Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project
expand article infoMaurizio Azzaro, Theodore T. Packard§, Luis Salvador Monticelli, Giovanna Maimone, Alessandro Ciro Rappazzo|, Filippo Azzaro, Federica Grilli, Ermanno Crisafi, Rosabruna La Ferla
‡ Institute of Polar Science, Messina, Italy
§ University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| National Interuniversity Consortium for Marine Sciences, Roma, Italy
¶ Institute for Biological Resources and Marine Biotechnologies, Ancona, Italy
Open Access


The Ross Sea is one of the most productive areas of the Southern Ocean and includes several functionally different marine ecosystems. With the aim of identifying signs and patterns of microbial response to current climate change, seawater microbial populations were sampled at different depths, from surface to the bottom, at two Ross Sea mooring areas southeast of Victoria Land in Antarctica. This oceanographic experiment, the XX Italian Antarctic Expedition, 2004-05, was carried out in the framework of the ABIOCLEAR project as part of LTER-Italy. Here, microbial biogeochemical rates of respiration, carbon dioxide production, total community heterotrophic energy production, prokaryotic heterotrophic activity, production (by 3H-leucine uptake) and prokaryotic biomass (by image analysis) were determined throughout the water column. As ancillary parameters, chlorophyll a, adenosine-triphosphate concentrations, temperature and salinity were measured and reported. Microbial metabolism was highly variable amongst stations and depths. In epi- and mesopelagic zones, respiratory rates varied between 52.4–437.0 and 6.3–271.5 nanol O2 l-1 h-1; prokaryotic heterotrophic production varied between 0.46–29.5 and 0.3–6.11 nanog C l-1 h-1; and prokaryotic biomass varied between 0.8–24.5 and 1.1–9.0 µg C l-1, respectively. The average heterotrophic energy production ranged between 570 and 103 mJ l-1 h-1 in upper and deeper layers, respectively. In the epipelagic layer, the Prokaryotic Carbon Demand and Prokaryotic Growth Efficiency averaged 9 times higher and 2 times lower, respectively, than in the mesopelagic one. The distribution of plankton metabolism and organic matter degradation was mainly related to the different hydrological and trophic conditions. In comparison with previous research, the Ross Sea results, here, evidenced a relatively impoverished oligotrophic microbial community, throughout the water column.


Microbial respiration, heterotrophic production, heterotrophic energy production, Ross Sea, Antarctica, LTER


The present work aims to explore the carbon fate through microbes in an area of the Ross Sea (RS) and to identify signs and patterns of microbial responses to current climate change.

The Southern Ocean (SO) plays an important role in world climate since it is considered the engine of the worldwide oceanic currents. The dramatic seasonal variability in environmental factors in SO generates a significant stress on the biota which must endure constant sunlight, oscillating temperatures and melting ice phenomena in spring-summer time. The annual pelagic primary production is largely confined to this period, when light and nutrient conditions are favourable for the phytoplankton growth. Studies on global change have revealed that the SO is becoming a larger carbon sink with over 30–40% of total carbon uptake occurring there (Lancelot 2007, Khatiwala et al. 2009). Recently, an early spring retreat of sea ice, along with a decrease in seasonal sea-ice cover, an increase of light availability and a presumed constraint on primary production, has also been reported (Dinasquet et al. 2018, Constable et al. 2014). Furthermore, the low iron-limited primary productivity ensures that SO falls into a high-nutrient low-chlorophyll (HNLC) type of environment (Minas et al. 1986, Dinasquet et al. 2018, Deppler and Davidson 2017).

One of the most productive and peculiar area of the SO continental shelf zone is the RS (Nelson et al. 1996). This region (1.55 million km2 of ocean bordering Antarctica from ice edge to deep ocean) was established as a Marine Protected Area (MPA) in December 2017. Here, a peculiar and relatively simply trophic web exists. The phytoplankton, dominated by either diatoms or Phaeocystis sp., depending on whether the euphotic zone is stratified or deeply mixed, is grazed either by krill or silver fish that, in turn, sustain the higher trophic levels (Deppler and Davidson 2017). The RS includes a mosaic of functionally different marine ecosystems mainly linked to sea ice distribution whose variability induces unpredicted cascade effects on trophic dynamics and carbon and nutrient drawdown (Catalano et al. 2006, Smith et al. 2007, Vichi et al. 2009). Accordingly, different fates involve the organic matter transfer, carbon export and depth sequestration. The relevance of high latitude oceans, such as the RS, to models and budgets of global carbon cycling has stimulated recent studies of organic matter degradation in the RS water column (Nelson et al. 1996, Carlson and Hansell 2003, Langone et al. 2003, Azzaro et al. 2006, Misic et al. 2017).

Catalano et al. (2006) confirmed that, in spring-summer periods, about 90% of the total carbon derived from new production was exported via higher trophic levels. Comparing the oxidation and sinking of organic matter through the deep water found that, according to sediment-traps estimates, 63% of organic carbon, remineralized to CO2 by microbial respiration, originated in the particulate organic matter (POC) pool (Azzaro et al. 2006). Such evidence highlighted POC as the main organic fuel in the RS biological pump. The oxidation rate, fuelling the dissolved organic fraction, was not measured by sediment traps (Jiao et al. 2010, Legendre et al. 2015), but it was a smaller fraction, here in the RS, than it was in other oceans (Azzaro et al. 2006).

Although microbes constitute the sentinel of ecosystem evolution (Dutta and Dutta 2016), to date, relatively little is known in the RS about the microbial contribution to the degradation of the carbon pool. Amongst the biological processes and metabolic activities, respiration has particularly been neglected despite its great impact on environmental ecology (Packard 2017). Respiration is controlled by the respiratory electron transport system activity (ETS) in all organisms on the planet (Packard 1969, Packard et al. 1971, Lane 2006, Packard et al. 2015). It functions in aerobic and anaerobic conditions, as well as in extreme or deep marine environments (Koppelmann et al. 2004, Azzaro et al. 2006, Packard and Codispoti 2007, Baltar et al. 2010). The ETS assay was originally designed by Packard (1971). The biochemical relationship amongst the biological energy currency, adenosine triphosphate (ATP) and ETS was completely unknown before the 1940s. In few words, in all living organisms, during cellular respiration, the electron transport system produces the bulk of the cell energy in the form of ATP molecules. However, earlier, the idea of capturing biologically usable energy from respiration was appreciated by the biophysicist, Alfred Lotka (1925). He felt that Darwinian natural selection was the result of competition between organisms for energy. Those individuals that extracted, stored and used energy most efficiently survived and reproduced more often than their competitors. Building on this concept, Howard Odum described energy flow in freshwater streams (Odum 1956). David Karl recently argued that biological energy production in the ocean should be assessed to understand ocean productivity better (Karl 2014).

Still, none of the earlier work calculated energy production from the biochemical processes that produce this energy. They used distantly related proxies such as biomass or, the more related one, heat production (Pamatmat et al. 1981). Here, we calculate the heterotrophic side of biological energy production (HEP) in the RS. HEP is the ATP generation that results from protons being pumped across the prokaryote plasmalemma or the eukaryote mitochondrial inner membrane by the respiratory ETS. This pumping stores energy in the form of a proton gradient across these membranes. It is manifest as both a hydrogen-ion gradient, with a pH change across the membrane as high as 1 pH unit (Procopio and Fornés 1997) and a voltage gradient with an electromotive force (EMF), equal to 170–225 mV. Both cross-membrane gradients, when there is no electron or proton leakage, generate a force across the membrane that is equal to the EMF in millivolts. These gradients, above about 225 mV, force protons back across the membrane, through the molecular motor, ATP synthetase (EC, to catalyse the phosphorylation of ADP to ATP (ADP + Pi + H+outATP + H2O + H+in) (Berg et al. 2002). This is the main way by which heterotrophic organisms produce their energy. The biochemistry of the connection between ATP and the ETS was poorly known until the Nobelist, Severo Ochoa, determined how three ATP molecule were produced for every oxygen atom (P/O ratio) used in respiratory oxygen consumption (Ochoa 1943). A decade later, Odum (1956) described energy flow in fresh water streams. Much later, working with the copepod Acartia tonsa, Dana 1849, Kiørboe et al. (1985) used modern biochemical understanding of the relationship between respiration and oxidative phosphorylation to calculate ATP production during growth and egg production. In this millenium, Karl (2014), cognisant of Odum’s thinking, as well as the ocean research of Holm-Hansen and Booth (1966) and Packard et al. (1971), advocated assessing biological energy production in the ocean to help understand regional differences in primary productivity. Here and in Packard et al. (2015), we build on this reasoning to calculate HEP and the ATP turnover rate for the seawater of the RS. This HEP is ATP produced by respiratory O2 consumption (RO2) both in the epipelagic microplankton community of phytoplankton, prokaryotes and protozoans and in the mesopelagic microplankton communities of prokaryotes and protozoans.

