Baseline studies on meiofauna in the Baltic Sea before bottom-trawl fisheries exclusion II. A comparative study using multi-gene metabarcoding and morphology

637 Baseline studies on meiofauna in the Baltic Sea before bottomtrawl fisheries exclusion II. A comparative study using multi-gene metabarcoding and morphology Sahar Khodami1, Alexandra Ostmann1, Jana Packmor1,2 , Kai Horst George1, Pedro Martínez Arbizu1 1 German Centre for Marine Biodiversity Research (DZMB), Senckenberg am Meer, Südstrand 44, 26382 Wilhelmshaven, Germany 2 Working Group Plant Biodiversity and Evolution, Fak. V, Institute for Biology and Environmental Sciences (IBU), Carl von Ossietzky University of Oldenburg, Carl-Von-Ossietzky-Str. 9-11, 26111 Oldenburg, Germany Corresponding author: Sahar Khodami ([email protected]) Copyright: © Sahar Khodami et al. This is an open access article distributed under terms of the Creative Commons Attribution License (Attribution 4.0 International – CC BY 4.0). Research Article Abstract Understanding how meiofaunal communities respond to spatial protection measures is essential for evaluating the effectiveness of marine conservation strategies, particularly in heavily impacted regions such as the Baltic Sea. Here, we present one of the first integrated assessments of meiofaunal diversity across marine protected areas (MPAs) and adjacent reference areas (REFs) in the Baltic Sea. Our study combines wholecommunity metabarcoding of two genetic markers—mitochondrial COI and ribosomal 18S—with classical morphological identification of harpacticoid copepods. Sampling was conducted in the Fehmarn Belt and Oderbank regions as part of the DAM pilot mission MGF Ostsee, prior to the exclusion of mobile bottom-contact fishing. We provide a valuable baseline of community composition under current conditions. Results highlight strong complementarity between the genetic markers: 18S offered broader taxonomic coverage and higher alpha diversity, particularly for Nematoda and Platyhelminthes, while COI enabled more precise species-level resolution, especially for Copepoda. Spatial analyses revealed distinct assemblages between Fehmarn Belt and Oderbank, with notably higher copepod diversity in Fehmarn Belt. Significant differences between MPAs and REFs were detected only in Oderbank. Morphological and molecular datasets showed substantial but incomplete overlap, with each method capturing unique taxa, demonstrating their combined value for comprehensive biodiversity assessments. Our findings suggest that meiofauna communities reflect both regional environmental gradients and high small-scale patchiness. This study underscores the utility of multi-marker metabarcoding alongside traditional taxonomy for monitoring meiobenthic diversity and highlights the importance of incorporating meiofauna into long-term ecological assessments. The dataset provides a crucial reference point for evaluating future recovery and management outcomes following trawling exclusion. Key words: Baltic sea, harpacticoid copepods, marine protected areas, meiofauna, MGF-Ostsee, multi-gene metabarcoding Academic editor: Kara Layton Received: 23 August 2025 Accepted: 27 November 2025 Published: 10 December 2025 Citation: Khodami S, Ostmann A, Packmor J, George KH, Martínez Arbizu P (2025) Baseline studies on meiofauna in the Baltic Sea before bottom-trawl fisheries exclusion II. A comparative study using multi-gene metabarcoding and morphology. Metabarcoding and Metagenomics 9: e169630. https://doi.org/10.3897/ mbmg.9.169630 Metabarcoding and Metagenomics 9: 637–662 (2025) DOI: 10.3897/mbmg.9.169630 638 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion Introduction Meiofaunal communities—comprising organisms passing through a 1-mm sieve but being retained in a 40-µm sieve—are fundamental components of benthic marine ecosystems. They play vital roles in nutrient cycling, organic matter decomposition, and secondary production (Danovaro et al. 2008). Meiofauna plays a key ecological role by transferring energy from microbial producers and detritus to higher trophic levels, serving as an important food source for juvenile stages of various benthic metazoans including fishes (Giere 2009; Schückel et al. 2013). Beyond their trophic significance, meiofaunal communities are increasingly recognized as sensitive indicators of ecosystem health and early responders to environmental shifts and their diversity and structure often exhibit rapid and pronounced changes in response to stressors (Vassallo et al. 2006; Mokievsky et al. 2010; Zeppilli et al. 2015; Kim et al. 2020). Despite their ecological importance, meiofauna have long been underrepresented in biodiversity assessments, primarily due to the technical complexity and taxonomic expertise required for traditional morphological identification (Maximov et al. 2017). Consequently, our understanding of their diversity, ecological functions, and responses to anthropogenic stressors remains limited, particularly in heavily impacted environments such as the Baltic Sea (Bradshaw et al. 2021). The Baltic Sea—with its pronounced salinity gradients, history of eutrophication, and legacy of industrial