Morphological Control of Microbial Ecosystems and Carbon Cycling in Greenlandic Cryoconite Holes

Morphological Control of Microbial Ecosystems and Carbon Cycling in Greenlandic Cryoconite Holes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Morphological Control of Microbial Ecosystems and Carbon Cycling in Greenlandic Cryoconite Holes Nozomu Takeuchi, Segawa Takahiro, Takumi Murakami, Koki Ishiwatari, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7116261/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract Cryoconite holes, small water-filled cylindrical pits on glacier surfaces, are crucial microbial habitats and play a pivotal role in darkening the Greenland Ice Sheet, potentially accelerating ice melt. To understand how their morphology influences microbial ecosystems and biogeochemical functions, we investigated cryoconite hole dimensions, microbial communities, and cryoconite characteristics across Issunguata Sermia Glacier, southwest Greenland. Our findings reveal distinct morphological gradients: cryoconite holes were shallower in the rough crevasse zone near the glacier margin and significantly deeper in the flat ice zone at the glacier's center. These morphological differences were strongly linked to disparities in phototrophic community composition and relative abundance, including filamentous cyanobacteria and glacier algae, between the deeper and shallower holes. Furthermore, cryoconite from deeper holes exhibited significantly higher organic content and enriched carbon stable isotope signatures, suggesting enhanced in-situ microbial productivity, despite consistent meltwater geochemistry and mineral compositions across all sites. Our results unequivocally demonstrate that glacier surface topography primarily drives cryoconite hole development, critically shaping localized microbial communities, carbon cycling, and albedo feedbacks. This study highlights the complex physical-biological interplay in glaciers, offering crucial insights into ice sheet melt and carbon dynamics in a changing polar region. Earth and environmental sciences/Climate sciences/Cryospheric science Biological sciences/Microbiology/Environmental microbiology/Water microbiology Biological sciences/Ecology/Microbial ecology Earth and environmental sciences/Biogeochemistry/Carbon cycle Earth and environmental sciences/Ecology/Stable isotope analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Cryoconite holes are ubiquitous water-filled cylindrical depressions on ablating glacier ice surfaces worldwide. Typically ranging from 1 to 30 cm deep and 1 to 50 cm in diameter, some can exceed 50 cm in diameter and 60 cm in depth (1). These holes form as dark-colored cryoconite sediment, accumulated at their base, absorbs solar radiation, enhancing localized ice melt (2). Beyond their physical formation, cryoconite holes are vital microhabitats for diverse microbial life, playing a crucial role in glacier ecosystems. Filled with stagnant meltwater, they provide essential environments for organisms thriving on glaciers. During the melting season, these holes host a rich community of phototrophs, including cyanobacteria and algae, alongside heterotrophs like micro-invertebrates, fungi, and bacteria (e.g., 2−8). Filamentous cyanobacteria, in particular, are instrumental in forming cryoconite granules – dark, granular mats composed of entangled particles, mineral dust, and organic matter (9−11). These granules constitute the primary sediment within the holes, contributing to their structural integrity (12). Cryoconite holes thus serve not only as stable microbial habitats but also as critical nutrient sinks, facilitating material recycling (13). Consequently, their formation and distribution significantly influence microbial production and biomass on glaciers (14, 15). The distribution and dimensions of cryoconite holes are highly heterogeneous across glacier surfaces, primarily controlled by local physical conditions. On the Greenland Ice Sheet, for instance, hole depth has been observed to increase with elevation (16), attributed to lower melt rates and reduced hydrological disturbance at higher altitudes due to declining air temperatures, while the melt rate at the hole floor remains largely governed by solar irradiance (1, 17, 18). Furthermore, hole formation and development are linked to ice surface roughness, slope angle, gradients, ice crystal size, and surface albedo (19, 20, 21), and their depth can be influenced by ice thermal conditions (22). Deep holes are prevalent in the lower ablation area of West Greenland, where cold ice from dry-snow accumulation zones emerges, contrasting with the higher ablation area, where warmer ice and a dirty, slushy surface without distinct holes are observed (23). Despite these insights, the comprehensive suite of factors governing the spatial variability of cryoconite holes on glaciers remains incompletely understood. Beyond their direct biological significance, the heterogeneous distribution of cryoconite holes and other ice surface structures profoundly impacts meltwater hydrology in the ablation zone. Meltwater initially permeates the porous surface weathering crust (24), subsequently flowing into cryoconite holes and/or supraglacial ponds before draining through complex supra-, en-, and sub-glacial systems (25, 26, 27). Therefore, the density and spatial arrangement of cryoconite holes likely influence meltwater residence time, thereby affecting the temporal patterns of glacier discharge (28). Moreover, microbes themselves migrate through meltwater pathways within the weathering crust, entering and exiting cryoconite holes (29, 30). This highlights the multifaceted role of cryoconite holes in not only regulating meltwater hydrology but also serving as critical conduits for nutrients, dust, and microbes on glaciers. In recent decades, the southwestern Greenland Ice Sheet has experienced a notable expansion of dark-colored ice surfaces (31, 32), contributing to accelerated mass loss. This darkening is primarily attributed to the blooming of pigmented glacier algae (e.g., 33), but also partially to the dispersion of cryoconite (2, 34, 35). While cryoconite within holes typically has limited direct impact on overall surface darkening due to low coverage (usually <1% of the ablation area; 36), the collapse of holes under specific weather conditions can disperse cryoconite, significantly contributing to dark ice expansion (34, 37). Numerical models suggest hole collapse occurs under warm, cloudy, and/or windy conditions (18). This process implies a strong association between surface darkening via cryoconite dispersion and the spatial variability in the density and dimensions of cryoconite holes across the ice sheet's ablation zone. Crucially, the dimensions of cryoconite holes can also directly influence the microbial community and productivity within them. The intensity and spectral properties of solar radiation reaching the bottom of the hole, a key factor for phototrophs, are highly dependent on hole depth (18). Given that different phototroph species possess specific pigment compositions optimized for various light conditions (38), their community structure may shift with depth. Furthermore, deeper holes tend to be more stable and persistent over longer durations, potentially ranging from days to years depending on dimensions (19, 28). Such variations in material residence time within the holes can significantly impact the cycling of dust, organic matter, and nutrients, consequently affecting microbial community composition, productivity, and ultimately the total abundance of cryoconite and surface albedo on the glacier. While previous research suggested that cryoconite holes can adapt their shapes to maintain stable autotrophic habitats, a concept termed biocryomorphic evolution (39), comprehensive studies demonstrating the spatial variabilities in cryoconite hole dimensions and their specific effects on cryoconite formation and microbial communities remain scarce. In this study, we addressed this knowledge gap by investigating the spatial variations in cryoconite hole dimensions, microbial communities, and cryoconite characteristics along a transect across Issunguata Sermia Glacier in southwest Greenland (Fig. 1). This glacier is an outlet glacier flowing westward from the Greenland Ice Sheet and is characterized by a central flat ice zone with relatively high ice movement velocity (mean 95 m year⁻¹, range 18–328 m year⁻¹) and a marginal zone featuring rough, crevassed surfaces (40) due to shear stresses resulting from spatially heterogeneous ice flow velocity (41). We aim to elucidate the intricate relationships between surface topography, hole morphology, microbial ecology, and carbon cycling within the bare ice area of the Greenland Ice Sheet. Results Variations in dimensions of cryoconite holes across a transect Cryoconite hole dimensions exhibited significant spatial variation along the transect (Figs. 2, 3), revealing two distinct morphological zones: a marginal crevasse zone (Sites S1–S12) and a central flat ice zone (S13–S20). The mean depth of cryoconite holes across the entire transect was 17.8 cm, ranging from 4.6 cm to 28.7 cm. A statistically significant difference in depth was observed between the two zones (t = 2.12, p < 0.01): the mean depth in the central flat zone (21.9 cm) was almost twice that in the marginal crevasse zone (11.0 cm). Consistent with depth, the mean water level from the hole bottom was 13.1 cm, with the flat zone showing approximately twice the water level of the crevasse zone (17.0 cm vs. 8.7 cm; t = 2.110, p < 0.01). Based on these clear distinctions, we refer to holes in the crevasse zone (S1–S12) as "shallower holes" and those in the flat zone (S13–S20) as "deeper holes" throughout this study (Fig. 2). Hole diameter showed less variation than depth, with a mean longest diameter of 4.2 cm (range: 2.5–7.5 cm) across the transect. While some exceptionally large holes (>20 cm) were observed, their limited number led to their exclusion from this specific dimensional analysis. The mean diameter in the crevasse zone was slightly smaller (3.2 cm) than in the flat zone (5.2 cm). Notably, relatively large holes (6.8–7.2 cm) were found at sites S13 and S14, which represent the transition area between the crevasse and flat zones. Phototroph Community Structure in Cryoconite Holes based on Microscopy Microscopic analysis identified various phototroph taxa within cryoconite samples (Fig. 4). These included typical glacier algae such as Ancylonema ( A .) nordenskiöldii , A. alaskana , and Cylindrocystis ( Cyl .) brébissonii . Additionally, at least two taxa of filamentous cyanobacteria were observed: Phormidesmis ( P .) priestleyi (thin filament, ~1.2 µm diameter), a common Arctic glacier species, and a thick, brown-colored Calothrix -like cyanobacterium, morphologically resembling Calothrix ( Cal .) parietina , widely reported in Arctic cryoconite (41). Both filamentous cyanobacteria frequently co-aggregated with mineral dust and organic matter to form cryoconite granules. The phototroph community structure, assessed by biovolume, varied significantly along the transect, showing clear distinctions between shallower and deeper holes (Fig. 5). In the shallower holes, phototroph communities were predominantly composed of the three glacier algae ( A. nordenskiöldii, A. alaskana, and Cyl. brébissonii ), with the exception of sites S2 and S3 (closest to the glacier margin), where P. priestleyi accounted for over 80% of the total cryoconite biovolume. Conversely, deeper holes were characterized by the predominance of Calothrix -like cyanobacteria, contributing 40–60% of the total biovolume at each site. 16S rRNA Gene Microbial Communities in Cryoconite Amplicon sequencing of the 16S rRNA gene from cryoconite samples yielded approximately 510,000 high-quality reads, clustered into 705 Amplicon Sequence Variants (ASVs). Taxonomic classification identified nine dominant phyla or classes: Cyanobacteria, Alphaproteobacteria, Armatimonadota, Chloroflexota, Gammaproteobacteria, Bacteroidota, Actinomycetota, Acidobacteriota, and Planctomycetota (Fig. 6a). Overall microbial community composition differed significantly between shallower and deeper cryoconite holes (PERMANOVA, p < 0.05; Fig. 6a). Crucially, cyanobacterial composition also varied significantly with hole depth (PERMANOVA, p < 0.01; Fig. 6b). All detected cyanobacterial ASVs were associated with Operational Taxonomic Units (OTUs) previously identified in phylogenetic studies of cryoconite-forming cyanobacteria (42) (Supplementary Fig. S1). In the deeper cryoconite holes, three cyanobacterial lineages predominated: P . priestleyi (OTU1), an unclassified cyanobacterium (OTU16), and Chamaesiphon (OTU3). Single-filament PCR analysis identified OTU16 as a Calothrix -like cyanobacterium (Fig. 6 and Supplementary Figs. S1–S2), although phylogenetic analysis revealed it belonged to an unidentified lineage distinct from canonical Calothrix . Thus, OTU16 is more appropriately referred to as an unidentified cyanobacterium despite its morphological resemblance to Cal . parietina . These findings suggest that cryoconite-forming cyanobacteria previously identified as Cal . parietina may encompass novel, morphologically cryptic lineages. In contrast, OTU16 and OTU3 were minor or nearly absent in shallower holes. Instead, OTU1, Pseudanabaena (OTU0 and OTU5), and Nostoc (OTU2) became predominant. These results collectively highlight a strong depth-dependent variation in cyanobacterial community structures. Several cyanobacterial OTUs, including OTU1, OTU3, and OTU16, comprised multiple ASVs, indicating species- or strain-level diversity within these lineages (Fig. S1). We observed that some closely related ASVs exhibited distinct distribution patterns. For instance, ASV_0 and ASV_2 (within OTU1), and ASV_8 (within OTU16) were prevalent in both deeper and shallower holes. However, closely related ASV_5 (within OTU1) and ASV_34, ASV_54, ASV_74, and ASV_106 (within OTU16) were specifically abundant in deeper holes (Supplementary Fig. S3). This demonstrates that cryoconite hole dimensions influenced cyanobacterial lineage composition at both the OTU and finer ASV levels. Carbon and Nitrogen Contents and Stable Isotope Ratios of Cryoconite Cryoconite carbon (C) and nitrogen (N) contents varied along the transect (Fig. 7, Supplementary Table S1). Overall, mean C and N contents were 2.09 ± 1.15% and 0.21 ± 0.11%, respectively, ranging from 0.31% to 4.09% for C and 0.02% to 0.37% for N. Both C and N contents generally increased from marginal to central sites, showing a significant difference between deeper and shallower holes (C: t = −5.95, p < 0.01; N: t = −5.32, p < 0.01). Specifically, the mean C and N contents in deeper holes were 2.3 and 2.1 times greater, respectively, than in shallower holes (C: 3.17% vs. 1.37%; N: 0.31% vs. 0.15%). Similarly, organic matter contents in cryoconite, ranging from 1.1% to 8.8% (mean 4.8%; Table S1), were significantly higher in deeper holes (7.22%) compared to shallower holes (3.22%). The mean C/N ratio of cryoconite organic matter was 9.9 ± 1.0. While it varied from 8.2 to 12.9 in shallower holes, it was relatively less variable (9.7 to 11.0) in deeper holes. No significant difference in the C/N ratio was found between deeper and shallower holes (t = −1.2635, p > 0.05). Cryoconite carbon and nitrogen stable isotope ratios also varied along the transect, generally increasing from the glacier margin to its central part. Carbon isotope ratios (δ¹³C) ranged from −22.0‰ to −19.9‰ (mean: −21.2 ± 1.31‰), and nitrogen isotope ratios (δ¹⁵N) ranged from −2.43‰ to +1.15‰ (mean: −0.25 ± 1.18‰). Both δ¹³C and δ¹⁵N were significantly higher in deeper holes (δ¹³C: −19.8‰ vs. −22.0‰; t = −7.46, p < 0.01; δ¹⁵N: +0.68‰ vs. −0.88‰; t = −4.49, p < 0.01). Isotope values showed greater variability in the crevasse zone but were relatively stable in the flat zone. Water Stable Isotopes and Major Soluble Ions of Meltwater in Cryoconite Holes The oxygen stable isotope (δ¹⁸O) of meltwater in cryoconite holes generally ranged from −28.3‰ to −24.5‰ (mean: −26.5 ± 2.0‰) across the transect, with an exceptionally low value of −33.5‰ observed at site S4 (Fig. 8a, Supplementary Table S1). Notably, δ¹⁸O values were slightly higher in deeper holes than in shallower holes (−25.5‰ vs. −27.2‰; t = −2.38, p < 0.05). The composition of major chemical solutes in meltwater also varied along the transect (Fig. 8b, Supplementary Table S1). Ca²⁺ was the most dominant solute near the glacier margin (S1–S5, 37.0–59.6% proportion), while Mg²⁺ and K⁺ dominated in the central part (S6–S20, 9.6–22.6% Ca²⁺ proportion). Concentrations of PO₄³⁻ and NO₃⁻ were slightly higher in the