Short-term dynamics of nutrients, planktonic abundances, and microbial respiratory activity in the Arctic Kongsfjorden (Svalbard, Norway)

Abstract

Atlantification of Arctic ocean is causing a sharp increase in temperature and salinity around Svalbard Islands and in Kongsfjorden. Such phenomenon and the input of sediment-rich glacial meltwater influence salinity, water column turbidity, and light penetration with ecological implications on the microbial features. With the aim to address the temporal variability of the microbial assemblage in relation to environmental variables, a 7-day study was carried out in Kongsfjorden, in late summer 2013. Abiotic (temperature, salinity, nutrients, total suspended matter, particulate inorganic, and organic carbon) and biotic (phyto -, picophyto-, bacterio-, and virioplankton abundance and microbial respiration) parameters were investigated at a station in the inner fjord area, ca. 8.5 km away from the glacier front. Phyto-, picophyto-, and virioplankton showed low abundance. Dinoflagellates and coccolithophorids dominated the phytoplankton community while Synechococcus sp. the picophytoplankton alone, in relation with Atlantic water. Low virus to bacteria ratios were detected, presumably linked to the high sedimentation rates. Interesting variability for picophytoplankton with depth, virioplankton with both time and depth, and respiratory rates with time were found. Moreover, the organic matter turnover was slower on the first sampling day compared to that of the following days. Planktonic abundance depended on the variability of both hydrology (seawater mass inflow) and freshwater runoff from the glacier (relative turbidity degree). Differently, the metabolic rates of respiration appeared to be linked with the particulate carbon pool. Over 1-week study, the diverse microbial dynamics appeared to be conditioned on complex forcing, emphasizing the importance of high-resolution experiment.

Introduction

Arctic warming is faster than in the rest of the world, a phenomenon called Arctic Amplification (Dai et al. 2019) involving both oceanic and terrestrial realms. The European side of the Arctic experienced a recent increase of warm water inputs from the Western Spitsbergen Current across Fram Strait (Cottier et al. 2007) and a corresponding reduction of sea ice cover (Lind et al. 2018; Promińska et al. 2018; Asbjørnsen et al. 2020). These changes in ocean properties make the Artic more similar to southern surrounding ocean (Polyakov et al. 2017) and there are clues of major changes in Arctic ecosystem that has been referred to as “Atlantification.” As a consequence, the Arctic Ocean experiencing weakened ocean stratification, enhancing vertical mixing and increasing upward fluxes of heat and salt that prevent sea ice formation and increase ocean heat content. All these changes in energy budget provoke also mixing of nutrients with potential impacts for biological systems in terms of nutrients availability (Armitage et al. 2020) and ecology of plants and animals (Grebmeier et al. 2006; Post et al. 2009). In turn, recent global warming has caused most land glaciers to retreat, increasing meltwater outflow and resulting in massive inputs of suspended sediment to the coastal marine environment (Zaborska et al. 2006). Nevertheless, according to Bogen et al. (2014), the higher in-land sedimentation and following forming of pro-glacial lakes complicate the discharge–sediment load relationship. For instance, the alteration the glaciers’ melting patterns modifies the runoff budget with temperature and/or salinity changes in the adjacent Atlantic and Arctic water with modifications in the fjords-ocean exchange processes (Svendsen et al. 2002), possibly leading to Atlantification. During glacier melting phases, the input of inorganic particulate matter strongly increases, reducing the proportion of POC in the total mass fluxes (Lalande et al. 2016; D’Angelo et al. 2018). In addition, sinking biogenic particles drive the flow of organic matter through the water column (Martin et al. 1987; Karl et al. 1987; Azzaro et al. 2006). These forthcoming change emphasize the need to improve our understanding of biological–physical coupling in glaciated fjords (Meire et al. 2017; Torsvik et al. 2019). Microbial processes, including population dynamics, phytoplankton taxonomic composition, primary production rates, as well as biogeochemical budget and trophic web (Kirchman et al. 2005; Schuur et al. 2009; Vincent 2010; Caroppo et al. 2017; Krajewska et al. 2020; Caruso et al. 2020), are also affected by Arctic warming. Viral, bacteria, and phytoplankton, playing a key role in carbon fluxes and nutrient regeneration (Brussaard 2004a; Zaccone et al. 2004; He et al. 2019), can be viewed as sentinels and amplifiers of global change (Vincent 2010). Previous studies have shown that viruses might be adsorbed on sinking particles, especially in sites with high sedimentation rates and transported with bacterioplankton to the sea floor (Schoemann et al. 2005). Moreover, this affinity for adsorbing to small particles may also substantially reduce the ability of viruses to infect host cells in regions with high loads of silt and clay-based terrestrial runoff (Murray and Jackson 1992; He et al. 2019; Maat et al. 2019). In Arctic areas, a small percentage of the surface primary production reaches the sea floor (Amiel et al. 2002; Sallon et al. 2011). Kongsfjorden is a fjord located on the west coast of Spitsbergen in the Svalbard archipelago (Norway) in the Arctic Ocean. It is oriented from south-east to north-west (78° 52′ –79° 04′ N, 11° 19′ –12° 30′ E) and is 20 km long with width from 4 to 10 km. The inner fjord is shallow with depths less than 100 m; the open-western zone is deeper than 300 m depth (Svendsen et al. 2002). The tidewater glaciers Kronebreen and Kongsvegen (extensions 445 and 165 km2, respectively; Trusel et al. 2010) ouflow in the inner fjord. A recent increase in melting of these tidewater glaciers has impacted the adjacent marine environment in its hydrography and biogeochemistry (Halbach et al. 2019). Also summer air high temperature affects the glacier thaw, which begins once there is sufficient surface melt to warm the glacier snowpack to the melting point, allowing water to make its way downward to the subglacial drainage network (Svendsen et al. 2002; Lydersen et al. 2014). When surface water enters this subglacial network, overpressure occurs, leading to lift-up and entrainment of basal sediments. As a result, the water coming out of the glacier tends to be sediment laden (Hodgkins et al. 1999). After meltwater discharges in the ocean from the base of the glacier, it rapidly forms a turbulent jet (Powell 1990). Because of relative density contrasts, the jet rises vertically and forms a buoyant and brackish surface overflow plume. Upwelling of the subglacial freshwater discharge plume at the Kronebreen glacier front entrained large volumes of ambient, nutrient-rich bottom waters which led to elevated surface concentrations of ammonium, nitrate, and partly silicic acid (Halbach et al. 2019). Sediment-rich surface water can strongly limit light penetration or causes nutrient fertilization, with important consequences on the plankton dynamics and remineralization (Krajewska et al. 2020) as well as trophic web dynamics. In sediment trap, the total mass fluxes (TMF) registered on annual scale in Kongsfjorden ranged between 6.8 × 103 and 1.5 × 104 g m−2 y−1 in the inner fiord (Svendsen et al. 2002; Zajaczkowski 2002; Aliani et al. 2004; D’Angelo et al. 2018), and reached 105 g m−2 y−1 at the Kronebreen glacier front (Trusel et al. 2010). However, particulate organic matters collected by sediment traps (Zajaczkowski 2008) or studies of the disequilibrium 234Th/238U (Aliani et al. 2004) do not consider the entire pool of oxidizable organic matter, which include both the particulate and dissolved forms. The study of microbial respiration rates instead could fill this gap, since respiration takes into account oxidation of both dissolved and particulate organic matter, providing an integrated estimate of the carbon utilization in the ocean (Azzaro et al. 2006). Viruses also can play an important role in controlling bacterial abundance and/or sustaining bacterial population by viral lysates (He et al. 2019; Zhang et al. 2020). Apart from microbial biodiversity, which is not within the scope of our research, our study hypothesized that, in September, the ocean water inflows and the glacial melting waters could have strong ecological implications on the microbial community abundance and respiration in Kongsfjorden. Moreover, in September, the collapse of the secondary phytoplankton bloom happens, releasing organic matter to be degraded by heterotrophic bacteria. To this aim, during late summer/early fall 2013, a high-resolution sampling over a short time scale (5 samplings in 7 days) was carried out to evaluate the planktonic abundance (phyto-, picophyto, bacterio-, and virioplankton) and the microbial remineralization rates as a function of environmental properties in a fixed station located ca. 8.5 km away from the glacier front. The novelty of this study is the interdisciplinary approach, which merges microbial data with total and organic matter, chlorophyll a, and hydrological variables along the water column (surface—down to 100 m depth).

Materials and methods

Water samples were collected every day from September 5 to 11, 2013, in Kongsfjorden at Mooring Dirigibile Italia station (MDI: 78° 54.86 ʹ N; 12° 15.41′ E, depth ~ 102 m) (Fig. 1). Meteorological data were obtained by the eKlima database, managed by the Norwegian Meteorological Institute (eKima.no).

Fig. 1
figure1

Map of the study area. In the small square, the location of the Svalbard Islands, together with their geographical coordinates, is reported. In the large square, Kongsfjorden area, its geographical coordinates, the locations of the Mooring Dirigibile Italia (MDI) station, and Ny-Alesund site are reported. The thick lines indicate the fronts of the Blomstrandbreen, Conwaybreen, and Kongsbreen glaciers during September 2013

Samples have been named according to the sampling date as follows: D05, D06, D09, D10, and D11. During sampling, photosynthetically active radiation (PAR: 400–700 nm), temperature in °C (T), and conductivity (then salinity and density, DEN) were recorded along the water column with a PNF-300 profiler (Biospherical Instruments, USA) and a SeaBird Electronics SBE-19 plus CTD profiler, respectively. Six depths (surface, 5, 25, 50, 75, and 100 m) were sampled with “10-L Niskin bottles,” to determine nutrients, total suspended matter, particulate inorganic and organic carbon, chlorophyll a, planktonic abundance (viruses, bacteria, picophytoplankton, and microphytoplankton), and microbial respiratory activity.

