Eukaryote diets in Arctic marine nematodes across seasons and shelf-to-basin gradients

Eukaryote diets in Arctic marine nematodes across seasons and shelf-to-basin gradients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Eukaryote diets in Arctic marine nematodes across seasons and shelf-to-basin gradients Snorre Flo, Bodil Annikki Bluhm, Camilla Svensen, Kim Praebel, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6252716/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Polar Biology → Version 1 posted 11 You are reading this latest preprint version Abstract Marine nematodes dominate the meiofauna of benthic sediments, but few studies have investigated their trophic roles. We studied the eukaryote diet composition of nematodes from surface sediments on the Arctic Barents Sea shelf, shelf break and adjacent Nansen Basin, during four seasons, using prey metabarcoding of the 18S ribosomal RNA gene. Monhysterida (35), Chromadorida (34), Araeolaimida (27) and Enoplida (22) nematodes were most frequently observed across the study area, and diets composed of diverse metazoan, fungal, and protist prey. In contrast to ambient sediment communities, prey followed a strong seasonal pattern, and ordination indicated two distinct seasonal prey clusters. In March and May prey were characterized by high relative abundances of fungi (42% and 48%, respectively). In comparison, August and December compositions had high relative abundances of arthropods (30% and 28%) and peritrich ciliates (11% and 9%, respectively). Other notable protist prey included chlorophytes and dinoflagellates, whereas diatoms – which were highly abundant in the ambient sediment communities, were virtually absent as prey. Nematode taxonomy and trophic groups explained little of the variation in prey, and the latter was only significant when applied at the level of family. Our results indicate that Arctic nematodes are generalists which can feed on a variety of eukaryote items despite differences in morphology. They further indicate that heterotrophs, such as fungi and arthropods, compose important dietary items for nematodes in the Barents Sea. Such trophic tendencies may enable nematodes to fuel continuous growth and reproduction in Arctic sediment communities where food items are seasonally varied. Marine nematodes Prey metabarcoding High-throughput sequencing Trophic groups Marine fungi Copepods Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Nematodes are best known for their pathogenic capabilities, causing serious disease in humans and animals, and considerable damage to food crops worldwide (Moens et al. 2013 ). However, the bulk of nematodes, both in terms of abundance and diversity, are found in soft marine and freshwater sediments, and terrestrial soils (Heip et al. 1985 ). Nematodes are perhaps especially important in marine sediments, where they comprise the majority of the meiofauna in most ocean regions (McIntyre 1971 ; Moens et al. 2013 ), and are “as dominant in the sediment as copepods are in the plankton” (Heip et al. 1985 ). While patchy in their distribution – likely due to local differences in food availability and sediment grain size, benthic marine nematodes can reach abundances of 5 million individuals per square meter (Soetaert et al. 2009 ), with abundances typically being highest in the uppermost surface sediment layers (Fonseca et al. 2010 ). Broadly speaking, meiofaunal abundance and diversity decrease with increasing water depth (Rex et al. 2006 ; Bessière et al. 2007 ; Soetaert et al. 2009 ), and it is likely that the decrease is due to dwindling food supply. The quality and quantity of pelagic food particles reaching the sea-floor decreases with increasing water depth (Billett et al. 1983 ; Schewe and Soltwedel 2003 ). Depth-related decrease in meiofaunal abundance is however less steep for nematodes than other, larger metazoans, leading to comparatively higher relative abundances of nematodes in the deep-sea than on shelves (Rex et al. 2006 ). Local factors may also be influential, with coarse grain-sized sediments favoring long nematodes, and sediments poor in organic materials favoring short and wide nematodes (Soetaert et al. 2009 ). Despite being small, slender and inconspicuous, nematodes take on different and important functional roles in the sediment. Marine nematodes can play a significant role in nutrient cycling through symbioses with various methanogenic, methanotroph, methylotroph, sulphate-reducing or nitrifying Archaea and Bacteria (Schuelke et al. 2018 ). Some nematodes feed on bacteria (Moens et al. 1999 ), while others prefer algae over bacteria (Tietjen and Lee 1973 ). Some are predators which may feed on other nematodes (Fonseca and Gallucci 2008 ; Dos Santos and Moens 2011 ), or other meiofauna including harpacticoid copepods, annelids, halacarid mites, ostracods and oligochaetes (Kennedy 1994 ). A considerable number of nematodes are believed to be omnivores, taking advantage of several different food sources including detritus and the sinking remains of planktonic organisms (Kennedy 1994 ). Marine nematodes have historically been divided into four trophic groups based on presence or absence of teeth or teeth-like structures, and their buccal cavity morphology (Wieser 1953 ). Species categorized as selective deposit feeders (1A) and non-selective deposit feeders (1B) both lack teeth and leave suitably sized food items undamaged, but are separated by small and large buccal cavities, respectively. In contrast, epistrate feeders (2A) and omnivore-predators (2B) both have buccal cavity teeth that are used to puncture prey and ingest their contents. However, omnivore-predators are separated from the former by having a hollow tooth-like structure (onchium) connected to a salivary gland, which enables concurrent secretion of enzymes and feeding on cell contents (Jensen 1987 ). While morphological structures like these are expected to influence the range of prey a nematode can accrue, it is still possible that different groups exploit the same food sources (Jensen 1987 ). Both epistrate and deposit feeders may for instance gain sustenance from microalgal cells (Moens and Vincx 1997 ), although the mechanisms of puncturing and feeding on cell contents (epistrate feeders) versus swallowing whole cells (deposit feeders) are different (Jensen 1987 ). Moreover, many aquatic nematodes have been suspected of being able to change feeding strategy in response to availability of particular food particles at a given time (Moens and Vincx 1997 ). Although nematodes are amongst the most diverse and abundant animals on earth, the scientific community is still largely in the dark with regards to their trophic interactions, and the impact they may have on their local benthic environments, or marine ecosystems as a whole. A handful of studies have conducted molecular microbiome profiling of nematodes, but these are typically from a limited geographic region, have targeted single nematode species or genera, or cover terrestrial soil species (Cheng et al. 2013 ; Baquiran et al. 2013 ; Dirksen et al. 2016 ). To the best of our knowledge, there have only been a few in situ studies of marine nematode trophic interactions to date, and none have been conducted for nematodes in the Eurasian Arctic. Using high throughput sequencing, 16S and 18S microbiome profiles were acquired from nematodes residing in the Arctic Beaufort Sea, Gulf of Mexico and Californian coast (Schuelke et al. 2018 ). A key finding Schuelke et al. ( 2018 ) reported, was that nematode microbiomes did not correlate distinctly to geographical region, nor to feeding group or host phylogeny. Hence, Wieser’s feeding groups were not a significant predictor of the nematode microbiome. Using stable isotope analysis on nematodes in a Portuguese estuary system Vafeiadou et al. ( 2014 ) found a considerable variation in trophic level among congeneric nematode species, and these authors, too, warned against depending solely on morphology when assessing feeding ecology. They for instance found that nematodes of the genus Paracomesoma , generally considered deposit-feeders due to a lack of teeth, had high δ 15 N levels as would rather be expected from omnivore-predators, indicating a higher trophic position than what would be expected from their morphology alone. The current study explores the trophic ecology of meiofaunal nematodes in the Eurasian Arctic. We specifically aimed to describe the key eukaryote prey organisms for individual nematodes sampled during different seasons and at spatially distinct locations on the Barents Sea shelf, shelf break and the adjacent Nansen Basin. The Barents Sea is one of the most productive regions on the Arctic continental shelf, and accounts for approximately 40% of the annual pan-Arctic net primary production (Sakshaug et al. 2009 ). The shelf regions are suggested to have particularly strong coupling of pelagic and benthic processes (Ambrose and Renaud 1995 ; Piepenburg 2005 ), making it an ideal study system for investigating pelagic food acquisition in benthic meiofauna. Pelagic carbon sources to the Barents Sea shelf have been characterized as mainly composed by phytodetritus (Renaud et al. 2008 ). The quantity and quality of organic matter produced depends on seasonally variable processes (Sakshaug et al. 2009 ), with most of the organic matter being produced and consumed during spring and summer when light is available. This material settles to the seafloor, with peaks in May and August in our study region (Bodur et al., 2023 ). While globally, nematodes are numerically dominant in the benthic meiofaunal community in the Arctic (Hoste et al. 2007 ; Oleszczuk et al. 2021 ), it is unclear if their diet varies seasonally in response to the settling phytodetritus. Nematodes may be used as biological indicators of sediment types, as morphometric attributes are related to environmental conditions such as food availability (Grzelak et al. 2016 ), and are key players in the carbon remineralization in the Barents Sea (Jorda-Molina, unpubl. data). We asked: Is nematode prey composition influenced by season, and if so – which prey are important in the respective seasons? Does prey composition match nematode buccal and odontial morphology (feeding guilds) as envisaged by (Wieser 1953 ), or is prey composition to some extent independent of host phylogeny and morphology of the buccal cavity (as suggested by Schuelke et al., 2018 ; Vafeiadou et al., 2014 )? Is prey composition reflecting the availability of putative prey as seen through metabarcoding of community samples, or is it related to environmental parameters like organic material (TOC), photosynthetic pigments (Chl a ), and phytodetritus (phaeopigments)? 2. Materials & Methods 2.1 Sample collection Sediment samples were collected along a transect east of Svalbard in the European Arctic from four different locations on the Barents Sea Shelf, along the Shelf Break and into the Nansen Basin (Fig. 1 ). Four cruises were conducted onboard the R/V Kronprins Haakon during August and December in 2019, and March and May in 2021, each targeting the same locations (Table 1 ). For clarity, we refer to each of the four cruise as distinct seasons (March, May, August and December) throughout the manuscript. Sediment was collected with a giant box-corer (0.25 m 2 ), from which the surface sediment (uppermost 1 cm, ca. 30 mL) was scraped off with a spoon. The box-corer was deployed three times on each station, and one surface sediment sample was obtained per box core sample. Surface sediment was immediately fixed on ice-cold ethanol (96%) and stored at − 20°C until further processing. 2.2 Picking and DNA extraction We extracted and concentrated the meiofauna from the sediment using colloidal silica and a modified decantation and flotation protocol (Heip et al. 1985 ; Fonseca and Fehlauer-Ale 2012 ; Dell’Anno et al. 2015 ). A silica solution was prepared with Ludox TM-40 (Sigma-Aldrich) by diluting with Milli-Q water (MQ) to the specific gravity of 1.13 (Giere 2009 ). The sediment was first washed vigorously with Milli-Q water onto a 64 µm sieve. Retained sediment and meiofauna were then resuspended with at least 5 parts colloidal silica, centrifuged at 1800 g for 5 minutes before decanting the supernatant with meiofauna over the same sieve. This was repeated three times per sediment sample. Nematodes were picked under a standard stereomicroscope using handmade Irwin loops (Schram and Davison 2012 ). All nematodes were individually rinsed by transfer through three Milli-Q droplets to minimize the effect of exogenous DNA, before being placed in tissue lysis (TL) buffer (E.Z.N.A. Tissue DNA kit, Omega Bio-tek). Final immersion in tissue-lysis buffer was performed with a flame-sterilized Irwin-loop. We picked 11 nematodes and added one extraction negative (TL buffer without material) from each sediment sample, making a grand total of 143 individual nematode samples and 13 extraction negatives which were stored at − 20°C until extraction. DNA was extracted per manufacturers protocol (Tissue Spin protocol, E.Z.N.A® Tissue DNA kit, Omega Bio-Tek), although with a lowered elution volume (2 x 50 µL), and by incubating on a thermocycler (300 rpm, 70°C, 3 hours or overnight). To verify that nematode samples were viable for sequencing, and that extraction negatives were indeed negative in PCR, all extracts were subsequently tested by PCR amplification of the V7 fragment of the 18S rRNA gene (~ 240 bp) using the universal eukaryotic primers 960F (5’-GGCTYAATTTGACTCAACRCG-3’, Gast et al., 2004 ) and modified 1200R (5’-GGGCATCACAGACCTG-3’, Cleary & Durbin, 2016 ) and 1% gel electrophoresis. Table 1 Overview of number of nematode (Nem) and sediment community (Com) samples, sampling month (Season), location (Station) with latitude (Lat.) and longitude (Lon.) and sample depth (Depth, m). Key properties in the upper 0–1 cm fraction of the sediments are shown with Total Organic Carbon (TOC, %), Chlorophyll a (Chl a , mg m − 2 ) and Phaeopigment (Phaeo, mg m − 2 ). NA indicates missing values either due to missing samples (Nem, Com) or due to lacking replicates for calculating standard deviations (SD). Season Station Lat. Lon. Depth Nem Com TOC ± SD a Chl a ± SD b Phaeo ± SD b March Shelf S. 76.0000 31.2193 325 11 3 2.13 ± 0.07 6.57 ± NA 18.67 ± NA March Shelf N. 79.7712 33.6121 327 11 3 1.36 ± 0.03 2.32 ± 0.89 18.93 ± 6.10 March Break 81.5467 30.8518 869 11 NA 1.54 ± 0.38 1.90 ± 0.78 12.66 ± 3.60 March Basin 81.7276 28.6712 2671 NA 2 1.40 ± 0.03 1.78 ± 0.30 13.40 ± 2.05 May Shelf S. 76.0004 31.2215 326 11 NA 1.98 ± 0.04 5.15 ± 0.92 23.12 ± 8.76 May Shelf N. 79.7508 34.0087 335 11 3 1.34 ± 0.05 2.65 ± 0.23 14.62 ± 2.18 May Break 81.5369 30.8676 824 11 NA 1.32 ± 0.02 1.79 ± 0.25 16.02 ± 2.44 May Basin 81.8420 30.7571 3103 11 3 1.31 ± 0.01 1.26 ± 0.31 9.02 ± 0.72 August Shelf S. 75.9997 31.2153 326 11 3 2.12 ± 0.09 4.79 ± 1.07 16.66 ± 0.61 August Shelf N. 79.7457 34.0169 334 11 3 1.49 ± 0.05 1.75 ± 0.50 9.59 ± 1.18 August Break 81.5452 30.8475 857 11 NA 1.44 ± 0.03 1.99 ± 0.54 10.02 ± 3.00 August Basin 81.7276 28.6712 2649 11 3 1.47 ± 0.01 1.33 ± 0.42 9.20 ± 1.65 Dec. Shelf N. 79.7585 33.9950 330 11 3 1.45 ± 0.03 2.98 ± 1.31 10.25 ± 2.08 Dec. Break 81.5428 30.9424 848 11 NA 1.31 ± 0.09 2.28 ± 1.12 8.92 ± 3.68 a . TOC data were obtained from published datasets (Ricardo de Freitas et al. 2022 ). b . Chl a and Phaeopigment data were obtained from published datasets (Akvaplan-niva, 2024a , 2024b , 2024c , 2024d ). DNA extraction of the ambient benthic eukaryote community was administered by Nansen Legacy colleagues at the University of Bergen (Prof. Lise Øvreaas). The respective sediment samples were obtained from the same up to three individual box-cores at each location. Vertical layers were separated from sub-cores, and immediately fixed on ice-cold ethanol (96%) and stored at – 80°C until further processing. In the lab, DNA was extracted from the uppermost 0–1 cm layer using the MO BIO PowerSoil DNA Isolation kit, following the manufacturer's protocol (v. 02232016) for centrifugation and with the following deviations. DNA was extracted from 0.30–0.35 g of surface sediment, 1 mL of C4 solution was used per sample, and at most 500 µL of sample was loaded onto the spin filter. Centrifugation for drying was increased to 2 min, and DNA was eluted twice (2 x 50 µL) to obtain a higher DNA quantity. 2.3 Sediment properties Sampling protocols for sediment properties are described in the sampling protocols from The Nansen Legacy, ( 2022 ) and sampling was supported by collaborators. Fluorometric pigment data from the same box-core sediment samples were acquired from Akvaplan-niva ( 2024a , 2024b , 2024c , 2024d ). We calculated the average Chlorophyll a (mg m − 2 ) and Phaeopigment (mg m − 2 ) concentrations in the uppermost 0–1 cm layer of sediment. Likewise, total organic carbon (TOC, %) and carbon isotopic composition (δ 13 C, ‰) from Isotope Ratio Mass Spectrometry (EA-IRMS) were acquired from Ricardo de Freitas et al. ( 2022 ) and calculated for the uppermost 0–1 cm layer of sediment. 2.4 Sequencing To determine the eukaryote diet of the nematodes, we amplified a short fragment of the 18S rRNA gene (V7, ~ 100–110 bp), using 18S_allshorts primers (Forward 5’-TTTGTCTGSTTAATTSCG-3’, and Reverse 5’-GCAATAACAGGTCTGTG-3’) (Guardiola et al. 2015 ). PCR primers were pre-tagged with 8-base oligonucleotides with at least 3 nucleotides difference to enable pooling of samples. The primers contained a variable number of degenerate nucleotides (N, 2–4) at the 5’-end to increase sequencing quality (Wangensteen et al. 2018 ). Amplicons were generated from extracts in 20 µL PCR reactions with 10 µL AmpliTaq Gold™ Master Mix (2x, Applied Biosystems), 0.16 µL Bovine Serum Albumin (BSA, 20 µg µL − 1 ), 5.84 µL ultrapure MQ water, 2.0 µL of 18S_allhorts forward and reverse primer mix (2.5 µM each), and 2.0 µL DNA template. PCR conditions included an initial denaturation (10 min, 95°C) and 35 cycles of denaturation (30 s, 95°C), annealing (30 s, 45°C) and elongation (30 s, 72°C). A subsample of real and negative samples from each PCR plate were observed on a 1% agarose gel to verify positive and negative amplification, respectively. Amplicons were subsequently purified with MinElute™ spin-columns (Qiagen, Hilden, Germany), and quantified using the fluorometric broad-range dsDNA assay (Qubit 4™, Invitrogen by Thermofischer). Sequencing libraries were prepared from quantified and purified pools following the NEXTflex™ PCR-Free DNA Sequencing Kit (Bioo scientific, Austin, Texas, USA). For each library, 3000 ng template was purified by size (≥ 150 bp) using magnetic beads (Agencourt AMPure XP beads; Beckman Coulter Genomics, California, USA). Sequences were adenylated and Illumina-compatible adaptors (NEXTflex™ DNA Barcode Adapter, Bioo Scientific) were ligated onto the adenylated ends. Libraries were quantified by qPCR using the NEBNext® Library Quant Kit for Illumina (New England Biolabs, Massachusetts, USA). Two libraries of nematode amplicons were sequenced using 150 bp paired end (PE) chemistry in a single lane (800 Gb output) on a Illumina NovaSeq6000 platform (Novogene Co., Ltd.). For sequencing of the benthic community, the Integrated Microbiome Resource (IMR, Halifax, Canada) was commissioned to amplify the V4 region of the 18S rRNA gene with the eukaryotic universal primers V4F_illumina (5’-CCAGCASCYGCGGTAATTCC–3') and V4R_AZig_illumina (5'-ACTTTCGTTCTTGATYRATGA–3', Piredda et al., 2017 ). PCR amplification and subsequent library preparation was conducted as described in Comeau & Kwawukume ( 2023 ), and sequencing was achieved on a Illumina MiSeq running with 300 bp PE chemistry. 2.5 Bioinformatics Approximately 3.1 billion raw paired end reads were acquired from 18S sequencing of nematode extracts, and processed using the OBITools (1.2.13, RRID:SCR_024141, Boyer et al., 2016 ), vsearch (2.22.1, RRID:SCR_024494, Rognes et al., 2016 ) and BLAST+ (2.13.0, RRID:SCR_004870, Altschul et al., 1990 ; Camacho et al., 2009 ) software. The raw data is available from NIRD-RDA (Flo 2024 ). Of the raw reads, 1.9 billion remained after pairing of forward and reverse reads (illuminapairedened), sample identification and demultiplexing (ngsfilter) and were non-ambiguous, of high-quality (obigrep -p ‘score > 40.00’) and within adequate length (80 ≤ 120 bp). 1.3 billion reads remained after subsequent dereplication (obiuniq), denoising (vsearch cluster_unoise, unoise_alpha 2) with removal of singletons (minsize 2) and removal of chimeras (vsearch uchime_denovo). Finally, 2.5 million zero-radius Operational Taxonomic Units (zOTUs) were taxonomically assigned to the protist ribosomal database (PR2, v.5.0.1) using BLAST+. From the sediment community samples, we acquired 414 698 raw paired end 18S V4 reads. The bioinformatic processing of sediment community sequence reads were identical to processing of prey reads with the exception of the few following steps. Since each sample had separate identifier oligos, the sediment reads did not require demultiplexing. Primers were trimmed with obicut (OBITools, -b 25) and reads within lengths of 360 ≤ 380 bp were retained. Approximately 179 000 reads divided amongst 4445 zOTUs were taxonomically assigned. 2.6 Curation of sequence-data Assigned nematode prey and sediment community zOTUs were imported to R-studio (v. 4.1.3), but prey zOTUs were processed through additional curation. By means of manual curation, we identified and discarded several taxa that were likely contaminants. For example, all arachnid zOTUs were discarded, after confirming with BLAST that neither of the 100 most abundant arachnid zOTUs were assigned to known marine taxa (e.g. Halacaridae). Certain terrestrial taxa whose presence could likely be explained by contamination, such as Homo , Embryophytes and Collembola, were discarded. Additionally, we employed the prevalence method of Decontam (Davis et al. 2018 ) to identify more cryptic contaminants, wherein identifications are based upon disproportional zOTU high abundances in extraction negatives compared to real samples. 2.7 Assigning nematodes to taxonomy and feeding group We identified the nematodes to family by curating the taxonomy of the zOTUs that contributed the maximum number of reads to each sample total. Given that the consumer DNA should contribute the most to the genetic material of each extract (e.g., Flo et al., 2024b ; Piñol et al., 2014 ), these zOTUs should represent the consumers themselves. The zOTU sequences were aligned to the NCBI nucleotide archive (NCBI-nt, RRID:SCR_004860) database by BLASTn (RRID:SCR_001598, Altschul et al. 1990 ; Camacho et al. 2009 ), and the output was imported to MEGAN6 (RRID:SCR_011942, Huson et al. 2007 ). The lowest common ancestor (LCA) was identified from all blast reports using the naive LCA algorithm for taxonomic binning (default with top percent = 5.0, min. support percent = 0.001). Due to sparse reference data for marine nematodes, we binned LCA at family-level taxonomy, even in cases where genera and species were confidently assigned by the algorithm (Supplementary Table S2). All taxonomy was confirmed and/or curated with the Nemys database (Nemys Eds., 2024). Eight samples identified to the freshwater family of Tobrilidae (superfamily Tobriloidea) were manually curated to Rhabdodemaniidae (superfamily Tobriloidea) whose members are the closest relatives with known presence in the marine Arctic meiobenthos (Van Gaever et al. 2009 ; Van Gaever and Vanreusel 2012 ). To confirm the taxonomic assignments, we compared our list of families to nematode taxonomy resulting from morphological examinations based on separate material from the same box core samples (Wernstrøm et al., in preparation). Based on the obtained nematode taxonomic identity, we further assigned each sample to a feeding group (1A – selective deposit, 1B – non-selective deposit, 2A – epistrate and 2B – omnivore-predators) according to (Jensen 1987 ) and (Wieser 1953 ). Only samples whose family were confidently assigned during taxonomic binning were assigned to a feeding group, and a few taxa whose buccal morphology are unknown (e.g. Ceramonematidae) were left unassigned (e.g. feeding group = NA, Supplementary Table S1 ). 2.8 Exploratory data analyses of nematode diet data We used tidyverse (Wickham et al. 2019 ) and phyloseq (1.36.0, RRID:SCR_013080, McMurdie and Holmes 2013 ) packages to expedite analysis and storage of data in R. We used primarily vegan (RRID:SCR_011950, Oksanen et al. 2019 ) to perform ecological analyses, and ggplot2 (RRID:SCR_014601, Wickham 2016 ) to prepare graphics. Sufficient sequencing depth was confirmed using rarefaction ( rarecurve , vegan) on prey data at both zOTU- and species-level. Filtered and curated prey data was first normalized to comparable values by transforming counts to relative read abundances (%, RRA). Prey RRA was used for generating bar plots, and to limit complexity we chose to plot data agglomerated to class and genus taxonomic levels, with taxa below a median sample abundance of 3 or 5% RRA being collected in “taxa < 3” or “taxa < 5%”, respectively. Due to the short marker gene (100–110 bp) and limited metazoan annotations in the database, we confirmed – and when required, curated all identified metazoan genera and classes by performing a BLAST search against the NCBI nucleotide archive (NCBI-nt). 2.9 Ordination of nematode prey and sediment communities Non-metric multidimensional scaling (NMDS) plots were made to explore how prey and community samples clustered across spatiotemporal parameters. We used two dissimilarity-based indices and data at zOTU- and Species-level agglomeration. Since NMDS ordination returned similar patterns with both zOTU and Species-level data, we suffice with only zOTU level ordinations in the current manuscript. Bray-Curtis dissimilarity was generated from relative abundance, while Jaccard distance was generated from presence-absence data with any zOTU > 1% relative read abundance for assigning presence. Indices were calculated using vegdist and scaled using metaMDS (vegan). To achieve low stress (< 0.15) while avoiding over-plotting, the number of dimensions for each scaling was selected based on permutational scree-plots. Subsequent NMDS plots were made using ggplot2, with centroids and confidence ellipses (95%, stat_ellipse , vegan). Canonical correspondence analysis (CCA) was computed using nematode prey relative abundances at family-level taxonomy, and a set of categorical and numerical constraints. Prior to analysis we evaluated each environmental parameter to exclude correlated variables with the pairs function. Through reverse model selection, starting with all seemingly uncorrelated variables, we reduced the model parameters stepwise, retaining only parameters that significantly explained inertia (anova.cca, p ≤ 0.001). The retained significant parameters included Phaeopigments (Phaeo, mg m − 2 ), Chlorophyll a :Phaeopigments ratio (Chl a :Phaeo, unitless), sample depth (Depth, m), sample location (Shelf S, Shelf N, Break and Basin), feeding group (1A, 1B, 2A and 2B), lowest common ancestor of nematode identified (ntax_lca) and sample month (March, May, August, December). Excluded variables were Latitude, Longitude, nematode order, Chl a concentration (mg m − 2 ), total organic carbon (TOC, %), carbon isotopic composition (δ 13 C, ‰) and day of year (DOY). The best fit model, explaining ~ 38% of the total inertia, was found when using square-root transformed relative abundances. Although a significant predictor of prey composition, lowest common ancestors of nematodes were not overlaid to limit overcrowding the plot. 2.10 PERMANOVA for testing significance of structuring parameters We used Permutational Multivariate Analysis of Variance (PERMANOVA) to test the relevance of parameters identified by CCA for describing nematode diets ( adonis2 , vegan). We used Bray-Curtis dissimilarity and Jaccard distance based on family-level taxonomy. However, since the two distance matrices yielded comparable results, we only show the statistical metrics derived from testing with Bray-Curtis dissimilarity. All terms were sequentially tested for between-group homogeneity of dispersion using betadisper (vegan, Table 4 ). 3. Results 3.1 Nematode taxonomy Approximately 84% of the nematodes sampled were confidently identified to family based on taxonomic binning of BLAST results, whereas 96% were assigned at the level of order (Fig. 2 ). Nematodes were assigned to seven orders with Monhysterida (35), Chromadorida (34), Araeolaimida (27) and Enoplida (22) being the orders most frequently observed. At the family level, 20 families were identified, and Chromadoridae (32, order Chromadorida), Comesomatidae (14, Araeolaimida), Oxystominidae (13, Enoplida) and Xyalidae (11, Monhysterida) were the most common. 