central part compared to the marginal parts. PO₄³⁻ was below 0.01 μEq L⁻¹ at the three marginal sites (S1–S3) but ranged from 0.21 to 2.8 μEq L⁻¹ at other sites (S4–S20). Similarly, NO₃⁻ was below 0.20 μEq L⁻¹ at marginal sites (S1–S3) but varied from 0.42 to 3.00 μEq L⁻¹ elsewhere (S4–S20). Importantly, no significant difference in these nutrient concentrations was observed between the shallower and deeper holes. At site S4, Ca²⁺ and SO₄²⁻ concentrations were distinctively high (34.4 and 14.7 μEq L⁻¹, respectively) compared to other sites (0.58–5.62 μEq L⁻¹ and 0.00–1.17 μEq L⁻¹, respectively). Concentrations of NH₄⁺, K⁺, Na⁺, and Cl⁻ varied across the transect but showed no clear trend. Mineralogical Compositions of Cryoconite XRD analysis of cryoconite samples revealed the presence of various silicate minerals (Fig. 9, Supplementary Fig. S4). Quartz, plagioclase, potassium-feldspar, and hornblende exhibited relatively intense peaks across all samples. Weak peaks of clay minerals, including kaolinite, chlorite, and illite, were also detected. Comparison of the relative peak intensities of the minerals among the study sites showed slight variations across the transect, but no significant differences were observed between shallower and deeper holes, or between marginal and central areas (Supplementary Fig. S5). Discussion Relationship between microbial communities and cryoconite hole dimensions Our findings reveal a significant difference in phototrophic communities between the two distinct cryoconite hole morphologies observed, despite minimal variation in the overall bacterial community at the phylum level. Both glacier algae and cyanobacteria, commonly found on Greenlandic and other Arctic glacier surfaces, were present in the holes. Specifically, deeper holes were dominated by Calothrix -like cyanobacteria, while shallower holes were characterized by a prevalence of glacier algae. Although 16S rRNA gene-based cyanobacterial communities did not perfectly align with microscopic biovolume data, they consistently showed distinct differences between the two hole types. Deeper holes exhibited dominance by three specific cyanobacterial OTUs (OTU 1, 16, and 3), whereas shallower holes supported a more diverse community (OTU 0, 1, 2, 3, 5, and 16). This divergence in phototrophic communities is likely driven by variations in light conditions at the hole bottom. Shallower and/or larger-diameter holes receive more direct and intense solar irradiance. In contrast, deeper holes experience shading by their walls, limiting direct sunlight, and receive attenuated indirect light transmitted through glacial ice ( 18 , 43 ). This attenuation is particularly strong at longer wavelengths due to ice absorption, meaning shorter wavelengths are more readily transmitted. Consequently, the spectral property and intensity of light differ significantly between shallow and deep holes. Phototrophs, such as those found in cryoconite, possess diverse pigments to optimize light harvesting for photosynthesis (e.g., 38, 44). Cyanobacteria, for instance, utilize phycobilisomes that can adapt to different light wavelengths ( 45 ). Therefore, it is plausible that phototrophs dominating shallower holes prefer direct, intense solar radiation, while those in deeper holes are adapted to weaker light and a limited wavelength range. The distinct distribution patterns of closely related cyanobacterial ASVs further support the notion that species- or strain-level ecological variations respond to these micro-environmental light differences. However, confirming this hypothesis requires further analysis of phototroph pigment compositions and direct in-situ light measurements within the holes. Beyond light conditions, the supply of cells from the surrounding ice surface may also influence community structure. Glacier algae, known to proliferate on bare ice, can be passively transported by meltwater through the ice weathering crust into cryoconite holes ( 30 ). The observed dominance of glacier algae in shallower holes could be partially explained by an abundant supply of algal cells from the adjacent ice surface, though further investigation into their abundance and movement on the bare ice is needed. Furthermore, differential hole persistence times between shallow and deep holes could contribute to the observed community structures. Shallower holes are more susceptible to collapse under varying weather conditions, leading to more frequent cycles of formation and collapse compared to the more stable and longer-persisting deeper holes ( 19 ). Given that phototroph species have varying growth rates, the stability and longevity of a habitat would favor the establishment of specific communities. The higher organic content found in cryoconite from deeper holes also aligns with this idea, suggesting a longer period for the accumulation of phototroph-derived organic matter. Sources of organic carbon and nitrogen in cryoconite The consistently lower C/N ratios (mean 9.9) of cryoconite compared to surrounding glacier forefield soils (mean 13.07; ( 46 )) strongly indicate that the organic matter within cryoconite holes is predominantly autochthonous, i.e., microbially produced on the glacier, rather than allochthonous (externally derived). Further supporting this, carbon stable isotope signatures (δ¹³C) of cryoconite also point to an internal microbial origin. While aeolian organics in West Greenland range from − 28 to − 27‰ ( 47 ) and terrestrial plants/marsh organics range from − 27 to − 22‰ ( 48 ), cryoconite in deeper holes exhibited distinctively enriched δ¹³C values (− 20.2 to − 19.5‰). This enrichment strongly suggests that carbon in deeper holes is primarily derived from cyanobacterial photosynthesis, consistent with the observed cyanobacterial dominance in these holes and findings from Antarctic glaciers ( 49 ) and previous Greenland Ice Sheet studies ( 50 ). The greater variability in δ¹³C values in shallower holes (− 23.6 to − 20.2‰) likely reflects the mixed contribution from both glacier algae and cyanobacteria, whose differing growth rates and turnover times could lead to varied isotopic signatures. Similarly, nitrogen stable isotope signatures (δ¹⁵N) of cryoconite are distinct from those in glacier forefield soils ( 46 ), further supporting microbial assimilation as the primary source of organic nitrogen in cryoconite. Values close to zero in cryoconite generally indicate nitrogen fixation by cyanobacteria ( 49 ). Although nitrogen fixation is considered limited on the Greenland Ice Sheet surface ( 51 ), the observed δ¹⁵N values likely reflect isotopic fractionation during microbial nitrogen assimilation. The enriched and less variable δ¹⁵N in deeper holes suggests nitrogen limitation within these environments, mirroring observations from Antarctic cryoconite ( 49 ). In contrast, the more variable δ¹⁵N in shallower holes may reflect different nitrogen compounds assimilated by glacier algae. Effect of exposures of basal ice and Pleistocene glacial ice on meltwater geochemistry, cryoconite, and microbial community While variations in microbial communities and organic abundance were observed, meltwater conditions in cryoconite holes, specifically water stable isotopes and chemical solutes, showed no significant difference between shallower and deeper holes. This is a crucial finding, as nutrient availability (e.g., nitrogen, phosphorus), generally scarce on glaciers ( 44 ), largely influences phototroph communities. The lack of significant nutrient differences across the transect (except near the glacier margin) strongly supports our hypothesis that the distinct phototroph communities are driven by hole depth and associated light conditions rather than nutrient availability. Notably, a significantly depleted oxygen stable isotope value (− 34‰) at site S4 indicated the presence of Pleistocene glacial ice, consistent with previous findings near the Greenland Ice Sheet margin ( 52 , 53 ). The area closer to the glacier margin (sites S1 − S3) is likely composed of basal refrozen ice ( 53 ). Although meltwater geochemistry in the marginal area (e.g., higher Ca²⁺ and SO₄²⁻ concentrations at site S4) slightly differed due to these ancient ice sources, its direct impact on microbial communities appeared limited. For instance, despite the distinct geochemistry at S4, no unique phototroph community was observed there (Fig. 5 ). Similarly, while basal ice areas (S1 − S3) showed Ca²⁺ dominance, overall solute concentrations were low. Interestingly, Nostoc-like cyanobacteria (OTU2) were uniquely detected near the glacier margin (S1, S3), suggesting a potential adaptation of this specific OTU to marginal ice conditions. Furthermore, mineral composition in cryoconite, a potential source of phosphorus for microbes ( 54 ), showed no significant variation across the transect based on XRD analysis. This suggests a common, possibly distant or outcropping ( 55 ) origin for these minerals, indicating limited local accumulation of windblown minerals. This consistency further supports the idea that mineral availability was not a primary driver of microbial community differentiation in this study. Two distinct equilibrium states of cryoconite holes and glacier topography The observed two distinct cryoconite hole depths can be conceptualized as different equilibrium states of melt rates between the hole floor and the surrounding ice ( 18 , 21 ). While equilibrium hole depth typically varies with atmospheric conditions (e.g., elevation, weather), such conditions cannot solely explain the depth differences observed across our comparatively small study area. In northeast Greenland, shallower holes have been attributed to the presence of Pleistocene ice characterized by smaller crystal size and dust content ( 20 ). However, in this study, the distribution of shallower holes did not correspond to the extent of Pleistocene ice suggested by lower oxygen stable isotopes. Furthermore, although a geometric interaction between hole depth and diameter has been suggested ( 56 ), our results (Fig. 3 ) did not support this relationship. Instead, our results strongly suggest that glacier surface topography is the primary determinant of these two distinct hole depths. The distribution of deeper holes precisely corresponded to the flat ice zone in the glacier's central part, whereas shallower holes were confined to the rough crevasse zones near the glacier margin. This direct correlation implies that the presence of crevasses significantly contributes to shallower hole formation, possibly by accelerating surface ablation ( 57 ) or altering light exposure due to irregular surface slopes ( 21 ). Further studies are needed to fully elucidate the complex relationship between surface topography and cryoconite hole formation, including thermal conditions potentially influenced by crevasses ( 22 , 23 ). The co-occurrence of distinctive phototroph communities and cryoconite properties with these two depth categories supports our hypothesis that these represent two different states of biocryomorphological equilibrium. The deeper holes, found in flat ice areas, are characterized by abundant cyanobacteria and higher carbon content, consistent with the "equilibrium state" previously suggested for Greenland Ice Sheet cryoconite holes ( 39 ). In contrast, the shallower holes in rough crevasse areas are dominated by glacier algae and exhibit lower, more variable cryoconite carbon content. The relative prevalence of these two equilibrium states across the ice sheet holds significant implications for overall microbial productivity and the carbon cycle. Given that crevasse-occupied areas in West Greenland have significantly expanded in recent decades (e.g., 42% of ablation area, increasing by 13% from 1985 − 2009; ( 58 )), such topography-driven shifts in cryoconite hole equilibrium states could induce substantial changes in carbon production. Therefore, changes in glacier dynamics and surface topography have the potential to significantly alter the spatial distribution of microbial communities and carbon cycling across the ice sheet, providing crucial insights into the localized drivers of ice sheet melt and carbon dynamics in a changing climate. Methods Field work on Issunguata Sermia Glacier The investigation was conducted on August 5th and 6th, 2017, on the ablation ice surface of the Issunguata Sermia Glacier in southwest Greenland (Fig. 1). Measurements of cryoconite hole dimensions and sample collections were performed at 20 distinct sites along a transect extending from the glacier margin towards its central part (Fig. 1). Sites S1 to S12 were situated within the crevasse zone, while sites S13 to S20 were in the flat zone. Cryoconite holes were present at all surveyed sites during the investigation period. At each of the 20 sites, the dimensions (depth, water level, diameter) of over 20 typical circular cryoconite holes were manually measured using a scale. Holes with irregular shapes or those connected to active meltwater flows were excluded from measurements and sampling, as such holes often exhibit distinct microbial activities ( 21 ). Cryoconite sediment at the bottom of the selected holes was collected using a pipette and preserved in plastic bottles. At Site S1, located very close to the glacier margin, the surface was heavily covered with dust and debris, and only shallow holes with irregular shapes were observed. Therefore, only cryoconite was collected at this site, and hole dimensions were not measured. Sample Preservation and Laboratory Analysis For subsequent molecular and microscopic analyses, approximately 1 mL (wet volume) of each cryoconite sample was separated, kept frozen during transport, and then stored at − 80°C until DNA extraction and preliminary microscopic examination. Another portion (approximately 1 g dry weight) of the cryoconite samples was air-dried at a laboratory in Kangerlussuaq International Science Support (KISS) for carbon and nitrogen stable isotope ratio analysis. The remaining cryoconite was fixed with formalin for comprehensive microscopic and mineralogical analysis and long-term preservation. Meltwater from cryoconite holes was also collected with a pipette at each site for analysis of water-stable isotopes and concentrations of major soluble chemical ions. All collected samples were subsequently transported to Chiba University, Japan, for comprehensive chemical, microbial, and mineralogical analyses. Microscopy of Phototrophs Phototrophic community structure was determined using microscopic analysis. To disaggregate cryoconite granules, which are complex aggregates of algae, mineral particles, organic matter, and filamentous cyanobacteria, samples were ultrasonicated. A 200 µL aliquot of the resulting microbial suspension in meltwater was then filtered onto a membrane filter (Omnipore, JH00013, Merck Millipore). Cells of glacier algae and cyanobacteria retained on the filter were enumerated using a fluorescent microscope. Biovolume (cell volume biomass) for each taxon was calculated from the geometric volume of individual cells and their respective counts. As absolute cell concentrations can vary significantly depending on the sampled sediment and meltwater volumes, the phototroph community structure is presented solely as the proportion of each taxon's biovolume relative to the total biovolume. 