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

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Journal of Marine System

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

Jérôme P. Payet a, Curtis A. Suttle a,b,c,⁎

a Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, BC, Canada V6T 1Z4b 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

Abstract

As part of the Canadian Arctic Shelf Exchange Study (CASES), we investigated the spatial and seasonal distributions of virusesin relation to biotic (bacteria, chlorophyll-a (chl a)) and abiotic variables (temperature, salinity and depth). Sampling occurred inthe southern Beaufort Sea Shelf in the region of the Amundsen Gulf and Mackenzie Shelf, between November 2003 and August2004. Bacterial and viral abundances estimated by epifluorescence microscopy (EFM) and flow cytometry (FC) were highlycorrelated (r2=0.89 and r2=0.87, respectively), although estimates by EFM were slightly higher (FC=1.08×EFM+0.12 andFC=1.07×EFM+0.43, respectively). Viral abundances ranged from 0.13×106 to 23×106 ml−1, and in surface waters were ~2-fold 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 regionsduring 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 inbacterial abundance (up to 22×105 ml−1). Using FC, two subgroups of viruses and heterotrophic bacteria were defined. A lowSYBR-green fluorescence virus subgroup (V2) representing ~71% of the total viral abundance, was linked to the abundance ofhigh nucleic acid fluorescence (HNA) bacteria (a proxy for bacterial activity), which represented 42 to 72% of the bacteria insurface layers. A high SYBR-green fluorescence viral subgroup (V1) was more related to high chl a concentrations that occurred insurface waters during spring and at stations near the Mackenzie River plume during the summer discharge. These results suggestthat V1 infect phytoplankton, while most V2 are bacteriophages. On the Beaufort Sea shelf, viral abundance displayed seasonal andspatial 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 abundanceimplies 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 GulfGeographic bounding coordinates: 69–72° N; 122–139° WKeywords: 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 604822 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

Please cite this article as: Payet, J.P., Suttle, C.A., Physical and biological correlates of virus dynamics in the southern Beaufort Sea andAmundsen Gulf, J. Mar. Syst. (2007), doi:10.1016/j.jmarsys.2007.11.002

mailto:[email protected]

http://dx.doi.org/10.1016/j.jmarsys.2007.11.002


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1. Introduction

The seas overlying the Arctic shelf encompass ~50kof the total area of the Arctic Ocean (Jakobsson et al.,2004). Thesewide shallow shelves are strongly influencedby coastal rivers, which discharge high amounts of inor-ganic and organic materials during summer (Racholdet al., 2004; Macdonald and Yu, 2006). They are alsoimportant areas for primary production in spring andsummer, as the water column stratifies and the ice edgeretreats. Additionally, they play key roles in global carboncycling (e.g. Stein and Macdonald, 2004, and referencestherein) and in processing organic matter, ultimatelyaffecting the water properties of the Arctic Ocean (e.g.Stein and Macdonald, 2004; Macdonald et al., 2005). TheArctic shelves are also among themost sensitive regions toglobal climate change and potentially to ecosystemchanges associated with warming, sea-ice reduction,river runoff and precipitation increase (e.g. Carmack andMacdonald, 2002; Stein and Macdonald, 2004). Suchchanges would likely affect the distribution, activity anddiversity of microbial communities and ultimately thecycling of organic matter on the shelves. Hence, knowingthe composition of microbial communities on the Arcticshelves and understanding the factors that influence theirdistribution is critical given their central role in globalcarbon cycling and other biogeochemical processes.

Viruses are the most abundant biological entities inthe sea. They affect mortality and thereby influence thediversity of autotrophic and heterotrophic marinemicrobial communities; in turn this affects globalgeochemical cycles (e.g. see reviews by Fuhrman,1999; Wommack and Colwell, 2000; Weinbauer, 2004;Suttle, 2005, 2007). Approximately 50% of bacterialproduction and up to 26% of the total organic carbonfixed by photosynthesis is lost through viral lysis eachday (Fuhrman, 1999; Wilhelm and Suttle 1999). Thelytic destruction of microbial cells shunts nutrientsbetween particulate and dissolved phases, suggestingthat viruses are a major driving force in the biogeochem-istry of the ocean. The few studies that have examinedviruses in the Arctic Ocean have found them to beabundant, 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; Anglyet al., 2006; Wells and Deming, 2006a,b). Therefore,viruses likely have a significant effect on the structure,productivity and function of Arctic-shelf microbialcommunities.

This paper documents the seasonal and spatialvariations in viral abundance as it relates to biotic andabiotic factors in the area of the Mackenzie River and

Please cite this article as: Payet, J.P., Suttle, C.A., Physical and biologiAmundsen Gulf, J. Mar. Syst. (2007), doi:10.1016/j.jmarsys.2007.11.002

Amundsen Gulf in the Canadian Arctic. The resultsdemonstrate that the viral abundance shows strongseasonal and spatial variability, and that differentsubsets of the viral community appear to be influencedmost strongly by different biotic and abiotic variables.

