Bacterial biomass and activity in the marginal ice zone of the northern Barents Sea

ARTICLE IN PRESS Deep-Sea Research II 55 (2008) 2199– 2209

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Bacterial biomass and activity in the marginal ice zone of the northern Barents Sea Helen Tammert a, Kalle Olli a,, Maria Sturluson b, Helene Hodal b a b

Institute of Ecology and Earth Sciences, University of Tartu, 51005 Tartu, Lai 40, Estonia Norwegian College of Fishery Science, Institute of Aquatic BioSciences, University of Tromsø, N-9037 Tromsø, Norway

a r t i c l e in fo

abstract

Available online 9 July 2008

Bacteria in the Arctic Waters are well adapted to low temperatures and play a key role in the transformation of organic matter. However, the activity of planktonic bacteria at cellular level remains poorly understood. In this study, we use fluorescent markers (40 ,60 -diamidino-2-phenylindole (DAPI), 5cyano-2,3-ditolyl tetrazolium chloride (CTC), Live/Dead BacLight viability kit) to discriminate between bacterial cells with a variety of physiological activities in the 0–200 m water column and sinking particles. During two field studies (July 2003 and 2004), we covered nine stations in the northern Barents Sea. The median bacterial abundance (DAPI staining) in the upper 50 m layer was 0.9  106 cells ml1 (range 0.2–3.2  106 cells ml1) in 2003 and 0.5  106 cells ml1 (range 0.2–1.0  106 cells ml1) in 2004. Bacteria with sufficient electron transport activity to be stained with CTC were on average 10% of the total count and ca. 20% of the total cells had intact cell membranes. In the water column, proxies of substrate availability (POC, PON, chlorophyll a, primary production) and bacterial production (thymidine and leucine uptake) correlated strongly with total bacterial count, CTC-stained cells and cells with ‘leaky’ membrane (stained with propidium iodine), but not with the concentration of cells with intact cell membrane. Contrary to expectations, the proportion of CTC-stained bacteria was not higher in the sinking particles (captured with sediment traps) compared to the ambient water. However, out of the bacteria with intact cell membranes, a higher proportion scored as CTC positive in the aggregates compared to the ambient water. Bacterial cells with ‘leaky’ cell membranes formed the largest part of total cell count in all samples, and accumulated in sites with high microbial activity (sinking aggregates, chlorophyll maxima, layers of high primary and bacterial production). We hypothesize that the source of the bacterial cells with ‘leaky’ cell membranes was metabolically the most active fraction of the bacterial assemblage (stained with CTC). & 2008 Elsevier Ltd. All rights reserved.

Keywords: Barents Sea Bacteria Arctic Waters Carbon cycling Aggregates

1. Introduction The total bacterial biomass in the planktonic environment consists of individuals with a wide variety of metabolic activity (Smith and del Giorgio, 2003) including dead or so-called ‘ghost cells’ (Zweifel and Hagstro¨m, 1995). The relatively low fraction of metabolically hyper-active bacteria in the oceanic upper layer (del Giorgio and Bouvier, 2002; Sherr et al., 1999a; Smith and del Giorgio, 2003) has led to the belief that a significant proportion of individual cells are not actively engaged in the bacterial activity and metabolism at the community level (Rodriques et al., 1992; Smith and del Giorgio, 2003). Bacterial activity is higher in organic aggregates and sinking particles (Huston and Deming, 2002; Kiørboe, 2003), leading to attenuation of the sedimentation

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E-mail address: [email protected] (K. Olli). 0967-0645/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.dsr2.2008.05.011

particle flux over short vertical distances below the productive layer (Andreassen and Wassmann, 1998; Olli et al., 2002; Wassmann et al., 2003). In tropical waters, planktonic bacteria appear to remineralize most of the sinking organic carbon (Cho and Azam, 1998), while in cold waters the role of bacteria has been questioned (e.g., Pomeroy, 1997). In polar seas, bacteria alleviate substrate limitation by increasing physical association with the higher concentrations of organic matter like detrital particles and aggregates (Junge et al., 2004), and by the expression of exoenzyme activity with low-temperature optima and longer enzyme lifetime (Huston and Deming, 2002; Huston et al., 2000). Sherr and Sherr (2003) found that bacterial abundance in the Arctic Ocean increased in tandem with phytoplankton biomass during spring and summer, suggesting substrate, rather than temperature limitation. Several studies have revealed a substantial bacterial activity in the Arctic Waters with an estimated bacterial carbon demand ranging from 10% to 4100% of the autochthonous

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primary production (Cota et al., 1996; Olli et al., 2007; Rich et al., 1997; Sherr and Sherr, 2003). An yet unresolved issue is the hitherto calculated unrealistically low bacterial growth rates (o0.02 day1) and long generation times (30–300 days) in the Arctic Waters (Anderson and Rivkin, 2001; Olli et al., 2007; Sherr and Sherr, 2003). We believe that these low growth rates are indications of heterogeneous activity within the bacterial community. As discussed in various papers (Choi et al., 1999; Davidson et al., 2004; Jugnia et al., 2000; Kirchman et al., 2007; Servais et al., 2003), only a relatively small fraction of bacteria is metabolically active, and it is probably this small fraction that is responsible for the majority of bacterial substrate uptake and respiration processes. Depending on the somewhat arbitrary definition of active fraction within the continuum of different metabolic properties among planktonic bacteria, a range of o2 to 50% have been reported (Smith and del Giorgio, 2003, and references therein). All methods describing cell-specific metabolic activity in bacteria have their drawbacks and virtues, and none have escaped criticism in scientific literature (Smith and del Giorgio, 2003). Although natural bacterial communities exhibit a range of physiological states—dead, alive, dormant and active—and cells can cycle in and out of high metabolic activity (Sherr et al., 1999b, 2001), the regulatory mechanisms remain poorly understood. Metabolically active bacteria are reportedly larger and preferred food for bacterivores compared to dormant cells (Gasol et al., 1995; Hahn and Ho¨fle, 2001; Monger and Landry, 1992). In parallel, dormancy induced by nutrient limitation constitutes a refuge from protozoan grazing and thus contributes to the survival of populations over times of low substrate availability. This suggests that in a patchy environment, we can expect to observe a higher proportion of active bacterial cells in layers of high chlorophyll and primary production, and conversely, more dormant cells when substrate supply rate is presumably low. Bacteria with high level of respiratory activity can be detected by the use of fluorogenic 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) (hereafter referred to as actively respiring cell (ARC)—after Lovejoy et al. (1996)). The main criticism of the tetrazolium dye method apparently stems from the low proportion of total cells scored as ARC, generally less than 20% (Smith and del Giorgio, 2003) and often just a few percent (Jugnia et al., 2000). However, Yager et al. (2001) demonstrated that up to 84% bacteria could be stained by CTC during Arctic phytoplankton blooms at 1.5 1C. In this paper, we follow the concept of Smith and del Giorgio (2003) that there is a nested hierarchy of physiological states within natural bacterial communities and that there is no inherent problem with some methods yielding low proportions of ‘reactive’ cells, because it is not unrealistic to think that a small fraction of the assemblage has much higher rates of activity than the rest. We believe that the ARC, having the highest metabolic activity, are responsible for the bulk of bacterial community metabolism in the cold Arctic Waters. Choi et al. (1996) reported that 50–93% of marine bacteria have compromised cell membranes and are likely to be in poor physiological condition or dead. However, the effectiveness of staining can vary and the issue of cell membrane permeability remains controversial (Davidson et al., 2004). Nevertheless, stains provide a rapid and ecologically valuable measure of bacterial activity (Sherr et al., 2001; Smith and del Giorgio, 2003), but care must be taken with interpretation. The aim of the present study was to investigate the activity of bacterial community in the marginal ice zone of the northern Barents Sea and in the mineralization of sinking particles. We hypothesized that the sinking organic particles captured in the sediment traps are hot spots of bacterial activity, reflected in larger proportion of metabolically hyper-active bacteria identified

