Supplemental Methods s7

1Supplemental Methods:

2 To determine whether our MEN comprised a non-random network, we compared the entire

3network, and the entire network minus the Archaea, to 100 randomly generated networks (Erdos-

4Renyi model; Erdös & Rényi 1959) of similar size using the Random Network tool in Cytoscape.

5The observed clustering coefficient and mean shortest path length (0.88 and 3.53, respectively)

6for the entire network including Archaea, were greater than the clustering coefficient and mean

7shortest path length for the random networks (0.06  4.0x10-4 and 1.97  4.8 x 10-4, respectively)

8indicating that our MEN was more organized than would be expected of a random network of

9similar size. Removal of the Archaea from the network increased the clustering coefficient to

100.89 and decreased the mean shortest path length to 3.39. Our clustering coefficients and mean

11shortest path lengths (with and without Archaea) were also greater than that observed for a three-

12domain association network from the open ocean (Steele et al. 2011). Similar to the results found

13by Steele et al. (2011), our network also exhibited the small world pattern (Watts & Strogatz

141998), which is characterized by few, highly connected nodes and expected of a non-random

15ecological network. Given the significant C-score for the complete dataset and the minor

16variation in path length and clustering coefficient between the total network and network minus

17Archaea, we chose to continue our analysis with the entire dataset, including the Archaea.

18 Module identification algorithms and/or the corresponding modularity metric (Q or M)

19have been used to identify important groups of nodes in pollinator networks (M = 0.52; Olesen

20et al. 2007), soil microbial communities (Q = 0.77; Barberán et al. 2012), the yeast interactome

21(Q and M not calculated; Kovács et al. 2010), and farm food webs (M = 0.70 to 0.88; Macfadyen

22et al. 2011). We calculated modularity for our complete network, using the ClusterMaker plugin

23(Morris et al. 2011) for Cytoscape, based on four different modularity methods: Markov Cluster

1 i 24Algorithm (MCL; Enright et al. 2002), GLay (Su et al. 2010), Connected Components Cluster

25(CCC; Morris et al. 2011), and Spectral Clustering of Protein Sequences (SCPS; Nepusz et al.

262010). These methods cover each of the classes of modularity algorithms discussed by Fortunato

27(2010). All four methods revealed modularity scores > 0.8, indicating that the FRX and WLB

28microbial communities have a modular structure (where a module is a group of connected nodes;

29Table 1), allowing us to use modules to simplify our MEN. To visualize the modules in our

30MEN, we used the ModuLand plugin (Szalay-Beko et al. 2012) for Cytoscape. The ModuLand

31calculation resulted in 27 modules ranging in size from 100 nodes with 705 connections to 2

32nodes with a single connection (Table S3 and Figure S2). ModuLand used the name of the node

33(OTU) with the maximum assignment value to each module as the module key. We subsequently

34assigned each module a number and both designations are used in the text (Table 2, Figure 5,

35Table S4).

36 The ClusterMaker plugin does not generate diagrams of modules, or provide information

37about the nodes contained in each module. In order to describe the modules, we used the

38ModuLand (Szalay-Beko et al. 2012) package in Cytoscape. ModuLand does not determine

39module structure based on modularity scores, and therefore does not calculate modularity scores;

40rather, it is based on a 3-D calculation of the community network where “hills” in the 3-D

41landscape (nodes with greater influence over the network structure) correspond to network

42modules. Details of the modularity calculations are provided by Kovács et al. (2010).

2 ii 47Supplemental Discussion of Significant Modules:

48 Module 22 contained nodes apparently related to the cycling of C1 compounds, including

49the most abundant putatively methanogenic OTUs (Archaea_03_102 and Archaea_03_50) in the

50total dataset, three bacterial OTUs related to candidate division OD1 and one related to candidate

51division OP9, which have been putatively connected to methane cycling (Orphan et al. 2001,

52Perua et al. 2012), five putatively methylotrophic lineages and one member of the

53Syntrophaceae, which are known to form syntrophic partnerships with hydrogenotrophic

54methanogens. Putative methanogens were most abundant at the deep chlorophyll maxima of both

55lakes (DCM; FRX 9 m and WLB 13 m) and decreased between summer and autumn (9.5% to

564.3% [FRX] and 18.0% to 10.2% [WLB]). Karl et al. (2008) suggested that aerobic methane

57production may be a side-effect of phosphorus limitation in surface seawater, while Grossart et

58al. (2011), and Damm et al. (2010) suggested a connection between by-products of

59photoautotrophy and methane production. High levels of particulate dimethylsulfoniopropionate

60(DMSP) at the WLB DCM (32 nmol L-1; Lee et al. 2004) may serve as a substrate or precursor to

61methanogenesis, via its degradation product, methanethiol (Kiene 1996; Damm et al. 2010).

