Physical and biological correlates of virus dynamics in the southern Beaufort Sea and Amundsen Gulf

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Journal of Marine Systems 74 (2008) 933 – 945 www.elsevier.com/locate/jmarsys

Physical and biological correlates of virus dynamics in the southern Beaufort Sea and Amundsen Gulf Jérôme P. Payet a , Curtis A. Suttle a,b,c,⁎ b

a Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, BC, Canada V6T 1Z4 Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada V6T 1Z4 c Department of Botany, University of British Columbia, Vancouver, BC, Canada V6T 1Z4

Received 10 April 2007; received in revised form 30 October 2007; accepted 18 November 2007 Available online 28 November 2007

Abstract As part of the Canadian Arctic Shelf Exchange Study (CASES), we investigated the spatial and seasonal distributions of viruses in relation to biotic (bacteria, chlorophyll-a (chl a)) and abiotic variables (temperature, salinity and depth). Sampling occurred in the southern Beaufort Sea Shelf in the region of the Amundsen Gulf and Mackenzie Shelf, between November 2003 and August 2004. Bacterial and viral abundances estimated by epifluorescence microscopy (EFM) and flow cytometry (FC) were highly correlated (r2 = 0.89 and r2 = 0.87, respectively), although estimates by EFM were slightly higher (FC = 1.08 × EFM + 0.12 and FC = 1.07 × EFM + 0.43, respectively). Viral abundances ranged from 0.13 × 106 to 23 × 106 ml− 1, and in surface waters were ~ 2fold higher during the spring bloom in May and June and ~ 1.5-fold higher during July and August, relative to winter abundances. These increases were coincident with a ~ 6-fold increase in chl a during spring and a ~ 4-fold increase in bacteria during summer. Surface viral abundances near the Mackenzie River were ~ 2-fold higher than in the Mackenzie Shelf and Amundsen Gulf regions during the peak summer discharge, concomitant with a ~ 5.5-fold increase in chl a (up to 2.4 μg l− 1) and a ~ 2-fold increase in bacterial abundance (up to 22 × 105 ml− 1). Using FC, two subgroups of viruses and heterotrophic bacteria were defined. A low SYBR-green fluorescence virus subgroup (V2) representing ~ 71% of the total viral abundance, was linked to the abundance of high nucleic acid fluorescence (HNA) bacteria (a proxy for bacterial activity), which represented 42 to 72% of the bacteria in surface layers. A high SYBR-green fluorescence viral subgroup (V1) was more related to high chl a concentrations that occurred in surface waters during spring and at stations near the Mackenzie River plume during the summer discharge. These results suggest that V1 infect phytoplankton, while most V2 are bacteriophages. On the Beaufort Sea shelf, viral abundance displayed seasonal and spatial variations in conjunction with chl a concentration, bacterial abundance and composition, temperature, salinity and depth. The highly dynamic nature of viral abundance and its correlation with increases in chl a concentration and bacterial abundance implies that viruses are important agents of microbial mortality in Arctic shelf waters. © 2007 Elsevier B.V. All rights reserved. Regional index terms: Canada; Beaufort Sea; Mackenzie Shelf; Amundsen Gulf Geographic bounding coordinates: 69–72° N; 122–139° W Keywords: Marine viruses; Bacteria; Chlorophyll a; Arctic; Flow cytometry; Seasonal variation; Spatial variation

⁎ Corresponding author. Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, BC, Canada V6T 1Z4. Tel.: +1 604 822 8610; fax: +1 604 822 6091. E-mail address: [email protected] (C.A. Suttle). 0924-7963/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2007.11.002

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1. Introduction The seas overlying the Arctic shelf encompass ~50k of the total area of the Arctic Ocean (Jakobsson et al., 2004). These wide shallow shelves are strongly influenced by coastal rivers, which discharge high amounts of inorganic and organic materials during summer (Rachold et al., 2004; Macdonald and Yu, 2006). They are also important areas for primary production in spring and summer, as the water column stratifies and the ice edge retreats. Additionally, they play key roles in global carbon cycling (e.g. Stein and Macdonald, 2004, and references therein) and in processing organic matter, ultimately affecting the water properties of the Arctic Ocean (e.g. Stein and Macdonald, 2004; Macdonald et al., 2005). The Arctic shelves are also among the most sensitive regions to global climate change and potentially to ecosystem changes associated with warming, sea-ice reduction, river runoff and precipitation increase (e.g. Carmack and Macdonald, 2002; Stein and Macdonald, 2004). Such changes would likely affect the distribution, activity and diversity of microbial communities and ultimately the cycling of organic matter on the shelves. Hence, knowing the composition of microbial communities on the Arctic shelves and understanding the factors that influence their distribution is critical given their central role in global carbon cycling and other biogeochemical processes. Viruses are the most abundant biological entities in the sea. They affect mortality and thereby influence the diversity of autotrophic and heterotrophic marine microbial communities; in turn this affects global geochemical cycles (e.g. see reviews by Fuhrman, 1999; Wommack and Colwell, 2000; Weinbauer, 2004; Suttle, 2005, 2007). Approximately 50% of bacterial production and up to 26% of the total organic carbon fixed by photosynthesis is lost through viral lysis each day (Fuhrman, 1999; Wilhelm and Suttle 1999). The lytic destruction of microbial cells shunts nutrients between particulate and dissolved phases, suggesting that viruses are a major driving force in the biogeochemistry of the ocean. The few studies that have examined viruses in the Arctic Ocean have found them to be abundant, dynamic and diverse (Maranger et al., 1994; Steward et al., 1996; Steward et al., 2000; Yager et al., 2001; Middelboe et al., 2002; Hodges et al., 2005; Angly et al., 2006; Wells and Deming, 2006a,b). Therefore, viruses likely have a significant effect on the structure, productivity and function of Arctic-shelf microbial communities. This paper documents the seasonal and spatial variations in viral abundance as it relates to biotic and abiotic factors in the area of the Mackenzie River and

