Antarctic snow algae: unraveling the processes underlying microbial community assembly during blooms formation

doi:10.21203/rs.3.rs-2066117/v1

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Antarctic snow algae: unraveling the processes underlying microbial community assembly during blooms formation

https://doi.org/10.21203/rs.3.rs-2066117/v1

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published 05 Sep, 2023

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Background and aims: At the West Antarctic Peninsula snow algae blooms are composed by complex microbial communities dominated by green microalgae and bacteria. During their progression, the assembly of these microbial communities occurs under harsh environmental conditions and a variable nutrient content due to fast snow melting. To date, it is still unclear what are the ecological mechanisms governing the composition and abundance of microorganisms during the formation of snow algae blooms. In this study, we aim to examine the main ecological mechanisms governing the assembly of snow algae blooms from early stages to colorful blooms.

Methods: The composition of the microbial communities within snow algae blooms was recorded in the West Antarctic Peninsula (O´Higgins base) during a 35-day period using 16S rRNA and 18S rRNA metabarcoding. In addition, the contribution of different ecological processes to the assembly of the microbial community was quantified using phylogenetic-bin-based null model analysis. Finally, using genome-scale metabolic models we assessed the potential for biotic interactions between algal species and bacteria trough the exchanging of metabolites.

Results: Our results showed that despite that dominant taxonomic groups changed during the formation of algae blooms neither diversity nor richness changed significatively. Likewise, in a nutrient rich environment, the content of macronutrients such as nitrate, ammonium, phosphate, and organic carbon did not play a major role in structuring the community. The quantification of ecological processes showed that the bacterial community assembly was governed by selective processes such as homogenous selection and potential biotic interaction between snow algae and dominant bacteria species. In contrast, the eukaryotic community was governed by stochastic processes such as dispersal limitation and drift, and in a lesser extent by homogenous selection.

Conclusions: Overall, our study highlights the differences in the microbial assembly between bacteria and eukaryotes in snow algae blooms and propose a model to integrate both assembly processes.

The Maritime Antarctic has been regarded as one of the most sensitive world regions to global warming, with an accelerated ice and snow melting in the last decades (King, 1994; Marshall et al., 2002; Vaughan et al., 2003). This scenario has caused strong variations in the physicochemical characteristics of the cryosphere (Hodson et al., 2017), providing favorable conditions for snow algae proliferation (Gray et al., 2020, Nowak et al., 2018). However, climate change might also impact processes such as dispersal phenology and environmental factors, further influencing successional trajectories (Makoto and Wilson, 2019) and the assembly of the microbial community of snow algae blooms. It has been reported that snow algae blooms might reduce the albedo up to 20 % at Maritime Antarctic snowfields (Huovinen et al., 2018). Thus, the presence of snow algae does not only contribute to the productivity and biodiversity of the cryosphere, but also affects the energy balance at a global level. However, despite the relevance of snow algae blooms, the processes governing the formation of these communities in their early stages are not well understood.

Although multiple mechanisms (e.g. selection, dispersal, drift, and speciation) may be responsible of controlling the diversity, distribution, and succession of the microbial community, quantifying their relative importance remains challenging (Ning et al., 2020). Moreover, in the case of Antarctic snow ecosystems, analysis of snow algae blooms imposes logistical and temporal restrictions that limit the study of these communities in their early stages. In the West Antarctic Peninsula, snow algae blooms are rapidly formed during the short spring period, which makes tracking the transition from fresh snow to colored snow algae blooms difficult.

Based on studies of visible and colorful snow algae blooms, it has been established that their functional profiles consistently exhibit genes involved in the tolerance to environmental stressors, which filter and select only adapted organisms (Soto et al., 2022). Likewise, nutrient stoichiometry also affects the microbial community structure of snow algae blooms. For instance, the proliferation of snow algae is favored by the release of elements such as Fe, Mn, and P from melting glaciers (Ganey et al., 2017), and snow algae species Chloromonas cf. alpina and Chloromonas polyptera have been shown to correlate positively with dissolved organic carbon (DOC) in the Arctic (Lutz et al., 2017).

