1
Supporting Online Material
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Supplementary Methods
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Sampling
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Seawater samples were collected on 72 occasions from January 2003-December 2008 from the
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L4 sampling site (50° 15.00' N, 4° 13.02') of the Western Channel Observatory
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(http://www.westernchannelobservatory.org.uk; Table S1, S2, S3). Nineteen environmental
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variables were obtained from the WCO database, available on the website; these included surface
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temperature, salinity, water density, silicate concentration, nitrate + nitrite concentration,
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chlorophyll
concentration,
total
organic
nitrogen
(TON)
concentration,
ammonium
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concentration, soluble reactive phosphorus (SRP) concentration, total organic carbon (TOC)
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concentration, photo-active radiation (PAR), phytoplankton and zooplankton taxa abundances,
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etc. Other variables measured were flow cytometric determination of bacterial abundance,
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microscope counts of cryptophyte, picoeukaryote, nanoeukaryote, coccolithophore and small
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dinoflagellate abundance. All methods for determining these variables are available on the WCO
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website (http://www.westernchannelobservatory.org.uk/all_parameters.html) and are detailed in
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(Widdicombe, Eloire et al. 2010).
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DNA extraction, 16S rDNA V6 amplification and Pyrosequencing
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Nucleic acid was extracted from 5 L seawater, collected from the surface and filtered
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immediately through a 0.22 µm Sterivex cartridge (Millipore), which was stored at -80 °C. DNA
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was isolated from each sample (Neufeld, Schafer et al. 2007) and then stored at -20 °C. DNA
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was used as a template for V6-region 16S rRNA amplification using the method of Huber et al.
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(Huber, Mark Welch et al. 2007) in sets of twelve. Each amplicon in set of twelve was labeled
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with a unique multiplex identifier (MID) sequence (Table S8). Subsequently, amplicon product
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pools for each year were pyrosequenced on ½ of a 454 pico-titre plate using the GS-flx platform.
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All sequences have been submitted to the NCBI short reads archive under SRA009436, and
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registered with the GOLD database (Gm00104). All data submitted are MIMARKS compliant
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(Yilmaz, Kottmann et al. 2010).
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Denoising or preclustering of pyrosequencing data
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From the complete raw dataset of 747,496 sequences, there were 65,059 unique
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sequences (100% nucleotide identity clustering (NIC)). To compensate for potential
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overestimation of diversity using pyrosequencing (Quince, Lanzen et al. 2009), the data set was
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further treated using a sequence clustering approach called Single Linkage Preclustering (SLP
35
(Huse, Welch et al. 2010)), and an algorithm based sff clustering approach called Denoiser V
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0.851 (Reeder and Knight 2010). Denoiser clusters OTUs at 3% sequence identity following
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alignment and clustering of the flowgrams. SLP uses a highly conservative 2% single-linkage
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pre-clustering approach, followed by an average-linkage clustering based on pair-wise
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alignments. From the combined 72 datasets, Denoiser produced 21,129 OTUs, while the
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considerably more conservative SLP resulted in 8,794 OTUs. In this study, the smallest dataset
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comprised 4101 sequences, whereas the largest dataset comprised 8 times the number of
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sequences (32,826). This means that the larger dataset had eight times the number of
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observations and hence could identify more novel OTUs purely on the basis of sample size.
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Therefore, to enable accurate analysis of species richness and diversity estimates between
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samples each dataset was randomly-resampled to the smallest sequencing effort using
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daisychopper (Gilbert, Field et al. 2009). Following random re-sampling and reanalysis using the
2
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denoising strategies, SLP generated 4,204 OTUs and Denoiser generated 10,215 OTUs. In both
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datasets, ~50% of the OTUs were represented by only a single sequence (singletons).