In order to analyse the role of prokaryotic metabolism as a regulator of the organic carbon budget, seawater samples were taken in a quadrilateral area of the RS where four mooring sites were located. Fifteen stations were sampled in the epipelagic layer (from surface to 100 m) and in the mesopelagic one (from > 100 m to 800 m) to broaden the evaluation of the whole area.

The prokaryotic biomass (PB) was detected by Image Analysis cell counts and volume measurements. The ETS assay was adopted to calculate respiration rates of microplankton (< 200 µm) in terms of oxygen utilisation (OUR), carbon dioxide production (CDPR) rates and heterotrophic energy production (HEP). In some stations, the prokaryotic heterotrophic activity and the heterotrophic carbon production by 3H-leucine uptake (PHA and PHP, respectively) were measured. Ancillary parameters were chlorophyll a (CHL), adenosine triphosphate (ATP) concentrations and the hydrological parameters. We tried to simultaneously analyse the bulk C metabolism of prokaryotic assemblage, by direct measurement of different independent parameters (respiration and heterotrophic production) and their interconnections (HEP, PCD and PGE).

The aims of the paper were 1) to monitor the role of microbes as regulators of the organic carbon transfer in the biogeochemical processes, 2) to compare the obtained data with other surveys in RS and 3) to use microbial respiratory and metabolic activity patterns as proxies to describe microbial ecosystem trends.


During the XX Italian PNRA (National Programme of Antarctic Research, year 2004/05) expedition, in the framework of the ABIOCLEAR project (Antartic BIOgeochemical cycles-CLimatic and palEoclimAtic Reconstructions), an oceanographic cruise was carried out from 4 January to 14 February 2005 aboard the Italian R/V Italica. In a quadrilateral area between four mooring sites (mooring A and B, monitored in the framework of LTER-Italy activity in Antarctica and moorings H and D), a total of fifteen stations were sampled throughout the water column, from surface to 800 m depth, using a Rosette sampler with 24, 12 l, Niskin bottles. The Rosette was mounted on a CTD equipped with a Sea-Bird 9/11 plus multiparametric probe (SeaBird Electronics) that sensed temperature (T), conductivity (salinity, S) and dissolved oxygen (DO [SBE 43]). In Figure 1, the map of the sampling area and in Table 1, the names of stations, their coordinates, maximum depths and studied parameters are reported. In Suppl. material 2: Table S1, the acronyms of the studied parameters and the link amongst some of them are reported.

Figure 1.

Map of the study area within the Ross Sea in the framework of the ABIOCLEAR Project in Summer 2005. The sampling stations are included within the polygon delimited by four mooring stations.

Station names, sampling dates, geographical coordinates, maximum depths and basic parameters. PA= prokaryotic abundance; PB= prokaryotic biomass; CHLa= chorophyll a; ATP= adenosine triphosphate; ETS= electron transport system activity; PHA= prokaryotic heterotrophic activity.

Station Date Latitude Longitude Depth Studied basic parameters
Abio09-D 1/10/2005 75°06.77'S, 164°25.55'E 1002 PA, PB, CHLa, ATP, ETS
Abio10 1/11/2005 75°20.96'S, 166°54.77'E 461 PA, PB, CHLa, ATP, ETS
Abio19 1/16/2005 75°50.81'S, 167°14.62'E 590 PA, PB, CHLa, ATP, ETS
Abio22-A 1/17/2005 76°41.49'S, 169°04.74'E 789 PA, PB, CHLa, ATP, ETS, PHA
Abio01-B 1/30/2005 74°00.53'S, 175°05.67'E 590 PA, PB, CHLa, ATP, ETS, PHA
H1 2/1/2005 75°58.20'S, 177°17.64'E 616 PA, PB, CHLa, ATP, ETS, PHA
Abio05 2/2/2005 75°00.00'S, 178°19.92'E 390 PA, PB, CHLa, ATP, ETS
Abio02 2/3/2005 74°17.85'S, 171°35.14'E 458 PA, PB, CHLa, ATP, ETS
Abio06 2/4/2005 74°57.31'S, 174°35.19'E 400 PA, PB, CHLa, ATP, ETS, PHA
Abio07 2/5/2005 75°04.92'S, 171°44.70'E 548 PA, PB, CHLa, ATP, ETS
Abio20 2/6/2005 76°06.97'S, 170°09.72'E 605 PA, PB, CHLa, ATP, ETS, PHA
Abio35 2/8/2005 76°30.67'S, 172°17.60'E 639 PA, PB, CHLa, ATP, ETS
Abio21 2/8/2005 76°14.06'S, 179°06.10'E 350 PA, PB, CHLa, ATP, ETS
Abio16 2/9/2005 75°58.39'S, 176°29.49'E 454 PA, PB, CHLa, ATP, ETS
Abio17 2/9/2005 75°50.94'S, 173°35.09'E 374 PA, PB, CHLa, ATP, ETS

Trophic measurements (ATP and CHLa)

For ATP measurements, 1 l of seawater was prefiltered through a 250 µm net and then filtered through a 0.22 µm membrane filter. The filter was immediately plunged into 3 ml boiling TRIS–EDTA phosphate buffer (pH 7.75) and the ATP was extracted at 10 °C for 3 min and kept frozen (-20 °C) until laboratory analysis in Italy (Holm-Hansen and Paerl 1972). The filtrate, from the ATP sample, was stored in sterile polycarbonate bottles. Extracts for ATP determination were prepared according to Holm-Hansen and Paerl (1972) and analysed by measuring the peak height of the firefly bioluminescence with a Lumat LB9507 luminometer by EG&G Berthold. The conversion factor, C/ATP = 250, was adopted to convert ATP values into carbon biomass units (C-ATP) according to Karl (1980). The accuracy of this conversion is ± 20% (Skjoldal and Båmstedt 1977). According to ATP concentrations, Karl (1980) classified the trophic status of marine systems as follows: oligotrophy when ATP is < 100 ng l-1; moderate trophism ATP > 100 < 500 ng l-1 and eutrophy ATP < 500 ng l-1.

CHLa concentrations, as an index of phytoplankton biomass, were determined in the water column from surface to a maximum of 160 m depth. The water samples (1 l) were filtered on Whatman GF/F glass-fibre filters, according to Lazzara et al. (1990). After filtration, the filters were immediately stored at -20 °C. CHLa was extracted in 90% acetone and read before and after acidification. Determinations were carried out with a Varian Eclipse spectrofluorometer. Maximum excitation and emission wavelengths (431 and 667 nm, respectively) were selected on a pre-scan with a solution of CHLa from Anacystis nidulans (Sigma-Aldrich Co). The conversion factor of C-CHL = 100 was adopted to convert CHLa values into carbon biomass units (Smith et al. 1998).

The Trophic State Index (TSI), applied to classify the stations according to their algal biomass, was calculated from the chlorophyll measurements (Carlson 1983: TSI (CHLa) = 9.81 ln(CHLa) + 30.6).

Prokaryotic determinations (abundance and biomass)

Samples for prokaryotic abundance (PA; including bacteria, archaea and cyanobacteria) were collected into sterile Falcon vials (50 ml). Each sample was immediately fixed in pre-filtered formaldehyde (0.2 µm porosity; 2% final concentration) and stored at 4 °C until analysis. Within three months, two replicates of each sample were filtered through polycarbonate black membrane filters (0.2 µm porosity; GE Water & Process Technologies) and stained for 10 min with 4’,6-diamidino-2-phenylindole (DAPI, Sigma, final concentration 10 µg ml-1) according to Porter and Feig (1980). Stained cells were counted under a Zeiss AXIOPLAN 2 imaging epifluorescence microscope (magnification: Plan-Neofluar 100× objective and 10× ocular; HBO 100 W lamp; filter sets: G365 excitation filter, FT395 chromatic beam splitter, LP420 barrier filter) equipped with the digital camera AXIOCAM-HR. Images were captured and digitised on a personal computer using the AXIOVISION 3.1 software. Linear dimensions of cells were measured and their shapes equated to standard geometric figures according to Lee and Fuhrman (1987), Fry (1990) and Massana et al. (1997). Volume of single cells (VOL, as µm3) was calculated according to Bratbak (1985). Cell carbon content (CCC, as fg C cell-1) was derived from cell volumes according to Loferer-Krößbacher et al. (1998). Total prokaryotic biomass (PB, as µg C l-1) was calculated by multiplying PA by CCC, locally derived from single cell VOL (La Ferla et al. 2015).