Amundsen Gulf in the Canadian Arctic. The results demonstrate that the viral abundance shows strong seasonal and spatial variability, and that different subsets of the viral community appear to be influenced most strongly by different biotic and abiotic variables. 2. Materials and methods 2.1. Sampling procedures The study was carried out onboard the CCGS Amundsen from 4 November 2003 to 10 August 2004 in the shelf area of the Mackenzie River and Amundsen Gulf in the south-eastern Beaufort Sea of the Canadian Arctic as part of the Canadian Arctic Shelf Exchange Study (CASES). From 4 November 2003 to 6 August 2004, water samples for a seasonal study were collected on 21 occasions at Station 200 (70°03′ N, 126° 30′ W; bottom depth ~230 m) in Franklin Bay (Fig. 1), where the Amundsen overwintered in first-year land-fast ice (~1 to 2 m) from 4 December 2003 to 31 May 2004. In addition, from 4 July to 10 August 2004 the Amundsen sampled eight stations (Stns 906, 912, 803, 718, 650, 415, 200 and 106) along a west–east transect extending from the Mackenzie River to the Amundsen Gulf (Fig. 1). These stations were within 69.5° to 71.5° N, and 122.3° to 138.6° W, and were chosen to explore the influence of the Mackenzie River on the hydrographical and microbial variables in summer. The stations were grouped by location into river-plume (RP), mid-shelf (MS) and gulf (G) (Fig. 1). At each station, vertical profiles of temperature (T) and salinity (S) were obtained with a CTD Sea-bird SBE 911 equipped with pressure, chlorophyll fluorescence, light transmission, oxygen, pH and PAR (Photosynthetically Active Radiation; 400–700 nm) probes. The CTD system was mounted on a carousel rosette carrying 24 12-l Niskin bottles. Water samples for chlorophyll a (chl a), viral abundance (VA) and heterotrophic bacterial abundance (BA) were collected from up to 8 depths. Samples (~0.5– 3 l) from each depth were immediately pre-filtered through 120-μm mesh-size Nitex to remove large particles and dispensed into acid-cleaned, sample-rinsed polyethylene 20-l carboys. Subsamples were then taken for determination of VA, BA and chl a concentrations as outlined below. The data were subdivided by season into late fall (4 November–20 December), winter (21 December–20 March), spring (21 March–24 June) and summer (24 June–10 August). In winter and spring, while the Amundsen was icelocked, a 5-l Go-Flo bottle was used to collect surface water (3 and 10 m) through an ice-hole that was located ~ 500 m from the ship, to avoid contamination. The water

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Fig. 1. Map of the study area on the SE Canadian Arctic Shelf, showing sampling stations on the Mackenzie Shelf/Amundsen Gulf system during CASES. Station 200 in Franklin Bay was sampled seasonally from November 2003 to August 2004. Stations 906 to 106 were sampled in July– August 2004 along the cruise track (black dashed arrow) from the Mackenzie River plume to the Amundsen Gulf. Stations were grouped in 3 regions according to their locations on the shelf: black circles, River plume (RP); white circles, Mid-shelf (MS); and grey circles, Gulf (G).

was transferred into acid-cleaned, sample-rinsed 20-l carboys, and gently transported back to the ship. 2.2. Chl a concentrations Chl a concentrations, a proxy for phytoplankton biomass, were provided by W.F. Vincent (Laval University). Seawater samples (0.1–2 l) were filtered onto Whatman GF/F filters, extracted for 24 h in 96% ethanol and analyzed on a calibrated Cary Eclipse spectrofluorometer as described in Garneau et al.(in press). 2.3. Enumeration of bacteria and viruses Subsamples (15 ml) were taken from the pre-filtered samples to determine VA and BA using flow cytometry (FC) and epifluorescence microscopy (EFM). For FC analysis, duplicate subsamples (1.8 ml) were dispensed into two sterile cryovials (2 ml), fixed with 0.5% glutaraldehyde (EM-grade), frozen in liquid nitrogen and stored at −80 °C. Once ashore, viruses and bacteria were stained with SYBR Green I (Invitrogen) and

enumerated separately by FC (FACSCalibur, BectonDickinson) according to Marie et al. (1999) and Brussaard (2004). To avoid coincidence, subsamples were diluted 20 to 50-fold for viruses and 1 to 10-fold for bacteria in sterile 0.02-μm filtered TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0). Yellow-green 0.92-μm beads (Fluoresbrite Microparticles, Polysciences) of known concentration (~2 × 105 ml− 1) were added in all samples as an internal standard. The virus-buffer suspension was stained for 10 min at 80 °C in the dark before cooling for 5 min at room temperature. Virus counts were corrected for background counts in TE buffer made in 0.02-μm filtered seawater and processed the same way as for the samples (Brussaard, 2004). The background counts were typically b 5% of the virus counts in the samples. Data were collected in list mode on log scale and then analyzed using WinMDI (Version 2.8, Trotter, http://facs.scripps.edu/ software.html) and Cell-Quest software (Becton-Dickinson). Before data acquisition, instrument performance was checked and manually optimized using BD CaliBRITE beads and FACSComp software (BD Biosciences). Cytograms and histograms obtained during

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Fig. 2. Typical flow cytograms and histograms produced by flow cytometer analysis showing (a,b) bacterial and (c,d) viral populations from a surface water sample collected in Franklin Bay in summer. Based on the density plots of SYBR-green fluorescence vs. side scatter and histograms of SYBRgreen fluorescence vs. number of events, two bacterial subgroups with high and low nucleic acid (HNA and LNA, respectively) and two virus subgroups with high and low SYBR-Green fluorescence (V1 and V2, respectively) were distinguished.

