Ice cover extent drives phytoplankton and bacterial community structure in a large north-temperate lake: implications for a warming climate.

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Environmental Microbiology (2015)

doi:10.1111/1462-2920.12819

Ice cover extent drives phytoplankton and bacterial community structure in a large north-temperate lake: implications for a warming climate

B. F. N. Beall,1† M. R. Twiss,2 D. E. Smith,2‡, B. O. Oyserman,1§ M. J. Rozmarynowycz,1 C. E. Binding,3 R. A. Bourbonniere,3 G. S. Bullerjahn,1 M. E. Palmer,4 E. D. Reavie,5 LCDR M. K. Waters,6 LCDR W. C. Woityra6 and R. M. L. McKay1* 1 Department of Biological Sciences, Bowling Green State University, Bowling Green, OH 43403, USA. 2 Department of Biology, Clarkson University, Potsdam, NY, USA. 3 Water Science & Technology Directorate, Environment Canada, Burlington, ON, Canada. 4 Sport Fish and Biomonitoring Unit, Ontario Ministry of the Environment and Climate Change, Toronto, ON, Canada. 5 Center for Water and the Environment, Natural Resources Research Institute, University of Minnesota Duluth, Duluth, MN, USA. 6 USCGC Neah Bay (WTGB 105), Cleveland, OH, USA. Summary Mid-winter limnological surveys of Lake Erie captured extremes in ice extent ranging from expansive ice cover in 2010 and 2011 to nearly ice-free waters in 2012. Consistent with a warming climate, ice cover on the Great Lakes is in decline, thus the ice-free condition encountered may foreshadow the lakes future winter state. Here, we show that pronounced changes in annual ice cover are accompanied by equally important shifts in phytoplankton and bacterial community structure. Expansive ice cover supported phytoplankton blooms of filamentous diatoms. By comparison, ice free conditions promoted the growth of smaller sized cells that attained lower total biomass. We Received 13 February, 2015; accepted 15 February, 2015. *For correspondence. E-mail [email protected]; Tel. +1 419 372 6873; Fax +1 419 372 2024. †Current address: ERM Rescan, Fifteenth Floor, 1111 W. Hastings Street, Vancouver, BC V6E 2J3, Canada. ‡ Current address: National Ecological Observatory Network, 1685 38th St., Ste. 100, Boulder, CO 80301, USA. §Current address: Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA.

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd

propose that isothermal mixing and elevated turbidity in the absence of ice cover resulted in light limitation of the phytoplankton during winter. Additional insights into microbial community dynamics were gleaned from short 16S rRNA tag (Itag) Illumina sequencing. UniFrac analysis of Itag sequences showed clear separation of microbial communities related to presence or absence of ice cover. Whereas the ecological implications of the changing bacterial community are unclear at this time, it is likely that the observed shift from a phytoplankton community dominated by filamentous diatoms to smaller cells will have far reaching ecosystem effects including food web disruptions. Introduction Lakes and reservoirs serve as rapid responding sentinels of human influence on the natural environment (Adrian et al., 2009; Williamson et al., 2009) rendering them powerful tools to advance our understanding of a changing climate on ecosystem structure and function. The Laurentian Great Lakes are especially valuable in this respect in that they share characteristics of both oceans and closed basin systems (Rao and Schwab, 2007) such that knowledge gained from their study can be used to gain insights for our coastal oceans. The effects of climate change have been especially pronounced in the Great Lakes where winter ice cover has declined by 71% over the past four decades (Wang et al., 2012). The decline is not constant; rather it is driven by high inter-annual variability combined with an increase in the frequency of years with low ice cover (Wang et al., 2012; Fujisaki et al., 2013). The manifestations of declining ice cover have likely far-reaching effects on biogeochemical cycles and ecosystem functioning in lakes. In Lake Superior where ice cover has declined by 79% since 1973, summer surface water temperatures have increased > 2.5°C over the same time period, a trend related to the decline in winter ice cover resulting in an earlier start to thermal stratification (Austin and Colman, 2007). Meteorological implications arise as a result of the weakened temperature gradient between air and water with stronger wind speeds possibly altering large-scale circulation patterns

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in the lake (Desai et al., 2009). Climate warming may also have important implications for eutrophication in the Great Lakes. In the lower Great Lakes, phosphorus concentrations were negatively correlated with the extent of winter ice cover with extremes of 200–300% greater concentrations than normal coinciding with strong El Niño years (Nicholls, 1998). Ice cover provides the time needed for settling and consolidation of suspended particles into sediments thereby reducing the extent of re-suspension following ice thaw (Kleeberg et al., 2013). The ecological integrity of aquatic systems is intimately tied to the activities of microbial consortia. Whereas we are accumulating knowledge of microbial diversity in the Great Lakes (e.g. Mou et al., 2013; Wilhelm et al., 2014), we know little about how these communities respond to the manifestations of climate change. Mid-winter surveys of Lake Erie conducted between 2010–2012 captured extremes in ice cover (Fig. S1). Expansive ice cover during 2011 coincided with a negative North Atlantic Oscillation (NAO) event together with La Niña, whereas the combined effect of La Niña and a positive NAO event resulted in negligible ice cover on the lake in winter 2012 (Fujisaki et al., 2013). Here, we show that pronounced changes in annual ice cover are accompanied by equally important shifts in phytoplankton and bacterial community composition and structure.

winter 2012 came from profiles of turbidity with median values (1 m depth) of 14.2 nephelometric turbidity units (NTU) an order of magnitude higher than measured in association with expansive ice cover during February 2011 (1.5 NTU) (two-tailed unpaired t-test, t = 10.70, degrees of freedom [DF] = 6, P < 0.0001) (Fig. 1). Accordingly, the higher turbidity measured in 2012 caused greater attenuation of photosynthetically active radiation (PAR) (Table S1). The increase in lake turbidity recorded in 2012 is consistent with recent models for Lake Erie that show deeper penetration of the vertical eddy viscosity and accelerated coastal current speed compared with winters with expansive ice cover (Fujisaki et al., 2013). These increases in vertical mixing and horizontal convection likely also have implications for eutrophication because wind-induced mixing will keep phosphorus and other nutrients in suspension (Nicholls, 1998). Indeed, analysis of nutrient chemistry from weekly water intakes data demonstrated elevated soluble reactive phosphorus and silicate concentrations during winter 2012 (Fig. 2).

Results and discussion Lake physico-chemical properties Mid-winter surveys of Lake Erie conducted from 2010– 2012 captured extremes in ice cover (Fig. S1). At 23%, Lake Erie possesses the highest median total accumulated ice coverage (TAC) among the Laurentian Great Lakes (Canadian Ice Service, 2010), a distinction consistent with its relative shallow bathymetry. Whereas ice conditions during winter 2010 (20.7% TAC) were comparable to the 33-year historical median, ice conditions in 2011 were more severe than normal (33.6% TAC) with total ice concentration exceeding nine-tenths coverage across much of the lake with the majority of this ice characterized as medium lake ice of 15–30 cm thickness. In sharp contrast, winter 2012 (1.4% TAC) was nearly ice free. The different ice conditions between years were likewise reflected in water column physicochemical parameters (Fig. 1). Vertical profiles taken at process stations occupied in mid-February portrayed near isothermal conditions with temperatures markedly depressed in 2011 (−0.2 to 0.4 °C) compared with 2012 (0.3 °C in the shallow western basin to 2.5 °C in the deeper eastern basin). Support for a thorough winddriven mixing regimen in the absence of ice during

Fig. 1. Vertical water quality profiles for a representative central basin station (EC1326) occupied during winter surveys of Lake Erie. Water column profiles of temperature, dissolved oxygen and turbidity were recorded during surveys in February 2011 (black) and 2012 (grey).