as ARC. We also hypothesized that the fraction of ARC in the water column follows the temporal and vertical variation of primary production. Finally, assuming that metabolically active bacteria are responsible for the bulk of bacterial production and carbon demand, we calculate more realistic growth rates and generation times of the natural bacterial assemblage.

2. Methods 2.1. Investigation period and cruise track This study was part of the international CABANERA project focusing on the pelagic–benthic coupling processes in the northern Barents Sea (Fig. 1). The fieldwork was carried out during two expeditions (8–22 July 2003; 20 July–3 August 2004) onboard R/V Jan Mayen (University of Tromsø, Norway). The station locations selection was guided by compare–contrast approach based on our knowledge on Atlantic and Arctic water regimes and ultimately constrained by the ice conditions. By large, the two expeditions took place during the same ecological season, but large variability from pre-bloom to post-bloom ecological settings was recovered at different locations, as well as a range from 30% to 80% ice cover. 2.2. Water column sampling and sediment trap deployments The station locations are given in Table 1. CTD profiles and water-column samples were taken at each station with a General Oceanic Rosette sampler equipped with 5-l Niskin bottles from fixed depths (1, 5, 10, 20, 30, 40, 50, 60, 90, 120, 150, 200 m, or as deep as the bottom topography allowed) and the chlorophyll maximum (determined by the fluorescence profile of the water column). In station VII (Nansen Basin, off the Barents Sea shelf) additional samples were obtained from 300, 400 and 500 m. To collect sinking aggregates, floating arrays of sediment traps (20, 30, 40, 50, 60, 90, 120, 150 and 200 m, bottom topography permitting) were moored to ice flows in each station and recovered ca. 24 h later. The sediment traps were parallel

Fig. 1. Polar stereographic projection map of the study area with station locations.

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Table 1 Location and description of the sampling stations Station

Date

Location (1N, 1E)

Bottom depth (m)

Ice cover (%)

Chl max (m)

Bloom stage

I II III IV VII IX X XI XIII

10 July 2003 13 July 2003 15 July 2003 18 July 2003 23 July 2004 25 July 2004 27 July 2004 29 July 2004 31 July 2004

75132.1, 30116.6 78113.9, 27119.2 79102.6, 25141.5 77103.2, 29109.7 82124.9, 29126.2 79123.9, 28141.7 79122.7, 28141.6 79149.4, 29143.6 79156.3, 30156.6

361 317 212 229 3500 300 300 190 119

40–70 40–70 50–70 40–70 80 70 40 30 30–40

3 24 28 10 0 ND 15 20 10

Late Peak Peak Early Early Peak Peak Peak Late

ND, no data.

cylinders (7.2 cm inner diameter, 45 cm height, H:D ratio 6.25) mounted in a gimbaled frame equipped with a vane. At moderate current velocities, the cylinders stay vertical and perpendicular to the current direction (Coppola et al., 2002). No bafflers were used in the cylinder opening and no poison was applied during the deployment. The trapping efficiency of 90–110% was revealed during an earlier Barents Sea experiment (Olli et al., 2002) by comparison with the 234Thorium method (Coppola et al., 2002). Upon retrieval, the contents of the sediment trap cylinders and Niskin bottles with water-column samples were drained into separate plastic containers and kept in dark at 0 1C until processing (o0.5 h).

All filtrations were done with Osmonics Inc. black membrane filters (0.22 mm pore size) on board the ship within an hour after the sampling, unless longer incubations were needed. Filters were air-dried, mounted into immersion oil and covered with a cover slip. The slides were kept frozen (22 1C) and dark until counting. All staining and incubations were done in darkness, at ambient pH, and near-ambient temperature (0 1C). Cell counts of bacteria and nanoflagellates were done within a 12-month period with Leica DMRB epifluorescence microscope with 100  oil immersion lens. Biomass estimates were converted to carbon units by using a conversion factor of 0.22 for HNF (Børsheim and Bratbak, 1987) and 20 fg C cell1 for bacteria (Lee and Fuhrman, 1987). The vertical fluxes were calculated to mg C m2 day1.