In Table 1, the acronyms of the studied variables are reported. Samples for nitrate (NO3), nitrite (NO2), ammonium (NH4+), and phosphate (PO43−) analysis were taken by filtering the collected water through GF/F glass fiber pre-combusted filters into 50 ml sample and kept frozen (− 20 °C) in falcon tubes (polyethylene) until analyses in the lab. Analytical determinations of NO3 and NO2 were performed according to Strickland and Parsons (1972); NH4+ was measured according to Aminot and Chaussepied’s method (1983). All nutrient concentrations were determined using a Varian Mod. Cary 50 spectrophotometer. Total dissolved nitrogen (Ntot) was determined by the sum of nitrates, nitrites, and ammonium.

Table 1 Acronyms of the studied parameters and link among some of them

Water samples for total suspended matter (TSM) were filtered on pre-weighed and pre-combusted GF/F filters, rinsed with Milli-Q water and oven-dried (50 °C) overnight, and then weighed to calculate the total suspended matter (TSM, mg L−1). Particulate total carbon (Ctot) and organic carbon (POC) were collected filtering on pre-combusted GF/F filters (nominal pore size 0.7 µm) stored at − 20 °C until analysis. Both Ctot and POC were analyzed using a FISONS NA2000 Elemental Analyzer (EA). POC filters were treated with 1.5 N hydrochloric acid to remove the inorganic carbon (Hedges and Stern 1984), and particulate inorganic carbon (PIC) was determined by subtracting the measured particulate organic carbon concentration (POC) to the measured Ctot contents and assuming that PIC is composed mainly of calcium carbonate (Monaco et al. 1990).

Water samples (1.0–2.0 L) for chlorophyll a (CHL) were sequentially filtered on polycarbonate filter (10.0 and 2.0 μm pore size), to separate the size classes: microphytoplankton (> 10 μm) and nano-phytoplankton (10–2.0 μm), and on Whatman GF/F glass fiber filter (nominal pore size 0.45 μm), to separate picophytoplankton (2.0–0.45 μm). Filters were stored in aluminum foil at − 20 °C until the laboratory analysis.

Chlorophyll a was extracted in a 90% acetone solution, at 4 °C for 24 h in the dark, and the concentration was measured with a spectrofluorometer (mod. Varian Cary Eclipse) before and after acidification (HCl solution 0.001 N). Excitation and emission wavelengths (429 and 669 nm) were selected after standardization with a solution of chlorophyll a extracted from Anacistys nidulans (by Sigma Co). The chlorophyll a concentration was calculated according to Lorenzen (1967) as described by Decembrini et al. (2009).

Phytoplankton samples were fixed with Lugol's iodine solution (1% of the total volume) and examined by an inverted microscope (Labovert FS Leitz) equipped with phase contrast. Subsamples (50–100 mL) were allowed to settle for 24–48 h. Counting of phytoplankton abundance (PHY) was performed following the Uthermöhl method (Edler and Elbrächter 2010) along transect (1–4); in addition, half of the Uthermöhl chamber was examined at a magnification of × 200, to count the less abundant (range 40–200 cells L−1) phytoplankton taxa (Zingone et al. 2010).

Bacterioplankton counts were performed on aliquots from 4 to 8 mL of seawater samples fixed with pre-filtered formaldehyde (0.2 µm porosity; final concentration 2%) and stored at 4 °C in the dark until lab treatment (La Ferla et al. 2010). The epifluorescence direct count technique was applied to estimate bacterioplankton abundance (BA) by using DAPI (4′6-diamidino-2-phenylindole, final concentration 10 μg mL−1 by Sigma-Aldrich) according to Porter and Feig (1980). An AXIOPLAN 2 Imaging microscope (Zeiss), equipped with a digital camera Axiocam (Zeiss) and an AXIOVISION 3.1 software, was used. Cell counts were performed on a minimum of 20 randomly selected fields in two replicate slides. When cell abundance was low, 30–40 random microscope fields were counted.

Samples for viral abundance (VA) were fixed on board with 0.5% glutaraldehyde, followed by freezing in liquid nitrogen, where they were kept until analysis (Brussaard 2004a). To be related with virus abundance in the virus to bacteria ratio, samples for bacterial counts by flow cytometry were fixed with paraformaldehyde (1%) and glutaraldehyde (0.05%) and freezed in liquid nitrogen, where they were kept until analysis (Gasol and del Giorgio 2000). At the laboratory, the samples for viral particles and bacteria were thawed and stained with SYBR Green I (at a final concentration of 5 × 10−5 of the commercial stock solution; Molecular Probes) (Brussaard 2004b) and analyzed using a FACSCalibur flow cytometer (BD Biosciences) equipped with an air-cooled argon ion laser emitting at 488 nm (power at 20 mW), fixed laser alignment, and fixed optical components. A 70 μm nozzle aspirated the sample, and sterile Milli-Q water (18.2 mΩ) was used as sheath fluid. Viral samples were then incubated in the dark at 80 °C for 10 min and at room temperature for an additional 5 min. Distinct viral groups were detected and quantified based on their cytometric signatures in a plot side scatter (X-axis, related by size) versus green fluorescence (Y-axis, green fluorescence from SYBR Green I related to nucleic acid content). The various autotrophic populations were distinguished using a combination of side scatter light and natural fluorescence (red and orange) issued by photosynthetic pigments (Marie et al. 2005). Fluorescent latex beads (Fluoresbrite YG carboxylate 1.0 μm, Polysciences) were added at a known abundance to each sample for the calibration of side scatter and green fluorescence signals and as internal standards for cytometric counts and measures. All data were obtained and analyzed using Cell Quest software (BD).

Respiratory activity was derived from the Electron Transport System assay (ETS) (Azzaro et al. 2012). Ten-liter samples were concentrated on GF/F Whatman glass fiber membranes and the filters were immediately frozen in liquid nitrogen to prevent enzyme degradation until analysis at the laboratory. The ETS activity assay was carried out in crude homogenate for access to the respiratory systems. The principle of this assay is to provide the NADH-, NADPH-, and succinate-dehydrogenases of the broken cells with an excess of their natural substrates (NADH, NADPH, and succinate) while collecting the transferred electrons with an artificial electron acceptor, a tetrazolium salt (INT: 2-(4-iodophenyl)-3-(4- nitrophenyl)-5-phenyltetrazolium chloride) which is reduced to formazan. ETS activity was calculated by the equation:

$$ {\text{ETSa }}\left( {\mu {\text{L O}}_{2} {\text{h}}^{ - 1} {\text{L}}^{ - 1} } \right) \, = \, \left( {H \, \times \, S \, \times \, C - OD} \right) \, \times \, \left( {1.42 \, \times \, V \, \times \, t} \right) $$

where H is homogenate volume (mL), S is volume of the final formazan solution, 60 is the minute-to-hour conversion factor, C-OD is optical density of the finale reaction mixture spectrophometrically read at 490 nm and corrected for blank absorbance, 1.42 converts the formazan formed to oxygen, V is the volume of seawater filtered (L), and t is the reaction time (20 min). The ETS Vmax was corrected for in situ temperature with the Arrhenius equation and with an activation energy of 15.8 kcal/mol (Packard et al. 1975). The ETS Vmax was expressed as C-ETS in carbon units, applying the following equation:

$$ C - {\text{ETS }}\left( {\mu {\text{g C h}}^{ - 1} {\text{L}}^{ - 1} } \right) \, = {\text{ETS }} \times \, 12/22.4 \, \times \, 122/172 $$

where 12 is the C atomic weight, 22.4 is the O2 molar volume, and 172/122 is the Takahashi oxygen/carbon molar ratio (Takahashi et al. 1985).

Statistical analyses

The statistical analyses were performed in the R statistical environment package v3.2.3. To study the association between the parameters, correlations between all pairs of data variables were estimated using a Spearman correlation coefficient followed by a significance test (p-value < 0.01) based on t-test. Differences in each dependent variable of the dataset were established using the two-way analysis of variance (ANOVA) (Holm-Sidak test) and considering time and depth as the main factors.

Results

Water column structure and light climate

During the 7-day experiment, rain precipitation occurred from 4 to 8 September (with the maximum on 5 September, 3.3 mm day−1), and also wind rapidly changed speed and direction from 1.95 (NNE) to 8.89 ms−1 (SSW) in 24 h only, between D05 and D06 when they reached the maximum values (Table 2).

Table 2 Meteorological parameters measured in the studied period

The incident PAR (E0+) values ranged between 575 and 766 µE m−2 s−1 whereas the underwater PAR attenuated in the upper water column and at 5 m depth, there was very low irradiance (~ 0.7% E + 0).

The vertical profiles (0–100 m) of temperature (T°C) and salinity are reported in Fig. 2. T°C ranged from 3.38 to 6.15 °C, increasing from the surface down to 50 m depth and then decreasing down to the bottom, where the minimum was detected. Salinity ranged between 29.66 and 35.06 and increased with depth. Significant differences in salinity and T°C versus depth were found day-by-day (Table 3). Higher T°C variability was mainly observed at 50 and 75 m depths.