3.2 Diet of nematodes and sediment community composition Approximately 1.3 billion prey sequence reads, divided amongst 2.5 million zOTUs were taxonomically assigned and thereafter analyzed. Sample completeness was verified with rarefaction analyses, and was found reasonably complete with average rarefaction curve slopes estimating 29 new zOTUs (0.029) and 2.6 new species (0.0026) per 1000 additional reads, and curves approximating plateau phase (Fig. S1 ). Putative dietary items belonged to a diverse set of eukaryote taxa, with high average relative read abundances (RRA) from metazoan, protist and fungal taxa. Metazoan prey was largely contributed by Arthropoda (average RRA = 23%), Craniata (7%) and Annelida (7%). Arthropoda reads as a whole were more abundant in August (30%) and December (28%) than in March (21%) and May (13%) (Fig. 3 A), and consisted mainly of copepod taxa, with Mysida, Cladocera and Balanomorpha contributing fewer, yet a substantial number of prey reads. Arthropod prey are explored in greater detail below. Craniata reads were abundant across all seasons (7%) and consisted mainly of taxa identified to Teleostei (6%). Genera including salmon, Salmo sp. (2%) and Atlantic cod, Gadus sp. (1%) contributed the greatest number of reads (Table S2), and Salmo sp. was particularly abundant in December (5%). Annelida (8%) were abundant particularly in August (10%) and December (10%) and were composed of taxa including Austrobdella sp. (2%) and Anguillosyllis sp. (1%). Fungi were also abundant and Ascomycota (23%), Basidiomycota (12%) and Mucoromycota (1%) composed the major fungal classes. In terms of seasonality, Ascomycota and Basidiomycota were less prevalent in August (15, and 8%) and December (19, and 9%) than in March (27, and 13%) and May (31, and 15%). For fungal taxa, we only report genera, since they are impossible to assign to species level based on a small 18S fragment. It was attempted to amplify and sequence prokaryote prey (16S rRNA V4) from nematode samples, but these efforts were unsuccessful, possibly due to low prokaryote DNA concentrations in single nematode DNA extracts. Table 2 Average relative read abundance (%, RRA) ± Standard Error of the Mean (SEM) of nematode prey classes in all samples, per month and per location. Average values greater than 1% are highlighted in bold, and values smaller than 0.1% are not shown. Subdivision Class All (N = 143) August (n = 44) December (n = 22) March (n = 33) May (n = 44) Shelf S (n = 33) Shelf N (n = 44) Break (n = 44) Basin (n = 22) Discosea_X Flabellinia 0.1 ± 0.1 0.3 ± 0.3 - - 0.1 ± 0.0 0.4 ± 0.4 - - - Chlorophyta_X Mamiellophyceae 0.1 ± 0.1 0.2 ± 0.1 0.2 ± 0.1 - - 0.1 ± 0.1 - 0.2 ± 0.2 0.1 ± 0.0 Trebouxiophyceae 3.8 ± 0.7 6.0 ± 1.8 3.7 ± 0.8 1.8 ± 0.4 3.2 ± 1.0 3.8 ± 1.7 3.3 ± 0.6 3.3 ± 1.1 5.9 ± 2.4 Picozoa_X Picozoa_XX 0.5 ± 0.1 0.6 ± 0.1 1.4 ± 0.4 0.4 ± 0.3 0.1 ± 0.0 0.5 ± 0.3 0.7 ± 0.2 0.5 ± 0.2 0.3 ± 0.1 Cryptophyta_X Cryptophyceae 0.1 ± 0.0 0.2 ± 0.1 0.1 ± 0.0 0.1 ± 0.1 - - 0.1 ± 0.0 0.2 ± 0.1 0.1 ± 0.0 Kathablepharida Kathablepharidea 0.2 ± 0.1 0.1 ± 0.0 - 0.2 ± 0.2 0.4 ± 0.2 0.1 ± 0.1 0.1 ± 0.1 0.3 ± 0.2 - Haptophyta_X Prymnesiophyceae 0.3 ± 0.1 0.1 ± 0.0 - 1.0 ± 0.6 0.1 ± 0.0 0.4 ± 0.3 0.1 ± 0.0 0.5 ± 0.4 0.1 ± 0.0 Filasterea Filasterea_X 0.2 ± 0.1 - - 0.1 ± 0.0 0.6 ± 0.5 0.1 ± 0.0 - 0.1 ± 0.0 1.0 ± 0.9 Fungi Ascomycota 23.3 ± 1.9 15.3 ± 3.0 19.0 ± 3.6 26.7 ± 3.1 30.9 ± 4.2 21.2 ± 3.7 22.3 ± 3.2 25.5 ± 3.5 23.9 ± 5.5 Basidiomycota 11.6 ± 1.2 8.2 ± 1.4 9.2 ± 3.5 12.6 ± 1.9 15.3 ± 2.9 15.2 ± 3.3 11.9 ± 1.9 11.0 ± 2.2 6.7 ± 1.6 Chytridiomycota 0.8 ± 0.5 1.8 ± 1.4 0.6 ± 0.4 0.2 ± 0.0 0.4 ± 0.1 2.3 ± 1.9 0.6 ± 0.3 0.2 ± 0.0 0.3 ± 0.1 Mucoromycota 1.4 ± 0.4 0.5 ± 0.2 0.1 ± 0.1 2.8 ± 1.5 1.9 ± 0.8 1.3 ± 0.3 2.5 ± 1.4 0.6 ± 0.2 0.8 ± 0.2 Metazoa Annelida 7.3 ± 1.1 9.8 ± 2.6 10.4 ± 3.7 5.4 ± 1.9 4.5 ± 0.8 4.4 ± 1.1 8.0 ± 2.0 8.8 ± 2.2 7.0 ± 3.7 Arthropoda 22.6 ± 1.6 30.4 ± 3.4 28.2 ± 3.7 20.6 ± 3.5 13.3 ± 1.6 17.9 ± 2.3 24.6 ± 3 22.6 ± 3.1 25.4 ± 4.9 Brachiopoda 0.1 ± 0.0 0.1 ± 0.0 0.3 ± 0.2 0.1 ± 0.0 0.2 ± 0.1 0.1 ± 0.0 0.1 ± 0.0 0.2 ± 0.1 0.3 ± 0.1 Bryozoa 1.0 ± 0.4 0.3 ± 0.1 0.6 ± 0.4 0.8 ± 0.1 2.1 ± 1.2 0.9 ± 0.1 1.7 ± 1.2 0.7 ± 0.2 0.6 ± 0.2 Cephalochordata 0.1 ± 0.1 - - - 0.4 ± 0.2 0.2 ± 0.2 - - 0.4 ± 0.4 Cnidaria 0.2 ± 0.1 0.2 ± 0.1 - 0.4 ± 0.1 0.3 ± 0.2 0.2 ± 0.1 0.4 ± 0.2 0.2 ± 0.1 0.1 ± 0.0 Craniata 7.4 ± 0.9 7.3 ± 1.5 7.1 ± 1.8 8.2 ± 1.8 6.9 ± 1.9 7.8 ± 1.8 7.0 ± 1.5 5.3 ± 1.1 11.4 ± 3.4 Echinodermata 0.2 ± 0.1 - 0.6 ± 0.6 0.2 ± 0.2 - - 0.3 ± 0.3 0.2 ± 0.1 - Hemichordata 0.2 ± 0.0 0.1 ± 0.0 - 0.2 ± 0.1 0.3 ± 0.1 0.4 ± 0.1 0.1 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 Mollusca 2.7 ± 0.3 3.0 ± 0.6 2.5 ± 0.7 1.9 ± 0.7 3.3 ± 0.6 3.2 ± 0.8 3.1 ± 0.6 2.3 ± 0.5 2.3 ± 0.6 Nemertea 0.8 ± 0.4 1.0 ± 0.7 2.9 ± 2.5 0.1 ± 0.0 0.1 ± 0.1 - 1.0 ± 0.7 1.4 ± 1.2 0.2 ± 0.1 Platyhelminthes 0.6 ± 0.1 0.9 ± 0.2 0.8 ± 0.2 0.2 ± 0.0 0.5 ± 0.1 0.7 ± 0.3 0.5 ± 0.1 0.6 ± 0.2 0.6 ± 0.2 Porifera 0.2 ± 0.1 - - 0.4 ± 0.4 0.4 ± 0.4 0.5 ± 0.5 - 0.3 ± 0.3 - Priapulida 0.2 ± 0.1 0.3 ± 0.1 0.1 ± 0.0 0.2 ± 0.1 0.1 ± 0.1 0.3 ± 0.1 0.3 ± 0.1 0.1 ± 0.0 0.1 ± 0.1 Rotifera 0.1 ± 0.0 0.1 ± 0.1 0.2 ± 0.1 0.1 ± 0.1 - 0.1 ± 0.0 0.1 ± 0.0 0.2 ± 0.1 0.1 ± 0.0 Urochordata 0.2 ± 0.1 0.1 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 0.5 ± 0.3 0.5 ± 0.3 0.1 ± 0.0 0.1 ± 0.0 0.3 ± 0.2 Ciliophora Oligohymenophorea 7.7 ± 1.0 5.8 ± 1.2 4.1 ± 1.2 10.8 ± 3 9.2 ± 1.6 9.1 ± 1.4 6.3 ± 1.4 7.8 ± 2.4 8.5 ± 2.1 Spirotrichea 0.7 ± 0.2 1.2 ± 0.7 0.2 ± 0.0 0.4 ± 0.1 0.6 ± 0.2 1.6 ± 0.9 0.2 ± 0.0 0.5 ± 0.2 0.6 ± 0.3 Dinoflagellata Dinophyceae 2.4 ± 0.5 3.4 ± 1.4 2.0 ± 0.8 1.9 ± 0.3 1.9 ± 0.3 4.1 ± 1.8 1.6 ± 0.2 2.2 ± 0.6 1.5 ± 0.4 Syndiniales 0.5 ± 0.3 0.3 ± 0.1 1.9 ± 1.6 - 0.6 ± 0.4 0.7 ± 0.6 0.2 ± 0.1 1.0 ± 0.8 0.1 ± 0.0 Cercozoa Filosa-Sarcomonadea 0.5 ± 0.1 0.3 ± 0.2 0.2 ± 0.2 0.5 ± 0.1 0.8 ± 0.3 0.5 ± 0.1 0.7 ± 0.3 0.4 ± 0.1 0.3 ± 0.1 Bigyra Sagenista 0.2 ± 0.1 0.5 ± 0.2 0.2 ± 0.0 0.1 ± 0.0 - - 0.2 ± 0.0 0.4 ± 0.2 0.1 ± 0.0 Gyrista Chrysophyceae 0.3 ± 0.1 0.1 ± 0.1 0.7 ± 0.6 0.2 ± 0.1 0.3 ± 0.1 0.2 ± 0.2 0.1 ± 0.1 0.5 ± 0.3 0.1 ± 0.0 Gyrista_X 0.1 ± 0.0 0.1 ± 0.0 0.3 ± 0.2 - - 0.1 ± 0.1 0.2 ± 0.1 0.1 ± 0.0 0.1 ± 0.0 Mediophyceae 0.4 ± 0.1 0.6 ± 0.2 1.2 ± 0.7 - 0.1 ± 0.0 0.4 ± 0.3 0.2 ± 0.0 0.7 ± 0.3 0.2 ± 0.1 Telonemia_X Telonemia_XX 0.2 ± 0.0 0.3 ± 0.1 0.1 ± 0.0 0.2 ± 0.2 0.1 ± 0.0 - 0.2 ± 0.1 0.3 ± 0.1 0.1 ± 0.0 Table 3 Average RRA (%) ± Standard Error of the Mean (SEM) of sediment community classes in all samples, per month and per location. Average values greater than 1% are highlighted in bold, and values smaller than 0.1% are not shown. Subdivision Class All (N = 26) August (n = 9) December (n = 3) March (n = 8) May (n = 6) Shelf S (n = 6) Shelf N (n = 12) Break (n = 0) Basin (n = 8) Chlorophyta_X Chlorophyceae - 0.1 ± 0.0 - - - - - - 0.1 ± 0.0 Mamiellophyceae 1.3 ± 0.3 0.2 ± 0.1 0.8 ± 0.4 2.0 ± 0.7 2.3 ± 0.8 0.1 ± 0.0 2.4 ± 0.5 - 0.6 ± 0.2 Pyramimonadophyceae 2.3 ± 0.8 2.4 ± 1.7 4.4 ± 1.9 0.9 ± 0.3 2.9 ± 1.4 0.1 ± 0.0 4.8 ± 1.3 - 0.1 ± 0.1 Trebouxiophyceae 0.1 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 - 0.1 ± 0.0 - 0.1 ± 0.0 Prasinodermophyta_X Prasinodermophyceae 0.1 ± 0.0 - - 0.1 ± 0.1 0.1 ± 0.1 - 0.2 ± 0.1 - - Haptophyta_X Prymnesiophyceae 0.3 ± 0.3 - - 0.1 ± 0.0 1.2 ± 1.1 0.1 ± 0.0 0.6 ± 0.6 - - Apusomonada_X Apusomonadidae 0.2 ± 0.1 0.2 ± 0.0 0.1 ± 0.0 0.5 ± 0.2 0.2 ± 0.1 - 0.2 ± 0.1 - 0.5 ± 0.2 Choanoflagellata Choanoflagellatea - - - - - - - - 0.1 ± 0.0 Fungi Chytridiomycota 4.2 ± 0.9 1.8 ± 0.7 8.2 ± 3.7 3.2 ± 1.0 6.9 ± 1.9 0.4 ± 0.1 7.5 ± 1.3 - 1.9 ± 0.3 Mucoromycota 0.4 ± 0.4 - - - 1.6 ± 1.4 - 0.8 ± 0.7 - - Metazoa Annelida 1.2 ± 0.6 - 5.6 ± 2.2 - 2.3 ± 1.5 - 1.5 ± 0.9 - 1.6 ± 1.2 Arthropoda 2.8 ± 1.3 0.5 ± 0.2 8.8 ± 1.9 0.5 ± 0.2 6.3 ± 4.7 0.5 ± 0.2 3.1 ± 1.1 - 4.1 ± 3.7 Cnidaria 0.3 ± 0.2 0.3 ± 0.2 1.5 ± 1.0 0.2 ± 0.1 - - 0.7 ± 0.3 - - Echinodermata - - 0.2 ± 0.1 - - - - - - Gastrotricha - - - 0.1 ± 0.0 - 0.1 ± 0.0 - - - Kinorhyncha 0.5 ± 0.3 1.0 ± 0.7 0.9 ± 0.6 0.1 ± 0.1 - 0.1 ± 0.1 1.0 ± 0.5 - - Metazoa_X 0.5 ± 0.2 0.3 ± 0.1 0.2 ± 0.1 0.4 ± 0.3 1.1 ± 0.6 - 0.6 ± 0.2 - 0.9 ± 0.4 Mollusca 2.5 ± 1.3 0.1 ± 0.1 4.9 ± 3.3 2.4 ± 2.1 5.0 ± 4.5 0.4 ± 0.2 2.7 ± 1.7 - 3.7 ± 3.5 Nematoda 5.7 ± 2.0 4.4 ± 1.2 2.3 ± 0.9 3.7 ± 1.4 12.0 ± 7.6 0.4 ± 0.2 4.4 ± 1.0 - 11.7 ± 5.6 Nemertea 2.6 ± 2.6 - 22.7 ± 18.5 - - - 5.7 ± 5.4 - - Platyhelminthes 0.5 ± 0.3 0.7 ± 0.6 0.1 ± 0.0 0.6 ± 0.4 0.2 ± 0.2 - 0.6 ± 0.3 - 0.8 ± 0.7 Opisthokonta_X Opisthokonta_XX 0.1 ± 0.0 0.1 ± 0.0 - 0.1 ± 0.1 - 0.1 ± 0.0 0.1 ± 0.0 - 0.1 ± 0.0 Rotosphaerida Rotosphaerida_X 0.2 ± 0.1 0.3 ± 0.2 - 0.3 ± 0.1 0.2 ± 0.1 - 0.2 ± 0.1 - 0.4 ± 0.2 Apicomplexa Coccidiomorphea 0.1 ± 0.0 0.1 ± 0.0 - - 0.1 ± 0.0 - - - 0.1 ± 0.0 Ciliophora Spirotrichea 1.8 ± 0.4 1.7 ± 0.4 3.1 ± 1.5 1.9 ± 0.6 1.3 ± 0.8 1.1 ± 0.2 3.0 ± 0.6 - 0.6 ± 0.3 Dinoflagellata Dinophyceae 15.7 ± 2.9 16.8 ± 6.3 20.4 ± 5.3 15.4 ± 4.6 12.2 ± 4.8 8.5 ± 1.9 27.1 ± 4.2 - 4.1 ± 1.7 Syndiniales 3.7 ± 0.7 1.4 ± 0.5 3.7 ± 1.0 4.4 ± 1.3 6.3 ± 1.5 0.5 ± 0.2 4.7 ± 0.7 - 4.7 ± 1.5 Perkinsea Perkinsida 3.5 ± 0.9 3.8 ± 1.8 1.0 ± 0.4 4.7 ± 1.8 2.7 ± 1.0 1.4 ± 0.6 1.9 ± 0.9 - 7.5 ± 1.8 Cercozoa Endomyxa - - - 0.1 ± 0.0 0.1 ± 0.0 - - - - Filosa-Imbricatea 0.1 ± 0.0 0.1 ± 0 - 0.1 ± 0.1 0.2 ± 0.1 0.1 ± 0.0 - - 0.3 ± 0.1 Filosa-Thecofilosea 0.2 ± 0.1 0.1 ± 0.1 0.1 ± 0.1 0.3 ± 0.1 0.3 ± 0.1 - 0.4 ± 0.1 - 0.2 ± 0.1 Bigyra Opalozoa - - - 0.1 ± 0.1 - - 0.1 ± 0.0 - - Sagenista 3.3 ± 0.7 1.1 ± 0.4 0.6 ± 0.3 4.1 ± 1.2 6.9 ± 1.6 0.3 ± 0.1 2.8 ± 0.7 - 6.3 ± 1.5 Gyrista Bacillariophyceae 1.3 ± 0.3 2.3 ± 0.7 2.6 ± 0.9 0.4 ± 0.1 0.4 ± 0.2 1.7 ± 0.7 1.9 ± 0.5 - 0.1 ± 0.0 Bolidophyceae 0.1 ± 0.0 - - 0.1 ± 0.0 0.1 ± 0.1 - - - 0.2 ± 0.0 Chrysophyceae 0.4 ± 0.1 0.4 ± 0.1 0.6 ± 0.3 0.4 ± 0.1 0.5 ± 0.2 0.1 ± 0.0 0.4 ± 0.1 - 0.7 ± 0.1 Coscinodiscophyceae - - 0.1 ± 0.1 0.1 ± 0.0 - 0.1 ± 0.0 - - - Gyrista_X 0.5 ± 0.1 0.2 ± 0.1 0.5 ± 0.2 0.8 ± 0.3 0.6 ± 0.3 0.3 ± 0.1 0.9 ± 0.2 - - Mediophyceae 42.2 ± 6.2 58.4 ± 10.6 5.7 ± 1.8 50.4 ± 10.5 25.0 ± 5.9 83.2 ± 3.1 18.2 ± 4.5 - 47.3 ± 9.4 Pelagophyceae 0.1 ± 0.1 0.2 ± 0.1 0.1 ± 0.1 - - - 0.2 ± 0.1 - - Peronosporomycetes 0.1 ± 0.1 - 0.1 ± 0.0 0.1 ± 0.1 0.3 ± 0.2 - 0.3 ± 0.1 - - Pirsoniales 0.2 ± 0.1 0.2 ± 0.1 0.1 ± 0.1 0.3 ± 0.1 0.3 ± 0.2 - 0.4 ± 0.1 - 0.1 ± 0.0 Stramenopiles_X Stramenopiles_XX 0.1 ± 0.0 0.1 ± 0.0 - 0.1 ± 0.1 0.1 ± 0.0 - - - 0.2 ± 0.1 Telonemia_X Telonemia_XX 0.2 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 0.2 ± 0.1 0.2 ± 0.1 - 0.1 ± 0.1 - 0.3 ± 0.1 Of protists, Oligohymenophorea were particularly abundant (8%), especially in March (11%) and May (9%), with peritrich ciliates from the genus Vorticella (8%) contributing the majority of the diet reads. Trebouxiophyceae (4%) chlorophytes were more abundant in August (6%) and December (4%) than in March (2%) and May (3%), with reads recruited primarily from Auxenochlorella sp. (4%). Dinophyceae (3%) were relatively abundant year-round, with Prorocentrum sp. (1%), Gyrodinium sp. (0.6%) and Heterodinium sp. (0.4%) contributing the bulk of the reads. Mediophyceae (0.4%) diatoms were practically neglible, with Thalassiosira sp. composing on average a meager 0.2%. Abundances of prey taxa present but with aggregated contribution of < 5% median abundance are listed in class and family level in the supplementary (Table S3 and S4). Approximately 179 000 reads divided amongst 4445 zOTUs were taxonomically assigned and analyzed to characterize the eukaryote sediment communities. Diatoms of the Mediophyceae class (42%) composed a much larger fraction of the reads recovered than in nematode prey (Fig. 3 B and Table 3 ). At genus level, the Mediophyceae were primarily identified to Chaetoceros sp. (40%). Dinoflagellate reads were also numerous, with even abundances year-round. Dinophyceae made up 16% of community reads, and Syndiniales (evenly contributed to by Dino-Group-II clade I and II) was responsible for 4% of the community reads. Metazoan classes found in the sediment were dominated by Nematoda (6% of community), Arthropoda (3%), and Nemertea (3%), and the latter was mostly composed of zOTUs identified to Cephalotrix sp. (3%). Arthropoda were markedly more abundant in the sediments at Shelf N (3%) and Basin (4%), than in the Shelf S station (0.5%). Of fungi, the dominating group was Chytridiomycota (4%), while Ascomycota (< 0.1%), Basidiomycota (< 0.1%) and Mucoromycota (0.4%) had negligible contributions. Abundances of community taxa present but with aggregated contribution of < 5% median abundances are listed in class and family level in the supplementary (Table S5 and S6). 3.3 Structure and drivers of prey composition We used NMDS to investigate patterns in prey and community composition based on the compositions themselves (Fig. 4 B-D), and CCA to identify the influence of potential drivers of prey diversity (Fig. 4 A). NMDS ordinations were also calculated at species-level, and these provided similar clustering patterns (Fig. S2 and Fig. S3). For the nematode diet data (Fig. 4 B and C), season sampled generated the strongest visible clusters, with August and December prey compositions clustering together, separate from March and May, when prey compositions also clustered together, regardless of dissimilarity or distance metric used. In the constrained ordination (Fig. 4 A), diet composition clustered similarly to the prey compositions in the NMDS (Fig. 4 B and C), with August and December forming a separate cluster distinct from March and May prey compositions. However, August prey was less homogeneous than prey composition in other months, and Shelf S prey compositions in this month in particular were more similar to those from March and May (Fig. 4 B and C). Along the y-axis several nematodes from shelf break and basin locations had different prey composition. For the sediment community in contrast, the primary clustering parameter was rather spatial, with stations Shelf S, Shelf N, and Basin forming separate clusters in the NMDS along axis 1 (shelf break was not sampled). Sampling season was of secondary importance in the sediment community, though some separation was seen by months along axis 2 within spatial clusters (Fig. 4 D and E). Statistically important numerical constraints (all with p > 0.001) for structuring prey compositions included phaeopigment concentration, chlorophyll a :phaeopigment ratio, and sampling depth as shown in Table 4 . Significant categorical constraints included the sample station, feeding group, lowest common ancestor, and season. March and May prey compositions were associated with high phaeopigment content, whereas August and December prey compositions were associated with higher Chlorophyll a :Phaeopigment ratio values. All parameters identified as significant through CCA, were likewise significant in subsequent PERMANOVA statistical tests (Table 4 ). Parameters that were not identified as influential through CCA and thereafter excluded to find the best model fit, included Latitude, Longitude, nematode order, Chl a concentration (mg m − 2 ), total organic carbon fraction (TOC, %), carbon isotopic composition (δ 13 C, ‰) and day of year (DOY). Table 4 Summary of key statistics for discerning the structuring parameters of nematode diets. CCA model constraints and PERMANOVA parameters are shown with respective F-statistic and accompanying p-value for significance. Bold PERMANOVA F- and p-values denote homogenous within-group dispersion as determined by betadisper tests. PERMANOVA statistics are shown for nematode prey data at three taxonomic levels: class, family and zOTU. CCA PERMANOVA (Bray-Curtis dissimilarity) Family Class Family zOTU Parameter Unit/levels Df F p F p F p F p Phaeo mg m-2 1 7.4 < 0.001 6.0 < 0.001 5.5 < 0.001 9.7 < 0.001 Chla:Phaeo ratio 1 2.2 < 0.001 2.6 < 0.05 2.8 < 0.05 2.1 < 0.05 Depth m 1 2.8 < 0.001 1.7 0.115 2.4 < 0.05 2.3 < 0.05 Station Shelf S, Shelf N, Break, Basin 3 1.9 < 0.001 1.5 0.072 1.7 < 0.05 1.8 < 0.001 Feeding Group 1A, 1B, 2A, 2B, NA 4 1.9 < 0.001 1.0 0.434 1.3 < 0.05 1.1 0.17 Taxonomy - 23 1.5 < 0.001 1.3 < 0.05 1.4 < 0.001 1.2 < 0.05 Season March, May, August, December 3 1.4 < 0.001 1.4 0.114 1.6 < 0.05 1.7 < 0.05 3.4 Prey composition across significant predictors According to PERMANOVA and CCA, the season, station, feeding group and nematode taxonomy (LCA) were all significant predictors of diet (Table 4 ). By averaging the prey according to these predictors, we identified the prey that were responsible for the differences in diet (Fig. 5 ). Important differences included an increased relative Arthropod abundance in August and December, and a shift towards increased relative abundance of Ascomycota, Basidiomycota and Oligohymenophorea in March and May. By station (Fig. 5 ), the relative abundance of Arthropoda prey reads was lower at the southern Barents Sea shelf (Shelf S., 18%) than at the northern stations (23–25%). Amongst feeding groups (Fig. 5 ), there are few notable distinctions, although putative omnivore-predators and epistrate feeders appear more dependent on metazoan prey compared to nematodes within the selective and non-selective deposit feeder groups. The greatest variation in prey composition is clearly found when averaging prey taxa for the taxonomic identity of nematodes (LCA, Fig. 5 ). However, the nematode taxonomy was also the parameter in which groups were composed of the most varying number of replicates (Fig. 1 ). 3.5 Arthropod fraction of nematode prey Arthropods were substantially more abundant amongst the putative nematode prey during August (30%) and December (28%) than in March (21%) and May (13%, Table 2 and Fig. 3 A). At the genus level it was apparent that the seasons were clearly distinguished in their arthropod composition (Fig. 6 ). In August and December, the arthropod prey of nematodes was mainly formed by zOTUs belonging to genera from Harpacticoida ( Parabradya sp., unidentified Harpacticoida_X), Cyclopoida and/or Harpacticoida ( Oithona sp., Microsetella sp.), and Siphonostomatoida and/or Misophrioida ( Lepeophtheirus sp., Misophriella sp.). In March and May, the prey composition shifted towards dominance of calanoid Calanus species, with additional contributions by Balanus sp., and sporadic major contributions from Lynceus sp. (Superorder Diplostraca) and the copepod species Oithona sp. and Microsetella sp. Additionally, a larger proportion of arthropod zOTUs belonged to various taxa whose median relative abundances were below 3% (Fig. 6 ). The mysid Erythrops sp. was relatively common regardless of season. 4. Discussion 4.1 Key ecological interactions The present study expands the nematode dietary profile catalogue to the Eurasian Arctic, characterizing eukaryote prey in free-living nematodes on the Barents Sea shelf, and adjacent Arctic Ocean Nansen basin. We also present the first profiles of prey from nematodes captured in deep sea sediments. By prey metabarcoding with a short 18S rRNA gene marker, we show that putative eukaryote prey were acquired from a broad range of metazoan, protist and fungal eukaryote taxa. Surprisingly, primary producers, which were extremely abundant in the sediment community, and are expected to form the basis for pelagic-benthic coupling in the Barents Sea region (Morata and Renaud 2008 ), were practically absent from the prey compositions of nematodes. Our data thus suggest that primary producers, are of lesser importance for the diet of nematode meiofauna. Instead, prey sequences were sourced from primarily heterotroph taxa, of which marine fungi and arthropods were major contributors. We can further hypothesize that fungi may play an important role as colonizers and recyclers of materials deposited from the pelagic realm (Lombard and Kiørboe 2010 ), and upon reaching the sea-floor – may have converted substantial portions of the sinking particles into fungal biomass. The current study thus support the emerging narrative that fungi, although enigmatic and still under-researched, play important roles in marine environments, not just as degraders, colonizers or parasites (Hassett et al., 2019 ; Hassett et al., 2017 ), but also as putative prey (e.g. Cleary et al., 2017 ; Flo et al., 2024a ). 16S rRNA sequencing was however not successful, and we suspect that the low success was due to very small DNA quantities from prokaryotes when DNA was extracted from individual nematodes. Low yields made subsequent PCR and sequencing prone to contamination. Future studies may find it beneficial to either extract DNA from pools of minute nematodes, or to develop primers that target smaller fragments within the 16S rRNA gene, thus making it easier to amplify a marker from partly digested material. Pooling of individuals (e.g. 10–20 individuals per extract) could raise quantities of prokaryote DNA that are adequate for PCR and sequencing but will inevitably also limit the ability to describe prey spectra of individuals. 4.2 Structuring patterns of nematode prey Our exploratory data analyses clearly indicated that temporal factors (season sampled, phaeopigments, Chl a :phaeopigment ratio) were of particular relevance for explaining variation in prey composition. In contrast, the sediment communities were more spatially structured, indicating that the sediment community composition was more distinct across sample sites and relatively stable across seasons. Weak seasonality has also been observed in surveys of macrobenthic community compositions at the same sites (Jordà-Molina et al. 2023 ), and in studies of benthic food web trophic structure (Ziegler et al. 2023 ). This is interesting, as it implies that the prey acquired was comparatively less influenced by sediment community composition than the input of pelagic material via vertical flux, which in the Arctic is much greater in the spring and summer than during the low-productive winter (Bodur et al. 2023 ). One of the other major objectives of the study was to investigate if host morphology (i.e. Weiser’s feeding groups) can be used as reliable predictors of diet in individual nematodes, or if denoting feeding ecology based on these factors alone leads to an arbitrary and oversimplified understanding as suggested by (Vafeiadou et al. 2014 ; Schuelke et al. 2018 ). Similarly to findings by Schuelke et al. ( 2018 ), we found no distinct partitioning of diets by feeding groups according to the prey relative read abundance. Likewise, NMDS analysis of prey did not return distinct clusters for feeding groups or nematode taxonomy (order and LCA), with the primary driver of prey sample clustering being the categorical seasons. In contrast, however, our CCA and PERMANOVA results indicated that the feeding groups were still a significant predictor of prey composition, but only significant when conducted at family-level taxonomy, and insignificant at both class and zOTU-levels. Hence, our data indicates that the nematodes surveyed acquire, to a large extent – a similar putative prey spectrum, wherein differences are primarily driven by seasonal variations, whereas feeding groups may have nuanced feeding preferences masked behind other more influential variables. 4.3 Microbial eukaryote prey Surprisingly, diatoms were highly abundant in the sediment community, but did not compose a great fraction of the prey reads recovered. This mismatch was particularly obvious for the genus Chaetoceros which was extremely abundant in the sediment community (~ 40%) but rarely represented in the prey (< 0.1%). The disparity in relative abundance could potentially be due to primer bias, since the prey and sediment community were sequenced using different primers targeting 18S V7 and V4 fragments (e.g. Parada et al., 2016 ). However, given the substantial contrast, we find it more likely that the disparity in abundance can be explained by aggregation of diatom material, such as dead and empty cells, heavily silicified resting spores or free DNA molecules aggregated at the sea floor, or in other words – from material unfit as nematode sustenance. Resting spores have been observed in Chaetoceros species, with substantial formation during conditions of high-cell density and/or low nutrient concentrations, and in both Antarctica and the Mediterranean Seas (Crosta et al. 1997 ; Pelusi et al. 2020 ). Aggregation of diatom DNA in sediments has also been hypothesized in other studies, with one citing diatom ASV abundances which exceeded what was expected based on metabarcoding of water-column samples (Nguyen et al. 2024 ). From metabarcoding of water column samples at the same stations and cruises, we likewise observed a much less dominant diatom fraction of zOTUs, and primarily abundant diatom taxa on the shelves during spring (April-May, Flo et al., 2024a ). If the culprit is free DNA molecules, it is likely that sediment samples, for which we did not employ pre-extraction filtering – will yield more diatom DNA than that of water-based samples, for which material is typically recovered on 0.22 µm filters. We must therefore argue that the abundance of diatom zOTUs recovered in our sediment samples likely do not accurately depict the number of diatoms viable for nematode ingestion. Nevertheless, these results indicate that diatoms, overall, are of lesser importance as putative prey for the nematodes studied here. Conversely, experimental observations of nematodes in shallow tidal flat sediments suggested high dependency on diatoms as a carbon source, wherein nematodes ingested whole cells (deposit and omnivore-predators), or pierced and sucked out cell contents (epistrate) (Moens et al. 2014 ). In contrast to tidal flats, however, nematodes on the Barents Sea shelves live on average at a depth of ~ 230 m (Sakshaug et al. 2009 ), and likely do not have access to the high-quality, active and “fresh” algal prey that exist on tidal flats. Thus, although community analyses indicated a clearly abundant Chaetoceros in the ambient sediment, they could be unviable as prey. The chain-like structure of Thalassiosira sp. and Chaetoceros sp. colonies may moreover offer defensive capabilities against deposit and omnivore-predators (whole cell ingestion), but then we would also expect nematodes who pierce and suck out contents (epistrate) to gain comparatively higher read counts from these taxa (Moens et al. 2014 ). We find no support for such a trend in the current data, and neither did Schuelke et al., ( 2018 ), wherein the most prevalent ochrophyte prey was found in a Desmoscolex selective deposit feeder. Instead, the consistently low or even lack of diatom reads speaks towards feeding group designations not being particularly representative of the in situ prey nematodes acquires. Oligohymenophorea were particularly abundant in the prey reads (~ 8% on average), with peritrich ciliates of the Vorticella genus comprising the bulk of the reads. Vorticella ciliates may exist both as a free-swimming telotroch, or as a sessile trophont, the latter having a contractile stalk that allows its bell-shaped body (zooid) with oral cilia to contract and expand, and to attach to a suitable substratum (Buhse et al. 2011 ). The amount of their contribution as prey items may, however, be exaggerated due to high rDNA copy-numbers in peritrich ciliates (Gong et al. 2013 ), and Vorticella are primarily known from soil and limnic habitats (France et al. 2017 ). Peritrich ciliates have been found as epibionts on Arctic amphipods (Arndt et al. 2005 ; Fernandez-Leborans et al. 2006 ), deep-sea isopods (Ólafsdóttir and Svavarsson 2002 ), and Vorticella are known to occur as epibionts on Chaetoceros colonies (Nanajkar et al. 2019 ). Hence, it is possible that they were epibionts on the arthropod taxa that were frequently observed in nematode prey, or on algae like Chaetoceros , which were very abundant in the sediment community, but not as prey themselves. Oligohymenophorea taxa were in contrast very rare in the sediment community samples (< 0.1%). Recent 18S metabarcoding surveys have found Vorticella in the plankton of the Baltic Sea, Kattegat and Skagerrak (Latz et al. 2024 ), and in the sediments of the Norwegian continental shelf (Lanzén et al. 2017 ). We were however unable to find any relevant sources on interaction between Vorticella and marine nematodes, nor did we observe any epibionts on any nematode individuals during sampling. Thus, we cannot deduce the nature of interaction between the two. Nevertheless, we argue that the Vorticella -nematode interaction may be a valuable, and possibly important new avenue for meiobenthic ecological research. 