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Environmental Microbiology (2015) 17(10), 3642–3661

doi:10.1111/1462-2920.12629

Seasonal assemblages and short-lived blooms in coastal north-west Atlantic Ocean bacterioplankton

Heba El-Swais,1 Katherine A. Dunn,2 Joseph P. Bielawski,2 William K. W. Li3 and David A. Walsh1* 1 Department of Biology, Concordia University, 7141 Sherbrooke St West, Montreal, QC H4B 1R6, Canada. 2 Department of Biology, Dalhousie University, 1355 Oxford St, Halifax, NS B3H 4R2, Canada. 3 Department of Fisheries and Oceans, Bedford Institute of Oceanography, Dartmouth, NS B2Y 4A2, Canada. Summary Temperate oceans are inhabited by diverse and temporally dynamic bacterioplankton communities. However, the role of the environment, resources and phytoplankton dynamics in shaping marine bacterioplankton communities at different time scales remains poorly constrained. Here, we combined time series observations (time scales of weeks to years) with molecular analysis of formalin-fixed samples from a coastal inlet of the north-west Atlantic Ocean to show that a combination of temperature, nitrate, small phytoplankton and Synechococcus abundances are best predictors for annual bacterioplankton community variability, explaining 38% of the variation. Using Bayesian mixed modelling, we identified assemblages of co-occurring bacteria associated with different seasonal periods, including the spring bloom (e.g. Polaribacter, Ulvibacter, Alteromonadales and ARCTIC96B-16) and the autumn bloom (e.g. OM42, OM25, OM38 and Arctic96A-1 clades of Alphaproteobacteria, and SAR86, OM60 and SAR92 clades of Gammaproteobacteria). Community variability over spring bloom development was best explained by silicate (32%) – an indication of rapid succession of bacterial taxa in response to diatom biomass – while nanophytoplankton as well as picophytoplankton abundance explained community variability (16–27%) over the transition into and out of the autumn bloom. Moreover, the seasonal structure

Received 25 June, 2014; revised 9 September, 2014; accepted 9 September, 2014. *For correspondence. E-mail david.walsh@ concordia.ca; Tel. (514) 848 2424 (ext. 3477); Fax 514-848-2881.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd

was punctuated with short-lived blooms of rare bacteria including the KSA-1 clade of Sphingobacteria related to aromatic hydrocarbon-degrading bacteria. Introduction Ocean bacterioplankton communities are highly diverse (Sogin et al., 2006), variable across space and time (Zinger et al., 2011; Giovannoni and Vergin, 2012), and integral players in global biogeochemical processes (Arrigo, 2005; Pomeroy et al., 2007). Variability in bacterioplankton diversity and community composition is often associated with changes in community function (DeLong et al., 2006; Fuhrman, 2009); therefore, an understanding of the distribution of bacterioplankton, and the factors governing these distributions, is essential if we are to understand marine ecosystem processes and responses to environmental change (Doney et al., 2012). Ocean time series studies have been invaluable in assessing environmental variability and discerning ecosystem responses (Ducklow et al., 2009). A number of studies situated in different oceanic provinces have assessed microbial plankton distributions in relation to environmental conditions over periods of one to many years (Ghiglione et al., 2007; Treusch et al., 2009; Gilbert et al., 2012; Giovannoni and Vergin, 2012; Chow et al., 2013). From these studies, a general observation is that bacterioplankton communities exhibit pronounced repeating patterns such as seasonal succession and annual reassembly (Fuhrman et al., 2006), and that seasonality can often be explained, in large part, by changes in environmental conditions (e.g. day-length, temperature) and available resources (e.g. nutrients, organic matter) (Steele et al., 2011; Gilbert et al., 2012). In addition to seasonality, certain (albeit much fewer) studies have evaluated temporal change of bacterioplankton across much longer and much shorter time scales. Assessments of sustained changes over the long term (decades to centuries) are relevant to questions on how climate change will impact marine ecosystems, while assessment of changes over the short term (days to weeks) are relevant to questions on how resistant and/or resilient bacterioplankton are to ecosystem disturbances, particularly those that are anthropogenic in nature. In a recent ground-breaking study, Vezzulli and colleagues

Bacterial diversity in Bedford Basin (2012) used formalin-fixed samples from a historical archive to show a 50 year trend of increasing Vibrio bacteria abundance in the North Sea that was linked to increased sea surface temperature. At the other end of the temporal spectrum, recent studies have demonstrated rapid succession of bacterioplankton communities in response to natural short-lived events such as phytoplankton blooms (Teeling et al., 2012), as well as major environmental disturbances such as the Deep Water Horizon oil spill in the Gulf of Mexico (Valentine et al., 2012). In addition to interacting with their physical and chemical environment, bacterioplankton also interact with other members of the biological community, including phytoplankton and other bacterioplankton mediated by trophic dependencies (Azam and Malfatti, 2007) or protists and viruses via grazing and cell lysis respectively (Jürgens, 2007; Suttle, 2007). Such biotic links are difficult to study because microbial interactions cannot easily be observed directly, as they readily can for plants and animals. One approach for identifying ecological interactions among microbes has been through the detection of timedependent correlations among microbial taxa, which are often visualized as microbial association networks (Fuhrman and Steele, 2008). Such studies have shown strong positive correlations between bacterial taxa (Gilbert et al., 2012), as well as among microbial taxa from all three domains of life (Steele et al., 2011). Studies have also reported on negative correlations among taxa that may be evidence for competition or predation (Steele et al., 2011). In surface waters of the temperate north Atlantic Ocean, the annual cycle of total phytoplankton abundance is well described and is in phase with the temperature cycle (Li et al., 2006a). In contrast to abundance, the cycle of phytoplankton biomass begins in the spring when a diatom bloom is induced because of stabilization of the nutrientrich water column; biomass is maintained at a lower level in summer by nutrients regenerated by bacterioplankton and viruses; a secondary bloom occurs in the autumn, fuelled by destabilization of the water column and nutrient upwelling; in winter, the water column is well mixed, and both phytoplankton abundance and biomass are at a minimum. However, the annual cycle of phytoplankton as a whole is different from that of its constituent parts. For example, large diatoms are most abundant in the spring, while small phytoplankton such as Synechococcus peak in the autumn. Associated with the annual variation in phytoplankton is a poorly described succession of bacterial populations that may reflect co-variation with phytoplankton to conditions such as temperature or nutrients and/or a direct response to phytoplankton succession mediated by specialization for phytoplankton-derived resources (Kujawinski, 2011).

