....................................................................................123 Discussion ...................................................................................................................126 Source of Sub-MIS Waters ..................................................................................127 Organic Carbon Sources ......................................................................................128 Dissolved Organic Matter Quality and Microbial Growth ..........................................................................................131 Conclusions .................................................................................................................133 Acknowledgements .....................................................................................................135 Supplemental Information ...........................................................................................136 References ...................................................................................................................137 6. A MICROBIAL ECOSYSTEM BENEATH THE WEST ANTARCTIC ICE SHEET .........................................................................................145 Contribution of Authors and Co-Authors ....................................................................145 Manuscript Information Page ......................................................................................147 A Microbial Ecosystem Beneath the West Antarctic Ice Sheet ..................................148 References ...................................................................................................................151 Acknowledgements .....................................................................................................151 Author Contributions ...................................................................................................151 Author Information ......................................................................................................151 WISSARD Science Team Members ...........................................................................151 Methods .......................................................................................................................152 Site Selection and Description .............................................................................152 vi TABLE OF CONTENTS – CONTINUED Hot Water Drilling and Clean Access to SLW ....................................................152 Temperature and Depth........................................................................................152 Water and Sediment Sampling.............................................................................152 Inorganic and Organic Chemistry ........................................................................152 Stable Isotope Analysis ........................................................................................152 Ph and Oxidation-Reduction Measurements .......................................................152 Cell and ATP Concentration ................................................................................152 Scanning Electron Microscopy ............................................................................152 Heterotrophic and Chemoautotrophic Production ...............................................153 Molecular and Phylogenetic Analysis of SSU Rrna Gene Sequences .................153 References ............................................................................................................153 Extended Data .............................................................................................................154 Supplementary Information .........................................................................................156 Supplementary Discussion ...................................................................................156 Solute Sources for SLW Waters .................................................................156 Molecular Analysis of SSU Gene Sequences .............................................156 Supplementary Discussion References .......................................................158 7. SUBGLACIAL CARBON AND NUTRIENT FLUXES FERTILIZE THE SOUTHERN OCEAN UNDER THE ROSS ICE SHELF ......................................................................................................159 Contribution of Authors and Co-Authors ....................................................................159 Manuscript Information Page ......................................................................................160 Subglacial Carbon and Nutrient Fluxes Fertilize the Southern Ocean Under The Ross Ice Shelf ............................................................................................161 Methods .......................................................................................................................170 Sample Collection ................................................................................................170 Organic Matter and Nutrients ..............................................................................171 Bacterial Carbon Demand and Respiration of Leucine ..................................................................................173 Flux Estimates ......................................................................................................174 Supplemental Information ...........................................................................................176 EEMS and PARAFAC Results and Analysis ......................................................176 References ...................................................................................................................180 8. CONCLUSIONS..........................................................................................................184 References ...................................................................................................................191 REFERENCES CITED ....................................................................................................193 vii LIST OF TABLES Table Page 2.1. Highest-level taxonomy definition for module keys ......................................29 2.2. S1. Environmental Data ..................................................................................43 2.3. S2. OTU and Diversity Data ...........................................................................44 2.4. S3. Module Assignments and Taxonomy .......................................................45 2.5. S4. Modularity Calculations for Lake Fryxell (FRX) and West Lobe Lake Bonney (WLB) Molecular Ecological Network (MEN) ...........................................................................69 2.6. S5. Major characteristics of modules with significant BC scores and those connected to the autumn proliferation of Archaea .........................69 3.1. Dark DIC-fixation as a percentage of total DIC-fixation ...............................76 3.2. Carbon balance for Lake Fryxell and East Lobe Lake Bonney ......................77 5.1. Water column inorganic chemistry from the AASW and mHSSW water masses sampled ....................................................................................118 5.2. Bacterioplankton and phytoplankton cell counts ..........................................119 5.3. Water column organic chemical composition and microbial activity ..........122 5.4. Fluorescence characteristics of dissolved organic matter .............................123 5.5. Prokaryotic diversity estimates for the different water masses sampled ......124 6.1. Biogeochemical data from the SLW borehole, water column, and surficial sediments ...................................................................................149 6.2. Extended Data Table 1 Crustal and seawater components to SLW waters ..................................................................................................154 6.3. Extended Data Table 2 Summary of parameters for SLW SSU gene sequence data .......................................................................................155 viii LIST OF TABLES - CONTINUED Table Page 7.1. Dissolved and particulate matter in SLW .....................................................163 7.2. Sources and sinks of organic carbon in SLW ...............................................167 7.3. Organic matter and nutrient supply to the Ross Ice Shelf cavity ..................169 ix LIST OF FIGURES Figure Page 1.1. Schematic view of aquatic environments discussed in this dissertation .............................................................................................5 2.1. Alpha diversity estimates ................................................................................25 2.2. Phylum-level diversity ....................................................................................26 2.3. Non-metric multidimensional scaling of bacterial, archaeal, and eukaryotic communities ...........................................................................26 2.4. Relative abundances of bacterial, archaeal, and eukaryotic OTUs .................27 2.5. Modules in the molecular ecological network ................................................28 2.6. S1. Profiles of oxygen, temperature, and conductivity ...................................41 2.7. S2. Complete molecular ecological network ..................................................42 3.1. Rates of DIC-fixation ......................................................................................75 4.1. Map showing the location of Subglacial Lake Whillans and its predicted flow path to the Ross Ice Shelf .......................................................88 4.2. Schematic showing flow directions for the field operation of the WISSARD water treatment system ................................................................88 4.3. Dye concentrations measured experimentally ................................................91 4.4. Fluorescent beads counted from the tank and ports ........................................92 4.5. Sediment concentration in the tank over time and at ports 1 and 2 ................92 4.6. Pond water cell and adenosine triphosphate concentrations ...........................93 4.7. Respiratory potential ......................................................................................93 5.1. Sampling site location ...................................................................................107 5.2. Water column profiles ...................................................................................117 x LIST OF FIGURES-CONTINUED Figure Page 5.3. Density plots of chl a versus FSC-A.............................................................120 5.4. Rarefaction curves, comparison of microbial communities, and relative abundance data .........................................................................126 5.5. S1. Potential temperature plotted over salinity .............................................136 6.1. Locator map of the WIS and SLW ...............................................................148 6.2. Phylogenetic analysis of SS gene sequences obtained from the SLW water column, surficial sediment, and drilling water ...........150 6.3. Morphological diversity of microbial cells in the SLW water column ........150 7.1. Profiles of organic matter and nutrients in SLW ..........................................166 7.2. S1. Fluorescence matrices for sediment porewater samples .........................177 7.3. S2. PARAFAC fingerprints and excitation emission spectra .......................178 7.4. S3. Representative water column EEM ........................................................179 8.1. Metabolic rates of heterotrophic microbial communities .............................185 xi ABSTRACT The research presented in this dissertation focused on microbially-mediated biogeochemical processes and microbial ecology in Antarctic lakes and seawater. The major objective of my research was to examine the impact of environmentally imposed energetic constraints on nutrient cycling in mirobially-dominated systems. I used three ice-covered aquatic environments as natural laboratories for my investigations. The permanently ice-covered lakes of the McMurdo Dry Valleys (MCM) are located in Victoria Land, East Antarctica. The MCM have been studied intensively as part of the McMurdo Long Term Ecological Research Project since 1993. My work built on the extensive MCM dataset via high-throughput DNA sequencing to examine microbial communities from all three domains of life during the transition to winter, and by quantifying rates of dark inorganic carbon-fixation. This worked showed the importance of flexible metabolisms in the microbial ecosystems of the MCM lakes. The ocean beneath the McMurdo Ice Shelf (MIS) is the gateway between the Ross Sea and the dark ocean of the Ross Ice Shelf cavity. The area supports a biological carbon pump that is important in ocean biogeochemistry. Ice shelves around Antarctica are under threat of collapse, but little is known about the ecosystems beneath them. My work used a combination of biogeochemical measurements and assessment of microbial community structure to characterize the ecosystem beneath the MIS and its connections to the open ocean. The data showed the importance of nutrients advected from open water to the MIS cavity and projected an organic carbon deficit farther from the ice shelf edge. Subglacial Lake Whillans lies 800 m beneath the surface of the West Antarctic Ice Sheet near the end of a hydrological continuum that terminates in the ocean beneath the Ross Ice Shelf. Primarily through the use of biogeochemical rate measurements and determinations of organic matter quantity and quality, this work established the presence of an active microbial ecosystem in the subglacial lake, and estimated the annual subglacial flux of carbon and nutrients to the ocean under the ice shelf. Together, these projects show the importance of microbial activity in regional biogeochemical processes and of metabolic flexibility under energy-limited conditions. 1 CHAPTER 1 INTRODUCTION Structure of the Dissertation Chapter 1 is an introduction to Antarctic aquatic environments, which establishes the background for and significance of my dissertation, Biogeochemical Processes in Antarctic Aquatic Environments: Linkages and Limitations. It provides descriptions of my research sites, and the overarching hypotheses addressed by my research. The remainder of my dissertation is comprised of six manuscripts. I used approaches ranging from the examination of microbial community structure, to geochemical characterization and the measurement of microbially-mediated transformations of carbon to examine biogeochemical processes in Antarctic aquatic environments and the linkages between and among organisms, processes, and systems. Chapters 2 and 3 focus on the permanently ice-covered lakes of the McMurdo Dry Valleys area of Antarctica. Chapter 2, published in the ISME Journal (Vick-Majors et al., 2014), describes changes in microbial community structure and uses network analysis to identify key potential metabolisms (those with many linkages to other organisms) during the transition to Antarctic winter (the Polar Night). Chapter 3, in review at Microbial Ecology, reports rates of dark inorganic carbon fixation in, and constructs organic carbon budgets for, two of the McMurdo Dry Valley lakes, highlighting the importance of chemolithoautotrophy as a source of organic carbon to these lakes. The next chapter begins a focus on subglacial aquatic environments and provides a methodological umbrella for the 2 remaining chapters. Chapter 4, published in Antarctic Science (Priscu et al., 2013), describes the methods and equipment used to ensure microbiologically clean access to pristine subglacial aquatic environments, which is important methodological background for the rest of the dissertation. Chapter 5, in press in Limnology and Oceanography, provides a rare look beneath the McMurdo Ice Shelf at microbial (bacterial and archaeal) community structure and biogeochemical processes in the sea under 80 m of ice. Chapter 6, published in Nature (Christner et al., 2014), describes a microbial ecosystem under the West Antarctic Ice Sheet (Subglacial Lake Whillans) in terms of activity, diversity, and geochemistry. Chapter 7, in preparation for Nature, builds on Chapter 6 to determine the sources and sinks for organic matter in Subglacial Lake Whillans and estimate the subglacial fertilization of the Southern Ocean beneath the Ross Ice Shelf. The final chapter (Chapter 8) summarizes my conclusions, and proposes future directions for research. Microbial Ecology in Antarctic Aquatic Environments Biological fluxes of the major elements (C, H, N, O, P, S) are largely driven by the activities of microorganisms (Falkowski et al., 2008). Of the estimated 4 - 6 x 1030 bacterial and archaeal cells on earth (Whitman et al., 1998), the greatest proportions are found in oceanic and terrestrial subsurface environments (3.0 x 1030 and 2.5 x 1030 cells, respectively; (Whitman et al., 1998)) and in subglacial environments (4.0 x 1029 cells; (Priscu et al., 2008)). These subsurface and sub-ice environments are aphotic, and are either completely removed from inputs of photosynthetically-derived organic carbon, are 3 separated from such carbon on timescales of thousands to millions of years (Lever et al., 2015), or receive very low fluxes (e.g. (Røy et al., 2012)). Low fluxes of energy and poor quality organic matter combine with other factors, such as nutrient limitation (of anabolism), that may be physiologically dissimilar to but difficult to disentangle from energy limitation (of catabolism) (Lever et al., 2015), to make most of the Earth’s biosphere appear energy limited (Morita, 1997; LaRowe and Amend, 2015). Much of the microbial biosphere and the activities associated with it are under sampled, either because of difficult to access locations (e.g. deep oceans, subglacial lakes) or high biodiversity, which is difficult to catalogue and understand (e.g. soils). As a consequence of under sampling, we have a limited understanding of how microorganisms in low energy environments interact and survive, and of their integration into global fluxes of carbon and nutrients (Rousk and Bengtson, 2014). Much of what is known about low energy environments comes from studies of deep subsurface and deep sea sediments, but Antarctic studies of aquatic environments also provide insight into microbial communities and microbially mediated biogeochemical processes under energy limitation. Surface aquatic environments in the Antarctic are subject to the bimodal light/dark cycle typical of high latitudes, providing an intermediate between the permanently dark subsurface and the usual diel light/dark cycles of moderate latitudes. Subglacial aquatic environments, on the other hand, are found under tens (mountain glaciers) to thousands (the Antarctic ice sheet) of meters of ice, leaving them permanently dark. A major consequence of lack of light is the loss of contemporaneous photosynthetic 4 primary production at the base of the food chain. Photosynthesis is the primary source of energy in the sunlit biosphere, fixing an estimated 60 Gt of carbon in the oceans per year (Behrenfeld et al., 2005). With the exception of deep sea hydrothermal vents, where strong chemical gradients support high rates of microbial activity e.g. (Orcutt et al., 2011), systems removed from local photosynthetic inputs often exhibit low metabolic rates (Røy et al., 2012). Antarctic aquatic environments, which are often dominated by microorganisms (see, for example Priscu et al., 1999; Cavicchioli, 2015, comprise relatively isolated and pristine environments in which to study microbial ecosystems that are either seasonally or permanently deprived of photosynthetic inputs. In spite of its reputation as a frozen mass of ice, the Antarctic provides numerous habitats for aquatic microbial life. The Southern Ocean, which surrounds the continent, contains diverse microbial communities in its waters and sea ice, with key roles in global biogeochemical processes (Cavicchioli, 2015). The Antarctic continent itself hosts lakes and ponds located in small, ice-free coastal areas of the continent such as the Vestfold Hills and the McMurdo Dry Valleys (~0.5% of the total land area; (Convey et al., 2014)). The microbially dominated lakes and ponds cover a range of nutrient conditions, fall on a salinity spectrum from fresh water to hyper saline, and range from oxygen over saturation to anoxia, and from seasonal to permanent ice covers (Spigel and Priscu, 1998), providing a range of natural conditions under which to study microbial processes. Subglacial lakes and wetlands, which were unknown until the late 20th century (Oswald and Robin, 1973), are an almost completely unexplored microbial habitat (Priscu et al., 2008). With 379 subglacial lakes discovered to date all over the Antarctic continent 5 (Wright and Siegert, 2012), the diversity of subglacial geochemical conditions and microbial communities is likely to rival that of the surface lakes. Figure 1 shows a schematic view of the aquatic environments examined in my dissertation. Figure 1. Schematic view of the aquatic environments discussed in this dissertation. The black line across the map of Antarctica shows where the cross-section was drawn from (~700 km). The Taylor Glacier represents the terminus of the East Antarctic Ice Sheet, which ends in the Transantarctic Mountains, leaving the McMurdo Dry Valleys free of ice cover except for it’s permanently ice-covered lakes (Chapters 2 and 3). The Dry Valleys terminate in the McMurdo Sound area of the Ross Sea, which is shown here partially covered with seasonal sea ice. The subglacial lakes (Chapters 6 and 7) shown beneath the Whillans Ice Stream portion of the West Antarctic Ice Sheet periodically drain into the ocean, indicated by subglacial outflow in the drawing. The grounding line indicates where the West Antarctic Ice Sheet leaves the continental land mass and begins to flow over the ocean, forming the Ross Ice Shelf. The Ross Ice Shelf joins the McMurdo Ice Shelf closer to the McMurdo Sound region and the two share an ice-shelf cavity (Chapter 5). The drawing is not to scale. The map of Antarctica (modified from OpenStreetMap) is used under the Creative Commons Attribution-ShareAlike 2.0 License. 6 McMurdo Dry Valley Lakes The perennially ice-covered lakes of the McMurdo Dry Valleys lie in East Antarctica in the coldest, driest desert on earth. The lakes are physicochemically stable environments and contain microbially dominated ecosystems (Priscu et al., 1999; Spigel and Priscu, 1998; Takacs and Priscu, 1998; Vick and Priscu, 2012). As the sole year- round source of liquid water, such lakes provide the only continuous habitat for aquatic life in the ice-free regions of the Antarctic continent. Permanent ice-covers on the lakes severely attenuate the penetration of solar irradiance to <1% of incident light (Lizotte and Priscu, 1992) and prohibit wind driven turbulence, propagating an environment continuously stratified with regard to solar energy and nutrients. The Taylor Valley is located in the McMurdo Dry Valleys, and contains four lakes, three of which are discussed in this dissertation. Lake Fryxell is located at the eastern end of Taylor Valley, near McMurdo Sound, while Lake Bonney is located at the western end of the valley, adjacent to the Taylor Glacier, the easternmost extent of the East Antarctic Ice Sheet. Lake Fryxell is an ~18 m deep, closed basin lake characterized by relatively nutrient-rich waters with an anoxic, brackish hypolimnion. The lake is in direct contact with the adjacent Canada Glacier. Lake Bonney is ~40 m deep, and its closed basin is divided into East Lobe and West Lobe Bonney. The deep waters of the two lobes are separated by a sill at ~13 m depth, while their surface waters exchange across the sill. The deep waters of West Lobe Bonney are anoxic, and the lake is influenced by subglacial outflow from Blood Falls beneath the Taylor Glacier (Mikucki et al., 2007); East Lobe Bonney has a suboxic hypolimnion and salinity ~10 times 7 seawater. The closed nature and disparate geochemistries of the Taylor Valley lakes make them important “natural laboratories” for the study of microbial responses to changing availability of energy sources and nutrients. Life Under Ice Shelves Ice shelves form where grounded ice sheets leave the land and float over the ocean; the waters beneath ice shelves are referred to as “marine cavities”. Seventy-five percent of Antarctica’s coastline is surrounded by ice shelves, covering 1.5 x 106 km2 of ocean (Rignot et al., 2013). West Antarctic ice shelves are thinning dramatically(Wouters et al., 2015), and together with intrusions of warm Circumpolar Deep Water beneath East Antarctic ice shelves such as the Totten Glacier (Greenbaum et al., 2015)make ice-shelf melt the single largest source of ablation in Antarctica (Rignot et al., 2013). Not only do these coastal ice shelves buttress the Antarctic Ice Sheet, which contains 58 m sea level equivalent worth of ice (Rignot et al., 2013), but seawater that flows beneath larger ice sheets (such as the Ronne) is modified to form Antarctic Bottom Water, which is key to cooling and ventilating oceans worldwide (Nicholls et al., 1991). The loss of Antarctic ice shelves is predicted to modify ocean circulation (Bougamont et al., 2007). The Southern Ocean is also an important sink for CO2. Changing atmospheric patterns are predicted to impact the functioning of the sink, but the magnitude and consequences of projected changes are not well known (Achterberg, 2014). Even less well known are the potential impacts of climate change on sub-ice shelf ecosystems, or how changing those ecosystems might impact the Southern Ocean carbon pump. Following the breakup of the Larsen A and B ice shelves, researchers found evidence for increased biological CO2 8 pumping (Bertolin and Schloss, 2009), and although the opening of preciously ice covered waters should increase CO2 pumping via new photosynthetic activity, it is difficult to interpret post-ice-shelf-breakup responses without baseline data. Baseline studies of sub-ice shelf ecosystems are almost entirely lacking due to the difficultly of accessing the ocean beneath thick ice; those data that do exist describe communities ranging from surprisingly diverse (Post et al., 2014) to scavenger-dominated (Lipps et al., 1979). The shift in nutrient concentrations from relatively nutrient rich water flowing into the Ross Ice Shelf cavity (Chapter 5) to the oligotrophic outflow from the cavity to McMurdo Sound (Hodson et al., 1981; J C Priscu et al., 1990) indicates significant biogeochemical modification of sub-ice shelf waters during residence time in the cavity. Understanding sub-ice shelf biogeochemical processes is important for predicting Southern Ocean responses to climate change, and provides new insight into microbial responses to the loss of locally produced photosynthetic carbon closer to the interior of the marine cavity. Subglacial Aquatic Environments In spite of being covered by 27 million km3 of ice up to ~3.5 km thick (Fretwell et al., 2013), the Antarctic continent hosts significant biological diversity (Chown et al., 2015). Perhaps the most important physical driver of habitability in Antarctic terrestrial environments is the availability of liquid water. Liquid water can be found on and near the surface of glaciers in the Antarctic, forming supraglacial habitats (Stibal et al., 2012) such as cryoconite holes that are hot spots of microbial activity (Foreman et al., 2007). Beneath the ice sheet, liquid water can form wherever temperatures reach the pressure 9 melting point, either due to geothermal heat flux (Fisher et al., 2015) or volcanic activity(Gaidos et al., 2009), although the latter has only been hypothesized in West Antarctica (Blankenship et al., 1993). The resulting subglacial lakes, streams, water saturated sediments, and wetlands are thought to contain ~106 km3 of water (J C Priscu et al., 2008) and serve as habitats for microbial life (J C Priscu, Adams, et al., 1999; Christner et al., 2006; Lanoil et al., 2009). Subglacial microbial life has been found in debris-rich basal ice, sediments, and water beneath temperate, Arctic, and Antarctic valley glaciers (Sharp et al., 1999; Skidmore et al., 2000; Mikucki et al., 2007). Biotic and abiotic weathering processes liberate nutrients and energy sources for microorganisms beneath glaciers (Montross et al., 2013), while overriden organic carbon or subglacially produced hydrogen (Telling et al., 2015) may fuel methanogenesis(Boyd et al., 2010; Wadham et al., 2012) and provide carbon and energy for methanotrophs (Dieser et al., 2014), Michaud et al., in prep). Chemolithotrophic metabolisms may be supported by iron and sulfur (Boyd et al., 2014; Purcell et al., 2014; Mikucki et al., 2009)or nitrogen (Boyd et al., 2011) (see also Chapter 6). Heterotrophic metabolism has received comparatively less attention than chemolithotrophic metabolism in subglacial environments, although the ultimate source of ammonium to support nitrification (Chapter 6) must be the remineralization of organic matter. Heterotrophic microorganisms may use recalcitrant or semi-labile relict organic matter present under glaciers as an energy source (Bardgett et al., 2007). Organic matter produced by chemoautotrophy can also support heterotrophic growth, however its production and 10 remineralization may be uncoupled, especially under conditions of energy limitation where cells are less likely to leak organic matter (Carlson et al., 2007). Subglacial Lake Whillans (SLW) is the only Antarctic subglacial lake to have been directly sampled to date (Tulaczyk et al., 2014). The lake lies 800 m beneath the surface of the Whillans Ice Stream (WIS) in West Antarctica. It is one of the “active” subglacial lakes along Antarctica’s Siple Coast, so named for the active hydrology that connects lakes in the region to each other and to the Ross Sea downstream (Fricker et al., 2007). The net fluxes of carbon and nutrients mediated by microorganisms in SLW, and other subglacial lakes, are important to quantify as a means of understanding subglacial contributions to downstream environments. The study of microorganisms in subglacial lakes adds to our understanding of microbial physiology, and of the potential for microbial life on icy worlds in our solar system and beyond (Mikucki et al., 2015). Significance of My Research My dissertation focuses on the microbial ecology of, and microbially mediated biogeochemical processes in, ice-covered Antarctic aquatic environments. The combination of liquid water and thick, permanent ice covers creates oases in the Antarctic desert(J C Priscu et al., 1998). The unique characteristics of ice-covered, liquid water habitats such as truncated ecosystems devoid of metazoans and/or photosynthetic primary producers, permanent darkness, and isolation from exchange with atmosphere make these desert oases excellent places to study microbial processes and interactions. I applied microbiological techniques to samples from two McMurdo Dry Valley lakes (Fryxell and 11 West Lobe Bonney) to provide the first in-depth, simultaneous description of microbial community structure from all three domains of life (Bacteria, Archaea, Eukarya) in these lakes and to examine microbial community shifts and interactions during the transition to the Polar Night. To better understand the potential importance of chemoautotrophic inorganic carbon fixation in the Dry Valley lakes, I studied rates of inorganic carbon fixation in Lake Fryxell and East Lobe Lake Bonney, and found that chemoautotrophy can help balance the organic carbon deficit imposed by the lack of photosynthetic activity during the winter. While the bimodal light/dark cycle associated with high latitudes has a direct impact on the McMurdo Dry Valley lakes, the waters beneath the McMurdo Ice Shelf and those in Subglacial Lake Whillans beneath the West Antarctic Ice Sheet, are permanently dark. I used microbiological and geochemical techniques to describe biogeochemical conditions under the McMurdo Ice Shelf, the first study of its kind. The baseline characterization of the sub-McMurdo Ice Shelf environment is especially important as Antarctic ice shelves are increasingly threatened with collapse (Rignot et al., 2013). My work on Subglacial Lake Whillans provided the first measurements of microbial activity, abundance and carbon and nutrients in an Antarctic subglacial lake and showed the potential for West Antarctic subglacial aquatic environments to impact Southern Ocean biogeochemical processes downstream. Together, the studies encompassed by my dissertation provide new insights into microbial diversity and activity in Antarctic aquatic systems and begin to address biogeochemical linkages with habitats beyond my study sites. 12 Hypotheses and Objectives The overarching question behind my dissertation is, “how does the ensemble of environmentally imposed energetic constraints impact nutrient cycling in microbially dominated systems?” The energetic constraints examined here include quality of organic matter and shifts in organic matter sources as a consequence of seasonal change or location. I discuss the implications for nutrient cycling directly (e.g. as rates of carbon transformations in Subglacial Lake Whillans) and indirectly (e.g. via shifts in microbial community structure and identification of key metabolisms in the McMurdo Dry Valley lakes). Hypothesis 1: The transition from summer to winter, characterized by the loss of summer sunlight, will be associated with shifts in microbial community structure and key potential metabolisms in Antarctic surface lakes. Associated objectives: 1. Characterize the bacterial, archaeal, and eukaryotic microbial communities present in Lake Fryxell and the West Lobe of Lake Bonney before and after the start of the seasonal sunset. 2. Determine whether microbial communities are different between summer and autumn. 3. Determine co-occurance patterns of microorganisms. 4. Identify important groups and putative functions within the communities. 5. Determine whether dark fixation of inorganic carbon (chemoautotrophy) can meet the estimated annual carbon deficit. 13 Hypothesis 2: Waters beneath the McMurdo Ice Shelf contain carbon, nutrients and energy sources advected from open water, which can support microbial communities beneath the ice. Associated objectives: 1. Compare measured current flow to published data on currents in the area to determine whether water at our sample site was likely advected from McMurdo Sound. 2. Determine the concentrations of nutrients and the quality and quantity of organic matter at the sample site. 3. Determine the abundance and diversity of prokaryotic microorgansims. 4. Determine whether phytoplankton are present under the ice and whether they contain chlorophyll. Hypothesis 3: Subglacial Lake Whillans is a nutrient-poor environment that hosts metabolically active microorgansims. Associated objectives: 1. Develop, test, and implement procedures for microbiologically clean access to the subglacial environment. 2. Determine whether microbial cells are present and quantify microbial cells in Subglacial Lake Whillans. 3. Determine whether microbial cells in Subglacial Lake Whillans are active. 4. Determine rates of heterotrophic and autotrophic activity. 5. Measure concentrations of inorganic nutrients and organic matter. 14 Hypothesis 4: Subglacial Lake Whillans is an important source of organic matter and nutrients to coastal environments downstream. Associated objectives: 1. Determine rates of dark CO2 incorporation (chemoautotrophic production of organic carbon). 2. Determine the heterotrophic demand for organic carbon (incorporation + respiration of carbon). 3. Estimate the annual contributions of chemoautotrophy, ice-melt, inflow from upstream, diffusion from sediment porewater, and inflow of groundwater to the Subglacial Lake Whillans organic carbon pool. 4. Using the sources and sinks above, calculate the accumulation time of the measured organic matter pool in SLW and compare to known hydrology. 5. Using the data derived above along with published data on water discharge, estimate the annual flux of organic matter and nutrients from the Siple Coast to the Southern Ocean. 15 References Achterberg EP. (2014). 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Priscu Contributions: Provided funding, oversight, samples, conceived the study, and aided in manuscript preparation. Co-Author: Linda A. Amaral-Zettler Contributions: Oversaw DNA sequencing and sequence data preparation, provided diversity statistics, provided funding, conceived the study, and aided in manuscript preparation. 21 Manuscript Information Page Trista J. Vick-Majors, John C. Priscu, Linda Amaral-Zettler The ISME Journal Status of Manuscript: ____ Prepared for submission to a peer-reviewed journal ____ Officially submitted to a peer-review journal ____ Accepted by a peer-reviewed journal _ X_ Published in a peer-reviewed journal Nature Publishing Group In Volume 8, 778-789, 2014 Reused according the Nature Publishing Group License Policy. NPG does not require authors of original (primary) research papers to assign copyright of their published contributions. Authors grant NPG an exclusive licence to publish, in return for which they can reuse their papers in their future printed work without first requiring permission from the publisher of the journal. ORIGINAL ARTICLE Modular community structure suggests metabolic plasticity during the transition to polar night in ice-covered Antarctic lakes Trista J Vick-Majors1, John C Priscu1 and Linda A Amaral-Zettler2,3 1Montana State University, Department of Land Resources and Environmental Sciences, Bozeman, MT, USA; 2The Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA, USA and 3Department of Geological Sciences, Brown University, Providence, RI, USA High-latitude environments, such as the Antarctic McMurdo Dry Valley lakes, are subject to seasonally segregated light–dark cycles, which have important consequences for microbial diversity and function on an annual basis. Owing largely to the logistical difficulties of sampling polar environments during the darkness of winter, little is known about planktonic microbial community responses to the cessation of photosynthetic primary production during the austral sunset, which lingers from approximately February to April. Here, we hypothesized that changes in bacterial, archaeal and eukaryotic community structure, particularly shifts in favor of chemolitho- trophs and mixotrophs, would manifest during the transition to polar night. Our work represents the first concurrent molecular characterization, using 454 pyrosequencing of hypervariable regions of the small-subunit ribosomal RNA gene, of bacterial, archaeal and eukaryotic communities in permanently ice-covered lakes Fryxell and Bonney, before and during the polar night transition. We found vertically stratified populations that varied at the community and/or operational taxonomic unit-level between lakes and seasons. Network analysis based on operational taxonomic unit level interactions revealed nonrandomly structured microbial communities organized into modules (groups of taxa) containing key metabolic potential capacities, including photoheterotrophy, mixotrophy and chemolithotrophy, which are likely to be differentially favored during the transition to polar night. The ISME Journal (2014) 8, 778–789; doi:10.1038/ismej.2013.190; published online 24 October 2013 Subject Category: Microbial population and community ecology Keywords: ice-covered lake; McMurdo Dry Valleys; microbial diversity; MIRADA-LTERS; network analysis; polar night Introduction Microbial diversity and function in aquatic ecosys- tems are tightly coupled to the physical and geochemical environment (Judd et al., 2006; Galand et al., 2008; Bielewicz et al., 2011). The importance of seasonal succession is increasingly being recognized within the context of geochemi- cally distinct environments (Crump et al., 2003; Andersson et al., 2010; Ghiglione and Murray, 2012; Grzymski et al., 2012). Polar environments are subject to strong seasonal light gradients, where 24-hour daylight drives continual photoautotrophic primary production during the summer, often coinciding with high rates of heterotrophic bacterioplankton production (Takacs and Priscu, 1998; Mora´n et al., 2001; Alonso-Sa´ez et al., 2008). Winter sampling is logistically difficult in the polar regions, and thus studies examining microbial dynamics during the darkness of winter and the spring and autumn transition periods are few. Recent studies have shown higher bacterial com- munity richness (Ghiglione and Murray, 2012) and the increased importance of chemolithotrophic Archaea in the Southern Ocean during winter (Grzymski et al., 2012; Williams et al., 2012), whereas others have shown that trophic plasticity is a key survival strategy for protists during the summer–winter transition in Antarctic lakes (Bielewicz et al., 2011). The perennially ice-covered lakes of the McMurdo (MCM) Dry Valleys, which lie in East Antarctica in the coldest, driest desert on earth, comprise physicochemically stable environments containing microbially dominated ecosystems (Spigel and Priscu, 1998; Takacs and Priscu, 1998; Correspondence: LA Amaral-Zettler, The Josephine Bay Paul Center, Marine Biological Laboratory, 7 MBL Street Woods Hole, Woods Hole, MA 02543, USA. E-mail: amaral@mbl.edu Received 15 May 2013; revised 23 August 2013; accepted 20 September 2013; published online 24 October 2013 The ISME Journal (2014) 8, 778–789 & 2014 International Society for Microbial Ecology All rights reserved 1751-7362/14 www.nature.com/ismej Priscu et al., 1999; Vick and Priscu, 2012). As the sole year-round source of liquid water, such lakes provide the only continuous habitat for aquatic life in the ice-free regions of the Antarctic continent. Permanent ice covers on the lakes severely attenuate the penetration of solar irradiance to between 1% and 2% of incident light (Lizotte and Priscu, 1994) and prohibit wind-driven turbulence, propagating an environment continuously stratified with regard to solar energy and nutrients. A few studies have examined the molecular diversity of bacterial, archaeal (Karr et al., 2005; Glatz et al., 2006; Karr et al., 2006) or protistan (Bielewicz et al., 2011; Kong et al., 2012a) communities in MCM lakes and found that communities are distinctly stratified by depth. The physicochemical stability of these environ- ments makes them excellent locations to examine the impact of seasonal light–dark cycles on micro- bial community dynamics. Most studies of the MCM lakes have been confined to summer, when phytoplankton primary production and glacial melt water streams supply 450% of the organic matter supporting hetero- trophic growth (Takacs et al., 2001), but a few have examined the activities of bacterioplankton (Takacs and Priscu, 1998; Vick and Priscu, 2012), phyto- plankton (Lizotte et al., 1996), flagellates (Thurman et al., 2012) and the diversity of protists (Bielewicz et al., 2011) during the transition periods flanking summer and winter. Mixotrophy, via the combined use of photosynthesis and phagotrophy, is a key adaptive strategy for phytoplankton in MCM lakes that likely allows populations to persist throughout the winter (McKnight et al., 2000; Laybourn-Parry, 2002; Bielewicz et al., 2011; Thurman et al., 2012), whereas metabolic plasticity, such as the ability to switch carbon substrates, is important for hetero- trophic bacterioplankton to remain active during winter when phytoplankton-produced organic car- bon is in short supply (Vick and Priscu, 2012). Clearly, trophic and metabolic versatilities are vital to the survival of given populations in these lakes and are important to the maintenance of overall ecosystem function. In addition to physicochemical controls on micro- bial community structure, recent studies have also shown that microbial co-occurrence patterns can help define ecologically meaningful interactions between species and across domains (Horner- Devine et al., 2007; Fuhrman and Steele, 2008; Steele et al., 2011) and that co-occurring species are often organized into groups, or modules, of func- tional significance (Chaffron et al., 2010; Barbera´n et al., 2012). In light of the physicochemical stability of the MCM lakes, we sought to determine the importance of both community succession and operational taxonomic unit (OTU)-level co-occur- rence patterns to overall community structure during the summer autumn transition period (November–March). We used pyrosequencing of the V6 (bacterial and archaeal) and V9 (eukaryotic) hypervariable regions of the small-subunit riboso- mal RNA gene on samples from two geochemically distinct MCM lakes, Lake Fryxell (FRX) and the West Lobe of Lake Bonney (WLB), during the austral summer (November) and autumn (March) to analyze microbial community composition, and implemen- ted molecular ecological network analysis to exam- ine inter- and intra-domain co-occurrence patterns. We provide the first evidence for seasonal shifts in bacterial, archaeal and eukaryotic communities in these Antarctic lakes. Our data also suggest the importance of metabolically plastic taxa in main- taining overall ecosystem function and document the proliferation of several archaeal lineages, which may be important primary producers during the darkness of winter. In combination with studies from the polar oceans (Grzymski et al., 2012; Ghiglione and Murray, 2012; Williams et al., 2012), our results reveal that shifts in community diversity may be characteristic of polar ecosystems during winter and have particular importance in fueling continued biogeochemical cycling in the absence of photosynthetic primary production. Methods Sample collection Duplicate samples for DNA extraction were collected from FRX (depth B18 m) and WLB (depthB38 m) during the Austral summer and autumn at depths of 6 m (bacterial and primary production maximum) and 9 m (chemocline; chlor- ophyll-a maximum) in FRX (2 November 2007 and 25 March 2008) and 13 m (chemocline; bacterial production, chlorophyll-a, primary production max- imum) and 18 m (hypersaline, bottom of trophogenic zone) in WLB (30 November 2007 and 12 March 2008). Corresponding environmental data were collected by the MCM Long-Term Ecological Research program B1 week before and 1 week after samples for DNA collection and interpolated to the DNA sample collection date (11 November and 5 December 2007, and 20 and 28 March for FRX; 25 November and 15 December 2007, and 7 and 14 March for WLB). In WLB, environmental data were collected at 17 and 20 or 25 m and interpolated