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Article

Microbial Ecology of Rotten Sea Ice: Implications for Arctic Carbon Cycling with Global Warming

by
Carie M. Frantz
1,*,
Byron C. Crump
2,*,
Shelly Carpenter
3,
Erin Firth
3,
Mónica V. Orellana
3,4,
Bonnie Light
3 and
Karen Junge
3
1
Department of Earth & Environmental Sciences, Weber State University, Ogden, UT 84408, USA
2
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
3
Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA 98109, USA
4
Institute for Systems Biology, Seattle, WA 98109, USA
*
Authors to whom correspondence should be addressed.
Microorganisms 2026, 14(2), 482; https://doi.org/10.3390/microorganisms14020482
Submission received: 1 January 2026 / Revised: 5 February 2026 / Accepted: 11 February 2026 / Published: 16 February 2026
(This article belongs to the Special Issue Polar Microbiome Facing Climate Change)
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Abstract

“Rotten” sea ice, ice in an advanced stage of melt, represents an important but understudied habitat in the rapidly changing Arctic. As Arctic warming accelerates, this late-season ice type will become more prevalent, yet little is known about its microbial inhabitants or their roles in Arctic marine biogeochemical cycles. We examined microbial communities (prokaryote and algal abundance, 16S and 18S rRNA gene and transcript sequencing) and biogeochemical properties of rotten sea ice and earlier-season ice near Utqiaġvik, Alaska, USA. Rotten ice was comparatively warm, isothermal, and largely drained of brine, with extensive, interconnected pore networks linked to melt ponds above and seawater below. Unlike earlier-season ice, fluids saturating rotten ice were vertically homogeneous in pH, dissolved inorganic carbon, prokaryote and phytoplankton abundance, and microbial community composition. However, particulate carbon and nitrogen exhibited strong vertical gradients, with the highest concentrations near the surface. Microbial communities in rotten ice were significantly different from those in earlier-season ice and varied between individual floes. These findings indicate that rotten ice constitutes a distinct microbial habitat and may serve as an important source of nutrient-rich particulate matter in the future Arctic Ocean during the summer melt season.

1. Introduction

1.1. “Rotten” Ice in a Changing Arctic

The Arctic is warming at more than twice the global average [1], and the past decade has seen sustained declines in sea ice extent, concentration, and thickness [2,3]. Longer melt seasons [4] are expected to increase the prevalence of “rotten” ice—ice in the very end stage of melt—during summer months [5]. Field observations suggest that rotten ice is more widespread than previously recognized [6,7], yet it remains understudied, particularly in the Arctic, because its fragile structure complicates collection and analysis.
Our team previously described the physical and optical properties of rotten first-year ice drifting near Utqiaġvik, Alaska. Briefly, rotten ice is warm (0 °C) and characterized by large, interconnected pore spaces with lower salinity than earlier-season brine inclusions, making it a distinct microbial habitat [8]. Despite these unusual properties, rotten ice has not been characterized as a microbial habitat, due in part to its extreme fragility, which makes sampling difficult. Indeed, one of the floes we sought to sample disintegrated during field work. This limitation is particularly consequential in lower-latitude, marginal shelf seas such as the Chukchi Sea, which supply nutrient-rich waters that sustain food webs throughout the Arctic Ocean [9,10,11,12] and may be among the first regions to experience widespread dominance of rotten ice.

1.2. Role of Rotten Ice in Arctic Biogeochemical Cycles

Sea ice plays a central role in Arctic primary productivity [13,14,15,16]. In spring, ice-bottom and under-ice algal blooms can contribute more than half of the fixed carbon fueling Arctic marine food webs in many regions [17]. Climate and phenology models predict that sea ice algal production will increase in a warmer Arctic [18], but the nutritional quality of these blooms may decline [19,20]. As ice melts in summer, algal biomass decreases and large amounts of organic carbon are released into the water column, influencing both the biological pump [21,22,23,24] and Arctic cloud formation via aerosol production [25,26]. Earlier summer melt could also create ecological mismatches if carbon release occurs before grazers are available to recycle it in the pelagic zone, potentially increasing carbon export to the benthos [15].
Prokaryotes—especially Bacteria—are integral to carbon and nitrogen cycling within sea ice [27,28,29,30] and are sensitive to climate-driven environmental change [31]. Although bacterial assemblages in sea ice are seasonally dynamic [16], comparatively little is known about these communities during late summer. A meta-analysis of Central Arctic pack ice data reported a shift towards increased relative abundances of Actinobacteria and Betaproteobacteria in brine occupying expanding channel spaces late in the melt season, though the implications of these changes for carbon cycling remain unclear [32].
In addition to shifts in community composition, both sea ice algae and prokaryotes produce extracellular polymeric substances (EPS), which may represent adaptive responses to the extreme and variable sea ice environment [33]. EPS can influence ice structure and potentially modify permeability and melt dynamics [34], linking microbial activity directly to the physical evolution of melting ice. However, the persistence and role of EPS in rotten ice have not been investigated, limiting our ability to predict how the increasing prevalence of rotten ice may influence microbial processes and the Arctic biogeochemical cycle.
Previous studies have documented sea ice environments earlier in the melt season [21,27,34,35,36,37], during late summer but in largely intact ice [38,39,40], or within unconsolidated brash ice [41]. Broader assessments of late-summer ice have also addressed nutrient distributions [42], ecological patterns [43], and physical characteristics derived from MOSAiC datasets [44,45]. However, environmental conditions and microbial communities associated with fully deteriorated (rotten) landfast sea ice remain uncharacterized.
Here we report the first biogeochemical and microbial community characterization of Arctic shelf rotten ice in the Chukchi Sea, comparing it with earlier-season landfast ice from the same region.