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Individual bacteria and algae interact with each other and with their physical-chemical environment at the short scales of microbial generation times and cellular distances. Yet plankton appear to exhibit system-level patterns at higher levels of biological organization (phylotypes, assemblage, community) and at time scales that transcend individual lives (months, season, year). In this paper, we discern these patterns by applying a new hierarchical modelling framework on a unique hierarchical observational time series. Specifically, we assessed bacterioplankton communities in a coastal inlet of the temperate north-west Atlantic Ocean over a 6 year period (2005–2010), including at a moderately high frequency (biweekly) over a single year, using high-throughput 16S rRNA gene sequencing. Multivariate analyses identified conditions and resources that most significantly influence the annual cycle of bacterioplankton community structure. We also identified novel associations between bacterioplankton and different size classes of phytoplankton by focusing our analyses on community variation across distinct periods of the annual cycle. Moreover, a combination of indicator analysis and Bayesian mixed modelling identified assemblages of co-occurring bacteria that exhibit strong seasonal preferences, including previously undescribed associations between bacterial taxa and either the spring or autumn phytoplankton bloom. The description of such links between the bacterioplankton and phytoplankton shed light on the ecological niches and resource preferences of a number of poorly characterized lineages of marine bacteria. Finally, we identified unexpected short-lived bloom events of typically rare bacteria that may provide insight into the impact of environmental perturbations on bacteria in the coastal ocean. Results Site description and environmental setting Bedford Basin is a temperate north-west Atlantic inlet located in Nova Scotia, Canada (Fig. 1A). Decades of weekly resolved times series observation have demonstrated that phytoplankton dynamics in the basin are coherent with the adjacent north-west Atlantic Ocean (Li et al., 2006a), and the general patterns are presented in Fig. 1B. During the winter, the water column is mixed, and nutrients are at a maximum concentration. As the nutrientrich water column stabilizes [around week 13, the spring equinox (SE)], a spring bloom dominated by large diatom species occurs. In the summer, phytoplankton are maintained, but at lower biomass as nutrients, particularly nitrate, are growth limiting (Li and Dickie, 2001). The peak in bacterioplankton cell abundance occurs in the early summer [around week 26, the summer solstice (SS)]. In the autumn, as the stability of the water column is eroded, a secondary phytoplankton bloom occurs [around week

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

3644 H. El-Swais et al.

A

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Nitrate 1

Diatoms Fraction of Chl a Spring bloom

Autumn secondary bloom

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3.5 x 10

Phytoplankton (< 20 um) Cells ml–1

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Bacterioplankton Cells ml–1

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-3

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Feb

Mar

Apr

Winter --> Spring

SE

May

Jun

Jul

Spring --> Summer

SS

Aug

Sep

Oct

Summer --> Autumn

AE

Nov

Dec Autum -->

WS

Fig. 1. (A) Location and bathymetry of Bedford Basin (B) plots of the average annual cycle of conditions and plankton in Bedford Basin surface waters. The plots were generated from the weekly averages calculated from > 20 years of collected data. The top panel presents the average physico-chemical conditions during the year. The bottom panels present the average annual cycle of selected plankton groups. The blue-green background gradient indicates average chlorophyll a concentrations. Important time points and seasonal transitions periods that were focused on in this study are indicated at the bottom of the figure.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

900 700 500

26

38

51

0.60

0.65

0.70

13

0.55

Phylotype evenness (Shannon)

2

B

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samples collected at biweekly intervals over a single full annual cycle (2009). Each sample was accompanied by a rich set of metadata consisting of 16 physico-chemical factors (i.e. temperature, salinity, inorganic nutrients, florescence and photosynthetically active radiation, or PAR) (Fig. S1, Table S1) and 11 biological parameters (i.e. Chl a, abundance of different phytoplankton groups and size classes, particulate organic nitrate and carbon, and bacterioplankton abundance) (Fig. S2, Table S2).

300

A

Phylotype richness (Chao-1)

Bacterial diversity in Bedford Basin

2

13

26

38

51

Week Fig. 2. Bacterioplankton 16S rRNA phylotype diversity in Bedford Basin. (A) Chao-1 estimates of bacterioplankton richness and (B) Shannon indices of bacterioplankton evenness. Box plots present the distribution of values from 2005 to 2010 for the SE (green), SS (red), AE (orange) and WS (blue) respectively. The ends of the box represent the 25th and 75th percentiles, the whiskers represent minimum and maximum range and black diamonds represent outliers. Hollow circles represent the biweekly samples from 2009.

38, the autumn equinox (AE)], consisting mainly of small phytoplankton species, particularly Synechococcus, but also dinoflagellates, cryptophytes and other chlorophyllpoor plankton taxa (Li and Dickie, 2001).

Bacterial 16S rRNA gene time series In order to understand bacterioplankton community dynamics in the context of environmental conditions and phytoplankton dynamics in the coastal north-west Atlantic Ocean, we performed a 6 year time series analysis of bacterial 16S rRNA gene diversity in surface waters of Bedford Basin. Bacterial communities were analysed at four distinct seasonal time points, consisting of the SE, SS, AE and winter solstice (WS) over the years 2005– 2010. The SE, SS, AE and WS samples were selected in order to assess bacterial community variability at seasonal and inter-annual time scales. We were also interested in assessing bacterial community variability over shorter time scales and therefore included additional

Bacterioplankton diversity The complete 16S rRNA gene dataset was comprised of 45 samples and a total of 1 060 533 sequences (9207– 79 881 sequences/sample) (Table S3). Bacterioplankton were comprised of typical temperate coastal ocean taxa, including Alphaproteobacteria, Betaproteobacteria and Gammaproteobacteria as well as Bacteroidetes and marine Actinobacteria (Fig. S3). Although it is common to define operational taxonomic units (OTUs; herein referred to as phylotypes) as sequence clusters exhibiting > 97% identity (Hanson et al., 2012), we chose to use a more phylogenetically broad phylotype definition (> 90% identity cut-off) in order to account for high variability among samples that may have resulted from either the small volume of seawater samples and/or error-prone polymerase chain reaction (PCR) associated with formalin preservation (see Discussion for more details). In general, bacterioplankton diversity exhibited a temporal pattern of decreasing diversity from the spring through summer followed by an increase in diversity through the autumn and winter (Fig. 2). Chao-1 estimates of bacterial richness ranged between 285 and 991 phylotypes. The minimum richness that we observed occurred during AE-2006 (285 phylotypes) and SE-2007 (346 phylotypes), while the maximum diversity was observed during WS-2010 (991 phylotypes) (Fig. 2A) The two periods of minimum richness also corresponded to times of low community evenness (AE-2010, 0.64; SE-2007, 0.54) (Fig. 2B), indicating that the decreased richness resulted from the blooming of a few specific bacterial phylotypes (more detail below). In contrast, high bacterial richness in the late autumn and winter was typically accompanied by high community evenness (> 0.73), suggesting the maintenance of a less active, but more diverse bacterioplankton community during the late autumn and cold winter months. Bacterioplankton community structure Temporal variability in bacterioplankton community structure was initially assessed and visualized using unconstrained non-metric multidimensional scaling (NMS) of bacterial 16S rRNA phylotypes. In the NMS ordination

