Single-cell imaging reveals efficient nutrient uptake and growth of microalgae darkening the Greenland Ice Sheet

Domain: earth_science
Published: 2025-02-19
Editorial outcome: minor_revision

Abstract
Blooms of dark pigmented microalgae accelerate glacier and ice sheet melting by reducing the surface albedo. However, the role of nutrient availability in regulating algal growth on the ice remains poorly understood. Here, we investigate glacier ice algae on the Greenland Ice Sheet, providing single-cell measurements of carbon:nitrogen:phosphorus (C:N:P) ratios and assimilation rates of dissolved inorganic carbon (DIC), ammonium and nitrate following nutrient amendments. The single-cell analyses reveal high C:N and C:P atomic ratios in algal biomass as well as intracellular P storage. DIC assimilation rates are not enhanced by ammonium, nitrate, or phosphate addition. Our combined results demonstrate that glacier ice algae can optimise nutrient uptake, facilitating the potential colonization of newly exposed bare ice surfaces without the need for additional nutrient inputs. This adaptive strategy is particularly important given accelerated climate warming and the expansion of melt areas on the Greenland Ice Sheet.

Introduction
The ablation zones of glacier and ice sheets surfaces are hotspots for microbial life 1–5. Dark-pigmented glacier ice algae (Ancylonema spp.) are the main primary producers on bare ice surfaces 4,6, forming extensive blooms during the summer melt season 7–9. These blooms lower the ice surface albedo, thereby accelerating ice melt 8–12. Algal blooms on the western margin of the Greenland Ice Sheet have been shown to contribute, on average, 10 to 13% to the surface ice melt 9. As the single largest contributor to global barystatic sea-level rise, the Greenland Ice Sheet plays a critical role in climate dynamics 13–16. Due to climate warming, the snow line on the ice sheet is migrating to higher elevations, exposing larger areas of bare ice 17. This expansion increases the potential habitat area for glacier ice algae, which, if able to colonize these newly exposed ice surfaces, could accelerate ice darkening and melting. However, the triggers and controls of algal bloom progression throughout the summer ablation season and the causes of inter-annual variations in bloom extent, remain poorly understood 6,8,18–20. Elucidating the mechanisms of algal bloom formation is crucial for predicting their future progression on bare ice surfaces 6,9,18,21, and for assessing their contribution to the melting of the Greenland Ice Sheet.

The highly oligotrophic conditions of the ice sheet’s ablation zone may limit the growth and expansion of algal blooms on ice surfaces. Glacier surfaces are characterized by low concentrations of inorganic dissolved macro-nutrients, with NH4+ and NO3− concentrations typically below 1 µM, and aqueous PO43- below 0.1 µM 22–24. In addition to algae, microbial communities on glacier surfaces often include protists such as ciliates and dinoflagellates, along with fungi, bacteria, and archaea 25–29, all of which compete for and drive the cycling of nutrients. McCutcheon et al. 23 found that phosphate can limit algal productivity and highlighted the positive association between phosphorus-bearing minerals, such as hydroxylapatite, and the accumulation of algal biomass. A potential relationship between minerals and algal growth has also been observed by Stibal et al. 30, who documented a positive correlation between dust loading and algal abundance.

Glacier ice algae inhabiting bare ice surfaces are likely to have evolved strategies to partially compensate for the low macro-nutrient concentrations typical of their habitats. These strategies may include maintaining a high carbon:nitrogen:phosphorus (C:N:P) biomass ratio, high nutrient uptake efficiency and/or intracellular nutrient storage capabilities. However, the nutrient content and uptake rates for cryophilicalgae and, in particular, glacier ice algae, remain unknown, hindering the mechanistic understanding of potential nutrient limitations on algal growth. Previous studies have focussed solely on bulk characterization of particulate organic matter (POM) from glacier ice algal-colonised ice 23,31. These data revealed a wide range of C:N:P ratios, spanning from 690:48:1 to 2615:196:1. The measured bulk C:P 23,31, and sometimes also bulk C:N ratios 31 in both glacier ice algae and snow algae-dominated POM samples from across the Arctic 29 were much higher than the Redfield C:N:P ratio of 116:16:1 commonly observed in marine ecosystems 32 This suggests either a relatively low macro-nutrient requirement for supraglacial algae, or potential limitations in P and N availability. However, the bulk C:N:P ratios of POM collected from ice surface samples are not likely to accurately reflect glacier ice algae biomass stoichiometries due to elemental contributions of atmospheric deposition-derived organic matter, necromass, extracellular polymeric substances, and other microorganisms (e.g. bacteria, other eukarioticalgae, fungi) in the POM filter fraction. Hence, bulk C:N:P ratios derived from surface ice POM will invariably span a large range. Only single-cell-specific measurements can accurately determine the C:N:P ratios of glacier ice algae. Additionally, single-cell activity and nutrient uptake assessments help identify potential rate-limiting factors imposed by nutrient availability on algal growth. Thus, targeted and cell-specific measurements are crucial for comprehending the nutrient demands that drive algal bloom progression and their future growth dynamics in glacier ecosystems.

In this study, we quantify the C:N:P ratios, assimilation rates of dissolved inorganic carbon (DIC), NO3- and NH4+, and growth rates of single glacier ice algal cells (Ancylonema spp.) from the Greenland Ice Sheet both under unamended and nutrient-amendment conditions. Our aim is to gain insights into the physiological responses of this key species to varying levels of nutrient availability. In addition to single-cell analyses, we also use bulk stable isotope biogeochemical rate measurements to quantify the C and N turnover, as well as the elemental composition of the microbial community on the Greenland Ice Sheet, which we further characterize by 16S and 18S rRNA gene amplicon sequencing. Together, our findings demonstrate that glacier ice algae are well adapted to the oligotrophic conditions of the Greenland ice sheet, and exhibit no significant changes in productivity in response to external nutrient additions. This suggests that as the ablation zones of glaciers and ice sheets expand due to climate warming, newly exposed bare ice surfaces can be readily colonized by the algae without nutrient limitation hindering their growth. Our study refines the understanding of how nutrient availability influences glacier ice algal bloom development and highlights the role of algal cells in primary production and nutrient cycling on glaciers and ice sheets.

Results
Physico-chemical conditions on the Greenland Ice Sheet
Dark snow-free surface ice with visibly high concentrations of particulates and algal biomass was collected from the southern tip of the Greenland Ice Sheet (Fig. 1a) to assess the in situ microbial community composition and to measure bulk and single-cell elemental ratios. Incubations were performed to evaluate bulk and single-cell C-fixation and inorganic N-assimilation rates (Fig. 1b). The ice samples were allowed to melt for ~36 h at an ambient air temperature of ~4 °C, under in situ light conditions (18 h of daylight). The initial concentrations of dissolved inorganic nutrients in the melted ice were 0.08 µM NH4+, 0.05 µM NO3−, and ~0.01 µM PO43- (Table 1). Dissolved organic nitrogen and phosphorous (DON and DOP) were present at concentrations ~5 and ~7 times higher than their respective inorganic counterparts.

Supraglacial community composition
The bare ice microbial community sampled for the incubation experiments had a mean glacier ice algal (phylum Phragmoplastophyta) abundance of 16.2±1.2×103 cells ml−1 based on microscopic analyses, of which ~66% were filamentous Ancylonema cf. nordenskiöldii and ~34% were unicellular A. cf. alaskanum (Table 1). Red-coloured snow algal cysts (phylum Chlorophyta), Chlamydomonas spp., were present at an abundance of 2.1±0.2×103 cells ml−1, and represented 12% of the total eukaryotic algal cells. In total, 76 amplicon sequence variants (ASVs) were found within the 18S rRNA gene amplicon data, confirming the dominance of glacier ice algae within the eukaryotic community. The phylum Phragmoplastophyta had the highest relative abundance among the eukaryotes (70%; Supplementary Fig. 1a) and was comprised solely of Mesotaeniaceae. Fungi contributed ~19% of the eukaryotic community, with the phyla Basidiomycota, Ascomycota, and Chytridiomycota dominating. Microscopic observations confirmed the presence of pigmented glacier ice algae with parasitic infections by Chytridiomycota (Supplementary Fig. 2). Other eukaryotic phyla found at lower relative abundances were Chlorophyta (9%), Cercozoa (1%), and Ciliophora (<1%). The bacterial community (determined by 16S rRNA gene amplicon sequencing) was dominated by Bacteroidia (37%), followed by Actinobacteria (26%), and Alphaproteobacteria (14%). Cyanobacteria represented 4% of the bacterial community. Overall, 71 bacterial ASVs were found.

