SPATIO-TEMPORAL DISTRIBUTION OF MICROBIAL COMMUNITIES IN THE LAURENTIAN GREAT LAKES

Mark Jeremy Rozmarynowycz

A Dissertation

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

December 2014

Committee:

R. Michael L. McKay, Advisor

William H. O'Brien Graduate Faculty Representative

George S. Bullerjahn

Scott O. Rogers

Zhaohui Xu

ii ABSTRACT

R. Michael L. McKay, Advisor

Freshwater microbial communities have received comparatively little attention compared to their marine counterparts, despite the importance of these systems. Using next-generation sequencing (Illumina itags), this study examined the microbial communities of the Laurentian

Great Lakes during both the summer-stratified period and during the winter. Additionally, the winter communities of the Laurentian Great Lakes were compared with the winter community of

Lake Onega, one of the largest freshwater lakes in Europe.

Winter communities were examined from 2010 through 2013. Lake Erie was examined during periods of high- (2010 and 2011) and low ice cover (2012). Lower ice-cover resulted in an 89% decrease in phytoplankton biovolume between years of expansive ice cover and nearly ice-free 2012. Principal coordinate analysis (PCoA) of UniFrac distance matrices revealed a strong separation between high-ice year 2010 and low-ice 2012, indicating a shift in microbial community structure. An examination of winter communities in 2013 from both Lake Erie and the upper Great Lakes revealed phylogenetically different communities for Lake Erie, Lake

Michigan, and the waters of the St Mary’s River. Samples from Lake Michigan and the Straits of Mackinac clustered with Lake Erie samples, which were correlated with concentrations of chloride and sulfate. The communities of the Laurentian Great Lakes were then compared with the communities of Lake Onega, and revealed strong differences in community structure.

Summer communities were examined from 2011 and 2012. A cruise from oligotrophic

Lake Superior to eutrophic Lake Erie revealed differences in community structure of the surface mixed layer. Concentrations of phosphorus and ammonium were correlated with the PCoA iii plots. A comparison of the surface waters of the upper Great Lakes with their oxygenated hypolimnions revealed a unique community in deep waters. This community had high abundances of Planctomycete and Chloroflexi reads, which were stable across spatially and temporally. Resampling in 2012 confirmed the stability of this community, and also examined cyanobacterial communities in both Lake Superior and Lake Erie. The community of Lake

Erie’s hypoxic ‘dead zone’ was also examined. iv

Dedicated to my wife, Clair

v ACKNOWLEDGMENTS

I would like to thank my advisor, Dr. R. Michael L. McKay, and the members of my committee, Dr. George S. Bullerjahn, Dr. Scott O. Rogers, Dr. Zhaohui Xu, and Dr. William H.

O'Brien. I would also like to thank Ben Beall, Ben Oyserman, Tim Davis, Caren Binding, Rick

Bourbonniere, Michelle Palmer, Nigel D’Souza, Steve Wilhelm, Matt Saxton, Euan Reavie, Bob

Sterner, Chip Small, Jacques Finlay, Sandy Brevold, Sue Watson, Michael Twiss, and Derek

Smith, as well as the Technical Operations personnel from Environment Canada. A special thanks to Nikolay Filatov, Sergey Komulaynen, Yuliya Slastina, Andrei Sharov, and everyone else at the Karelian Research Center of the Russian Academy of Sciences. I would also like to thank the officers and crews of CCGS Limnos, CCGS Griffon, USCGC Neah Bay, USCGC

Mackinaw, R/V Blue Heron, and R/V Lake Guardian.

This material is based upon work supported by the National Science Foundation under grant no. OCE- 0927512, 0927277, and 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 U.S. Department of Energy Joint Genome Institute was supported by the

Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and Community Sequencing Project 723 (RMLM, GSB, RAB).

vi

TABLE OF CONTENTS

Page

INTRODUCTION: HETEROTROPHIC BACTERIA OF FRESHWATER LAKES ...... 1

CHAPTER 1: ICE COVER EXTENT DRIVES MICROBIAL COMMUNITY STRUCTURE IN A LARGE NORTH-TEMPERATE LAKE: IMPLICATIONS FOR A WARMING CLIMATE ...... 8

1. INTRODUCTION ...... 8

1.1 MATERIALS AND METHODS ...... 9

1.1.1 STUDY SITE AND SAMPLING ...... 9

1.1.2 HIGH-THROUGHPUT MICROBIAL COMMUNITY ANALYSIS . 11

1.2 RESULTS AND DISCUSSION ...... 14

1.2.1 LAKE PHYSICO-CHEMICAL PROPERTIES ...... 14

1.2.2 PHYTOPLANKTON- AND MICROBIAL COMMUNITY SHIFTS

ASSOCIATED WITH ICE COVER ...... 15

1.2.3 ECOLOGICAL IMPLICATIONS OF LOW ICE COVER ...... 33

CHAPTER 2: TRANSITIONS IN MICROBIAL COMMUNITIES ALONG A 1,600 KM FRESHWATER TROPHIC GRADIENT ...... 35

2. INTRODUCTION ...... 35

2.1 MATERIALS AND METHODS ...... 37

2.1.1 STUDY SITE AND SAMPLING ...... 37

2.1.2 HIGH-THROUGHPUT MICROBIAL COMMUNITY ANALYSIS . 39

2.1.3 STATISTICAL ANALYSIS ...... 40

2.2 RESULTS AND DISCUSSION ...... 41

2.2.1 PHYSICO-CHEMICAL PROFILES OF SAMPLING LOCATIONS 41

2.2.2 MICROBIAL COMMUNITIES ...... 45

vii

2.2.3 PHYTOPLANKTON- AND MICROBIAL COMMUNITY SHIFTS

ASSOCIATED WITH LAKE TROPHIC STATE ...... 51

2.2.4 COMMUNITIES OF THE OXYGENATED HYPOLIMNION OF THE

UPPER GREAT LAKES ...... 57

2.2.5 RESAMPLING EFFORTS IN 2012 ...... 62

CHAPTER 3: SPATIO-TEMPORAL DYNAMICS OF WINTER MICROBIAL

COMMUNITIES IN LARGE FRESHWATER LAKES ...... 67

3. INTRODUCTION ...... 67

3.1 METHODS AND MATERIALS ...... 68

3.1.1 STUDY SITES AND SAMPLING ...... 68

3.1.2 HIGH-THROUGHPUT MICROBIAL COMMUNITY ANALYSIS . 70

3.1.3 STATISTICAL ANALYSIS ...... 72

3.2 RESULTS AND DISCUSSION ...... 73

3.2.1 ICE CONDITIONS AND SAMPLING ...... 73

3.2.2 ILLUMINA SEQUENCING RESULTS ...... 73

3.2.3 WINTER MICROBIAL COMMUNITIES OF LAKE ERIE ...... 78

3.2.4 MICROBIAL COMMUNITIES OF THE UPPER GREAT LAKES .. 80

3.2.5 UPPER GREAT LAKES VERSES LAKE ERIE ...... 83

3.2.6 LAKE ERIE CACHE SITES AND ICE SAMPLES ...... 90

3.2.7 MICROBIAL COMMUNITIES OF LAKE ONEGA ...... 91

CONCLUSIONS: ANTHROPOGENIC INFLUENCES ON FRESHWATER

MICROBIAL COMMUNITIES ...... 96

REFERENCES ...... 98

viii

APPENDIX A: TOP BLAST HITS FOR OTUS ABOVE 0.5% ABUNDANCE ...... 130

ix

LIST OF FIGURES

Figure Page

1 Mid-winter limnological surveys captured extremes of ice cover on Lake Erie ...... 10

2 Vertical water quality profiles for a representative central basin station (EC 1326)

occupied during winter surveys of Lake Erie...... 15

3 Phosphorus and silicate measurements ...... 16

4 Central basin phytoplankton chl a biomass ...... 17

5 Phytoplankton biomass accumulation during extremes of ice cover ...... 18

6 Rarefaction curves of observed species ...... 21

7 OTU abundance: Lake Erie 2010-12 ...... 23

8 Principal coordinate analysis: Lake Erie 2010-12 (unweighted) ...... 25

9 Principal coordinate analysis: Lake Erie 2010-12 (weighted) ...... 25

10 Maximum-likelihood tree of OTUs identified by supervised learning ...... 28

11 LDA effect size cladogram: Lake Erie 2010 and 2012 ...... 30

12 Sampling locations: July 2011 ...... 38

13 Rarefaction curves for bacteria and total reads: July 2011 ...... 46

14 Principal coordinate analysis (all samples) ...... 48

15 Principal coordinate analysis (surface mixed-layer) ...... 48

16 OTU abundance: July 2011 (surface mixed-layer) ...... 53

17 LDA effect size cladogram: July 2011 (surface mixed-layer) ...... 56

18 OTU abundance: July 2011 (hypolimnion) ...... 58

19 LDA effect size cladogram: July 2011 (hypolimnion) ...... 60

20 OTU abundance (2012 resampling) ...... 64

x

21 Sampling sites in the upper Great Lakes and Lake Erie (2013) ...... 69

22 Read abundance: Winter 2013 ...... 75

23 Rarefaction curves for bacterial OTUs ...... 76

24 OTU abundance: Lake Erie (winter 2013) ...... 79

25 OTU abundance: upper Great Lakes (winter 2013) ...... 81

26 Principal coordinate analysis: Laurentian Great Lakes (winter 2013) ...... 84

27 LDA effect size cladogram (Laurentian Great Lakes) ...... 88

28 OTU abundance: Lake Onega, Russia (winter 2013) ...... 92

29 Principal Coordinate Analysis of the Laurentian Great Lakes and Lake Onega ...... 94

30 LDA effect size cladogram: Laurentian Great Lakes and Lake Onega ...... 95 xi

LIST OF TABLES

Table Page

1 Sampling sites and physico-chemical parameters ...... 43

2 Alpha Diversity: July 2011 ...... 47

3 BEST analysis: July 2011 ...... 50

4 Mantel correlations: July 2011 ...... 50

5 Feature importance scores: surface mixed-layer (July 2011) ...... 56

6 Feature importance scores: hypolimnion (July 2011) ...... 60

7 Ice cover at sampling locations ...... 74

8 Alpha Diversity: Winter 2013 ...... 77

9 Chemical measurements for the Laurentian Great Lakes ...... 85

10 BEST and Mantel tests for the Laurentian Great Lakes samples (2013) ...... 86

11 Feature importance scores (Laurentian Great Lakes) ...... 88

12 Feature importance scores (Laurentian Great Lakes and Onega) ...... 95 1

INTRODUCTION: HETEROTROPHIC BACTERIA OF FRESHWATER LAKES

Bacteria have been recognized as integral components of all ecosystems, where they transform and cycle nutrients. While much attention has focused on marine microbial communities, little has been given to those of freshwater systems. Many freshwater systems suffer from nutrient loading and pollution stemming from anthropogenic inputs such as agricultural runoff. Increased nutrient loads can lead to eutrophication of such systems, the primary threat to ecological integrity (Schindler, 2006), and may lead to shifts in the composition of microbial communities. In freshwater communities, much of the focus has been on the

Cyanobacteria. Here we look at the heterotrophic members of freshwater communities, represented primarily by the Proteobacteria, Actinobacteria, Bacteroidetes, and members of the

PVC superphylum (Zwart et al., 2002; Newton et al., 2011)

The Proteobacteria are a highly diverse lineage, and represent the most studied of the bacterial phyla. This phylum contains 6 classes; the alpha-, beta-, delta-, gamma-, epsilon-, and zetaproteobacteria. Of these classes, the alpha-, beta-, and gammaproteobacteria are most often detected in freshwater and marine systems. Class Alphaproteobacteria, from which mitochondria are believed to have originated (Andersson et al., 1998), are ecologically important and are well known for nitrogen fixation in association with leguminous plants (Kersters et al.,

2006). Alphaproteobacteria are ubiquitous in freshwaters, although less numerous than in the oceans, where the SAR11 clade accounts for ~33% of surface water cells (Morris et al., 2002).

Members of the LD12 clade, a freshwater branch of the marine SAR11 clade, have since been discovered to be the most numerous of the Alphaproteobacteria in lakes (Logares et al., 2009), where they have adopted an oligotrophic lifestyle (Salcher et al., 2011). Along with their 2 efficient, but slow nutrient uptake, small size may be an adaptation to this lifestyle. It has been observed that increased grazing by size-selective protists resulted in an increase in

Alphaproteobacteria (Šimek et al., 1999; Salcher et al., 2005). Like Alphaproteobacteria,

Gammaproteobacteria are more abundant in marine systems than in freshwaters (Biers et al.,

2009). This class appears to lead a copiotrophic lifestyle, exhibiting fast growth rates in response to nutrient pulses (Šimek et al., 2006). In contrast to the Alpha- and

Gammaproteobacteria, Betaproteobacteria are found in low abundance in the ocean (Rusch et al., 2007). They are, however, often numerically dominant in freshwater (Glöckner et al., 2000;

Zwisler et al., 2003), where they are susceptible to flagellate grazing (Šimek et al., 2001).

Betaproteobacteria lead opportunistic lifestyles, responding rapidly to nutrient pulses (Šimek et al., 2006), and have been associated with extracellular phytoplankton production (Šimek et al.,

2008). Deltaproteobacteria, while not as abundant as other classes of Proteobacteria, are also ecologically important. Among its members are those that prey upon gram-negative bacteria

(Bdellvibrionales), as well as anaerobic sulfur-reducing bacteria. Among the Proteobacteria, phototrophic members utilizing proteorhodopsins can be found in the Alpha- (Rhodobacter),

Beta- (Rhodoferax), and Gammaproteobacteria (Chromatiales)(Kersters et al., 2006).

The Actinobacteria, another major bacterial lineage is found in a variety of habitats ranging from soils to marine and freshwater ecosystems. Previously thought to be exclusively soil bacteria (Goodfellow and Williams, 1983), freshwater and marine strains were discovered to be distinct from soil strains (Rappé et al., 1999; Newton et al., 2011). Moreover, they were often the most numerically abundant organisms in lakes, accounting for up to 63% of biomass attributed to bacterioplankton (Glöckner et al., 2000). Actinobacteria have been observed worldwide, retaining a strong 16S sequence similarity (Hahn and Pöckl, 2005; Zwart et al., 3

1998), suggesting similar clades across the globe, where they adapt to local conditions (Hahn and

Pöckl, 2005). Freshwater Actinobacteria have been divided into nine lineages representing 32 tribes (Newton et al., 2011). Two classes accounted for the vast majority of Actinobacteria in this study; Actinobacteria (acI lineage), and Acidomicrobia (acIV lineage). AcI Actinobacteria are the most abundant lineage in freshwater lakes, having been observed in abundances of >50% of the bacterial community (Allgaier et al., 2007; Warnecke et al., 2005; Glöckner et al., 2000).

Actinobacteria have a number of adaptations to freshwater lakes. Actinobacteria are generally accepted as having high GC content, ranging from 51-70% (Ventura et al., 2007), which has been suggested as a mechanism for UV protection (Warnecke et al., 2005). However, a study by Ghai et al., (2011) indicated that the numerically dominant acI clade, among other lineages, may have GC content as low as 37%. The discovery that Actinobacteria possess light- harvesting proteins similar to proteorhodopsins, dubbed actinorhodopsins, suggests that some members of the Actinobacteria may lead photoheterotrophic lifestyle in surface waters (Sharma et al., 2008). Recently, actinorhodopsins were identified in the genomes of several

Actinobacteria (Ghylin et al., 2014; Hahn et al., 2014). Size and cell wall structure may also be important mechanisms for survival. In culture, Actinobacteria were observed to bloom when in the presence of Ochromonas, a size-selective bacteriovore (Pernthaler et al., 2001; Hahn et al.,

2003). These studies identified cell size as well as cell wall structure, a feature known as the S- layer, as factors reducing grazing (Hahn et al., 2003). Supporting this, removal of the S layer resulted in increased grazing by Ochromonas by up to 5.2% (Tarao et al., 2009). Environmental conditions also play a role in the distribution of Actinobacteria, and have been observed to decrease in abundance with oxygen levels (Allgaier et al., 2006) and in culture with increased eutrophication (Haukka et al., 2006). The decrease in Actinobacteria in response to 4 eutrophication may be the result of a slow growth rate when compared to the growth rates of other freshwater bacteria (Šimek et al., 2006).

The Bacteroidetes are a metabolically diverse phylum found in a variety of habitats. It contains both chemoorganotrophs and phototrophs, the later being Flavobacteria utilizing proteorhodopsins (Gómez-Consarnau et al., 2007; González et al., 2008). Bacteroidetes are often particle associated, where they, along with Proteobacteria, are often numerous (Nold and

Zwart, 1998; Lemarchand et al., 2006). Bacteroidetes are known to degrade high molecular weight dissolved organic matter, such as chitin and cellulose, and as such are thought to have a major role in the environment. While this has been observed in the oceans (Fandino et al.,

2001), Betaproteobacteria have been observed to out-compete Bacteroidetes in some freshwater systems (Schweitzer et al., 2001; Böckelmann et al., 2000). Flavobacteria are found in high abundance during periods of cyanbacterial blooms, where they accounted for nearly 100% of the heterotrophic community during the decline of one bloom (Eiler and Bertilsson, 2007).

The PVC super phylum is an assemblage of bacteria that includes, among others, the

Planctomycetes, Verrucomicrobia, and Chlamydiae, from which the group derived its name.

The PVC super phylum is unique among bacteria for a number of archaea- and eukaryotic-like traits, including compartmentalization of cells (Lindsay et al., 2001; Lee et al., 2009), a lack of division protein FtsZ (Pilhofer et al., 2008), the production of sterol (Desmond and Gribaldo,

2009) and the lack of peptidoglycan in their cell walls. While these traits do not occur uniformly among members of the superphylum, they suggest a common ancestor of the superphylum

(Reynaud and Devos, 2011). Molecular and cellular functions of members of the PVC clade are poorly understood. Of those species which genomes have been sequenced, >50% of their genes are of unknown function (Devos, 2014). In this study, the phyla that occurred in significant 5 abundance were the Planctomycetes and Verrucomicrobia.

The Planctomycetes are another ubiquitous phylum, making up a significant portion of the bacterial community in a variety of environments, including soil (Buckley et al., 2006), fresh water , brackish, and marine habitats (Wang et al., 2002; Janssen, 2006; Gade et al., 2004,

Dedysh et al., 2005; Zeng et al., 2013; Vergin et al., 1998; Shu and Jiao, 2008). They have been found in diverse habitats ranging from epibacterial communities of macroalgae (Bondoso et al.,

2014), to deep-sea hydrothermal vent systems (Kato et al., 2010; Lanzén et al., 2011; Storesund and Øvreås, 2013), to the hyperarid Atacama Desert (Drees et al. 2006). Indeed, Planctomycetes were found in the highest known naturally occurring densities in the hindgut of soil-feeding termites (Köhler et al., 2008).

Planctomycetes are distinctive among bacteria. They display complex compartmentalization of cells, including a membrane-bound nucleoid. Furthermore, many members produce stalks and reproduce through budding. The environmental importance of these organisms is just beginning to be realized. Anaerobic ammonium oxidation (anammox) has been identified only in Planctomycetes (Strous et al, 1999) and it is estimated that they have generated as much as 50% of atmospheric N2 (Jetten, 2008). Interestingly, some possess genes for C1 transfer, typically found in methanogenic archaea and methanotrophic Proteobacteria, suggesting a possible role in the evolution of the methane cycle (Chistoserdova et al., 2004).

Of the samples analyzed in this study, two classes of Planctomycetes were represented in appreciable abundance, the Phycisphaerae and Planctomycetia. Class

Phycisphaerae, formerly group WPS1, was first discovered in PVC contaminated soils (Nogales et al., 2001). Uncultured representatives of this class have been associated with hypoxic conditions and include an anaerobic sulfur-rich spring (Elshahed et al., 2007), the suboxic zone 6 of the Baltic Sea (Fuchsman et al., 2011), and the sulfate-methane transition zone (Harrison et al., 2009). Two species have been brought into culture from this class, both isolated from marine algae. P. mikurensis is a facultatively anaerobic species (Fukunaga et al., 2009), while

Algisphaera agarilytica, is a strict aerobe (Yoon et al., 2013). While most planctomycetes reproduce by budding, both cultured species of this class reproduced by binary fission.

Class Planctomycetia is represented by a number of cultivated freshwater and marine strains. One cultured freshwater representative of this class, isolated from a lake in Michigan, was found in association with Daphnia (Staley, 1973). Members of this class may be important members of the sulfur cycle, and are capable of reducing elemental sulfur to sulfide (Elshahed et al., 2007). Indeed, comparative genomic analysis of 9 Rhodopirellula strains revealed a high diversity of genes annotated as sulfatases (>1,100)(Wegner et al., 2013). No representatives from the anammox clade, the Brocadiales, were detected in this study.

The Verrucomicrobia of the PVC superphylum are found both in water and soil habitats, as well as in association with eukaryotes (Sakai et al., 2003; Wang et al., 2005). They have been found in both surface and deep-water habitats, and have been associated with cyanobacterial blooms, where at times they have been observed to be the most abundant heterotrophic bacteria

(Kolmonen et al., 2004; Eiler and Bertilsson, 2004). In mesocosm experiments, their occurance has been correlated with eutrophic conditions (Haukka et al., 2006). Some have been discovered to have the ability to fix nitrogen, while other extremely acidophilic members have been found to oxidize methane (Dunfield et al., 2007; Islam et al., 2008, Pol et al., 2007).

In this study, 5 classes of Verrucomicrobia were present in appreciable numbers,

Pedosphaerae, Spartobacteria, Opitutae, Verrucomicrobiae, and Methylacidiphilae. The

Spartobacteria dominate verrucomicrobial communities in soils (Gaddy et al., 2011), and were 7 also observed as the dominant class in a humic lake (Arnds et al., 2010). In brackish regions of the Baltic Sea, a single OTU, ‘Spartobacteria baltica,’ was the dominant read (Herlemann et al.,

2011; Herlemann et al., 2013).

Other minor phyla may also have important ecological roles in freshwater lakes. A member of the Gemmatimonadetes isolated from a lake in the Gobi Desert was discovered to produce bacteriochlorophyll a (Zeng et al., 2014), and suggests a photoheterotrophic lifestyle for some members of this phylum. Another phylum, the Chloroflexi, may also be important in the ecology of deep, oxygenated hypolimnions. Members of the CL500-11, a subclass of the deep- ocean SAR202 clade, have been found to be the prominent group in the hypolimnions of both ultraoligotrophic Crater Lake, USA (Urbach et al., 2001) and mesotrophic Lake Biwa, Japan

(Okazaki et al., 2013), where it has been speculated to play a role in mineralization.

In this study, the bacterial communities of the Laurentian Great Lakes were explored using Illumina 16s tags. The influence of trophic status on these communities was examined along a gradient beginning in oligotrophic Lake Superior, the headwaters of the Laurentian Great

Lakes, and continuing to Lake Erie’s eutrophic Western Basin. Temporal resolution was provided by winter surveys of Lake Erie along with samples collected from the upper Great

Lakes. To provide insight into communities present in other large lakes in the Northern

Hemisphere, these winter communities were also compared with those of Lake Onega, one of the largest lakes in Europe. Additionally, the effects of global climate change were examined in

Lake Erie, comparing two years of high ice cover with a rare, nearly ice-free winter. 8

CHAPTER 1: ICE COVER EXTENT DRIVES MICROBIAL COMMUNITY

STRUCTURE IN A LARGE NORTH-TEMPERATE LAKE: IMPLICATIONS FOR A

WARMING CLIMATE

1. 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 4 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 in the lake (Desai et al., 2009). Climate warming may also have important implications for eutrophication in the Great Lakes. In the 9 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 resuspension 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. 1). 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.

1.1 MATERIALS AND METHODS

1.1.1 STUDY SITE AND SAMPLING

Lake Erie was sampled on surveys aboard CCGS Griffon and USCGC Neah Bay during winters 2010-2012 (Fig. 1). Early April monitoring surveys conducted by the U.S.

Environmental Protection Agency (EPA) were on board R/V Lake Guardian. Sampling aboard

CCGS Griffon was conducted during weeklong surveys in mid-February and included routine 10

Figure 1. Mid-winter limnological surveys captured extremes of ice cover on Lake Erie. A. Moderate Resolution Imaging Spectroradiometer (MODIS) image captured on 3 February, 2011 showing expansive ice cover. B. MODIS 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). MODIS images obtained from Great Lakes CoastWatch Program, NOAA-Great Lakes Environmental Research Lab (http://coastwatch.glerl.noaa.gov/).

