# Eukaryotic biodiversity of sub-ice water in the marginal ice zone of the European Arctic: A multi-marker eDNA metabarcoding survey

Ayla Murray, Simon Ramondenc, Simon Reifenberg, Meret Jucker, Mara Neudert, Rebecca Mcpherson, Wilken-Jon von Appen, Charlotte Havermans

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## ► To cite this version:

Ayla Murray, Simon Ramondenc, Simon Reifenberg, Meret Jucker, Mara Neudert, et al., Eukaryotic biodiversity of sub-ice water in the marginal ice zone of the European Arctic: A multi-marker eDNA metabarcoding survey. Science of the Total Environment, 2025, 968, pp.178840. 10.1016/j.scitotenv.2025.178840. hal-05513547

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# Eukaryotic biodiversity of sub-ice water in the marginal ice zone of the European Arctic: A multi-marker eDNA metabarcoding survey

Ayla Murray<sup>a,b,*†</sup>, Simon Ramondenc<sup>a,c</sup>, Simon F. Reifenberg<sup>a</sup>, Meret Jucker<sup>a,d</sup>, Mara Neudert<sup>a</sup>, Rebecca McPherson<sup>a</sup>, Wilken-Jon von Appen<sup>a</sup>, Charlotte Havermans<sup>a,b</sup>

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## HIGHLIGHTS

* Unique multi-marker (COI and 18S V1–2) eDNA metabarcoding of seawater from 0 to 5 m under sea ice

* Mix of sympagic and pelagic primary producers and metazoans detected

* High levels of uncharacterised eukaryotic diversity found in sub-ice water

* Community composition patterns linked to meltwater and sea ice concentration

* Multi-marker approach increases biodiversity uncovered in an under-studied and hard-to-reach environment

## GRAPHICAL ABSTRACT

### Eukaryotic biodiversity of sub-ice water in the Marginal Ice Zone of the European Arctic: A multi-marker eDNA metabarcoding survey

Properties of the meltwater stratification in the upper ocean, as well as sea-ice concentration and distance to the ice edge have significant impacts on eukaryotic diversity and community composition

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The MIZ is a highly dynamic habitat affected by the warming of the Arctic

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Metabarcoding of eDNA from sub-ice seawater with COI and 18S markers

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High taxa richness and spatial patterns of diversity uncovered

## ARTICLE INFO

Editor: Daniela Maria Pampanin

### Keywords:

Arctic Ocean
Environmental DNA metabarcoding
Fram Strait
COI
18S
Sea ice

## ABSTRACT

The effects of climate change, including warming waters, loss of sea ice habitat and the resulting changes in primary production, are inducing shifts in marine communities across the Arctic Ocean. The Arctic marginal ice zone (MIZ) is a highly dynamic ecosystem and is a transition zone between pack ice and the open ocean. It is habitat for a wide range of organisms, including sympagic and pelagic taxa, all of which are affected by the changing physical dynamics of the MIZ. Here we use a multi-marker (18S rRNA V1–2 and COI Leray-XT) approach to investigate eukaryotic biodiversity of the upper water column in this understudied habitat. Environmental DNA (eDNA) was sequenced from seawater samples collected directly beneath the sea ice and at a depth of 5 m, sourced from ice floes representing different ice regimes. To explore the abiotic factors influencing under-ice diversity, we combined satellite-derived environmental data with simultaneous in situ hydrographic measurements. Our analysis identified a range of sympagic and pelagic metazoans, along with primary producers typical of the region, as well as substantial uncharacterised diversity. Alpha diversity indices were higher immediately below the ice, and community composition differed across depths and ice floe stations. We show that the properties of the meltwater stratification in the upper ocean, along with sea ice concentration and

<footnote><sup>a</sup> Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany</footnote>

<footnote><sup>b</sup> Marine Zoologie, Fachbereich 2, University of Bremen, Bremen, Germany</footnote>

<footnote><sup>c</sup> Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche (LOV), Villefranche-sur-Mer, France</footnote>

<footnote><sup>d</sup> Institute of Terrestrial Ecosystems, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland</footnote>

<footnote>* Corresponding author at: Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany. E-mail address: ayla.murray@awi.de (A. Murray).</footnote>

<footnote n="1">¹ Permanent address: Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany.</footnote>
distance to the ice edge, significantly shape eukaryotic diversity and community composition. These findings highlight the effectiveness of eDNA metabarcoding for monitoring sub-ice communities and enhance our understanding of eukaryotic biodiversity in the MIZ.

# 1. Introduction

Marine environments are experiencing anthropogenic climate change at unprecedented rates. In the Arctic, warming is occurring up to four times faster than the rest of the world, and up to seven times faster in parts of the European Arctic (Rantanen et al., 2022). Sea surface and air temperatures are increasing, while sea ice thickness and extent are decreasing with entire ice-free periods predicted to occur in the Arctic as early as 2035 (reviewed in: Jahn et al., 2024). This loss of sea ice habitat, as well as the consequential changes in primary production, is inducing shifts in the community composition and distributions of marine species across the Arctic Ocean (Meredith et al., 2022). These alterations in the distribution and availability of suitable habitat for marine species often result in detrimental consequences on biodiversity and ecosystem functions (Hodapp et al., 2023; Meredith et al., 2022). Temperate taxa are expanding their ranges northward, while true Arctic and ice-associated taxa are exhibiting range contractions (Mueter et al., 2021) and overall increases in species richness have been observed across multiple parts of the food web (Alabia et al., 2023). As Arctic marine ecosystems continue to change, the need to monitor shifts in biodiversity is increasing (Bernatchez et al., 2024; Lannuzel et al., 2020).

The marginal ice zone (MIZ) is a highly dynamic ecosystem that can span from tens to hundreds of kilometres in width (Strong, 2012). It is a transition zone between pack ice and the open ocean, and is characterized here by ice concentrations between 15 and 80% (Dumont, 2022; Strong and Rigor, 2013). Strong near-surface stratification and the formation of a seasonal halocline is common in the MIZ, as a result of meltwater accumulation at the ice-ocean interface and at the surface in open water fractions around ice floes. Thus, the upper ocean can become dynamically decoupled from the interior ocean during the melt season, limiting the atmosphere-ocean exchange of heat, momentum and nutrients. This decoupling plays a crucial role in a range of local physical and biological processes across the MIZ (Smith et al., 2023). Rapid Arctic warming is inducing major changes to the MIZ with multi-year ice being replaced with thinner, first-year ice, significant poleward expansion of its interior edge (Aksenov et al., 2017; Strong and Rigor, 2013) and more open water expanses are occurring at its southern edge (Rolph et al., 2020).

The MIZ provides habitat for a wide range of organisms, including ice-associated (sympagic), sub-ice and pelagic taxa. In spring, as day length and light intensity increase, the ice begins to melt and sympagic algae grow in the bottom of the ice, signalling the onset of the spring bloom (Leu et al., 2015). Ice algae are a crucial high quality food source for sympagic taxa such as copepods, amphipods, appendicularians, and meiofauna (Falk-Petersen et al., 2009; Leu et al., 2011; Søreide et al., 2010). The pelagic bloom starts as the ice begins to break up to form the MIZ and enough light penetrates the water column (Galí et al., 2021). Increased near-surface stratification and light availability drive rapid growth and lead to highly concentrated patches of zooplankton in the surface layers (Joy-Warren et al., 2023). These are an important source of energy and nutrients for zooplankton grazers, which have their life cycles finely tuned to the timing and duration of these blooms (Falk-Petersen et al., 2009; Søreide et al., 2010). Sea-ice loss and light availability drive an increase in overall primary production, while bloom phenology and communities are shifting, leading to cascading changes throughout Arctic marine food webs (Castro de la Guardia et al., 2019; Lannuzel et al., 2020). Furthermore, the decline in the age and thickness of multi-year sea ice, which typically hosts higher levels of biodiversity and distinct assemblages of primary producers (e.g., Hop et al., 2020) and metazoans (e.g., Bluhm et al., 2017) than first-year ice, could result

in the loss of unique sympagic communities.

Due to the challenging environmental conditions and logistical limitations of deploying traditional survey equipment in the MIZ, few studies have investigated biodiversity of the water column directly beneath sea ice, where ice melt and intense biological activity occurs. Previous investigations that have targeted eukaryotic diversity in the MIZ, focus on - taxa in the sea ice (Hop et al., 2020; Kiko et al., 2017) or sample across large depth scales below it (Ribeiro et al., 2024; Vipindas et al., 2023; Xu et al., 2020). Most of these studies relied on morphological methods (e.g., David et al., 2015; Ehrlich et al., 2020; Hop et al., 2011), which may overlook key components of under-ice communities due to gear-related collection biases or low-resolution morphological identifications. For example, plankton nets and trawls can miss or destroy delicate yet ecologically important taxa like gelatinous zooplankton (GZP), leading to underestimates in both their abundance and diversity (Hosia et al., 2017; Murray et al., 2024). The development of environmental DNA (eDNA) metabarcoding allows us to overcome some of the aforementioned limitations of morphological sampling (Thomsen and Willerslev, 2015). It enables the identification of organisms based on the DNA they release into the environment through gametes, faeces and other excretion products, as well as shed cells, as opposed to capturing or visually surveying organisms. Furthermore, it can be used to detect individual species as well as entire communities and can be combined with abiotic data to investigate the environmental drivers of biodiversity (e.g., Adams et al., 2023; Murray et al., 2024). It has proven to be a cost-efficient and non-invasive technique for monitoring biodiversity in marine ecosystems (Adams et al., 2023; Bernatchez et al., 2024), including in the Arctic Ocean and its adjacent seas (Geraldi et al., 2024; Lacoursière-Roussel et al., 2018; Merten et al., 2023; Murray et al., 2024; Weydmann-Zwolicka et al., 2024). However, eDNA studies in sea ice habitats such as the Arctic MIZ are still lacking.