this study typically displayed two main subgroups of heterotrophic bacteria (Fig. 2a and b), with relative high and low nucleic acid fluorescence (HNA and LNA, respectively), and two main viral subgroups (Fig. 2c and d), with relative high and low SYBR green fluorescence (V1 and V2, respectively), similar to previous studies in other marine environments (Gasol et al., 1999; Marie et al., 1999; Lebaron et al., 2001; Brussaard, 2004). For EFM, duplicate slides for determining BA and VA were prepared immediately after collecting pre-filtered water samples following the protocols of Hennes and Suttle (1995). Briefly, viruses in unfixed seawater subsamples (0.9 ml) were filtered onto 0.02-μm poresize Anodisc filters (Whatman), immediately stained with Yo-Pro-1 (Invitrogen) for 48 h in the dark, rinsed with MilliQ water and mounted onto glass slides with glycerol. The slides were either frozen or counted immediately using a wide-blue filter set (excitation 450–480 nm, 515nm cutoff). For each sample, a minimum of 200 viruses or bacteria were counted in 20 randomly selected fields (Suttle, 1993). 2.4. Data analysis All the data were treated as a set of samples between 0 and 60 m, since most of the seasonal and spatial

variation in physical and biological parameters occurred over these depths. Data are presented as mean values with standard deviations (±SD). Nonparametric ANOVA on ranks (Kruskal–Wallis) with Dunn's test were used to test the seasonal and spatial variances of the different physical (T, S and depth) and biological variables (chl a, BA, HNA, LNA, VA, V1 and V2). The Spearman rank order coefficient (rs) was used to determine the associations between biotic and abiotic variables. Stepwise multiple regression (SMR), with a forward procedure (P-to-enter = 0.15), was then used to identify the best subsets of independent variables which contributed significantly to the observed variation in viral properties (VA, V1 and V2). Multi-colinearity among independent variables was avoided by examining tolerance values; no variable in the models had a tolerance b0.4 (values b 0.4 indicate serious problems with multicolinearity). The best model was obtained by combining stepwise procedures and manually selecting the independent variables possessing higher correlations with the dependent variables. Explained variance was measured as the adjusted r2 value (%) to account for the increased variance explained with increasing numbers of independent variables. Since the data revealed non-normality and heterogeneity of variance, they were log (x + 1) transformed prior to statistical analyses, except for T and S.

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Fig. 3. Vertical profiles of (a,c) temperature (T) and (b,d) salinity (S) in the upper 60 m, during a complete seasonal cycle in Franklin Bay (left panel) and along the west–east cruise track across the river plume (RP), mid-shelf (MS) and gulf (G) regions (c, d) (right panel). Black triangles indicate the station number. Black dots show the depth of the samples collected.

Analyses were performed using Systat 11® (Systat Software Inc.). 3. Results and discussion 3.1. Hydrographic features From 4 December 2003 to 31 May 2004, the Amundsen was immobilized in 1 to 2 m thick land-fast ice in Franklin Bay (Stn 200). During this period, ice covered most areas of the MS and G, although the ice started to melt earlier near the Mackenzie River. Surface waters at Stn 200 were still ice-covered until early July 2004. Most of the stations sampled on the west–east transect were icefree during July and August 2004, except for ice floes at Stn 650 in the MS region (Fig. 1). In Franklin Bay (e.g. at Stn 200), the water column was vertically stratified and the upper polar mixed layers (b 30 m) displayed strong seasonal variability, with highest S values (N31 psu) and lowest T (b −1.7 °C) in winter and early spring compared to fall and summer (Fig. 3a and b). The brine release due to ice growth during the winter–spring period could be responsible for this increase in S and decrease in T in near-surface layers;

whereas, freshwater flux due to melting ice likely resulted in a fresher upper ocean in early summer. In addition, inputs of warm freshwater due to the adjacent coastal river runoffs, such as the Horton River (70°00′ N, 126°.70′ W; e.g. Fig. 1), could also have influenced surface waters and further enhanced the stratification of the upper ocean in Franklin Bay. Another striking feature of the upper polar layers in Franklin Bay was the presence of a thin temperature inversion layer (b−1.2 °C) between 20 and 50 m during the winter–spring period (Fig. 3a), roughly coinciding with the depth of the pycnocline. Further seasonal hydrographic details for Stn 200 in Franklin Bay measured during this cruise can also be found elsewhere (Forest et al., 2007; Garneau et al., in press). Along the west–east cruise track during the summer, there was a pronounced horizontal gradient in surface waters due to the intrusion of relatively warm (T N 4 °C) and brackish freshwater (S b 20 psu) from the Mackenzie River into the colder (T b 2 °C) and denser (S N 20 psu) polar mixed waters of the Beaufort Sea shelf (Fig. 3c and d). At stations in the RP region, the surface waters were significantly warmer and fresher (7.1 ± 2.1 °C and 19.1 ± 4.3 psu, respectively) than at stations in the MS (1.9 ± 2.4 °C and 28.3 ± 2.1 psu, respectively) and G regions

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Fig. 4. Vertical profiles of chlorophyll a (chl a) concentrations in the upper 60 m, (a) during a complete seasonal cycle in Franklin Bay and (b) along the west–east cruise track across the river plume (RP), mid-shelf (MS) and gulf (G) regions. Black triangles indicate the station number. Black dots show the depth of the samples collected.