impacts—offers a unique setting for studying faunal responses to environmental stressors. Among the various anthropogenic pressures, mobile bottom trawling fisheries (Mobile grundberührende Fischerei, MGF) is a dominant driver of benthic change, exerting greater influence on seafloor ecosystems than most other stressors (Lampadariou et al. 2005; Micallef et al. 2018; Grip and Blomqvist 2020; Schönke et al. 2022). Bottom trawling physically disrupts seabed habitats and alters community composition, with well-documented impacts on larger benthic fauna (Schratzberger et al. 2002; Schratzberger and Jennings 2002; Clark et al. 2019; Bradshaw et al. 2021, 2024). However, its influence on sediment resuspension and biogeochemical cycling is less frequently examined (Bradshaw et al. 2021), and the response of smaller size classes—such as meiofauna— remains ambiguous, often attributed to their high resilience and rapid turnover rates (Schratzberger et al. 2002; Ingels et al. 2014). Despite their sensitivity to environmental disturbances and potential as bioindicators, meiobenthic communities have been relatively overlooked in impact assessments. This is particularly surprising given their known responses to anthropogenic stressors and established utility in ecological monitoring (Gray 1971; Marcotte and Coull 1974; Moore and Bett 1989; Kennedy and Jacoby 1999; Schratzberger et al. 2000, 2002; Schratzberger and Jennings 2002). Recent evidence suggests that long-term bottom trawling can substantially alter meiofaunal composition and energy flow within benthic ecosystems (Ingels et al. 2014). In light of accelerating biodiversity loss and climate change (Chapin et al. 2000; Singh et al. 2021; Hochkirch et al. 2023), the establishment of marine protected areas (MPAs) has become a widely adopted strategy for safeguarding marine biodiversity. In the Baltic Sea, MPAs have been designated to mitigate the effects of bottom trawling, with MGF officially excluded from these zones since 18 December 2024 (Amtsblatt der Europäischen Union, DE, Reihe 639 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion L: Delegierte Verordnung (EU) 2024/2943 der Kommission vom 17. September 2024, 28.11.2024). Nonetheless, the effectiveness of MPAs in protecting benthic biodiversity—particularly that of meiofauna—remains underexplored. This study contributes to the German Marine Research Alliance (Deutsche Allianz Meeresforschung, DAM) pilot mission MGF-Ostsee, which aims to evaluate the ecological effects of excluding MGF from Natura 2000 sites within Germany’s Exclusive Economic Zone (EEZ) of the Baltic Sea. Within this initiative, work package 2.4 specifically investigates the impacts of bottom trawling on meiobenthic communities. A core component of this investigation involves a comparative assessment between MPAs and adjacent reference areas (REFs) in the Fehmarn Belt (FB) and Oderbank (OB) regions. In the first phase of the study (MGF Ostsee-I), a comprehensive inventory of local meiofauna was established using both classical morphological techniques and high-throughput whole-community metabarcoding. Recent advances in DNA metabarcoding have revolutionized biodiversity assessments by enabling high-throughput, fine-resolution characterization of entire communities. However, limitations such as primer bias, incomplete reference databases, and marker-specific taxonomic gaps necessitate the use of multiple genetic markers. The mitochondrial COI and ribosomal 18S genes are widely applied in meiofaunal research, each offering distinct advantages. COI provides superior taxonomic resolution, often to species level, but can suffer from primer mismatches and uneven amplification across taxa. In contrast, 18S rDNA—particularly hypervariable regions such as V1–V2, V4 or V9—offers broader taxonomic coverage and more consistent amplification, albeit typically at lower taxonomic resolution (Günther et al. 2018; Rossel et al. 2019; Kapshyna et al. 2024). To overcome the limitations of single-marker approaches, many studies advocate for combining multiple markers to improve detection rates and taxonomic confidence (e.g. Zhang et al. 2018). Based on these evidences, we employed a multigene approach using both COI and 18S (V1–V2) markers to obtain a more comprehensive and robust characterization of meiofaunal diversity in the Baltic Sea. In this study, we present a detailed assessment of meiofaunal communities in the FB and OB regions of the Baltic Sea, comparing MPAs and REFs prior to the enforcement of bottom trawling restrictions. Focusing on harpacticoid copepods—one of the most dominant and ecologically important meiofaunal groups—we combined metabarcoding (COI and 18S) and morphological identification methods to: 1. Characterize the taxonomic composition and diversity of meiofaunal assemblages across MPAs and REFs; 2. Quantify congruence and discrepancies between morphological and molecular detection methods, particularly for harpacticoid copepods; 3. Evaluate the potential of MPAs to conserve meiofaunal diversity and maintain community structure. By integrating spatial comparisons and complementary methodological approaches, this study offers