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

3646 H. El-Swais et al.

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ee w

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0 2-09 0 4-09 51-05

51-06

2

2 6-10

Feb

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Apr

May Jun

Jul

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Sep

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Nov Dec

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Fig. 3. Bacterioplankton community similarity in Bedford Basin. (A) NMS ordination where samples are colour-coded by sampling month and labelled by week and year. (B) Community similarity-distance decay plot for samples separated by increasingly larger units of time. The ends of the box represent the 25th and 75th percentile, and the whiskers represent minimum and maximum range. (C) Community similarity values between samples collected 2 or 3 weeks apart in 2009.

plot, the SE, SS, AE and WS communities formed wellseparated clusters, which revealed a substantial seasonal signal within the Bedford Basin bacterioplankton community structure (Fig. 3A). Bacterioplankton communities sampled during the winter and spring were separated from those sampled in the summer and autumn along ordination axis 1, and of all environmental parameters, the strongest correlations with axis 1 included temperature (r1 = −0.812) and nitrate concentration (r1 = 0.63). To further quantify the importance of environmental variables in structuring bacterioplankton communities, we performed redundancy analysis (RDA) and constrained the ordination by the environmental variables (Fig. S4A). Similar to the NMS ordination, temperature and nitrate concentration were strong explanatory variables of community structure, explaining 24% and 10% of the community variation respectively (Table 1). In addition to these two physico-chemical factors, total phytoplankton (size class < 20 um) and Synechococcus abundances each explained 19% and 13% of community variation as well.

After controlling for autocorrelation between explanatory variables, we found that temperature, nitrate, phytoplankton and Synechococcus abundances cumulatively explained 38% of bacterial community variation and in combination were strongly correlated with bacterial community similarity (BIOENV, ρ = 0.66). The inclusion of samples collected across time intervals ranging from weeks to years allowed us to assess the community variability over multiple time scales within Bedford Basin bacterioplankton communities. On average, we detected a negative relationship between time and community similarity; samples collected 2 weeks apart exhibited a mean community similarity of 70% and community similarity decayed with time, reaching a minimum of 27% similarity between communities separated by 6 months (Fig. 3B). The high dissimilarity among communities separated by a period of 6 months was also supported by an Analysis of Similarity (ANOSIM) comparison among SE, SS, AE and WE samples. Comparison between communities sampled either during the

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

Bacterial diversity in Bedford Basin

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Table 1. Correlations of environmental and biological variables with bacterioplankton communities.

All samples

Winter to spring (week 6–17)

Spring to summer (week 20–30) Summer to autumn (week 32–44)

Autumn to winter (week 46-4)

ORDISTEP factors

Correlation

Variability explained (%)

Temp NO3– Phyto Syn Temp, Syn, NO3–, Phyto SiO4– Picoeuks SiO4–, Picoeuks n.a. LN-phyto O2 O2, LN-phyto SN-phyto NO2– O2 sat Picophyto Picophyto, SN-phyto, O2 sat, NO2–

0.64 0.14 0.45 0.41 0.66 0.40 0.47 0.68 n.a. 0.22 0.52 0.53 0.58 0.24 0.02 0.75 0.69

24 10 19 13 38 32 10 47 n.a. 16 20 33 18 12 4 27 51

LN-phyto, large nanophytoplankton; n.a., not applicable; O2 sat, oxygen saturation; Picoeuks, picoeukaryotes; Picophyto, picophytoplankton; Phyto, phytoplankton (> 20 um); SN-phyto, small nanophytoplankton; Syn, Synechococcus; Temp, temperature.

equinoxes (SE versus AE) or the solstices (SS versus WS) exhibited the highest and most significant ANOSIM values (Table 2). In addition, all ANOSIM comparisons between equinox and solstice communities were also significant except for the comparison between the SS and AE, which indicated that these two seasonal communities are less distinguishable from each other. Although we demonstrated a clear negative relationship between time and bacterioplankton community similarity, we also observed a very large variation in community similarity between samples separated by short time periods (i.e. 2 or 3 weeks) (Fig. 3B). In fact, during certain 2 week periods, we observed communities that were as dissimilar as those separated by 6 months, suggesting periods of rapid succession within the bacterioplankton community. To identify these periods of rapid succession, we plotted the community similarity values at a 2 week lag across the 2009 time series (Fig. 3C). Periods of highest community similarity general occurred during the winter and autumn, while periods of lowest similarity were generally confined to the spring and summer. The most rapid change in community structure (15% similarity) coincided with the crash of the spring phytoplankton bloom that occurred between 22 April and 13 May (week 17 and 20). Taxonomically, this dramatic shift was characterized by a transition from a Bacteroidetes (specifically Polaribacter and Cytophaga) dominated community (45% of sequences) to one dominated by the SAR11 clade (44% of sequences) (Fig. S5). A second striking instance of rapid community change occurred during the late summer, between 22 July and 5 August (week 30–32). This shift corresponded to an observed dominance of a single bacterial phylotype (KSA1-0071, 19.45% of sequences in week 32) that

belongs to the KSA1 clade, which is a deep-branching member of the Sphingobacteria family within the Bacteroidetes and an unusual bacterial group in the coastal ocean water column. The KSA1-0071 phylotype peaked in week 32 then decreased in relative abundance over the next month, and thereafter it was below the detection limit of our approach (Fig. S6). The identification of this unusual phylotype in marine surface waters suggests a possible short-term disturbance to the Bedford Basin ecosystem in August 2009.

Intra-seasonal variation in bacterial community structure As presented above, we identified a combination of factors (temperature, nitrate, total phytoplankton and Synechococcus abundances) that together strongly explained bacterioplankton community structure over the annual cycle in Bedford Basin. However, we hypothesized that additional environmental factors that may not be important at the annual scale may, nonetheless, be important in structuring bacterial communities during distinct periods of the year. To test the hypothesis, we divided the samples into four seasonal periods and performed RDA ordination on the subset of 16S rRNA phylotypes that Table 2. ANOSIM communities.