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Operational Taxonomic Unit picking and annotation
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Annotation of data was performed using the Visualization and Analysis of Microbial
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Population Structure project (VAMPS) website (http://vamps.mbl.edu/index.php). The VAMPS
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workflow was used to create a profile of the unique sequences, their taxonomic assignment and
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their abundance in each sample. OTUs in the each dataset were picked and annotated with the
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GAST process (Sogin, Morrison et al. 2006), which is available through the VAMPS website
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(http://vamps.mbl.edu/index.php). This process was used for all analyses in the study. However,
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OTU prediction was also performed on the Denoiser treated data to provide a comparison (Fig.
58
S1a). Following denoising with Denoiser v0.851, OTUs were picked using uclust (Edgar 2010)
59
version q1.2.15 at 97% sequence identity (maxaccepts=8, maxrejects=32). Representative
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sequences for each OTU were aligned with PyNAST 1.1 (Caporaso, Bittinger et al. 2010) (with
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min_length=50 to support the short read lengths), and a tree was built using FastTree (Price,
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Dehal et al. 2009). Unweighted UniFrac distances were calculated between samples using the
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FastTree tree and 5000 randomly selected sequences were used per sample to control for
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differences in UniFrac distances which might arise due to differences in sampling (sequencing)
65
depth. Principle Coordinates Analysis (PCoA) was applied to the UniFrac distance matrices, and
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plotted with an explicit time axis in units of days. Data analysis was performed using the
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Quantitative Insights Into Microbial Ecology (QIIME) toolkit (Caporaso, Kuczynski et al. 2010).
68
69
Analysis of OTU richness dynamics
3
70
Changes in community structure through time were analysed using nonparametric multivariate
71
methods (Clarke 1993) implemented in Primer v 6 (Clarke and Gorley 2006). Resampled
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abundances of OTUs (however defined) and subsets of OTUs (Proteobacteria, Bacterioidetes,
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Cyanobacteria, all other phyla combined) were Log(N+1)-transformed, to downweight
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contributions to intersample resemblances from the few most numerically abundant OTUs.
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Intersample resemblances (Clarke and Gorley 2006) were calculated using the Bray-Curtis
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similarity measure (Somerfield 2008) and the resemblance matrices ordinated using nonmetric
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multidimensional scaling (MDS). To examine the relative effects of seasonal and interannual
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variability samples were grouped into seasons (Winter: December, January, February; Spring:
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March, April, May; Summer: June, July, August; Autumn: September, October, November) and
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changes in community structure were analysed with 2-way permutation-based analysis of
81
variance (McArdle and Anderson 2001; Anderson, Gorley et al. 2008) using Season and Year as
82
factors, type III (partial) sums of squares and 999 permutations of residuals under a reduced
83
model.
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Two univariate measures of community structure, the number of different OTUs (S) and
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Simpson’s evenness index 1-’, were calculated for each sample. Variation in these indices, and
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environmental variables, were analysed using multiple linear regression (MLR) to determine
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temporal patterns.
88
regression on Serial Day (D), the number of days from 01/01/2003, a term representing a
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seasonal cycle mirroring day-length, peaking in midwinter (DX1 = cos(2(d/365)), where d is
90
the number of days from the 20th December, the shortest day in the previous year) and a further
91
orthogonal term to determine the nature of cycles with peaks at other times of the year (DX2 =
92
sin(2(d/365))).
Temporal trends and the form of seasonal cycles were determined by
Univariate measures were further regressed on a range of (sometimes
4
93
transformed) explanatory variables using stepwise MLR.
The relative goodness of fit of
94
different regression models was assessed using the Akaike Information Criterion (AIC). When
95
necessary, single variables were transformed to normalise residuals. Changes in community
96
structure were also analysed using stepwise distance-based linear modelling of variation among
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Bray-Curtis similarities. Analyses were performed using MINITAB v. 13 and the distance-based
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linear modelling (DistLM) module within PERMANOVA+ v. 1 add-in for PRIMER v. 6
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(McArdle and Anderson 2001; Anderson, Gorley et al. 2008). Inter-seasonal and inter-annual
100
variability among environmental variables were further analysed using correlation-based
101
principal components analysis (PCA).
