Photophysiological response of glacier ice algae to abiotic stressors

The glacier ice algae Ancylonema alaskanum and Ancylonema nordenskiöldii grow in harsh dynamic environments on bare ice surfaces. In these environments, they contribute to the continuous darkening of the ice surface, which in turn accelerates the ice melt. However, investigation into their adaptation and resilience in these environments is necessary in order to understand their robustness and potential for increasing the intensity of blooms. In this study, it was examined how variations in environmental parameters such as pH, salinity, light and temperature impacted the photophysiology of the glacier ice algae during a bloom in Greenland. Through in situ incubations and pulse-amplitude-modulation (PAM) fluorometric measurements, the photophysiological responses of algal cells were assessed. Results suggest that light intensity significantly influences glacier ice algae photophysiology, with cells exhibiting better performance (in terms of a higher theoretical maximum light coefficient and maximum quantum efficiency) under lower light intensity. Moreover, while light emerges as the primary driver of photophysiology, glacier ice algae demonstrate tolerance to a broad range of pH and temperatures four times higher than those experienced during Greenland’s summer.

1 Introduction

Glacier ice algae are known to occur in the top centimeters of surface ice in many high-latitude and -altitude areas, such as the Arctic (Yallop et al., 2012; Remias et al., 2012), Alps (Di Mauro et al., 2020), Himalayas (Yoshimura et al., 1997) and Antarctica (Ling and Seppelt, 1990). These algae belong to the Zygnematophyceae (Streptophyta) and are distinct from both snow and sea ice algae. Along the western margin of the Greenland ice sheet (GrIS), extensive algal blooms ($\sim$10⁴ cells mL^-1) that cover 4%–10% of the bare-ice area occur during summer ablation seasons when sunlight and liquid water are available for photosynthesis (Lutz et al., 2018; Williamson et al., 2018; Cook et al., 2020; Shimada et al., 2016). These blooms consist of the unicellular Ancylonema alaskanum (Kol) and filamentous Ancylonema nordenskiöldii Berggren (Remias et al., 2009; Procházková et al., 2021; Kol, 1942; Berggren, 1871), which both contain secondary phenolic pigments that accelerate surface melt by lowering bare-ice albedo and enhancing the absorption of solar energy (Shimada et al., 2016; Williamson et al., 2020). The reduction in albedo associated with glacier ice algae was estimated to have contributed up to 10%–13% of the total runoff in southwest Greenland during the summmer of 2017 (Cook et al., 2020). In the Kangerlussuaq region, approximately 58% of the ice was covered with glacier algae, contributing to darkening of the ice surface (Cook et al., 2020). As the melting of the GrIS is the primary cryospheric contributor to global eustatic sea-level rise (Bamber et al., 2018), which has been estimated at 13.7 $\pm$ 1.1 mm between 1972 and 2018 (Mouginot et al., 2019), there is an urgent need to improve understanding of how glacier ice algae are adapted to life in ice.

Glacier ice algae, being non-motile (Remias et al., 2009; Dial et al., 2018), must cope with the changes in the environment in which they live. Inhabiting some of the harshest environments on Earth, they must endure extreme conditions promoted by high and fluctuating light intensities, fluctuating pH levels, varying salinity, and temperature extremes (Williamson et al., 2020; Shetty et al., 2019). These environmental factors pose significant challenges to their survival and productivity (Shetty et al., 2019; Baker and Rosenqvist, 2004; Mott and Berry, 1986). For example, during the months of constant light in the Arctic summer season, glacier ice algae are subjected to intense solar radiation, with photosynthetic active radiation (PAR) that can exceed 1700 $\mu$mol photons ${\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$, alongside elevated UV radiation (Yallop et al., 2012; Williamson et al., 2020). Additionally, the pH of glaciers and ice sheets where the algae thrive can vary greatly, with measurements between 3.6 and 8.6 (Komárek and Komárek, 2001; Takeuchi and Kohshima, 2004; Remias et al., 2009; Remias et al., 2011). Moreover, changes in temperature (IPCC, 2023) and the potential for algae to be washed out into the ocean with ice melting may significantly impact their distribution and survival (Takeuchi, 2001).

While light serves as the primary energy source for photosynthesis in glacier algae, excessive radiation levels can induce photoinhibition and increase reactive oxygen species, which can damage DNA and proteins within the cell (Aro et al., 1993; Rippin et al., 2019; Williamson et al., 2020). A reduction in incoming light has been shown to alter the photophysiology of glacier ice algae (Williamson et al., 2020). Similarly, the external pH around algal cells plays a role in their photosynthetic performance (Yadav and Singh, 2021). Fluctuations in pH and salinity can disrupt vital cellular processes for algal growth and survival by, for example, damaging the photosystem or affecting the enzymatic processes involved in photosynthesis (Shetty et al., 2019; Mott and Berry, 1986). Additionally, temperature extremes influence enzymatic reactions and metabolic rates, thereby impacting algal productivity (Baker and Rosenqvist, 2004).

Despite the ecological significance of glacier ice algae, substantial gaps persist in understanding their responses to environmental perturbations, particularly regarding their photophysiology and productivity. Bridging this knowledge gap is crucial for predicting future dynamics in glacier ice ecosystems and assessing their vulnerability to ongoing environmental changes. A common and nondestructive method for assessing the fitness of plants and algal cells involves measuring chlorophyll fluorescence to estimate photosynthetic performance (Baker and Rosenqvist, 2004; Baker, 2008). This technique can be implemented using pulse-amplitude-modulation (PAM) fluorometry, which allows for in vivo examination of photosystem II (PSII). The electron transport through PSII serves as an indicator of the photosynthetic efficiency of a cell. Additionally, a reduced quantum yield, as indicated by the maximum quantum efficient (${\mathrm{F}}_v/{\mathrm{F}}_m$) ratio, may signify damage to PSII and is frequently employed as a proxy for cellular stress (Stibal et al., 2007; Baker and Rosenqvist, 2004).