Summer Autumn Winter

results

for

comparison

between

seasonal

Springa

Summer

Autumn

0.81, < 0.001 0.90, < 0.001 0.71, < 0.001

0.25, 0.032 0.88, 0.002

0.76, 0.002

a. Values reported correspond to the R-value, P-value of the ANOSIM results.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

3648 H. El-Swais et al. 90

Deltaproteobacteria Other Bacteria

80

Betaproteobacteria Acidimicrobidae

Number of indicators

70

Other Actinobacteridae Other Gammaproteobacteria Oceanospirillales

60

OM60 GSO

50

Alteromonadales SAR86

40

ARCTIC96B-16

30

KSA1

Other Bacteroidetes

Polaribacter Cytophaga

20

Flavobacteriales Other Alphaproteobacteria

10

SAR11 OM38

0 Winter --> Spring

Spring --> Summer

Summer --> Autumn

Autumn --> Winter

Rhodobacterales

Fig. 4. The number and taxonomic composition of strong indicator for the four distinct seasonal periods.

were present during these four distinct transitional periods (Fig. 1B). The four periods consisted of (i) the winter to spring transition characterized by the onset of stratification and the primary spring phytoplankton bloom (weeks 6–17 of 2009 and all SE samples), (ii) the spring to summer transition characterized by an increase in bacterial abundance (weeks 20–30 of 2009 and all SS samples), (iii) the summer to autumn transition characterized by the secondary autumn peak in phytoplankton (weeks 32–44 of 2009 and all AE samples) and (iv) the autumn to winter transition characterized by a breakdown in stratification and a decrease in bacterial abundance (week 46–4 of 2009 and all WS samples). RDA demonstrated that silicate (SiO4–) concentration was the strongest variable explaining bacterial community variation across the winter to spring transition, which in combination with the abundance of picoeukaryotes explained a total of 47% of the bacterioplankton variability (Table 1). The spring bloom is dominated by diatoms that assimilate silicate as they increase in abundance (Li and Dickie, 2001), thus reducing this nutrient to low concentrations in the water column. Because the timing of the spring phytoplankton bloom is variable from year to year, certain of our SE samples were collected early in the bloom (2008 and 2009) while others were collected at the bloom peak (2005) or post-bloom (2006, 2007 and 2010) (Fig. S2), which in combination with the biweekly sam-

pling in 2009 revealed the bacterial community response as the yearly spring bloom develops and then subsides. To identify the bacterial taxa most responsive to the spring bloom, we identified taxa associated with the spring transition using indicator analysis (Dufrene and Legendre, 1997). In total, we identified 41 spring indicator phylotypes, which were predominantly classified as Flavobacteria or Gammaproteobacteria (Fig. 4). The strongest indicator was a Polaribacter phylotype (Polaribacter-0005) (Fig. 5A). This phylotype also exhibited a strong negative Pearson correlation with silicate (rho = −0.61, P = 0.05), which indicated an association with late-bloom conditions in Bedford Basin. Additional flavobacterial phylotypes specifically associated with the spring bloom included Ulvibacter-0013 (Fig. 5B) and Psychroserpens-0012 (Fig. 5C). These two phylotypes were also negatively correlated with silicate concentration (Ulvibacter-0013, rho = −0.70, P = 0.01; Psychroserpens0012, rho = −0.60, P = 0.06). In contrast, we identified gammaproteobacterial phylotypes related to the Colliwelliaceae (Alteromonadales) that were strong indicators for the spring bloom (Colliwelliaceae-0010) (Fig. 5D) and exhibited a significant positive correlation with silicate (rho = 0.84, P = 0.002), which supports a strong association with early bloom conditions in Bedford Basin. Another strong spring indicator included a phylotype from the ARCTIC96B-16 group (Fig. 5E).

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

Bacterial diversity in Bedford Basin 14

30

A

Polaribacter-0005 SE IV=92.6, P=0.002

2007

Relative abundance (%)

Ulvibacter-0013 SE IV=65, P=0.0006

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Psychroserpens-0012 Psychroserpens 0012 SE IV=51.2 P=0.004

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Thiotrichales-0116 SS IV=71.8, P=0.0002

5

Oceanospirilalles-0345 SS IV=77.8, P=0.0002

H

SAR11-0079 SS IV=52.7,, P=0.0008

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SAR92-0008 AE IV=51.5, P=0.0002

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Marinobacterium-0165 AE IV=91.6, P=0.0002

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7

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ARCTIC96B-16-0020 SE IV=84.3, P=0.002

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1

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Oceanospirillales-0118 AE IV=80.3, P=0.0002

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KSA1-0071 AE IV=99.5, P=0.0002

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52

2 1

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Nitrospina-0369 WS IV=64.4, P 90% of the PD for the SS community (Fig. 6A). This suggests that the SS community might be a mixture of two communities, each having a different central tendency for the co-occurrence relationships of their principal phylotypes. The sharing of some principal phylotypes between V42 and V63 (e.g. Cytophaga-0030) is easily reconciled as the contribution of generalist lineages to both communities and highlights the value of probabilistically defining microbial assemblages (i.e. using soft rather than hard boundaries on assemblages). The AE-dominant assemblage (V100) contained 20 principal phylotypes (total PD of those phylotypes = 69%), most of which exhibited positive co-occurrence patterns with each other across the 2009 annual cycle (Fig. 6E). This highly structured pattern is analogous to the wellstructured SE assemblage; however, the identity of the phylotypes is different. A comparison between these two assemblages can provide insight into the interaction among bacterioplankton that differentiate the spring and autumn blooms. For example, alphaproteobacterial phylotypes were more prominent in the AE assemblage (V100) than the SE assemblage (V89). In particular, a network of co-occurring OM42-0001, OM25-0040, OM250085, OM38-0044 and Arctic96A-1-0007 phylotypes was apparent in the AE assemblage. Gammaproteobacterial phylotypes that distinguished the AE assemblage from the SE assemblage included SAR86-0041, Oceanospirilalles0118, OM60-0098, and SAR86-0100. Another noteworthy observation is the presence of two SAR92 phylotypes (SAR92-0029 and SAR92-008) that exhibit contrasting patterns among the five assemblages. SAR92-0008 was specific to the AE assemblage (and was also a strong indicator for the AE; Fig. 5J), while SAR92-0029 was found within the SE and SS assemblages, suggesting temporally defined niche partitioning between SAR92 phylotypes. The WS-specific (V48) assemblage was perhaps the least unique assemblage because most of its principle