(2.7 ± 0.1 °C and 24.6 ± 4.3 psu, respectively) (p b 0.05, K–W on ranks with Dunn's test). Within the Arctic, the Mackenzie River is the fourth largest river in terms of water discharge, with a maximum activity during the summer period (Macdonald et al., 1998). In July, the large warm river inflow from the Mackenzie River in RP increased the vertical salinity and thermal stratification of the surface layers (Fig. 3c and d). However, the influence of the Mackenzie River markedly decreased eastward in the MS, because of the gradual dilution of freshwater into the Arctic Ocean surface layers. In the G region, the surface hydrography was more similar to Stn 200 in Franklin Bay and the stratification corresponded to the upper Arctic Ocean layers. Overall, the seasonal and spatial profiles of T and S in the top 60 m of the water column in Franklin Bay and along the longitudinal transect were typical of those found in previous studies (Macdonald et al., 1998; Carmack and Macdonald, 2002; Garneau et al., 2006; Macdonald and Yu, 2006). 3.2. Chlorophyll-a The chl a concentrations were low (b 0.04 to 2.37 μg l− 1), but variable throughout the study, with surface (2– 6 m) or subsurface (25–50 m) peaks (Fig. 4). In Franklin Bay, surface chl a was almost undetectable in fall and winter with values ranging from b0.05 to 0.27 μg l− 1, whereas in summer, concentrations ranged from 0.21 to 0.61 μg l− 1 (Fig. 4a). Phytoplankton biomass increased by ~6-fold in the near surface in mid-May (up to 0.47 μg l− 1), although a few smaller peaks (b 0.26 μg l− 1) were detected earlier in April–May (Fig. 4a). Chl a concentrations were significantly higher in spring and summer (0.31 ± 0.08 μg l− 1 and 0.29 ± 0.17 μg l− 1,

respectively), than in fall and winter (0.08 ± 0.04 μg l− 1 and 0.07 ± 0.06 μg l− 1, respectively) (p b 0.05, K–W on ranks with Dunn's test). The depth of chl a maxima shifted from shallower surface peaks (~3 to 15 m) in spring to deeper peaks (e.g. ~45 m) in summer, corresponding with waters immediately above the pycnocline (e.g. Fig. 3b). In spring, the increase of chl a in the near surface was probably related to the release of algae from the bottom of the sea ice (Riedel et al., 2006; Renaud et al., 2007). Likewise, Renaud et al. (2007) measured a 10-fold increase in chl a on the bottom of the sea ice in May 2004 and a 7fold increase in chl a sedimentation rates in June 2004 at Stn 200 in Franklin Bay. With the spring increase of light, ice-algae developed and were released from the melting sea-ice. After the melt, and with the onset of stratification in the upper layers, the phytoplankton bloomed and depleted the nutrients above the pycnocline. Following the bloom, chl a accumulated near the pycnocline, possibly because of phytoplankton growth being supported by advection of nutrients from below the pycnocline. Similar to hydrographic features, chl a displayed a strong longitudinal gradient (Fig. 4b), with concentrations ~ 5.5-fold higher in the RP compared to the MS region, and a surface maximum of 2.37 μg l− 1 at Stn 912 near the Mackenzie River. On average, chl a in the upper layers was significantly higher in the RP compared to the MS and G regions (1.21 ± 0.96 μg l− 1, 0.26 ± 0.14 μg l− 1 and 0.21 ± 0.11 μg l− 1, respectively) (p b 0.05, K–W on ranks with Dunn's test). In contrast to the RP, where phytoplankton biomass was confined nearer to the surface, chl a in both the MS and G regions was generally lower in surface waters (b0.3 μg l− 1) but had deeper maxima (~ 20–50 m; up to 1.7 μg l− 1) than in the RP (Fig. 4b). The Mackenzie River may have directly affected (via dilution or enrichment of populations) and

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Fig. 5. Seasonal and spatial vertical profiles of (a,b) heterotrophic bacterial abundance (BA), (c,d) the abundance of high nucleic acid (HNA) bacteria, and (e,f) the abundance of low nucleic acid (LNA) bacteria in the upper 60 m during a complete seasonal cycle in Franklin Bay (left panel) and along the west–east cruise track across the river plume (RP), mid-shelf (MS) and gulf (G) regions (right panel). Black triangles indicate the station number. Black dots show the depth of the samples collected.

indirectly (via increasing nutrient supply) the surface phytoplankton biomass in the upper waters in RP region. Garneau et al. (2006) also observed a similar trend in chl a distributions with a remarkable decrease in chl a levels from the RP region towards the more oligotrophic and marine MS waters. 3.3. Heterotrophic bacteria There was good agreement between estimates of total heterotrophic bacterial abundances (BA) determined by flow cytometry (FC) and epifluorescence microscopy

(EFM) (r2 = 0.89, n = 204, p b 0.001, FC = 1.08 × EFM + 0.12) (data not shown), although, high background fluorescence made it difficult to count bacteria in the turbid RP surface waters (e.g. Stns 912 and 906). In addition, estimates of BA were more precise by FC than EFM, with mean coefficients of variation (CV) between duplicates of 4.1% and 9.3%, respectively. Flow cytometric estimates of BA in the upper 60 m ranged from 1.6 × 105 to 25 × 105 ml− 1 with a mean of 7.3 × 105 ± 5.5 × 105 ml− 1. Similarly to T, S and chl a, BA displayed strong spatial and seasonal variation (Fig. 5a and b) and generally followed phytoplankton distributions

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Table 1 Spearman's rank order correlation matrix for the relationship between biotic and abiotic variables for the upper 60 m at all stations (n = 191)