Fig. 2
figure2

Vertical distribution of salinity and temperature. Time series plot of temperature (°C) and salinity along water column at the Mooring Dirigibile Italia (MDI) station in the sampling days (D05, D06, D09, D10, and D11). Opaque area between D6 and D9 (2 days) is the calculate trend (gridding) of the plotted parameters

Table 3 Results of two-way ANOVA obtained from abiotic and biotic variables versus depth and time

To investigate changes of the main water masses, the T and salinity dataset and the mean values (± Standard Deviation) of the five samplings were plotted on the Fig. 3. Three main water masses were identified (Fig. 3) according to Cottier et al. (2005): the surface water (SW) of internal fjord origin (T > 1 °C and salinity < 34); the Atlantic water (AW) of external origin (T > 3 °C, salinity > 34.65 and D < 27.92); and finally, the intermediate water (IW) of mixed origin (T > 1 °C and salinity from 34.00 to 34.65). All water masses were sampled: three samplings in SW (surface, 5 and 25 m), one in IW (50 m), and two in AW (75 and 100 m). On the first and last days of sampling, salinity ranged between 33 and 34.6 (Fig. 3), when a plume of warmer water was recorded. During sampling at station D9, associated to the lowest salinity in surface waters, two low temperature peaks were recorded; the lowest temperature was at 50 m depth and the second low temperature at 75 m depth.

Fig. 3
figure3

Temperature–salinity diagram. Whole set of data and mean values ± S.D. for sampling depth (surface, 5, 25, 50, 75, and 100 m). SW, IW, and AW refer to surface water, intermediate water, and Atlantic water, respectively, at the Mooring Dirigibile Italia (MDI) station in the sampling days (D05, D06, D09, D10, and D11)

Inorganic nutrients, suspended particulate matter, and chlorophyll a

NO3 concentrations changed with depth and time (Table 3) (mainly at depths below SW) and the mean values between all the sampling days ranged from 0.10 ± 0.10 µmol L−1 (25 m depth) to 2.28 ± 1.19 µmol L−1 (100 m depth) (Fig. 4a). NO2 values were lower than NO3 values and they changed over time and depth (Table 3), mainly in SW, with the mean values ranging between 0.07 ± 0.02 µmol L−1 and 0.25 ± 0.22 µmol L−1 at 0 and 25 m, respectively (Fig. 4b). Generally, higher values of NO2, as for NO3, were observed in the depths below the SW. NH4+ mean values showed small variations over time and namely in SW, oscillating between 0.79 ± 0.41 µmol L−1 and 2.45 ± 2.27 µmol L−1, respectively, at 5 and 0 m depth (Fig. 4c). PO43− concentrations (Fig. 4d) changed trough the water column (Table 3) and, similarly to salinity and NO3, increased with depth, ranging between 0.53 ± 0.13 µmol L−1 (0 m depth) and 0.91 ± 0.09 µmol L−1 (100 m depth). N/P ratio varied with depth (Table 3) and ranged on average between 0.45 ± 0.12 and 0.89 ± 0.21 at 5 m and 0 m, respectively (Fig. 4e).

Fig. 4
figure4

Vertical distribution of inorganic nutrients. Nitrates (NO3, a), nitrites (NO2, b), ammonium (NH4+, c), phosphates (PO43−, d), expressed in µmol L−1, and nitrogen to phosphate ratio, calculated in Log (N/P, e), at the Mooring Dirigibile Italia (MDI) station at six depths (surface, 5, 25, 50, 75, and 100 m) in the sampling days (D05, D06, D09, D10, and D11)

TSM distribution showed high variability with depth (Table 3). Figure 5a describes the profiles of TSM that ranged from 0.52 to 5.0 mg L−1 (at 75 and 5 m depth, respectively). Peak TSM values were measured at the surface (avg. 3.81 ± 1.03 mg L−1), minimum values at 50 m depth (avg. 0.75 ± 0.13 mg l−1), and then TSM increased again down to the bottom. The lower and higher temporal variabilities were found in IW and SW, respectively. In SW (particularly at surface and 5 m depth), high values were determined on D05 and D11, and low values were determined on D09. In Fig. 5b and c, the bulk concentrations of PIC and POC along the water column are reported. They varied significantly with depth and time (Table 3). Like TSM, the highest averaged values were generally found at 5 m depth (PIC mean value: 82.2 ± 54.2 µg C L−1; POC mean value 166.7 ± 71.9 µg C L−1) where the highest variations of POC occurred. The lowest mean values of PIC and POC, instead, were determined at 50 and 75 m depth (PIC, 21.5 ± 21.1 µg C L−1; POC, 76.1 ± 25.8 µg C L−1), respectively. In Fig. 5d, the PIC/POC ratios are reported. Higher PIC/POC variability at 75 m depth was conditioned by high values of PIC determined in the first sampling day.

Fig. 5
figure5

Vertical distribution of total suspended matter, carbon pool, and chlorophyll a. Total suspended matter (TSM, a), expressed in mg L−1, particulate inorganic carbon (PIC, b), and particulate organic carbon (POC, c) in µg C L−1, particulate inorganic carbon to particulate organic carbon ratio, calculated in Log (PIC/POC, d) and chlorophyll a (CHL, e) in mg m−3 at the Mooring Dirigibile Italia (MDI) station at six depths (surface, 5, 25, 50, 75, and 100 m) in the sampling days (D05, D06, D09, D10, and D11)

The chlorophyll a concentration varied between a minimum of 0.034 and a maximum of 1.102 mg m−3 (st. D05-0 m and D06-0 m, respectively) with higher concentration close to the surface layer (0–5 m depth). In the first day (D05) of the measurement, the vertical distribution of chlorophyll a showed low surface value and a weak increasing at 50 m depth (Fig. 5e). Starting from the following day (D06), an inversion of its distribution is observed with the maximum at surface and a strong decrease already at 5 m depth. A similar trend continues in the following days with a decrease in both surface and towards the bottom.

Chlorophyll a varied between 0.034 and 1.102 mg m−3 measured on D05-0 m and D06-0 m, respectively, and the maximum variability was observed at surface layers (0–5 m depth). In the first day of the experiment, the vertical profile of chlorophyll a showed low values at surface and an increasing trend along the water column. Following, an inverse distribution was observed with time (D06 and D09) after that a decrease of chlorophyll a along the entire water column was detected (Fig. 5e).

Planktonic abundances (phyto-, picophyto-, bacterio-, and virioplankton)

Phytoplanktonic concentrations generally showed low values (on the order between 102–104 cells L−1, Fig. 6a) over the 7 days and throughout the water column, with the exception of three peaks at surface, 50 and 75 m depth (on the order of 105–106 cells L−1) measured on D09 (salinity minimum), D11 (salinity > 34.65), and D10 (salinity > 34.80), respectively. The community was dominated by dinoflagellates (i.e., Prorocentrum cf. gracile, Alexandrium minutum, A. tamarense, Scrippsiella acuminata, Gonyaulax spinifera, Prorocentrum micans, and P. cordatum), with total values ranging between 1.0 × 103 cells L−1 and 4.4 × 106 cells L−1. In particular, Alexandrium minutum and Prorocentrum cf. gracile were responsible for a bloom reaching the peak values of 1.8 × 106 cells L−1 and 4.7 × 106 cells L−1, respectively. Also coccolithophorids (Emiliania huxleyi and Syracosphaera pulchra) were important components of the phytoplankton community (range of the total coccolithophorids: 0.04 to 132.9 × 103 cells L−1). The less abundant diatoms (up to 4.5 × 103 cells L−1) were represented by cosmopolitan species (Cerataulina pelagica, Chaetoceros decipiens, Thalassiosira gravida, Pseudo-nitzschia delicatissima group) as well as by cold water taxa (Cylindrotheca closterium, Thalassiothrix longissima). More details on the phytoplankton community structure and taxa abundances are reported in Caroppo et al. (2017).

Fig. 6
figure6

Vertical distribution of plankton abundance. Phytoplankton abundance (PHY, a) expressed in Log cells × 103 L−1, picophytoplankton abundance (Synechococcus, b) in cells × 102 mL−1, bacterioplankton abundance (BA, c) in cells × 105 mL−1, and virioplankton abundance (VA, d) in VLP × 105 mL−1 at the Mooring Dirigibile Italia (MDI) station at six depths (surface, 5, 25, 50, 75, and 100 m) in the sampling days (D05, D06, D09, D10, and D11)

As concerns picophytoplankton, only Synechococcus sp. cells were detected and their abundance was in the order of 102 cells mL−1 (Fig. 6b). Its abundance was negligible at surface and increased within the depth interval 25–75 m, showing a significant variability with depth (Table 3). On the contrary, on D11, the distribution was uniform along the entire water column.

Bacterioplankton abundance was on the order of 105–106 cells mL−1 (Fig. 6c) and showed a distribution that changed little along the vertical in the different sampling days. The maximum mean value of bacterioplankton abundance was recorded at 25 m depth, where the maximum extension of the plume of warmer water on D05 and D11 occurred (Fig. 3a, salinity between 33 and 34.65). The minimum value of bacterioplankton abundance—averaged through the water column—was determined on D09, when the warm plume was at minimum. Virus amounted at 105 particles mL−1 (Fig. 6d) and V1 on average constituted the 92% of total viral count. Through the water column, viral abundance was low at surface layers and high at intermediate waters, after which it declined with increasing depth, showing a significant variability with depth (Table 3). The general temporal trend showed an important increase of the virus with time (Table 3), with the lowest counts registered on D05 and D06 and the highest on D09, D10, and D11. The virus to bacteria ratio (VBR)—calculated by flow cytometry analyses of viral particles/ bacteria—remained at or below 1 in the SW throughout the entire study, but was consistently > 1 below the SW, with maximal values at 50 m, from day D09 (Fig. 7).