In situ glacier ice algae C:N:P ratios
Elemental mapping of single glacier ice algal cells in the fresh ice melt yielded a mean in situ C:N biomass ratio of 19±2.9 and a C:P ratio of 509±149, exceeding the Redfield C:N (6.6) and C:P ratio (116) four- and three-fold, respectively 32 (Fig. 2; Table 2). The mean glacier algal N:P ratio was 26±5, higher than the Redfield N:P stoichiometry of 16. Overall, we observed a high variability in cellular C:P, N:P, and C:N stoichiometries. Notably, elemental mapping also revealed the presence of small (<1 µm) P-rich inclusions inside individual glacier ice algae cells (Fig. 2, white arrows in P elemental map).

C-fixation, N-assimilation, and the effect of nutrient additions in bulk samples and single algal cells
Incubations were performed under in situ conditions on the ice surface (Fig. 1b) to quantify the activity of both single glacier ice algae cells (Figs. 3; 4d,e; Table 2) and the bulk microbial community (Fig. 4a,b; Table 2) using stable isotope-based measurements of DIC and DIN uptake. All incubations received 13C-DIC to assess photoautotrophic C fixation, either with no nutrient amendments (control), or addition of 15N-NH4+, 15N-NO3−, PO43- or combined 15N-NH4++PO43-. Following isotope amendments, samples were taken after 6 h (T1) and 30 h (T2) of incubation. The incorporation rates of the C or N isotopes were calculated based on the change in isotopic composition of biomass from algal cells and bulk POM over the incubation period. The DIC concentration measured at T1 from the control bottle was 275 µM, much higher than 44–70 µM reported in Andrews et al. 33 or 15 µM observed in Yallop et al. 12, with the latter closer to values expected in dilute glacier ice melt in equilibrium with the atmosphere (Supplementary Note 1). The high initial DIC concentration might suggest that net heterotrophic activity and/or photooxidation occurred during the incubations and was likely impacted by the process of ice melting, but these values mitigate potential DIC limitations during the incubations.

Bulk and single-cell isotope incorporation measurements were obtained for T1 and T2 of the incubation period, except for single-cell analyses, where T1 measurements were only performed for the control and 15N-NH4+ treatments. In total, 244 glacier algal cells were analysed using high-resolution secondary ion mass spectrometry (HR-SIMS), with 24 of the 244 imaged cells (∼10%) not exhibiting any DIC uptake (Fig. 4d,e). The growth and assimilation data of algal cells described here and in Table 2 are based solely on the active fraction of the population (those showing DIC assimilation), while a comprehensive overview of the HR-SIMS data, including inactive cells, is provided in Supplementary Table 1.

Nutrient additions did not stimulate increases in bulk or single-cell C-based growth rates. Rather, the 15N-NH4+, 15N-NO3−, and combined 15N-NH4++PO43- additions resulted in significantly decreased bulk C-based growth rates (growth rates of 0.36±0.03, 0.29±0.05, 0.24±0.04 day−1, respectively) compared to the control (0.63±0.03 day−1; Kruskal-Wallis: chi-squared=12, p=0.02, df=4; Fig. 4a). Similarly, the mean C-based growth of single glacier ice algal cells also did not show any stimulation upon nutrient addition, but their C-based growth was significantly decreased in the PO43- and the 15N-NH4++PO43- treatments (0.20±0.11, 0.23±0.16 day−1, respectively; Kruskal-Wallis: chi-squared=47, p=1.56e−9, df=4) compared to the control (0.47±0.24 day−1) (Fig. 4d). The mean C-based growth rates for single glacier ice algal cells in the control treatment, with values of 0.65±0.36 at T1 and 0.47±0.24 day−1 at T2, correspond to mean C-based doubling times of ~2 days (Fig. 4d; Table 2).

The bulk and single-cell N-based growth rates were all higher at T1 compared to T2, which is consistent with the rapid depletion of the N-tracers in solution: <37% of NO3− in the 15N-NO3− treatment and <3% of the NH4+ in the 15N-NH4+ treatment remained after the first 6 h of incubation (T1) (Supplementary Fig. 4a,b). There was no statistically significant difference in N-based growth rates between the 15N-NH4+, 15N-NO3−, and 15N-NH4++PO43- additions in both bulk and single-cell measurements when compared at the same timepoint (Kruskal-Wallis: chi-squared=4.7, df=3, p=0.2 and Kruskal-Wallis: chi-squared=0.63, df=2, p=0.7) (Fig. 4b, e). The bulk N-based growth rates for both NO3− and NH4+ were 0.60 day−1 at T1, with assimilations of 44.8 and 32.4 µmol N L−1 day−1, respectively (difference in values is likely due to inhomogeneous biomass distribution; assimilation normalised to biomass: 28.5 and 28.6 µmol N mg−1 N POM day−1, respectively) (Fig. 4b, Table 2). The bulk N-based growth rates were 0.2 day−1 for T2 for both NO3− and NH4+. The N-based growth rates of the 15N-NH4+ and 15N-NO3− treatments were similar also for single glacier ice algal cells, at 0.07 day−1 at T2 (Fig. 4e, Table 2). The bulk POC:PON ratio in the nitrogen-spiked treatments decreased between T0 and T1 compared to the control and consistently remained lower, in line with the rapid depletion of the nitrogen tracers in solution (Fig. 4c).

Glacier ice algal cells assimilated C in excess of N and generally in excess of the Redfield C:N ratio (Fig. 5). C-fixation of the single algal cells continued at high rates, despite the measurable N-assimilation slowing down (due to the depletion of the 15N-tracers) at later time points, with mean C:N assimilation ratios of ~46 at T1 and ~84–113 at T2. There was no significant correlation between cell volume and C-based growth rates (Supplementary Fig. 5). Overall, we observed a high variability in C-fixation and N-assimilation rates between glacier ice algal cells (Figs. 4d,e; 5).

Contribution of glacier ice algae to bulk C and N uptake
Using the single-cell rate measurements, alongside bulk C and N uptake rates from DIC or NH4+, and the algal abundance, we estimate the glacier ice algae contribution to the total C and N uptake in bulk POM. These calculations are sensitive to variability in biomass distribution among incubation bottles, differences in single-cell activity rates, and variability in algal cell abundance between bottles and timepoints (Supplementary Note 2). The bulk POM measurements encompass all particulate matter retained on filters (3 and 0.2 µm), including not only glacier ice algae but also other organisms, such as dispersed cryoconite material containing cyanobacteria and organic matter (SEM images in Supplementary Fig. 6). Glacier ice algal assimilation accounted for ~7±6% to 15±12%, of the 13C from DIC recovered in POM, while glacier ice algae accounted between ~3±2% to 8±6% of the 15N from NH4+ recovered in POM (Supplementary Tables 2 and 3).