In addition, metagenomes of snow algae blooms have shown that some functions are redundant among microorganisms, suggesting that the stochastic processes could explain partially the assembly of microbial communities in snow algae blooms (Soto et al., 2022). Potential biotic interactions within snow algae blooms indicate that some members of the bacterial community likely compete for available resources and show a low potential for self-support their nutrient requirements only from the exchange of metabolites (Soto et al., 2022). However, in the microbiome of some alpine snow algae communities it was reported that members of Proteobacteria, like Oxalobacteraceae, Pseudomonadaceae, and Sphingomonadaceae show genus-specific co-occurrences with distinct species of microalgae (Krug et al., 2020). Thus, as some co-cultivation experiments suggest, inter-kingdom mutualistic relationships are likely to occur in these systems (Kazamia et al., 2012; Krug et al., 2020; Terashima et al., 2017).

Considering that the assembly of a community is a dynamic process, the mechanisms and assemblages of organisms may change over time (Gu et al., 2021). In the present study, we examine the main ecological mechanisms operating during the early stages of the assembly of the microbial community. In a 35-day field experiment we collected successively snow samples from a locality in the West Antarctic Peninsula and witnessed the transition from white snow to colorful snow algae blooms during the spring-summer season. Using 16S rRNA and 18S rRNA genes metabarcoding, the temporal patterns in microbial diversity and some key functional interactions during the formation of snow algae blooms were assessed. We hypothesize that the formation of colorful snow algae blooms is mostly driven by selective ecological mechanisms and in a lesser extent by stochastic processes. Thus, it is expected a decline of the microbial diversity during the formation of algal blooms because species that are not able to cope with environmental harsh conditions are filtered out. Likewise, cooperative interactions between algae and bacteria may be selecting and facilitating the colonization of the dominant species in snow algae blooms.

Study sites

Snow samples were collected from an area near the O’Higgins Base Station (Chile) located on the Isabel Riquelme Islet (63 ºS; 57º W) during the November and December 2019 (Fig. 1). Snow from four 2 x 2 m quadrats was sampled twice a week over a 35‑day period with a total of 46 samples.

Between the first and second sampling day, the snow of two sites melted completely, therefore the quadrats were moved closer to the snow-covered sites (see new coordinates in Supplementary Table 1). Because the progression of the snow algae blooms was unpredictable, two very light-green snow patches (Site 1 and 2) and two white snow patches were originally selected. The colors of the blooms changed during the experiment.

Sampling

For the molecular analyses (metabarcoding and metagenomics), superficial snow (~2 cm) was sampled into two 1260 mL Whirl‑Pak bags (Nasco, WI, USA) using sterile gloves and a small metallic shovel.

Temperature of each snow sampling site was measured with a Handheld Conductivity Meter (Pro40, YSI, OH, USA). For the determination of the nutrients content (NH₄⁺, NO₂, NO₃^-, and PO₄^-3), superficial snow samples (0-3 cm) were collected in a 1260 mL Whirl‑Pak bag. Additionally, for the determination of the content DOC, snow samples were collected using sterile 50 mL centrifuge tubes (Falcon). After collection, snow samples were transported to the laboratory at O’Higgins Base Station for further processing.

Sample Processing

For molecular analysis, the snow was melted and filtered through 0.22 μm mixed cellulose esters membranes (47 mm, Millipore). Depending on the intensity of the algal blooms, different volumes were filtered. For clear white snow 1 L was filtered, while for colored snow, only 100-150 mL was filterable before the filter saturation. Finally, the filters were stored at -20 °C inside individual aluminum foil envelopes and transported in a dry shipper to the laboratory at the Universidad Austral de Chile (Valdivia). Here, samples were stored at -80 °C until further DNA extraction.