to 18 m. Complete environmental data and methods are available on the MCM Long-Term Ecological Research website (http://www.mcmlter.org/) and are published elsewhere (Vick and Priscu, 2012), and the minimum information about a marker gene sequence-compliant (Yilmaz et al., 2011) environ- mental data are summarized in supplementary information. Representative vertical profiles of temperature, conductivity and oxygen from each lake are shown in Supplementary Figure S1, and environmental data in Supplementary Table S1. Temperature and conductivity were measured with a SBE 25 Sealogger CTD according to Spigel and Priscu (1998), and dissolved oxygen was measured Antarctic lake microbial communities during the polar night transition TJ Vick-Majors et al 779 The ISME Journal using the azide modification of the mini-Winkler titration. All water samples were collected through a borehole in the ice cover using a Niskin bottle. Samples for DNA extraction were filtered onto 0.2- mM Sterivex filters (Millipore, Billerica, MA, USA) and stored with 2.0 ml of Puregene lysis buffer at  20 1C until further processing. DNA was extracted as described previously (Amaral-Zettler et al., 2009), and water filtration and DNA extraction protocols can be found at http://amarallab.mbl.edu. Sequencing We amplified V6 hypervariable regions using pri- mers targeting positions (according to the E. coli numbering scheme) 947–1046 (Bacteria) and posi- tions 958–1048 (Archaea) of the 16S ribosomal RNA gene. For Eukarya, amplification of the V9 hyper- variable region followed established protocols (Amaral-Zettler et al., 2009). We multiplex- sequenced the resulting amplicons using bar-coded primers (Huber et al., 2007; Amaral-Zettler et al., 2009) on a 454 Genome Sequencer FLX (Roche, Switzerland) using the manufacturer’s recom- mended protocol. The number of reads obtained on a single sample ranged from 1724 to 23 334 (Archaea), 2997 to 15 552 (Bacteria) and 2602 to 12 504 (Eukarya) (Supplementary Table S2). Sequence processing Sequences were trimmed, and low-quality reads were removed according to Huse et al., 2007. Sequences were clustered into OTUs using ESPRIT, SLP and mothur to precluster (2%) sequences using single linkage and construct final clusters based on pairwise alignment and average linkage (Huse et al., 2010). Three-percent cluster widths were used for bacterial and archaeal analyses and 6% for eukar- yotic analyses. This clustering method is equally effective as ‘denoising’ data via methods such as Pyronoise to minimize OTU inflation (Quince et al., 2011). All of our sequence data are minimum information about a marker gene sequence-compli- ant (Yilmaz et al., 2011) and have been deposited in the National Center for Biotechnology Information normal and Sequence Read Archives under the accession number SRP028879. Taxonomy assignment Taxonomic classification was assigned using the Global Alignment for Sequence Taxonomy (GAST) method (Huse et al., 2008). Briefly, hypervariable tag reference sets (V6 or V9) were created from a ribosomal RNA reference database based on the SILVA database (Pruesse et al., 2007), with taxon- omy assigned by the RDP Classifier (Wang et al., 2007). We then aligned the tag sequences against the top 100 reference sequences using MUSCLE and considered the best GAST match. Smaller GAST distances equate to better matches. We assigned a tag to a given genus if two-thirds or more of the full- length reference ribosomal RNA sequences containing the exact hypervariable region shared the same genus. If there was no agreement, we moved up the tree one level to family and so on until a consensus was reached. Diversity calculations Parametric alpha diversity estimates for Bacteria and Archaea were calculated using CatchAll version 3.2 (Bunge et al., 2012), and eukaryotic nonparametric (Chao2) richness estimates (Chao, 1987) were calcu- lated with the program SPADE (Chao and Shen, 2010). All calculations were performed on pooled sequences from duplicate samples for bacteria and archaea and separate replicated samples for euka- ryotes. We performed these calculations using full data sets, as well as normalized data sets in which the number of sequences per sample was made equal through random resampling. Network analysis To determine associations between microbial populations and between microbial populations and the environment, we calculated Spearman correlations between environmental data, relative abundances of bacterial and archaeal OTUs and presence–absence of eukaryotic OTUs. Significant correlations (Po0.01; rX0.8) were extracted and the resulting matrix of correlation coefficients was loaded into the program Cytoscape (Shannon et al., 2003) for visualization. In total, we included 899 variables in our correlation analysis: 186 eukaryotic, 637 bacterial and 69 archaeal OTUs, as well as 7 environmental variables. Statistics Statistics were carried out in R (R Development Core Team, 2008). Beta diversity was examined and plotted using the function nmds (non-metric multi- dimensional scaling) in the R library labdsv (Roberts, 2010). The importance of environmental factors in partitioning of beta diversity was tested using permu- tational analysis of variance (Anderson, 2001) with the adonis function, and C-scores were calculated using the oecosimu function with nestedchecker (Stone and Roberts, 1990) and quasiswap (Miklo´s and Podani 2004) in the R library vegan (Oksanen et al., 2010). Network statistics, including modularity calcula- tions, were carried out using Cytoscape (Shannon et al., 2003; Supplementary Information). Results and discussion Seasonal variation in microbial communities Species richness (alpha diversity) was relatively low across all samples and all three domains, but it Antarctic lake microbial communities during the polar night transition TJ Vick-Majors et al 780 The ISME Journal followed trends observed in other environments with bacterial richness surpassing archaeal and eukaryotic richness by an order of magnitude (Huber et al., 2007, McCliment et al., 2012). Alpha diversity was generally higher in autumn samples for bacterial and eukaryotic communities in FRX, whereas the opposite was true in WLB (except for 18 m bacterial communities). Archaeal diversity was lower than bacterial and eukaryotic diversity (Figure 1), with coverage (observed/expected diver- sity) ranging from 53 to 90%. Eukaryotic coverage was highest, ranging from 87 to 98%, whereas bacterial coverage was comparatively low (15 to 50%; Supplementary Table S2). Actinobacteria and Bacteroidetes dominated the bacterial communities of both lakes. This is con- sistent with reports from other freshwater systems (Newton et al., 2011). The Proteobacteria was the next most abundant phylum in both lakes, with the class Betaproteobacteria dominating FRX and Gam- maproteobacteria dominating WLB, a difference that may be explained by the influence of Blood Falls, a Gammaproteobacteria-dominated subglacial feature that flows into the western terminus of WLB (Mikucki and Priscu, 2007). Marine Group I Cre- narchaeota dominated the archaeal communities, similar to the upper and intermediate waters of Arctic meromictic Lake A (Comeau et al., 2012), followed by Thermoplasmatales-related Euryarch- aeotes and Methanomicrobia. Eukaryotic community composition varied between lakes and depths, with Cryptomonadales and Ciliophora OTUs being the most frequently encountered in FRX, whereas WLB contained OTUs most frequently affiliated with Cryptomonadales, Stramenopiles and Dinoflagellata (Figure 2). Bacterial and eukaryotic communities both grouped by lake (permutational analysis of variance; P¼ 0.0010 for Bacteria, P¼ 0.0020 for Eukarya) and depth (P¼ 0.0010 for Bacteria and Eukarya). Season (summer vs autumn) was not significant alone, but when included in a model that first accounted for the interaction between lake and depth it explained a significant portion of the variation in the bacterial and eukaryotic communities (P¼ 0.013 and P¼ 0.042, respectively). These results indicate that within lake and depth, bacterial and eukaryotic communities were significantly different between seasons (Figure 3). Archaeal communities did not clearly partition by lake, depth or season, but a model accounting for lake and season indicated that depth was the most important factor explaining the variation between communities (P¼ 0.042; Figure 3). The effects of lake and season were marginally significant for the Archaea (P¼ 0.14 and 0.12, respectively). These results are similar to the seasonal and depth partitioning observed in Arctic meromictic Lake A, where bacterial and eukaryotic phyla varied as a function of both depth and time, and archaeal phylum-level seasonal changes were minor, but depth partitioning was strong (Charvet et al., 2012a, Comeau et al., 2012). Seasonal changes in community composition were apparent at the OTU level as the percentage of OTUs that went from being rare (o0.1% of community) or absent in summer samples to being abundant (40.1% of community; Crump et al., 2012) in autumn samples. The changes in composi- tion, shown in Figure 4 as points falling along the y axes, were especially pronounced for the Archaea, describing 28% and 23% of the 6 m and 9 m FRX communities, respectively, and 14% and 10% of the 13 m and 18 m WLB communities, respectively. OTU-level changes in community composition were comparatively small for the Bacteria (0.8 to 5.5% of the communities) and Eukarya (1.7 to 3.0% of OTUs). The use of a higher cutoff (1.0%) for the rare to abundant transition did not change the pattern, although it decreased the percentages (Archaea¼ 4 to 12%, Bacteria¼ 0.2 to 1.0%, Eukarya¼ 0 to 2.0%). Although they were not the dominant members of the communities overall, Stramenopiles (mostly chrysophytes) dominated the eukaryotic OTUs that became abundant during autumn. Charvet et al. (2012b) suggested that the generally small size of chrysophyte cells, relative to other phytoplankton, may account for their dominance in oligotrophic Figure 1 Alpha diversity estimates with Bonferroni-corrected confidence bounds calculated with CatchAll for Bacteria and Archaea and as the Chao2 index for Eukarya. Archaeal diversity estimates could not be calculated for the FRX 9 m and WLB 13 m summer samples owing to insufficient numbers of reads. Antarctic lake microbial communities during the polar night transition TJ Vick-Majors et al 781 The ISME Journal Arctic lakes; decreasing phosphorous concentra- tions in autumn samples (Vick and Priscu, 2012) may have favored the proliferation of chrysophytes in FRX and WLB. Lizotte et al. (1996) found that chrysophyte communities in the Lake Bonney photic zone were dominated by the mixotrophic genera Ochromonas, which may persist through the seasonal sunset by switching to phagotrophy. The Alphaproteobacteria dominated the autumn proliferation of bacterial OTUs. This contrast with the Actinobacteria- or Bacteroidetes-dominated total communities (summer and autumn together; Figure 2) indicates that the most numerically abundant phyla in the lakes are also capable of persisting through changing environmental condi- tions, whereas less abundant phyla may opportu- nistically increase under changing conditions. Sixty-eight percent of the archaeal OTUs that increased in density during autumn belonged to the Euryarchaeota; half of those grouped with marine or aquatic lineages, whereas the other half grouped with methanogenic clades. The non-methanogenic Euryarchaeota that became abundant during autumn were all members of the Marine Group II, which are known to form seasonal blooms in the surface waters of the North Sea (Pernthaler et al., 2002). One Marine Group II OTU (Archaea_03_5) increased from 3.0% and 0% to 27.3% and 16.5% of the archaeal sequences in the surface and 9 m waters, respectively, of FRX. Thirty-two percent of the autumn archaeal OTUs belonged to the Crenarch- aeota, which were dominated by terrestrial and soil groups (42.9%), followed by the Marine Group I Crenarchaeota (28.6%). The increase in euryarchaeal Figure 2 Phylum-level diversity of bacterial (a), archaeal (b) and eukaryotic (c) communities in Lakes FRX and WLB during summer (Sum) and autumn (Aut). Bacterial and archaeal OTUs were determined at 97% sequence similarity, and eukaryotic OTUs were determined at 94% sequence similarity. Alpha, Beta, Gamma and Delta refer to the subclasses of Proteobacteria. MGI refers to Marine Group I Crenarchaeota, TGC refers to Terrestrial Group Crenarchaeota and SGC refers to Soil Group Crenarchaeota. Figure 3 Non-metric multidimensional scaling of bacterial and archaeal (relative abundance; Bray-Curtis dissimilarity; stress¼ 2.49 and 4.97, respectively) and eukaryotic (presence- absence; Sørensen’s similarity; stress¼ 4.68) communities. Antarctic lake microbial communities during the polar night transition TJ Vick-Majors et al 782 The ISME Journal OTUs relative to crenarchaeal OTUs was distinct from the composition of the overall communities, which were dominated by Crenarchaeota rather than Euryarchaeota (Figure 2). The proliferation of archaeal phylotypes (Grzymski et al., 2012) and proteins associated with chemolithotrophic Archaea (Williams et al., 2012) was reported in Southern Ocean waters during the winter, suggesting that summer commu- nities dominated by photoautotrophy shift to chemolithotrophy during the polar night. Molecular and cultivation studies have revealed the presence of diverse chemolithotrophic microorganisms in FRX and WLB (Priscu et al., 1996, Voytek et al., 1999; Karr et al., 2005; Sattley and Madigan 2006; Kong et al., 2012a), and dark carbon fixation attributed to chemolithotrophs has been measured in these same lakes (Priscu et al., 1996, Vick and Priscu, unpublished data). Kong et al. (2012b) showed that Proteobacteria actively produced RubisCO in WLB during February and March, indicating that chemolithotrophic bacteria were active during the summer–autumn transition. Currently, there are no data regarding the activities of chemolithotrophic archaea in the photic zones of the MCM lakes, but the proliferation of archaeal sequences during autumn suggests that they may be important. Part of the autumn archaeal ‘bloom’ was also owing to the appearance of Terrestrial and Soil Group Crenarchaeota, indicating that allochthonous inputs may affect community structure. Eolian transport is an important dispersal mechanism in the MCM (Sˇabacka´ et al., 2012), and the downward migration of lake ice particulate matter (Squyres et al., 1991; Jepsen et al., 2010) may introduce microorganisms (Paerl and Priscu, 1998; Priscu et al., 1998; Gordon et al., 2000) into the water column. Similarly, mid-summer stream-flow is an important source of nutrients, particulate matter (Takacs et al., 2001; Foreman et al., 2004) and perhaps microorganisms (Vincent and Howard- Williams, 1986) to the lakes. Alternatively, the sequences may group with terrestrial lineages, but actually represent native aquatic organisms. Whether these putatively terrestrial sequences are transient, inactive or represent part of the active microbial assemblage is unknown. Putatively methanogenic lineages (Methanomicro- bia and Methanobacteria) accounted for 27% of the autumn proliferation of archaeal OTUs, although the most abundant methanogenic lineages decreased between summer and autumn. All of our samples were taken from oxygenated portions of the water column, but all known Methanomicrobia and Methanobacteria are strict anaerobes. Methano- microbial sequences in soils surrounding FRX and WLB (Takacs-Vesbach unpublished data) and func- tional methanogens in the deep waters of FRX (Karr et al., 2006) are possible sources of methanogenic sequences; however, a local BLAST search showed that none of the terrestrial or FRX sequences matched the sequences in our study (data not shown). It is possible that our methanogenic sequences are not functionally methanogens, but their average GAST distances were small (0 to 0.03), indicating 495% accuracy of GAST taxonomic assignments (Huse et al., 2008). Methane production has been documented in oxygenated seawater (Karl et al., 2008, Damm et al., 2010) and an oxygenated oligotrophic lake, where planktonic and phytoplankton-attached Archaea actively tran- scribed the methyl coenzyme M reductase A gene for methanogenesis (Grossart et al., 2011). Damm et al. (2010) and Grossart et al. (2011) both connected methanogenesis in oxygenated water to phytoplank- ton activity, and the high concentrations of DMSP in WLB at 13 m (Lee et al., 2004) may provide a substrate pool for methanogenesis through its degradation product methanethiol (Damm et al., 2010). Although the metabolic state of the putatively Figure 4 Relative abundances (%) of bacterial (a), archaeal (b) and eukaryotic (c) OTUs during summer and autumn. Bacterial and archaeal OTUs were calculated at 97% sequence similarity, and eukaryotic OTUs were calculated at 94% sequence similarity. Antarctic lake microbial communities during the polar night transition TJ Vick-Majors et al 783 The ISME Journal methanogenic cells in our study is unknown, their presence combined with the supersaturation of methane starting at 12 m in WLB (Priscu and Dore, unpublished data) suggests the possibility of active methanogenesis. Co-occurrence patterns and the molecular ecological network Nonrandom community assembly, denoted by non- random co-occurrence patterns, is characteristic of assemblages of organisms across domains of life (Gotelli and McCabe, 2002; Horner-Devine et al., 2007). We compared the co-occurrence patterns found in our bacterial, archaeal and eukaryotic sequence data with those of a null distribution, representing random co-occurrence, and used the C-score metric (Stone and Roberts 1990) to deter- mine whether our data differed significantly from a randomly assembled community. We observed non- random co-occurrence patterns for our whole data set (C-score¼ 1.56, P¼ 0.01) and for the Bacteria and Eukarya (C-score¼ 1.52, P¼ 0.01 and C-score¼ 1.61, P¼ 0.01, respectively), whereas the C-score for the Archaea alone was marginally significant (C-score¼ 1.41, P¼ 0.19). To describe the importance of biotic and abiotic interactions in explaining the nonrandom co-occur- rence patterns, we generated a molecular ecological network based on Spearman correlations (Pp0.01, rX0.8) between relative abundances of Bacterial and Archaeal OTUs, presence–absence of Eukaryotic OTUs and discrete values of environmental para- meters. In total, we found 20 793 significant correla- tions between 872 variables (Supplementary Figure S2). We used modularity to detect community structure in our network (Fortunato, 2010; Supplementary Information), resulting in 27 mod- ules containing groups of interconnected nodes (Figure 5; Supplementary Table S3). Each module was designated by a key (the OTU with the highest assignment value to the module) and numbered for convenience in the discussion (Table 1). The modules most important to the network structure were determined based on betweenness centrality (BC). Gonza´lez et al., 2010 showed that nodes with high BC scores were particularly important in maintaining the connectivity of an ecological net- work, and compared them with keystone species. Nine of 27 modules in our network had BC scores 40 (range 0.005–0.19; Table 1). We examined the modules with significant BC scores and the modules containing the autumn blooming Archaea in detail, and attempted to assign functions based on the putative physiologies of the organisms present (Supplementary Table S5). Bielewicz et al., (2011) suggested that trophic or metabolic plasticity allows organisms to be more successful in the MCM lakes, and our module analysis supports the importance of innovative energy capture and metabolic flexibility during the transition to polar night. On the basis of its high BC score (0.19; nearly twice the next highest BC), Module 15 forms the keystone (Gonza´lez et al., 2010) of the MCM network. Five OTUs (40% of the module) were related to taxa that can produce proteorhodopsins (Atamna-Ismaeel et al., 2008, Oh et al., 2011, Huggett and Rappe´, 2012), including the module key, Alphaproteobacteria_03_91 (Pelagibacter, GAST¼ 0.049), Bacteroidetes_03_1 (Flavobacter- iacea, GAST¼ 0.0029) and Gammaproteobacteria_03 _175 (Oceanospirillales, GAST¼ 0.0026). Proteorho- dopsins are light-driven proton pumps found mainly in marine and freshwater Alphaproteobac- teria, Gammaproteobacteria, Flavobacteria and some Euryarchaeota, which, along with heterotrophic metabolism, can generate energy for growth (‘photo- heterotrophy’; Giovannoni et al., 2005; Frigaard et al., 2006; Atamna-Ismaeel et al., 2008; DeLong and Be´ja`, 2010; Steindler et al., 2011) and provide a competitive advantage under conditions of organic carbon or nutrient limitation (Giovannoni et al., 2005), such as those found in the MCM lakes. Fluctuations in the quality and quantity of available organic carbon are thought to affect heterotrophic bacterioplankton metabolism in the MCM lakes during autumn (Vick and Priscu, 2012), and although nutrient limitation is perennial in these lakes phytoplankton activity results in a spring/ summer drawdown of N and P (Lizotte et al., 1996). In addition to putatively photoheterotrophic OTUs, which account for 40% of the module, Module 15 contains two OTUs of the genus Hydrogenophaga (GAST¼ 0.0014 and 0.002), which are typically facultatively autotrophic organisms capable of oxi- dizing hydrogen or using organic carbon to generate energy (for example, Yoon et al., 2008). In total, 53% of the module belongs to groups known to be metabolically flexible, suggesting that oligotrophy Figure 5 Modules in the molecular ecological network deter- mined by ModuLand. Each module is named according to the node with maximum assignment value to the module. Antarctic lake microbial communities during the polar night transition TJ Vick-Majors et al 784 The ISME Journal and the strong seasonality associated with the MCM lakes favor the ability to shift between energy resources in response to changing environmental conditions. In addition, Module 15’s keystone status indicates that these abilities are integral to the MCM lake ecosystem function. Module 17 contained five of the nodes represent- ing the autumn proliferation of archaeal phylotypes (Figure 4, Supplementary Table S3; Archaea_03_17), and it provides another example of competitive energy acquisition in MCM lakes. Two of the nodes belong to the Marine Group I Crenarchaeota and may signify an autumn shift in favor of chemolitho- trophic metabolisms similar to that found in the Southern Ocean (Grzymski et al., 2012; Williams et al., 2012). Nodes representing the Marine Group II of the Euryarchaeota also group with Module 17. Currently, there are no cultured representatives of Marine Group II, and thus little is known about their range of metabolic capabilities. However, a complete genome representing the Marine Group II Euryarchaeota was recovered from a Puget Sound metagenome (Iverson et al., 2012), revealing a photoheterotrophic, proteorhodopsin-containing organism. A PCR-based study of Archaea from the North Pacific Subtropical Gyre concluded that approximately 10% of Euryarchaeota contained proteorhodopsin genes (Frigaard et al., 2006). If the putative functions assigned to the Archaea in Module 17 are correct, the module provides further evidence for the importance of metabolic flexibility and suggests that chemolithotrophy may be impor- tant in fueling ecosystem production during the polar night. Protistan organisms often rely on mixotrophic lifestyles to cope with the oligotrophic conditions and seasonal light–dark cycles in MCM lakes (Laybourn-Parry 2002; Bielewicz et al., 2011; Thurman et al., 2012). Phototrophic nanoflagellates in WLB increased their grazing rates on fluores- cently labeled bacterial prey throughout the month of March (Thurman et al., 2012), supporting the suggestion of Bielewicz et al. (2011) that crypto- phyte populations use phagotrophy as an adaptive strategy during the summerwinter transition. Similarly, our results showed that Chrysophyceae, which are generally dominated by the mixotrophic genus Ochromonas in these lakes (Lizotte et al., 1996), likely increased in abundance during the autumn. Module 22 (BC¼ 0.04, Supplementary Table S3, Betaproteobacteria_03_51) contained 35% of the eukaryotic OTUs that became abundant during autumn, including all of the Chrysophyceae, the heterotrophic nanoflagellate Cryothecomonas and a ciliate. Module 26 (BC¼ 0.04) contained Actinobacteria (22% of the module), including members of the genus Microthrix (Actinobacteria_03_174; GAST¼ 0.0095). Microthrix and other Actinobacteria generate carbon and energy storage compounds (triacylglcerols), store polyphosphates and possess high-affinity Pst P-uptake systems, all of which may Table 1 Highest-level taxonomy definition for module keys with the average GAST distance for the reported taxonomy and the betweenness centrality score for each module Module ID Module Number Taxonomy Average GAST Distance Module Betweenness Centrality Score Acidobacteria_03_11260 1 Acidobacteriaceae 0.241 0 Acidobacteria_03_15770 2 Acidobacteriaceae 0.258 0 Acidobacteria_03_18 3 Geothrix 0.003 0 Acidobacteria_03_1982 4 Acidobacteriaceae 0.0165 0 Acidobacteria_03_231 5 Solibacter 0.0531 0 Acidobacteria_03_5976 6 Acidobacteriaceae 0.254 0 Actinobacteria_03_11036 7 Acidimicrobiaceae 0.105 0 Actinobacteria_03_3 8 Actinobacteria 0.0026 0 Actinobacteria_03_4 9 Sporichthyaceae 0.0016 0 Actinobacteria_03_91 10 Micrococcus 0.0031 0 Alphaproteobacteria_03_13239 11 Rhodospirilliaceae 0.281 0 Alphaproteobacteria_03_2014 12 Rickettsiaceae 0.0482 0 Alphaproteobacteria_03_62 13 Caulobacteraceae 0.0052 0 Alphaproteobacteria_03_6621 14 Sneathiella 0.0078 0 Alphaproteobacteria_03_91 15 Pelagibacter 0.0496 0.19 Archaea_03_117 16 Methanomicrobiales 0 0 Archaea_03_17 17 Thermoplasmatales 0.0102 0 Bacteroidetes_03_1296 18 Flexibacter 0.0188 0.07 Bacteroidetes_03_168 19 Croceibacter 0.0236 0.04 Bacteroidetes_03_262 20 Flavobacterium 0.0005 0.04 Betaproteobacteria_03_143 21 Herbaspirillum 0.0827 0.02 Betaproteobacteria_03_51 22 Methyloversatilis 0.0063 0.04 Eukarya_06_1718 23 Pteridomonas 0.023 0 Eukarya_06_1772 24 Chlorogonium 0.023 0 Gammaproteobacteria_03_4 25 Pseudomonas 0.0015 0.1 Planctomycetes_03_726 26 Planctomyces 0.0169 0.04 Verrucomicrobia_03_119 27 Opitutus 0.0077 0.005 Antarctic lake microbial communities during the polar night transition TJ Vick-Majors et al 785 The ISME Journal help the organisms compete under conditions of unbalanced growth and P-limiting conditions (McIlroy et al. 2013), such as those found in Lake Bonney (Dore and Priscu, 2001). In addition, Planktophila (Actinobacteria_03_32; GAST¼ 0.0028) are important polysaccharide degraders with the ability to mineralize N-acetylgucosamine, a break- down product of bacterial cell walls, which may assist in winter survival, and contain actinorhodop- sin (Garcia et al., 2013), suggesting a role for photoheterotrophy in Module 26. Module 21 (BC¼ 0.02; Key¼Betaproteobacteria_ 03_143) contained mostly heterotrophic bacteria and a few Archaea, and a majority of the OTUs in the module increased between the summer and autumn sampling points, especially in the shallower waters of the lakes. Typically, phytoplankton pro- duction is thought to draw down nutrient concen- trations during the summer, leading to increased nutrient depletion in the already oligotrophic waters of the FRX and WLB photic zones. Members of the Actinobacteria have been shown to proliferate under low-nutrient conditions (reviewed in Newton et al., 2010). The module also contains putative nitrogen-fixing bacteria, which may have increased in response to decreasing nutrient concentrations (Alphaproteobacteria_03_431, Alphaproteobacteria_ 03_15, Alphaproteobacteria_03_808 and Betaproteo- bacteria_03_143, the module key). Taken together, these modules provide evidence for the importance of metabolic and trophic plasticity and nutrient scavenging in the MCM lakes. Other significant modules are discussed in Supplementary Information and provide further examples of the adaptation to oligotrophic or changing environments, along with insights into organic matter processing and eukaryoteprokaryote interactions in lakes FRX and WLB. Conclusions Our study comprises the first high-throughput sequencing evaluation of the diversity of Bacteria, Archaea and Eukarya in permanently ice-covered lakes of the Antarctic MCM Dry Valleys. We found that these light- and nutrient-limited systems exhibit low diversity overall, but that the autumn decrease in solar radiation coincides with increases or shifts in microbial diversity across all three domains of life. The statistically significant partitioning of bacterial and eukaryotic communities by season within lake and depth suggests, in agreement with past studies, that these communities are strongly controlled by the vertically stratified water columns of lakes FRX and Bonney, but that they also respond to the change in season. The low archaeal diversity was offset by an autumn ‘bloom’ of archaeal OTUs, which likely stemmed from a combination of allochthonous inputs and proliferation of organisms adapted to the winter darkness. Similarly, we found OTUs whose closest relatives are adapted to low-nutrient environ- ments, photoheterotrophic and mixotrophic life- styles, to be particularly important in the modular community structure in these lakes. We suggest future studies focusing on functional gene analysis, metagenomics or transcription to examine the rela- tionships revealed by our molecular ecological net- work analysis. Conflict of Interest The authors declare no conflict of interest. Acknowledgements We would like to thank the MCM Microbial Observatory, Sukkyun Han, Chao Tang, Amy Chiuchiolo and Marie Sˇabacka´ for assistance with sample collection, the 2007–2008 McMurdo Long-Term Ecological Research limnology team for assistance with environmental data collection and Elizabeth McCliment for assistance with sequencing. Funding was provided by NSF DEB-0717390 to Linda A Amaral-Zettler (MIRADA-LTERS) and OPP- 1115254, OPP-0838953, OPP-1027284 and OPP- 0839075 to John C Priscu. The Montana Space Grant Consortium provided additional funding for Trista Vick-Majors. References Alonso-Sa´ez L, Sa´nchez O, Gasol JM, Balague´ V, Pedro´s-Alio C. (2008). 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The observed clustering coefficient and mean shortest path length (0.88 and 3.53, respectively) for the entire network including Archaea, were greater than the clustering coefficient and mean shortest path length for the random networks (0.06 ± 4.0x10-4 and 1.97 ± 4.8 x 10-4, respectively) indicating that our MEN was more organized than would be expected of a random network of similar size. Removal of the Archaea from the network increased the clustering coefficient to 0.89 and decreased the mean shortest path length to 3.39. Our clustering coefficients and mean shortest path lengths (with and without Archaea) were also greater than that observed for a three- domain association network from the open ocean (Steele et al. 2011). Similar to the results found by Steele et al. (2011), our network also exhibited the small world pattern (Watts & Strogatz 1998), which is characterized by few, highly connected nodes and expected of a non-random ecological network. Given the significant C-score for the complete dataset and the minor variation in path length and clustering coefficient between the total network and network minus Archaea, we chose to continue our analysis with the entire dataset, including the Archaea. Module identification algorithms and/or the corresponding modularity metric (Q or M) have been used to identify important groups of nodes in pollinator networks (M = 0.52; Olesen et al. 2007), soil microbial communities (Q = 0.77; Barberán et al. 2012), the yeast interactome (Q and M not calculated; Kovács et al. 2010), and farm food webs (M = 0.70 to 0.88; Macfadyen et al. 2011). We calculated modularity for our complete network, using the ClusterMaker plugin (Morris et al. 2011) for Cytoscape, based on four different modularity methods: Markov Cluster   ii   Algorithm (MCL; Enright et al. 2002), GLay (Su et al. 2010), Connected Components Cluster (CCC; Morris et al. 2011), and Spectral Clustering of Protein Sequences (SCPS; Nepusz et al. 2010). These methods cover each of the classes of modularity algorithms discussed by Fortunato (2010). All four methods revealed modularity scores > 0.8, indicating that the FRX and WLB microbial communities have a modular structure (where a module is a group of connected nodes; Table 1), allowing us to use modules to simplify our MEN. To visualize the modules in our MEN, we used the ModuLand plugin (Szalay-Beko et al. 2012) for Cytoscape. The ModuLand calculation resulted in 27 modules ranging in size from 100 nodes with 705 connections to 2 nodes with a single connection (Table S3 and Figure S2). ModuLand used the name of the node (OTU) with the maximum assignment value to each module as the module key. We subsequently assigned each module a number and both designations are used in the text (Table 2, Figure 5, Table S4). The ClusterMaker plugin does not generate diagrams of modules, or provide information about the nodes contained in each module. In order to describe the modules, we used the ModuLand (Szalay-Beko et al. 2012) package in Cytoscape. ModuLand does not determine module structure based on modularity scores, and therefore does not calculate modularity scores; rather, it is based on a 3-D calculation of the community network where “hills” in the 3-D landscape (nodes with greater influence over the network structure) correspond to network modules. Details of the modularity calculations are provided by Kovács et al. (2010).   iii   Supplemental Discussion of Significant Modules: Module 22 contained nodes apparently related to the cycling of C1 compounds, including the most abundant putatively methanogenic OTUs (Archaea_03_102 and Archaea_03_50) in the total dataset, three bacterial OTUs related to candidate division OD1 and one related to candidate division OP9, which have been putatively connected to methane cycling (Orphan et al. 2001, Perua et al. 2012), five putatively methylotrophic lineages and one member of the Syntrophaceae, which are known to form syntrophic partnerships with hydrogenotrophic methanogens. Putative methanogens were most abundant at the deep chlorophyll maxima of both lakes (DCM; FRX 9 m and WLB 13 m) and decreased between summer and autumn (9.5% to 4.3% [FRX] and 18.0% to 10.2% [WLB]). Karl et al. (2008) suggested that aerobic methane production may be a side-effect of phosphorus limitation in surface seawater, while Grossart et al. (2011), and Damm et al. (2010) suggested a connection between by-products of photoautotrophy and methane production. High levels of particulate dimethylsulfoniopropionate (DMSP) at the WLB DCM (32 nmol L-1; Lee et al. 2004) may serve as a substrate or precursor to methanogenesis, via its degradation product, methanethiol (Kiene 1996; Damm et al. 2010). Module 25 (BC = 0.1), which contained the highest abundance of putative methanogens after Module 22, also contained methylotrophic lineages that may be able to use dimethylsulfide (DMS) as a carbon source. Unlike DMSP, DMS concentrations are nearly below detection at the WLB DCM (Lee et al. 2004), perhaps because of quick utilization by methylotrophs. Members of the genus Loktanella, which are present in Module 25, have been shown to cleave DMSP to DMS and acrylate and genes for DMSP-degrading enzymes are widespread in other Alphaproteobacteria (see review by Moran et al. 2012). Modules 22 and 25 may have important roles in linking carbon and sulfur biogeochemistry in the MCM lakes.   iv   Module 19 (BC=0.04; key = Bacteroidetes_03_168, a member of the Crocibacter) is comprised primarily of heterotrophic bacteria, mainly of the Bacteroidetes and the Alpha and Gamma clades of the Proteobacteria in association with a few heterotrophic and phototrophic eukaryotic OTUs. Members of the Flavobacteria have been implicated in East Antarctic Southern Ocean processing of algal organic matter, which, once broken down, is then utilized by Alpha- and Gammaproteobacteria (Williams et al. 2012). Within the module, the nodes Gammaproteobacteria_03_1630 (Legionella), Verrucomicrobia_03_57 (Chthoniobacter), and Eukarya_06_2105 (Cyclonexis) formed a three-way positive interaction with no direct connection to any other members of the module. This three-way interaction is an example of a potential close association between a primary producer (Cyclonexis) and two heterotrophic bacterial OTUs. Cultivated members of the Chthoniobacter are known to grow on plant-related saccharides (Sangwan et al. 2004), and may break down organic matter excreted by Cyclonexis. Legionalla pneumophila has been found living in close association with cyanobacteria, apparently utilizing photosynthetic exudates as carbon and energy sources (Tison et al. 1980), but other studies with Legionella have shown that it does not effectively utilize large organic molecules (Chien et al. 2004). We suggest that this three-way interaction is bound by an initial breakdown of Cyclonexis-related organic matter by Chthoniobacter, followed by utilization of smaller molecules by Legionella. Module 20 (BC = 0.04; Key = Bacteroidetes_03_262) was characterized by tightly connected groups of eukaryotic and bacterial OTUs, 52% of which showed positive interactions between heterotrophic bacterial OTUs and phototrophic OTUs. The heterotrophic guilds were highly diverse, with OTUs from 8 phyla (Table S4), and differentiating their putative functions was not possible. Three phototrophic OTUs were represented in the module (Eukarya_06_1327   v   [Micractinium; GAST = 0.03], Eukarya_06_1388 [Goniochloris; GAST = 0.13], and Euk_9221 [Nannochloropsis;GAST = 0.06]). Micractinium (a chlorophyte) primarily interacted with members of the Bacteroidetes, followed by the Plantomycetes and Verrucomicrobia, while the Eustigmatophytes, Nannochloropsis and Goniochloris primarily interacted with Alphaproteobacteria, suggesting either preferential feeding on exudates from different phytoplankton, or similar responses to environmental conditions. Module 27 (BC = 0.005; Key = Verrucomicrobia_03_119) contained only five OTUs, two Bacteroidetes, one Actinobacteria, one Verrucomicrobia, and one eukaryote related to the frequent metazoan symbionts and parasites, the Apostomatia. Except for Bacteroidetes_03_457 (Algoriphagus) all of the bacterial OTUs are likely to be at least facultatively anaerobic and capable of fermentation. It is possible that Module 27 reflects a connection to the FRX and WLB metazoan communities, of which little is known. Sequence data from the MIRADA project suggested the presence of a few Maxillopoda and Brachiopoda. 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Profiles of oxygen, temperature and conductivity from Lake Fryxell (FRX) and the West Lobe of Lake Bonney (WLB) taken during March 2008. The solid and dashed horizontal lines represent the thickness of the ice- cover and the depths at which samples for molecular analysis were taken, respectively.   ix   Figure S2. Complete molecular ecological network (MEN), based on significant (P ≤ 0.01) Spearman correlations on ranks between bacterial, archaeal, and eukaryotic OTUs and environmental variables. Nodes correspond to individual OTUs or environmental parameters and colors correspond to modules. “Environmental” refers to physicochemical data used in the network analysis.   x     Table S1. Environmental Data Sample ID Collection Date Depth Lake Name Latitude Longitude Ammonium Pressure Chlorophyll UTC Meters Decimal Degree Decimal Degree µmol L-1 db µg L-1 MCM_1 11/22/07 6 Lake Fryxell -77.61031 85.53597 0.3 5.27 3.3 MCM_2 11/22/07 9 Lake Fryxell -77.61031 85.53597 0.17 8.2 5.04 MCM_3 11/30/07 13 West Lobe Bonney -77.72006 84.57732 0.96 12.23 4.03 MCM_4 11/30/07 18 West Lobe Bonney -77.72006 84.57732 172.9 17.51 0.94 MCM_5 3/17/08 13 West Lobe Bonney -77.72006 84.57732 0.67 12.3 5.53 MCM_6 3/17/08 18 West Lobe Bonney -77.72006 84.57732 157.75 17.42 0.77 MCM_7 3/25/08 6 Lake Fryxell -77.61031 85.53597 0.08 5.28 3.99 MCM_8 3/25/08 9 Lake Fryxell -77.61031 85.53597 0.32 8.2 10.05 Sample ID Conductivity Dissolved Inorganic Nitrogen Dissolved Organic Carbon Dissolved Organic Nitrogen Dissolved Oxygen Nitrate Nitrite Bacterial Cells mS cm-1 µmol L-1 µmol L-1 µmol L-1 µmol L-1 µmol L-1 µmol L-1 x106 ml-1 MCM_1 1.36 0.57 237.48 21.38515266 802.67 0.15 0.11 1.13 MCM_2 3.3 0.34 472.22 33.31 914.54 0.08 0.09 0.84 MCM_3 12.54 13.26 370 6.62 1591.72 11.21 1.09 0.05 MCM_4 65.13 189.12 1033.33 53.84 58.55 13.2 3.02 0.07 MCM_5 10.01 9.94 320 2.99 1562.62 9.53 0.45 0.09 MCM_6 64.08 174.08 758.1 56.41 68.72 14.68 2.32 0.04 MCM_7 1.83 0.31 170 11.58 807.32 0.1 0.14 1.42 MCM_8 2.04 0.58 514.29 36.4 764.52 0.09 0.17 1.25 Sample ID Particulate Carbon Particulat e Nitrogen pH Phosphate Salinity Silicate Temperatur e Water Column Depth µg L-1 µmol L-1 Log H+ µmol L-1 PSU µmol L-1 oC Meters MCM_1 370.16 3.78 8.02 0.13 1.26 200 1.42 18.64 MCM_2 437.44 4.32 7.73 0.28 3.12 220 2.17 18.64 MCM_3 339.4 2.01 7.05 0.05 13.55 200 1.03 41.45 MCM_4 241.8 1.78 5.87 0.27 90.02 220 -0.72 41.45 MCM_5 517.94 1.72 7.62 0.04 10.61 200 1.25 41.45 MCM_6 212.5 1.57 5.97 0.19 88.08 220 -0.65 41.45 MCM_7 533.62 3.29 8.18 0.06 1.82 200 1.31 18.64 MCM_8 1014.5 5.34 7.73 0.15 1.96 220 1.83 18.64   xi   Table S2. OTU and Diversity Data Dataset Number of Reads* Number of OTUs Coverage Number of Reads* Number of OTUs Coverage Number of Reads* Number of OTUs Coverage Bacteria Archaea Eukarya FRX6N 24662 363 0.33 25554 16 0.61 8299 69 0.94 FRX6N R 170 0.50 59 0.96 FRX6M 15466 464 0.15 8678 26 0.71 13393 74 0.94 FRX6M R 185 0.39 17 0.53 57 0.95 FRX9N 18252 363 0.31 9645 7336 57 0.97 FRX9N R 191 0.50 54 0.95 FRX9M 12046 298 0.19 15036 19 0.86 2074 85 0.96 FRX9M R 154 0.33 11 0.90 60 0.98 WLB13N 15545 229 0.31 14232 13929 78 0.94 WLB13N R 155 0.50 59 0.95 WLB13M 8632 161 0.38 3904 20 0.66 10572 56 0.95 WLB13M R 161 0.38 20 0.66 46 0.95 WLB18N 14587 270 0.30 20894 27 0.70 13173 102 0.88 WLB18N R 129 0.45 19 0.75 77 0.85 WLB18M R 18644 539 0.17 4183 14 0.74 9425 90 0.88 WLB18M 166 0.27 14 0.74 77 0.87 R = Resampled dataset *Replicates pooled Coverage = observed number of OTUs/expected number of OTUs   xii   Table S3. Module Assignments and Taxonomy. * = Module Key Node Module ID Taxonomy Acidobacteria_03_11260* 1 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Acidobacteria_03_118 1 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Acidobacteria_03_185 1 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Actinobacteria_03_137 1 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae;Planktophila Actinobacteria_03_189 1 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae Actinobacteria_03_229 1 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae Actinobacteria_03_9523 1 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae Bacteroidetes_03_622 1 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Rhodothermaceae Bacteroidetes_03_1309 1 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_3254 1 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_28519 1 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae;Sediminibacteriu m Cyanobacteria_03_5701 1 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionIII;Unassigned;Leptolyngbya OD1_03_14 1 Bacteria;OD1 OD1_03_2939 1 Bacteria;OD1 OP11_03_4956 1 Bacteria;OP11 Planctomycetes_03_7 1 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae Planctomycetes_03_10155 1 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae Planctomycetes_03_16886 1 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae Alphaproteobacteria_03_4 43 1 Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingop yxis Alphaproteobacteria_03_4 44 1 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Alphaproteobacteria_03_1 848 1 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Alphaproteobacteria_03_3 051 1 Bacteria;Proteobacteria;Alphaproteobacteria;Caulobacterales;Caulobacteraceae Alphaproteobacteria_03_4 780 1 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Acetobacteraceae;Roseomonas Betaproteobacteria_03_6 1 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Betaproteobacteria_03_13 13 1 Bacteria;Proteobacteria;Betaproteobacteria Betaproteobacteria_03_92 00 1 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Acidovorax Betaproteobacteria_03_10 877 1 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Rhodoferax Gammaproteobacteria_03_ 98 1 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomo nas Gammaproteobacteria_03_ 1234 1 Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Gammaproteobacteria_03_ 1291 1 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Coxiellaceae Gammaproteobacteria_03_ 2262 1 Bacteria;Proteobacteria;Gammaproteobacteria Gammaproteobacteria_03_ 2980 1 Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae;Brenneria;r ubrifaciens Gammaproteobacteria_03_ 22057 1 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae;Marinimicr obium   xiii   Deltaproteobacteria_03_13 1 1 Bacteria;Proteobacteria;Deltaproteobacteria;Bdellovibrionales;Bacteriovoraceae;Peredibacter Deltaproteobacteria_03_23 23 1 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales BacteriaNA_03_936 1 Bacteria Archaea_03_376 1 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Terrestrial_Miscellaneous_Group Archaea_03_392 1 Archaea;Crenarchaeota;Miscellaneous_Crenarchaeotic_Group Archaea_03_4088 1 Archaea;Crenarchaeota;Marine_Group_I Archaea_03_6978 1 Archaea;Crenarchaeota;Marine_Group_I Euk_1179 1 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;uncultured Euk_14612 1 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Prostomatea;Cryptocaryon Euk_22746 1 Eukaryota;Cryptophyceae;Cryptomonadales Euk_29005 1 Eukaryota;Opisthokonta;Fungi;LKM15 Eukarya_06_1006 1 Eukaryota;SAR;Stramenopiles;Chrysophyceae;LG21-05 Eukarya_06_1607 1 Eukaryota;Opisthokonta;Holozoa;Corallochytrium Eukarya_06_1710 1 Eukaryota;SAR;Stramenopiles Eukarya_06_1847 1 Eukaryota;SAR;Alveolata Eukarya_06_312 1 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Ochromonadales;Paraphysomonas Eukarya_06_329 1 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Ochromonadales;Paraphysomonas Eukarya_06_6423 1 Eukaryota;SAR;Stramenopiles;Synurales;Mallomonas Acidobacteria_03_15770* 2 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Actinobacteria_03_445 2 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales Actinobacteria_03_447 2 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales Actinobacteria_03_2306 2 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Micrococcaceae;Nesterenkonia Actinobacteria_03_10904 2 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae Bacteroidetes_03_30 2 