As a result, the aims of this study were to investigate the impact of light, pH, salinity, and temperature on the photophysiology of glacier ice algae via in situ incubations in Greenland, which allows for variable control, and provide valuable insights into their adaptive mechanisms and resilience in the face of changing environmental conditions.

2 Materials and methods

2.1 Study site and sample collection

Surface ice sampling was conducted at an elevation of 617 m a.s.l. $\sim$7.5 km from the S-Greenland ice sheet margin (61°06′05″N, 46°50′53″W) between 13 July and 8 August 2021 (Figures 1A–C). At this location, the microbial community was determined to consist of a diverse range of fungi, bacteria, and algae, including Streptophyte glacier ice algae (Jaarsma et al., 2023), which have been shown to lower bare ice albedo (Williamson et al., 2020). Approximately 100 kg of surface ice (top 1–2 cm) was collected using a field-conditioned metal ice axe. It was then transferred in equal amounts to sterile Whirl-Pak^® bags (Madison, WI) before being melted in a tent under low light conditions $(8.5{\text{W\hspace{0.17em}m}}^{-2})$ at 5°C–10°C over 72 h. Once melted, the samples in each bag were shaken by hand for 1 min. Composite samples were then created by transferring aliquots of 1,000 mL from each bag into fresh Whirl-Pak^® bags and shaking them again by hand for 1 min before using them in separate in situ incubation experiments.

Figure 1

Figure 1. An overview of four in situ incubation experiments that manipulated pH, salinity, light intensity and temperature. The ‘control’ samples represent the prevailing environmental conditions at the study site (A), an example of a field incubation for the pH experiment (B), and Copernicus Sentinel-2 optical satellite imagery of the fieldwork site (denoted by a blue star) in southern Greenland (61°06′05″N, 46°50′53″W) (C) during 2021 (The Danish Agency for Data Supply and Infrastructure, 2024).

2.2 Experimental design

In order to comprehensively examine the impact of environmental factors on the photophysiology of the microbial community, a series of controlled incubation experiments were conducted, manipulating pH, salinity, light and temperature. For each parameter, a gradient of five points was systematically created by transferring aliquots of 650 mL from one of the four bags to fresh 2,000-mL Whirl-Pak^® bags (Figure 1A). The environmental parameter of interest was then amended in all of the fresh bags except for one per replicate, which served as an unmodified “control”, representing the prevailing environmental conditions at the study site.

After determining that the pH of the surface ice was 6.7 $\pm$ 0.2 using an Orion™ Star A321 meter (Thermo Fisher Scientific, Waltham, MA), this value was established as a control. Subsequently, a pH gradient (3.58, 4.71, 6.7 [control], 8.07, and 9.21) was created by amending the samples with either sodium hydroxide (Sigma-Aldrich, Burlington, MA) or hydrochloric acid (VWR, Radnor, PA) in accordance with methods outlined by Jensen et al. (2023). A gradient of increasing salinity above baseline conditions (0‰[control], 5‰, 15‰, 35‰and 50‰) was created by adding increasing amounts of sodium chloride (m/v; Sigma-Aldrich) to the samples. A light intensity gradient was created by wrapping four out of five bags in generic solar control films of different optical performances capable of either excluding UV radiation or reducing incoming light. As confirmed using a UV–vis spectrometer coupled with an F600-VISNIR fiber optic cable (Black Comet CXR-25, StellarNet, Tampa, FL), this resulted in samples within the gradient being exposed to 89% (control), 75% minus UV (${75\%}_{-UV}$), 47%, 32%, and 14% ambient irradiance (Supplementary Figure S1; Supplementary Table S1). Measurements made for samples that had been incubated under 14% of ambient irradiance were excluded from further analysis, as they were corrupted by signal noise. Five replicate samples per point within the pH, salinity and light gradients (n = 5) were incubated on the bare ice surface (Figure 1B) under natural light $(105{\text{W\hspace{0.17em}m}}^{-2})$ for 48 h. All reagents used were of analytical reagent grade.

Lastly, a temperature gradient (5°C [control], 10°C, 15°C, 20°C, and 25°C) was created by placing the triplicate bags (n = 3) in heated water baths, where the temperature was manually controlled by adding hot water. Samples incubated at 5°C were considered to be controls, as this was the approximate air temperature (Fausto et al., 2021). Due to the challenges associated with manually maintaining the desired temperatures, the temperature experiments were conducted under low light conditions $(8.5{\text{W\hspace{0.17em}m}}^{-2})$ inside a tent for a shorter period of 4 h.

The photophysiology of the microbial community was assessed immediately within each sample at the end of its incubation period (48 h or 4 h) using the methods described in the following section. Prior to initiating the incubation experiments for pH, salinity, and light, baseline photophysiological measurements were taken for additional control samples that had been prepared using the same methods to establish a reference point after the melting process (${\mathrm{T}}_0$). Notably, no baseline for the temperature incubation experiment was needed, as the control sample remained under the initial low-light conditions for the brief 4-h incubation period.