critical insights into the value of meiofauna as bioindicators and advances the development of robust monitoring tools for marine conservation in a heavily impacted sea. 640 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion Methods Sample collection and laboratory procedures As part of the MGF Ostsee-I project and in preparation for whole-community metabarcoding, a total of 24 sediment samples were collected between 26 May and 3 June 2020, during the RV Elisabeth Mann Borgese cruise EMB 238/1 in the Fehmarn Belt (FB) region (Fig. 1). These included 12 replicates from MPAs and 12 from REFs. In the Oderbank (OB) region, an additional 26 stations were sampled during the EMB 267/1 cruise (1–16 June 2021), comprising 17 replicates from MPAs and 9 from REFs (Fig. 1). Meiofauna samples were collected using a multicorer equipped with 12 core liners (inner diameter 9.6 cm; area coverage per core, 74.2 cm2). The top five centimetres of sediment from two cores, along with the supernatant water (sieved through a 40-µm mesh), were transferred into Kautex vials and preserved in DESS (described by Yoder et al. 2006) at room temperature; upon arrival at the laboratory, all samples were stored at +4 °C. Sediment samples for metabarcoding were processed following the method of McIntyre and Warwick (1984), involving sieving through a 40-μm mesh and centrifugation with Kaolin and Levasil® (three times at 4,000 rpm for 5 minutes each). Samples were preserved in DESS at room temperature. From each sample of organisms, a 50 mL subsample was filtered using sterile glass fibre filters (2.7 μm pore size). Filters were rinsed with 96% denatured ethanol to remove DESS residues and transferred into sterile, DNA-free 1.5 mL Eppendorf tubes. The filters were then dried in a SpeedVac concentrator (Eppendorf) at 45 °C for one hour. Genomic DNA was extracted using the E.Z.N.A.® Mollusc DNA Kit (Omega Bio-Tek) for Fehmarn Belt samples and the DNeasy PowerSoil Pro Kit (Qiagen) for Oderbank samples, following manufacturer protocols. The two DNA extraction kits were used in different sampling years; the E.Z.N.A.® Mollusc DNA Kit occasionally required additional purification to remove PCR inhibitors, while the DNeasy PowerSoil Pro kit yielded cleaner extracts without further processing. Extracted DNA was further purified using AMPure XP magnetic beads (Beckman Coulter) when necessary. DNA concentration was quantified using a Qubit fluorometer and Qubit dsDNA High Sensitivity Assay Kit (Thermo Fisher) prior further processes. Two target gene regions were amplified via real-time PCR: the mitochondrial cytochrome c oxidase subunit I (COI) and the V1–V2 hypervariable region of the nuclear 18S rDNA gene. Amplification was performed using the primer pair F04/R22 for V1–V2 (Blaxter et al. 1998) and mLCOIintF/jgHCO2198 for COI (Geller et al. 2013; Leray et al. 2013), each tagged with partial Illumina Nextera adapter sequences. An indexing qPCR was conducted to attach Illumina Unique Dual Nextera Indexes and compatible Illumina adapters to each sample’s amplicons. PCR reactions were performed in 20 μL volumes with 10 μL SsoAdvanced Universal SYBR Green Supermix (Bio-Rad), 1 μL of each primer (10 pM/μL), 2 μL of template DNA, and 6 μL of molecular-grade water. Thermal cycling conditions included an initial denaturation at 98 °C for 2 minutes, followed by 25 cycles of denaturation at 98 °C for 15 seconds, annealing at 54 °C (COI) or 57 °C (V1–V2), and elongation at 72 °C for 1 minute. The indexing PCR included 10 cycles with denaturation at 98 °C and combined annealing/extension at 72 °C for 45 seconds. Both PCRs included a melt curve analysis (65–99 °C) to 641 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion assess amplification success in addition to Cq values and fluorescence signals. Indexed amplicons were pooled separately for each region and purified using 60% AMPure XP beads. Libraries were denatured and a 15% genomic PhiX spike-in was added prior to test sequencing using MiSeq Reagent Nano Kits v2 (250-cycle, paired-end). Final sequencing was performed using two Illumina MiSeq Reagent Kits v3 (300 cycles, paired-end) on an Illumina MiSeq platform at the DZMB Metabarcoding Laboratory in Wilhelmshaven, Germany. To compare morphological and molecular assessments of harpacticoid copepod diversity, we conducted a parallel analysis using species-level identifications from morphology and compared these with metabarcoding results from both gene fragments. For the FB region, morphological data were taken from the published dataset of Packmor et al. (2025), which provides information on species richness based on microscopy. For the OB region, we used the final morphological identifications, including species richness data. The latest dataset is submitted in a forthcoming manuscript Figure 1. The upper map shows the two investigated areas, Fehmarn Belt (purple box) and Oderbank (blue box), and their geographical position within the Baltic Sea. The lower panels display the sampling stations collected with a multicorer during cruises EMB 238/1 (2020; Fehmarn Belt) and EMB 267/1 (2021; Oderbank), as well as the Marine Protected Area (MPA) and Reference Area (REF) in each region. Bathymetric data were obtained from GEBCO 2025, Natural Earth, and HELCOM. The map was generated in QGIS version 3.24.1-Tisler (code revision 5709b824). 