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Cyclobacteriaceae;Algoriphagus Bacteroidetes_03_299 2 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae Bacteroidetes_03_1362 2 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Sphingobacteriaceae;Sphingobacteri aceae;Pedobacter Bacteroidetes_03_8459 2 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae Bacteroidetes_03_8909 2 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Chlamydiae_03_124 2 Bacteria;Chlamydiae;Chlamydiae;Chlamydiales;Simkaniaceae;Rhabdochlamydia Chlamydiae_03_379 2 Bacteria;Chlamydiae;Chlamydiae;Chlamydiales Cyanobacteria_03_5311 2 Bacteria;Cyanobacteria;Cyanobacteria Deferribacteres_03_6450 2 Bacteria;Deferribacteres;Deferribacteres;Deferribacterales;Unassigned;Caldithrix Firmicutes_03_5597 2 Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae OD1_03_5 2 Bacteria;OD1 OD1_03_326 2 Bacteria;OD1 OD1_03_921 2 Bacteria;OD1 OP3_03_32 2 Bacteria;OP3 OP3_03_470 2 Bacteria;OP3 OP11_03_2821 2 Bacteria;OP11 ProteobacteriaNA_03_5 2 Bacteria;Proteobacteria Alphaproteobacteria_03_2 46 2 Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Bradyrhizobiaceae;Bradyrhizobium   xiv   Alphaproteobacteria_03_4 17 2 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Alphaproteobacteria_03_1 113 2 Bacteria;Proteobacteria;Alphaproteobacteria;Caulobacterales;Caulobacteraceae;Caulobacter Alphaproteobacteria_03_1 267 2 Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sandaraki norhabdus Alphaproteobacteria_03_4 192 2 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Acetobacteraceae;Roseomonas Alphaproteobacteria_03_6 461 2 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Alphaproteobacteria_03_1 0317 2 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Holosporaceae;Holospora Alphaproteobacteria_03_2 4265 2 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Pseudoruegeri a Betaproteobacteria_03_11 6 2 Bacteria;Proteobacteria;Betaproteobacteria Betaproteobacteria_03_12 4 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Alcaligenaceae Betaproteobacteria_03_14 5 2 Bacteria;Proteobacteria;Betaproteobacteria Betaproteobacteria_03_17 1 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Alcaligenaceae Betaproteobacteria_03_18 6 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Betaproteobacteria_03_23 8 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Betaproteobacteria_03_24 0 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae Betaproteobacteria_03_38 1 2 Bacteria;Proteobacteria;Betaproteobacteria;Rhodocyclales;Rhodocyclaceae Betaproteobacteria_03_13 27 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Polynucleobacter Betaproteobacteria_03_37 04 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae;Duganella Betaproteobacteria_03_66 06 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae Betaproteobacteria_03_67 02 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae Betaproteobacteria_03_96 29 2 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae;Massilia Gammaproteobacteria_03_ 26 2 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Acinetobacter Gammaproteobacteria_03_ 463 2 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Coxiellaceae;Coxiella Gammaproteobacteria_03_ 2318 2 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae;Legionella Gammaproteobacteria_03_ 2982 2 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Coxiellaceae;Aquicella Gammaproteobacteria_03_ 3242 2 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales Gammaproteobacteria_03_ 4104 2 Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae Gammaproteobacteria_03_ 5304 2 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Coxiellaceae;Aquicella Deltaproteobacteria_03_49 2 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_15 4 2 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_35 0 2 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_70 7 2 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales   xv   Deltaproteobacteria_03_12 88 2 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales;Phaselicystidaceae;Phaselicystis Deltaproteobacteria_03_13 70 2 Bacteria;Proteobacteria;Deltaproteobacteria;Syntrophobacterales;Syntrophaceae;Desulfobacca Deltaproteobacteria_03_52 17 2 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales;Cystobacteraceae;Anaeromyxobacte r Deltaproteobacteria_03_19 667 2 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales TG-1_03_243 2 Bacteria;TG-1 TM7_03_78 2 Bacteria;TM7 Verrucomicrobia_03_115 2 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Prosthe cobacter Verrucomicrobia_03_146 2 Bacteria;Verrucomicrobia;Spartobacteria Verrucomicrobia_03_5195 2 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Prosthe cobacter;vanneervenii BacteriaNA_03_6189 2 Bacteria Archaea_03_3 2 Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales;Methanosarcinaceae;ANME-3 Archaea_03_13 2 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Archaea_03_85 2 Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales;Methanosarcinaceae Archaea_03_125 2 Archaea;Euryarchaeota;Methanobacteria;Methanobacteriales;Methanobacteriaceae Archaea_03_137 2 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;CCA47 Archaea_03_155 2 Archaea;Crenarchaeota;Miscellaneous_Crenarchaeotic_Group Archaea_03_164 2 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;CCA47 Archaea_03_262 2 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales;Methanolinea Archaea_03_333 2 Archaea;Crenarchaeota;Miscellaneous_Crenarchaeotic_Group Archaea_03_600 2 Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Deep_Sea_Hydrothermal_Vent_Group_6 Archaea_03_1643 2 Archaea;Euryarchaeota;Methanobacteria;Methanobacteriales;Methanobacteriaceae;Methanosph aera Euk_553 2 Eukaryota;SAR;Rhizaria;Cercozoa;Endomyxa;Novel Clade 10 Euk_597 2 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;WHOI-LI1-14 Euk_6684 2 Eukaryota;Excavata;Discoba;Jakobida;Andalucia Euk_21291 2 Eukaryota;SAR;Stramenopiles;Bicosoecida;P34.6 Eukarya_06_217 2 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Litostomatea;Mesodiniidae Eukarya_06_282 2 Eukaryota;Excavata;Discoba;Jakobida;Andalucia Eukarya_06_3268 2 Eukaryota;Opisthokonta;Fungi;Fungi;Basal fungi;Basal fungi;Chytridiomycota Eukarya_06_5199 2 Eukaryota;Opisthokonta;Fungi;Fungi;Basal fungi;Basal fungi;Chytridiomycota Eukarya_06_6667 2 Eukaryota;Archaeplastida;Chloroplastida;Chlorophyta;Chlorophyceae;Chlamydomonas Euk_43 2 Eukaryota;Incertae Sedis;Telonema Eukarya_06_20 2 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Ochromonadales;Paraphysomonas Eukarya_06_470 2 Eukaryota;SAR;Stramenopiles;Dictyochophyceae;Pedinellales;Pedinella Acidobacteria_03_18* 3 Bacteria;Acidobacteria;Holophagae;Holophagales;Holophagaceae;Geothrix Acidobacteria_03_6056 3 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Actinobacteria_03_8 3 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae Actinobacteria_03_304 3 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae;Microbacterium Actinobacteria_03_3291 3 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Actinomycetaceae;Actinomyces Bacteroidetes_03_96 3 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Salegentibacter Bacteroidetes_03_150 3 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Flammeovirgaceae   xvi   Bacteroidetes_03_171 3 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae Bacteroidetes_03_332 3 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteroidetes_03_803 3 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Flammeovirgaceae;Amoebophilus Bacteroidetes_03_929 3 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae Bacteroidetes_03_2759 3 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Cyclobacteriaceae;Algoriphagus Bacteroidetes_03_3255 3 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Saprospiraceae;Haliscomenobacter Chloroflexi_03_13604 3 Bacteria;Chloroflexi;Dehalococcoidetes;Unassigned;Unassigned;Dehalogenimonas Fibrobacteres_03_1037 3 Bacteria;Fibrobacteres;Fibrobacteria Firmicutes_03_5903 3 Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Acetivibrio Firmicutes_03_13458 3 Bacteria;Firmicutes;Bacilli;Bacillales Firmicutes_03_15022 3 Bacteria;Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacillus;durianis Nitrospirae_03_1 3 Bacteria;Nitrospirae;Nitrospira;Nitrospirales;Nitrospiraceae;Nitrospira Nitrospirae_03_39 3 Bacteria;Nitrospirae;Nitrospira;Nitrospirales OD1_03_220 3 Bacteria;OD1 Alphaproteobacteria_03_1 3 3 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Alphaproteobacteria_03_2 1 3 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Alphaproteobacteria_03_1 07 3 Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Bradyrhizobiaceae;Bradyrhizobium Alphaproteobacteria_03_1 027 3 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Rickettsiaceae;Rickettsia Alphaproteobacteria_03_4 657 3 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Acetobacteraceae;Acidocella Betaproteobacteria_03_7 3 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Rhodoferax Betaproteobacteria_03_12 43 3 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Burkholderia Gammaproteobacteria_03_ 3 3 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Acinetobacter Gammaproteobacteria_03_ 156 3 Bacteria;Proteobacteria;Gammaproteobacteria Gammaproteobacteria_03_ 335 3 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae;Legionella Gammaproteobacteria_03_ 955 3 Bacteria;Proteobacteria;Gammaproteobacteria;Chromatiales;Chromatiaceae Gammaproteobacteria_03_ 8491 3 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Coxiellaceae;Aquicella Deltaproteobacteria_03_1 3 Bacteria;Proteobacteria;Deltaproteobacteria;SAR324 Deltaproteobacteria_03_12 0 3 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfuromonadales;Geobacteraceae;Geobacter Deltaproteobacteria_03_25 0 3 Bacteria;Proteobacteria;Deltaproteobacteria;Bdellovibrionales;Bacteriovoraceae;Peredibacter Deltaproteobacteria_03_12 05 3 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_27 38 3 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales;Haliangiaceae;Haliangium Deltaproteobacteria_03_27 423 3 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Epsilonproteobacteria_03_ 1223 3 Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Arcoba cter Epsilonproteobacteria_03_ 3577 3 Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Helicobacteraceae;Wolinella Archaea_03_51 3 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Archaea_03_3746 3 Archaea;Crenarchaeota;Marine_Group_I   xvii   Eukarya_06_5182 3 Eukaryota;SAR;Alveolata;Dinoflagellata Eukarya_06_6352 3 Eukaryota;SAR;Stramenopiles;Labyrinthulomycetes;Thraustochytriaceae;E170 Eukarya_06_6504 3 Eukaryota;SAR;Alveolata;Dinoflagellata;Dinophyceae;Peridiniphycidae;Thoracosphaeraceae Eukarya_06_620 3 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Oligohymenophorea;Peritr ichia Acidobacteria_03_1982* 4 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Acidobacteria_03_3587 4 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Actinobacteria_03_563 4 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae;Sporichthya Bacteroidetes_03_6 4 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteroidetes_03_48 4 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteroidetes_03_383 4 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_852 4 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_919 4 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae Bacteroidetes_03_1324 4 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_12987 4 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium Bacteroidetes_03_16532 4 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium Chlamydiae_03_987 4 Bacteria;Chlamydiae;Chlamydiae;Chlamydiales;Parachlamydiaceae Chloroflexi_03_567 4 Bacteria;Chloroflexi;Dehalococcoidetes;Unassigned;Unassigned;Dehalogenimonas Chloroflexi_03_2908 4 Bacteria;Chloroflexi Nitrospirae_03_31 4 Bacteria;Nitrospirae;Nitrospira;Nitrospirales OD1_03_20 4 Bacteria;OD1 OD1_03_76 4 Bacteria;OD1 OD1_03_116 4 Bacteria;OD1 OD1_03_568 4 Bacteria;OD1 OD1_03_672 4 Bacteria;OD1 OD1_03_1868 4 Bacteria;OD1 OD1_03_2242 4 Bacteria;OD1 OD1_03_6826 4 Bacteria;OD1 OD1_03_7417 4 Bacteria;OD1 OP3_03_267 4 Bacteria;OP3 Alphaproteobacteria_03_4 70 4 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Unassigned;Captivus Alphaproteobacteria_03_1 358 4 Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sandaraki norhabdus Alphaproteobacteria_03_1 748 4 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Rickettsiaceae Alphaproteobacteria_03_4 617 4 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Acetobacteraceae;Rhodopila Alphaproteobacteria_03_1 3580 4 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae;Defluviicoccus Alphaproteobacteria_03_2 3303 4 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Unassigned;Captivus Betaproteobacteria_03_21 4 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Pelomonas Betaproteobacteria_03_13 78 4 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales Gammaproteobacteria_03_ 1258 4 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae Gammaproteobacteria_03_ 1913 4 Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Xanthomonadaceae;Rhodanob acter   xviii   Gammaproteobacteria_03_ 3943 4 Bacteria;Proteobacteria;Gammaproteobacteria Deltaproteobacteria_03_46 9 4 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_28 75 4 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_87 15 4 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_91 51 4 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_11 056 4 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_19 518 4 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Spirochaetes_03_1471 4 Bacteria;Spirochaetes;Spirochaetes;Spirochaetales TG-1_03_4 4 Bacteria;TG-1 Verrucomicrobia_03_2716 4 Bacteria;Verrucomicrobia;Opitutae;Puniceicoccales;Puniceicoccaceae;Coraliomargarita Archaea_03_61 4 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Archaea_03_356 4 Archaea;Crenarchaeota;terrestrial_group Archaea_03_2461 4 Archaea;Crenarchaeota;Marine_Group_I Archaea_03_4523 4 Archaea;Crenarchaeota;South_African_Gold_Mine_Group_1 Euk_6780 4 Unknown Euk_9317 4 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Cryothecomonas Euk_16319 4 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Cryothecomonas Euk_19952 4 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Cryothecomonas Euk_27629 4 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Cryothecomonas Eukarya_06_178 4 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Oligohymenophorea;Scuti cociliatia Eukarya_06_3142 4 Eukaryota;SAR;Stramenopiles;MH-XII Eukarya_06_415 4 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Kinetoplastea;Metakinetoplastina;Eubod onida Eukarya_06_7196 4 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Litostomatea;Trichostomatia;Polyplas tron Eukarya_06_125 4 Eukaryota;Archaeplastida;Chloroplastida;Chlorophyta;Chlorophyceae;Chlamydomonas Eukarya_06_963 4 Eukaryota;Opisthokonta;Fungi;Fungi;Basal fungi;Basal fungi;Chytridiomycota Acidobacteria_03_231* 5 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae;Solibacter Acidobacteria_03_343 5 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Acidobacteria_03_668 5 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Actinobacteria_03_135 5 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Cellulomonadaceae;Actinotalea Actinobacteria_03_467 5 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Kineosporiaceae Actinobacteria_03_977 5 Bacteria;Actinobacteria;Actinobacteria;Nitriliruptorales;Nitriliruptoraceae;Nitriliruptor Actinobacteria_03_1344 5 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae;Ferrithrix Actinobacteria_03_6586 5 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae;Aquiluna Actinobacteria_03_9488 5 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae;Frigoribacterium Bacteroidetes_03_51 5 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Cloacibacterium Bacteroidetes_03_1967 5 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteroidetes_03_3400 5 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Cytophagaceae Bacteroidetes_03_5762 5 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae;Sediminibacteriu m   xix   Bacteroidetes_03_18149 5 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Croceibacter Bacteroidetes_03_23763 5 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Croceibacter Chlamydiae_03_233 5 Bacteria;Chlamydiae;Chlamydiae;Chlamydiales;Simkaniaceae;Rhabdochlamydia Chlamydiae_03_835 5 Bacteria;Chlamydiae;Chlamydiae;Chlamydiales;Parachlamydiaceae;Neochlamydia Chlorobi_03_267 5 Bacteria;Chlorobi;Chlorobia;Chlorobiales Cyanobacteria_03_183 5 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionI;Unassigned;Gleocapsa Cyanobacteria_03_356 5 Bacteria;Cyanobacteria Firmicutes_03_578 5 Bacteria;Firmicutes;Clostridia;Halanaerobiales;Halanaerobiaceae;Halocella Firmicutes_03_2494 5 Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Acetivibrio Firmicutes_03_8830 5 Bacteria;Firmicutes;Bacilli;Bacillales;Bacillaceae;Virgibacillus;carmonensis OD1_03_124 5 Bacteria;OD1 OD1_03_230 5 Bacteria;OD1 OD1_03_1937 5 Bacteria;OD1 OD1_03_2137 5 Bacteria;OD1 OD1_03_7235 5 Bacteria;OD1 Planctomycetes_03_128 5 Bacteria;Planctomycetes;Phycisphaerae Alphaproteobacteria_03_9 5 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Alphaproteobacteria_03_7 30 5 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales Alphaproteobacteria_03_1 644 5 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Pseudorhodob acter Alphaproteobacteria_03_1 774 5 Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingop yxis Alphaproteobacteria_03_2 091 5 Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Hyphomicrobiaceae;Pedomicrobium Betaproteobacteria_03_98 5 Bacteria;Proteobacteria;Betaproteobacteria;Nitrosomonadales;Gallionellaceae Betaproteobacteria_03_12 3 5 Bacteria;Proteobacteria;Betaproteobacteria;Nitrosomonadales;Nitrosomonadaceae Betaproteobacteria_03_39 6 5 Bacteria;Proteobacteria;Betaproteobacteria;Hydrogenophilales;Hydrogenophilaceae;Thiobacillu s Gammaproteobacteria_03_ 8 5 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Pseudoalteromonadaceae;Pseu doalteromonas Gammaproteobacteria_03_ 10 5 Bacteria;Proteobacteria;Gammaproteobacteria Gammaproteobacteria_03_ 42 5 Bacteria;Proteobacteria;Gammaproteobacteria;Methylococcales;Methylococcaceae Gammaproteobacteria_03_ 89 5 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;Oceanospirillaceae Gammaproteobacteria_03_ 741 5 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Coxiellaceae;Aquicella Gammaproteobacteria_03_ 1550 5 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Coxiellaceae;Aquicella Gammaproteobacteria_03_ 3948 5 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;Halomonadaceae;Halomonas Gammaproteobacteria_03_ 4328 5 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Coxiellaceae;Aquicella Gammaproteobacteria_03_ 7239 5 Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Pasteurella;langaa ensis Gammaproteobacteria_03_ 23866 5 Bacteria;Proteobacteria;Gammaproteobacteria;Salinisphaerales;Salinisphaeraceae;Salinisphaera Deltaproteobacteria_03_94 5 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales;Polyangiaceae;Sorangium Deltaproteobacteria_03_41 0 5 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Desulfobulbaceae;Desulfobulbus   xx   Deltaproteobacteria_03_11 15 5 Bacteria;Proteobacteria;Deltaproteobacteria;Syntrophobacterales;Syntrophaceae;Syntrophus Deltaproteobacteria_03_20 121 5 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Deltaproteobacteria_03_21 828 5 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Desulfobulbaceae;Desulfocapsa TM6_03_361 5 Bacteria;TM6 Verrucomicrobia_03_31 5 Bacteria;Verrucomicrobia;Opitutae;Opitutales;Opitutaceae;Opitutus Verrucomicrobia_03_3477 5 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Verruc omicrobium BacteriaNA_03_909 5 Bacteria BacteriaNA_03_2028 5 Bacteria Archaea_03_329 5 Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales;Methanosarcinaceae;Methanosarc ina Archaea_03_570 5 Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Deep_Sea_Euryarcheotic_Group Euk_1861 5 Eukaryota;Centrohelida Euk_1899 5 Eukaryota;Archaeplastida;Chloroplastida;Chlorophyta;Mamiellophyceae;Monomastix Euk_7927 5 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Kinetoplastea;Prokinetoplastina;Ichthyo bodo Euk_7957 5 Eukaryota;RT5iin25 Euk_14491 5 Eukaryota;SAR;Rhizaria;Cercozoa Euk_18143 5 Eukaryota;Opisthokonta;Fungi;Fungi;Basal fungi;Basal fungi;Blastocladiomycota Euk_25112 5 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Kinetoplastea;Metakinetoplastina;Parab odonida Eukarya_06_1197 5 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Ochromonadales;Paraphysomonas Eukarya_06_1239 5 Eukaryota;Opisthokonta;Fungi;Basidiomycota;Pucciniomycotina;Microbotryomycetes Eukarya_06_15 5 Eukaryota;Archaeplastida;Chloroplastida;Charophyta;Phragmoplastophyta;Streptophyta;Embry ophyta;Tracheophyta Eukarya_06_1735 5 Eukaryota;SAR;Stramenopiles;Chrysophyceae Eukarya_06_2139 5 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Nassophorea;Obertrumia Eukarya_06_2422 5 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Litostomatea;Haptoria;Didinium Eukarya_06_2518 5 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Kinetoplastea;Metakinetoplastina;Neobo donida Eukarya_06_3727 5 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Oligohymenophorea;Penic ulia Eukarya_06_43 5 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Oligohymenophorea;Scuti cociliatia Eukarya_06_4827 5 Eukaryota;SAR;Alveolata;Dinoflagellata;Dinophyceae;Peridiniphycidae;Thoracosphaeraceae Eukarya_06_490 5 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Oligohymenophorea;Peritr ichia Eukarya_06_5165 5 Eukaryota;Opisthokonta;Fungi;Basidiomycota;Pucciniomycotina;Agaricostilbomycetes;Sterigm atomyces Eukarya_06_516 5 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Prostomatea;Cryptocaryon Acidobacteria_03_5976* 6 Bacteria;Acidobacteria;Acidobacteria;Acidobacteriales;Acidobacteriaceae Actinobacteria_03_9 6 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Mycobacteriaceae;Mycobacterium Actinobacteria_03_2432 6 Bacteria;Actinobacteria;Actinobacteria;Nitriliruptorales;Nitriliruptoraceae;Nitriliruptor Actinobacteria_03_7884 6 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales Actinobacteria_03_9272 6 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae Bacteroidetes_03_113 6 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteroidetes_03_1293 6 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Saprospiraceae;Lewinella Bacteroidetes_03_3460 6 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae   xxi   Bacteroidetes_03_17033 6 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales Bacteroidetes_03_27636 6 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Cyclobacteriaceae;Algoriphagus Chlamydiae_03_376 6 Bacteria;Chlamydiae;Chlamydiae;Chlamydiales;Simkaniaceae;Fritschea Cyanobacteria_03_61 6 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionIII;Unassigned;Leptolyngbya Firmicutes_03_435 6 Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus Firmicutes_03_5073 6 Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae Lentisphaerae_03_1145 6 Bacteria;Lentisphaerae;Lentisphaeria Lentisphaerae_03_1342 6 Bacteria;Lentisphaerae;Lentisphaeria Lentisphaerae_03_2153 6 Bacteria;Lentisphaerae;Lentisphaeria OD1_03_8148 6 Bacteria;OD1 OP3_03_247 6 Bacteria;OP3 Planctomycetes_03_1776 6 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae;Rhodopirellul a Alphaproteobacteria_03_8 2 6 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Alphaproteobacteria_03_3 67 6 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Roseovarius Alphaproteobacteria_03_1 553 6 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Alphaproteobacteria_03_1 654 6 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Alphaproteobacteria_03_2 853 6 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales Alphaproteobacteria_03_2 7270 6 Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Hyphomicrobiaceae;Pedomicrobium Gammaproteobacteria_03_ 2458 6 Bacteria;Proteobacteria;Gammaproteobacteria;Thiotrichales;Thiotrichaceae Gammaproteobacteria_03_ 3190 6 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae Gammaproteobacteria_03_ 16256 6 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae;Legionella Deltaproteobacteria_03_50 50 6 Bacteria;Proteobacteria;Deltaproteobacteria TM6_03_1232 6 Bacteria;TM6 Verrucomicrobia_03_228 6 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Luteoli bacter Archaea_03_18 6 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Archaea_03_91 6 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Archaea_03_443 6 Archaea;Crenarchaeota;terrestrial_group Archaea_03_1159 6 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Archaea_03_4463 6 Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Deep_Sea_Hydrothermal_Vent_Group_6 Archaea_03_4628 6 Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Halobacteriaceae;Halobaculum Archaea_03_6006 6 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales;Methanomicrobiaceae;Methano culleus Archaea_03_6367 6 Archaea;Crenarchaeota;terrestrial_group Euk_3921 6 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;NIF-3A7 Euk_12690 6 Eukaryota;RT5iin25 Euk_16481 6 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;BOLA322 Euk_21808 6 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Prostomatea;Cryptocaryon Euk_25078 6 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Cryothecomonas Eukarya_06_1220 6 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Ochromonadales;Epipyxis   xxii   Eukarya_06_842 6 Eukaryota;Opisthokonta;Fungi;Ascomycota;Saccharomycotina;Saccharomycetes;Saccharomyc etales Actinobacteria_03_11036* 7 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae Bacteroidetes_03_203 7 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_204 7 Bacteria;Bacteroidetes Bacteroidetes_03_223 7 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae;Ferruginibacter Bacteroidetes_03_273 7 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Winogradskyella Bacteroidetes_03_591 7 Bacteria;Bacteroidetes Bacteroidetes_03_1737 7 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales Bacteroidetes_03_2395 7 Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Rikenellaceae Bacteroidetes_03_2527 7 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Cryomorpha Bacteroidetes_03_2590 7 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_3273 7 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_4057 7 Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Marinilabiaceae Bacteroidetes_03_4142 7 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_6570 7 Bacteria;Bacteroidetes Bacteroidetes_03_24493 7 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Owenweeksia BRC1_03_1734 7 Bacteria;BRC1 Chlamydiae_03_79 7 Bacteria;Chlamydiae;Chlamydiae;Chlamydiales Chloroflexi_03_58 7 Bacteria;Chloroflexi;Caldilineae;Caldilineales;Caldilineaceae;Caldilinea Chloroflexi_03_330 7 Bacteria;Chloroflexi;Caldilineae;Caldilineales;Caldilineaceae;Caldilinea Chloroflexi_03_6266 7 Bacteria;Chloroflexi;Caldilineae;Caldilineales Firmicutes_03_3696 7 Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae Firmicutes_03_5065 7 Bacteria;Firmicutes;Bacilli;Bacillales;Paenibacillaceae;Paenibacillus Firmicutes_03_5402 7 Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Acetivibrio Firmicutes_03_6828 7 Bacteria;Firmicutes;Bacilli;Bacillales;Paenibacillaceae;Paenibacillus Firmicutes_03_12755 7 Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae Firmicutes_03_14822 7 Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Blautia Firmicutes_03_17205 7 Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Acetivibrio Firmicutes_03_32038 7 Bacteria;Firmicutes;Clostridia;Clostridiales Lentisphaerae_03_44 7 Bacteria;Lentisphaerae;Lentisphaeria Lentisphaerae_03_414 7 Bacteria;Lentisphaerae;Lentisphaeria Lentisphaerae_03_522 7 Bacteria;Lentisphaerae;Lentisphaeria Lentisphaerae_03_937 7 Bacteria;Lentisphaerae;Lentisphaeria Lentisphaerae_03_1945 7 Bacteria;Lentisphaerae;Lentisphaeria OD1_03_324 7 Bacteria;OD1 OD1_03_1098 7 Bacteria;OD1 OD1_03_2225 7 Bacteria;OD1 OP11_03_91 7 Bacteria;OP11 OP11_03_510 7 Bacteria;OP11 Planctomycetes_03_137 7 Bacteria;Planctomycetes;Phycisphaerae Planctomycetes_03_621 7 Bacteria;Planctomycetes;Phycisphaerae Planctomycetes_03_2319 7 Bacteria;Planctomycetes;Phycisphaerae   xxiii   Alphaproteobacteria_03_9 6 7 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Catellibacteriu m Alphaproteobacteria_03_1 19 7 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Alphaproteobacteria_03_2 88 7 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Rhodobacter Alphaproteobacteria_03_3 43 7 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales Alphaproteobacteria_03_3 46 7 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Alphaproteobacteria_03_6 51 7 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae;Defluviicoccus Alphaproteobacteria_03_2 223 7 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Acetobacteraceae;Roseomonas Alphaproteobacteria_03_3 014 7 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Alphaproteobacteria_03_1 4676 7 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Betaproteobacteria_03_17 7 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Rhodoferax Betaproteobacteria_03_42 7 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Betaproteobacteria_03_11 2 7 Bacteria;Proteobacteria;Betaproteobacteria;Hydrogenophilales;Hydrogenophilaceae Betaproteobacteria_03_14 1 7 Bacteria;Proteobacteria;Betaproteobacteria;Hydrogenophilales;Hydrogenophilaceae;Tepidiphilu s Betaproteobacteria_03_76 2 7 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Ottowia Betaproteobacteria_03_23 13 7 Bacteria;Proteobacteria;Betaproteobacteria Gammaproteobacteria_03_ 93 7 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Acinetobacter Gammaproteobacteria_03_ 99 7 Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Xanthomonadaceae Gammaproteobacteria_03_ 261 7 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae;Legionella Gammaproteobacteria_03_ 279 7 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae;Legionella Gammaproteobacteria_03_ 419 7 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae Gammaproteobacteria_03_ 426 7 Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Gammaproteobacteria_03_ 1678 7 Bacteria;Proteobacteria;Gammaproteobacteria;Thiotrichales;Piscirickettsiaceae;Methylophaga Gammaproteobacteria_03_ 3678 7 Bacteria;Proteobacteria;Gammaproteobacteria;Methylococcales;Methylococcaceae;Methylomo nas;fodinarum Deltaproteobacteria_03_60 7 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Desulfobulbaceae;Desulforhopal us Deltaproteobacteria_03_29 6 7 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Desulfobulbaceae;Desulfocapsa Deltaproteobacteria_03_14 89 7 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Desulfobacteraceae;Desulfofaba; hansenii Deltaproteobacteria_03_23 78 7 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Desulfobacteraceae Deltaproteobacteria_03_29 79 7 Bacteria;Proteobacteria;Deltaproteobacteria;Syntrophobacterales;Syntrophaceae;Smithella Deltaproteobacteria_03_78 91 7 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Desulfobulbaceae;Desulfurivibri o Deltaproteobacteria_03_33 077 7 Bacteria;Proteobacteria;Deltaproteobacteria;Syntrophobacterales;Syntrophaceae Epsilonproteobacteria_03_ 129 7 Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Helicobacteraceae;Sulfurimo nas   xxiv   Spirochaetes_03_147 7 Bacteria;Spirochaetes;Spirochaetes Spirochaetes_03_349 7 Bacteria;Spirochaetes;Spirochaetes Tenericutes_03_33 7 Bacteria;Tenericutes;Mollicutes;Acholeplasmatales;Acholeplasmataceae;Acholeplasma Verrucomicrobia_03_15 7 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae BacteriaNA_03_1049 7 Bacteria Archaea_03_1120 7 Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Deep_Sea_Hydrothermal_Vent_Group_6 Euk_71 7 Eukaryota;Incertae Sedis;Telonema Euk_2590 7 Eukaryota;SAR;Stramenopiles;Bicosoecida;Bicosoecidae;Bicosoeca Euk_17442 7 Eukaryota;Cryptophyceae;Cryptomonadales Eukarya_06_1539 7 Eukaryota;Haptophyta;Pavlovophyceae;Diacronema Eukarya_06_210 7 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Chromulinales;Uroglena Eukarya_06_4802 7 Eukaryota;Cryptophyceae;Cryptomonadales Eukarya_06_5629 7 Eukaryota;Archaeplastida;Chloroplastida;Chlorophyta;Chlorophyceae;Chlamydomonas Actinobacteria_03_3* 8 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Propionibacteriaceae;Propionibacteriu m Betaproteobacteria_03_30 8 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Pelomonas Euk_595 8 Eukaryota;SAR;Rhizaria;Cercozoa Eukarya_06_4386 8 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Spirotrichea;Hypotrichia;uncultured Firmicutes_03_1933 8 Bacteria;Firmicutes;Bacilli;Bacillales;Bacillaceae;Bacillus Actinobacteria_03_4* 9 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae Alphaproteobacteria_03_8 158 9 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Rickettsiaceae Eukarya_06_2698 9 Eukaryota;Opisthokonta;Holozoa;Choanomonada;Craspedida;Amb-18S-720 Betaproteobacteria_03_58 9 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Aquabacterium Actinobacteria_03_91* 10 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Micrococcaceae;Micrococcus Bacteroidetes_03_1875 10 Bacteria;Bacteroidetes Betaproteobacteria_03_50 3 10 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Alphaproteobacteria_03_1 3239* 11 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Bacteroidetes_03_400 11 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_1414 11 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Owenweeksia Bacteroidetes_03_2068 11 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Sphingobacteriaceae;Sphingobacteri aceae;Pedobacter Bacteroidetes_03_4593 11 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales Bacteroidetes_03_9103 11 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium Bacteroidetes_03_20155 11 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Croceibacter Firmicutes_03_11 11 Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus Firmicutes_03_7835 11 Bacteria;Firmicutes;Bacilli;Bacillales;Paenibacillaceae;Paenibacillus OD1_03_43 11 Bacteria;OD1 OD1_03_3041 11 Bacteria;OD1 OD1_03_6947 11 Bacteria;OD1 Planctomycetes_03_2812 11 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae;Rhodopirellul a Alphaproteobacteria_03_1 48 11 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Marinosulfom onas Alphaproteobacteria_03_1 11 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae   xxv   552 Deltaproteobacteria_03_90 11 Bacteria;Proteobacteria;Deltaproteobacteria Deltaproteobacteria_03_10 730 11 Bacteria;Proteobacteria;Deltaproteobacteria;Bdellovibrionales;Bdellovibrionaceae;Bdellovibrio; bacteriovorus Verrucomicrobia_03_25 11 Bacteria;Verrucomicrobia;Opitutae;Puniceicoccales;Puniceicoccaceae;Lentimonas Verrucomicrobia_03_410 11 Bacteria;Verrucomicrobia;Spartobacteria;Chthoniobacterales;Xiphinematobacteriaceae Verrucomicrobia_03_725 11 Bacteria;Verrucomicrobia;Opitutae;Opitutales;Opitutaceae;Opitutus Verrucomicrobia_03_3738 11 Bacteria;Verrucomicrobia;Spartobacteria;Chthoniobacterales;Xiphinematobacteriaceae Verrucomicrobia_03_7541 11 Bacteria;Verrucomicrobia;Opitutae;Puniceicoccales;Puniceicoccaceae;Coraliomargarita BacteriaNA_03_4150 11 Bacteria Archaea_03_87 11 Archaea;Crenarchaeota;Miscellaneous_Crenarchaeotic_Group Archaea_03_146 11 Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales;Methanosarcinaceae;Methanosarc ina Archaea_03_309 11 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales;Methanomicrobiaceae;Methano culleus Archaea_03_327 11 Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Deep_Sea_Hydrothermal_Vent_Group_6 Archaea_03_369 11 Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales;GOM_Arc_I Archaea_03_404 11 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales Archaea_03_1559 11 Archaea;Euryarchaeota;Methanobacteria;Methanobacteriales;Methanobacteriaceae;Methanobre vibacter Euk_27 11 Eukaryota;SAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniphycidae;Incertae Sedis;Cochlodinium Euk_222 11 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;WHOI-LI1-14 Euk_9498 11 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Euglenida;Heteronematina;Petalomonas Eukarya_06_1173 11 Eukaryota;SAR;Alveolata;Protalveolata;Syndiniales;Amoebophrya Eukarya_06_128 11 Unknown Eukarya_06_133 11 Unknown Eukarya_06_1448 11 Eukaryota;SAR;Stramenopiles;Peronosporomycetes;Phytophthora Eukarya_06_1671 11 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Kinetoplastea;Metakinetoplastina;Neobo donida Eukarya_06_180 11 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Spirotrichea;Choreotrichia;uncultured Eukarya_06_201 11 Eukaryota;SAR;Alveolata;Protalveolata;Syndiniales;Syndiniales Group II Eukarya_06_30 11 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Chromulinales;JBNA46 Eukarya_06_317 11 Eukaryota;Opisthokonta;Fungi;Basidiomycota;Ustilaginomycotina;Exobasidiomycetes;Malasse zia Eukarya_06_3194 11 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Protaspa Eukarya_06_3357 11 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Litostomatea;Haptoria Eukarya_06_39 11 Eukaryota;SAR;Alveolata;Dinoflagellata;Dinophyceae;Peridiniphycidae;Gonyaulacales;Neocer atium Eukarya_06_4087 11 Eukaryota;Opisthokonta;Fungi;LKM15 Eukarya_06_5063 11 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Euglenida;Heteronematina;Petalomonas Eukarya_06_512 11 Eukaryota;SAR;Alveolata;Dinoflagellata;Dinophyceae;Peridiniphycidae;Peridiniales;Protoperid inium Eukarya_06_5456 11 Eukaryota;SAR;Alveolata;Dinoflagellata;Dinophyceae;Peridiniphycidae;Thoracosphaeraceae Eukarya_06_7017 11 Eukaryota;Haptophyta;Prymnesiophyceae Eukarya_06_84 11 Eukaryota;SAR;Alveolata;Protalveolata;Syndiniales;Syndiniales Group I Eukarya_06_1160 11 Eukaryota;SAR;Stramenopiles;Dictyochophyceae;Pedinellales Alphaproteobacteria_03_2 014* 12 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Rickettsiaceae   xxvi   Planctomycetes_03_353 12 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae;Planctomyces Betaproteobacteria_03_87 3 12 Bacteria;Proteobacteria;Betaproteobacteria;Rhodocyclales;Rhodocyclaceae Eukarya_06_338 12 Eukaryota;Opisthokonta;Holozoa;Corallochytrium Alphaproteobacteria_03_6 2* 13 Bacteria;Proteobacteria;Alphaproteobacteria;Caulobacterales;Caulobacteraceae;Brevundimonas Euk_301 13 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Protaspa Alphaproteobacteria_03_6 621* 14 Bacteria;Proteobacteria;Alphaproteobacteria;Sneathiellales;Sneathiellaceae;Sneathiella Chloroflexi_03_474 14 Bacteria;Chloroflexi;Caldilineae;Caldilineales;Caldilineaceae;Caldilinea Cyanobacteria_03_38 14 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionIII Eukarya_06_515 14 Eukaryota;SAR;Stramenopiles;Diatomea;Bacillariophytina;Bacillariophyceae;Asterionellopsis Eukarya_06_723 14 Eukaryota;SAR;Stramenopiles;Diatomea;Bacillariophytina;Bacillariophyceae;Sellaphora Archaea_03_112 14 Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Halobacteriaceae;Halobaculum Eukarya_06_804 14 Eukaryota;Amoebozoa;Conosa;Protosporangiida;Protosporangiidae;Protosporangium Eukarya_06_285 15 Unknown Betaproteobacteria_03_28 15 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Hydrogenophag a Alphaproteobacteria_03_9 1* 15 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteroidetes_03_1 15 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Gammaproteobacteria_03_ 175 15 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales Alphaproteobacteria_03_9 75 15 Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Novosphi ngobium Betaproteobacteria_03_56 15 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Gammaproteobacteria_03_ 68 15 Bacteria;Proteobacteria;Gammaproteobacteria Verrucomicrobia_03_356 15 Bacteria;Verrucomicrobia;Opitutae;Opitutales;Opitutaceae;Opitutus Bacteroidetes_03_89 15 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Betaproteobacteria_03_46 15 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Hydrogenophag a Bacteroidetes_03_23 15 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteroidetes_03_416 15 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Betaproteobacteria_03_1 15 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Ralstonia Bacteroidetes_03_4 15 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium Archaea_03_117* 16 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales Archaea_03_14 16 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Archaea_03_141 17 Archaea;Crenarchaeota;Marine_Group_I Archaea_03_200 17 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales Archaea_03_17* 17 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Archaea_03_15 17 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Archaea_03_8 17 Archaea;Crenarchaeota;Marine_Group_I Bacteroidetes_03_1296* 18 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Cytophagaceae;Flexibacter Gammaproteobacteria_03_ 13 18 Bacteria;Proteobacteria;Gammaproteobacteria Planctomycetes_03_3822 18 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae Verrucomicrobia_03_300 18 Bacteria;Verrucomicrobia;Spartobacteria;Chthoniobacterales;Xiphinematobacteriaceae Gammaproteobacteria_03_ 18 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Pseudoalteromonadaceae;Pseu   xxvii   11 doalteromonas;ruthenica Euk_5461 18 Eukaryota;Centrohelida;M1-18D08 Verrucomicrobia_03_94 18 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae Bacteroidetes_03_121 18 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Tenacibaculum Bacteroidetes_03_251 18 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Salegentibacter Bacteroidetes_03_5 18 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium Bacteroidetes_03_168* 19 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Croceibacter Bacteroidetes_03_359 19 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Cyclobacteriaceae;Cyclobacterium Actinobacteria_03_140 19 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales Actinobacteria_03_15 19 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae;Microbacterium Bacteroidetes_03_12 19 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Firmicutes_03_7271 19 Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Acetivibrio OD1_03_291 19 Bacteria;OD1 Alphaproteobacteria_03_9 84 19 Bacteria;Proteobacteria;Alphaproteobacteria Gammaproteobacteria_03_ 120 19 Bacteria;Proteobacteria;Gammaproteobacteria;Unassigned;Unassigned;Thiohalophilus Gammaproteobacteria_03_ 236 19 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae;Unassigned ;Haliea Gammaproteobacteria_03_ 397 19 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Colwelliaceae;Colwellia;rossen sis Deltaproteobacteria_03_12 3 19 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfuromonadales;Geobacteraceae;Geopsychrob acter TM7_03_17 19 Bacteria;TM7 Euk_459 19 Eukaryota;SAR;Stramenopiles;Bicosoecida;Cafeteriidae;Cafeteria Euk_759 19 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Kinetoplastea;Metakinetoplastina;Parab odonida Euk_1165 19 Eukaryota;SAR;Stramenopiles;Labyrinthulomycetes;D2P04F01 Euk_4671 19 Eukaryota;RT5iin25 Eukarya_06_1003 19 Eukaryota;Archaeplastida;Chloroplastida;Chlorophyta;Chlorophyceae Eukarya_06_1331 19 Eukaryota;SAR;Stramenopiles;Bolidomonas Eukarya_06_150 19 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;NIF-3A7 Eukarya_06_3486 19 Eukaryota;Opisthokonta;Fungi;Fungi;Basal fungi;Basal fungi;Chytridiomycota Eukarya_06_3644 19 Unknown Eukarya_06_407 19 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Kinetoplastea;Metakinetoplastina;Parab odonida Eukarya_06_496 19 Eukaryota;Excavata;Discoba;Discicristata;Euglenozoa;Kinetoplastea;Metakinetoplastina;Neobo donida Eukarya_06_828 19 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Chromulinales;Spumella Actinobacteria_03_169 19 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Unassigned;Demequina Actinobacteria_03_370 19 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae;Frigoribacterium Bacteroidetes_03_198 19 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae;Gracilimonas Bacteroidetes_03_275 19 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Croceibacter Bacteroidetes_03_385 19 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae;Balneola Bacteroidetes_03_397 19 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Ulvibacter Bacteroidetes_03_824 19 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Brumimimicrobium Bacteroidetes_03_1289 19 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Psychroflexus   xxviii   Bacteroidetes_03_1516 19 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteroidetes_03_2401 19 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Saprospiraceae;Haliscomenobacter Bacteroidetes_03_2818 19 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Owenweeksia Alphaproteobacteria_03_3 6 19 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Alphaproteobacteria_03_1 69 19 Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Rhodobiaceae;Parvibaculum Alphaproteobacteria_03_1 599 19 Bacteria;Proteobacteria;Alphaproteobacteria;Caulobacterales;Hyphomonadaceae;Maricaulis Betaproteobacteria_03_53 19 Bacteria;Proteobacteria;Betaproteobacteria Betaproteobacteria_03_84 19 Bacteria;Proteobacteria;Betaproteobacteria;Rhodocyclales;Rhodocyclaceae;Azospira Gammaproteobacteria_03_ 2 19 Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae Gammaproteobacteria_03_ 7 19 Bacteria;Proteobacteria;Gammaproteobacteria Gammaproteobacteria_03_ 141 19 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales Gammaproteobacteria_03_ 197 19 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae Gammaproteobacteria_03_ 217 19 Bacteria;Proteobacteria;Gammaproteobacteria;Thiotrichales;Piscirickettsiaceae;Mariprofundus Gammaproteobacteria_03_ 369 19 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae Gammaproteobacteria_03_ 421 19 Bacteria;Proteobacteria;Gammaproteobacteria Gammaproteobacteria_03_ 427 19 Bacteria;Proteobacteria;Gammaproteobacteria;Salinisphaerales;Salinisphaeraceae;Salinisphaera Gammaproteobacteria_03_ 636 19 Bacteria;Proteobacteria;Gammaproteobacteria;Thiotrichales;Thiotrichaceae;Leucothrix Gammaproteobacteria_03_ 692 19 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae Gammaproteobacteria_03_ 894 19 Bacteria;Proteobacteria;Gammaproteobacteria;Thiotrichales;Piscirickettsiaceae;Methylophaga TM7_03_101 19 Bacteria;TM7 Verrucomicrobia_03_44 19 Bacteria;Verrucomicrobia;Opitutae;Puniceicoccales;Puniceicoccaceae;Lentimonas Verrucomicrobia_03_349 19 Bacteria;Verrucomicrobia;Opitutae;Puniceicoccales;Puniceicoccaceae;Coraliomargarita Verrucomicrobia_03_566 19 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Roseiba cillus Verrucomicrobia_03_918 19 Bacteria;Verrucomicrobia;Opitutae;Puniceicoccales;Puniceicoccaceae;Coraliomargarita Actinobacteria_03_36 19 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Iamiaceae;Iamia