Methods
Incubation experiment: C-fixation and N-uptake
We performed stable isotope incubation experiments to measure autotrophic C-fixation using 13C-DIC and N-assimilation from 15NO3− and 15NH4+ in the supraglacial community. Additionally, we tested the combined effect of 15NH4+ and PO43-, as well as the effect of PO43- alone (see Supplementary Fig. 3 for a graphical overview of the set-up). The melted surface ice, without any amendments, represents the T0 time point of our incubation experiment. The homogenized meltwater was then distributed into five 1L blue cap Schott bottles, in which four different treatments and one control were prepared. C-fixation by algae was traced by adding 30 µmol L−1 13C-labelled bicarbonate (13C-NaHCO3, ≥98 13C atom%; Sigma-Aldrich) to all treatments and control. N-assimilation was traced by adding 15N-labelled ammonium sulfate (15N-(NH4)2SO4) and 15N-labelled sodium nitrate (15N-NaNO3) to separate treatments (both ≥98 15N atom%, Sigma-Aldrich) at ~10 µM final concentration. The effect of PO43- availability on C and N uptake was assessed by adding potassium di-hydrogen phosphate at a final concentration of 10 µM to separate 13C-DIC-only and 13C-DIC+15N-NH4+ treatments. Once the tracers and nutrients were added, the liquid was gently homogenised and then further distributed from the 1L Schott bottles into triplicated 250 mL serum bottles, closed with butyl rubber stoppers and aluminium crimps, leaving a 10 mL air head-space. These bottles were then incubated under in situ conditions on the ice surface for ~30 h, where they received a total amount of short wave radiation of 346 W m−2 [69. The 1L Schott bottles with the remaining liquid for subsampling of the T1 time point were kept at ambient temperature and light, until sample processing finished.

Subsamples were taken at T0 from the melted glacier ice without nutrient or tracer addition, T1 (~6 h incubation time since nutrient or tracer addition, from the 1L Schott bottles) and at T2 (~30 h incubation time, from the triplicate serum bottles). The following subsamples were taken: (1) for measurements of the atom% of the DIC pool after 13C-DIC addition (T1 and T2 time points), a liquid sample was collected with a syringe without headspace and bubble formation into 5.9 ml exetainers (Labco, Wales, UK), containing 100 µL saturated ZnCl2 solution to stop the biological activity. The exetainers were stored in the dark at 4 °C until analysis; (2) For bulk C-fixation and N-assimilation measurements, the sample (145–193 mL) was filtered onto pre-combusted (450 °C) glass fibre filters (GF/F nominal pore size of 0.7 µm; Whatman, Maidstone, UK) and stored in plastic dishes at −80 °C until analysis. All bottles and laboratory equipment, such as filtration towers and forceps, were cleaned by soaking in 5% HCl overnight, followed by soaking and rinsing in Milli-Q. A filter rosette with one filter unit per treatment was used to avoid potential cross-contamination of isotopically labelled material; (3) For single-cell analyses by HR-SIMS (collected at T0 and T2, and for the control and 15N-NH4+ treatments at T1) and SEM-EDS (collected at T0), 5 mL subsamples were collected and fixed with 2% EM-grade paraformaldehyde (PFA; EMS, USA) for 24 h at 4 °C. The fixed cells were then filtered onto 3 µm pore size gold–coated polycarbonate filters (25 mm diameter; GTTP, Merck Millipore, Eschborn, Germany), washed three times with ~10 mL of 0.2 µm filtered glacier stream water and stored at −20 °C; (4) Samples for dissolved inorganic and organic nutrient measurements (collected at T0, T1, T2) were collected by filtering 30 mL through 0.2 µm PES filters (25 mm diameter, Merck Millipore) with a polypropylene syringe into pre-washed 30 mL HDPE Nalgene bottles. To avoid any contamination for ultra-trace ion analysis, the bottles and caps were previously soaked in 5% HCl overnight and thereafter soaked in fresh Milli-Q water (Millipore, USA) for three days, with Milli-Q water replacement every day 70. Once the nutrient samples were taken, the bottles were stored frozen at −20 °C until analysis; (5) For microscopy and cell counts (collected at T0), 2 mL of the sample liquid was preserved in duplicates in 2.5% EM-grade Glutaraldehyde (EMS, USA) and stored in the dark at 4 °C; (6) For amplicon sequencing (collected at T0), 500 mL of the melted ice surface sample was filtered onto a sterile, 0.2 µm cellulose nitrate filter (Thermo Scientific Nalgene), which was preserved in a sterile cryotube, flash-frozen and transported to the home laboratory in a cryo-shipper. The filter was stored at −80 °C until nucleic acid extraction.

Quantification of 13C-DIC, 15N-NH4+ and 15N-NO3− atom% and atom% excess
The abundance of heavy stable isotope tracers, expressed in percentage (‘atom%‘), depends on the concentration of the added heavy isotope and its dilution with the naturally occurring isotopes of the same compound (DIC, NH4+, NO3−). The concentration of the heavy isotope corrected for the naturally occurring heavy isotope already present in the sample before tracer addition is termed ‘atom% excess’. For determining the 13C-atom% of DIC, 2 ml subsamples from each ZnCl2 fixed exetainer were injected into helium-flushed exetainers and acidified with phosphoric acid following Torres et al. 71 to convert all DIC to CO2. Headspace subsamples were injected into GC-IRMS (iso-prime precision, precision ±0.1‰ for 13C-standards of 0–100 nM). The 13C-atom% of DIC in the ambient water was calculated from the measured concentrations of 13C-CO2 and 12C-CO2. Since the 13C-atom% of DIC between T1 and T2 decreased slightly (means of 4.4 to 3.6 atom% excess for all treatments), we used the mean value between T1 and T2 for each of the respective treatments for the T2 13C-label incorporation calculations (3.9 13C-atom% excess). For determining the 15N-atom% of NH4+ or NO3−, we used their added concentrations (10 µM with ≥98 15N-atom%) and corrected it for the dilution with naturally occurring NH4+ or NO3− at T0 (0.078 and 0.05 µM, respectively, with 0.36 15N-atom% natural abundance), yielding 98 and 97 atom% excess for 15N-NH4+ and 15N-NO3, respectively.

Isotopic analyses of bulk particulate organic matter
The C and N contents and the isotopic composition of bulk particulate organic matter (at T0, T1 and T2) were determined from the particulate material collected on GF/F filters, which were dried at 60 °C, decalcified overnight under 37% HCl fumes in a desiccator and again dried at 60 °C. One-quarter of each filter was packed into tin capsules and analysed by an elemental analyser (Thermo Flash EA1112) coupled to a continuous-flow Thermo Delta Plus XP isotope ratio mass spectrometer; Thermo Finnigan, Dreieich, Germany) (EA-IRMS) at the Max-Planck-Institute for Marine Microbiology (MPIMM), Germany. Caffeine was used as a standard for isotope ratio monitoring and C and N quantifications. The limit of detection (LOD) for isotopic enrichment was 1.078 13C-atom% and 0.365 15N-atom%.

Single-cell elemental ratios and HR-SIMS analyses
Single-cell elemental ratios were obtained at the T0 timepoint using scanning electron microscopy (SEM, Quanta FEG 250, Thermo Fisher Scientific) coupled to energy-dispersive X-ray spectroscopy (EDS, Bruker Nano GmbH) 72–76, at the MPIMM. To avoid charging effects through the presence of large numbers of minerals, cells had to be transferred from the GTTP filters (3 µm) onto filters with a thicker gold coating prior to analysis (25 mm, 0.8 µm pore size, 40/20 nm coating; APC, Eschborn Germany). This was done by adding one drop of Milli-Q onto the filter surface with algal cells, placing the new filter piece with thicker gold coating onto a drop of water, freezing both filters together for 2 min and once frozen, removing the old filter by peeling it off. This procedure transferred substantial amounts of the original filter material onto the new filter surface without the need to scrape off any cells. Additionally, filters were gently rinsed with Milli-Q to remove some minerals/sediment grains. For morphological and autofluorescence-based identification of algal cells, the gold-coated filters were cut into sections (approx. 5×5 mm) and areas of interest were marked and imaged using a laser micro-dissection (LMD) microscope (6000 B, Leica) prior to SEM-EDS measurements. The EDS system is equipped with two QUANTAX XFlash 6/30 (Bruker Nano GmbH, Germany) detectors. The detector area is 30 mm2 and the detectors have an energy resolution at Mn Kα line of <123 eV, allowing for the quantification of light elements. An NBS SRM 1155 ANSI 316 stainless steel standard was used to check the performance of the EDS system. 10 kV was used as a minimum accelerating voltage to analyse the sample for all major elements contained in the algal cells, also restricting the penetration depth to around 2 µm (demonstrated for cyanobacterial filaments in Schoffelen et al. 76), so reducing any potential signal from the filter surface. The analysis of the elemental content of algal cells was performed using the standardless P/B-ZAF method (Quantax 400 software, version 1.9; Bruker), suitable for samples with topography and allowing for measurements of light to heavy elements. Further details on the data processing can be found in Khachikyan et al. 73. Cells which were too thin for a robust signal were excluded from data processing by manually inspecting the obtained spectra. Single-cell relative C:P, C:N, and N:P atomic ratios were determined from the measurements in atom%, while the data in mass% was used to calculate the absolute elemental content of algal cells (see next section).