2. Materials and Methods

2.1. Sample Collection and Processing

Samples were collected from landfast first-year sea ice near the coast of Utqiaġvik, Alaska, in May and June, and from drifting ice in July of 2015 (Figure 1). Rotten ice was observed beneath melt ponds on both July floes. The 10 July floe (JY10) had a visibly high sediment load, whereas the 11 July floe (JY11) had low sediment comparable to earlier-season ice. Detailed site descriptions and field observations are provided in the Supplemental Materials. To maximize clarity, sample codes (summarized in Figure 1B) are avoided in the main text wherever possible but are retained in some figures and Supplemental Files.
The core sampling followed the protocol described in Frantz et al. (2019) [8]. Briefly, overlying snow was removed before drilling 9 cm diameter cores to the ice–seawater interface. Cores were sectioned on a sterile core cradle into ~20 cm top (T), middle (M), and bottom (B) horizons, placed into sterile Whirl-Pak bags (Nasco Sampling LLC, Pleasant Prairie, WI, USA), and transported in coolers to the Barrow Arctic Research Center (BARC). Additional opportunistic samples included: surface pond water (PW), sackhole percolate water (P1 = shallow, P2 = deep), fluid draining from rotten ice during collection (Drain), and below-ice seawater (SW). Because “seawater” samples were collected directly beneath the ice, they were likely influenced by ice brines and melts and may not represent true seawater. Core temperature profiling followed Eicken et al. (2009) [46]; this was not obtained for JY10 due to floe breakup during sampling.
At BARC, horizon subsections were either processed immediately or stored at in situ temperatures. Roughly half were melted whole (whole-horizon melts; H) at 4 °C in a 1:1 ratio with artificial seawater (as in Junge et al., 2004) [47]. The remainder were centrifuged at −5 °C for 5 min at 1500 rpm (Sorvall ST40R refrigerated centrifuge with a #6441 swinging 750 mL bucket rotor, Thermo Fisher Scientific™, Waltham, MA, USA) to separate brines, which were pooled (brine; B), and the spun-out ice was pooled and melted at 4 °C (ice-only melts; I). Resulting liquids were aliquoted for chemical, microbial, and molecular analyses. The sample naming scheme for ice core subsamples is outlined in Figure 1B.

2.2. Chemical and Biological Measurements

Salinity profiles of replicate cores were measured in 5 cm increments [8] using a conductivity meter (YSI model 30, Yellow Springs, Ohio; accuracy ±2%, resolution 0.1 ppt). Whole-horizon salinity values reflect the average across the corresponding ~20 cm sections. Salinity for other melts and liquid samples was measured using a handheld refractometer (Brix30, Reichert, Depew, NY, USA). pH was measured spectrophotometrically using m-cresol purple as an indicator [48].
Samples were filtered at 4 °C for analysis of suspended particulate matter (SPM), particulate organic carbon (POC), and particulate nitrogen (PN) following Kellogg and Deming (2009) [49]. Additional aliquots were analyzed for particulate extracellular polysaccharide substances (pEPS), quantified as glucose equivalents (glu-eq) via the phenol-sulfuric assay [50]. Dissolved organic carbon (DOC) was measured by the University of Washington Marine Chemistry Laboratory using a Shimadzu TOC-VCSH analyzer (Shimadzu Scientific Instruments, Inc., Columbia, MD, USA).
Phytoplankton were collected on glass fiber filters at 4 °C, dark-adapted for 15 min, and assessed for photosynthetic efficiency (Photosystem II quantum yield, Fv/Fm) via pulsed amplitude modulation fluorometry (JUNIOR-PAM, Heinz Walz GmbH, Effeltrich, Germany). Filters were stored frozen in the dark before pigment extraction in 90% acetone at 2 °C for 48 h. The total pigment to phaeopigment ratio (Fo/Fa) was measured fluorometrically with a Turner Designs TD-700 fluorometer (San Jose, CA, USA).
Samples for prokaryotic cell abundance were fixed with 2% formaldehyde, frozen, and then filtered onto 0.2 µm pore size black polycarbonate filters. Cells were dual stained with acridine orange and DAPI [51,52] and counted by epifluorescence microscopy (Axioscope.A1, Carl Zeiss Microscopy, LLC, White Plains, NY, USA). Counts were based on >200 cells per sample; standard deviations are from triplicate sample counts. The proportion of actively respiring cells was determined by the percentage of cells stained with CTC (5-cyano-2,3-ditolyl-tetrazolium chloride) after 1–3 h dark incubations [47].

2.3. Phytoplankton Abundance and Identification

Samples were fixed with Lugol iodine solution [53] and stored at 4 °C in glass jars. Protist cells > 2 μm were identified to the lowest possible taxonomic rank [54] using standard keys [55,56,57,58,59]. A minimum of 400 cells (accuracy ± 10%) were counted per settling chamber [60] using an Axiovert 10 microscope (Carl Zeiss Microscopy, LLC, White Plains, NY, USA).

2.4. Nucleotide Extraction, Amplification, Sequencing, and Processing

Detailed protocols are provided in the Supplemental Materials. Briefly, liquid samples were filtered (0.2 µm) at 4 °C onto Sterivex cartridge filters (Millipore Sigma, Burlington, MA, USA), preserved with RNAlater (Thermo Fisher Scientific™, Waltham, MA, USA), then stored at −80 °C. DNA was extracted from filter subsamples using sodium dodecyl sulfate (SDS) + phenol chloroform [61] and RNA with Ambion mirVana miRNA Isolation Kits (Thermo Fisher Scientific™, Waltham, MA, USA) followed by DNA digestion with Ambion TURBO DNA-free Kits (Thermo Fisher Scientific™, Waltham, MA, USA). cDNA was synthesized from RNA with SuperScript III First-Strand Synthesis SuperMix kits using random hexamers (ThermoFisher Scientific™, Waltham, MA, USA).
Amplicon sequencing was performed using modified Earth Microbiome Project protocols [62]. For Bacteria and Archaea, the 16S SSU rRNA gene region V4 was amplified with primers 515F [63] and 806R [64]. For Eukaryota, the 18S SSU rRNA gene region V9 was amplified using primers 1391F and EukBR [65]. Sequencing of amplicons from DNA and cDNA was performed on an Illumina MiSeq platform (San Diego, CA, USA) at the Oregon State University Center for Genome Research and Biocomputing (Corvallis, OR, USA).
Reads were processed in QIIME 2 (version 2025.4; [66]) with DADA2 [67,68], which grouped sequences into amplicon sequence variants (ASVs). Sequences were aligned using mafft [69,70], and rooted and unrooted trees were generated using FastTree2. Taxonomy was assigned using a naïve Bayesian classifier (Scikit-learn; [71]) trained on primer-trimmed sequences from the SILVA 138.2 SSU Ref NR 99 database [72,73,74]. Sequences classified as Eukaryota, mitochondria, or chloroplast were removed from the 16S dataset, and those classified as Bacteria, Archaea, and phyla that typically contain large (>1 mm) metazoans were removed from the 18S dataset. Ten samples were removed from the dataset due to quality concerns. Datasets were rarefied to 1000 sequences (16S) and 3600 sequences (18S). Beta-diversity metrics were calculated with weighted UniFrac distances [75].