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 U.S. 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 (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 Intake 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 et al., (2012). Briefly, surface water samples were collected at approximately 1 h intervals using a stainless steel sampling bottle and processed immediately onboard the ship. 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; 11 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 Inc.,

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 Inc., Bellevue, WA, USA) casts.

Samples were processed for determination of size-fractionated chlorophyll (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). Additional physico-chemical data from EPA annual spring surveys were obtained from the online Great Lakes Environmental Database

(GLENDA; accessed from http://www.epa.gov/cdx/).

1.1.2 HIGH-THROUGHPUT MICROBIAL COMMUNITY ANALYSIS

At three central basin hydrographic stations (EC 341, 880 and 1326) 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, 12 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. DNA 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. Primer design for universal amplification of the V4 region of 16S rDNA was based on Caporaso et al., (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 (Magoč and Salzberg, 2011). Resulting sequences were demultiplexed 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 (OTU) were picked using uclust at 97%

(Edgar, 2010) and OTUs represented 3 times or fewer were filtered. A representative set of sequences was generated for each site with taxonomy assigned using the 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 which were assigned to chloroplast and mitochondrial sequences, along with those not identifiable beyond Bacteria were 13 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 principle coordinates analysis

(PCoA) on both weighted and unweighted UniFrac matrices (Lozupone et al., 2007) rarified at

10,000 reads. Significance between PCoA clustering was tested using ANOSIM (Clark, 1993) with 999 permutations. Machine-learning approaches were 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 approaches were filtered to contain only OTUs that were present at an abundance of at least 1% in any sample. Samples that clustered together along weighted principal component 1 were used for analysis. Random forest analysis (Knights et al. 2012, Breiman, 1999) 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) which uses Kruskal-Wallis sum rank test (α=0.05) to identify significantly differential taxa and linear discriminant analysis

(LDA) to estimate the effect size of each. 14

1.2 RESULTS AND DISCUSSION

1.2.1 LAKE PHYSICO-CHEMICAL PROPERTIES

Mid-winter surveys of Lake Erie conducted in 2010/2011 and 2012 captured extremes in ice cover (Fig. 1). 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 physico-chemical parameters (Fig. 2). 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 wind-driven mixing regimen in the absence of ice during 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, DF = 6, P <

0.0001) (Fig. 2). Accordingly, the higher turbidity measured in 2012 caused greater attenuation of photosynthetically active radiation (PAR). 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 to winters with expansive ice cover

(Fujisaki et al., 2013). These increases in vertical mixing and horizontal convection 15

Figure 2. Vertical water quality profiles for a representative central basin station (EC 1326) occupied during winter surveys of Lake Erie. Water column profiles of temperature, dissolved oxygen and turbidity 620 were recorded during the February 2011 and 2012 surveys using a Model 660 Sonde (YSI Inc., Yellow Springs, OH, USA) lowered at approximately 0.1 m s-1. Whereas profiles varied distinctly between years of high (2011) vs. low (2012) ice, they showed only modest variation of physicochemical parameters with depth suggestive of isothermal mixing.

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 intake data demonstrated elevated soluble reactive phosphorus and silicate concentrations during winter 2012 (Fig. 3).

1.2.2 PHYTOPLANKTON- AND MICROBIAL 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 chl a biomass compared with

2011 that was measured as part of both mid-February and early April surveys (Fig. 4). 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 16

Figure 3. Phosphorus and silicate measurements. Soluble reactive phosphorus (SRP; top panel) and silicate (lower panel) measured near the inflow of the Detroit River into Lake Erie (Amherstburg), at a location in the central basin (Elgin) and near the outflow of the lake to the Niagara River (Rosehill). In each panel, the shaded region shows the interquartile range and the thick shaded bar shows the median. Three low ice years of 2002, 2006 and 2012 are then superimposed against the pooled data.

(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 non-motile 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 17

Figure 4. Central basin phytoplankton chl a biomass reported from U.S. EPA spring monitoring surveys.

exhibiting high transmittance of PAR (Bolsenga & 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) and 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 chlorophyll (Fig. 5a), 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 18

Figure 5. Phytoplankton biomass accumulation during extremes of ice cover. A. Winter surveys conducted on Lake Erie over two 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. 19

(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 3-fold. Likewise, results from the April EPA monitoring surveys of 10 central basin stations from 2010-2012 support these trends (Fig. 5b). 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. 5b). 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. 5b).

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. 3). Measurement of photosynthesis in early spring 2012 supported light-limitation of phytoplankton photosynthesis as Ik, the irradiance at 20 which photosynthesis becomes light-saturated, was > 4-fold higher than the calculated mean water column irradiance during the daylight period. Similar values of Ik were estimated for communities sampled in 2010 and 2011, but 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.

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 (Fig. 6b). For clarity, we 21

Figure 6. Rarefaction curves of observed species (97% OTUs) from 16S amplicons for A) Bacteria and 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. 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 (Fig. 6B).

Rarefaction curves indicated that diversity was not fully captured in most communities (Fig. 6A).

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. 6B). 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 22 contribution of 46% of total reads (Fig. 7). 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.,

2013a; b; 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 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 to 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 4-fold lower than in summer.

A single OTU of Aulacoseira sp. dominated the diatoms sampled during winter contributing nearly 80% of chloroplast reads and an average 37% of total reads (Fig. 7c). Based on sequencing data alone, the species-level identity of this dominant OTU is inconclusive, which likely reflects the paucity of diatom sequences available in the NCBI database. Our previous 23

Figure 7. OTU abundance: Lake Erie 2010-12 A. OTU abundances per station, colored by phylum, rarefied to 10,000 reads. B. OTU abundances per station, colored by class. C. Chloroplast OTU abundances as a percent of total chloroplast and bacterial reads. 24 surveys (Twiss et al., 2012) along with annual spring surveys of the EPA (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 PCoA of the bacterial community supported separation based on expanse of winter ice cover in both unweighted (qualitative) (Fig. 8) and weighted (quantitative) analyses

(Fig. 9). 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). The first two principal coordinates for the weighted UniFrac explained 67% and 95% of the variation for bacterial and chloroplast communities respectively (Fig. 5). PCoA of unweighted UniFrac distances revealed that 25% of variation (PC1) could be attributed to the expanse of ice cover. Likewise, 46% of variation

(PC1) could be explained by the expanse of ice cover in the weighted analysis when relative abundance of taxa was considered (Fig. 5a). Interestingly, samples collected during 2011 along with a sample collected from EC 341 during 2012 appear to be intermediates along PC1 and cluster separately along PC2. Satellite imagery revealed that sites sampled in 2011 were open water ~2 weeks prior to sampling, and may indicate a transitioning bacterial community. This clustering pattern is significant and reveals a high level of dissimilarity between clusters present in the weighted and unweighted UniFrac analysis (RANOSIM = 0.97, P = 0.002; unweighted

RANOSIM = 0.78, P = 0.002). Further examination of weighted UniFrac revealed that including the intermediate cluster with either the low ice or high ice cluster resulted in significant dissimilarity from the third group (high-ice RANOSIM = 0.58, P = 0.01; 25

Figure 8. Principal coordinate analysis: Lake Erie 2010-12 (unweighted). PCoA of unweighted UniFrac distance matrices for bacteria and chloroplast communities.

Figure 9. Principal coordinate analysis: Lake Erie 2010-12 (weighted). PCoA of weighted UniFrac distance matrices for bacteria and chloroplast communities. 26

low-ice RANOSIM = 0.86, P = 0.01), supporting a transitional condition. There was no significant difference between the intermediate cluster and a combined high- and low ice condition (RANOSIM

= 0.09, P = 0.25). The consistent ice-extent trend for all sites except EC 341 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 this 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 similarly revealed clustering by ice conditions (Figures

8 and 9). The first two principal components explained 35% and 95% of variation for unweighted and weighted UniFrac, respectively. Principal coordinate 1 alone accounted for 87% of the variation in the weighted analysis (Fig. 9). These UniFrac results are significant and reveal dissimilarity between low- and high ice conditions (weighted RANOSIM = 0.41, P = 0.019; unweighted RANOSIM = 0.42, P = 0.036), suggesting that specific taxa are associated with the ice cover. Interestingly, samples collected in 2011, which appear to be intermediates in the bacterial

PCoA, cluster with low-ice samples along weighted PC1, while clustering closer high ice samples along unweighted PC1, and may further point to a transition in the microbial community from open waters to ice. Even though stramenopiles are a dominant component of the microbial community in 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 ice-associated organisms (e.g., Aulacoseira icelandica). 27

Complementary machine learning approaches were used to identify discriminatory taxa for low-ice and high-ice conditions associated with years 2010 and 2012 (Knights et al. 2012,

Breiman, 1999). Samples for EC 880, EC 1326, and EC 341 were compared using supervised learning, a random forest algorithm (QIIME). Cross-validation estimates for 2010 and 2012 had an average probability of being guessed correctly 70% and 76% respectively. OTUs determined to be important features were identified using NCBI BLAST (Fig. 10). In total, OTUs that resulted in a decrease in accuracy in prediction accounted for an average of ~54% and ~44% of the bacterial community for 2010 and 2012 respectively. LEfSe LDA (Segata et al., 2011) was used to test for significant differences between low ice and high ice samples (Fig. 11). Both random forest and LDA algorithms identified OTUs within the Actinobacteria, Acidobacteria,

Bacteroidetes, Gemmatimonadetes, Planctomycetes, Proteobacteria, and Verrucomicrobia as important features differentiating low- and high-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 recently forming massive surface blooms in the western- and central basins of the lake (Allinger and Reavie 2013; Michalek et al.,

2013).

Among heterotrophic phyla, reads of Proteobacteria were uniformly dominant in winter regardless of ice expanse and contributed approximately 33% of bacterial reads in water samples.

Proteobacteria are arguably the best-studied phylum of environmentally relevant bacteria 28

Figure 10. Maximum-likelihood tree of OTUs identified by supervised learning. Important features for distinguishing between a high-ice year (2010) and a low-ice year (2012), identified using random forest analysis, along with associated BLAST hits. Percent change reflects changes in the community for low ice. 29 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., 2013b), 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 SAR11-cluster (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. 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). LDA indicated a significant decrease in Gammaproteobacteria under low-ice conditions. Supervised learning identified

OTUs from the Alpha-, Beta-, and Gamma- lineages as important features for distinguishing the low- and high ice years (Fig. 10). 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. 30

Figure 11. LDA effect size cladogram: Lake Erie 2010 and 2012. LDA cladogram comparing taxa of high-ice 2010 with those of low-ice 2012. Significantly discriminant nodes are colored, and branches are shaded by highest-ranking taxon.

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 (two-tailed 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. Supervised learning identified members of the

Sphingobacteria, Saprospireae, Cytophagia, and Flavobacteria as important features for 31 discriminating between high- and low ice years. Abundance of Bacteroidete 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% (Supp tree). LDA revealed significant decreases for Bacteroidetes, and indicated that Sphingobacteria and

Cytophagia, had significantly decreased under low-ice conditions.

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’ (Herlemann et al., 2013) identified as the most abundant bacterial read in brackish (salinities 5-10) regions of the Baltic

Sea (Herlemann et al., 2011). Random forest analysis identified this OTU as an important feature for distinguishing between high-ice and low-ice conditions, while LDA indicated a significant increase in the abundance of Spartobacteria during low ice conditions.

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 32 al., 2013; Poretsky et al., 2013), higher abundances of Verrucomicrobia have been reported.

Using CARD-FISH, Arnds et al. (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 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 under-recognized 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., 2013b), historically they have been associated with the terrestrial environment and their presence in lakes attributed primarily to runoff 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. 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 33 learning algorithms both indicated shifts in Actinobacteria between high- and low ice conditions.

Supervised learning indicated that two OTUs, both increasing under low-ice conditions, as discriminatory features, one of which shared close identity with Rhodococcus. Likewise, LDA indicated significant increases in Actinobacterial acI and acIV lineages during low ice conditions.

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. LDA also indicated significant decrease in Planctomycetes and an increase in Gemmatimonadetes.

1.2.3 ECOLOGICAL IMPLICATIONS OF LOW ICE COVER

Winter ice cover on the Great Lakes has declined by 71% over the past 4 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 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 Aulacoseira islandica will disrupt trophic transfer of carbon to zooplankton ultimately having a negative impact on fish recruitment. Suppression of winter diatom growth may also 34 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. Rather, late summer hypoxia followed the low ice years of 2002 and 2012. Whereas winter production is 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 and hypolimnetic volume and temperature. 35

CHAPTER 2: TRANSITIONS IN MICROBIAL COMMUNITIES ALONG A 1,600 KM

FRESHWATER TROPHIC GRADIENT

2. INTRODUCTION

Collectively, the Laurentian Great Lakes system holds 21% of global surficial and liquid freshwater making it arguably the most important natural resource on the North American continent. Historically, the lakes were exploited for their productive fisheries and as a transportation route, supporting the manufacturing activities that brought prosperity to America’s

“North Coast”. Today, they support a multi-billion dollar tourist and recreation industry, supply

40 million people with drinking water, and provide habitat for wildlife and dozens of species of fish. Unfortunately, these resources are at risk. Eutrophication is regarded as one of the primary threats to the ecological integrity of our freshwater resources (Schindler, 2006) and the

Laurentian Great Lakes have historically followed the pattern of eutrophication documented globally (Beeton, 1965). However, these transitions in trophic state are fluid, reflecting ecosystem responses not only to directed management efforts (Bunnell et al., 2014) but also to biological invasions (e.g. Hecky et al., 2004) and climate warming (Chapter 2). Indeed, recent evidence has accumulated describing both the oligotrophication (Barbiero et al., 2012) and re- eutrophication (Kane et al., 2014; Scavia et al., 2014) of lakes in the Great Lakes system.

Among the Great Lakes, Lakes Superior and Erie serve as end members in terms of trophic state (Chapra and Dobson, 1981; Sterner 2011). Lake Superior as the headwater of this system is an oligotrophic lake characterized by cold temperatures, deep mixing and a high stoichiometric imbalance between nitrogen and phosphorus (Sterner et al. 2007, Sterner 2011). It has continuous, near-saturation oxygen levels throughout the water column with oxygen 36 extending deep into surface sediments (Li et al. 2012). Its near pristine condition is supported by long term trends demonstrating stable profiles of total dissolved solids and major ions which serve as indicators of anthropogenic impacts on the system (Chapra et al., 2012). Lake Erie, in contrast, has a human-dominated watershed which is home to 13 million people, whose industrial and agricultural activities have contributed to the eutrophication of the western and central basins of the lake. Whereas the combined effects of directed management (Makarewicz and Bertram 1991) and the unintended consequences of invasive dreissenid mussels (Nicholls and Hopkins 1993) resulted in increased transparency of the lake in the 1980’s and 1990’s, the lake has since entered into a period of re-eutophication (Kane et al., 2014; Scavia et al., 2014) characterized by blooms of pelagic- (Twiss et al. 2012; Michalek et al. 2013) and benthic algae

(Higgins et al., 2005) and intensification of seasonal hypoxia (Hawley et al., 2006; Scavia et al.,

2014).

The activities of microbial populations and consortia have a direct and profound influence on the ecological integrity of aquatic systems. Microbes are primary and secondary producers. They facilitate nutrient cycling, remediate and detoxify contaminants, produce novel metabolites, and serve as keystone species in interactions with many other organisms. Yet our knowledge of microbial diversity in the Great Lakes is limited both spatially and seasonally.

With a few exceptions (summarized in Reed and Hicks, 2011), most of what we know about planktonic microbial diversity in the Great Lakes comes from recent pyrosequencing studies focused on Lake Erie (e.g. Mou et al., 2013a; Wilhelm et al., 2014), the lake most impacted by eutrophication. Here we describe planktonic microbial diversity captured as part of a 3,200 km round-trip limnological survey extending from western Lake Superior to eastern Lake Erie.

Conducted over a period of 2 weeks during the summer-stratified period, the survey provided a 37 unique opportunity to gain insights into how microbial communities reflect changes in the trophic state of the largest lake system on Earth.

2.1 METHODS AND MATERIALS

2.1.1 STUDY SITES AND SAMPLING

Samples were collected between July 14-29, 2011 aboard R/V Blue Heron during a 3,200 km round-trip survey through Lakes Superior, Huron, and Erie (Fig. 12). At each hydrographic station, sampling was preceded by a conductivity–temperature–depth (CTD) cast. Water was sampled from discrete depths using Niskin bottles attached to the CTD rosette. Samples were processed for determination of total chlorophyll (chl) a biomass (>0.2 µm) and for dissolved- (<

0.2 µm) and particulate nutrients. Sampling also included determination of elemental stoichiometry (C, N, P) for size-fractionated (<80 µm [total] and <2 µm [picoplankton]) planktonic seston.

Chl a biomass was measured by fluorometry following extraction in 90% (v/v) acetone at

-20 °C (Welschmeyer 1994). Subsamples for nutrient determination were frozen in polyethylene bottles on board ship for subsequent laboratory analysis. Nitrate was measured using a Lachat

QuickChem© 8500 Flow Injection Analysis System (Hach Co., Loveland, CO, USA) and a cadmium reduction column whereas measurement of total dissolved phosphate followed the ascorbic acid-molybdate method (Parsons et al. 1984). A fluorometric method was used to

+ measure NH4 as described previously (Kumar et al. 2007; Small et al. 2013). For particulate organic C, N, and P measurements, seston was collected onto precombusted 25 mm GF/F filters

(for P, filters were acid-rinsed prior to sample application), frozen, and then dried at 60 °C as 38

Figure 12. Sampling locations: July 2011. Sampling locations along a transect leaving from station CD-1 in Lake Superior and extending to station EC 879 in Lake Erie. described elsewhere (Sterner 2011). Particulate organic carbon and nitrogen were determined with a 2400 Series II CHN analyzer (Perkin Elmer, Waltham, MA, USA) using acetanilide as a standard. Samples for particulate organic phosphorus were digested in a 5% potassium persulfate solution and autoclaved for 30 min. Liberated soluble reactive phosphorus was analyzed with the ascorbic acid-molybdate method (Parsons et al., 1984).

Microbial biomass from water samples was concentrated on Sterivex cartridge filters

(0.22 µm; EMD Millipore, Billerica, MA, USA) and immediately frozen in liquid nitrogen. DNA was extracted from the Sterivex cartridges using the PowerWater Sterivex DNA Isolation Kit

(MO BIO Laboratories, Inc, Carlsbad, CA, USA) following manufacturer’s instructions. 39

2.1.2 HIGH-THROUGHPUT MICROBIAL COMMUNITY ANALYSIS

A comprehensive taxonomic analysis was completed by Illumina MiSeq targeting the

16S rRNA V4 hypervariable region of bacterial and plastid genomes. 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.

Primer design for universal amplification of the V4 region of 16S rDNA was based on (Caporaso et al., 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

(Magoč and Salzberg, 2011). Resulting sequences were demultiplexed 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 in QIIME 1.8.0 (Caporaso et al., 2010a) using default settings unless noted otherwise. OTUs were picked using uclust at 97% identity (Edgar, 2010) and

OTUs represented 3 or fewer times in the data set were filtered along with those shorter than 200 bp. A representative set of sequences was generated, where the most abundant sequence represented its respective OTU. Taxonomy was assigned to each representative sequence using the 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, reads that were assigned to chloroplast and mitochondrial sequences, those that were unclassified or unassignable, and those not identifiable beyond bacteria were filtered. Those reads matching 40

‘chloroplast’ were then used in subsequent analyses to examine the photosynthetic eukaryotic community. The combined total bacterial and chloroplast 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 for all bacterial OTUs was generated from the filtered alignment using FastTree 2.1.3 (Price et al., 2010). The samples were then rarefied in QIIME.

2.1.3 STATISTICAL ANALYSIS

Alpha and beta diversity were implemented through QIIME, and principal coordinates analysis (PCoA) on both weighted and unweighted UniFrac matrices (Lozupone and Knight,

2005) was done at a rarified depth of 20,000 reads. Significance of PCoA clustering was tested using ANOSIM with 999 permutations, each cluster being tested against the remainder of samples.

The importance of environmental features on PCoA clustering was explored using

QIIME’s BEST analysis, an implementation of BIOENV (Clarke & Ainsworth, 1993). Distance matrices were generated for each physiochemical parameter (QIIME), and correlations of physiochemical measurement matrices and UniFrac plots was examined using the Mantel test with 999 permutations. Machine-learning approaches were adopted to identify OTUs whose abundances were associated with UniFrac clustering patterns. To reduce noise due to a large number of OTUs, communities used were filtered to contain only OTUs that were present at an abundance of at least 1% in any sample. Shifts in community composition were investigated using LEfSe LDA (Segata et al., 2011), which uses a Kruskal-Wallis sum rank test (α=0.05) to identify significantly differential taxa and linear discriminant analysis (LDA) to estimate the 41 effect size of each. LEfSe LDA was used to identify significant differences in taxa between the communities of the open waters of the UGL, and those of Lake Erie as well as for identifying differences in community structure between surface and deep waters of the UGL. Communities of the surface waters of Michipicoton and CD-1 (sample B) (Lake Superior), along with EC 54,

EC 29, and EC 17 (Lake Huron) were compared against surface and metalimnion samples from

Lake Erie (91M, EC 879) as observed in UniFrac clustering of mixed layer samples. Samples from CD-1 (sample B), Michipicoton, EC 54, EC 17, and EC 29 were compared with their hypolimnion counterparts with the exception of EC 17, which did not cluster closely with other deep-water samples. Additionally, deep-water samples from EL-7, EL-0, and Sleeping Giant were used in the analysis. Random forest analysis (Knights et al. 2012, Breiman, 1999) was implemented using QIIME’s supervised learning script, and generated one thousand forests using leave-one-out cross validation for communities rarefied to 20,000 OTUs.

2.2 RESULTS AND DISCUSSION

2.2.1 PHYSICO-CHEMICAL PROFILES OF SAMPLING LOCATIONS

Thermal stratification was evident at most sites during the July multi-lake survey with surface mixed-layer temperatures following expected latitudinal trends ranging from 8 - 17 °C

(median: 13.4 °C) in Lake Superior, 18 – 22 °C in Lake Huron and 24 - 26 °C in Lake Erie. Of the physico-chemical parameters monitored, surface mixed-layer waters of the upper Great

Lakes (Superior and Huron) differed from deep waters primarily in temperature although there

+ - was evidence of modest surface water depletion of both NH4 and NO3 (Table 1) consistent with seasonal biological uptake (Sterner et al., 2007). In all cases, dissolved oxygen was near 42 saturation throughout the water column. Characteristic of the summer-stratified period in the upper Great Lakes (Barbiero and Tuchman, 2001), a prominent chlorophyll maximum existed at depth, located below the thermocline. In contrast to the upper Great Lakes, physico-chemical parameters in Lake Erie varied dramatically by basin consistent with the trophic gradient existing within the lake (Ludsin et al., 2001). Lake Erie’s shallow western basin was thoroughly mixed with elevated nitrogen compared to mixed layer water chemistry in the oligotrophic eastern basin. Whereas oxygen remained saturated throughout the water column in both the western- and eastern basins, our survey coincided with the onset of hypoxia in Erie’s central basin with dissolved oxygen declining to ~50% saturation in the hypolimnion.

Phytoplankton- and microbial communities sampled from the surface mixed layer in the upper Great Lakes were depleted in phosphorus compared to samples from depth, a pattern described previously during the summer-stratified period (Sterner 2011). For both total- (>0.2

µm) and picoplankton (0.2 – 2 µm) size fractions, molar ratios of N:P and C:P were ~2-fold higher in the epilimnion compared to the hypolimnion (two-tailed unpaired t-test, P < 0.0001).