642 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion (Ostmann, unpubl. data 1). Barcode reference sequences of morphologically identified harpacticoid copepods were generated from individual specimens in both regions and are submitted as part of a separate study (Ostmann, unpubl. data 2). These reference sequences enabled a comparison between morphologically confirmed taxa and metabarcoding detections across both areas. Bioinformatics and diversity analyses For community metabarcoding, demultiplexed sequencing reads from each gene fragment and sequencing run were trimmed to remove locus-specific primers using BBMap (sourceforge.net/projects/bbmap/). Denoising and quality filtering were conducted with the DADA2 pipeline (Callahan et al. 2016). Sequences were truncated at 190 bp (R1) and 183 bp (R2) for COI, and at 212 bp (R1) and 200 bp (R2) for V1–V2, before merging the paired reads into contigs. Chimeric sequences were identified and removed. Amplicon Sequence Variants (ASVs) were de-replicated and taxonomically annotated using a custom pipeline—SGN Metabarcoding Pipeline (https:// github.com/pmartinezarbizu) which incorporates the BLASTn tool. Each ASV was queried against the NCBI nucleotide database and the curated reference library of COI mtDNA and 18S rDNA barcodes from Ostmann unpub. data2. The top ten BLAST hits were retained, and metadata including percentage identity, query coverage, e-values, fragment length, GenBank accession number, and sample-specific read counts were recorded. Only true meiofaunal ASVs (belonging to Nematoda, Copepoda, Platyhelminthes, Arachnida, Ostracoda, Kinorhyncha, Gastrotricha, and Tardigrada) were retained for community analyses. For both sequencing runs ASVs from each gene fragment were merged and clustered into Operational Taxonomic Units (OTUs) using a neighbor-joining approach with a 3% similarity threshold for COI and V1–V2 using the R packages DECIPHER (Wright 2016) and dada2pp (Martínez Arbizu 2018). Taxonomic assignments were cross-validated against the World Register of Marine Species (WoRMS) database. The final curated OTU tables from COI and V1–V2 datasets were used for community analysis, employing a suite of R packages: ape (Paradis and Schliep 2019), ComplexUpset (Krassowski 2020), dada2pp (Martínez Arbizu 2018), dplyr (Wickham et al. 2023), effsize (Torchiano 2020), ggplot2 (Wickham 2016), ggpubr (Kassambara 2023), ggrepel (Slowikowski 2024), ggvenn (Yan 2023), pairwiseAdonis (Martínez Arbizu 2020), tidyverse (Wickham et al. 2019), UpSetR (Conway et al. 2017), and vegan (Oksanen et al. 2012). Alpha diversity was assessed using OTU richness, Shannon diversity, and Pielou’s evenness. Group differences were tested by Wilcoxon rank-sum tests and effect sizes were quantified using Cohen’s d. These metrics were calculated separately for the V1–V2 and COI datasets to evaluate marker-dependent differences in diversity. Shannon diversity and evenness were computed based on number of reads per OTU as proxy for abundances. PERMANOVA was used to test for differences in meiofaunal community composition between regions (FB & OB) and areas (MPAs and REFs), while Sørensen and Jaccard similarity indices were calculated to quantify Harpacticoida community overlap between methods (COI, V1–V2 and morphology). 643 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion Results Comparison of gene markers for meiofaunal community characterization Two sequencing runs produced approximately 19 million paired-end reads from the V1–V2 and COI gene fragments, covering samples from both, FB and OB. Analysis of the COI dataset revealed 1,108 meiofaunal ASVs, which were clustered into 312 OTUs across both study areas. In comparison, the V1–V2 dataset yielded 2,946 meiofaunal ASVs, clustered into 1,425 OTUs. At the class level, both gene fragments detected a broad spectrum of meiofaunal taxa, including Chromadorea and Enoplea (Nematoda), Copepoda (primarily Harpacticoida and Cyclopoida), Arachnida (mites only), free-living Platyhelminthes, Ostracoda, Gastrotricha, Allomalorhagida (Kinorhyncha), and Tardigrada. Fig. 2 illustrates the relative number of reads (Fig. 2A) and OTUs (Fig. 2B) for the major meiofauna groups based on metabarcoding data from both FB and OB. The V1–V2 marker identified Nematoda as the most diverse taxon, with 868 OTUs and the highest number of reads in both study regions. This was followed by Platyhelminthes (242 OTUs), Copepoda (110 OTUs), Ostracoda (100 OTUs), and Arachnida (29 OTUs), along with several less abundant taxa. In contrast, the COI marker revealed generally lower overall diversity and number of reads across most groups. For Copepoda, OTU richness was nearly comparable between markers (105 OTUs in COI vs. 110 in V1–V2), while Arachnida (mites) were more strongly represented in the COI dataset (57 OTUs vs. 29 in V1–V2). Nematoda were dramatically underrepresented in the COI data, with only 79 OTUs detected, while Platyhelminthes and Ostracoda were