Gammaproteobacteria_03_ 431 19 Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Xanthomonadaceae;Luteibact er Euk_1490 19 Unknown Eukarya_06_1125 19 Eukaryota;SAR;Stramenopiles;MAST-12 Betaproteobacteria_03_15 1 19 Bacteria;Proteobacteria;Betaproteobacteria Bacteroidetes_03_252 19 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Lutibacter Verrucomicrobia_03_62 19 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Roseiba cillus Verrucomicrobia_03_57 19 Bacteria;Verrucomicrobia;Spartobacteria;Chthoniobacter Eukarya_06_2105 19 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Chromulinales;Cyclonexis Gammaproteobacteria_03_ 1630 19 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae;Legionella Alphaproteobacteria_03_2 4 20 Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingop yxis   xxix   Euk_9221 20 Eukaryota;SAR;Stramenopiles;Eustigmatales;Nannochloropsis Eukarya_06_1327 20 Eukaryota;Archaeplastida;Chloroplastida;Chlorophyta;Trebouxiophyceae;Micractinium Node Module ID Taxonomy Eukarya_06_1388 20 Eukaryota;SAR;Stramenopiles;Eustigmatales;Goniochloris Eukarya_06_2908 20 Eukaryota;SAR;Rhizaria;Cercozoa Cyanobacteria_03_66 20 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionIV;Unassigned;Nostoc Planctomycetes_03_4 20 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae;Gemmata Alphaproteobacteria_03_1 89 20 Bacteria;Proteobacteria;Alphaproteobacteria;Caulobacterales;Caulobacteraceae;Phenylobacteriu m Verrucomicrobia_03_572 20 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Verruc omicrobium Eukarya_06_266 20 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Ochromonadales;Paraphysomonas Bacteroidetes_03_262* 20 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium Bacteroidetes_03_1099 20 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Fluviicola;taffensis Alphaproteobacteria_03_1 226 20 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Acetobacteraceae;Acidocella Verrucomicrobia_03_209 20 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Subdivision3 TM6_03_116 20 Bacteria;TM6 Alphaproteobacteria_03_3 32 20 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales Actinobacteria_03_62 20 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales Planctomycetes_03_1132 20 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae;Gemmata Eukarya_06_550 20 Eukaryota;SAR;Alveolata;Protalveolata;Colpodellida;Colpodella Actinobacteria_03_7 20 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae Eukarya_06_757 20 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Prostomatea;Cryptocaryon Bacteroidetes_03_933 20 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae;Ferruginibacter Alphaproteobacteria_03_3 4 20 Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Erythrobacteraceae Alphaproteobacteria_03_8 747 20 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae;Defluviicoccus Betaproteobacteria_03_16 20 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Gammaproteobacteria_03_ 748 20 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae;Legionella Euk_2670 20 Eukaryota;SAR;Rhizaria;Cercozoa;Clathrulinidae;Hedriocystis Bacteroidetes_03_485 20 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_1569 20 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Planctomycetes_03_317 20 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae Verrucomicrobia_03_335 20 Bacteria;Verrucomicrobia;Opitutae;Opitutales;Opitutaceae;Opitutus Verrucomicrobia_03_358 20 Bacteria;Verrucomicrobia;Opitutae;Opitutales;Opitutaceae;Opitutus Bacteroidetes_03_86 20 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae;Sediminibacteriu m Archaea_03_451 20 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Euk_16491 20 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Ochromonadales;Paraphysomonas Eukarya_06_605 20 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Prostomatea;Cryptocaryon Bacteroidetes_03_592 20 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Sphingobacteriaceae;Sphingobacteri aceae;Pedobacter Betaproteobacteria_03_29 20 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Alcaligenaceae Eukarya_06_850 20 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Litostomatea;Haptoria;Lacrymaria   xxx   Betaproteobacteria_03_13 4 20 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Bacteroidetes_03_774 20 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Betaproteobacteria_03_19 9 20 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Betaproteobacteria_03_27 20 Bacteria;Proteobacteria;Betaproteobacteria;Methylophilales;Methylophilaceae;Methylotenera Eukarya_06_1057 20 Eukaryota;SAR;Alveolata;Ciliophora;Postciliodesmatophora;Heterotrichea;Blepharisma Verrucomicrobia_03_4 20 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Luteoli bacter Actinobacteria_03_23 21 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae Bacteroidetes_03_357 21 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae Alphaproteobacteria_03_1 5 21 Bacteria;Proteobacteria;Alphaproteobacteria;Caulobacterales;Caulobacteraceae;Brevundimonas Actinobacteria_03_46 21 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae Archaea_03_23 21 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II Gammaproteobacteria_03_ 79 21 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Psychrobacter Actinobacteria_03_55 21 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae Alphaproteobacteria_03_6 08 21 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Gammaproteobacteria_03_ 1505 21 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales Bacteroidetes_03_866 21 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae;Sediminibacteriu m Alphaproteobacteria_03_4 31 21 Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Novosphi ngobium Alphaproteobacteria_03_8 08 21 Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Methylocystaceae;Methylocystis Archaea_03_449 21 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Terrestrial_Miscellaneous_Group Alphaproteobacteria_03_3 31 21 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Acetobacteraceae;Acidocella Archaea_03_32 21 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Terrestrial_Miscellaneous_Group Betaproteobacteria_03_8 21 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Polynucleobacter Betaproteobacteria_03_14 3* 21 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae;Herbaspirillum Archaea_03_192 21 Archaea;Crenarchaeota;terrestrial_group Verrucomicrobia_03_24 21 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae Bacteroidetes_03_21 21 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium Archaea_03_203 21 Archaea;Crenarchaeota;Miscellaneous_Crenarchaeotic_Group Archaea_03_288 21 Archaea;Crenarchaeota;terrestrial_group Eukarya_06_388 21 Eukaryota;SAR;Rhizaria;Cercozoa;Endomyxa;Novel Clade 10 Verrucomicrobia_03_224 21 Bacteria;Verrucomicrobia;Opitutae;Opitutales;Opitutaceae;Opitutus Betaproteobacteria_03_15 21 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Polynucleobacter OD1_03_1 21 Bacteria;OD1 Gemmatimonadetes_03_1 52 21 Bacteria;Gemmatimonadetes;Gemmatimonadetes;Gemmatimonadales;Gemmatimonadaceae;Ge mmatimonas Actinobacteria_03_1 22 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae Bacteroidetes_03_1130 22 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Saprospiraceae Verrucomicrobia_03_32 22 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Prosthe cobacter;vanneervenii Betaproteobacteria_03_51 * 22 Bacteria;Proteobacteria;Betaproteobacteria;Rhodocyclales;Rhodocyclaceae;Methyloversatilis   xxxi   Alphaproteobacteria_03_4 2 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Rhodobacter Actinobacteria_03_51 22 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae Actinobacteria_03_25 22 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae Betaproteobacteria_03_19 8 22 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Rhodoferax Deltaproteobacteria_03_16 1 22 Bacteria;Proteobacteria;Deltaproteobacteria;Myxococcales Actinobacteria_03_26 22 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae;Planktophila Actinobacteria_03_329 22 Bacteria;Actinobacteria;Actinobacteria;Solirubrobacterales;Conexibacteraceae;Conexibacter Cyanobacteria_03_113 22 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionIII;Unassigned;Leptolyngbya Actinobacteria_03_260 22 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales Euk_1654 22 Eukaryota;SAR;Stramenopiles;Bicosoecida;P34.6 Euk_2679 22 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Cryothecomonas Euk_3792 22 Eukaryota;Opisthokonta;Fungi;Basidiomycota;Agaricomycotina;Agaricomycetes Betaproteobacteria_03_4 22 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Rhodoferax Bacteroidetes_03_563 22 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Eukarya_06_1112 22 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Ochromonadales;Paraphysomonas Eukarya_06_1237 22 Eukaryota;Opisthokonta;Fungi;Fungi;Basal fungi;Basal fungi;Blastocladiomycota Eukarya_06_574 22 Unknown Eukarya_06_64 22 Eukaryota;Haptophyta;Prymnesiophyceae;Isochrysidales;Isochrysis Euk_384 22 Eukaryota;SAR;Stramenopiles;Labyrinthulomycetes;Thraustochytriaceae;E170 Eukarya_06_136 22 Eukaryota;SAR;Stramenopiles;Incertae Sedis;Pirsonia Eukarya_06_397 22 Eukaryota;Opisthokonta;Holozoa;Choanomonada;Craspedida;Lagenoeca Eukarya_06_472 22 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;NIF-3A7 Eukarya_06_856 22 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Cryothecomonas Gammaproteobacteria_03_ 32 22 Bacteria;Proteobacteria;Gammaproteobacteria Alphaproteobacteria_03_2 44 22 Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingop yxis Bacteroidetes_03_32 22 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium Alphaproteobacteria_03_1 505 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Rickettsiaceae;Rickettsia;endosymbi ont Gammaproteobacteria_03_ 1314 22 Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteroidetes_03_435 22 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Fluviicola Bacteroidetes_03_867 22 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Fluviicola Betaproteobacteria_03_9 22 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Eukarya_06_769 22 Eukaryota;SAR;Stramenopiles;Synurales;Mallomonas Bacteroidetes_03_1343 22 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Euk_367 22 Eukaryota;Opisthokonta;Fungi;LKM11 Eukarya_06_3 22 Eukaryota;SAR;Alveolata;Dinoflagellata;Dinophyceae;SL163A10 Actinobacteria_03_2 22 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Microbacteriaceae Alphaproteobacteria_03_2 597 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Rickettsiaceae;Rickettsia;endosymbi ont Alphaproteobacteria_03_1 36 22 Bacteria;Proteobacteria;Alphaproteobacteria;Caulobacterales;Hyphomonadaceae;Hyphomonas; Hyphomonas Alphaproteobacteria_03_5 71 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Rickettsiaceae;Rickettsia   xxxii   Actinobacteria_03_37 22 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae Alphaproteobacteria_03_1 43 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Rhodobacter Betaproteobacteria_03_13 5 22 Bacteria;Proteobacteria;Betaproteobacteria;Methylophilales;Methylophilaceae;Methylophilus Betaproteobacteria_03_13 22 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Rhodoferax Eukarya_06_391 22 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Chromulinales;Spumella Gammaproteobacteria_03_ 20 22 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomo nas OP9_03_4 22 Bacteria;OP9 Bacteroidetes_03_652 22 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Cytophagaceae;Dyadobacter Eukarya_06_74 22 Eukaryota;Archaeplastida;Chloroplastida;Chlorophyta;Prasinophytae;Pyramimonas Betaproteobacteria_03_99 22 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Hydrogenophag a Actinobacteria_03_481 22 Bacteria;Actinobacteria;Actinobacteria Bacteroidetes_03_960 22 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Cryomorpha Chlorobi_03_60 22 Bacteria;Chlorobi;Chlorobia;Chlorobiales OD1_03_45 22 Bacteria;OD1 OD1_03_79 22 Bacteria;OD1 Alphaproteobacteria_03_3 828 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Paracoccus Alphaproteobacteria_03_1 296 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Catellibacteriu m Bacteroidetes_03_828 22 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Fluviicola Bacteroidetes_03_1652 22 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Fluviicola Gammaproteobacteria_03_ 1166 22 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Enhydrobacter Deltaproteobacteria_03_28 3 22 Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Desulfobacteraceae;Desulfobacu la Alphaproteobacteria_03_5 047 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Rickettsiaceae;Rickettsia;endosymbi ont Alphaproteobacteria_03_6 733 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Acetobacteraceae;Acidocella Bacteroidetes_03_4962 22 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Crocinitomix;catalasitic a Betaproteobacteria_03_11 22 Bacteria;Proteobacteria;Betaproteobacteria;Methylophilales;Methylophilaceae TM6_03_287 22 Bacteria;TM6 Verrucomicrobia_03_27 22 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Roseiba cillus Bacteroidetes_03_2011 22 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Sphingobacteriaceae;Sphingobacteri aceae Bacteroidetes_03_1171 22 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Cryomorpha Archaea_03_50 22 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales;Unassigned;Candidatus_Methan oregula Eukarya_06_71 22 Eukaryota;SAR;Stramenopiles;Chrysophyceae;Chromulinales;Chrysamoeba Planctomycetes_03_30 22 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae Alphaproteobacteria_03_7 80 22 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Pseudorhodob acter Bacteroidetes_03_1888 22 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales Betaproteobacteria_03_19 22 Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae Archaea_03_102 22 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales Archaea_03_4 22 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine_Group_II   xxxiii   Euk_22 22 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea Deltaproteobacteria_03_11 60 22 Bacteria;Proteobacteria;Deltaproteobacteria;Syntrophobacterales;Syntrophaceae Archaea_03_241 22 Archaea;Crenarchaeota;terrestrial_group Eukarya_06_933 22 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Oligohymenophorea;Scuti cociliatia Deltaproteobacteria_03_42 6 22 Bacteria;Proteobacteria;Deltaproteobacteria;Bdellovibrionales;Bacteriovoraceae;Peredibacter Eukarya_06_140 22 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Spirotrichea;Hypotrichia;Oxytricha Archaea_03_324 22 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales;Rice_Cluster_II Euk_8710 22 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;NOR26 Eukarya_06_62 22 Eukaryota;SAR;Rhizaria;Cercozoa;Thecofilosea;Cryomonadida;Protaspidae;Cryothecomonas Euk_67 22 Eukaryota;Opisthokonta;Fungi;LKM11 Euk_1779 22 Eukaryota;Opisthokonta;Fungi;LKM11 Eukarya_06_630 22 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Litostomatea;Haptoria Lentisphaerae_03_2116 22 Bacteria;Lentisphaerae;Lentisphaeria OD1_03_3 22 Bacteria;OD1 Verrucomicrobia_03_26 22 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae Eukarya_06_1718* 23 Eukaryota;SAR;Stramenopiles;Dictyochophyceae;Pedinellales;Pteridomonas Gammaproteobacteria_03_ 2351 23 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Legionellaceae Eukarya_06_1772* 24 Eukaryota;Archaeplastida;Chloroplastida;Chlorophyta;Chlorophyceae;Chlorogonium Eukarya_06_1927 24 Eukaryota;Archaeplastida;Chloroplastida;Chlorophyta;Chlorophyceae;Chlamydomonas Actinobacteria_03_1592 25 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Unassigned;Microthrix OD1_03_126 25 Bacteria;OD1 Deltaproteobacteria_03_21 401 25 Bacteria;Proteobacteria;Deltaproteobacteria;Bdellovibrionales;Bacteriovoraceae;Peredibacter Euk_6458 25 Unknown Eukarya_06_651 25 Eukaryota;SAR;Stramenopiles;Chrysophyceae;E222 Actinobacteria_03_493 25 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Unassigned;Fodinicola Bacteroidetes_03_3718 25 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_4936 25 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales Bacteroidetes_03_5435 25 Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Rikenellaceae;Rikenella Chlorobi_03_216 25 Bacteria;Chlorobi;Chlorobia;Chlorobiales Alphaproteobacteria_03_3 99 25 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Pseudorhodob acter Verrucomicrobia_03_100 25 Bacteria;Verrucomicrobia;Spartobacteria Verrucomicrobia_03_938 25 Bacteria;Verrucomicrobia;Opitutae;Opitutales;Opitutaceae;Opitutus Archaea_03_104 25 Archaea;Crenarchaeota;Miscellaneous_Crenarchaeotic_Group Bacteroidetes_03_233 25 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Alphaproteobacteria_03_7 78 25 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Loktanella Gammaproteobacteria_03_ 4* 25 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomo nas Gammaproteobacteria_03_ 72 25 Bacteria;Proteobacteria;Gammaproteobacteria;Thiotrichales;Piscirickettsiaceae;Methylophaga TM6_03_130 25 Bacteria;TM6 Archaea_03_52 25 Archaea;Euryarchaeota;Methanomicrobia;Methanosarcinales;GOM_Arc_I   xxxiv   Archaea_03_167 25 Archaea;Crenarchaeota;Soil_Crenarchaeotic_Group Archaea_03_257 25 Archaea;Crenarchaeota;Miscellaneous_Crenarchaeotic_Group Euk_76 25 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Spirotrichea;Euplotia;Euplotes Eukarya_06_250 25 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Prostomatea;Cryptocaryon Actinobacteria_03_68 25 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae Actinobacteria_03_82 25 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales Alphaproteobacteria_03_6 84 25 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Archaea_03_159 25 Archaea;Crenarchaeota;Soil_Crenarchaeotic_Group Bacteroidetes_03_7251 25 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Fluviicola Archaea_03_240 25 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales;Unassigned;Candidatus_Methan oregula Archaea_03_263 25 Archaea;Euryarchaeota;Methanomicrobia;Methanomicrobiales;Methanocorpusculaceae;Methan ocorpusculum Eukarya_06_12 25 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Spirotrichea;Oligotrichia;uncultured Eukarya_06_255 25 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Oligohymenophorea;Penic ulia Actinobacteria_03_49 26 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae Spirochaetes_03_17 26 Bacteria;Spirochaetes;Spirochaetes;Spirochaetales Planctomycetes_03_726* 26 Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae;Planctomyces Actinobacteria_03_146 26 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Iamiaceae;Iamia Cyanobacteria_03_220 26 Bacteria;Cyanobacteria Euk_2599 26 Unknown Bacteroidetes_03_53 26 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium Bacteroidetes_03_343 26 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Lishizhenia Bacteroidetes_03_505 26 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Chitinophagaceae Alphaproteobacteria_03_1 912 26 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;Rickettsiaceae Bacteroidetes_03_544 26 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Fluviicola Chloroflexi_03_115 26 Bacteria;Chloroflexi;Caldilineae;Caldilineales Actinobacteria_03_1785 26 Bacteria;Actinobacteria;Actinobacteria;Solirubrobacterales;Conexibacteraceae;Conexibacter Actinobacteria_03_174 26 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Unassigned;Microthrix OD1_03_58 26 Bacteria;OD1 Verrucomicrobia_03_547 26 Bacteria;Verrucomicrobia;Opitutae;Puniceicoccales;Puniceicoccaceae;Coraliomargarita Actinobacteria_03_32 26 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Sporichthyaceae;Planktophila Gammaproteobacteria_03_ 145 26 Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Xanthomonadaceae Euk_169 26 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Spirotrichea;Euplotia;Euplotes Bacteroidetes_03_5975 26 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Sphingobacteriaceae;Sphingobacteri aceae;Pedobacter OD1_03_104 26 Bacteria;OD1 Gammaproteobacteria_03_ 2188 26 Bacteria;Proteobacteria;Gammaproteobacteria;Legionellales;Coxiellaceae;Aquicella Gammaproteobacteria_03_ 1728 26 Bacteria;Proteobacteria;Gammaproteobacteria;Thiotrichales;Piscirickettsiaceae;Methylophaga Actinobacteria_03_722 27 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Iamiaceae;Iamia Verrucomicrobia_03_119* 27 Bacteria;Verrucomicrobia;Opitutae;Opitutales;Opitutaceae;Opitutus Bacteroidetes_03_60 27 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Flavobacterium   xxxv   Eukarya_06_709 27 Eukaryota;SAR;Alveolata;Ciliophora;Intramacronucleata;Conthreep;Oligohymenophorea;Apos tomatia Bacteroidetes_03_457 27 Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Cyclobacteriaceae;Algoriphagus   xxxvi   Table S4. Modularity Calculations for the Lake Fryxell (FRX) and West Lobe Lake Bonney (WLB) Molecular 1   Ecological Network (MEN). 2   Method Modularity Number of modules MCL 0.87 45 GLay 0.87 19 CCC 0.86 16 SCPS 0.82 25 ModuLand - 27 3   Table S5. Major characteristics for modules with significant BC scores (>0) and those connected to the autumn 4   proliferation of Archaea. 5   Module Number Module Betweenness Centrality Score Main characteristics 15 0.19 Photoheterotrophy; energy acquisition 17 0 Chemoautotrophy; energy acquisition 18 0.07 DOM processing 19 0.04 DOM processing 20 0.04 Phytoplankton DOM 21 0.02 Nutrient processing 22 0.04 Mixotrophy 25 0.1 Carbon/Sulfur cycling 26 0.04 Anaerobic adaptation; energy storage 27 0.005 Metazoan- associated? 6   7   8   9   70 CHAPTER THREE PARTITIONING OF INORGANIC CARBON-FIXATION IN PERMANENTLY ICE- COVERED ANTARCTIC LAKES Contribution of Authors and Co-Authors Manuscript in Chapter 3 Author: Trista J. Vick-Majors Contributions: Designed and conducted experiments, collected samples, analyzed and interpreted data, prepared figures and tables, wrote the manuscript. Co-Author: John C. Priscu Contributions: Provided funding, designed experiments, provided input to manuscript. 71 Manuscript Information Page Trista J. Vick-Majors, John C. Priscu Microbial Ecology Status of Manuscript: ____ Prepared for submission to a peer-reviewed journal _X_ Officially submitted to a peer-review journal ____ Accepted by a peer-reviewed journal _ _ Published in a peer-reviewed journal Springer US Submitted November 12, 2015 72 PARTITIONING OF INORGANIC CARBON-FIXATION IN PERMANENTLY ICE- COVERED ANTARCTIC LAKES The following work has been submitted to Microbial Ecology Trista J. Vick-Majors1 and John C. Priscu1* 1Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USA *corresponding author Production of new carbon by phytoplankton photosynthesis forms the base of the food chain in most aquatic ecosystems. High latitude ecosystems are unique in their seasonal light-dark cycles, leading to continuous primary production during the summer months and no photoautotrophic primary production during the polar night [1]. This bimodality in photoautotrophy leads to a situation where annual respiratory consumption of organic carbon can exceed photoautotrophic organic carbon production [2]. Under these conditions chemolithoautotropic organic carbon production may contribute significantly to ecosystem processes. Lakes Fryxell and Bonney are permanently ice- covered lakes in the McMurdo Dry Valleys, the largest ice-free region of the Antarctic continent. These lakes are characterized by a paucity of metazoans and low light penetration through the ~ 4m thick permanent ice (<5% incident photosynthetically active radiation [PAR]). The water columns of both lakes are highly chemically stratified with oxygen over-saturated surface waters underlain by anoxic or suboxic saline layers [3] and water temperatures <6 oC. Past studies have examined the occurrence and expression carbon fixation genes [4, 5], microbial community structure [6], and phytoplankton [7, 8] dynamics during the transitions to and from the polar night, when photosynthetic primary production ceases or 73 begins. These studies found evidence for diverse carbon fixation strategies in Lake Fryxell and Lake Bonney, including chemolithoautotrophy, which may supplement a portion of the heterotrophic carbon demand that continues year-round [2, 9, 10]. Other researchers have detected chemolithoautotrophic carbon fixation genes [11] and ammonia-oxidizing bacteria in Lakes Fryxell and Bonney [12], estimated rates of ammonia oxidation [13], and isolated sulfur-oxidizing chemolithoautotrophic bacteria from Lake Fryxell [14]. The rates of inorganic carbon fixation by these chemolithoautotrophic populations remain unknown. Here, we present rates of dissolved inorganic carbon-fixation (DIC-fixation) from the water columns of Lake Fryxell (FRX) and the East Lobe of Lake Bonney (ELB) during two austral summers (2008-2009 and 2009-2010 [FRX only]), partitioned by size fractions and functional groups. FRX and ELB have been studied intensively since 1993 as part of the McMurdo Long Term Ecological Research program and data are publically available on the projects website (MCM LTER; mcmlter.org). We collected water samples directly below the ice covers, at the primary production maxima and at the bottom of the photic zones of each lake (ELB 6, 13, and 20 m, respectively; FRX 6, 10, and 12 m, respectively) through holes drilled in the ice covers. All depths are reported from the piezometric water level in the sampling hole. During 2008, glass bottles filled with lake water with no headspace were amended with [14C]-bicarbonate (stock concentration 133.9 uCi mL-1 in 2008, 144.1 uCi mL-1 in 2009). Final [14C]-bicarbonate concentrations were based on the concentrations of dissolved inorganic carbon at each sample depth (see MCM LTER methods at 74 http://www.montana.edu/priscu/dataproducts.php). Vials were sealed with teflon-lined caps, and incubated in the lake at the depth of sampling for 24 hours. Following incubation, samples were size-fractionated by filtering onto 3 uCi and 0.2 uCi polycarbonate filters, acidified with 3N hydrochloric acid and dried overnight. Radioactivity retained by particulate matter on the filters was measured with a calibrated scintillation counter and converted to rates of DIC-fixation according to the MCM LTER methods. Rates of dark DIC-fixation (chemolithoautotrophy + anapleurotic reactions) were determined from incubations conducted in opaque bottles, minus controls killed with trichloroacetic acid (final concentration ~5%). Photoautotrophic DIC-fixation was determined by subtracting opaque bottle activity from that in the light bottles. Incubations from 2009 were conducted in 33 ml test tubes in an environmental chamber (temperature = 2 oC; PAR ~ 90 umol m-2 s-1) because logistical constraints prevented us from incubating samples in the lake. In addition to the light, dark and killed treatments, the potential contribution of ammonia-oxidizing organisms to dark DIC- fixation was determined via addition of nitrapyrin (5 mg L-1 final concentration) to dark bottles. Nitrapyrin is known to inhibit the activity of ammonia-oxidizing archaea and bacteria (e.g. [15]). The entire volume was filter concentrated onto 0.2 um polycarbonate filters and inorganic 14C incorporation was determined by standard liquid scintillation spectrometry as described above. 75 Figure 1. Rates of DIC-fixation measured in (a) East Lobe Lake Bonney during December 2008, (b) Lake Fryxell during December 2008, and (c) Lake Fryxell during December 2009. Groups of bars are centered on the depth of sample collection, which is indicated to the right of each group. Representative profiles of photosynthetically active radiation (PAR) and dissolved oxygen are given for December in panels “a” and “b”. The difference between “dark DIC-fixation” and “nitrapyrin insensitive” can be attributed to DIC-fixation by ammonia- oxidizing bacteria. Error bars show the propagated standard error. The Lake Bonney ice cover was 3.5 m thick and the Lake Fryxell ice cover was 3.9 m thick (2008) and 4.1 m thick (2009) at the time of sampling. Photoautotrophy was the dominant pathway for DIC-fixation at surface and mid-depths in both lakes (6 m and 10 m FRX and 6 m and 12 m ELB), with the highest rates occurring in the 10 m FRX sample (Fig. 1). The majority of photoautotrophic DIC-fixation in FRX occurred in the small (0.2 – 3.0 um) size fraction dominated by small prokaryotes and pico-eukaryotes, whereas the majority of DIC-fixation in ELB occurred in the > 3 um size fraction. Higher rates of photoautotrophic DIC-fixation in 2009 may be the result of the higher PAR levels used in the incubator compared to the in situ PAR in 2008 (Table 1, Fig. 1; [16]. 76 LAKE DEPTH (m) TOTAL DIC- FIXATION DARK DIC- FIXATION DARK % TOTAL % NITRA ELB (2008) 6 0.98 0.04 3.8 - 13 1.1 0.10 8.3 - 20 0.30 0.09 30 - FRX (2008) 6 1.5 0.27 17 - 10 7.6 1.5 20 - 12 3.3 2.1 62 - FRX (2009) 6 4.8 0.45 9.1 63 10 12 1.4 11 30 12 1.9 0.25 13 N.S Table 1. Dark DIC-fixation (µg C L-1 d-1) as a percentage of total DIC-fixation (photoautotrophy + chemolithoautotrophy; µg C L-1 d-1). Size fractionated samples (2008) were summed to report total and dark DIC-fixation. Nitrapyrin sensitive dark DIC-fixation (% NITRA) is the potential contribution of ammonia-oxidizing bacteria/archaea. “-” = not measured. N.S. = No significant change versus unamended dark DIC-fixation (t-test, p < 0.05). See text for experimental details. The small size fraction dominated dark DIC-fixation in both lakes and accounted for 9% to 62% of total (sum of light and dark) primary production in FRX, and from 4% to 30% in ELB (Table 1). The highest rates of dark DIC-fixation were associated with the oxyclines (and chemoclines) of both lakes (13 m in ELB and 10 m in FRX), accounting for up to 20% of light DIC-fixation at these depths. Maximum photoautotrophic inorganic DIC-fixation and heterotrophic activity [9] was also associated with the oxycline. Photoautotrophic and chemolithoautotrophic metabolism in this zone of the lakes have been shown to be supported by the upward diffusion of inorganic S, N and P [14, 17]. Previous work indicated that FRX and ELB contain active populations of ammonia-oxidizing bacteria [12, 13, 18]. We treated samples with nitrapyrin, which 77 inhibits the conversion of ammonia to hydroxylamine, the initial step of ammonia- oxidation [19], to determine the relative contribution of ammonia-oxidizers to DIC- fixation in FRX in 2009. A majority of dark C-fixation (63%) at 6 m was sensitive to nitrapyrin (Table 1). The proportion was lower at 10 m (30%), and the effect of nitrapyrin was insignificant at 12 m. This decrease with depth likely represents the diminishing dissolved oxygen levels through this zone; oxygen is required for the aerobic oxidation of ammonium. LAKE DEMAND SOURCE BALANCE Heterotrophy (kg C y-1) Photoautotrophy (kg C y-1) Chemolithoautotrophy (kg C y-1) kg C y-1 Fryxell 2200 1800 6000 5600 East Lobe Bonney 1400 1400 1100 1100 Table 2. Carbon balance for Lake Fryxell and East Lobe Lake Bonney. Annual heterotrophic carbon demand (bacterial production+bacterial respiration; heterotrophy), extracellular release by phytoplankton based on modeled photosynthetic primary production (photoautotrophy), and chemolithoautotrophy (dark DIC-fixation). The balance represents the difference between the sum of photoautotrophic and chemolithoautotrophic fixation sources minus heterotrophic carbon demand. See supplemental methods for calculations and data sources. The extended periods of darkness in these high latitude lakes produce water column photosynthesis:respiration ratios < 1 [2], yielding organic carbon deficits of thousands of kilograms of carbon per year [20]. We compared annual heterotrophic carbon demand to new organic carbon production from chemolithoautotrophy (dark DIC- fixation) and photoautotrophy (see supplemental methods) in FRX and ELB to determine the degree to which sources of organic carbon production balance organic carbon 78 demand. Carbon production via phoautotrophic activity is equal to the demand from heterotrophic sinks in ELB, but not FRX (Table 2). However, the contribution of chemolithoautotrophic inorganic C-fixation brings the ratio of total inorganic carbon fixation to heterotrophic carbon demand to >1 (1.7) in ELB and in FRX (3.5), balancing the carbon budget for these lakes. In FRX, we can attribute 29% of the new production to inorganic carbon fixation by ammonia-oxidation. Much like has been suggested for Antarctic marine waters (e.g. [21, 22]), new carbon production via chemolithoautotrophy may supply an important source of new carbon to these ice-covered low light environments. Acknowledgements We thank the 2008-2009 and 2009-2010 McMurdo LTER limnology teams for assistance with sample collection and processing and Alexander Michaud and Pamela Santibañez for comments on the manuscript. This work was funded by NSF grants OPP-1115254, OPP-1340292, and OPP-7460252 to JCP. TJV received support from an American Association of University Women Dissertation Fellowship and a Montana Space Grant Consortium Graduate Fellowship. 79 Supplemental Methods The carbon balance was determined by subtraction of heterotrophic carbon demand from total DIC-fixation by photoautotrophs and chemolithoautotrophs in the trophogenic zone of each lake (5 – 12 m for Lake Fryxell, and 4.5 – 20 m for East Lobe Lake Bonney). Heterotrophic (bacterial and archaeal) production measurements (BP), determined via the incorporation of 3H-thymidine into biomass and converted to units of carbon, were compiled for spring (September and October 1995; [9]) and summer and autumn (November 2007 – April 2008; [10]). Where multiple measurements were made in a single month (February, March, and April 2008), monthly averages were used. Winter- time values, which have never been measured, were estimated by averaging late autumn (April 2008) and early spring (September 1995) values. Rates (mgC m-3 d-1) were determined either experimentally or by interpolating every half meter between experimentally measured values and multiplied by the volume (m3) of each half meter layer. The mg C d-1 for each layer were summed to determine a total for the trophogenic zone. Total monthly masses of carbon from bacterial production (January – December) were summed to determine the annual total. Heterotrophic (bacterial and archaeal) respiration (BR) was determined according the relationship used for Lake Fryxell and Lake Bonney [9], originally derived by [23]: BR = 3.7 X BP0.41 Total heterotrophic carbon demand was determined as the sum of BR and BP. 80 Dark inorganic carbon-fixation (chemolithoautotrophy) was assumed to be unaffected by the annual light cycle and remain consistent throughout the year. Dark DIC-fixation also includes cellular anaplerotic replenishment reactions. Values measured from dark bottle incubations (minus killed controls) were integrated over depth and converted to total mass within the trophogenic zone as described for BP. Annual photosynthetic primary production (kg C) was obtained by fitting a hyperbolic tangent model to depth and volume integrated water column productivity rates and 10 m PAR measured at 20 minute intervals throughout the year [2]. The values reported are averages of nine years between 1995 and 2007 for FRX and seven years for ELB. Extracellular release was calculated as 25% of photosynthetic primary production [20]. 81 References 1. Priscu JC, Priscu LR, Howard-Williams C, Vincent WF (1988) Diel patterns of photosynthate biosynthesis by phytoplankton in permanently ice-covered Antarctic lakes under continuous sunlight. J Plankton Res 10:333–340. doi: 10.1093/plankt/10.3.333 2. Priscu JC, Wolf CF, Takacs CD (1999) Carbon transformations in a perennially ice-covered Antarctic lake. 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Priscu JC (1990) Dynamics of ammonium oxidizer activity and nitrous oxide within and beneath Antarctic sea ice. 62:37–46. 83 22. Williams TJ, Long E, Evans F, et al. (2012) A metaproteomic assessment of winter and summer bacterioplankton from Antarctic Peninsula coastal surface waters. ISME J 6:1883–1900. doi: 10.1038/ismej.2012.28. 23. del Giorgio PA, Cole JJ (1998) Bacterial growth efficiency in natural aquatic systems. Annual Review of Ecology and Systematics 29:503–541. doi: 10.1146/annurev.ecolsys.29.1.503 84 CHAPTER FOUR A MICROBIOLOGICALLY CLEAN ACCESS STRATEGY FOR ACCESS TO THE WHILLANS ICE STREAM SUBGLACIAL ENVIRONMENT Contribution of Authors and Co-Authors Manuscript in Chapter 4 Author: John C. Priscu Contributions: Conceived the study, oversaw the study, obtained funding, and wrote the manuscript. Co-Author: Amanda M. Achberger Contributions: Conducted tests, performed lakewater pasteurization experiments, contributed to manuscript. Co-Author: Joel Cahoon Contributions: Analyzed data to determine system flow. Co-Author: Brent Christner Contributions: Conceived the study, oversaw the study, obtained funding, contributed to manuscript. Co-Author: Robert Edwards Contributions: Oversaw the study, managed the project, assisted with experimental design, assisted with sample collection, assisted with data analysis and manuscript preparation. Co-Author: Warren L. Jones Contributions: Analyzed data to determine system flow. Co-Author: Alexander B. Michaud 85 Contribution of Authors and Co-Authors-Continued Contributions: Conducted tests, collected samples, analyzed data, designed surface based cleaning experiments, cultivated bacteria for bacterial viability experiment, determined ATP concentrations, contributed to manuscript. Co-Author: Matthew R. Siegfried Contributions: Contributed hydrological information and map of Subglacial Lake Whillans to the manuscript. Co-Author: Mark L. Skidmore Contributions: Conducted tests, collected samples, analyzed data, contributed to manuscript. Co-Author: Robert H. Spigel Contributions: Performed data analysis for dye test. Co-Author: Gregg W. Switzer Contributions: Assembled filtration system and contributed operational information to manuscript. Co-Author: Slawek Tulaczyk Contributions: Obtained funding, contributed to the manuscript. Co-Author: Trista J. Vick-Majors Contributions: Conducted tests, collected samples, analyzed data, designed surface based cleaning experiments, performed microscopy for bacterial removal and bead removal experiments, calculated statistics for bead removal experiment, contributed to manuscript. 86 Manuscript Information Page John C. Priscu, Amanda M. Achberger, Joel E. Cahoon, Brent C. Christner, Robert L. Edwards, Warren L. Jones, Alexander B. Michaud, Matthew R. Siegfried. Mark L. Skidmore, Robert H. Spigel, Gregg W. Switzer, Slawek Tulaczyk, Trista J. Vick-Majors Antarctic Science Status of Manuscript: ____ Prepared for submission to a peer-reviewed journal ____ Officially submitted to a peer-review journal ____ Accepted by a peer-reviewed journal _X__ Published in a peer-reviewed journal Cambridge Journals In Volume 25, 637-647, 2013 Reused according the Cambridge University Press License Policy. License Number: 3752630339939 John C. Priscu, Amanda M. Achberger, Joel E. Cahoon, Brent C. Christner, Robert L. Edwards, Warren L. Jones, Alexander B. Michaud, Matthew R. Siegfried, Mark L. Skidmore, Robert H. Spigel, Gregg W. Switzer, Slawek Tulaczyk and Trista J. Vick- Majors, A microbiologically clean strategy for access to the Whillans Ice Stream subglacial environment, Antarctic Science 25(5): 637-647, Reproduced with permission. http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 Antarctic Science 25(5), 637–647 (2013) & Antarctic Science Ltd 2013 doi:10.1017/S0954102013000035 A microbiologically clean strategy for access to the Whillans Ice Stream subglacial environment JOHN C. PRISCU1, AMANDA M. ACHBERGER2, JOEL E. CAHOON3, BRENT C. CHRISTNER2, ROBERT L. EDWARDS1, WARREN L. JONES3, ALEXANDER B. MICHAUD1, MATTHEW R. SIEGFRIED4, MARK L. SKIDMORE5, ROBERT H. SPIGEL6, GREGG W. SWITZER1, SLAWEK TULACZYK7 and TRISTA J. VICK-MAJORS1 1Department of Land Resources and Environmental Science, Montana State University, Bozeman, MT 59717, USA 2Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA 3Department of Civil Engineering, Montana State University, Bozeman, MT 59717, USA 4Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093, USA 5Department of Earth Science, Montana State University, Bozeman, MT 59717, USA 6National Institute of Water and Atmospheric Research Ltd, Box 8602, Christchurch, New Zealand 7Department of Earth and Planetary Sciences, University of California, Santa Cruz, Santa Cruz, CA 95064, USA jpriscu@montana.edu Abstract: The Whillans Ice Stream Subglacial Access Research Drilling (WISSARD) project will test the overarching hypothesis that an active hydrological system exists beneath a West Antarctic ice stream that exerts a major control on ice dynamics, and the metabolic and phylogenetic diversity of the microbial community in subglacial water and sediment. WISSARD will explore Subglacial Lake Whillans (SLW, unofficial name) and its outflow toward the grounding line where it is thought to enter the Ross Ice Shelf seawater cavity. Introducing microbial contamination to the subglacial environment during drilling operations could compromise environmental stewardship and the science objectives of the project, consequently we developed a set of tools and procedures to directly address these issues. WISSARD hot water drilling efforts will include a custom water treatment system designed to remove micron and sub-micron sized particles (biotic and abiotic), irradiate the drilling water with germicidal ultraviolet (UV) radiation, and pasteurize the water to reduce the viability of persisting microbial contamination. Our clean access protocols also include methods to reduce microbial contamination on the surfaces of cables/hoses and down-borehole equipment using germicidal UV exposure and chemical disinfection. This paper presents experimental data showing that our protocols will meet expectations established by international agreement between participating Antarctic nations. Received 20 August 2012, accepted 25 November 2012, first published online 28 March 2013 Key words: clean access, environmental stewardship, hot water drilling, subglacial aquatic environments Introduction Recent discoveries of life under the thick ice sheet covering Antarctica have radically changed our view of the interior of the Antarctic continent. Subglacial exploration presents considerable challenges to the way we conduct science in an atmosphere of increasingly stringent environmental concerns (Priscu 2002, Priscu et al. 2003). Priscu et al. (1999) and Karl et al. (1999) were the first to show that there was microbial life in water from Subglacial Lake Vostok that had accreted to the bottom of the ice sheet. Since these seminal reports, others have confirmed the presence of microbial life both within and beneath Antarctica’s ice sheet (e.g. Christner et al. 2006, Priscu et al. 2007, Lanoil et al. 2009). As such, the basal zones of ice sheets are now thought to harbour active microbial ecosystems and our view of the extent of Earth’s biosphere has expanded substantially (Priscu & Christner 2004, Priscu et al. 2008). The pristine nature of Antarctic subglacial ecosystems has led to international concern about environmental and scientific stewardship during their exploration. Following more than ten years of deliberation by international and national committees (e.g. NRC 2007, Priscu et al. 2010), it has become clear that sampling of lakes and sediments beneath Antarctica’s ice sheets must be done in such a way that minimizes microbial and chemical contamination to the environment and to the samples being retrieved. All those involved in this research must recognize that environmental stewardship should take precedence over scientific endeavours. We can expect subglacial lakes to be at the forefront of the Antarctic tradition of melding interdisciplinary and international science in exploring one of the last unexplored frontiers on our planet (Priscu 2002). Largely through the efforts of scientific specialists organized by SCAR, three major subglacial drilling projects are now underway (Priscu et al. 2005). These include the 637 http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 Lake Vostok programme funded by the Russian Antarctic Federation (Lukin & Bulat 2011), the Lake Ellsworth programme funded by the United Kingdom’s Natural Environment Research Council (Ross et al. 2011), and the Whillans Ice Stream Subglacial Access Research Drilling (WISSARD) project, funded by the United States National Science Foundation (Fricker et al. 2011). All of these projects plan to access their subglacial targets within the next few years. Each project has proposed a suite of methods to ensure clean access to their respective systems, the details of which can be found on the Antarctic Treaty System’s website as informational papers or comprehensive environmental evaluations (http://www.ats.aq/ e/ats_keydocs.htm). The WISSARD project proposes to sample subglacial lake Whillans (SLW) during the 2012–13 summer field season and the subglacial environment on the lower Whillans Ice Stream in a region near the grounding zone (GZ) during the 2013–14 summer season. The GZ site will be in an area where drainage from SLW is thought to enter the marine environment beneath the Ross Ice Shelf (Fig. 1). Access to the subglacial environment will be accomplished using a hot water drilling system to penetrate the , 800m of ice overlying the basal water. To ensure that the borehole water Fig. 1. Map showing the location of Subglacial Lake Whillans (green star5 SLW, 84.2378S, 153.6148W) and its predicted flow path to the Ross Ice Shelf (heavy blue line). WISSARD sampling sites include the lake itself and near the end of the flow path close to the grounding line (heavy black line). The outlines of other lakes on the Whillans Ice Stream (yellow outlines) and their predicted flow paths (light blue lines) are also presented. Polar stereographic projection with true scale at -718S; gridlines are spaced at 50 km intervals. Background imagery and grounding line are from the MODIS Mosaic of Antarctica (Scambos et al. 2007); lake outlines from MODIS image differencing (Fricker & Scambos 2009); ice stream boundaries were determined from InSAR velocities (Rignot et al. 2011); hypothesized water flow paths were determined by tracing continuous local hydropotential minima (Carter et al. 2011, Carter & Fricker 2012). Fig. 2. Schematic (left panel, not to scale) showing the flow directions for the field operation of the Whillans Ice Stream Subglacial Access Research Drilling (WISSARD) water treatment system. The filtration and UV-treatment unit will be placed in-line between the borehole water return line and the heater modules. The enlarged inset (right panel) shows the treatment system components as set up for the laboratory tests. See text for details. 