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

Effect of ice cover on microbial community structure

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Week of Year Fig. 2. Soluble reactive phosphorus (SRP; top panel) and silicate (lower panel) in water samples collected at Ontario municipal water treatment plants. In each panel, the shaded region shows the interquartile range of concentrations for the entire period of 1999 to 2012 for the ice-season, defined as the first week of November (week 45) to the end of May (week 23). The separate lines show the nutrient concentrations during the 2009–2010, 2010–2011 and 2011–2012 ice years. Nutrient concentrations below analytical detection limits were replaced with values one-half of the reported analytical detection limit for the purposes of graphing and analysis. Nutrient concentrations measured at Amherstburg in 2012 were notable for the higher concentrations of SRP and silicate during the early weeks of the historical ice season. Historical SRP concentrations at a central basin site (Elgin) generally show little change throughout the ice season, but substantial changes were observed in winter 2012 with high early season SRP concentrations which returned to mean levels later in the season. In contrast, silicate concentrations in the central basin tended to decrease through the winter season consistent with nutritional uptake by diatoms (Twiss et al., 2012). Winter 2012 was an extreme example of this decreasing trend in silicate concentrations. The eastern outflow of Lake Erie, represented by sampling at Rosehill, generally showed a January peak in SRP and silicate concentrations with SRP concentrations generally outside the interquartile range.

Phytoplankton and bacterial community shifts associated with ice cover Expansive ice cover in winters 2010 and 2011 was associated with under-ice phytoplankton blooms dominated by physiologically robust, filamentous centric diatoms as reported previously (Twiss et al., 2012). Coincident with the mainly ice-free conditions of 2012 was a decline in excess of 70% of average total phytoplankton chlorophyll (chl) a biomass compared with 2011 that was measured as part of both mid-February and early April surveys (Fig. S2). At first glance, these results appear to be at odds with those of previous studies suggesting that high vernal phytoplankton biomass is linked to a positive NAO index resulting in earlier ice break-up (or negligible ice cover) during winter (Weyhenmeyer et al., 1999; Gerten and Adrian, 2000; Straile et al., 2003). While recognizing growth of phytoplankton under ice, these studies invoke turbulence as important in promoting the growth of diatoms during spring, a notion predicated on these nonmotile taxa requiring resuspension to maintain their position in the photic zone. For many lakes in areas of high snowfall, ice surfaces are likely to be snow-covered thereby restricting development of under-ice blooms due to lack of light penetration through the accumulated snow and ice. In contrast, Lake Erie may present a different winter environment given that an expansive, thick

snowpack is uncommon. Rather, snow falling on the ice surface of many large lakes is likely to accumulate in discrete drifts with the resulting snow-free ice exhibiting high transmittance of PAR (Bolsenga and Vanderploeg, 1992; Jewson et al., 2009). The PAR penetrating through the snow-free ice can support prolific growth of diatom microplankton biomass, which has been documented in Lake Erie (Twiss et al., 2012) as well as in Russia’s Lake Baikal (Bondarenko and Evstafyev, 2006; Jewson et al., 2008). Winter surveys conducted in 2011 demonstrated tight coupling between microphytoplankton (> 20 μm) chl a biomass and total (> 0.2 μm) chl a with microphytoplankton contributing a median 81% of total chl (Fig. 3a), consistent with dominance by filamentous diatoms (Twiss et al., 2012). In contrast, a strong departure from a microphytoplankton-dominated system was observed during the low ice winter of 2012, when pronounced declines in this size-class contributed a median of just 27% of total chl a biomass (two-tailed unpaired t-test, t = 13.73, DF = 146, P < 0.0001). Results obtained by assay of size-fractionated chl a were reinforced by flow cytometry which showed that cell abundance in the fraction containing large nanophytoplankton (6–30 μm) and smaller microphytoplankton (20–30 μm) declined more than threefold (Fig. S3). Likewise, results from the April US Environmental Protection Agency monitoring


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Fig. 3. Phytoplankton biomass accumulation during extremes of ice cover. A. Winter surveys conducted on Lake Erie over 2 years demonstrated tight coupling (r2 = 0.959) between microphytoplankton chl a biomass and total chl a during winters 2010 and 2011, years of expansive ice cover. Coincident with the mainly ice-free conditions of winter 2012 was a decline in total chl a biomass along with a strong departure from a microphytoplankton-dominated system (r2 = 0.758). B. During early spring monitoring surveys conducted immediately following the high ice years of 2010 and 2011, the filamentous diatom A. islandica contributed the majority of total phytoplankton biovolume in the lakes central basin (r2 = 0.982). Following the mild winter of 2012, both total phytoplankton biovolume and the share contributed by A. islandica declined precipitously.

surveys of 10 central basin stations from 2010–2012 support these trends (Fig. 3b). Aulacoseira islandica that emerges as the dominant species during winter persists into the early spring in Lake Erie (Barbiero and Tuchman, 2001; Reavie et al., 2014) where it contributed 86% of total phytoplankton biovolume following ice-out in 2010 and 2011 (Fig. 3b). A remarkable 89% decrease in total phytoplankton biovolume occurred with the transition from years of expansive ice (2010, 2011) to low ice (2012) with most of this decline attributed to a 95% decrease in A. islandica biovolume (two-tailed unpaired t-test, t = 4.1, DF = 28, P < 0.0005) (Fig. 3b).

Reduced cell size is recognized as a universal ecological response of phytoplankton to warming (Daufresne et al., 2009; Winder et al., 2009), although it usually marks a response to climate warming over multiple years as evidenced from sedimentary diatom assemblages (e.g. Rühland et al., 2008), not a single season as shown here. Based on the phytoplankton enumeration data, it is likely that the reduction in cell size documented here is related to the reduced dominance of A. islandica during the low ice winter combined with increased contributions to percent algal community biovolume by smaller cryptomonads and dinoflagellates (> 15-fold and > 4-fold respectively; E. Reavie, unpublished). What drove the decline in phytoplankton standing stock in winter 2012 is not known, although we suspect it to be related to light limitation of photosynthesis in a well-mixed, turbid water column since nutrients were abundant (Fig. 2). Measurement of photosynthesis in early spring 2012 supported light limitation of phytoplankton photosynthesis as Ik, the irradiance at which photosynthesis becomes light saturated, was > 4-fold higher than the calculated mean water column irradiance (Iwc) during the daylight period (Tables S1 and S2). Similar values of Ik were estimated for communities sampled in 2010 and 2011 (Table S2), but ironically, the presence of expansive ice cover in those years may have ensured adequate light fields for photosynthesis and growth. Previous studies suggest under-ice communities are subject to only limited mixing by convection, which would restrict the mixing of suspended phytoplankton deeper than Iz (Kelley, 1997), or that the communities are physically associated with ice cover (D’souza et al., 2013). Additional insights into microbial community dynamics were gleaned from temporally resolved short 16S rRNA tag (Itag) Illumina sequencing of samples collected at three central basin locations. The efficacy of Illumina sequencing has been demonstrated to show that known differences between microbial communities can be readily identified on Illumina platforms, making this approach suitable for high-throughput surveys of microbial communities (Caporaso et al., 2012). While recognizing the uncertainty of relying on a single sampling occasion from which to glean information on microbial diversity representative of the winter season, this approach reflects the myriad logistical challenges faced when conducting research during winter on large ice-covered lakes. Whereas partnership with coast guards offered greater temporal resolution for sampling physico-chemical parameters, logistical considerations generally prevented scientists from embedding during icebreaking operations leaving a week-long dedicated science survey during each year of the study as the only opportunity for targeted sample collection.