represented by 34 and 16 OTUs, respectively. Alpha diversity metrics were calculated using read numbers as a proxy for relative abundances and revealed marked differences between the two markers (Fig. 3A–C). The V1–V2 dataset exhibited significantly higher OTU richness, Shannon diversity, and Pielou’s evenness compared to the COI dataset (Wilcoxon p < 2e–16 for all comparisons, Fig. 3). On average, V1–V2 revealed a substantially greater number of OTUs and a more even distribution of reads across OTUs within samples. This pattern reflects the broad representation of numerous nematode OTUs with relatively similar read counts, which reduces dominance effects and results in higher calculated evenness values. Effect sizes were large, particularly for richness (Cohen’s d = –5.6) and Shannon diversity (d = –4.77), indicating strong marker-dependent differences in alpha diversity estimates. Overall, the combined patterns of relative number of OTUs and reads (Fig. 2), and alpha diversity metrics (Fig. 3) demonstrate that the choice of genetic marker can substantially influence diversity assessments in meiofauna metabarcoding studies. Taxonomic composition and diversity contributions differ broadly between gene fragments, potentially reflecting primer selectivity particularly for Nematoda or the influence of the gene divergent degrees on community structure. Compared to COI, V1–V2 provided a more consistent and comprehensive representation of major taxa and avoided some of the common amplification biases and gaps in reference databases associated with COI. Therefore, further comparisons between study sites and between MPAs and adjacent REFs were based on V1–V2 data to ensure robust ecological inference. However, detailed analyses focusing specifically on harpacticoid copepods were performed on both markers. 644 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion Community patterns across MPAs and REFs Community composition and diversity patterns varied significantly among regions. Nematoda dominated across all areas, with Copepoda contributing notably in FB REFs, whereas Platyhelminthes were more prevalent in OB (Fig. 4A). OTU richness varied across areas (Fig. 4B), with the MPAs of OB exhibiting the highest median richness, followed by the adjacent REFs of OB, FB, and finally the MPAs of FB. Venn diagrams revealed substantial overlap of OTUs between MPAs and REFs within each region (54.2% shared in FB, 19.0% in OB), but also indicated a notable proportion of unique OTUs associated with either MPAs or REFs particularly in OB (Fig. 4C). These patterns suggest both site-specific and small-scale patchiness of meiofaunal diversity and composition. Figure 2. Comparison of the relative number of OTUs and reads per sample across major meiofauna groups based on COI and V1–V2 gene metabarcoding data of the Fehmarn Belt and Oderbank in the Baltic Sea. A Relative taxonomic composition (based on the relative number of reads) at the major taxonomic level for each gene fragment. B Relative number of OTUs for each meiofauna group and gene fragment. In both panels the left bar plots are reflecting the COI analyses and the right bar plots are referring to the V1–V2 gene fragment. Samples are representing both the Fehmarn Belt and Oderbank, and are ordered by the same treatment structure, allowing for a direct visual comparison of diversity and dominance patterns between the two gene fragments. Color codes are consistent across panels and reflect the meiofaunal taxa and area. 645 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion Community composition was compared between the two study sites (PERMANOVA) and between the corresponding MPAs and REFs (pairwise PERMANOVA) using Hellinger-transformed number of reads per OTU with Euclidean distance (Table 1). Community composition differed significantly between FB and OB (PERMANOVA, p = 0.001). In the comparison of MPAs and REFs within each site, OB showed significant differences between areas (PERMANOVA, p = 0.001), whereas FB did not reveal any significant pattern (PERMANOVA, p = 0.146). Across ordination method, non-metric multidimensional scaling (nMDS, based on Hellinger transformed number of reads applying Euclidean distance) showed that samples from FB clustered distinctly from those in OB (Fig. 5A). This separation was especially pronounced along the first nMDS axis, which accounted for the majority of the observed compositional variance between the two sites. Within FB region, samples from MPAs and REFs showed high degree of overlap, while a clear and statistically significant separation was evident in OB (Fig. 5B; Table 1; p = 0.001). In FB, PERMANOVA did not detect a statistically significant difference between MPAs and REFs (Fig. 5A; Table 1; p= 0.146), which was also apparent from the nMDS ordination. Overall, these findings indicate that broad geographic differences between FB and OB (likely influenced by factors such as sediment grain size and salinity) Figure 3. Comparison of alpha diversity metrics between COI and V1–V2 markers across all samples. Violin plots show (A) OTU richness, (B) Shannon diversity, and (C) Pielou’s evenness. Black dots represent individual sample values. Statistical significance was assessed using Wilcoxon rank-sum tests; effect sizes are reported as Cohen’s d. All three metrics were significantly higher for the V1–V2 marker compared to COI (p < 2e–16), with large effect sizes for richness (d = –5.6), diversity (d = –4.77), and evenness (d = –2.92). 