2.5. Statistical Analyses in Python

All statistical analyses were performed in Python v. 3.7 (code available at https://github.com/cmfrantz/rottenice). Principal component analysis (PCA) was performed on physical and chemical parameters using scikit-learn (v. 0.23.1; [71]), and plots were generated using matplotlib (v. 3.2.1; [76]) and bokeh (v. 2.0.2; [77]). Heat maps were constructed from weighted UniFrac distance matrices using seaborn.clustermap (v. 0.10.1; [78]). Spearman correlations between metadata and dominant taxonomic groups were calculated using scipy.stats.spearmanr (v. 1.4.1; [79,80]).

3. Results

3.1. Physical and Chemical Properties

Biogeochemical measurements are summarized in Table S1 and Figure S1. Unless noted otherwise, results in the text refer to whole-horizon melt samples (Figure 2). Rotten ice was isothermal, warm (0 °C), and highly porous, with extensive channel interconnectivity [8]. Salinity in rotten ice brines and core drains (11–15 ppt) was much lower than for earlier-season ice (23–45 ppt in May and June brine), and similar to overlying pond water (14 ppt) and seawater collected from directly beneath the ice (12 ppt). July whole-horizon melts had an alkaline pH (8.3–8.7) comparable to June brine and sackhole percolates (8.3–9.2). SPM was especially high at the top of the 10 July dirty floe cores (0.1 mg L−1), reflecting the visibly high sediment load.
Across all samples except the 10 July dirty floe, total organic carbon was dominated by DOC (74–95%). DOC concentrations were generally lower in rotten ice than earlier-season ice (0.7–19.7 mg C L−1 in July vs. 1.2–53.7 mg C L−1 in June), with pond water, seawater, and core drain values in a similar range (1.0–1.8 mg C L−1). POC and pEPS increased from May to July, especially in middle and top core horizons (July POC: 0.3–1.8 mg C L−1 in ice vs. 0.3 mg C L−1 in seawater; pEPS: 0.1–0.3 mg glu-eq L−1 in ice vs. 0.04 glu-eq L−1 in seawater), and accounted for a small fraction of the total measured organic carbon. PN also increased from May to July in the middle and top core horizons (from 0.01–0.05 μg L−1 in May to 0.06–0.21 μg L−1 in July) and decreased in the bottom horizon. In all ice cores, bottom-horizon PN exceeded that of the underlying seawater. Principal component analysis (PCA) of physical and chemical parameters showed a consistent progression of all ice horizons towards the distinct properties of rotten ice (Figure 3).

3.2. Microscopy and Cell Counts

In May and June, the abundance of prokaryotes was much higher in the bottom horizon than in the middle and top horizons, peaking in bottom brines (~106 cells mL−1; Figure 2, Table S1). By July, prokaryotic cell abundance was similar across all horizons (>3 × 105 cells mL−1), and, while lower than in underlying seawater (9.2 × 105 cells mL−1), was higher than in the middle and bottom horizons of May and June (Figure 2). The proportion of actively respiring cells (CTC%) was ≤5% in most samples across all months, except in May and in 11 July clean floe bottom (~11%) and 10 July dirty floe top (12%).
Ice algae abundance also peaked in the bottom horizon in May and June, and consisted of a diverse assemblage of diatoms, flagellates, and other taxa (Figure 2 and Figure 4, Table S2). The ice-associated community in these months was distinct from that in underlying seawater, with diatoms much more abundant in ice. The ice-bottom community shifted from Chaetoceros-dominated in May to a diverse assemblage of pennate diatoms in June (Table S2). In July, ice algae were more uniformly distributed among horizons. In the 11 July clean floe, 40–60% of counted cells were empty diatom frustules, whereas the 10 July dirty floe was dominated by live diatoms (Table S2). Core drain communities more closely resembled overlying pondwater (10 July dirty floe) or underlying seawater (11 July clean floe) than ice fractions. The ice-only fraction of the 11 July clean floe contained a higher relative abundance of Chlorophyta than the corresponding whole-horizon melts.

3.3. Photopigments

Chlorophyll a and phaeopigment concentrations in May and June were substantially greater in the bottom horizon than in the middle and upper horizons (Figure 2) and than in the underlying seawater (Table S1). By July, concentrations were more uniform across horizons (0.3–2.0 μg L−1 in whole-horizon melts), and similar to those measured in May and June above the bottom horizon. Photosynthetic efficiency varied widely across fractions and samples (FV/FM: 0.7–0.3, and FO/FA: 1–5), with generally lower FO/FA values in the July rotten ice than in earlier-season samples. Since photosynthetic efficiency was measured in the field on spots on our ice cores, the probe likely captured readings from individual diatoms or small clusters rather than capturing horizon-side averages.