102
103
Analysis of community composition dynamics
104
Bacterial operational taxonomic units (OTUs) were formed from the sequence tags by single-
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linkage preclustering using pairwise alignments, followed by an average linkage clustering (SLP
106
/ PW-AL (Huse, Welch et al. 2010)). Taxonomic identities for bacteria are based off of 454 16S
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V6 sequence tags run through the Global Alignment for Sequence Taxonomy (GAST) process,
108
and represent the best agreed upon match for each OTU cluster within the SILVA98 ICOMM
109
database. Taxonomic IDs and representative sequences for the OTUs used in this study are
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included as supplementary tables (Tables SX). OTU data and sequence data used in this study is
111
archived
112
(http://vampsarchive.mbl.edu/diversity/diversity_old.php). Eukaryotic counts and environmental
113
data were gathered from the PML L4 dataset.
under
ICM_PML_Bv6-Western
English
Channel
114
For bacterial OTUs, the 300 most abundant (i.e. greatest average abundance), 300 most
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common (i.e. OTUs which occurred most often during the 6 years), and the 300 most variable
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(i.e. OTUs whose abundance changed the most over 6 years) were selected for downstream
117
analysis. Only those bacterial OTUs that occurred a minimum of least 7 months for bacteria were
118
included. Also included in the analysis were 76 eukaryotic variables that were present for a
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minimum of one third of the dates sampled.
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Discriminant Function Analysis
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Discriminant Function Analysis (DFA (Manly 2005)) and was used to identify variables (e.g.
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bacterial OTUs) that best predict the months (Fuhrman, Hewson et al. 2006). Bacterial OTUs
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and Eukaryotic variables were log(x+1) transformed and taken as percentages of the sample
125
total. Environmental variables were treated as described above. To avoid false patterns, the
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sample dates were randomized to disrupt the temporal relationships and the DFA was performed
127
on the randomized data, resulting in no similar patterns to those observed with the temporal
128
relationships intact. Time series analysis (Philippi 1993) was used to identify cyclical patterns
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and significant autocorrelations among months as evidence of a repeating pattern, based upon
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bacterial OTUs only. Multiple regression analyses (Rasmussen, Heissey et al. 2001) were used to
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test whether the temporally repeatable patterns in distribution and abundance are predictable by
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one or more environmental and eukaryotic factors. We used SYSTAT ® (v.11, Systat Software
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Inc, Chicago, IL, USA) and R 2.10.1 (Team 2010) to perform these analyses.
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Association networks
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To examine associations between the microbial populations and their environment, we analyzed
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the correlations, of the bacterial OTUs with each other, with eukaryotic counts, and with biotic
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and abiotic parameters over time using local similarity analysis (LSA, (Ruan, Dutta et al. 2006;
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139
Fuhrman and Steele 2008; Fuhrman 2009), Steele, J.A., Countway, P.D., Xia, L., Vigil, P.D.,
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Beman, J.M., et al. Marine bacterial, archaeal, and protistan association networks reveal
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ecological linkages. ISME J, In Press). LSA calculates contemporaneous and time-lagged
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correlations based on normalized ranked data and produces correlation coefficients that are
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analogous to a Spearman’s ranked correlation (Ruan, Dutta et al. 2006). Any parameters (OTUs
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or physico-chemical parameters) that occurred in fewer than 7 months were excluded from the
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analysis. We included 392 variables in each analysis: 300 bacterial OTUs, 74 Eukaryotic
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parameters, and 18 environmental parameters (Table S1,2,3). To sort through, condense, and
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visualize the correlations generated by the analysis, we used Cytoscape (Shannon, Markiel et al.
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2003) to create subnetworks (Figs. 6,7 S2-4) showing highly correlated subsets of the 398
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variables with significant local similarity correlations (all shown were p ≤ 0.005) with a false
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discovery rate (i.e. q-value) less than 0.003 (Storey 2002). False discovery rates are calculated
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from the observed p-value distribution and vary with the p-value (e.g. for the connections with p
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= 0.005, 0.001, q = 0.003, 0.0009, respectively). These correlation networks include nodes that
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consisted of bacterial OTUs, Eukaryotic OTUs, and the environmental parameters. The local
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similarity scores (rank correlations that may include a 1-3 month time lag) among the OTUs and
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the environmental parameters constituted the edges, i.e. connections between nodes.