In this study, we aim to survey eukaryotic biodiversity in the sub-ice water of the MIZ as well as compare the effectiveness of two common eDNA metabarcoding markers in capturing patterns of biodiversity in an ice-covered habitat. To achieve this, we conducted a multi-marker eDNA metabarcoding survey of the upper 5 m of the water column beneath ice floes representing differing ice conditions in the MIZ of Fram Strait. We hypothesized that, due to the limited number of previous studies targeting these communities, high levels of uncharacterized diversity would be prevalent in the eDNA datasets. We leveraged the resulting eDNA data to address the following questions:

1. What is the taxonomic breadth of the primary producer and metazoan communities in the sub-ice water column?

2. How do eukaryotic eDNA signals differ in alpha and beta diversity across vertical and horizontal scales in the MIZ?

3. Can we elucidate the drivers of community composition across different environmental conditions in the MIZ using eDNA signals?

4. How do the COI and 18S markers compare in terms of taxonomic coverage and patterns of diversity?

# 2. Methods

## 2.1. *Sampling area*

Fram Strait is the only deepwater inlet into the Arctic Basin and is a transition zone between the Arctic Ocean and the North Atlantic Ocean. The area is affected by the phenomenon known as the 'Atlantification' of the Arctic, which is largely driven by the increased transport of warm and saline Atlantic waters into the Arctic Ocean via the West Spitsbergen
Current (WSC). The WSC originates from the North Atlantic and travels northward via two main branches of the Norwegian Atlantic Current, joining in Fram Strait (Fig. 1). The WSC, along with the Atlantic water inflow through the Barents Sea opening, constitute the main sources of oceanic heat and salt to the Arctic Ocean (Tsubouchi and Kanzow, 2023). Furthermore, less sea ice is being transported to Fram Strait via the transpolar drift due to accelerated melting rates (Krumpen et al., 2019). The Yermak Plateau, where the sampling that forms the basis of this study took place, is a hydrographically dynamic region in Fram Strait where strong tides over the shallow bathymetry drive intense mixing of the Atlantic Water (Fer et al., 2010).

Sampling was undertaken during the PS131 expedition (Kanzow, 2023) on the R/V Polarstern in July 2022. Seawater samples were collected from the upper water column directly underneath selected ice floes in the MIZ, situated in northern Fram Strait (Fig. 1A). Three individual ice floes were visited repeatedly while they drifted across the Yermak Plateau. A fourth, floe F24_1, was situated over the eastern slope of the Yermak Plateau and visited only once. The ice floes were selected to be representative of different ice regimes typical of the MIZ, and the sample sites cover a range of different sea ice concentrations, and differ with respect to ice thickness, floe size, and distances to the ice edge. It must be noted that logistic and safety considerations such as ice stability played a role in the selection of these stations. Hence, the most outer floe being approximately 30 km from the open ocean and therefore not representative of the true outer edge of MIZ. We use the ice floe identifiers FN, FM, FS to reference the north, middle and south stations respectively, and F24_1 to identify the floe on which a 24-h sampling regime was conducted. The respective visit numbers are thus denoted as V1 to V3, i.e., FN_V2 is the second visit to the north floe FN (Fig. 1B). We treated the ice floe visits as nine separate ice floe stations, as it is the water underneath the ice that was sampled, which is continuously exchanged by the ice-relative ocean current.

## 2.2. Sample collection

### 2.2.1. Environmental data

To investigate the abiotic drivers of community composition, we

analysed environmental variables collected from instruments deployed on and under the ice, as well as satellite data (full details in Table S1). Salinity and temperature data were collected from directly under the ice to a depth of 10 m, using a RBRconcerto³ CTD. The CTD was deployed in the same ice hole from which the eDNA was sampled or within 10s of meters of it. For two sampling holes, we did not obtain CTD data (hole 1 on F24_1 and FM_V3), thus, we excluded these from analyses based on CTD data.

To describe the vertical density structure of the meltwater layer, data was taken from under-ice microstructure (MSS) measurements at each floe using a MSS90L profiler (Sea & Sun Technology). Based on these profiles, we aimed to quantify the meltwater distribution in the upper ocean by calculating the buoyancy deficit (BD) and the equivalent depth of this meltwater layer (hBD) following Randelhoff et al. (2017). BD is a proxy for the amount of meltwater accumulated in the upper ocean, while hBD describes how the meltwater is distributed vertically within the upper 50 m of the water column. For example, a larger (smaller) BD indicates that there is more (less) meltwater present. A high hBD indicates that this meltwater is more evenly distributed (mixed) while a low hBD indicates that it is accumulated in the upper layers (stratification). For a fixed BD, lower hBD values indicate that meltwater is accumulating closer to the surface. The BD was calculated by vertically integrating the density deviation from a reference density, defined as the density at 50 m depth (Fig. S1). The hBD was then derived by dividing BD by the difference between the shallowest density measurement from the MSS (taken at 3 m depth) and the reference density.

Sea ice concentration (SIC) and distance from the ice edge were calculated from Advanced Microwave Scanning Radiometer-2 (AMSR-2) sensor data. The SIC for each sample location was obtained by nearest-neighbour interpolation of the sample position to the gridded AMSR-2data (10 km resolution). The distance to the ice edge (DIST_SIC) was determined as the shortest distance of the sample position to the ice edge, with the ice edge defined as the 15 % SIC contour line.

### 2.2.2. Seawater for eDNA metabarcoding

Seawater samples were collected at two sampling depths through 1–3 drilled holes on each ice floe. The sampling point immediately

<figure><img src="image_8.png" /><figcaption>**Fig. 1.** Location of the sampling area and the ice floe stations in the MIZ. (A) Map of the Fram Strait area, with approximate sampling area indicated by black polygons. Red arrows represent the approximate flow of warm Atlantic Water on branches of the West Spitsbergen Current (WSC). The WSC is one of the currents that transports the Atlantic Water before half of it recirculates towards Greenland and the rest continues northwards into the Central Arctic. YP = Yermak Plateau. (B) Sentinel-1 Synthetic Aperture Radar (SAR) image of the ice floe stations in the marginal ice zone. The dotted white line is a visual estimate of the ice edge at the beginning of the sampling period and the hard-white line the end of the sampling period. Maps produced using the R package ggOceanMaps. SAR scenes are available from ESA, Copernicus Open Access Hub, last access: 30 August 2022.</figcaption></figure>
underneath the ice was taken from 0 to 60 cm below the ice, and the second at 5–5.6 m below the ice. These sampling depths are henceforth referred to as “0 m” and “5 m” respectively. A total of 6 L of water was collected per sampling point using a 4 L Niskin (Hydrobios, Kiel) bottle. The water was dispensed into pre-sterilized Kautex cannisters and kept on ice until filtration. Each sample was split across three filter triplicates of approximately 2 L of seawater. The water was filtered with a peristaltic pump across 0.22 µm Sterivex-GP filters (Merck Millipore). At each ice floe station, a field blank was collected by filtering MilliQ water instead of seawater. All tubes and Kautex cannisters used for sample collection were sterilized with a 1:10 Bleach/MilliQ water solution and then rinsed with MilliQ water between sampling events. New filtration tubing was used for each sample depth at every ice floe. Following filtration, the filters were sealed and stored at –80 °C for subsequent processing.

## 2.3. DNA extraction, library preparation and sequencing

Environmental DNA was extracted using the DNeasy Blood and Tissue kit (QIAGEN, Germany) and contamination prevention measures were observed following Murray et al. (2024). An extraction control was included with each batch to ensure reliability. The DNA extracts were stored at –20 °C until further processing. DNA metabarcoding library preparation and sequencing were carried out by AllGenetics & Biology SL. In the present study, we took a multi-marker approach to minimise the impacts of marker-specific limitations on biodiversity estimates (Clarke et al., 2017; Wangensteen et al., 2018b). The following two markers were amplified: (a) the 313 bp COI region with the forward primer mICOLintF-XT (5'GGWACWRGWGTGRACWITITAYCCYCC 3'; Wangensteen et al., 2018b) and the reverse jgHCO2198 (5'TAIA-CYTCIGGRTGICCRAARAAYCA 3'; Geller et al., 2013); and (b) the ~400 bp 18S V1–2 region with the forward primer SSU_F04 (5' GCTTGTCTCAAAGATTAAGCC 3'; Blaxter et al., 1998) and the reverse SSURmod (5' CCTGCTGCCTTCCTTRGA 3'; Sinniger et al., 2016). These primers have been successfully employed to detect a wide range of marine eukaryotes in eDNA studies (e.g., COI: Antich et al., 2023; Ershova et al., 2019; Murray et al., 2024 and 18S: Feng et al., 2022; He et al., 2021; Hestetun et al., 2021) and have comprehensive and curated reference databases publicly available. The PCR master mix and conditions followed Murray et al. (2024) for COI and Dischereit et al. (2024) for 18S rDNA. The final pool was sequenced on a PE250 flow cell using the NovaSeq platform (Illumina). Demultiplexing and removal of Illumina adaptors were performed by the sequencing company.