The T0 sample, one replicate per treatment of the T2 timepoint, and additionally the T1 of the control and the 15N-NH4+ amended treatments (due to rapid NH4+ cycling), were used for HR-SIMS analysis. The pre-imaged filter pieces from SEM-EDS analysis and additional filter pieces were mounted on a glass slide and coated with a 5 nm layer of gold prior to HR-SIMS analyses. Single-cell 15N and 13C assimilation rates of algal cells were determined by HR-SIMS (IMS 1280, CAMECA, Gennevilliers, France) at the Natural History Museum in Stockholm, Sweden. Areas of interest were pre-sputtered with a primary Cs+ ion beam of 3 nA for 240 s over an area of 80×80 µm and then analysed with a 100 pA beam over 70×70 µm at a spot size of 1 µm for 60 cycles. The HR-SIMS images (256×256 pixel) were recorded for 12C15N−, 13C14N− and 12C14N− ions with a peak-switching routine at a mass resolving power of 12,000 (M/ΔM) using a low-noise ion-counting electron multiplier. The detection limit was <0.01 counts per second (cps). For integration times of 60 s (12C14N−), 300 s (12C15N−) and 120 s (13C14N−) over 60 cycles, a run was expected to have total background count lower than 0.6, 3 and 1.2, respectively, not requiring any baseline correction. For the 256×256 pixel resolution, this approximates to background levels of 1e−5, 5e−5 and 2e−5 cps pixel−1, respectively. Images were processed using the CAMECA WinImage2 software. Secondary ion images were drift-corrected and accumulated for each measurement and the detector dead time, electronically gated at 44 ns, was processed on each pixel. Regions of interest (ROIs) were manually drawn around the algal cells. The 13C/(13C+12C) and 15N/(15N+14N) ratios were subsequently calculated as means for each ROI. Unlabelled (natural abundance) glacier ice algae cells from non-incubated samples were also measured (n=29) and mean isotope fractions (0.0037±0.00006 and 0.0111±0.00016 for 15N and 13C, respectively) were subtracted from the labelled samples to obtain ‘excess’ isotope fractions of the biomass. A. alaskanum and A. nordenskioeldii are grouped together as glacier ice algae within this study, due to their taxonomically close relationship and partial size overlap 77, which challenged an unambiguous species identification from microscopic images obtained for the filtered cells. We acknowledge that the fixation of the algal cells with PFA after incubations for HR-SIMS analysis may result in a decrease of 13C-enrichement of ca. 4–8%, and, to a lesser extent 15N-enrichment 78–80. However, this effect is likely considerably smaller than the differences observed in the C- and N-based growth rates between single-cell (fixed with PFA) and bulk (preserved by freezing) measurements in our study. We therefore chose not to apply any corrections to the measured enrichment values of the single-cell analyses. Cells were considered as enriched/active if their mean 13C/(13C+12C) enrichment exceeded the mean observed natural abundance value + 3x the standard deviation of unlabelled control cells 81 (1.15 13C-atom% for glacier ice algae).

Cellular biovolume, dry weight, and absolute elemental content of glacier ice algal cells
Cell dimensions were obtained from HR-SIMS images using ImageJ. Biovolumes were subsequently calculated by assuming cylindrical shapes for glacier ice algae after Hillebrand et al. 82. Cellular dry weights (pg cell−1) were calculated by multiplying the algal biovolumes (mean of 1414±873 µm−3 for all imaged algal cells, n=244) by the glacier ice algal-specific buoyant density of 1160 kg m−3 83 and a mean dry fraction of 0.28 (obtained from C. vulgaris 84). Absolute elemental contents of glacier ice algal cells (pg element cell−1) were determined by multiplying the median mass fraction of C, N and P in glacier ice algal cells (0.72, 0.04 and 0.04 C, N and P, respectively, derived from SEM-EDS, Supplementary Table 5) by the cellular dry weights (pg cell−1) 73.

C- and N-assimilation rates determined by EA-IRMS and HR-SIMS
Bulk C-assimilation rates were calculated using the following equation 85:

Cassimilationrate (µmolCL−1day−1) = 13Catom%excess POC × POC / (13Catom%excess DIC × Δt)

where 13C-atom% excess represents the 13C-atom% of incubated POC minus its natural abundance atom%, POC refers to the biomass concentration (µmol C L−1), 13C-atom% excess represents the 13C-atom% in DIC minus its natural abundance atom% and Δt represents the incubation period (in days, T0-T1 or T0-T2). We assume that the 13C-assimilation rates correspond to net photosynthesis, as any 13C fixed during the incubation (1.1 days including ~6 h of twilight) may have partially been respired again, which would not be measured by HR-SIMS.

Bulk N-assimilation rates from NH4+ or NO3− were calculated analogously from the 15N-atom% of incubated PON minus its natural abundance atom%, the corresponding PON concentration of the sample (µmol N L−1), the 15N-atom% of NH4+ or NO3− present in the incubation water minus their natural abundance atom%, and the incubation period, as described above.

Single-cell specific C-fixation rates were calculated according to the following equation 85:

Cfixationrate (pmolCcell−1day−1) = 13Catom%excess cell × C cell / (13Catom%excess DIC × Δt)

where 13C-atom% excess represents the 13C-atom% of single algal cells minus their natural abundance atom%, C cell represents the mean C content of single algal cells (pmol C cell−1, calculated as described above), 13C-atom% excess represents the 13C-atom% in DIC minus its natural abundance atom% and Δt represents the incubation period (in days, T0-T1 or T0-T2).

Single-cell specific N-assimilation rates from NH4+ or NO3− were calculated analogously from the 15N-atom% of single algal cells minus the natural abundance atom%, the 15N-atom% of NH4+ or NO3− present in the incubation water minus the natural abundance atom%, the corresponding mean N content of single algal cells (pmol N cell−1, calculated as described above), and the incubation period, as described above.

C- and N-based growth rates
Growth rates based on 13C-DIC, 15NH4 or 15NO3 isotope uptake were calculated for the bulk community (EA-IRMS measurements) or single algal cells (HR-SIMS measurements). C-based growth rates (day−1) were calculated following Martínez-Pérez et al. 74, based on Montoya et al. 85:

C-based growth rate (day−1) = log(13C atom% excess POC / (13C atom% excess POC − 13C atom% excess DIC)) × 1/Δt

where 13C-atom% excess represents the 13C-atom% in DIC minus its natural abundance atom%, 13C-atom% excess represents the 13C-atom% of incubated POC (of either bulk or single-cell biomass) minus its natural abundance atom%, and Δt representing the incubation period (in days, T0-T1 or T0-T2.

N-based growth rates were calculated analogously from the 15N-atom% excess of either NH4+ or NO3− in the incubation water, the 15N-atom% excess of PON of either bulk or single-cell biomass, and the incubation period, as described previously. The C or N-based growth rates assume exponential growth 74 and that all newly incorporated 13C or 15N are due to biomass increase 86, e.g. a growth rate of 1 day−1 means that cells double their C or N content once per day and, thus, divide once. The obtained growth rate estimates are independent of the biomass 85. A fraction of assimilated 13C or 15N may be allocated to C- or N-storage, recycling or replacing of cell components without net per cell growth. However, as this fraction is unknown, we do not consider it in our calculations. See Polerecky et al. 65 and Halbach 66 for more detail on assumptions for isotope uptake calculations. Population doubling times were calculated as 1/growth rate.