2.3 Bulk community photophysiology

The photophysiology of the microbial community was assessed using pulse-amplitude-modulation techniques following methods described by Perkins et al. (2006). Specifically, variable chlorophyll fluorescence was measured in 3-mL aliquots using a WaterPAM fluorometer combined with a blue emitter/detector cuvette system, with continuous stirring to prevent settling (Walz GmBH, Effeltrich, DE). All samples were dark-adapted for 30 min prior to rapid light curve assessments (RLCs), which consisted of nine 20-s steps of incrementally increasing actinic light intensities that ranged from 33 to 1,306 $\mu$mol photons ${\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$ followed by a saturating light pulse ($\sim$8,600 $\mu$mol photons ${\mathrm{m}}^{-2}$ ${\mathrm{s}}^{-1}$ for 600 m) at the end of each step. Maximum quantum efficiency ($F_v$/$F_m$) was calculated as the ratio of variable fluorescence ($F_v$), which is the difference between the minimum ($F_0$) and maximum ($F_m$) fluorescence yields in the dark-adapted state, to $F_m$. The relative rate of electron transport (rETR) was calculated as the product of the quantum yield of photosystem II (Y [PSII]) multiplied by the incident excitation and 0.5, thereby assuming an equal distribution of absorbed light energy between photosystems I and II. Relative maximum electron transport rates (${rETR}_{\mathit{max}}$), theoretical maximum light utilization coefficients $(\alpha )$ and light saturation coefficients ($E_k$) were derived from RLCs (rETR $\sim$ light intensity) constructed in R version 4.0.2 using an iterative curve fitting approach adapted from Williamson et al. (2020) to apply the Eilers and Peeters (1988) model. Outliers, which were defined as model residuals with z-scores greater than 2 or less than −2, were excluded from the data.

2.4 Statistical analysis

Statistical analysis of the data was performed in R version 4.0.2 using methods outlined by Crawley (2007). Assumptions of normality and homogeneity of variance were assessed through Shapiro-Wilk and Levene’s tests, respectively. One-way analysis of variance (ANOVA) was used to assess differences in photophysiological parameters across each of the gradients. Post-hoc pairwise comparisons were performed using Tukey’s HSD test when significant variations were detected. When parametric assumptions were met, the Student’s t-test was used to compare means between the ${\mathrm{T}}_0$ and control samples from the pH, salinity, and light experiments. In cases where assumptions of data normality and equal variance were violated, the Mann-Whitney U test (MW) was employed. For this study, the alpha level was set to 0.05. All errors reported in the text are one standard deviation about the mean.

3 Results

3.1 Technical challenges

Although, a series of incubation experiments aimed to investigate the individual effects of pH, salinity, light, and temperature on the photophysiology of glacier ice algae in south Greenland, the rapid light curve assessments, performed using PAM fluorometry, were insufficient to fully saturate PSII reaction centers within the microbial community (Supplementary Figures S2, S3). Consequently, it was not possible to derive values for ${rETR}_{\mathit{max}}$ and $E_k$ from any of the incubation experiments. However, we were able to derive values for $F_v$/$F_m$ and $\alpha$.

3.2 Ecophotophysiology

The photophysiological responses of the microbial community to variations in pH, salinity, light, and temperature appeared to differ among the four incubation experiments (Figures 2, 3). Within each specific experiment, both $F_v$/$F_m$ and $\alpha$ values exhibited similar trends. Most notably, variations in salinity and light exerted a more pronounced influence on algal photophysiology than either pH or temperature.

Figure 2

Figure 2. Maximal quantum yields ($F_v$/$F_m$) of electron transport through photosystem II for glacier ice algae that have been incubated under varying pH (A), salinity (B) and light (C) conditions for 48 h (n = 5), as well as different temperatures (D) for 4 h (n = 3). Gold columns represent control samples reflecting the prevailing environmental conditions of the study site on the Greenland ice sheet (61°06′N, 46°50′W) during 2021. Error bars indicate one standard deviation from the mean. Different lowercase letters denote significant differences at p $<$ 0.05 (Tukey’s HSD test).

Figure 3

Figure 3. Light utilization efficiencies $(\alpha )$ of glacier ice algae that have been incubated under varying pH (A), salinity (B) and light (C) conditions for 48 h (n = 5), as well as different temperatures (D) for 4 h (n = 3). Gold columns represent control samples reflecting the prevailing environmental conditions of the study site on the Greenland ice sheet (61°06′N, 46°50′W) during 2021. Error bars indicate one standard deviation from the mean. Different lowercase letters denote significant differences at p $<$ 0.05 (Tukey’s HSD test).

In the context of salinity, a 5‰ increase above ambient conditions resulted in a decline in $F_v$/$F_m$ from 0.41 $\pm$ 0.03 to 0.29 $\pm$ 0.02 and $\alpha$ decreased from 0.17 $\pm$ 0.01 to 0.12 $\pm$ 0.01 (both Tukey’s HSD, p $<$ 0.05; Figures 2B, 3B). Subsequently, a 35‰ increase in salinity above ambient levels led to significant decreases in both parameters: $F_v$/$F_m$ to 0.06 $\pm$ 0.02 and $\alpha$ to 0.02 $\pm$ 0.02 (both p $<$ 0.05). Furthermore, while the exclusion of UV light had no effect on $F_v$/$F_m$ or $\alpha$ values (both Tukey’s HSD, p $>$ 0.05; Figures 2C, 3C), both increased respectively from 0.48 $\pm$ 0.05 and 0.17 $\pm$ 0.01 to 0.58 $\pm$ 0.03 and 0.25 $\pm$ 0.03 when irradiance was reduced to 32% of ambient conditions (both Tukey’s HSD, p $<$ 0.05).

The photophysiology of the microbial community remained largely stable in response to variations in pH, with $F_v$/$F_m$ ranging from 0.29 $\pm$ 0.03 to 0.37 $\pm$ 0.06 (Figure 2A) and $\alpha$ ranging from 0.13 $\pm$ 0.02 to 0.16 $\pm$ 0.02 (Figure 3A) between pH 4.7 and 9.21 (both Tukey’s HSD, p $>$ 0.05). However, $F_v$/$F_m$ was lower for samples incubated at pH 3.5 (0.11 $\pm$ 0.02) relative to those incubated at pH 6.7 (control; p $<$ 0.05) and 9.21 (p $<$ 0.05). Additionally, temperature elevations showed no discernible impact on either $F_v$/$F_m$ or $\alpha$, with values ranging from 0.63 $\pm$ 0.03 to 0.68 $\pm$ 0.03 for $F_v$/$F_m$ (Figure 2D) and 0.30 $\pm$ 0.01 to 0.35 $\pm$ 0.01 for $\alpha$ (Figure 3D), as determined by ANOVA (p $>$ 0.05).