16S rRNA gene amplicon sequencing analysis DNA was extracted from 0.1 − 0.2 g of cryoconite samples following the protocol of Willerslev et al. ( 59 ), with the exception that bead-beating was performed using a Multi-beads Shocker at 2500 rpm for 30 s (Yasui Kikai, Japan) in 2-mL Matrix-E tubes (MP Biomedicals, USA). DNA extractions were conducted in a Class 100 clean bench (MHE-130AB3; PHCbi, Japan), and subsequent procedures were carried out in another Class 100 clean bench (MCV-131BNS; PHCbi). The absence of contaminating DNA was confirmed by performing the entire procedure (DNA extractions, PCR, library preparation) on blank controls without samples. Microbial community structures were analyzed by 16S rRNA gene amplicon sequencing using the primer pair Bakt 341F and Bakt 805R ( 60 ), each tagged with Illumina overhang adaptor sequences at the 5' ends. The 20 µL PCR mixture contained 1×KAPA HiFi HS ReadyMix (Kapa Biosystems), 0.2 µM of each primer, and 2 µL of template DNA. PCR amplification was conducted under the following conditions: initial denaturation at 95°C for 3 min; 20 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 60 s; and a final extension at 72°C for 5 min. Sample-specific indices and Illumina adapter sequences were added using the Nextera XT index kit v2 (Illumina, USA), and the index PCR was performed as described by Segawa et al. ( 61 ). PCR products were purified, pooled at an equimolar concentration, and mixed with the PhiX control DNA at a ratio of 80:20. Sequencing was performed on the Illumina MiSeq platform using 2 × 300 bp paired-end protocol with the MiSeq Reagent kit v3. The average read count per sample was approximately 51,000 for the 16S rRNA gene. Sequence analysis was performed using the DADA2 package v1.26 ( 62 ). The reads were denoised and clustered into amplicon sequence variants (ASVs), which represent unique biological sequences. Taxonomic assignments were performed by aligning ASVs against the SILVA 138 database using SINA v1.2.12 with a minimum similarity threshold of 0.85 ( 63 ). Similarity of bacterial community structures among samples was calculated based on Bray-Curtis dissimilarity of ASV composition, and structural differences between shallower and deeper cryoconite holes were statistically evaluated using permutational multivariate analysis of variance (PERMANOVA) implemented in the vegan package in R (v3.6.1). Then, ASVs which were differentially distributed between shallower and deeper cryoconite samples were identified using ALDEx2 v1.38.0 ( 64 ), and ASVs with an absolute effect size greater than 1 or a Benjamini-Hochberg-corrected Wilcoxon p-value below 0.1 were considered to be differentially distributed. To directly identify the cyanobacterial phylotypes comprising cryoconite granules in the deeper cryoconite holes, single-filament PCR analysis was conducted. Samples were placed on PPS membrane slides (Leica, Germany) and air-dried. Individual cyanobacterial filaments were isolated using a laser microdissection system (LMD 7000; Leica, Germany). Each dissected filament was transferred directly into a PCR tube containing a sterilized lysis buffer (10 mM Tris-HCl, pH 8.0; 0.1 mM EDTA; 0.1% Tween 20 in Milli-Q water). The tubes were subjected to three freeze–thaw cycles to lyse the cells, followed by incubation at 55°C for 2 hours. Whole genome amplification was then performed using the REPLI-g Single Cell Kit (Qiagen, Germany), and the amplified DNA was subsequently used as a template for PCR. PCR amplification of the 16S rRNA–ITS region was performed using 2× KAPA HiFi HotStart ReadyMix (Kapa Biosystems, USA) with primers 359F ( 65 ) and 23S30R. Thermal cycling conditions were as follows: initial denaturation at 95°C for 3 min; 40 cycles of 95°C for 30 s, 57°C for 30 s, and 72°C for 3 min; and a final extension at 72°C for 5 min. PCR products were purified using the MinElute PCR Purification Kit (Qiagen, Germany), and sequencing was performed using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, USA) on an ABI 3130xl Genetic Analyzer. To determine their phylogenetic affiliations, the obtained sequences were aligned using MAFFT v. 7.52 ( 66 ). A maximum likelihood tree was inferred using IQ-tree v. 1.6.12 ( 67 ) with the GTR + F + I + Γ4 model, and 1000 replications were carried out for standard bootstrap analysis. Analyses of organic matter of cryoconite To measure the organic content in cryoconite, the samples were dried (60ºC, 24 hours) in pre-weighed crucibles in the laboratory. Then, the dried samples were combusted for 1 hour at 500ºC in an electric furnace, and weighed again. The amount of organic matter was obtained from the difference in the weight between the dried and combusted samples. To assess the sources of carbon and nitrogen of organic matter in cryoconite, the carbon and nitrogen isotope ratios of the cryoconite samples were measured with an isotope ratio mass spectrometer (DELTA plus XP, Thermo Fisher Scientific, Inc.) connected to an elemental analyzer (Flash EA, Thermo Fisher Scientific, Inc.) in Research Institute for Humanity and Nature in Kyoto, Japan. Dried cryoconite samples were put in 1% HCl to remove carbonate, and rinsed with Milli-Q water five times, and are dried again. 20 mg of the samples was put in tin capsules and set in the EA sampler with standards. The carbon and nitrogen isotope ratios are expressed as follows: $$\:{\delta\:}{}^{13}\text{C}\:\:\text{o}\text{r}\:\:{\delta\:}{}^{15}\text{N}=\frac{{R}_{sample}-{R}_{standard}}{{R}_{standard}}\times\:1000\:\left(\text{‰}\right)$$ where R sample or R standard is the relative abundance of carbon or nitrogen isotopes ( 13 C/ 12 C or 15 N/ 14 N) of the sample or standard, respectively. The isotope ratios were corrected using multiple secondary standards carefully calibrated to international standards. Carbon isotope ratios were reported against the Vienna Pee Dee Belemnite (VPDB) scale, and nitrogen isotope ratios were reported against atmospheric N 2 . The standard errors of the measurements were less than ± 0.2‰ based on simultaneous measurements of the working standards. Geochemical analysis of meltwater in cryoconite holes To infer the source of meltwater in cryoconite holes, oxygen and hydrogen stable isotope ratios (δ 18 O and δD) of meltwater collected from cryoconite holes were analyzed with a liquid-water isotope analyzer (DLT-100, Los Gatos Research, USA) at Chiba University. The analytical precisions of δ 18 O and δD measurements were 0.05 and 0.5‰, respectively. The results of δ 18 O are presented in this paper. The major soluble chemical ions, including five cations (Na + , NH 4 + , K + , Mg 2+ , and Ca 2+ ) and four anions (Cl − , NO3 − , PO 4 3− , and SO 4 2− ) in meltwater, were also measured with an ion chromatography system (ICS1100, Thermo Fisher, USA). The ion separation columns used are AS12A and CS12A for anion and cation, respectively. Mineralogical analysis of cryoconite The mineralogical composition of the cryoconite was identified by powder X-ray diffraction analysis (XRD) using Geigerflex RAD-2B (RIGAKU, Japan) at Chiba University, Japan. Cryoconite samples were dried at 60°C and then powdered with an agate mortar. The X-ray target was CuKα, the tube voltage was 40 kV, and the tube current was 25 mA. Scans were performed from 2° to 40° (2θ) at a rate of 2° (2θ) per minute. Declarations Competing interests : The authors declare no competing interests. Author contributions: NT designed the study. KI, WA, and NT conducted the field observations. KI conducted analyses of the microscope, organics, and stable isotopes with the support of NT and WA. TS and TM performed the DNA and phylogenetic analyses of microbes in the samples. NT, TS and TM analysed the data and wrote the manuscript. All authors contributed to the discussion and reviewed the draft of the manuscript. Acknowledgments We would like to thank all members of the project of SIGMA2 for their support in the field campaign of 2017. We thank Ichiro Tayasu and Chikage Yoshimizu for their support of carbon and nitrogen stable isotope analysis, Noboru Furukawa for XRD analysis. This study was financially supported by Grant-in-Aids (23221004, 24H00260) and by the Arctic Challenge for Sustainability II (ArCS II, Program Grant Number JPMXD1420318865). Analyses of carbon and nitrogen isotopes were conducted by the support of Joint Research Grant for the Environmental Isotope Study of Research Institute for Humanity and Nature. Data availability: The raw Illumina sequence data have been deposited in the DDBJ Sequence Read Archive under accession number PRJDBXXX. Geochemical data are included in supplementary information files. The other datasets generated and analysed during the current study are available from the corresponding author on reasonable request. References Cook, J., Edwards, A., Takeuchi, N., and Irvine-Fynn, T.: Cryoconite: the dark biological secret of the cryosphere, Prog. Phys. Geograp., 40(1), 66–111, https://doi.org/10.1177/0309133315616574 (2016). Wharton, Jr. R. A., McKay, C. P., Simmons, Jr. G. M., and Parker, B. C.: Cryoconite holes on glaciers, BioScience, 499–503, https://doi.org/10.2307/1309818 (1985). Cameron, K. A., Hodson, A. J., and Osborn, A. M.: Structure and diversity of bacterial, eukaryotic and archaeal communities in glacial cryoconite holes from the Arctic and the Antarctic. FEMS microbiol. ecol., 82(2), 254–267, https://doi.org/10.1111/j.1574-6941.2011.01277.x (2012). Edwards, A., et al.: A distinctive fungal community inhabiting cryoconite holes on glaciers in Svalbard, Fungal Ecology, 6(2), 168–176, https://doi.org/10.1016/j.funeco.2012.11.001 (2013). Zawierucha, K., Kolicka, M., Takeuchi, N., and Kaczmarek, Ł.: What animals can live in cryoconite holes? A faunal review, J. Zool. 295(3), 159–169 https://doi.org/10.1111/jzo.12195 (2015). Kaczmarek, Ł., Jakubowska, N., Celewicz-Gołdyn, S., and Zawierucha, K.: The microorganisms of cryoconite holes (algae, Archaea, bacteria, cyanobacteria, fungi, and Protista): a review, Polar Record, 52(2), 176–203, https://doi.org/10.1017/S0032247415000637 (2016). Vonnahme, T. R., Devetter, M., Žárský, J. D., Šabacká, M., and Elster, J.: Controls on microalgal community structures in cryoconite holes upon high Arctic glaciers, Svalbard, Biogeosci., 13, 659–674, https://doi.org/10.5194/bg-13-659-2016 (2016). Kobayashi, K., Takeuchi, N., and Kagami, M. High prevalence of parasitic chytrids infection of glacier algae in cryoconite holes in Alaska. Scientific Reports, 13(1), 3973., https://doi.org/10.1038/s41598-023-30721-w (2023). Takeuchi, N., Kohshima, S., and Seko, K.: Structure, formation, darkening process of albedo reducing material (cryoconite) on a Himalayan glacier: a granular algal mat growing on the glacier, Arc. Antarc. Alp. Res., 33, 115–122, https://doi.org/10.2307/1552211 (2001). Langford, H., Hodson, A., Banwart, S., and Bøggild, C.: The microstructure and biogeochemistry of Arctic cryoconite granules, Ann. Glaciol., 51(56), 87–94, https://doi.org/10.3189/172756411795932083 (2010). Rozwalak, P., et al.: Cryoconite–From minerals and organic matter to bioengineered sediments on glacier's surfaces, Sci. Total Environment, 807, 150874, https://doi.org/10.1016/j.scitotenv.2021.150874 (2022). Cook, J., et al.: The mass–area relationship within cryoconite holes and its implications for primary production, Ann. Glaciol., 51, 106–110, https://doi.org/10.3189/172756411795932038 (2010). Hodson, A., et al.: The cryoconite ecosystem on the Greenland ice sheet, Ann. Glaciol., 51(56), 123–129, https://doi.org/10.3189/172756411795931985 (2010). Hodson, A. et al.: A glacier respires: quantifying the distribution and respiration CO2 flux of cryoconite across an entire Arctic supraglacial ecosystem, J. Geophys. Res. Biogeosci., 112(G4), https://doi.org/10.1029/2007JG000452 (2007). Anesio, A. M., Hodson, A., Fritz, A., Psenner, R., and Sattler, B.: High microbial activity on glacier importance to the global carbon cycle, Global Change Biol., 15, 955–960, https://doi.org/10.1111/j.1365-2486.2008.01758.x (2009). Gribbon, P. W. F.: Short Notes: Cryoconite Holes on Sermikavsak, West Greenland. J. Glaciol., 22(86), 177–181, https://doi.org/10.1017/S0022143000014167 (1979). McIntyre, N. F.: Cryoconite hole thermodynamics, Can. J. Earth Sci., 21, 152–156, https://doi.org/10.1139/e84-016 (1984). Onuma, Y., Fujita, K., Takeuchi, N., Niwano, M., and Aoki, T.: Modelling the development and decay of cryoconite holes in northwestern Greenland, The Cryosphere, 17, 3309–3328, https://doi.org/10.5194/tc-17-3309-2023 (2023). Takeuchi, N., Kohshima, S., Yoshimura, Y., Seko, K., and Fujita, K.: Characteristics of cryoconite holes on a Himalayan glacier, Yala Glacier Central Nepal. Bull. Glaciol. Res., 17, 51–59 (2000). Bøggild, C., Brandt, R. E., Brown, K. J., and Warren, S. G.: The ablation zone in northeast Greenland: ice types, albedos and impurities, J. Glaciol., 56(195), 101–113, https://doi.org/10.3189/002214310791190776 (2010). Cook, J. M., Sweet, M., Cavalli, O., Taggart, A., and Edwards, A.: Topographic shading influences cryoconite morphodynamics and carbon exchange, Arc. Antarc. Alp. Res., 50(1), S100014, https://doi.org/10.1080/15230430.2017.1414463 (2018). Ryser, C., et al.: Cold ice in the ablation zone: Its relation to glacier hydrology and ice water content, J. Geophys. Res.: Earth Surface, 118(2), 693–705, https://doi.org/10.1029/2012JF002526 (2013). Lüthi, M. P. et al.: Heat sources within the Greenland Ice Sheet: dissipation, temperate paleo-firn and cryo-hydrologic warming, The Cryosphere, 9, 245–253, https://doi.org/10.5194/tc-9-245-2015 , (2015). Müller, F., and Keeler, C. M.: Errors in short-term ablation measurements on melting ice surfaces. J. Glaciol., 8(52), 91–105, https://doi.org/10.1017/S0022143000020785 (1969). Bagshaw, E. A., et al.: Do cryoconite holes have the potential to be significant sources of C, N, and P to downstream depauperate ecosystems of Taylor Valley, Antarctica?, Arc. Antarc. Alp. Res., 45(4), 440–454. https://doi.org/10.1657/1938-4246-45.4.440 (2013). Edwards, A., Irvine-Fynn, T., Mitchell, A. C., and Rassner, S. M.: A germ theory for glacial systems?, Wiley Interdisciplinary Reviews: Water, 1(4), 331–340, https://doi.org/10.1002/wat2.1029 (2014). Cook, J. M., Hodson, A. J., and Irvine-Fynn, T. D. L.: Supraglacial weathering crust dynamics inferred from cryoconite hole hydrology, Hydrol. Process., 30, 433–446, https://doi.org/10.1002/hyp.10602 (2016). Fountain, A. G., Tranter, M., Nylen, T. H., Lewis, K. J., and Mueller, D. R.: Evolution of cryoconite holes and their contribution to meltwater runoff from glaciers in the McMurdo Dry Valleys, Antarctica. J. Glaciol., 50(168), 35–45, https://doi.org/10.3189/172756504781830312 (2004). Edwards, A., et al.: Possible interactions between bacterial diversity, microbial activity and supraglacial hydrology of cryoconite holes in Svalbard. The ISME journal, 5(1), 150–160, https://doi.org/10.1038/ismej.2010.100 (2010). Irvine-Fynn, T. D., et al.: Storage and export of microbial biomass across the western Greenland Ice Sheet, Nat. Commun., 12, 3960, https://doi.org/10.1038/s41467-021-24040-9 (2021). Shimada, R., Takeuchi, N., and Aoki, T.: 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, 43, https://doi.org/10.3389/feart.2016.00043 (2016). Tedesco, M., et al.: The darkening of the Greenland ice sheet: trends, drivers, and projections (1981–2100), The Cryosphere, 10, 477–496, https://doi.org/10.5194/tc-10-477-2016 (2016). Williamson, C. J., et al.: Algal photophysiology drives darkening and melt of the Greenland Ice Sheet, Proc. Nat. Acad. Sci., 117(11), 5694–5705, https://doi.org/10.1073/pnas.1918412117 (2020). Chandler, D. M., Alcock, J. D., Wadham, J. L., Mackie, S. L., and Telling, J.: Seasonal changes of ice surface characteristics and productivity in the ablation zone of the Greenland Ice Sheet, The Cryosphere, 9, 487–504, https://doi.org/10.5194/tc-9-487-2015 (2015). Takeuchi, N., Nagatsuka, N., Uetake, J., and Shimada, R.: Spatial variations in impurities (cryoconite) on glaciers in northwest Greenland, Bull. Glaciol. Res., 32, 85–94, https://doi.org/10.5331/bgr.32.85 (2014). Ryan, J. C. et al.: Dark zone of the Greenland Ice Sheet controlled by distributed biologically-active impurities, Nat. Commun., 9(1), 1065, https://doi.org/10.1038/s41467-018-03353-2 (2018). Takeuchi, N., et al.: Temporal variations of cryoconite holes and cryoconite coverage on the ablation ice surface of Qaanaaq Glacier in northwest Greenland, Ann. Glaciol., 59, 21–30, https://doi.org/10.1017/aog.2018.19 (2018). Perkins, R. G., et al.,: Photoacclimation by Arctic cryoconite phototrophs, FEMS Microbiology Ecology, 93(5), May 2017, fix018, https://doi.org/10.1093/femsec/fix018 (2017). Cook. J., Edwards, A., and Hubbard, A.: Biocryomorphology: integrating microbial processes with ice surface hydrology, topography, and roughness. Front. Earth Sci., 3, 78, https://doi.org/10.3389/feart.2015.00078 (2015). Jones, C., Ryan, J., Holt, T., and Hubbard, A.: Structural glaciology of isunguata sermia, West Greenland. J. Maps, 14(2), 517–527, https://doi.org/10.1080/17445647.2018.1507952 (2018). Gerdel, R. W., and Drouet, F.: The cryoconite of the Thule area, Greenland. Transact. Am. Microscop. Soc., 79(3), 256–272, https://doi.org/10.2307/3223732 (1960). Segawa, T., et al.: Biogeography of cryoconite forming cyanobacteria on polar and Asian glaciers, J. biogeography, 44(12), 2849–2861, https://doi.org/10.1111/jbi.13089 (2017). Cooper, M. G., et al.: Spectral attenuation coefficients from measurements of light transmission in bare ice on the Greenland Ice Sheet. The Cryosphere, 15(4), 1931–1953, https://doi.org/10.5194/tc-15-1931-2021 (2021). Hoham, R. W., and Remias, D.: Snow and glacial algae: a review, J. Phycol., 56(2), 264–282, https://doi.org/10.1111/jpy.12952 (2020). Murakami, T., et al.: Metagenomics reveals global-scale contrasts in nitrogen cycling and cyanobacterial light-harvesting mechanisms in glacier cryoconite, Microbiome, 10, 50, https://doi.org/10.1186/s40168-022-01238-7 (2022). Heindel, R. C., Governali, F. C., Spickard, A. M., and Virginia, R. A.: The role of biological soil crusts in nitrogen cycling and soil stabilization