Glacier ice algae contribution to bulk C and N uptake
Similar to previous studies 34,74, we estimated the relative contribution by active glacier ice algae to the total bulk C and N uptake (originating from 13C-DIC or 15NH4+) for the different time points:

Rel. contribution (%) = (assimilation cell × N cell) / assimilation bulk × 100

where assimilation cell is the mean assimilation rate of active glacier ice algae of the respective substrate (pmol element cell−1 day−1), N cell is the mean abundance of the active glacier ice algae (cells L−1) and assimilation bulk represents the assimilation rates of the bulk community of the corresponding time point (µmol element L−1 day−1). The active glacier ice algal cell numbers are derived from algal counts at T0, corrected for the active population fraction based on SIMS measurements of C fixation (90% active cells). Biomass distribution between incubation bottles was variable due to rapid sinking of particulate material, thus, the large uncertainty associated with the parameter of assimilation contributes to the uncertainty of relative contribution by glacier ice algae. To account for varying biomass between bottles and the potential varying algal abundance, we also performed the calculations using the algal abundance corrected by the fractional change in POC concentrations between T0 and T1, as well as T0 and T2. This revealed a consistently low contribution (7–12% for C from DIC and 3–4% for N from NH4+; Supplementary Tables 2 and 3). Uncertainties in the contribution of the glacier ice algal community assimilation to total assimilation derive from the combined uncertainties of each variable, following the laws of error propagation (Supplementary Note 2).

Dissolved nutrient analysis
Dissolved NO3−, NO2−, NH4+, and PO43- concentrations from T0, T1 and T2 were analysed on a Metrohm Ion chromatography system (883 Basic IC Plus and 919 Autosampler Plus) at Uppsala University, Sweden. The IC was equipped with a peristaltic pump to enable full loop injections (400 µl) to decrease the LOD and limit of quantifications (LOQ) 70. Sample tubes were stored with a lid in the autosampler to avoid contamination with N from air. LOD’s and LOQ’s were determined as 3× and 10× the standard deviation (STDEC) of the lowest nutrient concentrations from standards, according to the EPA procedure for method detection limit 87. LOD’s were 0.011, 0.008, 0.005 and 0.004 µM and LOQ’s were 0.022, 0.027, 0.018 and 0.007 µM, for NO2−, NO3−, PO43-, and NH4+, respectively. The corresponding mean precisions were ±3, ±8, ±5 and ±3% and accuracy −8, −12, −4 and −1%, for NO2−, NO3−, PO43-, and NH4+, respectively, as determined from a comparison of QC standards with 0.043, 0.026, 0.015, and 0.015 µM levels. Total dissolved nitrogen (TDN) was analysed on a Shimadzu TNM (Tokyo, Japan). DON was calculated as DON = TDN – DIN, where DIN is (NO3−+NH4++NO2−). Total dissolved phosphorus (TDP) was analysed by the molybdenum blue method after digestion with potassium persulfate and autoclaving at 121 °C for 60 min. DOP was then calculated as DOP = TDP − PO43-. There was insufficient liquid left from the first collected sample (T0) and the reported concentration was measured from a sampling location close to the experimental site, but three days later. The LOD’s were 0.83 and 0.03 µM and LOQs 0.03 and 0.07 µM for TDN and TDP, respectively. The accuracy for TDN was 12% and precision 5% with a standard of 0.03 µM N.

The T0/in situ samples for analysis of Na2+, Mg2+, K+ and Ca2+ were acidified using Aristar HNO3. The analyses of major, minor and trace element analyses were carried out with an inductively-coupled plasma mass spectrometer (ICP-MS; Thermo Fisher iCAP Qc). The precision of the analyses was between 1–5% and LOD’s for Na2+, Mg2+, K+ and Ca2+ were 0.2, 0.03, 0.65 and 0.46 µg L−1, respectively. The ICP-MS analyses were conducted by Stephen Reid at the University of Leeds, UK.

Community composition and algal abundance
The algal abundance and community composition at T0 were microscopically characterised from the glutaraldehyde preserved samples and algal cells counted on a B/W FlowCAM™ II (Fluid Imaging Technologies, Maine, USA) using a 100 µm × 2 mm flow cell, a 10× objective and the automated-imaging mode. A minimum of 760 total algal cells per sample were counted. Algal cells were subsequently taxonomically identified, using the Visual SpreadSheet (VISP). Additional pictures of the supraglacial community were taken from unfixed, fresh sample material using a Nikon Eclipse Ti microscope. Images of the fresh unfixed and fixed algal cells were screened for signs of fungal infections (Supplementary Fig. 2).

Amplicon sequencing was performed to determine the microbial composition of the sample prior to incubation. DNA extraction was performed using the DNeasy PowerSoil Pro Kit (Qiagen) according to the manufacturer’s protocol. Thereafter, DNA concentration was measured on a Qubit 3.0 (Invitrogen) with the broad-range dsDNA kit (Invitrogen). Amplification was performed for the bacterial 16S rRNA gene using Bakt_341F (5’-CCTACGGGNGGCWGCAG-3‘) and Bakt_805R (5’- GACTACHVGGGTATCTAATCC-3‘) 88 and for the 18S rRNA gene using 528F (5’- GCGGTAATTCCAGCTCCAA-3‘) and 706R (5’-AATCCRAGAATTTCACCTCT-3‘) 89. The amplicon library was built in a two-step PCR. Each reaction of the first PCRs contained 12.5 μL of 2x PCRBIO Ultra Mix (PCR Biosystems), 0.5 μL of forward and reverse primer from a 10 μM stock, 0.5 μL of bovine serum albumin (BSA) to a final concentration of 0.025 mg mL−1, 0.6 μL of sterile water and 5 μL of template DNA. For the first PCR, conditions were as follows: at 95 °C for 2 min, followed by 38 cycles of 95 °C for 15 s, 55 °C for 15 s, 72 °C for 40 s, with a final extension performed at 72 °C for 4 min. An electrophoresis 1% agarose gel was run with PCR products before proceeding. Samples were subsequently indexed in a second PCR. In the second PCR, 5 µl of product from the first PCR was used as template to add indexes and sequencing adaptors in a reaction consisting of 12.5 μl of 2× PCRBIO Ultra Mix (PCR Biosystems), 2 μl of each index primer (P5/P7), and DNase free water to a final volume of 28 µl. For the second PCR, conditions were as follows: pre-incubation at 98 °C for 1 min, followed by 13 cycles of 98 °C for 10 s, 55 °C for 20 s, and 72 °C for 40 s, and ending with a final step at 72 °C for 5 min.

The final PCR products were purified with 15 µl HighPrep PCR magnetic beads (MagBio Genomics Inc. Gaithersburg, Maryland, US) according to the manufacturer’s instructions and eluted in 27 µl TE buffer. Aliquots of the PCR products were run on a 1.5% agarose gel and checked under UV light. Concentrations of the amplified and purified DNA samples were measured on a Qubit 2.0 fluorometer (Invitrogen, Eugene, Oregon, US). The samples were then equimolarly pooled, and this final library was sequenced on an Illumina MiSeq using the V2 kit (Illumina Inc. San Diego, California, US) resulting in 2×250 bp reads.