Depth T S Chl a V2 V1 VA HNA LNA

T

S

Chl a

V2

V1

VA

HNA

LNA

BA

− 0.05

0.78 ⁎⁎⁎ −0.32⁎

−0.18⁎ 0.29⁎ −0.19⁎

− 0.18⁎⁎ 0.59⁎⁎⁎ − 0.32⁎ 0.54⁎⁎⁎

− 0.35⁎⁎ 0.33⁎⁎⁎ − 0.38⁎⁎⁎ 0.56⁎⁎⁎ 0.76⁎⁎⁎

−0.21⁎⁎ 0.56⁎⁎⁎ −0.35⁎ 0.55⁎⁎⁎ 0.98⁎⁎⁎ 0.81⁎⁎⁎

−0.16⁎ 0.62⁎⁎⁎ −0.32⁎ 0.62⁎⁎⁎ 0.84⁎⁎⁎ 0.73⁎⁎⁎ 0.84⁎⁎⁎

−0.21⁎ 0.63⁎⁎⁎ −0.41⁎⁎ 0.55⁎⁎⁎ 0.78⁎⁎⁎ 0.62⁎⁎⁎ 0.77⁎⁎⁎ 0.86⁎⁎⁎

−0.21⁎ 0.62⁎⁎⁎ −0.47⁎ 0.60⁎⁎⁎ 0.83⁎⁎⁎ 0.67⁎⁎ 0.80⁎⁎⁎ 0.97⁎⁎⁎ 0.94⁎⁎⁎

Variables: BA (bacterial abundance), LNA and HNA (low and high nucleic acid content bacteria), VA (viral abundance), V1 and V2 (low and high fluorescence viral subgroups), depth, Chl a (chlorophyll-a), T (temperature), S (salinity). Significance level: ⁎p b 0.05, ⁎⁎p b 0.01, ⁎⁎⁎ pb0.001.

(e.g. Figs. 3 and 4). BA, like chl a, was generally highest near the surface and often decreased with depth. In addition, for the overall data set, BA was positively correlated with chl a (rs = 0.60, p b 0.001, n = 191) and T (rs = 0.62, p b 0.001, n = 191), but negatively correlated to S (rs = −0.47, p b 0.05, n = 191) and depth (rs = −0.21, p b 0.05, n = 191) (Table 1). In Franklin Bay (Stn 200), BAwas significantly lower in fall and winter (5.54 × 10 5 ± 0.62 × 10 5 ml − 1 and 3.62 × 105 ± 0.67 × 105 ml− 1, respectively) than in spring and summer (12.6× 105 ± 2.3× 105 ml− 1 and 5.1 × 105 ± 1.5× 105 ml− 1, respectively) (p b 0.05, K–W on ranks, with Dunn's test). Following the spring phytoplankton bloom, BA markedly increased (~2-fold) in surface layers later in summer (Fig. 5a). In winter and spring, there was a succession of weak peaks in BA (b 11× 105 mL− 1) near the surface (b15 m) that were coincident with peaks in surface chl a and the temperature inversion layer (~20–40 m) (e.g. Figs. 3a, 4b and 5a). A greater and deeper peak in BA coincided with the pycnocline and chl a maximum in summer (e.g. Figs. 3b, 4a and 5a). In winter and spring, bacterial growth, and hence abundance, in the upper layers was likely reduced by low T, nutrients and organic carbon supplied by phytoplankton and river runoff, relative to the fall and particularly the summer. Further details about the seasonal bacterial distribution in Franklin Bay are discussed by Garneau et al. (in press). Similarly to chl a, BA (Fig. 5b) was significantly higher in the RP than in the MS and G regions (14.2 × 105 ± 6.6 × 105 ml− 1, 8.1 × 105 ± 4.1 × 105 ml− 1 and 10.1 × 105 ± 3.3 × 105 ml− 1, respectively) (p N 0.05, K–W on ranks with Dunn's test). Likely, the warm freshwater from the Mackenzie River and high phytoplankton biomass in RP region, resulted in higher BA in surface layers of the RP region. As previously reported in other marine environments (Marie et al., 1999; Gasol et al., 1999; Lebaron et al., 2001; Li & Dickie 2001; Zubkov et al., 2001a,b), two subgroups

of high nucleic acid fluorescence (HNA) and low nucleic acid fluorescence (LNA) bacteria were resolved by FC, based on relative SYBR-green fluorescence and side scatter (Fig. 2a and b). On average, HNA bacteria were more abundant in the upper 60 m than LNA bacteria (6.97 × 105 ± 3.51 × 10 5 ml − 1 and 5.20 × 105 ± 2.41 × 105 ml− 1, respectively) (Fig. 5c,d,e and f), but not significantly different (p N 0.05, t-test on ranks), comprising a mean proportion of 0.58± 0.10 of the total BA. HNA bacteria generally exceeded LNA bacteria in surface waters (b 50 m), but typically decreased with depth compared to LNA bacteria. At Stn 200 in Franklin Bay, the proportion of HNA cells was significantly higher in summer than in spring (p b 0.05, K–W on ranks, with Dunn's test), fall and winter (0.60 ± 0.08, 0.57 ± 0.09, 0.52 ± 0.07 and 0.48 ± 0.07, respectively) (Fig. 5c). This is coincident with the higher bacterial production at this time (Garneau et al., in press), which is likely supported by increased inputs of organic carbon from phytoplankton, river runoff and warmer surface T. Additionally, in winter and spring, HNA bacteria slightly increased at the temperature inversion layer (e.g. Figs. 3a and 5c), suggesting that a somewhat higher fraction of bacteria were active at this depth. Consistent with bacteria being more productive near the Mackenzie River as reported by Garneau et al. (2006), the abundance (Fig. 5d) and proportion (0.66) of HNA bacteria was higher in surface waters at Stn 912, although the proportion was not significantly different than observed for the MS and G regions (0.61 ± 0.08, 0.57 ± 0.06 and 0.56 ± 0.04, respectively) (p N 0.05, K–W on ranks). In the RP, warmer freshwaters and increased organic nutrients from the Mackenzie River and phytoplankton likely stimulated the productivity of the bacterial communities. The abundance of HNA bacteria increased significantly with chl a concentration (rs = 0.62, Table 1) in spring–summer at Stn 200 and also during the summer in