For the total seston fraction, mean mixed layer ratios of N:P and C:P of 35 and 295, respectively, suggested phosphorus-depleted surface communities and were consistent with the characterization of the upper Great Lakes as phosphorus deficient, oligotrophic environments

(Sterner 2011; Barbiero et al., 2012; Bunnell et al., 2014). Whereas both total- and picoplankton size fractions sampled from the surface mixed layer were depleted of phosphorus compared to deep water samples, C:N:P stoichiometry of the picoplankton fraction, which comprised most of 43

Table 1. Sampling sites and physico-chemical parameters Upper Great Lakes + Station Date Sample Depth (m) Site Depth (m) Temp (°C) DO (mg/L) Chl a (µg/L) DIC (mg/L) NH4 (µM) NO3 (µM) P (µM) Latitude Longitude CD-1 07/14/2011 5 250 8.5 13.1 0.46 9.4 0.2 25.8 0.05 47.063 -91.432 CD-1 07/29/2011 2 250 16.4 10.7 0.43 9.3 0.4 24.2 0.04 47.063 -91.432 CD-1 07/29/2011 150 250 3.8 14.0 0.05 9.3 0.6 26.6 0.03 47.063 -91.432 EC 17 07/24/2011 5 80 20.8 9.4 0.14 15.6 0.4 18.8 0.02 45.245 -80.864 EC 17 07/24/2011 73 80 4.1 14.5 0.09 16.1 0.5 20.6 0.03 45.245 -80.864 EC 29 07/24/2011 5 125 21.2 9.3 0.11 17.1 0.4 23.5 0.02 44.367 -81.833 EC 29 07/24/2011 119 125 3.8 14.3 0.02 17.2 0.6 23.2 0.02 44.367 -81.833 EC 54 07/17/2011 5 125 17.8 10.2 0.26 16.3 0.3 20.4 0.04 45.517 -83.417 EC 54 07/17/2011 100 125 3.9 14.5 0.06 17.1 0.5 22.7 0.03 45.517 -83.417 EC 9 07/17/2011 48 50 4.8 14.3 0.58 17.2 0.5 17.6 0.04 43.633 -82.378 EL-0 07/15/2011 145 165 3.8 14.4 0.09 9.2 0.4 26.5 0.05 47.75 -87.5 EL-7 07/26/2011 132 124 3.8 14.3 0.14 9.3 0.5 26.9 0.04 47.167 -85.015 Michipicoten 07/26/2011 5 80 16.7 10.5 0.46 9.0 0.3 23.4 0.04 47.908 -85.007 Michipicoten 07/26/2011 78 80 4.0 14.2 0.06 9.4 0.4 26.2 0.04 47.908 -85.007 Sleeping Giant 07/29/2011 240 272 3.6 14.1 0.11 9.2 0.4 26.5 0.03 48.223 -88.906

Upper Bays + Station Date Sample Depth (m) Site Depth (m) Temp (°C) DO (mg/L) Chl a (µg/L) DIC (mg/L) NH4 (µM) NO3 (µM) P (µM) Latitude Longitude Black Bay 2 07/28/2011 2 8 18.7 9.5 1.71 11.8 0.2 15.6 0.16 48.692 -88.397 Black Bay 1 07/28/2011 2 22 16.5 10.1 0.28 10.5 0.3 21.1 0.10 48.5 -88.608 Nipigon Bay 1 07/27/2011 5 27 17.8 9.8 0.58 11.0 0.3 16.1 0.08 48.933 -88.001

Lake Erie + Station Date Sample Depth (m) Site Depth (m) Temp (°C) DO (mg/L) Chl a (µg/L) DIC (mg/L) NH4 (µM) NO3 (µM) P (µM) Latitude Longitude 91M 07/18/2011 8 10 25.8 8.2 0.47 19.0 1.9 38.4 0.21 41.841 -82.917 EC 879 07/22/2011 5 60 23.5 9.1 0.52 20.5 0.6 6.7 0.11 42.507 -79.9 EC 879 07/22/2011 25 60 5.9 13.6 0.91 20.5 2.8 11.0 0.11 42.507 -79.9 EC 879 07/22/2011 60 60 4.6 12.3 0.02 21.0 2.8 12.1 0.02 42.507 -79.9 44 the microbial community analyzed in this study, was not strongly suggestive of phosphorus deficiency. While mean picoplankton N:P (19) and C:P (163) molar ratios were nominally above

Redfield stoichiometry (C:N:P = 106:16:1 by moles) identified for balanced growth, these values were diagnostic of only modest phosphorus deficiency for the Great Lakes (Guildford and

Hecky, 2000; Sterner 2011). Further, biomolecular modifications such as phospholipid substitution adopted by the microbial community in phosphorus-depleted environments such as

Lake Superior (Bellinger et al., 2014) may help to facilitate balanced growth despite non-

Redfield elemental stoichiometry.

Contrasting the situation in the upper Great Lakes, Lake Erie showed little in the way of depth- or seston size-related trends in N:P and C:P seston stoichiometry. Further, regardless of depth, samples showed no evidence of phosphorus deficiency with median elemental ratios lower than diagnostic thresholds (sensu Healy and Hendzel, 1980) for phosphorus limitation.

Water column profiles of seston C:N in the upper Great Lakes did not vary with depth for either total- (two-tailed unpaired t-test, t = 0.198, DF = 40, P = 0.844) or picoplankton (two- tailed unpaired t-test, t = 1.001, DF = 40, P = 0.323) size fractions. Further, mixed-layer seston

C:N was not different between total- and picoplankton size fractions (two-tailed unpaired t-test, t

= 0.562, DF = 47, P = 0.577). Likewise in Lake Erie, there was no discernible pattern of C:N with depth (two-tailed unpaired t-test, t = 0.741, DF = 17, P = 0.469) nor did mixed layer seston

C:N vary between total- and picoplankton size fractions (two-tailed unpaired t-test, t = 1.092, DF

= 17, P = 0.290). For all lakes, while median mixed layer C:N molar ratios (8.3 and 15 for the upper Great Lakes and Lake Erie, respectively) were higher than Redfield stoichiometry, the metric was diagnostic for nitrogen deficiency only for Lake Erie. Whereas Lake Erie has been historically characterized as a phosphorus-limited system (Lean et al. 1983; Guildford et al. 45

2005), evidence for seasonal nitrogen deficiency has accumulated in recent years (North et al.,

2007; Chaffin et al., 2013).

2.2.2 MICROBIAL COMMUNITIES

Insights into microbial community dynamics were gleaned from short 16S rRNA tag

(itag) Illumina sequencing targeting the V4 hypervariable region of bacterial and plastid 16S rRNA. 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). Reflecting the utility of this approach, the Earth Microbiome Project (Gilbert et al., 2010) has adopted it for rRNA amplicon sequencing.

Consistent with previous studies of freshwater lakes surveyed during summer using next generation sequencing approaches, reads of Bacteria dominated total sample reads (Fig. 13B)

(Oh et al., 2011; Eiler et al., 2013a; b; Mou et al., 2013; Parfenova et al., 2013; Wilhelm et al.,

2014). Chloroplast reads accounted for 8.1% of total reads by average and of the eukaryotic phytoplankton detected through plastid 16S sequences, diatoms were the most abundant chloroplast OTU at most sites in the upper Great Lakes consistent with previous characterization of summer phytoplankton communities in Lakes Superior and Huron (Barbiero and Tuchman,

2001). By contrast, communities in Lake Erie contained fewer diatoms and higher abundances of cryptomonad OTUs. This is in stark contrast to the winter and spring in Lake Erie where diatoms comprise a nearly monoalgal community and diatom sequences frequently surpass those of

Bacteria (Chapters 1 and 3).

Analysis of the bacterial community during the 3,200 km round-trip survey was based on a total of 1,411,554 16S rRNA tag sequences from 18 samples with the output from individual 46

A B Bacteria 200000 chloroplast 3000 bacteria

150000

2000 ed species v

total reads 100000

obse r 1000

50000

0 25000 50000 75000 100000 depth (reads) 0 EL-0 EL-7 Chloroplast EC 9 EC 29 EC 54 EC 91 EC 17 EC 879 CD-1 B CD-1 A CD-1 Black Bay 2 Black Bay 1 EC 879 DCL EC 29 Deep EC 54 Deep 500 EC 17 Deep EC 879 Deep CD-1 B Deep Michipicoton Nipigon Bay 1 Sleeping Giant Michipicoton Deep

400

300 ed species v

200 obse r

100

5000 10000 depth (reads)

Figure 13. Rarefaction curves and total reads: July 2011 A.Rarefaction curves for bacterial and chloroplast OTUs. B. Total reads obtained for each sampling location, with abundance for both chloroplast and bacterial reads. sites ranging between 27,426 to 142,364 reads per sample (Fig. 13B). Rarefaction curves indicated that diversity was not fully captured in most communities (Fig. 13A). This was likewise reflected by the Chao1 species richness estimator, which showed that 47% of all OTUs were identified in rarefied samples for Bacteria (Table 2). Shannon’s diversity index which combines species richness and abundance into a measure of evenness, did not vary for bacterioplankton communities by lake region or depth (one-way ANOVA; P = 0.36). Likewise, observed species counts did not vary (one-way ANOVA; P = 0.25) with surface mixed layer and deep samples across all sites averaging 1351 OTUs. The upper bays had the highest observed 47

Table 2. Alpha Diversity: July 2011

Upper Great Lakes Observed Species Shannon Simpson Chao1 PD surface waters 1347.1 6.1 0.94 2750 119.2 deep waters 1320 6.4 0.96 2919 139.9 EC 17 Deep 1194 6.2 0.96 2723 122.4 EC 9 1378 6.4 0.96 3256 135.1

Upper Bays Observed Species Shannon Simpson Chao1 PD Black Bay 1411 6.3 0.95 3056 140.8 Nipigon Bay 1573 6.4 0.96 3255 148.6

Lake Erie Observed Species Shannon Simpson Chao1 PD Western Basin 1099 5.9 0.93 2028 104.7 Eastern Basin Surface 1350.4 6.6 0.97 2972 123.7 Eastern Basin DCM 1230.3 6.1 0.95 2735 118.7 Eastern Basin Deep 1384 6.6 0.96 2992 126.5

*depth = 20,000 species counts, with 1,411 and 1,573 OTUs for Black Bay and Nipigon Bay respectively.

Likewise, Faith’s phylogenetic diversity was highest in Superior’s upper bays, while phylogenetic diversity was lowest in Erie’s western basin.

Principal Coordinate Analysis (PCoA) was used to examine community similarities between sites by comparing phylogenetic differences measured by UniFrac. Visualization of unweighted UniFrac revealed significant clustering of samples collected from the epilimnion of the upper Great Lakes (RANOSIM=0.46, P=0.001), the deep hypolimnetic waters of the upper

Great Lakes (RANOSIM=0.67, P=0.002), and Lake Erie (RANOSIM=0.48, P=0.003)(Fig. 14).

Weighted UniFrac also revealed a similar pattern of significant clustering of the hypolimnetic waters of the upper Great Lakes from all other samples (RANOSIM=0.87, P=0.001)(Fig. 14).

Hypolimnion samples from the upper Great Lakes clustered along PC1 in both unweighted and weighted analyses, which explained 18% and 46% of variation between samples respectively. 48

Unweighted Weighted

depth

epilimnion metalimnion

hypolimnion source

Lake Erie Upper Lakes

Figure 14. Principal coordinate analysis (all samples). PCoA of unweighted and weighted UniFrac distance matrices for all samples collected in 2012.

Unweighted Weighted

source

bays erie

upper

Figure 15. Principal coordinate analysis (surface mixed-layer). PCoA of unweighted and weighted UniFrac distance matrices for surface mixed-layer samples collected in 2012. 49

Both UniFrac plots revealed separation between surface waters of the upper Great Lakes to those of Lake Erie, indicating phylogenetic differences in community structure. To provide additional insight into community structure shifts across the great lakes trophic gradient, an additional

UniFrac analysis examined mixed layer samples (Fig. 15). PCoA of Weighted UniFrac distances of mixed layer samples revealed significant clustering of Lake Erie samples (RANOSIM=0.64,

P=0.02), as well as clustering of samples from open waters of the upper Great Lakes

(RANOSIM=0.38, P=0.02). No significance was detected for the upper bays (RANOSIM=2, P=0.17).

To determine what environmental factors may be responsible for the observed clustering patterns, a BEST correlation test was performed (Table 3). For UniFrac analyses containing all samples, depth and temperature ranked as the highest, with NO3 and P concentrations also ranking highly. Analysis of mixed layer samples revealed high ranks for both NH4 and P concentrations. To determine significance of the tested physiochemical parameters, distance matrices were generated from each parameter and were compared with UniFrac distance matrices using the Mantel test (Table 4). Of the parameters tested, NO3 concentrations were significant in all four UniFrac plots, while ammonium and particulate organic phosphorus were significant in all but the weighted UniFrac of all samples, where depth and temperature were highly significant. While notable differences were observed in community structure at station

CD-1 in samples collected at the start of the transect and again upon return (see below), no correlation was found for Julian day (Table 4).

LEfSe, which identifies taxa with significantly differential abundances and calculates an effect size for each using linear discriminant analysis, was used to examine the microbial communities associated with clusters identified with PCoA. Additionally, these clusters were then examined using Random Forest analysis as implemented in QIIME’s Supervised Learning 50

Table 3. BEST analysis: July 2011

Weighted (all sites) Unweighted (all sites) ρ ρ 0.60 7 0.58 5 0.61 1,7 0.67 1,5 0.62 1,5,7 0.67 1,5,9 0.62 1,3,5,7 0.67 1,2,5,9 0.62 1,3,5,7,9 0.67 1,2,5,8,9 0.62 1,3,5,7,8,9 0.67 1,2,4,5,8,9 0.62 1,2,3,5,7,8,9 0.66 1,2,3,4,5,8,9 0.62 1,2,3,4,5,7,8,9 0.60 1,2,3,4,5,6,8,9 0.62 1,2,3,4,5,6,7,8,9 0.29 1,2,3,4,5,6,7,8,9

Weighted (mixed layer) Unweighted (mixed layer) ρ ρ 0.52 2 0.63 9 0.53 2,9 0.65 4,9 0.52 2,8,9 0.66 2,4,9 0.49 1,6,8,9 0.66 2,4,8,9 0.49 1,2,6,8,9 0.61 1,2,3,5,9 0.48 1,2,3,6,8,9 0.60 1,2,3,5,8,9 0.46 1,2,3,4,6,8,9 0.59 1,2,3,4,5,8,9 0.41 1,2,3,4,5,6,8,9 0.51 1,2,3,4,5,6,8,9 0.27 1,2,3,4,5,6,7,8,9 0.40 1,2,3,4,5,6,7,8,9

- + NO3 = 1, NH4 = 2, DIC = 3, DO = 4, Temp = 5, Julian day = 6, Depth = 7, Chl a = 8, P = 9

Table 4. Mantel correlations: July 2011. Correlations of nutrient distance matrices and PCoA matrices (999 permutations)

Weighted Unweighted Weighted Unweighted (all sites) (all sites) (mixed layer) (mixed layer) R P R P R P R P - NO3 0.25 0.003 0.38 0.001 0.43 0.017 0.46 0.02 + NH4 0.15 0.084 0.26 0.04 0.52 0.001 0.5 0.003 P 0.15 0.063 0.28 0.013 0.5 0.007 0.63 0.001 DIC 0.21 0.013 0.29 0.003 0.28 0.024 0.28 0.025 DO 0.48 0.001 0.54 0.001 0.07 0.64 0.21 0.26 Chl a 0.09 0.376 0.07 0.53 0.09 0.62 0.08 0.7 Depth 0.52 0.001 0.22 0.024 -0.06 0.76 0.06 0.72 Temp 0.47 0.001 0.57 0.001 0.17 0.33 0.33 0.07 Julian day -0.05 0.436 -0.06 0.45 0.09 0.47 0.03 0.84 51 script. Supervised learning, which trains a classifier based on labeled training sets of OTU abundances, identified OTUs useful for discriminating between the mixed-layer of the upper

Great Lakes and both the hypolinmetic waters of the Upper Great Lakes and samples collected from Lake Erie. Samples from surface waters of the upper Great Lakes exhibited an average cross-validation estimate of 81%, while hypolimnetic samples had an average cross-validation estimate of 91%. The lower estimate of the surface waters is likely due to the inclusion of an early CD-1 site, the community of which bears resemblance to the communities of hypolimnetic waters (see below). Estimates for Lake Erie samples compared to upper Great Lakes surface waters resulted in average cross-validation estimates of 66% and 80% respectively. Both LEfSe and Random Forest analysis identified discriminant taxa in all of the major heterotrophic phyla,

Actinobacteria, Bacteroidetes, Proteobacteria, and Verrucomicrobia, along with the minor phyla

Chloroflexi and Planctomycetes.

2.2.3 PHYTOPLANKTON- AND MICROBIAL COMMUNITY SHIFTS ASSOCIATED

WITH LAKE TROPHIC STATE

The bacterial 16S rRNA tag sequences returned 28,277 OTUs affiliated with 131 unique bacterial orders within 36 phyla. While the high number of unique phyla and orders were suggestive of a diverse bacterial community in the Great Lakes, an average of 63% of the bacterial sequences from water samples were affiliated with only 8 bacterial orders belonging to

4 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) contributed an average of ~45% of total 52 bacterial reads and approached 70% at certain locations in the upper Great Lakes, 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.

Comparing rarefied samples, surface communities in the Great Lakes consisted of phyla typical of freshwater lakes (Newton et al. 2011; Zwart et al., 2002), represented primarily by

Proteobacteria, Bacteroidetes and Actinobacteria (Fig. 16). Surface mixed layer communities of the oligotrophic upper Great Lakes showed only modest variability, differing primarily in the abundance of Actinobacteria and Proteobacteria. The highest abundances of Proteobacteria were found in the upper Great Lakes, with OTUs belonging to the Betaproteobacteria dominant, comprising an average of 29% of phylum reads. Bacteroidete communities were stable across much of the upper lakes with median reads accounting for an average of 16% of total and with

Saprospirae the dominant class. Two notable departures from this trend were apparent in Lake

Huron where Bacteriodete abundance accounted for >25% of total reads. At station EC 54 in northern Lake Huron, the increase was attributed to a single OTU of Saprospirae whereas at EC

9, located in shallow waters of southern Lake Huron, Bacteroidetes accounted for ~28%, partially due to an increase in the abundance of Sphingobacteria.

Evidence for a temporal change in microbial community structure was provided by occupying western Lake Superior station CD-1 on both outbound- and return legs of the survey

(CD-1 A and CD-1 B) (Fig. 12, 16D and E). Namely, returning to the same site 15 days later revealed a 57% increase in Proteobacteria. This community shift was due to a 130% increase in 53

B.

Upper Upper Verrucomicrobia Bays Erie 100%

6% Other Verrucomicrobiae Spartobacteria Other Pedosphaerae 2% Opitutae 0 Methylacidiphilae Verrucomicrobia B C D E F G H I J K L

Proteobacteria Proteobacteria 60% 75% Other Planctomycetes Gamma Delta 20% Beta Nitrospirae 0 B C D E F G H I J K L

Gemmatimona- detes Bacteroidetes

Other 20% 50% Firmicutes Sphingobacteria Saprospirae 10% Flavobacteria Cytophagia Cyanobacteria 0 B C D E F G H I J K L

Chloroflexi

Actinobacteria 50% Chlorobi 30% Others 25% Actinobacteria 20% Bacteroidetes Acidimicrobia 10% 0 B C D E F G H I J K L Actinobacteria

Acidobacteria C. Chloroplasts

15% Other 0 10% B C D E F G H I J K L Stramenopiles Michipicoten Haptophyceae 5% Cryptophyta Chlorophyta 0 B C D E F G H I J K L

Figure 16. OTU abundance: July 2011 (surface mixed-layer) A. OTU abundances per station, colored by phylum, rarefied to 18,000 reads. B. OTU abundances per station, colored by class. C. Chloroplast OTU abundances as a percent of total chloroplast and bacterial reads. 54

Betaproteobacteria, primarily the photoheterotroph Rhodoferax, and an increase in predatory

Bdellovibrionales (Deltaproteobacteria), accompanied by a decrease in Alphaproteobacteria.

Actinobacteria decreased by ~30% at this time as well, due primarily to a decrease in class

Acidomicrobia. Other notable shifts in community structure after the 15-day span included decreases in Cyanobacteria, Planctomycetes, and Chloroflexi.

At a phylum level, no major phylogenetic differences were detected between the Nipigon

Bay and the surface waters of the upper Great Lakes. Community composition of CD-1 A, discussed above, resembled microbial communities of Black Bay and clustered with the upper bays in PCoA plots (Figures 14 and 15). Black Bay stood out from pelagic waters, containing noticeable increases in both Acidobacteria and Chloroflexi, and contained a high abundance of the order Methylophilales (Betaproteobacteria), accounting for ~6% of the community of the upper bays. Both bays contained the Actinobacterial class Acidobacteria, present only in low abundance in most open surface water samples. The community composition of Black Bay was similar between a sample taken July 28, 2011 and a second sample taken on June 28, 2012

(unpublished data).

At the end of the Great Lakes trophic gradient, Erie’s western basin contained the highest concentrations of nitrate. Erie’s western basin differed most notably from upper Great Lakes surface waters in that it had a 106% increase in the abundance of Actinobacteria and a ~46% decrease in Proteobacteria. Actinobacteria in Erie’s Western Basin accounted for 52% of the population, the most abundant OTU accounted for ~22% of the bacterial community and the most abundant OTU in the western basin. This OTU and a second abundant Actinobacteria

OTU both cluster in the Freshwater AC-I clade, and account for the most abundant

Actinobacteria OTUs at all sites sampled. While Proteobacteria in general declined in Erie’s 55

Western Basin, particularly Betaproteobacteria which decreased by ~69%, Deltaproteobacteria increased by ~80%. Verrucomicrobia abundance was lowest, accounting for ~1%, while

Bacteroidetes remained similar to populations found in the upper Great Lakes.

Lake Erie returned to oligotrophic status in its eastern Basin. Proteobacteria, while still lower than abundances in the upper Great Lakes, increased from western basin levels by ~24.5%.

The abundance of Verrucomicrobia is also higher in Erie’s Eastern basin than in the upper Great

Lakes. In contrast to Superior’s summer surface community, EC 879 contains higher abundance of Actinobacterial class Acidobacteria, nearly absent in the pelagic waters of the upper Great

Lakes. While the proportion of Bateroidetes remains similar, an increase in Sphingobacteria is apparent, with the composition of Bacteroidetes being similar to that of the upper bays. Samples taken at different depths at station EC 879 revealed an increasing abundance of Bacteroidetes with depth, mirrored by decreasing Actinobacteria abundance.

Machine learning comparisons of Lake Erie sites and surface waters of the upper Great

Lakes returned similar results. LEfSe identified significantly lower abundances of

Betaproteobacteria, Pedosphaerae (Verrucomicrobia), Cytophagales and Flavobacteriaceae

(Bacteroidetes) in Lake Erie samples (Fig. 17). Among taxa that increased significantly in Lake

Erie, Sediminibacterium, a member of the Saprospirae (Bacteroidetes), exhibited the highest

LDA effect score. Indeed Sediminibacterium was the most abundant OTU in the hypolimnion of

EC 879, accounting for 14.6% of the community. Other taxa that significantly increased in Erie included the Methylophilales (Betaproteobacteria), methylotrophic bacteria which have been associated with cyanobacterial blooms (Eiler & Bertilsson, 2004) and are thought to play a role in microcystin degradation (Mou et al., 2013b), along with members of the Actinobacterium

(Luna I), Armatimonadetes (Armatimonadaceae), Bacteroidetes 56

Figure 17. LDA effect size cladogram: July 2011 (surface mixed-layer). LDA cladogram comparing taxa of the upper Great Lakes with those of Lake Erie. Significantly discriminant nodes are colored, and branches are shaded by highest-ranking taxon.

Table 5. Feature importance scores: surface mixed-layer (July 2011). Feature importance scores expressed as mean decrease in accuracy (MDA) along with standard deviation for each taxon.

