Harnessing Environmental DNA Metabarcoding for the Detection and Mapping of Vulnerable Marine Ecosystems in the Mediterranean Sea

Harnessing Environmental DNA Metabarcoding for the Detection and Mapping of Vulnerable Marine Ecosystems in the Mediterranean Sea | 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 Harnessing Environmental DNA Metabarcoding for the Detection and Mapping of Vulnerable Marine Ecosystems in the Mediterranean Sea Simone Galli, Giulia Maiello, Nadia Marinchel, Paolo Carpentieri, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6907089/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The overexploitation of marine resources by commercial fisheries poses significant threats to marine biodiversity and ecosystem stability. Vulnerable Marine Ecosystems (VMEs) require urgent protection to mitigate the adverse impacts of fishing activities, especially deep-sea bottom trawling. Given our incomplete knowledge of the marine environment, rapid and precise localization of VMEs is a priority to protect them effectively. Traditional methods for identifying VMEs are often limited by logistical challenges, high costs, and potential sampling biases. In this study, we assess the effectiveness of environmental DNA (eDNA) metabarcoding as an innovative tool for detecting and mapping VMEs in the Mediterranean Sea. eDNA samples were gathered at 19 sampling sites during a scientific fishing campaign in the Eastern Ionian Sea. Through the amplification of the COI mitochondrial gene, we identified a total of 285 unique taxa. A total of seven VME Indicator (VMEI) taxa were detected. A Joint Species Distribution Model (JSDM), using Hierarchical Modelling of Species Communities (HMSC), was used to investigate possible relationships between VMEI taxa and environmental covariates. Predicted distribution patterns of VMEI taxa were used to calculate a richness-weighted VME index. Taxon richness was highest at sites with high VME Index values. These findings demonstrate the potential of eDNA metabarcoding to effectively map the distribution of VMEI taxa, identify key environmental drivers influencing their occurrence and assess overall ecosystem vulnerability. We argue that an integration of eDNA-based approaches with traditional fisheries surveys could significantly enhance biodiversity assessments and improve conservation strategies in data-limited marine environments. eDNA biodiversity Vulnerable Marine Ecosystems demersal fisheries Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Bottom trawling is one of the most widespread fishing activities globally (Eigaard et al. 2017 ; Amoroso et al. 2018 ), yet it poses significant threats to the benthic fauna and seafloor ecosystems. This fishing method involves dragging heavy nets along the seafloor, leading to habitat destruction, sediment resuspension, and drastic alterations of benthic environments and community structures (Johnson et al. 2015 ; Hiddink et al. 2019 ; De Borger et al. 2021 ). The Mediterranean Sea is one of the most intensely fished regions in the world, where bottom trawling has been a dominant fishing method for centuries (Osio, 2012 ). The semi-enclosed nature of the Mediterranean Sea, combined with high fishing pressure and limited enforcement of conservation measures in certain areas, has led to significant impacts on benthic habitats (Pusceddu et al. 2014 ). Bottom trawling in the Mediterranean Sea primarily targets highly valuable species such as European hake ( Merluccius merluccius ), red mullet ( Mullus barbatus ) and deep-water crustaceans (such as Parapenaeus longirostris , Aristeus antennatus and Nephrops norvegicus ), but its effects extend far beyond the targeted species (Guijarro et al. 2017 ; Tsagarakis et al. 2017 ). Repeated trawling operations lead to the depletion of structurally complex habitats, reducing biodiversity and altering trophic interactions (Peristeraki et al. 2020 ). Additionally, the resuspension of sediments can affect water quality and the availability of nutrients, further influencing ecosystem dynamics (Palanques et al. 2014 ). Given the importance of the Mediterranean Sea as a biodiversity hotspot (Coll et al. 2010 ), mitigating the effects of bottom trawling is critical for the long-term sustainability of marine resources. According to the FAO definition (FAO, 2009 ), Vulnerable Marine Ecosystems (VMEs) are groups of species, communities or habitats that may be vulnerable to impacts from fishing activities. Their sensitivity makes them important indicators, acting as sentinels of human impact at sea. Their vulnerability is assessed and measured through an evaluation of their uniqueness or rarity, functional significance, fragility, structural complexity and life-history traits that make the species recovery difficult (Morato et al. 2018 ; Carpentieri et al. 2021 ). Such features are shared by several deep-sea ecosystems, which are particularly vulnerable to the impact of bottom-contacting fishing gears (Ramirez-Llodra et al. 2011 ; Clark et al. 2016 ). VMEs are primarily identified by specific topographical or geological features, referred to as VME indicator features, as well as by the presence of several benthic organisms, called VME indicator (VMEI) species or taxa (FAO, 2009 ). In the Mediterranean Sea, several macro- and mega benthic invertebrates display aggregative behaviour, thus forming structurally complex environments that host rich fish and invertebrate communities (D’Onghia et al. 2010 ; Chimienti et al. 2019 ). These habitat-forming taxa, identified as VMEI by the General Fisheries Commission for the Mediterranean Sea (GFCM, 2017 ), are associated with highly valuable fish and crustacean species (Rueda et al. 2019 ; Sion et al. 2019 ) and are therefore particularly targeted by bottom trawl fisheries (Cau et al. 2017 ; D’Onghia et al. 2019 ). Recognizing the ecological threats posed by bottom trawling, the European Union (EU) has implemented a range of management measures aimed at protecting marine biodiversity and ensuring sustainable fisheries. New management objectives have been introduced with the EU Fisheries package (European Commission, 2023 ), which consists of 30% protection per Exclusive Economic Zone (EEZ) surface area, commonly referred to as the “30 x 30” strategy. Phasing out bottom trawling in all Natura 2000 sites by 2030 is a key action in this package. More recently, the European Union passed a nature restoration law (EU, 2024), aiming to restore 20% of degraded ecosystems by 2030 and 100% by 2050. Management measures include spatial restrictions on trawling, gear modifications, and the establishment of Marine Protected Areas (MPAs). No- take MPAs play a crucial role in these conservation efforts by providing shelters where marine ecosystems can recover from fishing pressures (Sciberras et al. 2013 ; Edgar et al. 2014 ). However, enforcement challenges and the need for improved monitoring tools highlight the importance of advancing detection methods for VMEs to enhance compliance and conservation effectiveness. Traditional approaches for identifying VMEs primarily rely on direct observations through remote-operated vehicles (ROVs), bottom trawl surveys, and diver-based assessments (Lauria et al. 2017 ; Chimienti et al. 2018a ; Stephenson et al. 2024 ). While these methods provide valuable information, they are often logistically complex, costly, and time-consuming. Moreover, some of them (such as trawl surveys) only capture certain organisms, therefore providing partial information on the whole marine community. Environmental DNA (eDNA) metabarcoding has emerged as a powerful complement - or alternative - for biodiversity monitoring, offering a reliable, non-invasive and cost-effective approach to detect marine