Metabolic rates

ETS measurements and relative rates

Respiratory rates were quantified according to the tetrazolium reduction technique (Packard 1971, 1985) as modified for the microplankton community (Kenner and Ahmed 1975). The ETS assay allowed an estimation of the maximum velocity (Vmax) of the dehydrogenases transferring electrons from their physiological substrates (NADH, NADPH and succinate) to a terminal electron acceptor (O2) through their associated electron transfer system. Details on our ETS measurements in Antarctic and peri-Antarctic areas have been described previously (Azzaro et al. 2006, Crisafi et al. 2010, Misic et al. 2017). Briefly, subsamples (from 2 to 20 l) were pre-filtered through a 250-μm mesh-size net and concentrated on GF/F-glass-fibre filters (nominal pore diameter 0.7 μm) at reduced pressure (< 1/3 atm). Although the filter porosity was specific for microplankton, GF/F filters retain also particles colonised by very small heterotrophs. The filters were immediately folded into cryovials and stored in liquid nitrogen to prevent the enzymatic decay (Ahmed et al. 1976) until they were analysed in the laboratory (< 3 months). The assays were performed in duplicate and homogenates were incubated for 30 min in the dark at the in situ temperature (± 0.5 °C) of the sample. The ETS was corrected for in situ temperature with the Arrhenius equation using a value for the activation energy of 11.0 kcal mol-1 (Packard et al. 1975, Arístegui and Montero 1995).

The specific standard deviation (i.e. the percentage of the standard deviation of the replicates on the average value of the same replicates), due to the analytical procedures and sample handling, was about 35%.

ETS (μl O2 l-1 h-1) was considered equal to the respiration rate in the epipelagic zone and converted to respiratory Carbon Dioxide Production Rates (CDPR) (μg C l-1 d-1) by using the following (Eq.1):

CDPR = (ETS*12/22.4) * (122/172) (Eq.1)

where 12 is the C atomic weight, 22.4 the O2 molar volume and 172/122 the Takahashi oxygen/carbon molar ratio (Takahashi et al. 1985). Real respiratory rates have been calculated using the conversion factors from ETS Vmax to CDPR as referred by La Ferla and Azzaro (2001b) and Azzaro et al. (2006), in the epi- and mesopelagic layers, respectively.

Cell specific respiratory rates (CSRR) were calculated by dividing normalised CDPR values to the normalised cell abundance values in each station by adopting a prokaryotic contribution of 50% and 80% at epipelagic and mesopelagic layers, respectively, assuming that the activities we measured were mainly due to the prokaryotic fraction and that all the cells have similar activity levels.

Heterotrophic energy production (HEP) determination

Today, the P/O ratio is thought to be closer to 2.5 rather than 3.0 (Ferguson 2010, Moran et al. 2012). With this ratio and using ETS activities to compute respiration, one can derive ATP production rates in a particular oceanic region through the determination of the HEP (Packard et al. 2015). HEP, in micro Joules (µJ), can be calculated from seawater respiration (R) in µmol O2 h-1 l1 using the following equation (Eq.2):

HEP (µJ h-1 l-1) = R (µmol O2 h-1 l-1) * 2 * 2.5 * 0.048 * 10-6 (Eq.2)

where 2 is the number of electron pairs that participate in reduction of one molecule of O2 to two molecules of H2O; 2.5 is the modified P/O of Ochoa (1943), the ATP produced by the flow of one electron pair; 0.048 is the Gibbs Free Energy (∆G) per micro mol ATP in J·(µmol ATP)-1 (Hinkle 2005, Ferguson 2010, Moran et al. 2012, Packard et al. 2015); and 10-6 converts Joules to micro Joules. If, HEP in milli Joules (mJ) is desired, simply replace the factor, 10-6, in Eq. 2, with the factor, 10-3. The turnover time (τ) of ATP in the microplankton community, from which came the sample, is the molar ratio of the ATP concentration to the ATP production rate, in the microplankton. The calculation steps are shown in Table 2.

Heterotrophic Energy production (HEP) and adenosine triphosphate (ATP) turnover time in microplankton in the Ross Sea water column.

Pelagic Zone Depth Interval Potential Respiration (Φ) ATP Respiration HEP (ATP Production) HEP (Energy Production) ATP Turnover Time (τ)
(m) (µmol O2 l-1 h-1) (ng l-1) (µmol O2 l-1 h-1) (µmol l-1 h-1) (mJ l-1 h-1) (min)
Euphotic 2–100 9.13 ± 5.93 123 ± 72.2 2.38 ± 1.54 11.88 ± 7.71 570 ± 370 1.25 ± 0.76
Epipelagic 2–160 8.97 ± 3.27 110 ± 71.0 2.33 ± 0.85 11.67 ± 4.25 560 ± 204 1.15 ± 0.77
Mesopelagic Upper (A) 100–500 4.02 ± 3.49 30.35 ± 31.68 1.05 ± 0.91 5.23 ± 4.54 251 ± 218 0.74 ± 0.49
Mesopelagic Upper (B) 160–500 3.03 ± 3.19 27.49 ± 32.58 0.86 ± 0.83 4.31 ± 4.15 207 ± 199 0.78 ± 0.53
Mesopelagic lower 500–800 1.65 ± 0.88 13.48 ± 6.76 0.43 ± 0.23 2.15 ± 1.15 103 ± 55 0.76 ± 0.53

Prokaryotic heterotrophic activity (PHA) and production (PHP)

PHA was evaluated by 3H-leucine incorporation rate assay using the microtubes method described by Smith and Azam (1992). Briefly, triplicate 1.7 ml subsamples and duplicate zero-time killed (trichloroacetic acid-TCA, 5% final concentration) blanks were incubated in 2 ml polypropylene microcentrifuge tubes (Safe-lock, Eppendorf) with L-[4,5-3H] leucine (Amersham, GE Healthcare, SA 61Ci/mmol) giving final concentration of 20 nM. Microtubes were distributed in floating racks into darkened containers with seawater disposed in a refrigerated box. Samples were incubated for 170–190 min at a temperature between -0.5 and -0.8 °C. Incubation was stopped with TCA (5% final concentration). Pellets were washed twice with 5% TCA and 80% ethanol and finally supplemented with 1 ml of liquid scintillation cocktail (Ultima Gold MV Perkin Elmer Life and Analytical Sciences). The liquid-scintillation analysis was effected in a Perkin Elmer Model Wallac 1414 WIN Spectral counter using the internal quenching control. Saturation curves analysis and time-course experiments were carried out at four different stations (from 2 to 300 m depth) with hourly controls for up to six hours of incubation. This procedure was established in a previous cruise in the RS, carried out within the framework of the Victoria Land Transect Project – VLTP-2004 (PNRA XIX Expedition). During PHA time-course experiments, linearity in the 3H-leucine uptake was observed in one sample (25 m depth) between 1 and 6 hours of incubation (r = 0.96), while in the other three samples (2, 270 and 310 m depths), linearity was observed only between 1 and 3 hours of incubation (r = 0.83 and 0.88, respectively).

Prokaryotic heterotrophic production (PHP) was calculated from the 3H-leucine incorporation rate (PHA) expressed in moles incorporated per unit time and volume (Kirchman et al. 1985) according to the equation PHP = PHA*CF where CF is a conversion factor expressed in kg C mol-1. CF were determined following a “semi-theoretical” approach using the molecular weight of leucine, the leucine content of cellular protein and the cellular carbon equivalent of the protein according to Simon and Azam (1989) and the isotope dilution (ID) in situ experimentally determined according the rectangular hyperbola fitting method of van Looij and Riemann (1993), as described by Pedrós-Alió et al. (2002). In particular, ID was determined from two samples collected at 20 and 45 m depth, respectively and two at 300 m depth. It varied between 1.04 and 1.37. The mean value of 1.25 was used to calculate the CF that was equivalent to 1.94 kg C (mol leucine)-1 (Table 3).

Isotopic Dilution (ID) detected from leucine incorporation rates (Vmax) in samples Abio19 (45 and 300 m depth) and Abio05 (20 and 300 m depth), respectively. ID mean value 1.25 ± 0.14.

Station depth (m) V max ± SD p mol l-1 h-1 ID confidence interval 95%
Abio19 45 18.058 ± 0.870 1.06 0.95–1.18
300 0.357 ± 0.069 1.24 1.00–1.48
Abio05 20 9.785 ± 4.813 1.37 0.69–2.04
300 0.932 ± 0.225 1.34 1.01–1.66

PHP was expressed as production of biomass (as C) per time unit and volume.