2. Materials and methods

2.1. Sampling procedures

The study was carried out onboard the CCGS Amund-sen from 4 November 2003 to 10 August 2004 in the shelfarea of the Mackenzie River and Amundsen Gulf in thesouth-eastern Beaufort Sea of the Canadian Arctic as partof the Canadian Arctic Shelf Exchange Study (CASES).From 4 November 2003 to 6 August 2004, water samplesfor a seasonal study were collected on 21 occasions atStation 200 (70°03′N, 126° 30′W; bottom depth ~230m)in Franklin Bay (Fig. 1), where the Amundsen over-wintered in first-year land-fast ice (~1 to 2 m) from 4December 2003 to 31 May 2004. In addition, from 4 Julyto 10 August 2004 the Amundsen sampled eight stations(Stns 906, 912, 803, 718, 650, 415, 200 and 106) along awest–east transect extending from theMackenzie River tothe Amundsen Gulf (Fig. 1). These stations were within69.5° to 71.5° N, and 122.3° to 138.6°W, andwere chosento explore the influence of the Mackenzie River on thehydrographical and microbial variables in summer. Thestations were grouped by location into river-plume (RP),mid-shelf (MS) and gulf (G) (Fig. 1).

At each station, vertical profiles of temperature (T) andsalinity (S) were obtained with a CTD Sea-bird SBE 911equipped with pressure, chlorophyll fluorescence, lighttransmission, oxygen, pH and PAR (PhotosyntheticallyActive Radiation; 400–700 nm) probes. The CTD systemwasmounted on a carousel rosette carrying 24 12-l Niskinbottles. Water samples for chlorophyll a (chl a), viralabundance (VA) and heterotrophic bacterial abundance(BA) were collected from up to 8 depths. Samples (~0.5–3 l) from each depth were immediately pre-filteredthrough 120-μm mesh-size Nitex to remove largeparticles and dispensed into acid-cleaned, sample-rinsedpolyethylene 20-l carboys. Subsamples were then takenfor determination of VA, BA and chl a concentrations asoutlined below. The data were subdivided by season intolate fall (4 November–20 December), winter (21December–20 March), spring (21 March–24 June) andsummer (24 June–10 August).

In winter and spring, while the Amundsen was ice-locked, a 5-l Go-Flo bottle was used to collect surfacewater (3 and 10m) through an ice-hole that was located ~500 m from the ship, to avoid contamination. The water

cal correlates of virus dynamics in the southern Beaufort Sea and


Fig. 1. Map of the study area on the SE Canadian Arctic Shelf, showing sampling stations on the Mackenzie Shelf/Amundsen Gulf system duringCASES. 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 regionsaccording to their locations on the shelf: black circles, River plume (RP); white circles, Mid-shelf (MS); and grey circles, Gulf (G).

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was transferred into acid-cleaned, sample-rinsed 20-lcarboys, and gently transported back to the ship.

2.2. Chl a concentrations

Chl a concentrations, a proxy for phytoplanktonbiomass, were provided by W.F. Vincent (Laval Uni-versity). Seawater samples (0.1–2 l) were filtered ontoWhatman GF/F filters, extracted for 24 h in 96% ethanoland analyzed on a calibrated Cary Eclipse spectro-fluorometer as described in Garneau et al.(in press).

2.3. Enumeration of bacteria and viruses

Subsamples (15 ml) were taken from the pre-filteredsamples to determine VA and BA using flow cytometry(FC) and epifluorescence microscopy (EFM). For FCanalysis, duplicate subsamples (1.8 ml) were dispensedinto two sterile cryovials (2 ml), fixed with 0.5%glutaraldehyde (EM-grade), frozen in liquid nitrogenand stored at −80 °C. Once ashore, viruses and bacteriawere stained with SYBR Green I (Invitrogen) and


enumerated separately by FC (FACSCalibur, Becton-Dickinson) according toMarie et al. (1999) andBrussaard(2004). To avoid coincidence, subsampleswere diluted 20to 50-fold for viruses and 1 to 10-fold for bacteria in sterile0.02-μmfilteredTE buffer (10mMTris, 1mMEDTA, pH8.0). Yellow-green 0.92-μm beads (Fluoresbrite Micro-particles, Polysciences) of known concentration(~2×105 ml−1) were added in all samples as an internalstandard. The virus-buffer suspension was stained for10 min at 80 °C in the dark before cooling for 5 min atroom temperature. Virus counts were corrected forbackground counts in TE buffer made in 0.02-μm filteredseawater and processed the same way as for the samples(Brussaard, 2004). The background counts were typicallyb5% of the virus counts in the samples. Data werecollected in listmode on log scale and then analyzed usingWinMDI (Version 2.8, Trotter, http://facs.scripps.edu/software.html) and Cell-Quest software (Becton-Dick-inson). Before data acquisition, instrument performancewas checked and manually optimized using BD Cali-BRITE beads and FACSComp software (BD Bios-ciences). Cytograms and histograms obtained during


http://facs.scripps.edu/software.html

http://facs.scripps.edu/software.html


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


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this study typically displayed two main subgroups ofheterotrophic bacteria (Fig. 2a and b), with relative highand low nucleic acid fluorescence (HNA and LNA,respectively), and two main viral subgroups (Fig. 2c andd), with relative high and low SYBR green fluorescence(V1 and V2, respectively), similar to previous studies inother marine environments (Gasol et al., 1999; Marieet al., 1999; Lebaron et al., 2001; Brussaard, 2004).