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Fig. 6. Linear regression (Model-II) of estimates of total viral abundance (VA) made by flow cytometry (FC) and epifluorescence microscopy (EFM); a) all the data and b) subset of the data. The dotted lines represent the best-fitted regressions and the solid lines a 1:1 relationship between FC and EFM estimates.

the RP region (p b 0.05, K–W on ranks, with Dunn's test). Overall, these results are consistent with suggestions that HNA bacteria are more metabolically active than LNA bacteria (e.g. Gasol et al., 1999; Lebaron et al., 2001), although LNA cells can account for a significant proportion of active cells in oligotrophic areas (Zubkov et al., 2001b; Jochem et al., 2004; Longnecker et al., 2005). 3.4. Viruses Estimates of viral abundance (VA) by FC and EFM were similar (r2 = 0.87, n = 204, p b 0.001, FC = 1.07 × EFM + 0.43), but slightly higher by FC (Fig. 6a). As for bacteria, high background fluorescence in the RP made counting by EFM difficult. When only the samples in which VA was lowest (b6 × 106 ml− 1) were used for the analysis, the correlation was significantly better (r2 = 0.91, n = 95, p b 0.001, FC = 1.02 × EFM + 0.40, e.g. Fig. 6b). Abundance estimates made on duplicate samples were more precise by FC (mean CV 7.3%) than by EFM (mean CV 15.4%). Similar to other biotic (chl a, BA, HNA and LNA) and abiotic (T and S) parameters, VA estimated by FC displayed strong spatial and seasonal variations with one or two subsurface peaks in the upper 60 m (Fig. 7a and b). The abundances ranged from ~1 × 106 to 27× 106 ml− 1 and exceeded BA by 5 to 60 times (16.2 ± 8.7). In the top 60 m of Franklin Bay (Stn 200), VA was significantly lower in fall and winter than in spring and summer (5.9 × 106 ± 2.5 × 106 ml− 1 , 5.3 × 106 ± 2.8 × 106 ml− 1, 10.2 × 106 ± 3.5 × 106 ml− 1 and 15.7 × 106 ± 3.9 × 106 ml− 1, respectively) (p b 0.05, K–W on ranks with Dunn's test). There were a few weak peaks in VA (b 12 × 106 ml− 1, e.g. Fig. 7a) at the surface and in the T inversion layer (~ 20–35 m) in late winter and spring, coincident with higher phytoplankton biomass and

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bacterial abundances (c.f. Figs. 4a and 5a), and indicating that viruses were a dynamic component of the microbial community even during the cold months. In summer, VA reached maxima (up to 19.8 × 106 ml− 1) at greater depths than in spring (b 15 m and ~ 45 m, respectively) (Fig. 7a), coincident with BA and chl a increases near the pycnocline (e.g. Figs. 3a, 4a and 5a). Furthermore, VA strongly increased in spring and summer (~ 2-fold and ~ 1.5-fold, respectively). Similar to the other biological variables, VA sharply increased (~3-fold) in surface waters in the RP (Fig. 7b) compared to the MS, and was significantly higher in the surface layers of the RP than in the MS and G (19.3 × 106 ± 5.3 × 106 ml− 1, 11.3 × 106 ± 4.8 × 106 ml− 1 and 14.8 × 106 ± 3.0 × 106 ml− 1, respectively) (p b 0.05, K–W on ranks, with Dunn's test). Furthermore, VA was significantly lower in the MS compared to the G (p b 0.05, K–W on ranks, with Dunn's test). The ratio of viruses to bacteria (VBR) in the upper 60 m varied strongly with season and region (range: 4.3– 40.2, mean ± SD: 14.4 ± 5.7) and increased with increases in autotrophic and heterotrophic host cells, particularly in spring–summer and at stations in the RP. For the seasonal data in Franklin Bay (Stn 200), the highest, but not significantly different (p N 0.05, K–W on ranks) VBR values were found in spring and in summer while the lowest values occurred in fall and winter (16.9 ± 8.6, 14.9 ± 4.7, 13.1 ± 6.4 and 13.7 ± 5.6, respectively). Previous studies found similar increases in the VBR during spring blooms in the region (Maranger et al., 1994; Yager et al., 2001). The VBR was also significantly higher in the RP than in the MS and G regions (17.7 ± 7.6, 12.5 ± 4.9 and 13.6 ± 5.0, respectively) (p b 0.05, K–W on ranks, with Dunn's test). As observed in previous studies in marine systems (Marie et al., 1999; Li and Dickie, 2001; Brussaard, 2004), low and high SYBR-Green fluorescence (V2 and V1, respectively) subgroups of viruses were clearly distinguished by FC (Fig. 2c and d). Overall, the abundance of V2 was significantly higher (p b 0.05, t-test on ranks) than V1 (5.42 × 10 6 ± 3.54 × 10 6 ml − 1 and 0.97 × 10 6 ± 0.86× 106 ml− 1, respectively) in the upper layers, and represented a greater proportion of the virioplankton (range: 0.69–0.97, mean ± SD: 0.87± 0.05). During the study, both V2 and V1 generally followed the same patterns (Fig. 7c,d,e and f), with marked increases in spring and in summer (Fig. 7c and e) and at stations in the RP region (Fig. 7d and f). However, V1 appeared to be tightly related to increases in chl a (e.g. Fig. 4a), and sharply decreased with depth. In Franklin Bay, both V2 and V1 displayed strong seasonal variation and increased in spring and summer (Fig. 7c and e), although V1

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Fig. 7. Seasonal and spatial vertical profiles of (a, b) viral abundance (VA), (c, d) the abundance of low SYBR-green fluorescence viruses (V2), (e,f) the abundance of high SYBR-green fluorescence viruses (V1) in the upper 60 m during a complete seasonal cycle in Franklin Bay (left panel) and along the west–east cruise track across the river plume (RP), mid-shelf (MS) and gulf (G) regions (right panel). Black triangles indicate the station number. Black dots show the depth of the samples collected.