Effect of ice cover on microbial community structure A total of 623 625 Itag sequences were recovered with the output from individual sites ranging between 47 269 to 109 653 total reads per sample (Table S3). For clarity, we conducted separate analyses of bacterial and chloroplast sequences. Use of rarefied samples of 10 000 random reads per site was based on the lowest return of bacterial reads (Table S3). Rarefaction curves indicated that diversity was not fully captured in most communities (Fig. S4A and B). A central finding from our winter surveys was that in 50% of the samples, chloroplast reads representing phytoplankton occurred in comparable (± 1%), or higher numbers than Bacteria (Fig. S4C). Of the eukaryotic phytoplankton, centric diatoms of class Coscinodiscophyceae, dominated the winter phytoplankton contributing 95% of all chloroplast reads as well as the majority of total reads during winter regardless of ice cover with an average contribution of 46% of total reads in rarefied samples (Fig. 4). This trend was supported by a recent pyrosequencing effort that demonstrated that reads of phototrophs were likewise dominant in 50% of the samples collected during a February 2010 survey of Lake Erie (Wilhelm et al., 2014). These results seemingly contradict other surveys of freshwater lakes using next-generation sequencing (NGS) approaches where reads of heterotrophic bacteria clearly dominate (Oh et al., 2011; Eiler et al., 2013; 2014; Mou et al., 2013; Parfenova et al., 2013), a feature likely related to seasonality because most limnological surveys are completed during the de facto spring-summer-fall field season. In support of this assertion, Wilhelm et al., (2014) showed that bacterial heterotrophs dominated sample reads at all sites surveyed in Lake Erie during summer 2010. Whereas the use of chloroplast small subunit ribosomal sequences can be misleading owing to interspecies differences in deoxyribonucleic acid (DNA) extraction efficiencies as well as differences in copy number of the SSU rRNA genes, comparison between microscopic abundance and NGS data demonstrate overall positive correspondence (Eiler et al., 2013). Moreover, some evidence suggests that diatoms can even be underestimated in NGS data due to inefficiencies in extracting DNA compared with other members of the community (Medinger et al., 2010). Further, while sequencing data alone cannot show that diatoms were numerically dominant over bacteria during winter sampling, analysis by flow cytometry showed bacterial abundance in winter to be over fourfold lower than in summer (Fig. S5). A single operational taxonomic unit (OTU) of Aulacoseira sp. dominated the diatoms sampled during winter contributing nearly 80% of chloroplast reads and an average 37% of total reads (Fig. 4C). Based on sequencing data alone, the species-level identity of this dominant OTU is inconclusive, which likely reflects the paucity of diatom sequences avail-

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able in the National Center for Biotechnology Information (NCBI) database. Our previous surveys (Twiss et al., 2012) along with annual spring surveys of the US Environmental Protection Agency (Barbiero and Tuchman, 2001; Reavie et al., 2014), however, conclusively demonstrated the winter phytoplankton to be dominated by A. islandica (O. Müller) Simonsen based on microscopic analysis. UniFrac principle coordinates analysis (PCoA) of the bacterial community supported separation based on expanse of winter ice cover in both weighted (Fig. 5A) and unweighted (Fig. S6A) analyses. The weighted analysis considers relative taxon abundance and the phylogenetic distance between observed OTUs from the different samples. By contrast, unweighted UniFrac is sensitive to factors affecting the presence or absence of taxa that may be otherwise obscured by abundance information (Lozupone et al., 2007). Weighted UniFrac analysis of bacterial communities revealed the strong influence of a single factor, with principal coordinate 1 (PC1) accounting for 46% of variation (Fig. 5A). Bacterial communities clustered by sample collection year, with 2010 and 2012 at opposite ends of PC1. In contrast, samples collected during 2011, along with a single sample collected in 2012, appear as intermediates along PC1. Moderate resolution imaging spectroradiometer images suggested that icecovered sites sampled in 2011 were characterized as open water ∼2 weeks prior to sampling, whereas the outlying 2012 sampling site (EC341) was in the vicinity of a rare ice field during that winter (Fig. S1b). These samples are intermediates along PC1, supporting ice cover as a driving factor behind clustering of bacterial communities along this axis, and suggestive of a transitioning community structure. Supporting this conclusion, a similar pattern was observed in an unweighted UniFrac plot, indicating a shift in taxa present during periods of high and low ice cover (Fig. S6A). Clustering patterns of these bacterial communities were significant in both weighted and unweighted analyses (RANOSIM = 0.97, P = 0.002; unweighted RANOSIM = 0.78, P = 0.002). Further examination of weighted UniFrac clustering revealed that when including the intermediate samples with either the high or low ice clusters maintained significant dissimilarity from the third group (intermediate included with: high ice RANOSIM = 0.58, P = 0.01; low ice RANOSIM = 0.86, P = 0.01), adding additional support for a transitional condition. The consistent ice extent trend for all sites except EC341 suggests that the observed changes in the composition of the community related to changes in the selective pressures in the environment. The changes in the relative abundance of Flavobacteria and Verrucomicrobia (discussed below) were symptomatic of these putative changes in selective processes. For example, Flavobacteria may preferentially use high-molecular weight substrates (Cottrell and Kirchman, 2003), and the


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Fig. 4. Lake Erie Itag microbial community composition during high ice (2010, 2011) and low ice (2012) winters represented by stacked bar plots showing OTU abundances per sampling station. A. Taxonomic breakdown of the bacterial communities by phylum. Each phylum is colour coded, and individual OTUs are separated by black lines. B. Bacterial classes within the dominant phyla. C. Abundance and taxonomic composition of chloroplast reads reflected as per cent of total reads. Representatives of four phyla dominated the bacterial communities. Single OTUs contributed up to 6% of total reads.