species by analyzing the corresponding genetic material released in the water column and/or sediment (Valentini et al, 2016 ; Mariani et al. 2019 ; Russo et al. 2021 ). eDNA metabarcoding thus represents a promising tool to obtain even more comprehensive results than conventional methods (Aglieri et al. 2021 ; Miya, 2022 ; Maiello et al. 2024 ). In this study, we harness the potential of eDNA metabarcoding to detect and map VMEs in the Eastern Ionian Sea. We integrate eDNA analysis with data collected from a scientific bottom trawl survey as part of the Mediterranean International Trawl Survey (MEDITS) campaign, with the specific aim to assess the presence and distribution of key VMEI taxa. The study employed a self-produced 3D-printed sampler, the metaprobe (Maiello et al. 2022 ), to collect eDNA samples directly from the trawl net, allowing for a more targeted approach to detecting benthic organisms associated with VMEs. By incorporating the data in a Joint Species Distribution Model (JSDM) approach, we predict the distribution of VMEIs across the study area, and evaluate the role of environmental factors in shaping it, thus identifying priority areas for protection and conservation strategies. Materials and methods Sampling Sampling was carried out in June 2023 in the Eastern Ionian Sea (FAO Geographical Sub Area 20) (Fig. 1 ), onboard a fishing vessel involved in the Mediterranean International Trawl Survey (MEDITS) campaign, that conducts annual bottom trawling surveys in different areas of the Mediterranean and Black Seas (Bertrand et al. 2002 ). Samples were gathered at 19 sites, covering a wide range of depths (41–751 m) and a coastal distance range (1.5–15.8 km) including both shelf and slope areas. For each haul, information about the abundance (i.e., biomass and number of individuals) of the organisms caught by the fishing net was recorded. eDNA was collected using a self-produced 3D-printed sampler, the metaprobe (Maiello et al. 2022 ). This hollow perforated sphere passively collects genetic material from the surrounding environment through three sterile gauze rolls placed inside it. The metaprobe was placed in the trawl net codend at the start of the fishing operations and retrieved once the net had been hauled and opened on board. Using sterile gloves and forceps, the gauze rolls were recovered and each one was placed in a separate 50 mL Falcon tube containing 99% ethanol for DNA preservation. Samples were frozen directly on board and then stored at -20°C in the laboratory before DNA extraction. Each of the three gauze was used as a field replica. At site ST46, one gauze roll was lost, resulting in only two replicates being available for that station. In total, 56 eDNA samples were collected across all sampling sites. eDNA processing DNA was extracted from the gauze rolls using a custom protocol. Initially, a small section (~ 2 × 2 cm) from the outer layer of the roll was cut into small pieces using sterile scissors. The pieces of gauze were placed in a 1.5 mL tube to allow the ethanol to evaporate. Each sample was then incubated at 55°C for 12 h in 1 mL EDTA (0.5 M pH 8) and 0.25 mg/mL Proteinase K. After lysis, samples were centrifuged, supernatant was collected and DNA was extracted with Roche Assembly Tubes silica columns (Dabney and Meyer, 2019 ). A total of four extraction negatives were processed along the samples, using portions of clean gauze. DNA extracts were amplified targeting a ~ 313bp fragment of the COI mitochondrial gene. Highly degenerated universal primers were used, in order to target all the metazoans (forward mICOIintF: 5′- GGWACWRGWTGRACWNTNTAYCCYCC-3′ (Leray et al. 2013 ); reverse jgHCO2198: 5′- TANACYTCNGGRTGNCCRAARAAYCA-3′ (Geller et al. 2013 )). To address potential contamination linked with laboratory procedures, a positive control ( Penaeus vannamei , a Pacific shrimp species not found in the Mediterranean Sea) and a negative PCR control were amplified along with the DNA samples. Each forward and reverse primer was tagged with a unique 8bp index to allow sample identification during bioinformatic analysis and reduce the likelihood of cross-contamination or tag switching during sequencing. Tags differed by at least three base pairs and were preceded by 2–4 degenerate bases to enhance sequence diversity. Each sample was PCR-amplified in triplicate using 20 µL reactions, including 10 µL MyFi™ Mix (Meridian Bioscience), 0.16 µL Bovine Serum Albumin (20 mg/mL, Thermo Fisher Scientific), 5.84 µL UltraPure™ Distilled Water (Invitrogen), 1 µL of each forward and reverse primer (10 µM, Eurofins), and 2 µL of template DNA. PCRs were performed under the following thermocycling conditions: polymerase activation at 95°C for 10 min, followed by 35 cycles of denaturation and amplification (94°C for 1 min, 45°C for 1 min, 72°C for 1 min), and a final elongation at 72°C for 5 min. PCR triplicates were pooled and visualised on a 2% agarose gel to assess the successful amplification of target sequences. PCR products were purified using Mag-Bind® TotalPure NGS magnetic beads (Omega Bio-tek Inc), adding a 0.8x ratio of magnetic beads to 30 µL of PCR product (Bronner and Quail, 2019 ). Purified PCR products were then quantified with a Qubit Flex™ 4.0 fluorometer with the Qubit™ dsDNA HS Assay Kit (Invitrogen). Based on the DNA concentration, PCR products were normalized and pooled in equimolar concentrations for library preparation. End-repair, adapter ligation, and library PCR amplification were performed using the NEXTFLEX® Rapid DNA-Seq Kit 2.0 for Illumina® platforms (PerkinElmer) following the manufacturer’s protocol. Fragment lengths were assessed with an Agilent 4200 TapeStation and High Sensitivity D1000 ScreenTape (Agilent Technologies), and secondary products such as adaptor dimers were removed through an additional 0.8:1 ratio magnetic bead clean-up. The library was quantified using a quantitative PCR (qPCR) on a Rotor-Gene Q (Qiagen) using the NEBNext® Library Quant Kit for Illumina® (New England Biolabs) and then diluted to 4 nM according to qPCR concentration. The final library and PhiX Control were re-quantified using qPCR before sequencing. The library was sequenced at 12.5 pM with 10% PhiX control using V3 chemistry (2 x 250 bp paired-end) on an Illumina MiSeq platform. The eDNA samples analyzed in this study were sequenced alongside samples from a separate project. Bioinformatics Bioinformatic analyses were conducted using the obitools software (Boyer et al. 2016 ). Read quality assessment was performed with fastqc , and low-quality ends were trimmed using obicut before downstream processing. Paired-end reads with a quality score exceeding 40 were merged using illuminapairedend , while sample demultiplexing was carried out with ngsfilter , using the unique barcodes and allowing for a single nucleotide mismatch. Sequence filtering was performed using obigrep to remove singletons and reads falling outside the expected length range (300–325 bp), followed by dereplication with obiuniq. We removed chimeras with uchime (Edgar et al. 2011 ), and clustered sequences into Molecular Operational Taxonomic Units (MOTUs) using swarm (Mahé et al. 2015 ) setting the threshold to d = 13 (Siegenthaler et al. 2019 ). Taxonomic assignment was performed with the sintax algorithm implemented in usearch (Edgar, 2016 ) against the MIDORI2 reference database for COI sequences (Leray et al. 2022 ). Contaminant removal was performed using blanks and negative controls with the microdecon (version 1.0.2; McKnight et al. 2019 ), an R package specifically designed for identifying and removing contaminants. Final refinement of the dataset consisted of removing MOTUs with a < 98% identity match and reads with an occurrence below 5 per sample, in