4.4 Copepod prey Arthropoda composed a high number of prey reads, and were particularly abundant during August (30.2%) and December (28.2%). Of these, both pelagic and benthic copepods composed the largest fraction, and although typically larger in size than nematodes, the abundance of copepods amongst prey reads may well be explained by predation. Through snap-freezing meiofauna and subsequently observing the meiofauna through a microscope, (Kennedy 1994 ) captured several meiofauna whose bodies were invaded, and apparently preyed upon by smaller nematodes. A range of nematode prey taxa were recorded in this way, including annelids, ostracods, halacarid mites and other nematodes, and photographs displayed harpacticoid copepods invaded by Monhystera spp. nematodes. On some occurrences, the author explained, all that remained was the indigestible prey carapace or cuticle, suggesting that the nematode may have acted as a scavenger, or was potentially feeding on internal bacteria (Kennedy 1994 ). Here, we identified two abundant harpacticoid taxa as prey including Parabradya spp. and an unidentified harpacticoid zOTU (Harpacticoida_X). While we can say little about the ecology of the latter, Parabradya spp. are epi-benthic harpacticoids, and have been found in sediments from both sub-littoral shores and the deep-sea (Seifried et al. 2007 ), and in the Arctic Laptev Sea (Chertoprud et al. 2018 ). In addition, a significant proportion of the arthropod reads were identified to Lepeophtheirus sp. and/or Misophriella sp.. Lepeophtheirus sp. include species known for parasitism, with both salmonids ( Lepeophtheirus salmonis ) and Pleurinectiformes flatfish ( Lepeophtheirus pectoralis ) on the menu (Boxshall 1976 ; Tully and Nolan 2002 ). Much less information exists for Misophriella sp. (Misophrioida), which are seldom found, and predominantly found in deep-sea hyperbenthic communities (Arbizu and Jaume 1999 ). We thus find Lepeophtheirus sp. a more probable source of the reads in our study. Intriguingly, high Lepeophtheirus / Misophriella abundances in August (6.6%) and December (8.0%) coincided with high abundances of Salmo sp. sequence reads in the same months (4.7 and 2.0%, respectively). The Salmo sp. reads, when BLASTed, has high affinity to several salmonids, including Salmo trutta (trout), Onchorynchus mykiss (rainbow trout) and O. gorbuscha (pink salmon). While wild O. mykiss is primarily located to the Pacific ocean in Asia and north-America (Light et al. 1989 )d trutta primarily occur along the coast-line and freshwater systems of mainland Norway, and thus unlikely suspects – the initially pacific O. gorbuscha has spread and now occur in great abundances along the Norwegian coastline, and also in the Barents Sea (Pauli et al. 2023 ), Greenland (Nielsen et al. 2020 ) and Ireland (Millane et al. 2019 ). In our study, both Lepeophtheirus/Misophriella and Salmo sp. were both more abundant at the northern Shelf N, and Shelf Break station than at Shelf S. Hence, it is possible that the nematodes surveyed may have acquired dietary material from Salmo sp. directly, or through secondary predation of their parasitic Lepeophtheirus sp., whom are known to feed on the mucus, skin and blood of salmonids (Brandal et al. 1976 ). Perhaps we can even hypothesize that infected and eventually incapacitated salmonids sink with their entourage of salmon lice which later falls prey to meiobenthic nematodes. Interestingly, a large portion of the arthropod prey reads were assigned to pelagic copepods such as Calanus spp., Pseudocalanus sp. and/or Microcalanus spp., Neocalanus sp. and Oithona spp. and/or Microsetella spp.. Apart from Neocalanus sp., whose distribution appears to be limited to lower latitudes in the North Atlantic, all of these taxa are known to be broadly and abundantly distributed in the Barents Sea (Wassmann et al. 2006 ; Hirche and Kosobokova 2011 ). In fact, all of these copepods were present in varying biomass and abundances in the above water masses during the Nansen Legacy cruises where this study’s material was collected (Wold et al. 2023 ). According to the pelagic study, mesozooplankton integrated water column biomass was at its height during the summer months of July (14.3 g m − 2 ) and August (11.3 g m − 2 ), and decreased dramatically in March and May (minimum ~ 1 g m − 2 ), which matches the observed lower relative abundances of arthropods in March and May nematode prey. In terms of integrated biomass, smaller pelagic copepods like Oithona similis , Microsetella norvegica and Pseudocalanus spp. were less prominent during the spring seasons, but increased through the summer months of July and August, and peaked in December (> 1 g m − 2 , Wold et al., 2023 ). Again, the trend sits well with the observed higher abundances of small copepods in nematode prey in August and December. Of the mesozooplankton genera studied, Calanus spp. were the main contributor to overall biomass in the water column, being particularly abundant during the summer and fall months (July, August, December), at the northern shelf station (Shelf N, 5.8–11.6 g m − 2 ) and at the Basin station in December (11.4 g m − 2 ). Our observations, however, indicate comparatively high Calanus spp. relative abundances in nematode prey reads at the Shelf S station, and during March and May. This could possibly be explained by seasonal migration patterns, with many Calanus entering diapause deep on the Barents Sea shelves between July and May (Aarflot et al. 2023 ), and which in the event of high mortality-rates (Daase and Søreide 2021 ) – may lead to high supply to benthos. The high relative abundances of Calanus spp. in nematode prey in March and May may be reasonably explained by a combination of two processes connected to the biological carbon pump: fecal pellet production and non-consumptive mortality. Dense and fast-sinking fecal pellets from Arctic copepods significantly contribute to the POC export from pelagic waters, with export rates increasing during the productive spring and summer (Bodur et al. 2023 ; Darnis et al. 2024 ). Particle attenuation by bacteria and pelagic invertebrates drastically limits the quantity of POC that reaches deeper waters, and only a small fraction reaches below 200 m (Svensen et al. 2012 , 2024 ). The rate of attenuation of fecal pellets is spatially and temporally variable, with the pelagic “retention filter” operating with higher efficiency in Atlantic Water (96% in May 1998) than in Arctic Water (50% in May 1998, Wexels Riser et al., 2002 ), and at higher efficiency during non-bloom scenarios (Wexels Riser et al. 2007 ). A smaller percentage of Calanus reads in the deep-sea basin sediments compared to shelf stations, may hence be explained by a combination of lower fecal pellet production, due to lower Calanus abundance and less primary production – and particle attenuation, in which both deceased copepods and fecal pellets may be attenuated before reaching the Nansen Basin. Some recent observations made from Svalbard and north of Svalbard showed that the dead fraction of high-Arctic zooplankton consisted mainly of copepods, with calanoid copepods (especially Calanus spp.) contributing more to the dead fraction during seasons of low productivity (Daase et al. 2014 ; Daase and Søreide 2021 ). In the Canadian Arctic, post-reproductive mortality of Calanus hyperboreus was estimated to drive comparatively higher downward particle flux in winter-early spring (16–91%) than in the remaining seasons (1–30%) (Sampei et al. 2009 ). Hence, a high biomass of Calanus spp. during summer months may be converted, through non-consumptive mortality events (e.g., caused by insufficient lipid storage for overwintering, parasitism or old age) – to putative food particles that sink and supply the epi- and meiobenthos over winter and early spring. We can further speculate in a certain lag-time from mortality to reaching the seafloor, especially at locations characterized by great depths or strong up-welling, and currents would certainly impact the site at which such particles would settle. This, in combination with lower fecal pellet production in the non-productive winter (Wexels Riser et al. 2002 ), could possibly explain why Calanus spp. were not more abundant in nematode prey in December, even though Calanus spp. were abundant amongst the mesozooplankton at this time in the waters above (Wold et al. 2023 ). That a combination of epibenthic and pelagic arthropods were identified in our study suggests that the nematodes feed as generalists, rather than selectively, on larger arthropods depending on their availability. In the high-Arctic, Calanus spp. appears particularly important as prey during seasons of low productivity, whereas benthic harpacticoids and smaller pelagic copepods are important during summer and early winter. 4.5 Fungal prey Fungal taxa assigned to Ascomycota, Basidiomycota and Mucoromycota were abundantly recovered from nematode prey year-round, and particularly in March and May. Although the roles and functions of fungi in the marine ecosystem remains severely understudied, there have been observations that underpin the possible importance of nematode-fungal interactions. Based on sheer abundance in sea-ice and water-samples, dikaryan fungi (including Basidiomycota and Ascomycota) and Chytridiomycota have been suggested to play an important role as sustenance at the base of the Arctic food web (Hassett et al., 2017 ). In a study of fungi-nematode interactions, fungal 18S sequences identified by Sanger sequencing of shallow and deep-sea nematode, were mainly assigned to Ascomycota and Basidiomycota (Bhadury et al. 2011 ). Likewise, Schuelke et al. ( 2018 ) observed that a major component of non-metazoan reads from single nematodes in the Arctic Beaufort Sea, Gulf of Mexico and Californian coast were of fungal origin, and noted that fungi – so commonly co-amplified with nematodes, must form some kind of important ecological association with them. One interesting observation was the high abundance of Mucoromycota, which, in contrast to Ascomycota and Basidiomycota, decreased dramatically at the Shelf Break and Nansen Basin compared to shelf stations. Mucoromycota are known to have parasitic, mycorrhizal and saprotrophic lifestyles, and as of now at least 16 genera of marine taxa are known (Calabon et al. 2023 ), but have not been identified as a major contributor to the Arctic marine fungal community (Hassett et al., 2017 ). While the exact nature of the fungi nematode associations remains unclear, we can be relatively confident that the fungal taxa somehow interact with marine nematodes as symbionts or prey. If ingested as a source of sustenance, the fungi can either be eaten directly, or indirectly via other organic particles (i.e. fecal pellets, dead and/or decaying organisms) that are continuously colonized and deposited on the sea floor in aquatic systems. Moreover, some fungal taxa seem more prone to occur in symbiotic associations than others. A recent review of fungal parasites noted for instance that Ascomycota could infect a diverse array of metazoan hosts, whereas comparatively few animal hosts were known for Mucoromycota and Chytridiomycota (Pang et al. 2021 ). 5. Conclusion The nematodes from the northern Barents Sea and Nansen Basin characterized here displayed significant overlaps in diet despite the differences in nematode phylogeny and morphology. Seasonality was clearly a more important structuring factor than teeth and buccal cavity morphology, with prey compositions clustering into distinct August and December or March and May groups which were separated by highly abundant arthropod or fungal prey reads, respectively. Combined, the high seasonality and low effect of feeding group morphology indicates that Arctic nematodes are generalists, who regardless of presence or absence of teeth, teeth-like structures or buccal cavity sizes obtain, to a great extent, similar eukaryote prey items. Our findings thus support the notion that the widely used morphological features have little power for explaining trophic roles of nematodes. Of the prey items that varied seasonally, the calanoid copepod prey were of particular interest, as they through mortality events may lead to possibly large and sudden inputs of pelagic biomass towards benthos. In addition, a surprisingly high prey abundance of metazoans and marine fungi and a lack of diatoms indicate that Arctic nematodes rely less on algae, and more on heterotroph prey. We moreover highlight the possibility of important novel interactions with Siphonostomatoid copepods and Vorticella-like peritrich ciliates and call for more experimental research to disentangle the nature of these associations. Declarations Financial or non-financial interests: The authors have no financial interests to disclose. Of non-financial interests, we declare that author Camilla Svensen is an editor for Polar Biology. Ethical approval: No approval of research ethics was required because the animals taken for analyses were unregulated invertebrate species. Funding: This work was supported by the Research Council of Norway (grant number 276730) through the Nansen Legacy project. Acknowledgements: We thank the Nansen Legacy project and Research Council of Norway for funding and support (RCN #276730) and fruitful discussions, and the crew of R/V Kronprins Haakon for providing us with the possibility to take samples in the Arctic. Thanks to Arunima Sen (Nord Univ.), Eric Jorda-Molina (Nord Univ.), Thaise Ricardo de Freitas (UiO), Silvia Hess (UiO) and Amanda Ziegler (UiT) for help acquiring sediment during the Nansen Legacy cruises. We give special thanks to Melissa Brandner (UiT), Hilde Rief Armo (UiB) and Lise Øvreås (UiB) for help with preparing samples for sequencing. Author contributions : All authors contributed to conceptualizing the study. SF, BAB and AV conducted the fieldwork, and SF performed the lab work with help from KP and AV. SF analyzed the data and wrote the manuscript, and all authors revised the manuscript and previous versions. All authors read and approved the manuscript. Data availability: The 18S metabarcoding data from nematodes generated for this study was published at the NIRD-RDA repository and are available at the following URL: https://doi.org/10.11582/2024.00105. References Aarflot JM, Eriksen E, Prokopchuk IP, et al (2023) New insights into the Barents Sea Calanus glacialis population dynamics and distribution. Prog Oceanogr 217:103106. https://doi.org/10.1016/j.pocean.2023.103106 Akvaplan-niva (2024a) Nansen Legacy Sediment Pigment Data Q1. Norstore. https://doi.org/10.11582/2024.00047 Akvaplan-niva (2024b) Nansen Legacy Sediment Pigment Data Q2. Norstore. https://doi.org/10.11582/2024.00046 Akvaplan-niva (2024c) Nansen Legacy Sediment Pigment Data Q3. Norstore. https://doi.org/10.11582/2024.00045 Akvaplan-niva (2024d) Nansen Legacy Sediment Pigment Data Q4. Norstore. https://doi.org/10.11582/2024.00044 Altschul SF, Gish W, Miller W, et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 Ambrose WG, Renaud PE (1995) Benthic response to water column productivity patterns: evidence for benthic-pelagic coupling in the Northeast Water Polynya. J Geophys Res 100:4411–4421. https://doi.org/10.1029/94JC01982 Arbizu PM, Jaume D (1999) New hyperbenthic species of Misophriopsis and Misophriella , first record of misophrioid copepods (Crustacea) from Antarctic waters. Helgol Mar Res 53:102–117. https://doi.org/10.1007/PL00012142 Arndt CE, Fernandez-Leborans G, Seuthe L, et al (2005) Ciliated epibionts on the Arctic sympagic amphipod Gammarus wilkitzkii as indicators for sympago-benthic coupling. Mar Biol 147:643–652. https://doi.org/10.1007/s00227-005-1599-4 Baquiran J-P, Thater B, Sedky S, et al (2013) Culture-Independent Investigation of the Microbiome Associated with the Nematode Acrobeloides maximus . PLoS One 8:e67425 Bessière A, Nozais C, Brugel S, et al (2007) Metazoan meiofauna dynamics and pelagic-benthic coupling in the Southeastern Beaufort Sea, Arctic Ocean. Polar Biol 30:1123–1135. https://doi.org/10.1007/s00300-007-0270-6 Bhadury P, Bik H, Lambshead JD, et al (2011) Molecular diversity of fungal phylotypes Co-Amplified alongside nematodes from coastal and Deep-Sea marine environments. PLoS One 6:e26445. https://doi.org/10.1371/journal.pone.0026445 Billett DSM, Lampitt RS, Rice AL, Mantoura RFC (1983) Seasonal sedimentation of phytoplankton to the deep-sea benthos. Nature 302:520–522. https://doi.org/10.1038/302520a0 Bodur Y V., Renaud PE, Goraguer L, et al (2023) Seasonal patterns of vertical flux in the northwestern Barents Sea under Atlantic Water influence and sea-ice decline. Prog Oceanogr 219:103132. https://doi.org/10.1016/j.pocean.2023.103132 Boxshall GA (1976) The host specificity of Lepeophtheirus pectoralis (Müller, 1776) (Copepoda: Caligidae). J Fish Biol 8:255–264. https://doi.org/https://doi.org/10.1111/j.1095-8649.1976.tb03949.x Boyer F, Mercier C, Bonin A, et al (2016) obitools: a unix-inspired software package for DNA metabarcoding. Mol Ecol Resour 16:176–182. https://doi.org/10.1111/1755-0998.12428 Brandal PO, Egidius E, Romslo I (1976) Host blood: a major food component for the parasitic copepod Lepeophtheirus salmonis Kroyeri, 1838 (Crustacea: Caligidae). Nor J Zool 24:341–343 Buhse HE, McCutcheon SM, Clamp JC, Sun P (2011) Vorticella . In: Encyclopedia of Life Sciences Calabon MS, Jones EBG, Pang KL, et al (2023) Updates on the classification and numbers of marine fungi. Bot Mar 66:213–238. https://doi.org/10.1515/bot-2023-0032 Camacho C, Coulouris G, Avagyan V, et al (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421. https://doi.org/10.1186/1471-2105-10-421 Cheng X-Y, Tian X-L, Wang Y-S, et al (2013) Metagenomic analysis of the pinewood nematode microbiome reveals a symbiotic relationship critical for xenobiotics degradation. Sci Rep 3:1869. https://doi.org/10.1038/srep01869 Chertoprud E, Abramova E, Korsun S, et al (2018) Composition of Harpacticoida (Crustacea, Copepoda) of the Laptev Sea in comparison with faunas of adjacent Arctic seas. Polar Biol 41:697–712. https://doi.org/10.1007/s00300-017-2229-6 Cleary AC, Durbin EG (2016) Unexpected prevalence of parasite 18S rDNA sequences in winter among Antarctic marine protists. J Plankton Res 38:401–417. https://doi.org/10.1093/plankt/fbw005 Cleary AC, Søreide JE, Freese D, et al (2017) Feeding by Calanus glacialis in a high arctic fjord: Potential seasonal importance of alternative prey. ICES J Mar Sci 74:1937–1946. https://doi.org/10.1093/icesjms/fsx106 Comeau A, Kwawukume A (2023) Preparing multiplexed 16S/18S/ITS amplicons for the Illumina MiSeq Crosta X, Pichon JJ, Labracherie M (1997) Distribution of Chaetoceros resting spores in modern peri-Antarctic sediments. Mar Micropaleontol 29:283–299. https://doi.org/10.1016/S0377-8398(96)00033-3 Daase M, Søreide JE (2021) Seasonal variability in non-consumptive mortality of Arctic zooplankton. J Plankton Res 43:565–585. https://doi.org/10.1093/plankt/fbab042 Daase M, Varpe Ø, Falk-Petersen S (2014) Non-consumptive mortality in copepods: occurrence of Calanus spp. carcasses in the Arctic Ocean during winter. J Plankton Res 36:129–144. https://doi.org/10.1093/plankt/fbt079 Darnis G, Geoffroy M, Daase M, et al (2024) Zooplankton fecal pellet flux drives the biological carbon pump during the winter–spring transition in a high-Arctic system. Limnol Oceanogr 69:1481–1493. https://doi.org/10.1002/lno.12588 Davis NM, Proctor DM, Holmes SP, et al (2018) Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6:226. https://doi.org/10.1186/s40168-018-0605-2 Dell’Anno A, Carugati L, Corinaldesi C, et al (2015) Unveiling the Biodiversity of Deep-Sea Nematodes through Metabarcoding: Are We Ready to Bypass the Classical Taxonomy? PLoS One 10:e0144928 Dirksen P, Marsh SA, Braker I, et al (2016) The native microbiome of the nematode Caenorhabditis elegans : gateway to a new host-microbiome model. BMC Biol 14:38. https://doi.org/10.1186/s12915-016-0258-1 Dos Santos GAP, Moens T (2011) Populations of two prey nematodes and their interaction are controlled by a predatory nematode. Mar Ecol Prog Ser 427:117–131. https://doi.org/10.3354/meps08991 Eds. N (2024) Nemys: World Database of Nematodes. https://nemys.ugent.be. Accessed 22 May 2024 Fernandez-Leborans G, Arndt CE, Gabilondo R (2006) Protozoan epibionts and their distribution on the Arctic ice-amphipod Gammarus wilkitzkii from Spitsbergen, Norway. Arctic, Antarct Alp Res 38:343–356. https://doi.org/10.1657/1523-0430(2006)38[343:PEATDO]2.0.CO;2 Flo S (2024) 18S metabarcoding of Arctic meiobenthic nematodes. Norstore. https://doi.org/10.11582/2024.00105 Flo S, Svensen C, Præbel K, et al (2024a) Dietary plasticity in small Arctic copepods as revealed with prey metabarcoding. J Plankton Res fbae042. https://doi.org/10.1093/plankt/fbae042 Flo S, Vader A, Præbel K (2024b) Brute force prey metabarcoding to explore the diets of small invertebrates. Ecol Evol 14:12. https://doi.org/10.1002/ece3.11369 Fonseca G, Fehlauer-Ale KH (2012) Three in one: Fixing marine nematodes for ecological, molecular, and morphological studies. Limnol Oceanogr Methods 10:516–523. https://doi.org/10.4319/lom.2012.10.516 Fonseca G, Gallucci F (2008) Direct evidence of predation in deep-sea nematodes: The case of Pontonema sp. Cah Biol Mar 49:295–297 Fonseca G, Soltwedel T, Vanreusel A, Lindegarth M (2010) Variation in nematode assemblages over multiple spatial scales and environmental conditions in Arctic deep seas. Prog Oceanogr 84:174–184. https://doi.org/10.1016/j.pocean.2009.11.001 France D, Tejada J, Matsudaira P (2017) Direct measurement of Vorticella contraction force by micropipette deflection. FEBS Lett 591:581–589. https://doi.org/10.1002/1873-3468.12577 Gast RJ, Dennett MR, Caron DA (2004) Characterization of Protistan Assemblages in the Ross Sea, Antarctica, by Denaturing Gradient Gel Electrophoresis. Appl Environ Microbiol 70:2028 LP – 2037. https://doi.org/10.1128/AEM.70.4.2028-2037.2004 Giere O (2009) Meiobenthology: The microscopic motile fauna of aquatic sediments Gong J, Dong J, Liu X, Massana R (2013) Extremely High Copy Numbers and Polymorphisms of the rDNA Operon Estimated from Single Cell Analysis of Oligotrich and Peritrich Ciliates. Protist 164:369–379. https://doi.org/10.1016/j.protis.2012.11.006 Grzelak K, Gluchowska M, Gregorczyk K, et al (2016) Nematode biomass and morphometric attributes as biological indicators of local environmental conditions in Arctic fjords. Ecol Indic 69:368–380. https://doi.org/10.1016/j.ecolind.2016.04.036 Guardiola M, Uriz MJ, Taberlet P, et al (2015) Deep-sea, deep-sequencing: Metabarcoding extracellular DNA from sediments of marine canyons. PLoS One 10:e0139633. https://doi.org/10.1371/journal.pone.0139633 Hassett B, Ducluzeau AL, Collins R, Gradinger R (2017) Spatial distribution of aquatic marine fungi across the western Arctic and sub-arctic. Environ Microbiol 19:475–484. https://doi.org/10.1111/1462-2920.13371 Hassett BT, Borrego EJ, Vonnahme TR, et al (2019) Arctic marine fungi: biomass, functional genes, and putative ecological roles. ISME J 13:1484–1496. https://doi.org/10.1038/s41396-019-0368-1 Heip C, Vincx M, Vranken G (1985) The ecology of marine nematodes. Oceanogr Mar Biol An Annu Rev 23:399–489 Hirche HJ, Kosobokova KN (2011) Winter studies on zooplankton in Arctic seas: the Storfjord (Svalbard) and adjacent ice-covered Barents Sea. Mar Biol 158:2359–2376. https://doi.org/10.1007/s00227-011-1740-5 Hoste E, Vanhove S, Schewe I, et al (2007) Spatial and temporal variations in deep-sea meiofauna assemblages in the Marginal Ice Zone of the Arctic Ocean. Deep Sea Res Part I Oceanogr Res Pap 54:109–129. https://doi.org/https://doi.org/10.1016/j.dsr.2006.09.007 Huson DH, Auch AF, Qi J, Schuster SC (2007) MEGAN analysis of metagenomic data. Genome Res 17:377–386. https://doi.org/10.1101/gr.5969107 Jensen P (1987) Feeding ecology of free-living aquatic nematodes. Mar Ecol Prog Ser 35:187–196. https://doi.org/10.1079/9780851990095.0105 Jordà-Molina È, Sen A, Bluhm BA, et al (2023) Lack of strong seasonality in macrobenthic communities from the northern Barents Sea shelf and Nansen Basin. Prog Oceanogr 219:103150. https://doi.org/https://doi.org/10.1016/j.pocean.2023.103150 Kennedy AD (1994) Predation within meiofaunal communities: Description and results of a rapid-freezing method of investigation. Mar Ecol Prog Ser 114:71–80. https://doi.org/10.3354/meps114071 Lanzén A, Lekang K, Jonassen I, et al (2017) DNA extraction replicates improve diversity and compositional dissimilarity in metabarcoding of eukaryotes in marine sediments. PLoS One 12:e0179443. https://doi.org/10.1371/journal.pone.0179443 Latz MAC, Andersson A, Brugel S, et al (2024) A comprehensive dataset on spatiotemporal variation of microbial plankton communities in the Baltic Sea. Sci Data 11:. https://doi.org/10.15468/cwjstg Light JT, Harris CK, Burgner RL (1989) Ocean Distribution and Migration of Steelhead ( Oncorhynchus mykiss , formerly Salmo gairdneri ). Int North Pacific Fish Comm 50 Lombard F, Kiørboe T (2010) Marine snow originating from appendicularian houses: Age-dependent settling characteristics. Deep Sea Res Part I Oceanogr Res Pap 57:1304–1313. https://doi.org/https://doi.org/10.1016/j.dsr.2010.06.008 McIntyre AD (1971) Observations on the status of subtidal meiofauna research. Smithson Contrib to Zool 76:149–153 McMurdie PJ, Holmes S (2013) phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One 8:e61217 Millane M, Walsh L, Roche WK, Gargan PG (2019) Unprecedented widespread occurrence of Pink Salmon Oncorhynchus gorbuscha in Ireland in 2017. J Fish Biol 95:651–654. https://doi.org/10.1111/jfb.13994 Moens T, Braeckman U, Derycke S, et al (2013) Ecology of free-living marine nematodes. In: Schmidt-Rhaesa A (ed) Nematoda. De Gruyter, pp 109–152 Moens T, Vafeiadou AM, De Geyter E, et al (2014) Diatom feeding across trophic guilds in tidal flat nematodes, and the importance of diatom cell size. J Sea Res 92:125–133. https://doi.org/10.1016/j.seares.2013.08.007 Moens T, Verbeeck L, Maeyer A de, Swings J (1999) Selective attraction of marine bacterivorous nematodes to their bacterial food . Mar Ecol Prog Ser 176:165–178 Moens T, Vincx M (1997) Observations on the feeding ecology of estuarine nematodes. J Mar Biol Assoc United Kingdom 77:211–227. https://doi.org/10.1017/s0025315400033889 Morata N, Renaud PE (2008) Sedimentary pigments in the western Barents Sea: A reflection of pelagic-benthic coupling? Deep Res Part II Top Stud Oceanogr 55:2381–2389. https://doi.org/10.1016/j.dsr2.2008.05.004 Nanajkar M, Fernandes V, Bogati K, Chatterjee T (2019) Gregarious true- colonies of ciliate Vorticella oceanica on a chain forming diatom Chaetoceros coarctatus : indicating change in the nature of association. Symbiosis 79:221–229. https://doi.org/10.1007/s13199-019-00640-4 Nguyen NL, Pawłowska J, Zajaczkowski M, et al (2024) Taxonomic and abundance biases affect the record of marine eukaryotic plankton communities in sediment DNA archives. Mol Ecol Resour 24:e14014. https://doi.org/10.1111/1755-0998.14014 Nielsen J, Rosing-Asvid A, Meire L, Nygaard R (2020) Widespread occurrence of pink salmon ( Oncorhynchus gorbuscha ) throughout Greenland coastal waters. J Fish Biol 96:1505–1507. https://doi.org/10.1111/jfb.14318 Oksanen J, Blanchet FG, Friendly M, et al (2019) Vegan: community ecology package. Ordination methods, diversity analysis and other functions for community and vegetation ecologists. R package ver, 2-3. Cran R Ólafsdóttir SH, Svavarsson J (2002) Ciliate (Protozoa) epibionts of deep-water asellote isopods (Crustacea): Pattern and diversity. J Crustac Biol 22:607–618. https://doi.org/10.1651/0278-0372(2002)022[0607:cpeodw]2.0.co;2 Oleszczuk B, Grzelak K, Kędra M (2021) Community structure and productivity of Arctic benthic fauna across depth gradients during springtime. Deep Res Part I Oceanogr Res Pap 170:103457. https://doi.org/10.1016/j.dsr.2020.103457 Pang KL, Hassett BT, Shaumi A, et al (2021) Pathogenic fungi of marine animals: A taxonomic perspective. Fungal Biol Rev 38:92–106. https://doi.org/10.1016/j.fbr.2021.03.008 Parada AE, Needham DM, Fuhrman JA (2016) Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 18:1403–1414. https://doi.org/10.1111/1462-2920.13023 Pauli BD, Berntsen HH, Thorstad EB, et al (2023) Geographic distribution, abundance, diet, and body size of invasive pink salmon ( Oncorhynchus gorbuscha ) in the Norwegian and Barents Seas, and in Norwegian rivers. ICES J Mar Sci 80:76–90. https://doi.org/10.1093/icesjms/fsac224 Pelusi A, Santelia ME, Benevenuto G, et al (2020) The diatom Chaetoceros socialis : spore formation and preservation. Eur J Phycol 55:1–10. https://doi.org/10.1080/09670262.2019.1632935 Piepenburg D (2005) Recent research on Arctic benthos: Common notions need to be revised. Polar Biol 28:733–755. https://doi.org/10.1007/s00300-005-0013-5 Piñol J, San Andrés V, Clare EL, et al (2014) A pragmatic approach to the analysis of diets of generalist predators: The use of next-generation sequencing with no blocking probes. Mol Ecol Resour 14:18–26. https://doi.org/10.1111/1755-0998.12156 Piredda R, Tomasino MP, D’Erchia AM, et al (2017) Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean Long Term Ecological Research site. FEMS Microbiol Ecol 93:1–14. https://doi.org/10.1093/femsec/fiw200 Renaud PE, Morata N, Carroll ML, et al (2008) Pelagic-benthic coupling in the western Barents Sea: Processes and time scales. Deep Res Part II Top Stud Oceanogr 55:2372–2380. https://doi.org/10.1016/j.dsr2.2008.05.017 Rex MA, Etter RJ, Morris JS, et al (2006) Global bathymetric patterns of standing stock and body size in the deep-sea