Environmental parameters

The concentrations of NH₄⁺, NO₃^- and PO₄^-3 in snow samples were quantified using a portable photometer (9300 Kit, YSI Inc., Yellow Springs, OH, USA). This instrument measures the change of color when chemical reagents react with the sample, and whose intensity is proportional to the concentration of the parameter under test. Previously, samples were melted and filtered through 0.45 µm mixed cellulose esters membranes (47 mm, Millipore). Afterwards, the filtered liquid was added to the commercial kit used for quantifying the nutrients following the manufacturer instructions. All samples were measured within 24 h after collection. For the determination of DOC, samples were stored at -80 ºC and transported in a dry shipper to the Centro de Investigación en Ecosistemas de la Patagonia (CIEP) in Coyhaique for quantification using the combustion catalytic oxidation / NDIR (Non-Dispersive Infra-Red) method. The chlorophyll a concentration was determined in 5 ml aliquots of melted snow filtered onto Whatman GFF filters. The filters were kept frozen at −20 °C and transported to the Universidad Austral de Chile in Valdivia, until extraction in 90% ethanol at 75 °C, and spectrophotometric measurements (SFS, 1993). No acid correction for phaeophytin was made.Daily records of maximum and minimum temperatures, accumulated snow, and fresh snow at O’Higgins Antarctic Base during the study period, were obtained from the Dirección Meteorológica de Chile (https://climatologia.meteochile.gob.cl/).

DNA extraction and metabarcoding

Total DNA from snow samples was extracted using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany) following the instructions from the manufacturer. DNA was extracted from filtered samples thawed in ice and cut into small pieces with a sterile scalpel. Half of the filters were used for DNA extraction and the other half was kept at −20 °C as a backup. Negative controls using ultrapure water were included during the DNA extraction (one negative control each 23 samples). Finally, DNA quantification was performed using the Qubit® dsDNA HS Assay Kit and a Qubit 3.0 fluorometer (Thermo Fisher Sci., Waltham, MA, USA). Samples were normalized to a DNA concentration of 5 ng/μl.

Metabarcoding libraries of the 16S rRNA and 18S rRNA genes were generated at the AUSTRAL-omics core research facility of the Universidad Austral de Chile according to the procedure detailed previously in Soto et al. (2022). For multiplexing, three pools (with 16, 18, and 18 samples per pool) of equimolar DNA concentration of 16S rRNA amplicons were generated. Each pool included two negative controls, one of them consisting of an indexed PCR product using ultrapure water as template and the other one a negative control obtained during the DNA extraction. For the 18S rRNA amplicons, three pools (27, 20, and 5 samples per pool) were generated. Two negative controls were included in each pool.

In the case of bioinformatic analyses of 16S rRNA and 18S rRNA gene amplicons, they also were conducted as detailed in Soto et al. (2022). After filtering and chimera's removal, a total of 1,536,943 unique sequences for the 16S rRNA gene amplicon were obtained and 2,450 ASVs were identified after removing singletons, sequences that occurred in only one sample, and potential contaminants. With regards to the 18S rRNA gene amplicon, we obtained a total of 3,511,719 unique sequences and 500 ASVs were identified. The resulting sequences were used to construct rarefaction curves verifying the sampling depth for the analysis of bacterial and eukaryotic communities (Supplementary Fig. 1).

Inferring community assembly mechanisms

Sequences alignments were conducted under the ClustalW method using the R package msa (Bodenhofer et al., 2015). In addition, maximum-likelihood phylogenetic trees were constructed using FastTree 2.1.11 (Price et al., 2010).

Quantification of the relative influences of community assembly processes were assessed using the R package iCAMP, which is a phylogenetic bin-based null model framework (Ning et al., 2020). iCAMP divided taxa into different groups (bins) based on their phylogenetic relationships. Then, the process governing each bin is identified based on null model analysis of the phylogenetic diversity using beta Net Relatedness Index (βNRI), and taxonomic β‑diversities using modified Raup–Crick metric (RC). For each bin, the fraction of pairwise comparisons with βNRI < −1.96 is considered as the percentages of homogeneous selection, and βNRI > +1.96 as the percentages of heterogeneous selection (Stegen et al., 2015). Additionally, pairwise comparisons with |βNRI| ≤ 1.96 and RC < −0.95 are treated as the percentages of homogenizing dispersal, while those with RC > + 0.95 as dispersal limitation (Stegen et al., 2015). The remainders with |βNRI| ≤ 1.96 and |RC| ≤ 0.95, represent the percentages of drift, diversification, weak selection, and/or weak dispersal (Ning et al., 2020; Zhou and Ning, 2017). Finally, to estimate the relative importance of individual processes at the whole community level, the fractions of individual processes across all bins are weighted by the relative abundance of each bin.