3.3 Photoinhibition

The photophysiology of the microbial community was assessed in all control samples. This evaluation occurred immediately after the melting process (${\mathrm{T}}_0$) and subsequently following a uniform 48-h incubation period for the pH, salinity, and light incubation experiments, ensuring consistent conditions across all samples. The $F_v$/$F_m$ values in the ${\mathrm{T}}_0$ samples, which ranged from 0.66 $\pm$ 0.01 to 0.72 $\pm$ 0.01, were consistently higher than in the control samples (0.37 $\pm$ 0.06 to 0.48 $\pm$ 0.05) after 48 h (Figure 4A) for all incubation experiments conducted (t-test for all, except light, for which MW was used, p $<$ 0.05). Similarly, $\alpha$ was significantly lower in the control samples (0.14 $\pm$ 0.02 to 0.17 $\pm$ 0.01) relative to the ${\mathrm{T}}_0$ samples (0.30 $\pm$ 0.04 to 0.34 $\pm$ 0.03; Figure 4B) in the three incubation experiments (t-test for all, p $<$ 0.05).

Figure 4

Figure 4. Maximal quantum yields ($F_v$/$F_m$) of electron transport through photosystem II (A) and light utilization efficiencies ($\alpha$); (B) of glacier ice algae from incubation experiments that examined the effects of pH, salinity (salt), and light on algal photophysiology. Incubation conditions reflect those of the study site on the Greenland ice sheet (61°06′N, 46°50′W) during 2021. Measurements were made directly after melting (${\mathrm{T}}_0$) and following a 48-h incubation under ambient conditions (control). Error bars indicate one standard deviation from the mean (n = 5). Different lowercase letters denote significant differences at p $<$ 0.05 (Student’s t-test for all, except Light in A, which used the Mann-Whitney U test).

4 Discussion

While glacier ice algae have been observed on glaciers and ice sheets globally (Takeuchi et al., 1998; Takeuchi et al., 2006; Takeuchi and Kohshima, 2004; Remias et al., 2012; Lutz et al., 2018; Di Mauro et al., 2020), very few attempts have been made to examine their photophysiology. Where investigations have been conducted, they have almost exclusively focused on the bulk photophysiology of the whole ice community (Yallop et al., 2012; Williamson et al., 2020; McCutcheon et al., 2021; Halbach et al., 2022; Perini et al., 2023; Doting et al., 2024). However, the photophysiology of both the unicellular (A. alaskana) and filamentous (A. nordenskiöldii) algae were analysed separately in a study by Procházková et al. (2021). Photophysiological data from selected studies that utilised PAM fluorometry (Williamson et al., 2020; McCutcheon et al., 2021; Halbach et al., 2022; Doting et al., 2024) have shown that the ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratios for glacier ice algae are often lower than the values typically reported for non-stressed green microalgae and plants (Table 1), which are around 0.7-0.8 (Masojídek et al., 2013; Björkman and Demmig, 1987). A lower ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratio implies low potential maximal photosynthetic activity, indicating potential stress or suboptimal conditions for photosynthesis. In this study, the ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratio of the control samples, incubated under in situ light conditions was between 0.37-0.48 for pH, salt, and light experiments, corresponding well with reported values from the literature for naturally occurring glacier algae dominated communities from Greenland, Table 1 (Williamson et al., 2020; McCutcheon et al., 2021; Halbach et al., 2022; Doting et al., 2024; Peter et al., 2024).

Table 1

Table 1. Overview of photophysiological parameters from other studies on glacier ice algae based on PAM measurements. All samples were without any treatment. The numbers are the average reported from each study.

From this study, it was determined that light history and intensity prior to and during the experiments exert a greater influence on the photophysiology of glacier ice algae. Three observations in this study supporting this statement: 1) the absolute high ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratios in the temperature experiment, 2) the differences between ${\mathrm{T}}_0$ and control in the light, pH and salinity experiments, and 3) the light experiment results.

The ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratio for the temperature experiment (${\mathrm{F}}_v/{\mathrm{F}}_m$ ratio of 0.7–0.8) is higher and similar to the values reported in Perini et al. (2023) and Peter et al. (2024), which was conducted under low light. The higher ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratio during the temperature experiment could be explained by the lower light intensity that the algae received, since these were conducted inside a laboratory tent, compared to the rest of the in situ incubation experiments that were outside under in situ high light intensity. This observation is also supported by the data from the ${\mathrm{T}}_0$ measurements (Figure 4A). The samples used for all the experiments were melted in a tent under low light conditions for 72 h, after which ${\mathrm{T}}_0$ was measured before the start of the light, pH, and salinity experiments, incubated under in situ light conditions. ${\mathrm{T}}_0$ had a ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratio of 0.66–0.72 (close to the ideal ratio of 0.7–0.8), dropping to 0.37-0.48 after incubations (control samples Figure 4A). Finally, in the light experiment, a clear pattern of higher ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratio in samples that received lower light is observed as also observed in Williamson et al. (2020). The glacier ice algae had an increased utilization of the light $(\alpha )$ when exposed to reduced incoming light (Figure 3C). This supports the hypothesis that the glacier ice algae benefit from a lower light intensity compared to the natural irradiation on the ice surface. Additional evidence of the glacier ice algae’s higher photosynthetic performance under lower light intensities comes from studies where glacier ice algae are cultured, under lower light intensities (max. 300 $\mu$mol photons ${\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$; Remias and Procházková, 2023; Jensen et al., 2023). Production of the purpurogallin pigment under high natural light conditions is suggested to be energetically expensive for the cell and, thus, under low light conditions, photosynthetic performance can be improved (Williamson et al., 2020; Williamson et al., 2021). Besides expending energy on pigment production under increased light intensities, algal cells may also need to manage reactive oxygen species that can damage components such as DNA and proteins within the cell (Rippin et al., 2019). This indicates that, even though glacier ice algae can tolerate irradiation up to 4,000 $\mu$mol photons ${\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$ (Williamson et al., 2020; McCutcheon et al., 2021), high light intensities are not their optimal growth condition.