For EFM, duplicate slides for determining BA and VAwere prepared immediately after collecting pre-filteredwater samples following the protocols of Hennes andSuttle (1995). Briefly, viruses in unfixed seawatersubsamples (0.9 ml) were filtered onto 0.02-μm pore-size Anodisc filters (Whatman), immediately stained withYo-Pro-1 (Invitrogen) for 48 h in the dark, rinsed withMilliQ water andmounted onto glass slides with glycerol.The slides were either frozen or counted immediatelyusing a wide-blue filter set (excitation 450–480 nm, 515-nm cutoff). For each sample, a minimum of 200 viruses orbacteria were counted in 20 randomly selected fields(Suttle, 1993).

2.4. Data analysis

All the data were treated as a set of samples between0 and 60 m, since most of the seasonal and spatial


variation in physical and biological parameters occurredover these depths. Data are presented as mean valueswith standard deviations (±SD).

Nonparametric ANOVA on ranks (Kruskal–Wallis)with Dunn's test were used to test the seasonal and spatialvariances of the different physical (T, S and depth) andbiological variables (chl a, BA, HNA, LNA, VA, V1 andV2). The Spearman rank order coefficient (rs) was used todetermine the associations between biotic and abioticvariables. Stepwise multiple regression (SMR), with aforward procedure (P-to-enter=0.15), was then used toidentify the best subsets of independent variables whichcontributed significantly to the observed variation in viralproperties (VA, V1 and V2). Multi-colinearity amongindependent variables was avoided by examining toler-ance values; no variable in the models had a toleranceb0.4 (values b0.4 indicate serious problems with multi-colinearity). The best model was obtained by combiningstepwise procedures and manually selecting the indepen-dent variables possessing higher correlations with thedependent variables. Explained variance was measured asthe adjusted r2 value (%) to account for the increasedvariance explained with increasing numbers of indepen-dent variables. Since the data revealed non-normality andheterogeneity of variance, they were log (x+1) trans-formed prior to statistical analyses, except for T and S.



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 thestation number. Black dots show the depth of the samples collected.


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Analyses were performed using Systat 11® (SystatSoftware Inc.).

3. Results and discussion

3.1. Hydrographic features

From 4 December 2003 to 31 May 2004, the Amund-sen was immobilized in 1 to 2 m thick land-fast ice inFranklin Bay (Stn 200). During this period, ice coveredmost areas of the MS and G, although the ice started tomelt earlier near the Mackenzie River. Surface waters atStn 200 were still ice-covered until early July 2004. Mostof the stations sampled on the west–east transect were ice-free during July and August 2004, except for ice floes atStn 650 in the MS region (Fig. 1).

In Franklin Bay (e.g. at Stn 200), the water columnwasvertically stratified and the upper polar mixed layers(b30 m) displayed strong seasonal variability, withhighest S values (N31 psu) and lowest T (b−1.7 °C) inwinter and early spring compared to fall and summer(Fig. 3a and b). The brine release due to ice growth duringthe winter–spring period could be responsible for thisincrease in S and decrease in T in near-surface layers;


whereas, freshwater flux due to melting ice likely resultedin a fresher upper ocean in early summer. In addition,inputs of warm freshwater due to the adjacent coastal riverrunoffs, such as the Horton River (70°00′ N, 126°.70′W;e.g. Fig. 1), could also have influenced surface waters andfurther enhanced the stratification of the upper ocean inFranklin Bay. Another striking feature of the upper polarlayers in Franklin Bay was the presence of a thintemperature inversion layer (b−1.2 °C) between 20 and50 m during the winter–spring period (Fig. 3a), roughlycoinciding with the depth of the pycnocline. Furtherseasonal hydrographic details for Stn 200 in Franklin Baymeasured 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 surfacewaters due to the intrusion of relatively warm (TN4 °C)and brackish freshwater (Sb20 psu) from the MackenzieRiver into the colder (Tb2 °C) and denser (SN20 psu)polar mixed waters of the Beaufort Sea shelf (Fig. 3c andd). At stations in the RP region, the surface waters weresignificantly 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



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) alongthe west–east cruise track across the river plume (RP), mid-shelf (MS) and gulf (G) regions. Black triangles indicate the station number. Black dotsshow the depth of the samples collected.