Fig. 7
figure7

Vertical distribution of virus to bacteria ratio. Virus to bacteria ratio (VBR) at the Mooring Dirigibile Italia (MDI) station at six depths (surface, 5, 25, 50, 75, and 100 m) in the sampling days (D05, D06, D09, D10, and D11)

Microbial respiratory activity

High respiration rates were detected and the values of ETS activity ranged between 0.139 and 5.035 µL O2 h−1 L−1 (on D05-75 m and D09-25 m, respectively). A great variability over time (Table 2) was observed so that, in the first day of the experiment, i.e., D05, ETS showed the lowest values uniformly distributed through the entire water column (Fig. 8a). Several peaks were observed at 50 (D06 and D010) and 25 m depths (D09). C-ETS (i.e., the amount of CO2 production rates by microbial respiration) varied between 0.053 and 1.913 µg C L−1 h−1 and was vertically and temporally distributed in accordance to ETS measurements (data not shown). The carbon turnover time due to respiration alone was estimated from the ratio POC content to respiration (POC/C-ETS) according to Ramírez et al. (2006) and La Ferla et al. (2005). The ratio varied from 0.49 to 1.56 days on D06 (75 m) and D05 (75 m), respectively (Fig. 8b). Longer times were observed in the first day of experiment; thereafter, the turnover time appeared to be lower and similar through the entire water column (Table 3).

Fig. 8
figure8

Vertical distribution of microbial respiration and carbon turnover. Microbial respiration determined by Electron Transport System activity (ETS, a) expressed in µL O2 h−1 L−1 and particulate organic carbon to respired carbon ratio (POC/C-ETS, b) in day calculated in Log, at the Mooring Dirigibile Italia (MDI) station at six depths (surface, 5, 25, 50, 75, and 100 m) in the sampling days (D05, D06, D09, D10, and D11)

Statistical analyses

Spearman correlation analysis was carried out among abiotic and biotic parameters (Table 4), where the significant correlation coefficients (r) are reported, together with their significance level (p) and data number (n). Analyzing all the samples together, few correlations were found. In particular, salinity showed positive correlations with depth, density, and Synechococcus sp. abundance and negative with chlorophyll a. Significant positive correlations between TSM and Ctot and negative with depth and VA also occurred. Moreover, POC showed to be positively related with ETS and Ntot whereas chlorophyll a. was negatively related with salinity, density, and depth.

Table 4 Spearman correlation analysis among abiotic and biotic parameters

Discussion

Short-term environmental variability at MDI station

Kongsfjorden is one of the most studied glacial fjords in the Arctic and is subject to both the hydrological dynamics of the Atlantic waters and the melting glacial ice with ecological implications at trophic and biogeochemical levels (Hop et al. 2002; Svendsen et al. 2002; Meire et al. 2017; Halbach et al. 2019). Every year, in July and September, the freshwater runoff from land and glaciers and their combination with Atlantic water inflow cause complex hydrographic features, including thermic and haline stratification at shallow depths (Iversen and Seuthe 2011). Moreover, strong surface outflow and subsurface inflow is established near the glacier fronts in summer (Cottier et al. 2005; Straneo et al. 2011). The observed variability of weather conditions and, in particular, the quick change of wind direction and shelf forcing in the study period could have altered the water column stability of MDI station. Cantoni et al. (2020) also observed how the short‐term weather patterns exerted a strong influence on freshwater content within the fjord in summer 2016. In Kongsfjorden, multiannual mean value as absolute measurements of wind speed (independent of wind direction) was calculated by Cisek et al. (2017) and it resulted to be 4.1 m s−1, over a period from 2005 to 2016; the multiannual monthly mean value in September was 3.4 m s−1. We are aware that having sampled only one station does not allow us to give certain picture of the plume location and/or extension. Nevertheless, during the study period, MDI site was influenced by surface low salinity (0–5 m depth), and by more saline Atlantic waters (< 34.5) in the layer from 75 to 100 m depth. An intermediate salinity comprised between 31.5 and 33 was measured around 30 m depth. We could hypothesize that, in the range of salinity between 33 and 34.6 (Fig. 3a), the temporary extension of a finger of warmer waters existed which withdrew over time from D05 to D09. Possibly, IW extension—corresponding to the sampling depth of 50 m—strongly changed during the week of sampling, with presumable implications also on the expansion of SW and AW.

With regard to nutrients, in the upper 20 m of the outer part of Kongsfjorden with summer conditions, Hop et al. (2002) reported nitrate (1.6–3.3 µmol L−1) and phosphate concentrations (0.5 µmol L−1) that were, respectively, higher than our measurements in the same depth range. In early summer, Piquet et al. (2014) measured nitrate + nitrite and phosphate values (in the upper 20 m) similar to and lower than our data, respectively. Iversen and Seuthe (2011) reported mean values of nitrate and phosphate, respectively, double and three times lower than our data in the upper 50 m (September 2006) at a station close to the settlement of Ny-Ålesund. Consequently, the N/P ratio was higher in September 2006 (6.2) compared to that determined in September 2013 (0.6). However, our study highlighted that a large percentage of nitrogen was available in the form of ammonium through the water column and mainly at surface, as observed by De Corte et al. (2011). In our study, ammonium averaged the 70.3% of the total nitrogen in SW, the 56.7% in IW, and the 37.8% in AW. Upwelling of bottom waters appears to be the main pathway for the supply of nitrate, phosphate, and ammonium in inner Kongsfjorden (Halbach et al. 2019), confirming that thermohaline variability might drive the variability of nutrients (De Corte et al. 2011; Iversen and Seuthe 2011) at MDI station also.

In Kongsfjorden, the concentration of suspended particulate matter mainly reflects the activity of glaciers which bring meltwater and particles into the fjord (Beszczyńska − Møller et al. 1997; D’Angelo et al. 2018). Moreover, since 2000, climate change has increased the meltwater input, causing shifts in loads of TSM entering the fjord (Zaborska et al. 2006). Furthermore, the analyses of long-term precipitation trends from the Svalbard region (Førland et al. 2011) indicates an increase in annual precipitation during the last 20 years resulting in an intensification of superficial runoff. According to Svendsen et al. (2002), the concentration of TSM and sedimentation processes in the Kongsfjorden have a direct influence on the extent of primary production and aggregation of particles. At MDI station, the measured TSM values were at the very low end of the range previously reported for the inner fjord and quite low respect to Zhu et al. (2016) observations despite that, during the study period, patches of reddish-brown waters were observed in surface layer. Presumably, this could likely be due to a high spatial and in time variability in the distribution of the plume. In the first 50 m, higher TSM values of particulate organic matter were detected than those reported by Svendsen et al. (2002) in September 1998. Differently, particulate carbon concentrations (both inorganic and organic) were lower than those reported by Svendsen et al. (2002) in July 1998 at similar distance as our station from the glacier (~ 10 km). However, our POC mean values were similar to those reported by Iversen and Seuthe (2011) in September 2006. Moreover, at 0 and 100 m, the inorganic component was high (PIC/POC = 0.6) but with a lower proportion than that reported by Svendsen et al. (2002). High variability of PIC (at surface and 75 m depth) was detected. In the Kongsfjorden, Svendsen et al. (2002) and De Corte et al. (2011) linked the distribution of suspended particles to the circulation patterns, but this was not our case.

Short-term planktonic variability in MDI station

The entire plankton assemblage, in terms of abundance, generally showed low values during the studied week. Anyway, the considered populations, i.e., phyto-, picophyto-, bacterio-, and virioplankton, exhibited specifically different trends and variability during the study period as well as along the water column. Several abiotic factors might have been responsible for these behaviors, including turbidity. Based on data collected in proximity to tidewater glacier fronts in Kongsfjorden, from July 26 to 31, 2017, Halbach et al. (2019) showed that the rapid retreat of tidewater glaciers and increasing meltwater discharge controlled the environmental suitability for phytoplankton growth with antagonistic effects of both light attenuation by glacier derived sediments and fertilization by nutrient input. Field data (collected in August 2014) and 18-year regional satellite records allowed a conceptual model for the impacts of warm water intrusion on the optical light field and primary production, within 10 km of tidewater glaciers. Firstly, sea ice loss allows earlier phytoplankton blooms to occur; secondly, glacial melting increased, reducing light availability thus primary production; finally, as tidewater glaciers retreat onto land, light availability will increase, allowing intense phytoplankton blooms (Payne and Roesler 2019).

Light conditions largely determine the level of primary production (Hop et al. 2002), even though Shikai et al. (2012) concluded that the phytoplankton communities in fjords in late summer are darkness adapted. Anyhow, turbidity had negative effects on autotrophic microorganisms and heterotrophic nanoflagellates (Sommaruga and Kandolf 2014) while it favored the mixotrophic species and dinoflagellate aggregation which acted as trophic link to bacteria (Caroppo et al. 2017). A study in the Kongsfjorden and near Krossfjorden suggested that decreased salinity (for eukaryotes and bacteria) and increased sediment load (for bacteria) are major factors of surface microbial community composition and diversity (Piquet et al. 2010). In addition to this, Caroppo et al. (2017) emphasized the important role of the pluviomentric regime to modify the turbidity, the light penetration, the depth of the euphotic zone (< 5 m depth in the present study), and, consequently, influencing also the spectral composition of penetrating radiation (Svendsen et al. 2002). In our study period, rain occurred from September 4 to 8, 2013, with the maximum (3.3 mm d–1) registered on 5th (D05).