order to reduce the effects of low-abundance false positives due to tag switching and/or cross-contamination. Terrestrial taxa likely resulting from human-associated contamination (e.g., fungi, insecta, Homo sapiens , Sus scrofa , Canis lupus ) were also removed from the final dataset. eDNA data validation In the first step of the data analysis was assessed the consistency between the data obtained from eDNA and those obtained from MEDITS catches. This step is crucial for laying the foundations of the analysis and the development of subsequent models. Statistical analyses were performed to compare eDNA-based taxon detections with catch data collected by the MEDITS campaign, according to the procedure described in Maiello et al. ( 2024 ). The eDNA dataset was filtered, retaining only the taxa assigned to the species level and removing algal, fungal and planktonic taxa. Cohen's Kappa coefficient was computed to assess inter-rater reliability between eDNA and fisheries catch data, with species presence-absence matrices used to evaluate agreement at the sampling site level. A Random Forest classification model was then employed to assess the predictive capacity of eDNA and catch data in distinguishing depth strata, using 100 iterations with 70% of the data allocated for training and the remaining 30% for the testing. Non-metric multidimensional scaling (nMDS) was applied using Jaccard dissimilarity to visualize differences in species assemblages detected by each of the two methods. First, only species common to both datasets (eDNA and catches) were considered, nMDS analysis and plotting were then performed taking into account common and non-common taxa to both datasets. Species occurrences were transformed into presence-absence data, and convex hulls were used to group sampling stations by depth strata. All statistical analyses and visualizations were conducted in R (v. 4.4.2) (R Core Team, 2024 ) using the vegan (Oksanen et al. 2022 ), randomforest (Liaw and Wiener, 2002 ), ggplot2 (Wickham, 2016 ), and yardstick (Kuhn and Vaughan, 2023 ) packages. Spatial analysis Subsequently, we gathered and collated spatial information relating the topographical and environmental features of the study area. The eDNA dataset was pre-processed to exclude taxa classified as non-marine, planktonic, or pelagic based on the ecological classification available on FishBase (Froese and Pauly, 2024 ) and SeaLifeBase (Palomares and Pauly, 2024 ). Taxonomic assignments were then aggregated at the order level to increase the robustness of spatial analysis. Finally, we excluded taxon orders occurring in only one sampling site to ensure sufficient data for modelling. The spatial analysis was conducted in R (v. 4.4.2) using the sf (Pebesma, 2018 ), terra (Hijmans, 2024 ), and gstat (Pebesma, 2004 ) packages. A 1x1 km cell grid covering the whole sampling area was established as a spatial object and projected using WGS84. A coastline shapefile was used to calculate the shortest Euclidean distance from each sample point to the coast. Environmental variables, including depth, slope, and oceanographic data (e.g., temperature, salinity, oxygen concentration), were incorporated into the analysis. Depth data were retrieved using the marmap (Pante and Simon-Bouhet, 2013 ) R package, which accesses the ETOPO 2022 database hosted on the NOAA website (NOAA National Centers for Environmental Information, 2022 ), and were subsequently spatially linked to each sampling point. Environmental variables were downloaded from the Copernicus Marine Data Store (Clementi et al. 2023; Feudale et al. 2023) and included values for: salinity, temperature, dissolved oxygen, chlorophyll, Particulate Organic Matter (POM), nitrates and phosphates. The final values of these variables were obtained by averaging the data across individual depth layers provided by the Copernicus database. The sampling station ST35 was removed from the analyses since it was located in an area (Gulf of Corinth) not covered by the Copernicus dataset. Slope was calculated from bathymetric data using the terrain function ( terra package). Global Fishing Watch (GFW) data (2013–2022) were used to assess spatial patterns of bottom trawling fishing effort (Global Fishing Watch, 2024 ). The effort was measured in fishing hours, as gathered by AIS data. Inverse Distance Weighting (IDW) was applied to interpolate continuous spatial surfaces for environmental parameters and fishing effort. The IDW method was implemented using the gstat package, with a power parameter of 2 and the maximum number of neighbouring points available. All spatial datasets were resampled to a common resolution and projected into the same coordinate reference system. Spatial joins were performed to associate sample locations with environmental variables, allowing for subsequent statistical analysis. Joint Species Distribution Modelling To investigate taxon-environment relationships and interspecific associations, a Joint Species Distribution Model (JSDM) was implemented using the Hierarchical Modeling of Species Communities (HMSC) framework, based on the work by Stephenson et al. ( 2024 ). This approach allows for the simultaneous modelling of multiple species while accounting for environmental covariates, spatial autocorrelation, and potential biotic interactions (Ovaskainen et al. 2017), and is available in the hmsc (Tikhonov et al. 2020 ) R package. The JSDM consists of a probit regression model for species presence-absence data and incorporates environmental predictors, spatial random effects and a residual covariance matrix to infer species co-occurrence patterns. Specifically, environmental covariates included in the model were: coastal distance , salinity , slope , temperature and trawling effort (Table S1 ). The other environmental variables included in the previous steps of the analysis showed high collinearity with the selected covariates and were therefore excluded to prevent model overfitting and loss of generality. We assessed multicollinearity among environmental predictors using the Variance Inflation Factor (VIF) implemented in the usdm (Naimi, 2015 ) R package. Predictors with a VIF ≥ 5 were iteratively removed, always discarding the variable with the highest VIF, until all selected variables had VIF values below this threshold. JSDM also introduces transect-level spatial random effects to account for the non-independence of observations due to the sampling design. A latent factor model was used to capture species co-occurrence patterns not explained by environmental variables, potentially indicative of biotic interactions or shared ecological preferences. Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling was employed for model fitting, ensuring robust posterior estimates. Weakly informative priors were specified to regularize the model and prevent overfitting. Multiple MCMC chains were executed, and convergence was assessed through Gelman-Rubin statistics and visual inspection of trace plots. Estimated effects of environmental variables, represented as β-coefficients, were derived from the model output. The value of each β-coefficient reflects the strength and direction of the relationship between the corresponding variable and the occurrence of VMEI taxa. To assess model performance, several validation techniques were applied: (i) the proportion of variance in taxon occurrence explained by the model was estimated through coefficient of determination (R²); (ii) predictive performance was evaluated through cross-validation, whereby subsets of data were withheld to compare predicted versus observed taxon occurrences. Finally, taxon-specific prediction errors were analyzed to determine the reliability of individual taxa predictions and identify areas for potential model refinement. Biodiversity analysis An overall Richness-Weighted VME Index was calculated for the study area, taking into account the probability of occurrence estimated by the JSDM for the individual taxa orders. To investigate the relationship between the overall VME index and the biodiversity, as assessed by the eDNA detections, a series of analyses were carried out. First, sampling sites were divided into three classes (Low (0–0.4), Medium (0.4–0.8), and High (0.8–2)) based on the corresponding VME Index value, so that each class contained the same number of sites. Using the presence/absence matrix of the eDNA dataset, taxon richness was calculated for each site. A boxplot was constructed to compare richness across VME index classes. Alpha diversity metrics were compared across VME Index classes using non-parametric Kruskal–Wallis tests and pairwise Wilcoxon rank-sum tests. A general linear model was used to assess the relationships between VME index values and taxon richness. A Venn diagram was generated using the eulerr (Larsson, 2020 ) R package to visualize taxon distribution and overlap among VME Index classes. Results eDNA data composition and validation The eDNA dataset, after bioinformatic processing, comprised a total of 4,851,074 reads (Table S2 ). After decontamination, data filtering and non-target taxa removal, the final dataset included 274 taxa, which were identified at different taxonomic resolutions: 244 at the species, 17 at genus, 3 at family, 7 at order and 3 at class level. The most prevalent phyla, in terms of taxon richness, were Chordata (44.5%), Arthropoda (14.6%), Cnidaria (12.4%), Mollusca (10.2%), and Annelida (3.6%), while in terms of read abundance, the distribution ranks were: Chordata (94%), Mollusca (4.1%), Cnidaria (1.2%), Arthropoda (0.5%), and Echinodermata (0.1%). The composition of the eDNA and catches datasets is reported in Supplementary material (Tables S3, S4) The comparison between eDNA and catch data revealed a substantial level of agreement. Confusion matrices describing the output of the Random Forest classification model revealed that both MEDITS catch data and eDNA data exhibited mean values of Cohen's Kappa of 0.79. (Fig. S1 ). Similar clustering patterns were observed from nMDS plots, when the analysis took into account only species common to both datasets (eDNA and catches) and common and non-common taxa to both datasets (Fig S2 A and S2B, respectively). In both nMDS plots, eDNA samples (red) and MEDITS samples (black) were closely associated, indicating a strong similarity in the species composition detected by both methods (Fig. S2 ). VMEI taxa According to the FAO definition (FAO, 2009 ) and the list provided by GFCM ( 2017 ), several VMEI taxa were present in the eDNA dataset. Specifically, 35 taxa belonging to 5 classes were identified: 1 Hexacorallia, 5 Octocorallia, 2 Crinoidea, 6 Demospongiae and 21 Hydrozoa. However, Hydrozoa were excluded from downstream analyses, as eDNA detection does not allow differentiation between the polypoid and medusoid life stages of the animal. The taxonomy referred to in this paper differs slightly from that indicated by the GFCM in its list of VMEI taxa (GFCM, 2017 ). In particular, the anthozoan subclass Octocorallia has recently undergone a major revision based on phylogenetic evidence, and is now regarded as a class; furthermore, the previous subdivision into Alcyonacea, Pennatulacea and Helioporacea has been replaced by a new subdivision into two orders only: Malacalcyonacea and Scleralcyonacea (McFadden et al. 2022 ). After filtration —which excluded taxonomic orders occurring at only one sampling site—, 7 taxa were retained, belonging to three orders: Malacalcyonacea, Scleralcyonacea and Comatulida (Table 1 ). Three non-Mediterranean taxa, Acanella arbuscula, Leptometra celtica and Spinimuricea atlantica , were present in the original dataset. In the Mediterranean, closely related species are known to occur: Isidella elongata, Leptometra phalangium and Spinimuricea klavereni respectively. However, as COI reference sequences for these species are not available in GenBank, we preferred to assign these sequences to the taxonomic level compatible with the detection of Mediterranean species, labelling them as “Keratoisididae”, “ Leptometra sp”. and “ Spinimuricea sp.”. Of the VMEI taxa considered in this work, only A. palmatum, A. mediterranea and Keratoisididae ( I. elongata ) were recorded in the catches (Table S4). Table 1 VMEI taxa retained for JSDM analysis after filtering. Taxa are categorized by phylum and order. Site occurrences and total reads are indicated. Phylum Order Taxon Sites occurring Total reads Echinodermata Comatulida Antedon mediterranea 1 16 Echinodermata Comatulida Leptometra sp. 3 1486 Cnidaria Malacalcyonacea Alcyonium palmatum 2 357 Cnidaria Malacalcyonacea Spinimuricea sp. 7 8546 Cnidaria Scleralcyonacea Keratoisididae 6 10674 Cnidaria Scleralcyonacea Funiculina quadrangularis 4 158 Cnidaria Scleralcyonacea Pennatula rubra 2 3047 Joint Species Distribution Modelling The Joint Species Distribution Modelling (JSDM) analysis provided key insights into how environmental drivers impact the distribution of the three VMEI orders considered: Malacalcyonacea, Scleralcyonacea and Comatulida. The model’s β-parameters (Fig. 2 ) indicate both the strength and direction of these relationships, demonstrating that the three taxa are influenced differently by each environmental factor. Malacalcyonacea displayed a pronounced positive association with temperature (β = 0.97), indicating that warmer waters favour its occurrence. Additionally, this order showed a positive dependency on trawling effort (β = 0.82). Conversely, negative relationships were observed with slope (β = -0.99), coastal distance (β = -0.85), and salinity (β = -0.74). Scleralcyonacea exhibited a strong positive dependency on trawling effort (β = 1), suggesting a distribution strongly associated with areas of high fishing activity. Positive relationships were also observed with coastal distance (β = 0.82) and slope (β = 0.8), while negative dependencies were detected with temperature (β = -0.72) and salinity (β = -0.54). Comatulida exhibited negative relationships with all the considered environmental variables. The strongest negative associations were found with slope (β = -0.97) and salinity (β = -0.85), followed by coastal distance (β = -0.79), trawling effort (β = -0.68), and temperature (β = -0.57). The proportion of explained variance (Fig. 3) provided further insights into the relative importance of each environmental factor. Malacalcyonacea were mainly influenced by slope and temperature, which together accounted for approximately 60% of the variance. Trawling effort emerged as the primary driver for Scleralcyonacea, explaining up to 45% of the observed patterns. Comatulida showed a more balanced distribution of explained variance, with slope and random effects playing a significant role. Random effects represent a measure of residual variance, which may be due to spatial or temporal autocorrelation. This presumably highlights the effect of variables not considered in the study. The species distribution models generated for Malacalcyonacea, Scleralcyonacea and Comatulida indicate distinct spatial patterns of predicted occurrence across the study area (Fig. 4 ). Figure 4 a displays the probability of presence (PA) for each taxon, revealing high prediction areas along the western Greek coastline. Malacalcyonacea exhibit localized high-probability zones, located along the coastline from northwestern Peloponnese to the Straits of Corfu, favouring enclosed waters (inner Ionian archipelago). Notably, Scleralcyonacea show the most widespread distribution, with