Derived parameters as Specific Growth Rate d-1 [SGR (µ) = PHP/PB (Prokaryotic biomass)] and Biomass Turnover Time (days) [BTT (g) = ln(2)/µ] were calculated according to Kirchman (2001).

In comparison, PHPD, PBD, SGRD and BTTD were calculated using 107 fg C µm-3 cell and an ID = 1 according to Ducklow et al. (2001). Cell specific incorporation rates (CSIR) were calculated by dividing normalised PHP values to the normalised cell abundance values in each station.

The prokaryotic C requirement was computed as Prokaryotic Carbon Demand (PCD), i.e. PHP+CDPR by using normalised data, assuming the contribution of prokaryotes to total microbial community respiration as 50% in the epipelagic layer and a contribution of 80% in the mesopelagic ones (La Ferla et al. 2005, del Giorgio et al. 2011, Baltar et al. 2015). The Prokaryotic Growth Efficiency (PGE) was calculated as PHP/PCD and the isotope dilution (ID) in situ was experimentally determined according the rectangular hyperbola fitting method of van Looij and Riemann (1993), as described by Pedrós-Alió et al. (2002) using HYPER 32 fitting software.

Data processing

In order to detect possible influences between environmental factors and microbial variables, Spearman Rank correlation coefficients were calculated for the microbiological data and environmental parameters using the SigmaStat software V3.0 and the Mann and Whitney test using the PAST.exe (Hammer et al. 2001). The analyses of variance (one-way ANOVA and Kruskal-Wallis) were applied to some parameters to assess the significance of the differences between depth layers and stations.

Data were integrated with depth according to the trapezoidal method and normalised to the depth: from 2 to 100 m for the epipelagic layer; from 100 to 800 m for the mesopelagic layer.

The depth-integrated rate (ʃR dz in mg C m2 d-1 l-1) for the water column was calculated within the depth interval between Z1 and Z2 using the following equation (Eq. 3):

ʃR dz = y (Z2(x+1) – Z1(x+1) / (x+1) (Eq.3)


Temperature ranged between -2.01 (H1, 500 m) and 1.48 °C (Abio09-D, 5 m) and salinity between 34.11 (Abio10, 25 m) and 34.79 (Abio09-D, 600 m). In Suppl. material 1: Figure S1, the temperature and salinity at surface and 200 m depth layers are shown. In the surface layer, a frontal structure was evidenced by temperature (T) and salinity gradients. Temperature clearly decreased eastwards from 1.5 to -0.6 °C, corresponding to mooring stations Abio09-D and H1, respectively. Salinity was similarly distributed along a non-linear front that cut eastwards and southwards through the RS with a salinity of 34.3. At the 200 m depth-horizon, temperature showed a wedge of relatively warm water, with temperatures ranging from -1.3 to -0.7 °C, that had crept between two cooler water masses, with temperatures ranging from -1.9 to -1.3 °C. Salinity, in Suppl. material 1: Figure S1, showed an eastwards decreasing trend (from 34.66 to 34.46).

In Table 4, the ranges, mean values and standard deviations of the trophic analyses, prokaryotic determinations and metabolic rates detected in the epipelagic and mesopelagic layers are reported. CHLa showed very low concentrations throughout the study area (mean values 0.14 and 0.058 mg m-3 in the epi- and mesopelagic layers, respectively). In the epipelagic layer, the minimum and maximum values were detected at stations Abio20 (5 m) and Abio09-D (5 m), respectively; in the mesopelagic layer, they occurred at stations Abio10 (150 m) and Abio02 (110 m), respectively.

Range, mean and standard deviations, sampling numbers (n) of trophic parameters (CHLa, C-CHLa, ATP and C-ATP), prokaryotic abundance and biomass (PA and PB) and metabolic rates (ETS, CDPR and PHP); detected in the epipelagic (0–100 m) and mesopelagic (>100<800 m) depth layers. CHLa= cholrophyll a; C-CHLa= cholrophyll a in carbon units; ATP= adenosine triphosphate; C-ATP= adenosine triphosphate in carbon units; PA= prokaryotic abundance; PB= prokaryotic biomass; ETS= electron transport system activity; CDPR= carbon dioxide production rate; PHP= carbon prokaryotic heterotrophic production.

mg m-3 mg C m-3 ng l-1 ng C l-1 cells ml-1 µg C l-1 µl O2 l-1 h-1 µg C l-1 h-1 µg C l-1 h-1
0–100 m depth
min 0.019 1.89 42.76 10689 5.70E+4 0.8 0.052 0.0043 0.0005
max 0.539 53.90 380.49 95123 1.44E+6 24.5 0.437 0.1660 0.0295
mean 0.141 14.11 123.78 30944 2.87E+5 5.2 0.201 0.0740 0.0100
SD 0.116 11.56 70.26 17564 2.55E+5 4.5 0.076 0.0323 0.0077
n 67 67 39 39 67 67 66 66 25
100–800 m depth
min 0.007 0.67 4.77 1193 5.66E+4 0.8 0.006 0.0002 0.0003
max 0.244 24.44 130.69 32673 6.76E+5 15.1 0.271 0.0965 0.0061
mean 0.058 5.77 29.04 7260 1.51E+5 3.1 0.086 0.0122 0.0019
SD 0.051 5.14 30.66 7664 8.45E+4 1.9 0.076 0.0244 0.0015
n 25 25 47 47 78 78 81 81 36

The average CHLa value in the epipelagic layer was 2.4 times higher than in the mesopelagic one. In Figure 2a, the C-CHLa vertical distribution is reported. TSI were always lower than 30, categorising the RS as oligotrophic for Summer 2005 (Suppl. material 3: Table S2).

Figure 2.

Depth profiles of mean values and standard deviations of the carbon content derived from chlorophyll a (C-CHLa, a) and adenosine triphosphate (C-ATP, b), the prokaryotic biomass (PB, c) and the carbon dioxide production rates derived from ETS activity (CDPR, d) in the Ross Sea water column.

ATP sharply decreased with depth (Figure 2b) by a factor of 4 (Table 4). On average, ATP ranged between 124 and 29 ng l-1 in the epi- and mesopelagic layers. The minimum and maximum concentrations in the epipelagic layer were observed in stations H1 (100 m) and Abio09-D (5 m), respectively; in the mesopelagic layer, at stations H1 (500 m) and Abio02 (110 m), respectively. The biological carbon, calculated from the ATP (C-ATP), decreased with depth from a mean of 31 micro g C l-1 in the epipelagic euphotic zone to a mean of 7 micro g C l-1 in the mesopelagic zone between 100 and 800 m (Table 4 and Figure 2b). According to Karl’s classification, stations H1, Abio09-D, Abio06, Abio05 and Abio02 manifested moderate trophism (> 100 < 500 ng l-1) whereas Abio07, Abio20, Abio35 manifested oligotrophy (ATP < 100 ng l-1).

The calculation of the average TSI, chosen to establish the station trophic state, ranged from a high of 24 at H1, the most offshore station, to a low of 16 at Abio20, a station much closer to shore (Suppl. material 3: Table S2, Figure 1). Stations H1, Abio09-D, Abio21, and Abio05 were richer than the other stations. Their averaged TSI surpassed 23. Stations H1, Abio21 and Abio05 grouped together as the three most offshore stations, but station Abio09-D was the most inshore one. At the other end of the scale, station Abio20, as mentioned above, had the lowest TSI. A one-way ANOVA analysis confirmed this variability amongst the stations (P < 0.000314, n = 80).

Prokaryote Abundance (PA) was in the order of 104–106 cell ml-1 in the epipelagic layer and 104–105 cell ml-1 in the mesopelagic layer (Table 4). The Cell Carbon Content (CCC) showed a discrete variability in both the studied layers and ranged between 11 and 36 and between 13 and 45 fg C cell-1 in epi- and mesopelagic layers, respectively (Suppl. material 4: Table S3). PB modulated by both PA and CCC, showed a decreasing trend and high variability in the upper layers, particularly at 25 m (Figure 2c). The minimum value was detected at 500 m and an increase below occurred. The values in the epipelagic layer were 1.7 times higher than in the mesopelagic one. In the near-surface layer, PB min and max values were detected at stations Abio05 (100 m) and Abio09-D (25 m), respectively. In the deep layer, PB min and max values were obtained at stations Abio05 (100 m) and Abio22-A (100 m), respectively.