Data analysis
Statistical analysis and plotting was undertaken in R (version 4.4.2) 90. The non-parametric Kruskal-Wallis t-test was used to explore the similarity of data for individual treatments generated by HR-SIMS and EA-IRMS, followed by a post-hoc test of multiple comparisons using the Fisher’s least significant difference criterium and Holm’s p-adjustment method. Data were considered significantly different at p<0.05. Inactive cells (i.e. those with no significant 13C-DIC incorporation) were excluded from statistical tests involving cell activity. Results are presented as mean ± standard deviation. The 16S and 18S rRNA gene amplicons were pre-processed and analysed using the DADA2 R package 91 for ASVs. Taxonomic assignment was made using the SILVA (V148) rRNA gene database 92. Detailed documentation of the pipelines, including parameter setups, is available in Trivedi et al. 93. Results were visualized using the phyloseq v1.36.0 94 and ggplot2 v3.3.5 R packages 95. Classes and phyla with <1% mean relative abundance were grouped as Others for 16S and 18S rRNA gene data representation, respectively.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Halbach et al. present a rigorous investigation of macronutrient uptake and bulk growth rate metrics, combined with\nexceptional individual cell imaging to understand if macronutrient amendments stimulate growth and biochemical\nreconfigurations within. This is an incredibly detailed study and warrants publication after consideration of comments and\nadditional discussion. The paper is well-written and experimental setup is thoroughly complete. Since their recognition as\nimportant regulators of ice sheet melt balance (Halbach et al., 2022), it is fantastic to see this work attempt to test various\nhypotheses about the biogeochemical controls. Although, I believe that this work represents just the beginning. It is,\nhowever, a great contribution to the field.\nOverall, I do believe this is a unique study with important implications for the success and fitness of a foundational algal\nspecies. I think the results in the study support their conclusion that the cells are efficient at optimizing their cellular demand\nfor N, while demonstrating luxury uptake of P and storage as granules within the cell. I do believe, however, that given these\nalgae are probably limited by the transport of P-rich minerals to the ice, this would also include micronutrients, which were\nnot investigated. I hope that the authors consider my comments as a possible future direction for understanding the bottom-\nup control of inorganic nutrients.\nThe non-Redfieldian ratios highlighted in their study could use some more discussion about what kind of biomolecules are\ncontributing to elevated C:N:P. For example, the production of pigments or polymeric molecules would tend towards high\nC:N:P. What is known about their lipid content to handle the cold temperatures? I felt discussion of the physiological traits to\nbe quite limited.\nGiven that this is an excellent study, and their results are irrefutable in light of the amount of data presented, I raise the\nquestion, what are these algae limited by? It is hard to argue that light might limit their growth on a highly reflective surface\nwith 18 hours of sunlight per day. In fact, this intensity of irradiation could cause detrimental effects through inefficient\nelectron transport (production of ROS) or excessive heat (although this is compensated by the cold temperatures, see\nStrzepek et al., 2019). Inefficient light harvesting could lead to higher rates of\nmortality, and necessarily drive quotas for\nelectron transporters or superoxide dismutase to be higher, certainly among the microbial community. Meta-transcriptomics\nwould be very useful to tease apart these biochemical responses. I wonder if there exists an ROS process that effectively\ncaps the ability for the organisms to grow given these nutrient amendments. I think it is possible that a mechanism to retain\nessential micronutrients on the ice surface would be prerequisite for efficient N and P uptake/storage. There is no large\ncontribution of micronutrients from meltwaters (glacial ice, besides basal ice in contact with bedrock, has very low\nconcentrations of soluble iron, see Hopwood et al., 2017), that micronutrients such as iron could be a limiting factor. It seems\nlike iron could be diverted within the cell to uptake nitrate. Without sufficient iron for nitrate reductase, nitrate assimilation\ncould be inhibited. Further, such efficient regeneration of nutrients in oligotrophic regions require a mechanism to retain iron\nto support high rates of heterotrophy and turnover of DON and DOP (although alkaline phosphatases are typically Zn- and\nMg- dependent). This points towards an emergent hypothesis that turnover of organic pools of N and P are extremely\nimportant in this system, and so the growth of glacier ice algae is dependent on close symbioses with heterotrophs – as\nalluded to in the text. I am currently unsure how diffusion might affect these transient communities living in meltwater pools or\nice surfaces. In other aquatic environments, it is presumably more turbulent, and so the flux of nutrients to the cell surface is\ngreater than on the ice surface.\nWhile such a study on micronutrients is outside of the scope of this paper, I do think it is worth a short discussion as to the\nrole of trace-metals in supporting communities optimized for low macronutrient conditions. In the very least, it does appear\nthat the glacier ice algae are able to divert intracellular metals toward N-assimilation instead of C-fixation. Again,\nmeta-transcriptomics would be immensely helpful to investigate responses to nutrient amendments.\nSpecific in-line comments:\n[99-100] remove “the yields insights into”\n[172] I was hoping that cell-specific pigments would also be measured as it could drive the variability seen between cells,\nalthough I’m not sure how that would change over 30 hours.\n[Fig. 4] Are there no T1 single cell C or N based growth rates for nitrate and phosphate? Alternatively, should the ammonium\nbe calculated as a rate from T0 given that results for T1 are displayed, or is that for consistency to the other nutrient\namendments?\n[241] So it does appear they are approaching Redfield ratio as the allocation of C and N are reconfigured in the cell. This is\nshown in Fig. 5, where the data, still with lots of scatter, shows that at high nutrient additions, they opportunistically take up N\ninto the cell, but that does not translate to higher C fixation. At low N availability, C-fixation drops to where many cells are\nclose to Redfield. However, most cells lie above this line, so I agree with the authors interpretation.\n[311] The reference requires another bracket “[47]\n[346] DOC retained on filters might require a reference, if that is known.\nReferences:\nStrzepek, R. F., Boyd, P. W., and Sunda, W. G., 2019, Photosynthetic adaptation to low iron, light, and temperature in\nSouthern Ocean phytoplankton: Proceedings of the National Academy of Sciences, v. 116, p. 4388–4393.\nHopwood, M. J., Cantoni, C., Clarke, J. S., Cozzi, S., and Achterberg, E. P., 2017, The heterogeneous nature of Fe delivery\nfrom melting icebergs: Geochemical Perspectives Letters, v. 3, p. 200–209.", "reviewer_2": "The manuscript presents single-cell data on elemental composition and C and N assimilation of glacial ice algae, providing\nimportant new insights into their ecophysiology as well as the ecological and biogeochemical functioning of this system. The\nresults are presented clearly, experiments seem mostly carefully conducted and discussion and conclusions adequate,\nhowever I have one concern regarding the experimental design that requires clarification and some questions/suggestions\nregarding interpretation (see below)\nSpecifically, based on the text in l. 420-421, and l. 426, do I understand it correctly that 1L of sample was spiked in a Schott\nbottle, then 750 ml were transferred to serum bottles for 30h incubations, while ca 250 ml remained in the Schott bottle for\nthe 6h incubation? If so, it seems the conditions in 6h and 30h incubations were quite different (especially given the large\nheadspace in the Schott bottle which, for instance, may have affected gas exchange, incl. DIC and 13C labelling). Couldn’t\nthis difference in incubation conditions explain the difference in C and N assimilation rates between t1 and t2?\nThe authors conclude from the nutrient addition experiments that the algae are not nutrient limited (e.g. l. 386) and further\nthat they can readily colonize new ice surfaces (ll. 390-394) – I guess nutrients are only part of the picture, doesn’t this also\ndepend on ecological interactions (i.e. how growth rates compare to those of potential competitors)? Also, at least in theory\nthe lack of a response to the nutrient additions could indicate that some other (micro)nutrient (other than those tested) is\nlimiting, can this be briefly discussed?\nCuriously, the study shows lower growth rates in nutrient-amended bottles than in controls. As an explanation, in l. 301 it is\nsuggested that these algal populations might be sensitive to high nutrient loads – what would be the mechanism here, could\nthis be explained in a little more detail?\nOther minor comments:\nTable 2 shows mean values for two time points but SD only for one of them. I suggest showing both time points with their\nrespective SD.\nfig. S2b and fig. 1c are the same – I believe one could be omitted?