In this study, no significant differences were identified for the ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratio or light utilization efficiency $(\alpha )$ in the pH experiment, indicating that the algae can cope with a broad range of pH conditions, both acidic and alkaline. The pH of the ice at the sampling time was 6.7 $\pm$ 0.2. Glacier ice algae have successfully been cultured in media with pH 5 and 6 (Remias and Procházková, 2023; Jensen et al., 2023) and both alkaline (8.6) and acidic (3.6) pH levels have been measured at different glaciers, where glacier ice algae have been observed (Komárek and Komárek, 2001; Takeuchi and Kohshima, 2004; Remias et al., 2011; Procházková et al., 2021), supporting the assertion that glacier ice algae can cope with a broad pH range.

In addition to growth across a broad range of pH, glacier ice algae were also able to withstand increasing temperatures. No significant differences were observed between the increased temperatures (10°C–25°C) and the control (5°C). However, this study was only conducted over a period of 4 h (Figures 2D, 3D). The algae naturally experience a fluctuation in temperature during a day with temperatures fluctuating between 1.5°C at night and 6° during the summer at the field site (Fausto et al., 2021). The maximum temperature for the summer of 2021 at the field camp was 6.21°C (Fausto et al., 2021), indicating that, for at least a short period, the glacier ice algae are still actively photosynthesizing at a temperature four times higher than the maximum air temperature at the study site. Growth and high photosynthetic performance at high temperatures have also been observed in both Arctic and Antarctic microalgae, suggesting that many polar microalgae are psychrotolerant rather than psychrophilic (Chen et al., 2012; Teoh et al., 2004; Stibal and Elster, 2005). The glacier ice algae, however, was strongly impacted by high salinity. Increasing salt concentrations are a known abiotic stressor, where elevated levels of reactive oxygen species interfere with photosynthesis by damaging photosystem I (PSI) and weakening of PSII (Shetty et al., 2019). Furthermore, transcriptomic data from the green microalgae Chlamydomonas reinhardtii have shown that downregulation of PSI light-harvesting complex genes leads to reduced photosynthetic performance in the cells (Shetty et al., 2019). Some bacterial and algal species have a cross-tolerance between salt stress and stress from freezing since those stressors are both related to osmotic changes within the cell (Wilson et al., 2012; Schmid et al., 2009; Tanaka et al., 2001; Raymond et al., 2020). However, in this study, the ${\mathrm{F}}_v/{\mathrm{F}}_m$ ratio and the light utilization efficiency $(\alpha )$ significantly dropped with increasing salt concentration (Figures 2B, 3B).

This study was conducted using in situ incubations with a single condition change to examine its affect on the photophysiology of glacier ice algae. With fresh cultures of glacier ice algae (Remias and Procházková, 2023; Jensen et al., 2023), it would be possible to conduct a similar incubation study in a more controlled environment for an extended duration, allowing for the investigation of prolonged effects, such as temperature variations on photophysiology. Furthermore, laboratory studies with these cultures could enable the exploration of interactive effects, where multiple environmental parameters, such as increasing light and temperature, are varied simultaneously to better simulate and understand their combined impact on the algae.

In this study, it was demonstrated that glacier ice algal photophysiology is highly influenced by solar irradiance while performing well under a wide range of other physicochemical conditions. Solar irradiance significantly impacts the photophysiology and fitness of glacier ice algae, potentially serving as the primary driver of photophysiological variability under natural conditions. Despite their sensitivity to light, as expected from a photosynthetic organism, and increasing salt conditions, glacier ice algae were able to sustain in situ electron transport under a broad range of pH and temperature conditions. Based on the incubation experiments under different temperature conditions, it was shown that the glacier ice algae may not be truly psychrophilic but rather psychrotolerant. However, these experiments were only conducted for a short period of time and thus, it is possible that more pronounced differences in photophysiology under different temperature conditions could emerge over an extended duration. Despite this, it is argued that the adaptation of glacier ice algae to such a broad range of physico-chemical conditions is advantageous, considering their non-motile nature and the dynamic environment of the ice surface, which can undergo rapid changes over short periods.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

MJ: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing–original draft, Writing–review and editing. TT-J: Data curation, Formal Analysis, Methodology, Validation, Writing–original draft, Writing–review and editing. MT: Funding acquisition, Project administration, Writing–original draft, Writing–review and editing. LB: Funding acquisition, Project administration, Writing–original draft, Writing–review and editing. AA: Conceptualization, Funding acquisition, Project administration, Supervision, Writing–original draft, Writing–review and editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The study was financially supported by the European Research Council Synergy Grant DEEP PURPLE under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 856416).