652 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion (Pawlowski et al. 2016) but also supports the development of DNA-based biotic indices for ecological monitoring (Pawlowski et al. 2018). Although in our study the read numbers were used as a proxy for relative abundances when calculating alpha diversity metrics, we acknowledge that sequencing reads do not necessarily reflect true organismal abundances. Variations in body size, gene copy number, primer affinity, and amplification efficiency can bias the number of reads per taxon. In this study, however, only bona fide meiofaunal taxa with broadly comparable body sizes were considered, which minimizes extreme differences in template DNA quantity across taxa driven by body mass. Therefore, while read numbers cannot be equated directly with absolute abundances, they are considered a reasonable proxy for the relative abundances of taxa within samples in this dataset. This interpretation is further supported by empirical evidence from Rossel et al. (2019), who demonstrated that, when using a complete reference library and applying appropriate data transformations, read numbers per OTU reliably reflect relative species abundances in metabarcoding analyses. In addition, our data extend previous observations to the context of Baltic Sea meiofauna—an environmentally variable and still understudied region, demonstrate that metabarcoding represents a robust alternative to time-intensive traditional analyses for ecological monitoring and emphasize the importance of selecting genetic markers according to specific study objectives—whether focused on community-wide surveys, taxon-specific assessments, or functional monitoring. Spatial structuring of meiofauna at the MPAs and REFs Our comparative analysis between FB and OB revealed a clear spatial structuring of meiofaunal communities. This was consistently supported by PERMANOVA results and ordination plots—particularly for the 18S dataset and morphological data. The regional signal was strong, with distinct clusters forming between FB and OB across all taxonomic levels. These differences in meiofauna community are likely driven by environmental gradients such as sediment grain size, organic content, and salinity, which vary substantially between the two regions, as documented in the MGF-Ostsee project reports (Gogina and Schönke 2020; Feldens et al. 2021). Similar patterns have been observed elsewhere: Wang et al. (2023) showed that salinity and sediment composition were key predictors of intertidal meiofaunal community shifts in the Yellow Sea. In our study, the contrasting diversity and assemblage patterns between FB and OB may reflect the environmental differences between the two regions. FB sediments contain a much higher proportion of fine particles (<62.5 µm; ~50% in FB compared to ~0.5% in OB), substantially higher organic matter content (~6% in FB vs. ~0.4% in OB), and higher salinity (Gogina and Schönke 2020; Feldens et al. 2021). These differing conditions might contribute to the distinct meiofaunal assemblages observed in the two areas. Comparable environmentally driven patterns have been reported for sublittoral habitats in the southern North Sea and Baidaratskaya Bay of the Kara Sea, where depth and sediment type strongly influenced meiofaunal composition (Udalov et al. 2017; Schratzberger et al. 2000). Further support for the role of sediment characteristics comes from recent assessments of sediment-disturbed environments. Kapshyna et al. (2024) suggested that meiofaunal community differences can arise from sediment mismatches caused by grain-size variation following nourishment events. They noted that increases in the quantity 653 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion and heterogeneity of coarser sediments can positively affect post-impact meiofaunal abundances and richness (see also Glueck 2023; Fegley et al. 2020). This aligns well with our findings, underscoring that even moderate shifts in sediment texture can substantially influence meiofaunal community structure. In addition, we observed statistically significant differences (PERMANOVA test) between MPAs and REFs at OB. Given that fisheries exclusion had not yet been implemented at the time of sampling, these differences are likely attributable to the naturally patchy distribution of meiofaunal communities. This interpretation is consistent with the observations of Folkers and George (2011), who documented habitat partitioning among harpacticoid species in the western Baltic sublittoral, suggesting that substantial species turnover can occur over relatively short spatial scales. In contrast, we detected no significant differences in the meiofauna community between areas within the Fehmarn Belt, in line with the findings of Packmor et al. (2025), who also reported no significant differences between MPAs