ETS showed a decreasing trend with depth and the values in the epipelagic layer were 3 times higher than in the mesopelagic one (Table 4). In the upper layer, ETS minimum and maximum values were detected at stations Abio 22-A (80 m) and Abio10 (25 m), respectively. In the deeper layer, they were observed at stations Abio16 (400 m) and Abio10 (150 m), respectively.

CDPR showed a decreasing trend with depth with a discrete variability in the deep layers (Figure 2d). It sharply decreased with depth by a factor of 6 (Table 4). The minimum value in the epipelagic layer was achieved at station Abio16 (2 m) and the maximum value at Abio10 (25 m). In the mesopelagic layer, minimum and maximum values were observed at Abio16 (400 m) and Abio17 (120 m), respectively.

The cell specific respiratory rate (CSRR) calculated on depth-integrated and normalised data (Figure 3), in the epipelagic layer varied between 10.1×10-3 (Abio16) and 390×10-3 (at station Abio02) fg C cell-1; in the mesopelagic waters, they varied between 1.46×10-3 (Abio16) and 50.1×10-3 (Abio17) fg C cell-1.

Figure 3.

Cell Specific Respiratory Rates (CSRR) and SD for each station in the epipelagic and mesopelagic layers (0–100 m, 100–800 m). In the upper and deeper layers, the prokaryotic contributions to total respiration were considered to be the 50% and 80%, respectively, of the total carbon dioxide production rates (CDPR).

The HEP calculations for the epipelagic and mesopelagic waters of RS are given in Table 2. The average HEP (and standard deviation) in epipelagic zone down to 160 m was 560 ± 20 µJ h-1 l-1. Over this depth range, HEP ranged from as high of 108 µJ h-1 l-1, at station Abio06 (2 m depth), to 15 µJ h-1 l-1, at station Abio22-A (80 m). The HEP calculations for the euphotic zone (upper 100 m) were the same (Line 1, Table 2). The range was 108 to 15 µJ h-1 l-1 and the average and standard deviation was 570 ± 370 µJ h-1 l-1. In mesopelagic waters below 160 m down to 500 m, HEP was 80% lower, averaging 118 ± 90 µJ h-1 l-1. If we make the calculation from and including 100, the HEP down to 500 m was larger. It averaged 251 ± 218 µJ h-1 l-1. The database for the mesopelagic waters between 500 and 800 m was smaller than the one between 100 and 500 m, in only 15 compared to 74 measurements. These measurements yielded an average deep-mesopelagic HEP of 103 ± 55 µJ h-1 l-1. The ATP turnover rate in the cells of the microplankton was remarkably constant with depth, in the order of magnitude of a minute. It only decreased 39% from the euphotic zone to the lower mesopelagic zone, from 1.25 ± 0.76 to 0.76 ± 0.53 minutes.

The prokaryotic heterotrophic activity (PHA) – in term of leucine incorporation rates – varied between 0.213 and 19.035 pmol l-1 h-1 (Figure 4). Peaks of activity were detected in surface (Abio01-B, Abio20 and Abio06 stations) or at 20–25 m depth (Abio22-A and H1 stations). Decreasing PHA was detected with depth followed by an increase of activity in the bottom samples (Figure 4). In station Abio22-A, normalised PHA in the epipelagic layer (12.4 nmol m-3 h-1) was one order of magnitude higher than that observed in the other stations (Table 5). PHA observed in 0–50 m layers was on average 8.05 ± 4.52 pmol l-1 h-1 (n = 15). The cell specific incorporation rate (CSIR) varied between 1.992 and 70.844 z mol leucine cell-1 h-1 with the highest values in the upper 50 m depth (28.20 ± 15.97 z mol leucine cell-1 h-1) while, in mesopelagic waters, they turned out to be one order of magnitude less (6.48 ± 3.63 z mol leucine cell-1 h-1, n = 26). Throughout the different water columns, CSIR strictly reflected what has already been observed in PHA (see Figure 4).

Figure 4.

Leucine incorporation rates (PHA) at the five stations in the Ross Sea, Summer 2005.

Leucine incorporation rates (PHA expressed in nmol m-3 h-1) integrated in the depth intervals 1–100 m (epipelagic layer) and 100 m-bottom (mesopelagic layer) and normalized.

Station depth (m) PHA
1–100 m 100 m – botton (m)
Abio22-A 789 12.420 2.093 (600)
Abio01-B 590 2.651 0.463 (585)
H1 616 5.398 0.799 (600)
Abio06 400 3.805 2.589 (390)
Abio20 605 3.926 1.353 (500)

PHP showed the same distribution of PHA throughout the water column. In the epipelagic layer, PHP varied between 0.46 and 29.51 ng C l-1 h-1 with the highest value at station Abio22-A at 25 m depth. In mesopelagic waters, PHP varied 0.33 and 6.11 ng C l-1 h- 1 and it was, on average, 5 times lower than in upper layer (Table 4). In Table 6, the PHP, PB, SGR and BTT calculated at the ABIO22, ABIO01, H1, ABIO06 and ABIO20 stations are reported. In the euphotic layer, SGR day-1 ranged from 0.005 to 0.18 with a mean of 0.053. With the conversion factors used by Ducklow et al. (2001), SGRD day-1 ranged from 0.013 to 0.44, with a mean of 0.128. Contrary to the SGR, the BTT (days) ranged from 3.79 to 128.31 (mean = 28.1) and BTTD ranged from 1.55 to 52.04 (mean = 11.6). In mesopelagic waters, SGR and BTT were lower and higher, respectively, than those observed in the upper 100 m of the epipelagic zone.

Prokaryotic heterotrophic production (PHP), Prokaryotic biomass (PB), Prokaryotic specific growth rate (SGR) and Biomass turnover time (BTT) calculated in the five indagated stations (ABIO22, ABIO01, H1, ABIO06 and ABIO20). PHPD, PBD, SGRD and BTTD were calculated using a different factor (107 fg C µm-3 cell-1 and ID = 1) according to Ducklow et al. (2001).

Depth n Mean SD Range
PHP (ng C l-1 h-1) 0–100 m 22 12.182 9.492 0.575–36.883
100–800 m 26 2.027 1.629 0.413–6.441
PHPD (ng C l-1 h-1) 0–100 m 22 9.746 7.593 0.460–29.506
100–800 m 26 1.622 1.303 0.330–5.153
PB (µg C l-1) 0–100 m 20 5.179 3.594 1.921–15.127
100–800 m 25 3.222 1.939 1.074–9.028
PBD (µg C l-1) 0–100 m 20 1.735 1.242 0.608–5.125
100–800 m 25 1.077 0.676 0.338–3.101
SGR day-1 0–100 m 20 0.053 0.047 0.005–0.183
100–800 m 25 0.016 0.011 0.003–0.038
SGRD day-1 0–100 m 20 0.128 0.116 0.013–0.446
100–800 m 25 0.040 0.027 0.006–0.095
BTT (days) 0–100 m 20 28.12 30.43 3.79–128.31
100–800 m 25 65.29 50.44 18.24–254.05
BTTD (days) 0–100 m 20 11.63 12.50 1.55–52.04
100–800 m 25 27.458 23.152 7.26–118.26

The normalised prokaryotic C demand (PCD) ranged between 0.035 and 0.055 mg C h-1 m-3 in the epipelagic layer with high values at Abio22-A and Abio06. In the mesopelagic layer, it ranged between 0.003 and 0.008 mg C h-1 m-3 with the highest value at Abio20 (Table 7). Comparing the two layers, the averaged PCD was about 9 times higher in the epipelagic layer than in mesopelagic one (Kruskal-Wallis One-Way ANOVA: P < 0.003).

Prokaryotic carbon demand (PCD) and prokaryptic growth efficiency (PGE) depth-integrated and normalized values in the epi- and mesopelagic depth layers.

Station 0–100 m depth 100–800 m depth
mg C m-3 h-1 % mg C m-3 h-1 %
Abio09-D 0.0069 26.23
Abio22-A 0.051 37.82 0.0052 60.21
Abio01-B 0.0354 11.62 0.0052 22.28
H1 0.0417 19.87 0.0045 27.58
Abio06 0.0551 18.99 0.0029 26.21
Abio20 0.0457 13.31 0.0079 26.71
Abio16 0.0037 88.49

The normalised PGE ranged between 12 and 37% in epipelagic layer, with the lowest value at Abio01-B and the highest at Abio22-A. In the mesopelagic layer, PGE ranged between 22 and 88% with the minimum value at station Abio01-B and the maximum at Abio16. In contrast to the PCD, the averaged PGE was twice as high in the mesopelagic zone as in the epipelagic one (Kruskal-Wallis One-Way ANOVA: P < 0.048).