displayed a sharper increase in spring (~2.5-fold). As well, V2 and V1 were significantly higher in surface waters in the RP compared to the MS and G (pb 0.05, K–W on ranks, with Dunn's test), with both V2 and V1 being ~3fold higher in the RP compared to the MS and ~1.2-fold higher compared to the G (Fig. 7d and f). 3.5. Relationships between viruses and other variables Spearman's rank correlation on the entire data set for the upper 60 m (n = 191) indicated that VA was positively correlated with BA (rs = 0.83, p b 0.01), chl a (rs = 0.57, p b 0.01) and T (rs = 0.56, p b 0.01), and negatively correlated with S (rs = −0.35, p b 0.01) and depth (rs =

−0.35, p b 0.01) (Table 1). Both V1 and V2 were significantly correlated with chl a, HNA and LNA (Table 1). Furthermore, correlation analyses for different seasons and regions in the upper 60 m revealed a strong tendency for viruses to increase with phytoplankton and bacterial abundances. To identify which independent variables (BA, HNA, LNA, chl a, T and S) best explained the variation in viral properties (VA, V1 or V2) in the upper 60 m, stepwise multiple regressions (SMR) were performed using either the entire data set or the seasonal and spatial data sets (Table 2). With the entire data set, 56% of the total variation in VA was explained by BA, chl a and S, with the highest β values for BA and a negative β coefficient for S (Table 2). For V1 and V2, 61% and 53%

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Table 2 Results of stepwise multiple regression (SMR) analysis with viral properties (VA, V1, V2) as the dependent variables and bacterial properties (BA, HNA, LNA), chlorophyll-a (Chl a) concentration, depth, temperature (T) and salinity (S) as independent variables Overall model

Independent variables Chl a

BA

HNA

S

T

β

Cr

β

Cr

β

Cr

β

Cr

β

Cr2

All Stns VA 0.56⁎⁎⁎ V1 0.61⁎⁎⁎ V2 0.53⁎⁎⁎

0.372⁎⁎ 0.432⁎⁎ 0.352⁎⁎

0.54 0.59 0.52

0.701⁎⁎⁎ – –

0.51 – –

– 0.541⁎⁎⁎ 0.701⁎⁎⁎

– 0.44 0.48

−0.183⁎ −0.243⁎ −0.123⁎

0.55 0.66 0.58

– – –

– – –

Fall VA V1 V2

0.45⁎⁎⁎ 0.46⁎⁎ 0.38⁎⁎

– – –

– – –

0.452⁎⁎⁎ – –

0.42 – –

– 0.482⁎⁎ 0.592⁎⁎⁎

– 0.72 0.37

−0.621⁎⁎⁎ −0.611⁎⁎⁎ −0.651⁎⁎⁎

0.30 0.49 0.35

– – –

– – –

Winter VA V1 V2

0.29⁎ 0.30⁎⁎ 0.26⁎

– – –

– – –

0.323⁎ – –

0.39 – –

– – 0.353⁎⁎

– – 0.31

−0.522⁎⁎ −0.681⁎⁎ −0.472⁎⁎⁎

0.37 0.28 0.22

0.591⁎⁎ 0.482⁎⁎ 0.561⁎⁎

0.25 0.23 0.25

Spring VA V1 V2

0.61⁎⁎⁎ 0.63⁎⁎⁎ 0.62⁎⁎⁎

0.521⁎⁎⁎ 0.731⁎⁎⁎ 0.581⁎⁎⁎

0.58 0.64 0.52

– – –

– – –

– – –

– – –

−0.302⁎⁎ – −0.342⁎⁎⁎

0.62 – 0.60

– – –

– – –

Summer VA 0.88⁎⁎⁎ V1 0.71⁎⁎⁎ V2 0.77⁎⁎⁎

– 0.412⁎⁎ –

– 0.76 –

0.641⁎⁎⁎ – –

0.88 – –

– 0.651⁎⁎⁎ 0.721⁎⁎⁎

– 0.65 0.78

– – –

– – –

– – –

– – –

River plume VA 0.77⁎⁎⁎ V1 0.75⁎⁎⁎ V2 0.72⁎⁎⁎

0.521⁎⁎⁎ 0.561⁎⁎⁎ 0.532⁎⁎

0.57 0.52 0.58

0.462⁎⁎⁎ – –

0.67 – –

– 0.472⁎⁎⁎ 0.561⁎⁎⁎

– 0.72 0.58

– – –

– – –

0.403⁎⁎⁎ 0.383⁎⁎⁎ 0.473⁎⁎⁎

0.81 0.80 0.79

Mid-shelf VA 0.69⁎⁎⁎ V1 0.72⁎⁎⁎ V2 0.73⁎⁎⁎

0.332⁎⁎ 0.462⁎⁎⁎ 0.352⁎⁎⁎

0.70 0.74 0.65

0.571⁎⁎⁎ – –

0.56 – –

– 0.541⁎⁎⁎ 0.611⁎⁎⁎

– 0.57 0.62

−0.273⁎⁎⁎ −0.293⁎⁎ −0.283⁎⁎

0.72 0.70 0.75

– – –

– – –

Gulf VA V1 V2

0.312⁎⁎ 0.352⁎⁎⁎ 0.292⁎⁎

0.77 0.90 0.76

0.721⁎⁎⁎ – –

0.51 – –

– 0.771⁎⁎⁎ 0.711⁎⁎⁎

– 0.66 0.68

– – –

– – –

– – –

– – –

2

r

0.76⁎⁎⁎ 0.88⁎⁎⁎ 0.74⁎⁎⁎

2

2

2

2

All SMR analyses were performed on data from the upper 60 m and were run separately. SMR was first performed with the entire data set and the subsequent runs divided into 4 seasons (fall, winter, spring, summer) and into 3 regions (river plume, mid-shelf and gulf). Because LNA and HNA sometimes displayed low tolerance values (t b 0.4), only the best predictor was retained in the model, which was HNA. Depth was also discarded from SMR analysis since it was highly correlated to S. Overall model adjusted r2 and significance are presented, together with the standardized multiple regression coefficients (β) and cumulative coefficient (Cr2) of the variables, which were retained in the model after a stepwise procedure (⁎⁎⁎p b 0.001, ⁎⁎p b 0.01, ⁎p b 0.05). The subscript number indicates the rank order in which dependent variables were retained in the model. Those dependent variables that explain at least 50% (cumulatively) of the overall model r2 are indicated in bold.