Effect of ice cover on microbial community structure

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Fig. 5. Phylogenetic clustering of winter samples by principle coordinates analysis (PCoA) of weighted UniFrac distances for bacterial (A) and chloroplast (B) communities.

observed decrease in Flavobacteria during the low-ice year may be the indirect result of decreases in organic matter production from diatoms. Analysis of chloroplast sequences likewise revealed clustering by ice conditions (Fig. 5B; Fig. S6B), with PC1 of the weighted UniFrac analysis explaining 87% of variation and clustering patterns showing significant dissimilarity between high and low ice years (2010–11 versus 2012) (weighted RANOSIM = 0.41, P = 0.019; unweighted RANOSIM = 0.42, P = 0.036). These results point towards specific eukaryotic taxa also being associated with the ice cover. Samples collected in 2011 clustered with low-ice samples along weighted PC1, whereas in unweighted analysis they clustered closer to high ice samples. The increased abundance of high ice-associated taxa (weighted UniFrac), while still retaining taxa associated with low ice communities (unweighted UniFrac) further points to a transitioning community structure in the eukaryotic community. Whereas stramenopiles are a dominant component of the microbial community during winter, regardless of ice cover, the strong association between ice extent and the variation in the UniFrac PCoA points to a phylogenetically distinct group of iceassociated organisms (e.g. Aulacoseira icelandica). Complementary machine learning approaches were used to determine which taxa were responsible for the clustering associated with the years 2010 and 2012. Random forest analysis was adopted to identify discriminatory taxa for high and low ice conditions (Breiman, 2001; Knights et al., 2011). Samples for EC880, EC1326 and EC341 were compared using a random forest

algorithm (QIIME). Cross-validation estimates for 2010 and 2012 had an average probability of being guessed correctly based on their bacterial communities 70% and 76% respectively. OTUs determined to be important features were identified using NCBI BLAST (Fig. S7). In total, OTUs that resulted in a decrease in accuracy in prediction (Table S3), and thus discriminative for either the high or low ice conditions, accounted for an average of 54% and 44% of the bacterial community for 2010 and 2012 respectively. Linear discriminant analysis (LDA) effect size (LEfSe) (Segata et al., 2011) was used to test for significant differences between high and low ice samples (Fig. S8). Both random forest and LDA algorithms identified OTUs within the Actinobacteria, Acidobacteria, Bacteroidetes, Gemmatimonadetes, Planctomycetes, Proteobacteria and Verrucomicrobia as important features differentiating high and low ice conditions. The dominant phyla among Bacteria during winter in Lake Erie were similar to previous reports for freshwater lakes (Zwart et al., 2002; Newton et al., 2011). A lack of abundant Cyanobacteria sequences was likely related to seasonality. Indeed, Cyanobacteria can be abundant components of the bacterioplankton during summer in Lake Erie, accounting for 10% of bacterial sequences in Sandusky Bay (Mou et al., 2013) and forming massive surface blooms in the western and central basins of the lake (Allinger and Reavie, 2013; Michalak et al., 2013). Among heterotrophic phyla, reads of Proteobacteria were uniformly dominant in winter regardless of ice


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expanse and contributed approximately 33% of bacterial reads in water samples. Proteobacteria are arguably the best studied phylum of environmentally relevant bacteria reflecting their ubiquity and abundance as well as their biochemical and physiological diversity (Newton et al., 2011). Comparing abundance of proteobacterial classes during winter surveys, the Betaproteobacteria were dominant comprising 57% of all proteobacterial reads followed by Alphaproteobacteria sequences which contributed 28% (Fig. 4B). These trends in abundance follow the general sense of the literature whereby Betaproteobacteria can be numerically dominant among bacterioplankton in lakes (Glöckner et al., 1999; 2000; Eiler et al., 2014; Skopina et al., 2015), even during periods of ice cover (Pernthaler et al., 1998). While ubiquitous in freshwaters (Newton et al., 2011), Alphaproteobacteria more frequently prevail in marine environments where they are dominated by the SAR11cluster (Morris et al., 2002). Members of the freshwater LD12 lineage (now classified as SAR11 subclade IIIb; Grote et al., 2012) dominate the Alphaproteobacteria in most lakes (Zwart et al., 2002; Newton et al., 2011). Likewise in the present study, a sequence that clusters with members of the LD12 clade dominated the Alphaproteobacteria with a single OTU that accounted for 66% of all alphaproteobacterial reads and over 6% of all bacterial reads (Fig. S9A). Whereas LD12 bacteria are ubiquitous in lakes, studies of seasonal bloom dynamics generally show a reduction in their abundance during winter (Pernthaler et al., 1998; Salcher et al., 2011; Heinrich et al., 2013). Linear discriminant analysis indicated a significant decrease in Gammaproteobacteria, particularly the Xanthomonadales, along with a significant increase in the genus Methylibium under low ice conditions (Fig. S8). Random forest identified OTUs from the Alpha, Beta and Gamma lineages as important features for distinguishing the high and low ice years. Shifts in discriminatory Proteobacteria during 2012 resulted in an average net decrease of 7% of the bacterial community, of which a decrease of 5% was attributed to an OTU with a high identity to the genus Pelagibacter, a member of the SAR11 clade (Fig. S7). Following the Proteobacteria in abundance were the phenotypically and metabolically diverse Bacteriodetes that contributed 27% of heterotroph reads under conditions of expansive ice cover (Fig. 4B). Negligible ice cover encountered in February 2012 was accompanied by a 23% decline in Bacteriodetes reads which could be attributed in part to a 48% decrease in reads of Flavobacteria between years of high and low ice (twotailed unpaired t-test, t = 3.46, DF = 6, P < 0.05). In previous studies of lakes (Eiler and Bertilsson, 2007; Zeder et al., 2009), Flavobacteria responded positively to pulses of phytoplankton production, so it is likely that

the large decline in chl a biomass measured between high and low ice years in the present study contributed to the decline of this bacterial class. Random forest identified members of the Sphingobacteria, Saprospireae, Cytophagia, and Flavobacteria as important features for discriminating between high and low ice years (Fig. S7). Abundance of Bacteroidetes OTUs identified by supervised learning as important features exhibited an average net decrease of 8% of the bacterial community, of which decreases in OTUs belonging to the Sphingobacteria accounting for 2% while decreases in Saprospireae resulted in a decrease of 3% (Fig. S7). Linear discriminant analysis revealed significant decreases for Bacteroidetes, and indicated that Sphingobacteria and Cytophagia, had significantly decreased under low ice conditions (Fig. S8). In contrast to the Bacteriodetes, reads of Verrucomicrobia increased by 30% between years of high and low ice with the increase attributed to increases in the reads of class Verrucomicrobiae (Fig. 4B). A single OTU belonging to the Spartobacteria contributed 50% of all Verrucomicrobia reads in water samples and > 8% of total bacterial reads in water samples regardless of ice cover making it the single most abundant bacterial OTU in our surveys. This OTU showed close identity (89%) to a putative ‘Spartobacteria baltica’, part of the spartobacterial lineage ‘LD29’ (Herlemann et al., 2013) identified as highly abundant in brackish (salinities 5–10) regions of the Baltic Sea (Herlemann et al., 2011; Bergen et al., 2014). Random forest analysis identified this OTU as an important feature for distinguishing between high ice and low ice conditions (Fig. S7), whereas LDA indicated a significant increase in the abundance of Spartobacteria during low ice conditions (Fig. S8). Verrucomicrobia are important representatives of soil communities accounting for nearly 25% of all soil bacteria reads in a recent survey (Bergmann et al., 2011). Likewise, Verrucomicrobia are ubiquitous members of aquatic environments (Zwart et al., 2003; Herlemann et al., 2011; Freitas et al., 2012), where they may be active in the hydrolysis of diverse polysaccharides (Martinez-Garcia et al., 2012). While typically present as a minor phylum in aquatic systems accounting for < 2% of bacteria in diverse marine environments (Freitas et al., 2012) and generally < 6% in freshwater lakes (Newton et al., 2011; Parfenova et al., 2013; Poretsky et al., 2013), higher abundances of Verrucomicrobia have been reported. Using Catalyzed Reporter Deposition - Fluorescence In Situ Hybridization, Arnds and colleagues (2010) showed that Verrucomicrobia contributed up to 19% to the microbial community in a dystrophic lake in Germany. The characterization in the present study of the Verrucomicrobia as abundant members of the Lake Erie winter community, accounting for 18% of total bacterial