MDA SD Taxonomy 0.012 0.003 Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; f__C111; g__; s__ 0.011 0.003 Verrucomicrobia; c__[Methylacidiphilae]; o__Methylacidiphilales; f__LD19; g__; s__ 0.010 0.002 Verrucomicrobia; c__Opitutae; o__Opitutales; f__Opitutaceae; g__Opitutus; s__ 0.010 0.004 Chloroflexi; c__SL56; o__; f__; g__; s__ 0.010 0.004 Bacteroidetes; c__Cytophagia; o__Cytophagales; f__Cytophagaceae; g__Emticicia; s__ 0.010 0.003 Proteobacteria; c__Deltaproteobacteria; o__Bdellovibrionales; f__Bacteriovoracaceae; g__; s__ 0.009 0.003 Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__; g__; s__ 0.009 0.003 Proteobacteria; c__Betaproteobacteria; o__MWH-UniP1; f__; g__; s__ 0.008 0.003 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Comamonadaceae 0.008 0.002 Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Microbacteriaceae; g__Candidatus Rhodoluna; s__ 0.008 0.002 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Comamonadaceae; g__Hydrogenophaga; s__ 0.008 0.002 Proteobacteria; c__Betaproteobacteria; o__Methylophilales; f__Methylophilaceae; g__; s__ 0.008 0.002 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Alcaligenaceae; g__; s__ 0.007 0.003 Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__ACK-M1; g__; s__ 0.006 0.003 Bacteroidetes; c__[Saprospirae]; o__[Saprospirales]; f__Chitinophagaceae; g__; s__ 57

(Sphingobacteria), Chloroflexi (SL56), and Verrucomicrobia (Opitutae). Random forest analysis (Table 5) underscored these results, with many of the taxa identified by LDA also being identified as important features for the supervised learning classifier.

2.2.4 COMMUNITIES OF THE OXYGENATED HYPOLIMNION OF THE UPPER

GREAT LAKES

Deep-water samples from Lake Superior and Lake Huron displayed a similar bacterial community throughout the upper Great Lakes, which was more stable temporally and spatially than communities of surface waters (Fig. 18). Samples appeared similar after a passage of 14 days (EL-0, July 15; CD-1, July 29) and across multiple years (2011, 2012) at multiple sites, as far south as EC 29, Lake Huron. While deep waters contained ~42% lower abundances of

Proteobacteria, they remained the dominant phyla, accounting for an average of ~29%. A notable decrease in Betaproteobacteria was observed, due for the most part due to a ~79% decrease in Rhodoferax, the most abundant OTU in surface waters. Gammaproteobacteria were present in higher abundance. The abundance of phylum Actinobacteria did not change dramatically with depth and was the second most abundant phylum at the surface as well as at depth. While abundance was similar at the phyla level, changes in class abundance were apparent. Class Actinobacteria decreased by an average of ~37%, while class Acidomicrobia increased at depth. Bacteroidetes were less abundant in the deep waters of the upper Great

Lakes, dropping from the third most abundant phyla at the surface (~17%) to the sixth most abundant in the deep (~10%). The Verrucomicrobia, a major phyla also observed in abundance during the winter in Lake Erie (Chapters 1 and 3), increased from ~4% to ~10% of the community at depth. 58

Verrucomicrobia

20% Other Verrucomicrobiae Epilimnion Hypolimnion Erie Spartobacteria 100% 10% Pedosphaerae Opitutae Other Methylacidiphilae 0 A B C D E F G H I J K L M

Verrucomicrobia Proteobacteria

Proteobacteria Other 75% Gamma Delta 20% Planctomycetes Beta Alpha

0 A B C D E F G H I J K L M Nitrospirae

Bacteroidetes Gemmatimona- detes 50% 30% Other Firmicutes Sphingobacteria 20% Saprospirae Flavobacteria 10% Cytophagia Cyanobacteria

0 A B C D E F G H I J K L M

Chloroflexi Actinobacteria

25% Chlorobi 30% Others Actinobacteria 20% Bacteroidetes Acidimicrobia 10%

0 Actinobacteria A B C D E F G H I J K L M

Acidobacteria Chloroplasts 0 A B C D E F G H I J K L M 15% Other 150 100

Stramenopiles

Depth 5 5 5 5 5 5 (m) 10% Haptophyceae Cryptophyta Michipicoten Michipicoten Chlorophyta Station 5%

0 A B C D E F G H I J K L M Figure 18. OTU abundance: July 2011 (hypolimnion) A. OTU abundances per station, colored by phylum, rarefied to 18,000 reads. B. OTU abundances per station, colored by class. C. Chloroplast OTU abundances as a percent of total chloroplast and bacterial reads. 59

The deep water community differed from surface waters most notably in that they had a high abundance of Planctomycetes, accounting for an average of ~16% of the bacterial community, elevating the Planctomycetes to the third most abundant phylum in deep waters. The

Planctomycetes were dominated by two OTUs, belonging to the class Phycisphaerae, together account for an average of 13% of the bacterial community. A third OTU was also present and belonged to the class Planctomycetia, accounting for an additional 1.3% of the community. A significant increase in Chloroflexi was also a major difference between surface and deep waters, where they accounted for an average of ~11%, and was dominated by a single OTU. This OTU, a member of the Anaerolineae, is more abundant in the deep waters and the most abundant OTU at depth.

LEfSe LDA results showed significant increases in Chloroflexi (Anaerolineae), and

Planctomycetes (Planctomycetia, Phycisphaerae) in hypolimnion of the upper Great Lakes, along with increases in Bacteroidetes (Sphingobacteria), Actinobacteria (Thermoleophilia), and

Verrucomicrobia (Pedosphaerae, Opitutae, Methylacidiphilae) (Fig. 19). Significant decreases in Proteobacteria (Betaproteobacteria), Bacteroidetes (Cytophagia, Flavobacteria),

Actinobacteria (Actinobacteria). Random forest analysis returned OTUs belonging to significant taxa identified by LEfSe, underscoring these results (Table 6).

While known for their contributions to the nitrogen cycle through anammox (Strous et al.,

1999), little is known about their role in the environment. Their abundances are generally low in lakes, but abundances of up to 11% have been documented (Gade et al., 2004). Indeed, planctomycetes were found to be one of the most numerically dominant phyla in Sphagnum bog

(Dedysh et al., 2005) The highest abundance in Superior was observed from samples taken near

Michipicoton, July 2011, where they accounted for 18.3% of the community. 60

Figure 19. LDA effect size cladogram: July 2011 (hypolimnion). LDA cladogram comparing taxa of the upper Great Lakes epilimnion communities and those of the hypolimnion. Significantly discriminant nodes are colored, and branches are shaded by highest-ranking taxon.

Table 6. Feature importance scores: hypolimnion (July 2011). Top ranking feature importance scores expressed as mean decrease in accuracy (MDA) along with standard deviation for each taxon.

MDA SD Taxonomy 0.013 0.002 Verrucomicrobia; c__Opitutae; o__Opitutales; f__Opitutaceae; g__Opitutus; s__ 0.012 0.003 Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__Flavobacteriaceae; g__Flavobacterium; s__ 0.011 0.003 Planctomycetes; c__Planctomycetia; o__Gemmatales; f__Gemmataceae; g__; s__ 0.011 0.002 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Oxalobacteraceae; g__Polynucleobacter; s__ 0.011 0.003 Bacteroidetes; c__Sphingobacteriia; o__Sphingobacteriales; f__; g__; s__ 0.010 0.002 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Comamonadaceae 0.010 0.002 Bacteroidetes; c__Sphingobacteriia; o__Sphingobacteriales; f__; g__; s__ 0.010 0.003 Verrucomicrobia 0.010 0.001 Planctomycetes; c__Phycisphaerae; o__Phycisphaerales; f__; g__; s__ 0.010 0.003 Verrucomicrobia; c__[Pedosphaerae]; o__[Pedosphaerales]; f__R4-41B; g__; s__ 0.009 0.003 Proteobacteria; c__Alphaproteobacteria; o__Sphingomonadales; f__; g__; s__ 0.009 0.002 Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__Flavobacteriaceae; g__Flavobacterium; s__ 0.009 0.003 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Comamonadaceae; g__Rhodoferax; s__ 0.009 0.003 Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__ACK-M1; g__; s__ 0.009 0.003 Bacteroidetes; c__[Saprospirae]; o__[Saprospirales]; f__Saprospiraceae; g__Aquirestis; s__calciphila 0.009 0.003 Planctomycetes; c__Phycisphaerae; o__Phycisphaerales; f__; g__; s__ 0.009 0.003 Proteobacteria; c__Betaproteobacteria; o__Methylophilales; f__Methylophilaceae; g__; s__ 0.009 0.003 Chloroflexi; c__Anaerolineae; o__H39; f__; g__; s__ 0.009 0.003 Bacteroidetes; c__[Saprospirae]; o__[Saprospirales]; f__Chitinophagaceae; g__; s__ 0.009 0.003 Bacteroidetes; c__[Saprospirae]; o__[Saprospirales]; f__Chitinophagaceae; g__; s__ 61

Planctomycetes have been shown to be important factors in the decomposition of dissolved organic matter (DOM) in nutrient-poor waters when supplied with inorganic N (Tadonléké,

2006). However, estimates of Planctomycete growth rates (Fuerst, 1995; Pollet et al., 2014) have shown them to be slow growing organisms with generation times in excess of 100 hours, and as such, may not be able to respond quickly to environmental changes.

On average, the most abundant OTU in the deep waters of the upper Great Lakes belonged to the Chloroflexi. Interestingly, this OTU clusters with the CL500-11 clade, a subclass of the deep-ocean SAR202 clade. This clade is a predominant group found in the oxygenic hypolimnions of both Crater lake, USA (Urbach et al., 2001), and Lake Biwa, Japan (Okazaki et al., 2013).

Together with the dominant Planctomycetes, these three OTUs accounted for ~24% of deep- water microbial communities. Of the conditions measured, temperature seems to be the most influential factor on abundance of these OTUs, and was identified by BEST analysis identified temperature, along with depth, as the highest ranking environmental variables explaining the clustering of these communities (see above). Indeed, after an interval of 14 days and a temperature increase of 7.9 C, surface samples at station CD-1 containing these OTUs exhibit a

10-fold decrease in Planctomycete abundance and a ~30-fold decrease in the dominant

Cholorflexi OTU. The decrease in these Planctomycete and Chloroflexi OTUs over time at station CD-1 suggests that the surface waters may be transitioning from a winter community where these OTUs may play a larger role in surface waters. Supporting this view, a winter sample taken at Sault Ste. Marie, where water flows from Lake Superior to the lower lakes, revealed a high abundance of the dominant Plactomycete and Chloroflexi OTUs in surface waters (Chapter 3). If temperature is a regulator of these OTUs, than rising water temperatures 62 in Superior may be a major influence on their temporal abundance and distribution in surface waters. Recent models have estimated that surface waters in Superior are rising at a rate of (11 ±

6) × 10−2°C yr−1, pushing the stratification of the lake 12 hours earlier each year (Austin and

Coleman, 2007). Interestingly, these dominant Planctomycete and Chloroflexi OTUs appear to be widespread, occurring in all of the Laurentian Great Lakes, as well as in a European great lake, Lake Onega (Chapter 3).

Lake Huron’s Georgian Bay (station EC 17) stands out from the other sites in the upper Great

Lakes in that, at the time of sampling, it did not display the same abundances of Planctomycete and Chloroflexi OTUs present throughout hypolimnionetic waters of Lakes Huron and Superior, despite a comparable depth. It instead exhibited a high abundance of Verrucomicrobia, accounting for >25% of the community. The most abundant OTU belonged to the

Verrucomicrobia (class Opitutae; family Cerasicoccaceae), and accounted for ~14% of the population. While this OTU is present in the hypolimnion throughout the upper Great Lakes, it averages only ~1% of the community. These abundances resemble the Verrucomicrobia abundances of Lake Erie Winter communities (Chapter 1). However, the communities in Erie were composed primarily of Spartobacteria and Verrucomicrobiae, while the dominant classes present in Georgian Bay are Opitutae and Pedosphaerae. Higher abundances of Bacteroidetes

(Saprospirae) accounted for an additional ~8% of the community. The reason for this dramatic difference in community composition is unknown. The recorded temperature was higher than the average for deep-water, at ~4℃, and had a nitrate concentration was lower than that of the rest of the samples collected from the upper Great Lakes (20.6 µM/L).

2.2.5 RESAMPLING EFFORTS IN 2012 63

Limited resampling in 2012 provided an opportunity to examine temporal differences in community structure in both the upper Great Lakes and in Lake Erie’s central basin. It also provided an opportunity to examine the abundance of Cyanobacteria, which were present only in low abundance at the time of sampling in 2011. A depth profile was obtained from station WM in Lake Superior on August 15th, 2012 at depths of 5m, 90m, and 195m. Additionally, epilimnion (1m) and hypolimnion (22m) samples were collected from EC 1326 in Lake Erie’s

Central Basin on July 26, 2012 and an additional surface sample was collected on August 17th.

Surface waters collected in August at station WM differed most notably in that they contained a high abundance of Cyanobacteria, which accounted for ~24% of the community

(Fig. 20). The majority of Cyanobacteria, ~67%, belonged to a single picocyanobacteria OTU.

This OTU matches the Synechococcus LSI and LSII clades, novel picocyanobacterial clades isolated from Lake Superior (Ivanikova et al., 2007). The second largest OTU matches

Synechococcus sp. MH301, a species found throughout the lakes.

Communities in the oxygenated hypolimnion and nepheloid layer were generally consistent with communities observed in hypolimnetic communities from 2011, and contained similar abundances of both Chloroflexi and Planctomycetes. An increase in Nitrospira abundance was observed in deep water samples with the highest abundance in the nepheloid layer, where they made up ~6% of the community.

Lake Erie’s central basin has been experiencing an expansive, annually occurring hypoxic ‘dead zone’, and has been a feature of public concern (Hawley et al., 2006). Depth profiles collected in 2012 provided insight into the community of Erie’s hypoxic zone.

Conditions recorded at the nearby Cleveland Central Bouy (NOAA GLERL) revealed stratification of the Central Basin with decreasing oxygen levels (1.6 mg/L) and temperatures of 64

A. B. Verrucomicrobia

10% Superior Erie Other 100% Verrucomicrobiae Spartobacteria 5% Pedosphaerae Other Opitutae Methylacidiphilae

Verrucomicrobia 0 A B C D E F

Proteobacteria Proteobacteria

75% 30% Other Planctomycetes Gamma 20% Delta Beta 10% Alpha Nitrospirae

0 A B C D E F Gemmatimona- detes Bacteroidetes 20% 50% Firmicutes 15% Other Sphingobacteria 10% Saprospirae Cyanobacteria Flavobacteria 5% Cytophagia

0 Chloroflexi A B C D E F Percentage of bacterial community Actinobacteria Chlorobi 25% 40%

30% Bacteroidetes Others 20% Actinobacteria Acidimicrobia 10% Actinobacteria 0 A B C D E F

Acidobacteria 0 C. A B C D E F Chloroplasts 185 90 22

Depth 5 1 1 (m) 15% Other Stramenopiles Haptophyceae WM A WM A WM A EC 880 J EC 880 J EC 1326 A 10% Station Cryptophyta Chlorophyta 5% ug ug ug uly uly

ug 0 A B C D E F

Figure 20. OTU abundance (2012 resampling) A. OTU abundances per station, colored by phylum, rarefied to 18,000 reads. B. OTU abundances per station, colored by class. C. Chloroplast OTU abundances as a percent of total chloroplast and bacterial reads. 65

for ~25°C in the epilimnion and ~13°C in the hypolimnion.

Surface waters at station EC 880 (July) and EC 1326 (August) in 2012 differed from the

July 2011 communities in that they also contained higher abundances of Cyanbacteria, accounting ~13% and ~8% of the community respectively. These stations had a diverse cyanobacterial community, more so than surface waters at station WM in Lake Superior. Lake

Erie also displayed the highest diversity of Cyanobacteria, containing 196 and 173 OTUs for

July and August respectively. Cyanobacterial populations were primarily composed of

Synechococcus, and included the LSI and LS II strains of Lake Superior. Oscillatoriophycideae and Nostocophycideae were also abundant in surface waters of Lake Erie. Algal taxa that contain toxic species, Microcystis, Planktothrix, and Lyngbya, were present in low abundance.

Thirteen OTUs matched Microcystis, and a single OTU each matched Planktothrix and Lyngbya.

Both Microcystis and Planktothrix had OTUs that were detected as far north as Lake Superior.

The community of the hypoxic zone differed in composition from that of the oxic surface waters. The community present in the hypoxic hypolimnion resembled those of winter communities of Lake Erie (chapter 2) and contained higher abundances of Spartobacteria

(Verrucomicrobia) and Sphingobacteria (Bacteroidetes). Actinobacteria, which accounted for

~40% of the surface community, decreased to ~20% at depth. Armatimonadetes increased in abundance 26-fold in the hypolimnion and accounted for ~1.5% of the community. The

Proteobacteria generally increased in the hypolimnion, with increases in Beta- (1.8 fold), Delta-

(8.4 fold), and Gammaproteobacteria (1.5 fold). An increase in the family Crenotrichaceae

(Gammaproteobacteria) was detected, known methanotrophs of the order Methylococcales 66

(Hansen and Hanson, 1996). Crenotrichaceae had an abundance of 0.02% in the epilimnion and their abundance increased to ~ 6% of the community in the hypolimnion. Chloroflexi increased by ~7-fold in the hypoxic zone, the majority of which were classified as SL56, a freshwater sponge associated clone (Gernert et al., 2005). A small increase was observed for OTUs matching the Deltaproteobacterium Geothrix (Desulfuromonadales), a sulfate-reducing bacterium also capable of utilizing Fe3+ (Kersters et al., 2006). 67

CHAPTER 3: SPATIO-TEMPORAL DYNAMICS OF WINTER MICROBIAL

COMMUNITIES IN LARGE FRESHWATER LAKES

3. INTRODUCTION

Despite reductions in the expanse and mass of the global cryosphere in response to climate warming (Derksen et al., 2012), with nearly 70% of freshwater in the form of ice, the cryosphere serves an important role in the Earth’s water cycle. While much of the cryosphere is characterized by persistent features such as permafrost, ice-caps, and glaciers, seasonal ice covers wide zones around the globe and can play an important role in coastal zone primary production (Arrigo et al., 1997).

Sea ice, an ephemeral environment covering upwards of ~13% of the surface of the planet (Lizotte, 2001), has been recognized as a biologically productive environment. Inside the channels of its semi-solid matrix, sea ice supports a variety of both prokaryotic and eukaryotic organisms. These include protists, metazoans, and algae, of which low-light adapted diatoms make up the majority of sea-ice assemblages and are the major primary producers in this environment (Garrison, 2001). It also hosts active microbial communities, the abundance of which have been correlated with chlorophyll (Kivi and Kuosa, 1994; Staley et al., 1989;

Sullivan and Palmisano, 1984). The heterotrophic activities of these assemblages result in anoxic zones exhibiting high rates of denitrification (Rysgaard and Glud, 2004). With liquid water in the form of concentrated brines, and temperatures that may drop below -20 C, sea ice is an environment inhabited by extremophiles (Thomas and Dieckmann, 2002).

Winter communities of temperate freshwater systems on the other hand, have not received as much attention, despite major bodies of water experiencing extensive and prolonged 68 ice-cover. The Laurentian Great Lakes, the largest freshwater system in the world holding ~21% of surficial surface water, and the great lakes of Europe, Ladoga and Onega, are such systems.

In North America, Lake Erie can experience an annual maximum ice concentration of 94%

(Assel et al., 2003). Beneath its ice, Lake Erie supports annual winter diatom blooms that drive winter productivity, predominantly composed of the centric diatom Aulacoseira islandica

(Chandler, 1940; Twiss et al., 2012; Saxton et al, 2012). Bacterial epiphytes associated with diatoms obtained from these blooms have been demonstrated to exhibit ice-nucleation activity at temperature as high as 3 C, and are thought to play a role in the formation of these blooms

(D’sousa et al., 2013).

In this study, the winter microbial communities are examined through the lens of 16S rRNA tag (itag) Illumina sequencing. The communities of Lake Erie’s waters, ice, and diatom blooms (CACHE sites) are examined and compared with communities of the oligotrophic upper

Great Lakes. Communities of the Laurentian Great Lakes are then compared to the communities of Petrozavodsk Bay, Lake Onega.

3.1 METHODS AND MATERIALS

3.1.1 STUDY SITES AND SAMPLING

The upper Laurentian Great Lakes were sampled aboard the USCGC Mackinaw between

January 29 and February 2, 2013 on a transect that spanned ~200 km from Sault Ste. Marie to northern Lake Michigan (Fig. 21). Lake Underway sampling of Lake Erie was done aboard the

CCG Griffon on an annual survey of hydrographic stations from February 21 through February

22, 2012 (Fig. 21). Underway sampling followed the procedures of Binding et al., (2012).

Surface water samples, along with ice samples if applicable, were collected at hourly 69

Figure 21. Sampling sites in the upper Great Lakes and Lake Erie (2013). 70 intervals along the transect using a stainless steel sampling bottle for surface water or a plastic crate for ice samples. Ice samples were rinsed with 0.2 µm filtered lake water. Samples were processed immediately aboard the ship. Lake Onega samples were collected at two near-shore sites in Petrozavodsk Bay, March 30, 2013. The ice core taken from station L1 measured ~1m in depth, and was divided into three roughly equal parts representing surface ice, mid-layer ice, and bottom ice. A second ice core (station L2) was melted and analyzed as a single sample. Water was collected from beneath the ice at both stations L1 and L2 using a Van Dorn bottle. Extant and characterization of ice cover at each location was recorded.

Microbial biomass from water samples was concentrated on Sterivex cartridge filters

(0.22 µm; EMD Millipore, Billerica, MA, USA) and immediately frozen in liquid nitrogen. DNA was extracted from the Sterivex cartridges using the PowerWater Sterivex DNA Isolation Kit

(MO BIO Laboratories, Inc, Carlsbad, CA, USA) following manufacturer’s instructions.

Samples were processed for dissolved and particulate nutrients (< 0.2 µm) as well as for the determination of size-fractionated chlorophyll a (chl) biomass on 0.2 and 20 um polycarbonate filters. Chl a biomass was measured by fluorometry following extraction in 90% (v/v) acetone at

-20 °C (Welschmeyer 1994). Subsamples for nutrient determination were frozen in polyethylene bottles on board ship for subsequent laboratory analysis. Nutrients were measured by the

National Center for Water Quality Research (Heidelberg University, Tiffin, OH, USA) using standardized techniques.

3.1.2 HIGH-THROUGHPUT MICROBIAL COMMUNITY ANALYSIS

A comprehensive taxonomic analysis was completed by Illumina MiSeq targeting the

16S rRNA V4 hypervariable region of bacterial and plastid genomes. Short 16S rRNA tag (itag) 71 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.

Primer design for universal amplification of the V4 region of 16S rDNA was based on Caporaso et al., (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

(Magoč and Salzberg, 2011). Resulting sequences were demultiplexed 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 QIIME 1.8.0 (Caporaso et al., 2010a) using default settings unless noted otherwise. OTUs were picked using uclust at 97% identity (Edgar, 2010) and

OTUs represented 3 or fewer times in the data set were filtered along with those shorter than 200 bp. A representative set of sequences was generated with the most abundant sequence representing its respective OTU. Taxonomy was assigned to each representative sequence using the 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, reads that were assigned to chloroplast and mitochondrial sequences, those that were unclassified or unassignable, and those not identifiable beyond bacteria were filtered. Those reads matching

‘chloroplast’ were then used in subsequent analyses to examine the photosynthetic eukaryotic community. The combined total bacterial and chloroplast reads are referred to as ‘total reads’.

The representative sequences were aligned to the Greengenes core reference alignment (Desantis 72 et al., 2006) using PyNAST (Caporaso et al., 2010b) and gaps in the resulting alignment were filtered. A phylogenetic tree for all bacterial OTUs was generated from the filtered alignment using FastTree 2.1.3 (Price et al., 2010).

3.1.3 STATISTICAL ANALYSIS

Alpha and beta diversity were implemented through QIIME, and principal coordinates analysis (PCoA) on both weighted and unweighted UniFrac matrices (Lozupone et al, 2005) was done at a rarified depth of 18,000 reads. Sample 1365 ice A was excluded from statistical analysis due to low abundance of bacterial reads, returning only 6,145 bacterial reads.

Significance of PCoA clustering was tested using ANOSIM using 999 permutations with each cluster being tested against the remainder of samples.

The importance of environmental features on PCoA clustering patterns was explored using QIIME’s BEST analysis, an implementation of BIOENV (Clarke & Ainsworth, 1993).

Distance matrices were generated for each physiochemical parameter (QIIME), and correlations of physiochemical measurement matrices and UniFrac plots was examined using the Mantel test with 999 permutations. Machine-learning approaches were adopted to identify OTUs whose abundances were associated with UniFrac clustering patterns. To reduce noise due to the presence of a large number of low abundance OTUs, communities used were filtered to contain only OTUs that were present at an abundance of at least 1% in any sample. Shifts in community composition were investigated using LEfSe (Segata et al., 2011), which uses a Kruskal-Wallis sum rank test (α=0.05) to identify significantly differential taxa and linear discriminant analysis

(LDA) to estimate an effect size for each differential taxon. LEfSe LDA was used to identify significant differences in taxa between the communities of the Laurentian Great Lakes based on 73

PCoA clustering patterns as well as taxonomic differences between Lake Onega and the

Laurentian Great Lakes. Random forest analysis (Knights et al. 2012, Breiman, 1999) was implemented using QIIME’s supervised learning script, and generated 5,000 forests using leave- one-out cross validation for communities rarefied to 18,000 OTUs.