benthos. Mar Ecol Prog Ser 317:1–8. https://doi.org/10.3354/meps317001 Ricardo de Freitas T, Hess S, Alve E, et al (2022) Seabed sediment data (upper 6 cm) on water content, total nitrogen, total carbon, total organic carbon, total inorganic carbon, carbon and nitrogen isotopic composition from the Nansen Legacy seasonal cruise 2021704 (Q2). Nor Mar Data Cent. https://doi.org/https://doi.org/10.21335/NMDC-350572235 Rognes T, Flouri T, Nichols B, et al (2016) VSEARCH: A versatile open source tool for metagenomics. PeerJ 4:e2584: https://doi.org/10.7717/peerj.2584 Sakshaug E, Johnsen G, Kristiansen S, et al (2009) Ecosystem Barents Sea. Ecosyst Barents Sea 587 Sampei M, Sasaki H, Hattori H, et al (2009) Significant contribution of passively sinking copepods to the downward export flux in arctic waters. Limnol Oceanogr 54:1894–1900. https://doi.org/10.4319/lo.2009.54.6.1894 Schewe I, Soltwedel T (2003) Benthic response to ice-edge-induced particle flux in the Arctic Ocean. Polar Biol 26:610–620. https://doi.org/10.1007/s00300-003-0526-8 Schram MD, Davison PG (2012) Irwin Loops—A History and Method of Constructing Homemade Loops. Trans Kansas Acad Sci 115:35–40. https://doi.org/10.1660/062.115.0106 Schuelke T, Pereira TJ, Hardy SM, Bik HM (2018) Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats. Mol Ecol 27:1930–1951. https://doi.org/10.1111/mec.14539 Seifried S, Plum C, Schulz M (2007) A new species of Parabradya Lang, 1944 (Copepoda: Harpacticoida: Ectinosomatidae) from the abyssal plain of the Angola Basin. Zootaxa 1–21. https://doi.org/10.11646/zootaxa.1432.1.1 Soetaert K, Franco M, Lampadariou N, et al (2009) Factors affecting nematode biomass, length and width from the shelf to the deep sea. Mar Ecol Prog Ser 392:123–132. https://doi.org/10.3354/meps08202 Svensen C, Iversen M, Norrbin F, et al (2024) Impact of aggregate-colonizing copepods on the biological carbon pump in a high-latitude fjord. Limnol Oceanogr 69:2029–2042. https://doi.org/10.1002/lno.12641 Svensen C, Riser CW, Reigstad M, Seuthe L (2012) Degradation of copepod faecal pellets in the upper layer: Role of microbial community and Calanus finmarchicus . Mar Ecol Prog Ser 462:39–49. https://doi.org/10.3354/meps09808 The Nansen Legacy (2022) Sampling Protocols: Version 10. Nansen Leg Rep Ser 32:. https://doi.org/10.7557/nlrs.6684 Tietjen JH, Lee JJ (1973) Life history and feeding habits of the marine nematode, Chromadora macrolaimoides steiner. Oecologia 12:303–314. https://doi.org/10.1007/BF00345045 Tully O, Nolan DT (2002) A review of the population biology and host–parasite interactions of the sea louse Lepeophtheirus salmonis (Copepoda: Caligidae). Parasitology 124:165–182. https://doi.org/DOI: 10.1017/S0031182002001889 Vafeiadou AM, Materatski P, Adão H, et al (2014) Resource utilization and trophic position of nematodes and harpacticoid copepods in and adjacent to Zostera noltii beds. Biogeosciences 11:4001–4014. https://doi.org/10.5194/bg-11-4001-2014 Van Gaever S, Olu K, Derycke S, Vanreusel A (2009) Nematode abundances in sediments from the Storegga Slide (Norwegian Sea) obtained during the VICKING expedition in 2006. Van Gaever S, Vanreusel A (2012) Nematode abundance in cold seep sediments of the Nordic Margins (Nyregga, Styregga, Haakon Mosby Mud Volcano) Wangensteen OS, Palacín C, Guardiola M, Turon X (2018) DNA metabarcoding of littoral hard-bottom communities: high diversity and database gaps revealed by two molecular markers. PeerJ 6:e4705–e4705. https://doi.org/10.7717/peerj.4705 Wassmann P, Reigstad M, Haug T, et al (2006) Food webs and carbon flux in the Barents Sea. Prog Oceanogr 71:232–287. https://doi.org/https://doi.org/10.1016/j.pocean.2006.10.003 Wexels Riser C, Reigstad M, Wassmann P, et al (2007) Export or retention? Copepod abundance, faecal pellet production and vertical flux in the marginal ice zone through snap shots from the northern Barents Sea. Polar Biol 30:719–730. https://doi.org/10.1007/s00300-006-0229-z Wexels Riser C, Wassmann P, Olli K, et al (2002) Seasonal variation in production, retention and export of zooplankton faecal pellets in the marginal ice zone and central Barents Sea. J Mar Syst 38:175–188. https://doi.org/10.1016/S0924-7963(02)00176-8 Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis, 2nd edn. Springer-Verlag New York Wickham H, Averick M, Bryan J, et al (2019) Welcome to the tidyverse. J Open Source Softw 4:1686 Wieser W (1953) Die Beziehung zwischen Mundhöhlengestalt, Ernährungsweise und Vorkommen bei freilebenden marinen Nematoden: eine ökologisch-morphologische Studie. Ark für Zool 4:439–484 Wold A, Hop H, Svensen C, et al (2023) Atlantification influences zooplankton communities seasonally in the northern Barents Sea and Arctic Ocean. Prog Oceanogr 219:. https://doi.org/10.1016/j.pocean.2023.103133 Ziegler AF, Bluhm BA, Renaud PE, Jørgensen LL (2023) Weak seasonality in benthic food web structure within an Arctic inflow shelf region. Prog Oceanogr 217:103109. https://doi.org/https://doi.org/10.1016/j.pocean.2023.103109 Additional Declarations Competing interest reported. Author Camilla Svensen is currently an editor for Polar Biology. Supplementary Files SIPBSF.docx Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Polar Biology → Version 1 posted Editorial decision: Revision requested 16 Dec, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers invited by journal 02 Apr, 2025 Editor assigned by journal 02 Apr, 2025 Submission checks completed at journal 18 Mar, 2025 First submitted to journal 18 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6252716","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444759624,"identity":"1f0557eb-ea2c-4755-bf37-535cf888f20e","order_by":0,"name":"Snorre Flo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYPACORkG9gYgbWBBtBZjHgaeAyAtEqRokUgAMYjQIj+7+djnigoDHn7J51c3/CiQYOBv707Aq8XgzrHkmWfOGPBIzs4pu9kDdJjEmbMb8GuRyDFmbGz7w2NwOyftBg9Qi4FELn4t8jNAWv4Z8BjcPJN28w8xWhhugLQ0ALXcYD92myhbDG6kJTM2HAP6pSeH7baMgQQPQb/Iz0g+zNhQYyDHz3782c03f2zk+Nt7CTgMAXgMwCSxykGA/QEpqkfBKBgFo2AEAQDb7EG69jxtAQAAAABJRU5ErkJggg==","orcid":"","institution":"University Centre in Svalbard","correspondingAuthor":true,"prefix":"","firstName":"Snorre","middleName":"","lastName":"Flo","suffix":""},{"id":444759625,"identity":"636ae644-4b5d-454c-af46-87a7751d7d38","order_by":1,"name":"Bodil Annikki Bluhm","email":"","orcid":"","institution":"UiT The Arctic University of Norway","correspondingAuthor":false,"prefix":"","firstName":"Bodil","middleName":"Annikki","lastName":"Bluhm","suffix":""},{"id":444759626,"identity":"ae711e50-d9fe-4c0d-8e50-6fe6b8d5ad53","order_by":2,"name":"Camilla Svensen","email":"","orcid":"","institution":"UiT The Arctic University of Norway","correspondingAuthor":false,"prefix":"","firstName":"Camilla","middleName":"","lastName":"Svensen","suffix":""},{"id":444759627,"identity":"834dee7c-51a0-408c-bb63-0461ab902fb2","order_by":3,"name":"Kim Praebel","email":"","orcid":"","institution":"UiT The Arctic University of Norway","correspondingAuthor":false,"prefix":"","firstName":"Kim","middleName":"","lastName":"Praebel","suffix":""},{"id":444759628,"identity":"59cdbcc0-c6e1-46a1-9382-ff8ddc03e1a3","order_by":4,"name":"Anna Vader","email":"","orcid":"","institution":"University Centre in Svalbard","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Vader","suffix":""}],"badges":[],"createdAt":"2025-03-18 11:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6252716/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6252716/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00300-026-03475-0","type":"published","date":"2026-04-30T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81149223,"identity":"485a8d1a-05c5-4722-9030-064ab9ce91da","added_by":"auto","created_at":"2025-04-22 19:09:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73020,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area on the southern and northern Barents Sea shelf (Shelf S and Shelf N, S = South, N = North, respectively), shelf break (Break) and Nansen Basin (Basin) sampled during four seasonal cruise campaigns (in March, May, August and December).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6252716/v1/d05a5f0ea1616d584d82550b.jpg"},{"id":81148812,"identity":"028805a5-7db1-48dd-8a49-3ef01ca25235","added_by":"auto","created_at":"2025-04-22 19:01:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84217,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree of nematode identity based on compiled BLAST results for the top scoring zOTU in each sample. Taxonomy is binned at family level and summarized at order level (right side), with number of samples assigned to each taxa indicated. Numbers at branching points indicate the number of samples assigned at higher taxonomic levels, and circle sizes are scaled according to the number of specimens assigned to branching points. A few samples could only be confidently assigned to higher taxonomic levels (e.g. Enoplia; 2 and Chromadorea; 2).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6252716/v1/5ed37fefd1f6f66087a59e03.jpg"},{"id":81149226,"identity":"36bad743-2416-49a4-89bb-daf67907af77","added_by":"auto","created_at":"2025-04-22 19:09:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144593,"visible":true,"origin":"","legend":"\u003cp\u003eNematode prey (a) and sediment community (b) composition across seasons and locations in the northern Barents Sea (Shelf_S, Shelf_N), Barents Sea shelf break (Break) and Nansen Basin (Basin). All bars are compositions of individual samples. All taxa are agglomerated at class taxonomic level, and taxa with median abundances less than 5% across all samples are collected under “Taxa \u0026lt;5%”.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6252716/v1/b8cc49d23e1c5750abe92740.jpg"},{"id":81149224,"identity":"00732da9-783c-4218-9707-486eb0a0b790","added_by":"auto","created_at":"2025-04-22 19:09:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":86012,"visible":true,"origin":"","legend":"\u003cp\u003eSimilarity patterns of nematode prey compositions (a, b, c) and sediment community compositions (d, e) on the Barents Sea shelf and adjacent Nansen Basin. In a) Canonical correspondence analysis (CCA) constrained by significant numerical and categorial parameters. Numerical parameters (chlorophyll \u003cem\u003ea\u003c/em\u003e - Chl_\u003cem\u003ea\u003c/em\u003e, phaeopigments - Phaeo, sample depth - Depth) are shown with arrows, and categorical parameters (feeding groups; 1A - selective deposit, 1B - nonselective deposit, 2A - epistrate, 2B - omnivore-predators, UFG - unknown feeding group, stations; Shelf S, Shelf N, Break and Basin) are shown as centroids with red crosses. In b) and c), similarity patterns of prey compositions are shown using Non-metric multidimensional scaling (NMDS) of Bray-Curtis (b), and Jaccard (c) dissimilarities. In d) and e), similarity patterns of sediment community compositions are shown using NMDS of Bray-Curtis (d), and Jaccard (e) dissimilarities. All dissimilarity matrices shown were generated from compositional data at zOTU level taxonomy. In all panels, sample points are colored according to month, and shapes denote the station sampled. Ellipses indicate 95% confidence levels, with centroids showing the average within-month (b, c) or within-station (d, e) position.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6252716/v1/e0c5fcdaaced6c44a4f7891c.jpg"},{"id":81148814,"identity":"faffd590-d69e-482a-b4ed-1fa86f505d0a","added_by":"auto","created_at":"2025-04-22 19:01:43","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104679,"visible":true,"origin":"","legend":"\u003cp\u003eAverage nematode prey composition on the Barents Sea shelf and in the adjacent Nansen Basin during seasons (Season), stations (Station), feeding groups (FG), and nematode taxonomy in the form of lowest common ancestor (LCA) and order (Order). All taxa with median relative abundance less than 5% across samples are collected under “Taxa \u0026lt;5%”. Numbers in parentheses reflect the number of individuals in average prey compositions.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6252716/v1/5a6cd4d7a0528b913e069533.jpg"},{"id":81148817,"identity":"2ca4d857-e7e9-417a-9bb4-d2fc01709a75","added_by":"auto","created_at":"2025-04-22 19:01:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":126203,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of arthropod genera in nematode prey by season and scaled according to a) the arthropod fraction, and b) all prey in diet compositions. For some of the assignments the barcode marker sequence was equally similar to multiple genera, and we thus report both possible taxa (e.g. \u003cem\u003eOithona\u003c/em\u003e/\u003cem\u003eMicrosetella\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6252716/v1/aecc4eda224f197c391708c0.jpg"},{"id":108438084,"identity":"dcc5e525-e893-4825-aada-dfc1eb286a7e","added_by":"auto","created_at":"2026-05-04 16:07:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1775275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6252716/v1/73513b51-f672-4610-b218-8f76219d55e5.pdf"},{"id":81148818,"identity":"3b3a2394-d9b8-478f-a67d-ab77d4a7a3ae","added_by":"auto","created_at":"2025-04-22 19:01:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1071695,"visible":true,"origin":"","legend":"","description":"","filename":"SIPBSF.docx","url":"https://assets-eu.researchsquare.com/files/rs-6252716/v1/43d11c489b69174046ad2aa5.docx"}],"financialInterests":"Competing interest reported. Author Camilla Svensen is currently an editor for Polar Biology.","formattedTitle":"Eukaryote diets in Arctic marine nematodes across seasons and shelf-to-basin gradients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNematodes are best known for their pathogenic capabilities, causing serious disease in humans and animals, and considerable damage to food crops worldwide (Moens et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, the bulk of nematodes, both in terms of abundance and diversity, are found in soft marine and freshwater sediments, and terrestrial soils (Heip et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Nematodes are perhaps especially important in marine sediments, where they comprise the majority of the meiofauna in most ocean regions (McIntyre \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Moens et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and are \u0026ldquo;as dominant in the sediment as copepods are in the plankton\u0026rdquo; (Heip et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). While patchy in their distribution \u0026ndash; likely due to local differences in food availability and sediment grain size, benthic marine nematodes can reach abundances of 5\u0026nbsp;million individuals per square meter (Soetaert et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), with abundances typically being highest in the uppermost surface sediment layers (Fonseca et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Broadly speaking, meiofaunal abundance and diversity decrease with increasing water depth (Rex et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bessi\u0026egrave;re et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Soetaert et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and it is likely that the decrease is due to dwindling food supply. The quality and quantity of pelagic food particles reaching the sea-floor decreases with increasing water depth (Billett et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Schewe and Soltwedel \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Depth-related decrease in meiofaunal abundance is however less steep for nematodes than other, larger metazoans, leading to comparatively higher relative abundances of nematodes in the deep-sea than on shelves (Rex et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Local factors may also be influential, with coarse grain-sized sediments favoring long nematodes, and sediments poor in organic materials favoring short and wide nematodes (Soetaert et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite being small, slender and inconspicuous, nematodes take on different and important functional roles in the sediment. Marine nematodes can play a significant role in nutrient cycling through symbioses with various methanogenic, methanotroph, methylotroph, sulphate-reducing or nitrifying Archaea and Bacteria (Schuelke et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Some nematodes feed on bacteria (Moens et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), while others prefer algae over bacteria (Tietjen and Lee \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). Some are predators which may feed on other nematodes (Fonseca and Gallucci \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dos Santos and Moens \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), or other meiofauna including harpacticoid copepods, annelids, halacarid mites, ostracods and oligochaetes (Kennedy \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). A considerable number of nematodes are believed to be omnivores, taking advantage of several different food sources including detritus and the sinking remains of planktonic organisms (Kennedy \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMarine nematodes have historically been divided into four trophic groups based on presence or absence of teeth or teeth-like structures, and their buccal cavity morphology (Wieser \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1953\u003c/span\u003e). Species categorized as selective deposit feeders (1A) and non-selective deposit feeders (1B) both lack teeth and leave suitably sized food items undamaged, but are separated by small and large buccal cavities, respectively. In contrast, epistrate feeders (2A) and omnivore-predators (2B) both have buccal cavity teeth that are used to puncture prey and ingest their contents. However, omnivore-predators are separated from the former by having a hollow tooth-like structure (onchium) connected to a salivary gland, which enables concurrent secretion of enzymes and feeding on cell contents (Jensen \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). While morphological structures like these are expected to influence the range of prey a nematode can accrue, it is still possible that different groups exploit the same food sources (Jensen \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Both epistrate and deposit feeders may for instance gain sustenance from microalgal cells (Moens and Vincx \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), although the mechanisms of puncturing and feeding on cell contents (epistrate feeders) versus swallowing whole cells (deposit feeders) are different (Jensen \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Moreover, many aquatic nematodes have been suspected of being able to change feeding strategy in response to availability of particular food particles at a given time (Moens and Vincx \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough nematodes are amongst the most diverse and abundant animals on earth, the scientific community is still largely in the dark with regards to their trophic interactions, and the impact they may have on their local benthic environments, or marine ecosystems as a whole. A handful of studies have conducted molecular microbiome profiling of nematodes, but these are typically from a limited geographic region, have targeted single nematode species or genera, or cover terrestrial soil species (Cheng et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Baquiran et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dirksen et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To the best of our knowledge, there have only been a few \u003cem\u003ein situ\u003c/em\u003e studies of marine nematode trophic interactions to date, and none have been conducted for nematodes in the Eurasian Arctic. Using high throughput sequencing, 16S and 18S microbiome profiles were acquired from nematodes residing in the Arctic Beaufort Sea, Gulf of Mexico and Californian coast (Schuelke et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A key finding Schuelke et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported, was that nematode microbiomes did not correlate distinctly to geographical region, nor to feeding group or host phylogeny. Hence, Wieser\u0026rsquo;s feeding groups were not a significant predictor of the nematode microbiome. Using stable isotope analysis on nematodes in a Portuguese estuary system Vafeiadou et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found a considerable variation in trophic level among congeneric nematode species, and these authors, too, warned against depending solely on morphology when assessing feeding ecology. They for instance found that nematodes of the genus \u003cem\u003eParacomesoma\u003c/em\u003e, generally considered deposit-feeders due to a lack of teeth, had high δ\u003csup\u003e15\u003c/sup\u003eN levels as would rather be expected from omnivore-predators, indicating a higher trophic position than what would be expected from their morphology alone.\u003c/p\u003e \u003cp\u003eThe current study explores the trophic ecology of meiofaunal nematodes in the Eurasian Arctic. We specifically aimed to describe the key eukaryote prey organisms for individual nematodes sampled during different seasons and at spatially distinct locations on the Barents Sea shelf, shelf break and the adjacent Nansen Basin. The Barents Sea is one of the most productive regions on the Arctic continental shelf, and accounts for approximately 40% of the annual pan-Arctic net primary production (Sakshaug et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The shelf regions are suggested to have particularly strong coupling of pelagic and benthic processes (Ambrose and Renaud \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Piepenburg \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), making it an ideal study system for investigating pelagic food acquisition in benthic meiofauna. Pelagic carbon sources to the Barents Sea shelf have been characterized as mainly composed by phytodetritus (Renaud et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The quantity and quality of organic matter produced depends on seasonally variable processes (Sakshaug et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), with most of the organic matter being produced and consumed during spring and summer when light is available. This material settles to the seafloor, with peaks in May and August in our study region (Bodur et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While globally, nematodes are numerically dominant in the benthic meiofaunal community in the Arctic (Hoste et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Oleszczuk et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), it is unclear if their diet varies seasonally in response to the settling phytodetritus. Nematodes may be used as biological indicators of sediment types, as morphometric attributes are related to environmental conditions such as food availability (Grzelak et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and are key players in the carbon remineralization in the Barents Sea (Jorda-Molina, unpubl. data). We asked: Is nematode prey composition influenced by season, and if so \u0026ndash; which prey are important in the respective seasons? Does prey composition match nematode buccal and odontial morphology (feeding guilds) as envisaged by (Wieser \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1953\u003c/span\u003e), or is prey composition to some extent independent of host phylogeny and morphology of the buccal cavity (as suggested by Schuelke et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vafeiadou et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)? Is prey composition reflecting the availability of putative prey as seen through metabarcoding of community samples, or is it related to environmental parameters like organic material (TOC), photosynthetic pigments (Chl \u003cem\u003ea\u003c/em\u003e), and phytodetritus (phaeopigments)?\u003c/p\u003e"},{"header":"2. Materials \u0026 Methods","content":"\u003cp\u003e2.1 Sample collection\u003c/p\u003e\n\u003cp\u003eSediment samples were collected along a transect east of Svalbard in the European Arctic from four different locations on the Barents Sea Shelf, along the Shelf Break and into the Nansen Basin (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Four cruises were conducted onboard the R/V Kronprins Haakon during August and December in 2019, and March and May in 2021, each targeting the same locations (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For clarity, we refer to each of the four cruise as distinct seasons (March, May, August and December) throughout the manuscript. Sediment was collected with a giant box-corer (0.25 m\u003csup\u003e2\u003c/sup\u003e), from which the surface sediment (uppermost 1 cm, ca. 30 mL) was scraped off with a spoon. The box-corer was deployed three times on each station, and one surface sediment sample was obtained per box core sample. Surface sediment was immediately fixed on ice-cold ethanol (96%) and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until further processing.\u003c/p\u003e\n\u003cp\u003e2.2 Picking and DNA extraction\u003c/p\u003e\n\u003cp\u003eWe extracted and concentrated the meiofauna from the sediment using colloidal silica and a modified decantation and flotation protocol (Heip et al. \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e; Fonseca and Fehlauer-Ale \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dell\u0026rsquo;Anno et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). A silica solution was prepared with Ludox TM-40 (Sigma-Aldrich) by diluting with Milli-Q water (MQ) to the specific gravity of 1.13 (Giere \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The sediment was first washed vigorously with Milli-Q water onto a 64 \u0026micro;m sieve. Retained sediment and meiofauna were then resuspended with at least 5 parts colloidal silica, centrifuged at 1800 g for 5 minutes before decanting the supernatant with meiofauna over the same sieve. This was repeated three times per sediment sample. Nematodes were picked under a standard stereomicroscope using handmade Irwin loops (Schram and Davison \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). All nematodes were individually rinsed by transfer through three Milli-Q droplets to minimize the effect of exogenous DNA, before being placed in tissue lysis (TL) buffer (E.Z.N.A. Tissue DNA kit, Omega Bio-tek). Final immersion in tissue-lysis buffer was performed with a flame-sterilized Irwin-loop. We picked 11 nematodes and added one extraction negative (TL buffer without material) from each sediment sample, making a grand total of 143 individual nematode samples and 13 extraction negatives which were stored at \u0026minus;\u0026thinsp;20\u0026deg;C until extraction. DNA was extracted per manufacturers protocol (Tissue Spin protocol, E.Z.N.A\u0026reg; Tissue DNA kit, Omega Bio-Tek), although with a lowered elution volume (2 x 50 \u0026micro;L), and by incubating on a thermocycler (300 rpm, 70\u0026deg;C, 3 hours or overnight). To verify that nematode samples were viable for sequencing, and that extraction negatives were indeed negative in PCR, all extracts were subsequently tested by PCR amplification of the V7 fragment of the 18S rRNA gene (~\u0026thinsp;240 bp) using the universal eukaryotic primers 960F (5\u0026rsquo;-GGCTYAATTTGACTCAACRCG-3\u0026rsquo;, Gast et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e) and modified 1200R (5\u0026rsquo;-GGGCATCACAGACCTG-3\u0026rsquo;, Cleary \u0026amp; Durbin,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and 1% gel electrophoresis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverview of number of nematode (Nem) and sediment community (Com) samples, sampling month (Season), location (Station) with latitude (Lat.) and longitude (Lon.) and sample depth (Depth, m). Key properties in the upper 0\u0026ndash;1 cm fraction of the sediments are shown with Total Organic Carbon (TOC, %), Chlorophyll \u003cem\u003ea\u003c/em\u003e (Chl \u003cem\u003ea\u003c/em\u003e, mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and Phaeopigment (Phaeo, mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). NA indicates missing values either due to missing samples (Nem, Com) or due to lacking replicates for calculating standard deviations (SD).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLat.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLon.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepth\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCom\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTOC\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChl \u003cem\u003ea\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhaeo\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShelf S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.2193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;NA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.67\u0026thinsp;\u0026plusmn;\u0026thinsp;NA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShelf N.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.7712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.6121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.93\u0026thinsp;\u0026plusmn;\u0026thinsp;6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.5467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.8518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.7276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.6712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShelf S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.2215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.12\u0026thinsp;\u0026plusmn;\u0026thinsp;8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShelf N.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.7508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.0087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.5369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.8676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.8420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.7571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShelf S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.9997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.2153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShelf N.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.7457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.0169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.5452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.8475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.7276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.6712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDec.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShelf N.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.7585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.9950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDec.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.5428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.9424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e. TOC data were obtained from published datasets (Ricardo de Freitas et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e. Chl \u003cem\u003ea\u003c/em\u003e and Phaeopigment data were obtained from published datasets (Akvaplan-niva, \u003cspan class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024c\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024d\u003c/span\u003e).\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eDNA extraction of the ambient benthic eukaryote community was administered by Nansen Legacy colleagues at the University of Bergen (Prof. Lise \u0026Oslash;vreaas). The respective sediment samples were obtained from the same up to three individual box-cores at each location. Vertical layers were separated from sub-cores, and immediately fixed on ice-cold ethanol (96%) and stored at \u0026ndash; 80\u0026deg;C until further processing. In the lab, DNA was extracted from the uppermost 0\u0026ndash;1 cm layer using the MO BIO PowerSoil DNA Isolation kit, following the manufacturer\u0026apos;s protocol (v. 02232016) for centrifugation and with the following deviations. DNA was extracted from 0.30\u0026ndash;0.35 g of surface sediment, 1 mL of C4 solution was used per sample, and at most 500 \u0026micro;L of sample was loaded onto the spin filter. Centrifugation for drying was increased to 2 min, and DNA was eluted twice (2 x 50 \u0026micro;L) to obtain a higher DNA quantity.\u003c/p\u003e\n\u003cp\u003e2.3 Sediment properties\u003c/p\u003e\n\u003cp\u003eSampling protocols for sediment properties are described in the sampling protocols from The Nansen Legacy, (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and sampling was supported by collaborators. Fluorometric pigment data from the same box-core sediment samples were acquired from Akvaplan-niva (\u003cspan class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024c\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024d\u003c/span\u003e). We calculated the average Chlorophyll \u003cem\u003ea\u003c/em\u003e (mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and Phaeopigment (mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) concentrations in the uppermost 0\u0026ndash;1 cm layer of sediment. Likewise, total organic carbon (TOC, %) and carbon isotopic composition (\u0026delta;\u003csup\u003e13\u003c/sup\u003eC, \u0026permil;) from Isotope Ratio Mass Spectrometry (EA-IRMS) were acquired from Ricardo de Freitas et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and calculated for the uppermost 0\u0026ndash;1 cm layer of sediment.\u003c/p\u003e\n\u003cp\u003e2.4 Sequencing\u003c/p\u003e\n\u003cp\u003eTo determine the eukaryote diet of the nematodes, we amplified a short fragment of the 18S rRNA gene (V7, ~\u0026thinsp;100\u0026ndash;110 bp), using 18S_allshorts primers (Forward 5\u0026rsquo;-TTTGTCTGSTTAATTSCG-3\u0026rsquo;, and Reverse 5\u0026rsquo;-GCAATAACAGGTCTGTG-3\u0026rsquo;) (Guardiola et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). PCR primers were pre-tagged with 8-base oligonucleotides with at least 3 nucleotides difference to enable pooling of samples. The primers contained a variable number of degenerate nucleotides (N, 2\u0026ndash;4) at the 5\u0026rsquo;-end to increase sequencing quality (Wangensteen et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Amplicons were generated from extracts in 20 \u0026micro;L PCR reactions with 10 \u0026micro;L AmpliTaq Gold\u0026trade; Master Mix (2x, Applied Biosystems), 0.16 \u0026micro;L Bovine Serum Albumin (BSA, 20 \u0026micro;g \u0026micro;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), 5.84 \u0026micro;L ultrapure MQ water, 2.0 \u0026micro;L of 18S_allhorts forward and reverse primer mix (2.5 \u0026micro;M each), and 2.0 \u0026micro;L DNA template. PCR conditions included an initial denaturation (10 min, 95\u0026deg;C) and 35 cycles of denaturation (30 s, 95\u0026deg;C), annealing (30 s, 45\u0026deg;C) and elongation (30 s, 72\u0026deg;C). A subsample of real and negative samples from each PCR plate were observed on a 1% agarose gel to verify positive and negative amplification, respectively. Amplicons were subsequently purified with MinElute\u0026trade; spin-columns (Qiagen, Hilden, Germany), and quantified using the fluorometric broad-range dsDNA assay (Qubit 4\u0026trade;, Invitrogen by Thermofischer). Sequencing libraries were prepared from quantified and purified pools following the NEXTflex\u0026trade; PCR-Free DNA Sequencing Kit (Bioo scientific, Austin, Texas, USA). For each library, 3000 ng template was purified by size (\u0026ge;\u0026thinsp;150 bp) using magnetic beads (Agencourt AMPure XP beads; Beckman Coulter Genomics, California, USA). Sequences were adenylated and Illumina-compatible adaptors (NEXTflex\u0026trade; DNA Barcode Adapter, Bioo Scientific) were ligated onto the adenylated ends. Libraries were quantified by qPCR using the NEBNext\u0026reg; Library Quant Kit for Illumina (New England Biolabs, Massachusetts, USA). Two libraries of nematode amplicons were sequenced using 150 bp paired end (PE) chemistry in a single lane (800 Gb output) on a Illumina NovaSeq6000 platform (Novogene Co., Ltd.).\u003c/p\u003e\n\u003cp\u003eFor sequencing of the benthic community, the Integrated Microbiome Resource (IMR, Halifax, Canada) was commissioned to amplify the V4 region of the 18S rRNA gene with the eukaryotic universal primers V4F_illumina (5\u0026rsquo;-CCAGCASCYGCGGTAATTCC\u0026ndash;3\u0026apos;) and V4R_AZig_illumina (5\u0026apos;-ACTTTCGTTCTTGATYRATGA\u0026ndash;3\u0026apos;, Piredda et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). PCR amplification and subsequent library preparation was conducted as described in Comeau \u0026amp; Kwawukume (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), and sequencing was achieved on a Illumina MiSeq running with 300 bp PE chemistry.\u003c/p\u003e\n\u003cp\u003e2.5 Bioinformatics\u003c/p\u003e\n\u003cp\u003eApproximately 3.1 billion raw paired end reads were acquired from 18S sequencing of nematode extracts, and processed using the OBITools (1.2.13, RRID:SCR_024141, Boyer et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), vsearch (2.22.1, RRID:SCR_024494, Rognes et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and BLAST+ (2.13.0, RRID:SCR_004870, Altschul et al., \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e; Camacho et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) software. The raw data is available from NIRD-RDA (Flo \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Of the raw reads, 1.9\u0026nbsp;billion remained after pairing of forward and reverse reads (illuminapairedened), sample identification and demultiplexing (ngsfilter) and were non-ambiguous, of high-quality (obigrep -p \u0026lsquo;score\u0026thinsp;\u0026gt;\u0026thinsp;40.00\u0026rsquo;) and within adequate length (80\u0026thinsp;\u0026le;\u0026thinsp;120 bp). 1.3\u0026nbsp;billion reads remained after subsequent dereplication (obiuniq), denoising (vsearch cluster_unoise, unoise_alpha 2) with removal of singletons (minsize 2) and removal of chimeras (vsearch uchime_denovo). Finally, 2.5\u0026nbsp;million zero-radius Operational Taxonomic Units (zOTUs) were taxonomically assigned to the protist ribosomal database (PR2, v.5.0.1) using BLAST+.\u003c/p\u003e\n\u003cp\u003eFrom the sediment community samples, we acquired 414 698 raw paired end 18S V4 reads. The bioinformatic processing of sediment community sequence reads were identical to processing of prey reads with the exception of the few following steps. Since each sample had separate identifier oligos, the sediment reads did not require demultiplexing. Primers were trimmed with obicut (OBITools, -b 25) and reads within lengths of 360\u0026thinsp;\u0026le;\u0026thinsp;380 bp were retained. Approximately 179 000 reads divided amongst 4445 zOTUs were taxonomically assigned.\u003c/p\u003e\n\u003cp\u003e2.6 Curation of sequence-data\u003c/p\u003e\n\u003cp\u003eAssigned nematode prey and sediment community zOTUs were imported to R-studio (v. 4.1.3), but prey zOTUs were processed through additional curation. By means of manual curation, we identified and discarded several taxa that were likely contaminants. For example, all arachnid zOTUs were discarded, after confirming with BLAST that neither of the 100 most abundant arachnid zOTUs were assigned to known marine taxa (e.g. Halacaridae). Certain terrestrial taxa whose presence could likely be explained by contamination, such as \u003cem\u003eHomo\u003c/em\u003e, Embryophytes and Collembola, were discarded. Additionally, we employed the prevalence method of Decontam (Davis et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) to identify more cryptic contaminants, wherein identifications are based upon disproportional zOTU high abundances in extraction negatives compared to real samples.\u003c/p\u003e\n\u003cp\u003e2.7 Assigning nematodes to taxonomy and feeding group\u003c/p\u003e\n\u003cp\u003eWe identified the nematodes to family by curating the taxonomy of the zOTUs that contributed the maximum number of reads to each sample total. Given that the consumer DNA should contribute the most to the genetic material of each extract (e.g., Flo et al., \u003cspan class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Pi\u0026ntilde;ol et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), these zOTUs should represent the consumers themselves. The zOTU sequences were aligned to the NCBI nucleotide archive (NCBI-nt, RRID:SCR_004860) database by BLASTn (RRID:SCR_001598, Altschul et al. \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e; Camacho et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), and the output was imported to MEGAN6 (RRID:SCR_011942, Huson et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The lowest common ancestor (LCA) was identified from all blast reports using the \u003cem\u003enaive\u003c/em\u003e LCA algorithm for taxonomic binning (default with top percent\u0026thinsp;=\u0026thinsp;5.0, min. support percent\u0026thinsp;=\u0026thinsp;0.001). Due to sparse reference data for marine nematodes, we binned LCA at family-level taxonomy, even in cases where genera and species were confidently assigned by the algorithm (Supplementary Table S2). All taxonomy was confirmed and/or curated with the Nemys database (Nemys Eds., 2024). Eight samples identified to the freshwater family of Tobrilidae (superfamily Tobriloidea) were manually curated to Rhabdodemaniidae (superfamily Tobriloidea) whose members are the closest relatives with known presence in the marine Arctic meiobenthos (Van Gaever et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Van Gaever and Vanreusel \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). To confirm the taxonomic assignments, we compared our list of families to nematode taxonomy resulting from morphological examinations based on separate material from the same box core samples (Wernstr\u0026oslash;m et al., in preparation).\u003c/p\u003e\n\u003cp\u003eBased on the obtained nematode taxonomic identity, we further assigned each sample to a feeding group (1A \u0026ndash; selective deposit, 1B \u0026ndash; non-selective deposit, 2A \u0026ndash; epistrate and 2B \u0026ndash; omnivore-predators) according to (Jensen \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e) and (Wieser \u003cspan class=\"CitationRef\"\u003e1953\u003c/span\u003e). Only samples whose family were confidently assigned during taxonomic binning were assigned to a feeding group, and a few taxa whose buccal morphology are unknown (e.g. Ceramonematidae) were left unassigned (e.g. feeding group\u0026thinsp;=\u0026thinsp;NA, Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e2.8 Exploratory data analyses of nematode diet data\u003c/p\u003e\n\u003cp\u003eWe used tidyverse (Wickham et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and phyloseq (1.36.0, RRID:SCR_013080, McMurdie and Holmes \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) packages to expedite analysis and storage of data in R. We used primarily vegan (RRID:SCR_011950, Oksanen et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) to perform ecological analyses, and ggplot2 (RRID:SCR_014601, Wickham \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) to prepare graphics. Sufficient sequencing depth was confirmed using rarefaction (\u003cem\u003erarecurve\u003c/em\u003e, vegan) on prey data at both zOTU- and species-level. Filtered and curated prey data was first normalized to comparable values by transforming counts to relative read abundances (%, RRA). Prey RRA was used for generating bar plots, and to limit complexity we chose to plot data agglomerated to class and genus taxonomic levels, with taxa below a median sample abundance of 3 or 5% RRA being collected in \u0026ldquo;taxa\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026rdquo; or \u0026ldquo;taxa\u0026thinsp;\u0026lt;\u0026thinsp;5%\u0026rdquo;, respectively. Due to the short marker gene (100\u0026ndash;110 bp) and limited metazoan annotations in the database, we confirmed \u0026ndash; and when required, curated all identified metazoan genera and classes by performing a BLAST search against the NCBI nucleotide archive (NCBI-nt).\u003c/p\u003e\n\u003cp\u003e2.9 Ordination of nematode prey and sediment communities\u003c/p\u003e\n\u003cp\u003eNon-metric multidimensional scaling (NMDS) plots were made to explore how prey and community samples clustered across spatiotemporal parameters. We used two dissimilarity-based indices and data at zOTU- and Species-level agglomeration. Since NMDS ordination returned similar patterns with both zOTU and Species-level data, we suffice with only zOTU level ordinations in the current manuscript. Bray-Curtis dissimilarity was generated from relative abundance, while Jaccard distance was generated from presence-absence data with any zOTU\u0026thinsp;\u0026gt;\u0026thinsp;1% relative read abundance for assigning presence. Indices were calculated using \u003cem\u003evegdist\u003c/em\u003e and scaled using \u003cem\u003emetaMDS\u003c/em\u003e (vegan). To achieve low stress (\u0026lt;\u0026thinsp;0.15) while avoiding over-plotting, the number of dimensions for each scaling was selected based on permutational scree-plots. Subsequent NMDS plots were made using ggplot2, with centroids and confidence ellipses (95%, \u003cem\u003estat_ellipse\u003c/em\u003e, vegan).\u003c/p\u003e\n\u003cp\u003eCanonical correspondence analysis (CCA) was computed using nematode prey relative abundances at family-level taxonomy, and a set of categorical and numerical constraints. Prior to analysis we evaluated each environmental parameter to exclude correlated variables with the \u003cem\u003epairs\u003c/em\u003e function. Through reverse model selection, starting with all seemingly uncorrelated variables, we reduced the model parameters stepwise, retaining only parameters that significantly explained inertia (anova.cca, p\u0026thinsp;\u0026le;\u0026thinsp;0.001). The retained significant parameters included Phaeopigments (Phaeo, mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), Chlorophyll \u003cem\u003ea\u003c/em\u003e:Phaeopigments ratio (Chl \u003cem\u003ea\u003c/em\u003e:Phaeo, unitless), sample depth (Depth, m), sample location (Shelf S, Shelf N, Break and Basin), feeding group (1A, 1B, 2A and 2B), lowest common ancestor of nematode identified (ntax_lca) and sample month (March, May, August, December). Excluded variables were Latitude, Longitude, nematode order, Chl \u003cem\u003ea\u003c/em\u003e concentration (mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), total organic carbon (TOC, %), carbon isotopic composition (\u0026delta;\u003csup\u003e13\u003c/sup\u003eC, \u0026permil;) and day of year (DOY). The best fit model, explaining\u0026thinsp;~\u0026thinsp;38% of the total inertia, was found when using square-root transformed relative abundances. Although a significant predictor of prey composition, lowest common ancestors of nematodes were not overlaid to limit overcrowding the plot.\u003c/p\u003e\n\u003cp\u003e2.10 PERMANOVA for testing significance of structuring parameters\u003c/p\u003e\n\u003cp\u003eWe used Permutational Multivariate Analysis of Variance (PERMANOVA) to test the relevance of parameters identified by CCA for describing nematode diets (\u003cem\u003eadonis2\u003c/em\u003e, vegan). We used Bray-Curtis dissimilarity and Jaccard distance based on family-level taxonomy. However, since the two distance matrices yielded comparable results, we only show the statistical metrics derived from testing with Bray-Curtis dissimilarity. All terms were sequentially tested for between-group homogeneity of dispersion using \u003cem\u003ebetadisper\u003c/em\u003e (vegan, Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Nematode taxonomy\u003c/p\u003e\u003cp\u003eApproximately 84% of the nematodes sampled were confidently identified to family based on taxonomic binning of BLAST results, whereas 96% were assigned at the level of order (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nematodes were assigned to seven orders with Monhysterida (35), Chromadorida (34), Araeolaimida (27) and Enoplida (22) being the orders most frequently observed. At the family level, 20 families were identified, and Chromadoridae (32, order Chromadorida), Comesomatidae (14, Araeolaimida), Oxystominidae (13, Enoplida) and Xyalidae (11, Monhysterida) were the most common.\u003c/p\u003e\u003cp\u003e3.2 Diet of nematodes and sediment community composition\u003c/p\u003e\u003cp\u003eApproximately 1.3\u0026nbsp;billion prey sequence reads, divided amongst 2.5\u0026nbsp;million zOTUs were taxonomically assigned and thereafter analyzed. Sample completeness was verified with rarefaction analyses, and was found reasonably complete with average rarefaction curve slopes estimating 29 new zOTUs (0.029) and 2.6 new species (0.0026) per 1000 additional reads, and curves approximating plateau phase (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Putative dietary items belonged to a diverse set of eukaryote taxa, with high average relative read abundances (RRA) from metazoan, protist and fungal taxa. Metazoan prey was largely contributed by Arthropoda (average RRA = 23%), Craniata (7%) and Annelida (7%). Arthropoda reads as a whole were more abundant in August (30%) and December (28%) than in March (21%) and May (13%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), and consisted mainly of copepod taxa, with Mysida, Cladocera and Balanomorpha contributing fewer, yet a substantial number of prey reads. Arthropod prey are explored in greater detail below. Craniata reads were abundant across all seasons (7%) and consisted mainly of taxa identified to Teleostei (6%). Genera including salmon, \u003cem\u003eSalmo\u003c/em\u003e sp. (2%) and Atlantic cod, \u003cem\u003eGadus\u003c/em\u003e sp. (1%) contributed the greatest number of reads (Table S2), and \u003cem\u003eSalmo\u003c/em\u003e sp. was particularly abundant in December (5%). Annelida (8%) were abundant particularly in August (10%) and December (10%) and were composed of taxa including \u003cem\u003eAustrobdella\u003c/em\u003e sp. (2%) and \u003cem\u003eAnguillosyllis\u003c/em\u003e sp. (1%). Fungi were also abundant and Ascomycota (23%), Basidiomycota (12%) and Mucoromycota (1%) composed the major fungal classes. In terms of seasonality, Ascomycota and Basidiomycota were less prevalent in August (15, and 8%) and December (19, and 9%) than in March (27, and 13%) and May (31, and 15%). For fungal taxa, we only report genera, since they are impossible to assign to species level based on a small 18S fragment. It was attempted to amplify and sequence prokaryote prey (16S rRNA V4) from nematode samples, but these efforts were unsuccessful, possibly due to low prokaryote DNA concentrations in single nematode DNA extracts.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"±\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage relative read abundance (%, RRA) ± Standard Error of the Mean (SEM) of nematode prey classes in all samples, per month and per location. Average values greater than 1% are highlighted in bold, and values smaller than 0.1% are not shown.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubdivision\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(N = 143)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003cp\u003e(n = 44)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003cp\u003e(n = 22)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003cp\u003e(n = 33)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003cp\u003e(n = 44)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eShelf S\u003c/p\u003e \u003cp\u003e(n = 33)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eShelf N\u003c/p\u003e \u003cp\u003e(n = 44)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBreak\u003c/p\u003e \u003cp\u003e(n = 44)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBasin\u003c/p\u003e \u003cp\u003e(n = 22)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscosea_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlabellinia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChlorophyta_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMamiellophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTrebouxiophyceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.8 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.0 ± 1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.7 ± 0.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.8 ± 0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3.2 ± 1.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.8 ± 1.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.3 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e3.3 ± 1.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e5.9 ± 2.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePicozoa_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePicozoa_XX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.5 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.4 ± 0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCryptophyta_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCryptophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKathablepharida\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKathablepharidea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaptophyta_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrymnesiophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.0 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFilasterea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFilasterea_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6 ± 0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.0 ± 0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAscomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e23.3 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e15.3 ± 3.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e19.0 ± 3.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e26.7 ± 3.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e30.9 ± 4.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e21.2 ± 3.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e22.3 ± 3.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e25.5 ± 3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e23.9 ± 5.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBasidiomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11.6 ± 1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.2 ± 1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9.2 ± 3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e12.6 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e15.3 ± 2.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e15.2 ± 3.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e11.9 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e11.0 ± 2.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e6.7 ± 1.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChytridiomycota\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.8 ± 0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.8 ± 1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.3 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMucoromycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.4 ± 0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.8 ± 1.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.9 ± 0.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.3 ± 0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.5 ± 1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.8 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003eMetazoa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnnelida\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.3 ± 1.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e9.8 ± 2.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e10.4 ± 3.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.4 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4.5 ± 0.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4.4 ± 1.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e8.0 ± 2.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e8.8 ± 2.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e7.0 ± 3.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eArthropoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e22.6 ± 1.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e30.4 ± 3.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e28.2 ± 3.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e20.6 ± 3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e13.3 ± 1.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e17.9 ± 2.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e24.6 ± 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e22.6 ± 3.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e25.4 ± 4.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrachiopoda\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBryozoa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.0 ± 0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.1 ± 1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.7 ± 1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.6 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCephalochordata\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCnidaria\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCraniata\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.4 ± 0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e7.3 ± 1.