In this study, results were summarized for each group of samples collected the same date, and then the succession of relative importance of each ecological process was compared between consecutive sampling dates. The significance of differences was based on bootstrapping (1000 replications).

Determining the potential for biotic interactions and metabolites exchange

SMETANA (Zelezniak et al., 2015) was used to assess the potential for biotic interactions and the exchange of metabolites between bacteria and algal species. For this analysis, we used Polaromonas, Pedobacter, Cryobacterium bacteria genome-scale metabolic models — previously generated in Soto et. al (2022) — and Chlamydomonas sp. ICE-L and Chlamydomonas sp. UWO 241 reconstructed genome-scale metabolic models that were generated using CarveMe (Machado et al., 2018).

Genome assemblies of Chlamydomonas sp. ICE-L (accession number PRJNA636631) (Zhang et al., 2020) and Chlamydomonas sp. UWO 241 (accession number PRJNA547753) (Kalra et al., 2020) were obtained from GenBank ^® genetic sequence database at NCBI. Protein-coding sequences were predicted from both genomes assemblies using AUGUSTUS (Stanke and Morgenstern, 2005) and the genome of Chlamydomonas reinhardtii as the model training set for gene prediction.

Assuming a minimal medium, the SMETANA score was calculated to estimate the potential exchange of metabolites in the microbial snow algae community (Zelezniak et al., 2015). Only exchanged compounds with scores > 0.5 following SMETANA were included in this study, meaning that at least in 50% of the simulations those compounds were exchanged between bacterial and algal species.

Statistical Analysis

Rarefaction curves were generated using Vegan package under R v3.6.1 (Oksanen et al., 2007). In addition, Shannon-Wiener index and Chao 1 Richness was calculated and illustrated using the packages phyloseq (McMurdie and Holmes, 2013) and ggplot2 (Wickham, 2016). Non-parametrical Kruskal-Wallis test was used to compare diversity and richness values during the formation of the algae blooms, using sampling date as categorical variable.

Metabarcoding RDA were based on Hellinger distance (Legendre and Gallagher, 2001) which was calculated using the R package BiodiversityR (Kindt and Kindt, 2015) and the reads count of bacterial and eukaryotic ASVs. RDA models were constructed considering the concentration of NH₄⁺, NO₃^-, PO₄^-3 and DOC as constraining variables and test the significance of each variable performing an ANOVA like permutation test for redundancy analysis (permutations = 999).

Correlation (Pearson correlation) matrices of bacterial genera against eukaryotes were computed and plotted using the Hmisc and corrplot R packages, respectively (Harrell Jr and Harrell Jr, 2019; Wei et al., 2017). For this analysis we used taxa that significantly contributed to the assembly of the microbial communities and were taxonomically assigned at genus level.

Finally, the figures were generated using the R packages ggplot2.

Temporal variation on the microbial composition of snow algae blooms

During the field experiment, 60 cm of accumulated snow melted in a 35-day period (Fig. 2). In addition, snowing led to accumulation of 2-3 cm of fresh snow, which melted fast due to the increase of air temperatures above 0 ºC during the second half of December (Fig. 2). This increase was also associated with the development of the snow algae blooms, reflected in increase of Chl a towards the end December (Fig. 2), producing both green and red blooms (Fig. 3).

At the beginning of the temporal series the most abundant bacterial genus was Psychrobacter accounting up to 48 % (mean relative abundance), followed by Tissierella (10 %) and Sporosarcina (10 %) (Fig. 4). However, their abundances declined with time. In contrast, at the end of December, the most abundant genus was Polaromonas reaching up to 27 %, followed by the genera Flavobacterium (24 %), and Rhodoferax (16 %) (Fig. 4). Finally, bacterial alpha diversity (Shannon-Wiener index and Chao1 richness) did not vary significantly during field experiment (Supplementary Fig. 2).