3.2 RESULTS AND DISCUSSION

3.2.1 ICE CONDITIONS AND SAMPLING

Ice thickness was similar for both the upper Great Lakes and Lake Erie, ranging from ~1 cm to a maximum of ~20 cm (Table 7). Ice cover for sampled sites in the upper Great Lakes was

100% for all but a few stations, while ice cover in Lake Erie was variable, ranging from ~1% to

100% with most sites having greater than 80% coverage. Ice cover at Petrozavodsk Bay was

100% and had a thickness of ~1m at sampled locations. Snow cover was variable for both the upper Great Lakes and Lake Erie, ranging from 0% to 80% coverage, while snow cover at

Petrozavodsk Bay was 100%.

3.2.2 ILLUMINA SEQUENCING RESULTS

Insights into microbial community dynamics were gleaned from short 16S rRNA tag

(itag) Illumina sequencing targeting the V4 hypervariable region of bacterial and plastid 16S rRNA. 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). Reflecting the utility of this approach, the Earth Microbiome Project (Gilbert et al., 2010) has adopted it for rRNA amplicon sequencing.

The abundance of chloroplast reads, based on a total of 654,781 reads, differed for each 74

Table 7. Ice cover at sampling locations

Ice Snow Ice Station Ice Type Cover Cover Thickness Lake Erie EC 67 100% 0% - 2" EC 938 1% 0% - - EC 937 20% 0% - 0.5" EC 879 60% 0% fresh 1 mm CACHE 2 100% 50% thin + cakes 1-8" EC 1365 100% 40% - 5" CACHE 1 100% 30% thin 4-8" EC 880 80% 60% - 4-6" EC 1326 90% 20% - 5" EC 341 - - - - EC 970 - - - -

Upper Great Lakes M001 100% - - - M002 100% 100% plate 4" M004 100% 10% brash 4" M006 0 0% NA 0 brash/panca M007 60% 60% ke 3" M009 100% 20% brash 1" M012 10% 70% brash 2" M016 100% 0% plate 8" M018 100% 80% plate 6"

Lake Onega L1 100% 100% fast 36" L2 100% 100% fast 24" 75

Figure 22. Read abundance: Winter 2013. Total reads obtained for each sampling location with abundance of both bacterial and chloroplast reads region. Chloroplast reads accounted for ~13% of total reads by average for the upper Great

Lakes (Fig. 22). Of the eukaryotic phytoplankton detected through plastid 16S sequences, diatoms were the most abundant chloroplast OTU at most sites in the upper Great Lakes.

Cryptophytes dominated at two sampled sites; M009 in Lake Michigan and M006 in the Straits of Mackinac. Lake Erie was dominated by diatoms, as previously reported (Chapter 1), with the exception of station EC 67, where chlorophytes were the dominant chloroplast OTUs. Lake Erie waters contained chloroplast reads averaging ~21% of total reads, while CACHE sites averaged

~28%. Ice samples averaged ~30%, with some ice samples containing abundances as high as 76

Figure 23. Rarefaction curves for bacterial OTUs

48%. By contrast, chloroplast reads in Lake Onega averaged only 0.1% in ice samples and 0.2% in water samples.

Analysis of the bacterial community for all sites was based on a total of 2,364,252 16S rRNA tag sequences from 32 samples with the output from individual sites ranging between

18,914 to 135,954 reads per sample (Fig. 22). Alpha diversity measures were obtained for each sample. Observed species plots indicated that samples did not reach saturation (Fig. 23). This was likewise reflected by Chao1, which estimated that <50% of all OTUs were identified in 77

Table 8. Alpha Diversity: Winter 2013

Lake Erie water Observed Species Shannon Simpson Chao1 PD EC 67 1733 6.04 0.92 3918 173 EC 341 1222 6.40 0.97 2658 135 EC 879 1843 6.98 0.96 4216 182 EC 880 1556 6.99 0.98 3405 164 EC 938 1359 6.12 0.94 3084 149 EC 970 1884 7.10 0.97 4242 188 EC 1326 1584 6.97 0.97 3491 170 CACHE A 2836 9.53 1.00 4789 253 CACHE B 2761 9.44 1.00 4605 246

Lake Erie ice Observed Species Shannon Simpson Chao1 PD Pigeon Bay ice 2906 9.44 0.99 4773 254 EC 67 ice 2160 7.09 0.95 4171 201 EC 879 ice 2168 7.83 0.98 4426 209 EC 880 ice 2822 9.62 1.00 4584 252 EC 938 ice 2678 8.92 0.99 4954 237 EC 1365 ice B 1240 6.48 0.97 2602 136

Upper Great Lakes Observed Species Shannon Simpson Chao1 PD M001 1112 6.59 0.97 2427 127 M002 1130 6.42 0.97 2495 130 M004 1361 6.73 0.97 3251 146 M006 999 6.02 0.95 2206 116 M007 1840 7.28 0.98 4320 189 M009 786 4.99 0.92 1883 94 M012 1968 6.97 0.96 3904 194 M016 984 5.95 0.95 2207 114 M018 2199 7.75 0.98 5065 207

Lake Onega Observed Species Shannon Simpson Chao1 PD L1 upper ice 1494 6.22 0.93 2987 134 L1 middle ice 1486 6.38 0.94 3083 134 L1 bottom ice 1716 6.59 0.94 3347 149 L1 sub-ice water 1544 6.38 0.94 3189 101 L1 bottom water 1406 6.30 0.92 2862 131 L2 ice 1163 5.97 0.94 2262 100 L2 sub-ice water 1178 6.31 0.95 2143 137

*depth = 18,000 78 most rarefied samples for Bacteria (Table 8). Shannon index was generally similar for most samples and ranged between ~6-8, with the exception of Lake Michigan site M009, where the

Shannon index was lowest (~5), and Erie’s CACHE sites and Lake Erie ice samples, where it was highest (>9). The Simpson index revealed high diversity in all samples, with CACHE and

CACHE-like ice samples approaching 1. Phylogenetic diversity was also high in CACHE and

CACHE-like ice, where observed species counts where also highest, averaging ~2500 OTUs. .

The bacterial 16S rRNA tag sequences returned 13,640 OTUs affiliated with 224 unique bacterial orders within 53 phyla. While the high number of unique phyla and orders were suggestive of a diverse bacterial community in the Laurentian Great Lakes, an average of ~87% of the bacterial sequences from the Laurentian Great Lakes were affiliated with only 4 phyla. Of these phyla, two orders (Actinomycetales and Burkholderiales) contributed an average of ~30% of total bacterial reads.

3.2.3 WINTER MICROBIAL COMMUNITIES OF LAKE ERIE

Hi-throughput sequencing surveys of winter microbial communities of Lake Erie were conducted for 2010 through 2012 (Chapter 1) and communities of the Central- and Western

Basins were consistent with communities obtained in this study. Proteobacteria were the dominant phylum throughout the lake (Fig. 24). Their abundances in the central basin accounted for an average of ~32% of the community while abundances in the Western and Eastern basins were appreciably higher, accounting for 50% or more of the bacterial community. One sampled site, EC 67, exhibited an abundance of 74%. Alpha- and Betaproteobacteria were most abundant classes in the central basin, averaging ~27% and ~58% of Proteobacteria respectively.

Betaproteobacteria were more abundant in the Western and Eastern basins, where 79

Figure 24. OTU abundance: Lake Erie (winter 2013) A. OTU abundances per station, colored by phylum, rarefied to 18,000 reads. B. OTU abundances per station, colored by class. C. Chloroplast OTU abundances as a percent of total chloroplast and bacterial reads. 80

they accounted for ~70% and 65% of Proteobacteria respectively. Three OTUs were uniformly dominant members in this phylum, each accounting for 5-20% of the total bacterial community.

An Alphaproteobacterium of the LD12 clade, and two Betaproteobacteria with high identities to

Undibacterium (98%) and Rhodoferax (100%) respectively.

Bacteroidetes were most abundant in the Western and Central basins where they accounted for an average of ~20% of the community. The average abundance of Bacteroidetes decreased sharply in the eastern basin to ~9%, due in part to a decrease in Flavobacteria. Although abundance of Flavobacteria was lower in the Western basin, Bacteroidete abundance was bolstered by increases in Sphingobacteria and Cytophagia. Generally, the abundance of

Flavobacteria followed that of diatom read abundance, as was previously observed (Chapter 1).

Verrucomicrobia were in highest abundance in the Central Basin where diatom abundances were highest, and averaged ~13% of the community. A single Spartobacteria OTU was dominant at most sites, with the exception of the Western Basin and EC 67 in the Eastern Basin, also mirroring diatom abundances at those sites.

Actinobacteria accounted for an average of 18% of the community throughout the lake.

Communities of Actinobacteria were composed of members of both AC-I and AC-IV clusters, representing class Actinobacteria and class Acidimicrobia respectively. While class

Actinobacteria was dominant at all sites, class Acidobacteria exhibited its highest abundances in the Central basin and appeared to be influenced by diatom abundance (Figure 24B, C, and D).

3.2.4 MICROBIAL COMMUNITIES OF THE UPPER GREAT LAKES

Water samples collected along a ~220 km winter survey at the convergence of three Great

Lakes, Superior, Huron, and Michigan, offered a glimpse into the winter microbial communities 81

Figure 25. OTU abundance: upper Great Lakes (winter 2013) A. OTU abundances per station, colored by phylum, rarefied to 18,000 reads. B. OTU abundances per station, colored by class. C. Chloroplast OTU abundances as a percent of total chloroplast and bacterial reads. 82 of the upper great lakes (Fig. 25). Water from Lake Superior, the headwaters of the Laurentian

Great Lakes, flows through the St. Mary’s River and drains into Lake Huron. Communities throughout the St Mary’s River resembled those of summer hypolimnetic waters of Lake

Superior (Chapter 2). Abundances of Proteobacteria and Actinobacteria were similar to Erie’s

Central Basin, accounting for an average of ~31% and ~14% of the community respectively, and both groups had a similar composition at the class level. Bacteroidete abundance was highest in the river, accounting for ~14% of the population, with upwards of ~22% near Potagannissing

Bay, where the St Mary’s River flows into Lake Huron. While Verrucomicrobia abundance was similar to Lake Erie, accounting for an average of ~15% of the population, the abundance of

Verrucomicrobial classes in the St Mary’s River were different than those in Lake Erie, with higher abundances of the classes Opitutae and Pedosphaerae and lower abundances of

Spartobacteria and Verrucomicrobiae. Planctomycetes were more abundant in the St Mary’s

River, accounting for an average of ~14% of the community. The dominant Planktomycete

OTUs throughout the river have been previously observed in the hypolimnetic waters throughout the upper Great Lakes during summer sampling (Chapter 2). A Chloroflexi OTU of the CL500 clade was also present, and was previously observed as the most abundant OTU in hypolimnetic waters of the upper Great Lakes.

Community structure differed in Lake Michigan and the Straits of Mackinac. Proteobacteria were in higher abundance and averaged ~46% of the community, due in part to an increase in

Gammaproteobacteria abundances, particularly within the Straits of Mackinac. Abundances of

Verrucomicrobia and Planctomycetes were lower, averaging only ~6% and ~2% respectively, a stark contrast to the abundances seen in the St. Mary’s River. Chloroflexi, however, were more abundant in the waters of Lake Michigan and the Straits of Mackinac, accounting for upwards of 83

10% of the population. Firmicutes were detected at higher abundances in several sites and were most prominent where diatom abundances were high, at station M007, near Mackinaw City, and to a lesser extant at station M018 in Lake Michigan. At these stations, Firmicutes comprised

~9% and ~3% of the community respectively, and was dominated by a single OTU with a high identity to the genus Exiguobacteria (100%).

3.2.5 UPPER GREAT LAKES VERSES LAKE ERIE

To elucidate differences in microbial communities of the upper Great Lakes verses those of eutrophic Lake Erie, water samples collected during the winter of 2013 were compared. Lake

Erie, the most anthropogenically influenced of the Laurentian Great Lakes, has been a subject of interest due to heavy anthropogenic influence in its western basin, where planktonic blooms may contribute to an expansive summer hypoxic zone (Hawley et al., 2006). Lake Superior on the other hand, sustains the least anthropogenic influence of the Great Lakes. It is, however, experiencing a century-long increase in nitrate (Sterner et al., 2007).

Principal Coordinate Analysis revealed significant clustering of Great Lakes water samples into three clusters for both weighted (RANOSIM = 0.62, P = 0.001) and unweighted UniFrac

(RANOSIM = 0.9, P = 0.001)(Fig. 26). Unweighted UniFrac PCoA revealed clustering for the

Saint Mary’s River and eastern Mackinac Straits (M001, M002, M004, M006) (RANOSIM = 0.54,

P = 0.005), Lake Michigan (M009, M016) (RANOSIM = 0.54, P = 0.02), and a third cluster consisting of a mix of samples from Mackinac Straits (M012, M007), Lake Michigan (M018), and all samples from Lake Erie (RANOSIM = 0.54, P = 0.007). Weighted UniFrac PCoA revealed similar clustering of the St. Mary’s River (M001, M002, M004) (RANOSIM = 0.44, P = 0.005),

Lake Michigan and Mackinac Straits samples (M006, M009, M012, M016) (RANOSIM = 0.37, P = 84

Unweighted Weighted

source

bays erie

upper

Figure 26. Principal coordinate analysis: Laurentian Great Lakes (winter 2013).

0.005), and a third cluster of Lake Erie samples along with M018 (Lake Michigan) and M007

(Mackinac Straits) (RANOSIM = 0.25, P = 0.023).

To examine environmental influences on these clustering patterns, BEST analysis was performed on UniFrac distance matrices for both weighted and unweighted analyses against physiochemical measurements (Table 9). BEST analysis revealed high ranks for both sulfate

(SO4) and chloride (CL) in both weighted and unweighted UniFrac, while chlorophyll readings also ranked highly in the weighted analysis. Permutational Mantel tests were also performed comparing UniFrac distance matrices and distance matrices derived from physiochemical parameters. Highly significant correlations were obtained for both SO4 and CL for both weighted (SO4, P = 0.003; CL, P = 0.001) and unweighted UniFrac (SO4, P = 0.001; CL, P =

0.001). Significant correlations for Chlorophyll A (0.02 micron) (P = 0.05), NO3 (P = 0.04), and

SIO2 (P = 0.02) were observed for weighted UniFrac, while SRP was significant for unweighted

UniFrac (P = 0.04) (Table 10). SO4 and CL were the most significant correlations for both weighted and unweighted UniFrac. Chloride and sulfate measurements were lowest in the St. 85

Table 9. Chemical measurements for the Laurentian Great Lakes

Upper Great Lakes NH CL SO NO NO SIO SRP Total Phosphorus Stn 3 4 2 3 2 (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) M001 0.022 1.8 3.3 0 0.27 1.82 0.003 0.03 M002 0.018 2.8 3.3 0 0.27 2.16 0.001 0.02 M004 0.021 1.8 2.7 0 0.21 1.32 0.003 0.02 M006 0.022 7 11.4 0 0.2 1.48 0.002 0.02 M007 0.022 9.9 18.5 0 0.22 2.05 0.001 0.01 M009 0.016 6.9 10.4 0 0.11 0.97 0.001 0.01 M012 0.019 8.5 15.4 0 0.2 1.69 0.002 0.01 M016 0.015 4.9 5.6 0 0.09 0.64 0.001 0.02 M018 0.021 11.8 20.8 0 0.2 1.89 0.000 0.02

Lake Erie NH CL SO NO NO SIO SRP Total Phosphorus Stn 3 4 2 3 2 (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) 67 0.026 19.8 21.9 0 0.33 0.91 0.012 0.03 938 0.005 15.6 18 0.01 0.24 0.81 0.010 0.02 938 ice 0.01/0.082 5.2/4.6 5.7/5.6 0/0 0.05/0.05 0.28/0.16 0.0109/0.0009 0.03 879 0.015 14.1 14.9 0 0.15 0.72 0.010 0.03 879 ice 0.012/0.069 6.1/5.5 7.1/7 0/0 0.06/0.06 0.28/0.28 0.008/0.0007 0.02 CACHE2 0.032 12.2 12.3 0 0.11 0.64 0.015 0.10 1365 0.002/0.058 11.5/16 11.4/21.4 0/0 0.03/0.05 0/0.05 0.0047/0.0004 0.04 1365 ice 0.022 0.7 0.6 0 0.02 -0.02 0.028 0.13 CACHE1 0.008 13 12.1 0 0.09 0.71 0.010 0.18 880 0.012/0.034 2.1/16.7 2.3/22.2 0/0 0.06/0.14 0.05/0.58 0.013/0.0036 0.03 880 ice 0.005 12.7 13 0 0.08 0.43 0.013 0.14 1326 0.012 14.9 17.9 0 0.08 0.23 0.007 0.02 341 0.014 16 20.3 0 0.06 0.01 0.006 0.03 970 0.053 10.2 12.3 0 0.31 1.61 0.004 0.03 86

Table 10. BEST and Mantel tests for the Laurentian Great Lakes samples (2013).

Unweighted UniFrac Weighted UniFrac ρ ρ 0.50 10 0.37 9 0.53 9,10 0.48 4,10, 0.53 1,9,10 0.48 4,6,10, 0.53 1,2,9,10 0.48 4,5,6,10, 0.53 1,2,7,9,10 0.48 2,4,5,6,10, 0.53 1,2,7,8,9,10 0.48 2,4,5,6,7,10, 0.53 1,2,7,8,9,10,11 0.48 2,4,5,6,7,8,10, 0.53 1,2,6,7,8,9,10,11 0.48 2,4,5,6,7,8,10,11, 0.50 1,2,5,6,7,8,9,10,11 0.48 1,2,4,5,6,7,8,10,11, 0.43 1,2,4,5,6,7,8,9,10,11 0.47 1,2,4,5,6,7,8,9,10,11, 0.04 1,2,3,4,5,6,7,8,9,10,11 0.03 1,2,3,4,5,6,7,8,9,10,11,

NO3 = 1, NH3 = 2, Depth = 3, Chl 0.2 = 4, Chl 20 = 5, SIO2 = 6, Total P = 7, SRP = 8, CL = 9,

S04 = 10, NO2 = 11

Unweighted UniFrac Weighted UniFrac R P R P Chl 0.2 -0.11 0.432 0.22 0.049 Chl 20 -0.10 0.437 0.17 0.161 CL 0.50 0.001 0.40 0.001 Depth -0.12 0.288 -0.17 0.102

NH3 -0.06 0.609 -0.10 0.454

NO2 -0.11 0.449 -0.12 0.427

NO3 0.10 0.322 0.20 0.037

SIO2 0.07 0.444 0.27 0.016

SO4 0.49 0.001 0.38 0.003 SRP 0.23 0.039 0.13 0.211 TP 0.08 0.457 0.05 0.598 87

Mary’s River, averaging ~2 mg/L and ~3 mg/L respectively, and highest in the Lake Erie cluster, where averages of ~13.5 mg\L and ~17 mg/L were obtained for CL and SO4 respectively, and are consistent with previous studies (Chapra et al., 2009; Chapra et al., 2012) The Lake

Michigan associated cluster averaged ~7 mg/L chloride and ~11 mg/L sulfate, lower than expected for either Lake Michigan or Lake Huron, but may be influenced by water flowing out of Lake Superior. Interestingly, upper Great Lakes samples that clustered with Lake Erie in both weighted and unweighted analyses averaged ~11 mg\L chloride and~20 mg/L sulfate, similar to concentrations obtained from Lake Erie. LEfSe LDA was used to detect significant shifts in taxa between clustering observed in Weighted UniFrac PCoA. Discriminatory taxonomic features generally mirrored observed shifts in read abundance. In the St. Mary’s River, The

Planctomycetes, along with the order Phycisphaerales were ranked highest. Of the

Bacteroidetes, members of both the Flavobacteria and Saprospirae were identified. The genus

Sediminibacterium (Saprospirales) ranked highly and interestingly was also identified by LDA as an important feature for identifying summer Lake Erie samples from those of the upper Great

Lakes, where Sediminibacterium was highly abundant Erie’s oligotrophic eastern basin (Chapter

2). The genus Fluviicola of the Cryomorphaceae (Flavobacteria), identified by LEfSe as discriminatory, while Verrucomicrobia ranked highly as well, and two orders were identified as discriminatory, the Pedosphaereales (Pedosphaerae) and the Cerasicoccales (Opitutae).

Synechococcus (Cyanobacteria) was in highest abundance in the river, and likewise was also identified as discriminatory.

Taxonomic features associated with the Lake Michigan cluster identified by LEfSe included members of the Proteobacteria, Actinobacteria, and Chloroflexi, also reflecting observed abundances of reads. Proteobacteria were discriminatory in general and included members of 88

Figure 27. LDA effect size cladogram (Laurentian Great Lakes). LDA cladogram comparing taxa of the upper Great Lakes with those of Lake Erie. Significantly discriminant taxa are represented by colored nodes,and branches are shaded by highest ranking taxon.

Table 11. Feature importance scores (Laurentian Great Lakes). Expressed as mean decrease in accuracy (MDA) along with standard deviation (SD) for each taxon.

MDA SD Taxonomy 0.029 0.003 Chloroflexi; c__Anaerolineae; o__H39 0.028 0.003 Verrucomicrobia; c__[Pedosphaerae]; o__[Pedosphaerales]; f__Ellin517 0.026 0.003 Verrucomicrobia; c__[Pedosphaerae]; o__[Pedosphaerales]; f__R4-41B 0.015 0.005 Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae 0.013 0.005 Cyanobacteria; c__Synechococcophycideae; o__Synechococcales; f__Synechococcaceae; g__Synechococcus 0.013 0.004 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Oxalobacteraceae 0.013 0.004 Bacteroidetes; c__[Saprospirae]; o__[Saprospirales]; f__Saprospiraceae 0.012 0.005 Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__Cryomorphaceae; g__Fluviicola 0.011 0.004 Planctomycetes; c__Phycisphaerae; o__Phycisphaerales 89 the Alpha- and Gammaproteobacteria. Of the Alphaproteobacteria, the Pelagibacteraceae

(SAR11 clade), a group that is extremely common in the oceans (Morris et al., 2002), ranked highest. Of the Gammaproteobacteria, OM60 of the Alteromondales, along with the

Sinobacteraceae (Xanthomonadales) were identified. The classes Anaerolineae and SL50

(Chloroflexi) both ranked highly, mirroring increased Chloroflexi abundance at these locations.

In Lake Erie samples, LEfSe identified members of the Proteobacteria, Bacteroidetes,

Verrucomicrobia, Acidobacteria, and Gemmatimonadetes as highly ranking taxa. For the

Verrucomicrobia, the Chthoniobacterales (Spartobacteria) and Verrucomicrobiales

(Verrucomicrobiae) both ranked. Xiphinematobacter (Spartobacteria) was the highest-ranking taxon for Lake Erie, and has been observed in high abundance in previous winter surveys of

Lake Erie (Chapter 1). Ranking taxa of the Alphaproteobacteria included the

Methylobacteriaceae and Bradyrhizobiacea, both orders of the Rhizobiales. The

Oxalobacteraceae (Burkholderiales) of the Betaproteobacteria, along with the Xanthomonadales of the Gammaproteobacteria also ranked. The only member of the Bacteroidetes to rank was identified as Aquirestris (Saprospiraceae). Members of the minor phyla Acidimicrobia (The

Holophagales) and Gemmatimonodetes (Gemmatimonadales) were also identified as important discriminatory taxa.

Random Forest analysis was used to identify OTUs important for distinguishing between samples obtained from the upper Great Lakes and those from eutrophic Lake Erie. The results underscored the findings of LEfSe, and identified members of the Choroflexi, Verrucomicrobia,

Proteobacteria, Cyanobacteria, and Planctomycetes as highly discriminatory features for each cluster (Table 11). 90

3.2.6 LAKE ERIE CACHE SITES AND ICE SAMPLES

CACHE sites, areas experiencing localized winter diatom blooms, contained a high diversity of OTUs, and as such, no individual OTUs were notably abundant (Fig. 24). The abundance of the most dominant OTU accounted for only ~3% of the population. Due to the low abundance of

OTUs in these samples, Lake Erie ice and CACHE samples were not included in LEfSe and random forest analyses of the Great Lakes.