\nAlso, I think it would be helpful to indicate any chytrids in the microscope images by arrows. To non-specialists it is not clear\nwhether the spherical ‘cells’ are algal cysts or chytrids\nl. 370 discusses the role of variable activity for adapting to environmental gradients over time - what about spatial\n(micro)environmental gradients in the ice, are those relevant?\nTable 1 – state the units\nl. 99 typo – delete ‘highlights the’\nl. 339-341 revise phrasing, ‘despite’ does not seem to make sense\nl. 477-478 typo - delete ‘again’\nl. 776 journal name is missing\nl. 930, 931 please correct spelling of N2 fixation\nSupplement l. 24 ‘assumingly’ - presumably?"}, "author_response": "Response to reviewer comments by Halbach et al.\nResponse to reviewers’ comments on ‘Single-cell imaging reveals efficient nutrient uptake\nand growth of microalgae darkening the Greenland Ice Sheet’.\nWe would like to thank the reviewers for their helpful and constructive comments and\nsuggestions. We have addressed all issues that were raised, which further improved the\nmanuscript. Please find below our responses to the specific points. In our resubmission,\nwe provide a clean, updated manuscript file, as well as one where all changes are indicated.\nThe line numbers mentioned here refer to the “clean version” of the manuscript file without markup.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nHalbach et al. present a rigorous investigation of macronutrient uptake and bulk\ngrowth rate metrics, combined with exceptional individual cell imaging to understand\nif macronutrient amendments stimulate growth and biochemical reconfigurations\nwithin. This is an incredibly detailed study and warrants publication after\nconsideration of comments and additional discussion. The paper is well-written and\nexperimental setup is thoroughly complete. Since their recognition as important\nregulators of ice sheet melt balance (Halbach et al., 2022), it is fantastic to see this\nwork attempt to test various hypotheses about the biogeochemical controls.\nAlthough, I believe that this work represents just the beginning. It is, however, a great\ncontribution to the field.\nOverall, I do believe this is a unique study with important implications for the success\nand fitness of a foundational algal species. I think the results in the study support\ntheir conclusion that the cells are efficient at optimizing their cellular demand for N,\nwhile demonstrating luxury uptake of P and storage as granules within the cell. I do\nbelieve, however, that given these algae are probably limited by the transport of P-\nrich minerals to the ice, this would also include micronutrients, which were not\ninvestigated. I hope that the authors consider my comments as a possible future\ndirection for understanding the bottom-up control of inorganic nutrients.\nWe thank the reviewer for their encouraging comments and interesting thoughts! We agree\nthat future studies are needed to shed light on the actual controls of ice algae growth, and\nhave included a discussion of possible future research avenues in the revised manuscript\n(please see detailed answers below), while trying to stay within the word limit and keeping\nthe manuscript still focused on macronutrient limitation.\nThe non-Redfieldian ratios highlighted in their study could use some more discussion\nabout what kind of biomolecules are contributing to elevated C:N:P. For example, the\nproduction of pigments or polymeric molecules would tend towards high C:N:P.\nWhat is known about their lipid content to handle the cold temperatures? I felt\ndiscussion of the physiological traits to be quite limited.\nInvestigation of the specific physiological adaptations and which specific compounds cause\nthe elevated C:N:P content lies outside the scope of this paper. However, we agree with the\nreviewer that biomolecule accumulation may contribute towards the elevated C:N:P content\nin glacier ice algae, and we have edited the manuscript to explicitly discuss this (line: 326),\nas it provides an important avenue for future investigations. We agree that the C-rich\nphenolic pigments likely contribute significantly to the elevated C content (lines: 283-290).\nPeters et al. (2024) studied the lipids in glacier ice algal dominated algal communities, and\nthat study shows that lipids dominate the endometabolites. We added the reference to the\ntext (line 287). It is likely that lipids play an important role in the adaptation by glacier ice\nalgae to the cold temperatures of their habitat.\nGiven that this is an excellent study, and their results are irrefutable in light of the\namount of data presented, I raise the question, what are these algae limited by?\nIt is indeed an intriguing question of what the ultimate limiting factors for ice-algae growth\nare, and we have now incorporated a more extended discussion of potential controls in the\nrevised manuscript (discussion: lines 247-253). Please additionally see our detailed answers\nto potential controls below.\nIt is hard to argue that light might limit their growth on a highly reflective surface with\n18 hours of sunlight per day. In fact, this intensity of irradiation could cause\ndetrimental effects through inefficient electron transport (production of ROS) or\nexcessive heat (although this is compensated by the cold temperatures, see\nStrzepek et al., 2019). Inefficient light harvesting could lead to higher rates of\nmortality, and necessarily drive quotas for electron transporters or superoxide\ndismutase to be higher, certainly among the microbial community. Meta-\ntranscriptomics would be very useful to tease apart these biochemical responses. I\nwonder if there exists an ROS process that effectively caps the ability for the\norganisms to grow given these nutrient amendments.\nWe agree that the ice-algal cells may experience stress from excess irradiation, and that this\ncould contribute to their mortality (we estimate that ca. 10% of the ice-algal cells are not\nactive), in addition to the observed fungal infections. Future metatranscriptomics analyses\nlooking specifically into regulation of ROS defense mechanisms under varying light\nconditions, as well as direct measurements of ROS concentrations in the melt water, could\nindeed yield further insights into this.\nI think it is possible that a mechanism to retain essential micronutrients on the ice\nsurface would be a prerequisite for efficient N and P uptake/storage. There is no\nlarge contribution of micronutrients from meltwaters (glacial ice, besides basal ice in\ncontact with bedrock, has very low concentrations of soluble iron, see Hopwood et\nal., 2017), that micronutrients such as iron could be a limiting factor. It seems like iron\ncould be diverted within the cell to uptake nitrate. Without sufficient iron for nitrate\nreductase, nitrate assimilation could be inhibited. Further, such efficient regeneration\nof nutrients in oligotrophic regions require a mechanism to retain iron to support high\nrates of heterotrophy and turnover of DON and DOP (although alkaline phosphatases\nare typically Zn- and Mg- dependent). This points towards an emergent hypothesis\nthat turnover of organic pools of N and P are extremely important in this system, and\nso the growth of glacier ice algae is dependent on close symbioses with heterotrophs\n– as alluded to in the text.\nBoth reviewers mentioned the possibility of micronutrients to be acting as potential controls\nof glacial algae growth. Although this was not tested in our experimental setup, we fully\nagree that limitation by micronutrients is very important to be considered. As the reviewer\npoints out, micronutrients such as iron and other metals could be a key factor limiting algal\ngrowth, and the mechanisms for retaining and efficiently using these within the ice surface\ncommunity should be addressed in future studies. These could include studying\nmicronutrient availability on a microscale in relation to biofilm formation and DON/DOP\nturnover, combined with metatranscriptomic analyses of the community under different\nmicro- and macronutrient-loading regimes. We have, therefore, added a section in the\ndiscussion specifically about micronutrients (discussion: lines 247-253).\nI am currently unsure how diffusion might affect these transient communities living in\nmeltwater pools or ice surfaces. In other aquatic environments, it is presumably more\nturbulent, and so the flux of nutrients to the cell surface is greater than on the ice\nsurface.\nThis is an interesting point. How advection and turbulence are impacting the nutrient fluxes\non the ice surface deserves further studies. Certainly, continuous melting of the ice during\nthe daytime could supply nutrients. The biofilm around the algal communities and other\nmicrorganisms present also needs to be considered when trying to understand the nutrient\nfluxes on the microscale. Note, however, that we have refrained from speculating too much\nin the paper.\nWhile such a study on micronutrients is outside of the scope of this paper, I do think it\nis worth a short discussion as to the role of trace-metals in supporting communities\noptimized for low macronutrient conditions. In the very least, it does appear that the\nglacier ice algae are able to divert intracellular metals toward N-assimilation instead\nof C-fixation. Again, meta-transcriptomics would be immensely helpful to investigate\nresponses to nutrient amendments.\nWe appreciate the thoughts of the reviewer on potential factors controlling ice-algae growth,\nand, as suggested, we have added a new discussion point of the potential relevance of trace\nmetal cycling in glacier ice algae habitats to the revised manuscript (discussion: lines 247-\n253, and conclusion 353).\nSpecific in-line comments:\n[99-100] remove “the yields insights into”\nAgreed and revised accordingly.