2.3. Sample processing and analysis

2.4. Chlorophyll a, primary and bacterial production

Prior to sub-sampling, each canister containing the water column or sediment trap samples was thoroughly mixed by gentle rotation. Aliquots were taken with a clean pipette and dispensed into 2-ml eppendorf tubes for bacterial activity incubations. Separate 20-ml aliquots for nanoflagellate counts were taken in scintillation vials and preserved with 0.2-mm filtered glutaraldehyde (2.5% final concentration) and stained with 40 ,60 -diamidino2-phenylindole (DAPI) (Porter and Feig, 1980) for cell counts. Unidentified nanoflagellates were counted into size classes and heterotrophic forms were separated from the phototrophs based on the lack of chlorophyll autofluorescence. Choanoflagellates, which could be distinguished from other cells, were counted separately. ARC were determined as those capable of reducing the nonfluorescent tetrazolium salt (CTC; Polysciences Inc.) to its fluorescent counterpart (Rodriques et al., 1992). CTC was added at a final concentration of 4 mmol l1 to 2-ml sample aliquots dispensed into plastic vials, followed by a 4-h incubation. The incubation was terminated with buffered formaldehyde (2% final concentration) and the samples were counter stained with DAPI (Polysciences Inc.) for 5 min (10 mg ml1 final concentration). ARC were determined by red fluorescence under green excitation light and DAPI-stained cells (hereafter referred to as total bacteria) were counted from the same filter under UV-violet (Leica filter cube D BP355–425 excitation filter; LP470 barrier filter). From a separate filter, bacterial cells with compromised cell membrane (hereafter referred to as PI-stained cells) and intact cell membrane (hereafter referred to as intact bacteria) were distinguished by using LIVE/DEAD BacLight viability kit (Molecular Probes). One component of the kit, green-fluorecing SYTO 9, stains all bacterial cells and serves as a viability marker. The second component, redfluorescing propidium iodine (PI) stains cells with damaged cell membrane. Two-ml aliquot samples were incubated for 15 min after simultaneous addition of both stains (1.5 mg ml1 final concentration).

Chlorophyll a (Chl) was measured by filtering 50–200 ml sample onto 0.7-mm Whatman GF/F filters and extracting in methanol for 24 h in the darkness. Samples were read with a Turner Fluorometer AU-10 calibrated with pure chlorophyll (Sigma Inc. C6144) according to Holm-Hansen et al. (1965). Algal 14CO2 fixation was measured in situ by the 14C method (Steeman Nielsen, 1952). Water samples were incubated in light and dark bottles in the respective depths for 24 h with a concentration of 0.0125 mCi ml1; after filtration, the filters were frozen and analyzed within 2 months from sampling on Ultima Gold XP counter (for details see Hodal and Kristiansen (2008)). Bacterial production was determined with a dual labeling technique based on thymidine (Tdr) and leucine (Leu) incorporation as specified in Fuhrman and Azam (1982) and Kirchman et al. (1985), respectively. Three 10-ml replicates per sample were incubated for 2 h with 10 nmol l1 3H-Tdr and 50 nmol l1 14C-Leu. Thymidine incorporation was converted to cell production (mg C m3 day1) by the factor 1.1 1018 cells mol1 3H incorporated (Riemann et al., 1987). Leucine incorporation was converted to protein production using the fractions 0.073 Leu/protein and 0.86 C/protein according to Simon and Azam (1989). Additional 14 C-Leu incorporation to carbon production calculation was applied by a dilution factor of 2, as some isotope dilution is always present (Simon and Azam, 1989) and the correction factor of 1.27 because of the dual labeling approach with 3H-Tdr (Chin-Leo and Kirchman, 1988).

3. Results 3.1. Station characteristics All stations were ice covered (30–80%) and due to ice melting had a lower-salinity (32.5–33.1) upper layer. Salinity increased to 35 at 200 m depth in most stations. The physical conditions of the

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sampling stations are described in detail by Kivima¨e (2007) and Sundfjord et al. (2007). In summary, several stations (I, III, XI, XIII) had nutrient-depleted surface layer, while others (IV, VII) had nitrate throughout the water column (Hodal and Kristiansen, 2008). The vertical distribution of Chl (Fig. 2) and location of Chl maxima (Table 1) reflected nutrient availability. Chl was low in stations with nutrients still present in the surface layer, indicating an early stage of bloom development (IV, VII), while subsurface Chl maxima indicated deep nutricline and later phase of the phytoplankton bloom (I, XIII). Most of the stations were considered at peak bloom stage (II, III, IX, X, XI) based on mineral nutrient, Chl, and phytoplankton biomass distribution. Sundfjord et al. (2007) clustered the stations according to geographic location, water mass properties and large-scale current features as northern (VII), interior (II, III, X, XI) and southern marginal ice zone (MIZ) stations (I, IV). The southern marginal ice zone stations had a strong signal of the Atlantic water, while the interior stations were more influenced by Arctic Waters.

3.2. Primary and bacterial production Primary production (Fig. 2) was confined to the upper 50-m layer in all the stations, with pronounced sub-surface maxima in several stations (stations I–III). The integrated (0–50 m) primary production ranged generally from 170 (station X) to 450 mg C m2 day1 (station XI) (Table 2). Productivity was exceptionally high in station II (770 mg C m2 day1) due to elevated activity at 30–40 m layer, and lowest in the off-shelf Nansen Basin station VII (100 mg C m2 day1).

Incorporation rates of Tdr and Leu co-varied (Fig. 3), but gave different estimates of bacterial production when common conversion factors were used (see Section 2). Bacterial production estimated with Leu incorporation method was nearly fourfold higher on average than the Tdr-based estimate. Almost similar difference between the two methods (by a factor of 3 or higher) was reported by Rich et al. (1997) during a transect study over the central Arctic Ocean. For this reason, results with both methods are considered as follows. Integrated (0–50 m) Tdr- and Leu-based bacterial production ranged between 21 and 155 mg C m2 day1, and 68 and 570 mg C m2 day1, respectively, being lowest in station VII (Table 2). In several stations elevated bacterial production (Leu 41 mg C m3

Table 2 Net phytoplankton production (PP; mg C m2 day1) and bacterial production (BP; mg C m2 day1) based on thymidine (Tdr) and leucine (Leu) methods, and the ratio of bacterial to particulate primary production Station

PP

BPTdr

BPLeu

BPTdr:PP

BPLeu:PP

I II III IV VII IX X XI XIII

282(724) 767(775) 399(735) 392(731) 99(76) ND 167(714) 454(786) 368(7110)

37(73) 83(74) 154(714) 52(74) 21(70.7) 55(73) 60(72) 99(76) 140(715)

85(76) 359(730) 463(729) 140(77) 69(75) 300(715) 324(711) 570(739) 529(763)

0.13(70.01) 0.11(70.01) 0.39(70.05) 0.13(70.01) 0.21(70.01) ND 0.36(70.03) 0.22(70.05) 0.38(70.13)

0.30(70.03) 0.47(70.06) 1.16(70.12) 0.36(70.03) 0.70(70.07) ND 1.94(70.17) 1.26(70.26) 1.44(70.50)

Rates were integrated through 0–50 m. 790% confidence intervals were calculated with Monte Carlo simulations assuming normal error distribution of replicate measurements. ND, no data.