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phylotypes also made substantial contributions to at least one other assemblage. In fact, the WS assemblage seemed to be a mixture of some principle phylotypes that were important in either the AE and SE assemblages. For example Cytophaga-0015, Colwelliaceae-0010, Ulvibacter-0013, OM38-0017 and GSO-0004 were common with the SE assemblage. Likewise, ARCTIC97A1-0007, OM1-0021 and SAR11-0024 were common with the AE assemblage. V48 is characterized by 20 principal phylotypes responsible for a total probability density of 70%. Among these there were several principal phylotypes specific to the WS assemblage, including SAR11-0028, GSO-0002 and Rhodobacter-0077. The high contribution of V48 to the winter community indicates that winter co-occurrence patterns differ from the other seasons. However, the sharing of many principal phylotypes with other assemblages supports the notion that bacterial phylotypes can be maintained during the winter months and exhibit successive blooms through the spring to autumn period. Discussion Seasonal dynamics in plankton communities are of particular current interest owing to the relationship between seasonality and global climate warming (Giovannoni and Vergin, 2012). The main effect of warming on the oceans is an increase in water column stratification (Doney et al., 2012), a process that occurs each year in temperate regions such as the coastal north-west Atlantic Ocean site that we have studied here. Indeed, times series observation of plankton in Bedford Basin has shown that climate variation, propagated through interannual variation in stratification, can influence both phytoplankton and bacterioplankton abundances over the long term (Li and Harrison, 2008). Similarly, multiyear observations on the Scotian shelf of Eastern Canada and the Labrador Sea have shown sustained changes in bacterioplankton abundances in the spring and autumn that are linked to phytoplankton biomass (Li et al., 2006b). Recently, an inventory of plankton microbes was generated for the Gulf of Maine area that includes Bedford Basin (Li et al., 2011); however, little information exists on the distribution of bacterial taxa in time. Based on these observations, we were motivated to investigate the temporal variability of bacterioplankton communities in Bedford Basin across time scales ranging from a few weeks to many years, in order to better constrain the role of environmental conditions, resources and ecological interactions in shaping marine bacterioplankton communities in time. Seasonality in coastal Atlantic Ocean bacterioplankton Similar to other studies of temperate and-high latitude marine ecosystems, we identified seasonal fluctuations in

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

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bacterial diversity consisting of high richness and evenness in the late autumn and winter, and low richness and evenness in the spring and summer. In a comparable 6-year time series study of the Western English Chanel, Gilbert and colleagues (2012) reported diversity maxima in the winter and minima in the summer. Moreover, modelling of global marine bacterial diversity also predicts that diversity peaks in the winter at temperate latitudes (Ladau et al., 2013). One potential explanation for this seasonality in diversity involves water column stratification. In surface waters of temperate and high-latitude oceans, we hypothesize that allochthonous input of bacterial taxa from deep water can increase diversity estimates. Previous work has shown that during periods of water column stratification, the bottom water of Bedford Basin supports a bacterial community that is phylogenetically distinct to that residing in the surface water (Georges et al., 2014). As stratification erodes in the late autumn, bacterial taxa are presumably mixed into the surface water. Indeed numerous winter indicator taxa such as GSO and Nitrospina probably find their true niche in the deeper layer during stratification but are located in the surface winter water merely due to mixing of the water column. A study recently performed by Chow and colleagues (2013) at the San Pedro Ocean Time Series station in Southern California also showed that the bacterial community was vertically homogenized in the winter but underwent vertical divergence of bacterial taxa as the water column warmed and stratified. Future time series work that integrates temporal and vertical sampling of the bacterial community over the seasonal stratification cycle will contribute to our understanding of the relationship between season and richness at temperate and northern latitudes. The annual cycle of phytoplankton and nutrients in the North Atlantic is driven by temperature stratification. Therefore, it is not surprising that we identified temperature as the single strongest explanatory factor for the annual variability in bacterial community structure in Bedford Basin. Temperature is a well-known predictor of community structure variability at both spatial (Pommier et al., 2007; Yilmaz et al., 2012) and temporal scales (Chow et al., 2013) in the oceans. However, in addition to temperature, we also determined that a combination of factors, including nitrate concentration, phytoplankton and Synechococcus abundances, collectively explained a large degree of variability in the annual cycle of bacterioplankton community composition. Moreover, when we focused on factors structuring bacterioplankton communities during distinct periods of the year, we identified links with other phytoplankton groups including diatoms and picoeukaryotes (the SE period), nanophytoplankton (the AE period) and small nanophytoplankton (the WS period). In an earlier study (Ning et al., 2005),

statistical evidence based on partial coefficients derived from multiple regression indicate that the association between bacterioplankton and phytoplankton is different among phytoplankton (chlorophyll a) size fractions. Moreover, the association is also different among taxonomic groups within the picophytoplankton size fraction. Hence, the direct and indirect trophic processes that link phytoplankton to bacterioplankton presumably depend on distinct biological characteristics of the interacting components. For example, differential susceptibility of phytoplankton to viral lysis or grazing mortality might be the bases for differential flux of phytoplankton lysate towards heterotrophic demand by bacterioplankton. Bacterioplankton assemblages associated with spring and autumn blooms Our work supports the notion that bacterial communities exhibit annually reoccurring patterns in the ocean (Fuhrman et al., 2006; Gilbert et al., 2012). Such seasonality in bacterioplankton community structure results, at least in part, from differences in the ecology and metabolic capabilities of bacterial taxa. In this study, we aimed to better constrain the ecological niches of coastal temperate ocean bacterioplankton by identifying consistent temporal patterns of bacterial taxa using a combination of indicator analysis (Dufrene and Legendre, 1997) and Bayesian modelling (Shafiei et al., submitted), followed by analysis of co-occurrence patterns (Ruan et al., 2006) of principle bacterial taxa over the 2009 biweekly time series. Although indicator analysis is a powerful approach at identifying taxa that characterize a specific environment or season (Fortunato et al., 2013), it treats taxa as independent units of diversity and is unable to detect interactions between members of the community. In contrast, the Bayesian approach models microbial communities as statistical mixtures of phylotypes (i.e. assemblages). This approach is fundamentally different than the Bayesian approach of Larsen and colleagues (2012), who had different analytical objectives. Larsen and colleagues (2012) assume that community structure is non-hierarchical and attempt to model the data as just a single realization of an interaction network. Within BioMiCo, Shafiei and colleagues treat the observed data as constrained by some underlying community structure, with each sample representing one of many possible realizations of that structure. This framework is required when the primary goal is to infer how overlapping mixtures of microbial assemblages are associated with various environmental conditions. We believe that probabilistic graphical modelling, such as employed by Shafiei and colleagues (2014a) and by Larsen and colleagues (2012), provide solutions to many of the analytical challenges faced within environmental microbial diversity;