As concerns the abundance of phytoplankton, our data showed values of 103 cells L−1 with the exception of three monospecific bloom registered at three different depths and days (Caroppo et al. 2017). These events were associated with the presence of the mixotrophic species and, in particular, to Prorocentrum cf gracile (first finding in Arctic waters) and Alexandrium minutum (harmful species) (Caroppo et al. 2017), to the detriment of other autotrophic species (diatoms, micro-sized flagellates, and coccolithophores). In a context of poor light availability and low nitrate concentration in the Arctic late summer, phytoplankton biomass decreases and diatoms appear to be rare (Kubiszyn et al. 2014). However, another reason of diatoms paucity might be the silicic acid limitation after the spring bloom (Krause et al. 2019). Our data confirmed the dominance of dinoflagellates and Phaeocystis sp. in the summer period as found by Hop et al. (2002) and Seuthe et al. (2011). In Summer 1971, in the outer part of the fjord, Halldal and Halldal (1972) reported phytoplanktonic abundance lower than our estimates (32–64 × 106 and 44 × 109 cells m−2, respectively) while in the upper 50 m depth of the fjord in September 2006, Iversen and Seuthe (2011) reported pico- and nanophytoplanktonic abundance one order of magnitude higher (230 × 109cells m−2) than our phytoplankton abundance determined in the same month at MDI station, 8.5 km away from the glacier.

The occurrence of picophytoplankton in Arctic water disagrees with the general understanding that it is almost absent in polar oceans due to low temperatures (Pedrós-Alió et al. 2015). Nevertheless, just Synechococcus sp. has already been suggested as a bio-indicator of the Atlantic waters advection into the Arctic Ocean in the eastern part of the Fram Strait (North-west of Svalbard), wherein averaged cell counts of 104 cells mL−1 were found in Atlantic water in August 2014 (Paulsen et al. 2016). According to these authors, temperature is the main driver of Synechococcus genus abundance and diversity in Arctic Ocean, where only sequences related to clade I and IV were retrieved. In our study, Synechococcus sp. was almost lacking at surface layers of MDI station and the greatest abundance was found in IW and AW in relation with increasing salinity. The variability in the strength of the Atlantic inflow combined with varying extension of the glacial melt water could have determined the spread of Synechococcus sp. in our sampling station. In a warmer Arctic ocean, a greater contribution of Synechococcus sp. at the expense of larger phytoplankton species might imply a reduced contribution of primary production to the Arctic food chain.

As concerns bacterioplankton, the abundance obtained throughout this study was in accordance with De Corte et al. (2011) and Iversen and Seuthe (2011) estimates. Perhaps, due to different kind of counting (by image analysis and cytometry), our bacterioplankton averaged value was slightly lower than those determined in July 2008 in surface and 20–100 layers (2.2 and 1.4 times, respectively) by De Corte et al. (2011) and those reported by Iversen and Seuthe (2011) within the 0–50 layer in September 2006 (1.9 times). When compared with other image analysis direct counts, our values were even slightly lower than those found by Caruso et al. (2020) along a transect in Konsfjorden from off-shore stations towards the Kronebreen glacier. Since, in spring phytoplankton, population prevails on the bacterial community, the different season of investigation (May 2016) could explain the different counts. Differently, in the same fjord, Jiang et al. (2005) counted averaged value of 3.53 × 105 cells mL−1 in the 0–50 layer within the inner fjord, on August 2004 (range 1.84–4.01 × 105), slightly lower than ours. This variable distribution of the bacterioplankton during the different observation periods (months and years) could be due to both the variation in phytoplankton distribution and water masses. Anyway, in MDI, bacterioplankton abundance showed the highest values in AW, i.e., during the apparent maximum extension of the plume (D05 and D11) while the minimum was measured on D09 when the plume was at its minimum.

Viruses are considered the numerically dominant component in all aquatic systems (Jacquet et al. 2010), capable of infecting bacteria and phytoplankton (Brussaard 2004a). It is well known that viruses can influence their dynamics, through reduction of population, mainly during blooms, or by preventing these communities from reaching high abundances (Brussaard 2004a). Viral infection is known to increase as host cell density increases (Wiggins and Alexander 1985) but a relationship between virus and bacterial counts was not observed in the present study, notwithstanding the bacteriophages (V1 group) were the most numerous. De Corte et al. (2011) observed that this relationship was weak over the melting season and attributed it to UV radiation and suspended particles differently affecting viral and bacterial abundance in fjords. As concerns the prolonged exposure time to solar radiation during the Arctic summer, the viral production and decay rates are affected more seriously than bacterioplankton abundance (Weinbauer et al. 1999; Wihelm et al. 2003; De Corte et al. 2011). This evidence is in agreement with our results that detected the lowest values of viral abundance in the surface layer of MDI station. In our study period (September) and MDI station, virus count was lower than that reported in a transect along the same fjord in the June–July 2008 (De Corte et al. 2011). One more conceivable explanation for the low viral abundance in MDI station might be due to the scavenging of viruses by suspended inorganic particles via the runoff from the adjacent Kronebreen glacier (Schoemann et al. 2005). Previous studies have shown that free virus might be adsorbed to sinking particles (Proctor and Fuhrman 1991; Maat et al. 2019), especially in sites with high sedimentation rates, and transferred with prokaryotes to the sea floor (Schoemann et al. 2005). In our study, the negative correlation between viral count and TSM reinforced this hypothesis. The affinity for adsorbing to small particles may also substantially reduce the ability of free viruses to infect host cells in regions with high loads of silt and clay-based terrestrial runoff (Murray and Jackson 1992). Alternatively, the low viral abundance might be also caused by a high dissolved aminopeptidase activity released by particle-associated bacteria, capable of cleaving the proteins of the viral capsid (Simon et al. 2002).

Beside of virus abundance, virus to bacteria ratio that explains different relations between virus and bacteria with time and depths also was low in our study. Analogous results were found by Wróbel et al. (2013) that calculated VBR in the range of 0.2–1.2 in sediment cores collected in Hornsund fjord (Svalbard). Low VBR in polar environments might be indicative of prokaryotic activity that is low (e.g., with temperature as a limiting factor for prokaryotic metabolism) to maintain a high level of virus production (Kirchman et al. 2009; Middelboe et al. 2012). Nevertheless, in June to July 2008, De Corte et al. (2011) reported higher VBR in the range of 7.2 to 3.1 presumably in relation with a post-bloom period (Hodal et al. 2012). An explanation of our low VBR could also be related to the month of collection (September), i.e., in a period following a secondary peak in productivity, as well as to the water stratification. Drewes et al. (2016), in a study carried out in glacier-fed turbid lakes, explained that the relatively lower variability in free virus abundance and the lower virus to prokaryote ratio, found in the turbid lakes than in the clear one, were due to a rather low turnover and thus, to a reduced impact on microbial communities.

Microbial respiration values determined by ETS, resulted to be high in MDI. As concerns the organic matter oxidation, very few papers exist in Arctic Sea and they mostly refer to studies that use the difference between the oxygen concentration in the clear bottles after incubation and the initial oxygen concentration (Nguyen and Maranger 2011; Vaquer-Sunyer et al. 2013). In the Barents Sea, Martinez (1991) measured respiration rates with the use of ETS assay and the values of carbon respired C-ETS resulted to be one–two times lower than ours. The oxidative rates detected in MDI showed elevated metabolic activity that might be supported by the organic matter in the detrital particles (Turley 1999). In fact, a great variability of organic matter oxidation over time was observed together with a close relationship between ETS and the POC distribution through the water column.

Moreover, the N/P ratios highlighted the role of respiration in remineralization of nutrients in this season in MDI station. Despite the well-known uncertainties of carbon turnover estimated by empirical calculation from the ratio of the POC content and the respiration (Azzaro et al. 2019), it resulted to be fast (mean value 0.85 days) with the only exception of that detected on D05 in the water column below 25 m depth. However, these values did not take into account the dissolved pool of organic matter and were determined by using not real but potential rates.

To our knowledge, in peculiar environments like the polar seas (par excellence cold, dark, and with low microbial abundances) no papers exist on the true physiological rate (ETS/R) other than that of Crisafi et al. (2010) who adopted the factor of 0.15, determined for the euphotic zone of sub-Antarctic areas. We might hypothesize that the integrated microbial respiration (averaged data 0–50 m) would potentially be 0.18 g C m−2 d−1 (range 0.03–0.27 g C m−2 d−1) in MDI. Speculatively, this finding suggests an imbalance with the primary production rates estimated in the same season in the Kongsfjorden by Hop et al. (2002; 1.2 g C m−2 d−1) as well as with those reported by Hodal et al. (2012) during the spring bloom in 2002 (0.47–0.58 g C m−2 d−1).

In this context, the MDI station in September 2013 seemed to work as CO2 sink. Similarly, Caruso et al. 2020, estimating the potential organic matter turnover by enzyme activity rates, suggested that the Kongsfjorden ecosystem acted in the late spring–early summer period mostly as a carbon sink rather than as a source since only a low percentage of POC (< 0.67%) was metabolized by the microbial community in May 2016.