## 2.4. Bioinformatic analyses

Given the differing natures of the two genes (COI is mitochondrial and 18S is nuclear), we used separate pipelines and optimised metabarcoding units to process the sequencing reads accordingly. While denoising and amplicon sequence variants (ASVs) output have been employed for COI-based studies, there is the risk of overlooking intra-specific variation if clustering into molecular operational taxonomic units (MOTUs) is not used (Antich González et al., 2021). In contrast, applying clustering to 18S ASVs can underestimate biodiversity in a dataset (Giebner et al., 2020). Hence, we used denoising and then clustering with MOTUs as a unit for the COI data (Antich González et al., 2021) and denoising with ASVs for the 18S data.

The COI sequence reads were processed using a bioinformatics pipeline based ‘OBITools 3’ (Boyer et al., 2016) following Antich et al. (2023). Taxonomic assignment to the lowest common ancestor was done by *ecotag*, which is a phylogenetic tree-based method. We used a curated reference database with a focus on marine metazoans, containing 148,932 sequences from BOLD, NCBI and inhouse (available at: https://github.com/adriantich/NJORDR-MJOLNIR3). Taxonomic assignment was improved where possible using BOLDigger2 (Buchner and Leese, 2020) to query on BOLD (Ratnasingham and Hebert, 2007). This

allows for the utilization of the latest COI barcodes that may not be included in the database used in the bioinformatics pipeline. When improved matches and extra taxonomic information were available, they were adjusted in the dataset. The following identity match thresholds were applied: > 97 % for species, 95 % for genus, 90 % for family, 85 % for order and 75 % for class (following Ershova et al., 2023). Identity matches <70 % were marked as unassigned. The remaining MOTUs were named according to the highest taxonomic level possible, and with a unique number in cases where more than one MOTU had the same assignment.

The 18S raw sequence reads were processed using DADA2 v1.18 (Callahan et al., 2016) in Rstudio v 4.0.4. Forward reads were trimmed at 220 bp and reverse reads at 210 bp and filtered with a maximum expected error threshold of 2.2 and 2.1, for forward and reverse reads respectively. Sequences were denoised and paired ends were merged using a minimum overlap of 25 bp and zero mismatches when aligning. Singletons were discarded and chimeras filtered out. We chose to use a zooplankton-focused database for taxonomic assignment in order to avoid known classification issues of other popular databases such as SILVA, where zooplankton are underrepresented (Questel et al., 2021). Taxonomic assignment of the resulting ASVs was done using the MetaZooGene database (O'Brien et al., 2024). The version "MZGb 3.0" containing 68,484 18S rRNA sequences containing genus and species data from any ocean was used (Mode C). A minimum identification bootstrap value of 75 % was set for taxonomic assignment, and lower values were set as unassigned. Assignments to higher taxonomic levels (genus and species) were not considered for metazoans in the 18S due to its conservative nature, which increases the chance of incorrect assignments at such high resolution (Giebner et al., 2020; Tang et al., 2012; Wangensteen et al., 2018a). This is especially true for copepods (Wu et al., 2015), which were the most dominant taxa in the dataset. However, taxonomic matches to genus level were acknowledged in the Chromista and Plantae kingdoms.

After taxonomic assignment was completed, we removed reads assigned to (i) bacteria, non-eukaryotes, fungi (18S only) and obvious contaminants (e.g., insects and terrestrial taxa). Blank corrections and further data curation steps were done for both datasets, following Murray et al. (2024).

## 2.5. Statistical analysis

All statistical analyses were conducted using R version 4.3.0 (R Core Team, 2023), and the majority of the visualizations were generated using the *ggplot2* (Wickham, 2016) and *phyloseq* packages (McMurdie and Holmes, 2013). Rarefaction curves, which assess the effectiveness of sequencing depth in capturing biodiversity within the samples, were performed on the raw MOTU and ASV data using the *vegan* package (Oksanen et al., 2019). To account for uneven sequencing depth in the alpha diversity analyses, samples were normalized to the lowest individual sample read count for both datasets (COI and 18S). This was done using the scaling with ranked subsampling method (SRS: Beule and Karlovsky, 2020) from the SRS package (Heidrich et al., 2021). Rarefied MOTUS/ASV richness (S) and Shannon-Wiener Index (H') were calculated and we tested whether alpha diversity was significantly different between sampling depths and ice floes. This was done using Kruskal-Wallis rank tests and post-hoc Dunn pairwise comparisons using the *rstatx* package (Kassambara, 2023).

To explore the relationship between community composition and environmental variables, we conducted multivariate analyses. Ordination with non-metric multidimensional scaling (NMDS) was used to investigate broad patterns of community compositional across different sampling depths and ice floes in the MIZ. Aitchison's distances, which consider the compositional nature of metabarcoding data (Quinn et al., 2018), were calculated by transforming raw read count data with centre log ratio (CLR, with pseudocount =1) and using Euclidean distance matrices. Then, we conducted Permutation Analysis of Variance
(PERMANOVA) tests to assess significant differences in community compositions for the groupings.

The effects of environmental drivers on the community composition were further investigated through a sparse partial least squares (sPLS) analysis using the mixOmics package (Rohart et al., 2017). Prior to the analysis, MOTUs/ASVs were filtered for zero variance and the environmental variables were standardized to enhance normality and homoscedasticity. The sPLS analyses were performed in canonical mode on the CLR-transformed sequence datasets. The clustered image maps (CIM) of the first two components were produced to show Pearson's correlation between MOTUs or ASVs and environmental variables. A cutoff of 0.4. and 'average' clustering method were used.

# 3. Results

## 3.1. Variability of oceanographic and sea ice conditions

The origin of the sampled ice floes was determined by calculating back-trajectories based on daily ice velocity data (Krumpen et al., 2020). All ice floes in the present study were second-year ice, originating from the Laptev Sea and transported towards Fram Strait via the Transpolar Drift (Fig. S2).

The ice floe stations exhibited SIC ranging from 58 % to 81 %, with the highest concentration at floe F24_1 (81 %) and the lowest at floe FS_V3 (58%) (Table 1). Station FN_V1 was located furthest from the ice edge (130 km), while FS_V2 was the closest to the ice edge (39 km). The water temperatures throughout the upper 5 m at all stations were generally below -1.4 °C, with low salinities (< 33 psu) near the ice-ocean interface dominating the upper-ocean density structure (Fig. S3 and Table S1). The profiles indicate that the upper water column was determined by sea ice melt at all stations and maintained by relatively low levels of mixing (e.g., Reifenberg et al., 2025). The buoyancy deficit (BD) ranged between 11.6 and 29.1 kg/m² and the meltwater layer depth (hBD) was between 11.1 m and a maximum of 25.4 m at station F24_1, indicating that the melt water was mixed. The hBD values were the lowest at the stations closest to the ice edge, indicating stronger stratification with a surface-intensified melt water layer (Table 1).

## 3.2. Sequencing summary

We sequenced a total of 90 samples and 20 controls (field blanks, extraction controls and PCR controls). After bioinformatic processing and filtration steps, we obtained 5,776,833 reads assigned to 522 MOTUs with COI, and 18,191,862 reads assigned to 1428 ASVs with 18S (Table S2). Rarefaction curves based on reads plateaued for all samples for both markers, indicating that the sequencing depth was sufficient to capture the full biodiversity of each sample (Fig. S4). In COI, 95 % of MOTUs were assigned to Kingdom level, 94 % to Phylum, 73 % to Class, 18 % to Order, 13 % to Family, 11 % to genus and 5 % to species. For 18S, 98 % were assigned to Kingdom level, 95 % to Phylum, 89 % to Class, 75 % to Order and 69 % to Family.

## 3.3. Taxonomic composition

### 3.3.1. Overall composition

Common detections by both markers were highest at Kingdom level and decreased with taxonomic resolution (Fig. S5 and Table S3). Fungi were recovered only by COI and Protozoa only by 18S, while Plantae, Chromista and Metazoa were found in both datasets. Overlapping detections were the lowest at family level, where only 22 out of 122 taxa were recovered by both markers. All major marine metazoan phyla were detected by COI, but 18S failed to detect Annelida, Mollusca, Nemertea and Porifera. In contrast, 18S detected more Chromista phyla than COI, including Cercozoa, Ciliophora, Heliozoa and Radiozoa.