In the case of the eukaryotic community, at the beginning the most abundant organisms were fungi of the genera Davidiella (relative abundance 42 %), Teratosphaeriaceae (13 %), and Paraconiothyrium fungi (9 %) (Fig. 4). However, the abundances of all these species abruptly declined three days later (28-11-2019) to less than 1 % when the community was dominated by algae of the genus Chloromonas (74 %) (Fig. 4). As the field experiment progressed, Chloromonas algae dominated the eukaryotic communities with relative abundances between 10 % and 74 %. However, since mid-December, snow algae of the genus Chlamydomonas increased their abundance reaching up to 36 % in the last sampling day (29-12-2019) (Fig. 4). Finally, like the bacterial community, alpha diversity estimated from Shannon-Wiener index and Chao 1 richness did not vary significantly during the formation of snow algae blooms (Supplementary Fig. 3).

Influence of nutrients content in the microbial community structure

The effect of the levels of nutrients (NH₄⁺, NO₃^- and PO₄^-3, and DOC) in the snow on the community structure of bacteria and eukaryotes inhabiting snow algae blooms was assessed trough an RDA model and ANOVA like permutation tests. Our results showed that the bacterial community structure was affected significatively by the levels of NO₃^-(F_1,41= 2.1012, p=0.011), and DOC (F_1,41= 1.9364, p=0.016), while eukaryotes community structure was affected only by the levels of DOC (F_1,41= 2.9688, p=0.003). However, our RDA models captured a small percentage (bacteria= 7.9 %; eukaryotes= 7.7 %) of the variation of bacteria and eukaryotes (Fig. 5).

Ecological processes governing the microbial assembly in snow algae blooms

Based on the principle of the null models employed by iCAMP, our results show that both deterministic and stochastic processes drive the temporal variation of snow algae blooms. However, the contribution of each ecological process differed between the bacterial and the eukaryotic community. For bacteria, the estimated importance of stochasticity (dispersal limitation, homogenizing dispersal, and drift) during the formation of algae blooms, averaged 29 %. Interestingly, the contribution of dispersal limitation to the assembly of the community, declined from a mean value of 25 %, in the first half of December, to 8 % at the end of December (Fig. 6). In contrast, the importance of deterministic selection (homogeneous and heterogeneous selection) averaged 71 % along the whole sampling period and reached a peak of ~ 80 % towards the end of December. In the case of the eukaryotic community, changes in diversity due to stochasticity averaged a 61 %, with dispersal limitation and drift being the most relevant processes (25 % and 33 %, respectively), and selection importance accounted for the 39 % (Fig. 7). The importance of the ecological processes governing the assembly of the eukaryotic communities fluctuate during the formation of the algae blooms. For instance, the importance of homogeneous selection reached its lower point (13 %) on at the end of December, being between 32 % and 58 % the rest of the time. Likewise, drift increased its importance from 18 %, at the beginning of the field experiment to ~ 40 % towards the end of December. However, this increase was not linear, and the importance of drift fluctuated over time between ~20 % and ~40 %, stabilizing on contributions higher than 40 % towards the end of December. Finally, dispersal limitation importance also varied during the formation of algae blooms reaching its peak at the beginning (late November) and the end of the field experiment (end of December) (Fig. 7).

Taxonomic groups influencing the microbial assembly and potential biotic interactions

The taxonomic groups that most contributed to homogenous selection process were bacteria of the genera Flavobacterium, Polaromonas, Marinobacter, and Psychrobacter. Flavobacterium bacteria impacted the microbial assembly during the whole period of snow algae blooms formation, while its contribution increased towards the end of the field experiment (Fig. 8). In addition, the contribution of Psychrobacter and Marinobacter to homogenous selection was greater at early stages of the bloom formation, contrasting with Polaromonas bacteria that played a more prominent role in the microbial assembly during the last half of December.