Conclusions

Our paper underscores the importance of high-resolution studies in refining the understanding in polar environments. In fact, the study strategy, i.e., high-resolution sampling series over a short time scale in a fixed station, allowed to detect rapid changes in abiotic and biotic parameters. The planktonic components (in terms of abundance of phyto-, picophyto-, bacterio-, and virioplankton) showed to be differently tied to the dynamic of the environmental variables. In particular, a high temporal variability of the viral abundance and respiratory rates was observed; Synechococcus sp. and viral abundance, POC/C-ETS ratio, and TSM highly varied with depth. Total suspended matter and salinity changes differently affected the microbial communities. The freshwater runoff of the melting glaciers, together with the inflow of Atlantic water from the open ocean, appeared to influence the occurrence of microbial pulses. Moreover, the low viruses to bacteria ratio was presumably linked to the high sedimentation of particles in the studied station. Detailed ecological studies are important to give insights of the dynamic of biological processes in areas affected by climate change.

Data Availability

Material described in the manuscript, including all relevant raw data, will be freely available to any researcher wishing to use them for non-commercial purposes.

References

  1. Aliani S, Bartholini G, Degl’Innocenti F, Delfanti R, Galli C, Lazzoni E, Lorenzelli R, Malaguti A, Meloni R, Papucci C, Salvi S, Zaborska A (2004) Multidisciplinary investigations in the marine environment of the inner Kongsfjord, Svalbard Islands (September 2000 and 2001). Chem Ecol 20:19–28

    Google Scholar 

  2. Amiel D, Cochran JK, Hirschberg DJ (2002) 234Th/238U disequilibrium as an indicator of the seasonal export flux of particulate organic carbon in the North Water. Deep Sea Res II 49:5191–5209

    CAS  Google Scholar 

  3. Aminot A, Chaussepied M (1983) Manuel des analyses chimiques en milieu marin. CNEXO, Editions Jouvre, Paris, p 395

    Google Scholar 

  4. Armitage TWK, Manucharyan GE, Petty AA, Kwok R, Thompson AF (2020) Enhanced eddy activity in the Beaufort Gyre in response to sea ice loss. Nat Comm. https://doi.org/10.1038/s41467-020-14449-z

    Article  Google Scholar 

  5. Asbjørnsen H, Arthun M, Skagseth Ø, Eldevik T (2020) Mechanisms underlying recent Arctic Atlantification. Geophys Res Lett. https://doi.org/10.1029/2020GL088036

    Article  Google Scholar 

  6. Azzaro M, La Ferla R, Azzaro F (2006) Microbial respiration in the aphotic zone of the Ross Sea (Antarctica). Mar Chem 99:199–209

    CAS  Google Scholar 

  7. Azzaro M, La Ferla R, Maimone G, Monticelli LS, Zaccone R, Civitarese G (2012) Prokaryotic dynamics and heterotrophic metabolism in a deep convection site of Eastern Mediterranean Sea (the Southern Adriatic Pit). Cont Shelf Res 44:106–118

    Google Scholar 

  8. Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. Nat Conserv 34:441–475

    Google Scholar 

  9. Bogen J, Xu M, Kennie P (2014) The impact of pro-glacial lakes on downstream sediment delivery in Norway. Earth Surf Process Landf. https://doi.org/10.1002/esp.3669

    Article  Google Scholar 

  10. Brussaard CPD (2004a) Viral control of phytoplankton population—a review. J Eukaryot Microbiol 51:125–138

    PubMed  Google Scholar 

  11. Brussaard CPD (2004b) Optimization of procedures for counting viruses by flow cytometry. Appl Environ Microbiol 70:1506–1513

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Cantoni C, Hopwood MJ, Clarke JS, Chiggiato J, Achterberg EP, Cozzi S (2020) Glacial drivers of marine biogeochemistry indicate a future shift to more corrosive conditions in an Arctic fjord. J Geophys Res Biogeosci. https://doi.org/10.1029/2020JG005633

    Article  Google Scholar 

  13. Caroppo C, Pagliara P, Azzaro F, Miserocchi S, Azzaro M (2017) Late summer phytoplankton blooms in the changing polar environment of the Kongsfjorden (Svalbard, Arctic). Cryptogam Algol 38:53–72

    Google Scholar 

  14. Caruso G, Madonia A, Bonamano S, Miserocchi S, Giglio F, Maimone G, Azzaro F, Decembrini F, La Ferla R, Piermattei V, Piazzolla D, Marcelli M, Azzaro M (2020) Microbial abundance and enzyme activity patterns: response to changing environmental characteristics along a transect inKongsfjorden (Svalbard Islands). J Mar Sci Eng. https://doi.org/10.3390/jmse8100824

    Article  Google Scholar 

  15. Cisek M, Makuch P, Petelski T (2017) Comparison of meteorological conditions in Svalbard fjords: Hornsund and Kongsfjorden. Oceanologia 59:413–421

    Google Scholar 

  16. Cottier F, Tverberg V, Inall M, Svendsen H, Nilsen F, Griffith C (2005) Water mass modification in an Arctic fjord through cross-shelf exchange: the seasonal hydrography of Kongsfjorden, Svalbard. J Geophys. https://doi.org/10.1029/2004JC002757

    Article  Google Scholar 

  17. Cottier FR, Nilsen F, Inall ME, Gerland S, Tverberg V, Svendsen H (2007) Wintertime warming of an Arctic shelf in response to large-scale atmospheric circulation. Geophys Res Lett. https://doi.org/10.1029/2007GL029948

    Article  Google Scholar 

  18. Crisafi E, Azzaro M, Lo Giudice A, Michaud L, La Ferla R, Maugeri TL, De Domenico M, Azzaro F, Acosta Pomar MLC, Bruni V (2010) Microbiological characterization of a semi-enclosed sub-Antarctic environment: the Strait of Magellan. Polar Biol 33:1485–1504

    Google Scholar 

  19. Dai A, Luo D, Song M, Liu J (2019) Arctic amplification is caused by sea-ice loss under increasing CO2. Nat Commun. https://doi.org/10.1038/s41467-018-07954-9

    Article  PubMed  PubMed Central  Google Scholar 

  20. D’Angelo A, Giglio F, Miserocchi S, Sanchez-Vidal A, Aliani S, Tesi T, Viola A, Mazzola M, Langone L (2018) Multi-year particle fluxes in Kongsfjorden, Svalbard. Biogeosciences 15:5343–5363

    Google Scholar 

  21. De Corte D, Sintes E, Yokokawa T, Herndl GJ (2011) Changes in viral and bacterial communities during the ice-melting season in the coastal Arctic (Kongsfjorden, Ny-Ålesund). Environ Microbiol 13:1827–1841

    PubMed  Google Scholar 

  22. Drewes F, Peter H, Sommaruga R (2016) Are viruses important in the plankton of highly turbid glacier-fed lakes? Sci Rep 6:24608. https://doi.org/10.1038/srep24608

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Decembrini F, Caroppo C, Azzaro M (2009) Size structure and production of phytoplankton community and carbon pathways channelling in the Southern Tyrrhenian Sea (Western Mediterranean). Deep Sea Res II 56:687–699

    CAS  Google Scholar 

  24. Edler L, Elbrächter M (2010) The Utermöhl method for quantitative phytoplankton analysis. Intergovernmental Oceanographic Commission of UNESCO, Paris

    Google Scholar 

  25. Førland EJ, Benestad R, Hanssen-Bauer I, Haugen JE, Skaugen TE (2011) Temperature and precipitation development at Svalbard 1900–2100. Adv Meteorol. https://doi.org/10.1155/2011/893790

    Article  Google Scholar 

  26. Gasol JM, de Giorgio PA (2000) Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities. Sci Mar 64:197–224

  27. Grebmeier JM, Overland JE, Moore SE, Farley EV, Carmack EC, Cooper LW, Frey KE, Helle JH, McLaughlin FA, McNutt SL (2006) A major ecosystem shift in the northern Bering Sea. Science 311:1461–1464

    CAS  PubMed  Google Scholar 

  28. Halbach L, Vihtakari M, Duarte P, Everett A, Granskog MA, Hop H, Kauko HM, Kristiansen S, Myhre PI, Pavlov AK, Pramanik A, Tatarek A, Torsvik T, Wiktor JM, Wold A, Wulff A, Steen H, Assmy F (2019) Tidewater gaciers and bedrock characteristics control the phytoplankton growth environment in a Fjord in the Arctic. Front Mar Sci. https://doi.org/10.3389/fmars.2019.00254

    Article  Google Scholar 

  29. Halldal P, Halldal K (1972) Phytoplankton, chlorophyll, and submarine light conditions in Kings Bay, Spitsbergen, Juy 1971. Norw J Bot 20:99–108

    Google Scholar 

  30. He L, Yin K, Yuan X (2019) Double maximum ratios of viruses to bacteria in the water column: implications for different regulating mechanisms. Front Microbiol. https://doi.org/10.3389/fmicb.2019.01593

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hedges JI, Stern JH (1984) Carbon and nitrogen determination of carbonate-containing solids. Limnol Oceanogr 19:984–989

    Google Scholar 

  32. Hodal H, Falk-PetersenS HH, Kristiansen S, Reigstad M (2012) Spring bloom dynamics in Kongsfjorden, Svalbard: nutrients, phytoplankton, protozoans and primary production. Polar Biol 35:191–203

    Google Scholar 

  33. Hodgkins R, Hagen JO, Hamran SE (1999) 20th century mass balance and thermal regime change at Scott Turnerbreen, Svalbard. Ann Glaciol 28:216–220