The Spearman-Rank correlation analysis of the whole dataset yielded the outputs shown in Table 8; only the significant correlation coefficients (r) are reported, together with their significance level (P) and data number (n). Numerous significant correlations were computed amongst the hydrological, trophic and microbial parameters. PB, CDPR and PHP showed the largest number of highly significant correlations with most of the hydrological, trophic and microbiological parameters. No significant relations were detected between CCC vs. PHP and CHL or between PHP and ATP. In Table 9, the stations and depths, which showed the minimum and maximum values of each parameter in the epi- and mesopelagic layers, are summarised. Finally, the depth integrated data of standing stock - in terms of prokaryotic and autotrophic biomass (PB and C-CHLa), total biomass (C-ATP) and remaining heterotrophic biomass (HB) – and rates – in terms of respiratory and prokaryotic heterotrophic production rates (CDPR and PHP) – are reported in Suppl material 5: Table S4, C-CHLa, PB and C-ATP, amounted to 1545, 1681 and 4605 mg C m-2, respectively. The remaining heterotrophic component (HB) presumably accounted for a biomass of 1379 mg C m-2. CDPR remineralised 8.279 mg C m-2 h-1 with higher rates in the upper layers while PHP accounted for 1.697 mg C m-2 h-1 with a similar weight in the epi- and mesopelagic layers.

Spearman–Rank correlations among microbial and environmental parameters in the whole data set. O2= dissolved oxygen; S= salinity; DEN= density; PA= prokaryotic abundance; PB= prokaryotic biomass; CCC= cell carbon content; ATP= adenosine triphosphate; CHLa= chorophyll a; PHP= prokaryotic heterotrophic production; CDPR= carbon dioxide production rates.

CHLa vs. r P n ATP vs. r P n
Depth -0.545 0.0000 80 Depth -0.836 0.0000 79
T °C 0.5 0.0000 80 T °C 0.776 0.0000 78
O2 0.626 0.0000 73 O2 0.543 0.0000 67
S -0.505 0.0000 73 S -0.762 0.0000 67
DEN -0.522 0.0000 73 DEN -0.747 0.0000 67
PA 0.347 0.0024 75 PA 0.399 0.0000 78
PB 0.318 0.0056 75 CCC -0.255 0.0243 78
ATP 0.899 0.0000 42 PB 0.229 0.0437 78
PHP 0.46 0.0139 28 CHLa 0.899 0.0000 42
CDPR 0.346 0.0028 73 CDPR 0.775 0.0000 76
PB vs. r P n CDPR vs. r P n PHP vs. r P n
Depth -0.285 0.0000 134 Depth -0.851 0.0000 136 Depth -0.683 0.0000 56
T °C 0.197 0.023 134 T °C 0.714 0.0000 136 T °C 0.47 0.0000 56
O2 0.459 0.0000 122 O2 0.607 0.0000 123 O2 0.784 0.0000 47
S -0.215 0.0174 122 S -0.731 0.0000 123 S -0.545 0.0000 47
DEN -0.205 0.0237 122 DEN -0.726 0.0000 123 DEN -0.554 0.0000 47
PA 0.898 0.0000 134 PA 0.304 0.0000 125 PA 0.662 0.0000 56
CCC 0.258 0.0027 134 CCC -0.256 0.0040 125 PB 0.618 0.0000 56
ATP 0.229 0.0437 78 PB 0.216 0.0157 125 CHLa 0.46 0.0139 28
CHLa 0.318 0.0056 75 CHLa 0.346 0.0028 73 ETS 0.615 0.0000 54
PHP 0.618 0.0000 56 ATP 0.775 0.0000 76 CDPR 0.657 0.0000 54
CDPR 0.216 0.0157 125 PHP 0.657 0.0000 54

Stations and depth with minimum and maximum values of each parameters in the epi- and mesopelagic layers. CHLa= chorophyll a; ATP= adenosine triphosphate; PA= prokaryotic abundance; CCC= cell carbon content; PB= prokaryotic biomass; ETS= electron transport system activity; CDPR= carbon dioxide production rates; PHA= prokaryotic heterotrophic activity.

0–100 m 100–800 m
min max min max
CHLa Abio20 – 5m Abio09(D) – 5m Abio10 – 150m Abio02 – 110m
ATP H1 – 100m Abio09(D) – 5m H1 – 500m Abio02 – 110m
PA Abio05 – 100m Abio09(D) – 25m Abio22(A) – 100m Abio07 – 500m
CCC Abio20 – 2m H1 – 2m Abio05 – 200m Abio09(D) – 800m
PB Abio05 – 100m Abio09(D) – 25m Abio05 – 100m Abio22(A) – 100m
ETS Abio22(A) – 80m Abio10 – 25m Abio16 – 400m Abio10 – 150m
CDPR Abio16 – 2m Abio10 – 25m Abio16 – 400m Abio17 – 120m
HEP Abio22(A) – 80m Abio06 – 2m Abio16 – 400m Abio10 – 150m
PHA Abio01(B) – 100m Abio22(A) – 25m Abio06 – 270m Abio16 – 250m


Trophic conditions and prokaryotic biomass

The environmental assessment revealed a general picture of low trophism on a spatial scale as exemplified by the CHLa concentration. Analysis of the ATP concentrations also indicated modest or poor trophism. Considering ATP as a quantitative proxy for total living biomass (Holm-Hansen and Paerl 1972, Karl 1980), a relatively homogeneous biomass occurred throughout the RS. However, according to Karl’s classification, most stations manifested moderate trophism or oligotrophy. In a previous study carried out in January-February 2001 in the RS, an extensive algal biomass and variable ATP estimates corresponding to different trophic statuses were observed (Azzaro et al. 2006, La Ferla et al. 2015). In particular, marked peaks of ATP were found (up to 1752 ng l-1), revealing strong eutrophy according to Karl (1980). In our study, ATP determinations confirmed the trophic evaluation derived by TSI. In the 0–100 m depth layer, the autotrophic biomass (in terms of C-CHLa) accounted for 20–59% (mean value 44 ± 19%) of the total biomass (in terms of C-ATP). The highest and the lowest ratios of autotrophic biomass occurred at stations Abio09-D and Abio20, respectively, thus corroborating the previous statements about TSI and ATP estimates. In the epipelagic layer, the integrated CHLa values revealed a low phytoplankton standing stock varying from 4.76 to 23.68 mg m-2. In contrast, in austral summer 2014, Mangoni et al. (2017) detected high autotrophic biomass (integrated CHLa up to 371 mg m-2) in the epipelagic layer of the RS.

PA was in a range comparable to similar measurements made in several Antarctic marine environments (see table S1 in La Ferla et al. 2015). Comparison to previous studies in the RS confirmed the results of Monticelli et al. (2003) in Terra Nova Bay and Celussi et al. (2009) in Cape Adare.

PB decreased with depth by a factor of 1.7. However, it was higher than other prokaryotic measurements made previously in the RS (Buitenhius et al. 2012, Carlson et al. 1999, Steward and Fritsen 2004). The utilisation of a variable cell carbon content (from 11 to 45) to calculate the biomass in each sample could partially explain this pattern. Generally, two standard PA to biomass conversion factors are utilised in ecological marine studies: 20 fg C cell-1 (Lee and Fuhrman 1987) or 9.1 fg C cell-1 (Buitenhuis et al. 2012). Since cell carbon content is directly linked to cell-size variability (Young 2006), the utilisation of unvarying factors to convert abundance to biomass might distort the evaluations by overestimating or, in this case, underestimating the actual carbon amounts (La Ferla et al. 2015).

Respiration and CO2 production

The ETS assay, originally designed by Packard (1971), continues to be successfully adopted in oceanic regions because of its high sensitivity and resolution levels that are not attainable with other methods based on sample incubation (del Giorgio and Williams 2005). Moreover, the response to the bias derived by the utilisation of empirical conversion factors from the ETS Vmax into actual rates of O2 consumption and metabolic CO2 production has largely been discussed and countered (del Giorgio and Williams 2005, La Ferla et al. 2010, Packard et al. 2015, Filella et al. 2018). Independently, good correlations between ETS and in vivo respiration rates were obtained in surface seawater samples in the framework of the ABIOCLEAR cruise. In the aphotic zone between 100 and 600 m depth of the RS, Azzaro et al. (2006) reported decreasing ETS activity throughout the water column and, despite the algal bloom in 2001, the ETS activity fell in a narrower range (0.017–0.170 µl O2 l-1 h-1 than our data in the mesopelagic layer. In Summer 2014, Misic et al. (2017) observed different ETS - POM relationships, but ones consistent with the characteristics of a phytoplanktonic bloom of the Phaeocystis type. Moreover, their results featured averaged ETS values twice ours in the epipelagic and mesopelagic layers. CDPR was also twice ours in the upper layers and 4.6 times higher than ours in the mesopelagic one. The comparison between these findings evidenced great differences in the metabolic rates on an inter-annual scale and corroborated the importance of heterotrophic signals in understanding climate trends.