CACHE site samples collected in the Eastern and Central basins were composed of similar bacterial communities. CACHE sites had notably lower abundances of Actinobacteria than water samples, accounting for less than 10% of the population and also contained higher concentrations of Nitrospirae. Proteobacteria were the dominant phylum in the communities and accounted for ~48% of reads. CACHE sites contained lower abundances of both Alpha- and Betaproteobacteria than did non-CACHE water samples, but displayed increases in the abundances of both Gamma- and Deltaproteobacteria. These classes are markedly lower at non-

CACHE sites, averaging only ~8% and ~3% for Gamma- and Deltaproteobacteria respectively.

Gammaproteobacteria, while being poorly represented in the underlying water, are well known dominant members of sea-ice microbial communities (Bowman et al., 2012). While epiphytic

Gammaproteobacteria have been suggested as a mechanism of resuspension of diatoms into the water column, previously identified ice-nucleating bacterial isolates (D’sousa et al., 2013), were present only in low abundance (<0.02% of bacteria) at CACHE sites.

Ice collected in Lake Erie during 2013, along with a rare ice-field encountered at Pigeon Bay during the low-ice winter of February 2012, offered a glimpse into microbial partitioning to the ice phase. While the communities of some ice samples resembled those of the surrounding water, other ice samples closely resembled the communities present in CACHE sites (EC 880, 91

Pigeon Bay, EC 938).

3.2.7 MICROBIAL COMMUNITIES OF LAKE ONEGA

Lake Onega, an oligotrophic lake in the Karalia region of Russia, is Europe’s second largest

Great Lake. Petrozavodsk Bay, a northern bay of Lake Onega, is similar to Lake Erie in that it is of comparable average depth and is subject to appreciable anthropogenic influence from the city of Petrozavodsk and inputs from the Suna River (Sabylina et al., 2011). Lake Onega is typically ice-covered through mid-May, and ice was substantially thicker here than in Lake Erie, measuring over a meter at both sites at the time of sampling. While Lake Erie maintains a pH of

~8.4, Lake Onega samples were more acidic, with an average pH of 6.6.

The winter microbial community of Lake Onega contrasted sharply with the Laurentian

Great Lakes (Fig. 28). Chloroplast reads were vanishingly low, and large differences in the abundances of bacteria were apparent. The most abundant phyla in Petrozavodsk Bay were the

Proteobacteria, which averaged ~75% of the community, and the Bacteroidetes, which accounted for 13%. The Actinobacteria, which accounted for ~14% of Larentian communities, were drastically lower, accounting for ~4% of the community and Verrucomicrobia accounted for <1%.

Orders comprising the majority of the Proteobacteria were the Burkholderiales and the

Pseudomonadales, accounting for 28% and ~29% of the community respectively.

Gammaproteobacteria accounted for upwards of 44.5% of the community in Petrozavodsk Bay, of which Acinetobacter was the most abundant OTU. An exception was water sampled from station L2, in which Pseudomonas was the most abundant gamma OTU. The

Gammaproteobacteria contain known ice-nucleating species, and indeed, ice nucleation proteins 92

Figure 28. OTU abundance: Lake Onega, Russia (winter 2013) A. OTU abundances per station, colored by phylum, rarefied to 18,000 reads. B. OTU abundances per station, colored by class. C. Chloroplast OTU abundances as a percent of total chloroplast and bacterial reads. 93 were detected in these samples (unpublished data). Moreover, a Pseudomonas OTU identified as an ice-nucleator in Lake Erie (DeSouza et al., 2013), was present in higher abundance in the ice of Petrozavodsk Bay, with an abundance of ~1%. Unlike the Laurentian Great Lakes, there were no highly abundant Alphaproteobacteria, although general abundances of the class were similar.

The most abundant Betaproteobacteria in Lake Onega belonged to the family Oxalobacteraceae.

Firmicutes were present in higher numbers in the bottom ice, and waters just beneath. The dominant Firmicute OTU in Russian ice most closely matched Tumebacillus permanentifrigoris strain Eur1 9.5, a strain originally isolated from permafrost in Canada and is known to grow chemolithoautotrophically on inorganic sulfur compounds (Steven et al., 2008). This OTU is also present in the waters of the Upper Great Lakes at stations M007 and M018. Waters beneath the ice at station L1 differed in that the dominant Firmicute was an Exiguobacteria, which was also the dominant Firmicute OTU where present in waters of the Upper Great Lakes. While minor groups in Lake Onega, the Planctomycete and Chloroflexi OTUs found in high abundance in the upper Laurentian Great Lakes were present in low abundance in the waters beneath the ice.

Ice and water samples from Lake Onega, Russia were analyzed against ice and water samples from the Laurentian Great Lakes (Fig. 29). PCoA of UniFrac distances revealed significant clustering of Lake Onega samples from the Laurentian Great Lakes for both weighted (RANOSIM

= 0.92, P = 0.001) and unweighted (RANOSIM = 0.91, P = 0.001) analyses. Taxonomic differences between Onega and the Laurentian Great Lakes were examined using LEfSe LDA analysis.

Mirroring observed abundances of reads, LEfSe identified members of the Protebacteria,

Bacteroidetes, and Firmicutes as discriminating taxa. Proteobacteria dominated Petrozavodsk

Bay and likewise contained the most discriminatory taxa. The Gammaproteobacteria contained 94

Figure 29. Principal Coordinate Analysis of the Laurentian Great Lakes and Lake Onega. the most discriminatory taxa, and indeed, the Pseudomonadales obtained the highest effect score. The Pseudomonadales identified by LEfSe as discriminatory were the families Pseudomonadaceae and Moraxellaceae. Other Gammaproteobacteria were identified as well, including the families Xanthomonadaceae and the Enterobacteriaceae.

Members of the Alpha- and Betaproteobacteria a had high effect scores as well, where the families Sphingomonadaceae, Bradyrhizobiaceae, and Caulobacteraceae represented the Alphaproteobacteria, and the families Rhodocyclaceae and Oxalobacteraceae represented the Betaproteobacteria. Two families belonging to the Firmicutes were

Exiguobacteriaceae and the Alicyclobacillaceae. The Bacteroidetes were represented by the family Flavobactereaceae. Random Forest Analysis underscored these results, with

OTUs from the Pseudomonadales and Oxalobacteraceae ranking amongst the most discriminating taxa (Table 12). 95

Figure 30. LDA effect size cladogram: Laurentian Great Lakes and Lake Onega. LDA cladogram comparing taxa of the upper Great Lakes with those of Lake Erie. Significantly discriminant nodes are colored, and branches are shaded by highest-ranking taxon.

Table 12. Feature importance scores (Laurentian Great Lakes and Onega). Expressed as mean decrease in accuracy (MDA) along with standard deviation () for each taxon.

MDA SD Taxonomy 0.014 0.002 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Comamonadaceae; g__Methylibium; s__ 0.014 0.002 Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__; g__; s__ 0.014 0.002 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Oxalobacteraceae; g__Janthinobacterium; s__ 0.013 0.001 Proteobacteria; c__Gammaproteobacteria; o__Pseudomonadales; f__Pseudomonadaceae; g__Pseudomonas; s__fragi 0.012 0.002 Proteobacteria; c__Gammaproteobacteria; o__Pseudomonadales; f__Moraxellaceae 0.007 0.001 Bacteroidetes; c__Sphingobacteriia; o__Sphingobacteriales; f__; g__; s__ 0.007 0.002 Bacteroidetes; c__Flavobacteriia; o__Flavobacteriales; f__Flavobacteriaceae; g__Flavobacterium; s__ 0.007 0.002 Gemmatimonadetes; c__Gemmatimonadetes; o__Gemmatimonadales; f__Gemmatimonadaceae; g__Gemmatimonas 0.007 0.001 Verrucomicrobia; c__Opitutae; o__[Cerasicoccales]; f__[Cerasicoccaceae]; g__; s__ 0.006 0.002 Proteobacteria; c__Gammaproteobacteria; o__Pseudomonadales; f__Moraxellaceae 0.006 0.002 Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__ACK-M1; g__; s__ 0.005 0.001 Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; f__C111; g__; s__ 0.005 0.002 Proteobacteria; c__Gammaproteobacteria; o__Pseudomonadales; f__Pseudomonadaceae; g__Pseudomonas 0.005 0.002 Proteobacteria; c__Gammaproteobacteria; o__Alteromonadales; f__OM60 96

CONCLUSIONS: ANTHROPOGENIC INFLUENCES ON FRESHWATER

MICROBIAL COMMUNITIES

This study examined the microbial communities of the Great Lakes, the largest freshwater system in the world. The results of this study highlighted microbial community shifts associated with anthropogenic influences, and perhaps provides a glimpse into the future of the

Laurentian Great Lakes.

Shifts in community structure caused by decreasing ice cover, the result of a warming climate, were observed in chapter 1. Lower ice-cover resulted in an 89% decrease in phytoplankton biovolume between years of expansive ice cover and nearly ice-free 2012.

Principal coordinate analysis (PCoA) of UniFrac distance matricies revealed a significant separation between the communities of high-ice year 2010 and low-ice 2012, indicating a shift in microbial community structure. In 2011 Lake Erie’s central basin was open-water two weeks prior to sampling, resulting in a community that appeared to be transitioning from low-ice conditions to high-ice conditions, suggesting that observed shifts in community structure may happen comparatively rapidly with the onset of ice.

Microbial community shifts were also observed during the summer-stratified period during a survey of the Laurentian Great Lakes in 2011 (Chapter 2). Communities were observed along a transect originating from the oligotrophic waters of Lake Superior and followed a trophic gradient to Lake Erie, the most anthropogenically influenced of the Great Lakes. PCoA revealed significant phylogenetic differences between the waters of the upper Great Lakes and those of

Lake Erie. Statistical analyses revealed that, of the physico-chemical measurements recorded 97 during the cruise, increases in ammonium and particulate phosphorus concentrations were highly significant.

Additionally, the possible influence of anthropogenic inputs on microbial communities of the upper Great Lakes was observed during a winter survey in 2013 (Chapter 3). PCoA clustering revealed that two sites in Lake Michigan grouped with samples collected from Lake

Erie, a lake heavily influenced by anthropogenic inputs. Statistical analyses indicated that concentrations of sulfate, primarily associated with fossil-fuel usage, and chloride, associated with industrial discharges and road salt runoff, best explained the phylogenetic differences between PCoA clustering for both weighted and unweighted PCoA. Indeed, sulfate and chloride measurements for these two sites displayed levels expected for Lake Erie. Both Lake Michigan and Lake Huron are experiencing strong upward trends in chloride. While Lake Michigan has experienced a significant increase in sulfate, which suggested a moderate upward trend, a lack of data beyond 1992 has made the current state of the lake unclear (Chapra et al., 2012).

These studies highlighted potentially important shifts in microbial communities associated with physico-chemical measurements, and indicated that anthropogenic influences on the lakes as well as the atmosphere may be responsible and offer a glimpse into the potential future the Great Lakes. Future work will focus on further examination of taxa whose abundance increased significantly in anthropogenically-influenced communities. With the majority of environmental sequences originate from unculturable taxa, techniques such as single-cell sequencing may provide valuble insight into their metabolic functionality. Additionally, identified chemical parameters identified as best explaining the bacterial community shifts observed in this study must be further tested, and additional parameters affecting these communities identified. 98

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APPENDIX A: TOP BLAST HITS FOR OTUS ABOVE 0.5% ABUNDANCE

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__Nitrosopumilus;s__ 451 bits 251/254 1/254 denovo858 253 gb|HQ331116.1 1358 (244) 2.00E-123 (99%) (0%) Nitrosopumilaceae archaeon MY1 16S ribosomal RNA gene k__Bacteria;p__Acidobacteria;c__[Chloracidobacteria];o__RB41;f__Ellin6075;g__;s__ 429 bits 246/253 0/253 denovo52233 253 gb|GU187039.1 1395 (232) 7.00E-117 (97%) (0%) Acidobacteria bacterium IGE-018 16S ribosomal RNA gene k__Bacteria;p__Acidobacteria;c__Acidobacteria-6;o__CCU21;f__;g__;s__ 324 bits 227/253 0/253 denovo42852 253 gb|GU187027.1 1415 (175) 3.00E-85 (90%) (0%) Acidobacteria bacterium IGE-011 16S ribosomal RNA gene k__Bacteria;p__Acidobacteria;c__Holophagae;o__Holophagales;f__Holophagaceae;g__;s__ 412 bits 243/253 0/253 denovo139953 253 ref|NR_036779.1 1448 (223) 7.00E-112 (96%) (0%) Geothrix fermentans strain H5 16S ribosomal RNA gene k__Bacteria;p__Acidobacteria;c__RB25;o__;f__;g__;s__ 318 bits 227/254 2/254 denovo69535 253 gb|GQ287504.1 1061 (172) 2.00E-83 (89%) (1%) Endosymbiont of Ridgeia piscesae clone P1s-297 16S ribosomal RNA gene k__Bacteria;p__Acidobacteria;c__Solibacteres;o__Solibacterales;f__Solibacteraceae;g__Candidatus Solibacter;s__ 462 bits 252/253 0/253 denovo122162 253 gb|JF488162.1 740 (250) 7.00E-127 (99%) (0%) Acidobacteria bacterium SCGC AAA204-D14 16S ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Acidimicrobiia;o__Acidimicrobiales;f__C111;g__;s__ 470 bits 254/254 0/254 denovo47748 254 gb|HQ663608.1 417 (254) 4.00E-129 (100%) (0%) Actinobacterium SCGC AAA278-P20 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Acidimicrobiia;o__Acidimicrobiales;f__C111;g__;s__ 470 bits 254/254 0/254 denovo68510 254 gb|HQ663408.1 778 (254) 4.00E-129 (100%) (0%) Actinobacterium SCGC AAA043-M07 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Acidimicrobiia;o__Acidimicrobiales;f__C111;g__;s__ 436 bits 248/254 0/254 denovo125113 254 gb|GQ369058.1 1519 (236) 4.00E-119 (98%) (0%) Iamia sp. T2-YC6790 16S ribosomal RNA gene 131

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Actinobacteria;c__Acidimicrobiia;o__Acidimicrobiales;f__C111;g__;s__ 366 bits 233/250 1/250 denovo34861 253 gb|HQ663608.1 417 (198) 6.00E-98 (93%) (0%) Actinobacterium SCGC AAA278-P20 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Acidimicrobiia;o__Acidimicrobiales;f__C111;g__;s__ 470 bits 254/254 0/254 denovo78190 254 gb|HQ663477.1 432 (254) 4.00E-129 (100%) (0%) Actinobacterium SCGC AAA044-G14 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__;g__;s__ 468 bits 253/253 0/253 denovo2578 253 gb|HQ663289.1 427 (253) 2.00E-128 (100%) (0%) Actinobacterium SCGC AAA028-N15 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__;g__;s__ 468 bits 253/253 0/253 denovo19681 253 gb|HQ663442.1 427 (253) 2.00E-128 (100%) (0%) Actinobacterium SCGC AAA044-C20 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 418 bits 244/253 0/253 denovo72575 253 gb|HQ663405.1 782 (226) 2.00E-113 (96%) (0%) Actinobacterium SCGC AAA043-L13 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 464 bits 252/253 0/253 denovo47064 253 gb|HQ663436.1 438 (251) 2.00E-127 (99%) (0%) Actinobacterium SCGC AAA044-C06 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 451 bits 250/253 0/253 denovo48409 253 gb|HQ663632.1 436 (244) 2.00E-123 (99%) (0%) Actinobacterium SCGC AAA280-L02 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 401 bits 241/253 0/253 denovo47747 253 gb|JF488148.1 831 (217) 2.00E-108 (95%) (0%) Actinobacterium SCGC AAA208-D13 16S ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 468 bits 253/253 0/253 denovo3739 253 gb|HQ663645.1 419 (253) 2.00E-128 (100%) (0%) Actinobacterium SCGC AAA280-P13 16S small subunit ribosomal RNA gene 132

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 440 bits 248/253 0/253 denovo114581 253 gb|HQ663632.1 436 (238) 3.00E-120 (98%) (0%) Actinobacterium SCGC AAA280-L02 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 468 bits 253/253 0/253 denovo41035 253 gb|HQ663605.1 430 (253) 2.00E-128 (100%) (0%) Actinobacterium SCGC AAA278-P08 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 468 bits 253/253 0/253 denovo94249 253 gb|JF488148.1 831 (253) 2.00E-128 (100%) (0%) Actinobacterium SCGC AAA208-D13 16S ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 424 bits 245/253 0/253 denovo70884 253 dbj|AB529716.1 709 (229) 3.00E-115 (97%) (0%) Bacterium KW-45 gene for 16S ribosomal RNA k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__ACK-M1;g__;s__ 468 bits 253/253 0/253 denovo94247 253 gb|JF488157.1 805 (253) 2.00E-128 (100%) (0%) Actinobacterium SCGC AAA208-N15 16S ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Microbacteriaceae 468 bits 253/253 0/253 denovo14234 253 gb|KF295696.1 717 (253) 2.00E-128 (100%) (0%) Cryobacterium sp. Y4_112_2 16S ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Microbacteriaceae;g__;s__ 429 bits 246/253 0/253 denovo106703 253 gb|KF160638.1 790 (232) 7.00E-117 (97%) (0%) Bacterium 175_xplvp267 16S ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Microbacteriaceae;g__;s__ 468 bits 253/253 0/253 denovo104827 253 gb|HQ663509.1 428 (253) 2.00E-128 (100%) (0%) Actinobacterium SCGC AAA044-M17 16S small subunit ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Microbacteriaceae;g__Candidatus Rhodoluna;s__ 468 bits 253/253 0/253 denovo101369 253 gb|HQ663292.1 421 (253) 2.00E-128 (100%) (0%) Actinobacterium SCGC AAA028-P02 16S small subunit ribosomal RNA gene 133

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Micrococcaceae;g__Arthrobacter 468 bits 253/253 0/253 denovo69358 253 gb|KM252929.1 1516 (253) 2.00E-128 (100%) (0%) Arthrobacter globiformis strain SQ5-54 16S ribosomal RNA gene k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Mycobacteriaceae;g__Mycobacterium;s__ 468 bits 253/253 0/253 denovo33956 253 gb|KJ624419.1 1138 (253) 2.00E-128 (100%) (0%) Mycobacterium sp. prunus-01 16S ribosomal RNA gene k__Bacteria;p__Armatimonadetes;c__Armatimonadia;o__Armatimonadales;f__Armatimonadaceae;g__;s__ 392 bits 240/254 0/254 denovo72819 254 gb|JF488159.1 834 (212) 9.00E-106 (94%) (0%) Bacterium SCGC AAA202-N08 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes 468 bits 253/253 0/253 denovo78270 253 gb|HQ663240.1 429 (253) 2.00E-128 (100%) (0%) Bacteroidetes bacterium SCGC AAA028-D13 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae 401 bits 241/253 0/253 denovo53069 253 gb|HQ231219.1 1402 (217) 2.00E-108 (95%) (0%) Sediminibacterium sp. HME6815 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 440 bits 248/253 0/253 denovo53849 253 gb|JF488165.1 830 (238) 3.00E-120 (98%) (0%) Bacteroidetes bacterium SCGC AAA204-N13 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 446 bits 249/253 0/253 denovo25281 253 dbj|AB529691.1 710 (241) 7.00E-122 (98%) (0%) Bacterium MI-10 gene for 16S ribosomal RNA

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 468 bits 253/253 0/253 denovo33059 253 gb|HQ663099.1 433 (253) 2.00E-128 (100%) (0%) Bacteroidetes bacterium SCGC AAA024-O02 16S small subunit ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 457 bits 251/253 0/253 denovo46929 253 gb|JF488139.1 832 (247) 3.00E-125 (99%) (0%) Bacteroidetes bacterium SCGC AAA206-I05 16S ribosomal RNA gene 134

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 418 bits 244/253 0/253 denovo76668 253 gb|JF488165.1 830 (226) 2.00E-113 (96%) (0%) Bacteroidetes bacterium SCGC AAA204-N13 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 424 bits 247/255 4/255 denovo97260 253 gb|KC690141.2 1468 (229) 3.00E-115 (97%) (2%) Ferruginibacter sp. HME8881 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 418 bits 244/253 0/253 denovo121042 253 gb|JF488134.1 833 (226) 2.00E-113 (96%) (0%) Bacteroidetes bacterium SCGC AAA206-D16 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 449 bits 247/249 0/249 denovo115054 253 gb|HQ663662.1 426 (243) 5.00E-123 (99%) (0%) Bacteroidetes bacterium SCGC AAA487-E10 16S small subunit ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 418 bits 244/253 0/253 denovo15029 253 gb|KJ634465.1 1462 (226) 2.00E-113 (96%) (0%) Parasegetibacter sp. SGM2-10 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__;s_ _ 446 bits 249/253 0/253 denovo38638 253 ref|NR_041250.1 1431 (241) 7.00E-122 (98%) (0%) Terrimonas lutea strain DY 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__Flavisolibacter;s __ 429 bits 246/253 0/253 denovo138626 253 gb|HQ663132.1 432 (232) 7.00E-117 (97%) (0%) Bacteroidetes bacterium SCGC AAA027-D22 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__Sediminibacterium;s__ 457 bits 251/253 0/253 denovo84042 253 gb|HQ663407.1 791 (247) 3.00E-125 (99%) (0%) Bacteroidetes bacterium SCGC AAA043-M05 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Chitinophagaceae;g__Sediminibacterium;s__ 468 bits 253/253 0/253 denovo115615 253 gb|JF488135.1 742 (253) 2.00E-128 (100%) (0%) Bacteroidetes bacterium SCGC AAA206-E07 16S ribosomal RNA gene 135

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Saprospiraceae;g__;s__ 420 bits 246/255 1/255 denovo113619 254 gb|AF530981.1 397 (227) 4.00E-114 (96%) (0%) Bacteroidetes bacterium so46 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Saprospiraceae;g__Aquirestis;s__calciphila 468 bits 253/253 0/253 denovo99617 253 gb|HQ663092.1 438 (253) 2.00E-128 (100%) (0%) Bacteroidetes bacterium SCGC AAA024-M07 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales 279 bits 220/254 2/254 denovo25088 253 dbj|AB681038.1 1436 (151) 8.00E-72 (87%) (1%) Flexibacter flexilis gene for 16S rRNA, , strain: NBRC 16027 k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales 291 bits 222/254 2/254 denovo110945 253 ref|NR_113726.1 1436 (157) 3.00E-75 (87%) (1%) Flexibacter flexilis strain NBRC 15060 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales;f__[Amoebophilaceae];g__SC3-56;s__ 357 bits 233/253 0/253 denovo99786 253 emb|AM408791.1 1479 (193) 3.00E-95 (92%) (0%) Endosymbiont of Acanthamoeba sp. EI4 partial 16S rRNA gene k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales;f__Cyclobacteriaceae;g__;s__ 468 bits 253/253 0/253 denovo78955 253 gb|HQ663036.1 427 (253) 2.00E-128 (100%) (0%) Bacteroidetes bacterium SCGC AAA024-A13 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales;f__Cytophagaceae 468 bits 253/253 0/253 denovo36443 253 gb|HQ663208.1 435 (253) 2.00E-128 (100%) (0%) Bacteroidetes bacterium SCGC AAA027-O08 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales;f__Cytophagaceae;g__;s__ 442 bits 250/255 2/255 denovo3881 253 dbj|AB127723.1 560 (239) 9.00E-121 (98%) (1%) Bacterium PE03-7G4 gene for 16S rRNA k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales;f__Cytophagaceae;g__;s__ 401 bits 242/254 2/254 denovo139537 253 gb|EF636197.1 978 (217) 2.00E-108 (95%) (1%) Sphingobacteriales bacterium TP498 16S ribosomal RNA gene 136