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

Bacterial diversity in Bedford Basin they offer a statistically rigorous framework for learning from noisy or sparse data; they provide formal mechanisms for describing uncertainty; and they can adapt to the data to avoid over-fitting. Without BioMiCo, we would not have been able to identify and explain the difference between spring and autumn blooms in terms of several ecologically relevant assemblages within the bacterial community. With regards to the ecological differences between taxa, it is informative to compare the distinct assemblages of bacteria and their co-occurrence patterns over the spring and autumn phytoplankton blooms respectively. Both blooms were comprised of phylotypes within the Gammaproteobacteria and Flavobacteria, but at finer taxonomic scale, we identified clear partitioning of taxa to either the spring or autumn bloom. The spring bloom assemblage (V89) was specifically dominated by known biopolymer-degrading members of the Flavobacteria (e.g. Polaribacter and Ulvibacter) (Kirchman, 2002; Gómez-Pereira et al., 2010) and Gammaproteobacteria (Alteromonadales) (Ivars-Martinez et al., 2008; Shi et al., 2012; Pedler et al., 2014), as well as the poorly characterized ARCTIC96B-16 clade. Recently, the bacterial response to a spring bloom in the North Sea was studied, and a rapid succession of bacterial taxa specialized for successive decomposition of algal derived organic matter was described (Teeling et al., 2012). In Bedford Basin, we found that silicate was a strong predictor of bacterial community structure across the spring bloom. Because silicate is an inversely related proxy for diatom biomass, it is evident that there is a similarly rapid succession in bacterioplankton occurring over the spring bloom in the coastal north-west Atlantic Ocean. Indeed, Alteromonadales phylotypes were associated with early bloom conditions suggesting a preference for organic matter exuded from living phytoplankton. In contrast, Polaribacter and Ulvibacter phylotypes were associated with late bloom conditions, suggesting a preference for organic matter released from decaying phytoplankton. Our observations are also in accordance with a recent study by Sarmento and Gasol (2012) that experimentally demonstrated that Alteromonadales and Bacteroidetes utilize diatom-derived dissolved organic carbon. In contrast to the spring bloom, gammaproteobacterial phylotypes associated with the autumn bloom assemblage (V100) were members of the SAR86, OM60 and SAR92 clades. These taxa have been previously described as associated with nutrient-poor conditions in the coastal ocean (Dupont et al., 2012) such as those encountered in Bedford Basin during the late summer and autumn. The SAR11 and Rhodobacterales clades of alpha Proteobacteria were also dominant lineage in Bedford Basin late in the year. The occurrence of high

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relative abundance of SAR11 in the summer and autumn is comparable with observations made off the coast of California (Chow et al., 2013) and in the Sargasso sea (Treusch et al., 2009), but is in contrast to the winter peaks of SAR11 observed in the North Pacific subtropical gyre (Eiler et al., 2009) and the Western English channel (Gilbert et al., 2012). Physiology and genome analysis of the SAR11 clade have shown that these bacteria are small in size, their genomes have undergone extensive streamlining as an energy and nutrient conserving adaptation, and they possess genes for proteorhodopsin and high-affinity membrane transporters (Rappé et al., 2002; Giovannoni et al., 2005). The importance of these transporters for nutrient scavenging is supported by metaproteomic studies in Bedford Basin (Georges et al., 2014) as well as in the North Sea (Teeling et al., 2012) where high expression of organic substrate transporters from the SAR11 clade was observed post-bloom. Interestingly, we also identified flavobacterial phylotypes associated with the autumn bloom, specifically the indicator taxa Psychroflexus-0192, as well as assemblage members Cytophaga-0132 and Cytophaga-0174 and Flavobacteriales-0070, which suggests certain members of the marine Flavobacteria may also be ecologically specialized for low nutrients or the use of specific phytoplankton-derived organic compounds during autumn conditions, as supported by recent single-cell genome analyses (Woyke et al., 2009). Rare bloom events in Bedford Basin Many bacterial phylotypes exhibited seasonal structure, yet we also identified phylotypes that exhibited rare and episodic blooms. We find these taxa to be of considerable interest because short-lived high relative abundance may reflect a response to some infrequent environmental change that occurred in the ecosystem that, moreover, may be anthropogenic in origin. For example, we observed a transient bloom of the Sphingobacteria KSA1-0071 phylotype on two separate occasions (5 August 2009 and SE-2007). KSA-0071 phylotype was 93% identical to 16S rRNA sequences from members of the Balneola genus within the family Sphingobacteria (Urios et al., 2006; 2008). Balneola has been implicated in the degradation of toluene and benzene because members of this genus were the dominant organisms in enriched toluene media from contaminated sediment off the East China Sea (Li et al., 2012). These observations make it tempting to speculate that the KSA-0071 phylotype may serve as a bioindicator, responding to the release of hydrocarbon contamination into the Bedford Basin, which is a strong possibility owing to the high level of marine industry and shipping traffic in and around Bedford Basin (Ducharme, 2000; Ducharme and Turner,