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Supplementary results
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Comparing OTU richness across 6-years using SLP, Denoiser and no-treament
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The robust seasonal pattern was very similar whether the raw data, Denoiser-treated data or SLP-
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treated data was used for analysis (correlations based on observed richness in all 72 time points;
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raw-denoised 0.74; raw-SLP 0.76; denoised-SLP 0.78; Figure S1), which indicates that the
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while these denoising methods have a significant impact on the estimation of richness, they have
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little impact on the observed seasonal patterns in richness (S – OTU abundance).
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Alpha and Beta-diversity analysis based on Denoiser results
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Alpha diversity, measured in observed OTUs (S) and Phylogenetic Diversity (PD) (Faith 1992),
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was relatively constant across the time-series within a defined range, but showed distinct cyclical
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patterns with maxima in winter and minima in summer (Figure S5a). These trends were
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consistent irrespective of the applied denoising algorithm. The mean S per time point was 694,
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with an average minimum of 282 in summer and maximum of 1157 in winter. A cyclic pattern
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was observed in beta diversity, measured in UniFrac distances (Lozupone and Knight 2005)
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between the samples (Figure S5b). UniFrac distances, or the fraction of branch length in a
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phylogenetic tree representing the reads observed in a pair of samples that is unique to either
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sample, were smallest between time points from the same seasons and larger when comparing
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different seasons. For example, January of 2003 is more similar to January of 2008 in terms of
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phylogenetic composition of the samples than either is to July of 2003.
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Community Composition Dynamics
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Diatoms were not correlated with any bacterial OTUs (Fig. S2), yet diatoms correctly predicted
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the bacterial community pattern in all but the 300 most common OTUs, which was significantly
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predicted by phytoplankton counts, although the variance explained by eukaryotes (adjusted r2 =
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0.18-0.25) was lower for eukaryotic counts compared to environmental factors (Table S6). The
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correlation networks reveal that diatoms and phytoplankton are correlated to primary production,
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PAR, and day-length. The diatoms, and other phytoplankton, may also be reacting to a seasonal
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shift in nutrients or energy and providing a change in community to which the bacteria are
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responding. It is also likely that the broad view that these counts are providing misses
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interactions between individual eukaryotic and bacterial taxa, but that comparing community-
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wide indicators (e.g. DFA, dbLM) is allowing the signals to be detected.
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Supplementary References
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Table S1 – Sampling dates, and environmental conditions for each date. Data are MIMARKS
compliant (Yilmaz, Kottmann et al. 2010). DX1 and DX2 were calculated using the equations
outlined in the methodology. Na – data not used in analysis. Am – Ammonia (µmol L-1); Chla Chlorophyll a (µg/L); NOx – NO2+NO3 (µmol L-1); Sa – Salinity (Practical Salinity Units); Si –
Silicate (µmol L-1); SRP – Soluble Reactive Phosphate (µmol L-1); Tmp – Temperature (°C);
TOC – Total Organic Carbon (µmol L-1); TON – Total Organic Nitrogen (µmol L-1); SD – Serial
Day; DL – Day Length (hours); PAR – Photosynthetically Active Radiation (µmol
photons/m2/s); NAO – North Atlantic Oscilation; PP – Primary Productivity; dPP – Daily
Primary Productivity; MLD – Mixed Layer Depth (m).