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales;f__Cytophagaceae;g__;s__ 468 bits 253/253 0/253 denovo46762 253 dbj|AB308367.1 1415 (253) 2.00E-128 (100%) (0%) Bacterium TG141 gene for 16S ribosomal RNA k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales;f__Cytophagaceae;g__;s__ 335 bits 230/254 2/254 denovo70684 253 ref|NR_042235.1 1445 (181) 2.00E-88 (91%) (1%) Adhaeribacter aquaticus strain MBRG1.5 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales;f__Cytophagaceae;g__Emticicia ;s__ 464 bits 253/254 0/254 denovo48707 254 gb|KF309174.1 1427 (251) 2.00E-127 (99%) (0%) Emticicia sp. KACC 17466 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales 418 bits 244/253 0/253 denovo47692 253 gb|HQ663214.1 437 (226) 2.00E-113 (96%) (0%) Bacteroidetes bacterium SCGC AAA027-P14 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__[Weeksellaceae];g__Chryseobacterium;s__ 468 bits 253/253 0/253 denovo48145 253 gb|JX287903.1 1446 (253) 2.00E-128 (100%) (0%) Chryseobacterium sp. T115F.09.B.CHS.MI.SRW.W.Kidney.D 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Cryomorphaceae;g__;s __ 340 bits 230/253 0/253 denovo9507 253 gb|JF488529.1 787 (184) 3.00E-90 (91%) (0%) Bacteroidetes bacterium SCGC AAA160-A21 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Cryomorphaceae;g__;s __ 462 bits 252/253 0/253 denovo26241 253 gb|HQ663214.1 437 (250) 7.00E-127 (99%) (0%) Bacteroidetes bacterium SCGC AAA027-P14 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Cryomorphaceae;g__Fluviicola;s__ 457 bits 252/254 1/254 denovo12120 253 gb|HQ663199.1 436 (247) 3.00E-125 (99%) (0%) Bacteroidetes bacterium SCGC AAA027-M20 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Cryomorphaceae;g__Fluviicola;s__ 412 bits 246/256 5/256 denovo128376 253 gb|HQ663199.1 436 (223) 7.00E-112 (96%) (2%) Bacteroidetes bacterium SCGC AAA027-M20 16S small subunit ribosomal RNA gene 137

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Cryomorphaceae;g__Fluviicola;s__ 444 bits 242/243 0/243 denovo111334 253 gb|JF488655.1 479 (240) 3.00E-121 (99%) (0%) Bacterium SCGC AAA074-P13 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Cryomorphaceae;g__Fluviicola;s__ 468 bits 253/253 0/253 denovo77705 253 gb|HQ663147.1 437 (253) 2.00E-128 (100%) (0%) Bacteroidetes bacterium SCGC AAA027-F10 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Cryomorphaceae;g__Fluviicola;s__ 457 bits 251/253 0/253 denovo9927 253 gb|HQ663127.1 436 (247) 3.00E-125 (99%) (0%) Bacteroidetes bacterium SCGC AAA027-D14 16S small subunit ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacteriu m 462 bits 252/253 0/253 denovo115904 253 gb|KF499995.1 1373 (250) 7.00E-127 (99%) (0%) Flavobacterium sp. S1(2013) 16S ribosomal RNA gene

k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacteriu m 446 bits 249/253 0/253 denovo95783 253 emb|FR772225.1 1468 (241) 7.00E-122 (98%) (0%) Flavobacterium sp. R-38284 partial 16S rRNA gene, strain R-38284 k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__ 468 bits 253/253 0/253 denovo30403 253 gb|HM776989.1 1367 (253) 2.00E-128 (100%) (0%) Flavobacterium sp. HME6134 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__ 468 bits 253/253 0/253 denovo48235 253 gb|JN622004.1 1329 (253) 2.00E-128 (100%) (0%) Flavobacterium sp. HME7816 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__ 468 bits 253/253 0/253 denovo109339 253 gb|KF160572.1 748 (253) 2.00E-128 (100%) (0%) Bacterium 109_oclvp514 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__ 446 bits 249/253 0/253 denovo71185 253 gb|KC986993.1 1481 (241) 7.00E-122 (98%) (0%) Flavobacterium sp. TP-Snow-C63 16S ribosomal RNA gene 138

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__ 446 bits 249/253 0/253 denovo140198 253 gb|HQ000017.1 1383 (241) 7.00E-122 (98%) (0%) Flavobacterium sp. HME6144 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__ 462 bits 252/253 0/253 denovo140816 253 gb|KF499997.1 1331 (250) 7.00E-127 (99%) (0%) Flavobacterium sp. W2 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__ 433 bits 246/252 0/252 denovo111036 253 emb|FR744830.1 1397 (234) 5.00E-118 (98%) (0%) Cellulophaga sp. SW1-18 partial 16S rRNA gene, isolate SW1-18

k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__succinican s 468 bits 253/253 0/253 denovo102005 253 gb|KJ810593.1 1411 (253) 2.00E-128 (100%) (0%) Flavobacterium sp. THW-Z16 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ gb|AF233293.1|AF 368 bits 235/253 0/253 denovo67412 253 233293 1422 (199) 2.00E-98 (93%) (0%) Tuber borchii symbiont b-1BO 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 316 bits 226/253 2/253 denovo21471 253 gb|HQ290503.1 804 (171) 6.00E-83 (89%) (1%) Bacterium SCGC AAA018-K8 small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ gb|AF233293.1|AF 385 bits 238/253 0/253 denovo32943 253 233293 1422 (208) 2.00E-103 (94%) (0%) Tuber borchii symbiont b-1BO 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 348 bits 233/255 2/255 denovo81430 253 gb|HQ663372.1 431 (188) 2.00E-92 (91%) (1%) Bacteroidetes bacterium SCGC AAA041-P16 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 329 bits 229/254 2/254 denovo11072 253 gb|FJ377412.1 469 (178) 7.00E-87 (90%) (1%) Bacteroidetes bacterium Ana2 16S ribosomal RNA gene 139

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 302 bits 224/254 2/254 denovo90158 253 gb|HQ663373.1 437 (163) 2.00E-78 (88%) (1%) Bacteroidetes bacterium SCGC AAA041-P17 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 357 bits 233/253 0/253 denovo52292 253 gb|HQ290503.1 804 (193) 3.00E-95 (92%) (0%) Bacterium SCGC AAA018-K8 small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 436 bits 249/255 2/255 denovo13905 253 gb|HQ663372.1 431 (236) 4.00E-119 (98%) (1%) Bacteroidetes bacterium SCGC AAA041-P16 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 318 bits 226/253 0/253 denovo13081 253 dbj|AB540005.1 1369 (172) 2.00E-83 (89%) (0%) Flexibacteraceae bacterium Kor gene for 16S rRNA k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ gb|AF233293.1|AF 390 bits 239/253 0/253 denovo14345 253 233293 1422 (211) 3.00E-105 (94%) (0%) Tuber borchii symbiont b-1BO 16S ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 361 bits 233/252 0/252 denovo88567 253 gb|HQ290503.1 804 (195) 3.00E-96 (92%) (0%) Bacterium SCGC AAA018-K8 small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 381 bits 239/255 2/255 denovo95153 253 gb|HQ663373.1 437 (206) 2.00E-102 (94%) (1%) Bacteroidetes bacterium SCGC AAA041-P17 16S small subunit ribosomal RNA gene k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__;g__;s__ 318 bits 227/254 2/254 denovo89220 253 dbj|AB529714.1 711 (172) 2.00E-83 (89%) (1%) Bacterium KW-39 gene for 16S ribosomal RNA k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__Sphingobacteriaceae;g__;s__ 468 bits 253/253 0/253 denovo63630 253 gb|FJ897516.1 1375 (253) 2.00E-128 (100%) (0%) Pedobacter sp. B4a-b5 16S ribosomal RNA gene 140

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__Sphingobacteriaceae;g__Pedobacter;s__ 468 bits 253/253 0/253 denovo116317 253 emb|HF937000.1 780 (253) 2.00E-128 (100%) (0%) Pedobacter sp. 2P2A7 partial 16S rRNA gene, isolate 2P2A7

k__Bacteria;p__Chlorobi;c__OPB56;o__;f__;g__;s __ 468 bits 253/253 0/253 denovo70627 253 gb|HQ663022.1 427 (253) 2.00E-128 (100%) (0%) Bacteroidetes bacterium SCGC AAA023-M10 16S small subunit ribosomal RNA gene k__Bacteria;p__Chloroflexi;c__Anaerolineae;o__H39;f__;g__;s__ 372 bits 233/249 0/249 denovo115607 253 gb|HQ675640.1 873 (201) 1.00E-99 (94%) (0%) Chloroflexi bacterium SCGC AAA240-O05 small subunit ribosomal RNA gene k__Bacteria;p__Chloroflexi;c__Chloroflexi;o__[Roseiflexales];f__;g__;s__ 211 bits 206/251 4/251 denovo88782 253 gb|DQ812550.1 1252 (114) 3.00E-51 (82%) (2%) Chloroflexi bacterium Ver9Iso2 16S ribosomal RNA gene

k__Bacteria;p__Chloroflexi;c__SL56;o__;f__;g__; s__ 468 bits 253/253 0/253 denovo121125 253 gb|HQ663375.1 718 (253) 2.00E-128 (100%) (0%) Firmicutes bacterium SCGC AAA043-A02 16S small subunit ribosomal RNA gene k__Bacteria;p__Cyanobacteria 346 bits 231/253 0/253 denovo100381 253 gb|KM462867.1 126694 (187) 7.00E-92 (91%) (0%) Pedinomonas tuberculata culture-collection SAG:42.84 chloroplast, complete genome k__Bacteria;p__Cyanobacteria;c__Nostocophycideae;o__Nostocales;f__Nostocaceae;g__Dolichospermum;s__ 440 bits 248/253 0/253 denovo16213 253 gb|KC242835.1 1164 (238) 3.00E-120 (98%) (0%) Dolichospermum lemmermannii 08-01 16S ribosomal RNA gene k__Bacteria;p__Cyanobacteria;c__Nostocophycideae;o__Nostocales;f__Nostocaceae;g__Dolichospermum;s__ 468 bits 253/253 0/253 denovo38284 253 gb|KM376423.1 396 (253) 2.00E-128 (100%) (0%) Aphanizomenon flos-aquae AP1 16S ribosomal RNA gene k__Bacteria;p__Cyanobacteria;c__Synechococcophycideae;o__Synechococcales;f__Synechococcaceae;g__Synechococcus;s__ 468 bits 253/253 0/253 denovo42568 253 gb|AY151243.1 1560 (253) 2.00E-128 (100%) (0%) Synechococcus sp. MW6C6 16S ribosomal RNA gene 141

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Cyanobacteria;c__Synechococcophycideae;o__Synechococcales;f__Synechococcaceae;g__Synechococcus;s__ 468 bits 253/253 0/253 denovo78294 253 gb|HQ663058.1 416 (253) 2.00E-128 (100%) (0%) Cyanobacterium SCGC AAA024-D22 16S small subunit ribosomal RNA gene

k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillale s 468 bits 253/253 0/253 denovo32527 253 gb|KM241844.1 960 (253) 2.00E-128 (100%) (0%) Bacillus sp. S11 16S ribosomal RNA gene

k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillale s 366 bits 234/252 0/252 denovo49201 252 gb|KF555361.1 1459 (198) 6.00E-98 (93%) (0%) Bacterium KB54 16S ribosomal RNA gene k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__[Exiguobacteraceae];g__Exiguobacterium;s__ 468 bits 253/253 0/253 denovo14743 253 dbj|LC008359.1 1442 (253) 2.00E-128 (100%) (0%) Exiguobacterium sp. DG1 gene for 16S ribosomal RNA

k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Alicyclobacillaceae;g__Alicyclobacillus;s __ 468 bits 253/253 0/253 denovo72735 253 ref|NR_043849.1 1407 (253) 2.00E-128 (100%) (0%) Tumebacillus permanentifrigoris strain Eur1 9.5 16S ribosomal RNA gene, k__Bacteria;p__Gemmatimonadetes;c__Gemmatimonadetes;o__Gemmatimonadales;f__Gemmatimonadaceae;g__Gemmatimonas;s__ 457 bits 251/253 0/253 denovo19676 253 ref|NR_074708.1 1527 (247) 3.00E-125 (99%) (0%) Gemmatimonas aurantiaca strain T-27 16S ribosomal RNA gene k__Bacteria;p__Gemmatimonadetes;c__Gemmatimonadetes;o__KD8-87;f__;g__;s__ 346 bits 231/253 0/253 denovo137988 253 gb|KF481682.1 1525 (187) 7.00E-92 (91%) (0%) Gemmatimonas sp. AP64 16S ribosomal RNA gene

k__Bacteria;p__Nitrospirae;c__Nitrospira;o__Nitrospirales;f__Nitrospiraceae;g__Nitrospira;s_ _ gb|AF155155.1|AF 407 bits 243/254 2/254 denovo37386 253 155155 1420 (220) 3.00E-110 (96%) (1%) Nitrospira cf. moscoviensis SBR2046 16S ribosomal RNA gene

k__Bacteria;p__Nitrospirae;c__Nitrospira;o__Nitrospirales;f__Nitrospiraceae;g__Nitrospira;s_ _ 468 bits 253/253 0/253 denovo138467 253 gb|KF724505.1 1499 (253) 2.00E-128 (100%) (0%) Nitrospira sp. BS10 16S ribosomal RNA gene 142

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Planctomycetes;c__Phycisphaerae;o__Phycisphaerales;f__;g__;s__ 357 bits 233/253 0/253 denovo37527 253 gb|HQ663340.1 450 (193) 3.00E-95 (92%) (0%) Planctomycetes bacterium SCGC AAA041-F18 16S small subunit ribosomal RNA gene k__Bacteria;p__Planctomycetes;c__Phycisphaerae;o__Phycisphaerales;f__;g__;s__ 468 bits 253/253 0/253 denovo128019 253 gb|HQ663340.1 450 (253) 2.00E-128 (100%) (0%) Planctomycetes bacterium SCGC AAA041-F18 16S small subunit ribosomal RNA gene k__Bacteria;p__Planctomycetes;c__Phycisphaerae;o__Phycisphaerales;f__;g__;s__ 257 bits 217/255 4/255 denovo43852 253 gb|HQ663340.1 450 (139) 4.00E-65 (85%) (2%) Planctomycetes bacterium SCGC AAA041-F18 16S small subunit ribosomal RNA gene k__Bacteria;p__Planctomycetes;c__Phycisphaerae;o__Phycisphaerales;f__;g__;s__ 268 bits 217/253 0/253 denovo63041 253 gb|HQ663340.1 450 (145) 2.00E-68 (86%) (0%) Planctomycetes bacterium SCGC AAA041-F18 16S small subunit ribosomal RNA gene k__Bacteria;p__Planctomycetes;c__Phycisphaerae;o__Phycisphaerales;f__;g__;s__ 296 bits 222/253 0/253 denovo42552 253 gb|HQ663340.1 450 (160) 8.00E-77 (88%) (0%) Planctomycetes bacterium SCGC AAA041-F18 16S small subunit ribosomal RNA gene k__Bacteria;p__Planctomycetes;c__Phycisphaerae;o__Phycisphaerales;f__;g__;s__ 307 bits 225/254 2/254 denovo51108 253 gb|HQ663340.1 450 (166) 3.00E-80 (89%) (1%) Planctomycetes bacterium SCGC AAA041-F18 16S small subunit ribosomal RNA gene k__Bacteria;p__Planctomycetes;c__Phycisphaerae;o__Phycisphaerales;f__;g__;s__ 268 bits 217/253 0/253 denovo91248 253 gb|HQ663340.1 450 (145) 2.00E-68 (86%) (0%) Planctomycetes bacterium SCGC AAA041-F18 16S small subunit ribosomal RNA gene k__Bacteria;p__Planctomycetes;c__Planctomycetia;o__Gemmatales;f__Gemmataceae;g__;s__ 318 bits 226/253 0/253 denovo108557 253 gb|GQ889484.1 1344 (172) 2.00E-83 (89%) (0%) Gemmata sp. Wa1-6 16S ribosomal RNA gene k__Bacteria;p__Planctomycetes;c__Planctomycetia;o__Gemmatales;f__Gemmataceae;g__;s__ 468 bits 253/253 0/253 denovo12523 253 gb|JF488132.1 826 (253) 2.00E-128 (100%) (0%) Planctomycetes bacterium SCGC AAA206-C13 16S ribosomal RNA gene 143

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Planctomycetes;c__Planctomycetia;o__Pirellulales;f__Pirellulaceae;g__;s__ 296 bits 222/253 0/253 denovo43987 253 gb|JF443760.1 1412 (160) 8.00E-77 (88%) (0%) Planctomycete DDSe1305 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria 272 bits 216/250 2/250 denovo9649 253 ref|NR_074118.1 1437 (147) 1.00E-69 (86%) (1%) Desulfomonile tiedjei strain DSM 6799 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria 344 bits 230/252 0/252 denovo133726 253 gb|KC878691.1 795 (186) 3.00E-91 (91%) (0%) Bdellovibrio sp. JS422 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Caulobacterales;f__Caulobacterace ae 468 bits 253/253 0/253 denovo16622 253 gb|KP072774.1 1054 (253) 2.00E-128 (100%) (0%) Brevundimonas nasdae strain PVL06 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Caulobacterales;f__Caulobacteraceae;g__;s__ 446 bits 249/253 0/253 denovo22317 253 gb|KF360053.1 1350 (241) 7.00E-122 (98%) (0%) Caulobacter sp. DS48-6-3 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Caulobacterales;f__Caulobacteraceae;g__Caulobacter;s__ 468 bits 253/253 0/253 denovo122431 253 gb|KM287535.1 900 (253) 2.00E-128 (100%) (0%) Caulobacter sp. MNA-Slr-4 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Caulobacterales;f__Caulobacteraceae;g__Mycoplana;s__ 468 bits 253/253 0/253 denovo127218 253 gb|KP072768.1 1091 (253) 2.00E-128 (100%) (0%) Brevundimonas subvibrioides strain PVS11 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales 460 bits 249/249 0/249 denovo34154 253 gb|HQ663587.1 402 (249) 3.00E-126 (100%) (0%) Alpha proteobacterium SCGC AAA278-N02 16S small subunit ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__;g__;s__ 468 bits 253/253 0/253 denovo113757 253 gb|HM124367.1 1341 (253) 2.00E-128 (100%) (0%) Hyphomicrobium sp. 16-60 16S ribosomal RNA gene 144

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__;g__;s__ 427 bits 243/249 0/249 denovo110384 253 gb|HQ663551.1 403 (231) 3.00E-116 (98%) (0%) Alpha proteobacterium SCGC AAA278-F22 16S small subunit ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Bradyrhizobiaceae;g__Bradyrhizobium;s__ 468 bits 253/253 0/253 denovo103976 253 gb|KF933598.1 1455 (253) 2.00E-128 (100%) (0%) Bradyrhizobium sp. ADU18 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Methylobacteriaceae;g__Methylobacterium;s__ 468 bits 253/253 0/253 denovo45500 253 gb|KM083547.1 1381 (253) 2.00E-128 (100%) (0%) Methylobacterium sp. W-NB-14 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodobacterales;f__Rhodobacterac eae 462 bits 252/253 0/253 denovo78571 253 gb|EU979473.1 1433 (250) 7.00E-127 (99%) (0%) Rhodobacter sp. CR07-5 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodobacterales;f__Rhodobacteraceae;g__Rhodobacter;s__ 468 bits 253/253 0/253 denovo34956 253 gb|HQ663371.1 730 (253) 2.00E-128 (100%) (0%) Alpha proteobacterium SCGC AAA041-P11 16S small subunit ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodospirillales 363 bits 235/254 2/254 denovo124817 253 gb|KM083695.1 756 (196) 7.00E-97 (93%) (1%) Oleomonas sp. O3 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodospirillales;f__Acetobacterace ae 468 bits 253/253 0/253 denovo70273 253 gb|HQ663072.1 410 (253) 2.00E-128 (100%) (0%) Alpha proteobacterium SCGC AAA024-G04 16S small subunit ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodospirillales;f__Acetobacterace ae 407 bits 243/254 1/254 denovo133072 253 gb|HQ663381.1 688 (220) 3.00E-110 (96%) (0%) Alpha proteobacterium SCGC AAA043-C09 16S small subunit ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodospirillales;f__Acetobacteraceae;g__;s__ 401 bits 241/253 0/253 denovo89763 253 gb|KF160606.1 707 (217) 2.00E-108 (95%) (0%) Bacterium 143_xplvp334 16S ribosomal RNA gene 145

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rickettsiales;f__Pelagibacteraceae;g__;s__ 468 bits 253/253 0/253 denovo41034 253 gb|JF488156.1 806 (253) 2.00E-128 (100%) (0%) Alpha proteobacterium SCGC AAA208-M13 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__;g__;s__ 468 bits 253/253 0/253 denovo60271 253 ref|NR_109465.1 1428 (253) 2.00E-128 (100%) (0%) Sphingorhabdus planktonica strain G1A_585 16S ribosomal RNA gene,

k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__Sphingomonadaceae;g__;s __ 468 bits 253/253 0/253 denovo125114 253 gb|JF297619.1 1188 (253) 2.00E-128 (100%) (0%) Sandarakinorhabdus limnophila strain 407 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__Sphingomonadaceae;g__Sphingomonas;s_ _ 468 bits 253/253 0/253 denovo71637 253 gb|CP009571.1 1479 (253) 2.00E-128 (100%) (0%) Sphingomonas taxi strain ATCC 55669, complete genome

k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__Sphingomonadaceae;g__Sphingomonas;s_ _ 468 bits 253/253 0/253 denovo65619 253 gb|KM272391.1 1359 (253) 2.00E-128 (100%) (0%) Sphingomonas mucosissima strain JS 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria 418 bits 244/253 0/253 denovo100437 253 gb|EU434484.1 1462 (226) 2.00E-113 (96%) (0%) Azoarcus sp. b303 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria 457 bits 251/253 0/253 denovo126640 253 dbj|AB529670.1 697 (247) 3.00E-125 (99%) (0%) Bacterium YO-16 gene for 16S ribosomal RNA k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__;f__;g__;s__ 401 bits 241/253 0/253 denovo16515 253 ref|NR_104816.1 1460 (217) 2.00E-108 (95%) (0%) Nitrosovibrio tenuis strain Nv1 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales 440 bits 248/253 0/253 denovo80937 253 gb|KJ878612.1 1217 (238) 3.00E-120 (98%) (0%) Herbaspirillum sp. ES2-54 16S ribosomal RNA gene 146

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales 424 bits 245/253 0/253 denovo90487 253 gb|JF488131.1 785 (229) 3.00E-115 (97%) (0%) Beta proteobacterium SCGC AAA206-C11 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__;g__;s__ 468 bits 253/253 0/253 denovo77822 253 gb|JF488131.1 785 (253) 2.00E-128 (100%) (0%) Beta proteobacterium SCGC AAA206-C11 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Alcaligenaceae;g__;s__ 435 bits 247/253 0/253 denovo41637 253 dbj|AB127733.1 526 (235) 2.00E-118 (98%) (0%) Bacterium PE03-7G14 gene for 16S rRNA k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Alcaligenaceae;g__;s__ 468 bits 253/253 0/253 denovo30377 253 gb|HQ663178.1 442 (253) 2.00E-128 (100%) (0%) Beta proteobacterium SCGC AAA027-K18 16S small subunit ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Alcaligenaceae;g__;s__ 446 bits 249/253 0/253 denovo124318 253 gb|KM079614.1 1417 (241) 7.00E-122 (98%) (0%) Bordetella sp. CLR2013-1 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadacea e 468 bits 253/253 0/253 denovo5131 253 gb|HQ663122.1 441 (253) 2.00E-128 (100%) (0%) Beta proteobacterium SCGC AAA027-C19 16S small subunit ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadacea e 424 bits 245/253 0/253 denovo55465 253 gb|GU572363.1 1349 (229) 3.00E-115 (97%) (0%) Leptothrix sp. B8 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadacea e 462 bits 252/253 0/253 denovo144236 253 gb|HQ601950.1 478 (250) 7.00E-127 (99%) (0%) Bacterium 5a.2 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Comamonas;s__ 468 bits 253/253 0/253 denovo9994 253 gb|KM067129.1 1484 (253) 2.00E-128 (100%) (0%) Comamonas sp. JB 16S ribosomal RNA gene 147