Heterotrophic energy production

HEP calculations in the microplankton quantify the energy generation due to the decomposition of ATP by a group of enzymes (ATPases) in plasmalemma membranes of constituent bacteria and archaea as well as in constituent eukaryote mitochondrion. It represents a new metric in oceanographic analysis. The only other oceanic region, for which HEP has been calculated, is the Peru Current Upwelling at 15° S (Pisco, Peru). The HEP calculations for the epipelagic and mesopelagic waters of the Peru Upwelling at 15° S are given in Packard et al. (2015). The average HEP (and standard deviation) in the epipelagic zone down to 150 m was 24 ± 30 × 103 µJ h-1 l-1. In a transect (C-Line) for 185 km across the Peru Upwelling, epipelagic HEP ranged from a high of 108 × 103 µJ h-1 l-1, at station C10, over the Peru Trench, 71 km from the coast, to a low of 2 × 103 µJ h-1 l-1, 22 km further offshore at station C12. In mesopelagic waters below 150 m down to 1000 m, HEP was 96% lower than it was in the epipelagic zone, averaging only 84 ± 59 µJ h-1 l-1. These values, from the Peru Upwelling system (Packard et al. 2015), are more than an order of magnitude higher than the values from the RS.

Prokaryotic heterotrophic activity and production

Time course experiments on PHA showed results in agreement with those detected in the VLTP-2004 project (Monticelli, personal communication). Linearity occurred within 1 and 6 hours for samples collected at 25 m depth and in a smaller time lapse for the others. Along the water column, higher PHA was always observed in the photic layers while reduced activity in the aphotic waters occurred. An increase in activity was always observed in the bottom samples, i.e. those taken a few metres from the sea floor. The increase of heterotrophic bacteria metabolisms in benthic boundary layers is a known phenomenon observed in other water columns (Packard and Christensen 2004).

At station Abio22-A, PHA was particularly high at all depths, with the highest normalised rates (12.442 nmol m-3 h-1) in the photic layer. This was one order of magnitude higher than equivalent normalised PHA calculations observed at the other four stations. The mean leucine incorporation rate observed in the 0 - 50 m layer was 8.05 pmol l-1 h-1 (sd = 4.52, n = 15), the same order of magnitude as observed by Ducklow et al. (2001) in the RS during late spring period. The PHA observed in our cruise was also in accordance with that observed by Pedrós-Alió et al. (2002) in the Gerlache strait (Antarctic Peninsula) in late spring and summer cruises. The cell specific incorporation rate (CSIR) strictly reflected the distribution of PHA throughout the water column with the highest values in the surface to 50 m depth layer.

In our experiment, an isotope dilution of 1.25 was used to calculate the CF. It was equivalent to 1.94 kg C mol leu-1. Considering the variability observed in ID determinations and the coefficient of variation (CV%) detected in the triplicate samples for leucine incorporation-rate analysis (mean = 14.3%, sd = 11.3%, n = 84), the ID should be not too far from 1 (assuming no isotope dilution). That value corresponds to a theoretical CF = 1.55 kg C mol leu-1 (Simon and Azam 1989). Similar ID values (mean = 1.27 corresponding to CF = 1.96 kg C mol leu-1) were detected in the Antarctic Peninsula area in late spring-summer where the empirical method carried out simultaneously produced on average CF = 0.81 kg C mol leu-1 (Pedrós-Alió et al. 2002). In the framework of experiments conducted in subtropical northeast Atlantic Ocean, Baltar et al. (2010) discussed the incongruence often observed between empirical and theoretical CFs estimates as well as their variability in the mesopelagic water column (range 0.13–0.85 kg C mol-1 leu). They argued that, in the deep domain, the carbon limitation and the slower cell growth take place, further reducing the deep water CFs as compared to the theoretical ones. The choice of theoretical, semi-theoretical or empirical CF can markedly affect the PHP estimation and, consequently, the derived parameters. In the case of a significantly different CF, it would be appropriate to use both values to furnish the best information about the carbon flux in the prokaryotic compartment. PHP followed the same distribution of PHA throughout the water column with a maximum value corresponding to a production peak at 25 m depth at station Abio22-A.

In the euphotic layer, SGR d-1 calculated with our CFs, resulted 2.4 times lower than SGRD d-1 calculated using the conversion factors used by Ducklow et al. (2001). Contrary to the SGR, the BTT (days) was 2.4 times higher than that calculated with Ducklow’s CFs. In mesopelagic waters, SGR and BTT were the opposite of those observed in the upper 100 m of the epipelagic zone.

From mean hourly values, the CDPR/PHP ratios in the epi- and mesopelagic layers were 11.75 and 0.80 µg C l-1 h-1, respectively.

Prokaryotic metabolic patterns

Prokaryotic (bacterial and archaeal) activity is often measured using the PGE that defines the balance between catabolic and anabolic prokaryotic processes (Baltar et al. 2015). It corresponds to the proportion of dissolved organic carbon (DOC) that is converted by microorganisms into biomass and might be consumed by higher trophic levels (Eichinger et al. 2010). PGE is considered the most important factor affecting the C budget (Van Wambeke et al. 2002) and it indicates the efficiency of organic substrates recycling by prokaryotes (Mazuecos et al. 2015). The determination of PGE depended on the choice of the methodological procedures and approach, i.e. mostly respiration estimates and leucine to carbon CF. Indirect respiration estimates were often obtained from the sinking biogenic particles (Sweeney et al. 2000), from the bacterial production experimental data and from an empirical contribution of respiration (Ducklow et al. 2001; Ducklow 2003) or by sediment traps (DiTullio et al. 2000, Langone et al. 2000, Langone et al. 2003, Nelson et al. 1996). Sometimes, PGE is arbitrarily considered to be 30% or 36% (Manganelli et al. 2009) and a few papers have simultaneously identified the actual respiration and heterotrophic production rates (La Ferla et al. 2005, Zaccone et al. 2003, del Giorgio et al. 2011, Baltar et al. 2009, 2015 and references herein) in pelagic waters. In deep waters in the Atlantic Ocean, Baltar et al. (2010) determined PGE variations in the range < 1–34%. This high variability resulted in being highly correlated to the empirical conversion factors determined and adopted to calculate PHP (Baltar et al. 2010). High variability of PGE, on both space and time scales in ocean samples, has often been assessed (Lemée et al. 2002, Reinthaler and Herndl 2005) and low values of PGE (< 15%) have been associated with oligotrophic conditions (Biddanda et al. 2001, del Giorgio et al. 2011). In short, at low growth efficiency rates, more dissolved organic matter is remineralised, keeping the nutrients cycling within the microbial cycle; conversely, at high growth efficiency rates, the dissolved organic matter is more efficiently transferred into the particulate phase thus strengthening the carbon distribution throughout the trophic food web (Cajal-Medrano and Maske 2005). In our study, the highest PGE values were determined at stations where oligotrophic conditions at the sea-surface were evidenced by TSI. Typically, most of the primary production in low-productivity environments is respired by bacteria (Biddanda et al. 2001). In epipelagic layers, the prokaryotic respiratory processes exceeded heterotrophic production, whilst high PGE was surprisingly calculated in the mesopelagic layer showing consistent differences with the upper layer. In addition, excluding the Abio16 and Abio22-A stations, where high PHPs were determined, the averaged PGE value in the mesopelagic layer surpassed the value in the epipelagic one. These findings were consistent with the high values detected in the deep layers of the Mediterranean Sea (La Ferla et al. 2005 and 2010) where PGE data did not correlate with primary production, but rather confirmed that the PCD could not be sustained solely by the DOC of autochtonous origin and/or by phytoplankton exudation. In the RS, Azzaro et al. (2006) compared estimates of carbon flux by sediment traps and found that about 63% of organic carbon, remineralised by respiration, was derived from the POC pool, confirming the decomposition rates of Ducklow et al. (2001) in the vertical POC flux. Weak bacteria-primary production coupling has also been ascribed to temperature restriction of metabolic utilisation (Pomeroy and Wiebe 2001) or to grazing together with the lack of bio-available dissolved organic matter (Bird and Karl 1999). The relatively high PGE probably also reflected the capability of prokaryotic cells to individually divide (fast growing communities) or to increase in size. We found that, in our samples, volumetric determinations in the mesopelagic layer were higher than in the epipelagic layer. They fell into the range of 0.038 to 0.196 µm3 (data not shown). Furthermore, the circulatory dynamics of the water masses could also explain the high PGE at depth. When vertical convective processes occur with the sinking of surface water masses, a consequent enrichment of fresh organic matter in the deep layers happens (La Ferla and Azzaro 2001a, Azzaro et al. 2012). In temperate seas, the lateral advection of newly-formed water masses (both intermediate and deep) from convective regions as well as the lateral injection in the winter, of organic matter from the canyons and shelves, enhanced C respiration in deep layers (Packard et al. 2008, La Ferla et al. 2010). The horizontal PGE variability in the water layers suggested that, in mesopelagic waters, prokaryotes are able to use the available organic matter and convert it into biomass more efficiently than in epipelagic ones. This finding could be interpreted as an adaptive physiological response. It reflects the ability of deep-water microbes to efficiently exploit the available DOC at great depths. Placenti et al. (2018) suggested this mechanism for the deep Mediterranean Sea. Another aspect, that was unfortunately not considered, concerns the maintenance of the prokaryotic biomass and metabolism in terms of energetics when assessing the role of microbes in oceanic carbon cycles (Eichinger et al. 2010). According to Baltar et al. (2015), a combination of environmental stressors could enhance the proportion of the energy flux devoted to cell maintenance, inducing increases in cell specific respiration and decreases in PGE. In incubation experiments, slow-growing bacterial communities tended to have low PGE and to respire a high portion of the secondary production in terms of leucine uptake (del Giorgio et al. 2011). Moreover, in oligotrophic systems, low PGE may result from the maintenance of active transport and from the production of exoenzymatic hydrolysis with high bacterial energy demand (del Giorgio and Cole 1998).