Acknowledgments

The authors would like to thank the 2021 Deep Purple team for their support in the field, Christopher J Williamson for his guidance on processing PAM fluorometry data, and Jonas K Andersen for his assistance with producing the site map and conducting spectrometric measurements. The authors would also like to thank Hans Jakobsen for lending the spectrophotometer.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgeoc.2024.1436488/full#supplementary-material

References

Aro, E. M., Virgin, I., and Andersson, B. (1993). Photoinhibition of Photosystem II. Inactivation, protein damage and turnover. Biochimica Biophysica Acta (BBA) - Bioenergetics 1143, 113–134. doi:10.1016/0005-2728(93)90134-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Baker, N. R. (2008). Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 59, 89–113. doi:10.1146/annurev.arplant.59.032607.092759

PubMed Abstract | CrossRef Full Text | Google Scholar

Baker, N. R., and Rosenqvist, E. (2004). Applications of chlorophyll fluorescence can improve crop production strategies: an examination of future possibilities. J. Exp. Bot. 55, 1607–1621. doi:10.1093/jxb/erh196

PubMed Abstract | CrossRef Full Text | Google Scholar

Bamber, J. L., Westaway, R. M., Marzeion, B., and Wouters, B. (2018). The land ice contribution to sea level during the satellite era. Environ. Res. Lett. 13, 1–21. doi:10.1088/1748-9326/aadb2c

CrossRef Full Text | Google Scholar

Berggren, S. (1871). Alger från Grönlands inlandis. Öfversigt af kongl. Vetenskaps-akademiens Förhandlingar, 293–297.

Google Scholar

Björkman, O., and Demmig, B. (1987). Photon yield of O2 evolution and chlorophyll fluorescence characteristics at 77 K among vascular plants of diverse origins. Planta 170, 489–504. doi:10.1007/BF00402983

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, Z., He, C., and Hu, H. (2012). Temperature responses of growth, photosynthesis, fatty acid and nitrate reductase in Antarctic and temperate Stichococcus. Extremophiles 16, 127–133. doi:10.1007/s00792-011-0412-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Cook, J. M., Tedstone, A. J., Williamson, C., McCutcheon, J., Hodson, A. J., Dayal, A., et al. (2020). Glacier algae accelerate melt rates on the south-western Greenland Ice Sheet. Cryosphere 14, 309–330. doi:10.5194/tc-14-309-2020

CrossRef Full Text | Google Scholar

Crawley, M. J. (2007). The R book. John Wiley & Sons, Ltd.

Google Scholar

Dial, R. J., Ganey, G. Q., and Skiles, S. M. K. (2018). What color should glacier algae be? An ecological role for red carbon in the cryosphere. FEMS Microbiol. Ecol. 94, 1–9. doi:10.1093/femsec/fiy007

PubMed Abstract | CrossRef Full Text | Google Scholar

Di Mauro, B., Garzonio, R., Baccolo, G., Franzetti, A., Pittino, F., Leoni, B., et al. (2020). Glacier algae foster ice-albedo feedback in the European Alps. Sci. Rep. 10, 4739. doi:10.1038/s41598-020-61762-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Doting, E. L., Jensen, M. B., Peter, E. K., Ellegaard-Jensen, L., Tranter, M., Benning, L. G., et al. (2024). The exometabolome of microbial communities inhabiting bare ice surfaces on the southern Greenland Ice Sheet. Environ. Microbiol. 26, e16574–13doi. doi:10.1111/1462-2920.16574

PubMed Abstract | CrossRef Full Text | Google Scholar

Eilers, P. H., and Peeters, J. C. (1988). A model for the relationship between light intensity and the rate of photosynthesis in phytoplankton. Ecol. Model. 42, 199–215. doi:10.1016/0304-3800(88)90057-9

CrossRef Full Text | Google Scholar

Fausto, R. S., van As, D., Mankoff, K. D., Vandecrux, B., Citterio, M., Ahlstrøm, A. P., et al. (2021). Programme for monitoring of the Greenland ice sheet (promice) automatic weather station data. Earth Syst. Sci. Data 13, 3819–3845. doi:10.5194/essd-13-3819-2021

CrossRef Full Text | Google Scholar

Halbach, L., Chevrollier, L.-A., Doting, E. L., Cook, J. M., Jensen, M. B., Benning, L. G., et al. (2022). Pigment signatures of algal communities and their implications for glacier surface darkening. Sci. Rep. 12, 17643. doi:10.1038/s41598-022-22271-4

PubMed Abstract | CrossRef Full Text | Google Scholar

IPCC (2023). “Summary for policymakers,” in Climate change 2023: synthesis report. contribution of working groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change, Editor Core Writing Team, H. Lee, and J. Romero (Geneva, Switzerland: IPCC), pp. 1–34. doi:10.59327/IPCC/AR6-9789291691647.001

CrossRef Full Text | Google Scholar

Jaarsma, A. H., Sipes, K., Zervas, A., Jiménez, F. C., Ellegaard-Jensen, L., Thøgersen, M. S., et al. (2023). Exploring microbial diversity in Greenland Ice Sheet supraglacial habitats through culturing-dependent and -independent approaches. FEMS Microbiol. Ecol. 99, fiad119–16. doi:10.1093/femsec/fiad119

PubMed Abstract | CrossRef Full Text | Google Scholar

Jensen, M. B., Perini, L., Halbach, L., Jakobsen, H., Haraguchi, L., Ribeiro, S., et al. (2023). The dark art of cultivating glacier ice algae. Bot. Lett., 1–10. doi:10.1080/23818107.2023.2248235

CrossRef Full Text | Google Scholar

Kol, E. (1942). The snow and ice algae of Alaska. Smithson. Misc. Collect. 101, 1–36.

Google Scholar

Komárek, O., and Komárek, J. (2001). Contribution to the taxonomy and ecology of green cryosestic algae in the summer season 1995-96 at King George Island, S. Shetland Islands. Nova Hedwig. Beih. 123, 121–140.