3.4. 16S rRNA Gene and Transcript Sequences: Bacteria and Archaea

All samples analyzed were dominated by sequences of Bacteria from six orders—Flavobacteriales, Pelagibacterales, Rhodobacterales, Burkholderiales, Enterobacterales, and Pseudomonadales—which together accounted for an average of 85% of 16S rRNA gene sequences in both DNA and cDNA datasets (Figure 5, Table S3). The order Enterobacterales in this classification is dominated by sequences affiliated with genera commonly associated with sea ice environments, with the four most abundant genera being Colwellia, Glaciecola, Paraglaciecola, and Psychromonas. While these genera are placed within Enterobacterales in the SILVA database, they are classified within Alteromonadales in other taxonomic frameworks, including the List of Prokaryotic names with Standing in Nomenclature (LPSN) [81]. Members of domain Archaea accounted for ≤3% of DNA sequences and ≤1% of all cDNA sequences, except in May seawater, where a single ASV of Nitrosopumilus, an ammonia-oxidizing member of Archaea, comprised 8–25% of DNA and cDNA sequences (Figure S2).
In May and June, communities were dominated by diverse Gammaproteobacteria, with Alphaproteobacteria (primarily Rhodobacterales) and Flavobacteriales—especially Polaribacter—making up most of the remainder (Figure 5, Figure S2). This pattern was consistent across ice horizons and fractions, although differences emerged at finer taxonomic levels. For example, May bottom-horizon active communities (cDNA) were rich in diverse Pseudomonadales, whereas upper-horizon communities were dominated by Colwellia (Enterobacterales; 43–50% in top and middle whole-horizon melts, 23–29% in bottom) and Polaribacter (Flavobacterales; 21–28% in top and middle whole-horizon melts, 6–8% in bottom), both of which were rare in July samples (Figure 5b). June communities largely resembled those in May at the class level, though some differences between months and fractions emerged at finer taxonomic levels.
In July, community composition was relatively uniform across ice horizons, but differed strongly between floes (Figure 5, Figure S2). The 11 July clean floe resembled earlier-season assemblages, except for lower Colwellia and Polaribacter and greater representation of Flavobacterium, Rhodobacterales, and Comamonadaceae (Burkholderiales). In contrast, the 10 July dirty floe was more distinct, with Rhodobacterales and Burkholderiales together comprising ~60% of whole-horizon melt cDNA sequences (vs. <15% in May and June). Overall, both rotten ice floes exhibited a marked shift from common sea ice lineages Colwelliaceae, Alteromonadaceae, Psychromonadaceae, and other members of the Enterobacterales in earlier-season ice towards dominance by lineages within the Burkholderiales (Figure 5, Figure S2).
Weighted UniFrac distance analyses reflected the compositional shifts described above, showing significant month-to-month differences, with sampling month and floe exerting a stronger influence on the community composition than ice horizon (Figure 6 and Figure 7). For May, cDNA data revealed that bottom-horizon communities were distinct from those in the top and middle horizons. June samples clustered with May top- and middle-horizon samples (Figure 6, Figure S3). In contrast, microbial communities from both whole-horizon and ice-only melts in the two July floes were distinct from one another, and distinct from communities in earlier months (Figure 6a and Figure 7a).
In May and June, communities from all ice-associated materials, including core fluids (brines and percolates), were compositionally distinct from May seawater. In contrast, in July’s rotten ice floes, core fluids (brines and core drain samples) closely resembled July seawater communities (Figure 7a, Figure S3). This similarity was evident even in the 10 July clean floe, where ice-only and whole-horizon melts differed strongly from underlying seawater, but core drain fluids did not. Pondwater from the 10 July dirty floe also closely matched July seawater composition.

3.5. 18S rRNA Gene and Transcript Sequences: Eukaryota

Figure 5c,d and Figure S4 show the relative abundance of major eukaryotic taxonomic groups for whole-horizon melts assessed with 18S rRNA gene and transcript sequences, and Figure S5 shows beta diversity patterns for 18S rRNA transcript sequences.
Eukaryotic communities were dominated by members of the SAR supergroup (Stramenopiles, Alveolates, and Rhizaria) which accounted for more than half of all sequences in most samples. This group included diatoms, dinoflagellates, ciliates, and cercozoans. Cryptophyceae and Chlorophyta were also abundant in some samples (Figure 5, Figure S4). Fungal sequences were present at low relative abundance, generally accounting for <1% of 18S rRNA gene and transcript sequences across samples. Sequences from phyla composed mostly of non-microbial organisms, such as copepods, were rare in non-seawater samples and were excluded from downstream analyses.
Primary producers were particularly abundant and active in all samples. Diverse Diatomea were present in every sample, with Psammodictyon, Chaetoceros, Planktoniella, Pleurosigma, Fragilariopsis, and Pleurosigma ranking among the 20 most abundant taxa in the dataset. Other common primary producers included the chlorophyte Pyramimonas, the dinoflagellate Scrippsiella, and several cryptomonads, all of which occurred in notable proportions in most samples. Among non-photosynthetic microorganisms, the most abundant taxa were ciliates, the protist predator Cryothecomonas (the most abundant taxon in the dataset), and other predatory cercozoans. Despite these commonalities, the relative abundance and phylogenetic composition of communities varied considerably from month to month.
Weighted UniFrac analysis of 18S rRNA gene and transcript sequences showed patterns similar to those seen for 16S: community composition differed markedly between months, with month and floe exerting stronger influence than sample fraction or horizon (Figure 6b, Figure 7b and Figure S5). In May, communities from all fractions were similar to each other but distinct from underlying seawater. In June, top, middle, and bottom samples diverged from one another, but clustered broadly with other early-season samples. In July, rotten ice samples formed distinct groups largely different from the May and June communities. An exception was the ice-only fraction from the 10 July dirty floe, which closely resembled earlier-season ice. Core fluids from rotten ice in July were compositionally similar to seawater.

3.6. Comparing DNA to cDNA

Communities identified with cDNA sequences, representing the metabolically active fraction, often differed markedly from those identified from DNA, which captures both active and inactive organisms. Weighted UniFrac sample distances between DNA and cDNA pairs varied widely, ranging from 0.05 to 0.6 for both the 16S rRNA and 18S rRNA datasets, with most between 0.2–0.3 (Figure S6). In the 16S rRNA dataset, the largest differences occurred in samples from the 10 July dirty floe, with all distances > 0.3, driven primarily by a high proportion of Bacteroidota, especially Flavobacterium, in DNA sequences that were absent or rare (<5%) in cDNA sequences, while Gammaproteobacteria were proportionately more abundant in cDNA (Figure 5, Figure S2). This pattern of reduced Bacteroidota and increased Gammaproteobacteria in cDNA compared to DNA was consistent across much of the dataset. In the 18S dataset, the greatest DNA–cDNA differences were observed in May bottom samples and several 10 July dirty floe samples, also with distances > 0.3 (Figure S6), largely reflecting higher representation of diatom sequences in cDNA relative to DNA (Figure 5, Figure S2).