in Kangerlussuaq, West Greenland. Ecosystems, 22, 243–256, https://doi.org/10.1007/s10021-018-0267-8 (2019). Musilova, M., Tranter, M., Bennett, S. A., Wadham, J., and Anesio, A. M.: Stable microbial community composition on the Greenland Ice Sheet, Front. Microbiol., 6, 193, https://doi.org/10.3389/fmicb.2015.00193 (2015). Leng, M. J., et al.: Deglaciation and catchment ontogeny in coastal south-west Greenland: implications for terrestrial and aquatic carbon cycling. J. Quaternary Sci., 27(6), 575–584, https://doi.org/10.1002/jqs.2544 (2012). Schmidt, S. K., et al.: Microbial biogeochemistry and phosphorus limitation in cryoconite holes on glaciers across the Taylor Valley, McMurdo Dry Valleys, Antarctica, Biogeochemistry, 158(3), 313–326, https://doi.org/10.1007/s10533-022-00900-4 (2022). Stibal, M., et al.: Environmental controls on microbial abundance and activity on the Greenland ice sheet: a multivariate analysis approach, Microbial Ecol., 63, 74–84, https://doi.org/10.1007/s00248-011-9935-3 (2012). Telling, J., et al.: Microbial nitrogen cycling on the Greenland Ice Sheet, Biogeosciences, 9, 2431–2442, https://doi.org/10.5194/bg-9-2431-2012 (2012). Reeh, N., Oerter, H., & Thomsen, H. H.: Comparison between Greenland ice-margin and ice-core oxygen-18 records. Annals of Glaciology, 35, 136–144, https://doi.org/10.3189/172756402781817365 (2002). MacGregor, J. A., et al.: The age of surface-exposed ice along the northern margin of the Greenland Ice Sheet, J. Glaciol., 66(258), 667–684, https://doi.org/10.1017/jog.2020.62 (2020). McCutcheon, J., et al.: Mineral phosphorus drives glacier algal blooms on the Greenland Ice Sheet, Nat. Commun., 12(1), 570, https://doi.org/10.1038/s41467-020-20627-w (2021). Nagatsuka, N., et al.: Variations in Sr and Nd Isotopic Ratios of Mineral Particles in Cryoconite in Western Greenland, Front. Earth Sci, 4, 93, https://doi.org/10.3389/feart.2016.00093 (2016). Banerjee, A., et al.: A scaling relation for cryoconite holes. Geophys. Res. Let., 50(22), e2023GL104942, https://doi.org/10.1029/2023GL104942 (2023). Cathles, L. M., Abbot, D. S., Bassis, J. N., and MacAYEAL, D. R.: Modeling surface-roughness/solar-ablation feedback: application to small-scale surface channels and crevasses of the Greenland ice sheet. Ann. Glaciol., 52(59), 99–108, https://doi.org/10.3189/172756411799096268 (2011). Colgan, W., et al.: An increase in crevasse extent, West Greenland: Hydrologic implications. Geophys. Res. Lett., 38(18), https://doi.org/10.1029/2011GL048491 (2011). Willerslev, E., et al.: Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science, 300(5620), 791–795, https://doi.org/10.1126/science.1084114 (2003). Herlemann, D. P. R., et al.: Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. The ISME Journal, 5(10), 1571–1579, https://doi.org/10.1038/ismej.2011.41 (2011). Segawa, T., et al.: Evolution of snow algae, from cosmopolitans to endemics, revealed by DNA analysis of ancient ice. The ISME Journal, 17(4), https://doi.org/491-501.10.1038/s41396-023-01359-3 (2023). Callahan, B. J., et al.: DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13, 581–583, https://doi.org/10.1038/nmeth.3869 (2016). Pruesse, E., Peplies, J., and Glöckner, F. O.: SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 28(14), 1823–1829, https://doi.org/10.1093/bioinformatics/bts252 (2012). Fernandes, A. D., et al.: Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome , 2, 15, https://doi.org/10.1186/2049-2618-2-15 (2014). Nübel, U., Garcia-Pichel, F., and Muyzer, G.: PCR primers to amplify 16S rRNA genes from cyanobacteria. Applied and environmental microbiology, 63(8), 3327–3332. https://doi.org/10.1128/aem.63.8.3327-3332.1997 (1997). Katoh, K, and Standley, D. M.: MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol., 30, 772–780, https://doi.org/10.1093/molbev/mst010 (2013). Nguyen, L-T., Schmidt, H. A., von Haeseler, A., and Minh, BQ.: IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Molecular Biology and Evolution, 32: 268–274. https://doi.org/10.1093/molbev/msu300 (2015). Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7116261","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":487590998,"identity":"fb90a4fa-8671-42cc-a72b-50a492e000ce","order_by":0,"name":"Nozomu Takeuchi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYHACZhCRwM98AEgVgNg8DIchMgewa2CDapFsSwBSBgYkaDE4hqSFGZ+rzOWbHxt8bLPLMz7G/PAzj8GfxAb2swcPF9QwJDYwnsVqjWUbm3HizLbkYrNjbMbSPAYGiQ08eQmHZxwDamE4l4BNi8ExBuPDvG0HErfdbzBjBmuR4DE4zMP2H6jljAF2LeyfD/8Fatncxv4NScs/BjxaeIyTGYFaNrDxINnC24ZPS06xYc+55MQZx3iKJecYGBu38eQYHJ7Zx2Dchssvh49vlvhRZpfY38a+8cObCjnZfvYzxp8LvjHI9ktgDzEM4NgGY7FJnCFKB4M9gsnfQ5yWUTAKRsEoGO4AAEMyYYfbxqCLAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3267-5534","institution":"Chiba University","correspondingAuthor":true,"prefix":"","firstName":"Nozomu","middleName":"","lastName":"Takeuchi","suffix":""},{"id":487590999,"identity":"09764df0-2039-4d43-bb53-c441606c3b38","order_by":1,"name":"Segawa Takahiro","email":"","orcid":"https://orcid.org/0000-0002-3111-708X","institution":"University of Yamanashi","correspondingAuthor":false,"prefix":"","firstName":"Segawa","middleName":"","lastName":"Takahiro","suffix":""},{"id":487591000,"identity":"ee767af0-a706-44e6-bb11-68bc89da5c6e","order_by":2,"name":"Takumi Murakami","email":"","orcid":"https://orcid.org/0000-0002-4738-0464","institution":"Tokyo Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Takumi","middleName":"","lastName":"Murakami","suffix":""},{"id":487591001,"identity":"a0a1122a-18aa-4c1b-a5a7-df13a51f09e4","order_by":3,"name":"Koki Ishiwatari","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Koki","middleName":"","lastName":"Ishiwatari","suffix":""},{"id":487591002,"identity":"465f35a9-3083-48f5-9679-3c22e75d7ab5","order_by":4,"name":"Akane Watanabe","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Akane","middleName":"","lastName":"Watanabe","suffix":""}],"badges":[],"createdAt":"2025-07-14 02:45:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7116261/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7116261/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-025-03045-y","type":"published","date":"2025-12-01T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87323260,"identity":"da827c84-aba3-44bd-8c53-637ff0481008","added_by":"auto","created_at":"2025-07-22 16:57:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118076,"visible":true,"origin":"","legend":"\u003cp\u003e(a) A Sentinel-2 satellite image of Issunguata Sermia Glacier in southwest Greenland showing the location of 20 study sites along a transect line (S1 – S20, Yellow dots). (b) Aerial photograph of the crevasse zone near the glacier margin including sites S1–S4. (c) Aerial photograph of the central flat zone including the sites S14–S20. The aerial photographs were captured using a drone (Mavic 2, DJI) on August 6th, 2017.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/51ed39ef247d07eb85946491.jpg"},{"id":87323261,"identity":"2da210ed-ce0a-49da-a770-c4fa3749a0d0","added_by":"auto","created_at":"2025-07-22 16:57:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68448,"visible":true,"origin":"","legend":"\u003cp\u003ePhotographs of cryoconite holes on Issunguata Sermia Glacier. (a), (b) Shallower holes at site S5. (c), (d) Deeper holes at site S16.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/6626d5c96aeb97042d7d9ed9.jpg"},{"id":87323511,"identity":"c553fa20-a5d7-473d-bbbc-92d589b8c5cf","added_by":"auto","created_at":"2025-07-22 17:05:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59073,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variations in dimensions of cryoconite holes across the transect line on Issunguata Sermia Glacier (mean and standard deviation).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/90f3aee98b4e5bca5e00094c.jpg"},{"id":87323262,"identity":"1e27a8c0-bf25-4925-896e-a4ea39d5f3cc","added_by":"auto","created_at":"2025-07-22 16:57:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66684,"visible":true,"origin":"","legend":"\u003cp\u003eMicroscopic photographs of five major phototroph taxa observed in cryoconite holes on Issunguata Sermia Glacier. (a) \u003cem\u003eAncylonema nordenskiöldii\u003c/em\u003e, (b) \u003cem\u003eAncylonema\u003c/em\u003e \u003cem\u003ealaskana\u003c/em\u003e, (c) \u003cem\u003eCylindrocystis\u003c/em\u003e \u003cem\u003ebrébissonii\u003c/em\u003e. (d) \u003cem\u003ePhormidesmis\u003c/em\u003e \u003cem\u003epriestleyi\u003c/em\u003e, (e) \u003cem\u003eCalothrix\u003c/em\u003e-like cyanobacteria. Scale bar: 10 μm.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/37e2a7f76c9ee6f1bcf7552f.jpg"},{"id":87324239,"identity":"9159448f-5356-4052-8d8a-943fee2efc76","added_by":"auto","created_at":"2025-07-22 17:13:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":88590,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in phototroph community structure (biovolume) across the transect line on Issunguata Sermia Glacier.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/fece1b05edea14d4f5f6a5b8.jpg"},{"id":87323264,"identity":"3df0020a-6b76-4f44-b296-747ec8fc20ba","added_by":"auto","created_at":"2025-07-22 16:57:07","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":81740,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in the relative abundance of (a) major bacterial phyla or classes and (b) cyanobacterial taxa based on 16S rRNA gene amplicon sequencing of cryoconite samples from Issunguata Sermia Glacier.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/cef433a753b80e7d4feb8ed5.jpg"},{"id":87324741,"identity":"7451f394-2b4e-4a8c-86a3-4b2a7df23868","added_by":"auto","created_at":"2025-07-22 17:21:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":77190,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in C/N ratio, contents, and stable isotopes of organic carbon and nitrogen in cryoconite across the transect on Issunguata Sermia Glacier.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/7a242c1e0a1fe75849e6dfc5.jpg"},{"id":87323513,"identity":"900fa352-174c-4faf-9258-e771c91d3dd7","added_by":"auto","created_at":"2025-07-22 17:05:08","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":126114,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in (a) water-oxygen stable isotope, (b) composition of major soluble ions, and (c) concentrations of major soluble ions in meltwater of cryoconite holes across the transect on Issunguata Sermia Glacier.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/f29365cb7b42804dadb1ac5b.jpg"},{"id":87323293,"identity":"8442a3ee-5af6-4bd2-a711-a3687e599169","added_by":"auto","created_at":"2025-07-22 16:57:08","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":45234,"visible":true,"origin":"","legend":"\u003cp\u003eX-ray diffraction patterns of mineral composition in cryoconite from five representative sites on Issunguata Sermia Glacier.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/78281e3152ad4767027956cf.jpg"},{"id":99677032,"identity":"3eb6f8c4-78cc-4a21-8f70-51ef734635e2","added_by":"auto","created_at":"2026-01-07 08:13:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1703183,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/171e8101-43b2-4b10-8587-b877743c74ef.pdf"},{"id":87323282,"identity":"97fa4f12-b2c4-473c-992c-e59502e7e58d","added_by":"auto","created_at":"2025-07-22 16:57:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":991433,"visible":true,"origin":"","legend":"Supplemental table and figures","description":"","filename":"Supplimentaryv13.docx","url":"https://assets-eu.researchsquare.com/files/rs-7116261/v1/0df097eb00a9ac2f6ff9bedd.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Morphological Control of Microbial Ecosystems and Carbon Cycling in Greenlandic Cryoconite Holes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCryoconite holes are ubiquitous water-filled cylindrical depressions on ablating glacier ice surfaces worldwide. Typically ranging from 1 to 30 cm deep and 1 to 50 cm in diameter, some can exceed 50 cm in diameter and 60 cm in depth (1). These holes form as dark-colored cryoconite sediment, accumulated at their base, absorbs solar radiation, enhancing localized ice melt (2).\u003c/p\u003e\n\u003cp\u003eBeyond their physical formation, cryoconite holes are vital microhabitats for diverse microbial life, playing a crucial role in glacier ecosystems. Filled with stagnant meltwater, they provide essential environments for organisms thriving on glaciers. During the melting season, these holes host a rich community of phototrophs, including cyanobacteria and algae, alongside heterotrophs like micro-invertebrates, fungi, and bacteria (e.g.,\u0026nbsp;2−8). Filamentous cyanobacteria, in particular, are instrumental in forming cryoconite granules – dark, granular mats composed of entangled particles, mineral dust, and organic matter (9−11). These granules constitute the primary sediment within the holes, contributing to their structural integrity (12). Cryoconite holes thus serve not only as stable microbial habitats but also as critical nutrient sinks, facilitating material recycling (13). Consequently, their formation and distribution significantly influence microbial production and biomass on glaciers (14, 15).\u003c/p\u003e\n\u003cp\u003eThe distribution and dimensions of cryoconite holes are highly heterogeneous across glacier surfaces, primarily controlled by local physical conditions. On the Greenland Ice Sheet, for instance, hole depth has been observed to increase with elevation (16), attributed to lower melt rates and reduced hydrological disturbance at higher altitudes due to declining air temperatures, while the melt rate at the hole floor remains largely governed by solar irradiance (1, 17, 18). Furthermore, hole formation and development are linked to ice surface roughness, slope angle, gradients,\u0026nbsp;ice crystal size,\u0026nbsp;and\u0026nbsp;surface\u0026nbsp;albedo (19, 20, 21), and their depth can be influenced by ice thermal conditions (22). Deep holes are prevalent in the lower ablation area of West Greenland, where cold ice from dry-snow accumulation zones emerges, contrasting with the higher ablation area, where warmer ice and a dirty, slushy surface without distinct holes are observed (23). Despite these insights, the comprehensive suite of factors governing the spatial variability of cryoconite holes on glaciers remains incompletely understood.\u003c/p\u003e\n\u003cp\u003eBeyond their direct biological significance, the heterogeneous distribution of cryoconite holes and other ice surface structures profoundly impacts meltwater hydrology in the ablation zone. Meltwater initially permeates the porous surface weathering crust (24), subsequently flowing into cryoconite holes and/or supraglacial ponds before draining through complex supra-, en-, and sub-glacial systems (25, 26, 27). Therefore, the density and spatial arrangement of cryoconite holes likely influence meltwater residence time, thereby affecting the temporal patterns of glacier discharge (28). Moreover, microbes themselves migrate through meltwater pathways within the weathering crust, entering and exiting cryoconite holes (29, 30). This highlights the multifaceted role of cryoconite holes in not only regulating meltwater hydrology but also serving as critical conduits for nutrients, dust, and microbes on glaciers.\u003c/p\u003e\n\u003cp\u003eIn recent decades, the southwestern Greenland Ice Sheet has experienced a notable expansion of dark-colored ice surfaces (31, 32), contributing to accelerated mass loss. This darkening is primarily attributed to the blooming of pigmented glacier algae (e.g.,\u0026nbsp;33), but also partially to the dispersion of cryoconite (2, 34, 35). While cryoconite within holes typically has limited direct impact on overall surface darkening due to low coverage (usually \u0026lt;1% of the ablation area;\u0026nbsp;36), the collapse of holes under specific weather conditions can disperse cryoconite, significantly contributing to dark ice expansion (34, 37). Numerical models suggest hole collapse occurs under warm, cloudy, and/or windy conditions (18). This process implies a strong association between surface darkening via cryoconite dispersion and the spatial variability in the density and dimensions of cryoconite holes across the ice sheet's ablation zone.