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.1 ± 1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e8.2 ± 1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6.9 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7.8 ± 1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e7.0 ± 1.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e5.3 ± 1.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e11.4 ± 3.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEchinodermata\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 ± 0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHemichordata\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMollusca\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.7 ± 0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.0 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.5 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.9 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3.3 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.2 ± 0.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.1 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2.3 ± 0.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e2.3 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNemertea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.8 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.0 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.9 ± 2.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0 ± 0.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.4 ± 1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatyhelminthes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.6 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.6 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePorifera\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5 ± 0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePriapulida\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotifera\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrochordata\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCiliophora\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOligohymenophorea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.7 ± 1.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.8 ± 1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.1 ± 1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e10.8 ± 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e9.2 ± 1.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e9.1 ± 1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e6.3 ± 1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e7.8 ± 2.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e8.5 ± 2.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpirotrichea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.7 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.2 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.6 ± 0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.6 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDinoflagellata\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDinophyceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.4 ± 0.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.4 ± 1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.0 ± 0.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.9 ± 0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.9 ± 0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4.1 ± 1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.6 ± 0.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2.2 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.5 ± 0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSyndiniales\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.5 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.9 ± 1.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7 ± 0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.0 ± 0.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCercozoa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFilosa-Sarcomonadea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.5 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBigyra\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSagenista\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGyrista\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChrysophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7 ± 0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGyrista_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMediophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.2 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelonemia_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTelonemia_XX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage RRA (%) ± Standard Error of the Mean (SEM) of sediment community classes in all samples, per month and per location. Average values greater than 1% are highlighted in bold, and values smaller than 0.1% are not shown.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubdivision\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(N = 26)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003cp\u003e(n = 9)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003cp\u003e(n = 3)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003cp\u003e(n = 8)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003cp\u003e(n = 6)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eShelf S\u003c/p\u003e \u003cp\u003e(n = 6)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eShelf N\u003c/p\u003e \u003cp\u003e(n = 12)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBreak\u003c/p\u003e \u003cp\u003e(n = 0)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBasin\u003c/p\u003e \u003cp\u003e(n = 8)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eChlorophyta_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChlorophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMamiellophyceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.3 ± 0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.0 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.3 ± 0.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.4 ± 0.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.6 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePyramimonadophyceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.3 ± 0.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.4 ± 1.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.4 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.9 ± 1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e4.8 ± 1.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrebouxiophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrasinodermophyta_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrasinodermophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaptophyta_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrymnesiophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.2 ± 1.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6 ± 0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApusomonada_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApusomonadidae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChoanoflagellata\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChoanoflagellatea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChytridiomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.2 ± 0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.8 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.2 ± 3.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.2 ± 1.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6.9 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e7.5 ± 1.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.9 ± 0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMucoromycota\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.6 ± 1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8 ± 0.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eMetazoa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnnelida\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.2 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.6 ± 2.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.3 ± 1.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.5 ± 0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.6 ± 1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eArthropoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.8 ± 1.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.8 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6.3 ± 4.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.1 ± 1.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e4.1 ± 3.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCnidaria\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5 ± 1.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEchinodermata\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGastrotricha\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKinorhyncha\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.0 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9 ± 0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.0 ± 0.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetazoa_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.1 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.9 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMollusca\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.5 ± 1.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.9 ± 3.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.4 ± 2.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5.0 ± 4.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.7 ± 1.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e3.7 ± 3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNematoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.7 ± 2.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.4 ± 1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.3 ± 0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.7 ± 1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e12.0 ± 7.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e4.4 ± 1.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e11.7 ± 5.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNemertea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.6 ± 2.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e22.7 ± 18.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e5.7 ± 5.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatyhelminthes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 ± 0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.8 ± 0.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpisthokonta_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpisthokonta_XX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotosphaerida\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotosphaerida_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApicomplexa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoccidiomorphea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCiliophora\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSpirotrichea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.8 ± 0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.7 ± 0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.1 ± 1.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.9 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.3 ± 0.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.1 ± 0.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.0 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.6 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDinoflagellata\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDinophyceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e15.7 ± 2.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e16.8 ± 6.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e20.4 ± 5.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e15.4 ± 4.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e12.2 ± 4.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e8.5 ± 1.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e27.1 ± 4.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e4.1 ± 1.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSyndiniales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.7 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.4 ± 0.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.7 ± 1.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.4 ± 1.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6.3 ± 1.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e4.7 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e4.7 ± 1.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerkinsea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePerkinsida\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.5 ± 0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.8 ± 1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.0 ± 0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.7 ± 1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.7 ± 1.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.4 ± 0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.9 ± 0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e7.5 ± 1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCercozoa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndomyxa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFilosa-Imbricatea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFilosa-Thecofilosea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBigyra\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpalozoa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSagenista\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.3 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.1 ± 0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.1 ± 1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6.9 ± 1.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.8 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e6.3 ± 1.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eGyrista\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBacillariophyceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.3 ± 0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.3 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.6 ± 0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.7 ± 0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.9 ± 0.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBolidophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChrysophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.7 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoscinodiscophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGyrista_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6 ± 0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.9 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMediophyceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e42.2 ± 6.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e58.4 ± 10.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.7 ± 1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e50.4 ± 10.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e25.0 ± 5.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e83.2 ± 3.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e18.2 ± 4.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e47.3 ± 9.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePelagophyceae\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeronosporomycetes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePirsoniales\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3 ± 0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStramenopiles_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStramenopiles_XX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelonemia_X\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTelonemia_XX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 ± 0.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3 ± 0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eOf protists, Oligohymenophorea were particularly abundant (8%), especially in March (11%) and May (9%), with peritrich ciliates from the genus \u003cem\u003eVorticella\u003c/em\u003e (8%) contributing the majority of the diet reads. Trebouxiophyceae (4%) chlorophytes were more abundant in August (6%) and December (4%) than in March (2%) and May (3%), with reads recruited primarily from \u003cem\u003eAuxenochlorella\u003c/em\u003e sp. (4%). Dinophyceae (3%) were relatively abundant year-round, with \u003cem\u003eProrocentrum\u003c/em\u003e sp. (1%), \u003cem\u003eGyrodinium\u003c/em\u003e sp. (0.6%) and \u003cem\u003eHeterodinium\u003c/em\u003e sp. (0.4%) contributing the bulk of the reads. Mediophyceae (0.4%) diatoms were practically neglible, with \u003cem\u003eThalassiosira\u003c/em\u003e sp. composing on average a meager 0.2%. Abundances of prey taxa present but with aggregated contribution of \u0026lt; 5% median abundance are listed in class and family level in the supplementary (Table S3 and S4).\u003c/p\u003e\u003cp\u003eApproximately 179 000 reads divided amongst 4445 zOTUs were taxonomically assigned and analyzed to characterize the eukaryote sediment communities. Diatoms of the Mediophyceae class (42%) composed a much larger fraction of the reads recovered than in nematode prey (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At genus level, the Mediophyceae were primarily identified to \u003cem\u003eChaetoceros\u003c/em\u003e sp. (40%). Dinoflagellate reads were also numerous, with even abundances year-round. Dinophyceae made up 16% of community reads, and Syndiniales (evenly contributed to by Dino-Group-II clade I and II) was responsible for 4% of the community reads. Metazoan classes found in the sediment were dominated by Nematoda (6% of community), Arthropoda (3%), and Nemertea (3%), and the latter was mostly composed of zOTUs identified to \u003cem\u003eCephalotrix\u003c/em\u003e sp. (3%). Arthropoda were markedly more abundant in the sediments at Shelf N (3%) and Basin (4%), than in the Shelf S station (0.5%). Of fungi, the dominating group was Chytridiomycota (4%), while Ascomycota (\u0026lt; 0.1%), Basidiomycota (\u0026lt; 0.1%) and Mucoromycota (0.4%) had negligible contributions. Abundances of community taxa present but with aggregated contribution of \u0026lt; 5% median abundances are listed in class and family level in the supplementary (Table S5 and S6).\u003c/p\u003e\u003cp\u003e3.3 Structure and drivers of prey composition\u003c/p\u003e\u003cp\u003eWe used NMDS to investigate patterns in prey and community composition based on the compositions themselves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D), and CCA to identify the influence of potential drivers of prey diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). NMDS ordinations were also calculated at species-level, and these provided similar clustering patterns (Fig. S2 and Fig. S3). For the nematode diet data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and C), season sampled generated the strongest visible clusters, with August and December prey compositions clustering together, separate from March and May, when prey compositions also clustered together, regardless of dissimilarity or distance metric used. In the constrained ordination (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), diet composition clustered similarly to the prey compositions in the NMDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and C), with August and December forming a separate cluster distinct from March and May prey compositions. However, August prey was less homogeneous than prey composition in other months, and Shelf S prey compositions in this month in particular were more similar to those from March and May (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and C). Along the y-axis several nematodes from shelf break and basin locations had different prey composition.\u003c/p\u003e\u003cp\u003eFor the sediment community in contrast, the primary clustering parameter was rather spatial, with stations Shelf S, Shelf N, and Basin forming separate clusters in the NMDS along axis 1 (shelf break was not sampled). Sampling season was of secondary importance in the sediment community, though some separation was seen by months along axis 2 within spatial clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and E).\u003c/p\u003e\u003cp\u003eStatistically important numerical constraints (all with \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.001) for structuring prey compositions included phaeopigment concentration, chlorophyll \u003cem\u003ea\u003c/em\u003e:phaeopigment ratio, and sampling depth as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Significant categorical constraints included the sample station, feeding group, lowest common ancestor, and season. March and May prey compositions were associated with high phaeopigment content, whereas August and December prey compositions were associated with higher Chlorophyll \u003cem\u003ea\u003c/em\u003e:Phaeopigment ratio values. All parameters identified as significant through CCA, were likewise significant in subsequent PERMANOVA statistical tests (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Parameters that were not identified as influential through CCA and thereafter excluded to find the best model fit, included Latitude, Longitude, nematode order, Chl \u003cem\u003ea\u003c/em\u003e concentration (mg m\u003csup\u003e− 2\u003c/sup\u003e), total organic carbon fraction (TOC, %), carbon isotopic composition (δ\u003csup\u003e13\u003c/sup\u003eC, ‰) and day of year (DOY).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of key statistics for discerning the structuring parameters of nematode diets. CCA model constraints and PERMANOVA parameters are shown with respective F-statistic and accompanying p-value for significance. Bold PERMANOVA F- and p-values denote homogenous within-group dispersion as determined by betadisper tests. PERMANOVA statistics are shown for nematode prey data at three taxonomic levels: class, family and zOTU.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCCA\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e \u003cp\u003ePERMANOVA (Bray-Curtis dissimilarity)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ezOTU\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParameter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnit/levels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDf\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhaeo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg m-2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e5.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e9.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChla:Phaeo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.115\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShelf S, Shelf N, Break, Basin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.072\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding Group\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1A, 1B, 2A, 2B, \u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.434\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxonomy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarch, May, August, December\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e3.4 Prey composition across significant predictors\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to PERMANOVA and CCA, the season, station, feeding group and nematode taxonomy (LCA) were all significant predictors of diet (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). By averaging the prey according to these predictors, we identified the prey that were responsible for the differences in diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Important differences included an increased relative Arthropod abundance in August and December, and a shift towards increased relative abundance of Ascomycota, Basidiomycota and Oligohymenophorea in March and May. By station (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the relative abundance of Arthropoda prey reads was lower at the southern Barents Sea shelf (Shelf S., 18%) than at the northern stations (23–25%). Amongst feeding groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), there are few notable distinctions, although putative omnivore-predators and epistrate feeders appear more dependent on metazoan prey compared to nematodes within the selective and non-selective deposit feeder groups. The greatest variation in prey composition is clearly found when averaging prey taxa for the taxonomic identity of nematodes (LCA, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, the nematode taxonomy was also the parameter in which groups were composed of the most varying number of replicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e3.5 Arthropod fraction of nematode prey\u003c/p\u003e\u003cp\u003eArthropods were substantially more abundant amongst the putative nematode prey during August (30%) and December (28%) than in March (21%) and May (13%, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). At the genus level it was apparent that the seasons were clearly distinguished in their arthropod composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In August and December, the arthropod prey of nematodes was mainly formed by zOTUs belonging to genera from Harpacticoida (\u003cem\u003eParabradya\u003c/em\u003e sp., unidentified Harpacticoida_X), Cyclopoida and/or Harpacticoida (\u003cem\u003eOithona\u003c/em\u003e sp., \u003cem\u003eMicrosetella\u003c/em\u003e sp.), and Siphonostomatoida and/or Misophrioida (\u003cem\u003eLepeophtheirus\u003c/em\u003e sp., \u003cem\u003eMisophriella\u003c/em\u003e sp.). In March and May, the prey composition shifted towards dominance of calanoid \u003cem\u003eCalanus\u003c/em\u003e species, with additional contributions by \u003cem\u003eBalanus\u003c/em\u003e sp., and sporadic major contributions from \u003cem\u003eLynceus\u003c/em\u003e sp. (Superorder Diplostraca) and the copepod species \u003cem\u003eOithona\u003c/em\u003e sp. and \u003cem\u003eMicrosetella\u003c/em\u003e sp. Additionally, a larger proportion of arthropod zOTUs belonged to various taxa whose median relative abundances were below 3% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The mysid \u003cem\u003eErythrops\u003c/em\u003e sp. was relatively common regardless of season.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e4.1 Key ecological interactions\u003c/p\u003e \u003cp\u003eThe present study expands the nematode dietary profile catalogue to the Eurasian Arctic, characterizing eukaryote prey in free-living nematodes on the Barents Sea shelf, and adjacent Arctic Ocean Nansen basin. We also present the first profiles of prey from nematodes captured in deep sea sediments. By prey metabarcoding with a short 18S rRNA gene marker, we show that putative eukaryote prey were acquired from a broad range of metazoan, protist and fungal eukaryote taxa. Surprisingly, primary producers, which were extremely abundant in the sediment community, and are expected to form the basis for pelagic-benthic coupling in the Barents Sea region (Morata and Renaud \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), were practically absent from the prey compositions of nematodes. Our data thus suggest that primary producers, are of lesser importance for the diet of nematode meiofauna. Instead, prey sequences were sourced from primarily heterotroph taxa, of which marine fungi and arthropods were major contributors. We can further hypothesize that fungi may play an important role as colonizers and recyclers of materials deposited from the pelagic realm (Lombard and Ki\u0026oslash;rboe \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and upon reaching the sea-floor \u0026ndash; may have converted substantial portions of the sinking particles into fungal biomass. The current study thus support the emerging narrative that fungi, although enigmatic and still under-researched, play important roles in marine environments, not just as degraders, colonizers or parasites (Hassett et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hassett et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), but also as putative prey (e.g. Cleary et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Flo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). 