638 JOHN C. PRISCU et al. http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 meets the cleanliness requirements promoted by a recent US National Research Council report (NRC 2007) and the scientific integrity of samples (water and sediments) is maintained, the project will include a borehole water treatment system designed to eliminate particles . 0.2mm in diameter, and reduce the concentration of viable microbial cells present in the borehole water (Fig. 2) and attached to deployed equipment and instrumentation. This document presents: i) results from experiments designed to test the efficacy of the filtration system, and ii) disinfection protocols to be employed at all proposed WISSARD drilling sites where biological clean access is a requirement. These results are discussed within the context of the hydrology of SLW. Methods Borehole filtration and germicidal treatment system The filtration component of the water treatment system consists of a 2mm prefilter (pleated polypropylene cartridges, 15 cm diameter x 203 cm long, Champion Process Inc) followed by 0.2mm filtration through a polyethersulfone membrane cartridge (7 cm diameter x 76 cm long, Champion Process Inc). After filtration, the clarified water was subsequently exposed in sequence to two separate ultraviolet (UV) irradiation modules (Glasco UV) that use 185 nm ozone-producing lamps followed by exposure to 254 nm germicidal lamps (wavelengths denote peak spectral output). Based on data supplied by the manufacturer, the tandem filtration strategy is designed to decrease the number of particles . 0.2mm by 99.98% (i.e. 4-log reduction). The two UV systems provide a 185 nm dosage . 40000mW-sec cm-2 and 254 nm germicidal dosage .175 000mW-sec cm-2. Doses of 40 000mW-sec cm-2 are typically used for water disinfection, and depending on the UV transmission properties of the water and the sensitivity of the microorganisms to UV, exposures in this range have been shown to reduce the number of viable cells by 1-log to nearly 6-log (Aquafine Corp). The internal volume of the entire system (filters plus UV units) is 572 l. In a field scenario, the treated water will undergo a final pasteurization step where water temperature will progressively reach 908C before exiting the 152m long heating coils of the drilling unit. In summary, the WISSARD borehole water treatment system will use three complementary technologies to reduce microbial contaminants in the borehole water: i) filtration to remove particles . 0.2mm, ii) UV irradiation (185 and 254 nm), and iii) pasteurization in the boilers of the hot water drill. In field operations, hot water is pumped to the bottom of a 30 cm diameter borehole at high pressure (the diameter will be controlled by adjusting the water heating rate), flows upward in the hole and is pumped from the top, as shown schematically in the left-hand panel of Fig. 2. After water is pumped from the top of the borehole, it flows into a 14 000 l holding tank, then through the filtration system followed by the heater modules before again being pumped to the drilling nozzle at the bottom of the borehole. The drilling speed is expected to be 1mmin-1 through most of the ice and the filtration system will be run continuously during drilling operations. The liquid water in the hole- drill-treatment system thus forms a recirculating system, with a liquid volume that increases as drilling proceeds. A 1900 l insulated melt tank will provide start-up water for the field system by melting snow using heated glycol pumped through a series of heat-radiating immersion plates that form a recirculation system. The components of the treatment system are illustrated in the right-hand panel of Fig. 2 as set up for laboratory testing. The following laboratory tests were conducted to determine the efficacy of the system: 1) Dye study to determine the system flow characteristics. 2) Fluorescent micro-bead (1–5mm) and microbial cell removal. 3) Silt (1.2–63mm particle size) removal. 4) Effect of UV exposure on viability of a model bacterium (Escherichia coli strain K-12). 5) Lakewater test to examine the filtration efficiency and disinfecting properties of the UV lamps on populations of natural aquatic microorganisms. 6) Lakewater pasteurization test to assess the influence of the heat generated by the hot water drill on microbial viability. Dye test A dye test was performed to examine the hydraulic residence-time characteristics of water in the treatment system. The system was filled with water containing fluorescent Rhodamine WT (water tracing) dye at a concentration of , 9mg l-1, and then flushed with dye-free tap water at 95 lmin-1 for 25min. The flow rate of 95 lmin-1 is within the range of flow to be used during actual drilling operations. At this rate, the treatment system has a mean residence time (volume 572 l divided by a flow rate of 95 lmin-1) of 6min. The test duration of 25min therefore corresponds to 4.2 residence times. During the course of the test, discrete samples were collected to examine the dilution of the dye at port 7. Data from port 7 represents the integrated flow characteristics of the entire system (filter plus UV lamp canisters). All fluorescence measurements were made on a calibrated Turner Model 112 fluorometer fitted with a C5-60 excitation filter, a C56 emission filter, and F4T-4 lamp. Bead removal experiment Fluorescent beads (manufacturer nominal size range: 1–5mm, mean5 2.2mm) were added to the feed tank SUBGLACIAL CLEAN ACCESS 639 http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 containing 1325 l of tap water to a final concentration of , 2 x 105 beads ml-1 and mixed thoroughly. The bead mixture in the feed tank was then pumped through the filtration and UV canisters (the UV lamps were off for this test) at , 94 lmin-1 until 1113 l had passed through the system (, 12min). The filtered water was sent to the drain (i.e. it was not recirculated through the system). Samples were then collected in clean quadruplicate vials from the source tank and from ports 1, 2, and 4 along the flow path (Fig. 2). The samples were vortexed vigorously for , 30 s and prepared for microscopic enumeration by filtering 10ml of sample onto a black polycarbonate 0.2mm filter. A Nikon 80i microscope with a Nikon B2-A filter cube was used to enumerate all samples. Percent efficacy of bead removal from each port was calculated as: ððGeometric mean bead count in the tank -Geometric mean bead count at a selected portÞ= Geometric meanbead count in the tankÞ  100; where the geometric mean5 the Nth root of the product of N replicates. Silt removal experiment Local mineral soil was mixed with ,13 l of tap water and sieved through 63mm mesh to form a silt/clay slurry. The slurry was added to 440 l of water in the holding tank to a final concentration of 0.395 g l-1 and pumped through the 2mm filtration unit at 95 lmin-1. The 0.2mm filter and the UV systems were bypassed in this test. Outflow water from the filtration unit was returned to the holding tank yielding a recirculating system with constant volume. Samples were collected from the tank six times over a 40min period, with a final sample collected 15 hours after the pump was turned on. Additional samples were collected at ports 1 and 2, four times over the 15 hour experiment. All samples were filtered onto Whatman GF/C filters (effective particle retention 1.2mm), dried for 24 hours at 1008C and weighed. Standard particle-size classifications (Vanoni 1975) typically specify a range of particle diameters of 4–62mm for silt, and 0.24–4mm for clay. The Stokes settling velocity for the largest silt particle (62mm) is less than 0.2 cm s-1, and is much smaller than the upward average flow velocity in the 30 cm diameter ice borehole of 2.2 cms-1 corresponding to a flow of 95 lmin-1. Silt entering the ice hole with meltwater during field operations would therefore be maintained in suspension and carried into the filtration unit. Bacterial culture removal and viability test Cultures of Escherichia coli strain K-12 were grown in 25% nutrient broth and added to the holding tank to a final concentration of ,106 cells ml-1. After mixing with 1330 l of water in the holding tank for 30min, triplicate samples were collected from the tank to estimate the bulk bacterial concentration in the test water before it passed through the UV treatment system. The pump was turned on and 227 l was allowed to pass through the UV system in three single pass experiments at flow rates of 19 lmin-1, 76 lmin-1, and 152 lmin-1, with triplicate samples collected from port 7 (post UV treatment) in autoclaved 20ml vials. Flushing the system with 227 l before sampling provided adequate time for the UV lamps to reach full output capacity, and allowed water in the UV system to be replaced five times before samples were taken. This ensured that all of the standing water in the UV canisters (total canister volume5 45 l) was replaced with amended tank water before sampling began. Colony forming units (CFU) from the initial inoculum and each sample were enumerated by standard dilution spread plating on nutrient agar followed by overnight incubation at 378C, and again after two weeks of incubation. The two week incubation allowed assessment of the potential for the bacteria to recover from UV irradiation. The limit of quantification for this method is 300 CFUml-1 for aqueous samples. Lakewater bacterial removal and viability Experiments were conducted on lakewater from the Montana State University campus. Lakewater (760 l) was sieved through a 125mm mesh net to remove large debris and brought to 950 l with tap water in the holding tank. The cell count in the original pond water was 1.90x 106 cells ml-1 and 1.52 x 106 cells ml-1 following dilution in the tank. The UV lamps were switched on for 5min (allowing them to reach full capacity) before recirculating the water through all filters and both UV lamps at 76 lmin-1. Four replicate 10ml samples were collected from the tank over a period of 90min at selected times following pump start-up for bacterial counts and determination of intracellular adenosine triphosphate (ATP) concentration. Bacterial samples were collected in 10ml sterile glass vials, fixed with formalin (5% v/v final concentration), stained using the deoxyribonucleic acid (DNA) dye SYBR GoldTM, and enumerated by epifluorescence microscopy (Lisle & Priscu 2004). Cellular ATP concentration, a proxy for microbial biomass and viability (Karl 1980), was determined by concentrating cells in a 10ml water sample on a 0.2mm acrodisk syringe filter (Pall Corp). Adenosine triphosphate was extracted with the Microbial ATP Kit HS (BioThema Inc), and luminosity was measured with a Glomax 20/20 luminometer. The limit of detection for this method was 10-15mol ATP ml-1 based on two standard deviations above the y-intercept in the ATP standard curve. Lakewater pasteurization test Water collected from a lake on the Louisiana State University campus containing an original cell density of 2.5 x 105 (estimated by SYBR- GoldTM epifluorescence 640 JOHN C. PRISCU et al. http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 microscopic counts) was serially diluted with filtered lakewater to create two identical sets of cell concentrations ranging from 2.5 x104 to 25 total cells ml-1. A set of control dilutions was incubated at , 258C while the other was heated to 858C for 2min to mimic the effect of pasteurization in the boilers of the hot water drill. Following exposure to heat, cell viability was assayed by measuring the respiratory reduction of the tetrazolium dye 2, 3-bis-(2-methoxy-4-nitro-5sulphenyl)-(2H)- tetrazolium-5-carboxanilide (XTT) to formazan (Roslev & King 1993). This tetrazolium salt is reduced by active respiratory electron transport, forming a water soluble formazan dye measureable as absorbance at 490nm and serves as a proxy for cell viability. Surface-based cleaning experiments Triplicate flat stainless steel (2.5 cmx 10 cm) and transparent thermoplastic Poly(methyl methacrylate) (PMMA) (5 cmx 10cm) coupons, representative of materials comprising the down-borehole instrumentation, were contaminated with either E. coli K-12 or the endospore- former Bacillus subtilis. The coupons were then analysed for cell viability following treatment with 3% hydrogen peroxide. Before inoculation with bacteria, all materials were rinsed with sterile Type 1 (18.2MV cm-1) water and autoclaved. The coupons were immersed in cell inocula (1 x 107 cell ml-1 E. Coli, 4 x 106 cell ml-1 B. subtilis) for 1min and allowed to air dry for 5min before being sprayed five times with 3% H2O2 (which completely wetted the surfaces) and allowed to react with the bacteria on the coupons for 1min. Controls consisted of triplicate contaminated coupons not sprayed with H2O2. A 10 cm2 area of all control and H2O2-treated samples were swabbed in triplicate using sterile polyurethane swabs moistened with sterile phosphate-buffered saline (PBS). Swabs were immediately placed into 10ml of sterile PBS where they were sonicated and vortexed to release cells into the buffer. The 10ml PBS solution containing the cells was then serially diluted (five dilutions) into sterile PBS. One-hundred mL of each dilution was spread plated in triplicate onto nutrient agar. The cultures were incubated aerobically at 378C and colonies were enumerated following 18 hours and 72 hours of incubation. The limits of detection for this method, accounting for the area swabbed and the ensuing dilution is 300 CFU cm-2. Results Dye test results A dye test was conducted for 25min (4.1 times the mean residence time of the system) to examine the rate at which the dye decreased in the system following a clean water flush. This is the same method as that used to experimentally determine the residence time distribution for a reactor (Dankwerts 1953), except that the step-change in dye concentration here is negative. Nevertheless, the results yield the same information. Changes in dye concentration from test 2 showed an exponential decrease of dye concentration over time (Fig. 3) and were fitted with Eq. (1): CðtÞ ¼ C1 - ðC1 -C0Þ  ð1- expð- ktÞÞ; ð1Þ where C(t)5 concentration at a given time (t), C15 initial dye concentration at t50, C1 - C05 the change in concentration between time t50 and t5 infinity (C0 represents the final, tap-water concentration), and k5 exponential decay factor, which denotes 1/residence time (1/TR). The fit to the experimental points is excellent (r25 0.99), and as shown in Fig. 3, is very similar to the theoretical dilution curve predicted for the flushing of a continuously stirred tank reactor (CSTR). This can be expressed in non- dimensional form, in terms of the fraction of fluid in the tank that remains to be replaced or flushed out at time t, by: ðCðtÞ -C0Þ=ðC1-C0Þ ¼ 1- expð- t=TRÞ ¼ 1- expð- ktÞ; ð2Þ with parameters C1 and C0 equal to those of the fitted curve, but decay constant k5 1/TR5 0.166 lmin-1. Here TR is the mean residence time (6.0min), computed from Fig. 3. Dye concentrations measured experimentally at Port 7 (filled circles); fitted exponential decay curve (solid line); the curve (dashed line) for a continuously stirred tank reactor (CSTR) with the same initial and final concentrations (C1 and C0) as for the fitted curve, but with the decay constant k computed from the mean residence time (TR5V/Q,5 6.02min, where V5 treatment system volume (572 l) and Q5 flow rate (95 lmin-1), as k5 1/TR); and the curve (dashed-dot line) for a plug-flow reactor with the same initial and final concentrations (C1 and C0) as for the fitted curve, and residence time TR5V/Q5 6.02min. SUBGLACIAL CLEAN ACCESS 641 http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 the treatment system volume V and the experimental flow rate Q, as TR5V/Q (Dankwerts 1953). Also shown in Fig. 3 is the theoretical flushing curve for plug-flow through the treatment system, which does not provide a good match to the treatment system’s observed mixing and dilution behaviour. This is not surprising, given the complexity of internal flow paths through the various components of the treatment system. The turnover or replacement fractions at various multiples of t/TR can be computed from Eq. (2), showing that one residence time (t/TR5 1) yields a turnover or replacement percent of 63%, and that it requires 3.0 and 4.6 residence times to replace 95% and 99%, respectively, of the fluid in the system. This equates to 95% and 99% replacement times for the fluid in the system of 21min and 32min, respectively for the flow of 95 lmin-1. Bead removal experiment Bead concentrations at ports 1 and 2 were not significantly different (P. 0.05) from those in the tank after 1113 l of water had passed through the 2mm filter (Figs 2 & 4). However, bead concentration decreased by almost four orders of magnitude at port 4 after the sample water had passed the 0.2mm filter (P, 0.05). The efficacy of bead removal by the 2mm filter was essentially zero (there was no significant difference in bead concentration between the tank and samples a ports 1 and 2) while the 0.2mm filter had a removal efficiency of . 99%. Silt removal experiment The recirculation test using the 2mm filter only showed that suspended sediment in the holding tank decreased exponentially from 0.4–0.01 g l-1 over 40min at a flow rate of 95 lmin-1 (Fig. 5a). The residence time of the system, calculated as 1/k from the fitted equation in Fig. 5a was 7.3min. Both the shape of the removal curve and the value computed for the e-folding removal time closely match the flushing behaviour of the dye study. By analogy with the flushing described by Eq. (2), 95% and 99% of the sediment in the system would be removed in 22 and 34min, respectively, under the experimental conditions. Data from the inlet (port 1) and outlet (port 2) from the 2mm filter system, showed that the dry weight of the sediment particles (63–1.2mm in diameter) increased during the first 4min interval after the pump was turned on, then dropped to background levels in the tank (Fig. 5b). These data indicate rapid mixing before filtration (port 1 data) and that a large number of particles were not retained in the initial pass through the filter. After 15 hours, no silt and clay particles were detectable (detection limit5 0.002 g l-1) in samples from the tank, Fig. 4. Fluorescent beads counted from the tank and selected sample ports after filtering 1113 l through the system. The flow rate was , 94 lmin-1. Error bars5 standard deviation (SD) (n5 4). Fig. 5. a. Sediment concentration in the tank over time, and b. in the tank and ports 1 and 2 at specific times up to 15 hours. Water was recirculated through the tank during this experiment, passing only through the 2mm filter, at a flow rate of 95 lmin- 1 . 642 JOHN C. PRISCU et al. http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 port 1, or port 2 (Fig. 5b). The final sampling time at 15 hours is 75 times the theoretical hydraulic retention time of the system. UV exposure and cell viability Circulation of the E. coli suspension through the 185 nm and 254 nm UV irradiation modules reduced the number of viable E. coli cells in the tank (initial concentration of 7.2 x 106 CFU ml-1) to below the methodological limit of quantification (, 300 CFU ml-1) on a single pass through the UV lamps at flow rates of 19, 76 and 152 lmin-1, which represents 2.7, 0.68 and 0.34min of UV exposure as the sample passed through the UV system (based on a UV canister volume of 52 l). After two weeks of incubation at room temperature (, 228C), the CFUs on the plates remained below 300 CFU ml-1, providing evidence that the bacteria were killed by the UV treatment as opposed to incurring a level of sub-lethal damage that allowed them to remaining viable and recover. Lakewater bacterial removal and viability Viable lakewater bacteria within the tank were either physically removed or their DNA was irreversibly damaged by UV radiation during a 90min experiment, reaching levels similar to that observed in procedural blanks by the end of the experiment (Fig. 6a). Based on the 76 lmin-1 flow rate used in this experiment and the exponential curve fit shown in Fig. 6a, 99% of all bacteria were removed or killed following 78min of run time (51min for 95% removal). Adenosine triphosphate measurements from the tank over the course of the experiment revealed that cellular ATP decreased by three orders of magnitude (to the limits of detection of the ATP method) following 15min of circulation (Fig. 6b). Based on the exponential model used to fit the data, 99% of ATP-containing cells were eliminated from the system (through a combination of cell removal by the filters and decomposition by the UV lamps) in 27min (18min for 95% removal) at the 76 lmin-1 flow rate used in the experiment. The almost three times greater reduction in ATP concentration relative to total cell number indicates that intracellular ATP was actually destroyed by the system, presumably by the UV lamps and Fig. 6. a. Pond water cell, and b. adenosine triphosphate (ATP) concentrations in the tank at a flow rate of 76 lmin-1 with the samples passing through both filter systems and both UV lamps. Each time point represents the mean and standard error (s.e.) (n5 3). Cell concentrations and ATP levels were below levels of detection after 60min and 15min, respectively. Fig. 7. Respiratory potential, detected as tetrazolium dye 2, 3-bis-(2-methoxy-4-nitro-5sulphenyl)-(2 H)-tetrazolium-5- carboxanilide (XTT) reduction to formazan dye (measured as absorption at 490 nm) of pasteurized (858C for 2min) lakewater samples relative to unpasteurized lakewater controls (no heat treatment) following 21 hours of incubation with XTT. The data represent a dilution series made from the original lakewater sample. Bars represent mean and 1 SD (n5 3). Asterisks denote that pasteurization significantly (P, 0.001) reduced cell viability in all dilutions. SUBGLACIAL CLEAN ACCESS 643 http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 associated ozone. The reduced levels of intracellular ATP could also be the result of the synthesis of ATP-dependent enzymes used to repair UV damaged cells. Lakewater pasteurization test The pasteurization experiment (858C for 2min) conducted on lakewater showed that heating to the temperatures expected in the boiler of the hot water drill (at least 858C) reduced respiration potential (XTT reduction to formazan dye) of the organisms significantly (P, 0.001) at cell densities ranging from 25–25 000 cell ml-1 (Fig. 7). These results indicate that the boilers in the hot water drill can produce up to a 2-log reduction in the number of viable cells. Surface cleaning experiments Following experimental contamination with E. coli and B. subtilis, the stainless steel coupons averaged ( ± SD) 8.7 x 104 ± 8.6 x 104 CFU cm-2 and 7.2 x 104 ± 4.0 x 104 CFU cm-2, respectively. The averages ( ± SD) for these same organisms on the plastic coupons were 6.2 x 105 ± 1.8 x 105 CFU cm-2 and 4.0 x 105 ± 3.9 x 105 CFU cm-2, respectively. A one-way analysis of variance (ANOVA) followed by a multi-comparison test revealed that a significantly (P, 0.001) greater proportion of E. coli and B. subtilis adhered to the plastic coupons per unit area than the stainless steel coupons. No significant difference (P. 0.05) in adherence was evident between the test organisms. Following treatment with H2O2, plate counts after 18 hours and 72 hours of incubation were below the limits of quantification (300 CFU cm-2; i.e. no colonies formed), indicating a 2 to 3-log reduction in the concentration of viable cells. Discussion A flow rate of 95 lmin-1 and a total volume for the entire treatment system (filtration plus UV components) of 572 l yields a mean retention time of 6min, which represents the time when 63% of the water in the system is replaced for a CSTR. Timescales for the system for 95% and 99% water replacement are 21min and 32min, respectively, at this flow rate. Results from the dye study yielded a dilution curve for the treatment system that was very similar to that for a CSTR. Moreover, the filtration-disinfection-heater- borehole forms a recirculating system, whereby water in the borehole continually recirculates through the treatment system as the borehole volume increases during drilling operations (i.e. the borehole is deepened). The expected drilling rate of , 1mmin-1 (producing an increase in the water volume in a 0.3m diameter borehole at a rate of 71 lmin-1) is slower than the upward velocity of water in the 30 cm diameter borehole (, 1.3mmin-1 at a flow rate of 95 lmin-1). Hence, water in the borehole is being pumped through the filtration system at a faster rate than the liquid volume of the borehole is increasing. The nature of mixing of water in the borehole itself is uncertain. Assuming an ice borehole depth and diameter of 800m and 0.3m, respectively (yielding a borehole volume of 57m3), and a system pumping rate of 95 lmin-1, the mean residence time for borehole water would be ten hours. This would be the actual time for complete replacement of the water in the borehole if plug-flow results from water being pumped into the bottom of a relatively smooth borehole and removed from the surface at the same rate where it enters the filtration/UV system and boilers before being returned to the bottom of the borehole. Although flow in the actual borehole will be complicated by buoyancy effects (it is likely to be turbulent; Reynolds number , 4500), its mixing and replacement-time characteristics will resemble plug-flow much more closely than that of a CSTR (Dankwerts 1953). Hence, it is reasonable to assume that the timescale for complete replacement of borehole water will be of the same order as the mean residence time (ten hours), rather than that given for a CSTR (i.e. 4.6 times the mean residence time for 99% replacement). As the heated water enters the bottom of the borehole, it will mix with newly melted water. The mixed water will start to flow upward in the borehole to maintain continuity with the rate at which water is being withdrawn at the surface for treatment and reheating. The question of how much, and how often, water in the borehole is treated is difficult to answer, because of this mixing, the complicated flow in the borehole, and the fact that the borehole volume grows with time. If the filtration system is run continuously during borehole drilling, as planned, the water volume in the borehole will be much less than 57m3 during the early stages of drilling when the borehole depth is considerably shallower than 800m, and borehole water originally near the surface will be passed through the water treatment system many times before the subglacial environment is entered. However, as the hole becomes very deep, a point will be reached beyond which there is not enough time for water melted at the bottom of the hole to reach the surface before the hole is melted to the base of the ice. Importantly, all of the heated water introduced into the bottom of the borehole will have passed through the treatment system at least once as the borehole reaches its maximum depth of , 800m. Based on our experimental results with sediment removal (Fig. 5b), one pass through the 2mm filter of the filtration system alone will not eliminate all of the sediment particles in the borehole, particularly if the sinking rate of sediment particles (. 63mm) exceeds the upward velocity of water through the borehole. However, the UV component of the system, which was shown to reduce E. coli by 4-log units following a single pass through the system at 95 lmin-1 and to reduce cellular ATP levels to below the limits of detection (10-15mol ATP ml-1) 644 JOHN C. PRISCU et al. http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 following 15min at 76 lmin-1, should significantly reduce the number of viable microbial cells. Hence, both systems working in tandem will effectively eliminate most of the relatively buoyant particles, including cells, in the system and kill a majority of the cells as melting proceeds from the surface to the bottom of the ice stream (, 800m). Our pasteurization tests showed that a lake microbial assemblage exposed to 858C for 2min significantly (P, 0.001) reduced the respiratory potential of the assemblage, resulting in up to a 2-log reduction in the number of viable cells. These conditions are similar to the temperatures and retentions of the boilers on the hot water drill. As such, the heating system alone on the WISSARD drill should kill the vast majority of bacteria that pass through it, particularly if they are not heat tolerant (e.g. not endospore-forming bacteria), which we expect to be the case for most of the bacteria that exist in the ice we will melt to generate the borehole water (e.g. Christner et al. 2008, Priscu et al. 2008). In summary, our test results indicate that one passage of a volume of water through the filtration component of the system will reduce the total number of microbes by more than 4-log units. Any cells remaining in the water after filtration will be reduced another 3.5-log units by combined effect of UV irradiance and pasteurization. To place this into context, if we assume an initial borehole cell concentration of 106 cell ml-1 (equivalent to coastal ocean waters), the system as tested would be capable of reducing the microbial burden to , 100 cells ml-1 in the borehole water after one borehole residence time (, ten hours at a 95 lmin-1 pumping rate). Because we will treat all down borehole equipment with 3% H2O2, which produced a 2 to 3-log reduction in our endospore and non-endospore forming test organisms, any cellular contamination introduced into the drilling and water treatment system during our operation is expected to be minimal. In addition to the safeguards discussed above, all hoses and cables on the WISSARD drilling system will pass through a clamp-on high pressure hot water cleaning system followed by a UV collar as they are deployed down the borehole. The UV collar will provide a disinfection dose of 40 000mW-sec cm-2 at a maximum cable/hose deployment rate of 60mmin-1. In addition to the filtration/UV system and decontamination protocols that will be implemented during borehole drilling, the hydraulic nature of the lake itself provides a safeguard against permanent contamination by drilling procedures. Subglacial Lake Whillans belongs to the category of active subglacial lakes, which are located and defined on the basis of anomalously fast ice surface changes attributed to large water volume fluctuations in subglacial lake basins. Active subglacial lakes were discovered by InSAR (Gray et al. 2005) and subsequent studies using satellite altimetry have demonstrated that there are . 120 such lakes in Antarctica (Wingham et al. 2006, Fricker et al. 2007, Fricker & Scambos 2009, Smith et al. 2009). Active subglacial lakes are different from many previously described Antarctic subglacial lakes (e.g. review in Siegert et al. 2005) in a number of important ways. The defining feature of active subglacial lakes is that they actively fill and drain, undergoing volume changes large enough to cause localized, anomalously high (up to several metres) deformation of the ice surface above them that can be detected by a space borne instrument (e.g. Fricker et al. 2007). They also tend to occur within fast flowing parts of the Antarctic ice sheet (ice streams and outlet glaciers) as opposed to non-active lakes that are concentrated near ice divides, where ice flow is sluggish (Siegert et al. 2005, Smith et al. 2009). The active subglacial lakes discovered thus far tend to have a smaller area, and presumably volume, than their non-active counterparts. This may be largely due to observational biases because non-active lakes have been traditionally identified from airborne ice-penetrating radar surveys, which are more likely to encounter large subglacial lakes than small ones (e.g. Siegert et al. 2005). At the same time, active subglacial lakes have only been mapped using anomalous ice surface elevation changes occurring within the last decade, when new airborne/satellite altimetry and InSAR data provided sufficiently precise measurements of ice surface topography to reveal their existence. Hence, detection of such lakes is more likely if they have relatively small water residence time (i.e. high water throughput rates combined with low total basin volume). Analyses of satellite data by Fricker et al. (2007) and Fricker & Scambos (2009) indicated that SLW has an area of 59 km2 ± 12 km2 and it has experienced two drain-fill cycles between 2003 and 2009. These authors estimated that each of the drain-fill cycles resulted in lake volume fluctuations of about 0.1 km3 of water. The WISSARD surface geophysics team completed a high-density survey focused on the SLW basin in the 2010–11 field season. Improved constraints on ice geometry indicate that the subglacial water volume change during fill-drain cycles of SLW is 0.15 km3 (unpublished data), which is 50% greater than the previous estimates of Fricker et al. (2007) and Fricker & Scamos (2009). Geophysical estimates of the depth of the lake at the time of the field survey, when the lake was drained, reveals that it is , 8m (Horgan et al. 2012, Christianson et al. 2012). The relatively shallow depth of the SLW basin is consistent with its location within a region of gently undulating basal topography and low ice surface slope (e.g. Shabtaie et al. 1987). Given the area of the lake basin, the lake volume can be estimated to be , 0.5 km3. Consequently, it would take fewer than three to four fill-drain cycles to exchange the total lake volume. Given that SLW has undergone two complete fill-drain cycles over a six year period, we estimate a water residence time for the lake of the order of ten years, or less. SUBGLACIAL CLEAN ACCESS 645 http://journals.cambridge.org Downloaded: 20 Aug 2014 IP address: 153.90.122.211 The decadal scale flushing time for SLW is nearly 100 times faster than that predicted for Subglacial Lake Ellsworth (SLE) (750 years; Siegert et al. 2012) and about 1000 times faster than the water residence time estimated for Subglacial Lake Vostok (, 10 000 years; Bell et al. 2002). Clearly, SLW is fundamentally different from SLE and Vostok, which contain large water volumes and do not show evidence of significant water volume changes over the period of instrumental observations. Subglacial Lake Whillans can be considered a small temporary storage basin for water draining beneath the Whillans Ice Stream and any disturbance resulting from drilling and sampling operations should have a minor and transitory impact. Acknowledgements We are grateful to the Department of Civil Engineering for allowing us use of the Hydraulics Laboratory at MSU. P.W. Adkins assisted with the experiments and laboratory work and R. Powell commented on the manuscript. This work was supported by NSF-OPP grants 0838933 to JCP, 0838941 to BC and 0839142 to ST as part of the Whillans Ice Stream Subglacial Access Drilling (WISSARD) Project. We appreciate the support and contributions of the WISSARD science team, and drilling and operational support contractors in accomplishing our goals. 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Vick-Majors Contributions: Collected samples, oversaw sample collection, performed microbiological activity assays, performed microscopy, performed excitation-emission matrix spectroscopy, performed water mass analysis, performed particulate carbon and nitorogen measurements, assisted with dissolved nutrient analysis, analyzed data, prepared figures and tables, and wrote the manuscript. Co-Author: Amanda M. Achberger Contributions: Collected samples, extracted DNA and analyzed microbial community structure data, provided bar graph and diversity statistics, and assisted with manuscript preparation. Co-Author: Pamela Santibáñez Contributions: Conducted flow cytometry analyses, performed microscopy, analyzed flow cytometry data, and assisted with manuscript preparation. Co-Author: John E. Dore Contributions: Performed dissolved nutrients analyses and assisted with manuscript preparation. Co-Author: Timothy Hodson Contributions: Collected and analyzed current meter data, assisted with water mass analysis, commented on manuscript. Co-Author: Alexander B. Michaud Contributions: Collected samples, processed samples in the field, assisted with nutrients analysis, assisted with manuscript preparation. 99 Contribution of Authors and Co-Authors-Continued Co-Author: Brent C. Christner Contributions: Conceived the project, provided funding for DNA sequence sample collection and processing, oversaw DNA sequence sample collection and processing, commented on the manuscript. Co-Author: Jill Mikucki Contributions: Collected samples, collected CTD data, provided funding, commented on the manuscript. Co-Author: Mark L. Skidmore Contributions: Collected samples, provided geochemical data, oversaw analysis of DOC samples, assisted with manuscript preparation. Co-Author: Ross Powell Contributions: Oversaw and carried out deployment of current meter, provided current meter data, provided funding, commented on manuscript. Co-Author: W. Peyton Adkins Contributions: Collected and processed samples in the field, assisted with microscopy, commented on manuscript. Co-Author: Carlo Barbante Contributions: Provided geochemical data and commented on manuscript. Co-Author: Andrew Mitchell Contributions: Provided geochemical data and commented on manuscript. Co-Author: Reed Scherer Contributions: Collected sediment samples, performed sediment diatom analyses and assisted with manuscript preparation. Co-Author: John C. Priscu Contributions: Conceived the study, provided funding, assisted with manuscript preparation. 100 Manuscript Information Page Trista J. Vick-Majors, Amanda Achberger, Pamela Santibáñez, John E. Dore, Timothy Hodson, Alexander B. Michaud, Brent C. Christner, Jill Mikucki, Mark L. Skidmore, Ross Powell, W. Peyton Adkins, Carlo Barbante, Andrew Mitchell, Reed Scherer, John C. Priscu Limnology and Oceanography Status of Manuscript: ____ Prepared for submission to a peer-reviewed journal ____ Officially submitted to a peer-review journal _X Accepted by a peer-reviewed journal _ __ Published in a peer-reviewed journal John Wiley & Sons Ltd. January, 2016 Reused with permission: Permission is granted for you to use the material requested for your thesis/dissertation subject to the usual acknowledgements (author, title of material, title of book/journal, ourselves as publisher) and on the understanding that you will reapply for permission if you wish to distribute or publish your thesis/dissertation commercially. You must also duplicate the copyright notice that appears in the Wiley publication in your use of the Material; this can be found on the copyright page if the material is a book or within the article if it is a journal. Permission is granted solely for use in conjunction with the thesis, and the material may not be posted online separately. Any third party material is expressly excluded from this permission. If any of the material you wish to use appears within our work with credit to another source, authorisation from that source must be obtained. 101 BIOGEOCHEMISTRY AND MICROBIAL DIVERSITY IN THE MARINE CAVITY BENEATH THE MCMURDO ICE SHELF, ANTARCTICA The following work is in press at Limnology and Oceanography. Trista J. Vick-Majors1, Amanda Achberger2, Pamela Santibáñez 1, John E. Dore1, Timothy Hodson3, Alexander B. Michaud1, Brent C. Christner2, Jill Mikucki4, Mark L. Skidmore5, Ross Powell3, W. Peyton Adkins2, Carlo Barbante6, Andrew Mitchell7, Reed Scherer3, John C. Priscu1* 1Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USA 2Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA 3Department of Geological and Environmental Sciences, Northern Illinois University, DeKalb, IL, USA 4Department of Biology, Middlebury College, Middlebury, VT, USA 5Department of Earth Sciences, Montana State University, Bozeman, MT, USA 6Institute for the Dynamics of Environmental Processes – CNR, Venice, and Department of Environmental Sciences, University of Venice, Venice, Italy 7Department of Geography and Earth Sciences, Aberystwyth University, Ceredigion, UK *Corresponding author Abstract Ice shelves surround ~ 75% of Antarctica’s coastline and are highly sensitive to climate change; several have recently collapsed and others are predicted to in the near future. Marine waters beneath ice shelves harbor active ecosystems, while adjacent seas can be important areas of bottom water formation. Despite their oceanographic 102 significance, logistical constraints have resulted in few opportunities to directly sample sub-ice shelf cavities. Here, we present the first data on microbial diversity and biogeochemistry beneath the McMurdo Ice Shelf (MIS) near Ross Island, Antarctica. Physicochemical profiles obtained via a 56 m deep borehole through the MIS revealed three vertically layered water masses (Antarctic Surface Water [AASW], Ice Shelf Water [ISW], and modified High Salinity Shelf Water [mHSSW]). Metabolically active, moderately diverse (Shannon diversity from 2.06 to 5.74) microbial communities were detected in the AASW and mHSSW. Heterotrophic bacterial production and dissolved organic matter concentrations were higher (12% to 37% and 24%, respectively) in mHSSW relative to AASW. Chemoautotrophic production was 5.3 and 6.0 nmol C L-1 d-1 in the AASW and mHSSW, respectively. Phytoplankton cells were more abundant and larger in the mHSSW sample relative to the AASW, which indicates sinking of phytoplankton produced in surface waters and, together with southerly flowing currents (0.09 to 0.16 m s-1), horizontal advection of phytoplankton from McMurdo Sound. Advected phytoplankton carbon together with in situ chemoautotrophic production provide important sources of organic matter and other reduced compounds to support ecosystem processes in the dark waters in the ice shelf cavity. Introduction Ice shelves, which form where glaciers leave the land and float on the ocean surface, cover more than 1.5 x106 km2 of coastal ocean in Antarctica (Rignot et al. 2013). Yet, only a few studies have directly measured physical or biological processes beneath 103 Antarctic ice shelves, in the sub-ice shelf marine waters (e.g. Azam et al. 1979; Riddle et al. 2007; Robinson et al. 2010; Carr et al. 2013). The Ross Ice Shelf (RIS) is the most extensive ice shelf on the planet, accounting for about one-third of the surface area of all Antarctic ice shelves (Fox and Cooper 1994; Depoorter et al. 2013). The RIS covers about half of the Ross Sea, an important area of Antarctic Bottom Water formation, which is vital to the global-scale thermohaline circulation (Jacobs et al. 1970; Budillon et al. 2011). The McMurdo Ice Shelf (MIS) comprises the northwestern portion of the RIS, and although they form a unified sub-ice shelf cavity and are often referred to as a single entity, the MIS originates from different source glaciers than the main body of the RIS (Robinson et al. 2010). The RIS front is typically >300 m thick and presents a significant inflow barrier to the sub-ice cavity, whereas the thinner (~20-100 m) MIS front provides an important hydrologic conduit linking the waters of the Ross Sea to the sub-RIS cavity (Robinson et al. 2010). Waters beneath the RIS are also influenced by subglacial water from continental Antarctica, making the sub-ice shelf waters an important intermediate between the open Ross Sea and subglacial outflow from the Antarctic continent (Horgan et al. 2013). The Ross Sea is one of the most biologically productive and best-studied marine regions in Antarctica (Smith Jr. et al. 2012). Multiple studies have documented the onset of rapid phytoplankton growth during spring concurrent with the loss of winter sea-ice cover (Arrigo and McClain 1994; Smith Jr. and Gordon 1997; Arrigo and van Dijken 2004). The annual phytoplankton bloom peaks in surface waters between mid-December and early-January, with chlorophyll a (chl a) concentrations in excess of 10 µg L-1 (Smith 104 Jr. and Gordon 1997). The bloom is dominated initially by Phaeocystis antarctica (Smith Jr. and Gordon 1997) followed by a mixed diatom assemblage (Arrigo 1999). The summer phytoplankton blooms are sustained by high nutrient levels and shallow mixed layers initiated by melting sea-ice (Smith and Comiso 2008). Decomposition of phytoplankton biomass in this region is associated with one of the largest blooms of bacterioplankton recorded, reaching biomass levels of >10 mmol C m-2 (Ducklow et al. 2001). This productive sea comprises the source of the sub-MIS waters, via McMurdo Sound (Robinson et al. 2010). In contrast to the relatively well-studied Ross Sea, sub ice-shelf cavities (marine waters beneath ice-shelves) have received far less attention, largely due to logistical constraints associated with accessing the ocean beneath thick, floating ice. The first oceanographic data from beneath the MIS was collected in 2003 and revealed net transport from McMurdo Sound to the MIS cavity, a diurnal tidal cycle, and evidence for tidally driven melting near the ice shelf front (Robinson et al. 2010). The Ross Ice Shelf Project (in 1977; Clough and Hansen 1979) was the first to make oceanographic measurements (Gilmour 1979; Jacobs et al. 1979; Williams and Robinson 1979) and detect sub-ice shelf biological communities (Azam et al. 1979; Lipps et al. 1979) beneath the RIS (420 m ice thickness) at Station J9, 450 km from the edge of the RIS. Microorganisms in the water column and seafloor sediments at Station J9 metabolized added organic substrates (Azam et al. 1979) and dark carbon dioxide (CO2) fixation (1.5 g C m-2 y-1) was attributed to the activity of nitrifying microorganisms (Horrigan 1981). Underwater camera observations and traps yielded fish and crustaceans (Lipps et al. 105 1979), suggesting the existence of an active food web under the ice shelf, although the benthic community consisted mainly of scavengers. More recently, samples from beneath the RIS and MIS near Ross Island led to the discovery of a new species of sea anemone that embeds in and inhabits the base of the ice shelf (Daly et al. 2013), and provided information on the microbial community composition of marine sediments below this region of the ice shelf (Carr et al. 2013). Underwater camera observations showed that a diverse benthic community, unlike that detected at Station J9 beneath the RIS, exists beneath the Amery Ice Shelf (Post et al. 2014). These studies suggest that sub-ice shelf biota are involved in Southern Ocean biogeochemical processes, but that we have a poor understanding of both the diversity of life and habitats beneath ice shelves. As Antarctic ice shelves are increasingly threatened with collapse induced by changing climate (Joughin et al. 2014, Wouters et al. 2015), the need to develop an understanding of sub-ice shelf biogeochemical processes becomes progressively more important. Studies following the breakup of the Larsen A and B Ice Shelves documented changes in biological CO2 