Effect of ice cover on microbial community structure reads during February 2012 and possessing a single OTU identified as the most abundant sequence in our surveys further underscores the need to elucidate the roles assumed by this globally distributed but underrecognized group. The final major phylum represented in our study, the Actinobacteria, collectively represented greater than 17% of all bacterial reads regardless of ice cover (Fig. 4B). Whereas representatives from class Actinobacteria dominated in all water samples, sequences of subclass Acidomicrobidae consistently accounted for ∼25% of the reads within the phylum. Whereas Actinobacteria can be numerically dominant in lakes (Glöckner et al., 2000; Eiler et al., 2014), historically they have been associated with the terrestrial environment, and their presence in lakes attributed primarily to run-off and Aeolian deposition. Distinct aquatic clades have been recognized (Warnecke et al., 2004) with the acI lineage (class Actinobacteria) accounting for > 90% of all Actinobacteria reads in some lakes (Warnecke et al., 2005). Consistent with the results of previous studies (Newton et al., 2011), the dominant Actinobacteria OTUs in the present study cluster with the acI- and acIV (predominantly Acidomicrobidae) lineages which typically dominate freshwater environments (Fig. S9B). Whereas a lack of cultured representatives leaves questions regarding the role of Actinobacteria in the environment, culture-independent approaches support the existence of both photo- and heterotrophic life styles with genes coding for energy-generating actinorhodopsins identified associated with members of the acI lineage sampled from the photic zone of Lake Erie during summer (Sharma et al., 2009). The machine learning algorithms both indicated shifts in Actinobacteria during high and low ice conditions. Random forest analysis indicated that two OTUs, both increasing under low ice conditions, as discriminatory features, one of which shared close identity with Rhodococcus (Fig. S7). Likewise, LDA indicated significant increases in actinobacterial acI and acIV lineages during low ice conditions (Fig. S8). Shifts in minor taxa were also apparent between high and low ice conditions. Both LDA and supervised learning pointed to members of the Acidobacteria, with LDA indicating a significant increase in Solibacterales during low ice conditions (Fig. S8). Linear discriminant analysis also indicated a significant decrease in Planctomycetes and an increase in Gemmatimonadetes (Fig. S8). Ecological implications of low ice cover Winter ice cover on the Great Lakes has declined by 71% over the past four decades (Wang et al., 2012). While ice decline on Lake Erie has been lower (50%) than the system-wide average, ice cover models incorporating 2 × CO2 scenarios predict a future with markedly

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reduced, or even no ice on the lake (Assel, 1991). Thus, the condition of negligible ice cover encountered during winter 2012 provided a window to a future ‘low ice’ state of Lake Erie. Our surveys demonstrated tangible and potentially important shifts in phytoplankton and microbial community structure between winters of high and low ice, the most striking of which was the precipitous decline in standing stock of filamentous diatom microplankton in winter 2012. The shift away from a phytoplankton community dominated by filamentous diatoms is likely to have far reaching ecosystem effects including food web disruptions where we predict that the sharp decline in A. islandica will disrupt trophic transfer of carbon to zooplankton ultimately having a negative impact on fish recruitment. Suppression of winter diatom growth may also have important biogeochemical implications for events during summer in Lake Erie. Abundant diatom growth combined with low measured rates of bacterial decomposition results in export of algal biomass to the benthos (Wilhelm et al., 2014). As the hypolimnion warms during summer, bacterial remineralization of the exported diatom biomass accelerates, which depletes the hypolimnion of oxygen and results in formation of the Lake Erie ‘dead zone’, the expanse of which can exceed 10 000 km2 (Hawley et al., 2006). However, a prediction that lower phytoplankton biomass accumulation during low ice winters would lessen the extent and magnitude of central basin hypoxia is not supported by available data (Fig. S10). Rather, late summer hypoxia followed the low ice years of 2002 and 2012 (Zhou et al., 2015). Whereas winter production can be an important driver of late summer hypoxia (Wilhelm et al., 2014), other factors contribute to the formation of hypoxia including the length of the stratified period, hypolimnetic volume and temperature and meteorological factors (Zhou et al., 2015).

Experimental procedures Study site and sampling Lake Erie was sampled on surveys aboard CCGS Griffon and USCGC Neah Bay during winters 2010–2012 (Fig. S1). Early April monitoring surveys conducted by the US EPA were on board R/V Lake Guardian. Sampling aboard CCGS Griffon was conducted during week-long surveys in mid-February and included routine hydrographic stations as well as underway sampling (2011–2012 only). Sampling aboard USCGC Neah Bay comprised underway water collection through the ice-breaking season (January–March) in a partnership with the US Coast Guard described elsewhere (Oyserman et al., 2012). At each sampling location, the extent and characterization of ice cover, if present, along with meteorological conditions were recorded. Also included in the analysis were weekly water intake data collected at municipal water treatment plants near the inflow of the Detroit River into Lake Erie