16S rRNA sequencing was however not successful, and we suspect that the low success was due to very small DNA quantities from prokaryotes when DNA was extracted from individual nematodes. Low yields made subsequent PCR and sequencing prone to contamination. Future studies may find it beneficial to either extract DNA from pools of minute nematodes, or to develop primers that target smaller fragments within the 16S rRNA gene, thus making it easier to amplify a marker from partly digested material. Pooling of individuals (e.g. 10\u0026ndash;20 individuals per extract) could raise quantities of prokaryote DNA that are adequate for PCR and sequencing but will inevitably also limit the ability to describe prey spectra of individuals.\u003c/p\u003e \u003cp\u003e4.2 Structuring patterns of nematode prey\u003c/p\u003e \u003cp\u003eOur exploratory data analyses clearly indicated that temporal factors (season sampled, phaeopigments, Chl \u003cem\u003ea\u003c/em\u003e:phaeopigment ratio) were of particular relevance for explaining variation in prey composition. In contrast, the sediment communities were more spatially structured, indicating that the sediment community composition was more distinct across sample sites and relatively stable across seasons. Weak seasonality has also been observed in surveys of macrobenthic community compositions at the same sites (Jord\u0026agrave;-Molina et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and in studies of benthic food web trophic structure (Ziegler et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is interesting, as it implies that the prey acquired was comparatively less influenced by sediment community composition than the input of pelagic material via vertical flux, which in the Arctic is much greater in the spring and summer than during the low-productive winter (Bodur et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the other major objectives of the study was to investigate if host morphology (i.e. Weiser\u0026rsquo;s feeding groups) can be used as reliable predictors of diet in individual nematodes, or if denoting feeding ecology based on these factors alone leads to an arbitrary and oversimplified understanding as suggested by (Vafeiadou et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schuelke et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly to findings by Schuelke et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we found no distinct partitioning of diets by feeding groups according to the prey relative read abundance. Likewise, NMDS analysis of prey did not return distinct clusters for feeding groups or nematode taxonomy (order and LCA), with the primary driver of prey sample clustering being the categorical seasons. In contrast, however, our CCA and PERMANOVA results indicated that the feeding groups were still a significant predictor of prey composition, but only significant when conducted at family-level taxonomy, and insignificant at both class and zOTU-levels. Hence, our data indicates that the nematodes surveyed acquire, to a large extent \u0026ndash; a similar putative prey spectrum, wherein differences are primarily driven by seasonal variations, whereas feeding groups may have nuanced feeding preferences masked behind other more influential variables.\u003c/p\u003e \u003cp\u003e4.3 Microbial eukaryote prey\u003c/p\u003e \u003cp\u003eSurprisingly, diatoms were highly abundant in the sediment community, but did not compose a great fraction of the prey reads recovered. This mismatch was particularly obvious for the genus \u003cem\u003eChaetoceros\u003c/em\u003e which was extremely abundant in the sediment community (~\u0026thinsp;40%) but rarely represented in the prey (\u0026lt;\u0026thinsp;0.1%). The disparity in relative abundance could potentially be due to primer bias, since the prey and sediment community were sequenced using different primers targeting 18S V7 and V4 fragments (e.g. Parada et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, given the substantial contrast, we find it more likely that the disparity in abundance can be explained by aggregation of diatom material, such as dead and empty cells, heavily silicified resting spores or free DNA molecules aggregated at the sea floor, or in other words \u0026ndash; from material unfit as nematode sustenance. Resting spores have been observed in \u003cem\u003eChaetoceros\u003c/em\u003e species, with substantial formation during conditions of high-cell density and/or low nutrient concentrations, and in both Antarctica and the Mediterranean Seas (Crosta et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Pelusi et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Aggregation of diatom DNA in sediments has also been hypothesized in other studies, with one citing diatom ASV abundances which exceeded what was expected based on metabarcoding of water-column samples (Nguyen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). From metabarcoding of water column samples at the same stations and cruises, we likewise observed a much less dominant diatom fraction of zOTUs, and primarily abundant diatom taxa on the shelves during spring (April-May, Flo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). If the culprit is free DNA molecules, it is likely that sediment samples, for which we did not employ pre-extraction filtering \u0026ndash; will yield more diatom DNA than that of water-based samples, for which material is typically recovered on 0.22 \u0026micro;m filters. We must therefore argue that the abundance of diatom zOTUs recovered in our sediment samples likely do not accurately depict the number of diatoms viable for nematode ingestion.\u003c/p\u003e \u003cp\u003eNevertheless, these results indicate that diatoms, overall, are of lesser importance as putative prey for the nematodes studied here. Conversely, experimental observations of nematodes in shallow tidal flat sediments suggested high dependency on diatoms as a carbon source, wherein nematodes ingested whole cells (deposit and omnivore-predators), or pierced and sucked out cell contents (epistrate) (Moens et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast to tidal flats, however, nematodes on the Barents Sea shelves live on average at a depth of ~\u0026thinsp;230 m (Sakshaug et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and likely do not have access to the high-quality, active and \u0026ldquo;fresh\u0026rdquo; algal prey that exist on tidal flats. Thus, although community analyses indicated a clearly abundant \u003cem\u003eChaetoceros\u003c/em\u003e in the ambient sediment, they could be unviable as prey. The chain-like structure of \u003cem\u003eThalassiosira\u003c/em\u003e sp. and \u003cem\u003eChaetoceros\u003c/em\u003e sp. colonies may moreover offer defensive capabilities against deposit and omnivore-predators (whole cell ingestion), but then we would also expect nematodes who pierce and suck out contents (epistrate) to gain comparatively higher read counts from these taxa (Moens et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We find no support for such a trend in the current data, and neither did Schuelke et al., (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), wherein the most prevalent ochrophyte prey was found in a \u003cem\u003eDesmoscolex\u003c/em\u003e selective deposit feeder. Instead, the consistently low or even lack of diatom reads speaks towards feeding group designations not being particularly representative of the \u003cem\u003ein situ\u003c/em\u003e prey nematodes acquires.\u003c/p\u003e \u003cp\u003eOligohymenophorea were particularly abundant in the prey reads (~\u0026thinsp;8% on average), with peritrich ciliates of the \u003cem\u003eVorticella\u003c/em\u003e genus comprising the bulk of the reads. \u003cem\u003eVorticella\u003c/em\u003e ciliates may exist both as a free-swimming telotroch, or as a sessile trophont, the latter having a contractile stalk that allows its bell-shaped body (zooid) with oral cilia to contract and expand, and to attach to a suitable substratum (Buhse et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The amount of their contribution as prey items may, however, be exaggerated due to high rDNA copy-numbers in peritrich ciliates (Gong et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and \u003cem\u003eVorticella\u003c/em\u003e are primarily known from soil and limnic habitats (France et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Peritrich ciliates have been found as epibionts on Arctic amphipods (Arndt et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fernandez-Leborans et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), deep-sea isopods (\u0026Oacute;lafsd\u0026oacute;ttir and Svavarsson \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and \u003cem\u003eVorticella\u003c/em\u003e are known to occur as epibionts on \u003cem\u003eChaetoceros\u003c/em\u003e colonies (Nanajkar et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, it is possible that they were epibionts on the arthropod taxa that were frequently observed in nematode prey, or on algae like \u003cem\u003eChaetoceros\u003c/em\u003e, which were very abundant in the sediment community, but not as prey themselves. Oligohymenophorea taxa were in contrast very rare in the sediment community samples (\u0026lt;\u0026thinsp;0.1%). Recent 18S metabarcoding surveys have found \u003cem\u003eVorticella\u003c/em\u003e in the plankton of the Baltic Sea, Kattegat and Skagerrak (Latz et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and in the sediments of the Norwegian continental shelf (Lanz\u0026eacute;n et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We were however unable to find any relevant sources on interaction between \u003cem\u003eVorticella\u003c/em\u003e and marine nematodes, nor did we observe any epibionts on any nematode individuals during sampling. Thus, we cannot deduce the nature of interaction between the two. Nevertheless, we argue that the \u003cem\u003eVorticella\u003c/em\u003e-nematode interaction may be a valuable, and possibly important new avenue for meiobenthic ecological research.\u003c/p\u003e \u003cp\u003e4.4 Copepod prey\u003c/p\u003e \u003cp\u003eArthropoda composed a high number of prey reads, and were particularly abundant during August (30.2%) and December (28.2%). Of these, both pelagic and benthic copepods composed the largest fraction, and although typically larger in size than nematodes, the abundance of copepods amongst prey reads may well be explained by predation. Through snap-freezing meiofauna and subsequently observing the meiofauna through a microscope, (Kennedy \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) captured several meiofauna whose bodies were invaded, and apparently preyed upon by smaller nematodes. A range of nematode prey taxa were recorded in this way, including annelids, ostracods, halacarid mites and other nematodes, and photographs displayed harpacticoid copepods invaded by \u003cem\u003eMonhystera\u003c/em\u003e spp. nematodes. On some occurrences, the author explained, all that remained was the indigestible prey carapace or cuticle, suggesting that the nematode may have acted as a scavenger, or was potentially feeding on internal bacteria (Kennedy \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Here, we identified two abundant harpacticoid taxa as prey including \u003cem\u003eParabradya\u003c/em\u003e spp. and an unidentified harpacticoid zOTU (Harpacticoida_X). While we can say little about the ecology of the latter, \u003cem\u003eParabradya\u003c/em\u003e spp. are epi-benthic harpacticoids, and have been found in sediments from both sub-littoral shores and the deep-sea (Seifried et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and in the Arctic Laptev Sea (Chertoprud et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, a significant proportion of the arthropod reads were identified to \u003cem\u003eLepeophtheirus\u003c/em\u003e sp. and/or \u003cem\u003eMisophriella\u003c/em\u003e sp.. \u003cem\u003eLepeophtheirus\u003c/em\u003e sp. include species known for parasitism, with both salmonids (\u003cem\u003eLepeophtheirus salmonis\u003c/em\u003e) and Pleurinectiformes flatfish (\u003cem\u003eLepeophtheirus pectoralis\u003c/em\u003e) on the menu (Boxshall \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Tully and Nolan \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Much less information exists for \u003cem\u003eMisophriella\u003c/em\u003e sp. (Misophrioida), which are seldom found, and predominantly found in deep-sea hyperbenthic communities (Arbizu and Jaume \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). We thus find \u003cem\u003eLepeophtheirus\u003c/em\u003e sp. a more probable source of the reads in our study. Intriguingly, high \u003cem\u003eLepeophtheirus\u003c/em\u003e/\u003cem\u003eMisophriella\u003c/em\u003e abundances in August (6.6%) and December (8.0%) coincided with high abundances of \u003cem\u003eSalmo\u003c/em\u003e sp. sequence reads in the same months (4.7 and 2.0%, respectively). The \u003cem\u003eSalmo\u003c/em\u003e sp. reads, when BLASTed, has high affinity to several salmonids, including \u003cem\u003eSalmo trutta\u003c/em\u003e (trout), \u003cem\u003eOnchorynchus mykiss\u003c/em\u003e (rainbow trout) and \u003cem\u003eO. gorbuscha\u003c/em\u003e (pink salmon). While wild \u003cem\u003eO. mykiss\u003c/em\u003e is primarily located to the Pacific ocean in Asia and north-America (Light et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1989\u003c/span\u003e)d \u003cem\u003etrutta\u003c/em\u003e primarily occur along the coast-line and freshwater systems of mainland Norway, and thus unlikely suspects \u0026ndash; the initially pacific \u003cem\u003eO. gorbuscha\u003c/em\u003e has spread and now occur in great abundances along the Norwegian coastline, and also in the Barents Sea (Pauli et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Greenland (Nielsen et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Ireland (Millane et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In our study, both \u003cem\u003eLepeophtheirus/Misophriella\u003c/em\u003e and \u003cem\u003eSalmo\u003c/em\u003e sp. were both more abundant at the northern Shelf N, and Shelf Break station than at Shelf S. Hence, it is possible that the nematodes surveyed may have acquired dietary material from \u003cem\u003eSalmo\u003c/em\u003e sp. directly, or through secondary predation of their parasitic \u003cem\u003eLepeophtheirus\u003c/em\u003e sp., whom are known to feed on the mucus, skin and blood of salmonids (Brandal et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Perhaps we can even hypothesize that infected and eventually incapacitated salmonids sink with their entourage of salmon lice which later falls prey to meiobenthic nematodes.\u003c/p\u003e \u003cp\u003eInterestingly, a large portion of the arthropod prey reads were assigned to pelagic copepods such as \u003cem\u003eCalanus\u003c/em\u003e spp., \u003cem\u003ePseudocalanus\u003c/em\u003e sp. and/or \u003cem\u003eMicrocalanus\u003c/em\u003e spp., \u003cem\u003eNeocalanus\u003c/em\u003e sp. and \u003cem\u003eOithona\u003c/em\u003e spp. and/or \u003cem\u003eMicrosetella\u003c/em\u003e spp.. Apart from \u003cem\u003eNeocalanus\u003c/em\u003e sp., whose distribution appears to be limited to lower latitudes in the North Atlantic, all of these taxa are known to be broadly and abundantly distributed in the Barents Sea (Wassmann et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hirche and Kosobokova \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In fact, all of these copepods were present in varying biomass and abundances in the above water masses during the Nansen Legacy cruises where this study\u0026rsquo;s material was collected (Wold et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to the pelagic study, mesozooplankton integrated water column biomass was at its height during the summer months of July (14.3 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and August (11.3 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), and decreased dramatically in March and May (minimum\u0026thinsp;~\u0026thinsp;1 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), which matches the observed lower relative abundances of arthropods in March and May nematode prey. In terms of integrated biomass, smaller pelagic copepods like \u003cem\u003eOithona similis\u003c/em\u003e, \u003cem\u003eMicrosetella norvegica\u003c/em\u003e and \u003cem\u003ePseudocalanus\u003c/em\u003e spp. were less prominent during the spring seasons, but increased through the summer months of July and August, and peaked in December (\u0026gt;\u0026thinsp;1 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, Wold et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Again, the trend sits well with the observed higher abundances of small copepods in nematode prey in August and December. Of the mesozooplankton genera studied, \u003cem\u003eCalanus\u003c/em\u003e spp. were the main contributor to overall biomass in the water column, being particularly abundant during the summer and fall months (July, August, December), at the northern shelf station (Shelf N, 5.8\u0026ndash;11.6 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and at the Basin station in December (11.4 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). Our observations, however, indicate comparatively high \u003cem\u003eCalanus\u003c/em\u003e spp. relative abundances in nematode prey reads at the Shelf S station, and during March and May. This could possibly be explained by seasonal migration patterns, with many \u003cem\u003eCalanus\u003c/em\u003e entering diapause deep on the Barents Sea shelves between July and May (Aarflot et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and which in the event of high mortality-rates (Daase and S\u0026oslash;reide \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) \u0026ndash; may lead to high supply to benthos.\u003c/p\u003e \u003cp\u003eThe high relative abundances of \u003cem\u003eCalanus\u003c/em\u003e spp. in nematode prey in March and May may be reasonably explained by a combination of two processes connected to the biological carbon pump: fecal pellet production and non-consumptive mortality. Dense and fast-sinking fecal pellets from Arctic copepods significantly contribute to the POC export from pelagic waters, with export rates increasing during the productive spring and summer (Bodur et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Darnis et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Particle attenuation by bacteria and pelagic invertebrates drastically limits the quantity of POC that reaches deeper waters, and only a small fraction reaches below 200 m (Svensen et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The rate of attenuation of fecal pellets is spatially and temporally variable, with the pelagic \u0026ldquo;retention filter\u0026rdquo; operating with higher efficiency in Atlantic Water (96% in May 1998) than in Arctic Water (50% in May 1998, Wexels Riser et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and at higher efficiency during non-bloom scenarios (Wexels Riser et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A smaller percentage of \u003cem\u003eCalanus\u003c/em\u003e reads in the deep-sea basin sediments compared to shelf stations, may hence be explained by a combination of lower fecal pellet production, due to lower \u003cem\u003eCalanus\u003c/em\u003e abundance and less primary production \u0026ndash; and particle attenuation, in which both deceased copepods and fecal pellets may be attenuated before reaching the Nansen Basin. Some recent observations made from Svalbard and north of Svalbard showed that the dead fraction of high-Arctic zooplankton consisted mainly of copepods, with calanoid copepods (especially \u003cem\u003eCalanus\u003c/em\u003e spp.) contributing more to the dead fraction during seasons of low productivity (Daase et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Daase and S\u0026oslash;reide \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Canadian Arctic, post-reproductive mortality of \u003cem\u003eCalanus hyperboreus\u003c/em\u003e was estimated to drive comparatively higher downward particle flux in winter-early spring (16\u0026ndash;91%) than in the remaining seasons (1\u0026ndash;30%) (Sampei et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Hence, a high biomass of \u003cem\u003eCalanus\u003c/em\u003e spp. during summer months may be converted, through non-consumptive mortality events (e.g., caused by insufficient lipid storage for overwintering, parasitism or old age) \u0026ndash; to putative food particles that sink and supply the epi- and meiobenthos over winter and early spring. We can further speculate in a certain lag-time from mortality to reaching the seafloor, especially at locations characterized by great depths or strong up-welling, and currents would certainly impact the site at which such particles would settle. This, in combination with lower fecal pellet production in the non-productive winter (Wexels Riser et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), could possibly explain why \u003cem\u003eCalanus\u003c/em\u003e spp. were not more abundant in nematode prey in December, even though \u003cem\u003eCalanus\u003c/em\u003e spp. were abundant amongst the mesozooplankton at this time in the waters above (Wold et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). That a combination of epibenthic and pelagic arthropods were identified in our study suggests that the nematodes feed as generalists, rather than selectively, on larger arthropods depending on their availability. In the high-Arctic, \u003cem\u003eCalanus\u003c/em\u003e spp. appears particularly important as prey during seasons of low productivity, whereas benthic harpacticoids and smaller pelagic copepods are important during summer and early winter.\u003c/p\u003e \u003cp\u003e4.5 Fungal prey\u003c/p\u003e \u003cp\u003eFungal taxa assigned to Ascomycota, Basidiomycota and Mucoromycota were abundantly recovered from nematode prey year-round, and particularly in March and May. Although the roles and functions of fungi in the marine ecosystem remains severely understudied, there have been observations that underpin the possible importance of nematode-fungal interactions. Based on sheer abundance in sea-ice and water-samples, dikaryan fungi (including Basidiomycota and Ascomycota) and Chytridiomycota have been suggested to play an important role as sustenance at the base of the Arctic food web (Hassett et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In a study of fungi-nematode interactions, fungal 18S sequences identified by Sanger sequencing of shallow and deep-sea nematode, were mainly assigned to Ascomycota and Basidiomycota (Bhadury et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Likewise, Schuelke et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) observed that a major component of non-metazoan reads from single nematodes in the Arctic Beaufort Sea, Gulf of Mexico and Californian coast were of fungal origin, and noted that fungi \u0026ndash; so commonly co-amplified with nematodes, must form some kind of important ecological association with them.\u003c/p\u003e \u003cp\u003eOne interesting observation was the high abundance of Mucoromycota, which, in contrast to Ascomycota and Basidiomycota, decreased dramatically at the Shelf Break and Nansen Basin compared to shelf stations. Mucoromycota are known to have parasitic, mycorrhizal and saprotrophic lifestyles, and as of now at least 16 genera of marine taxa are known (Calabon et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but have not been identified as a major contributor to the Arctic marine fungal community (Hassett et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While the exact nature of the fungi nematode associations remains unclear, we can be relatively confident that the fungal taxa somehow interact with marine nematodes as symbionts or prey. If ingested as a source of sustenance, the fungi can either be eaten directly, or indirectly via other organic particles (i.e. fecal pellets, dead and/or decaying organisms) that are continuously colonized and deposited on the sea floor in aquatic systems. Moreover, some fungal taxa seem more prone to occur in symbiotic associations than others. A recent review of fungal parasites noted for instance that Ascomycota could infect a diverse array of metazoan hosts, whereas comparatively few animal hosts were known for Mucoromycota and Chytridiomycota (Pang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe nematodes from the northern Barents Sea and Nansen Basin characterized here displayed significant overlaps in diet despite the differences in nematode phylogeny and morphology. Seasonality was clearly a more important structuring factor than teeth and buccal cavity morphology, with prey compositions clustering into distinct August and December or March and May groups which were separated by highly abundant arthropod or fungal prey reads, respectively. Combined, the high seasonality and low effect of feeding group morphology indicates that Arctic nematodes are generalists, who regardless of presence or absence of teeth, teeth-like structures or buccal cavity sizes obtain, to a great extent, similar eukaryote prey items. Our findings thus support the notion that the widely used morphological features have little power for explaining trophic roles of nematodes. Of the prey items that varied seasonally, the calanoid copepod prey were of particular interest, as they through mortality events may lead to possibly large and sudden inputs of pelagic biomass towards benthos. In addition, a surprisingly high prey abundance of metazoans and marine fungi and a lack of diatoms indicate that Arctic nematodes rely less on algae, and more on heterotroph prey. We moreover highlight the possibility of important novel interactions with Siphonostomatoid copepods and \u003cem\u003eVorticella-like\u003c/em\u003e peritrich ciliates and call for more experimental research to disentangle the nature of these associations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFinancial or non-financial interests:\u003c/strong\u003e The authors have no financial interests to disclose. Of non-financial interests, we declare that author Camilla Svensen is an editor for Polar Biology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e No approval of research ethics was required because the animals taken for analyses were unregulated invertebrate species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Research Council of Norway (grant number 276730) through the Nansen Legacy project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Nansen Legacy project and Research Council of Norway for funding and support (RCN #276730) and fruitful discussions, and the crew of \u003cem\u003eR/V Kronprins Haakon\u003c/em\u003e for providing us with the possibility to take samples in the Arctic. Thanks to Arunima Sen (Nord Univ.), Eric Jorda-Molina (Nord Univ.), Thaise Ricardo de Freitas (UiO), Silvia Hess (UiO) and Amanda Ziegler (UiT) for help acquiring sediment during the Nansen Legacy cruises. We give special thanks to Melissa Brandner (UiT), Hilde Rief Armo (UiB) and Lise \u0026Oslash;vre\u0026aring;s (UiB) for help with preparing samples for sequencing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All authors contributed to conceptualizing the study. SF, BAB and AV conducted the fieldwork, and SF performed the lab work with help from KP and AV. SF analyzed the data and wrote the manuscript, and all authors revised the manuscript and previous versions. All authors read and approved the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 18S metabarcoding data from nematodes generated for this study was published at the NIRD-RDA repository and are available at the following URL: https://doi.org/10.11582/2024.00105.