pumping mediated by water column primary production (Bertolin and Schloss 2009) and disruption and succession of benthic communities beneath the newly open water (Domack et al. 2005; Fillinger et al., 2013). Still, a paucity of baseline data limits the context within which the ramifications of ice shelf collapse for biogeochemical processes in the Southern Ocean can be interpreted. Here, we integrate physical and chemical data with the first microbiological data obtained from the sub-MIS water column. Our results identify biogeochemical linkages between the Ross Sea via McMurdo Sound and the dark sub-ice environment beneath the MIS and RIS. 106 Methods Site Description The Ross Ice Shelf stretches ~850 km from the Antarctic coast to the open ocean, with water column depths ranging from ~50 m to ~1000 m (Greischar and Bentley 1980). The MIS comprises the thinnest area of the ice shelf, with ice thicknesses generally <100 m thick at MIS compared to up to >800 m thick near the southern extent of the RIS (most of the RIS is ~200-250 m thick; Griggs and Bamber 2011). A coastal current flows along the front of the RIS until it reaches Ross Island, where some of the water turns north along the Victoria Land coast, and some flows south, through the eastern part of McMurdo Sound and beneath the MIS (Barry, 1988). Our drill site was located on the MIS (77.8902 S, 167.0083 E; Figure 1) near Ross Island, approximately 8 km from the edge of the transition from ice shelf to the seasonal and multi-year fast sea-ice of McMurdo Sound and ~2 km from the ANDRILL HWD-1 site described by Robinson et al. (2010) and Robinson (2004), which we use for comparison in this paper. At the time of sampling, the transition from sea-ice to the open water of McMurdo Sound was approximately 48 km to the north of the sampling site. The 56 m deep borehole (~60 cm diameter) that penetrated into the sub-ice shelf cavity was created with a clean access hot water drill (Blythe et al. 2014, Burnett et al. 2014, and Rack et al. 2014) on 18 December 2012. The sub-ice shelf cavity is defined as the ocean beneath the ice-shelf. The water column depth below the borehole was 872 m from the ice-water interface (928 m from the ice surface) to the seafloor. All depths are reported from the bottom of the ice shelf (i.e. the ice-water interface). MODIS satellite 107 imagery from around the time of sampling (not shown; data available from the U.S. Geological Survey EarthExplorer) indicated that we did not collect samples during the Ross Sea phytoplankton bloom. Figure 1. Sampling site location. Map by Bradley Herried, Polar Geospatial Center. Physical and Chemical Parameters Depth profiles of temperature and conductivity were measured with a SBE 19plusV2 SeaCAT Profiler CTD (Sea-bird Electronics, Inc.) sonde. Potential temperature relative to the surface was derived according to UNESCO equations (IOC, SCOR and IAPSO, 2010). Water masses observed in the study area were characterized according to Stover (2006) and Jacobs and Fairbanks (1985) (see also Celussi et al. 2010) as: (i) 108 Antarctic Surface Water (AASW) had potential temperature above the freezing point with relatively low salinity (34.2 and 34.4); (ii) Ice Shelf Water (ISW) had potential temperature below the surface freezing point and salinities between 34.5 and 34.7, and (iii) modified High Salinity Shelf Water (mHSSW) had potential temperature near the surface freezing point with salinities between 34.6 and 34.8. These water masses were graphically identified using Ocean Data View 4.5.6 (Schlitzer 2013), as shown in Figure S1. Current velocity and direction were obtained with a Contros Oceanline HS-2X series electromagnetic current meter and the appearance of the surface sediments was documented with an attached camera. Discrete water samples were collected through the borehole with a 10 L General Oceanics Niskin bottle (AASW [30 m] sample) and a 5 L Go-Flo bottle (mHSSW [850 m] sample). Both sampling bottles were disinfected with 3% hydrogen peroxide before deployment. The water was decanted immediately through acid leached silicone tubing into appropriate vessels. Samples for dissolved oxygen and dissolved inorganic carbon (DIC) were stabilized on site with a stock solution of sodium azide (1% w/v), potassium iodide (10% w/v), sodium hydroxide (32% w/v) (dissolved oxygen) or chloroform (DIC). All water samples were stored at ~ 4 oC in the dark until return to McMurdo Station (MCM) for processing (~ 4 h). Shallow sediment samples were collected from the top surface of a geothermal probe that penetrated the surface sediments in an operational test, and the resulting data are reported qualitatively. Diatom frustules in fine-grained sediments were processed using standard micropaleontological techniques (e.g. Scherer 1991), using 15% H2O2 and 109 mounted as strewn slides in Norland optical adhesive for light microscopy, and the results presented are qualitative. Samples for DOC and three dimensional spectrofluorometric characterization of organic matter (excitation-emission matrix spectroscopy; EEMS) were decanted into 1% hydrochloric acid and deionized water rinsed fluorinated high density polyethylene (HDPE) carboys (Thermo Scientific, Nalgene, Waltham, MA) and filtered through acid- leached and combusted (>4 h at 450 °C) 25 mm glass fiber filters (GF/F, effective retention size 0.7 µm). The filtrate was collected in acid washed and combusted (>4 h at 450 °C) 125 ml amber borosilicate glass bottles fitted with polytetrafluoroethylene (PTFE) lined caps and stored at 4 °C until analysis. DOC concentrations were determined using a Shimadzu TOC-V Series TOC analyzer following acidification with hydrochloric acid to pH ≤ 2 to remove inorganic carbon as CO2. EEMS were determined with a Horiba Jobin Yvon Fluoromax-4 Spectrophotometer (Horiba, Ltd., Japan) equipped with a Xe light source using a 1 cm path length quartz cuvette. Excitation data were measured every 10 nm from 240 nm to 450 nm, and emission data every 2 nm from 300 nm to 560 nm. Measurements were corrected for background (0.2 µm filtered Milli-Q water) and Raman scattering, and for inner-filter effects using absorbance spectra collected between 190 nm and 1100 nm with a Genesys 10 Series Spectrophotometer (1 cm path length, Thermo Scientific) as described by McKnight et al. (2001). The following indices were derived to reveal the character of the DOC: (i) Fluorescence Index (the ratio of the emission at 470 nm to the emission at 520 nm at an excitation of 370 nm; McKnight et al. 2001, Cory and 110 McKnight 2005), (ii) Freshness Index (the emission at 380 nm divided by the maximum emission between 420 and 435 nm at an excitation 370 nm; Huguet et al. 2009), and (iii) Humification Index (the area of the peak under emission 435 to 480 nm divided by the peak area under emission 330 to 345 nm; Zsolnay et al. 1999). To compare fluorescence intensities in the two samples, standardized maximum fluorescence was calculated as follows: (value-min)/(max-min), where “value” is the maximum fluorescence in the sample, and “max” and “min” are the maximum and minimum fluorescence for the two samples combined. Suspended matter collected on the filters from DOC and EEMS filtration was stored frozen (-20°C) in the dark until acid-fuming (to remove inorganic C) and analysis of particulate organic C (PC) and nitrogen (PN) on a CE Instruments Flash EA 112 (ThermoQuest, San Jose, CA). Dissolved oxygen and DIC were measured in MCM using the azide modification of the mini-Winkler titration and infrared gas analysis of acid sparged samples, respectively, according to the limnological methods used by the McMurdo LTER (http://www.mcmlter.org/data_methods2.htm). Samples for dissolved inorganic nutrients (soluble reactive phosphorus [SRP], nitrate + nitrite [N+N], and ammonium [NH4+] were filtered through combusted GF/F filters into 1% hydrochloric acid leached and deionized water rinsed HDPE bottles and frozen at -20°C until analysis. The concentrations of these compounds were determined colorimetrically according to Strickland and Parsons (1968) and Solórzano (1969). 111 Biological Parameters Chlorophyll a (chl a) concentrations were determined on 200 mL samples filtered onto 25 mm diameter GF/F filters; the filters were stored at -20°C in the dark immediately upon return to MCM. The samples were extracted in 90% acetone (v:v; acetone:water) for ~24 h in the dark at 4 oC and analyzed with a calibrated fluorometer (Turner 10-AU-10) as described by Welschmeyer (1994). Dark carbon fixation (chemoautotrophy) was determined on quadruplicate live and trichloroacetic acid [TCA; ~ 5% final concentration] killed 40 mL samples (Christner et al. 2014). The samples were incubated in the dark in glass bottles filled to the top (no head-space) and capped with PTFE lined caps. Each sample was amended with 14C- bicarbonate (stock solution = 114.41 µCi mL-1) to a final radioactive concentration of 10 µCi mL-1 and incubated in the dark at 4 °C for 94 h. Long incubation times and high specific activity substrates were used in anticipation of low rates of biological activity in the sub-ice shelf cavity, and were similar to those used in other studies aimed at detecting chemoautotrophic carbon fixation (48 h, Horrigan 1981; 168 h, Yakimov et al. 2011). Incubations were terminated by the addition of ice-cold 100% TCA (2.5% final concentration) and size fractionated onto GF/F filters (to capture larger, aggregated and filamentous cells) and 0.2 µm polycarbonate filters (to capture smaller or non-aggregated cells). Filters were placed in 20 mL scintillation vials, acidified with 1.5 ml of 3N HCl and dried at 60 °C. Radioactivity incorporated into cellular carbon retained on the filters was counted on a calibrated scintillation counter after addition of 10 mL Cytoscint ES scintillation cocktail (MP Biomedicals). 112 Heterotrophic bacterial (where “bacterial” can include bacteria and archaea throughout the text) production was determined using [3H]methyl-thymidine incorporation into DNA (Fuhrman and Azam 1982) and [3H]L-leucine incorporation into protein (Kirchman et al. 1985). Samples (1.5 mL; 5 live and 5 TCA killed) were incubated with 20 nM radio-labeled thymidine (specific activity 20 Ci mmol-1) or leucine (specific activity 84 Ci mmol-1) at 4 °C in the dark for ~20 h. Incubations were terminated by the addition of 100% cold TCA (5% final). Following centrifugation, a series of extractions with cold (~4°C) 5% TCA and cold (~4°C) 80% ethanol were performed and the residual pellet was dried overnight at room temperature. One mL of Cytoscint ES scintillation cocktail was added to each vial and the samples were counted on a calibrated scintillation counter. Resulting thymidine and leucine incorporation rates (nM TdR d-1or nM Leu d-1, respectively) at the incubation temperature (4o C) were converted to rates at in situ temperatures (-1.7 to -1.9o C) using energy of activation of 12,600 kcal mol-1 determined from Antarctic lakes (Takacs and Priscu 1998). Temperature corrected rates were converted to heterotrophic bacterial production (BP TdR and BP Leu) using published conversion factors of 2.0x1018 cells mol-1 thymidine (Bell 1993) and 1.42x1017 cells mol-1 leucine (Chin-Leo and Kirchman 1988) and a cellular carbon content of 11 fg C cell-1 (Kepner et al. 1998). Cell specific activity (mg C cell-1 d-1) was determined by dividing heterotrophic bacterial production by bacterioplankton cell abundance (see below) for each sample. Heterotrophic bacterial respiration was measured in the 850 m sample by adding 60 mL of sample water to an autoclaved amber HDPE bottle (Nalgene) followed by the 113 addition of uniformly labeled 14C-L-leucine (specific activity >300 mCi mmol-1; final leucine concentration 60 nM; final activity 0.0180 µCi mL-1) (del Giorgio et al. 2011). Five-milliliter aliquots of the radiolabeled sample were added to autoclaved 25 mL glass side arm flasks (6 live and 6 TCA killed controls; 250 µL of cold 100% TCA). The top of the flask was sealed with a butyl rubber septum holding a small basket containing a folded GF/C filter suspended above the aqueous phase (Christner et al. 2006); the sidearm was sealed with a butyl rubber septum. Following incubation in the dark for 39 h at 2-4 °C, the reactions in live incubations were terminated by injecting cold 100% TCA (final concentration 5%) into the sample through the sidearm which lowered the pH to ≤ 2. β-phenylethylamine (100 µL; Sigma, catalog number P2641) was added to the GF/C filter through the septum with a needle and syringe to trap respired CO2. Killed samples were maintained at 4 oC for five days with occasional gentle swirling to liberate and trap all respired CO2 from the aqueous phase. Cellular 14C incorporation was determined on the liquid fraction following filtration onto 0.2 µm polycarbonate filters. The GF/C and polycarbonate filters were placed in 20 mL scintillation vials followed by the addition of 10 mL of Cytoscint-ES and the 14C activity was determined using a calibrated scintillation counter. Bacterial growth efficiency was calculated using the leucine respiration data (described above) as ((BP Leu)/(BP Leu + Leu respiration)) x100. Leu:TdR ratios are based on molar incorporation rates. Samples for phytoplankton (autofluorescent cells) and bacterioplankton (non- autofluorescent bacteria and archaea) enumeration were preserved with sodium borate buffered formalin (5% v/v) and stored at 4 °C in the dark. Samples were filtered through 114 30 μm mesh using a sterile BD Falcon 12 x 75 mm tube with cell strainer cap to eliminate large particles. Cell density was determined with a PhytoCyt flow cytometer (Turner Designs) at a flow rate of 50μL min-1 and core size of 12 μm. Unstained seawater samples were used for the detection of phytoplankton. Bacterioplankton were enumerated using seawater samples stained with SYBR® Green I (SGI; Molecular Probes; supplied at 10,000X) at a final concentration of 1X original (Marie et al. 2001). Bacterioplankton cells were identified as a distinct population on a density plot of SGI emission-height (excitation 488 nm, emission 515-545 nm) versus forward light scatter-area (FSC-A: 0˚±15˚). High nucleic acid (HNA) and low nucleic acid (LNA) bacterioplankton were identified according to the intensity of their SGI emission (Gasol and del Giorgio 2000; Lebaron et al. 2001; Bouvier et al. 2007). Because FSC-A and SGI emission from small phytoplankton cells may overlap with those of stained bacterioplankton, we also examined small phytoplankton (<2 µm chl-a autofluorescing cells) in stained samples by plotting FSC-A vs. orange fluorescence (excitation 488 nm, emission 565-605 nm), blue laser-dependent red fluorescence (excitation 488 nm, emission >670 nm; chl a) and red fluorescence (excitation 640 nm; emission 650-700). Small phytoplankton cells determined using these criteria were subtracted from the total counts in this region to obtain bacterioplankton cell counts. Bacterioplankton cell counts were verified on selected samples by epifluorescence microscopy of SYBR® Gold- stained cells using the protocol of Lisle and Priscu (2004). Larger phytoplankton cells (>2 µm) were enumerated on a density plot of FSC-A versus chl a using 2 mL of unstained sample. The larger phytoplankton population was 115 divided into subpopulations (a, b, and c) based on density plots of FSC versus chl a. The presence of each distinct population was verified by measuring the mean emission ratios of red fluorescence/chl a and orange fluorescence/red fluorescence. To determine cell sizes, the larger phytoplankton were also examined with an epifluorescence microscope (Nikon Eclipse 80i) equipped with a Retiga-2000R Fast 1394 camera under blue excitation cube filter (excitation 450-490 nm; emission >515 nm) following the method of Hillebrand et al. (1999). Samples were prepared for microscopy by filtering 1.6 ml (30 m sample) and 2.2 ml (850 m sample) through 0.2 µm 13 mm hydrophilic PTFE membrane filters (Millipore, catalog number JHWP013000) and mounting the filters on glass slides with 1:1 (v:v) glycerol/water. Sixty photographs were taken of random fields at 1000x magnification and the diameter of ~200 cells were measured in each sample (standard error <2% of the mean) using ImageJ software (Schneider et al. 2012). Gating strategies for the different classes of planktonic cells are available in the MIFlowCyt-Compliant Items (Lee et al. 2008; supplemental information) and archived with the data at http://www.flowrepository.org under the ID number FR-FCM-ZZK3. A discrete water sample for nucleic acid extraction was collected in situ at 850 m with a Large Volume Water Transfer System (WTS-LV; McLane Research Laboratories, East Falmouth, MA). This system was fitted with a stacked 143 mm diameter filter housing that allowed the sample to be size fractionated into 10µm, 3 µm, and 0.2 µm classes (Supor Membrane filters were used; Pall Inc.). This system allowed us to concentrate 295 L of seawater on 142 mm filters during a four hour in situ deployment. An additional discrete water sample (300 mL) from 30 m was obtained for nucleic acid 116 extraction from a 10 L Niskin bottle sample that was filtered through a 47 mm 0.2 µm Supor membrane filter (Pall Inc.). Filters from the WTS-LV were preserved as described previously (Christner et al. 2014) while the entire 47 mm filter from the 30 m sample was placed in a 7 mL cryovial and preserved with 7 mL of a solution of 40 mM EDTA pH 8.0, 50 mM Tris pH 8.3, and 0.73 M Sucrose to prevent cell lysis during during storage. Nucleic acids were extracted from the 10 µm, 3 µm, and 0.2 µm filters from 850 m and the 0.2 µm filter from 30 m using a MO BIO PowerWater DNA Isolation Kit according to the manufactures instructions. The V4 hypervariable region of the small subunit (SSU) rRNA gene was amplified using the primers 515F and 806R (Caporaso et al. 2012) and sequenced on an Illumina MiSeq as described by Chirstner et al. (2014). Sequence reads were assembled and quality filtered using the Mothur (v1.33.2; Schloss et al. 2009) phylogenetic pipeline following the recommended MiSeq standard operating procedure. Sequences were clustered into operational taxonomic units (OTUs; cluster.split command) based on a pairwise distance matrix (dist,seq) calculated using default settings. OTUs were defined based on 97% sequence similarity cutoff. Preliminary classification of resulting SSU rRNA gene sequences was accomplished using the SILVA database as implemented within the SILVA Incremental Aligner (SINA). Poorly classified sequences were further examined using NCBI Blastn. Statistics were calculated using Mothur (v1.33.2; Schloss et al. 2009) according to Chao (1984), Good (1953) and Magurran (1988). Estimates of diversity and richness include singletons; singletons were removed from other analyses. SSU sequences obtained from chloroplasts were removed from the analyses. Sequences were uploaded to SRA under 117 accession number PRJNA278982. We note that recent efforts have shown that the primers used in our study underestimate the abundance and diversity of the ubiquitous SAR11 clade of bacteria. A comprehensive discussion of the primers can be found in Appril et al. (2015). Results Water Column Structure The upper ~95 m of the water column (relative to the bottom of the ice shelf) was characterized by relatively warm, less saline water (-1.70 oC to -1.89 oC, Salinity 34.45 to 34.61) (Figure 2A). A colder layer (-1.90 oC to -1.93 oC) of intermediate salinity (34.60 to 34.70) was present from ~95 m to 315 m. Figure 2. Profiles of salinity (practical salinity units; PSU) and temperature (A), current velocity and direction relative to true north (B). Sidebar in panel (A) designates three water layers: Antarctic Surface Water (AASW), Ice Shelf Water (ISW), and modified High Salinity Shelf Water (mHSSW). 118 Water below ~315 m had temperatures ranging from -1.88 oC to -1.90 oC and was relatively higher in salinity (34.66 to 34.75). These water masses were similar to those described previously in this area (Jacobs and Fairbanks 1985; Stover 2006; Celussi et al. 2010): AASW, ISW and mHSSW (Figure S1). Current flow was generally to the south- southeast with true headings (declination corrected) of 140o, 161o and 162o for the AASW, ISW and mHSSW layers, respectively (Figure 2B). Current velocities for these respective layers averaged 0.21, 0.11 and 0.14 m s-1. Current velocity in the mHSSW layer showed a clear demarcation at 500 m with average velocity in the upper part of this water layer averaging 0.09 m s-1 while that below 500 m averaged 0.14 m s-1. Inorganic Chemistry Concentrations of NH4+, N+N, and SRP were 40%, 9% and 7%, respectively, higher in the AASW (30 m sample) relative to the mHSSW (850 m sample). Dissolved oxygen concentrations were 330 µmol L-1 and 370 µmol L-1, representing 92% and 97% of air saturation, respectively (Table 1). The concentration of DIC was 2.2 mmol L-1 in both samples. The molar ratio of dissolved inorganic N (N+N + NH4+) to SRP was 16.1 in the AASW and 15.7 mHSSW water layers. Sample DIC mmol L-1 NH4+ µmol L-1 N+N µmol L-1 SRP µmol L-1 DO µmol L-1 AASW 2.2 0.7 25.1 1.6 333 mHSSW 2.2 0.5 23.0 1.5 367 Table 1. Water column inorganic chemistry from the AASW (30 m) and mHSSW (850 m) water masses sampled. DIC=dissolved inorganic carbon; SRP=soluble reactive phosphorus; DO=dissolved oxygen N+N=nitrate+nitrite. 119 Microbiological Characteristics Bacterioplankton cell densities were 1.2 x 108 cells L-1 in the AASW and 1.1 x 108 cells L-1 in the mHSSW water layers (Table 2), and similar counts were determined via epifluorescent microscope (data not shown). High-nucleic acid and low-nucleic acid (HNA and LNA) bacterioplankton cells comprised ~35% and ~64% of total bacterioplankton cells in the AASW and mHSSW, respectively. Sample Bacterio - plankton (x108) LNA (x107) HN A (x107) Phyto < 2 um (x106) Phyto >2 um (x106) Phyto a (x106 ) Phyto b (x106) Phyto c (x106) Total Phyto (x106) AASW 1.2 7.5 4.1 1.5 4.5 0.92 2.5 0.59 6.0 mHSSW 1.1 7.2 3.9 0.0010 6.7 0.41 2.9 2.7 6.7 Table 2. Bacterioplankton and phytoplankton cell counts (cells L-1). Counts determined by flow cytometry for samples collected in the AASW (30 m sample) and mHSSW (850 m sample). Bacterioplankton cell counts are the sum of low nucleic acid (LNA) and high nucleic acid (HNA) cells. Phytoplankton include autofluorescent cells of <2µm and >2µm diameter. Mean intensity is fluorescence intensity (relative units) per cell excited with the blue laser and read at emission of >670 nm (chl a). Phytoplankton >2 um were divided into three populations: Phyto a, Phyto b, and Phyto c based on size (FSC-A). Total phytoplankton are the sum of all autofluorescing organisms. Small (<2 µm) phytoplankton were more abundant in the surface water layer than the deep layer (1.5 x 106 cells L-1 in AASW, 1.0 x 104 cells L-1 in mHSSW), while the densities of larger (>2 µm) phytoplankton were 33% higher at depth (Table 2). Microscopic enumeration showed that 83% to 85% of phytoplankton cells in both samples were Phaeocystis antarctica; the remainder consisted of diatoms. Total phytoplankton (large plus small) were 10% higher in the mHSSW relative to densities in the AASW. 120 Large phytoplankton populations a, b, and c had distinctly different FSC-A intensities, where greater intensity is related to greater cell size. The mean FSC-A per cell in population b was approximately twice that of population a. The mean FSC-A per cell for population c was approximately twice that of b (Figure 3). The AASW water sample was dominated by population b (56% of total phytoplankton), while the mHSSW was dominated by populations b and c (43% and 40%, respectively; Table 2, Figure 3). Figure 3. Density plots of chl a fluorescence (relative intensity units) versus forward scatter-area (FSC-A; relative intensity units) in the AASW water (30 m) (A) and mHSSW water (850 m) (B). Both samples show three distinct populations (a, b, and c) based on differences in FSC-A mean emission intensity for each population. Mean emission intensity for each group is given in the lower right corner of each plot. The increase in population c relative abundance indicates an increase in cell size (FSC-A) at depth. Microscopic examination of the AASW and mHSSW >2 μm phytoplankton showed that the between water mass size difference was small (mean =3.27 and 3.44 µm for AASW and mHSSW, respectively) but significant (t = -4.06, n = 121 410, P<0.001), supporting the increase in size indicated by the shift towards population c dominated phytoplankton at depth. The mean emission ratios of red fluorescence/chl a fluorescence and orange fluorescence/chl a fluorescence remained constant within populations indicating a similar phytopigment composition was present at each depth and only the phytoplankton abundances and sizes changed. Rates of both Leu and TdR incorporation were higher in the deep mHSSW waters but the difference was proportionally greater for TdR, leading to a lower Leu:TdR molar ratio in the deep waters (14 in the AASW, 9 in the mHSSW; Table 3). In terms of bacterial carbon production, BP-TdR and BP-Leu ranged from 34.5 nmol C L-1 d-1 (AASW Leu) to 63.6 nmol C L-1 d-1 (mHSSW TdR). Cell specific activity was also higher at depth (3.5 x 10-12 mg C cell-1 d-1 (Leu) and 4.0 x 10 -12 mg C cell-1 d-1 (TdR) in the AASW versus 4.8 x 10-12 mg C cell-1 d-1 (Leu) and 6.9 x 10 -12 mg C cell-1 d-1 (TdR) in the mHSSW. Increased activity at depth was also associated with relatively high bacterial growth efficiency (70%). Dark 14C-bicarbonate incorporation ranged from 1.6 nmol C L-1 d-1 (AASW >3 µm size fraction) to 3.1 nmol C L-1 d-1 (mHSSW 0.2 – 3µm size fraction) and was higher in the 0.2 – 3 µm than the >3 µm size fraction (12% greater in AASW and 6% greater in mHSSW). The summed size fractionated dark 14C-bicarbonate incorporation was greater in the mHSSW (11% difference) and ranged from 9% (mHSSW BP-TdR) to 15% (AASW BP-Leu) of heterotrophic bacterial production. 122 Sample PC µM PN µM C:N DOC µM chl a µg L-1 BGE Leu TdR Leu: TdR 14C-bicarb nM C d-1 nM d-1 nM d-1 nM d-1 nM C d-1 0.2-3µm >3 µm sum AASW 28.4 2.2 11.3 42.1 2.4 ND 0.27 (0.02) 35.1 (2.8) 0.022 (0.003) 39.5 (6.0) 12 2.8 (0.2) 2.5 (0.5) 5.3 (0.5) mHSSW 26.1 1.9 12.0 31.8 2.9 0.7 0.36 (0.08) 47.0 (10.0) 0.035 (0.0003) 63.6 (9.0) 10 3.1 (0.6) 2.9 (1.0) 6.0 (1.0) Table 3. Water column organic chemical composition and microbial activity (± propagated standard error) for samples collected in the AASW (30 m) and mHSSW (850 m) water masses. Particulate organic carbon and nitrogen (PC and PN), the molar PC to PN ratio (C:N), dissolved organic carbon (DOC), chlorophyll a (chl a), bacterial growth efficiency (BGE) rates of 3H-leucine (Leu) and 3H-thymidine (TdR) incorporation and bacterial production (nmol C L-1 d-1) the molar ratio of Leu:TdR (nmol Leu L-1 d-1: nmol TdR L-1 d-1), rate of 14C-bicarbonate incorporation in the dark (0.2 – 3µm and >3µm size fractions and the sum of the two size fractions). ND = Not determined. The molar ratio of particulate organic C:N was slightly higher in the mHSSW (10.5 vs. 10.0), while DOC concentrations were higher in the AASW (42.1 µmol L-1 vs. 31.8 µmol L-1). Chl a was 2.4 µg L-1 in the shallow water and 2.9 µg L-1 in the deep water (Table 4), and PC:chl a (µg C L-1: µg chl a L-1) was 109 in the AASW and 91 in the mHSSW. The Fluorescence and Freshness Indices for DOM were 2% and 21% higher, respectively, in the mHSSW sample than the AASW sample while the Humification Index was 55% higher in the AASW sample (Table 4). The maximum excitation/emission of fluorescent DOM (Exmax/Emmax) occurred in the range of tryptophan-like fluorescence (240/322-324 in the AASW and 240/334-352 in the mHSSW), and standardized maximum fluorescence revealed 1.7 times greater tryptophan-like fluorescence in the mHSSW sample compared to the AASW (Table 4) 123 Sample Fluorescence Index Humification Index Freshness Index Max Fluor AASW 1.65 0.31 1.35 0.56 mHSSW 1.70 0.14 1.72 0.97 Table 4. Fluorescence characteristics of dissolved organic matter (DOM) in the two water masses sampled. A higher Fluorescence Index indicates DOM of more microbial character, a higher Humification Index indicates a higher degree of humification, and a higher Freshness Index indicates a higher proportion of recently produced DOM. Standardized maximum fluorescence (Max Fluor) occurred in the region of tryptophan- like fluorescence for both samples (excitation 240 nm and emission 334-352 (30 m) and 322-334 (850 m)). The seafloor at the sample site was characterized by a rocky lag deposit, indicative of continuous or episodic strong bottom currents. Fine-grained sediment, including diatom frustules, was present interstitially between rocks and pebbles. The diatom assemblage recovered from near surficial sediments was characterized by Southern Ocean and Ross Sea pelagic species including Eucampia antarctica, Thalassiosira oliverana, Thalassiosira tumida, Stelarima microtrias, Thalassiosira lentiginosa, Thlassiosira gracilis, Actinocyclus actinochilus and Fragilariopsis obliquicostata. Taxa characteristic of the northern polar frontal zone and lower latitudes, including Thalassionema nitzschioides, Thalassiosira oestrupii and Actinocyclus octinarius, were also represented. Fragilariopsis curta accounted for less than 1% of the diatom flora in the MIS sediment. Bacterial and Archaeal Diversity and Community Structure Microbial diversity, as indicated by the Shannon diversity index (H’) and the inverse Simpsons diversity index (1/λ), was greatest in the >10 µm size fraction of the 124 mHSSW sample (Table 5). Good’s coverage, a non-parametric coverage estimator, indicated that the majority of the OTUs in the samples were identified through sequencing, (>99%; Table 5) with projected between 140 and 500 (calculated as 1/(1- Good’s coverage)) more sequencing reads required to detect an additional OTU from each sample (Figure 4). Sample # of reads # of OTUs Good’s Coverage Chao1 H’ 1/λ AASW 238448 1475 99.8% 2140 2.06 3.79 mHSSW 0.2 to 3 µm 193331 1805 99.6% 2995 4.26 23.37 mHSSW 3 to 10 µm 101768 2208 99.3% 2960 5.27 75.10 mHSSW >10 µm 391658 3442 99.7% 4880 5.74 117.10 Table 5. Prokaryotic diversity estimates for the different water masses sampled. OTUs were calculated based on 97% similarity. H’ = Shannon diversity index, 1/λ = inverse Simpsons diversity index. Good’s coverage is a non-parametric coverage indicator. Chao1 gives the expected number of OTUs. The total numbers of OTUs observed in the SSU sequence libraries represented between 60% (mHSSW 0.2 to 3 µm size fraction) and 75% (mHSSW 3 to10 µm size fraction) of the expected number of OTUs predicted by the Chao1 richness estimator (Table 5). The three size fractions of mHSSW were dominated by phylotypes that classify within the Gammaproteobacteria and Bacteroidetes, with greater proportions of Bacteroidetes (predominantly Flavobacteria) occurring in the >10 µm and 3 to 10 µm fractions relative to the 0.2 to 3 µm fraction (Figure 4). Together, the Gammaproteobacteria and Bacteroidetes accounted for ~53% of the OTUs in the 125 mHSSW sample (size fractions pooled), with the Gammaproteobacteria alone accounting for nearly 75% of the OTUs observed at the sampling depth in the AASW. The most abundant operational taxonomic unit (OTU; 3.7% of sequence reads) in the mHSSW sample was most closely related (96% identity) to Candidatus Vesicomyosocius okutanii, a sulfide-oxidizing symbiont of a deep-sea clam. An OTU most closely related (98% sequence similarity) to the ammonia oxidizer Nitrosopumilus maritimus, was the third most common in the mHSSW sample, comprising 3.5% of total sequencing reads. Members of the Thuamarchaeota comprised a total of 4.1% of the mHSSW community (10.9%, 2.6% and 1.3% of reads from the 0.2 to 3 µm, 3 to 10 µm and >10 µm size fractions, respectively). The majority of sequences in the AASW were related to Oceanospirillales sp. (most abundant OTU, 42.4% of reads) and members of the SAR92 clade (second most abundant OTU, 28.9% of reads). OTUs most closely related to the SAR11 clade were rare, comprising 0.13% of the total library in the AASW and 1.2% in the mHSSW. A total of ~9.0% of the sequence reads from the AASW were closely related to Polaribacter sp., a marine genus reported to possess gas vacuoles. Approximately 10% of the OTUs identified in all samples were present in both the AASW and the mHSSW (Figure 4). Eighteen of these OTUs were present at relative abundances >0.1% in both water masses. In general, OTUs that were common in one water mass (≥1%) were rare in the other water mass. Exceptions to this were in OTUs related to the Oceanospirillaceae, which comprised 42.4% and 4.1% of the AASW and mHSSW populations, respectively, an OTU related to the SAR92 cluster (28.9% and 126 2.3% in the AASW and mHSSW, respectively), and Flavobacteriaceae (2.6% and 1.0% in the AASW and mHSSW, respectively). Figure 4. (a) Rarefaction curves for OTU richness of samples from AASW (30 m) and mHSSW (850 m); (b) comparison of microbial communities from the AASW water mass and mHSSW water mass samples showing a 10% overlap in community composition at the OTU level (97% SSU sequence identity); (c) and relative abundance data for AASW and mHSSW. Discussion Sub-ice shelf habitats are among the least-studied ecosystems in the world’s oceans. Our results show an active microbial ecosystem under the MIS, supported in part via chemoautotrophic activity and in part via advection of nutrients and biomass from eastern McMurdo Sound. Thick ice prevents the penetration of sunlight, leaving sub-ice shelf waters devoid of the primary production typical of open ocean photic zones, therefore horizontal advection from adjacent open waters should play a proportionally 127 greater role in sub-ice shelf biogeochemistry. Sub-ice shelf habitats can be oligotrophic, such as the J9 site beneath the RIS, (Lipps et al. 1979) where chemoautotrophically produced new carbon may be an important food source (Horrigan, 1981). Conversely, the diverse assemblage that comprised the benthic community beneath the Amery Ice Shelf indicated a nutrient rich environment likely sustained by advection of phytoplankton produced organic matter (Riddle et al. 2007, Post et al. 2014). The J9 and Amery sites differ in their distance to open water (~400 km for J9 and ~100 km for Amery). These results, along with those of our study, indicate that biogeochemical processes beneath an ice shelf are controlled by the proximity to open water, the trophic state of the source waters and new (chemoautotrophic) carbon production beneath the ice shelf. Source of Sub-MIS Waters The currents observed at our sampling site (Fig. 2B) are in agreement with previous observations of a strong southward flow component from the Ross Sea to the MIS via the eastern side of McMurdo Sound (Robinson et al. 2010). Our biological and physicochemical measurements support an eastern McMurdo Sound source for the sub- MIS waters at our sample site. Bacterioplankton abundances in eastern McMurdo Sound were similar to those at our site (Table 2; ~108 cells L-1, Hodson et al. 1981 and Rivkin 1991). In contrast, the oligotrophic western side, which is a mixture of sub-MIS outflow and water circulating from the Ross Sea, contained an order of magnitude fewer cells (Hodson et al. 1981). Nutrient concentrations (SRP and DIN; Table 1) were within a few µmol L-1 of those measured during December in eastern McMurdo Sound (Rivkin 1991). These data imply that the transport time from the open waters of McMurdo Sound to our 128 sample site (~ 2 days; Robinson 2004) did not result in major changes to the concentrations of inorganic nutrients or microbial cells. Given the similarity between the biological and physicochemical character of our samples and eastern McMurdo Sound waters, the prevailing current direction we conclude that nutrient and biomass-rich water advected from eastern McMurdo Sound likely plays an important role in sub-MIS biogeochemical processes. Sedimentary diatoms can also provide tracers of water column advection, however, strong bottom currents (Fig. 2B) at our sample site suggest that little deposition occurs there at present. The most common diatoms we observed (multiple species of Thalassiosira, A. actinochilus, and F. obliquicostata) are also found in sediments from the eastern side of McMurdo Sound (Leventer and Dunbar 1988), however the near absence of F. curta, which is commonly observed in McMurdo Sound, is striking. No viable diatoms were recovered in our surface sediment samples, suggesting that the diatoms we observed may represent earlier deposition, or that they are the result of advection from pelagic sources. Together, these data indicate that diatom deposition may not be an accurate tracer of modern water sources at this site. Organic Carbon Sources Sub-ice shelf systems are unique from open ocean environments in their dependence on horizontal advection of food sources or in situ chemoautotrophic production, rather than on vertical fluxes of phytoplankton-derived food sources (Gutt et al. 2011). To determine whether intact phytoplankton cells are advected beneath the MIS, and consider their potential importance as a food source, we examined chl a 129 concentrations and phytoplankton abundances in the AASW and mHSSW. Chlorophyll a concentrations in both samples were similar to values measured in McMurdo Sound during mid-December (Rivkin 1991), suggesting that phytoplankton loss rates (i.e. due to sedimentation, cell lysis, grazing) were low by the time water from McMurdo Sound reached our sampling site. Our CTD data revealed the presence of three distinct water masses beneath the MIS, rather than a well-mixed water column that would result in even distribution of chl a, so the simplest explanation for the similar concentrations of chl a we measured in the AASW and mHSSW (2.4 and 2.9 µg L-1, respectively) is settling of chl a containing cells into the aphotic mHSSW during the ~2 day transport time to our sample site. Indeed, our flow cytometry and microscopy data showed that chl a containing phytoplankton cells were present in both water masses. Phytoplankton cells were larger in the mHSSW than in the AASW and had higher chl a emission per cell, suggesting that intact cells or colonies were transported to depth, a phenomenon that has been previously reported for P. antarctica (DiTullio et al. 2000). The PC:chl a (ug L-1: ug L-1) measured in our samples (109 in the AASW and 91 in the mHSSW) was similar to that measured in active populations from the Ross Sea (92; Ditullio and Smith, 1996), supporting our contention that cells transported to depth were intact. Intact phytoplankton cells can be advected great distances under ice from open water (Holm-Hansen et al. 1978), where they may later be decomposed as a source of nutrition for heterotrophic growth of sub-ice shelf microorganisms. 130 To our knowledge, no measurements of P. antarctica decomposition rates have been made for the MIS/RIS cavity, although DiTullio et al. (2000) tracked P. antarctica export in the Ross Sea and showed that intact cells are rapidly exported to depth. Based on a phytoplankton decomposition rate of 0.032% d-1 for a cold saline Antarctic lake near McMurdo Sound (Priscu 1992), 100% of the phytoplankton particulate carbon we observed would be decomposed after 10 years. This potential decadal scale decomposition suggests that sinking and advected phytoplankton are important sources of organic matter beneath the ice shelf, and may continue to be as they are advected farther from the ice shelf front. Bulk DOC concentrations in our samples were relatively low, similar to Ross Sea wintertime background concentrations (e.g. Ducklow 2003). Based on our determinations of bacterial growth efficiency and bacterial carbon production, the heterotrophic bacterial carbon demand (bacterial carbon production + bacterial carbon respiration) in our samples was ~ 50 to 90 nmol C L-1 d-1. Assuming that the entire DOC pool is bioavailable and the system in steady-state, these rates would be expected to deplete the standing stock of DOC within 1 – 2 years. The total DOC pool is likely an overestimate of the bioavailable fraction, as the labile pool is consumed quickly, concurrent with the phytoplankton bloom (Ducklow 2003). Residence times in the MIS/RIS cavity may vary between water masses and are not well constrained, but estimates range from ~ 2 to 8 years (Smethie Jr. and Jacobs 2005, Reddy et al. 2010). Based on this, our data imply that another organic carbon source would be required to maintain heterotrophic potential beneath the ice shelf during the water residence time. 131 The production of organic matter via fixation of CO2 into biomass (chemoautotrophy) may also be important under the ice. Our microbial diversity, in concert with results from other studies (e.g., Gryzmski et al. 2012; Williams et al. 2012; Christner et al. 2014) show that aerobic ammonia-oxidizing microorganisms are widespread in Antarctic aquatic environments, and may be key sources of new organic carbon to ecosystems shielded from sunlight. Isolated representatives of the proposed phylum Thaumarchaeota, a major group identified in our mHSSW sample, are aerobic ammonia oxidizers that fix CO2 (Könneke et al. 2014). We measured dark CO2 fixation of ~3 nmol C L-1 d-1 in the 0.2 – 3 µm size fraction in both the shallow AASW and deep mHSSW samples. We attribute this CO2 fixation to bacteria and/or archaea, rather than phytoplankton-mediated anapleurotic reactions because our flow cytometry data showed that the 0.2 – 3 µm size fraction was chiefly comprised of bacterioplankton. Still, dark CO2 fixation was an order of magnitude less than heterotrophic bacterial carbon production at our study site, implying that heterotrophic demand in this region of the MIS must also rely on some combination of the previously discussed carbon sources (advected organic matter and biomass or the DOC pool). Dissolved Organic Matter Quality and Microbial Growth Dissolved organic carbon concentrations, while higher in the surface waters (42.1 µmol L-1) than at depth (31.8 µM) were similar to Ross Sea wintertime background values (40-42 µmol L-1; Ducklow 2003). With terrestrial sources limited by a paucity of land plants, autochthonous production dominates the DOM pool in Antarctic waters (e.g 132 McKnight et al. 2001). Fluorescence indices derived from our EEMS datasets were consistent with those of microbially produced organic matter, where a high index (~ 1.8) corresponds to DOM of microbial origin, and a low index (~ 1.2) corresponds to DOM of terrestrial origin (McKnight et al. 2001, Cory and McKnight 2005). Humification indices increase with the presence of more humified and/or older DOM (up to >10 for fulvic acids; Zsolnay et al. 1999), while a Freshness index >1 corresponds to recently produced microbial DOM (Huguet et al. 2009). Both the Humification and Freshness indices calculated for our samples are consistent with DOM of recent microbial origin. The 55% higher Humification and 22% lower Freshness Indices in the AASW water mass relative to the deeper mHSSW water mass reveal that the near-surface DOM was more degraded, perhaps due to high rates of heterotrophic activity associated with phytoplankton production in the photic zone of McMurdo Sound before being advected into the sub-ice cavity. Ducklow (2003) showed that labile DOC produced during the summer Ross Sea phytoplankton bloom is quickly consumed, leaving a mainly recalcitrant pool available for consumption during the winter. This is consistent with our findings of more degraded near-surface DOM. All three indices of biological activity (3H-TdR, 3H-Leu, and 14C- bicarbonate uptake) revealed increased metabolic rates in the deeper mHSSW water mass. Increased biological activity may explain the stronger signal for freshly produced DOM, including the elevated maximum tryptophan-like fluorescence in the mHSSW relative to the AASW. 133 Although the ability to take up leucine and thymidine can differ among bacterial groups (Pérez et al. 2010), the ratio of Leu (protein production or biomass maintenance) to TdR (DNA synthesis or cell division) incorporation rates can be used as a metric for understanding changes in rates of biomass maintenance relative to reproduction (Chin- Leo and Kirchman, 1988; Shiah and Ducklow, 1997; Vick and Priscu, 2012). The lower Leu:TdR we detected in the mHSSW sample may indicate faster heterotrophic bacterioplankton growth rates at depth relative to the surface, possibly a product of the availability of higher quality DOM. Conclusions The sub-McMurdo Ice Shelf cavity provides an important conduit for organisms and nutrients between the Ross Sea and the sub-Ross Ice Shelf cavity. Our results show that the decomposition of phytoplankton coupled with chemoautotrophic production of new carbon may be important in supporting ecosystems beneath the MIS. While inputs from phytoplankton blooms are available only during the austral summer, chemoautotrophic production is available year-round. Our sequence-based community analysis corroborates our contention and those of previous reports suggesting that new carbon produced by chemoautotrophic ammonia-oxidizing organisms can supply organic carbon to partially sustain ecosystem processes beneath Antarctic ice shelves such as the MIS and RIS. Other abundant phylotypes in our deep-water sample are related to a chemoautotrophic sulfide oxidizer, suggesting that reduced sulfur compounds may also be an important energy source to fuel chemoauotrophic production beneath the MIS. 134 Given that many of the ice shelves surrounding Antarctica’s coastline are thinning at increasing rates (Paolo et al. 2015) and are susceptible to collapse (Joughin et al. 2014), their losses should lead to significant changes in the biogeochemistry of Antarctic coastal systems supported by sub-ice shelf processes. Changes in Southern Ocean ice cover can impact carbon biogeochemistry at local as well as global (see review by Sigman et al. 2010) scales. In the case of the RIS/MIS, the relative biogeochemical importance of chemoautotrophic production and material advected beneath the ice from the Ross Sea would decrease if photosynthetic primary production began to occur in newly open water. Assuming that the rates we measured are relatively constant over the year, the sum of heterotrophic and autotrophic C production under the MIS is currently <1% of the photosynthetic C production in the open water of the Ross Sea (per km2 per year in the top 100 m of water column; Ross Sea estimates in Arrigo et al., 2008). Considering such disparity in productivity between ice shelf covered and open water, the loss of ice shelves can be expected to have cascading effects on elemental cycling in the region via shifts in dominant biological processes. In a less dramatic scenario than massive ice-shelf loss, climate change induced changes to winds and temperatures that modulate incursions of Circumpolar Deep Water onto the continental shelf could severely disrupt the Ross Sea food web (Smith Jr. et al 2014), leading to major effects on sub-MIS biogeochemistry. Baseline data from existing sub ice shelf environments, such as the MIS, inform the prediction of the biogeochemical impacts of ice shelf collapse and climate change in the Southern Ocean. 