probability values reaching up to 1 particularly in offshore regions along the outer borders of the study area, as well as in the Gulf of Patras and the waters between the Peloponnese and the Ionian Islands. Comatulida follow a trend similar to Malacalcyonacea, but with generally lower predicted occurrence levels. Figure 4 b presents the standard deviation (SD) of the predictions, illustrating the model’s uncertainty. Higher SD values, concentrated near the edges of high-prediction areas, suggest potential variability in environmental suitability or lower confidence due to limited data coverage. In contrast, core high-probability areas exhibit lower uncertainty, indicating robust model predictions in these regions. Biodiversity Further integrating the JSDM predictions, the Richness-Weighted VME Index (Fig. 5 ) highlights key conservation priority areas by combining VMEI taxa richness with the associated probability of occurrence. The highest VME index values are concentrated in the Gulf of Patras and along the northwestern Peloponnesian coast, coinciding with regions where VMEI taxa exhibit high predicted occurrence. Additionally, elevated values are observed in the Straits of Corfu and along offshore waters surrounding the Ionian Islands, indicating potential biodiversity hotspots for VMEs. Areas with moderate index values may serve as transition zones, while lower index values along the southwestern study area suggest either limited species presence or unsuitable habitat conditions. Taxon richness varied significantly across the three VME Index classes (Kruskal-Wallis test, p = 0.0034; Fig. 6 a). Pairwise comparisons indicated that richness was significantly higher in High VME areas compared to both Medium ( p = 0.026) and Low VME classes ( p = 0.0016), while the difference between Low and Medium was not significant ( p = 0.23). Taxon richness showed a weak but positive correlation with the VME Index (Fig. 6 b). This trend was observed across all three VME Index classes, with the highest diversity values more frequently associated with High VME sites. The Venn diagram (Fig. 6 c) illustrates the distribution of unique and shared taxa among VME Index classes. A core group of 74 taxa was shared across all classes, while High VME areas hosted the highest number of unique taxa (67). These findings indicate that areas with higher VME Index values support greater and more distinct taxonomic richness. Discussion In this study, eDNA data collected during a scientific fishing campaign were used to detect and map VMEI taxa presence in the Eastern Ionian Sea. The use of self-produced samplers placed inside bottom-trawl nets allowed gathering more comprehensive information on the biological communities occurring at the sampling sites. Overall, a total of seven VMEI species, belonging to two phyla and three orders, were considered in the study. The JSDM revealed how the spatial distribution of each one of the three orders is primarily influenced by specific environmental factors. Taking into account these findings, it was ultimately possible to identify the areas most likely to host these species and, therefore, to be considered as VME areas. These areas were indeed shown to be associated with a higher overall biodiversity compared to those in which the predicted occurrence of VMEI taxa was low. The effectiveness and the reliability of the metaprobes have already been stated in various studies, whether they have been integrated with trawling (Maiello et al. 2022 , Maiello et al. 2024 ) or purse seine (Marinchel et al. unpublished data) fishing activities. In the context of the MEDITS campaign, the inclusion of a metaprobe in the fishing net adds a layer of information, flanking the traditional assessment based on visual inspection of the net content. In particular, Maiello et al. ( 2022 ) demonstrated how the placement of the metaprobe within the trawl net allows the collection of eDNA associated not only with the contents of the net itself but also with benthic and pelagic organisms that are present in the surrounding environment and are not found in the catch. These results make the trawl-associated metaprobe an effective tool for investigating a set of organisms that possess a low degree of catchability, as is the case for several VMEI taxa (Auster et al. 2011 ). In this regard, Geange et al. (2019) have shown that catchability of VMEI taxa in trawls is taxa-dependent and generally low (below 1%). This low catchability can be particularly important for fragile species like sponges or soft corals, which are prone to fragmentation and may not be retained in trawl nets (Freese et al. 1999 ). Chimienti et al. ( 2018a ) reported that in a trawl survey in the Ionian Sea, despite the heavy damage caused to specimens of Pennatula rubra , few individuals ended up in the trawling net (0.9%). Two taxa of the order Malacalcyonacea were identified in this study: Spinimuricea sp. and Alcyonium palmatum . The genetic sequence for Spinimuricea sp. was initially assigned, after bioinformatic procedures, to S. atlantica , an Atlantic Sea gorgonian whose presence has been recently recorded in the Alboran Sea (Ocaña et al. 2017 ) and the Gulf of Lion (Digenis et al. 2024 ). Its presence in the study area is however questionable, especially considering the scarce information available on the species distribution and its close resemblance with the congeneric S. klavereni (Carpine and Grasshoff, 1975 ). S. klavereni is a rare species, endemic to the Mediterranean Sea, where it occurs with a scattered distribution. Its presence is recorded in the Aegean and Adriatic Seas (Macic et al. 2021), which makes its occurrence in the study area more likely. For S. klavereni there is no available COI reference sequence available, hence it is probable that our S. atlantica record could correspond to its Mediterranean congeneric. S. klavereni occurs in relatively high abundance in coastal areas of the Sea of Marmara (Topçu and Öztürk, 2016 ), where it forms colonies at shallow depths (20–45m) in light of its opportunistic behaviour and high resistance to anthropogenic pressures that negatively affect the presence of other gorgonian species (Topçu and Öztürk, 2015 ). A. palmatum , or red dead man’s fingers, is a Mediterranean soft coral that may occur on hard substrates or biogenic detritus (Enrichetti et al. 2019 ), but is typical of continental shelf soft bottoms, where it acts as an engineering species contributing to the creation of structural complexity (Ambroso et al. 2013 ). Scleralcyonacea are represented in our samples by Keratoisididae, Funiculina quadrangularis and Pennatula rubra . Isidella elongata , also known as bamboo coral, is the only Keratoisididae of the Mediterranean Sea (Vafidis et al. 1994 ; Oceana, 2009 ), so it is safe to assume that the sequence identified by the eDNA samples belong to this taxon, also considering of the lack of COI reference sequences for this species. This species forms dense aggregations on bathyal mud up to depths of 1600 m and is typically anchored to small stones embedded into the sediment (Lauria et al. 2017 ). Given its slow growth rate and particularly fragile structure, it is highly susceptible to bottom trawling activities (Carbonara et al. 2020 ), which drastically damage the habitat of this species (Georges et al. 2024 ). Of the two pennatulacean species found in our samples, the tall sea pen, F. quadrangularis , lives embedded in soft mud sediments up to depths of 2000m (Greathead et al. 2007 ), where it can form dense aggregations representing essential habitats for several crustacean species (Fabri et al. 2014 ). The red sea pen, P. rubra , instead, is widespread on Mediterranean muddy and sandy-mud bottoms, and possesses a withdrawal behaviour