3.7. Relating Environmental Data to Microbial Communities

Spearman correlation analysis identified many environmental parameters that were significantly associated (p ≤ 0.05) with the relative abundance of different taxonomic groups (Figure S7a). For 16S rRNA transcript cDNA, the strongest correlations (Spearman coefficients ≤−0.7 or ≥+0.7) were with seasonal factors including sampling date, temperature, and ice density. As ice warmed from May to July, Flavobacterium, Pseudomonas, and members of Comamonadaceae increased in relative abundance, while Polaribacter, Colwellia, and Psychromonas declined. Several nutrient parameters also covaried with microbial taxa, but these correlations were generally weaker.
Similarly, for 18S rRNA transcript cDNA sequences, temperature correlated strongly with changes in abundant taxa (Figure S7b). Diatoms, particularly Chaetoceros, along with diverse Bacillariophyceae and Mamiellophyceae, increased in relative abundance with increasing temperature, whereas members of MOCH-2 (Ochrophyta), Cryptomonadales, and other taxa decreased. Nutrient parameters showed stronger correlations in the 18S dataset than in the 16S dataset. DOC correlated most strongly with diatoms in Bacillariophyceae (S = 0.56, 0.46), POC with the green algae Pyramimonas (S = 0.46), and pEPS with diatoms in Mediophyceae and Planktoniella (S = 0.58 and 0.56, respectively). Interestingly, diatom counts did not correlate with the relative abundance of Diatomea taxa in the 18S data, likely because other eukaryotic groups also responded strongly to conditions that favor diatom growth (Figure 4).

4. Discussion

4.1. Rotten Ice Is a Distinct Habitat from Earlier-Season Ice

Rotten ice represents a distinct habitat for microbial life, serving as a transitional environment between the cold, hypersaline brine inclusions of winter ice and the summertime surface waters of the Arctic Ocean. It is relatively warm (~0 °C), isothermal, and largely drained of brine [8] with large, interconnected pore spaces (mm to cm scale) that communicate with overlying melt ponds and underlying seawater. These pore waters were substantially less saline than the brine inclusions typical of earlier-season ice (11–15 ppt vs. 23–45 ppt), and composed a much larger volume due to a roughly 10× to 50× increase in ice porosity.
In earlier-season (May and June) ice, prokaryotic and eukaryotic microbial communities varied with vertical position in the ice, forming distinct algal bloom communities with very high concentrations of prokaryotic cells, algal cells, and chlorophyll in the more porous bottommost few centimeters of ice (Figure 1c, Figure 2 and Figure 4). In July rotten ice, much lower concentrations of chlorophyll and algal cells in bottom ice reflect the loss of the ice-bottom algal bloom (Figure 2 and Figure 4).
Comparing ice horizons above the ice-bottom bloom, rotten ice had higher levels of particulate nutrients (POC, pEPS, PN) than earlier-season ice, though our POC and pEPS concentrations were lower than those reported for winter- and springtime ice in other Arctic regions [21,23].
DOC levels, however, were similar. Chlorophyll concentrations and algal cell counts in rotten ice were also similar to the upper horizons of earlier-season ice. However, prokaryotic cell counts were much higher in rotten ice than in non-bloom horizons of earlier-season ice, indicating an important shift in the structure of the microbial community.

4.2. Rotten Ice Retains Particulate Carbon and Nitrogen

In both sampled floes, concentrations of POC, pEPS, and PN were higher in rotten ice than in overlying pondwater and underlying seawater, and were highest in middle and upper horizons of the ice. This preferential enrichment of particulate nutrients in rotten ice may reflect a combination of physical retention and continued production during late-season melt.
Laboratory studies using EPS from cultures of the diatom Melosira arctica amended with Xanthum gum indicate that EPS can modulate gas and fluid permeability in melting ice and may even alter melt behavior [34]. EPS can promote cell attachment to brine channel walls [82,83]. Thus, sticky, carbohydrate-rich organic material (e.g., pEPS) within the porous ice matrix, perhaps adhered to the ice, could account for the retention of particulate nutrients, even when other nutrients are flushed through the interconnected fluid-filled channels that characterize rotten ice.
Eventually, however, progressive melt could overwhelm this binding through enhanced flushing, or the ice can melt entirely, dispersing the carbohydrate-rich polymeric network and exporting biomass and nutrients into the surrounding ocean, stimulating microbial growth in the under-ice water [84]. Melting ice could play a larger role in Arctic carbon cycling than currently recognized by coupling late-season particle retention with episodic export [21,24,84,85,86].

4.3. Comparison of Rotten Ice with Other Late-Season Sea Ice Environments

Direct comparisons between our measurements of rotten ice in the Chukchi Sea and previously published late-season sea ice observations are challenging because available datasets remain sparse and highly variable across geographic regions, melt stages, and ice horizons. Nevertheless, several broad patterns emerged when our results were evaluated alongside prior studies (Table S4).
Biogeochemical and biological conditions in rotten ice were broadly consistent with established differences between Arctic sea ice and the underlying seawater [35]. Chlorophyll concentrations in our rotten ice samples were comparatively low, similar to values published for brash ice [41] and other late-season sea ice environments [38,39]. These concentrations were one to two orders of magnitude below the concentrations commonly observed in earlier-season, less permeable ice [21,27,40].
Prokaryotic cell abundances in rotten ice were comparable to published values for other late-season ice horizons [35,38], although they were nearly an order of magnitude lower than the highest values published [21,40]. Diatom concentrations were approximately two orders of magnitude lower than those found in the few available comparable data points [40].
Intercomparison of pEPS concentrations is inherently challenging because values are sensitive to both methodological differences and post-sampling handling [87]. The one study that used comparable methods [38] suggests that pEPS concentrations in our rotten ice samples are similar in magnitude to those observed in other late-season sea ice. However, the vertical distribution we observed differs from their findings, with the highest pEPS concentrations occurring in the upper ice horizons in our rotten ice samples.
POC, DOC, and PON concentrations in rotten ice generally fell within or slightly above ranges reported for other late-season sea ice environments [34,36,37,38,85]. An exception is the notably high POC concentrations reported from Franklin Bay, which coincided with very high concentrations of chlorophyll and prokaryotic cells [21].