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(2.7±0.1 °C and 24.6±4.3 psu, respectively) (pb0.05,K–W on ranks with Dunn's test). Within the Arctic, theMackenzie River is the fourth largest river in terms ofwater discharge, with a maximum activity during thesummer period (Macdonald et al., 1998). In July, the largewarm river inflow from the Mackenzie River in RPincreased the vertical salinity and thermal stratification ofthe surface layers (Fig. 3c and d). However, the influenceof the Mackenzie River markedly decreased eastward inthe MS, because of the gradual dilution of freshwater intothe Arctic Ocean surface layers. In the G region, thesurface hydrography was more similar to Stn 200 inFranklin Bay and the stratification corresponded to theupper Arctic Ocean layers.

Overall, the seasonal and spatial profiles of T and Sin the top 60 m of the water column in Franklin Bay andalong the longitudinal transect were typical of thosefound 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 (b0.04 to 2.37 μgl−1), but variable throughout the study, with surface (2–6 m) or subsurface (25–50 m) peaks (Fig. 4).

In Franklin Bay, surface chl awas almost undetectablein fall and winter with values ranging from b0.05 to0.27 μg l−1, whereas in summer, concentrations rangedfrom 0.21 to 0.61μg l−1 (Fig. 4a). Phytoplankton biomassincreased by ~6-fold in the near surface inmid-May (up to0.47 μg l−1), although a few smaller peaks (b0.26 μg l−1)were detected earlier in April–May (Fig. 4a). Chl aconcentrations were significantly higher in spring andsummer (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) (pb0.05, K–W onranks withDunn's test). The depth of chl amaxima shiftedfrom shallower surface peaks (~3 to 15 m) in spring todeeper peaks (e.g. ~45 m) in summer, corresponding withwaters immediately above the pycnocline (e.g. Fig. 3b). Inspring, the increase of chl a in the near surface wasprobably related to the release of algae from the bottom ofthe sea ice (Riedel et al., 2006; Renaud et al., 2007).Likewise, Renaud et al. (2007)measured a 10-fold increasein chl a on the bottom of the sea ice in May 2004 and a 7-fold increase in chl a sedimentation rates in June 2004 atStn 200 in Franklin Bay. With the spring increase of light,ice-algae developed and were released from the meltingsea-ice. After themelt, andwith the onset of stratification inthe upper layers, the phytoplankton bloomed and depletedthe nutrients above the pycnocline. Following the bloom,chl a accumulated near the pycnocline, possibly because ofphytoplankton growth being supported by advection ofnutrients from below the pycnocline.

Similar to hydrographic features, chl a displayed astrong longitudinal gradient (Fig. 4b), with concentra-tions ~5.5-fold higher in the RP compared to the MSregion, and a surface maximum of 2.37 μg l−1 at Stn 912near the Mackenzie River. On average, chl a in the upperlayers was significantly higher in the RP compared to theMS 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) (pb0.05, K–W onranks with Dunn's test). In contrast to the RP, wherephytoplankton biomass was confined nearer to thesurface, chl a in both the MS and G regions wasgenerally lower in surface waters (b0.3 μg l−1) but haddeeper maxima (~20–50 m; up to 1.7 μg l−1) than in theRP (Fig. 4b). The Mackenzie River may have directlyaffected (via dilution or enrichment of populations) and



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 alongthe 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.


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indirectly (via increasing nutrient supply) the surfacephytoplankton biomass in the upper waters in RP region.Garneau et al. (2006) also observed a similar trend in chla distributions with a remarkable decrease in chl a levelsfrom the RP region towards the more oligotrophic andmarine MS waters.

3.3. Heterotrophic bacteria

There was good agreement between estimates of totalheterotrophic bacterial abundances (BA) determined byflow cytometry (FC) and epifluorescence microscopy


(EFM) (r2 =0.89, n=204, pb0.001, FC=1.08×EFM+0.12) (data not shown), although, high backgroundfluorescence made it difficult to count bacteria in theturbid RP surface waters (e.g. Stns 912 and 906). Inaddition, estimates of BAwere more precise by FC thanEFM, with mean coefficients of variation (CV) betweenduplicates of 4.1% and 9.3%, respectively.

Flow cytometric estimates of BA in the upper 60 mranged from 1.6×105 to 25×105 ml−1 with a mean of7.3×105±5.5×105 ml−1. Similarly to T, S and chl a, BAdisplayed strong spatial and seasonal variation (Fig. 5aand b) and generally followed phytoplankton distributions



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

T S Chl a V2 V1 VA HNA LNA BA

Depth −0.05 0.78⁎⁎⁎ −0.18⁎ −0.18⁎⁎ −0.35⁎⁎ −0.21⁎⁎ −0.16⁎ −0.21⁎ −0.21⁎T −0.32⁎ 0.29⁎ 0.59⁎⁎⁎ 0.33⁎⁎⁎ 0.56⁎⁎⁎ 0.62⁎⁎⁎ 0.63⁎⁎⁎ 0.62⁎⁎⁎