However, using mean hourly normalised values, the ratio CDPR/PHP (µg C l-1 h-1) in the epipelagic layer was 3 times higher than in the mesopelagic one, presumably due to the occurrence of autotrophic respiration (Marra and Barber 2004). The prokaryotic carbon requirement (PCD) was particularly low in the mesopelagic layer where autotrophic production was lacking.

The significant relationship between CDPR and other physical and chemical parameters measured, suggests that respiration is strictly interconnected with environmental forces. Respiration varied in response to changes in hydrology according to Rivkin and Legendre (2001). In addition, it co-varied with different microbial parameters showing the consistent patterns of diverse aspects of microbial metabolism, as previously postulated by del Giorgio et al. (2011). The close link between the diverse aspects of prokaryotic patterns would imply that changes in the metabolic variables synergistically mediate the fate of organic matter by influencing the composition of organic material reaching the sediments (Catalano et al. 2006), the distribution of particulate and dissolved matter with depth (Carlson et al. 2000, Fabiano et al. 2000, Misic et al. 2017) and the remineralisation through the water column (Azzaro et al. 2006, 2012).

In all the stations, CSRR was surprisingly higher in the epipelagic layer than in the mesopelagic one. This suggests that a valuable contribution of organic matter of phytoplanktonic origin might sustain the heterotrophic metabolism in the upper layer. When we calculate CSRR by ETS Vmax, i.e. without utilisation of ETS to carbon conversion factors, almost all the mesopelagic values would be lower than surface ones with averaged CSRR value of 0.34 and 0.29 fg C cell-1 in the epi- and mesopelagic layers, respectively (data not shown). Although different from other reports from temperate seas (Placenti et al. 2018, Baltar et al. 2009), the CSRR in epipelagic water was higher than in the mesopelagic one, suggesting more actively respiring cells in the upper layers. Nevertheless, high CSRR values were found at stations where TSI was low, suggesting the importance of cell-specific respiration in oligotrophic conditions, in agreement with the findings of Baltar et al. (2015) for subantarctic waters. Conversely, CSRR in Summer 2014 was higher in the deeper layers than in the surface ones (Misic et al. 2017). Nevertheless, high CSRR values were found at stations where TSI was low, which also corroborates the importance of prokaryotic respiration in the surface layer.

The cell-specific incorporation rate (CSIR) strictly reflected the distribution of PHA throughout the water column with the highest values in the surface to 50 m depth layer. The average CSIR was similar to that detected by Ducklow et al. (2001) and Pedrós-Alió et al. (2002) in the upper 50 m layer of the RS during Summer 1997 as well as in the Antarctic Peninsula, respectively. A decreased availability of organic carbon for synthesising new biomass could explain this finding (Baltar et al. 2015). The CSIR-temperature Spearman-rank relationships were positive in the photic layer and negative in the aphotic one (data not shown). The positive correlation observed in the epipelagic layer amongst heterotrophic rates, CHLa and respiration, allowed us to consider a direct or indirect (previous exoenzymatic hydrolysis) flux of labile DOC from phytoplankton biomass and detritus towards the new prokaryotic biomass. In the XIX PNRA expedition (VLTP-2004 project), a mean of 2.35 µg C l-1 h-1 potentially mobilised by leucine aminopeptidase + ß-glucosidase activities, was detected in the photic layer off Victoria Land (Monticelli, personal communication). In the photic zone of Terra Nova Bay (RS), during summer 2000, the daily flow of C towards new prokaryotic biomass was equivalent to 0.2% of C, potentially mobilised by exoenzymatic activities (Monticelli et al. 2003).

Overall, in Summer 2005, the investigated area of the RS contributed in different ways to the epi- and mesopelagic layer carbon metabolism. Per sea-surface area, the autotrophic (by C-CHLa), prokaryotic (PB) and total standing stocks (C-ATP) amounted to 1545, 1681 and 4605 mg C m-2, respectively. The remaining heterotrophic component (HB) presumably accounted for a biomass of 1379 mg C m-2. The prokaryotic biomass appeared to be predominant in the mesopelagic layer with respect to the epipelagic one (depth integrated PB ratio epi/meso: 0.4). The entire heterotrophic production accounted for 1.697 mg C m-2 h-1 with a similar weight in the epi- and mesopelagic layers (depth integrated PHP ratio epi/meso was 1.03). Respiration remineralised 8.279 mg C m-2 h-1 with higher rates in the upper layers (depth integrated CDPR ratio epi/meso was 2.7).


This study was carried out within a time series of research conducted since the nineties in the Ross Sea. Through their metabolic rates, microorganisms worked as regulators of the organic carbon transfer in the Ross Sea and impacted Antarctic biogeochemical cycles. In this experiment, highly variable microbial metabolism was detected at all stations and depth layers. At the same time, coherent metabolic patterns were detected using different, independent, methodological approaches. The distribution of plankton metabolism and organic matter degradation was mainly related to the general oligotrophic conditions occurring during Summer 2005. The processes of heterotrophic production, respiration and growth efficiency revealed relatively low levels of carbon remineralisation. Compared with other cruises carried out in the Ross Sea, dramatic changes were found on an inter-annual scale. Monitoring the heterotrophic microbial patterns in long term series is proving to be an interesting approach in furthering understanding of biogeochemical trends. In contexts such as the mooring sites of LTER-Italy, it needs to be better known due to the climate-change implication of Antarctic Ocean on the global scale.


The research was funded by the XX Italian PNRA (National Programme of Antarctic Research, year 2004/05) expedition in the framework of the ABIOCLEAR project (Antartic BIOgeochemical cycles-CLimatic and palEoclimAtic Recostructions, coord. Dr. Mariangela Ravaioli of CNR-ISMAR, Institute of Marine Science) and received the financial support of P-ROSE project (Plankton biodiversity and functioning of the ROss Sea Ecosystems in a changing southern ocean, funded by PNRA, National Programme of Antarctic Research, year 2016/18, coord. Prof. Olga Mangoni of CoNISMa, National Interuniversity Consortium for Marine Sciences) and of CELEBeR project (CDW effects on glacial melting and on bulk of Fe in the Western Ross Sea, funded by PNRA, National Programme of Antarctic Research, year 2016/18, coord. Prof. Paola Rivaro of University of Genoa). The authors thank all the staff of R/V Italica for the logistics help and support. PHA saturation curves analysis and time courses were fixed in a precedent project (Victoria Land Transect Project, VLTP-2004) funded by XIX PNRA Expedition. The T.T. Packard’s contribution was supported by TIAA-CREF (USA) and Social Security (USA). The authors also thank the Editor and reviewers for their very useful comments and Mr. Alessandro Cosenza (ISP Institute of Polar Science) for figure processing.


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