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Hydrogenophaga;s__ 468 bits 253/253 0/253 denovo41023 253 gb|KM199760.1 1400 (253) 2.00E-128 (100%) (0%) Hydrogenophaga sp. IDSBO-1 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Limnohabitans;s__ 462 bits 252/253 0/253 denovo6224 253 gb|HQ662997.1 431 (250) 7.00E-127 (99%) (0%) Beta proteobacterium SCGC AAA023-F15 16S small subunit ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Methylibium;s__ 468 bits 253/253 0/253 denovo16913 253 gb|JX402118.1 1527 (253) 2.00E-128 (100%) (0%) Methylibium sp. IFP 2052 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Polaromonas;s__ 468 bits 253/253 0/253 denovo33614 253 gb|KF295827.1 790 (253) 2.00E-128 (100%) (0%) Polaromonas sp. Z96 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Rhodoferax;s__ 468 bits 253/253 0/253 denovo95002 253 gb|KF441679.1 1525 (253) 2.00E-128 (100%) (0%) Albidiferax sp. 7B-403 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Rubrivivax;s__ 457 bits 251/253 0/253 denovo52929 253 dbj|AB529685.1 690 (247) 3.00E-125 (99%) (0%) Bacterium MI-1 gene for 16S ribosomal RNA k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Rubrivivax;s__ 468 bits 253/253 0/253 denovo128944 253 emb|FM886864.1 1219 (253) 2.00E-128 (100%) (0%) Comamonadaceae bacterium OTSZ_A_290 partial 16S rRNA gene, strain OTSZ_A_290 k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae 459 bits 248/248 0/248 denovo5870 253 gb|KJ396164.1 953 (248) 9.00E-126 (100%) (0%) Bacterium TC1_29 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae 424 bits 245/253 0/253 denovo53810 253 gb|KM056763.1 1393 (229) 3.00E-115 (97%) (0%) Pandoraea sp. sk44 16S ribosomal RNA gene 148

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae 462 bits 252/253 0/253 denovo80658 253 gb|JX154210.1 872 (250) 7.00E-127 (99%) (0%) Herbaspirillum sp. AR1(3) 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__;s__ 468 bits 253/253 0/253 denovo87451 253 gb|KM035973.1 1444 (253) 2.00E-128 (100%) (0%) Undibacterium sp. THG-DN7.3 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__Cupriavidus;s__ 468 bits 253/253 0/253 denovo52339 253 gb|KM083805.1 1423 (253) 2.00E-128 (100%) (0%) Cupriavidus sp. JPR10 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__Janthinobacterium 462 bits 252/253 0/253 denovo64399 253 emb|LK391529.1 1395 (250) 7.00E-127 (99%) (0%) Janthinobacterium lividum partial 16S rRNA gene, isolate S8 k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__Janthinobacterium;s__ 468 bits 253/253 0/253 denovo91005 253 gb|KF301576.1 1046 (253) 2.00E-128 (100%) (0%) Beta proteobacterium P21 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__Janthinobacterium;s__ 468 bits 253/253 0/253 denovo12219 253 gb|KP072772.1 1046 (253) 2.00E-128 (100%) (0%) Massilia varians strain PVL04 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__Polynucleobacter;s__ 468 bits 253/253 0/253 denovo90366 253 ref|NR_125545.1 1491 (253) 2.00E-128 (100%) (0%) Polynucleobacter acidiphobus strain MWH-PoolGreenA3 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Methylophilales;f__Methylophilaceae 462 bits 252/253 0/253 denovo9600 253 gb|KF911346.1 1372 (250) 7.00E-127 (99%) (0%) Methylophilus methylotrophus strain HME9441 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Methylophilales;f__Methylophilaceae;g__;s__ 440 bits 248/253 0/253 denovo2914 253 gb|KC577611.1 1313 (238) 3.00E-120 (98%) (0%) Methylotrophic bacterium RS-M7 16S ribosomal RNA gene 149

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Methylophilales;f__Methylophilaceae;g__;s__ 468 bits 253/253 0/253 denovo21631 253 dbj|AB529715.1 712 (253) 2.00E-128 (100%) (0%) Bacterium KW-40 gene for 16S ribosomal RNA k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Methylophilales;f__Methylophilaceae;g__;s__ 468 bits 253/253 0/253 denovo78952 253 gb|JF488150.1 846 (253) 2.00E-128 (100%) (0%) Beta proteobacterium SCGC AAA208-G15 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__MWH-UniP1;f__;g__;s__ 468 bits 253/253 0/253 denovo117551 253 emb|AJ565422.1 1448 (253) 2.00E-128 (100%) (0%) Beta proteobacterium MWH-UniP4 partial 16S rRNA gene, isolate MWH-UniP4 k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Nitrosomonadales;f__Nitrosomonadaceae 468 bits 253/253 0/253 denovo110742 253 gb|AY123800.1 1498 (253) 2.00E-128 (100%) (0%) Nitrosospira briensis 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Rhodocyclales;f__Rhodocyclaceae;g__Dechloromonas;s__ 468 bits 253/253 0/253 denovo53969 253 gb|KF441656.1 1530 (253) 2.00E-128 (100%) (0%) Ferribacterium sp. 7A-631 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Rhodocyclales;f__Rhodocyclaceae;g__Sulfuritalea ;s__ 446 bits 249/253 0/253 denovo101339 253 gb|HQ117920.1 521 (241) 7.00E-122 (98%) (0%) Sideroxydans sp. PN032 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__SBla14;f__;g__;s__ 468 bits 253/253 0/253 denovo142064 253 gb|DQ839562.1 1484 (253) 2.00E-128 (100%) (0%) Candidatus Nitrotoga arctica clone 6680 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__SC-I-84;f__;g__;s__ 435 bits 247/253 0/253 denovo59753 253 dbj|AB127737.1 624 (235) 2.00E-118 (98%) (0%) Bacterium PE03-7G18 gene for 16S rRNA k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria 307 bits 228/257 8/257 denovo10808 253 dbj|AB081539.1 540 (166) 3.00E-80 (89%) (3%) Bacterium Oil-M2-6 gene for 16S rRNA 150

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__[Entotheonellales];f__;g__;s__ 309 bits 221/247 4/247 denovo99115 253 ref|NR_074350.1 1494 (167) 1.00E-80 (89%) (2%) Candidatus Koribacter versatilis Ellin345 strain Ellin345 16S ribosomal RNA

k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Bdellovibrionales;f__Bacteriovoraca ceae 429 bits 246/253 0/253 denovo93422 253 gb|AY294224.1 613 (232) 7.00E-117 (97%) (0%) Bacteriovorax sp. PC2 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Bdellovibrionales;f__Bacteriovoracaceae;g__;s__ 429 bits 246/253 0/253 denovo93230 253 gb|AY294224.1 613 (232) 7.00E-117 (97%) (0%) Bacteriovorax sp. PC2 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Bdellovibrionales;f__Bacteriovoracaceae;g__;s__ 363 bits 235/254 2/254 denovo22541 253 gb|JF488363.1 799 (196) 7.00E-97 (93%) (1%) Delta proteobacterium SCGC AAA166-N08 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Bdellovibrionales;f__Bacteriovoracaceae;g__Peredibacter;s__star rii 418 bits 244/253 0/253 denovo140831 253 gb|GQ179642.1 813 (226) 2.00E-113 (96%) (0%) Bdellovibrio sp. SD2GS 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Desulfobacterales;f__Desulfobulbaceae;g__Desulfobulbus;s__ 451 bits 250/253 0/253 denovo57283 253 emb|AJ012591.1 1449 (244) 2.00E-123 (99%) (0%) sulfate-reducing bacterium R-PropA1 16S rRNA gene k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Myxococcales 401 bits 241/253 0/253 denovo14225 253 gb|AY146669.1 456 (217) 2.00E-108 (95%) (0%) Delta proteobacterium K18 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Myxococcales;f__OM27;g__;s__ 335 bits 229/253 0/253 denovo27423 253 emb|HF543825.1 1547 (181) 2.00E-88 (91%) (0%) Kofleria flava partial 16S rRNA gene, strain DSM:14620, clone K2 k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Myxococcales;f__OM27;g__;s__ 268 bits 219/255 4/255 denovo61897 253 gb|KM088092.1 1428 (145) 2.00E-68 (86%) (2%) Cronobacter sp. BYP 16S ribosomal RNA gene 151

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Myxococcales;f__OM27;g__;s__ 267 bits 216/252 0/252 denovo88868 253 dbj|AB246771.1 1236 (144) 6.00E-68 (86%) (0%) Myxobacterium AT1-01 gene for 16S rRNA k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Spirobacillales;f__;g__;s__ 451 bits 250/253 0/253 denovo42656 253 gb|EU220836.1 1394 (244) 2.00E-123 (99%) (0%) Spirobacillus cienkowskii 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria 440 bits 248/253 0/253 denovo72827 253 gb|AF531001.1 563 (238) 3.00E-120 (98%) (0%) Bacteroidetes bacterium zo30 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Alteromonadales;f__[Chromatiaceae];g__Rheinheimera;s__ 468 bits 253/253 0/253 denovo65909 253 gb|KM220013.1 738 (253) 2.00E-128 (100%) (0%) Rheinheimera tangshanensis strain AO 0117 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Alteromonadales;f__125ds10;g__; s__ 379 bits 238/254 2/254 denovo104321 253 gb|GU124598.1 1401 (205) 7.00E-102 (94%) (1%) Haliea sp. SY02 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Alteromonadales;f__211ds20;g__; s__ 401 bits 241/253 0/253 denovo142444 253 gb|EU346444.1 847 (217) 2.00E-108 (95%) (0%) Marine sponge bacterium FILTER11C104m 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Alteromonadales;f__Alteromonadaceae;g__Cellvibrio;s__ 446 bits 249/253 0/253 denovo45335 253 gb|EF692635.1 1529 (241) 7.00E-122 (98%) (0%) Cellvibrio sp. KY-YJ-2 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Alteromonadales;f__OM60 446 bits 249/253 0/253 denovo38079 253 gb|GU124598.1 1401 (241) 7.00E-122 (98%) (0%) Haliea sp. SY02 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Alteromonadales;f__OM60;g__;s __ 440 bits 248/253 0/253 denovo94562 253 gb|EU346595.1 766 (238) 3.00E-120 (98%) (0%) Marine sponge bacterium PLATEsixfor-(1)-24 16S ribosomal RNA gene 152

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacteriales;f__Enterobacteriaceae 468 bits 253/253 0/253 denovo85257 253 dbj|LC008364.1 1465 (253) 2.00E-128 (100%) (0%) Rahnella sp. M107 gene for 16S ribosomal RNA k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Methylococcales;f__Crenotrichaceae;g__Crenothrix;s__ 418 bits 245/254 1/254 denovo96024 253 gb|HQ290497.1 817 (226) 2.00E-113 (96%) (0%) Bacterium SCGC AAA018-I2 small subunit ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Oceanospirillales;f__Halomonadaceae;g__Halomonas;s__ 468 bits 253/253 0/253 denovo90578 253 gb|KJ956967.1 921 (253) 2.00E-128 (100%) (0%) Halomonas sp. MCCC1A08148 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellace ae 462 bits 252/253 0/253 denovo142705 253 gb|EU636038.1 1402 (250) 7.00E-127 (99%) (0%) Antarctic bacterium 3C8 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellace ae 433 bits 247/253 1/253 denovo136329 253 gb|JF715439.1 1369 (234) 5.00E-118 (98%) (0%) Acinetobacter sp. IMCC12751 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__ 468 bits 253/253 0/253 denovo128316 253 gb|KM349497.1 847 (253) 2.00E-128 (100%) (0%) Acinetobacter junii strain LFN3 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__ 468 bits 253/253 0/253 denovo105099 253 emb|LM994723.1 933 (253) 2.00E-128 (100%) (0%) Acinetobacter radioresistens genomic DNA containing 16S-23S intergenic spacer region, strain PuiC5.3 k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__ 468 bits 253/253 0/253 denovo3527 253 gb|KF441705.1 1520 (253) 2.00E-128 (100%) (0%) Acinetobacter sp. 7B-866 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__guillouiae 453 bits 247/248 0/248 denovo821 253 gb|KJ806412.1 1385 (245) 4.00E-124 (99%) (0%) Acinetobacter soli strain S-X6A 16S ribosomal RNA gene 153

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__lwof fii 453 bits 247/248 0/248 denovo30463 253 gb|KP072748.1 1069 (245) 4.00E-124 (99%) (0%) Acinetobacter lwoffii strain PVR03 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Alkanindiges;s__ 453 bits 247/248 0/248 denovo35378 253 ref|NR_025254.1 1533 (245) 4.00E-124 (99%) (0%) Alkanindiges illinoisensis strain MVAB Hex1 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Pseudomonadaceae;g__Pseudomonas 462 bits 252/253 0/253 denovo114409 253 gb|GQ169380.1 1438 (250) 7.00E-127 (99%) (0%) Pseudomonas thivervalensis strain IEHa 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Pseudomonadaceae;g__Pseudomonas 468 bits 253/253 0/253 denovo49407 253 gb|KF747045.1 700 (253) 2.00E-128 (100%) (0%) Pseudomonas sp. MY-CA99-1 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Pseudomonadaceae;g__Pseudomonas;s__fragi 468 bits 253/253 0/253 denovo56232 253 emb|LN624760.1 1451 (253) 2.00E-128 (100%) (0%) Pseudomonas sp. BSTT44 partial 16S rRNA gene, strain BSTT44 k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Thiotrichales;f__Piscirickettsiaceae;g__;s__ 379 bits 237/253 0/253 denovo140050 253 gb|HQ675407.1 430 (205) 7.00E-102 (94%) (0%) Gamma proteobacterium SCGC AAA003-F05 small subunit ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Vibrionales;f__Pseudoalteromonadaceae;g__Pseudoalteromonas;s__ 468 bits 253/253 0/253 denovo7680 253 emb|LK391521.1 1416 (253) 2.00E-128 (100%) (0%) Pseudoalteromonas marina partial 16S rRNA gene, isolate M7 k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Sinobacteraceae 418 bits 244/253 0/253 denovo113512 253 dbj|AB819626.1 1531 (226) 2.00E-113 (96%) (0%) Pseudomonas sp. VT1B gene for 16S ribosomal RNA k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Sinobacteraceae;g__;s__ 407 bits 242/253 0/253 denovo132633 253 gb|KF595153.1 1468 (220) 3.00E-110 (96%) (0%) Steroidobacter sp. JC2953 16S ribosomal RNA gene 154

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae 446 bits 249/253 0/253 denovo67787 253 emb|HE616177.1 1467 (241) 7.00E-122 (98%) (0%) Arenimonas sp. PYM3-14 partial 16S rRNA gene, isolate PYM3-14 k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae 462 bits 252/253 0/253 denovo95020 253 gb|KC921149.1 1380 (250) 7.00E-127 (99%) (0%) Lysobacter sp. LWQ61 16S ribosomal RNA gene

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__; s__ 457 bits 251/253 0/253 denovo9365 253 gb|KF746926.1 1505 (247) 3.00E-125 (99%) (0%) Arenimonas sp. YT8 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Dokdonella;s__ 468 bits 253/253 0/253 denovo96911 253 gb|KF378756.1 1429 (253) 2.00E-128 (100%) (0%) Dokdonella ginsengisoli strain CP14 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Stenotrophomonas;s__ 468 bits 253/253 0/253 denovo31144 253 gb|JQ073893.1 1228 (253) 2.00E-128 (100%) (0%) Stenotrophomonas sp. Am049 16S ribosomal RNA gene k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Thermomonas;s__fusca 468 bits 253/253 0/253 denovo23724 253 dbj|AB578884.1 1475 (253) 2.00E-128 (100%) (0%) Gamma proteobacterium 7-13 gene for 16S rRNA k__Bacteria;p__Proteobacteria;c__TA18;o__PHOS-HD29;f__;g__;s__ 311 bits 190/201 0/201 denovo87087 253 gb|FJ460151.1 557 (168) 3.00E-81 (95%) (0%) Actinobacterium MS-B-41 16S ribosomal RNA gene k__Bacteria;p__Verrucomicrobia 322 bits 225/250 2/250 denovo118963 253 gb|HQ663696.1 425 (174) 1.00E-84 (90%) (1%) Verrucomicrobia bacterium SCGC AAA487-O09 16S small subunit ribosomal RNA gene k__Bacteria;p__Verrucomicrobia 318 bits 226/253 0/253 denovo97164 253 gb|JF488114.1 862 (172) 2.00E-83 (89%) (0%) Verrucomicrobia bacterium SCGC AAA204-G18 16S ribosomal RNA gene 155

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length

k__Bacteria;p__Verrucomicrobia;c__[Methylacidiphilae];o__Methylacidiphilales;f__LD19;g__; s__ 468 bits 253/253 0/253 denovo129321 253 gb|GU936927.1 1486 (253) 2.00E-128 (100%) (0%) Chlorophyta symbiont of Lubomirskia sp. isolate R54 16S ribosomal RNA gene, partial sequence; chloroplast

k__Bacteria;p__Verrucomicrobia;c__[Methylacidiphilae];o__Methylacidiphilales;f__LD19;g__; s__ 468 bits 253/253 0/253 denovo121122 253 gb|HQ663079.1 428 (253) 2.00E-128 (100%) (0%) Verrucomicrobia bacterium SCGC AAA024-I18 16S small subunit ribosomal RNA gene

k__Bacteria;p__Verrucomicrobia;c__[Pedosphaer ae] 381 bits 237/253 0/253 denovo117953 253 gb|JF488102.1 787 (206) 2.00E-102 (94%) (0%) Verrucomicrobia bacterium SCGC AAA204-A11 16S ribosomal RNA gene

k__Bacteria;p__Verrucomicrobia;c__[Pedosphaer ae] 324 bits 227/253 0/253 denovo57669 253 dbj|AB360418.1 1296 (175) 3.00E-85 (90%) (0%) Bacterium RS25A gene for 16S ribosomal RNA k__Bacteria;p__Verrucomicrobia;c__[Pedosphaerae];o__[Pedosphaerales] 401 bits 241/253 0/253 denovo69999 253 gb|AY960777.1 1443 (217) 2.00E-108 (95%) (0%) Bacterium Ellin514 16S ribosomal RNA gene

k__Bacteria;p__Verrucomicrobia;c__[Pedosphaerae];o__[Pedosphaerales];f__auto67_4W;g__;s __ 435 bits 247/253 0/253 denovo84854 253 gb|HQ290510.1 830 (235) 2.00E-118 (98%) (0%) Bacterium SCGC AAA018-N22 small subunit ribosomal RNA gene k__Bacteria;p__Verrucomicrobia;c__[Pedosphaerae];o__[Pedosphaerales];f__Ellin515;g__;s__ 424 bits 245/253 0/253 denovo112499 253 gb|AY234519.1 1447 (229) 3.00E-115 (97%) (0%) Bacterium Ellin5102 16S ribosomal RNA gene k__Bacteria;p__Verrucomicrobia;c__[Pedosphaerae];o__[Pedosphaerales];f__R4-41B;g__;s__ 363 bits 234/253 0/253 denovo52336 253 gb|HQ290510.1 830 (196) 7.00E-97 (92%) (0%) Bacterium SCGC AAA018-N22 small subunit ribosomal RNA gene k__Bacteria;p__Verrucomicrobia;c__[Pedosphaerae];o__[Pedosphaerales];f__R4-41B;g__;s__ 468 bits 253/253 0/253 denovo47251 253 gb|JF488119.1 862 (253) 2.00E-128 (100%) (0%) Verrucomicrobia bacterium SCGC AAA204-K10 16S ribosomal RNA gene 156

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length

k__Bacteria;p__Verrucomicrobia;c__[Spartobacteria];o__[Chthoniobacterales];f__[Chthoniobacteraceae];g_ _;s__ 438 bits 245/249 0/249 denovo37961 253 gb|HQ663667.1 430 (237) 1.00E-119 (98%) (0%) Verrucomicrobia bacterium SCGC AAA487-F13 16S small subunit ribosomal RNA gene

k__Bacteria;p__Verrucomicrobia;c__[Spartobacteria];o__[Chthoniobacterales];f__[Chthoniobacteraceae];g__Candidatus Xiphinematobacter;s__ 357 bits 234/254 2/254 denovo126815 253 gb|GU129926.1 1418 (193) 3.00E-95 (92%) (1%) Spartobacteria bacterium NM5 16S ribosomal RNA gene

k__Bacteria;p__Verrucomicrobia;c__[Spartobacteria];o__[Chthoniobacterales];f__[Chthoniobacteraceae];g__Candidatus Xiphinematobacter;s__ 433 bits 244/249 0/249 denovo3696 253 gb|HQ663543.1 421 (234) 5.00E-118 (98%) (0%) Verrucomicrobia bacterium SCGC AAA278-E15 16S small subunit ribosomal RNA gene

k__Bacteria;p__Verrucomicrobia;c__[Spartobacteria];o__[Chthoniobacterales];f__[Chthoniobacteraceae];g__Chthoniobacte r;s__ 451 bits 250/253 0/253 denovo29217 253 gb|AY960770.1 1448 (244) 2.00E-123 (99%) (0%) Bacterium Ellin507 16S ribosomal RNA gene

k__Bacteria;p__Verrucomicrobia;c__Opitutae;o__[Cerasicoccales];f__[Cerasicoccaceae];g__;s_ _ 453 bits 250/253 1/253 denovo89761 253 gb|HQ663618.1 409 (245) 4.00E-124 (99%) (0%) Verrucomicrobia bacterium SCGC AAA280-C19 16S small subunit ribosomal RNA gene

k__Bacteria;p__Verrucomicrobia;c__Opitutae;o__[Cerasicoccales];f__[Cerasicoccaceae];g__;s_ _ 468 bits 253/253 0/253 denovo52295 253 gb|HQ663236.1 437 (253) 2.00E-128 (100%) (0%) Verrucomicrobia bacterium SCGC AAA028-D03 16S small subunit ribosomal RNA gene k__Bacteria;p__Verrucomicrobia;c__Opitutae;o__Opitutales;f__Opitutaceae;g__;s__ 429 bits 246/253 0/253 denovo78194 253 gb|EF636029.1 916 (232) 7.00E-117 (97%) (0%) Verrucomicrobia bacterium TP675 16S ribosomal RNA gene k__Bacteria;p__Verrucomicrobia;c__Opitutae;o__Opitutales;f__Opitutaceae;g__Opitutus;s__ 424 bits 245/253 0/253 denovo104760 253 gb|EF636028.1 712 (229) 3.00E-115 (97%) (0%) Verrucomicrobia bacterium TP376 16S ribosomal RNA gene k__Bacteria;p__Verrucomicrobia;c__Opitutae;o__Opitutales;f__Opitutaceae;g__Opitutus;s__ 468 bits 253/253 0/253 denovo117286 253 gb|HQ341784.1 748 (253) 2.00E-128 (100%) (0%) Verrucomicrobia bacterium WD61 16S ribosomal RNA gene 157

Query Subj RDP taxonomy/OTU/Top BLAST Hit ID Score Expect Percent ID Gaps Length Length

k__Bacteria;p__Verrucomicrobia;c__Verrucomicrobiae;o__Verrucomicrobiales;f__Verrucomicrobiaceae;g__Luteolibacter;s __ 440 bits 248/253 0/253 denovo32185 253 ref|NR_109500.1 1444 (238) 3.00E-120 (98%) (0%) Luteolibacter luojiensis strain DR4-30 16S ribosomal RNA gene

k__Bacteria;p__Verrucomicrobia;c__Verrucomicrobiae;o__Verrucomicrobiales;f__Verrucomicrobiaceae;g__Luteolibacter;s __ 446 bits 249/253 0/253 denovo141573 253 dbj|AB677319.1 1485 (241) 7.00E-122 (98%) (0%) Luteolibacter algae gene for 16S rRNA, , strain: H18

k__Bacteria;p__Verrucomicrobia;c__Verrucomicrobiae;o__Verrucomicrobiales;f__Verrucomicrobiaceae;g__Luteolibacter;s __ 457 bits 251/253 0/253 denovo59044 253 emb|FN554389.1 1323 (247) 3.00E-125 (99%) (0%) Verrucomicrobiaceae bacterium CHC8 partial 16S rRNA gene, strain CHC8

k__Bacteria;p__Verrucomicrobia;c__Verrucomicrobiae;o__Verrucomicrobiales;f__Verrucomicrobiaceae;g__Prosthecobacte r;s__ 451 bits 250/253 0/253 denovo10162 253 emb|AJ966883.1 5039 (244) 2.00E-123 (99%) (0%) Prosthecobacter vanneervenii strain DSM 12252