which, through the closure of the polyps and the expulsion of part of the water contained within the colony, allows it to partially or completely burrow in the sediment (Chimienti et al. 2018b ). Pennatulaceans can form monospecific or mixed environment structuring aggregations called “sea pen fields” (Mastrototaro et al. 2013 ; Porporato et al. 2014 ). The order Comatulida includes two crinoid taxa: Leptometra sp. and Antedon mediterranea . As in the case of pennatulaceans, these benthic organisms aggregate to form the so-called “crinoid fields”, playing a role in the structuring of soft bottoms and hosting a rich associated community (Fanelli et al. 2007 ). This is particularly true in the case of L. phalangium , which typically occurs in shelf break and canyon head areas representing important areas for the juvenile stages of several species (Colloca et al. 2004 ). L. phalangium has no COI reference sequences available; the congeneric L. celtica , identified in our eDNA samples, occurs in the Atlantic Ocean, and has been reported in the Western Mediterranean Sea (Koukoras et al. 2007; Kaïdi et al. 2024 ). However, these observations are poorly documented in the literature which largely focuses on L. phalangium when referring to the Leptometra genus in the Mediterranean (Millot et al. 2024 ). A. mediterranea has a high variability of habitat preference, also occurring on abandoned fishing gear or organic substrates as an epibiont (Toma et al. 2024 ). JSDM estimates a different relationship with the environmental variables under consideration for the three VMEI orders, which results in a different prediction of their spatial distribution. The order Malacalcyonacea is positively influenced by the trawling effort and the seabed temperature and negatively by its slope and the coastal distance. This is probably associated with the fact that the two species representing this order are known to occur at shallow depths and close to the coastline (Topçu and Öztürk, 2016 ; Enrichetti et al. 2019 ). The relatively shallow, warm and flat seabed of the continental shelf extending between the Gulf of Patras and the island of Kefalonia, as well as in the Straits of Corfu, could therefore represent a suitable area for these species. Furthermore, the genus Spinimuricea is known to be particularly resilient and tolerant to anthropogenic stresses (Topçu and Öztürk, 2016 ), which is well suited to its positive association with trawling effort and its presence in areas particularly affected by fishing and maritime transport activities. More intriguing is the very strong positive association between trawling intensity and Scleralcyonacea distribution. F. quadrangularis can be indeed found in areas that are exploited by fisheries since its flexibility likely allows it to temporarily lie flat under the passage of fishing gears (Lauria et al, 2017 ; Sbrana et al. 2024 ). Meanwhile, the withdrawal behaviour of P. rubra could give this species a certain degree of resistance to bottom-contacting gears (Chimienti et al. 2018b ). On the other hand, Keratoisididae are heavily threatened by seafloor exploitation by fisheries and the highest concentrations of this taxon in the Mediterranean are found in refuge habitats inaccessible to fishing activities, such as underwater canyons (Mastrototaro et al. 2017 ). A possible explanation of the positive association of scleralcyonacean species with trawling could stand in the fact that both bottom trawling fishery and the taxa belonging to this order are closely associated with soft bottoms, further strengthening the importance of trawling effort in VME conservation and protection plans. The negative effect of seafloor slope in predicting Comatulida distribution may also be related to the fact that these taxa were found in sampling sites associated with soft bottoms of the continental shelf, mainly in the Straits of Corfu. The importance of random effects in explaining the variance for the spatial distribution of this order, as well as the generally low predicted occurrence, probably depend on variables not considered in this study or on the scarcity of sites where these taxa were found. Indeed, random effects represent a measure of residual variance, which may be due to spatial or temporal autocorrelation. Overall, the areas with the highest concentration of VMEI taxa, as identified by the JSDM, are mostly located in soft bottoms located next to the Gulf of Patras and the Straits of Corfu. These enclosed and shallow waters are affected by significant anthropogenic pressure, both from fishing activities and ship traffic centred in the busy ports of Patras and Corfu. Altough the mere occurrence of VMEI taxa in these sites does not necessarily imply the presence of a structured and functional VME, we were nevertheless able to observe a correlation between the VME index predicted by our model and the species richness of the area (Fig. 6 ). This work aimed at enhancing the value of integrating eDNA metabarcoding-based methodologies within scientific fishing campaigns such as MEDITS. Regardless of the significant advantages, eDNA metabarcoding comes with some limitations which should be taken into account when interpreting the data. Although eDNA metabarcoding is nowadays widely used as a reliable tool for species richness assessments, raw eDNA data cannot provide solid quantitative estimates of the abundance and biomass of the species under investigation (Shelton et al. 2023 ; Blackman et al. 2024 ). The number of reads yielded by sequencers is influenced by various environmental and experimental factors, which prevent a direct correlation between DNA sequences and the associated biomass (Barnes et al. 2016; Kelly et al. 2019 ). Primer choice is one of the factors that need to be taken into account. The cytochrome c oxidase subunit I (COI) primers used in this study are highly degenerated in order to amplify the DNA of all metazoans, ensuring a broad taxonomic spectrum of investigation (Porter and Hajibabei, 2020). This is particularly useful for a comprehensive approach to the identification of VMEI taxa, which cover a broad spectrum of animal phyla, from Porifera to Echinodermata. Some taxonomic groups are however known to poorly amplify with COI primers, leading to false negatives in the metabarcoding results (Elbrecht and Leese, 2017 ; Collins et al. 2019 ). Focusing on specific taxonomic groups, or even on single species, would allow the use of more selective and specific primers that are likely to yield more accurate data on species presence and even abundance (Govender et al. 2022 ; Pont et al. 2023 ). Notwithstanding all this, it must be considered that for several VMEI taxa, there are no reference sequences online, or they are only available for certain genetic regions. The rapid progress being made in eDNA metabarcoding is likely to make up for these shortcomings in the foreseeable future. The rarity of VMEI taxa, coupled with the use of non-specific primers for their identification, led to few occurrence records for these species in our eDNA data. To strengthen the JSDM model, it was therefore deemed appropriate to aggregate the VMEI presence data at the order level. This inevitably introduces a degree of generalisation in establishing the role of environmental variables in shaping the spatial distribution of VMEI. As in the case of Scleralcyonacea, VMEI orders may contain organisms with different ecology, such as gorgonians and sea pens. These limitations could be overcome by increasing sampling sites, sampling effort and generally adopting a more targeted approach towards the identification of VMEI species, even at the cost of taxonomically narrowing the field of investigation. Conclusion In conclusion, eDNA metabarcoding represents