0 20 40 60 80 100 0

Stn I

Stn II

Stn III

Stn IV

Stn VII

Stn IX

Depth (m)

20 40 60 80 100 0

PP Chl Tdr

20 40 60 80 Stn X

100 0

25 50 Primary production (mg C m−3 d−1)

Stn XI

0

25 50 Bacterial production (10×mg C m−3 d−1)

Stn XIII

0

25 50 Chl (5×mg Chl m−3)

Fig. 2. Vertical profiles of primary production, thymidine uptake based bacterial production and chlorophyll a concentration.

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Table 3 Median suspended biomass (range in parenthesis) of different bacterial physiological groups (mg C l1) in the upper (1–50 m) and deeper (60–200 m) layers

2

Station Layer (m) Total

ARC

I

1–50 60–200

12.7 (6.1–26.2) 5.4 (4.8–8.3)

1.4 (0.3–2.6) 1.9 (1.5–3.6) 0.6 (0.2–1.2) 2.4 (1.9–2.5)

II

1–50 60–200

22.7 (11.6–45.7) 1.5 (0.6–5.0) 5.2 (3.6–5.8) 26.1 (14.7–36.6) 11.6 (5.3–14.9) 1.3 (0.6–1.7) 4.8 (3.6–5.6) 10.0 (8.2–19.1)

III

1–50 60–200

27.7 (19.1–48.8) 1.8 (0.8–4.5) ND 13.2 (10.3–26.6) 1.3 (0.7–4.3) ND

27.9 (26.5–29.3) ND

IV

1–50 60–200

30.9 (5.9–63.5) 1.6 (0.8–3.7) ND 23.4 (22.4–24.4) 0.9 (0.9–1.0) ND

ND ND

VII

1–50 60–200

8.6 (6.1–10.3) 8.4 (6.0–11.1)

0.8 (0.7–1.1) 2.7 (2.0–3.8) 0.8 (0.6–1.1) 3.1 (2.2–7.2)

8.0 (4.9–9.1) 5.0 (4.8–6.7)

IX

1–50 60–200

10.7 (6.2–13.4) 4.6 (3.8–9.7)

0.7 (0.3–1.1) 1.5 (1.3–2.0) 0.9 (0.6–1.1) 1.3 (1.0–1.7)

8.8 (7.2–10.7) 5.8 (4.2–6.2)

Fig. 3. Log–log plot of the two methods for estimating bacterial production. 1:1 line with 0 intercept has been added.

X

1–50 60–200

9.0 (7.3–14.6) 4.3 (3.7–9.3)

0.8 (0.1–1.3) 1.7 (1.4–2.2) 0.4 (0.4–0.5) 1.7 (1.5–2.2)

8.9 (5.4–11.7) 4.8 (4.5–9.4)

day1; Tdr40.3 mg C m3 day1) ranged well below the euphotic layer down to 90 m (stations II, III) or even to 120 m (Fig. 2). Bacterial production matched a substantial fraction of the primary production, with large differences between the stations. Over the productive layer (0–50 m) the integrated BPLeu:PP ranged from 0.3 to 1.9 and BPtdr:PP from 0.1 to 0.4 (Table 2).

XI

1–50 60–200

11.4 (5.8–19.9) 10.1 (7.9–12.5)

0.9 (0.8–1.7) 1.7 (1.2–2.9) 0.9 (0.8–1.2) 1.9 (1.7–2.3)

14.8 (10.6–18.5) 7.5 (6.7–12.9)

XIII

1–50 60–200

10.8 (6.7–14.0) 14.8 (8.0–18.7)

0.9 (0.8–2.1) 1.3 (0.8–1.7) 14–1 (9.2–20.2) 1.0 (0.8–1.0) 1.0 (0.9–1.6) 18.9 (16.4–20.2)

3.3. Water column bacterial biomass and abundance

rates were in the off-shelf station VII (o5 mg C m2 day1) and highest in station IV (415 mg C m2 day1). Also, in station IV the contribution of total bacterial biomass to the POC sedimentation was highest (median 13%), while this contribution was lowest in stations II and X (medians 2.3% and 2.5%, respectively). The fraction of ARC from the total count in the sediment traps was on average 0.09 (range 0.02–0.20). This fraction did not change much with depth, but differences between stations were notable. In stations I and II on average, 12% of the bacteria in the sediment traps were ARC, while in station IV the percentage was lowest (3%). Overall, the fraction on ARC was not significantly different between the water column and sediment traps (p ¼ 0.69; d.f. ¼ 83; t-test on log-transformed data). However, in station IV, the fraction of ARC was lower than in the water column (po0.005, Wilcox test). The fraction of intact bacteria in the sediment traps (median 0.18, range 0.07–0.41) was not significantly different from the values in the water column. The difference was significant (po0.001, Wilcox test) only in the off-shelf station VII, being higher (median 0.41) in the water column and lower (median 0.14) in the sediment traps. Generally, the majority of intact bacteria in the sediment traps were also ARC (median 0.60, range 0.24–1.07), and this proportion was significantly higher (po0.05, Wilcox test) than in the water column (median 0.44).

Log Leu production (µg C l−1 d−1)

3

1

0

−1

−2

−3 −4

−3

−2

−1

0

1

Log Tdr production (µg C l−1 d−1)

The average biomasses of the various bacterial physiological groups are detailed in Table 3 and the vertical distribution is depicted in Fig. 4. In general, the total bacterial abundance varied from o0.2 to 3  106 cells ml1, with substantial differences between stations. In stations VII, IX and X, total bacterial abundance was mostly o0.5  106 cells ml1 throughout all depths. Total bacterial abundance was highest (42  106 cells ml1) in the upper 50 m of stations II, III and IV. The various bloom stages were reflected in the depth distribution of peak bacterial abundance (Fig. 4). In stations II, III and IV, we observed a clear maximum in the upper 1–20 m layer, while stations I, XI and, to some extent, X were characterized by a sub-surface bacterial abundance peak at 20–50 m layer (Fig. 5). The abundance of ARC (median 0.5  105 cells ml1, range 0.07–2.5  105 cells ml1) was a minor fraction (median 0.09, range 0.03–0.24) of the total bacterial count, but in significant correlation (spearman rho ¼ 0.7, d.f. ¼ 103, po0.001) with the latter. The abundance of intact bacteria (median 0.9  105 cells ml1, range 0.4–3.6  105 cells ml1) was a modest fraction (median 0.2, range 0.06–0.82) of the total bacteria and correlated poorly (spearman rho ¼ 0.2, d.f. ¼ 83, p40.05) with the latter. The median ratio of ARC to intact bacteria was 0.44 (range 0.08–1.7). PI-stained bacteria comprised a major fraction (median 1, range 0.43–2.1) of the total bacterial counts with a highly significant correlation (spearman rho ¼ 0.8, d.f. ¼ 83, po0.001).