\n[172] I was hoping that cell-specific pigments would also be measured as it could\ndrive the variability seen between cells, although I’m not sure how that would change\nover 30 hours.\nWe agree that the observed inter-cellular variability in pigmentation (Figure 1c and Figure\nS2) is highly interesting and could explain the observed differences in single-cell activities.\nUnfortunately, using the methods available to us (and the samples that were taken during\nthe study), we were not able to resolve pigment type or content of individual cells.\n[Fig. 4] Are there no T1 single cell C or N based growth rates for nitrate and\nphosphate? Alternatively, should the ammonium be calculated as a rate from T0\ngiven that results for T1 are displayed, or is that for consistency to the other nutrient\namendments?\nDue to practical constraints and the intensiveness of single-cell measurements using SIMS,\nand had to prioritize samples for analyses. We specifically chose to measure the T1 sample\nin addition to T2 for the ammonium incubation, because NH4 was rapidly taken up in our\nincubations.\nWe would like to clarify that the NH4 uptake rates at T1 take into consideration the activity\nbetween tracer addition (T0) and the first biomass sampling time point after ca 6h (T1), while\nrates at T2 are calculated between tracer addition (T0) and the second biomass sampling\ntime point after ca 30h (T2).\n[241] So it does appear they are approaching Redfield ratio as the allocation of C and\nN are reconfigured in the cell. This is shown in Fig. 5, where the data, still with lots of\nscatter, shows that at high nutrient additions, they opportunistically take up N into the\ncell, but that does not translate to higher C fixation. At low N availability, C-fixation\ndrops to where many cells are close to Redfield. However, most cells lie above this\nline, so I agree with the authors interpretation.\nWe are happy that the reviewer agrees with our interpretation.\n[311] The reference requires another bracket “[47]\nThank you, done.\n[346] DOC retained on filters might require a reference, if that is known.\nWe now specify this to highlight that the DOC can be retained on the POM on the filters,\ninstead of directly on the filters. Particularly exopolymeric substances can be very sticky\n(Nagar et al. 2021, Extracellular polymeric substances in Antarctic environments: A review of\ntheir ecological roles and impact on glacier biogeochemical cycles) and are visible around\nglacier surface biological material (McCutcheon et al. 2021, Mineral phosphorus drives\nglacier algal blooms on the Greenland Ice Sheet).\nReferences:\nStrzepek, R. F., Boyd, P. W., and Sunda, W. G., 2019, Photosynthetic adaptation to\nlow iron, light, and temperature in Southern Ocean phytoplankton: Proceedings of the\nNational Academy of Sciences, v. 116, p. 4388–4393.\nHopwood, M. J., Cantoni, C., Clarke, J. S., Cozzi, S., and Achterberg, E. P., 2017,\nThe heterogeneous nature of Fe delivery from melting icebergs: Geochemical\nPerspectives Letters, v. 3, p. 200–209.\nReviewer #2 (Remarks to the Author):\nThe manuscript presents single-cell data on elemental composition and C and N\nassimilation of glacial ice algae, providing important new insights into their\necophysiology as well as the ecological and biogeochemical functioning of this\nsystem. The results are presented clearly, experiments seem mostly carefully\nconducted and discussion and conclusions adequate, however I have one concern\nregarding the experimental design that requires clarification and some\nquestions/suggestions regarding interpretation (see below)\nSpecifically, based on the text in l. 420-421, and l. 426, do I understand it correctly\nthat 1L of sample was spiked in a Schott bottle, then 750 ml were transferred to\nserum bottles for 30h incubations, while ca 250 ml remained in the Schott bottle for\nthe 6h incubation? If so, it seems the conditions in 6h and 30h incubations were quite\ndifferent (especially given the large headspace in the Schott bottle which, for\ninstance, may have affected gas exchange, incl. DIC and 13C labelling). Couldn’t this\ndifference in incubation conditions explain the difference in C and N assimilation\nrates between t1 and t2?\nThe description by reviewer #2 of the experimental setup is correct. Note that the Schott\nbottles were kept at ambient light and temperature until the first samples could be taken\n(information added in lines 470-471). We do not think that the observed differences in rates\nare the result of methodological inconsistencies, as we took into consideration the potential\ndifference in labeling% between incubation bottles by taking dedicated labeling% samples\nfrom both Schott (T1) and serum bottles (T2). We used the labeling% measured Schott\nbottles (T1) for all calculations of rates between T0 (tracer addition) and T1\n(biomass/medium sampling from Schott bottles after 6h). For all calculations of rates\nbetween T0 (tracer addition) and T2 (biomass/medium sampling from serum bottles after\n30h), we used the average of labeling% from Schott Bottles (T1) and serum bottles (T2), as\nthe cells experienced changing conditions during the incubation. For DIC, we observed no\nconcentration differences (but slight difference in 13C-DIC labeling%) between Schott and\nSerum bottle samples. As both ammonium and nitrate are dissolved in the liquid phase, we\ndo not expect varying headspace volumes to affect the community activity.\nPlease also note that the C-based growth rate estimates are very similar for rates calculated\nbetween T0-T1 and T0-T2. There is however a difference between T0-T1 and T0-T2 N-\nbased growth rates, which likely stems from the rapid uptake and depletion of the added 15N\ntracer (leaving little N for assimilation between T1 and T2). We interpret this as the\ncommunity efficiently scavenging bioavailable N sources, but being able to sustain\nconsistently high C-based growth also in absence of additional N (lines 280-287 in\ndiscussion).\nThe authors conclude from the nutrient addition experiments that the algae are not\nnutrient limited (e.g. l. 386) and further that they can readily colonize new ice\nsurfaces (ll. 390-394) – I guess nutrients are only part of the picture, doesn’t this also\ndepend on ecological interactions (i.e. how growth rates compare to those of\npotential competitors)? Also, at least in theory the lack of a response to the nutrient\nadditions could indicate that some other (micro)nutrient (other than those tested) is\nlimiting, can this be briefly discussed?\nWe agree with the reviewer that other factors may also play a role in limiting algal growth.\nWhile our focus has been on macronutrient limitation, we now acknowledge that\nmicronutrients could limit algal growth if not available in sufficient concentrations. We have\nadded a section addressing this point in the discussion (discussion: lines 247-253).\nCuriously, the study shows lower growth rates in nutrient-amended bottles than in\ncontrols. As an explanation, in l. 301 it is suggested that these algal populations\nmight be sensitive to high nutrient loads – what would be the mechanism here, could\nthis be explained in a little more detail?\nThank you for addressing the missing mechanistic explanation. We can only speculate, as\nwe did not study these responses on a metabolic level. Even though, the ~10 µM of added\nNH concentration is below the threshold commonly considered toxic (most microalgae\n4\ntolerate NH concentrations <100 µM (Collos and Harrison, 2014)) and 10 µM NH4\n4\nconcentrations were also used in McCutcheon et al. (2021), we cannot exclude that the\nglacial microbes thriving at ultra oligotrophic conditions could be sensitive to high nutrient\nloads. We clarified that a high N load may negatively affect the algae metabolism\n(discussion, line 241).\nOther minor comments:\nTable 2 shows mean values for two time points but SD only for one of them. I\nsuggest showing both time points with their respective SD.\nFrom T1, we only have one biological replicate and thus no SD.\nfig. S2b and fig. 1c are the same – I believe one could be omitted?\nThank you for spotting this. We removed Figure S2b.\nAlso, I think it would be helpful to indicate any chytrids in the microscope images by\narrows. To non-specialists it is not clear whether the spherical ‘cells’ are algal cysts\nor chytrids\nWe marked the spheres that we identify as chytrids (sporangia) in Figure S2c-e.\nl. 370 discusses the role of variable activity for adapting to environmental gradients\nover time - what about spatial (micro)environmental gradients in the ice, are those\nrelevant?\nYes, the observed variability very likely plays a role in adaptation to spatial environmental\ngradients on the ice, and within the biofilm on the ice surface. We added this information to\nthe revised manuscript (L 330-331).\nTable 1 – state the units\nThank you, we added units to nutrient concentrations.\nl. 99 typo – delete ‘highlights the’\nThank you, done.\nl. 339-341 revise phrasing, ‘despite’ does not seem to make sense\nWe revised the sentence accordingly.\nl. 477-478 typo - delete ‘again’\nThank you, done.\nl. 776 journal name is missing\nThank you, we added the journal name.\nl. 930, 931 please correct spelling of N2 fixation\nWe corrected the mistake.\nSupplement l. 24 ‘assumingly’ - presumably?\nThank you, done as suggested."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Thank you for carefully considering my comments on the previous version of the manuscript. My questions and concerns\nhave been adequately addressed. I believe the revisions have improved the manuscript and look forward to seeing it\npublished."}, "author_response": ""}]

Decision letter