135 Acknowledgements The Whillans Ice Stream Subglacial Access Research Drilling (WISSARD) project was funded by National Science Foundation grants (0838933, 0838896, 0838941, 0839142, 0839059, 0838885, 0838855, 0838763, 0839107, 0838947, 0838854, 0838764 and 1142123) from the Office of Polar Programs. Partial support was also provided by funds from NSF award 1023233 (B.C.C.), NSF award 1115245 (J.C.P.), the American Association of University Women Dissertation Fellowship (T.J.V.), the NSF’s Graduate Research Fellowship Program (1247192; A.M.A.), the Chilean Fulbright-CONICYT Scholarship (P.S), the Italian National Antarctic Program (C.B.), and fellowships from the NSF’s IGERT Program (0654336) and the Montana Space Grant Consortium (A.B.M.). The authors would like to thank two anonymous reviewers for helpful comments, Robert Edwards for project management, Andrew T. Fisher and Kenneth D. Mankoff for providing sediment samples, the WISSARD Science Team, the individuals working as part of the Antarctic Support Contractor managed by Lockheed-Martin, for logistical support, as well as K. Welch and A. Chiuchiolo for analytical and laboratory assistance and I. Alekhina for field support. The drilling was directed by F.Rack and implemented by D.Blythe, J.Burnett, C.Carpenter, D.Duling (chief driller), D.Gibson, J. Lemery, A. Melby and G. Roberts. 136 Supplemental Information Figure S1. Potential temperature (oC, relative to the surface) plotted over salinity (PSU) showing the presence of three distinct water layers at our sampling site: Antarctic Surface Water (AASW), Ice Shelf Water (ISW), and modified High Salinity Shelf Water (mHSSW). 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Chemosphere 38: 45–50. 145 CHAPTER SIX A MICROBIAL ECOSYSTEM BENEATH THE WEST ANTARCTIC ICE SHEET Contribution of Authors and Co-Authors Manuscript in Chapter 6 Author: Brent C. Christner Contributions: Oversaw sample collection and sequence data analyses, provided funding, prepared figures and tables, wrote the manuscript. Co-Author: John C. Priscu Contributions: Oversaw sample collection and data analyses, conceived the study, provided funding, analyzed DIC samples, wrote the manuscript. Co-Author: Amanda M. Achberger Contributions: Collected and analyzed samples for molecular data, contributed to manuscript preparation. Co-Author: Carlo Barbante Contributions: Collected samples, conducted and interpreted chemical measurements. Co-Author: Sasha P. Carter Contributions: Provided geophysical data. Co-Author: Knut Christianson Contributions: Provided geophysical data. Co-Author: Alexander B. Michaud Contributions: Collected samples, contributed sediment biogeochemical data, ATP concentration, and dissolved oxygen concentration data. 146 Contribution of Authors and Co-Authors-Continued Co-Author: Jill A. Mikucki Contributions: Collected samples, collected and examined CTD data. Co-Author: Andrew C. Mitchell Contributions: Collected samples, conducted and interpreted chemical measurements. Co-Author: Mark L. Skidmore Contributions: Collected samples, conducted and interpreted chemical measurements. Co-Author: Trista J. Vick-Majors Contributions: Collected samples, conducted microbiological rate experiments, assisted with nutrient and DOC analyses, performed microscopy. 147 Manuscript Information Page Brent C. Christner, John C. Priscu, Amanda M. Achberger, Carlo Barbante, Sasha P. Carter, Knut Christianson, Alexander B. Michaud, Jill A. Mikucki, Andrew C. Mitchell, Mark L. Skidmore, Trista J. Vick-Majors Nature Status of Manuscript: ____ Prepared for submission to a peer-reviewed journal ____ Officially submitted to a peer-review journal _ Accepted by a peer-reviewed journal _ X_ Published in a peer-reviewed journal Nature Publishing Group In Volume 512, 310-313, 2014 Reused according the Nature Publishing Group License Policy. NPG does not require authors of original (primary) research papers to assign copyright of their published contributions. Authors grant NPG an exclusive licence to publish, in return for which they can reuse their papers in their future printed work without first requiring permission from the publisher of the journal. LETTER doi:10.1038/nature13667 A microbial ecosystem beneath the West Antarctic ice sheet Brent C. Christner1, John C. Priscu2, Amanda M. Achberger1, Carlo Barbante3, Sasha P. Carter4, Knut Christianson5{, Alexander B. Michaud2, Jill A. Mikucki6, Andrew C. Mitchell7, Mark L. Skidmore8, Trista J. Vick-Majors2 & theWISSARD Science Team{ Liquidwater has beenknown tooccurbeneath theAntarctic ice sheet formore than40 years1, but only recently have these subglacial aque- ous environments been recognized asmicrobial ecosystems thatmay influence biogeochemical transformations on a global scale2–4. Here wepresent the first geomicrobiological descriptionofwater andsur- ficial sediments obtained from direct sampling of a subglacial Ant- arctic lake.SubglacialLakeWhillans (SLW) liesbeneathapproximately 800m of ice on the lower portion of theWhillans Ice Stream (WIS) in West Antarctica and is part of an extensive and evolving subgla- cial drainagenetwork5.Thewater columnofSLWcontainedmetabol- ically activemicroorganisms andwas derived primarily from glacial icemelt with solute sources from lithogenicweathering and aminor seawater component.Heterotrophic andautotrophicproductiondata together with small subunit ribosomal RNA gene sequencing and biogeochemicaldata indicate thatSLWisa chemosyntheticallydriven ecosystem inhabited by a diverse assemblage of bacteria and archaea. Our results confirm that aquatic environments beneath theAntarctic ice sheet support viablemicrobial ecosystems, corroboratingprevious reports suggesting that they contain globally relevant pools of carbon andmicrobes2,4 that canmobilize elements fromthe lithosphere6 and influence Southern Ocean geochemical and biological systems7. Almost 400 subglacial lakes have been identified beneath theAntarc- tic ice sheet8. Speculationon thepresence of functionalmicrobial ecosys- temswithin these lakes followed their discovery1 andmotivated the initial studies of samples originating from Subglacial Lake Vostok (SLV)9,10. However, the body of microbiological data from SLV has been a point of contention, primarily because all studies were based on analyses of frozen (that is, accreted) lakewater samples recovered from a borehole containing a contaminated hydrocarbondrilling fluid3.Our report doc- uments the first analysis ofwater andsurficial sediments collecteddirectly froma subglacial lake beneath theWestAntarctic ice sheet (WAIS) using microbiologically clean drilling and sampling techniques11. The water residence time for SLV exceeds 10,000 years12, while that for ‘active’ lakes such as SLW is on the order of years to decades5,8. SLW ispart of a networkof threemajor reservoirs beneath the lower ice plain of the WIS that regulate water transport to a subglacial estuary at the grounding zone, linking thehydrological system to the sub-ice-oceancav- ity beneath the Ross Ice Shelf 5,13 (Fig. 1). During two separate drainage events in 2006 and 2009, SLWdischarged,0.15 km3 of water over two six-monthperiods, each time lowering the lake level by about 5m5,14. The drilling location to access SLWwas selected using reflection seismology13 and ice-penetrating radar14 data, and corresponded to the regionofmax- imum predicted water column thickness, lowest hydropotential, and largest satellite-measured surface elevation changes (Fig. 1). Ahotwater drilling systemwasused to create a,0.6mdiameter bore- hole through the overlying ice sheet into SLW, allowing for physical measurements and the direct collection ofwater column and sediment samples.Drilling and lake entry procedures followed recommendations for environmental protectionof subglacial aquatic environments11, incor- porating rigorous measures to reduce the introduction of foreign micro- biota andmaterial into SLWand the interconnected subglacial drainage {Lists of participants and their affiliations appear at the end of the paper. 1Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana 70803, USA. 2Department of Land Resources and Environmental Science, Montana State University, Bozeman, Montana 59717, USA. 3Institute for the Dynamics of Environmental Processes – CNR, Venice, andDepartment of Environmental Sciences, Informatics and Statistics, Ca9Foscari University of Venice, Venice 30123, Italy. 4Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, USA. 5Physics Department, St Olaf College, Northfield, Minnesota 55057, USA. 6Department of Microbiology, University of Tennessee, Knoxville, Tennessee 37996, USA. 7Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK. 8Department of Earth Science, Montana State University, Bozeman, Montana 59717, USA. {Present address: Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA (K.C.). 0 20 40 60 80 100 km Whillans Ice Stream Ross Ice Shelf Engelhardt Ice Ridge Mercer Ice Stream SLW SLC USLC SLM Lake 10 Lake 7 Lake 8 –1 64 ° –1 64 ° –1 56 ° –1 56 ° –1 48 ° –1 48 ° –84° –84.8° –83.6° –84.4° –1 60 ° –84.4° –83.6° –1 52 ° –1 52 ° Ice flow direction 350 37 5 325 300 350 325400 3503 75 275 350 375 4 00 2.50 5 km Hydropotential (kPa) Drill site 300 400 Figure 1 | Locator map of the WIS and SLW. The yellow box and star indicate the general location of the lake and the drill site; maximum extent of SLW and other lakes28 under the ice stream are shaded in blue; predicted subglacial water flowpaths through SLW and other subglacial lakes are represented by blue lines with arrows; the black line denotes the ice-sheet grounding line at the edge of the Ross Ice Shelf29. Inset (expanded from area in yellow box) shows details of SLW with both maximum (solid blue line) and minimum lake extent (shaded blue area), hydropotential contours (white isolines; 25 kPa interval), and drill site (yellow star; 84.240u S 153.694u W). Background imagery is MODIS MOA30. 3 1 0 | N A T U R E | V O L 5 1 2 | 2 1 A U G U S T 2 0 1 4 Macmillan Publishers Limited. All rights reserved©2014 system.Video inspectionof theborehole and temperaturemeasurements revealed that the ice–water interface occurred at 8016 1m below the surface (mbs) and the lake depth at the borehole site was,2.2m at the time of sampling. Two borehole deployments of a conductivity, tem- perature anddepth (CTD) sonde togetherwith data from three discrete hydrocasts showed that SLW had an average in situ temperature of 20.49 uC, pH of 8.1, and conductivity of 720mS cm21; properties that were distinctly different from the borehole water (Table 1). Water fromthree discrete hydrocasts in SLWhadnear identical geo- chemical compositions on the basis of major ion chemistry (Table 1) and all showedoxygenunder-saturation (,16%of air-saturatedwater). Since there is nodefinitive evidence of lakewater freezing to the bottom of the overlying ice sheet as in SLV12, it is unlikely that lake water con- stituents in SLW are influenced significantly by freeze concentration. The d18O ofH2O for SLW(238.0%) was similar to glacial ice sampled approximately 10m above the ice–water interface from the neighbour- ingKambIce Stream15 (KIS;238 to239%), indicating that glacialmelt was the dominant water source for SLW.A considerable fraction of the major anions and cations originated from mineral weathering, with a minor seawater component based onCl2 to Br2 ratios (ExtendedData Table 1).Crustallyderivednon-seawater solutes in SLWshowedadom- inance ofweatheringproducts from silicateminerals (Na1 1K1) over carbonateminerals (Mg21 1Ca21), similar to other sub ice-sheet sys- tems inGreenland andAntarctica6,7 (SupplementaryDiscussion). The dominant non-seawater anions (SO4 22 and HCO3 2) were probably products of sulphide oxidation, carbonation reactions, and carbonate dissolution7. Sulphide oxidation and carbonation reactions have been demonstrated to bemicrobially driven in subglacial systems and linked to enhanced rates ofmineralweathering16. Although clayminerals are a potential sourceof the relativelyhighF2 concentrations inSLW(Table1), subglacial volcanism in the upstream catchment supplying SLW17may also contribute. Ammonium accounted for 73% of the dissolved inorganic nitrogen poolwithin thewatercolumnofSLW(Table1).Given thatmineral sources of ammonium are minor, the majority of the ammonium is probably frommicrobialmineralization. Soluble reactive phosphorus levelswere similar to the total inorganic nitrogen pool (dissolved N:P molar ratio of 1.1), implying a biologically nitrogen-deficient environment, relative to phosphorus. Unfortunately, sample limitations precluded measure- ment of dissolved organic N and P concentrations to assess their nutri- tional contribution. In addition to its nutritional role, ammonium is also an energy source for chemolithoautotrophic ammonium-oxidizing bac- teria andarchaea.Evidence for complete nitrification in the aerobic SLW water columnwas supported byD17O ofNO3 values (20.1% to 0.2%) that indicated microbial processes rather than atmospheric input was thedominant source for nitrate in the lake18. Particulate organicC (PC) to N (PN) molar ratios in the water column exceeded that of actively growingbacteriabyalmost15-fold, suggestiveof elevated levelsofnitrogen- poordetritus.Dissolvedorganic carbon (DOC) in thewater columnaver- aged 2216 55mmol l21, which is about five times greater than average values for the deep ocean19 and similar to themaximumrange estimate for SLV9,20 (86–160mmol l21). Acetate and formate concentrations in the water column averaged 1.3 and 1.2mmol l21, respectively, indicat- ing that at least a portion of theDOCpool was labile. The conductivity andmicrobiological data (Table 1 andFig. 2a) showed that littlemixing occurred between the borehole water and lake, supporting the hypo- thesis that DOC in thewater column originated from SLW. The lack of winnowing in sediment cores from SLW, in concert with the fact that similar DOC concentrations were obtained as the overlying ice moved ,4mduring the course of our operations, provided evidence thatwater column DOC did not result from sediment disturbance during drilling operations. TheDOC in SLWmost likely originated from upward dif- fusionofDOCassociatedwith ancientmarine sediments4 (SLWsediment surface area:depth ratio< 30,000), chemoautotrophic production, or from a combination of both sources. The average cell density in the SLWwater column was 1.33 105 cells ml21 (Table 1); microscopy revealed the presence of numerousmorpho- types, approximately 10% of which were filamentous (Fig. 3). Cellular ATP, aproxy for viablebiomass, in SLWwas3.7 pmolATP l21 (Table1). Cell and ATP concentrations were 188- and 93-fold higher, respect- ively, than those observed in the boreholewater before breakthrough to SLW. Carbon biomass estimates for SLWwater based on theATP data (4806100ngC l21)were3- to50-foldhigher than thoseobservedbeneath theRoss Ice Shelf at site J9 (ref. 21). Analysis of small subunit ribosomal RNA (SSU rRNA) sequences amplified from thewater columnsamples showed that the community was similar among replicate lake samples, was distinct from thedrillingwater (Fig. 2a), and contained at least 3,931 operational taxonomic units (OTUs; Extended Data Table 2). AnOTU closely related to the nitrite oxidizing betaproteobacterium ‘Candidatus Nitrotoga arctica’22 comprised 13% of the sequence data, and many of the most abundant phylotypes were closely related to chemolitho- autotrophic species that use reduced nitrogen, iron or sulphur com- pounds as energy sources (Fig. 2b; SupplementaryDiscussion). Twoof Table 1 | Biogeochemical data from the SLW borehole, water col- umn, and surficial sediments Parameter Borehole* Water column{ Sediments{ Physical Temperature (uC)1 20.17 (0.25) 20.49 (0.03) n.d. Conductivity (mS cm21 @ 25 uC)I 5.3 720 (10) 860 pHI 5.4 8.1 (0.1) 7.3 Redox (mV (SHE))I n.d. 382 n.d. Microbiological Cell density (cellsml21) 6.9 3102 (51.0) 1.3 3105 (0.4 3105) n.d. Cellular ATP (pmol l21) 0.04 (0.002) 3.70 (1.00) n.d. [3H]thymidine" n.d. 13.7 (1.3) 46.6 (5.6) [3H]leucine" n.d. 2.9 (0.4) 0.9 (0.04) 14C-bicarbonate (ng C l21 d21) n.d. 32.9 (4.2) n.d. Carbon and nutrients Dissolved oxygen (mmol l21) n.d. 71.9 (12.5) n.d. DIC (mmol l21) n.d. 2.11 (0.03) n.d. DOC (mmol l21) n.d. 221 (55) n.d. Acetate (mmol l21) n.d 1.3 (0.2) n.d. Formate (mmol l21) n.d 1.2 (0.3) n.d. PC# n.d. 78.5 (7.4) 384.2 (37.0) PN# n.d. 1.2 (0.4) 21.5 (1.7) PC:PN (molar) n.d. 65.4 (0.3) 17.9 (0.4) NH4 1 (mmol l21) n.d. 2.4 (0.6) n.d. NO2 2 (mmol l21) n.d. 0.1 (0.1) n.d. NO3 2 (mmol l21) n.d. 0.8 (0.5) 9.1 PO4 32 (mmol l21) n.d. 3.1 (0.7) 7.3 DIN:SRP (molar) n.d. 1.1 (0.4) n.d. Major ions (meq l21) Na1 n.d. 5,276 (18) 6,977 K1 n.d. 186 (4.2) 293 (1.0)w Mg21 n.d. 507 (12) 596 (101)w Ca21 n.d 859 (29) 860 (104)w F2 n.d. 31.5 (0.4) 34.0 Cl2 n.d. 3,537 (3.4) 4,943 Br2 n.d. 6 (0.01) 7 (0.4)w SO4 22 n.d. 1,111 (0.4) 1,230 HCO3 2 n.d 2,111 (35) 2,238** Stable isotopes{{ d18O of H2O n.d. 238.0% 237.5% D17O of NO3 2 n.d. 20.1 to 0.2% n.d. *Borehole water sampled by hydrocast at 672mbs before lake entry. {Water columndata represent averages (6 s.d.) fromhydrocasts collectedon28 January2013 (cast 1), 30 January (cast 2) and 31 January (cast 3) 2013, except for [3H]leucine incorporation, which is an average of cast 1 and 3 only. {The sediment data correspond to measurements from the upper 2 cm of surficial sediments. 1Average (6 s.d.) of in situmeasurements made through the lake water column at ,10 cm intervals with a SBE 19plusV2 SeaCAT Profiler CTD on 28 January and 30 January 2013. IBased on measurements from discrete water samples brought to the surface. "Macromolecular incorporation rates of tritiumwere converted to cellular carbon and presented along with bicarbonate incorporation as average ng C l21 d21 (6 s.d.) for water or average ng C d21 gram dry weight 21 (6 s.d.) of sediment. #Average (6 s.d.) mmol l21 for water and average (6 s.d.) mmol g dry weight sed21 for surficial sediment. wSurficial sediment porewater major ions are the average (6 range) of two replicates. **Calculated based on charge balance. {{Values are per thousand and reported relative to V-SMOW. The range of 2measurements is given for D17O of NO3 2. n.d., no data available. LETTER RESEARCH 2 1 A U G U S T 2 0 1 4 | V O L 5 1 2 | N A T U R E | 3 1 1 Macmillan Publishers Limited. All rights reserved©2014 the abundant water column OTUs had high identity (.99%) to SSU sequencespreviously reported fromsediments sampledbeneath theKIS23 (Fig. 2b). Preliminary attempts to detect eukaryotic SSU sequences in the SLW water column were unsuccessful. Average dark [14C]bicarbonate incorporation in the water column samples (32.9 ng C l21 d21; Table 1) exceeded average rates of hetero- trophicproductionbasedon [3H]thymidine (13.7 ngC l21 d21) and [3H] leucine (2.9 ngC l21 d21) incorporation by 2- and 11-fold, respectively. Assuming that the thymidine and leucine values represent net incorpo- ration, and that respiratory losseswere 87%ofnet incorporation (which are average values forAntarcticMcMurdoDryValley lakes24), the gross bacterial carbon demand (net productivity 1 respiration) would be 105 and 23 ngC l21 d21, respectively. If dark [14C]bicarbonate incorp- oration represents new organic carbon production via chemoautotro- phy, the observed rates would meet between 31% and 143% of the heterotrophic carbondemand in the system. It should be noted that the effect of pressure (, 8MPa in SLW) was not tested andmay influence the absolute rates of metabolism measured. Pore water conductivity (860mS cm21) and pH (7.3) in SLW’s surfi- cial sedimentswerewithin20%of the lakewater values (Table1).Upward diffusion of ions from sediment pore water is presumably the primary source of the ions in the water column. Average surficial sediment PC andPNconcentrationswere384.2 and21.5mmolgdryweight21, respec- tively, and represented 0.43% and 0.03% of sediment dry weight. The molar PC:PN ratio in the surficial sediment layer (17.9) was 3.7-fold lower than that in the water column (Table 1), indicative of nitrogen- enriched sedimentary particulate organicmatter, with respect towater columnsuspensoids.On thebasis of ratesof thymidine and leucine incor- poration, averageheterotrophic production in the surficial sedimentwas 46.6 and 0.9 ngC d21 g dryweight21, respectively.Approximately 75% of the OTUs from the surficial sediments classified within the Proteo- bacteria (Fig. 2a).Althoughmanyphylotypes in thewater columnwere also abundant in the surficial sediments (Fig. 2b),,70% of the OTUs were unique to the sediment environment. The nearest neighbours of the most abundant phylotypes in the surface sediments were chemoli- thoautotrophs or species that useC1hydrocarbons as carbonand energy sources (Fig. 2b, Supplementary Discussion). Our data show that SLW supports a metabolically active and phylo- genetically diverse ecosystem that functions in the dark at sub-zero tem- peratures, confirming more than a decade of circumstantial evidence regarding the presence of life beneathAntarctica’s ice sheet9,10,20,23. Rate experiments revealed that chemoautotrophicprimaryproduction inSLW a b OTU 1756 ( , )4.8% 12% KIS clone B77 (EU30485.1) Sideroxydans lithotrophicus ES-1 (NR074731.1) OTU 1901 ( , )<0.1% 5.3% ‘Candidatus Nitrotoga arctica’ 6680 (DQ839562) OTU 10327 ( , )13% 7.8% Candidatus Nitrotoga sp. (EF562070.1.1368) KIS clone B83 (EU30487.1) OTU 2522 ( , )5.0% 1.8% Polaromonas glacialis Cr4-12 (HM583568) Thiobacillus denitrificans (NR074417.1) OTU 1767 ( , )<0.1% 6.0% KIS clone B26 (EU030484.1) OTU 5861 ( , )<0.1% 2.2% Methylobacter tundripaludum SV96 (NR042107.1) Ferroglobus placidus (NR074531.1) ‘Candidatus Nitrososphaera viennensis’ (FR773158.1) ‘Candidatus Nitrososphaera gargensis’ Ga9.2 (NR102916.1) OTU 1005 ( , )2.5% <0.1% ‘Candidatus Nitrosoarchaeum koreensis’ MY1 (HQ331116) ‘Candidatus Cenarchaeum symbiosum’ (AF083072) ‘Candidatus Nitrosopumilus maritimus’ SCM1 (NR102913.1) B etap roteob acteria Gammaproteo- bacteria Thaum archaeota 83 48 86 74 58 89 100 92 100 84 94 99 94 99 94 99 740.05 100 Alphaproteobacteria Deltaproteobacteria Unclassified Proteobacteria Actinobacteria Firmicutes Chloroflexi Lentisphaerae Betaproteobacteria Gammaproteobacteria Bacteriodetes Thaumarchaeota Planctomycetes Verrucomicrobia Unclassified Cast 1 Cast 2 Cast 3 SedimentDrill water 1.0 0.8 0.6 0.4 0.2 0 R el at iv e ab un d an ce Figure 2 | Phylogenetic analysis of SSU gene sequences obtained from the SLW water column, surficial sediment (0–2 cm) and drilling water. a, Cluster analysis of the microbial phylogenetic structure in the samples (top) and the relative abundance of bacterial and archaeal phyla in the water and sediment samples (bottom). The Proteobacteria were split into classes for greater detail. The asterisk indicates statistical significance (analysis of molecular variance, AMOVA, P value, 0.001). b, Phylogenetic analysis of bacterial and archaeal OTUs abundant in the SLW water column and sediments. The accession numbers of nearest neighbours and reference taxa are listed parenthetically. Bootstrap values are shown at the nodes. SLWphylotypes are bolded and followed by the percentage each represented in the water column (blue) and sediment (red) libraries. The scale bar indicates the number of nucleotide substitutions per position. a b d c Figure 3 | Morphological diversity of microbial cells in the SLW water column. a, Epifluorescencemicrograph showing a variety of cell morphotypes, whichwas confirmed by scanning electronmicroscopy (SEM; b–d). The yellow arrows in the SEM images indicate cells with rod (b), curved rod (c) and coccoid (d) morphologies. Scale bar, 2 mm. RESEARCH LETTER 3 1 2 | N A T U R E | V O L 5 1 2 | 2 1 A U G U S T 2 0 1 4 Macmillan Publishers Limited. All rights reserved©2014 is adequate to support heterotrophicmetabolism in the subglacial eco- system. The abundance of taxa related to nitrifiers22,25 in concert with elevated ammonium and D17O of NO3 values near 0% in the water column (Table 1) implies that nitrificationmay be a fundamental che- moautotrophic pathway of new organic carbon production in SLW. Similar conclusions regarding the ecological significance of nitrifica- tion have been drawn for thewater columnbeneath the Ross Ice Shelf26 and in McMurdo Sound27. Given the prevalence of subglacial water in Antarctica8, our data from SLW lead us to contend that aquatic micro- bial ecosystems are common features of the subsurface environment that exists beneath the,107 km2 Antarctic ice sheet. 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Acknowledgements The Whillans Ice Stream Subglacial Access Research Drilling (WISSARD) project was funded by National Science Foundation grants (0838933, 0838896, 0838941, 0839142, 0839059, 0838885, 0838855, 0838763, 0839107, 0838947, 0838854, 0838764 and 1142123) from the Division of Polar Programs. Partial support was also provided by funds from NSF award 1023233 (B.C.C.), NSF award 1115245 (J.C.P.), theNSF’s GraduateResearchFellowship Program (1247192; A.M.A.), the Italian National Antarctic Program (C.B.), and fellowships from the NSF’s IGERT Program (0654336) and the Montana Space Grant Consortium (A.B.M.). Logisticswereprovidedby the139thExpeditionaryAirlift Squadronof theNewYorkAir National Guard, Kenn Borek Air, and bymany dedicated individuals working as part of the Antarctic Support Contractor, managed by Lockheed-Martin. The drilling was directedbyF.Rack;D.Blythe, J.Burnett, C.Carpenter,D.Duling (chiefdriller),D.Gibson, J. Lemery, A. Melby and G. Roberts provided drill support at SLW. L. Geng, B. Vandenheuvel, A. Schauer and E. Steig provided assistance with the stable isotopic analyses. We thank J. Dore for assistance with the nutrient analysis. Author Contributions The manuscript was written by B.C.C. and J.C.P.; A.M.A. generated and analysed the molecular data; C.B., A.C.M. and M.L.S. conducted and interpreted the chemical measurements; S.P.C. and K.C. provided geophysical data; J.A.M. obtainedandexamined theCTDdata; A.B.M. andT.J.V. contributedandanalysed physiological and biogeochemical data; M.L.S. conducted and interpreted the isotopic analyses; and T.J.V. provided the micrographs. All authors contributed to the study design and acquisition of samples and/or data. Author Information The SSU sequence data are deposited in the NCBI SRA database under the accession number SRP041285. Reprints and permissions information is available at www.nature.com/reprints. The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper. Correspondence and requests for materials should be addressed to B.C.C. (xner@lsu.edu) or J.C.P. (jpriscu@montana.edu). WISSARD Science Team Members W. P. Adkins1, S. Anandakrishnan2, G. Barcheck3, L. Beem3, A. Behar4, M. Beitch3, R. Bolsey3, C. Branecky3, R. Edwards5, A. Fisher3, H. A. Fricker6, N. Foley3, B. Guthrie7, T. Hodson7, R. Jacobel8, S. Kelley5, K. D. Mankoff3, E. McBryan4, R. Powell7, A. Purcell9, D. Sampson3, R. Scherer7, J. Sherve5, M. Siegfried6 & S. Tulaczyk3 1Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana 70803,USA. 2Department ofGeosciences, Pennsylvania StateUniversity, University Park, Pennsylvania 16802, USA. 3Department of Earth and Planetary Sciences, University of California, Santa Cruz, Santa Cruz, California 95064, USA. 4School of Earth and Space Exploration, Arizona State University, Tempe, Arizona 85287, USA. 5Department of Land Resources and Environmental Science, Montana State University, Bozeman, Montana 59717, USA. 6Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, USA. 7Department of Geology and Environmental Geosciences, Northern Illinois University, DeKalb, Illinois 60115, USA. 8Physics Department, St Olaf College, Northfield, Minnesota 55057,USA. 9Department ofMicrobiology, University of Tennessee, Knoxville, Tennessee 37996, USA. LETTER RESEARCH 2 1 A U G U S T 2 0 1 4 | V O L 5 1 2 | N A T U R E | 3 1 3 Macmillan Publishers Limited. All rights reserved©2014 METHODS Site selection and description. SLWwas discovered using satellite laser altimetry and initially identified as a region (596 12 km2) of temporally varying surface ele- vation; it is one of 11 active subglacial lakes documented beneath the WIS5. SLW fills and drains every few years as part of a series of hydrologically linked subglacial lakes in the area, eventually draining to the ocean5,28,31. Ice-penetrating radar and active-source seismic data estimated that themaximum lake depth does not exceed 8 and 15m at low- and high-stand, respectively14,32. A lake-level rise of,5m from the low-stand lake level plus ice-flexural effects are sufficient to initiate flow over a drainage divide and trigger lake drainage. During a drainage event,,0.15 km3 of waterdrains in a six-month timeframeat awater fluxof,10m3 s21 (refs 5, 14). Thus, SLW is a shallow active hydrological reservoir beneath an active ice stream. The deepest point in the seismically detected water columnwas selected as the drill site (84.240u S 153.694uW;Fig. 1).Drilling and subglacial lake access occurredduring a near low-stand state in late January 201333. Hotwater drilling and clean access to SLW.Ahotwater drilling systemwas used between 23–27 January 2013 to melt through the,801m thick ice sheet, creating an access borehole (minimum diameter,60 cm) for direct sampling and to con- duct in situ measurements of the SLW water column and sediments. Microbial cells in the drilling water and on exposed surfaces of the hose, cables, and deployed equipment were reduced and killed through the use of four complementary tech- nologies: (1) filtration, (2) ultraviolet irradiation, (3) pasteurization, and (4) disin- fection with 3% w/v H2O2 (ref. 11). The drilling water, derived from the overlying ice sheet,was continuously circulated through awater treatment system that removed micron and sub-micron sized particles (.0.2mm), irradiated the drillingwaterwith two germicidalwavelengthsofultraviolet radiation (185nm,40,000mWs21 cm22 and 254 nm,175,000mWs21 cm22), and pasteurized the water at 90 uC to reduce the viability of persisting microbial contamination. Ports were plumbed along the system’s flow path, allowing discrete water samples to be obtained before and after each stage11. The drill hose and instrument cableswere deployed at a rate nogreater than 1m s21 through a custom borehole collar that contained 12 amalgam pellet ultraviolet lamps, providing a cumulative germicidal ultraviolet dosage of at least 40,000mWs21 cm22 (Arapahoe SciTech). All borehole sampling tools and instru- ments were spray-saturated with 3% w/v H2O2 and staged in sealed polyethylene bags until tool deployment. Single-use protective apparel (Tyvek) was worn by all personnel during borehole science operations. The efficacy of the clean access tech- nology andprocedureswere tested thoroughly before use in the field and are detailed elsewhere11. Drilling was conducted at a flow rate of,135 lmin21 to,700mbs, whereupon the drill was withdrawn, the borehole was inspected with video, and a hydrocast was conducted at 672mbs tomeasure the chemical andmicrobiological properties of the borehole water. To ensure that borehole water did not enter the lake upon breakthrough, the borehole hydrostatic pressurewas reduced by,35%(that is, the water levelwas lowered from80 to 108mbs)below the expected equilibration level for 800mof ice14. Drilling subsequently proceeded at the reduced flow rate of 19 lmin21, and at 08:02 on 27 January (UTC112), the load on the hose diminished as the drill reached,801mbs. Two minutes later, the head above the borehole water return pump (stationed at 110mbs) rose rapidly and remained at ,80mbs, confirming hydrostatic equilibration between the borehole and lakewater (that is, breakthrough to SLW). Importantly, the rise in borehole water confirmed that no drilling water entered the subglacial environment during breakthrough. To maintain the bore- hole and offset freeze back, thermal energywas added to the borehole by redeploy- ing the drill at a flow rate of,135 lmin21. Borehole reaming was conducted after breakthrough to the lake by slowly withdrawing the drill (,1mmin21). A second 24h reaming occurred 32 h after initial penetration of the lake to ensure successful deployment of all sampling tools. All in situmeasurements and discrete sampling occurred over a 3-day period. Temperature and depth. A SBE 19plusV2 SeaCAT Profiler CTD (Seabird Elec- tronics, Inc.) was used tomeasure temperature and depth within the borehole and lake water column. The instrumentwas deployed in profilingmode and lowered at a rate of,0.5m s21. Borehole depths are referenced to the snow surface in prox- imity to theborehole. Thewater columndepth inSLW(that is, thedistancebetween the ice–water interface and underlying sediments)was estimatedusingCTDdata to distinguish differences inwater mass upon entry to the lake water column from the borehole. Lake depthwas obtained from the top of the lake water mass to the depth where the sonde contacted the bottom.This depth estimatewas corroboratedwith a calibrated cable attached to a real-time borehole video camera. Water and sediment sampling. Following ref. 11, discrete samples of the drilling water (,20 l)were obtained at two timepoints during the drillingprocess. Samples ofwater from the input to the filtrationmodule, input to the borehole,water return- ing from the borehole, and a hydrocast at 672mbs before lake entry were collected and concentrated onto 142mm0.2mmSupormembrane filters (Pall Corporation). The filters were processed identically to those from the SLW water column (see below). Three discrete water samples were collected between 28 and 31 January 2013 at approximately mid-depth in the,2.2m SLW water column. Bulk water was col- lected using 10 l Niskin bottles and transferred via acid (10% HCl) leached silicon tubing tocleanbottles following the limnologicalproceduresoutlinedby theMcMurdo Long Term Ecological Research (LTER) Program34. SLW water column particulate matter for nucleic acid analysis was filter con- centrated in situ using a LargeVolumeWater Transfer System (WTS-LV) thatwas modified to fit theminimumborehole diameter of 30 cm (McLane Research Labo- ratories Inc.). TheWTS-LV has a 3-tier 142mm filter holder that accepts filters in series for size fractionation of particulates in the sample water. There were three separate casts of theWTS-LV in SLWand between 4.9 and 7.2 l of water was filter- concentrated during each 2 h deployment. In cast 1, the filter housing was loaded with a 10mmnylonmesh screen together with 3mm and 0.2mm Supormembrane filters. The filters for cast 2 and 3 had pore sizes of 3.0mm, 0.8mm, and 0.2mm. Imme- diately after recovery, the filter housingunitwas detached from thepumpandopened in a class 100 laminar flow hood. The filters were placed in sterile 142mm Petri dishes, sliced into quarters with a clean scalpel, and transferred to a cryovial that contained 7ml of DNA lysis solution (40mM EDTA pH8.0, 50mM Tris pH 8.3, 0.73M sucrose). The preserved samples were immediately frozen for transport to McMurdo Station and stored at 280 uC. Surficial sediments were collected using a multicoring device (Uwitec) that had a core barrel inner diameter of 59.5mm. Sediment porewaterwas obtained by insert- ingRhizon samplers35 (0.2mmpore size) through predrilled holes in the core barrel liner and extracted under negative pressure createdwith a 10ml sterile syringe. Sur- ficial sediment (0 to 2 cmdepth) from the coreswas sampled inside a class 100 clean hood using a cleaned core cutter (Uwitec). The sediment samples for molecular biological analysis were placed in 60ml sterile Nalgene bottles containing 10ml of the DNA lysis solution and frozen. Specific electrical conductivity (at 25 uC) and pH of the lake and sediment pore water were determined using a YSI model 3252 probe connected to a YSI model 3100 conductivitymeter and aBeckmanmodel 200pHmeter. Bothprobe andmeter combinationswere calibrated immediately before samplemeasurementsweremade. Inorganic and organic chemistry. Particulate organic C (PC) and N (PN) sam- ples from the water columnwere vacuum (,0.3 atm) filtered onto pre-combusted (450 uC for 4 h)WhatmanGF/F filters and analysed on aCE Instruments Flash EA 112 (ThermoQuest, San Jose, CA). The filters and sediment samples whichhad been dewatered via centrifugation were fumed for 24 h over fresh 12M HCl to remove inorganic carbon and dried for 24 h at 90 uC before analysis. Dissolved oxygen was measured using the azide modification of the mini-Winkler titration36. Dissolved inorganic carbon was measured by infrared gas analysis of acid sparged samples. Samples for dissolved inorganicN andPwere filtered through pre-combusted and 1% v/v HCl leached GF/F filters, collected in 1% HCl leached HDPE bottles, and frozen for shipment to theUSwhere nitrate, nitrite, ammonium, and soluble reac- tivePwere analysed colorimetrically34.Major ions andorganic acids fromSLWwater and sediment porewater were analysed on aMetrohm ion chromatograph using a C4 cation column and an aSupp5 anion column. Stable isotope analysis. Stable isotopemeasurementswere conducted at the Isolab (University ofWashington, Seattle).Measurements of oxygen isotope ratios of lake water and pore water samples were made using a Picarro cavity ring-down laser spectrometer. Nitrate for D17O determination in the water samples was concen- trated using an anionic resin37 followed by the bacterial reduction and thermal decomposition method38,39.D17O of NO3 was analysed with a Finnigan Delta Plus Advantage isotope ratio mass spectrometer. Isotope measurements are reported using standard dnotation in per thousand relative toVienna StandardMeanOcean Water (VSMOW). pH and oxidation-reductionmeasurements. Sediment pHwasmeasured with a Microelectrodes Inc. MI-407P needle pH electrode and aMI 401 Ag/AgCl2 micro reference electrode, calibrated with Orion low ionic strength buffers (pH4, 7, 10). Oxidation-reductionpotential (ORP)wasmeasured inSLWwaterwitha glass epoxy platinum electrode and a MI 401 Ag/AgCl2 micro reference electrode calibrated with Zobell’s solution and corrected to the standard hydrogen electrode (SHE). Cell and ATP concentration. Samples for cell enumeration from water and sedi- mentwere collected in combusted glass bottles and fixed in sodiumborate-buffered formalin (2%v/v). Sub-sampleswere filtered on black 0.2mmpolycarbonatemem- brane filters, stainedwith SYBRGold (Life Technologies), and immediately counted via epifluorescencemicroscopy. Sediment interference didnowallowaccurate deter- mination of cell density in sediment samples. Cellular ATP was measured in trip- licate as previously described11 and viable biomass was estimated from the ATP concentration using a carbon to ATP ratio of 250 by weight10,21. Scanning electronmicroscopy. Samples for scanning electronmicroscopy (SEM) were fixed with either 2% (w/v) formalin or 0.5% (w/v) glutaraldehyde and filtered RESEARCH LETTER Macmillan Publishers Limited. All rights reserved©2014 onto a 13mm diameter 0.2mm polytetrafluoroethylene (PTFE) filters. Following ethanol dehydration and critical point drying, the filters were attached to an alu- minium stub, coated with either gold or palladium, and observed on a Zeiss Supra 55VP Field Emission Scanning Electron Microscope. Heterotrophic and chemoautotrophic production. Heterotrophic productivity was measured using [3H]methyl-thymidine incorporation into DNA40 and [3H] leucine incorporation intoprotein41. Samples (1.5ml; 10 and5 live and10and5 tri- chloroacetic acid (TCA)-killed controls for casts 1 and 3, respectively) were incu- batedwith20nMradiolabelled thymidine (specific activity 20Cimmol21) or leucine (specific activity 84Cimmol21) at 4 uC in the dark for 175h (average). A separate time-course experiment (data not shown) revealed that incorporation was linear over this incubationperiod. Incubationswere terminatedby the additionof 100%w/v cold TCA (5% final). Following centrifugation, a series of washes with cold 5%w/v TCAandcold 80%v/v ethanolwereperformed.The final pelletwas dried overnight at,25 uC.Radioactivity in the pellet was determinedwith a calibrated liquid scin- tillation counter following the addition of 1ml of Cytoscint ES (MP Biomedicals). The rates of thymidine and leucine incorporation (nM TdR d21 or nM Leu d21) obtained at the incubation temperature (4 uC)were converted to the in situ temper- ature of20.49 uCusing an energy of activation of 48,821 Jmol21 determined from temperature gradient experiments (data not shown). Rates of macromolecular syn- thesiswere converted to carbonproductionusing 2.03 1018 cellsmol21 thymidine42 and 1.423 1017 cells mol21 leucine43, in concert with a cellular carbon content of 11 fg C cell21 (ref 44). For the sediment assays, a slurry was created by adding 1 g wet weight of sediment to 10ml of 0.2mm-filtered SLW water. The processing of the sediment slurries was identical to water samples except a total of three 80% v/v ethanol rinseswere performed to enhance the removal of unincorporated substrate. After drying, 200ml of tissue solubilizer (ScintiGest; FisherChemical)was added to eachvial. Themetabolic rate datawerenormalizedper gramdryweight of sediment. Dark CO2 fixation was determined in sterile 40ml glass vials filled to the top with sample (leavingnoheadspace) and cappedwithPTFE lined caps (10 and5 live and 10 and 5 TCA-killed for casts 1 and 3, respectively). The vials were amended with sterile [14C]bicarbonate (stock concentration5 0.1144 mCiml21) to a final experimental concentration of 1 mCiml21 and incubated in the dark at 4 uC for 281h (average). A separate time-course experiment (data not shown) revealed that incorporationwas linear over this incubation period. Incubationswere terminated by the additionof coldTCA(2.5%w/v final concentration) and filteringonto 0.2mm polycarbonate filters. The filters were placed in 20ml scintillation vials, acidified with 0.5ml of 3NHCl, and dried at 60 uC for 24 h. Radioactivity on the filters was determined with a calibrated liquid scintillation counter following the addition of 10ml of Cytoscint ES (MP Biomedicals). Molecular and phylogenetic analysis of SSU rRNA gene sequences. DNA was extracted from a portion of each filter (1/8 of a 142mm filter) using the Power Water DNA Isolation Kit and from sediments (,0.5 g wet weight) with the Power Soil DNA isolation kit (MO BIO Laboratories, Inc.). The extraction procedures followed those recommended by the manufacturer. The SSU rRNA gene was amplified using the oligonucleotide primers 515F and 806R, asdescribedpreviously45.Amplification reactions (50ml each)wereperformed using 5 units of AmpliTaqGoldDNApolymerase LD (Invitrogen), 13 PCRGold Buffer (Invitrogen), 3.5mMMgCl2, 10 pmol of each primer, 200mMdNTPs, and 0.1–3 ng of DNA template. After 9min of heat activation at 94 uC (AmpliTaq Gold DNApolymerase is a chemical hot-start enzyme), 35 cycles of PCRwere performed using the following amplification conditions: denaturation at 94 uC for 45 s, anneal- ing for 90 s at 50 uC, and elongation at 72 uC for 90 s, with a terminal elongation at 72 uC for 10min. The optimum number of cycles for PCRwas determined by suc- cessively lowering the cycle number so that false positive amplification was pre- vented while amplification was possible for the lowest biomass samples analysed. The concentration of the PCR products were determined using the Quant-iT Pico Green dsDNA Assay Kit (Invitrogen). The amplicons were pooled and cleaned with the MoBio UltraClean PCR Clean-Up Kit. Sequencing was performed using the Illumina MiSeq platform (Selah Genomics, Greenville, SC). Paired end sequence readswere assembled and quality filtered using theMothur46 phylogenetic analysis pipeline (v1.33.2). The sequenceswere alignedwith the SILVA IncrementalAligner47 (SINAv1.2.11; database release 115). The aligned readswere checked for chimaeras using theUchime algorithm48, as implementedwithinMothur, and chimaeric sequences were removed from the data. Sequences with.97% SSU rRNA gene sequence similarity were clustered into an OTU and representative sequences for each OTUwere chosen for classification using the SILVA database. Diversity and richness estimateswerecalculated inMothur46. Singletonswere excluded fromfurther analyses, and for simplicity of presentation, phyla representedby,1% of the sequence reads were grouped into the unclassified category (Fig. 2a). Com- munity comparisons using Yue and Clayton theta similarity coefficient analysis and Weighted Unifrac were also performed within Mothur. MEGA 5.2 software was used for phylogenetic analysis using maximum likelihood, the Jukes–Cantor nucleotide substitution model (1,000 iterations), and a 253 nucleotide alignment. Attempts to detect SSU sequences from eukaryotes were based on previously published methods50. 31. Carter, S. P. & Fricker, H. A. The supply of subglacial meltwater to the grounding line of the Siple Coast, West Antarctica. Ann. Glaciol. 53, 267–290 (2012). 32. Horgan, H. J. et al. Subglacial Lake Whillans—Seismic observations of a shallow active reservoir beneath a West Antarctic ice stream. Earth Planet. Sci. Lett. 331– 332, 201–209 (2012). 33. Siegfried,M. R., Fricker, H. A., Roberts,M., Scambos, T. A. & Tulaczyk, S. A decade of West Antarctic subglacial lake interactions from combined ICESat and CryoSat-2 altimetry. Geophys. Res. Lett. 2013GL058616, doi:10.1002/2013GL058616 (2014). 34. Priscu, J. C. LTER Limno Methods Manual – MCM_Limno_Methods_current.pdf. http://www.mcmlter.org/data/lakes/MCM_Limno_Methods_current.pdf (2013). 35. Seeberg-Elverfeldt, J., Schlu¨ter, M., Feseker, T. & Ko¨lling, M. Rhizon sampling of porewaters near the sediment-water interface of aquatic systems. Limnol. Oceanogr. Methods 3, 361–371 (2005). 36. American Public Health Association. Standardmethods for the examination of water and waste water (American Public Health Society Press, 1995). 37. Costa, A. W. et al. Analysis of atmospheric inputs of nitrate to a temperate forest ecosystem fromD17O isotope ratiomeasurements.Geophys. Res. Lett.38, L15805 (2011). 38. Casciotti. K. L. Sigman, D. M., Galanter Hastings, M., Bohlke, J. K. & Hilkert, A. Measurement of the oxygen isotopic composition of nitrate in seawater and freshwater using the denitrifier method. Anal. Chem. 74, 4905–4912 (2002). 39. Kaiser, J., Hastings, M. G., Houlton, B. Z., Rockmann, T. & Sigman, D. M. Triple oxygen isotope analysis of nitrate using the denitrifier method and thermal decomposition of N2O. Anal. Chem. 79, 599–607 (2007). 40. Fuhrman, J. & Azam, F. Thymidine incorporation as a measure of heterotrophic bacterioplankton production in marine surface waters: evaluation and field results.Mar. Biol. 66, 109–120 (1982). 