S −0.19⁎ −0.32⁎ −0.38⁎⁎⁎ −0.35⁎ −0.32⁎ −0.41⁎⁎ −0.47⁎Chl a 0.54⁎⁎⁎ 0.56⁎⁎⁎ 0.55⁎⁎⁎ 0.62⁎⁎⁎ 0.55⁎⁎⁎ 0.60⁎⁎⁎

V2 0.76⁎⁎⁎ 0.98⁎⁎⁎ 0.84⁎⁎⁎ 0.78⁎⁎⁎ 0.83⁎⁎⁎

V1 0.81⁎⁎⁎ 0.73⁎⁎⁎ 0.62⁎⁎⁎ 0.67⁎⁎

VA 0.84⁎⁎⁎ 0.77⁎⁎⁎ 0.80⁎⁎⁎

HNA 0.86⁎⁎⁎ 0.97⁎⁎⁎

LNA 0.94⁎⁎⁎

Variables: BA (bacterial abundance), LNA and HNA (low and high nucleic acid content bacteria), VA (viral abundance), V1 and V2 (low and highfluorescence viral subgroups), depth, Chl a (chlorophyll-a), T (temperature), S (salinity). Significance level: ⁎pb0.05, ⁎⁎pb0.01, ⁎⁎⁎pb0.001.


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(e.g. Figs. 3 and 4). BA, like chl a, was generally highestnear the surface and often decreased with depth. Inaddition, for the overall data set, BA was positivelycorrelated with chl a (rs=0.60, pb0.001, n=191) and T(rs=0.62, pb0.001, n=191), but negatively correlated toS (rs=−0.47, pb0.05, n=191) and depth (rs=−0.21,pb0.05, n=191) (Table 1).

In FranklinBay (Stn 200), BAwas significantly lower infall and winter (5.54 ×105 ± 0.62× 105 ml− 1 and3.62×105±0.67×105 ml−1, respectively) than in springand summer (12.6×105±2.3×105 ml−1 and 5.1×105±1.5×105 ml−1, respectively) (pb0.05,K–Won ranks, withDunn's test). Following the spring phytoplankton bloom,BA markedly increased (~2-fold) in surface layers later insummer (Fig. 5a). In winter and spring, there was asuccession ofweak peaks in BA (b11×105 mL−1) near thesurface (b15 m) that were coincident with peaks in surfacechl a and the temperature inversion layer (~20–40 m) (e.g.Figs. 3a, 4b and 5a). A greater and deeper peak in BAcoincided with the pycnocline and chl a maximum insummer (e.g. Figs. 3b, 4a and 5a). In winter and spring,bacterial growth, and hence abundance, in the upper layerswas likely reduced by low T, nutrients and organic carbonsupplied by phytoplankton and river runoff, relative to thefall and particularly the summer. Further details about theseasonal bacterial distribution in FranklinBay are discussedby Garneau et al. (in press).

Similarly to chl a, BA (Fig. 5b) was significantlyhigher 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) (pN0.05,K–W on ranks with Dunn's test). Likely, the warmfreshwater from the Mackenzie River and high phyto-plankton biomass in RP region, resulted in higher BA insurface 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 nucleicacid 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 moreabundant in the upper 60 m than LNA bacteria(6.97×105 ±3.51×105 ml−1 and 5.20×105 ±2.41×105 ml−1, respectively) (Fig. 5c,d,e and f), but notsignificantly different (pN0.05, t-test on ranks), comprisinga mean proportion of 0.58±0.10 of the total BA. HNAbacteria generally exceeded LNAbacteria in surface waters(b50 m), but typically decreased with depth compared toLNA bacteria.

At Stn 200 in Franklin Bay, the proportion of HNAcells was significantly higher in summer than in spring(pb0.05,K–Won ranks, with Dunn's test), fall andwinter(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 higherbacterial production at this time (Garneau et al., in press),which is likely supported by increased inputs of organiccarbon from phytoplankton, river runoff and warmersurface T. Additionally, in winter and spring, HNAbacteria slightly increased at the temperature inversionlayer (e.g. Figs. 3a and 5c), suggesting that a somewhathigher fraction of bacteria were active at this depth.

Consistent with bacteria being more productive nearthe Mackenzie River as reported by Garneau et al.(2006), the abundance (Fig. 5d) and proportion (0.66) ofHNA bacteria was higher in surface waters at Stn 912,although the proportion was not significantly differentthan observed for the MS and G regions (0.61±0.08,0.57±0.06 and 0.56±0.04, respectively) (pN0.05, K–Won ranks). In the RP, warmer freshwaters and increasedorganic nutrients from the Mackenzie River andphytoplankton likely stimulated the productivity of thebacterial communities.

The abundance of HNA bacteria increased signifi-cantly with chl a concentration (rs=0.62, Table 1) inspring–summer at Stn 200 and also during the summer in



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


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the RP region (pb0.05,K–Won ranks, with Dunn's test).Overall, these results are consistent with suggestions thatHNA bacteria are more metabolically active than LNAbacteria (e.g. Gasol et al., 1999; Lebaron et al., 2001),although LNA cells can account for a significantproportion of active cells in oligotrophic areas (Zubkovet al., 2001b; Jochem et al., 2004; Longnecker et al., 2005).