    Google Scholar 

  34. Hop H, Pearson TH, Hegseth EN, Kovacs KM, Wiencke C, Kwasniewski S, Eiane K, Mehlum F, Gulliksen B, Wlodarska-Kowalczuk M, Lydersen C, Weslawski JM, Cochrane S, Gabrielsen GW, Leakey R, Lonne OJ, Zajaczkowski M, Falk-Petersen S, Kendall MA, Wängberg S-Å, Bischof K, Voronkov A, Kovaltchouk NA, Wiktor J, Poltermann M, di Prisco G, Papucci C, Gerland S (2002) The marine ecosystem of Kongsfjorden, Svalbard. Polar Res 21:167–208

    Google Scholar 

  35. Iversen KR, Seuthe L (2011) Seasonal microbial processes in a high-latitude fjord (Kongsfjorden, Svalbard): I. Heterotrophic bacteria, picoplankton and nanoflagellates. Polar Biol 34:731–749

    Google Scholar 

  36. Jiang X, Jianfeng HE, Caim I (2005) Abundance and biomass of heterotrophic microbes in the Kongsfjorden, Svalbard. Acta Oceanol Sin 24:143–152

    Google Scholar 

  37. Jacquet S, Miki T, Noble R, Peduzzi P, Wilhelm S (2010) Viruses in aquatic ecosystems: important advancements of the last 20 years and prospects for the future in the field of microbial oceanography and limnology. Adv Oceanogr Limnol. https://doi.org/10.1080/19475721003743843

    Article  Google Scholar 

  38. Karl DM, Knauer GA, Martin JH (1987) Downward flux of particulate organic matter in the ocean: a particle decomposition paradox. Nature 332:438–441

    Google Scholar 

  39. Kirchman DL, Malmstrom RR, Cottrell MT (2005) Control of bacterial growth by temperature and organic matter in the Western Arctic. Deep Sea Res II. https://doi.org/10.1016/j.dsr2.2005.09.005

    Article  Google Scholar 

  40. Kirchman DL, Xosé AG, Morán HDucklow (2009) Microbial growth in the polar oceans — role of temperature and potential impact of climate change. Nature Reviews Microbiology 7(6):451–459

    CAS  PubMed  Google Scholar 

  41. Krajewska M, Szymczak-Żyła M, Tylmann W, Kowalewska G (2020) Climate change impact on primary production and phytoplankton taxonomy in Western Spitsbergen fjords based on pigments in sediments. Glob Planet Change. https://doi.org/10.1016/j.gloplacha.2020.103158

    Article  Google Scholar 

  42. Krause JW, Schulz IK, Rowe KA, Dobbins W, Winding MHS, Sejr MK, Duarte CM, Agustí S (2019) Silicic acid limitation drives bloom termination and potential carbon sequestration in an Arctic bloom. Sci Rep. https://doi.org/10.1038/s41598-019-44587-4

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kubiszyn M, Piwosz K, Wiktor JM Jr, Wiktor JM (2014) The effect of inter-annual Atlantic water inflow variability on the planktonic protist community structure in the West Spitsbergen waters during the summer. J Plankton Res 36:1190–1203

    Google Scholar 

  44. La Ferla R, Azzaro F, Azzaro M, Caruso G, Decembrini F, Leonardi M, Maimone G, Monticelli LS, Raffa F, Santinelli C, Zaccone R, Ribera d’Alcalà M (2005) Microbial contribution to carbon biogeochemistry in the Central Mediterranean Sea: Variability of activities and biomass. J Mar Syst 57:146–166

    Google Scholar 

  45. La Ferla R, Azzaro M, Budillon G, Caroppo C, Decembrini F, Maimone G (2010) Distribution of the prokaryotic biomass and community respiration in the main water masses of the Southern Tyrrhenian Sea (June and December 2005). Adv Oceanogr Limnol 2:235–257

    Google Scholar 

  46. Lalande C, Nöthig EM, Bauerfeind E, Hardge K, Beszczynska-Möller A, Fahl K (2016) Lateral supply and downward export of particulate matter from upper waters to the seafloor in the deep eastern Fram Strait. Deep-Sea Res. https://doi.org/10.1016/j.dsr.2016.04.014

    Article  Google Scholar 

  47. Lind S, Ingvaldsen RB, Furevik T (2018) Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import. Nat Climate Change 8:634–639

    Google Scholar 

  48. Lydersen C, Assmy P, Falk-Petersen S, Kohler J, Kovacs KM, Reigstad M, Steen H, Strøm H, Sundfjord A, Varpe O, Walczowski W, Weslawski JM, Zajaczkowski M (2014) The importance of tidewater glaciers for marine mammals and seabirds in Svalbard, Norway. J Mar Syst 129:452–471

    Google Scholar 

  49. Lorenzen CI (1967) Determination of chlorophyll and phaeopigments spectrophotometric equations. Limnol Oceanogr 12:343–346

    CAS  Google Scholar 

  50. Maat DS, Prins MA, Brussaard CPD (2019) Sediments from arctic tide-water glaciers remove coastal marine viruses and delay host infection. Viruses. https://doi.org/10.3390/v11020123,2019

    Article  PubMed  PubMed Central  Google Scholar 

  51. Marie D, Simon N, Vaulot D (2005) Phytoplankton cell counting by flow cytometry. In: Andersen RA (ed) Algal culturing techniques. Physiological Society of America, Oxford (UK), pp 253–267

    Google Scholar 

  52. Martin JH, Knauer GA, Karl DM, Broenkow WW (1987) VERTEX: carbon cycling in the northeast Pacific. Deep Sea Res 34:267–285

    CAS  Google Scholar 

  53. Martinez R (1991) Biomass and respiratory ETS activity of microplankton in the Barents Sea. Polar Res. https://doi.org/10.3402/polar.v10i1.6738200

    Article  Google Scholar 

  54. Meire L, Mortensen J, Meire P, Juul-Pedersen T, Sejr MK, Rysgaard S, Nygaard R, Huybrechts P, Meysman FJR (2017) Marine-terminating glaciers sustain high productivity in Greenland fjords. Glob Chang Biol 23:5344–5357

    PubMed  Google Scholar 

  55. Middelboe M, Glud RN, Sejr MK (2012) Bacterial carbon cycling in a subarctic fjord: a seasonal study on microbial activity, growth efficiency, and virus-induced mortality in Kobbefjord, Greenland. Limnol Oceanogr 57:1732–1742

    CAS  Google Scholar 

  56. Monaco A, Courp T, Heussner S, Carbonne J, Fowler SW, Deniaux B (1990) Seasonality and composition of particulate fluxes during ECOMARGE-I, western Gulf of Lions. Cont Shelf Res 10:959–987

    Google Scholar 

  57. Murray AG, Jackson GA (1992) Viral dynamics: a model of the effects of size, shape, motion and abundance of single-celled planktonic organisms and other particles. Mar Ecol Prog Ser 89:103–116

    Google Scholar 

  58. Nguyen D, Maranger R (2011) Respiration and bacterial carbon dynamics in Arctic sea ice. Polar Biol 34:1843–1855

    Google Scholar 

  59. Packard TT, Devol AH, King FD (1975) The effect of temperature on the respiratory electron transport system in marine plankton. Deep Sea Res 22:237–249

    CAS  Google Scholar 

  60. Payne CM, Roesler CS (2019) Characterizing the influence of Atlantic water intrusion on water mass formation and phytoplankton distribution in Kongsfjorden Svalbard. Cont Shelf Res. https://doi.org/10.1016/j.csr.2019.104005

    Article  Google Scholar 

  61. Paulsen ML, Doré H, Garczarek L, Seuthe L, Müller O, Sandaa R-A, Bratbak G, Larsen A (2016) Synechococcus in the Atlantic gateway to the Arctic Ocean. Front Mar Sci. https://doi.org/10.3389/fmars.2016.0019

    Article  Google Scholar 

  62. Pedrós-Alió C, Potvin M, Lovejoy C (2015) Diversity of planktonic microorganisms in the Arctic Ocean. Prog Oceanogr 139:233–243

    Google Scholar 

  63. Piquet AMT, Scheepens JF, Bolhuis H, Wiencke C, Buma AGJ (2010) Variability of protistan and bacterial communities in two Arctic fjords (Spitsbergen). Polar Biol 33:1521–1536

    Google Scholar 

  64. Piquet AMT, van de Poll WH, Visser RJW, Wiencke C, Bolhuis H, Buma AGJ (2014) Springtime phytoplankton dynamics in Arctic Krossfjorden and Kongsfjorden (Spitsbergen) as a function of glacier proximity. Biogeosciences 11:2263–2279

    Google Scholar 

  65. Polyakov IV, Pnyushkov AV, Alkire MB, Ashik IM, Baumann TM, Carmack EC, Goszczko I, Guthrie J, Ivanov VV, Kanzow T, Krishfield R, Kwok R, Sundfjord A, Morison J, Rember R, Yulin A (2017) Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean. Science 356:285–291

    CAS  PubMed  Google Scholar 

  66. Porter KG, Feig YS (1980) The use of DAPI for identifying and counting aquatic microflora. Limnol Oceanogr 25:943–948

    Google Scholar 

  67. Post E, Forchhammer MC, Bret-Harte MS, Callaghan TV, Christensen TR, Elberling B, Fox AD, Gilg O, Hik DS, Høye TT, Ims RA, Jeppesen E, Klein DR, Madsen J, McGuire AD, Rysgaard S, Schindler DE, Stirling I, Tamstorf MP, Tyler NJ, van der Wal R, Welker J, Wookey PA, Schmidt NM, Aastrup P (2009) Ecological dynamics across the arctic associated with recent climate change. Science 325:1355–1358