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(Amherstburg), at a location in the central basin (Elgin) and near the outflow of the lake to the Niagara River (Rosehill). These data were acquired through the Great Lakes Intakes Program, an initiative of the Ontario Ministry of the Environment and Climate Change (Nicholls et al., 2001). Underway winter sampling from CCGS Griffon and USCGC Neah Bay followed the procedure detailed in Binding and colleagues (2012). Briefly, surface water samples were collected at approximately 1 h intervals using a stainless steel sampling bottle and processed immediately onboard the ship. Turbulence associated with icebreaker motion resulted in mixing of surface waters consistent with flat profiles of physicochemical parameters measured at hydrographic stations (Fig. 1). Sampling at routine hydrographic stations occupied by CCGS Griffon and R/V Lake Guardian was as described elsewhere (Twiss et al., 2012 and Reavie and Barbiero, 2013 respectively). Briefly, water column profiles of temperature, dissolved oxygen and conductivity were recorded during the February CCGS Griffon surveys using a Model 660 Sonde (YSI, Yellow Springs, OH, USA) lowered at approximately 0.1 m s−1. Aboard R/V Lake Guardian, depth-resolved sampling was preceded by conductivity, temperature, depth (CTD; SBE 911plus; Sea-Bird Electronics, Bellevue, WA, USA) casts. Samples were processed for determination of sizefractionated chl a biomass (0.2 and 20 μm polycarbonate filters) and for dissolved (< 0.2 μm) and particulate nutrients. Chl a biomass was measured by fluorometry following extraction in 90% (v/v) acetone at −20°C (Twiss et al., 2012). Nutrients were measured by the National Laboratory for Environmental Testing (Environment Canada, Burlington, ON, Canada), the Laboratory Services Branch (Ontario Ministry of the Environment and Climate Change, Toronto, ON, Canada) and the National Center for Water Quality Research (Heidelberg University, Tiffin, OH, USA) using standardized techniques (NLET, 1994; Chow et al., 2010; and U.S. EPA, 1979 respectively). Sample preparation for microscopic analysis conducted as part of spring EPA surveys was as described elsewhere (Reavie and Barbiero, 2013; Reavie et al., 2014). Briefly, formalin-preserved phytoplankton samples integrated from depths of 1, 5, 10 and 20 m were split and analysed separately for soft-bodied algae and diatoms. Analysis of soft algae used the quantitative Utermöhl method using an inverted microscope (Utermöhl, 1958). The sample for diatom analysis was digested in nitric acid and subsequently in peroxide leaving diatom frustules which were then plated on slides and counted using oil immersion (1000 × or higher) to identify taxa. Cell dimensions were recorded so that algal biovolumes could be calculated. Additional physico-chemical data from EPA annual spring surveys were obtained from the online Great Lakes Environmental Database (accessed from http://www.epa.gov/cdx/).

High-throughput microbial community analysis At three central basin hydrographic stations (EC341, EC880 and EC1326; coordinates provided in Table S1) occupied during annual February surveys by CCGS Griffon, a comprehensive taxonomic analysis was completed by Illumina MiSeq targeting the 16S rRNA V4 hypervariable region of

bacterial and plastid genomes. Targeting the larger V4 region over V6, which has been typically used in such studies, should better reflect the microbial diversity present (Degnan and Ochman, 2012). Microbial biomass from surface water (and ice melt) was concentrated on Sterivex cartridge filters (0.22 μm; EMD Millipore, Billerica, MA, USA) and immediately frozen in liquid nitrogen. Deoxyribonucleic acid was extracted from the Sterivex cartridges using the PowerWater Sterivex DNA Isolation Kit (MO BIO Laboratories, Inc, Carlsbad, CA, USA) following manufacturer’s instructions. Short 16S rRNA tag (Itag) sequencing was completed at the Joint Genome Institute (Walnut Creek, CA, USA) using an Illumina MiSeq benchtop sequencer (2 × 250 bp reads) incorporating a PhiX library control. Resulting raw sequence data were deposited to the NCBI Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/Traces/sra) as SRP050963. Primer design for universal amplification of the V4 region of 16S ribosomal DNA was based on Caporaso and colleagues (2011), with the forward primer remaining unchanged and 96 variations of the reverse primer, each having 0–3 nucleotides added between the padding and the V4 sequence. PhiX reads and contaminating Illumina adaptor sequences were filtered, and unpaired reads were discarded. Sequences were then trimmed to 165 bp and assembled using FLASH software (Magocˇ and Salzberg, 2011). Resulting sequences were de-multiplexed and filtered for quality: sequences were trimmed using a sliding window of 10 bp, required a mean quality score of 30 and contained less than 5 Ns or 10 bases with a quality score less than 15. Itags were processed using default settings of QIIME 1.8.0 (Caporaso et al., 2010a) unless noted otherwise. Operational taxonomic units were picked using UCLUST at 97% (Edgar, 2010), and OTUs represented three times or fewer were filtered. A representative set of sequences was generated for each site with taxonomy assigned using the Ribosomal Database Project (RDP) classifier (Wang et al., 2007) with a minimum confidence of 80% for taxonomy assignment. Assignment was based on the Greengenes taxonomy (McDonald et al., 2012) and reference database version 12_10 (Werner et al., 2012). For analysis of bacterial populations alone, reads that were assigned to chloroplast and mitochondrial sequences, along with those not identifiable beyond Bacteria were filtered. Reads matching ‘chloroplast’ were used in subsequent analyses to examine the phytoplankton community. The combined bacterial and eukaryotic reads are referred to as ‘total reads’. The representative sequences were aligned to the Greengenes core reference alignment (DeSantis et al., 2006) using PYNAST (Caporaso et al., 2010b) and gaps in the resulting alignment were filtered. A phylogenetic tree was generated from the filtered alignment using FASTTREE 2.1.3 (Price et al., 2010) following which the samples were rarefied in QIIME. Alpha diversity, which assesses the diversity within each sample, was calculated from the resulting files and collated. Phylogenetic measures of community beta diversity, a measure of diversity between different environments, were calculated using PCoA on both weighted and unweighted UniFrac matrices (Lozupone et al., 2007) rarefied at 10 000 reads. Significance for PCoA clustering was tested using Analysis of Similarities (ANOSIM) (Clarke, 1993) with 999 permutations. Machine-learning approaches (random forest analysis, LEfSe LDA) were


Effect of ice cover on microbial community structure adopted to identify OTUs whose abundances were associated with ice conditions present in 2010 and 2012. To reduce noise due to a large number of OTUs, communities used with machine-learning algorithms were filtered to contain only OTUs that were present at an abundance of at least 1% in at least one sample. Additional replicates collected in 2012 were used to increase sample number in statistical sample sets. Random forest analysis (Breiman, 2001; Knights et al., 2011) was done using QIIME’s supervised learning script. One thousand forests were generated using ‘leave-one-out’ cross validation for communities rarefied to 20 000 OTUs. Discriminatory taxa between high ice and low ice conditions was also investigated using LEfSe (Segata et al., 2011) (default parameters) which uses Kruskal–Wallis sum rank test (α = 0.05) to identify significantly differential taxa and LDA to estimate the effect size of each.

Acknowledgements We thank the officers and crews of CCGS Griffon, USCGC Neah Bay and R/V Lake Guardian along with Technical Operations personnel from Environment Canada who ably assisted with the sampling program. The manuscript benefited from the insights of many colleagues, including R. Adrian, T. Glavina de Rio, K. Rühland, J. Saros, E. Silow, D. Straile and S. Wilhelm. This material is based upon work supported by the National Science Foundation under grant no. OCE-1230735 (RMLM, GSB). Additional support was provided by the Ohio Sea Grant College Program (grant R/ER-081 to RMLM and GSB), New York Sea Grant (grant R-CE-29 to MRT), the Lake Erie Protection Fund (grant 430-12 to RMLM) and the U.S. Environmental Protection Agency (grant GL-00E00790-2 to EDR). The work conducted by the US Department of Energy Joint Genome Institute was supported by the Office of Science of the US Department of Energy under Contract No. DE-AC0205CH11231 and Community Sequencing Project 723 (RMLM, GSB, RAB).

Conflict of interest The authors declare no conflict of interest.