The assembly of eukaryotic communities of snow algae blooms was mostly impacted by dispersal limitation, drift, and homogenous selection. The greatest contributions to dispersal limitation were algal species of genus Chlamydomonas and the order Chlamydomonadales. In contrast, algae of the genus Chloromonas and the order Prasiolales, mostly contributed to the processes of drift and homogenous selection (Fig. 8). Finally, fungi of the family Chytridiomycetes impacted drift and homogenous selection.

Correlations between bacterial and eukaryotic species that contributed to the microbial assembly revealed positive and significant correlations between Chlamydomonas algae and Polaromonas, and Cryobacterium bacteria (Fig. 9). Likewise, Davidiella fungi correlated with bacteria of the genus Sporosarcina. In addition, Polaromonas bacteria positively co‑occurred with bacteria of the genera Flavobacterium and Cryobacterium.

In addition, some of the species that positively correlated had the potential of exchanging compounds. For instance, Chlamydomonas algae can provide Fe (III), uracil, D-ribose, nicotinate, oxygen, benzoate, L-arginine, H₂O, and glycerol‑3‑phosphate to bacteria of the genus Polaromonas. Likewise, Chlamydomonas algae might be source of ornithine, oxygen, and thiamin to bacteria of the genus Cryobacterium. Finally, when using Chlamydomonas reinhardtii (a model organism used for predicting genes with AUGUSTUS), only Fe (III), oxygen, oxidized glutathione, and benzoate were predicted to be transferred from algae to bacterial species (Fig. 10).

During the 2019/2020 austral summer, exceptional high temperatures were recorded across Antarctica, leading to melting of ice and exposure of new ice-free areas (Robinson et al., 2020). These exceptional conditions were driven by anomalously long periods of sustained air temperatures ≥0 ∘C (55 to 90 h) from late November onwards, which affected meltwater refreezing (Banwell et al., 2021). Our results showed that at O’Higgins (in the northern part of the West Antarctic Peninsula), these warm conditions conducted to a fast development of snow algae blooms during December 2019. Moreover, along with the establishment of snow algae blooms we also observed a rapid color change from green to red in a two-weeks period. Thus, as previously reported, the appearance of snow algae is likely determined by absence of snowfall and air temperatures above melting point (Onuma et al., 2016).

In this rapid progression of the snow algae bloom formation, we could record the temporal variation in the microbial composition and the dynamics of the main mechanisms that governed the community assembly highlighting that both the species and the ecological process change with time.

Deterministic processes govern the assembly of bacterial communities

During the formation of snow algae blooms, the bacterial community assembly was mostly governed by selective pressure resulting from consistent environmental conditions (homogeneous selection). One of the environmental factors that might impose a selective pressure on the community is the availability and levels of nutrients. For instance, phosphorus has commonly been regarded as a major nutrient controlling freshwater productivity, whereas nitrogen drives the productivity of marine coastal waters (Crain, 2007; Paerl, 2009). However, in this study we showed that although DOC and NO₃^-impacted the community structure of bacteria inhabiting snow algae blooms, their contribution was low. During the summer season, some coastal snowfields across the Maritime Antarctic receive a high input of these nutrients due to the presence of penguin colonies (Abakumov, 2018). Therefore, it could consistently be argued that snow algae blooms in this region flourish in a macro nutrient-saturated environment and the variation of nutrients levels has a minor role in the assembly of the bacterial community.

Another possible selective force is environmental stressors filtering. This type of filtering selects related taxa that share evolutionarily conserved traits that allow them to tolerate harsh environmental factors, while taxa lacking these traits fail to become established (HilleRisLambers et al., 2012). It has been reported that bacteria inhabiting snow algae blooms share a consistent functional profile with features related to cold environments (Soto et al., 2022), thus factors such as low temperatures, high solar radiation, changing water availability, play a major role selecting the species that form the bacterial community (Soto et al., 2022).