a promising, reliable and non-invasive approach for detecting and mapping VMEs and unveiling their role in shaping biodiversity across the sea floor. Our findings underscore the potential of eDNA metabarcoding to complement traditional survey methods. The implementation of an opportunistic sampling based on the association of the metaprobe with pre-existing activities could represent a low-cost strategy able to provide indications of areas worthy of further investigation with more targeted approaches, such as ROV surveys or taxon-specific eDNA metabarcoding. As anthropogenic pressures continue to threaten marine biodiversity, integrating eDNA-based monitoring into conservation strategies will be essential for the establishment of solid management plans to ensure the protection of VMEs. Future research should focus on refining methodological frameworks, standardizing protocols, and expanding reference databases to maximize the accuracy and applicability of this innovative technique. Declarations Acknowledgements These research activities were carried out in the framework of the LETTER OF AGREEMENT between the Food and Agriculture Organization of the United Nations (‘FAO’) under the General Fisheries Commission for the Mediterranean (GFCM) project MTF/INT/943/MUL - Baby 32 & Baby 33 - Select Activities of the strategies towards the sustainability of fisheries and sustainable aquaculture development implemented in the Mediterranean and the Black Sea and the Department of Biology of the University of Rome “Tor Vergata”, with the support of DGMARE. The views expressed in this publication are those of the authors and do not necessarily reflect the views or policies of the Food and Agriculture Organization of the United Nations. Funding Food and Agriculture Organization of the United Nations—General Fisheries Commission for the Mediterranean (GFCM), MTF/INT/943/MUL Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation and data collection were performed by Archontia Chatzispyrou, Konstantinos Charalampous, Dimitrios Damalas, andCaterina Stamouli. Laboratory procedures were performed by Simone Galli, Giulia Maiello and Stefano Mariani. Data analysis was conducted by Simone Galli, Nadia Marinchel, Tommaso Russo and Alice Sbrana. Paolo Carpentieri, Dimitrios Damalas, Stefano Mariani, Tommaso Russo and Alice Sbrana supervised the work. The first draft of the manuscript was written by Simone Galli and Alice Sbrana. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. 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Mol Ecol 25(4):929–942. https://doi.org/10.1111/mec.13428 Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis . Springer-Verlag, New York. R package version 3.4.4. https://ggplot2.tidyverse.org Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx TableS3.csv TableS4.csv Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6907089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475052636,"identity":"7eb2eb2a-3bda-4bea-9297-7df3914f423c","order_by":0,"name":"Simone 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05:45:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51137,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated effects of environmental variables, measured as β-parameters, on the occurrence of VMEI orders. Positive and negative relations are represented in red and blue, respectively. The value of the β-coefficient indicates the strength of the relati relationship. Values \u0026gt; 0.95 are indicated by a bold frame.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6907089/v1/2ea913e48f2f56e57778eee5.png"},{"id":89625540,"identity":"26d18440-1992-460d-87cb-684a736f55c1","added_by":"auto","created_at":"2025-08-22 05:45:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53991,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of variance explained by environmental variables for the three VMEI orders.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6907089/v1/9a401717478289d1f6335e3d.png"},{"id":89624849,"identity":"39601551-dd1b-48a7-b3bc-2f81f689fe31","added_by":"auto","created_at":"2025-08-22 05:37:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":287903,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probability of occurrence (top row) and associated standard deviation (bottom row) for the three VMEI orders. High values are shown in brighter shades of orange, while low values are indicated in deep purple.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6907089/v1/0701fb105b10448af5ae1141.png"},{"id":89625542,"identity":"914be821-3f23-4431-b569-9677666acd56","added_by":"auto","created_at":"2025-08-22 05:45:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89991,"visible":true,"origin":"","legend":"\u003cp\u003eRichness-weighted VME index, integrating predicted species richness with probability of occurrence for VMEI taxa. High values are shown in brighter shades of orange, while low values are indicated in deep purple.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6907089/v1/af0ffeebae7578a412898c8e.png"},{"id":89624862,"identity":"de94b420-d6dc-4613-af59-ab75224ba102","added_by":"auto","created_at":"2025-08-22 05:37:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":77804,"visible":true,"origin":"","legend":"\u003cp\u003eBiodiversity patterns across sampling sites. a) Scatterplot of taxon richness versus continuous VME Index, with points colored by VME class and a fitted regression line. b) Boxplot showing taxon richness by VME Index class, with results of Kruskal-Wallis and post-hoc pairwise tests. c) Venn diagram illustrating the number of unique and shared taxa among Low, Medium, and High VME Index classes\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6907089/v1/f3f1ef550998b225fe14e761.png"},{"id":103879723,"identity":"b81b6e43-22ad-4f44-b0f2-9d65e71f0aa7","added_by":"auto","created_at":"2026-03-04 04:55:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1392841,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6907089/v1/62153d1d-f80d-40c3-ad90-9782a0702935.pdf"},{"id":89624842,"identity":"7e41c920-b13a-4ab9-b0b7-58c2baf78977","added_by":"auto","created_at":"2025-08-22 05:37:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14509,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6907089/v1/4e3f5b7b00a86f8c7616836f.docx"},{"id":89624840,"identity":"19e8721b-56af-4b50-9fa6-2165cd8ed199","added_by":"auto","created_at":"2025-08-22 05:37:25","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11440,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.csv","url":"https://assets-eu.researchsquare.com/files/rs-6907089/v1/2b61773d693925bd94951dab.csv"},{"id":89624844,"identity":"26a90e1e-b343-4226-8bae-0de0f3e8667a","added_by":"auto","created_at":"2025-08-22 05:37:25","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7381,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.csv","url":"https://assets-eu.researchsquare.com/files/rs-6907089/v1/b688defb9cd9f6fc62885a74.