Table S2 – Eukaryotic organism abundance measurements used in this study. H – Heterotrophic;
A – Autotrophic; M – Mixotrophic; H – Herbivores; O – Omnivores; C(B)
Carnivores(Bacteriovores); C – Carnivores; na – data not used in analysis. Ac - Acartia clausii;
Apc – Appendicularia; Bra - Brachyura; Bry - Bryozoa (Cyphonautes larvae); Cal - Calanoida;
Ch - Calanus helgolandicus; Ch(f) - Calanus helgolandicus (female); Ch(m) - Calanus
helgolandicus (male); Cet - Centropages spp.; Cty - Centropages typicus; Cha - Chaetognatha;
Cho - Chordata; Cil - CILIATES; Cin - Cirripede nauplii; Cir - Cirripedia; Cla - Cladocera; Cni Cnidaria; Coc - Coccolithophorids; Cod - Colorless Dinophycaea; CGy - Colourless Gyrodinium
sp. Med.; CGy(s) - Colourless Gyrodinium sp. Small; Cgn - Copepod eggs and nauplii; Con Copepod nauplii; Cop – Copepoda; Ca - Corycaeus anglicus; Cr - Crustacean; Crp Cryptomonad; Cyc - Cyclopoida (Oithona spp.); Del - Decapod larvae; De - Decapoda; Dia DIATOMS; Ech - Echinodermata; Echl - Echinodermata larvae; Ehux - Emiliania huxleyi; Eua Euterpina acutifrons; Eva - Evadne spp.; FE - Fish eggs; Fla2 - Flagellate 2µm; Fla FLAGELLATES; Fla5 - Flagellates 5µm; Gud - Guinardia delicatula; Ha - Harpacticoida; Hy Hydromedusae; Ka - Katodinium sp. Small; Ll - Lamellibranch larvae; Ma - Malacostraca; MiZ
- MICROZOOPLANKTON; Mo – Mollusca; Myr - Myrionecta rubra; Nic - Nitzschia
closterium; On - Oncaea spp.; Ppc - Para-Pseudo-Clauso-Ctenocalanus; Pap - Paracalanus
parvus; Pas - Paralia sulcata; Pyt - PHYTOPLANKTON; Po - Podon spp.; Poe Poecilostomatoida; Pol - Polychaeta; Pll - Polychaete larvae; Pnd - Pseudo-nitzschia
delicatissima; Pce - Pseudocalanus elongates; Sar - SARCODINA; Sct - Scripsiella trochoidea;
Sip - Siphonophorae; Stm - Stauroneis membranacea; StL - Strombidium sp. Large; StM Strombidium sp. Medium; StS - Strombidium sp. Small; Tl - Temora longicornis; Tr Torodinium cf. robustum; Uc - Unidentified Chaetognath; Uo - Unidentified Oncaea; Ur Urochordata; Zf - ZOO-FLAGELLATES.
TABLE S3 – Minimal Information about a MARKer gene Sequence (MIMARKS) compliant
data for sample information and sequencing protocol information.
Table S4 Permutation-based analysis of variance tests of differences among seasons and years
for different taxonomic groupings, using Bray-Curtis similarities calculated from Log(N+1)transformed abundances.
Table S5 Discriminant function analysis results from the first discriminant axis (DFA1) for the
bacterioplankton community sampled monthly from the English Channel. The OTU
identification given is the best consensus identification for the OTU cluster based on the ICoMM
SILVA98 taxonomy (http://vampsarchive.mbl.edu/diversity/diversity_old.php).
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Table S6 Multiple Regression Analysis results for DFA1 from the bacterioplankton community
predicted by environmental and eukaryotic parameters.
Table S7. Summary of results from stepwise distance-based linear modeling of inter-sample
similarities (or distances for single variables) variables on temporal variables (serial day = D,
solstice term = DX1, equinox term = DX2) and measured or modeled variables. S – Species
richness or number of operational taxonomic units. 1-λ' is the Simpsons dominance coefficient,
1-(1- λ a') refers to the indicative evenness of the community.
Table S8 – Bacterial 16S rRNA V6 specific primers used for amplification of the V6 region of
16S rRNA gene. Lowercase base pairs indicate the 454-GS-FLX A (for forward primers) or B
(for reverse primers) adapter. Large uppercase base pairs indicate the multiplex identifier (MID)
label used to differentiate the 9 primers into 12 groups for pooling. Primers originate from Huber
et al (2007)
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Figure S1 – A - Comparison of OTU abundance of raw (blue) and error-corrected sequence data
(Red – denoiser, Green – SRP) from all 72 time points in the L4 data series. Each time point was
resampled to an equal sequencing effort of 4505 sequences. The red line demonstrates OTU
abundance following the Denoiser software, while the green line demonstrates the OTU
abundance following the more conservative Single Linkage Preclustering approach. B –
Comparison of S, and diversity estimators, caho1 and ACE.