The overall read assemblage of the COI dataset was dominated by Chromista (52 % of reads) followed by Metazoa (41 %), Plantae (7 %),

Fungi (0.04 %) and reads unassigned at Kingdom level (0.05 %) (Fig. S6A). In the 18S dataset, Metazoa were the most dominant (79 % of reads), followed by Chromista (18 %), Plantae (3 %), Protozoa (0.2 %) and unassigned ASVs to Kingdom level (0.01 %) (Fig. S6B). We identified the top 15 MOTUs and ASVs by read abundance in each dataset to gain insights into the dominant taxa overall, as well as across different ice floe stations and depths (Fig. 2A and B). The 15 most abundant MOTUs comprised 78 % of the COI reads and the most abundant ASVs 91 % of the 18S reads (Table S4). The complete taxonomic information of all MOTUs and ASVs in the eDNA datasets can be found in Tables S5 and S6.

### 3.3.2. Primary producers

Proportions of Chromista were higher under the ice than at 5 m in both datasets, except at the floe F24_1. Plantae abundances were the highest at 0 m in the COI dataset. The Plantae proportion of the 18S dataset was largely at F24_1, where it was also higher at 0 m than at 5 m. The most abundant Chromista phylum in the COI dataset was Heterokontophyta, making up 42.0 % of the reads. The second was Ochrophyta (22.3 %), then Haptophyta (17.7 %), Myzozoa (11.8 %) and the remaining phyla (<1 % each). The highest MOTU richness was found in Ochrophyta (103 MOTUs), followed by Heterokontophyta (72), Oomycota (55), Myzozoa (48), and Haptophyta (25) (Table S7). The MOTU richness was <10 for each of the other three Chromista phyla. In the 18S dataset the phyla Myzozoa had the most reads (63.5 %) and highest ASV richness (382), then Heterokontophyta (20.4 % and 81 ASVs), and Ochrophyta (25.6 % and 150 ASVs). The remaining nine phyla had <5 % relative read abundance each (Table S4). We detected Chromista genera typically found in the region including the diatom genera *Thalassiosira*, *Chaetoceros*, *Pseudo-nitzschia*, *Nitzschia* and the haptophyte genera *Phaeocystis* and *Chrysochromulina*.

Three Plantae phyla were recovered with COI, with Chlorophyta being the most dominant (83 % of Plantae reads), followed by Rhodophyta (11.7 %) and Streptophyta (5.3 %). Rhodophyta had the highest MOTU richness with 27 MOTUs, followed by Chlorophyta (15). Chlorophyta was the only Plantae phylum in the 18S dataset and had 31 ASVs. In the COI dataset, we found the picoplankton genera *Bathycoccus* and *Micromonas* (order: Mamiellales) to be the most dominant Plantae MOTUs, which were present at every ice floe. The most abundant Plantae ASVs in the 18S dataset also belonged to the genus *Micromonas* and was also present at all ice floes.

In the top 15 MOTUs, a MOTU in the diatom family of *Chaetocerotaceae* (11.4 % of reads from all kingdoms) was the most dominant non-metazoan, followed by a haptophyte MOTU in the Phaeocystales order (5.2 %). In the 18S dataset, two dinoflagellate ASVs of the *Gyrodinium* genus were the most abundant Chromista (2.9 % and 2.5 %) and an ASV from the Plantae genus of *Micromonas* (2.5 %) (Fig. 2).

### 3.3.3. Metazoans

The proportion of metazoan reads in the overall dataset were larger

<table><caption>**Table 1**
Location, ice and meltwater conditions at each ice floe station. SIC = sea ice concentration, BD = buoyancy deficiency and hBD = depth of meltwater layer.</caption><thead><tr><th>Ice Floe Station</th><th>Longitude (decimal)</th><th>Latitude (decimal)</th><th>SIC (%)</th><th>Distance to ice edge (km)</th><th>BD (kg/m²)</th><th>hBD (m)</th></tr></thead><tbody><tr><td>F24_1</td><td>1.286</td><td>81.325</td><td>81</td><td>100</td><td>24.4</td><td>25.4</td></tr><tr><td>FN_V1</td><td>6.813</td><td>81.596</td><td>68</td><td>130</td><td>14.6</td><td>21.5</td></tr><tr><td>FN_V2</td><td>4.779</td><td>81.501</td><td>75</td><td>81</td><td>17.2</td><td>17.5</td></tr><tr><td>FN_V3</td><td>2.36</td><td>81.179</td><td>68</td><td>87</td><td>17.7</td><td>16.5</td></tr><tr><td>FM_V2</td><td>5.127</td><td>81.351</td><td>71</td><td>67</td><td>14.5</td><td>11.2</td></tr><tr><td>FM_V3</td><td>2.123</td><td>81.069</td><td>63</td><td>80</td><td>21.0</td><td>15.0</td></tr><tr><td>FS_V1</td><td>7.728</td><td>81.183</td><td>72</td><td>84</td><td>11.6</td><td>15.3</td></tr><tr><td>FS_V2</td><td>5.425</td><td>81.189</td><td>70</td><td>39</td><td>14.1</td><td>12.1</td></tr><tr><td>FS_V3</td><td>3.784</td><td>80.927</td><td>58</td><td>51</td><td>29.1</td><td>14.5</td></tr></tbody></table>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 2.** The 15 most dominant MOTUs/ASVs and alpha diversity indices at each ice floe station. (A) Relative read abundance of the 15 most abundant MOTUs in the COI dataset. MOTUs are labelled to the highest taxonomic resolution possible (up to species level). All remaining MOTUs are grouped together as "Other". (B) The 15 most abundant ASVs in the 18 s dataset. Metazoan ASVs are labelled to the order level and Chromista and Plantae to genus level where possible. All remaining ASVs are grouped together as "Other".</figcaption></figure>

at 5 m than right beneath the ice at all ice floe stations, for both markers, except at FS_V3 (outer) in the COI dataset (Fig. S5). However, some individual taxa (e.g., Annelida, Anthozoa, and Rotifera) were more abundant at 0 m (Fig. S7). Reads from the Arthropoda phylum made up 62.1 % of the metazoan component of the COI dataset, followed by Annelida (12.2 %), Cnidaria (12.1 %), and Chordata (12.1 %). Each of the remaining phyla accounted for <1.1 % of the total reads. The phyla with the highest MOTU richness were Arthropoda and Cnidaria (27

MOTUs each) and the remaining metazoan phyla had <10 MOTUs each. In the 18S dataset, Arthropoda was by far the most dominant metazoan phylum, comprising 99.9 % of the reads and exhibiting the highest ASV richness with 474 ASVs. The remaining metazoan phyla made up <0.1 % of the reads and all had an ASV richness lower than 5 (Table S7).

Metazoan MOTUs accounted for five of the top 15 most abundant MOTUs in the COI dataset (all kingdoms combined). These included the MOTU assigned to the cyclopoid copepod *Oithona similis* (18.7 % of the
total COI reads), followed by the calanoid copepod *Pseudocalanus minutus* (7.5 %). The top 15 ASVs in the 18S dataset contained four metazoan ASVs and were strongly dominated by a single ASV from the Calanidae family (71.5 % of all 18S reads), followed by another Calanidae ASV (3.0 %) (Table S4 and Fig. 2).

We detected MOTUs from the major gelatinous zooplankton (GZP) classes; Scyphozoa (6), Hydrozoa (17) and the phylum Ctenophora (4). The highest GZP richness was recovered by COI while only a single hydrozoan ASV (family: Rhopalonematidae) and three ctenophore ASVs (family: Bolinopsidae, Mertensiidae and Euplokamididae) were in the 18S dataset. The ctenophore family Bolinopsidae was detected by both markers. Finally, a single appendicularian ASV (family: Oikopleuridae) was recovered in the 18S dataset (Tables S5 and S6).