41. Kirchman, D., K’nees, E. & Hodson, R. Leucine incorporation and its potential as a measure of protein synthesis by bacteria in natural aquatic systems.Appl. Environ. Microbiol. 49, 599–607 (1985). 42. Bell, R. T. Estimating production of heterotrophic bacterioplankton via incorporation of tritiated thymidine. In: Kemp, P. F., Sherr, B. F., Sherr, E. B. & Cole, J. J. (eds) Handbook of Methods in Aquatic Ecology (Lewis, 1993). 43. Chin-Leo,G.&Kirchman,D.Estimatingbacterialproduction inmarinewaters from the simultaneous incorporation of thymidine and leucine. Appl. Environ. Microbiol. 54, 1934–1939 (1988). 44. Kepner, R. L., Wharton, R., Jr & Suttle, C. A. Viruses in Antarctic Lakes. Limnol. Oceanogr. 43, 1754–1761 (1998). 45. Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012). 46. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009). 47. Pruesse, E., Peplies, J. & Glo¨ckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012). 48. Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection.Bioinformatics27,2194–2200 (2011). 49. Holland, H. D. The Chemistry of the Atmosphere and Oceans (Wiley, 1978). 50. Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes.PLoSONE 4, e6372 (2009). LETTER RESEARCH Macmillan Publishers Limited. All rights reserved©2014 Extended Data Table 1 | Crustal and seawater components to SLW waters *Average values for hydrocasts 1, 2 and 3. {Calculated using Cl2 concentrations and ratios of each species to Cl2 in seawater in meq l21; Na1 0.859, K1 0.019, Mg21 0.195, Ca21 0.037, F2 0.00013, SO4 22 0.103, and HCO3 2 0.004 (ref. 49). {Calculated by subtracting the seawater component from the average SLW solute concentration for each ion. 1Negative values indicate the potential for ion exchange of Mg21 with other cations on clay minerals present in suspended sediments of SLW. RESEARCH LETTER Macmillan Publishers Limited. All rights reserved©2014 Extended Data Table 2 | Summary of parameters for the SLW SSU gene sequence data *Sequences remaining after quality filtering, and removal of chimaeric sequences and singletons. {OTUs that passed quality filtering, excluding singletons. {Calculated using Mothur46. LETTER RESEARCH Macmillan Publishers Limited. All rights reserved©2014 W W W. N A T U R E . C O M / N A T U R E | 1 SUPPLEMENTARY INFORMATION doi:10.1038/nature13667 Supplementary Discussion Solute sources for SLW waters. The Cl- to Br- ratios of SLW waters averaged 0.00164, which is close to that for seawater (0.00156)49. Thus a parsimonious assumption is that all Cl- and Br- in SLW was from a seawater source. The average Cl- concentration of 3.5 mmol L-1 in SLW represents a dilution relative to seawater of ~154-fold, indicating that seawater was a volumetrically minor contribution to the lake water. The seawater component for other major anions and cations can then be calculated using Cl− concentrations and ratios of each species to Cl− in seawater in μeq L-1 (Extended Data Table 1). The crustally-derived component of solute to SLW was determined by subtracting the seawater values for individual ions from the average SLW composition (Extended Data Table 1). This calculation results in negative values for Mg2+, indicating a deficit of Mg2+ relative to seawater ratios. A process that could account for the Mg2+ deficit is an ion exchange reaction with other cations on clay minerals present in suspended sediments of SLW. Theoretical and observational data indicate that seawater may penetrate no further than a few kilometers inland of the low-tide grounding line, making a seawater incursion to SLW (~100 km from the grounding line) extremely unlikely. Therefore, we hypothesize that the seawater source is from pre-existing marine pore waters in sediments beneath and upstream of SLW. The inorganic nitrogen pool within the water column of SLW was dominated by ammonium relative to nitrite and nitrate (Table 1). Since mineral sources of ammonium are minor, the majority of the ammonium is presumed to originate from the microbial mineralization of nitrogen-containing organic material in the sediments, which could diffuse into the water column and also be transported to SLW from upstream portions of the subglacial hydrological network. A 1:1 relationship would be expected between NH4+ loss and NO3- gain unless N2O or other intermediates were being produced by nitrification. Given the low oxygen concentrations, high NH4+, and SSU sequence data suggesting that nitrifying taxa were abundant in the SLW water column (Fig. 2b), the production of N2O is likely51. Unfortunately, we do not have N2O concentration data for SLW water. The unexpectedly low nitrate level may also result from denitrification in the sediment surface layers or in low oxygen microzones associated with suspended sediment particles. Our sequence data revealed the presence of known denitrifiers (Thiobacillus denitrificans) and nitrate reducers (e.g., species of Polaromonas), supporting this contention. Molecular analysis of SSU gene sequences. Paired-end sequencing of the V4 region of the SSU gene generated 3,556,417 sequences from the SLW water column, 1,361,815 from the drill and borehole water, and 561,966 from the surficial sediment samples. After quality filtering and removal of chimeric sequences, 2,686,526, 984,412, and 333,600, respectively, reads were used for phylogenetic analysis (Extended Data Table 2). Calculation of sequence coverage (Extended Data Table 2) and collector curves (data not shown) indicated the depth of sequencing to be sufficient to describe the abundant members in the SLW water and sediment communities. SUPPLEMENTARY INFORMATION 2 | W W W. N A T U R E . C O M / N A T U R E RESEARCH Analysis of molecular variance (AMOVA) in data obtained from three casts of the WTS- LV showed no statistical difference amongst the casts (pair wise p-values ≥ 0.69); therefore, all molecular data from the water column were compiled. Estimations of species diversity in the lake water revealed a community diversity comparable to many surface aquatic environments52. Of the 3,931 OTUs identified in the SLW water column, 3,105 (87% of the total sequence reads) and 30 (3.6% of the total sequence reads) classified within the Bacteria and Archaea, respectively, while 793 OTUs were not classified. The bacterial and archaeal OTUs were classified into 32 and 2 phyla, respectively (Fig. 2a). The majority of OTUs were taxonomically affiliated with the Proteobacteria (1,893 OTUs; 53% of all sequences) and Actinobacteria (401 OTUs; 11% of all sequences). Within the proteobacterial OTUs from the water column, 84% of the sequences classified within the beta- and delta- classes. Phylotypes most closely related to species in the genera ‘Candidatus Nitrotoga’, Polaromonas, and Sideroxydans were the 1st, 2nd, and 3rd, respectively, most abundant OTUs in the dataset. Highly abundant actinobacterial phylotypes were most closely related to SSU gene sequences reported previously from polar lake environments (e.g., ref 53). Most of the archaeal phylotypes were classified as Thaumarchaeota, with one OTU from this group representing the 5th most abundant phylotype. Phylotypes that were abundant in the water column and surficial sediment (Fig. 2b) were very rare (OTU 1756, 0.003%; OTU 10327, 0.007%; OTU 2522, 0.002%; and OTU 1767, 0.001%) or not observed (OTU 1901, OTU 5861, and OTU 1005) in data obtained from the drill water assemblage. Nearly all the SSU gene sequences characterized from the surficial sediment were bacterial (1,935 OTUs; 94%), with only 0.3% classifying within the Archaea. Proteobacteria were the most abundant phylum, with the beta- and gamma- classes representing 65% of the OTUs within this group. Similar to observations for the SLW water column, phylotypes most closely related to species of Sideroxydans and ‘Candidatus Nitrotoga’ were the most abundant OTUs (1st and 2nd, respectively). However, phylotypes that classified within the genera Thiobacillus, Nitrosospira, and Methylobacter were enriched in the sediments relative to the water column (<0.7% of all sequences in the water column). Cluster analysis of the water column, sediment, and drilling water community structure indicated that the SLW water and surficial sediments were not statistically different; however, the drilling water was statistically different from the water column and sediment environment (Figure 2a). Samples of the drilling water contained no OTUs that classified as archaeal, and only 41 OTUs (<1%) were unclassifiable at the domain level. The Proteobacteria and the Firmicutes were the most abundant phyla in the dataset, representing 70% and 20% of the sequences, respectively. The most abundant phylotypes were most closely related to species of Janthinobacterium and Tumebacillus, with each representing ~19% of the dataset. Many of the other abundant OTUs in the drilling water were closely related to sequences and isolates observed previously in icy environments, including Antarctic ice cores54. W W W. N A T U R E . C O M / N A T U R E | 3 SUPPLEMENTARY INFORMATION RESEARCH Supplementary Discussion References 51. Goreau, R. E. et al. Production of NO2- and N2O by nitrifying bacteria at reduced concentrations of oxygen. Appl. Environ. Microbiol. 40, 526–532 (1980). 52. Biers, E. J., Sun, S. & Howard, E. C. Prokaryotic genomes and diversity in surface ocean waters: interrogating the global ocean sampling metagenome. Appl. Environ. Microbiol. 75, 2221-2229 (2009) 53. Mosier, A. C., Murray, A. E. & Fritsen, C. H. Microbiota within the perennial ice cover of Lake Vida, Antarctica. FEMS Microbiol. Ecol. 59, 274-288 (2007) 54. Raymond, J. A., Christner, B.C. & Schuster, S. C. A bacterial ice-binding protein from the Vostok Ice Core. Extremophiles 12, 713-717 (2008) 159 CHAPTER SEVEN SUBGLACIAL CARBON AND NUTRIENT FLUXES FERTILIZE THE SOUTHERN OCEAN UNDER THE ROSS ICE SHELF Contribution of Authors and Co-Authors Manuscript in Chapter 7 Author: Trista J. Vick-Majors Contributions: Collected samples, oversaw sample collection, performed microbiological activity assays, performed phosphorus analyses, assisted with nitrogen analyses, performed excitation-emission matrix spectroscopy, performed statistical analyses, analyzed data, prepared figures and tables, and wrote the manuscript. Co-Author: Alexander B. Michaud Contributions: Performed sediment porewater extractions, assisted with nitrogen analyses, commented on manuscript. Co-Author: John C. Priscu Contributions: Oversaw the study and sample analyses, commented on the manuscript. 160 Manuscript Information Page Trista J. Vick-Majors, Alexander B. Michaud, John C. Priscu Nature Status of Manuscript: _X_ Prepared for submission to a peer-reviewed journal ____ Officially submitted to a peer-review journal ____ Accepted by a peer-reviewed journal ____ Published in a peer-reviewed journal Nature Publishing Group 161 Subglacial carbon and nutrient fluxes fertilize the Southern Ocean under the Ross Ice Shelf The following work is prepared to be submitted to Nature. Trista J. Vick-Majors1, Alexander B. Michaud1, John C. Priscu1 1Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT Active subglacial water systems, once thought to be sealed beneath the Antarctic Ice Sheet (AIS), ultimately drain into the Southern Ocean1 forming a conduit between subglacial and marine carbon and nutrient pools and connecting continental and oceanic biogeochemical processes. Antarctic subglacial water is estimated to contain 5.1 Pg of organic carbon2, equivalent to the ~5.4 Pg contained in the entire Antarctic Ice Sheet3, while relict marine sediments beneath the AIS may contain ten times the amount in northern permafrost4. The recent discovery of microbial ecosystems beneath the AIS in Subglacial Lake Whillans (SLW) 5 allows us to include the activities of subglacial microorganisms in estimates of subglacial-to-ocean carbon and nutrient fluxes. Here we show that subglacial organic matter can contribute substantially to the dark ocean waters beneath the Ross Ice Shelf (RIS), where the availability of organic matter may limit rates of heterotrophic carbon mineralization6. Our data reveal that subglacial carbon sources can explain dissolved organic carbon concentrations in the SLW water column within predicted water residence times. We demonstrate that fluxes of biologically relevant solutes from subglacial aquatic environments along the Siple Coast are sufficient to drive heterotrophic biological activity locally (at the grounded margin of the West Antarctic Ice Sheet), and perhaps regionally (in the RIS subglacial cavity). We conclude that microbial 162 activity in hydraulically active subglacial aquatic environments plays a key role in the fixation and mobilization of organic matter from beneath the AIS to the Southern Ocean. Subglacial water from the Siple Coast drains to coastal embayments beneath the Ross Ice Shelf (RIS), forming subglacial estuaries7. Subglacial outflow accounts for a substantial portion of the variability in the RIS cavity freshwater budget and is significant in the overall freshwater budgets of the coastal embayments8, but there is substantial uncertainty regarding the potential importance of biologically relevant solutes contained in subglacial outflow. An estimated 5.4 Pg of organic carbon is stored in Antarctic subglacial aquatic environments2, but estimates of subglacial-to-ocean fluxes have been hindered by lack of quantitative information on actual subglacial concentrations of carbon and nutrients. Quantifying carbon and nutrient fluxes is key to understand sub-ice shelf biogeochemical processes. Life in the darkness beneath the RIS9, where water residence times span ~1 - 8 years10,11, depends on carbon and nutrients advected from open water (>1000 km away from the ice shelf interior), carried from the continent by the ice itself and/or subglacial water, or combinations of both sources. As Antarctic ice shelves are increasingly threatened with climate-change-induced collapse12, understanding sub-ice-shelf biogeochemical processes and their linkages with the open ocean and the Antarctic continent become progressively more important. Subglacial Lake Whillans (SLW), an active subglacial lake along the Siple Coast1 is the only Antarctic subglacial lake directly sampled in a microbiologically and chemically clean manner to date13. SLW lies 800 m beneath the surface of the Whillans Ice Stream (WIS) and hosts an active microbial ecosystem supported by 163 chemoautotrophic activity5. The WIS is the largest contributor to subglacial groundwater along the Siple Coast, and its long sedimentary (till) pore water residence times (1000 - 10,000 years) indicate that it should be rich in solutes that can contribute both to subglacial biological activity and nutrient rich subglacial outflow. The residence time for the bulk water column in SLW is much shorter (years to decades) 1,14. Basal ice melt ( ~1.8 cm y-1, ref. 15) at the lake and upstream of the lake comprises the primary source of water to SLW5, which has filled and drained three times during the past 12 years14,16. The sampling of SLW in early 2013 occurred when the lake was at relatively low stand during the slow fill phase. The temporal variability of biological activity and biologically relevant solutes are not known, however, the waters and sediments of SLW were characterized by relatively high levels of organic matter, nutrients (Table 1), and biological activity at the time of sampling. Table 1. Dissolved and particulate matter in SLW. DOC = dissolved organic carbon, POC = particulate organic carbon, DON = dissolved organic nitrogen, PN = particulate organic nitrogen, DIN = dissolved inorganic nitrogen, DOP = dissolved organic phosphorus, PP = particulate phosphorus, SRP = soluble reactive phosphorus *reported in Christner et al. The dissolved organic carbon (DOC) concentration measured in SLW was similar to other Antarctic terrestrial aquatic systems (i.e., trophogenic zones of perennially ice covered lakes in the McMurdo Dry Valleys17). Concentrations were ~7 times higher than Parameter DOC* POC* DON PN* DIN* DOP PP SRP* Concentration ( g l-1) 2650 942 33.6 16.8 46.2 189 46.5 96.0 Total Pool size (x105 kg) 3.2 1.1 0.04 0.02 0.06 0.23 0.06 0.12 164 wintertime values in the Ross Sea18 and summer time concentrations under the northeastern edge of the McMurdo Ice Shelf 19 (~0.4 mg l-1), where water enters the RIS cavity from McMurdo Sound. DOC serves as the primary source of energy and biomass for heterotrophic bacteria and archaea in the sea. Low concentrations of DOC in water advected under the RIS suggest that a DOC-rich source from subglacial outflow may be biologically important to heterotrophic bacteria and archaea in the Southern Ocean, which may be carbon-limited6. Subglacial dissolved organic matter (DOM) is N-poor relative to C (mass ratio of DOC:DIN = 57, DOC:DON = 79; Table 1), indicating a relatively recalcitrant nature. Recalcitrant or semi-labile DOC is biologically reactive, but remineralization usually occurs over long timescales (months to decades) 20. Mixing with nutrient-rich water can stimulate remineralization of recalcitrant DOM; N-rich water (molar ratio of DOC:DIN ~1.7, DIN 0.35 mg l-1) entering the RIS cavity from McMurdo Sound19 may supplement C-rich subglacial outflow to support biological activity beneath the RIS. Waters beneath the AIS are not atmospherically ventilated and, similar to other Antarctic aquatic environments, do not receive terrestrial (vegetation-derived) inputs of DOM or particulate organic matter (POM). The ultimate source of organic matter beneath the WIS is thought to be relict marine organic matter deposited during previous incursions of sea water21, chemoautotrophic production by bacteria and/or archaea inhabiting the subglacial waters and sediments, or a combination of both. Reworking of organic matter by heterotrophic bacteria and/or archaea may be a secondary source of (often refractory) DOM22. Parallel factor analysis (PARAFAC) of fluorescence signatures 165 of DOM (FDOM; derived from excitation-emission matrix spectroscopy “EEMS”) in SLW sediment pore waters (Figure S1) showed the presence of four fluorophores (Figure S2), including two amino acid-like fluorophores (tyrosine and tryptophan, F3 and F4, respectively), which indicate microbial production of DOM. F3 (ex/em 270/300 nm) and F4 (ex/em <250,270/336 nm) accounted for 69.8% of total FDOM in the surface sediments (0-2 cm), and decreased in relative abundance with depth, down to 49.5% at the bottom of the profile (36-38 cm). While the relative abundance of F3 and F4 decreased with depth, two humic-like fluorophores, F1 (ex/em 250,300/380 nm) and F2 (ex/em 250,340/454 nm), increased at a rate of 0.5% cm-1, with F1 dominating most of the depths below 18 cm (Figure 1). Humic-like fluorophores may indicate the presence of more recalcitrant DOM deeper in the sediments, however, recalcitrant DOM may still be microbially produced23. Indeed, F1 (ex/em 300/380) was most similar to a microbially derived component from a McMurdo Dry Valley lake24, which also shared characteristics with the typical marine humic peak25 and is associated with recent biological activity in the ocean26, and with processing of organic matter by microbial communities in Antarctic mountain glaciers27. F2 was most similar to the mixture of humic peaks “A” and “C” identified in ref. 25, which are commonly associated with coastal environments. A similar peak was also identified in permanently ice-covered Antarctic McMurdo Dry Valley lakes, where it was associated with microbial production of DOM24, and in the deep ocean, where it was related to microbial processing of organic matter26. In the water column, a single FDOM peak was present, with tyrosine-like fluorescence of ex/em 240/310 (Figure S3), as 166 opposed to the tyrosine-like F3 observed in the sediments (ex/em 270/300), implying different processing of DOM in the water column versus the sediments. Figure 1. Profiles of organic matter and nutrients in SLW. Dissolved organic carbon (DOC), dissolved organic nitrogen (DON), ammonium, nitrate, phosphate (mg l-1) and FDOM components (as % of total fluorescence) from SLW sediment porewater and water column. Water column FDOM could not be modeled with sediment porewater FDOM. The single FDOM peak present in the water column was most similar to sediment component F3. Tyr = tyrosine-like fluorescence, trip = tryptophan-like fluorescence. The dashed line indicates the sediment-water interface. The FDOM data indicate that the ultimate source of DOM (or DOC) in SLW is microbial activity, however, the size of the observed DOC pool in SLW (3.2 x 105 kg C; Table 1) cannot be explained by microbial activity in the lake alone. Likely other sources of DOC to the SLW water column are surface flow during lake filling (see methods), inflow of till porewater (see methods), chemoautotrophic production by prokaryotes5, upward flux from the sediments, and ice melt3,15 (Table 2), which total 2.7 x 104 kg y-1 . 167 Chemoautotrophic activity comprises 5% of the annual DOC supply, with inflow from upstream and water flowing out of the till comprising the major sources of DOC (92%). The major sink for DOC in the lake is biological activity. Sources x 103 kgC y-1 Sinks x 103 kgC y-1 Surplus x 103 kgC y-1 Accumulation time (years) Inflow Till water Chemoauto- trophy Sediment flux Ice melt Heterotrophic Production Heterotrophic respiration 26.0 12.3 18.6 6.25 1.44 0.50 0.16 0.083 0.876 Table 2. Sources and sinks of organic carbon in SLW. The accumulation time for the observed DOC pool is based on the annual surplus. The “prokaryotic carbon demand” (PCD = 959 kg y-1, where PCD is the sum of carbon respired and the carbon incorporated into biomass by heterotrophic bacteria/archaea;) in SLW is 28 times less than the DOC supplied to the lake on an annual basis, resulting in most of the DOC inputs accumulating as surplus (Table 2). Annual DOC sources exceed the size of the PCD sink by two orders of magnitude, explaining the accumulation of the observed DOC pool in SLW in 12 years (Table 2). A 12 year accumulation time is remarkably similar to the decadal scale fill- drain cycles predicted for this region14. The agreement in residence and accumulation time estimates indicates that our carbon budget for the lake is reasonable; even the removal of either of the two largest and least well-characterized sources (inflow from upstream, for which the actual DOC concentration was estimated, and outflow of till porewater to the surface) increases the accumulation time to 43 or 16 years, respectively, while the removal of both increases accumulation time to 279 years. The magnitude of the biological sources and sinks of N and P in the SLW water column are unknown, but 168 inflow of till water containing the average SLW till porewater concentrations of N and P (Figure 1) would produce the observed N and P pools in <10 years and >100 years, respectively. Subglacial water drainage from the Siple Coast ice streams crosses the grounding line of the West Antarctic Ice Sheet during lake discharge events and enters the ocean cavity beneath the RIS at a rate of between 0.82 and 15.8 km3 y-1 (average, 1.9 km3 y-1, ref. 8). Although, concentrations of biologically relevant solutes in subglacial water may vary across the Siple Coast values from SLW allow us to constrain the importance of subglacial water in fertilizing the sub-RIS cavity. Over the range of outflows reported for the Siple Coast, the input of organic carbon to the sub-RIS cavity ranges from 2.9 x 106 kg y-1 to 42 x 106 kg y-1, with an average of 6.8 x 106 kg y-1 (74% in the dissolved fraction, 26% in the particulate fraction). Inputs of N and P are 10 fold lower than for carbon, and both are dominated by organic fractions. The mass of organic carbon supplied to the RIS from the Siple Coast is ~three orders of magnitude lower than the mass of organic carbon supplied annually to the Southern Ocean from primary production (~4400 Tg, ref. 28), however, fluxes from subglacial environments can provide important carbon, energy and nutrient sources to the biological community in the sub-RIS cavity9. Heterotrophic prokaryotic production under the RIS, based on the incorporation of 3H- thymidine at the Ross Ice Shelf Project J9 borehole, was 0.73 ng C l-1 d-1 (ref. 9). Assuming bacterial stoichiometry of 45:9:1, (C:N:P , ref. 29) N and P uptake can be estimated to be 0.17 ng N l-1 d-1 and 0.04 ng P l-1 d-1. Based on our model, subglacial outflow estimates from the Siple Coast can meet between 8% and 169% (avg 20%) of C 169 demand, 1% to 19% (avg 2%) of N demand, and 14% to 273% (avg 33%) of P demand under the entire RIS (Table 3). Nutrient kg y -1 outflow (x 106) BP demand (ng l-1 d-1) % of BP met by outflow Avg Min Max Avg Min Max Organic C 6.8 2.9 42 0.73 20.4% 8.8% 169.8% DIN 0.09 0.04 0.73 0.17 2.4% 1.0% 19.6% Organic N 0.10 0.04 0.80 SRP 0.18 0.08 1.5 0.04 32.8% 14.2% 273.0% Organic P 0.45 0.19 3.7 Table 3. Organic matter and nutrient supply to the Ross Ice Shelf cavity. Outflow from the Siple Coast is compared to estimated carbon and nutrient demand by heterotrophic bacteria and/or archaea (BP demand). Estimated BP demand was determined from thymidine turnover times reported in ref. 9 and the bacterial C:N:P stoichiometry reported in ref. 29. DIN=dissolved inorganic nitrogen. SRP=soluble reactive phosphorus. Organic C, N, and P are the sum of dissolved and particulate fractions. Our data show that the relatively high concentrations of organic matter and nutrients observed in SLW are microbially derived and can accumulate within a reasonable water residence time, based on known hydrology. The accumulated pools of biologically-relevant solutes can be released in subglacial outflow from the Siple Coast of West Antarctica to fertilize the sub-RIS cavity at biologically significant rates. The utilization of subglacially released DOC by heterotrophic bacteria/archaea and the degradation of POC by macrofauna30 or microbial extracellular enzymes31 may support life under the RIS. Microbial activity supported by subglacial outflow under the RIS should result in the further production of recalcitrant or semi-labile DOM23,32, giving 170 subglacial outflow an important role in ocean biogeochemistry and carbon storage in the ocean. Methods Sample Collection Subglacial Lake Whillans (SLW) water and sediment were collected through a ~0.6 m diameter borehole which was created with a microbiologically-clean, hot water drill 33-35. Lake water samples were collected with a 10 L Niskin bottle and sediments were recovered using a gravity multicorer (Uwitec). Full details of the clean access protocol, drilling and sample recovery are described elsewhere13,36. Briefly, three discrete 10 L water samples were collected at mid-depth in the ~2.2 m water column using a Niskin bottle on January 28 and 30, 2013 and returned to the on-site laboratory for processing. After inverting the Niskin bottle 3 times, water was decanted into acid- washed (1% hydrochloric acid; rinsed 5X with ultra-pure water) and autoclaved opaque high density polyethylene (HDPE) bottles (biological assays), acid-washed low density polyethylene (LDPE) bottles (dissolved N and P), or either acid washed fluorinated HDPE bottles (Thermo Scientific, Nalgene, Waltham, MA) or acid washed and combusted glass bottles (particulate and dissolved organic matter). Sediment porewater samples were extracted from a sediment core (multicore 3B) collected on 30 January 2013 for analysis of nitrate, phosphate, and dissolved organic matter using Rhizon porewater samplers (Rhizosphere) 37. Rhizon samplers (0.1 µm filter pore size) were soaked in MilliQ water prior to installation through pre-drilled holes in 171 the sediment core liner. A 10 ml syringe was attached to the outlet and the plunger locked to maintain a vacuum. After 14 h of extraction, the porewater was dispensed into cleaned vials (nitrate and phosphate: 10% HCl acid washed, MilliQ rinsed (6X); dissolved organic matter: 10% HCl acid washed, MilliQ rinsed (6X), combusted for 4 hr at 450 oC). Procedural blanks using MilliQ water were handled in parallel to samples and used to determine background introduced by the Rhizon extraction. Background was subtracted out of all analyses. Nitrate and phosphate samples were stored frozen at -20 oC and DOM samples were stored at 4 oC and returned to Montana State University for analysis. The sediment core used for determination of porewater ammonium was frozen immediately after collection on January 30, 2013, and stored at -20 oC until processing at Montana State University. Organic Matter and Nutrients WMter column samples for DOC and three dimensional spectrofluorometric characterization of dissolved organic matter (excitation-emission matrix spectroscopy; EEMS) were filtered through acid-leached and combusted (>4 h at 450 °C) 25 mm glass fiber filters (GF/F, effective retention size 0.7 μm). The filtrate was collected in acid washed and combusted (>4 h at 450 °C) 125 ml amber borosilicate glass bottles fitted with polytetrafluoroethylene (PTFE) lined caps and stored at 4 °C until analysis. DOC and total nitrogen (TN) concentrations were determined in water column and sediment porewater samples using a Shimadzu TOC-V Series TOC analyzer following acidification with hydrochloric acid to pH ≤ 2 to remove inorganic carbon as CO2. 172 Dissolved organic nitrogen (DON) was determined by subtracting NO2-, NO3-, and NH4+ (water column5) or NO3- and NH4+ (sediment porewater) from TN. EEMS were determined with a Horiba Jobin Yvon Fluoromax-4 Spectrophotometer (Horiba, Ltd., Japan) equipped with a Xe light source using a 1 cm path length quartz cuvette. Excitation data were measured every 10 nm from 240 nm to 450 nm, and emission data every 2 nm from 300 nm to 560 nm. Measurements were corrected for background (0.2 µm filtered Milli-Q water), Raman scattering, and inner-filter effects using absorbance spectra collected between 190 nm and 1100 nm with a Genesys 10 Series Spectrophotometer (1 cm path length, Thermo Scientific) 38. We used parallel factor analysis (PARAFAC) to decompose the trilinear EEMS arrays and derive a four component model describing the fluorescence characteristics of the porewater DOM using the drEEM toolbox (version 0.2.0)39 for Matlab. Porewater NO3- and PO4- concentrations were determined via ion chromotography (Metrohm, described in Michaud et al, 2015 submitted). Sediments for dissolved NH4+ were thawed at Montana State University in an ISO class 1000 cold clean room at 4°C and subsampled every 2 cm by extruding and slicing. The 2 cm sections were transferred to acid washed (10% HCl), MilliQ water-rinsed (6X), combusted (4h at 450°C) glass vials with polytetrafluoroethylene lined caps, frozen at -20°C and thawed prior to analysis. Sediments were transferred from the glass vials to acid washed and MilliQ rinsed 50 mL conical centrifuge tubes and centrifuged at 3500 x g for 20 minutes. The supernatant was transferred to acid washed and MilliQ rinsed 15 ml conical centrifuge tubes and spun for an additional 20 min at 4500 x g to pellet any remaining fine 173 particulates. The clean supernatant from the 15 mL centrifuge tube was transferred to an acid washed and MilliQ rinsed glass vial. The supernatant was diluted (1:10) to a final volume of 5 mL with MilliQ water for colorimetric analysis40. Total dissolved and particulate phosphorus (TDP and PP) were determined colorimetrically41 on water column samples. Soluble reactive phosphorus (3.1 µm) reported previously5 was subtracted from TDP (soluble non-reactive phosphorus) to approximate dissolved organic phosphorus (DOP). Bacterial Carbon Demand and Respiration of Leucine Heterotrophic bacterial respiration was measured by adding 60 mL of sample water to an autoclaved amber HDPE bottle (Nalgene) followed by the addition of uniformly labeled 14C-L-leucine (specific activity >300 mCi mmol-1; final leucine concentration 60 nM; final activity 0.0180 µCi mL-1) 42. Five-milliliter aliquots of the radiolabeled sample were added to autoclaved 25 mL glass side arm flasks (6 live and 6 TCA killed controls; 250 μL of cold 100% TCA). The top of the flask was sealed with a butyl rubber septum holding a small basket containing a folded GF/C filter suspended above the aqueous phase; the sidearm was sealed with a butyl rubber septum. Following incubation in the dark for 105 h at 2-4 °C (linearity of the rate of label incorporation was determined elsewhere), the reactions in live incubations were terminated by injecting cold 100% TCA (final concentration 5%) into the sample through the sidearm which lowered the pH to ≤ 2. β-phenylethylamine (100 μl) was added to the GF/C filter through the septum with a needle and syringe to trap respired CO2. Killed samples were maintained at ~25 oC for 174 24 hours with occasional gentle swirling to liberate respired CO2 from the aqueous phase. Care was taken not to splash the aqueous phase of the incubation onto the GF/C filter or basket holding the filter. Cellular 14C incorporation was determined on the liquid fraction following filtration onto 0.2 μm polycarbonate filters. The GF/C and polycarbonate filters were placed in 20 mL scintillation vials followed by the addition of 10 mL of Cytoscint- ES and the 14C activity was determined using a calibrated scintillation counter. 14C- leucine uptake and respiration were converted to units of carbon as described in ref. 19, and total heterotrophic prokaryotic carbon demand was calculated as the sum of respired carbon and carbon fixed into biomass. Flux Estimates We converted all chemical concentrations to total mass (kg) using a total SLW volume of 0.12 km3 at the time of sampling (water column depth, 2.2 m, ref. 5; lake surface area ~60 km2, ref. 43). The mass of carbon supplied to SLW annually was determined as follows: DOC concentration in water flowing in from upstream was assumed to be the same as that determined for SLW5 and the volume of water entering the lake was determined for the year before sampling14,16; till porewater inflow concentration was assumed to be the average of sediment porewater values measured in this study, and the volume of till porewater inflow was reported previously44 and modified to include inflow only over the surface area of SLW; the contribution from chemoautotrophic production was determined previously5; sediment porewater to water column fluxes were determined using Fick’s first law with tortuosity and temperature corrected diffusion coefficients45,46 assuming a well-mixed water column, from 1 cm 175 below the sediment surface to the surface of the sediments; DOC from ice melt was determined based on melt rate (1.728 cm y-1; ref. 15) and the published DOC concentration for the AIS (0.15 mg l-1; ref. 3). The total prokaryotic carbon demand (PCD) was subtracted from the sum of the organic carbon sources to determine the annual carbon surplus in the lake, assuming a steady state system (dDOC/dt = inflow + till water + chemoautotrophy + porewater diffusion + ice melt - PCD = 0). The surplus was then divided by the total mass of the SLW DOC pool to determine accumulation time. To determine potential outflow to the sub-RIS cavity, we used the total mass of each carbon and nutrient pool in SLW to calculate a concentration of each solute per km3 of water. We then used the range of annual water outflow volumes reported previously for the Siple Coast ice streams8 to calculate the total mass of each solute carried across the grounded margin of the ice sheet into the the sub-RIS cavity annually. To determine the contribution per unit water volume under the RIS, we used a previously reported11 sub-RIS volume estimate of 125,333 km3. 176 Supplemental Information EEMS and PARAFAC Results and Analysis Our 4-component PARAFAC model (Figure S2) explained 99.7% of the variability in the sediment porewater EEMS dataset and had a core consistency of 58.3%. Our relatively small number of available samples (19, of which 7 water column samples were excluded from the model as outliers) may have limited our ability to validate models with more components, however, the low residuals (99.7% variability explained), along with manual analysis of the corrected EEMS profiles indicated that 4 components is reasonable. The four component model also minimized the sum of squares error (SSE=0.15) and converged after 62 iterations with random starts and non-negativity constraints. The model was successfully split-half validated using alternating splits of 6 samples per split. We were unable to include water column samples in our model, as their fluorescence characteristics made them outliers relative to the sediments (determined through visual examination of the spectra and preprocessing with the drEEM toolbox) and the small number of water column samples precluded the construction of a water column only PARAFAC model. However, manual “peak picking” (i.e. 25) revealed the presence of a single peak, indicative of protein-like fluorescence (ex/em 240/310; Figure S3). 177 Figure S1. Fluorescence matrices for sediment porewater samples. The EEMS matrix (corrected as described in Methods), the modeled matrix (PARAFAC) and the residuals from the PARAFAC model are shown from left to right. Randomly distributed residuals in combination with a high proportion of variability explained (99.7%) indicated a good model fit. The color scale in the lower right corner indicates the intensity of relative fluorescence units. 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Geophys. Res. Lett. 41, 2003–2010 (2014). 45. Li, Y.-H. & S, G. Diffusion of ions in sea water and in deep-sea sediments. Geochimica et Cosmochimica Acta 38, 703–714 (1974). 46. Shen, L. & Chen, Z. Critical review of the impact of tortuosity on diffusion. Chemical Engineering Science 62, 3748–3755 (2007). 184 CHAPTER EIGHT CONCLUSIONS Slow-growth under energy limited conditions is likely the most common physiological state of microorganisms on Earth (Lever et al., 2015), yet little is known about how microbial communities persist under such conditions, or how they impact biogeochemical processes (LaRowe and Amend, 2015). Even the definition of energy- limitation is a gray area, with no consensus on the minimum amount of energy that is required to maintain a cell or for a cell to undergo cell division for population growth or maintenance (e.g. Hoehler and Jørgensen, 2013; Lever et al., 2015). The “deep biosphere”, which includes ecosystems and organisms living beneath the upper few meters of the Earth’s surface, receives low energy fluxes and is generally considered to be energy limited and/or nutrient starved (Hoehler and Jørgensen, 2013; Jørgensen, 2011). Rates of metabolic activity in these environments are low compared to surface, or nutrient-rich, environments. Metabolic activity in Subglacial Lake Whillans (SLW) is similar to that found in the subsurface, while activity beneath the McMurdo Ice Shelf (MIS) is similar to other surface and oceanic environments and that of the McMurdo Dry Valley lakes (MCM) is intermediate (Figure 8.1). Collectively, the three environments examined by my dissertation provide a useful energetic gradient along which to study microbially-mediated processes. 185 Figure 8.1. Metabolic rates of heterotrophic microbial communities in Subglacial Lake Whillans (SLW; Chapter 6), under the McMurdo Ice Shelf (MIS; Chapter 5), and the McMurdo Dry Valley Lakes during the Polar Night transition (MCM; Chapter 2 and (Vick and Priscu, 2012) compared to those of deep-biosphere (black) and surface (gray) environments. The figure is modified from (Jørgensen, 2011). Photosynthetic microorganisms in the MCM are adapted to the low-light environment beneath the lake ice during summer (Priscu et al., 1990), but the darkness of winter is a major loss of energy to the lakes. Obligate photosynthetic phytoplankton maintain downregulated photosynthetic machinery during the winter (Morgan-Kiss et al., 2015), while mixotrophic protists (capable of photosynthesis and phagotrophy) increase their abundance and activity (Chapter 2, Thurman et al., 2012). Bacterial and eukaryotic phylum-level community compositions differ significantly between summer and autumn within the MCM lakes, indicating a seasonal shift in community composition. Archaeal communities, on the other hand, differ between depths rather than seasons, indicating that 186 within-lake geochemical conditions are more important in determining community structure, although OTU-level diversity differed between seasons (Chapter 2). The proliferation of putatively chemolithotrophic OTU’s and the keystone status of metabolically flexible OTU’s are suggestive of the importance of shifts in community composition and flexible metabolisms in these nutrient and light limited lakes. Supporting evidence for the importance of non-photosynthetic metabolisms comes from the high rates of chemoautotrophic inorganic carbon fixation in the lakes (Chapter 3). Nutrient limitation and/or seasonal shifts in carbon sources may lead to relatively low rates of heterotrophic bacterial and archaeal activity in the MCM (Figure 8.1). Alternatively, a tradeoff in favor of metabolic flexibility could result in perceived low growth rates and the low growth efficiencies estimated for MCM bacterioplankton (Takacs et al., 2001), as energy is funneled to the maintenance of enzymes that allow organisms to respond quickly to changing conditions (Teixeira de Mattos and Neijssel, 1997). Metabolic flexibility and availability of energy sources to support chemoautotrophic carbon fixation (Chapters 2 and 3) may be important in defining the lakes’ intermediate position between the low-energy deep biosphere and high-energy surface environments (Figure 8.1). The waters beneath the MIS are permanently dark, but connected via ocean circulation to the open waters of McMurdo Sound. The relatively short distance between the sample collection site and ice-free waters leads to quantitative similarities in nutrient and cell concentrations (Chapter 4). Along with dissolved organic matter and nutrients, chlorophyll a-containing phytoplankton cells are advected under the ice, where they may 187 release energy-rich organic matter similar to phytoplankton in open water, or conserve it to support their own metabolism in the dark, releasing it when they eventually lyse (Thornton, 2014). This access to phytoplankton-derived organic matter and nutrients may be key to the MIS site’s energetic similarity to the open ocean and other surface environments (Figure 8.1). Chemoautotrophic carbon-fixation also occurs under the MIS, but can account for only a small proportion of the heterotrophic activity measured at the site, making it proportionally less important than in MCM. Seasonal access to sunlight and connections with ice-free waters differentiate MCM and MIS from SLW, which is permanently dark and contains an ecosystem dependent on energy sources buried in the sediments or carried by ice-melt, the ultimate source of the lake water. SLW lies in an area of West Antarctica that may be occasionally inundated with seawater while maintaining its ice cover, however, the late-Pleistocene collapse of the West Antarctic Ice Sheet (Scherer et al., 1998) presumably provided the last input of photosynthetically produced organic matter to the region ~123,000 years ago. During the intervening time, microbial activity has led to a net loss of nitrogen and net gain of organic carbon in the subglacial environment, resulting in the high C:N ratios observed in the dissolved and particulate matter pools in SLW (Chapters 6 and 7). The organic matter in SLW is microbially produced (Chapter 7), however, there may be important differences in the timescales of organic matter supply and demand, or limitations imposed by the quality of the organic matter, which lead to the energy-limited heterotrophic growth of microorganisms in SLW (Figure 8.1). Energy-limited cells are not expected to leak organic matter, a common means of energy dissipation (Carlson et 188 al., 2007), which may explain the disparity between the amount of inorganic carbon fixed by chemoautotrophy in SLW and that used to support heterotrophic production (Chapters 6 and 7). Where the availability of freshly produced organic matter is limited, heterotrophic bacteria or archaea produce polysaccharide capsules and ectoenzymes to scavenge organic molecules (Carlson et al., 2007). This microbially produced organic matter can lead to the accumulation of recalcitrant organic matter, which may be bioavailable over timescales of thousands of years (Jiao et al., 2010). Thus, the balance of chemoautotrophic and heterotrophic activity in SLW can explain the large pool of dissolved organic matter present in the lake and the low heterotrophic activity SLW shares with deep-biosphere environments (Figure 8.1). Together, the five core papers of this dissertation address the overarching theme, “how does the ensemble of environmentally imposed energetic constraints impact nutrient cycling in microbially dominated systems?” My data show that, even in energy limited SLW, microbial activity shapes the geochemical environment and has the potential to impact biogeochemical processes in hydrologically connected environments. Much of the work in this dissertation was exploratory; the McMurdo Dry Valley lakes were sampled for the first time during the seasonal austral sunset, data collected from under the McMurdo Ice Shelf comprised the first biogeochemical dataset from that location, and Subglacial Lake Whillans became the first subglacial lake to be directly sampled. As such, these papers should open many avenues for future research. Our comprehension of the McMurdo Dry Valley lakes ecosystems will increase with targeted analyses of functional genes and meta-“omics” studies focused on the microbial 189 interactions and metabolic strategies putatively identified here. A more holistic understanding of sub-ice shelf ecosystems can be achieved through studies that also incorporate the heterotrophic eukaryotic components of the microbial communities beneath ice-shelves. Sampling under ice shelves is logistically challenging, but transects from the edge of the ice-shelf to the grounding zone would provide important information on sub-ice-shelf biogeochemistry with progressive distance from open water, as well as physical oceanographic data. The active subglacial lakes of West Antarctica are dynamic and likely comprise environments that are highly variable over time and space. The knowledge gained from studying Subglacial Lake Whillans is an important first step, allowing comparative studies to be designed and targeted hypotheses developed. Future studies aimed at determining the age and source of subglacial organic matter can be combined with information about microbial activity to inform the field of microbial physiology and energetics, and with geochemical information to further our understanding of the dynamics of the West Antarctic Ice Sheet and when previous incursions of sea water may have occurred. 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