\u003c/p\u003e\n\u003cp\u003eCrucially, the dimensions of cryoconite holes can also directly influence the microbial community and productivity within them. The intensity and spectral\u0026nbsp;properties of solar radiation reaching the bottom of the hole, a key factor for phototrophs, are highly dependent on hole depth\u0026nbsp;(18). Given that different phototroph species possess specific pigment compositions optimized for various light conditions\u0026nbsp;(38), their community structure\u0026nbsp;may\u0026nbsp;shift with depth. Furthermore, deeper holes tend to be more stable and persistent over longer durations, potentially ranging from days to years depending on dimensions (19, 28). Such variations in material residence time within the holes can significantly impact the cycling of dust, organic matter, and nutrients, consequently affecting microbial community composition, productivity, and ultimately the total abundance of cryoconite and surface albedo on the glacier. While previous research suggested that cryoconite holes can adapt their shapes to maintain stable autotrophic habitats, a concept termed biocryomorphic evolution (39), comprehensive studies demonstrating the spatial variabilities in cryoconite hole dimensions and their specific effects on cryoconite formation and microbial communities remain scarce.\u003c/p\u003e\n\u003cp\u003eIn this study, we addressed this knowledge gap by investigating the spatial variations in cryoconite hole dimensions, microbial communities, and cryoconite characteristics along a transect across Issunguata Sermia Glacier in southwest Greenland (Fig. 1). This glacier is an outlet glacier flowing westward from the Greenland Ice Sheet and is characterized by a central flat ice zone with relatively high ice movement velocity (mean 95 m year⁻¹, range 18–328 m year⁻¹) and a marginal zone featuring rough, crevassed surfaces (40) due to shear stresses resulting from spatially heterogeneous ice flow velocity (41). We aim to elucidate the intricate relationships between surface topography, hole morphology, microbial ecology, and carbon cycling within the bare ice area of the Greenland Ice Sheet.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eVariations in dimensions of cryoconite holes across a transect\u003c/h2\u003e\n\u003cp\u003eCryoconite hole dimensions exhibited significant spatial variation along the transect (Figs. 2, 3), revealing two distinct morphological zones: a marginal crevasse zone (Sites S1\u0026ndash;S12) and a central flat ice zone (S13\u0026ndash;S20). The mean depth of cryoconite holes across the entire transect was 17.8 cm, ranging from 4.6 cm to 28.7 cm. A statistically significant difference in depth was observed between the two zones (t = 2.12, p \u0026lt; 0.01): the mean depth in the central flat zone (21.9 cm) was almost twice that in the marginal crevasse zone (11.0 cm). Consistent with depth, the mean water level from the hole bottom was 13.1 cm, with the flat zone showing approximately twice the water level of the crevasse zone (17.0 cm vs. 8.7 cm; t = 2.110, p \u0026lt; 0.01). Based on these clear distinctions, we refer to holes in the crevasse zone (S1\u0026ndash;S12) as \u0026quot;shallower holes\u0026quot; and those in the flat zone (S13\u0026ndash;S20) as \u0026quot;deeper holes\u0026quot; throughout this study (Fig. 2).\u003c/p\u003e\n\u003cp\u003eHole diameter showed less variation than depth, with a mean longest diameter of 4.2 cm (range: 2.5\u0026ndash;7.5 cm) across the transect. While some exceptionally large holes (\u0026gt;20 cm) were observed, their limited number led to their exclusion from this specific dimensional analysis. The mean diameter in the crevasse zone was slightly smaller (3.2 cm) than in the flat zone (5.2 cm). Notably, relatively large holes (6.8\u0026ndash;7.2 cm) were found at sites S13 and S14, which represent the transition area between the crevasse and flat zones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhototroph Community Structure in Cryoconite Holes based on Microscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicroscopic analysis identified various phototroph taxa within cryoconite samples (Fig. 4). These included typical glacier algae such as \u003cem\u003eAncylonema\u003c/em\u003e (\u003cem\u003eA\u003c/em\u003e.) \u003cem\u003enordenski\u0026ouml;ldii\u003c/em\u003e, \u003cem\u003eA. alaskana\u003c/em\u003e, and \u003cem\u003eCylindrocystis\u003c/em\u003e (\u003cem\u003eCyl\u003c/em\u003e.) \u003cem\u003ebr\u0026eacute;bissonii\u003c/em\u003e. Additionally, at least two taxa of filamentous cyanobacteria were observed: \u003cem\u003ePhormidesmis\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e.) \u003cem\u003epriestleyi\u003c/em\u003e (thin filament, ~1.2 \u0026micro;m diameter), a common Arctic glacier species, and a thick, brown-colored \u003cem\u003eCalothrix\u003c/em\u003e-like cyanobacterium, morphologically resembling \u003cem\u003eCalothrix\u003c/em\u003e (\u003cem\u003eCal\u003c/em\u003e.) \u003cem\u003eparietina\u003c/em\u003e, widely reported in Arctic cryoconite (41). Both filamentous cyanobacteria frequently co-aggregated with mineral dust and organic matter to form cryoconite granules.\u003c/p\u003e\n\u003cp\u003eThe phototroph community structure, assessed by biovolume, varied significantly along the transect, showing clear distinctions between shallower and deeper holes (Fig. 5). In the shallower holes, phototroph communities were predominantly composed of the three glacier algae (\u003cem\u003eA. nordenski\u0026ouml;ldii, A. alaskana, and Cyl. br\u0026eacute;bissonii\u003c/em\u003e), with the exception of sites S2 and S3 (closest to the glacier margin), where \u003cem\u003eP.\u003c/em\u003e \u003cem\u003epriestleyi\u003c/em\u003e accounted for over 80% of the total cryoconite biovolume. Conversely, deeper holes were characterized by the predominance of \u003cem\u003eCalothrix\u003c/em\u003e-like cyanobacteria, contributing 40\u0026ndash;60% of the total biovolume at each site.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e16S rRNA Gene Microbial Communities in Cryoconite\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmplicon sequencing of the 16S rRNA gene from cryoconite samples yielded approximately 510,000 high-quality reads, clustered into 705 Amplicon Sequence Variants (ASVs). Taxonomic classification identified nine dominant phyla or classes: Cyanobacteria, Alphaproteobacteria, Armatimonadota, Chloroflexota, Gammaproteobacteria, Bacteroidota, Actinomycetota, Acidobacteriota, and Planctomycetota (Fig. 6a).\u003c/p\u003e\n\u003cp\u003eOverall microbial community composition differed significantly between shallower and deeper cryoconite holes (PERMANOVA, p\u0026nbsp;\u0026lt;\u0026nbsp;0.05; Fig. 6a). Crucially, cyanobacterial composition also varied significantly with hole depth (PERMANOVA, p\u0026nbsp;\u0026lt;\u0026nbsp;0.01; Fig. 6b). All detected cyanobacterial ASVs were associated with Operational Taxonomic Units (OTUs) previously identified in phylogenetic studies of cryoconite-forming cyanobacteria (42) (Supplementary\u0026nbsp;Fig. S1).\u003c/p\u003e\n\u003cp\u003eIn the deeper cryoconite holes, three cyanobacterial lineages predominated: \u003cem\u003eP\u003c/em\u003e. \u003cem\u003epriestleyi\u003c/em\u003e (OTU1), an unclassified cyanobacterium (OTU16), and Chamaesiphon (OTU3). Single-filament PCR analysis identified OTU16 as a \u003cem\u003eCalothrix\u003c/em\u003e-like cyanobacterium (Fig. 6 and Supplementary\u0026nbsp;Figs. S1\u0026ndash;S2), although phylogenetic analysis revealed it belonged to an unidentified lineage distinct from canonical \u003cem\u003eCalothrix\u003c/em\u003e. Thus, OTU16 is more appropriately referred to as an unidentified cyanobacterium despite its morphological resemblance to \u003cem\u003eCal\u003c/em\u003e. \u003cem\u003eparietina\u003c/em\u003e. These findings suggest that cryoconite-forming cyanobacteria previously identified as \u003cem\u003eCal\u003c/em\u003e. \u003cem\u003eparietina\u003c/em\u003e may encompass novel, morphologically cryptic lineages.\u003c/p\u003e\n\u003cp\u003eIn contrast, OTU16 and OTU3 were minor or nearly absent in shallower holes. Instead, OTU1, Pseudanabaena (OTU0 and OTU5), and Nostoc (OTU2) became predominant. These results collectively highlight a strong depth-dependent variation in cyanobacterial community structures.\u003c/p\u003e\n\u003cp\u003eSeveral cyanobacterial OTUs, including OTU1, OTU3, and OTU16, comprised multiple ASVs, indicating species- or strain-level diversity within these lineages (Fig. S1). We observed that some closely related ASVs exhibited distinct distribution patterns. For instance, ASV_0 and ASV_2 (within OTU1), and ASV_8 (within OTU16) were prevalent in both deeper and shallower holes. However, closely related ASV_5 (within OTU1) and ASV_34, ASV_54, ASV_74, and ASV_106 (within OTU16) were specifically abundant in deeper holes (Supplementary Fig. S3). This demonstrates that cryoconite hole dimensions influenced cyanobacterial lineage composition at both the OTU and finer ASV levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon and Nitrogen Contents and Stable Isotope Ratios of Cryoconite\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCryoconite carbon (C) and nitrogen (N) contents varied along the transect (Fig. 7,\u0026nbsp;Supplementary\u0026nbsp;Table S1). Overall, mean C and N contents were 2.09 \u0026plusmn; 1.15% and 0.21 \u0026plusmn; 0.11%, respectively, ranging from 0.31% to 4.09% for C and 0.02% to 0.37% for N. Both C and N contents generally increased from marginal to central sites, showing a significant difference between deeper and shallower holes (C:\u0026nbsp;t\u0026nbsp;=\u0026nbsp;\u0026minus;5.95,\u0026nbsp;p\u0026nbsp;\u0026lt;\u0026nbsp;0.01;\u0026nbsp;N:\u0026nbsp;t\u0026nbsp;=\u0026nbsp;\u0026minus;5.32,\u0026nbsp;p\u0026nbsp;\u0026lt;\u0026nbsp;0.01). Specifically, the mean C and N contents in deeper holes were 2.3 and 2.1 times greater, respectively, than in shallower holes (C: 3.17% vs. 1.37%; N: 0.31% vs. 0.15%). Similarly, organic matter contents in cryoconite, ranging from 1.1% to 8.8% (mean 4.8%; Table S1), were significantly higher in deeper holes (7.22%) compared to shallower holes (3.22%).\u003c/p\u003e\n\u003cp\u003eThe mean C/N ratio of cryoconite organic matter was 9.9 \u0026plusmn; 1.0. While it varied from 8.2 to 12.9 in shallower holes, it was relatively less variable (9.7 to 11.0) in deeper holes. No significant difference in the C/N ratio was found between deeper and shallower holes (t = \u0026minus;1.2635, p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eCryoconite carbon and nitrogen stable isotope ratios also varied along the transect, generally increasing from the glacier margin to its central part. Carbon isotope ratios (\u0026delta;\u0026sup1;\u0026sup3;C) ranged from \u0026minus;22.0\u0026permil; to \u0026minus;19.9\u0026permil; (mean: \u0026minus;21.2 \u0026plusmn; 1.31\u0026permil;), and nitrogen isotope ratios (\u0026delta;\u0026sup1;⁵N) ranged from \u0026minus;2.43\u0026permil; to +1.15\u0026permil; (mean: \u0026minus;0.25 \u0026plusmn; 1.18\u0026permil;). Both \u0026delta;\u0026sup1;\u0026sup3;C and \u0026delta;\u0026sup1;⁵N were significantly higher in deeper holes (\u0026delta;\u0026sup1;\u0026sup3;C: \u0026minus;19.8\u0026permil; vs. \u0026minus;22.0\u0026permil;; t = \u0026minus;7.46, p \u0026lt; 0.01; \u0026delta;\u0026sup1;⁵N: +0.68\u0026permil; vs. \u0026minus;0.88\u0026permil;; t = \u0026minus;4.49, p \u0026lt; 0.01). Isotope values showed greater variability in the crevasse zone but were relatively stable in the flat zone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWater Stable Isotopes and Major Soluble Ions of Meltwater in Cryoconite Holes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe oxygen stable isotope (\u0026delta;\u0026sup1;⁸O) of meltwater in cryoconite holes generally ranged from \u0026minus;28.3\u0026permil; to \u0026minus;24.5\u0026permil; (mean: \u0026minus;26.5 \u0026plusmn; 2.0\u0026permil;) across the transect, with an exceptionally low value of \u0026minus;33.5\u0026permil; observed at site S4 (Fig. 8a, Supplementary Table S1). Notably, \u0026delta;\u0026sup1;⁸O values were slightly higher in deeper holes than in shallower holes (\u0026minus;25.5\u0026permil; vs. \u0026minus;27.2\u0026permil;; t = \u0026minus;2.38, p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eThe composition of major chemical solutes in meltwater also varied along the transect (Fig. 8b,\u0026nbsp;Supplementary\u0026nbsp;Table S1). Ca\u0026sup2;⁺ was the most dominant solute near the glacier margin (S1\u0026ndash;S5, 37.0\u0026ndash;59.6% proportion), while Mg\u0026sup2;⁺ and K⁺ dominated in the central part (S6\u0026ndash;S20, 9.6\u0026ndash;22.6% Ca\u0026sup2;⁺ proportion).\u003c/p\u003e\n\u003cp\u003eConcentrations of PO₄\u0026sup3;⁻ and NO₃⁻ were slightly higher in the central part compared to the marginal parts. PO₄\u0026sup3;⁻ was below 0.01 \u0026mu;Eq L⁻\u0026sup1; at the three marginal sites (S1\u0026ndash;S3) but ranged from 0.21 to 2.8 \u0026mu;Eq L⁻\u0026sup1; at other sites (S4\u0026ndash;S20). Similarly, NO₃⁻ was below 0.20 \u0026mu;Eq L⁻\u0026sup1; at marginal sites (S1\u0026ndash;S3) but varied from 0.42 to 3.00 \u0026mu;Eq L⁻\u0026sup1; elsewhere (S4\u0026ndash;S20). Importantly, no significant difference in these nutrient concentrations was observed between the shallower and deeper holes. At site S4, Ca\u0026sup2;⁺ and SO₄\u0026sup2;⁻ concentrations were distinctively high (34.4 and 14.7 \u0026mu;Eq L⁻\u0026sup1;, respectively) compared to other sites (0.58\u0026ndash;5.62 \u0026mu;Eq L⁻\u0026sup1; and 0.00\u0026ndash;1.17 \u0026mu;Eq L⁻\u0026sup1;, respectively). Concentrations of NH₄⁺, K⁺, Na⁺, and Cl⁻ varied across the transect but showed no clear trend.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMineralogical Compositions of Cryoconite\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXRD analysis of cryoconite samples revealed the presence of various silicate minerals (Fig. 9, Supplementary Fig. S4). Quartz, plagioclase, potassium-feldspar, and hornblende exhibited relatively intense peaks across all samples. Weak peaks of clay minerals, including kaolinite, chlorite, and illite, were also detected. Comparison of the relative peak intensities of the minerals among the study sites showed slight variations across the transect, but no significant differences were observed between shallower and deeper holes, or between marginal and central areas (Supplementary Fig. S5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eRelationship between microbial communities and cryoconite hole dimensions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur findings reveal a significant difference in phototrophic communities between the two distinct cryoconite hole morphologies observed, despite minimal variation in the overall bacterial community at the phylum level. Both glacier algae and cyanobacteria, commonly found on Greenlandic and other Arctic glacier surfaces, were present in the holes. Specifically, deeper holes were dominated by \u003cem\u003eCalothrix\u003c/em\u003e-like cyanobacteria, while shallower holes were characterized by a prevalence of glacier algae. Although 16S rRNA gene-based cyanobacterial communities did not perfectly align with microscopic biovolume data, they consistently showed distinct differences between the two hole types. Deeper holes exhibited dominance by three specific cyanobacterial OTUs (OTU 1, 16, and 3), whereas shallower holes supported a more diverse community (OTU 0, 1, 2, 3, 5, and 16).\u003c/p\u003e\u003cp\u003eThis divergence in phototrophic communities is likely driven by variations in light conditions at the hole bottom. Shallower and/or larger-diameter holes receive more direct and intense solar irradiance. In contrast, deeper holes experience shading by their walls, limiting direct sunlight, and receive attenuated indirect light transmitted through glacial ice (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This attenuation is particularly strong at longer wavelengths due to ice absorption, meaning shorter wavelengths are more readily transmitted. Consequently, the spectral property and intensity of light differ significantly between shallow and deep holes. Phototrophs, such as those found in cryoconite, possess diverse pigments to optimize light harvesting for photosynthesis (e.g., 38, 44). Cyanobacteria, for instance, utilize phycobilisomes that can adapt to different light wavelengths (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Therefore, it is plausible that phototrophs dominating shallower holes prefer direct, intense solar radiation, while those in deeper holes are adapted to weaker light and a limited wavelength range. The distinct distribution patterns of closely related cyanobacterial ASVs further support the notion that species- or strain-level ecological variations respond to these micro-environmental light differences. However, confirming this hypothesis requires further analysis of phototroph pigment compositions and direct in-situ light measurements within the holes.