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Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Fig. S1. Mid-winter limnological surveys captured extremes of ice cover on Lake Erie. A. Moderate resolution imaging spectroradiometer image captured on 3 February, 2011 showing expansive ice cover. B. Moderate resolution imaging spectroradiometer image captured 15 February showing the mainly ice-free condition of Lake Erie during winter 2012. Sampling locations are shown for surveys onboard CCGS Griffon (mid-February) and USCGC Neah Bay (January– March). Moderate resolution imaging spectroradiometer images obtained from Great Lakes CoastWatch Program, NOAA-Great Lakes Environmental Research Lab (http:// coastwatch.glerl.noaa.gov/). Fig. S2. Central basin phytoplankton chl a biomass reported from US EPA spring monitoring surveys. Aulacoseira islandica and Stephanodiscus spp. that emerge as dominant phytoplankton during winter persist into the early spring in Lake Erie and are routinely documented as abundant flora during the EPA’s annual spring (April) survey of the lake (Barbiero and Tuchman, 2001; Reavie et al., 2014). Notably, the major decline in chl a biomass between high and low ice years documented during our February surveys persisted into early April based on comparison of 2011-2012 EPA monitoring results (two-tailed t-test, DF = 18, P < 0.0001). Likewise, review of historical EPA data obtained from the online Great Lakes Environmental Database (accessed from http:// www.epa.gov/cdx/) showed a similar large (> 80%) decline in chl a biomass comparing early April surveys following years of high ice in 2001 and 2003 with the extreme low ice winter of 2002 (two-tailed t-test, DF = 18, P < 0.0005). Data are presented as box and whisker plots showing median extractive chl a concentration. Mean values for chl a are shown with a dashed line. Vertical boxes around each median show the upper and lower quartiles, whereas whiskers extend from the 5th to 95th percentile. Fig. S3. The phytoplankton community structure was examined using a flow cytometric size-based analysis (Marie et al., 2005). The approach measured cells in size ranging from approximately 0.8 μm to ≤ 50 μm in length, excluding large cells and cells in chains which dominated the Lake Erie community during winter 2011. Cells were classified into two phytoplankton size groups: A, C. ∼6 to 30 μm diameter and B, D. ∼2 to 6 μm diameter, and by the presence of orange fluorescent phycoerythrin (C, D). Samples were fixed in buffered formaldehyde (final concentration 1% v/v) and flashfrozen in liquid nitrogen. Samples were kept in liquid nitrogen or at −80 °C until thawing and immediate analysis. Flow cytometry samples were analysed on a FACSCalibur flow


Effect of ice cover on microbial community structure cytometer (Becton–Dickinson, San Jose CA, USA.). All data acquisitions were done with logarithmic signal amplification. Cytometer sample flow rates were calibrated using bead stocks of known concentration (Calibrite beads, BectonDickinson) and particle sizes were calibrated using beads of known diameter (Flow Cytometry Size Bead Kit, Invitrogen, Life Technologies, Grand Island, NY, USA). Eukaryotic phytoplankton were distinguished by size and red fluorescence (instrument settings: Forward Scatter = E- 01, and FL3 = 350). Cell abundances were calculated from acquisition duration, the number of events, and instrument flow rate. Results obtained by flow cytometry showed that cell abundance in the combined fraction containing large nanophytoplankton (6–30 μm) and smaller microphytoplankton (20–30 μm) (A) declined more than 3-fold during the low ice winter 2012. A similar decline was observed among phycoerythrin (PE)-rich taxa from the same size class (C). In contrast, small nanophytoplankton (2–6 μm; B) increased by 1.3-fold, whereas PE-rich small nanophytoplankton did not vary between years (D). Fig. S4. Rarefaction curves of observed species (97% OTUs) from 16S amplicons for (A) Bacteria and (B) chloroplast sequences. Use of rarefied samples of 10 000 random reads per site was based on the lowest return of bacterial reads. C. Breakdown of Bacteria and chloroplast sequences recovered from each site. A central finding from our winter surveys was that in 50% of the samples, chloroplast reads representing phytoplankton occurred in comparable (± 1%) or higher numbers than Bacteria. The bacterial 16S rRNA Itag sequences returned 3929 OTU’s affiliated with 362 unique bacterial orders within 35 phyla. Of the phyla represented, 19 were established lineages, whereas the remaining 16 were Greengenes-defined candidate phyla (McDonald et al., 2012). Likewise, only 99 of the bacterial orders were established lineages with the remainder representing candidate orders. While the high number of unique phyla and orders are suggestive of a diverse bacterial community in Lake Erie, even during winter, 75% of the bacterial sequences from water samples where affiliated with only 8 bacterial orders belonging to four phyla. These orders included Actinomycetales (Actinobacteria), Burkholderiales, Methylophilales and Rickettsiales (Proteobacteria), Chthoniobacterales (Verrucomicrobia), and Flavobacteriales, Saprospirales and Sphingobacteriales (Bacteriodetes). Three orders (Actinomycetales, Burkholderiales and Rickettsiales) consistently contributed > 30% of total bacterial reads, consistent with their dominance reported from a recent pyrosequencing survey of Western Lake Erie and Sandusky Bay during summer (Mou et al., 2013). However, seasonal similarities ended there with different orders comprising the remaining dominant taxa by season. Fig. S5. Seasonal and depth-resolved abundance of bacterial heterotrophs in Lake Erie for 2011 and 2012. Samples were divided into summer (July/August 2010 and 2011) and winter (February 2010, 2011, and 2012) and divided by depth into epilimnetic (E) and hypolimnetic (H) samples based on summer thermal stratification profiles. Observations were pooled across years; however, summer samples were not available from 2012. Samples were fixed in buffered formaldehyde (final concentration 1% v/v) and flash-frozen in liquid