Figure S2 - Most connected cluster from a highly correlated (r > 0.7, p < 0.001, q<0.0015) subset
of the 300 most variable bacterial OTUs. The correlation network shows a loosely connected
cluster (A) and a highly connected cluster (B). The loosely connected cluster consists of OTUs
from the Bacteroidetes (light orange) and Proteobacteria phyla (blue) along with eukaryotes
(red), phytoplankton (green), clustered around environmental variables (grey). The highly
connected cluster consists entirely of bacterial OTUs including bacteroidetes, proteobacteria
(blue shades), deferribacteres (dark orange), cyanobacteria (light green). Solid lines represent
postive correlations, dashed lines represent negative correlations. Black lines show no time
delay, red arrows are delayed one month. Figure S7 contains a key for color ids.
Figure S3A - Subnetwork of correlated (r > 0.5, p < 0.005, q<0.0048) variables built around
mixotrophic eukaryotes and the variables directly connected to mixotrophic eukaryotes from the
300 most abundant bacterial OTUs. The zooplankton are red, phytoplankton are green,
environmental variables are grey, proteobacteria are blue, bacteroidetes are orange,
verrucomicrobia are purple, cyanobacteria are light green. Black lines show no time delay and
red arrows are delayed one month. Solid lines represent positive correlations; red lines represent
negative correlations. S3B - Subnetwork of correlated (r > 0.5, p < 0.005, q<0.0048) variables
built around autotrophic eukaryotes and the variables directly connected to autotrophic
eukaryotes from the 300 most abundant bacterial OTUs. The zooplankton are red, phytoplankton
are green, environmental variables are grey, proteobacteria are blue, bacteroidetes are orange,
verrucomicrobia are purple, cyanobacteria are light green. Black lines show no time delay and
red arrows are delayed one month. Solid lines represent positive correlations; red lines represent
negative correlations.
Figure S4A - Subnetwork of highly correlated (r > 0.7, p < 0.001, q<0.0008) variables built
around environmental parameters and the variables directly connected to the environmental
parameters from the 300 most variable bacterial OTUs. The zooplankton are red, phytoplankton
are green, environmental variables are grey, proteobacteria are blue, bacteroidetes are orange.
Black lines show no time delay. Red arrows are delayed one month, green arrows are delayed
two months, blue arrows are delayed 3 months. Solid lines represent positive correlations; red
lines represent negative correlations. S4B - Subnetwork of highly correlated (r > 0.7, p < 0.001,
q<0.0015) variables built around environmental parameters from the 300 most common bacterial
OTUs. The zooplankton are red, phytoplankton are green, environmental variables are grey,
proteobacteria are blue, bacteroidetes are orange. Black lines show no time delay. Red arrows are
delayed one month, green arrows are delayed two months, blue arrows are delayed 3 months.
Solid lines represent positive correlations; red lines represent negative correlations.
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Appendum to Figures: Color key showing identifications for the variables used in the correlation
networks. Bacterial OTUs are defined by class, eukaryotes are divided into zooplankton and
phytoplankton in Figures 4, 5, S2-S4.
Figure S5 - (a) Alpha diversity (observed OTUs: solid line, left axis; phylogenetic diversity:
dashed line, right axis) and (b) beta diversity (unweighted UniFrac) by month spanning six years
of marine water sampling at the L4 site in the Western English Channel. A cyclic pattern is
observed in both alpha diversity, with species richness peaking in the winter months; and beta
diversity with, for example, samples from the winter (red/blue) of 2003 being more similar in
phylogenetic composition to the winter of 2008 than either is to the summer (green) of 2003.
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