Intact

PI stained 10.1 (5.7–27.5) 4.8 (4.0–8.7)

ND, no data

3.5. Relationships between variables in the water column and sediment traps

3.4. Bacterial biomass in sinking particles On average, the total bacterial biomass (7.7 mg C m2 day1, range 2.9–38.2 mg C m2 day1) was a small, but measurable fraction (median 3.3%, range 0.9–22.1%) of the POC vertical flux, with pronounced variability between the stations. The lowest

To elucidate the mechanisms of bacterial community responses to the variable conditions in the water column, we correlated the abundance of various bacterial physiological groups with each other, primary and bacterial production rates and the concentration of particulate organic nutrients and Chl (Table 4).

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0 20 40 60 80 100 0

Stn I

Stn II

Stn III

Stn IV

Stn VII

Stn IX

Depth (m)

20 40 60 80 100 0

Total PI Alive ARC

20 40 60 80 Stn X

100 0

25

50

Stn XI

0

25

50

Stn XIII

0

25

50

Bacterial biomass (µg C l−1) Fig. 4. Vertical profiles of concentrations of total, PI-stained, intact bacteria and ARC.

Notably, most of the relationships were statistically significant, due to the large sample size. Remarkably, the concentration of intact bacteria did not correlate with most of the measured parameters. In the sediment trap material, all the parameters covaried positively (Table 4).

heterotrophic group Bicosoecales were present in low numbers in most stations.

3.6. Bacterial growth rates

There are not many studies from the Arctic Waters where the proportion of bacteria with different physiological characteristics have been quantified comparatively from the water column and sediment traps. Our data show that the metabolic activity and membrane permeability of natural bacterioplankton communities in the northern Barents Sea are heterogeneous. Commonly, estimates of bacterial activity in the Arctic Waters are derived from incorporation rates of radiolabeled amino acids and rates of production are then normalized to concentrations of bacteria using DAPI or Acridine Orange to determine cell-specific rates of bacterial activity (Olli et al., 2007; Rich et al., 1997; Sherr and Sherr, 2003). Our results agree with several other studies from cold waters (Davidson et al., 2004; Huston and Deming, 2002; Sherr et al., 2001) that the inherent assumption that all bacteria contribute equally to microbial activity and production is not valid. Servais et al. (2001) found that ARC were responsible for o60% of production by the bacterial community, but their results may have been influenced by the apparent inhibition of protein synthesis by CTC. Other studies suggest that much of the bacterial production in natural marine communities is attributable to o10% of the bacterial standing stock (Choi et al., 1996; Karner and Fuhrman, 1997). Davidson et al. (2004) suggested that only 10–15% of the natural bacterioplankton in the Southern Ocean exhibited high levels of respiratory activity, and that o30% of the bacteria were responsible for most of the bacterial metabolism, production, growth and division. Thus, the cycling of matter in the

As we have two estimates of bacterial production (Tdr and Leu methods) and the growth rates can be normalized to several physiological groups, there are also more than one way to express bacterial growth rates. The bacterial growth rates in the euphotic layer are detailed in Table 5; below the euphotic layer (450 m), bacterial growth rates decreased and were generally o0.2 and o0.5 day1 for intact bacteria and ARC, respectively, based on Tdr uptake.

3.7. Biomass and composition of heterotrophic nanoflagellates Heterotrophic nanoflagellate (cell size o20 mm) abundance was on average 160 cells ml1 (range 3–625 cells ml1) in 2003 and 580 cells ml1 (range 75–2100 cells ml1) in 2004. Despite the difference in cell abundance, the biomass values were relatively similar between the 2 years (Fig. 6). In 2004, more cells were in the smaller size range, while in 2003, the biomass was dominated by relatively large cells of choanoflagellates (Fig. 6). At least two different choanoflagellate species were present, both single celled forms. In 2004, choanoflagellates were less abundant, and the species were mostly colonial forms. Heterotrophic dinoflagellates were relatively frequent in all stations and both years, but were not counted separately. In addition, a few cells from the

4. Discussion

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20

Depth (m)

40 60 80 100 120

Stn I

Stn II

Stn III

Stn IV

20

Depth (m)

40 60 Total PI

80

Alive

100

ARC

120

Stn VII

0

Stn X

Stn XI

30 0 15 15 15 30 0 30 Vertical flux of bacterial biomass (µg C l−1d−1)

Fig. 5. Vertical flux of bacterial biomass showing the contribution of cells with different physiological states.

Table 4 Spearman correlation coefficients (rho) between pairs of bacterial abundance, primary and bacterial production, Chl and particulate nutrients in the water column Water column

Total

ARC

Alive

PI stained

ARC Alive PI stained Leu Tdr PP Chl POC PON

0.67 0.21 0.79 0.47 0.50 0.24 0.66 0.63 0.72

0.21 0.61 0.37 0.39 0.44 0.40 0.61 0.62

0.10 0.15 0.15 0.10 0.21 0.24 0.15

0.69 0.71 0.24 0.66 0.63 0.72

Sediment trap ARC Alive PI stained POC PON

0.55 0.61 0.80 0.47 0.50

0.65 0.70 0.47 0.42

0.81 0.65 0.57

0.68 0.67

 po0.05;  po0.01;  po0.001.

microbial loop may be supported by a relatively small proportion of the bacterial standing stock also in polar waters.

4.1. Similar ARC in the water column and sinking particles A major goal of this study was to determine if sinking particles in the cold waters of the northern Barents Sea host a higher proportion of active and intact bacteria, as has been shown in

several studies from both, cold and temperate waters (Huston and Deming, 2002; Martinez et al., 1996). During a study in a very productive North Water polynya, Huston and Deming (2002) observed the percentage of ARC to be two to four times higher in sediment trap samples compared to the water column. This contrasts our results, where no difference in the proportion of ARC in the suspended and aggregate associated populations was found. The mechanisms for this discrepancy are not obvious. The most significant difference between the water column and sediment traps was the proportion of ARC from the intact bacteria: once the cells had intact cell membrane, they were more likely to be classified as ARC in the sinking aggregates. Possibly, the discrepancy with respect to ARC enrichment stems from the source of the sinking particles. The high proportion of ARC in the North Water study might have originated from the substantial material flux from the sea-ice community (Huston and Deming, 2002). In our study, the sea-ice algal communities were not well-developed and POC vertical flux below the ice was relatively low (Reigstad et al., 2008; Tamelander et al., 2008). Neither was the ice–water interface considered as a significant source of labile DOC (Gasˇparovic´ et al., 2007). Theoretical (Kiørboe, 2003) and experimental evidence (Kiørboe et al., 2004) suggest that after reaching a steady state (within 0.5–1 day for bacteria and flagellates), the colonization and detachment rates of bacteria dominate over growth and mortality within marine snow type aggregates by almost an order of magnitude. This consideration, if relevant to the sinking aggregates of primarily planktonic origin in the northern Barents Sea, could explain the similar proportions of ARC within the aggregates and in the ambient water. The intense exchange of cells between the aggregates and ambient water masks the signal derived from microscale bacterial dynamics within the sinking particles. However, the microbial dynamics within aggregates