62 Module 25 (BC = 0.1), which contained the highest abundance of putative methanogens

63after Module 22, also contained methylotrophic lineages that may be able to use dimethylsulfide

64(DMS) as a carbon source. Unlike DMSP, DMS concentrations are nearly below detection at the

65WLB DCM (Lee et al. 2004), perhaps because of quick utilization by methylotrophs. Members

66of the genus Loktanella, which are present in Module 25, have been shown to cleave DMSP to

67DMS and acrylate and genes for DMSP-degrading enzymes are widespread in other

68Alphaproteobacteria (see review by Moran et al. 2012). Modules 22 and 25 may have important

69roles in linking carbon and sulfur biogeochemistry in the MCM lakes.

3 iii 70 Module 19 (BC=0.04; key = Bacteroidetes_03_168, a member of the Crocibacter) is

71comprised primarily of heterotrophic bacteria, mainly of the Bacteroidetes and the Alpha and

72Gamma clades of the Proteobacteria in association with a few heterotrophic and phototrophic

73eukaryotic OTUs. Members of the Flavobacteria have been implicated in East Antarctic

74Southern Ocean processing of algal organic matter, which, once broken down, is then utilized by

75Alpha- and Gammaproteobacteria (Williams et al. 2012). Within the module, the nodes

76Gammaproteobacteria_03_1630 (Legionella), Verrucomicrobia_03_57 (Chthoniobacter), and

77Eukarya_06_2105 (Cyclonexis) formed a three-way positive interaction with no direct

78connection to any other members of the module. This three-way interaction is an example of a

79potential close association between a primary producer (Cyclonexis) and two heterotrophic

80bacterial OTUs. Cultivated members of the Chthoniobacter are known to grow on plant-related

81saccharides (Sangwan et al. 2004), and may break down organic matter excreted by Cyclonexis.

82Legionalla pneumophila has been found living in close association with cyanobacteria,

83apparently utilizing photosynthetic exudates as carbon and energy sources (Tison et al. 1980),

84but other studies with Legionella have shown that it does not effectively utilize large organic

85molecules (Chien et al. 2004). We suggest that this three-way interaction is bound by an initial

86breakdown of Cyclonexis-related organic matter by Chthoniobacter, followed by utilization of

87smaller molecules by Legionella.

88 Module 20 (BC = 0.04; Key = Bacteroidetes_03_262) was characterized by tightly

89connected groups of eukaryotic and bacterial OTUs, 52% of which showed positive interactions

90between heterotrophic bacterial OTUs and phototrophic OTUs. The heterotrophic guilds were

91highly diverse, with OTUs from 8 phyla (Table S4), and differentiating their putative functions

92was not possible. Three phototrophic OTUs were represented in the module (Eukarya_06_1327

4 iv 93[Micractinium; GAST = 0.03], Eukarya_06_1388 [Goniochloris; GAST = 0.13], and Euk_9221

94[Nannochloropsis;GAST = 0.06]). Micractinium (a chlorophyte) primarily interacted with

95members of the Bacteroidetes, followed by the Plantomycetes and Verrucomicrobia, while the

96Eustigmatophytes, Nannochloropsis and Goniochloris primarily interacted with

97Alphaproteobacteria, suggesting either preferential feeding on exudates from different

98phytoplankton, or similar responses to environmental conditions.

99 Module 27 (BC = 0.005; Key = Verrucomicrobia_03_119) contained only five OTUs,

100two Bacteroidetes, one Actinobacteria, one Verrucomicrobia, and one eukaryote related to the

101frequent metazoan symbionts and parasites, the Apostomatia. Except for Bacteroidetes_03_457

102(Algoriphagus) all of the bacterial OTUs are likely to be at least facultatively anaerobic and

103capable of fermentation. It is possible that Module 27 reflects a connection to the FRX and WLB

104metazoan communities, of which little is known. Sequence data from the MIRADA project

105suggested the presence of a few Maxillopoda and Brachiopoda.

106 Module 18, (BC = 0.07) contained mostly heterotrophic bacteria, and was dominated by

107OTUs thought be capable of flexibility in substrate utilization (the module key is Flexibacter,

108GAST = 0.0188), including many members of the Flavobacteria, which are known to degrade

109large carbon compounds, making smaller molecules available as substrate for other heterotrophs

110(Williams et al 2012).

111 112 113 114 115 116 117 118 119 120

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