Selective pressures also arise from biotic interactions (Benkman, 2013) and highly abundant microbial taxa may inhibit other microbes or may benefit from positive interactions with different community members (Cosetta and Wolfe, 2019). Our results showed that bacteria diversity and richness did not vary significantly during the formation of the blooms. However, as snow algae increased their abundance, the bacterial community was dominated by a couple of taxa frequently reported in snow algae blooms such as Polaromonas and Flavobacterium (Darcy et al., 2011; Soto et al., 2020; Yakimovich et al., 2020). Here, bacterial taxa that significantly contributed with the assembly of the community such as Polaromas correlated positively with Chlamydomonas, as well as bacteria of the genus Cryobacterium, suggesting a potential biotic interaction.

According to our genomic-reconstructed models, an inter-kingdom exchange of vitamins, amino acids, sugars, and inorganic ions can be settled between Chlamydomonas algae and Polaromonas bacteria. These exchanges were simulated in a minimum medium and highlight the benefits of the algae-bacteria interactions in case of nutrient limitation. For instance, Chlamydomonas algae can donate D‑Ribose and glycerol 3-phosphate to Polaromonas bacteria. D-Ribose is a pentose sugar commonly found as a component of nucleic acids that can be utilized by many bacteria as a sole carbon source (Oh et al., 1999), and glycerol 3‑phosphate plays a major role in glycolysis and phospholipid biosynthesis in the cell (Lemieux et al., 2004). These findings support the idea that biotic interactions between Antarctic Polaromonas strains that lack the ability to degrade complex carbohydrates and algal cells able to metabolize complex carbohydrates are drivers of Polaromonas bacteria colonization in Antarctic ecosystems (Gawor et al., 2016).

Finally, the exchange of certain metabolites might contribute to the tolerance to harsh environmental conditions. For instance, Chlamydomonas algae may act as a supplier of thiamin and ornithine to Cryobacterium bacteria. Thiamin, known as vitamin B1, may have an indirect role alleviating oxidative stress, by providing NADH and NADPH to the antioxidant network, or directly, by acting as an antioxidant (Asensi-Fabado and Munné-Bosch, 2010). Likewise, ornithine may be involved in the stress tolerance of Cryobacterium bacteria because it is a precursor of putrescine (Di Martino et al., 2013), a polyamine that accumulated under o conditions such as low pH, anoxia, heavy metals, low Mg⁺², cold or osmotic stress (Cui et al., 2020).

Stochastic processes govern the assembly of eukaryotic communities

Despite the contribution of selective forces (homogenous selection) to the assembly of the eukaryotic community in snow algae blooms, stochasticity (dispersal limitation and drift) can be regarded as the most relevant factor. Dispersal is predominantly passive (Nemergut et al., 2013) and when selection pressure is weak, and the dispersal rate between communities is limiting, the dynamics of turnover within a community are subject to stochastic changes in species abundance and random variations in population (Evans et al., 2017).

At first, this finding might result contradictory because the ubiquitous presence of snow algal species supports the idea that there is a global dispersal mechanism influencing the distribution of these organisms (Brown and Jumpponen, 2019; Segawa et al., 2018). Moreover, snow algae have a consistent functional profile related with the tolerance to the environmental stressors in cold habitats (Gálvez et al., 2021; Procházková et al., 2020, 2018; Soto et al., 2022) and it has been proposed that highly specialized taxa may be more likely to be cosmopolitan (Brown and Hovmøller, 2002; Davison et al., 2015). However, regarding their proliferation patterns, it has been shown that snow algae colonize discrete snow patches producing colorful blooms, and that each species within a patch is highly clonal (Brown et al., 2016).

A key factor to integrate the cosmopolitan nature of snow algae and the dispersal limitation recorded in this study might be their life cycle. Snow algae of the order Chlamydomonadales have a distinct life cycle consisting in a vegetative flagellate stage and an immobile cyst or spore stage (Remias, 2012). The encysted phase of snow algae is characterized by the accumulation of secondary carotenoids that may act like photoprotective compounds by absorbing visible wavelengths and dissipating the excess radiant energy as heat (Bidigare et al., 1993; Dial et al., 2018). In addition, encysted cells have a high abundance of storage and reserve metabolites likely to face stressful conditions (Lutz et al., 2015). In contrast, during the vegetative phase of snow algae, their metabolic profile is characterized by key metabolites involved in growth and proliferation and the dominant pigment is chlorophyll.