Google Scholar

Ling, H., and Seppelt, R. (1990). Snow algae of the Windmill Islands, continental Antarctica. Mesotaenium berggrenii (Zygnematales, Chlorophyta) the alga of grey snow. Antarct. Sci. 2, 143–148. doi:10.1017/S0954102090000189

CrossRef Full Text | Google Scholar

Lutz, S., McCutcheon, J., McQuaid, J. B., and Benning, L. G. (2018). The diversity of ice algal communities on the Greenland Ice Sheet as revealed by oligotyping. Microb. genomics 4, 0001599–e210. doi:10.1099/mgen.0.000159

PubMed Abstract | CrossRef Full Text | Google Scholar

Masojídek, J., Torzillo, G., and Koblížek, M. (2013). Photosynthesis in microalgae. Handb. Microalgal Cult., 21–36. doi:10.1002/9781118567166.ch2

CrossRef Full Text | Google Scholar

McCutcheon, J., Lutz, S., Williamson, C., Cook, J. M., Tedstone, A. J., Vanderstraeten, A., et al. (2021). Mineral phosphorus drives glacier algal blooms on the Greenland Ice Sheet. Nat. Commun. 12, 570. doi:10.1038/s41467-020-20627-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Mott, K. A., and Berry, J. A. (1986). Effects of pH on activity and activation of ribulose 1,5-bisphosphate carboxylase at air level CO 2. Plant Physiol. 82, 77–82. doi:10.1104/pp.82.1.77

PubMed Abstract | CrossRef Full Text | Google Scholar

Mouginot, J., Rignot, E., Bjørk, A. A., van den Broeke, M., Millan, R., Morlighem, M., et al. (2019). Forty-six years of Greenland Ice Sheet mass balance from 1972 to 2018. Proc. Natl. Acad. Sci. 116, 9239–9244. doi:10.1073/pnas.1904242116

PubMed Abstract | CrossRef Full Text | Google Scholar

Perini, L., Gostinčar, C., Likar, M., Frisvad, J. C., Kostanjšek, R., Nicholes, M., et al. (2023). Interactions of fungi and algae from the Greenland ice sheet. Microb. Ecol. 86, 282–296. doi:10.1007/s00248-022-02033-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Perkins, R. G., Mouget, J. L., Lefebvre, S., and Lavaud, J. (2006). Light response curve methodology and possible implications in the application of chlorophyll fluorescence to benthic diatoms. Mar. Biol. 149, 703–712. doi:10.1007/s00227-005-0222-z

CrossRef Full Text | Google Scholar

Peter, E. K., Jaeger, C., Lisec, J., Peters, R. S., Mourot, R., Rossel, P., et al. (2024). Endometabolic profiling of pigmented glacier ice algae on the Greenland Ice Sheet. Metabolomics 20, 98. doi:10.1007/s11306-024-02147-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Procházková, L., Řezanka, T., Nedbalová, L., and Remias, D. (2021). Unicellular versus filamentous: The glacial alga ancylonema alaskana comb. et stat. nov. and its ecophysiological relatedness to ancylonema nordenskioeldii (zygnematophyceae, streptophyta). Microorganisms 9, 1103. doi:10.3390/microorganisms9051103

PubMed Abstract | CrossRef Full Text | Google Scholar

Raymond, J. A., Morgan-Kiss, R., and Stahl-Rommel, S. (2020). Glycerol is an osmoprotectant in two antarctic Chlamydomonas species from an ice-covered saline lake and is synthesized by an unusual bidomain enzyme. Front. Plant Sci. 11, 1259–1268. doi:10.3389/fpls.2020.01259

PubMed Abstract | CrossRef Full Text | Google Scholar

Remias, D., Holzinger, A., Aigner, S., and Lütz, C. (2011). Ecophysiology and ultrastructure of ancylonema nordenskiöldii (zygnematales, streptophyta), causing brown ice on glaciers in svalbard (high arctic). Polar Biol. 35, 899–908. doi:10.1007/s00300-011-1135-6

CrossRef Full Text | Google Scholar

Remias, D., Holzinger, A., and Lütz, C. (2009). Physiology, ultrastructure and habitat of the ice alga Mesotaenium berggrenii (Zygnemaphyceae, Chlorophyta) from glaciers in the European Alps. Phycologia 48, 302–312. doi:10.2216/08-13.1

CrossRef Full Text | Google Scholar

Remias, D., and Procházková, L. (2023). The first cultivation of the glacier ice alga Ancylonema alaskanum (Zygnematophyceae, Streptophyta): differences in morphology and photophysiology of field vs laboratory strain cells. J. Glaciol. 69, 1080–1084. doi:10.1017/jog.2023.22

CrossRef Full Text | Google Scholar

Remias, D., Schwaiger, S., Aigner, S., Leya, T., Stuppner, H., and Lütz, C. (2012). Characterization of an UV- and VIS-absorbing, purpurogallin-derived secondary pigment new to algae and highly abundant in Mesotaenium berggrenii (Zygnematophyceae, Chlorophyta), an extremophyte living on glaciers. FEMS Microbiol. Ecol. 79, 638–648. doi:10.1111/j.1574-6941.2011.01245.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Rippin, M., Pichrtová, M., Arc, E., Kranner, I., Becker, B., and Holzinger, A. (2019). Metatranscriptomic and metabolite profiling reveals vertical heterogeneity within a Zygnema green algal mat from Svalbard (High Arctic). Environ. Microbiol. 21, 4283–4299. doi:10.1111/1462-2920.14788

PubMed Abstract | CrossRef Full Text | Google Scholar

Schmid, B., Klumpp, J., Raimann, E., Loessner, M. J., Stephan, R., and Tasara, T. (2009). Role of cold shock proteins in growth of Listeria monocytogenes under cold and osmotic stress conditions. Appl. Environ. Microbiol. 75, 1621–1627. doi:10.1128/AEM.02154-08