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Table 5 Median bacterial growth rates (day1) in the water column (0–50 m) Station

AliveTdr

ARCTdr

TotTdr

AliveLeu

ARCLeu

TotLeu

I II III IV VII IX X XI XIII

0.35(70.05) 0.35(70.04) 1.17(70.35) ND 0.24(70.04) 0.98(70.13) 0.91(70.09) 1.13(70.11) 2.47(70.34)

0.64(70.09) 1.03(70.11) 1.64(70.23) 0.63(70.09) 0.69(70.11) 1.76(70.27) 1.90(70.92) 1.89(70.29) 2.65(70.62)

0.05(70.01) 0.07(70.01) 0.10(70.01) 0.07(70.01) 0.07(70.01) 0.13(70.01) 0.17(70.01) 0.18(70.01) 0.26(70.03)

0.81(70.14) 1.47(70.20) 3.79(70.94) ND 0.85(70.14) 5.42(70.79) 4.65(70.47) 6.19(70.80) 9.29(71.39)

1.31(70.33) 4.44(70.49) 5.27(70.84) 1.98(70.28) 2.38(70.41) 9.56(71.39) 9.70(74.16) 10.33(71.69) 9.95(72.46)

0.11(70.02) 0.32(70.04) 0.31(70.04) 0.25(70.03) 0.25(70.03) 0.69(70.08) 0.87(70.07) 0.96(70.10) 0.99(70.11)

Rates are calculated from thymidine and leucine incorporation rates, and normalized to various physiological groups of bacteria. 790% confidence intervals as in Table 2.

0

Tot HNF Choano

20 40 60 80 100

Stn I

Stn II

Stn III

Stn IV

Stn VII

Stn IX

Stn X

Stn XI

Stn XIII

0

Depth (m)

20 40 60 80 100 0 20 40 60 80 100 0

5

10

0

5

10

0

5

10

Nanoflagellate biomass (µg C l−1) Fig. 6. Vertical profiles of heterotrophic nanoflagellates and the contribution of choanoflagellates. Error bars represent the microscopy counting errors (95% confidence intervals). Replicate sampling revealed a coefficient of variation of 12% between the samples (not shown).

originating largely from ice-algae (Huston and Deming, 2002) may be very different. Overall, the bacterial activity in the northern Barents Sea was very high, as deduced from the high bacterial to primary production ratios (Table 2). The BP:PP, being on average about 0.2 in open oceans (Ducklow and Carlson, 1992) can vary from o0.03 to 41 in the Arctic Waters (Cota et al., 1996; Olli et al., 2007; Rich et al., 1997), depending on stage of the phytoplankton bloom and bacterial substrate supply rates. As deduced from the vertical profiles of bacterial production (maxima commonly found in sub-surface layers), the main source of planktonic bacterial substrate probably originated from the suspended algal biomass and production, with only minor contribution from the relatively weakly developed ice-algal communities (Gasˇparovic´ et al., 2007; Tamelander et al., 2008). The ice-algal contribution to primary production and as a source of bacterial substrate may vary

substantially in the Arctic Waters. Rich et al. (1997) reported a very high bacterial production (occasionally exceeding planktonic primary production) in the central Arctic Ocean, supported by a conspicuous ice-associated algal production (Gosselin et al., 1997). In contrast, Sherr and Sherr (2003) and Olli et al. (2007) reported relatively low bacterial production (compared to the primary production) in the central Arctic Ocean in combination with a poorly developed ice-algal community.

4.2. Low abundance of ARC Overall, the ARC comprised ca. 10% of the total cell count (Table 3), with no notable increase in sites of high microbial activity. Similar to Davidson et al. (2004), the ARC in our study comprised approximately half of the intact bacterial population. The low

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proportion of ARC was likely due to the high electron transport activity required for bacteria to become stained with CTC (Sherr et al., 1999a). Choi et al. (1999) argued that the percentage of ARC increases with increasing substrate availability. This is intuitively understandable, but contrasts our results where ARC did not reveal particular concentration increase, e.g., compared to the PI-stained cells, in sites of elevated algal and bacterial activity, like the sub-surface chlorophyll and bacterial production maxima. If nano-heterotrophs preferentially ingest actively growing and dividing bacteria, and discriminate against dormant and dead ones, as have been demonstrated in numerous studies (Gasol et al., 1995; Hahn and Ho¨fle, 2001; Monger and Landry, 1992), the latter may accumulate relative to the active ones in environments with elevated bacterial production and grazing rates like the sinking aggregates or chlorophyll and primary production maxima. Grazing selectivity can reportedly result in active bacteria being consumed at rates around four times that of inactive cells (del Giorgio et al., 1996; Gasol and del Giorgio, 2000). Preferential grazing on actively growing and dividing cells could partly explain the reduced abundance of ARC in sites of high microbial activity and concomitant accumulation of PI-stained cells. Of the heterotrophic protists, choanoflagellates, a morphologically easily recognized phylogenetic group of heterotrophic protists, are voracious bacterivores and their abundances in Arctic Waters are often found to be higher (up to order of 105 cells ml1) than for other types of colorless flagellates (Olli et al., 2007; Sherr et al., 1997). Choanoflagellates clearly dominated the heterotrophic nanoflagellate biomass in all the stations during the 2003 cruise, but were less abundant and sporadically present in 2004. Sherr et al. (1997) estimated bacterivory by heterotrophic nanoflagellates (inc. choanoflagellates) in the Arctic Ocean via uptake of fluorescently labeled bacteria. Using their cell-specific bacterial ingestion rates and the total heterotrophic nanoflagellate abundance in our study, we can approximate a mean rate of bacterial ingestion in the upper 50 m layer as 60 ng C l1 day1 (25%, 50% and 75% percentiles: 34, 50, 101 ng C l1 day1, respectively) in 2003, and 180 ng C l1 day1 (percentiles 120, 148, 228 ng C l1 day1, respectively) in 2004. These are close to or higher than the values estimated by Sherr et al. (1997). The 2003 estimates are probably conservative, because a large proportion of the heterotrophic protists were choanoflagellates, which are reportedly more efficient bacterivores than other forms (Sherr et al., 1997). Furthermore, our calculations do not account for mixotrophy by phototrophic species, which are known to be abundant in Arctic Waters (Olli et al., 2007). If the bacterivory rates are in the right order, we can approximate the grazing loss rate of bacterial production. Based on Tdr uptake, the interquartile range of bacterial production lost to grazing was 2–12% (median 5%) in 2003 and 5–20% (median 10%) in 2004. Thus, a measurable, but not excessive fraction of the bacterial production in our study was grazed by bacterivores. Finally, we cannot ignore the potential role of marine bacteriophages on the bacterial production (Middelboe et al., 2002; Tuomi et al., 1999). High bacterial growth rate and accumulation of cells increases the host–phage contact rate (Wells and Deming, 2006), which could have led to the high proportion of PI-stained bacteria in the sinking aggregates and layers of high microbial activity during our study.