### 3.4. Alpha and beta diversity

Alpha diversity analyses revealed that both MOTU richness (COI: $x^2 = 34.9, p < 0.01$ and 18S: $x^2 = 11.6, p < 0.01$) and the Shannon index (COI: $x^2 = 29.36, p < 0.01$ and 18S: $x^2 = 7.29, p < 0.01$) were significantly higher at 0 m than 5 m in both datasets (Fig. S8). A higher Shannon index indicates greater evenness, reflecting a wider spread of dominant taxa, whereas a lower Shannon index suggests that a smaller number of taxa dominated, resulting in reduced evenness. We found that ice floe station had a significant effect on MOTU richness (COI: $x^2 = 29.24, p \le 0.01$ and 18S: $x^2 = 33.24, p \le 0.01$) and Shannon index (COI: $x^2 = 26.11, p \le 0.01$ and 18S: $x^2 = 37.47, p \le 0.01$) (Fig. S9). For COI, pairwise comparisons showed that MOTU richness was significantly higher at FS_V3 than FN_V3 and F24_1 and FM_V3 had significantly higher richness than FN_V3. Shannon index was also significantly higher in FS_V3 than FN_V3 (Fig. S9). In the 18S dataset, ASV richness was higher at the F24_1 station than at FN_1, FN_V2 and FM_V3 (Fig. S9). The F24_1 station also had significantly higher Shannon index than FM_V3, while the outer stations FS_V2 and FS_V1, had higher Shannon index than the middle station FM_V3 (Fig. S9). The variability in Shannon index and the corresponding spread can be clearly seen at FS_V3 in the COI dataset (high Shannon index), where there is a high proportion of 'Other' MOTUs (Fig. 2A) and a correspondingly high Shannon index value (Fig. S9B). In contrast, FM_V3 is dominated by a single ASV (Fig. 2B) and has a significantly low Shannon index (Fig. S9D). As beta diversity analyses, the non-metric Multidimensional Scaling with Aitchison's distances of community composition showed congruent results for both datasets, with significant clustering by both ice floe station and depth (Fig. 3). Ice floe F24_1 showed the most obvious separation from all the other stations for the COI and 18S datasets (Fig. 3). Differences in community composition by depth was most apparent in the COI dataset (Fig. 3A and B). Subsequent PERMANOVA analysis supported these ordination results, indicating that both station and depth, as well as an interaction, were significant variables influencing community composition (Table 2).

### 3.5. Community signatures of different environmental conditions in the MIZ

In the sPLS analyses, 34 % of the COI MOTUs, and 31 % of the 18S ASVs were significantly linked to environmental variables (Fig. 4). Among these MOTUs and ASVs, Chromista were predominant in both the COI (66.0 %) and 18S (75.1 %) datasets, while Metazoa comprised 13.3 % and 12.9 %, respectively. Two independent hierarchical clustering analyses (one for each marker) grouped these MOTUs and ASVs into four clusters, each representing distinct communities associated with varying environmental conditions within the MIZ (Fig. 4). Detailed taxonomic information on the significant MOTUs and ASVs from the sPLS analyses, including their correlations with environmental variables, clusters assignments, and abundances at each ice floe station, is provided in Tables S8 and S9.

#### 3.5.1. sPLS analysis of the COI dataset

In the COI dataset, cluster 1 was characterized by strong positive correlations (>0.4) with sea ice concentration and hBD, as well as with distance from the ice edge. This suggests high sea ice coverage and stations located farther from the ice edge with high hBD, characteristics typical of inner MIZ ice floes. There were also significant relationships with lower temperature and longitude (Fig. 4A). This cluster consisted of 46 MOTUs and was the most dominant at F24_1 (Fig. 4B). Metazoan MOTUs in this cluster included those assigned to the Malacostraca, Anthozoa and Scyphozoa classes. Additionally, the green algae *Bathycaoccus prasinos* and *Micromonas* sp. (Family: Mamiellophyceae were present in the highest proportions at floe F24_1 and FS_V1 (Table S8).

Cluster 2 showed strong positive correlations with BD and hBD, indicating a high concentration of meltwater distributed over a deeper layer. Moreover, this cluster showed negative correlations with temperature, which is also indicative of meltwater, as well as longitude, suggesting the more western stations (Fig. 4A). The proportion of cluster 2 reads was highest at the westernmost stations F24_1, FM_V3 and FS_V3 (Fig. 4B). This cluster contained 31 MOTUs, more than half of which were algae, with three belonging to metazoans (phyla Arthropoda, Scyphozoa and Actinopterygii) (Table S8).

Cluster 3 showed negative correlations with SIC, hBD, distance to ice edge and salinity. The MOTUs in this cluster were positively correlated with temperature and most were positively correlated with BD (Fig. 4A and Table S8). The low sea ice cover and distance to the ice edge are indicative of the outer MIZ, while the low hBD and high BD show shallow meltwater stratification. The MOTUs in this cluster that correlated with low hBD but high BD are more likely to be found at stations with a stratified meltwater layer. This cluster contained 47 MOTUs, which were present at outer and middle MIZ stations (FS) and at FM_V3 (Fig. 4B). It was almost absent at F24_1, where the meltwater layer was deeper. The metazoans in this cluster included single MOTUs from classes Hydrozoa and Scyphozoa, as well as one each from Arthropoda and Porifera. Algal representatives included one MOTU assigned to *Pseudo-nitzschia*, seven MOTUs from the brown algae class Phaeophyceae, and five MOTUs from the Plantae class Mamiellophyceae (Table S8).

Cluster 4 showed the strongest negative correlations with BD and positive correlations with salinity, indicating low concentrations of meltwater. Positive correlations with longitude and latitude, although less pronounced, suggest a connection to the north-eastern stations. The highest proportion of cluster 4 reads were found at FN_V1 and FS_V1, located in the north-eastern part of our sampling area (Fig. 4B). This cluster contained 42 MOTUs, including the sea ice diatom genera *Fragilariopsis* and *Nitzschia*, the diatom family Skeletonomataceae and the symbiotic dinoflagellate genus *Symbiodinium* (Table S8).

#### 3.5.2. sPLS analysis of the 18S dataset

Cluster 1 in the 18S dataset showed the strongest positive correlations with SIC and hBD, followed by BD, and negative correlations with temperature (Fig. 5A). These patterns are characteristics of the inner MIZ (high ice cover) and a higher concentration of colder meltwater distributed deeper in the upper water column. The cluster also showed weaker correlations (both positive and negative) with distance to the ice edge, latitude and salinity. Comprising 134 ASVs, this cluster was the most dominant at F24_1, where the highest hBD was measured (Table 1; Fig. 5B). ASVs positively correlated with distance to the ice edge, latitude and salinity were most abundant at F24_1. In contrast, ASVs with negative correlations to salinity and strong positive correlations with other factors were more prevalent at FS_V3. Plantae ASVs, including those assigned to the picoplankton genera *Bathycaoccus* (1 ASV) and *Micromonas* (3 ASVs), were most abundant at F24_1 (Table S9).

Cluster 2 showed strong negative correlations with SIC, hBD, salinity, distance to the ice edge and latitude, along with weak positive correlations with BD and temperature. These patterns are characteristic of outer MIZ sites with a meltwater layer beneath thinner ice. This
cluster included 78 ASVs, mostly Chromista, as well as four Protozoa, one Plantae and one Metazoa ASV. Reads from this cluster were most abundant at FS_V3, where low sea ice cover and a stratified meltwater layer were observed (Table 1). ASVs from the dinoflagellate genus *Gyrodinium* (14), and the diatom genera *Pseudo-nitzschia* (3), and *Chaetoceros* (4) were identified in this cluster (Table S9).

Cluster 3 was the smallest cluster in the 18S dataset with 23 ASVs, all of which were Chromista, along with a single ASV each from Protozoa and Plantae (Table S9). This cluster showed positive correlations with salinity, SIC, distance to the ice edge, as well as weaker positive correlations with latitude and hBD. It also showed negative correlations with BD (Fig. 5A). These characteristics are indicative of stations with high ice coverage in the inner MIZ and well mixed meltwater in the upper water column. The reads in this cluster were most prevalent at F24_1 and FS_V1 (Fig. 5B).

Cluster 4 was the largest in terms of ASVs and was mainly characterized by negative correlations with BD and hBD, along with positive correlations with temperature and salinity (Fig. 5A). These patterns

suggest conditions with weak but shallow meltwater layers and less ice cover. This cluster included 195 ASVs and was highly dominant at all stations except F24_1 and FS_V3 (Fig. 5B). Apart from a single ASV assigned to Metazoa in cluster 2, this cluster was the only cluster containing Metazoans (54 ASVs), including an ASV assigned to the GZP family Rhopalonematidae, mainly abundant at FM_V3 and FN_V3, and an ascidian from the family Perophoridae, which was mostly found at FS_V2. Moreover, 52 ASVs were assigned to the copepod family Calanidae (Table S9).