of the total variation was explained by HNA, followed by chl a and S (Table 2). Segregation of the data by region or season generally increased the overall proportion of the total variance explained in viral properties, except for fall and winter, in which less was explained (Table 2). In fall, both S and BA explained 45% of the variation in VA, with

high negative β coefficients for S accounting for ~30% of the variation (Table 2). In winter, BA, T and S together explained 29% of the variation in VA, with T accounting for 25% of the variation. Subsurface T peaks (up to −1.3 °C) in the temperature inversion layer, in winter and spring, likely explained the positive association with VA

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and BA. In spring, chl a and S explained 61% of the total variability in VA, with chl a accounting for 58% of variation. In summer, BA explained 88% of total variation in VA. For viral properties, chl a was the best predictor for V1 and V2 variations in spring (64% and 52%, respectively) (Table 2); whereas, HNA bacteria was the best predictor for V2 and to a lesser extent V1 (78% and 65%, respectively) in summer (Table 2). For the spatial data, BA, chl a and T accounted for 77% of the variability in VA in the RP, with chl a and BA explaining 57% and 10% of the variation, respectively (Table 2). On the MS, similar predictors of VA were found, except that T was replaced by S in the model. This is likely the result of a shift in environmental conditions at the surface to more oceanic water. In the G, BA and chl a explained 76% of the variability in VA, with BA accounting for 51% of the variation. In terms of viral properties, V1 was closely associated with chl a in the RP, which explained 52% of the variation. Furthermore, when moving toward the G, chl a explained less of the variation in V1, but compared to V2, V1 remained more closely associated with chl a. HNA bacteria were the best predictors of V2 variation in all the regions, with greater β coefficients in the G (Table 2). Overall, SMR indicated clear seasonal and spatial variations in viral abundance that was tightly coupled to shifts in environmental and biological parameters. 4. Conclusions This study is the first to report a strong relationship between virus abundance estimated by flow cytometry (FC) and epifluorescence microscopy (EFM) in a large number (n = 204) of environmental samples. These data revealed pronounced seasonality and spatial gradients in viral abundance on the Beaufort Sea shelf, and demonstrated that these shifts were related to changes in the abundance of potential microbial hosts, trophic status and environmental variables. Chl a concentrations, and bacterial and viral abundances were highest in the surface layers although within the ranges reported for the area (Steward et al., 1996; Yager et al., 2001; Middelboe et al., 2002; Hodges et al., 2005; Garneau et al., 2006; (in press); Wells and Deming, 2006a,b). As well, freshwater inputs from the Mackenzie River in summer resulted in greater surface stratification, higher phytoplankton biomass, and more HNA bacteria and viruses. In contrast, the influence of the Mackenzie River was negligible eastward in the MS and G regions, where surface waters were more typical of the upper Arctic Ocean with low phytoplankton biomass and bacterial and viral abundances.

In Franklin Bay, there was high seasonal variability. In summer, melting ice and freshwater runoff from the Horton River increased surface stratification, and likely the supply of nutrients and organic carbon. As well, increasing irradiance from spring through summer triggered higher phytoplankton biomass, and a near-surface bloom when ice-algae were released during ice break-up. Increased phytoplankton biomass in surface waters was associated with enhanced viral and bacterial abundances, although the highest bacterial abundances were in summer when the surface temperature was warmer. A greater proportion of the putatively more active HNA bacteria occurred at the surface and subsurface during the summer. The abundances of V2 and V1 increased with trophic status, as evidenced by higher chl a levels and HNA bacterial abundances in spring–summer and at stations influenced by the Mackenzie River. V2 was most abundant and tightly tied to bacteria, especially to HNA bacteria, suggesting that they infect heterotrophic bacteria. On the other hand, V1 was tightly coupled to chl a concentration, supporting the idea that they infect eukaryotic phytoplankton. Overall, these results demonstrate that viruses are abundant and dynamic components of the aquatic microbial communities of the Canadian Arctic Shelf. As obligate pathogens, this implies that they are also significant agents of microbial mortality and thus affect the abundance, dynamics and diversity of microbial communities with consequent implications for carbon and nutrient cycling. Acknowledgements We gratefully thank A.I. Culley, A.M. Comeau, C. Pedrós-Alió, C. Lovejoy and C. Martineau for their efforts in field sampling, A.C. Ortmann and A.M. Chan for their logistic support and facilitation during the expedition. We are also grateful to M.-É. Garneau and W.F. Vincent for providing the chlorophyll a data. The assistance of the officers and crew of the CCGS Amundsen is greatly appreciated. We also thank the reviewers and editors who provided comments that improved the manuscript. This study was funded by NSERC through the Canadian Arctic Shelf Exchange Study (CASES) project and a Discovery Grant to CAS. This research is a contribution to the CASES project under the overall direction of L. Fortier. References Angly, F.E., et al., 2006. The marine viromes of four oceanic regions. PLoS Biol 4, 2121–2131. Brussaard, C.P.D., 2004. Optimization of procedures for counting viruses by flow cytometry. Appl. Environ. Microbiol. 70, 1506–1513.

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