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nitrogen. Samples were kept in liquid nitrogen or at −80°C until thawing and immediate analysis. Flow cytometry samples were analysed on a FACSCalibur flow cytometer (Becton-Dickinson, San Jose CA, USA.). All data acquisitions were done with logarithmic signal amplification. Cytometer sample flow rates were calibrated using bead stocks of known concentration (Calibrite beads, Becton-Dickinson). Bacterial heterotrophs were identified by their size using the side scatter channel (SSC) and SYBR Green fluorescence (FL1). SYBR Green I (Invitrogen, Life Technologies, Grand Island, NY, USA) is cell-permeable and binds to doublestranded DNA. Fixed samples were thawed and then incubated at room temperature in the dark for 15 minutes with SYBR Green I (Marie et al., 1997). Cell abundances were calculated from acquisition duration, the number of events, and instrument flow rate. The abundance of bacterial heterotrophs was generally greater, but more variable, in summer than in winter in Lake Erie. No evidence was observed for significant differences in the abundance of heterotrophic bacteria related to the extent of ice cover. Numbers shown below the axis refer to the number of discrete samples analysed. Fig. S6. Phylogenetic clustering of winter samples by principle coordinates analysis (PCoA) of unweighted UniFrac distances for bacterial (A) and chloroplast (B) communities. Fig. S7. Maximum-likelihood tree of OTUs identified by random forest analysis as important features for distinguishing between a high-ice year (2010) and a low ice year (2012), along with associated BLAST hits. Percent change reflects changes in abundance from high ice to low ice conditions. Fig. S8. Linear discriminant analysis effect size cladogram comparing the taxa of high ice winter 2010 communities with those of low ice winter 2012. Significantly discriminant nodes are coloured by year with the highest mean abundance, and branches are shaded by highest ranking taxon. Fig. S9. Maximum-likelihood trees showing phylogenetic placement of dominant OTUs from Lake Erie winter samples within (A) Alphaproteobacteria and (B) Actinobacteria. Trees were generated with bootstrap values based on 1000 replications. Alignments and trees for determining the phylogenetic placement of dominant OTUs were done with MEGA 5.2.2 (Tamura et al., 2011) and based on sequences from Van den Wyngaert et al., (2011) and Warnecke and colleagues (2004); respectively. A. A single OTU dominated the Alphaproteobacteria accounting for 66% of the reads for this class and 18% of reads of all Proteobacteria. B. Dominant OTUs of Actinobacteria cluster with the acI and acIV lineages which dominate freshwater environments. Fig. S10. Dissolved oxygen concentrations reported from US EPA spring and summer monitoring surveys of 10 central basin stations. From each station, data from the ‘bottom minus 2 m’ depth was recorded and plotted as box and whisker plots showing median dissolved oxygen concentration. Vertical boxes around each median show the upper and lower quartiles whereas whiskers extend from the 5th to 95th percentile. Use of the ‘bottom minus 2 m’ depth ensured that the hypolimnion was represented during the August surveys. Regardless of Lake Erie ice expanse between 2011 and 2013, hypoxia developed in the central basin hypolimnion by the August survey date. Likewise, review of historical EPA data obtained from the online Great Lakes Environmental


16


Database (GLENDA; accessed from http://www.epa.gov/ cdx/) showed that hypoxia developed following the low ice winter of 2002. Table S1. In 2012, light extinction coefficients in water not covered directly by ice were measured using a freefalling hyperspectral Optical Profiler (Satlantic, Halifax, NS, Canada). The light extinction coefficient (KdPAR) was calculated from spectral down-welling irradiance measurements integrated over PAR. The mean daily scalar irradiance just beneath the surface was estimated for PAR using radiative transfer model HYDROLIGHT (v 5.2; Sequoia Scientific, Bellevue, WA, USA) which in combination with measured vertical attenuation coefficients allowed estimates of the mean water column irradiance using the formula I = [E0 (1 − eKT)] [KT]−1 (Riley 1957), where E0 is the mean solar flux at the surface of the lake integrated over 24 h, K is the vertical light extinction coefficient, and T is the depth of mixing. To measure light penetration through ice in 2011, a LI-192 Underwater Quantum Sensor (LI-COR, Lincoln, NE, USA) was lowered through holes augered through the ice in order to approximate in situ measures of light attenuation. Light extinction coefficients measured in February 2012 ranged widely from 0.8–5.6 m−1 with a 12 station average of 2.05 m−1. Only stations where mean water column irradiance (Iwc) could be calculated are shown below. The mean KdPAR measured during the 2012 survey was > 2-fold higher than the average light extinction coefficient measured during surveys from 2008-2010 (Twiss et al., 2012) when the lake was predominantly ice-covered (two-tailed unpaired t-test, t = 2.89, DF = 24, P < 0.01). Further, light extinction coefficients reported in Twiss and colleagues (2012) may have been overestimated as light profiles were measured after the icebreaker had cleared ice from the sampling ocation. An effort to measure light attenuation using an underwater PAR sensor deployed through holes augered through plate ice having a thickness of > 20 cm in February 2011 reinforced this notion. Whereas the ice was shown to attenuate PAR by ∼60%, the light extinction coefficient measured through the top 10 m of the water column was 0.48 m−1, 45% lower than the mean extinction coefficient measured from 2008–2010. Table S2. Photosynthetic rates were measured by tracing the acid-stable uptake of radiolabelled 14C by photoautotrophs from the dissolved inorganic form. Briefly, in each year of the study, samples were collected from central basin site EC 1326 (41° 44′ 00” N; 81° 41′ 52” W) as USCGC Neah Bay was returning to port. The sampling site is located 10 nm from the homeport of Cleveland, OH. Once the vessel had returned to port, samples were immediately retrieved from the vessel and kept on ice during transport to our lab at BGSU. Samples were generally stored overnight in the dark at 4°C prior to measuring photosynthetic carbon uptake. In

darkness, NaH14CO3 ([60 μCi; specific activity: 58 mCi mmol−1] MP Biomedicals, Solon, OH, USA) was added to dark-adapted samples. The cell suspension was distributed as 1 mL aliquots into 7 mL chilled glass scintillation vials that were incubated simultaneously under 24 different light intensities for 2–3 h using a temperature-controlled photosynthetron (CHPT Mfg. Inc., Georgetown, DE, USA) as described previously (McKay et al., 1997). The reaction was terminated by the addition of 50 μL of formaldehyde to each sample. Acid-stable 14C assimilation was measured by liquid scintillation counting following the addition of 4.5 mL of Ecolite (+) cocktail (MP Biomedicals) to each vial. Total activity of the added 14C was determined by adding 20 μL of the sample at t = 0 to scintillation cocktail containing 200 μL of ß-phenylethylamine (Sigma, St. Louis, MO, USA). Background activity was determined at t = 0 by dispensing a sample aliquot directly into formaldehyde prior to adding scintillation cocktail. Photosynthetic rates, normalized to chl a biomass, were used to construct photosynthesis – irradiance curves using a non-linear regression curve fitting function (SIGMAPLOT 12.5, SYSTAT Software, San Jose, CA) based on the equation of Platt and colleagues (1980). The model returned three parameters: Pmax, the maximum photosynthetic rate at light saturation (g C g Chl a−1 h−1), α, the slope of the curve at low irradiances (g C g Chl a−1 h-1 [μmol quanta−1 m−2), and β, the slope of the curve associated with photoinhibition at high irradiance. From these parameters, we could calculate Ik (μmol quanta m−2 s−1) to estimate the irradiance at which photosynthesis becomes light saturated. Where replicates were measured, values are provided as the mean ± SD. Our results indicated moderate rates of production in winter and early spring similar to rates reported in Fahnenstiel and colleagues (2000) for the spring isothermal period. Calculated values of Ik suggested that photosynthesis saturated at low PAR as might be expected due to low seasonal insolation as well as the inhibitory effects of ice (10 Feb 2010, 3 March 2011) and turbidity (5 April 2012), respectively, on light penetration. Table S3. Summary of Itag sequences, numbers of operational taxonomic units (OTUs; 97% sequence identity) and alpha-diversity estimates for A) Bacteria and B) chloroplasts. For each analysis, an equal number of sequences (10 000) from each community was randomly selected. Values reported as mean ± S.D. The Chao1 species richness estimator showed that diversity was not fully captured in most communities with 52% of all OTUs identified in rarefied samples for Bacteria. Shannon’s diversity index which combines species richness and abundance into a measure of evenness did not vary for bacterioplankton communities assayed during high- and low-ice years (two-tailed unpaired t-test, t = 2.21, DF = 8, P = 0.058).


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