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Powell RD (1990) Glacimarine processes at grounding-line fans and their growth to ice-contact deltas. GSL. https://doi.org/10.1144/GSL.SP.1990.053.01.03

    Article  Google Scholar 

  69. Proctor LM, Fuhrman JA (1991) Roles of viral infection in organic particle flux. Mar Ecol Prog Ser 69:133–142

    Google Scholar 

  70. Promińska A, Falck E, Walczowski W (2018) Interannual variability in hydrography and water mass distribution in Hornsund, an Arctic fjord in Svalbard. Pol Res. https://doi.org/10.1080/17518369.2018.1495546

    Article  Google Scholar 

  71. Ramírez T, Liger E, Mercado JM, Cortés D, Vargas-Yañez M, Sebastián M, Reul A, Aguilera J, Bautista B (2006) Respiratory ETS activity of plankton in the northwestern Alboran Sea: seasonal variability and relationship with hydrological and biological features. J Plank Res 28:629–641

    Google Scholar 

  72. Sallon A, Michel C, Gosselin M (2011) Summertime primary production and carbon export in the southeastern Beaufort Sea during the low ice year of 2008. Polar Biol 34:989–2005

    Google Scholar 

  73. Schoemann V, Becquevort S, Stefels J, Rousseau V, Lancelot C (2005) Phaeocystis blooms in the global ocean and their controlling mechanisms: a review. J Sea Res 53:43–66

    CAS  Google Scholar 

  74. Schuur EAG, Vogel JG, Crummer KG, Lee H, Sickman JO, Osterkamp TE (2009) The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature 459(7246):556–559

    CAS  PubMed  Google Scholar 

  75. Seuthe L, Iversen KR, Narcy F (2011) Microbial processes in a high-latitude fjord (Kongsfjorden, Svalbard): II Ciliates and dinoflagellates. Polar Biol 34:751–766

    Google Scholar 

  76. Shikai C, Jianfeng HE, Peimin HE, Fang Z, Ling L, Yuxin M (2012) The adaptation of Arctic phytoplankton to low light and salinity in Kongsfjorden (Spitsbergen). Adv Pol Sci 23:19–24

    Google Scholar 

  77. Simon M, Grossart HP, Schweitzer B, Ploug H (2002) Microbial ecology of organic aggregates in aquatic ecosystems. Aquat Microb Ecol 28:175–211

    Google Scholar 

  78. Sommaruga R, Kandolf G (2014) Negative consequences of glacial turbidity for the survival of freshwater planktonic heterotrophic flagellates. Sci Rep. https://doi.org/10.1038/srep04113

    Article  PubMed  PubMed Central  Google Scholar 

  79. Straneo F, Curry RG, Sutherland DA, Hamilton GS, Cenedese C, Våge K, Stearns LA (2011) Impact of fjord dynamics and glacial runoff on the circulation near Helheim Glacier. Nat Geosci 4:322–327

    CAS  Google Scholar 

  80. Strickland JDH, Parson TR (1972) A practical handbook of seawater analysis. J Fish Res Board Can 167:1–311

    Google Scholar 

  81. Svendsen H, Beszczynska-Moller A, Hagen JO, Lefauconnier B, Tverberg V, Gerland S, Orbaek JB, Bischof K, Papucci C, Zajaczkowski M, Azzolini R, Bruland O, Wiencke C, Winther JG, Dallmann W (2002) The physical environment of Kongsfjorden-Krossfjorden, an Arctic fjord system in Svalbard. Polar Res 21:133–166

    Google Scholar 

  82. Takahashi T, Broecker WS, Langer S (1985) Redfield ratio based on chemical data from isopycnal surfaces. J Geophys Res 90(C4):6907–6924

    CAS  Google Scholar 

  83. Torsvik T, Albretsen J, Sundfjord A, Kohler J, Sandvik AD, Skarðhamar J, Lindbäck K, Everett A (2019) Impact of tidewater glacier retreat on the fjord system: modeling present and future circulation in Kongsfjorden, Svalbard. Estuar Coast Shelf Sci 220:152–165

    Google Scholar 

  84. Trusel LD, Powell RD, Cumpston RM, Brigham-Grette J (2010) Modern glacimarine processes and potential future behaviour of Kronebreen and Kongsvegen polythermal tidewater glaciers, Kongsfjorden, Svalbard. Geol Soc Lond 344:89–102

    Google Scholar 

  85. Turley CM (1999) The changing Mediterranean Sea—a sensitive ecosystem? Prog Oceanogr 44:387–400

    Google Scholar 

  86. Vaquer-Sunyer R, Duarte CM, Regaudie-De-Gioux A, Holding JM, García-Corral LS, Reigstad M, Wassmann P (2013) Seasonal patterns in Arctic planktonic metabolism (Fram Strait - Svalbard region). Biogeosciences 10:1451–1469

    Google Scholar 

  87. Vincent WF (2010) Microbial ecosystem responses to rapid climate change in the Arctic. ISME J 4:1089–1091

    Google Scholar 

  88. Weinbauer MG, Wilhelm SW, Suttle CA, Pledger RJ, Mitchell DL (1999) Sunlight-induced DNA damage and resistance in natural viral communities. Aquat Microb Ecol 17:111–120

    Google Scholar 

  89. Wiggins B, Alexander M (1985) Minimum bacterial abundance for bacteriophage replication: implications for significance of bacteriophages in natural ecosystems. Appl Environ Microbiol 49:19–23

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Wilhelm SW, Jeffrey WH, Dean AL, Meador JJ, Pakulski D, Mitchell DL (2003) UV radiation induced DNA damage in marine viruses along a latitudinal gradient in the southeastern Pacific Ocean. Aquat Microb Ecol 31:1–8

    Google Scholar 

  91. Wróbel B, Filippini M, Piwowarczyk J (2013) Low virus to prokaryote ratios in the cold: benthic viruses and prokaryotes in a subpolar marine ecosystem (Hornsund, Svalbard). Int Microbiol 16:45–52

    PubMed  Google Scholar 

  92. Zaborska A, Pempkowiak J, Papucci C (2006) Some sediment characteristics and sedimentation rates in an Arctic Fjord (Kongsfjorden, Svalbard). Ann Environ Protect 8:79–96

    Google Scholar 

  93. Zaccone R, Caroppo C, La Ferla R, Zampino D, Caruso G, Leonardi M, Maimone G, Azzaro M, Sitran R (2004) Deep-chlorophyll maximum time series in the Augusta Gulf (Ionian Sea): microbial community structures and functions. Chem Ecol 20:267–284

    Google Scholar 

  94. Zajaczkowski M (2002) On the use of sediment traps in sedimentation measurements in glaciated fjords. Polar Res 23:61–174

    Google Scholar 

  95. Zajaczkowski M (2008) Sediment supply and fluxes in glacial and outwash fjords, Kongsfjorden and Adventfjorden, Svalbard. Pol Polar Res 29:59–72

    Google Scholar 

  96. Zhang R, Li Y, Yan W, Wang Y, Cai L, Luo T, Li H, Weinbauer MG, Jiao N (2020) Viral control of biomass and diversity of bacterioplankton in the deep sea. Commun Biol. https://doi.org/10.1038/s42003-020-0974-5

    Article  PubMed  PubMed Central  Google Scholar 

  97. Zingone A, Totti C, Sarno D, Cabrini M, Caroppo C, Giacobbe MG, Lugliè S, Nuccio C, Socal G (2010) Fitoplancton: metodiche di analisi quali-quantitativa. In: Socal G, Buttino I, Cabrini M, Mangoni O, Penna A, Totti C (eds) Metodologie di studio del plancton marino, vol 56. Manuali e Linee Guida, pp 213–223

  98. Zhu ZY, Wu Y, Liu SM, Wenger F, Hu J, Zhang J, Zhang RF (2016) Organic carbon flux and particulate organic matter composition in Arctic valley glaciers: examples from the Bayelva River and adjacent Kongsfjorden. Biogeosciences. https://doi.org/10.5194/bg-13-975-2016

    Article  Google Scholar 

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Acknowledgements

We want to thank the Editor and three anonymous reviewers for their substantial suggestions. Many thanks to Dr. Mariarosa Maimone for friendly revising the English language.

Funding

This work was supported by ARCA project (ARtico: cambiamento Climatico Attuale ed eventi estremi del passato) of DSSTTA (Dipartimento Scienze del Sistema Terra e Tecnologie per l’Ambiente) del Consiglio Nazionale delle Ricerche (CNR) and by Short-Term Mobility of CNR, AMMCNT prot. N. 0061011 of 14/09/2016. A. S. Cabral was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

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AM conceived and designed research; AM and MG wrote the manuscript; AM, AS, GF, and MS conducted field work and data analyses; MG, DF, CC, and CAS performed lab experiments and data analyses, LL, PR, and LR revised and improved the manuscript; CA performed statistics; RAC, AF, and MM performed lab experiments.

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Correspondence to M. Azzaro.

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Azzaro, M., Aliani, S., Maimone, G. et al. Short-term dynamics of nutrients, planktonic abundances, and microbial respiratory activity in the Arctic Kongsfjorden (Svalbard, Norway). Polar Biol 44, 361–378 (2021). https://doi.org/10.1007/s00300-020-02798-w

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Keywords

  • Short-term dynamics
  • Phytoplankton
  • Bacterioplankton
  • Virioplankton
  • Microbial respiration
  • Kongsfjorden