3.4. Viruses

Estimates of viral abundance (VA) by FC and EFMwere similar (r2=0.87, n=204, pb0.001, FC=1.07×EFM+0.43), but slightly higher by FC (Fig. 6a). As forbacteria, high background fluorescence in the RP madecounting by EFM difficult. When only the samples inwhich VAwas lowest (b6×106 ml−1) were used for theanalysis, the correlationwas significantly better (r2=0.91,n=95, pb0.001, FC=1.02×EFM+0.40, e.g. Fig. 6b).Abundance estimates made on duplicate samples weremore precise by FC (mean CV 7.3%) than by EFM (meanCV 15.4%).

Similar to other biotic (chl a, BA, HNA and LNA) andabiotic (T and S) parameters, VA estimated by FCdisplayed strong spatial and seasonal variations with oneor 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 andsummer (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) (pb0.05, K–W on rankswith Dunn's test). There were a few weak peaks in VA(b12×106 ml−1, e.g. Fig. 7a) at the surface and in the Tinversion layer (~20–35 m) in late winter and spring,coincident with higher phytoplankton biomass and


bacterial abundances (c.f. Figs. 4a and 5a), and indi-cating that viruses were a dynamic component of themicrobial community even during the cold months. Insummer, VA reached maxima (up to 19.8×106 ml−1) atgreater depths than in spring (b15 m and ~45 m,respectively) (Fig. 7a), coincident with BA and chl aincreases near the pycnocline (e.g. Figs. 3a, 4a and 5a).Furthermore, VA strongly increased in spring andsummer (~2-fold and ~1.5-fold, respectively).

Similar to the other biological variables, VA sharplyincreased (~3-fold) in surface waters in the RP (Fig. 7b)compared to the MS, and was significantly higher in thesurface 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) (pb0.05,K–W on ranks, with Dunn's test). Furthermore, VA wassignificantly lower in theMS compared to the G (pb0.05,K–W on ranks, with Dunn's test).

The ratio of viruses to bacteria (VBR) in the upper60m varied strongly with season and region (range: 4.3–40.2, mean±SD: 14.4±5.7) and increasedwith increasesin autotrophic and heterotrophic host cells, particularlyin spring–summer and at stations in the RP. For theseasonal data in Franklin Bay (Stn 200), the highest, butnot significantly different (pN0.05, K–Won ranks) VBRvalues were found in spring and in summer while thelowest values occurred in fall and winter (16.9±8.6,14.9±4.7, 13.1±6.4 and 13.7±5.6, respectively). Pre-vious studies found similar increases in the VBR duringspring blooms in the region (Maranger et al., 1994; Yageret al., 2001). The VBR was also significantly higher inthe RP than in the MS and G regions (17.7±7.6, 12.5±4.9 and 13.6±5.0, respectively) (pb0.05, K–W onranks, with Dunn's test).

As observed in previous studies in marine systems(Marie et al., 1999; Li andDickie, 2001; Brussaard, 2004),low and high SYBR-Green fluorescence (V2 and V1,respectively) subgroups of viruses were clearly distin-guished by FC (Fig. 2c and d). Overall, the abundance ofV2 was significantly higher (pb0.05, t-test on ranks) thanV1 (5.42 ×106 ± 3.54× 106 ml− 1 and 0.97 ×106 ±0.86×106 ml−1, respectively) in the upper layers, andrepresented a greater proportion of the virioplankton(range: 0.69–0.97, mean±SD: 0.87±0.05). During thestudy, both V2 and V1 generally followed the samepatterns (Fig. 7c,d,e and f), with marked increases inspring and in summer (Fig. 7c and e) and at stations in theRP region (Fig. 7d and f). However, V1 appeared to betightly related to increases in chl a (e.g. Fig. 4a), andsharply decreased with depth. In Franklin Bay, both V2and V1 displayed strong seasonal variation and increasedin spring and summer (Fig. 7c and e), although V1



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) andalong the west–east cruise track across the river plume (RP), mid-shelf (MS) and gulf (G) regions (right panel). Black triangles indicate the stationnumber. Black dots show the depth of the samples collected.


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displayed a sharper increase in spring (~2.5-fold). Aswell,V2 and V1 were significantly higher in surface waters inthe RP compared to the MS and G (pb0.05, K–W onranks, with Dunn's test), with both V2 and V1 being ~3-fold higher in the RP compared to the MS and ~1.2-foldhigher 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 forthe upper 60 m (n=191) indicated that VAwas positivelycorrelated with BA (rs=0.83, pb0.01), chl a (rs=0.57,pb0.01) and T (rs=0.56, pb0.01), and negativelycorrelated with S (rs=−0.35, pb0.01) and depth (rs=