4.4. Late-Season Shifts in Sea Ice Microbial Communities

In May, the prokaryotic and algal communities in upper ice horizons were concentrated in small brine inclusions. The algal community composition was consistent throughout the ice, whereas prokaryotic communities varied somewhat with depth, particularly at the ice bottom, which was rich in diverse Pseudomonadales. Ice-bottom brines contained much higher concentrations of both prokaryotic and algal cells than the underlying seawater, reflecting the presence of the visible ice-bottom algal bloom and an associated heterotrophic community of prokaryotes.
By June, the ice-bottom eukaryotic community had begun to shift, with a turnover in the diatom community and reduced photopigment concentrations. In contrast, prokaryotic cell counts increased, indicating a June heterotrophic “bloom” in the ice bottom and underlying seawater, which likely consumed the algal die-off. Meanwhile, algal counts and photopigment concentrations increased somewhat in the upper ice layers, suggesting an ice-interior bloom in June.
By July, rotten floes exhibited microbial communities that were substantially different from earlier-season ice. This shift in community composition in rotten vs. earlier-season ice was especially pronounced in the 16S (prokaryotic) community. Prokaryotic cell counts were uniformly high throughout the ice, and similar to concentrations in the underlying seawater. Community composition was relatively homogeneous within each ice flow, but differed between the two rotten ice floes sampled, as well as from earlier-season ice. These differences may reflect distinct trajectories of community change, different initial ice communities, and/or differences in sediment load between the 10 July dirty floe and the 11 July clean floe. Environmental shifts during the formation of rotten ice likely drive rapid change in the composition and activity of sympagic microbial communities.
Despite these variations between our two rotten ice floes, general patterns emerged. The “typical sea ice” taxa of Bacteria that were common in earlier-season ice—such as Colwellia and Polaribacter—were rare in rotten ice (Figure 5, Figure S2). Rotten ice, in contrast, had a greater abundance of Rhodobacterales and Burkholderiales in the prokaryotic community.
Environmental changes associated with rot, especially increasing temperature and decreasing salinity, correlated with significant shifts in microbial community composition. Warmer conditions appeared to favor Flavobacterium, Pseudomonas, and Comamonadaceae, while reducing Polaribacter, Cowellia, and Psychromonas. A limitation of our study is that we did not explicitly analyze light availability, which likely covaried with temperature and salinity, and influenced sympagic microbial communities [88]. Observed correlations may therefore partly be driven by increased light from May to July.
The similarity between pond water, core drain fluids, and seawater in our July rotten ice samples likely reflects the high connectivity of rotten ice and exchange with external waters. Phytoplankton patterns suggest that, as rotten ice melts and brine mixes with seawater, algal cells—mainly empty frustules—are flushed out, potentially sinking to the benthos.
In summary, rotten ice hosts microbial communities that differ fundamentally from those in earlier-season ice: biomass is greater and more evenly distributed, and community composition is distinct. Development of rotten ice communities may be unpredictable; our two sampled floes had communities that were as different from each other, as they were from earlier-season ice.

4.5. Differences in the Active (cDNA) and Latent (DNA) Microbial Communities

Compositional differences between DNA and cDNA sequences were evident across all samples, indicating distinctions between the “latent” (DNA) and “active” (cDNA) communities. These differences were most apparent in samples with high microbial activity (measured as CTC uptake and photosynthesis), suggesting that some taxa in the DNA pool were inactive or significantly less active than other members. Alternative explanations for these observed differences include (1) shifts in environmental conditions during the processing of extracted cores favoring the activity of select members of the community, (2) extraction biases due to the use of different kits for DNA and RNA extraction, or (3) some combination of processing artifacts and genuine in situ differences in organismal activity.

5. Conclusions

This study provides the first comprehensive biogeochemical and microbial characterization of Arctic shelf rotten ice and demonstrates that rotten ice constitutes a distinct microbial habitat within the Arctic sea ice system. Compared to earlier-season ice and underlying seawater, rotten ice supports a restructured microbial community influenced by increased porosity, loss of vertical biogeochemical gradients, and enhanced connectivity with underlying seawater. Although microbial community composition varied between sampled floes, rotten ice consistently exhibited features characteristic of advanced melt, including reduced algal biomass, altered vertical structure, and persistence of particle-associated organic matter. As ice decay progressed, particulate constituents, including POC, PN, and pEPS, were retained in the upper horizons of rotten ice, suggesting an EPS-mediated retention mechanism within the degraded ice matrix. Together, these concurrent drainage and retention processes position rotten ice as both a conduit and a transient reservoir for organic matter. As melt seasons lengthen and rotten ice becomes more prevalent, the terminal breakup of this habitat may deliver episodic pulses of particulate nutrients to surface waters, with implications for microbial community structure and carbon cycling in the future Arctic Ocean.
The limited availability of data for late-season sea ice highlights the need for more data collection that would enable a better understanding of the distribution, observed differences, and factors that govern in the biogeochemistry and microbial ecology of late-season ice compared to the more commonly studied earlier season sea ice [89]. We anticipate that upcoming data from the MOSAiC expedition and the upcoming Tara Polaris expeditions [90], when published, will further illuminate the emerging ecological shifts in a warming Arctic sea ice habitat.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms14020482/s1, Supplemental Materials document containing supplemental table legends, supplemental figures with legends, descriptions of supplemental datasets S1–S4, detailed field observations, and detailed methods [61,64,65,69,70,71,91,92,93,94,95,96,97]; Figure S1a: Metadata heatmaps, Part I; Figure S1b: Metadata heatmaps, Part II; Figure S2: 16S rRNA gene sequencing taxonomic barplot for all samples; Figure S3: 18S rRNA gene sequencing taxonomic barplot for all samples; Figure S4: 16S rRNA gene sequencing PCoA plot from the cDNA transcripts for all samples; Figure S5: 18S rRNA gene sequencing PCoA from cDNA transcripts for all samples; Figure S6: cDNA vs. DNA sample distances; Figure S7: Spearman correlations between abundant taxonomic groups and field and lab measurements; Table S1: Sample metadata and biogeochemical data; Table S2: Microscopic algae identification and counts; Table S3: ASV tables; Table S4: Meta-Analysis of biogeochemistry.

Author Contributions

Conceptualization, C.M.F., B.C.C. and K.J.; methodology, S.C., E.F., M.V.O., B.L., K.J. and B.C.C.; software, C.M.F. and B.C.C.; formal analysis, C.M.F. and B.C.C.; investigation, C.M.F., S.C., E.F., M.V.O., B.L., B.C.C. and K.J.; resources, C.M.F., S.C., E.F., M.V.O., B.L., B.C.C. and K.J.; data curation, C.M.F., S.C. and B.C.C.; writing—original draft preparation, C.M.F. and B.C.C.; writing—review and editing, S.C., E.F., M.V.O., B.L. and K.J.; visualization, C.M.F. and B.C.C.; supervision, B.C.C. and K.J.; project administration, C.M.F., B.C.C. and K.J.; funding acquisition, B.C.C., K.J., B.L. and M.V.O. All authors have read and agreed to the published version of the manuscript.