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3656 H. El-Swais et al. 2001). Moreover, we also identified a strong positive correlation between KSA1-0071 and a phylotype from a putative methane-oxidizing gammaproteobacterial phylotype (Methylophaga-0312), which further supports the notion of transient hydrocarbon contamination in the basin. A second example of an intermittent bloom that we observed was that of an actinobacterial OM1 phylotype at the 2010 autumn equinox (24% of the bacterial sequences). Earlier studies have shown that members of the OM1 clade usually represent about 5% of surface bacterioplankton communities (Morris et al., 2012) and that OM1 is associated with the deep chlorophyll maximum during the spring picophytoplankton bloom in the Sargasso Sea (Giovannoni and Vergin, 2012). The OM1 bloom in Bedford Basin occurred 1 week after the 2010 autumn peak in picophytoplankton, which further supports a strong interaction between OM1 and picophytoplankton in the ocean. Interpretation of results from filter PCR-amplified formalin-fixed seawater In this study, we generated 16S rRNA gene sequence data from less than 1 ml of formalin-fixed seawater (105– 106 bacterioplankton) using the filter PCR methodology (Kirchman et al., 2001). This allowed us to analyse bacterioplankton diversity using an archive of samples originally collected for the purpose of flow cytometricbased analysis of plankton abundances. Although clearly informative with regards to temporal dynamics of bacterioplankton in the coastal ocean, there are a number of important caveats associated with our approach. The first is the question of how representative such a small volume of seawater is of the surface community given the observation that bacterioplankton communities can vary remarkably over very small spatial scales (Long and Azam, 2001; Hewson et al., 2006). Owing to such microheterogeneity, the spatial scale of some of the temporal distributions described above are unknown, and we advise that future filter PCR-based approaches use replication in order to differentiate community variability at different spatial scales. Indeed, Kirchman and colleagues (2001) proposed that filter PCR could, in fact, be used to examine hypotheses about bacterioplankton variation at small spatial scales. We attempted to minimize and control for this variation in two ways. First, we filtered out particles because particle-associated bacteria are believed to be patchier than free-living bacteria (Azam and Malfatti, 2007). Second, we used a phylogenetically broad phylotype definition (90%), with the expectation that some of the variability would be collapsed into phylotypes that would be more broadly distributed in the water column and over time.

There are well-known biases associated with all steps of PCR-based methods, including cell lysis, nucleic acid extraction, PCR amplification and sequencing (von Wintzingerode et al., 1997; Berry et al., 2011). For filter PCR specifically, results could be biased by unequal lysis of bacterial taxa, which is analogous to the problem of bias associated with different DNA extraction methods. No obvious bias associated with unequal cell lysis was evident because we identified all expected major marine bacterioplankton lineages; however, caution should be taken if such an approach was to be employed in an environment dominated by difficult to lyse cells such as Gram-positive bacteria. Another limitation here is that prolonged formalin fixation contributes to DNA degradation and modification, which is associated with decreased PCR yield, a failure to amplify longer templates and (most treacherously) error-prone PCR (Greer et al., 1991; Quach et al., 2004). In our study, we minimized the impact of DNA degradation by targeting a short PCR amplicon (V5 region, 100 bp), while formalin-induced PCR errors most likely did not lead to inflation of diversity because we used such a broad phylotype definition. We believe that as long as these limitations are recognized, the proven ability to work with small volumes of formalin-fixed seawater should facilitate molecular analysis of other sample archives associated with long-term oceanographic monitoring programmes. Concluding remarks In conclusion, this study examined changes in coastal ocean bacterioplankton communities over time scales ranging from a few weeks to many years. We demonstrated that there is temporal variability in bacterioplankton communities that is strongly linked to changes in environmental conditions as well as changes in the phytoplankton community. Hence, as phytoplankton community structure varies with climate change, we predict that this variation will propagate through to changes in bacterioplankton communities. For example, if global warming disproportionally favours small phytoplankton over larger phytoplankton as was recently reported (Li et al., 2009), then we may also see a favouring of ‘autumn’ bacterial taxa over ‘spring’ bacterial taxa as the oceans warm. However, this thinking is highly speculative at the moment and must be informed by future time series observations of bacterioplankton community composition in the ocean. Experimental procedures Sample collection Small volume seawater samples (2 ml) were collected from surface water (1 m depth) at the compass buoy station in

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

Bacterial diversity in Bedford Basin Bedford Basin (44° 41′ 30″ N, 63° 38′ 30″ W) as part of the Bedford Basin Monitoring Program. Seawater samples were fixed in 1% paraformaldehyde for 15 min at room temperature and then stored at −80°C for later analysis. Details of sampling and collection of physicochemical and phytoplankton data can be found in Li and Dickie (2001).

Filter PCR and 16S rRNA gene sequencing For each sample, 1 ml of fixed seawater was pre-filtered through a 25 mm borosilicate microfibre filter with a 2.7 μm pore size (AMD Manufacturing) to remove large particles and eukaryotes, then microbial biomass was collected on a 25 mm polycarbonate filter with a 0.2 μm pore size (GE) using vacuum filtration (100 kPa). Filters were washed three times with 5 ml of sterile MilliQ ultra-pure water to remove residual formaldehyde preservative. Filters were sectioned and an eighth of a filter was transferred to a 200 μl PCR tube. Direct PCR amplification of the V5 region on the 16S rRNA gene was conducted similar to the filter PCR approach first reported by Kirchman and colleagues (2001). We used universal primers for the V5 region of the 16S rRNA gene (DW786F 5′-GATTAGATACCCTSGTAG-3′and DW926R 5′CCGTCAATTCMTTTRAGT-3′) that were modified from Baker and colleagues (2003) to eliminate bias against marine Alphaproteobacteria. PCR reactions (50 μl total volume) contained 0.5 μM each primer, 1 X Phire Reaction Buffer containing 1.5 mM MgCl2 (Finnzymes Thermofischer Scientific), 0.2 mM deoxynucleotide triphosphates and 1 U of Phire Hot Start II DNA Polymerase (Finnzymes Thermofischer Scientific). Cycling conditions involved an initial 3 min cell lysis/denaturing step, followed by 30 cycles of 5 s at 98°C, 5 s at 49°C and 10 s at 72°C, and a final elongation step of 1 min at 72°C. Reverse primer were barcoded with a specific IonXpress sequence to identify samples. PCR products were purified using QIAquick Gel Extraction Kit (Qiagen), quantified using Quantifluor dsDNA System (Promega), pooled at equimolar concentration and sequenced using an Ion Torrent PGM system on a 316 chip with the Ion Sequencing 200 kit as described in Sanschagrin and Yergeau (2014). The 16S rRNA sequence data is available at the European Nucleotide Archive under the accession number PRJEB7573.

Bioinformatic analysis of 16S rRNA gene sequences V5 16S rRNA sequences were analysed using the opensource MOTHUR pipeline (Schloss et al., 2009). Sequences with an average quality of < 17, length < 100 bp or that did not match the IonXpress barcode and both the PCR forward and reverse primer sequences were discarded. Potential chimeric sequences were identified using UCHIME (Edgar et al., 2011) and also discarded. Sequences were clustered into OTUs (herein referred to as phylotypes) at 90% identity using the furthest neighbour algorithm. Sequences and phylotypes were assigned to taxonomic groups using the Silva database (Quast et al., 2012) and the GreenGenes taxonomy (DeSantis et al., 2006), the Wang method and a bootstrap value cut-off of > 60% (Wang et al., 2007).