4.3. Bacteria with ‘leaky’ cell membrane accumulate in high microbial activity spots Most of the bacteria in our stations had compromised cell membranes, suggesting that only a small fraction of bacterioplankton contributed to respiration and re-mineralization. The

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observed accumulation of PI-stained cells in layers of high bacterial activity are not unique. In earlier studies, the proportion of PI-stained bacteria has been shown to vary between 65% and 93% (Choi et al., 1996) or 51–62% (Davidson et al., 2004). Recently Davidson et al. (2004, Fig. 7) showed a substantial accumulation of PI-stained cells in the euphotic zone with high bacterial activity in the cold waters off the Amery Ice Shelf (Prydz Bay, Antarctica). The proportion of PI-stained bacteria depends on a variety of conditions, like pH, temperature, staining time and PI concentration (Davidson et al., 2004; Williams et al., 1998). There is little consensus on the effectiveness of stains used to discriminate live, dead and active bacteria (Davidson et al., 2004; Nebe-von-Caron et al., 1998; Ullrich et al., 1999). Williams et al. (1998) reported weak PI staining of ‘leaky’ viable cells and Stevenson (1978) and Luna et al. (2002) reported high proportion of ‘dead’ bacteria that can be ‘reactivated’ by adding nutrients. Apparently, PI, although used to determine ‘dead’ bacteria, can penetrate the membranes of cells that have recently divided or been exposed to physical, chemical and mechanical changes or starvation (Choi et al., 1996; Davidson et al., 2004; Nebe-von-Caron et al., 1998). However, the main question that remains is: Why do we observe in this, and in many other studies, so many PI-stained bacterial cells accumulating in sites of high microbial activity? Even if the leaky cells are just metabolically inactive, there must be a strong source for this fraction of cell. The PI-stained cells in the water column correlated well with the proxies of substrate availability like Chl, POC, PON, but also with bacterial production estimates. Also, ARC had a positive correlation with POC, PON and somewhat weaker correlation with Chl, bacterial and primary production. However, the intact bacterial fraction had no correlation with bacterial production or any substrate availability proxy apart from the weak correlation with POC (Table 4). This indicates that the principal source for the PI-stained cells in the water column can only be the most actively growing bacterial fraction (e.g., the ARC). Even if we know the source, it does not tell us what physiological mechanisms lead to the significant production of cells with leaky membranes. We also do not know the loss rates of this accumulated biomass, at what rate are they grazed, lysed or degraded. Given these limitations, we can propose only a speculative scenario, which cannot be verified with the available restricted data. If the loss rates are constant or comparable at different stations, then the biomass accumulation is a function of production rate. In sites of high substrate supply and microbial activity (which are estimated as bulk properties), also the consumption of substrate is high, and at any time-point a fraction of the actively metabolizing cells can experience substrate limitation at a microbial space scale. Marine bacteria inhabit microenvironments defined on diffusive scales in the order of less than a few millimeters (Seymour et al., 2004). It is at this microscale that important ecological processes including nutrient uptake, starvation, infection and predation take place, and that patchiness of substrates and organic particles exist (Seuront et al., 2002). Micro-scale heterogeneity in bacterial activity could have critical implications for calculations of bulk bacterial growth and biogeochemical transformation rates. Specific micro-zones or ‘hotspots’, where bacterial activity significantly exceeds background levels, will inevitably be missed by bulk analysis techniques (Seymour et al., 2004). Enhanced activity in ‘hotspots’ could lead to rapid micro-scale substrate limitation, starvation and slow-down of the metabolism, switching the cells into an inactive state. Metabolic inactivation can provide a refugee from protistan grazing and possibly also from viral lysis compared to actively growing cells. The net result can be an accumulation of low-metabolic-activity cells in layers of high microbial activity. If low-metabolic-activity cells are permeable to PI, as suggested by

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numerous studies (Gasol et al., 1995; Hahn and Ho¨fle, 2001; Monger and Landry, 1992), it could explain the accumulation of cells with compromised membranes in our study, as well as in earlier works (e.g., Davidson et al., 2004). 4.4. High growth rates of suspended bacteria The bulk bacterial growth rates in the surface layer, based on Tdr incorporation and total cell counts, ranged from 0.04 to 0.16 (0.27 in station XIII; Table 5). Our data are within the typical reported values in the Arctic surface waters ranging from 0.05 to 0.5 day1, with an average of about 0.1 day1 (Rich et al., 1997). Focusing on the intact and ARC fraction of the bacterial population gives substantially higher growth rates than the bulk properties, up to 2 day1, or even higher in station XIII. Bacterial growth rates based on Leu incorporation were clearly higher. When normalized to intact or ARC bacteria, growth rates exceeded 5 day1 in many occasions, suggesting that a small, but metabolically most active fraction of bacteria may indeed grow at a very high rate in the cold Arctic Waters. The discrepancy between the growth rates stems from the different estimates of bacterial production. The high BPleu to BPtdr ratio could indicate substantial protein turnover and perhaps high respiration relative to biomass production, suggesting low growth efficiencies for the bacteria (Rich et al., 1997). Following the logic of Rich et al., BPthr to BPleu ratio would give us a rough estimate of bulk level bacterial growth efficiency, which in our study would be approximately 26%. It is poorly known how bacterial growth efficiencies vary in different oceanic regimes (del Giorgio and Cole, 2000), and it has recently been suggested that the growth efficiency varies substantially due to shifting between auto- and allochthonous C sources (Kritzberg et al., 2005). We believe that similar to bacterial activity, there is a range of growth efficiencies at a single cell level and the bulk estimate necessarily gives a poor estimate of the actual microbial processes.