Funding

This project was supported by U.S. National Science Foundation Awards PLR-1304228 to K.J., B.L., M.V.O. and 1656026 to B.C.C.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data presented in the study are included in the article and Supplementary Material. The sequencing data for 16S and 18S rRNA gene and transcript amplicons were deposited in the Sequence Read Archive (SRA) of the NCBI under BioProject ID: PRJNA1253678. Additional original data associated with this study are openly available in Open Science Framework at https://doi.org/10.17605/OSF.IO/D8MHU.

Acknowledgments

We thank Julianne Yip for help with sample collection and processing, Allison Cusick for help with troubleshooting nucleotide extraction, and Brynli Tattersall and Rachael Carter for preliminary sequence analysis work. Algal cell identification and enumeration for this work was done by Sylvie Lessard. Logistical support was provided by CH2MHill Polar Services. We also extend our deep gratitude to the Ukpeaġvik Iñupiat Corporation Science staff and affiliates in Utqiaġvik for making our work at BARC and in the field possible. During the preparation of this manuscript/study, the author(s) used Microsoft Copilot, December 2025 update for the purposes of translation and language refinement, grammar polishing, readability improvement, and sentence structure enhancement. No GenAI tools were used for data analysis, statistical procedures, or the generation of original scientific results. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
rRNARibosomal Ribonucleic Acid
EPSextracellular polymeric substances
pEPSparticulate extracellular polymeric substances
SPMsuspended particulate matter
POCParticulate organic carbon
PNParticulate nitrogen
DOCDissolved organic carbon
glu-eqglucose equivalents
DAPI4′,6-diamidino-2-phenylindole
CTC5-Cyano-2,3-ditolyl tetrazolium chloride
ASVsamplicon sequence variants