and REFs when comparing meiofauna communities at major taxonomic levels. Our study was conducted in proposed MPAs and REFs to evaluate their suitability for future protection measures and the contrasting results between FB and OB underscore the importance of spatially explicit, context-aware assessments when evaluating MPAs effectiveness after exclusion of fishery. As fisheries exclusion had been implemented neither in FB nor in OB at the time of sampling, our study represents the ‘Before’ phase of a BACI-style (Before-After-Control-Impact) framework. By incorporating proposed MPAs and adjacent REFs, our design establishes a critical ecological baseline to assess future impacts of trawling exclusion. As Seger et al. (2021) emphasize, BACI designs are essential for disentangling natural variability from human-induced effects. Our data serve as a foundational reference point for upcoming ‘After’ assessments, enabling robust evaluations of fauna turnover and potential recovery. In this way, our study complements similar BACI-based initiatives (Gogina and Schönke 2020; Feldens et al. 2021) that aim to assess the ecological effectiveness of trawling bans and broader conservation interventions in Baltic MPAs. Congruence and complementarity of morphology and metabarcoding Our detailed comparison of morphological and molecular data for harpacticoid copepods revealed both complementary strengths and notable discrepancies. In FB, morphological analysis recovered a higher number of species per sample, particularly less abundant taxa such as Normanella obscura Lee W. & Huys, 1999, Stylicletodes longicaudatus (Brady & Robertson, 1876), Haloschizopera pygmaea (Norman & Scott T., 1905), and Monopenicillus anke George, Zey & Packmor, 2023. These species were absent from one or both metabarcoding datasets, likely due to PCR bias, inefficient DNA extraction, or the absence of reference barcodes. Conversely, both metabarcoding methods detected several potential cryptic species-level OTUs, for example, within Cletodes tenuipes, Enhydrosoma curticauda, Tachidius discipes Giesbrecht, 1881, Nannopus palustris, Pseudobradya sp. and Parabradya dilatata. Notably, the last taxa were detected only by the V1–V2 marker and were absent from the morphological and COI dataset, possibly because the V1– V2 region is relatively conserved, limiting its ability to discriminate among closely related species. These findings align with those of Rossel and Martínez Arbizu (2019), where morphological and MALDI-TOF mass spectra data revealed several distinct lineages within the harpacticoid community of the North Sea, the specimens of N. 654 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion palustris from different seas (Garlitska et al. 2012) and several other harpacticoid species (e.g., Rocha-Olivares et al. 2001; Vakati et al. 2019). In our study, while the overlap between methods was strong, it remained incomplete. Several dominant taxa—including Danielssenia typica, Halectinosoma gothiceps, and Cletodes tenuipes—were consistently detected by both approaches, underscoring their ecological relevance and confirming the robustness of both methods while method-specific detections also revealed technical limitations: morphology may overlook small, fragile, or early-stage individuals, whereas molecular methods may miss taxa due to primer binding biases or gaps in reference databases, as also noted by Zeppilli et al. (2015). When comparing individual gene fragments to morphology, the V1–V2 marker failed to detect certain taxa—such as Proameira simplex (Norman & Scott T., 1905) and Echinolaophonte horrida (Norman, 1876) —that were abundantly recovered through morphology and COI. This might highlight the limited resolution of the V1–V2 fragment for species-level discrimination. Similar method-specific differences were documented in different multi-gene studies e.g., Bucklin et al. (2016), Cahill et al. (2018) and Gielings et al. (2021), where both approaches captured broad-scale community patterns but varied in taxonomic depth and precision. Many studies such as Aylagas et al. (2014), Bucklin et al. (2021) and Gielings et al. (2021) further emphasized the value of integrating morphological expertise with molecular tools to achieve accurate and reproducible biodiversity assessments. Implications for monitoring and conservation This study reinforces the value of meiofauna as indicators for ecological monitoring in marine systems. Our observation of region-specific OTUs, significant community differences between sites, and sensitivity of certain copepod taxa to environmental conditions point to the ecological signal contained within these small-bodied yet functionally important organisms, aligning with other related studies (Bik et al. 2012; Fonseca et al. 2017). As Zeppilli et al. (2015) argue, meiofauna possess several ideal traits for biomonitoring, including ubiquity, short generation times, and diverse functional roles. From a methodological standpoint, our findings reiterate the importance of accurate reference databases and tailored marker selection. The limitations encountered with COI—particularly for underrepresented taxa like nematodes