\u003c/p\u003e\u003cp\u003eBeyond light conditions, the supply of cells from the surrounding ice surface may also influence community structure. Glacier algae, known to proliferate on bare ice, can be passively transported by meltwater through the ice weathering crust into cryoconite holes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The observed dominance of glacier algae in shallower holes could be partially explained by an abundant supply of algal cells from the adjacent ice surface, though further investigation into their abundance and movement on the bare ice is needed.\u003c/p\u003e\u003cp\u003eFurthermore, differential hole persistence times between shallow and deep holes could contribute to the observed community structures. Shallower holes are more susceptible to collapse under varying weather conditions, leading to more frequent cycles of formation and collapse compared to the more stable and longer-persisting deeper holes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Given that phototroph species have varying growth rates, the stability and longevity of a habitat would favor the establishment of specific communities. The higher organic content found in cryoconite from deeper holes also aligns with this idea, suggesting a longer period for the accumulation of phototroph-derived organic matter.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSources of organic carbon and nitrogen in cryoconite\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe consistently lower C/N ratios (mean 9.9) of cryoconite compared to surrounding glacier forefield soils (mean 13.07; (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)) strongly indicate that the organic matter within cryoconite holes is predominantly autochthonous, i.e., microbially produced on the glacier, rather than allochthonous (externally derived).\u003c/p\u003e\u003cp\u003eFurther supporting this, carbon stable isotope signatures (δ¹³C) of cryoconite also point to an internal microbial origin. While aeolian organics in West Greenland range from − 28 to − 27‰ (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) and terrestrial plants/marsh organics range from − 27 to − 22‰ (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), cryoconite in deeper holes exhibited distinctively enriched δ¹³C values (− 20.2 to − 19.5‰). This enrichment strongly suggests that carbon in deeper holes is primarily derived from cyanobacterial photosynthesis, consistent with the observed cyanobacterial dominance in these holes and findings from Antarctic glaciers (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) and previous Greenland Ice Sheet studies (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The greater variability in δ¹³C values in shallower holes (− 23.6 to − 20.2‰) likely reflects the mixed contribution from both glacier algae and cyanobacteria, whose differing growth rates and turnover times could lead to varied isotopic signatures.\u003c/p\u003e\u003cp\u003eSimilarly, nitrogen stable isotope signatures (δ¹⁵N) of cryoconite are distinct from those in glacier forefield soils (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), further supporting microbial assimilation as the primary source of organic nitrogen in cryoconite. Values close to zero in cryoconite generally indicate nitrogen fixation by cyanobacteria (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Although nitrogen fixation is considered limited on the Greenland Ice Sheet surface (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), the observed δ¹⁵N values likely reflect isotopic fractionation during microbial nitrogen assimilation. The enriched and less variable δ¹⁵N in deeper holes suggests nitrogen limitation within these environments, mirroring observations from Antarctic cryoconite (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). In contrast, the more variable δ¹⁵N in shallower holes may reflect different nitrogen compounds assimilated by glacier algae.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffect of exposures of basal ice and Pleistocene glacial ice on meltwater geochemistry, cryoconite, and microbial community\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile variations in microbial communities and organic abundance were observed, meltwater conditions in cryoconite holes, specifically water stable isotopes and chemical solutes, showed no significant difference between shallower and deeper holes. This is a crucial finding, as nutrient availability (e.g., nitrogen, phosphorus), generally scarce on glaciers (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), largely influences phototroph communities. The lack of significant nutrient differences across the transect (except near the glacier margin) strongly supports our hypothesis that the distinct phototroph communities are driven by hole depth and associated light conditions rather than nutrient availability.\u003c/p\u003e\u003cp\u003eNotably, a significantly depleted oxygen stable isotope value (− 34‰) at site S4 indicated the presence of Pleistocene glacial ice, consistent with previous findings near the Greenland Ice Sheet margin (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The area closer to the glacier margin (sites S1 − S3) is likely composed of basal refrozen ice (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Although meltwater geochemistry in the marginal area (e.g., higher Ca²⁺ and SO₄²⁻ concentrations at site S4) slightly differed due to these ancient ice sources, its direct impact on microbial communities appeared limited. For instance, despite the distinct geochemistry at S4, no unique phototroph community was observed there (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Similarly, while basal ice areas (S1 − S3) showed Ca²⁺ dominance, overall solute concentrations were low. Interestingly, Nostoc-like cyanobacteria (OTU2) were uniquely detected near the glacier margin (S1, S3), suggesting a potential adaptation of this specific OTU to marginal ice conditions.\u003c/p\u003e\u003cp\u003eFurthermore, mineral composition in cryoconite, a potential source of phosphorus for microbes (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), showed no significant variation across the transect based on XRD analysis. This suggests a common, possibly distant or outcropping (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) origin for these minerals, indicating limited local accumulation of windblown minerals. This consistency further supports the idea that mineral availability was not a primary driver of microbial community differentiation in this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTwo distinct equilibrium states of cryoconite holes and glacier topography\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe observed two distinct cryoconite hole depths can be conceptualized as different equilibrium states of melt rates between the hole floor and the surrounding ice (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). While equilibrium hole depth typically varies with atmospheric conditions (e.g., elevation, weather), such conditions cannot solely explain the depth differences observed across our comparatively small study area. In northeast Greenland, shallower holes have been attributed to the presence of Pleistocene ice characterized by smaller crystal size and dust content (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, in this study, the distribution of shallower holes did not correspond to the extent of Pleistocene ice suggested by lower oxygen stable isotopes. Furthermore, although a geometric interaction between hole depth and diameter has been suggested (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) did not support this relationship.\u003c/p\u003e\u003cp\u003eInstead, our results strongly suggest that glacier surface topography is the primary determinant of these two distinct hole depths. The distribution of deeper holes precisely corresponded to the flat ice zone in the glacier's central part, whereas shallower holes were confined to the rough crevasse zones near the glacier margin. This direct correlation implies that the presence of crevasses significantly contributes to shallower hole formation, possibly by accelerating surface ablation (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) or altering light exposure due to irregular surface slopes (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Further studies are needed to fully elucidate the complex relationship between surface topography and cryoconite hole formation, including thermal conditions potentially influenced by crevasses (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe co-occurrence of distinctive phototroph communities and cryoconite properties with these two depth categories supports our hypothesis that these represent two different states of biocryomorphological equilibrium. The deeper holes, found in flat ice areas, are characterized by abundant cyanobacteria and higher carbon content, consistent with the \"equilibrium state\" previously suggested for Greenland Ice Sheet cryoconite holes (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In contrast, the shallower holes in rough crevasse areas are dominated by glacier algae and exhibit lower, more variable cryoconite carbon content. The relative prevalence of these two equilibrium states across the ice sheet holds significant implications for overall microbial productivity and the carbon cycle. Given that crevasse-occupied areas in West Greenland have significantly expanded in recent decades (e.g., 42% of ablation area, increasing by 13% from 1985 − 2009; (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e)), such topography-driven shifts in cryoconite hole equilibrium states could induce substantial changes in carbon production. Therefore, changes in glacier dynamics and surface topography have the potential to significantly alter the spatial distribution of microbial communities and carbon cycling across the ice sheet, providing crucial insights into the localized drivers of ice sheet melt and carbon dynamics in a changing climate.\u003c/p\u003e\n\n\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eField work on Issunguata Sermia Glacier\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe investigation was conducted on August 5th and 6th, 2017, on the ablation ice surface of the Issunguata Sermia Glacier in southwest Greenland (Fig.\u0026nbsp;1). Measurements of cryoconite hole dimensions and sample collections were performed at 20 distinct sites along a transect extending from the glacier margin towards its central part (Fig.\u0026nbsp;1). Sites S1 to S12 were situated within the crevasse zone, while sites S13 to S20 were in the flat zone. Cryoconite holes were present at all surveyed sites during the investigation period.\u003c/p\u003e\u003cp\u003eAt each of the 20 sites, the dimensions (depth, water level, diameter) of over 20 typical circular cryoconite holes were manually measured using a scale. Holes with irregular shapes or those connected to active meltwater flows were excluded from measurements and sampling, as such holes often exhibit distinct microbial activities (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Cryoconite sediment at the bottom of the selected holes was collected using a pipette and preserved in plastic bottles. At Site S1, located very close to the glacier margin, the surface was heavily covered with dust and debris, and only shallow holes with irregular shapes were observed. Therefore, only cryoconite was collected at this site, and hole dimensions were not measured.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample Preservation and Laboratory Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor subsequent molecular and microscopic analyses, approximately 1 mL (wet volume) of each cryoconite sample was separated, kept frozen during transport, and then stored at − 80°C until DNA extraction and preliminary microscopic examination. Another portion (approximately 1 g dry weight) of the cryoconite samples was air-dried at a laboratory in Kangerlussuaq International Science Support (KISS) for carbon and nitrogen stable isotope ratio analysis. The remaining cryoconite was fixed with formalin for comprehensive microscopic and mineralogical analysis and long-term preservation.\u003c/p\u003e\u003cp\u003eMeltwater from cryoconite holes was also collected with a pipette at each site for analysis of water-stable isotopes and concentrations of major soluble chemical ions. All collected samples were subsequently transported to Chiba University, Japan, for comprehensive chemical, microbial, and mineralogical analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicroscopy of Phototrophs\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePhototrophic community structure was determined using microscopic analysis. To disaggregate cryoconite granules, which are complex aggregates of algae, mineral particles, organic matter, and filamentous cyanobacteria, samples were ultrasonicated. A 200 µL aliquot of the resulting microbial suspension in meltwater was then filtered onto a membrane filter (Omnipore, JH00013, Merck Millipore). Cells of glacier algae and cyanobacteria retained on the filter were enumerated using a fluorescent microscope. Biovolume (cell volume biomass) for each taxon was calculated from the geometric volume of individual cells and their respective counts. As absolute cell concentrations can vary significantly depending on the sampled sediment and meltwater volumes, the phototroph community structure is presented solely as the proportion of each taxon's biovolume relative to the total biovolume.\u003c/p\u003e\u003ch3\u003e16S rRNA gene amplicon sequencing analysis\u003c/h3\u003e\u003cp\u003eDNA was extracted from 0.1 − 0.2 g of cryoconite samples following the protocol of Willerslev et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), with the exception that bead-beating was performed using a Multi-beads Shocker at 2500 rpm for 30 s (Yasui Kikai, Japan) in 2-mL Matrix-E tubes (MP Biomedicals, USA). DNA extractions were conducted in a Class 100 clean bench (MHE-130AB3; PHCbi, Japan), and subsequent procedures were carried out in another Class 100 clean bench (MCV-131BNS; PHCbi). The absence of contaminating DNA was confirmed by performing the entire procedure (DNA extractions, PCR, library preparation) on blank controls without samples.\u003c/p\u003e\u003cp\u003eMicrobial community structures were analyzed by 16S rRNA gene amplicon sequencing using the primer pair Bakt 341F and Bakt 805R (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), each tagged with Illumina overhang adaptor sequences at the 5' ends. The 20 µL PCR mixture contained 1×KAPA HiFi HS ReadyMix (Kapa Biosystems), 0.2 µM of each primer, and 2 µL of template DNA. PCR amplification was conducted under the following conditions: initial denaturation at 95°C for 3 min; 20 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 60 s; and a final extension at 72°C for 5 min.\u003c/p\u003e\u003cp\u003eSample-specific indices and Illumina adapter sequences were added using the Nextera XT index kit v2 (Illumina, USA), and the index PCR was performed as described by Segawa et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). PCR products were purified, pooled at an equimolar concentration, and mixed with the PhiX control DNA at a ratio of 80:20. Sequencing was performed on the Illumina MiSeq platform using 2 × 300 bp paired-end protocol with the MiSeq Reagent kit v3. The average read count per sample was approximately 51,000 for the 16S rRNA gene.\u003c/p\u003e\u003cp\u003eSequence analysis was performed using the DADA2 package v1.26 (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). The reads were denoised and clustered into amplicon sequence variants (ASVs), which represent unique biological sequences. Taxonomic assignments were performed by aligning ASVs against the SILVA 138 database using SINA v1.2.12 with a minimum similarity threshold of 0.85 (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Similarity of bacterial community structures among samples was calculated based on Bray-Curtis dissimilarity of ASV composition, and structural differences between shallower and deeper cryoconite holes were statistically evaluated using permutational multivariate analysis of variance (PERMANOVA) implemented in the vegan package in R (v3.6.1). Then, ASVs which were differentially distributed between shallower and deeper cryoconite samples were identified using ALDEx2 v1.38.0 (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), and ASVs with an absolute effect size greater than 1 or a Benjamini-Hochberg-corrected Wilcoxon p-value below 0.1 were considered to be differentially distributed.