# 4. Discussion

## 4.1. Eukaryotic biodiversity in the sub-ice water column

In the present study, we implemented multi-marker metabarcoding to detect high levels of eukaryotic richness from relatively small volumes of sub-ice water in the MIZ. However, despite including a marker that

<figure><img src="image_11.png" /><figcaption>**Fig. 3.** Non-metric Multi-dimensional scaling (NMDS) ordination plots and Iris plots of eukaryotic community composition. (A) COI NMDS plot and (B) iris plot of relative abundance of the 10 most abundant MOTUs. (C) 18S NMDS plot and (D) iris plot of relative abundance of the 10 most abundant ASVs. NMDS were performed on CLR transformed data and Euclidean distance matrices (Aitchison's distance). Shapes indicate depth, colours indicate ice floes. Order of bars on Iris plots are arranged according to the NMDS ordination axes.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 4.** Sparse least square analyses (sPLS) indicating relationships between community composition and environmental variables in the COI dataset. (A) Clustered image maps (CIM) of the two components of sparse partial least squares (sPLS) regression analysis. Pairwise correlations between MOTUs (x-axis) and environmental variables (y-axis). Colour indicates correlation: browns show negative correlations and blues positive, with colour intensity representing strength of correlation. Hierarchical clusters are displayed on the top (clustering method: average linkage, distance method: Pearson's correlation). Cut-offs = -0.4 and 0.4. Environmental parameters: Temperature (°C), Buoyancy deficiency (BD), depth of buoyancy deficiency (hBD), Latitude (Lat), Longitude (Lon), Sea ice concentration (SIC; %) and distance to ice edge (Dist_SIC; km). (B) Pie plots showing the proportions of reads assigned to each of the sPLS clusters at each ice floe station.</figcaption></figure>

allows for high resolution taxonomic assignments (COI), large pro-
portions of the sequences recovered could only be resolved to broad
taxonomic levels. The prevalence of low-resolution assignments, or
uncharacterized diversity, highlights a significant gap in our under-
standing of eukaryotic diversity in the upper water column of the MIZ.
This is likely in part explained by a large number of eukaryotes in the
MIZ that have yet to be identified with molecular methods, as well as the
limitations of reference databases that remain a major challenge for
eDNA metabarcoding studies. The high numbers of unassigned eukary-
otes are consistent with findings from previous eDNA studies of pelagic
eukaryotic communities, which report considerable unknown diversity
in other oceans (Bakker et al., 2019; López-Escardó et al., 2018) as well
as in the Arctic Ocean (Questel et al., 2021). A recent COI-based eDNA
study in Fram Strait found lower levels of unassigned metazoans
throughout the water column compared to our study (Murray et al.,
2024), suggesting that this hidden diversity is higher directly under the
ice than in open waters in Fram Strait.

4.1.1. *Primary producers*

Primary producers in the MIZ consist of ice algae, which typically
live inside and on the lower surface of sea ice, and phytoplankton found
in the sub-ice water column. As the Arctic sea ice declines in both
thickness and distribution, primary producers are being exposed to
different environmental conditions, including changes in light regimes.
This has led shifts in the timing and duration of the ice-algae and
phytoplankton blooms in the MIZ (reviewed in Barber et al., 2015).

Recent evidence shows phytoplankton blooms are increasingly occur-
ring under the sea ice due to thin first-year ice and consequentially
higher light availability (Ardyna et al., 2020; Assmy et al., 2017), rather
than being restricted to patches of open water in the MIZ.

We detected a combination of ice algae and phytoplankton with both
eDNA markers, with MIZ-typical phytoplankton taxa showing high
sequence read abundance and richness. Sympagic blooms are generally
dominated by the sea ice specialist diatom genera such as *Nitzschia*,
*Navicula* and *Melosira* (Barber et al., 2015; Poulin et al., 2011). In our
samples, we observed low read abundances of *Nitzschia* and *Navicula*,
but we did not detect *Melosira*. This could be due to our sampling
occurring after the peak of the spring bloom when the abundances of
these taxa had significantly declined. Pelagic blooms in the MIZ are
typically dominated by the cryo-pelagic diatom genera such as *Tha-
lasiostira*, *Chaetoceros* and *Fragilariopsis*, all of which we found in our
samples with high read abundances. Furthermore, we detected a num-
ber of dinoflagellates (class: Dinophyceae), the haptophyte family
Phaeocystales (genus *Phaeocystis* with 18S), and two prasinophyte
genera (*Micromonas* and *Bathyccoccus*) that are typical of Arctic summer
waters (Benner et al., 2020; Freyria et al., 2021; Joli et al., 2017).
Interestingly, we also found a number of MOTUs and ASVs belonging to
the brown algae class Phaeophyceae. While these are not necessarily
part of the primary producer community in the MIZ, they are present in
nearby Svalbard fjords (e.g., Fredriksen et al., 2019) and evidence of
macroalgae transport well beyond coastal habitats has been shown in
metabarcoding studies in other oceans (Ortega et al., 2019). We
<figure><img src="image_13.png" /><figcaption>**Fig. 5.** Sparse least square analyses (sPLS) indicating relationships between community composition and environmental variables in the 18S dataset. (A) Clustered image maps (CIM) of the two components of sparse partial least squares (sPLS) regression analysis. Pairwise correlations between ASVs (x-axis) and environmental variables (y-axis). Colour indicates correlation: browns show negative correlations and blues positive, with colour intensity representing strength of correlation. Hierarchical clusters are displayed on the top (clustering method: average linkage, distance method: Pearson's correlation). Cut-offs = -0.4 and 0.4. Environmental parameters: Temperature (°C), Buoyancy deficiency (BD), depth of buoyancy deficiency (hBD), Latitude (Lat), Longitude (Lon), Sea ice concentration (SIC; %) and distance to ice edge (Dist_SIC; km). (B) Pie plots showing the proportions of 18S reads assigned to each of the sPLS clusters at each ice floe station.</figcaption></figure>

<caption>**Table 2**</caption>

Statistical results of PERMANOVA analyses for the effect of depth and ice floe station on community composition.

<table><thead><tr><th colspan="6">COI</th></tr><tr><th></th><th>DF</th><th>SS</th><th>R<sup>2</sup></th><th>F</th><th>p-value</th></tr></thead><tbody><tr><td>Depth</td><td>1</td><td>4297.76</td><td>0.087</td><td>16.19</td><td>0.001</td></tr><tr><td>Floe name</td><td>8</td><td>20,638.53</td><td>0.42</td><td>9.67</td><td>0.001</td></tr><tr><td>Depth x Floe name</td><td>8</td><td>5345.08</td><td>0.11</td><td>2.5</td><td>0.001</td></tr><tr><td>Residual</td><td>72</td><td>19,209.45</td><td>0.39</td><td></td><td></td></tr><tr><td>Total</td><td>89</td><td>49,490.81</td><td>1</td><td></td><td></td></tr></tbody></table>

<table><thead><tr><th colspan="6">18S</th></tr><tr><th></th><th>DF</th><th>SS</th><th>R<sup>2</sup></th><th>F</th><th>p-value</th></tr></thead><tbody><tr><td>Depth</td><td>1</td><td>8742.71</td><td>0.06</td><td>8.66</td><td>0.001</td></tr><tr><td>Floe name</td><td>8</td><td>53,510.07</td><td>0.35</td><td>6.62</td><td>0.001</td></tr><tr><td>Depth x Floe name</td><td>8</td><td>17,439.33</td><td>0.11</td><td>2.16</td><td>0.001</td></tr><tr><td>Residual</td><td>72</td><td>72,722.25</td><td>0.48</td><td></td><td></td></tr><tr><td>Total</td><td>89</td><td>152,414.35</td><td>1</td><td></td><td></td></tr></tbody></table>

Notes: DF = degrees of freedom, SS = sums of squares, R² = R-squared value, F = F-statistic.

hypothesize that these detections are due to gametophytes or tissue being advected north from the Svalbard coast towards the Yermak Plateau by the West Spitsbergen Current (Fig. 1A).

### 4.1.2. Metazoans

Sea ice-associated fauna live in the lower centimetres of the sea ice and its brine channels, as well the lower surface or in the water column directly below. Arctic sea ice fauna are typically a combination of pelagic, benthic and sympagic taxa, which are a mix of arctic-endemic, arctic-boreal and boreal biogeographic origins (Bluhm et al., 2010; Kosobokova et al., 2011). Here, we observed both ice-associated and pelagic metazoans typical of the region (Barber et al., 2015; Hop et al., 2021; Weydmann et al., 2014), including meiofauna (e.g., Rotifera, Arthropoda, Annelida), zooplankton (copepods, amphipods and gelatinous zooplankton), potential meroplankton (e.g., polychaetes, gastropods and poriferans) and vertebrates (fish: *Boreogadus saida* and whales: *Balaena mysticetus* and *Monodon monoceros*). Overall, the proportion of metazoan reads was highest at 5 m at all stations, with the exception of a single station in the outer MIZ. Despite this general trend, several metazoan taxa (i.e., Rotifera, Tunicata and Annelida) were found in higher read abundances at 0 m than 5 m, suggesting a higher abundance or biomass of these taxa closer to the ice surface. Indeed, larvae of Tunicata and Annelida species, for example, are known to settle and feed on the lower surface of the ice (Barber et al., 2015; Bluhm et al., 2010, 2017).

Arthropods, particularly copepods and amphipods, are the most dominant zooplankton in the Arctic in terms of richness, biomass and abundance. This was evident in both of our eDNA datasets, where Arthropoda had the highest relative read abundances and MOTU
richness (COI only) compared to the other phyla. In terms of amphipods, we detected the sympagic *Apherusa glacialis*, but not *Onisimus* spp. nor *Gammarus wilkitzkii*. These are well-known ice-associated amphipod taxa in the region, along with the pelagic amphipod genus *Themisto*, which was also absent from both datasets (David et al., 2015; Hop et al., 2011, 2021). We would have expected to detect more of these amphipods, as the ice floes samples were second year ice (i.e., multi-year) and Hop et al. (2011) found the highest abundances of ice-amphipods such as *G. wilkitzkii* and *A. glacialis* in multi-year sea ice. Therefore, the absence of amphipod genera such as *Themisto* and *Gammarus* in our results is likely due to factors such as primer or sequencing biases, rapid degradation, or dilution of extra-cellular DNA, rather than a true absence of the organisms.