PubMed Abstract | CrossRef Full Text | Google Scholar

Shetty, P., Gitau, M. M., and Maróti, G. (2019). Salinity stress responses and adaptation mechanisms in eukaryotic green microalgae. Cells 8, 1657–1716. doi:10.3390/cells8121657

PubMed Abstract | CrossRef Full Text | Google Scholar

Shimada, R., Takeuchi, N., and Aoki, T. (2016). Inter-annual and geographical variations in the extent of bare ice and dark ice on the Greenland ice sheet derived from MODIS satellite images. Front. Earth Sci. 4, 1–10. doi:10.3389/feart.2016.00043

CrossRef Full Text | Google Scholar

Stibal, M., and Elster, J. (2005). Growth and morphology variation as a response to changing environmental factors in two Arctic species of Raphidonema (Trebouxiophyceae) from snow and soil. Polar Biol. 28, 558–567. doi:10.1007/s00300-004-0709-y

CrossRef Full Text | Google Scholar

Stibal, M., Elster, J., Šabacká, M., and Kaštovská, K. (2007). Seasonal and diel changes in photosynthetic activity of the snow alga Chlamydomonas nivalis (Chlorophyceae) from Svalbard determined by pulse amplitude modulation fluorometry. FEMS Microbiol. Ecol. 59, 265–273. doi:10.1111/j.1574-6941.2006.00264.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Takeuchi, N. (2001). The altitudinal distribution of snow algae on an Alaska glacier (Gulkana Glacier in the Alaska Range). Hydrol. Process. 15, 3447–3459. doi:10.1002/hyp.1040

CrossRef Full Text | Google Scholar

Takeuchi, N., and Kohshima, S. (2004). A snow algal community on tyndall glacier in the southern patagonia icefield, Chile. Arct. Antarct. Alp. Res. 36, 92–99. doi:10.1657/1523-0430(2004)036[0092:asacot]2.0.co;2

CrossRef Full Text | Google Scholar

Takeuchi, N., Kohshima, S., and Fujita, K. (1998). Snow algae in Himalyan glaciers, Glacier AX010 East Nepal: relationship with glacier summer mass balance. Bullettin Glaciol. Res. 16, 43–50.

Google Scholar

Takeuchi, N., Uetake, J., Fujita, K., Aizen, V. B., and Nikitin, S. D. (2006). A snow algal community on Akkem glacier in the Russian Altai mountains. Ann. Glaciol. 43, 378–384. doi:10.3189/172756406781812113

CrossRef Full Text | Google Scholar

Tanaka, S., Ikeda, K., and Miyasaka, H. (2001). Enhanced tolerance against salt-stress and freezing-stress of Escherichia coli cells expressing algal bbc1 gene. Curr. Microbiol. 42, 173–177. doi:10.1007/s002840010199

PubMed Abstract | CrossRef Full Text | Google Scholar

Teoh, M. L., Chu, W. L., Marchant, H., and Phang, S. M. (2004). Influence of culture temperature on the growth, biochemical composition and fatty acid profiles of six Antarctic microalgae. J. Appl. Phycol. 16, 421–430. doi:10.1007/s10811-004-5502-3

CrossRef Full Text | Google Scholar

The Danish Agency for Data Supply and Infrastructure (2024). Satellite imagery Greenland

Google Scholar

Williamson, C. J., Anesio, A. M., Cook, J., Tedstone, A., Poniecka, E., Holland, A., et al. (2018). Ice algal bloom development on the surface of the Greenland Ice Sheet. FEMS Microbiol. Ecol. 94, fiy025–10. doi:10.1093/femsec/fiy025

PubMed Abstract | CrossRef Full Text | Google Scholar

Williamson, C. J., Cook, J., Tedstone, A., Yallop, M., McCutcheon, J., Poniecka, E., et al. (2020). Algal photophysiology drives darkening and melt of the Greenland Ice Sheet. Proc. Natl. Acad. Sci. 117, 5694–5705. doi:10.1073/pnas.1918412117

PubMed Abstract | CrossRef Full Text | Google Scholar

Williamson, C. J., Turpin-Jelfs, T., Nicholes, M. J., Yallop, M. L., Anesio, A. M., and Tranter, M. (2021). Macro-nutrient stoichiometry of glacier algae from the southwestern margin of the Greenland ice sheet. Front. Plant Sci. 12, 673614–673618. doi:10.3389/fpls.2021.673614

PubMed Abstract | CrossRef Full Text | Google Scholar

Wilson, S. L., Frazer, C., Cumming, B. F., Nuin, P. A., and Walker, V. K. (2012). Cross-tolerance between osmotic and freeze-thaw stress in microbial assemblages from temperate lakes. FEMS Microbiol. Ecol. 82, 405–415. doi:10.1111/j.1574-6941.2012.01404.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Yadav, N., and Singh, D. P. (2021). Photosynthetic efficiency and compositional alterations in microalgae Chlorella vulgaris in response to changes in the pH condition. Vegetos 34, 119–126. doi:10.1007/s42535-021-00186-1

CrossRef Full Text | Google Scholar

Yallop, M. L., Anesio, A. M., Perkins, R. G., Cook, J., Telling, J., Fagan, D., et al. (2012). Photophysiology and albedo-changing potential of the ice algal community on the surface of the Greenland ice sheet. ISME J. 6, 2302–2313doi. doi:10.1038/ismej.2012.107

PubMed Abstract | CrossRef Full Text | Google Scholar

Yoshimura, Y., Kohshima, S., and Ohtani, S. (1997). A community of snow algae on a Himalayan glacier: change of algal biomass and community structure with altitude. Arct. Alp. Res. 29, 126–137. doi:10.2307/1551843

CrossRef Full Text | Google Scholar