5. Conclusions Like in many other studies (Davidson et al., 2004; Sherr et al., 1999a, 2001) we found that stains for bacterial metabolism gave an ecologically meaningful indication of the physiological state of natural bacterial communities. We observed positive correlation between bacterial production, Chl and the concentration of ARC. However, overall our results do not conform with the general perception that environments with elevated microbial activity, like sinking marine aggregates and layers of high Chl or primary production host a higher proportion of actively respiring bacteria. Similar to Davidson et al. (2004), we observed elevated concentration of PI-stained cells, particularly in spots of high microbial activity. These observations suggest that important, but yet poorly understood microbial mechanisms have a major influence on shifting the physiological state of natural bacterioplankton communities. We propose that the immediate source of the PI-stained cells is the most active fraction of the bacterioplankton. In the high bacterial activity micro-environments, the rapid uptake of substrate can lead to resource limitation at the diffusive space scale relevant to individual cells, and this can lead to metabolic inactivation and subsequent accumulation of PI-stained cells. The PI-stained cell pool, which was substantial in our study, is in balance between the source and loss through decomposition, grazing or aggregate bound sedimentation. If bacterivores discriminate against metabolically inactive cells, we could envisage a scenario where PI-stained cells accumulate in layers with elevated bulk bacterial activity. This would explain the substantial positive

correlation of PI-stained cell pool with Chl and bacterial production during our study.

Acknowledgments We thank the captain and crew on the R/V Jan Mayen (University of Tromsø) for cruise logistical support as well as helpful colleagues in the field. The work is part of the CABANERA project (NFR 155936/700) under the NORKLIMA program financed by the Norwegian Research Council. The Estonian Science Foundation (6470) supported H. Tammert and K. Olli. The inspiring discussions with Dr. Llyd Wells clarified several issues. Constructive critics from three anonymous reviewers improved the manuscript. References Anderson, M.R., Rivkin, R.B., 2001. Seasonal patterns in grazing mortality of bacterioplankton in polar oceans: a bipolar comparison. Aquatic Microbial Ecology 25 (2), 195–206. Andreassen, I.J., Wassmann, P., 1998. Vertical flux of phytoplankton and particulate biogenic matter in the marginal ice zone of the Barents Sea in May 1993. Marine Ecology Progress Series 170, 1–14. Børsheim, K.Y., Bratbak, G., 1987. Cell volume to cell carbon conversion factors for a bacterivorous Monas sp encirched from seawater. Marine Ecology Progress Series 36, 171–175. Chin-Leo, G., Kirchman, D.L., 1988. Estimating bacterial production in marine waters from the simultaneous incorporation of thymidine and leucine. Applied and Environmental Microbiology 54, 1934–1939. Cho, B.C., Azam, F., 1998. Major role of bacteria in biogeochemical fluxes in the ocean’s interior. Nature 332, 441–443. Choi, J.W., Sherr, E.B., Sherr, B.F., 1996. Relation between presence–absence of a visible nucleoid and metabolic activity in bacterioplankton cells. Limnology and Oceanography 41, 1161–1168. Choi, J.W., Sherr, E.B., Sherr, B.F., 1999. Dead or alive? A large fraction of ETSinactive marine bacterioplankton cells as assessed by reduction of CTC, can become ETS-active cells with incubation and substrate addition. Aquatic Microbial Ecology 18, 105–115. Coppola, L., Roy-Barman, M., Wassmann, P., Jeandel, C., 2002. Calibration of sediment traps and particulate organic carbon export using 234Th in the Barents Sea. Marine Chemistry 80, 11–26. Cota, G.F., Pomeroy, L.R., Harrison, W.G., Jones, E.P., Peters, F., Sheldon, W.M., Weingartner, T.R., 1996. Nutrients, primary production and microbial heterotrophy in the south-eastern Chukchi Sea: Arctic summer nutrient depletion and heterotrophy. Marine Ecology Progress Series 136, 247–258. Davidson, A.T., THomson, P.G., Westwood, K., van den Enden, R., 2004. Estimation of bacterioplankton activity in Tasmanian coastal waters and between Tasmania and Antarctica. Aquatic Microbial Ecology 37, 33–45. del Giorgio, P.A., Bouvier, T., 2002. Linking the physiologic and phylogenetic successions in free-living bacterial communities along an estuarine salinity gradient. Limnology and Oceanography 47, 471–486. del Giorgio, P.A., Cole, J.J., 2000. Bacterial energetics and growth efficiency. In: Kirchman, D.L. (Ed.), Microbial Ecology of the Oceans. Wiley-Liss, New York, pp. 289–325. del Giorgio, P.A., Gasol, J.M., Vague´, D., Mura, P., Augustı´, S., Duarte, C.M., 1996. Bacterioplankton community structure: protists control net production and the proportion of active bacteria in a coastal marine community. Limnology and Oceanography 41, 1169–1179. Ducklow, H.W., Carlson, C.A., 1992. Oceanic bacterial production. Advances in Microbial Ecology 12, 113–181. Fuhrman, J.A., Azam, F., 1982. Thymidine incorporation as a measure of heterotrophic bacterioplankton productivity in marine surface waters: evaluation and field results. Marine Biology 66, 109–120. Gasol, J.M., del Giorgio, P.A., 2000. Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities. Scientia Marina 64, 197–224. Gasol, J.M., del Giorgio, P.A., Massana, R., Duarte, C.M., 1995. Active vs inactive bacteria: size-dependence in a coastal marine plankton community. Marine Ecology Progress Series 128, 91–97. Gasˇparovic´, B., Plavsˇic´, M., Bosˇkovic´, N., C`osovic´, B., Reigstad, M., 2007. Organic matter characterization in Barents Sea and eastern Arctic Ocean during summer. Marine Chemistry 105, 151–165. Gosselin, M., Levasseur, M., Wheeler, P.A., Horner, R.A., Booth, B.C., 1997. New measurements of phytoplankton and ice algal production in the Arctic Ocean. Deep-Sea Research II 44, 1623–1644. Hahn, M.W., Ho¨fle, M.G., 2001. Grazing of protozoa and the effect on populations of aquatic bacteria. FEMS Microbiology, Ecology 35, 113–121.

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