In the summer surface waters, the copepod species *Calanus glacialis* and *C. finmarchicus* typically represent the highest copepod biomass, while smaller species such as *O. similis*, *Triconia borealis* and *Pseudocalanus* spp. have the highest abundances (Hop et al., 2006). To a certain extent, these patterns were reflected in the eDNA signals in the present study. In the COI dataset, *O. similis* and *P. minutus* were two of the most dominant MOTUs in terms of read abundance. We also detected *Calanus* species typical to the region, albeit at lower read abundances compared to the former. In a morphological study, Kaiser et al. (2021) found *Calanus* spp., *Pseudocalanus* spp. and *Oithona* spp. when sampling the water column under an ice filament in the MIZ. *Oithona* spp. was observed in the highest abundances in the upper water column at the centre of the ice filament, where ice concentration was the highest. Another MIZ study, Kiko et al. (2017) also found *Oithona* spp. to be one of the most abundant metazoans in the sub-ice water column. In the 18S dataset, calanoid copepods were by far the most dominant in both read abundance and ASV richness. Oithonidae (the family that includes *Oithona* spp.) and the order Harpaticoida were also present, but in low read abundances. While these eDNA-based detections of copepod taxa reflect previous morphological findings in the MIZ, the differing patterns of read abundances demonstrate the well-known limitations of interpreting read abundances in eDNA as direct representations of organism abundance or biomass (Rourke et al., 2022; Ruppert et al., 2019). While semi-quantitative patterns (i.e., relative read abundances) provide more reliable insights than absolute read counts, they must still be interpreted with some caution.

Gelatinous zooplankton (GZP), especially appendicularians and ctenophores, have been reported to be dominant components of the pelagic community in the MIZ (Bluhm et al., 2010; David et al., 2015; Ehrlich et al., 2020). In our study, we indeed detected these major GZP groups, albeit with relatively low taxonomic resolution and reads. For example, reads assigned to the appendicularian family Oikopleuridae and the ctenophore families Bolinopsidae, Mertensiidae and Euplokmididae were all recovered in the 18S dataset. Species in these families have been found in areas with high ice cover in the Arctic (e.g., David et al., 2015). One explanation for the low reads could be the patchy distribution typical of these groups (e.g., David et al., 2015), with a lack of aggregations occurring at or near the ice floe stations around the sampling time. Additionally, previous studies have also failed to amplify appendicularians with the COI Leray marker used (Albaina et al., 2024; Ershova et al., 2021; Murray et al., 2024), suggesting it may not be suitable for this taxonomic group.

We recovered a number of sympagic meiofauna with high resolution taxonomy. These included *Pseudocalanus* spp. and members of the order Harpacticoida (e.g., *Tisbe furcata*), which are typical sympagic copepods to the region (Arndt and Swadling, 2006; Kiko et al., 2017). The Arctic ice hydroid *Sympagohydra tuuli*, which inhabits brine channels and has been found in previous morphological studies (Kiko et al., 2017; Siebert et al., 2009) was not detected. Nevertheless, we did recover a large number of MOTUs assigned to the class Hydrozoa, some of which could belong to sympagic hydrozoans. However, *S. tuuli* is poorly represented in public reference databases so further barcoding efforts are necessary to enhance its detectability in eDNA samples. Ice meiofauna remains a

largely understudied group (Patrohay et al., 2022), but with optimisation efforts, eDNA metabarcoding could become a useful tool furthering our understanding of their diversity and community dynamics within the changing MIZ.

## 4.2. Spatial variations of alpha and beta diversity observed in the MIZ

Sea ice affects both light availability (through its cover and thickness) and stratification in the upper meters of the water column (e.g., from its meltwater input), thereby playing a crucial role in shaping the dynamics of primary production and consumers. Despite the high proportions of low-resolution assignments, the alpha and beta diversity analyses in the present study are essentially taxon-free (Berry et al., 2019; Feng et al., 2022), which allowed us to detect aspects of the ecology of many of the MOTUs and ASVs in relation to depth, location and sea ice conditions.

### 4.2.1. Vertical patterns of diversity

Regarding vertical patterns, we found significantly higher alpha diversity indices at 0 m, along with distinct variations in community composition between the two sampled depths in both the COI and 18S datasets. The proportion of primary producer sequence reads (Chromista and Plantae) was higher at 0 m than at 5 m (both datasets) across all stations except the F24_1 ice floe, whereas the opposite was true for Chromista. Metazoans as a whole were more abundant at 5 m, but distinct patterns emerged for individual taxa, with the most abundant metazoan ASVs and MOTU showing differences in relative abundances between depths. For example, in the COI dataset, some MOTUs (e.g., Annelida, Ascidiacea and Monogononta) were present in larger relative read abundances at 0 m than at 5 m. The reverse was true for the pelagic copepods *O. similis* and *P. minutus*. In the 18S dataset, this was the most obvious in the single Calanoida ASV (ASV_1) that made up 99 % of the metazoan reads, which was in larger proportions at 5 m depth (Fig. 2). These small-scale vertical variations in diversity and composition could be affected by the input of in-ice and melt-pond communities into the meltwater layer immediately under the ice, either as eDNA or the release of whole organisms with melting. This enrichment of the sub-ice community by taxa from sea ice melt is supported by previous findings in the Arctic (Smith et al., 2023; Xu et al., 2020). Significant differences in metazoan composition between in-ice and sub-ice water were also found by a morphological study in the MIZ on the Russian side of the Arctic (Kiko et al., 2017). Here, they observed meiofauna in the sub-ice water re-colonising the sea ice, both passively as ice re-formed and actively, depending on the taxa. Meiofauna taxa have high osmotic tolerance, allowing them to survive in a range of salinities that occur in brine channels and meltwater, whereas pelagic taxa may be sensitive to lower salinities in the meltwater (Grainger and Mohammed, 1990; Smith et al., 2023). Moreover, the community at 5 m depth is likely influenced by pelagic taxa from deeper waters or by advection from nearby open water areas adjacent to the MIZ.

### 4.2.2. Horizontal patterns of diversity

Location also had a significant effect on eukaryotic diversity and community composition in both the COI and 18S datasets, with the ice floe stations F24_1 and FS_V3 showing the greatest differences. Alpha diversity was generally higher towards the outer edge of the MIZ in both datasets, with the exception of floe F24_1, which had high richness and Shannon diversity for 18S. Furthermore, the ordination analyses and subsequent iris plots showed that F24_1 and FS_V3 formed the most distinct clusters for both datasets (Fig. 3). These differences in diversity and community composition are likely explained by the distinct location and environmental conditions observed at the inner and outer MIZ floes. The ice floe F24_1 was the only one positioned on the slope of the Yermak plateau, had the deepest seafloor depth (1400 m) and was where we measure the highest sea ice concentration and the deepest distribution of meltwater. In contrast, FS_V3 was located towards the outer edge of the
MIZ, and was characterized by the lowest sea ice cover and had a shallower meltwater layer depth compared to the floes in the inner MIZ. These distinct patterns in diversity and community assemblages over relatively small scales, fit with previous morphology-based findings, which indicate that Arctic sea ice habitats and their associated communities are highly dynamic, even over small spatial gradients (David et al., 2015; Ehrlich et al., 2020; Ribeiro et al., 2024).

### 4.3. Distinct community signatures shaped by environmental conditions in MIZ

When analysing the community assemblages linked to different environmental conditions (Figs. 4 and 5), we identified meltwater dynamics and sea ice concentration as key environmental drivers. The identification of four cluster in each dataset, with congruent patterns for both markers, underscores the strong influence of environmental gradients within the MIZ. The dominance of conditions typical of the inner MIZ (i.e., high sea ice coverage and greater distance to the ice edge) and outer MIZ (i.e., low ice coverage and close to ice edge) highlights the role of these region in shaping the community composition. Variations in clusters associated with meltwater conditions between the markers suggest that differences in the depth and volume of meltwater distribution may further modulate these ecological patterns, reflecting the complexity of environmental drivers in the MIZ.

Primary producers made up the largest proportion of the taxa with significant correlations to environmental variables, highlighting the crucial role that the environmental conditions play in shaping their diversity and distribution in the MIZ. Overall, the majority of taxa showed strong correlations with either the high ice cover and the deeper meltwater layer present at F24_1, or to the outer MIZ conditions characterized by lower ice cover and meltwater at FS_V3. For instance, in the phytoplankton community, the most abundance ASV assigned to the *Phaeocystis* genus was found across all ice floes, with its highest abundance at F24_1. This suggests a potential affinity of *Phaeocystis* for inner MIZ conditions, further emphasizing the influence of environmental factors on community distribution within the MIZ. This is in line with previous finding by Assmy et al. (2017), who detected under-ice phytoplankton blooms dominated by *Phaeocystis* in the same region in 2015. The sequences assigned to the green algae genera *Micromonas* and *Bathycoccus* showed a strong relationship with low temperature and high sea ice concentration, with the highest read abundance observed at F24_1, followed by FS_V1. This was further illustrated in the sPLS analyses where both genera were part of the signature communities (cluster 1 in both Figs. 4 and 5) for high sea ice cover and meltwater content. Arctic *Micromonas* species include different thermo-types, such as cold-water specialists, which could explain their higher abundance at the station with inner MIZ conditions (Demory et al., 2019). However, sequencing longer gene fragments would be required to confirm whether the observed organisms are indeed the cold-water specialists referenced.