−0.35, pb0.01) (Table 1). Both V1 and V2 were sig-nificantly correlated with chl a, HNA and LNA (Table 1).Furthermore, correlation analyses for different seasonsand regions in the upper 60 m revealed a strong tendencyfor viruses to increase with phytoplankton and bacterialabundances. To identify which independent variables(BA, HNA, LNA, chl a, T and S) best explained thevariation in viral properties (VA, V1 or V2) in the upper60 m, stepwise multiple regressions (SMR) wereperformed using either the entire data set or the seasonaland spatial data sets (Table 2). With the entire data set,56% of the total variation in VAwas explained by BA, chla and S, with the highest β values for BA and a negative βcoefficient for S (Table 2). For V1 and V2, 61% and 53%



Table 2Results 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

Overallmodel

Independent variables

Chl a BA HNA S T

r2 β Cr2 β Cr2 β Cr2 β Cr2 β Cr2

All StnsVA 0.56⁎⁎⁎ 0.372⁎⁎ 0.54 0.701⁎⁎⁎ 0.51 – – −0.183⁎ 0.55 – –V1 0.61⁎⁎⁎ 0.432⁎⁎ 0.59 – – 0.541⁎⁎⁎ 0.44 −0.243⁎ 0.66 – –V2 0.53⁎⁎⁎ 0.352⁎⁎ 0.52 – – 0.701⁎⁎⁎ 0.48 −0.123⁎ 0.58 – –

FallVA 0.45⁎⁎⁎ – – 0.452⁎⁎⁎ 0.42 – – −0.621⁎⁎⁎ 0.30 – –V1 0.46⁎⁎ – – – – 0.482⁎⁎ 0.72 −0.611⁎⁎⁎ 0.49 – –V2 0.38⁎⁎ – – – – 0.592⁎⁎⁎ 0.37 −0.651⁎⁎⁎ 0.35 – –

WinterVA 0.29⁎ – – 0.323⁎ 0.39 – – −0.522⁎⁎ 0.37 0.591⁎⁎ 0.25V1 0.30⁎⁎ – – – – – – −0.681⁎⁎ 0.28 0.482⁎⁎ 0.23V2 0.26⁎ – – – – 0.353⁎⁎ 0.31 −0.472⁎⁎⁎ 0.22 0.561⁎⁎ 0.25

SpringVA 0.61⁎⁎⁎ 0.521⁎⁎⁎ 0.58 – – – – −0.302⁎⁎ 0.62 – –V1 0.63⁎⁎⁎ 0.731⁎⁎⁎ 0.64 – – – – – – – –V2 0.62⁎⁎⁎ 0.581⁎⁎⁎ 0.52 – – – – −0.342⁎⁎⁎ 0.60 – –

SummerVA 0.88⁎⁎⁎ – – 0.641⁎⁎⁎ 0.88 – – – – – –V1 0.71⁎⁎⁎ 0.412⁎⁎ 0.76 – – 0.651⁎⁎⁎ 0.65 – – – –V2 0.77⁎⁎⁎ – – – – 0.721⁎⁎⁎ 0.78 – – – –

River plumeVA 0.77⁎⁎⁎ 0.521⁎⁎⁎ 0.57 0.462⁎⁎⁎ 0.67 – – – – 0.403⁎⁎⁎ 0.81V1 0.75⁎⁎⁎ 0.561⁎⁎⁎ 0.52 – – 0.472⁎⁎⁎ 0.72 – – 0.383⁎⁎⁎ 0.80V2 0.72⁎⁎⁎ 0.532⁎⁎ 0.58 – – 0.561⁎⁎⁎ 0.58 – – 0.473⁎⁎⁎ 0.79

Mid-shelfVA 0.69⁎⁎⁎ 0.332⁎⁎ 0.70 0.571⁎⁎⁎ 0.56 – – −0.273⁎⁎⁎ 0.72 – –V1 0.72⁎⁎⁎ 0.462⁎⁎⁎ 0.74 – – 0.541⁎⁎⁎ 0.57 −0.293⁎⁎ 0.70 – –V2 0.73⁎⁎⁎ 0.352⁎⁎⁎ 0.65 – – 0.611⁎⁎⁎ 0.62 −0.283⁎⁎ 0.75 – –

GulfVA 0.76⁎⁎⁎ 0.312⁎⁎ 0.77 0.721⁎⁎⁎ 0.51 – – – – – –V1 0.88⁎⁎⁎ 0.352⁎⁎⁎ 0.90 – – 0.771⁎⁎⁎ 0.66 – – – –V2 0.74⁎⁎⁎ 0.292⁎⁎ 0.76 – – 0.711⁎⁎⁎ 0.68 – – – –

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 thesubsequent runs divided into 4 seasons (fall, winter, spring, summer) and into 3 regions (river plume, mid-shelf and gulf). Because LNA and HNAsometimes displayed low tolerance values (tb0.4), only the best predictor was retained in the model, which was HNA. Depth was also discarded fromSMR analysis since it was highly correlated to S. Overall model adjusted r2 and significance are presented, together with the standardized multipleregression coefficients (β) and cumulative coefficient (Cr2) of the variables, which were retained in the model after a stepwise procedure(⁎⁎⁎pb0.001, ⁎⁎pb0.01, ⁎pb0.05). The subscript number indicates the rank order in which dependent variables were retained in the model. Thosedependent variables that explain at least 50% (cumulatively) of the overall model r2 are indicated in bold.