\u003c/p\u003e\u003cp\u003eTo directly identify the cyanobacterial phylotypes comprising cryoconite granules in the deeper cryoconite holes, single-filament PCR analysis was conducted. Samples were placed on PPS membrane slides (Leica, Germany) and air-dried. Individual cyanobacterial filaments were isolated using a laser microdissection system (LMD 7000; Leica, Germany). Each dissected filament was transferred directly into a PCR tube containing a sterilized lysis buffer (10 mM Tris-HCl, pH 8.0; 0.1 mM EDTA; 0.1% Tween 20 in Milli-Q water). The tubes were subjected to three freeze–thaw cycles to lyse the cells, followed by incubation at 55°C for 2 hours. Whole genome amplification was then performed using the REPLI-g Single Cell Kit (Qiagen, Germany), and the amplified DNA was subsequently used as a template for PCR. PCR amplification of the 16S rRNA–ITS region was performed using 2× KAPA HiFi HotStart ReadyMix (Kapa Biosystems, USA) with primers 359F (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e) and 23S30R. Thermal cycling conditions were as follows: initial denaturation at 95°C for 3 min; 40 cycles of 95°C for 30 s, 57°C for 30 s, and 72°C for 3 min; and a final extension at 72°C for 5 min. PCR products were purified using the MinElute PCR Purification Kit (Qiagen, Germany), and sequencing was performed using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, USA) on an ABI 3130xl Genetic Analyzer.\u003c/p\u003e\u003cp\u003eTo determine their phylogenetic affiliations, the obtained sequences were aligned using MAFFT v. 7.52 (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). A maximum likelihood tree was inferred using IQ-tree v. 1.6.12 (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) with the GTR + F + I + Γ4 model, and 1000 replications were carried out for standard bootstrap analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalyses of organic matter of cryoconite\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo measure the organic content in cryoconite, the samples were dried (60ºC, 24 hours) in pre-weighed crucibles in the laboratory. Then, the dried samples were combusted for 1 hour at 500ºC in an electric furnace, and weighed again. The amount of organic matter was obtained from the difference in the weight between the dried and combusted samples.\u003c/p\u003e\u003cp\u003eTo assess the sources of carbon and nitrogen of organic matter in cryoconite, the carbon and nitrogen isotope ratios of the cryoconite samples were measured with an isotope ratio mass spectrometer (DELTA plus XP, Thermo Fisher Scientific, Inc.) connected to an elemental analyzer (Flash EA, Thermo Fisher Scientific, Inc.) in Research Institute for Humanity and Nature in Kyoto, Japan. Dried cryoconite samples were put in 1% HCl to remove carbonate, and rinsed with Milli-Q water five times, and are dried again. 20 mg of the samples was put in tin capsules and set in the EA sampler with standards.\u003c/p\u003e\u003cp\u003eThe carbon and nitrogen isotope ratios are expressed as follows:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\delta\\:}{}^{13}\\text{C}\\:\\:\\text{o}\\text{r}\\:\\:{\\delta\\:}{}^{15}\\text{N}=\\frac{{R}_{sample}-{R}_{standard}}{{R}_{standard}}\\times\\:1000\\:\\left(\\text{‰}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cem\u003eR\u003c/em\u003e\u003csub\u003esample\u003c/sub\u003e or \u003cem\u003eR\u003c/em\u003e\u003csub\u003estandard\u003c/sub\u003e is the relative abundance of carbon or nitrogen isotopes (\u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC or \u003csup\u003e15\u003c/sup\u003eN/\u003csup\u003e14\u003c/sup\u003eN) of the sample or standard, respectively. The isotope ratios were corrected using multiple secondary standards carefully calibrated to international standards. Carbon isotope ratios were reported against the Vienna Pee Dee Belemnite (VPDB) scale, and nitrogen isotope ratios were reported against atmospheric N\u003csub\u003e2\u003c/sub\u003e. The standard errors of the measurements were less than ± 0.2‰ based on simultaneous measurements of the working standards.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeochemical analysis of meltwater in cryoconite holes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo infer the source of meltwater in cryoconite holes, oxygen and hydrogen stable isotope ratios (δ\u003csup\u003e18\u003c/sup\u003eO and δD) of meltwater collected from cryoconite holes were analyzed with a liquid-water isotope analyzer (DLT-100, Los Gatos Research, USA) at Chiba University. The analytical precisions of δ\u003csup\u003e18\u003c/sup\u003eO and δD measurements were 0.05 and 0.5‰, respectively. The results of δ\u003csup\u003e18\u003c/sup\u003eO are presented in this paper.\u003c/p\u003e\u003cp\u003eThe major soluble chemical ions, including five cations (Na\u003csup\u003e+\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, and Ca\u003csup\u003e2+\u003c/sup\u003e) and four anions (Cl\u003csup\u003e−\u003c/sup\u003e, NO3\u003csup\u003e−\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3−\u003c/sup\u003e, and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2−\u003c/sup\u003e) in meltwater, were also measured with an ion chromatography system (ICS1100, Thermo Fisher, USA). The ion separation columns used are AS12A and CS12A for anion and cation, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMineralogical analysis of cryoconite\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe mineralogical composition of the cryoconite was identified by powder X-ray diffraction analysis (XRD) using Geigerflex RAD-2B (RIGAKU, Japan) at Chiba University, Japan. Cryoconite samples were dried at 60°C and then powdered with an agate mortar. The X-ray target was CuKα, the tube voltage was 40 kV, and the tube current was 25 mA. Scans were performed from 2° to 40° (2θ) at a rate of 2° (2θ) per minute.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003e\u003cb\u003eCompeting interests\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor contributions:\u003c/h2\u003e\u003cp\u003eNT designed the study. KI, WA, and NT conducted the field observations. KI conducted analyses of the microscope, organics, and stable isotopes with the support of NT and WA. TS and TM performed the DNA and phylogenetic analyses of microbes in the samples. NT, TS and TM analysed the data and wrote the manuscript. All authors contributed to the discussion and reviewed the draft of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eWe would like to thank all members of the project of SIGMA2 for their support in the field campaign of 2017. We thank Ichiro Tayasu and Chikage Yoshimizu for their support of carbon and nitrogen stable isotope analysis, Noboru Furukawa for XRD analysis. This study was financially supported by Grant-in-Aids (23221004, 24H00260) and by the Arctic Challenge for Sustainability II (ArCS II, Program Grant Number JPMXD1420318865). Analyses of carbon and nitrogen isotopes were conducted by the support of Joint Research Grant for the Environmental Isotope Study of Research Institute for Humanity and Nature.\u003c/p\u003e\u003ch2\u003eData availability:\u003c/h2\u003e\u003cp\u003eThe raw Illumina sequence data have been deposited in the DDBJ Sequence Read Archive under accession number PRJDBXXX. Geochemical data are included in supplementary information files. The other datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCook, J., Edwards, A., Takeuchi, N., and Irvine-Fynn, T.: Cryoconite: the dark biological secret of the cryosphere, Prog. Phys. Geograp., 40(1), 66\u0026ndash;111, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0309133315616574\u003c/span\u003e\u003cspan address=\"10.1177/0309133315616574\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWharton, Jr. R. A., McKay, C. P., Simmons, Jr. G. M., and Parker, B. C.: Cryoconite holes on glaciers, BioScience, 499\u0026ndash;503, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1309818\u003c/span\u003e\u003cspan address=\"10.2307/1309818\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1985).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCameron, K. A., Hodson, A. J., and Osborn, A. M.: Structure and diversity of bacterial, eukaryotic and archaeal communities in glacial cryoconite holes from the Arctic and the Antarctic. FEMS microbiol. ecol., 82(2), 254\u0026ndash;267, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1574-6941.2011.01277.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1574-6941.2011.01277.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdwards, A., et al.: A distinctive fungal community inhabiting cryoconite holes on glaciers in Svalbard, Fungal Ecology, 6(2), 168\u0026ndash;176, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.funeco.2012.11.001\u003c/span\u003e\u003cspan address=\"10.1016/j.funeco.2012.11.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZawierucha, K., Kolicka, M., Takeuchi, N., and Kaczmarek, Ł.: What animals can live in cryoconite holes? A faunal review, J. Zool. 295(3), 159\u0026ndash;169 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jzo.12195\u003c/span\u003e\u003cspan address=\"10.1111/jzo.12195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaczmarek, Ł., Jakubowska, N., Celewicz-Gołdyn, S., and Zawierucha, K.: The microorganisms of cryoconite holes (algae, Archaea, bacteria, cyanobacteria, fungi, and Protista): a review, Polar Record, 52(2), 176\u0026ndash;203, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0032247415000637\u003c/span\u003e\u003cspan address=\"10.1017/S0032247415000637\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVonnahme, T. R., Devetter, M., Ž\u0026aacute;rsk\u0026yacute;, J. D., Šaback\u0026aacute;, M., and Elster, J.: Controls on microalgal community structures in cryoconite holes upon high Arctic glaciers, Svalbard, Biogeosci., 13, 659\u0026ndash;674, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/bg-13-659-2016\u003c/span\u003e\u003cspan address=\"10.5194/bg-13-659-2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKobayashi, K., Takeuchi, N., and Kagami, M. High prevalence of parasitic chytrids infection of glacier algae in cryoconite holes in Alaska. Scientific Reports, 13(1), 3973., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-30721-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-30721-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakeuchi, N., Kohshima, S., and Seko, K.: Structure, formation, darkening process of albedo reducing material (cryoconite) on a Himalayan glacier: a granular algal mat growing on the glacier, Arc. Antarc. Alp. Res., 33, 115\u0026ndash;122, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1552211\u003c/span\u003e\u003cspan address=\"10.2307/1552211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLangford, H., Hodson, A., Banwart, S., and B\u0026oslash;ggild, C.: The microstructure and biogeochemistry of Arctic cryoconite granules, Ann. Glaciol., 51(56), 87\u0026ndash;94, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3189/172756411795932083\u003c/span\u003e\u003cspan address=\"10.3189/172756411795932083\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRozwalak, P., et al.: Cryoconite\u0026ndash;From minerals and organic matter to bioengineered sediments on glacier's surfaces, Sci. Total Environment, 807, 150874, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.150874\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.150874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook, J., et al.: The mass\u0026ndash;area relationship within cryoconite holes and its implications for primary production, Ann. Glaciol., 51, 106\u0026ndash;110, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3189/172756411795932038\u003c/span\u003e\u003cspan address=\"10.3189/172756411795932038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHodson, A., et al.: The cryoconite ecosystem on the Greenland ice sheet, Ann. Glaciol., 51(56), 123\u0026ndash;129, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3189/172756411795931985\u003c/span\u003e\u003cspan address=\"10.3189/172756411795931985\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHodson, A. et al.: A glacier respires: quantifying the distribution and respiration CO2 flux of cryoconite across an entire Arctic supraglacial ecosystem, J. Geophys. Res. Biogeosci., 112(G4), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2007JG000452\u003c/span\u003e\u003cspan address=\"10.1029/2007JG000452\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnesio, A. M., Hodson, A., Fritz, A., Psenner, R., and Sattler, B.: High microbial activity on glacier importance to the global carbon cycle, Global Change Biol., 15, 955\u0026ndash;960, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2008.01758.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2008.01758.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGribbon, P. W. F.: Short Notes: Cryoconite Holes on Sermikavsak, West Greenland. J. Glaciol., 22(86), 177\u0026ndash;181, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0022143000014167\u003c/span\u003e\u003cspan address=\"10.1017/S0022143000014167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1979).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcIntyre, N. F.: Cryoconite hole thermodynamics, Can. J. Earth Sci., 21, 152\u0026ndash;156, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1139/e84-016\u003c/span\u003e\u003cspan address=\"10.1139/e84-016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1984).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOnuma, Y., Fujita, K., Takeuchi, N., Niwano, M., and Aoki, T.: Modelling the development and decay of cryoconite holes in northwestern Greenland, The Cryosphere, 17, 3309\u0026ndash;3328, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/tc-17-3309-2023\u003c/span\u003e\u003cspan address=\"10.5194/tc-17-3309-2023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakeuchi, N., Kohshima, S., Yoshimura, Y., Seko, K., and Fujita, K.: Characteristics of cryoconite holes on a Himalayan glacier, Yala Glacier Central Nepal. Bull. Glaciol. Res., 17, 51\u0026ndash;59 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026oslash;ggild, C., Brandt, R. E., Brown, K. J., and Warren, S. G.: The ablation zone in northeast Greenland: ice types, albedos and impurities, J. Glaciol., 56(195), 101\u0026ndash;113, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3189/002214310791190776\u003c/span\u003e\u003cspan address=\"10.3189/002214310791190776\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook, J. M., Sweet, M., Cavalli, O., Taggart, A., and Edwards, A.: Topographic shading influences cryoconite morphodynamics and carbon exchange, Arc. Antarc. Alp. Res., 50(1), S100014, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15230430.2017.1414463\u003c/span\u003e\u003cspan address=\"10.1080/15230430.2017.1414463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRyser, C., et al.: Cold ice in the ablation zone: Its relation to glacier hydrology and ice water content, J. Geophys. Res.: Earth Surface, 118(2), 693\u0026ndash;705, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2012JF002526\u003c/span\u003e\u003cspan address=\"10.1029/2012JF002526\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026uuml;thi, M. P. et al.: Heat sources within the Greenland Ice Sheet: dissipation, temperate paleo-firn and cryo-hydrologic warming, The Cryosphere, 9, 245\u0026ndash;253, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/tc-9-245-2015\u003c/span\u003e\u003cspan address=\"10.5194/tc-9-245-2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;ller, F., and Keeler, C. M.: Errors in short-term ablation measurements on melting ice surfaces. J. Glaciol., 8(52), 91\u0026ndash;105, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0022143000020785\u003c/span\u003e\u003cspan address=\"10.1017/S0022143000020785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1969).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBagshaw, E. A., et al.: Do cryoconite holes have the potential to be significant sources of C, N, and P to downstream depauperate ecosystems of Taylor Valley, Antarctica?, Arc. Antarc. Alp. Res., 45(4), 440\u0026ndash;454. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1657/1938-4246-45.4.440\u003c/span\u003e\u003cspan address=\"10.1657/1938-4246-45.4.440\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdwards, A., Irvine-Fynn, T., Mitchell, A. C., and Rassner, S. M.: A germ theory for glacial systems?, Wiley Interdisciplinary Reviews: Water, 1(4), 331\u0026ndash;340, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wat2.1029\u003c/span\u003e\u003cspan address=\"10.1002/wat2.1029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook, J. M., Hodson, A. J., and Irvine-Fynn, T. D. L.: Supraglacial weathering crust dynamics inferred from cryoconite hole hydrology, Hydrol. Process., 30, 433\u0026ndash;446, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hyp.10602\u003c/span\u003e\u003cspan address=\"10.1002/hyp.10602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFountain, A. G., Tranter, M., Nylen, T. H., Lewis, K. J., and Mueller, D. R.: Evolution of cryoconite holes and their contribution to meltwater runoff from glaciers in the McMurdo Dry Valleys, Antarctica. J. Glaciol., 50(168), 35\u0026ndash;45, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3189/172756504781830312\u003c/span\u003e\u003cspan address=\"10.3189/172756504781830312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdwards, A., et al.: Possible interactions between bacterial diversity, microbial activity and supraglacial hydrology of cryoconite holes in Svalbard. The ISME journal, 5(1), 150\u0026ndash;160, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ismej.2010.100\u003c/span\u003e\u003cspan address=\"10.1038/ismej.2010.100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIrvine-Fynn, T. D., et al.: Storage and export of microbial biomass across the western Greenland Ice Sheet, Nat. Commun., 12, 3960, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-021-24040-9\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-24040-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShimada, R., Takeuchi, N., and Aoki, T.: 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, 43, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2016.00043\u003c/span\u003e\u003cspan address=\"10.3389/feart.2016.00043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTedesco, M., et al.: The darkening of the Greenland ice sheet: trends, drivers, and projections (1981\u0026ndash;2100), The Cryosphere, 10, 477\u0026ndash;496, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/tc-10-477-2016\u003c/span\u003e\u003cspan address=\"10.5194/tc-10-477-2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliamson, C. J., et al.: Algal photophysiology drives darkening and melt of the Greenland Ice Sheet, Proc. Nat. Acad. Sci., 117(11), 5694\u0026ndash;5705, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1918412117\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1918412117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChandler, D. M., Alcock, J. D., Wadham, J. L., Mackie, S. L., and Telling, J.: Seasonal changes of ice surface characteristics and productivity in the ablation zone of the Greenland Ice Sheet, The Cryosphere, 9, 487\u0026ndash;504, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/tc-9-487-2015\u003c/span\u003e\u003cspan address=\"10.5194/tc-9-487-2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakeuchi, N., Nagatsuka, N., Uetake, J., and Shimada, R.: Spatial variations in impurities (cryoconite) on glaciers in northwest Greenland, Bull. Glaciol. Res., 32, 85\u0026ndash;94, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5331/bgr.32.85\u003c/span\u003e\u003cspan address=\"10.5331/bgr.32.85\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRyan, J. C. et al.: Dark zone of the Greenland Ice Sheet controlled by distributed biologically-active impurities, Nat. Commun., 9(1), 1065, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-018-03353-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-018-03353-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakeuchi, N., et al.: Temporal variations of cryoconite holes and cryoconite coverage on the ablation ice surface of Qaanaaq Glacier in northwest Greenland, Ann. Glaciol., 59, 21\u0026ndash;30, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/aog.2018.19\u003c/span\u003e\u003cspan address=\"10.1017/aog.2018.19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerkins, R. G., et al.,: Photoacclimation by Arctic cryoconite phototrophs, FEMS Microbiology Ecology, 93(5), May 2017, fix018,\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/femsec/fix018\u003c/span\u003e\u003cspan address=\"10.1093/femsec/fix018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook. J., Edwards, A., and Hubbard, A.: Biocryomorphology: integrating microbial processes with ice surface hydrology, topography, and roughness. Front. Earth Sci., 3, 78, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2015.00078\u003c/span\u003e\u003cspan address=\"10.3389/feart.2015.00078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones, C., Ryan, J., Holt, T., and Hubbard, A.: Structural glaciology of isunguata sermia, West Greenland. J. Maps, 14(2), 517\u0026ndash;527, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17445647.2018.1507952\u003c/span\u003e\u003cspan address=\"10.1080/17445647.2018.1507952\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerdel, R. W., and Drouet, F.: The cryoconite of the Thule area, Greenland. Transact. Am. Microscop. Soc., 79(3), 256\u0026ndash;272, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3223732\u003c/span\u003e\u003cspan address=\"10.2307/3223732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1960).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSegawa, T., et al.: Biogeography of cryoconite forming cyanobacteria on polar and Asian glaciers, J. biogeography, 44(12), 2849\u0026ndash;2861, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jbi.13089\u003c/span\u003e\u003cspan address=\"10.1111/jbi.13089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCooper, M. G., et al.: Spectral attenuation coefficients from measurements of light transmission in bare ice on the Greenland Ice Sheet. The Cryosphere, 15(4), 1931\u0026ndash;1953, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/tc-15-1931-2021\u003c/span\u003e\u003cspan address=\"10.5194/tc-15-1931-2021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoham, R. W., and Remias, D.: Snow and glacial algae: a review, J. Phycol., 56(2), 264\u0026ndash;282, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jpy.12952\u003c/span\u003e\u003cspan address=\"10.1111/jpy.12952\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurakami, T., et al.: Metagenomics reveals global-scale contrasts in nitrogen cycling and cyanobacterial light-harvesting mechanisms in glacier cryoconite, Microbiome, 10, 50, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40168-022-01238-7\u003c/span\u003e\u003cspan address=\"10.1186/s40168-022-01238-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeindel, R. C., Governali, F. C., Spickard, A. M., and Virginia, R. A.: The role of biological soil crusts in nitrogen cycling and soil stabilization in Kangerlussuaq, West Greenland. Ecosystems, 22, 243\u0026ndash;256, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10021-018-0267-8\u003c/span\u003e\u003cspan address=\"10.1007/s10021-018-0267-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMusilova, M., Tranter, M., Bennett, S. A., Wadham, J., and Anesio, A. M.: Stable microbial community composition on the Greenland Ice Sheet, Front. Microbiol., 6, 193, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2015.00193\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2015.00193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeng, M. J., et al.: Deglaciation and catchment ontogeny in coastal south-west Greenland: implications for terrestrial and aquatic carbon cycling. J. Quaternary Sci., 27(6), 575\u0026ndash;584, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jqs.2544\u003c/span\u003e\u003cspan address=\"10.1002/jqs.2544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchmidt, S. K., et al.: Microbial biogeochemistry and phosphorus limitation in cryoconite holes on glaciers across the Taylor Valley, McMurdo Dry Valleys, Antarctica, Biogeochemistry, 158(3), 313\u0026ndash;326, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10533-022-00900-4\u003c/span\u003e\u003cspan address=\"10.1007/s10533-022-00900-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStibal, M., et al.: Environmental controls on microbial abundance and activity on the Greenland ice sheet: a multivariate analysis approach, Microbial Ecol., 63, 74\u0026ndash;84, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00248-011-9935-3\u003c/span\u003e\u003cspan address=\"10.1007/s00248-011-9935-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTelling, J., et al.: Microbial nitrogen cycling on the Greenland Ice Sheet, Biogeosciences, 9, 2431\u0026ndash;2442, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/bg-9-2431-2012\u003c/span\u003e\u003cspan address=\"10.5194/bg-9-2431-2012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReeh, N., Oerter, H., \u0026amp; Thomsen, H. H.: Comparison between Greenland ice-margin and ice-core oxygen-18 records. Annals of Glaciology, 35, 136\u0026ndash;144, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3189/172756402781817365\u003c/span\u003e\u003cspan address=\"10.3189/172756402781817365\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacGregor, J. A., et al.: The age of surface-exposed ice along the northern margin of the Greenland Ice Sheet, J. Glaciol., 66(258), 667\u0026ndash;684, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/jog.2020.62\u003c/span\u003e\u003cspan address=\"10.1017/jog.2020.62\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCutcheon, J., et al.: Mineral phosphorus drives glacier algal blooms on the Greenland Ice Sheet, Nat. Commun., 12(1), 570, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-020-20627-w\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-20627-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagatsuka, N., et al.: Variations in Sr and Nd Isotopic Ratios of Mineral Particles in Cryoconite in Western Greenland, Front. Earth Sci, 4, 93, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2016.00093\u003c/span\u003e\u003cspan address=\"10.3389/feart.2016.00093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBanerjee, A., et al.: A scaling relation for cryoconite holes. Geophys. Res. Let., 50(22), e2023GL104942, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2023GL104942\u003c/span\u003e\u003cspan address=\"10.1029/2023GL104942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCathles, L. M., Abbot, D. S., Bassis, J. N., and MacAYEAL, D. R.: Modeling surface-roughness/solar-ablation feedback: application to small-scale surface channels and crevasses of the Greenland ice sheet. Ann. Glaciol., 52(59), 99\u0026ndash;108, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3189/172756411799096268\u003c/span\u003e\u003cspan address=\"10.3189/172756411799096268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eColgan, W., et al.: An increase in crevasse extent, West Greenland: Hydrologic implications. Geophys. Res. Lett., 38(18), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2011GL048491\u003c/span\u003e\u003cspan address=\"10.1029/2011GL048491\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWillerslev, E., et al.: Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science, 300(5620), 791\u0026ndash;795, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1084114\u003c/span\u003e\u003cspan address=\"10.1126/science.1084114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerlemann, D. P. R., et al.: Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. The ISME Journal, 5(10), 1571\u0026ndash;1579, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ismej.2011.41\u003c/span\u003e\u003cspan address=\"10.1038/ismej.2011.41\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSegawa, T., et al.: Evolution of snow algae, from cosmopolitans to endemics, revealed by DNA analysis of ancient ice. The ISME Journal, 17(4), https://doi.org/491-501.10.1038/s41396-023-01359-3 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCallahan, B. J., et al.: DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13, 581\u0026ndash;583, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nmeth.3869\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.3869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePruesse, E., Peplies, J., and Gl\u0026ouml;ckner, F. O.: SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 28(14), 1823\u0026ndash;1829, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/bts252\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/bts252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFernandes, A. D., et al.: Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. \u003cem\u003eMicrobiome\u003c/em\u003e, 2, 15, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/2049-2618-2-15\u003c/span\u003e\u003cspan address=\"10.1186/2049-2618-2-15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eN\u0026uuml;bel, U., Garcia-Pichel, F., and Muyzer, G.: PCR primers to amplify 16S rRNA genes from cyanobacteria. Applied and environmental microbiology, 63(8), 3327\u0026ndash;3332.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/aem.63.8.3327-3332.1997\u003c/span\u003e\u003cspan address=\"10.1128/aem.63.8.3327-3332.1997\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1997).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKatoh, K, and Standley, D. M.: MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol., 30, 772\u0026ndash;780, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/molbev/mst010\u003c/span\u003e\u003cspan address=\"10.1093/molbev/mst010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen, L-T., Schmidt, H. A., von Haeseler, A., and Minh, BQ.: IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Molecular Biology and Evolution, 32: 268\u0026ndash;274. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/molbev/msu300\u003c/span\u003e\u003cspan address=\"10.1093/molbev/msu300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7116261/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7116261/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCryoconite holes, small water-filled cylindrical pits on glacier surfaces, are crucial microbial habitats and play a pivotal role in darkening the Greenland Ice Sheet, potentially accelerating ice melt. To understand how their morphology influences microbial ecosystems and biogeochemical functions, we investigated cryoconite hole dimensions, microbial communities, and cryoconite characteristics across Issunguata Sermia Glacier, southwest Greenland. Our findings reveal distinct morphological gradients: cryoconite holes were shallower in the rough crevasse zone near the glacier margin and significantly deeper in the flat ice zone at the glacier's center. These morphological differences were strongly linked to disparities in phototrophic community composition and relative abundance, including filamentous cyanobacteria and glacier algae, between the deeper and shallower holes. Furthermore, cryoconite from deeper holes exhibited significantly higher organic content and enriched carbon stable isotope signatures, suggesting enhanced in-situ microbial productivity, despite consistent meltwater geochemistry and mineral compositions across all sites. Our results unequivocally demonstrate that glacier surface topography primarily drives cryoconite hole development, critically shaping localized microbial communities, carbon cycling, and albedo feedbacks. This study highlights the complex physical-biological interplay in glaciers, offering crucial insights into ice sheet melt and carbon dynamics in a changing polar region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Morphological Control of Microbial Ecosystems and Carbon Cycling in Greenlandic Cryoconite Holes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 16:57:03","doi":"10.21203/rs.3.rs-7116261/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c6f6ec7f-1f99-4ba4-b01a-1b6baa5b5ba9","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51769990,"name":"Earth and environmental sciences/Climate sciences/Cryospheric science"},{"id":51769991,"name":"Biological sciences/Microbiology/Environmental microbiology/Water microbiology"},{"id":51769992,"name":"Biological sciences/Ecology/Microbial ecology"},{"id":51769993,"name":"Earth and environmental sciences/Biogeochemistry/Carbon cycle"},{"id":51769994,"name":"Earth and environmental sciences/Ecology/Stable isotope analysis"}],"tags":[],"updatedAt":"2026-01-07T08:13:11+00:00","versionOfRecord":{"articleIdentity":"rs-7116261","link":"https://doi.org/10.1038/s43247-025-03045-y","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2025-12-01 05:00:00","publishedOnDateReadable":"December 1st, 2025"},"versionCreatedAt":"2025-07-22 16:57:03","video":"","vorDoi":"10.1038/s43247-025-03045-y","vorDoiUrl":"https://doi.org/10.1038/s43247-025-03045-y","workflowStages":[]},"version":"v1","identity":"rs-7116261","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7116261","identity":"rs-7116261","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}