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Figure 1. Summary of sample collection: (A) Map of sampling locations in May (M-CS), June (JN-CS), and July (JY10 and JY11) 2015. The inset shows the location of Utqiaġvik, Alaska. BARC is the Barrow Arctic Research Center. (B) Schematic illustrating the subsamples collected and the naming scheme for this study. For example, M-CS-HT-1 refers to aliquot 1 of the May core top whole-horizon melt sample; JN-CS-IM-2 refers to aliquot 2 of the June core middle ice-only sample; JY10-BB-3 refers to aliquot 3 of the 10 July core bottom brine sample; JY11-SW-2 refers to aliquot 2 of the 11 July floe seawater sample. (C) Photos of locations and representative cores from May (purple outline), including a core bottom showing the ice-bottom algal bloom; June (blue outline); 10 July (dirty rotten ice floe: JY10; green outline), including a core bottom with no visible ice-bottom algal bloom; and 11 July (clean rotten ice floe: JY11; yellow outline). Full core images (horizontal cores) are shown approximately to scale.
Figure 1. Summary of sample collection: (A) Map of sampling locations in May (M-CS), June (JN-CS), and July (JY10 and JY11) 2015. The inset shows the location of Utqiaġvik, Alaska. BARC is the Barrow Arctic Research Center. (B) Schematic illustrating the subsamples collected and the naming scheme for this study. For example, M-CS-HT-1 refers to aliquot 1 of the May core top whole-horizon melt sample; JN-CS-IM-2 refers to aliquot 2 of the June core middle ice-only sample; JY10-BB-3 refers to aliquot 3 of the 10 July core bottom brine sample; JY11-SW-2 refers to aliquot 2 of the 11 July floe seawater sample. (C) Photos of locations and representative cores from May (purple outline), including a core bottom showing the ice-bottom algal bloom; June (blue outline); 10 July (dirty rotten ice floe: JY10; green outline), including a core bottom with no visible ice-bottom algal bloom; and 11 July (clean rotten ice floe: JY11; yellow outline). Full core images (horizontal cores) are shown approximately to scale.
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Figure 2. Box-and-whisker plots of field and laboratory measurements from whole-horizon melt samples. For each variable, the box represents the interquartile range (IQR), the horizontal line denotes the median, and whiskers extend to 1.5× IQR. Individual samples are overlaid as points, color-coded by sampling month and shape-coded by ice horizon, illustrating the distribution of measurements within and across horizons and sampling periods. Abbreviations: suspended particulate material (SPM), particulate nitrogen (PN), carbon to nitrogen ratio (C/N), dissolved organic carbon (DOC), particulate organic carbon (POC), particulate extracellular polymeric substances (pEPS), total pigment to phaeopigment ratio (Fo/Fa), photosystem II quantium yield (Fv/Fm), proportion of actively respiring cells determined by the percentage of cells stained with 5-cyano-2,3-ditolyl-tetrazolium chloride (% CTC).
Figure 2. Box-and-whisker plots of field and laboratory measurements from whole-horizon melt samples. For each variable, the box represents the interquartile range (IQR), the horizontal line denotes the median, and whiskers extend to 1.5× IQR. Individual samples are overlaid as points, color-coded by sampling month and shape-coded by ice horizon, illustrating the distribution of measurements within and across horizons and sampling periods. Abbreviations: suspended particulate material (SPM), particulate nitrogen (PN), carbon to nitrogen ratio (C/N), dissolved organic carbon (DOC), particulate organic carbon (POC), particulate extracellular polymeric substances (pEPS), total pigment to phaeopigment ratio (Fo/Fa), photosystem II quantium yield (Fv/Fm), proportion of actively respiring cells determined by the percentage of cells stained with 5-cyano-2,3-ditolyl-tetrazolium chloride (% CTC).
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Figure 3. Principal components analysis (PCA) of selected field and lab measurements from whole-horizon melt samples showing the seasonal evolution of upper, middle and bottom ice horizons from May to June to into July’s rotten ice (dashed arrows). Percent variance explained by each principal component (PC) is shown in parentheses. Only measurements with complete data coverage across all whole-horizon melts were included in the PCA. Vectors for included variables (gray arrows) depict their relative contributions to the ordination based on their magnitude and direction. Although temperature was excluded from the PCA because it was not measured in the 10 July floe, it nevertheless correlated strongly with sample date (r = 0.9).
Figure 3. Principal components analysis (PCA) of selected field and lab measurements from whole-horizon melt samples showing the seasonal evolution of upper, middle and bottom ice horizons from May to June to into July’s rotten ice (dashed arrows). Percent variance explained by each principal component (PC) is shown in parentheses. Only measurements with complete data coverage across all whole-horizon melts were included in the PCA. Vectors for included variables (gray arrows) depict their relative contributions to the ordination based on their magnitude and direction. Although temperature was excluded from the PCA because it was not measured in the 10 July floe, it nevertheless correlated strongly with sample date (r = 0.9).
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Figure 4. Abundance and taxonomic makeup of sea ice communities based on microscopy: (a) Relative abundance of major taxonomic groups; (b) Cell counts of taxonomic groups. Unless otherwise specified, results are from whole-horizon melt samples. Only live organisms are included; empty frustules and cysts were excluded from counts.
Figure 4. Abundance and taxonomic makeup of sea ice communities based on microscopy: (a) Relative abundance of major taxonomic groups; (b) Cell counts of taxonomic groups. Unless otherwise specified, results are from whole-horizon melt samples. Only live organisms are included; empty frustules and cysts were excluded from counts.
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Figure 5. Relative abundance of sequences from major taxonomic groups in whole-horizon melt samples. The upper two panels show results from (a) 16S rRNA gene DNA sequencing and (b) 16S rRNA transcript cDNA sequencing after removal of sequences identified as Eukaryota, mitochondria, chloroplast, or unclassified. In these two plots, the “sea ice genera in o__Enterobacterales” combines four genera commonly associated with sea ice: Glaciecola, Paraglaciecola, Colwellia, and Psychromonas. The lower panels show results from (c) 18S rRNA gene DNA sequencing and (d) 18S rRNA transcript cDNA sequencing for the eight most abundant phyla in the dataset, along with additional phyla identified as primary producers. For all panels, values for replicate samples in the quality-controlled dataset were averaged.
Figure 5. Relative abundance of sequences from major taxonomic groups in whole-horizon melt samples. The upper two panels show results from (a) 16S rRNA gene DNA sequencing and (b) 16S rRNA transcript cDNA sequencing after removal of sequences identified as Eukaryota, mitochondria, chloroplast, or unclassified. In these two plots, the “sea ice genera in o__Enterobacterales” combines four genera commonly associated with sea ice: Glaciecola, Paraglaciecola, Colwellia, and Psychromonas. The lower panels show results from (c) 18S rRNA gene DNA sequencing and (d) 18S rRNA transcript cDNA sequencing for the eight most abundant phyla in the dataset, along with additional phyla identified as primary producers. For all panels, values for replicate samples in the quality-controlled dataset were averaged.
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Figure 6. Principal coordinates analysis (PCoA) plots based on weighted UniFrac distances for whole-horizon melt and seawater samples: (a) 16S rRNA transcript cDNA sequencing; (b) 18S rRNA transcript cDNA sequencing. Colors indicate sampling month or specific floe, and marker shapes indicate the sampled horizon (with seawater shown as circles).
Figure 6. Principal coordinates analysis (PCoA) plots based on weighted UniFrac distances for whole-horizon melt and seawater samples: (a) 16S rRNA transcript cDNA sequencing; (b) 18S rRNA transcript cDNA sequencing. Colors indicate sampling month or specific floe, and marker shapes indicate the sampled horizon (with seawater shown as circles).
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Figure 7. Sample clustering dendrogram and sample distance heatmap based on weighted UniFrac distances of 16S and 18S rRNA transcript cDNA amplicon sequences from whole-horizon melt samples: (a) Results of 16S sequencing after removal of sequences identified as mitochondria, chloroplasts, or unclassified; (b) Results for 18S sequencing after removal of sequences identified as Bacteria, Archaea, unclassified, or belonging to phyla with mostly non-microbial (≥1 mm) members. In heatmaps, cool colors (blue) indicate small sample distances (more similar communities) while warmer colors (yellow) indicate large sample distances (more distinct communities). Colors in the top row indicate sample months: May (indigo), June (blue), 10 July (light green), and 11 July (yellow). Colors in the left columns indicate the sample fraction (see key). Major clusters and their associated sample types are outlined with white boxes. Notably, while samples from the same month generally cluster together, samples from the same fraction collected in different months do not.
Figure 7. Sample clustering dendrogram and sample distance heatmap based on weighted UniFrac distances of 16S and 18S rRNA transcript cDNA amplicon sequences from whole-horizon melt samples: (a) Results of 16S sequencing after removal of sequences identified as mitochondria, chloroplasts, or unclassified; (b) Results for 18S sequencing after removal of sequences identified as Bacteria, Archaea, unclassified, or belonging to phyla with mostly non-microbial (≥1 mm) members. In heatmaps, cool colors (blue) indicate small sample distances (more similar communities) while warmer colors (yellow) indicate large sample distances (more distinct communities). Colors in the top row indicate sample months: May (indigo), June (blue), 10 July (light green), and 11 July (yellow). Colors in the left columns indicate the sample fraction (see key). Major clusters and their associated sample types are outlined with white boxes. Notably, while samples from the same month generally cluster together, samples from the same fraction collected in different months do not.
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MDPI and ACS Style

Frantz, C.M.; Crump, B.C.; Carpenter, S.; Firth, E.; Orellana, M.V.; Light, B.; Junge, K. Microbial Ecology of Rotten Sea Ice: Implications for Arctic Carbon Cycling with Global Warming. Microorganisms 2026, 14, 482. https://doi.org/10.3390/microorganisms14020482

AMA Style

Frantz CM, Crump BC, Carpenter S, Firth E, Orellana MV, Light B, Junge K. Microbial Ecology of Rotten Sea Ice: Implications for Arctic Carbon Cycling with Global Warming. Microorganisms. 2026; 14(2):482. https://doi.org/10.3390/microorganisms14020482

Chicago/Turabian Style

Frantz, Carie M., Byron C. Crump, Shelly Carpenter, Erin Firth, Mónica V. Orellana, Bonnie Light, and Karen Junge. 2026. "Microbial Ecology of Rotten Sea Ice: Implications for Arctic Carbon Cycling with Global Warming" Microorganisms 14, no. 2: 482. https://doi.org/10.3390/microorganisms14020482

APA Style

Frantz, C. M., Crump, B. C., Carpenter, S., Firth, E., Orellana, M. V., Light, B., & Junge, K. (2026). Microbial Ecology of Rotten Sea Ice: Implications for Arctic Carbon Cycling with Global Warming. Microorganisms, 14(2), 482. https://doi.org/10.3390/microorganisms14020482

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