We also detected significant relationships between a number of metazoan taxa and environmental variables, although these were in lower proportions compared to the primary producers and were restricted to low resolution assignments (i.e., class-level for COI). They included a range of groups including the Annelida, Arthropoda, Chordata and Cnidaria phyla. In the COI dataset, the highest number of MOTUs with significant relationships were found in the phylum Cnidaria, including the classes Anthozoa, Hydrozoa and Scyphozoa. In the 18S dataset, the majority of significant relationships were ASVs assigned to Arthropoda, with the exception for a single hydrozoan (family: Rhopalonematidae) and an ascidian (family: Perophoridae) ASV. As sea ice conditions change due to warming and as the phenology and composition of ice-algae and phytoplankton blooms shift, metazoans with life cycles linked to blooms will also be affected. However, with limited taxonomic resolution of the 18S gene and the high levels of uncharacterised diversity resulting in a low number of species-level

detections with COI, we are constrained to conclusions at the genus or family level and cannot yet identify specific indicator species or strains.

### 4.4. Multi-marker approach to surveying under-ice biodiversity with eDNA

Marker choice and associated primer pairs can be a significant source of bias on the biodiversity captured by eDNA metabarcoding (Clarke et al., 2017; Wangensteen et al., 2018b). Thus, using multiple markers can increase taxonomic coverage and the chances of detecting a more complete picture of the biodiversity present. Although using two markers increased the number of eukaryotic taxa detected in the present study, both of the markers employed have benefits and known limitations that must be taken into consideration. The Leray-XT primer pair (Wangensteen et al., 2018b), was designed for eukaryotes, with a focus on metazoans, allows for high taxonomic precision and therefore a high number of species-level identifications due to the fast evolutionary rates of the COI gene (Tang et al., 2012; Wangensteen et al., 2018b). Furthermore, the Leray fragment is useful for detecting levels of intra-specific diversity, allowing us to not only detect different species but also investigate haplotypic diversity (e.g., Antich et al., 2023). It has well-stocked and curated public reference databases available for taxonomic assignment (Andújar et al., 2018), such as the BOLD database (Ratnasingham and Hebert, 2007). The 18S rRNA gene is more conservative than COI, but can amplify a wide taxonomic range of eukaryotes, including those where COI is limited (Questel et al., 2021). For example, ctenophores and appendicularians are thought to be often missed by COI, but successfully detected by 18S (e.g., Dischereit et al., 2024). In our study, this was true except for a ctenophore family detected by both (Bolinopsidae). The 18S marker also has high representation of public reference databases for a wide range of eukaryotic taxa. However, a trade-off of the high level of universality of 18S is its slow-evolving nature, which hampers its accuracy at higher taxonomic resolutions with the most reliable rates of identification being limited to the order or family level for metazoans (Questel et al., 2021; Tang et al., 2012; Wu et al., 2015). So, although we captured these GZP families for example, we cannot yet say with certainty which species the eDNA originated from.

Using two different markers can also result in contrasting quantitative patterns detected within the same samples (Bakker et al., 2019), and there remain limitations to interpreting relative read abundances as a proxy for biomass or abundance. Here, we detected similar patterns in alpha and beta diversity, which are in principle taxonomy-free analyses. We found the same elevated diversity under the ice compared to 5 m depth, as well as differences in community composition between depths and stations (especially F24_1) in both datasets. However, the relative read abundances of dominant taxa were not always congruent between the two markers. For example, the cyclopoid copepod species *O. similis* was the most dominant MOTU for COI, while the 18S dataset was dominated by a single calanoid copepod ASV. Although a single ASV assigned to Oithonidae was indeed in the top ASVs for 18S, it had a relative read abundance of <1 %. Relative read abundances are semi-quantitative representations of abundances or biomass of the organisms, but to which degree is both gene and taxon-dependent and currently not easily resolved (Lamb et al., 2019). Correlations between biomass and read abundance has been found for a number of zooplankton taxa when analysing the COI marker and bulk samples (Ershova et al., 2023; Ershova et al., 2021). These findings support the assumption that our COI-based patterns for zooplankton can be interpreted with more confidence than in the 18S dataset. Another factor affecting read counts is some taxa having higher numbers of gene copies than others. For instance, dinoflagellates have a high number of 18S copies which can inflate read abundances recovered in metabarcoding (Ribeiro et al., 2024). Furthermore, the strong dominance of a single calanoid ASV in our 18S reads suggests that primer or sequencing biases have a significant effect on read abundances in this dataset that needs to
be resolved. In summary, there is a need for more taxa- and marker-specific work, including linking extracellular DNA reads (i.e. eDNA) and biomass for specific taxa, and ecological patterns linked to these relative read abundance must still be interpreted with caution. In future studies, increasing the volume, temporal and spatial resolution of sampling would likely increase the number of taxa detected by both markers. Additionally, the use of passive filtration systems has been shown to improve biodiversity capture in tropical and temperate marine systems (Bessey et al., 2021) and in the deep sea (Govindarajan et al., 2022). For instance, using static filter membranes placed beneath the ice at different depths could increase exposure to larger volumes of water and, consequently higher concentration of eDNA. This approach could significantly enhance the effectiveness of eDNA as a biodiversity tool in the MIZ, regardless of which marker(s) is employed.

## 5. Conclusions

This study provides the first high-resolution, multi-marker metabarcoding analysis of eukaryotic biodiversity in the upper five meters of sub-ice water in the Arctic MIZ. The taxonomic composition of eukaryotes observed in this study was largely consistent with previous morphology-based studies in the MIZ, demonstrating eDNA as an effective tool for assessing biodiversity in sub-ice water. The use of the two markers enhanced taxonomic coverage, resolution, and the biodiversity detected. However, our findings highlight persistent gaps in reference databases that need to be addressed to achieve high taxonomic resolution, particularly in the centimetres directly beneath the ice. We demonstrated that depth, meltwater, and sea ice properties are key drivers of diversity and community composition, with consistent patterns observed across two widely used universal metabarcoding markers. Furthermore, we highlighted the potential of multi-marker surveys to identify signature communities associated with specific environmental states in ice-covered regions, such as the MIZ. In future studies, increasing the sampling volume of sea-water as well as the temporal and spatial resolution of sampling would likely increase the number of taxa detected by both markers. Future studies should prioritize sampling across seasons and wider parts of the MIZ to track the dynamics of eukaryotic communities over time and across different locations and environmental variables.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.178840.

### CRediT authorship contribution statement

**Ayla Murray:** Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Data curation, Conceptualization.
**Simon Ramondenc:** Writing – review & editing, Visualization, Validation, Formal analysis.
**Simon F. Reifenberg:** Writing – review & editing, Visualization, Resources, Formal analysis, Data curation.
**Meret Jucker:** Writing – review & editing, Validation, Investigation.
**Mara Neudert:** Writing – review & editing, Visualization, Investigation, Data curation.
**Rebecca McPherson:** Writing – review & editing, Resources, Investigation, Conceptualization.
**Wilken-Jon von Appen:** Writing – review & editing, Resources, Project administration, Investigation, Funding acquisition, Conceptualization.
**Charlotte Havermans:** Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

This study has been conducted in the framework of the Helmholtz Young Investigator Group "ARJEL – Arctic Jellies" with the project number VH-NG-1400, led by CH and funded by the Helmholtz Society and the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research. The concept and eDNA data for this manuscript were produced as part of the ICE-Jelly project with the Polarstern group number AWI_PS131_10. The sea ice physics sampling was part of grant number AWI_PS131_02. The oceanographic sampling was part of grant number AWI_PS131_07.

### Data availability

All MSS measurements are available at: https://doi.pangaea.de/10.1594/PANGAEA.956086.

All CTD data is available at: https://doi.org/10.1594/PANGAEA.971721

Reference database for COI marker is available at: https://github.com/adriantich/NJORDR-MJOLNIR3/tree/main.

Reference database for 18S marker is available at: https://www.st.nmfs.noaa.gov/copepod/collaboration/metazoogene/atlas/html-src/data_T4000000_000.html.

The raw metabarcoding sequence data are used in this project are publicly available at NCBI on the SRA database under the BioProject ID: PRJNA1135808.

Advanced Microwave Scanning Radiometer-2 (AMSR-2) sensor data downloaded from: OSI SAF Global Sea Ice Concentration (AMSR-2), OSI-408-a, DOI: 10.15770/EUM_SAF_OSI_NRT_2023. EUMETSAT Ocean and Sea Ice Satellite Application Facility. Data extracted from OSI SAF FTP server.

The presented work contains primary and altered Sentinel1 data products.

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