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAarflot JM, Eriksen E, Prokopchuk IP, et al (2023) New insights into the Barents Sea \u003cem\u003eCalanus glacialis\u003c/em\u003e population dynamics and distribution. Prog Oceanogr 217:103106. https://doi.org/10.1016/j.pocean.2023.103106\u003c/li\u003e\n\u003cli\u003eAkvaplan-niva (2024a) Nansen Legacy Sediment Pigment Data Q1. Norstore. https://doi.org/10.11582/2024.00047\u003c/li\u003e\n\u003cli\u003eAkvaplan-niva (2024b) Nansen Legacy Sediment Pigment Data Q2. Norstore. https://doi.org/10.11582/2024.00046\u003c/li\u003e\n\u003cli\u003eAkvaplan-niva (2024c) Nansen Legacy Sediment Pigment Data Q3. Norstore. https://doi.org/10.11582/2024.00045\u003c/li\u003e\n\u003cli\u003eAkvaplan-niva (2024d) Nansen Legacy Sediment Pigment Data Q4. Norstore. https://doi.org/10.11582/2024.00044\u003c/li\u003e\n\u003cli\u003eAltschul SF, Gish W, Miller W, et al (1990) Basic local alignment search tool. J Mol Biol 215:403\u0026ndash;410. https://doi.org/10.1016/S0022-2836(05)80360-2\u003c/li\u003e\n\u003cli\u003eAmbrose WG, Renaud PE (1995) Benthic response to water column productivity patterns: evidence for benthic-pelagic coupling in the Northeast Water Polynya. J Geophys Res 100:4411\u0026ndash;4421. https://doi.org/10.1029/94JC01982\u003c/li\u003e\n\u003cli\u003eArbizu PM, Jaume D (1999) New hyperbenthic species of \u003cem\u003eMisophriopsis\u003c/em\u003e and \u003cem\u003eMisophriella\u003c/em\u003e, first record of misophrioid copepods (Crustacea) from Antarctic waters. Helgol Mar Res 53:102\u0026ndash;117. https://doi.org/10.1007/PL00012142\u003c/li\u003e\n\u003cli\u003eArndt CE, Fernandez-Leborans G, Seuthe L, et al (2005) Ciliated epibionts on the Arctic sympagic amphipod \u003cem\u003eGammarus wilkitzkii\u003c/em\u003e as indicators for sympago-benthic coupling. Mar Biol 147:643\u0026ndash;652. https://doi.org/10.1007/s00227-005-1599-4\u003c/li\u003e\n\u003cli\u003eBaquiran J-P, Thater B, Sedky S, et al (2013) Culture-Independent Investigation of the Microbiome Associated with the Nematode \u003cem\u003eAcrobeloides maximus\u003c/em\u003e. PLoS One 8:e67425\u003c/li\u003e\n\u003cli\u003eBessi\u0026egrave;re A, Nozais C, Brugel S, et al (2007) Metazoan meiofauna dynamics and pelagic-benthic coupling in the Southeastern Beaufort Sea, Arctic Ocean. Polar Biol 30:1123\u0026ndash;1135. https://doi.org/10.1007/s00300-007-0270-6\u003c/li\u003e\n\u003cli\u003eBhadury P, Bik H, Lambshead JD, et al (2011) Molecular diversity of fungal phylotypes Co-Amplified alongside nematodes from coastal and Deep-Sea marine environments. PLoS One 6:e26445. https://doi.org/10.1371/journal.pone.0026445\u003c/li\u003e\n\u003cli\u003eBillett DSM, Lampitt RS, Rice AL, Mantoura RFC (1983) Seasonal sedimentation of phytoplankton to the deep-sea benthos. Nature 302:520\u0026ndash;522. https://doi.org/10.1038/302520a0\u003c/li\u003e\n\u003cli\u003eBodur Y V., Renaud PE, Goraguer L, et al (2023) Seasonal patterns of vertical flux in the northwestern Barents Sea under Atlantic Water influence and sea-ice decline. Prog Oceanogr 219:103132. https://doi.org/10.1016/j.pocean.2023.103132\u003c/li\u003e\n\u003cli\u003eBoxshall GA (1976) The host specificity of \u003cem\u003eLepeophtheirus pectoralis\u003c/em\u003e (M\u0026uuml;ller, 1776) (Copepoda: Caligidae). J Fish Biol 8:255\u0026ndash;264. https://doi.org/https://doi.org/10.1111/j.1095-8649.1976.tb03949.x\u003c/li\u003e\n\u003cli\u003eBoyer F, Mercier C, Bonin A, et al (2016) obitools: a unix-inspired software package for DNA metabarcoding. Mol Ecol Resour 16:176\u0026ndash;182. https://doi.org/10.1111/1755-0998.12428\u003c/li\u003e\n\u003cli\u003eBrandal PO, Egidius E, Romslo I (1976) Host blood: a major food component for the parasitic copepod \u003cem\u003eLepeophtheirus salmonis\u003c/em\u003e Kroyeri, 1838 (Crustacea: Caligidae). Nor J Zool 24:341\u0026ndash;343\u003c/li\u003e\n\u003cli\u003eBuhse HE, McCutcheon SM, Clamp JC, Sun P (2011) \u003cem\u003eVorticella\u003c/em\u003e. In: Encyclopedia of Life Sciences\u003c/li\u003e\n\u003cli\u003eCalabon MS, Jones EBG, Pang KL, et al (2023) Updates on the classification and numbers of marine fungi. Bot Mar 66:213\u0026ndash;238. https://doi.org/10.1515/bot-2023-0032\u003c/li\u003e\n\u003cli\u003eCamacho C, Coulouris G, Avagyan V, et al (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421. https://doi.org/10.1186/1471-2105-10-421\u003c/li\u003e\n\u003cli\u003eCheng X-Y, Tian X-L, Wang Y-S, et al (2013) Metagenomic analysis of the pinewood nematode microbiome reveals a symbiotic relationship critical for xenobiotics degradation. Sci Rep 3:1869. https://doi.org/10.1038/srep01869\u003c/li\u003e\n\u003cli\u003eChertoprud E, Abramova E, Korsun S, et al (2018) Composition of Harpacticoida (Crustacea, Copepoda) of the Laptev Sea in comparison with faunas of adjacent Arctic seas. Polar Biol 41:697\u0026ndash;712. https://doi.org/10.1007/s00300-017-2229-6\u003c/li\u003e\n\u003cli\u003eCleary AC, Durbin EG (2016) Unexpected prevalence of parasite 18S rDNA sequences in winter among Antarctic marine protists. J Plankton Res 38:401\u0026ndash;417. https://doi.org/10.1093/plankt/fbw005\u003c/li\u003e\n\u003cli\u003eCleary AC, S\u0026oslash;reide JE, Freese D, et al (2017) Feeding by \u003cem\u003eCalanus glacialis\u003c/em\u003e in a high arctic fjord: Potential seasonal importance of alternative prey. ICES J Mar Sci 74:1937\u0026ndash;1946. https://doi.org/10.1093/icesjms/fsx106\u003c/li\u003e\n\u003cli\u003eComeau A, Kwawukume A (2023) Preparing multiplexed 16S/18S/ITS amplicons for the Illumina MiSeq\u003c/li\u003e\n\u003cli\u003eCrosta X, Pichon JJ, Labracherie M (1997) Distribution of \u003cem\u003eChaetoceros\u003c/em\u003e resting spores in modern peri-Antarctic sediments. Mar Micropaleontol 29:283\u0026ndash;299. https://doi.org/10.1016/S0377-8398(96)00033-3\u003c/li\u003e\n\u003cli\u003eDaase M, S\u0026oslash;reide JE (2021) Seasonal variability in non-consumptive mortality of Arctic zooplankton. J Plankton Res 43:565\u0026ndash;585. https://doi.org/10.1093/plankt/fbab042\u003c/li\u003e\n\u003cli\u003eDaase M, Varpe \u0026Oslash;, Falk-Petersen S (2014) Non-consumptive mortality in copepods: occurrence of \u003cem\u003eCalanus\u003c/em\u003e spp. carcasses in the Arctic Ocean during winter. J Plankton Res 36:129\u0026ndash;144. https://doi.org/10.1093/plankt/fbt079\u003c/li\u003e\n\u003cli\u003eDarnis G, Geoffroy M, Daase M, et al (2024) Zooplankton fecal pellet flux drives the biological carbon pump during the winter\u0026ndash;spring transition in a high-Arctic system. Limnol Oceanogr 69:1481\u0026ndash;1493. https://doi.org/10.1002/lno.12588\u003c/li\u003e\n\u003cli\u003eDavis NM, Proctor DM, Holmes SP, et al (2018) Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6:226. https://doi.org/10.1186/s40168-018-0605-2\u003c/li\u003e\n\u003cli\u003eDell\u0026rsquo;Anno A, Carugati L, Corinaldesi C, et al (2015) Unveiling the Biodiversity of Deep-Sea Nematodes through Metabarcoding: Are We Ready to Bypass the Classical Taxonomy? PLoS One 10:e0144928\u003c/li\u003e\n\u003cli\u003eDirksen P, Marsh SA, Braker I, et al (2016) The native microbiome of the nematode \u003cem\u003eCaenorhabditis elegans\u003c/em\u003e: gateway to a new host-microbiome model. BMC Biol 14:38. https://doi.org/10.1186/s12915-016-0258-1\u003c/li\u003e\n\u003cli\u003eDos Santos GAP, Moens T (2011) Populations of two prey nematodes and their interaction are controlled by a predatory nematode. Mar Ecol Prog Ser 427:117\u0026ndash;131. https://doi.org/10.3354/meps08991\u003c/li\u003e\n\u003cli\u003eEds. N (2024) Nemys: World Database of Nematodes. https://nemys.ugent.be. Accessed 22 May 2024\u003c/li\u003e\n\u003cli\u003eFernandez-Leborans G, Arndt CE, Gabilondo R (2006) Protozoan epibionts and their distribution on the Arctic ice-amphipod \u003cem\u003eGammarus wilkitzkii\u003c/em\u003e from Spitsbergen, Norway. Arctic, Antarct Alp Res 38:343\u0026ndash;356. https://doi.org/10.1657/1523-0430(2006)38[343:PEATDO]2.0.CO;2\u003c/li\u003e\n\u003cli\u003eFlo S (2024) 18S metabarcoding of Arctic meiobenthic nematodes. Norstore. https://doi.org/10.11582/2024.00105\u003c/li\u003e\n\u003cli\u003eFlo S, Svensen C, Pr\u0026aelig;bel K, et al (2024a) Dietary plasticity in small Arctic copepods as revealed with prey metabarcoding. J Plankton Res fbae042. https://doi.org/10.1093/plankt/fbae042\u003c/li\u003e\n\u003cli\u003eFlo S, Vader A, Pr\u0026aelig;bel K (2024b) Brute force prey metabarcoding to explore the diets of small invertebrates. Ecol Evol 14:12. https://doi.org/10.1002/ece3.11369\u003c/li\u003e\n\u003cli\u003eFonseca G, Fehlauer-Ale KH (2012) Three in one: Fixing marine nematodes for ecological, molecular, and morphological studies. Limnol Oceanogr Methods 10:516\u0026ndash;523. https://doi.org/10.4319/lom.2012.10.516\u003c/li\u003e\n\u003cli\u003eFonseca G, Gallucci F (2008) Direct evidence of predation in deep-sea nematodes: The case of \u003cem\u003ePontonema\u003c/em\u003e sp. Cah Biol Mar 49:295\u0026ndash;297\u003c/li\u003e\n\u003cli\u003eFonseca G, Soltwedel T, Vanreusel A, Lindegarth M (2010) Variation in nematode assemblages over multiple spatial scales and environmental conditions in Arctic deep seas. Prog Oceanogr 84:174\u0026ndash;184. https://doi.org/10.1016/j.pocean.2009.11.001\u003c/li\u003e\n\u003cli\u003eFrance D, Tejada J, Matsudaira P (2017) Direct measurement of \u003cem\u003eVorticella\u003c/em\u003e contraction force by micropipette deflection. FEBS Lett 591:581\u0026ndash;589. https://doi.org/10.1002/1873-3468.12577\u003c/li\u003e\n\u003cli\u003eGast RJ, Dennett MR, Caron DA (2004) Characterization of Protistan Assemblages in the Ross Sea, Antarctica, by Denaturing Gradient Gel Electrophoresis. Appl Environ Microbiol 70:2028 LP \u0026ndash; 2037. https://doi.org/10.1128/AEM.70.4.2028-2037.2004\u003c/li\u003e\n\u003cli\u003eGiere O (2009) Meiobenthology: The microscopic motile fauna of aquatic sediments\u003c/li\u003e\n\u003cli\u003eGong J, Dong J, Liu X, Massana R (2013) Extremely High Copy Numbers and Polymorphisms of the rDNA Operon Estimated from Single Cell Analysis of Oligotrich and Peritrich Ciliates. Protist 164:369\u0026ndash;379. https://doi.org/10.1016/j.protis.2012.11.006\u003c/li\u003e\n\u003cli\u003eGrzelak K, Gluchowska M, Gregorczyk K, et al (2016) Nematode biomass and morphometric attributes as biological indicators of local environmental conditions in Arctic fjords. Ecol Indic 69:368\u0026ndash;380. https://doi.org/10.1016/j.ecolind.2016.04.036\u003c/li\u003e\n\u003cli\u003eGuardiola M, Uriz MJ, Taberlet P, et al (2015) Deep-sea, deep-sequencing: Metabarcoding extracellular DNA from sediments of marine canyons. PLoS One 10:e0139633. https://doi.org/10.1371/journal.pone.0139633\u003c/li\u003e\n\u003cli\u003eHassett B, Ducluzeau AL, Collins R, Gradinger R (2017) Spatial distribution of aquatic marine fungi across the western Arctic and sub-arctic. Environ Microbiol 19:475\u0026ndash;484. https://doi.org/10.1111/1462-2920.13371\u003c/li\u003e\n\u003cli\u003eHassett BT, Borrego EJ, Vonnahme TR, et al (2019) Arctic marine fungi: biomass, functional genes, and putative ecological roles. ISME J 13:1484\u0026ndash;1496. https://doi.org/10.1038/s41396-019-0368-1\u003c/li\u003e\n\u003cli\u003eHeip C, Vincx M, Vranken G (1985) The ecology of marine nematodes. Oceanogr Mar Biol An Annu Rev 23:399\u0026ndash;489\u003c/li\u003e\n\u003cli\u003eHirche HJ, Kosobokova KN (2011) Winter studies on zooplankton in Arctic seas: the Storfjord (Svalbard) and adjacent ice-covered Barents Sea. Mar Biol 158:2359\u0026ndash;2376. https://doi.org/10.1007/s00227-011-1740-5\u003c/li\u003e\n\u003cli\u003eHoste E, Vanhove S, Schewe I, et al (2007) Spatial and temporal variations in deep-sea meiofauna assemblages in the Marginal Ice Zone of the Arctic Ocean. Deep Sea Res Part I Oceanogr Res Pap 54:109\u0026ndash;129. https://doi.org/https://doi.org/10.1016/j.dsr.2006.09.007\u003c/li\u003e\n\u003cli\u003eHuson DH, Auch AF, Qi J, Schuster SC (2007) MEGAN analysis of metagenomic data. Genome Res 17:377\u0026ndash;386. https://doi.org/10.1101/gr.5969107\u003c/li\u003e\n\u003cli\u003eJensen P (1987) Feeding ecology of free-living aquatic nematodes. Mar Ecol Prog Ser 35:187\u0026ndash;196. https://doi.org/10.1079/9780851990095.0105\u003c/li\u003e\n\u003cli\u003eJord\u0026agrave;-Molina \u0026Egrave;, Sen A, Bluhm BA, et al (2023) Lack of strong seasonality in macrobenthic communities from the northern Barents Sea shelf and Nansen Basin. Prog Oceanogr 219:103150. https://doi.org/https://doi.org/10.1016/j.pocean.2023.103150\u003c/li\u003e\n\u003cli\u003eKennedy AD (1994) Predation within meiofaunal communities: Description and results of a rapid-freezing method of investigation. Mar Ecol Prog Ser 114:71\u0026ndash;80. https://doi.org/10.3354/meps114071\u003c/li\u003e\n\u003cli\u003eLanz\u0026eacute;n A, Lekang K, Jonassen I, et al (2017) DNA extraction replicates improve diversity and compositional dissimilarity in metabarcoding of eukaryotes in marine sediments. PLoS One 12:e0179443. https://doi.org/10.1371/journal.pone.0179443\u003c/li\u003e\n\u003cli\u003eLatz MAC, Andersson A, Brugel S, et al (2024) A comprehensive dataset on spatiotemporal variation of microbial plankton communities in the Baltic Sea. Sci Data 11:. https://doi.org/10.15468/cwjstg\u003c/li\u003e\n\u003cli\u003eLight JT, Harris CK, Burgner RL (1989) Ocean Distribution and Migration of Steelhead (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e, formerly \u003cem\u003eSalmo gairdneri\u003c/em\u003e). Int North Pacific Fish Comm 50\u003c/li\u003e\n\u003cli\u003eLombard F, Ki\u0026oslash;rboe T (2010) Marine snow originating from appendicularian houses: Age-dependent settling characteristics. Deep Sea Res Part I Oceanogr Res Pap 57:1304\u0026ndash;1313. https://doi.org/https://doi.org/10.1016/j.dsr.2010.06.008\u003c/li\u003e\n\u003cli\u003eMcIntyre AD (1971) Observations on the status of subtidal meiofauna research. Smithson Contrib to Zool 76:149\u0026ndash;153\u003c/li\u003e\n\u003cli\u003eMcMurdie PJ, Holmes S (2013) phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One 8:e61217\u003c/li\u003e\n\u003cli\u003eMillane M, Walsh L, Roche WK, Gargan PG (2019) Unprecedented widespread occurrence of Pink Salmon \u003cem\u003eOncorhynchus gorbuscha\u003c/em\u003e in Ireland in 2017. J Fish Biol 95:651\u0026ndash;654. https://doi.org/10.1111/jfb.13994\u003c/li\u003e\n\u003cli\u003eMoens T, Braeckman U, Derycke S, et al (2013) Ecology of free-living marine nematodes. In: Schmidt-Rhaesa A (ed) Nematoda. De Gruyter, pp 109\u0026ndash;152\u003c/li\u003e\n\u003cli\u003eMoens T, Vafeiadou AM, De Geyter E, et al (2014) Diatom feeding across trophic guilds in tidal flat nematodes, and the importance of diatom cell size. J Sea Res 92:125\u0026ndash;133. https://doi.org/10.1016/j.seares.2013.08.007\u003c/li\u003e\n\u003cli\u003eMoens T, Verbeeck L, Maeyer A de, Swings J (1999) Selective attraction of marine bacterivorous nematodes to their bacterial food . Mar Ecol Prog Ser 176:165\u0026ndash;178\u003c/li\u003e\n\u003cli\u003eMoens T, Vincx M (1997) Observations on the feeding ecology of estuarine nematodes. J Mar Biol Assoc United Kingdom 77:211\u0026ndash;227. https://doi.org/10.1017/s0025315400033889\u003c/li\u003e\n\u003cli\u003eMorata N, Renaud PE (2008) Sedimentary pigments in the western Barents Sea: A reflection of pelagic-benthic coupling? Deep Res Part II Top Stud Oceanogr 55:2381\u0026ndash;2389. https://doi.org/10.1016/j.dsr2.2008.05.004\u003c/li\u003e\n\u003cli\u003eNanajkar M, Fernandes V, Bogati K, Chatterjee T (2019) Gregarious true- colonies of ciliate \u003cem\u003eVorticella oceanica\u003c/em\u003e on a chain forming diatom \u003cem\u003eChaetoceros coarctatus\u003c/em\u003e: indicating change in the nature of association. Symbiosis 79:221\u0026ndash;229. https://doi.org/10.1007/s13199-019-00640-4\u003c/li\u003e\n\u003cli\u003eNguyen NL, Pawłowska J, Zajaczkowski M, et al (2024) Taxonomic and abundance biases affect the record of marine eukaryotic plankton communities in sediment DNA archives. Mol Ecol Resour 24:e14014. https://doi.org/10.1111/1755-0998.14014\u003c/li\u003e\n\u003cli\u003eNielsen J, Rosing-Asvid A, Meire L, Nygaard R (2020) Widespread occurrence of pink salmon (\u003cem\u003eOncorhynchus gorbuscha\u003c/em\u003e) throughout Greenland coastal waters. J Fish Biol 96:1505\u0026ndash;1507. https://doi.org/10.1111/jfb.14318\u003c/li\u003e\n\u003cli\u003eOksanen J, Blanchet FG, Friendly M, et al (2019) Vegan: community ecology package. Ordination methods, diversity analysis and other functions for community and vegetation ecologists. R package ver, 2-3. Cran R\u003c/li\u003e\n\u003cli\u003e\u0026Oacute;lafsd\u0026oacute;ttir SH, Svavarsson J (2002) Ciliate (Protozoa) epibionts of deep-water asellote isopods (Crustacea): Pattern and diversity. J Crustac Biol 22:607\u0026ndash;618. https://doi.org/10.1651/0278-0372(2002)022[0607:cpeodw]2.0.co;2\u003c/li\u003e\n\u003cli\u003eOleszczuk B, Grzelak K, Kędra M (2021) Community structure and productivity of Arctic benthic fauna across depth gradients during springtime. Deep Res Part I Oceanogr Res Pap 170:103457. https://doi.org/10.1016/j.dsr.2020.103457\u003c/li\u003e\n\u003cli\u003ePang KL, Hassett BT, Shaumi A, et al (2021) Pathogenic fungi of marine animals: A taxonomic perspective. Fungal Biol Rev 38:92\u0026ndash;106. https://doi.org/10.1016/j.fbr.2021.03.008\u003c/li\u003e\n\u003cli\u003eParada AE, Needham DM, Fuhrman JA (2016) Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 18:1403\u0026ndash;1414. https://doi.org/10.1111/1462-2920.13023\u003c/li\u003e\n\u003cli\u003ePauli BD, Berntsen HH, Thorstad EB, et al (2023) Geographic distribution, abundance, diet, and body size of invasive pink salmon (\u003cem\u003eOncorhynchus gorbuscha\u003c/em\u003e) in the Norwegian and Barents Seas, and in Norwegian rivers. ICES J Mar Sci 80:76\u0026ndash;90. https://doi.org/10.1093/icesjms/fsac224\u003c/li\u003e\n\u003cli\u003ePelusi A, Santelia ME, Benevenuto G, et al (2020) The diatom \u003cem\u003eChaetoceros socialis\u003c/em\u003e: spore formation and preservation. Eur J Phycol 55:1\u0026ndash;10. https://doi.org/10.1080/09670262.2019.1632935\u003c/li\u003e\n\u003cli\u003ePiepenburg D (2005) Recent research on Arctic benthos: Common notions need to be revised. Polar Biol 28:733\u0026ndash;755. https://doi.org/10.1007/s00300-005-0013-5\u003c/li\u003e\n\u003cli\u003ePi\u0026ntilde;ol J, San Andr\u0026eacute;s V, Clare EL, et al (2014) A pragmatic approach to the analysis of diets of generalist predators: The use of next-generation sequencing with no blocking probes. Mol Ecol Resour 14:18\u0026ndash;26. https://doi.org/10.1111/1755-0998.12156\u003c/li\u003e\n\u003cli\u003ePiredda R, Tomasino MP, D\u0026rsquo;Erchia AM, et al (2017) Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean Long Term Ecological Research site. FEMS Microbiol Ecol 93:1\u0026ndash;14. https://doi.org/10.1093/femsec/fiw200\u003c/li\u003e\n\u003cli\u003eRenaud PE, Morata N, Carroll ML, et al (2008) Pelagic-benthic coupling in the western Barents Sea: Processes and time scales. Deep Res Part II Top Stud Oceanogr 55:2372\u0026ndash;2380. https://doi.org/10.1016/j.dsr2.2008.05.017\u003c/li\u003e\n\u003cli\u003eRex MA, Etter RJ, Morris JS, et al (2006) Global bathymetric patterns of standing stock and body size in the deep-sea benthos. Mar Ecol Prog Ser 317:1\u0026ndash;8. https://doi.org/10.3354/meps317001\u003c/li\u003e\n\u003cli\u003eRicardo de Freitas T, Hess S, Alve E, et al (2022) Seabed sediment data (upper 6 cm) on water content, total nitrogen, total carbon, total organic carbon, total inorganic carbon, carbon and nitrogen isotopic composition from the Nansen Legacy seasonal cruise 2021704 (Q2). Nor Mar Data Cent. https://doi.org/https://doi.org/10.21335/NMDC-350572235\u003c/li\u003e\n\u003cli\u003eRognes T, Flouri T, Nichols B, et al (2016) VSEARCH: A versatile open source tool for metagenomics. PeerJ 4:e2584: https://doi.org/10.7717/peerj.2584\u003c/li\u003e\n\u003cli\u003eSakshaug E, Johnsen G, Kristiansen S, et al (2009) Ecosystem Barents Sea. Ecosyst Barents Sea 587\u003c/li\u003e\n\u003cli\u003eSampei M, Sasaki H, Hattori H, et al (2009) Significant contribution of passively sinking copepods to the downward export flux in arctic waters. Limnol Oceanogr 54:1894\u0026ndash;1900. https://doi.org/10.4319/lo.2009.54.6.1894\u003c/li\u003e\n\u003cli\u003eSchewe I, Soltwedel T (2003) Benthic response to ice-edge-induced particle flux in the Arctic Ocean. Polar Biol 26:610\u0026ndash;620. https://doi.org/10.1007/s00300-003-0526-8\u003c/li\u003e\n\u003cli\u003eSchram MD, Davison PG (2012) Irwin Loops\u0026mdash;A History and Method of Constructing Homemade Loops. Trans Kansas Acad Sci 115:35\u0026ndash;40. https://doi.org/10.1660/062.115.0106\u003c/li\u003e\n\u003cli\u003eSchuelke T, Pereira TJ, Hardy SM, Bik HM (2018) Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats. Mol Ecol 27:1930\u0026ndash;1951. https://doi.org/10.1111/mec.14539\u003c/li\u003e\n\u003cli\u003eSeifried S, Plum C, Schulz M (2007) A new species of \u003cem\u003eParabradya\u003c/em\u003e Lang, 1944 (Copepoda: Harpacticoida: Ectinosomatidae) from the abyssal plain of the Angola Basin. Zootaxa 1\u0026ndash;21. https://doi.org/10.11646/zootaxa.1432.1.1\u003c/li\u003e\n\u003cli\u003eSoetaert K, Franco M, Lampadariou N, et al (2009) Factors affecting nematode biomass, length and width from the shelf to the deep sea. Mar Ecol Prog Ser 392:123\u0026ndash;132. https://doi.org/10.3354/meps08202\u003c/li\u003e\n\u003cli\u003eSvensen C, Iversen M, Norrbin F, et al (2024) Impact of aggregate-colonizing copepods on the biological carbon pump in a high-latitude fjord. Limnol Oceanogr 69:2029\u0026ndash;2042. https://doi.org/10.1002/lno.12641\u003c/li\u003e\n\u003cli\u003eSvensen C, Riser CW, Reigstad M, Seuthe L (2012) Degradation of copepod faecal pellets in the upper layer: Role of microbial community and \u003cem\u003eCalanus finmarchicus\u003c/em\u003e. Mar Ecol Prog Ser 462:39\u0026ndash;49. https://doi.org/10.3354/meps09808\u003c/li\u003e\n\u003cli\u003eThe Nansen Legacy (2022) Sampling Protocols: Version 10. Nansen Leg Rep Ser 32:. https://doi.org/10.7557/nlrs.6684\u003c/li\u003e\n\u003cli\u003eTietjen JH, Lee JJ (1973) Life history and feeding habits of the marine nematode, \u003cem\u003eChromadora macrolaimoides\u003c/em\u003e steiner. Oecologia 12:303\u0026ndash;314. https://doi.org/10.1007/BF00345045\u003c/li\u003e\n\u003cli\u003eTully O, Nolan DT (2002) A review of the population biology and host\u0026ndash;parasite interactions of the sea louse \u003cem\u003eLepeophtheirus salmonis\u003c/em\u003e (Copepoda: Caligidae). Parasitology 124:165\u0026ndash;182. https://doi.org/DOI: 10.1017/S0031182002001889\u003c/li\u003e\n\u003cli\u003eVafeiadou AM, Materatski P, Ad\u0026atilde;o H, et al (2014) Resource utilization and trophic position of nematodes and harpacticoid copepods in and adjacent to \u003cem\u003eZostera noltii\u003c/em\u003e beds. Biogeosciences 11:4001\u0026ndash;4014. https://doi.org/10.5194/bg-11-4001-2014\u003c/li\u003e\n\u003cli\u003eVan Gaever S, Olu K, Derycke S, Vanreusel A (2009) Nematode abundances in sediments from the Storegga Slide (Norwegian Sea) obtained during the VICKING expedition in 2006.\u003c/li\u003e\n\u003cli\u003eVan Gaever S, Vanreusel A (2012) Nematode abundance in cold seep sediments of the Nordic Margins (Nyregga, Styregga, Haakon Mosby Mud Volcano)\u003c/li\u003e\n\u003cli\u003eWangensteen OS, Palac\u0026iacute;n C, Guardiola M, Turon X (2018) DNA metabarcoding of littoral hard-bottom communities: high diversity and database gaps revealed by two molecular markers. PeerJ 6:e4705\u0026ndash;e4705. https://doi.org/10.7717/peerj.4705\u003c/li\u003e\n\u003cli\u003eWassmann P, Reigstad M, Haug T, et al (2006) Food webs and carbon flux in the Barents Sea. Prog Oceanogr 71:232\u0026ndash;287. https://doi.org/https://doi.org/10.1016/j.pocean.2006.10.003\u003c/li\u003e\n\u003cli\u003eWexels Riser C, Reigstad M, Wassmann P, et al (2007) Export or retention? Copepod abundance, faecal pellet production and vertical flux in the marginal ice zone through snap shots from the northern Barents Sea. Polar Biol 30:719\u0026ndash;730. https://doi.org/10.1007/s00300-006-0229-z\u003c/li\u003e\n\u003cli\u003eWexels Riser C, Wassmann P, Olli K, et al (2002) Seasonal variation in production, retention and export of zooplankton faecal pellets in the marginal ice zone and central Barents Sea. J Mar Syst 38:175\u0026ndash;188. https://doi.org/10.1016/S0924-7963(02)00176-8\u003c/li\u003e\n\u003cli\u003eWickham H (2016) ggplot2: Elegant Graphics for Data Analysis, 2nd edn. Springer-Verlag New York\u003c/li\u003e\n\u003cli\u003eWickham H, Averick M, Bryan J, et al (2019) Welcome to the tidyverse. J Open Source Softw 4:1686\u003c/li\u003e\n\u003cli\u003eWieser W (1953) Die Beziehung zwischen Mundh\u0026ouml;hlengestalt, Ern\u0026auml;hrungsweise und Vorkommen bei freilebenden marinen Nematoden: eine \u0026ouml;kologisch-morphologische Studie. Ark f\u0026uuml;r Zool 4:439\u0026ndash;484\u003c/li\u003e\n\u003cli\u003eWold A, Hop H, Svensen C, et al (2023) Atlantification influences zooplankton communities seasonally in the northern Barents Sea and Arctic Ocean. Prog Oceanogr 219:. https://doi.org/10.1016/j.pocean.2023.103133\u003c/li\u003e\n\u003cli\u003eZiegler AF, Bluhm BA, Renaud PE, J\u0026oslash;rgensen LL (2023) Weak seasonality in benthic food web structure within an Arctic inflow shelf region. Prog Oceanogr 217:103109. https://doi.org/https://doi.org/10.1016/j.pocean.2023.103109\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"polar-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pobi","sideBox":"Learn more about [Polar Biology](http://link.springer.com/journal/300)","snPcode":"300","submissionUrl":"https://submission.nature.com/new-submission/300/3","title":"Polar Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Marine nematodes, Prey metabarcoding, High-throughput sequencing, Trophic groups, Marine fungi, Copepods","lastPublishedDoi":"10.21203/rs.3.rs-6252716/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6252716/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMarine nematodes dominate the meiofauna of benthic sediments, but few studies have investigated their trophic roles. We studied the eukaryote diet composition of nematodes from surface sediments on the Arctic Barents Sea shelf, shelf break and adjacent Nansen Basin, during four seasons, using prey metabarcoding of the 18S ribosomal RNA gene. Monhysterida (35), Chromadorida (34), Araeolaimida (27) and Enoplida (22) nematodes were most frequently observed across the study area, and diets composed of diverse metazoan, fungal, and protist prey. In contrast to ambient sediment communities, prey followed a strong seasonal pattern, and ordination indicated two distinct seasonal prey clusters. In March and May prey were characterized by high relative abundances of fungi (42% and 48%, respectively). In comparison, August and December compositions had high relative abundances of arthropods (30% and 28%) and peritrich ciliates (11% and 9%, respectively). Other notable protist prey included chlorophytes and dinoflagellates, whereas diatoms \u0026ndash; which were highly abundant in the ambient sediment communities, were virtually absent as prey. Nematode taxonomy and trophic groups explained little of the variation in prey, and the latter was only significant when applied at the level of family. Our results indicate that Arctic nematodes are generalists which can feed on a variety of eukaryote items despite differences in morphology. They further indicate that heterotrophs, such as fungi and arthropods, compose important dietary items for nematodes in the Barents Sea. Such trophic tendencies may enable nematodes to fuel continuous growth and reproduction in Arctic sediment communities where food items are seasonally varied.\u003c/p\u003e","manuscriptTitle":"Eukaryote diets in Arctic marine nematodes across seasons and shelf-to-basin gradients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 19:01:38","doi":"10.21203/rs.3.rs-6252716/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-16T18:39:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T17:09:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277302809876051546952577900551886314098","date":"2025-11-01T14:13:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T20:54:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285297648747236449225031121112391580417","date":"2025-09-24T08:05:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1396824060389136135108544807764092540","date":"2025-09-19T16:21:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307531950960591275327580923523756757891","date":"2025-05-13T21:25:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T09:03:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-02T08:51:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T11:40:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Polar Biology","date":"2025-03-18T11:26:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"polar-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pobi","sideBox":"Learn more about [Polar Biology](http://link.springer.com/journal/300)","snPcode":"300","submissionUrl":"https://submission.nature.com/new-submission/300/3","title":"Polar Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"74b7c79e-f442-45b9-ba02-a015091872a6","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:07:08+00:00","versionOfRecord":{"articleIdentity":"rs-6252716","link":"https://doi.org/10.1007/s00300-026-03475-0","journal":{"identity":"polar-biology","isVorOnly":false,"title":"Polar Biology"},"publishedOn":"2026-04-30 15:57:56","publishedOnDateReadable":"April 30th, 2026"},"versionCreatedAt":"2025-04-22 19:01:38","video":"","vorDoi":"10.1007/s00300-026-03475-0","vorDoiUrl":"https://doi.org/10.1007/s00300-026-03475-0","workflowStages":[]},"version":"v1","identity":"rs-6252716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6252716","identity":"rs-6252716","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}