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of the total variation was explained by HNA, followed bychl a and S (Table 2). Segregation of the data by region orseason generally increased the overall proportion of thetotal variance explained in viral properties, except for falland 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% ofthe variation (Table 2). In winter, BA, T and S togetherexplained 29% of the variation in VA, with T accountingfor 25% of the variation. Subsurface T peaks (up to−1.3 °C) in the temperature inversion layer, in winter andspring, likely explained the positive association with VA




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and BA. In spring, chl a and S explained 61% of the totalvariability in VA, with chl a accounting for 58% ofvariation. In summer, BA explained 88% of total variationin VA. For viral properties, chl awas the best predictor forV1 and V2 variations in spring (64% and 52%,respectively) (Table 2); whereas, HNA bacteria was thebest predictor for V2 and to a lesser extent V1 (78% and65%, respectively) in summer (Table 2).

For the spatial data, BA, chl a and T accounted for77% of the variability in VA in the RP, with chl a and BAexplaining 57% and 10% of the variation, respectively(Table 2). On the MS, similar predictors of VA werefound, except that Twas replaced by S in the model. Thisis likely the result of a shift in environmental conditionsat the surface to more oceanic water. In the G, BA and chla explained 76% of the variability in VA, with BAaccounting for 51% of the variation. In terms of viralproperties, V1 was closely associated with chl a in theRP, which explained 52% of the variation. Furthermore,when moving toward the G, chl a explained less of thevariation in V1, but compared to V2, V1 remained moreclosely associated with chl a. HNA bacteria were thebest predictors of V2 variation in all the regions, withgreater β coefficients in the G (Table 2).

Overall, SMR indicated clear seasonal and spatialvariations in viral abundance that was tightly coupled toshifts in environmental and biological parameters.

4. Conclusions

This study is the first to report a strong relationshipbetween virus abundance estimated by flow cytometry(FC) and epifluorescence microscopy (EFM) in a largenumber (n=204) of environmental samples. These datarevealed pronounced seasonality and spatial gradients inviral abundance on the Beaufort Sea shelf, and demon-strated that these shifts were related to changes in theabundance of potential microbial hosts, trophic status andenvironmental variables.

Chl a concentrations, and bacterial and viral abun-dances were highest in the surface layers although withinthe 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 MackenzieRiver in summer resulted in greater surface stratification,higher phytoplankton biomass, and more HNA bacteriaand viruses. In contrast, the influence of the MackenzieRiver was negligible eastward in the MS and G regions,where surface waters were more typical of the upperArctic Ocean with low phytoplankton biomass andbacterial and viral abundances.


In Franklin Bay, there was high seasonal variability. Insummer, melting ice and freshwater runoff from theHortonRiver increased surface stratification, and likely thesupply of nutrients and organic carbon.Aswell, increasingirradiance from spring through summer triggered higherphytoplankton biomass, and a near-surface bloom whenice-algae were released during ice break-up. Increasedphytoplankton biomass in surface waters was associatedwith enhanced viral and bacterial abundances, althoughthe highest bacterial abundances were in summerwhen thesurface temperature was warmer. A greater proportion ofthe putatively more active HNA bacteria occurred at thesurface and subsurface during the summer.

The abundances of V2 and V1 increased with trophicstatus, as evidenced by higher chl a levels and HNAbacterial abundances in spring–summer and at stationsinfluenced by the Mackenzie River. V2 was mostabundant and tightly tied to bacteria, especially to HNAbacteria, suggesting that they infect heterotrophic bac-teria. On the other hand, V1 was tightly coupled to chl aconcentration, supporting the idea that they infect eukary-otic phytoplankton.

Overall, these results demonstrate that viruses areabundant and dynamic components of the aquatic micro-bial communities of the Canadian Arctic Shelf. As obligatepathogens, this implies that they are also significant agentsof microbial mortality and thus affect the abundance,dynamics and diversity of microbial communities withconsequent implications for carbon and nutrient cycling.

Acknowledgements

We gratefully thank A.I. Culley, A.M. Comeau, C.Pedrós-Alió, C. Lovejoy andC.Martineau for their effortsin field sampling, A.C. Ortmann and A.M. Chan for theirlogistic support and facilitation during the expedition. Weare also grateful to M.-É. Garneau and W.F. Vincent forproviding the chlorophyll a data. The assistance of theofficers and crew of the CCGS Amundsen is greatlyappreciated. We also thank the reviewers and editors whoprovided comments that improved the manuscript. Thisstudywas funded byNSERC through theCanadianArcticShelf Exchange Study (CASES) project and a DiscoveryGrant to CAS. This research is a contribution to theCASES project under the overall direction of L. Fortier.

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

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