During the melting season, snow algae usually germinate from underlying resting spores on the ground surface that have been dormant from the previous melting season (Hoham et al., 2006; Jones et al., 2001). The ability of germinate from dormant cysts is likely a key factor in the dispersion of snow algae. In fact, dispersion has been regarded as key mechanism connecting very distant communities, and is particularly relevant for microorganisms with dormant phases in their life cycle (Mestre and Höfer, 2021). Thus, our findings suggest that dispersal limitation and drift govern the snow algae community assembly while the cells are in vegetative state, while the cosmopolitan feature of snow algae is related with the dormant phase.

Concluding remarks

Our results revealed that the assembly of bacterial and eukaryotic community within snow algae blooms is driven by different ecological mechanisms. At first, we propose that snowing events allow the aeolian deposition of microorganisms. Then, in the case of bacteria, the first selective force is likely the environmental filtering that selects taxa highly adapted regardless that they are less likely to have dispersal barriers. Thus, post‑depositional selection occurs because factors like low temperatures, rapid osmotic shifts, and high radiation select taxa able to tolerate these abiotic stressors. In addition, snow algae cells remain dormant until the environmental conditions are propitious for their flourish. Finally, in a macro nutrient rich environment, selection is driven by the constant environment’s constraints present in Antarctic snow (Initial Selection, Fig. 11).

In a second stage, snow melting increases the water availability that along with high radiation during the spring-summer season, allow the development of snow algae. Consequently, encysted dormant cells change to vegetative and increase their abundance forming colored blooms. Although snow algae should cope with harsh environmental conditions, selective forces related with stress filtering are weaker in this stage than processes such as dispersion limitation and drift. When algal propagules successfully establish under favorable conditions they reproduce asexually with a low potential to dispersal in this vegetative stage. Alternatively, snow algae flourishing may be a result of reestablishment of algae in situ from the previous growing season (Flourishment of snow algae, Fig. 11). In contrast, bacteria remain governed by selective pressure with the potential of generating biotic interactions (e.g Chlamydomonas algae and Polaromonas bacteria). Meanwhile, fungal species increase their abundance likely because the higher nutrient availability.

Finally, after snow algae complete their life cycle, cells increase the production and accumulation of secondary carotenoids, loose their flagella, and become dormant. In this state, snow algae may remain in the same site until the next melting season or be air transported to other sites (End of the cycle, Fig.11). Therefore, despite the increasing occurrence of snow algae blooms in maritime Antarctica, these microbial communities are threatened if the snow melts faster than the transition of snow algae from vegetative cells to dormant propagules.

Acknowledgements

Logistic support supplied by the Instituto Antártico Chileno (INACh) and the staff at the scientific station Professor Julio Escudero during the ECA 56 are greatly acknowledged. In addition, we acknowledge the logistic support at O’Higgins Base of the Chilean Army endowment 2020 during ECA 56. Finally, we thank AUSTRAL-omics for the service of metabarcoding.

Authors contribution

Daniela F. Soto designed the study and methodology, performed the formal analysis, generated all figures, and wrote the original draft. Iván Gómez participated in designing the study, reviewed and edited the manuscript, and supported the funding acquisition. Pirjo Huovinen participated in designing the study, reviewed and edited the manuscript, and supported the funding acquisition.

Funding

This work was funded by the Fondecyt grants #1201053 and the Fondap IDEAL #15150003.

Availability of data and materials

Raw metabarcoding snow datasets are deposited in the European Nucleotide archive under study number PRJEB55994. Metagenome-Assembled Genomes (MAGs) can be found under the study number PRJEB42843.

Ethics approval and consent to participate

The research in the Antarctic was carried out under the permission granted by INACh in accordance with the Protocol on Environmental Protection to the Antarctic Treaty.

Consent for publication

All authors consent to the publication of this manuscript and declare that declare no conflicts of interest.

Competing interests

The authors declare that they have no competing interests.

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Antarctic snow algae: unraveling the processes underlying microbial community assembly during blooms formation

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