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Statistical analyses Estimates of phylotype richness (Chao-1 index) and evenness (Shannon) were generated from 5400 rarefied phylotypes using MOTHUR. NMS ordination of phylotype relative abundances was performed using Bray–Curtis distances forced to two axes with 250 iterations and a stability criterion of 10–4, implemented in PC-ORD (McCune and Mefford, 2011). ANOSIM (Clarke, 1993) was calculated to compare intraversus inter-sample diversity on Yue and Clayton’s theta similarity index using MOTHUR. To identify phylotypes characteristic of each season we performed indicator species analysis (ISA) implemented in PC-ORD. All data were used in the ISA, and samples were partitioned to include the solstice and equinox samples and the flanking sample weeks in 2009 as explained in the Results section. Phylotypes with indicator values > 50 and P < 0.005 were considered to be strong indicators. Prior to RDA, environmental data was tested for normality (Shapiro–Wilks test, P < 0.05), and variables that were not normally distributed were transformed to a near normal distribution. All phytoplankton and bacterial abundances were log transformed. To adjust for high kurtosis, particulate organic carbon was square root transformed. RDA and partitioning of variance was used to determine the per cent of community variation explained by environmental variables (Legendre and Anderson, 1999). RDA was conducted on whole community and groups of samples as described in the Results section. Environmental factors were forward selected based on explanatory power for the whole community and seasonal periods using ORDISTEP Spearman correlations between Euclidean distances of the forward selected variables and Bray–Curtis similarity of the species matrices were calculated for whole community and seasonal samples using BIOENV. RDA/ORDISTEP/BIOENV analyses were run in R (v.2.15.3, R core team, 2011) using the VEGAN package (Oksanen et al., 2013).

Bayesian modelling and network analysis The Bayesian modelling framework called BioMiCo was used to infer how assemblages of phylotypes are mixed to form communities. A complete description of the model is provided in Shafiei and colleagues (M. Shafiei, K.A. Dunn, E. Boon, S. MacDonald, D.A. Walsh, H. Gu and J.P. Bielawski, submitted). The model was supplied with 16S rRNA phylotype abundance data, and the community composition of each sample was modelled by applying a Dirichlet prior to the parameters of the multinomial distribution. Under BioMiCo, inference of community structure employs supervised learning. Collapsed Gibbs sampling (2000 iterations) was used to learn the posterior distribution of the mixture weights given the assigned time points (SE, SS, AE and WS) for the 24 samples collected from 2005 through 2010. It is during this ‘training phase’ that the model learns which assemblages are associated with the seasonal time points. A large posterior mixing probability is taken as evidence of an association between an assemblage and a seasonal time point. The user must specify the number of assemblages in the model. As long as the number of assemblages is not too small, the results should be robust to the user-supplied value. This is because redundant

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661

3658 H. El-Swais et al. assemblages carry very little weight (very low posterior mixing probability). To guard against unnecessary community complexity, we choose the concentration parameter values for the Dirichlet prior to be close to zero (αθ = 0.01). Because we had no prior preference for a particular assemblage, we used a symmetric Dririchlet prior. The sparsity introduced by this prior solves model identifiability issues, reduces model variance and improves model interpretability (Shafiei et al., submitted). Indeed, using the prior in this way was effective for these data. The model was run by assuming 100 possible assemblages. The same strategy was employed with the prior placed on the phylotype composition of an assemblage; i.e. we employed a sparse symmetric Dirichlet prior with concentration parameters set close to zero (αθ = 0.01). The trained model is a predictive model; it can be used to predict the factor labels for independent data for which there are no factor assignments. We took advantage of this to evaluate the trained model. We employed ‘leave-one-out’ cross-validation as a means of measuring the prediction error on new data (Hastie et al., 2009). Cross-validation is widely recognized as a rigorous technique for validating a predictive model (Hastie et al., 2009). Starting with the 24 samples collected at the SE, SS, AE and WS time points from 2005 to 2010, we removed one sample and trained the model on the remaining 23 samples. This trained model was then used to predict the time point of the one sample that was not included in the training data (this one sample is referred to as the ‘test sample’). We computed the posterior probability that the test sample came from each season, and the provenance of the test sample was classified according to the maximum of these posterior probabilities. The procedure was then repeated for each of the 24 samples. As both the predicted and the true time points are known for each sample at the end of the leave-one-out rotation, prediction accuracy is easily measured as the percent of samples that were correctly classified. Classification according to the maximum posterior probability yielded 100% accuracy. LSA was conducted on the principal phylotypes of the five major assemblages (Ruan et al., 2006) using the ELSA virtual machine created by Xia and colleagues (2013) and no time lag. Correlation networks were visualized using CYTOSCAPE (Shannon et al., 2003).

Acknowledgements We would like to acknowledge Kevin Pauley and Jeff Anning for logistical support in collecting samples from Bedford Basin. We thank Joseph R. Mingrone for helpful discussions about the computational challenges posed by our analysis,and for his direct assistance with the computational resources. This work was supported by National Sciences and Engineering Researh Council of Canada (DG402214-2011 and DG298294-2009), Canada Research Chairs Program (950-221184) and Canadian Institutes of Health Research (CMF-108026) research grants and the Bedford Basin Monitoring Program of Fisheries and Oceans Canada, the Atlantic Computational Excellence Network (ACEnet) and the Tula Foundation. The authors have no conflict of interest.

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Bacterial diversity in Bedford Basin Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Fig. S1. Physico-chemical conditions in Bedford Basin during 2005–2010. The black line shows the weekly data from the Bedford Basin monitoring programme, and red points indicate samples used in this study. Fig. S2. Plankton variability in Bedford Basin during 2005– 2010. The black line shows the weekly data from the Bedford Basin monitoring programme, and red points indicate samples used in this study. Fig. S3. The taxonomic composition of bacterioplankton communities at the spring equinox, summer solstice, autumn

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equinox and winter solstice averaged over a 6 year period (2005–2010). Fig. S4. Partial RDA and explanatory factors for (A) all samples, as well as the (B) spring transition, (C) autumn transition and (D) winter transition periods. Fig. S5. Taxonomic composition of bacterioplankton communities sampled biweekly over the 2009 annual cycle. Fig. S6. Temporal dynamics of the KSA1-0071 phylotype across the 2009 annual cycle. Table S1. Physico-chemical data. Table S2. Data on plankton abundances. Table S3. 16S rRNA gene data summary.

© 2014 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3642–3661