and platyhelminths—highlight the need for using broader gene fragments and ongoing reference library development for different taxon groups (also indicated by Sinniger et al. 2016; Gielings et al. 2021; Ficetola et al. 2024). In the Baltic Sea, where novel or cryptic species are frequently encountered (Packmor et al. 2025), such efforts are particularly urgent. The integration of molecular tools into routine ecological monitoring has been recommended by other studies, emphasizing the scalability of metabarcoding and the potential of molecular approaches to detect ecosystem change across diverse spatial and temporal scales (e.g. Pawlowski et al. 2016; 2018). Incorporating such frameworks could significantly enhance meiofaunal monitoring programs in the Baltic and other dynamic marine systems. Our data demonstrate that MPAs can serve as valuable biodiversity reservoirs, even for organisms that are rarely considered in conservation assessments. As marine protection strategies evolve, integrating meiofaunal data into long-term monitoring programs—particularly using metabarcoding—can enhance detection of ecosystem change and improve management outcomes. 655 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion Acknowledgements The authors are indebted to the captain and the crew of RV ELISABETH MANN BORGESE for their valuable support and help during cruise EMB238 & EMB 267. Furthermore, we would like to thank Dr. Manon Dunn (Universität zu Köln, Germany) and Sven Hofmann (Senckenberg am Meer, Germany) for sampling the meiobenthic material and laboratory assistants Denisse Carolina GalarzaVerkovitch, MSc Eileen Deeken, BA Kim-Wiebke Redlich and BSc Ann-Kathrin Wessels (Senckenberg am Meer, Wilhelmshaven, Germany) for their contribution to reference barcode library preparation and centrifugation of the metabarcoding samples. This is publication number 107 that uses data from the Senckenberg am Meer Metabarcoding and NGS laboratory. Additional information Conflict of interest The authors have declared that no competing interests exist. Ethical statement Sampling and field studies: All necessary permits for sampling and observational field studies have been obtained by the cruise leader of EMB238 and 267 from the competent authority (Bundesamt fur Seeschifffahrt und Hydrographie (BSH)). Use of AI English wording was polished using ChatGPT (OpenAI), when needed. Funding This study was funded by the Federal Ministry of Education and Research (BMBF) Grant numbers: 03F0848A and 03F0848C as part of MGF Ostsee project. Author contributions Sahar Khodami: Writing – original draft, Writing – review & editing, Visualization, Investigation, Data curation, Formal analysis, Methods. Alexandra Ostmann: Data curation, Methods, Writing – review & editing. Jana Packmor: Data curation, Methods, Writing – review & editing. Kai Horst George: Funding acquisition, Data curation, Methods, Writing – review & editing, Investigation, Conceptualization, Supervision. Pedro Martínez Arbizu: Review & editing, Formal analysis, Conceptualization, Investigation Author ORCIDs Sahar Khodami https://orcid.org/0000-0002-7944-4004 Jana Packmor https://orcid.org/0009-0002-6858-9977 Kai Horst George https://orcid.org/0000-0001-6464-0099 Pedro Martínez Arbizu https://orcid.org/0000-0002-0891-1154 Data availability The metabarcoding raw sequence data are publicly available in the NCBI Sequence Read Archive (SRA) under BioProject ID PRJNA1309109, with SRA accession numbers SRR35078194–SRR35078336. The OTU tables generated for each genetic fragment are provided as Suppl. materials 1, 2, where the first 21 columns include detailed taxonomic 656 Metabarcoding and Metagenomics 9: 637–662 (2025), DOI: 10.3897/mbmg.9.169630 Sahar Khodami et al.: Baltic Sea meiofauna before trawl exclusion information for each OTU, including NCBI accession numbers (“accn”), BLASTn percentage identity (“pident”), query coverage (“qcov”), and E-value (“eval”). A list of harpacticoid species and OTUs detected by each method (Morphology, COI, and V1–V2) is available in Suppl. material 3. In this table, columns correspond to species/OTU names (“Species”), morphologically detected species in Fehmarn Belt and Oderbank (“Morphology_FB” and “Morphology_OB”), and OTUs detected by each fragment (COI and V1–V2) in each study area (“COI_FB”, “COI_ OB”, “V1–V2_FB”, and “V1–V2_OB”). The study is compliant with CBD and Nagoya protocols. 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Evolutionary Applications 15: 1901–1914. https://doi.org/10.1111/eva.12694 Supplementary material 1 V1–V2 OTU table Authors: Sahar Khodami, Alexandra Ostmann, Jana Packmor, Kai Horst George, Pedro Martínez Arbizu Data type: csv Explanation note: A list of detected OTUs by V1–V2 gene marker from all studied stations including taxonomic assignments and related blast values. The first 21 columns include detailed tax onomic information for each OTU, including NCBI accession numbers (“accn”), BLASTn percentage identity (“pident”), query coverage (“qcov”), and E-value (“eval”). Copyright notice: This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited. Link: https://doi.org/10.3897/mbmg.9.169630.suppl1