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Harnessing Environmental DNA Metabarcoding for the Detection and Mapping of Vulnerable Marine Ecosystems in the Mediterranean Sea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBottom trawling is one of the most widespread fishing activities globally (Eigaard et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Amoroso et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), yet it poses significant threats to the benthic fauna and seafloor ecosystems. This fishing method involves dragging heavy nets along the seafloor, leading to habitat destruction, sediment resuspension, and drastic alterations of benthic environments and community structures (Johnson et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hiddink et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; De Borger et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Mediterranean Sea is one of the most intensely fished regions in the world, where bottom trawling has been a dominant fishing method for centuries (Osio, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The semi-enclosed nature of the Mediterranean Sea, combined with high fishing pressure and limited enforcement of conservation measures in certain areas, has led to significant impacts on benthic habitats (Pusceddu et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Bottom trawling in the Mediterranean Sea primarily targets highly valuable species such as European hake (\u003cem\u003eMerluccius merluccius\u003c/em\u003e), red mullet (\u003cem\u003eMullus barbatus\u003c/em\u003e) and deep-water crustaceans (such as \u003cem\u003eParapenaeus longirostris\u003c/em\u003e, \u003cem\u003eAristeus antennatus\u003c/em\u003e and \u003cem\u003eNephrops norvegicus\u003c/em\u003e), but its effects extend far beyond the targeted species (Guijarro et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tsagarakis et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Repeated trawling operations lead to the depletion of structurally complex habitats, reducing biodiversity and altering trophic interactions (Peristeraki et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, the resuspension of sediments can affect water quality and the availability of nutrients, further influencing ecosystem dynamics (Palanques et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Given the importance of the Mediterranean Sea as a biodiversity hotspot (Coll et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), mitigating the effects of bottom trawling is critical for the long-term sustainability of marine resources.\u003c/p\u003e\u003cp\u003eAccording to the FAO definition (FAO, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Vulnerable Marine Ecosystems (VMEs) are groups of species, communities or habitats that may be vulnerable to impacts from fishing activities. Their sensitivity makes them important indicators, acting as sentinels of human impact at sea. Their vulnerability is assessed and measured through an evaluation of their uniqueness or rarity, functional significance, fragility, structural complexity and life-history traits that make the species recovery difficult (Morato et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Carpentieri et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such features are shared by several deep-sea ecosystems, which are particularly vulnerable to the impact of bottom-contacting fishing gears (Ramirez-Llodra et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Clark et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). VMEs are primarily identified by specific topographical or geological features, referred to as VME indicator features, as well as by the presence of several benthic organisms, called VME indicator (VMEI) species or taxa (FAO, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In the Mediterranean Sea, several macro- and mega benthic invertebrates display aggregative behaviour, thus forming structurally complex environments that host rich fish and invertebrate communities (D\u0026rsquo;Onghia et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chimienti et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These habitat-forming taxa, identified as VMEI by the General Fisheries Commission for the Mediterranean Sea (GFCM, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), are associated with highly valuable fish and crustacean species (Rueda et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sion et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and are therefore particularly targeted by bottom trawl fisheries (Cau et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; D\u0026rsquo;Onghia et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecognizing the ecological threats posed by bottom trawling, the European Union (EU) has implemented a range of management measures aimed at protecting marine biodiversity and ensuring sustainable fisheries. New management objectives have been introduced with the EU Fisheries package (European Commission, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which consists of 30% protection per Exclusive Economic Zone (EEZ) surface area, commonly referred to as the \u0026ldquo;30 x 30\u0026rdquo; strategy. Phasing out bottom trawling in all Natura 2000 sites by 2030 is a key action in this package. More recently, the European Union passed a nature restoration law (EU, 2024), aiming to restore 20% of degraded ecosystems by 2030 and 100% by 2050. Management measures include spatial restrictions on trawling, gear modifications, and the establishment of Marine Protected Areas (MPAs). No- take MPAs play a crucial role in these conservation efforts by providing shelters where marine ecosystems can recover from fishing pressures (Sciberras et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Edgar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, enforcement challenges and the need for improved monitoring tools highlight the importance of advancing detection methods for VMEs to enhance compliance and conservation effectiveness.\u003c/p\u003e\u003cp\u003eTraditional approaches for identifying VMEs primarily rely on direct observations through remote-operated vehicles (ROVs), bottom trawl surveys, and diver-based assessments (Lauria et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chimienti et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Stephenson et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While these methods provide valuable information, they are often logistically complex, costly, and time-consuming. Moreover, some of them (such as trawl surveys) only capture certain organisms, therefore providing partial information on the whole marine community. Environmental DNA (eDNA) metabarcoding has emerged as a powerful complement - or alternative - for biodiversity monitoring, offering a reliable, non-invasive and cost-effective approach to detect marine species by analyzing the corresponding genetic material released in the water column and/or sediment (Valentini et al, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mariani et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Russo et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). eDNA metabarcoding thus represents a promising tool to obtain even more comprehensive results than conventional methods (Aglieri et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Miya, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maiello et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we harness the potential of eDNA metabarcoding to detect and map VMEs in the Eastern Ionian Sea. We integrate eDNA analysis with data collected from a scientific bottom trawl survey as part of the Mediterranean International Trawl Survey (MEDITS) campaign, with the specific aim to assess the presence and distribution of key VMEI taxa. The study employed a self-produced 3D-printed sampler, the \u003cem\u003emetaprobe\u003c/em\u003e (Maiello et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), to collect eDNA samples directly from the trawl net, allowing for a more targeted approach to detecting benthic organisms associated with VMEs. By incorporating the data in a Joint Species Distribution Model (JSDM) approach, we predict the distribution of VMEIs across the study area, and evaluate the role of environmental factors in shaping it, thus identifying priority areas for protection and conservation strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSampling\u003c/h2\u003e\u003cp\u003eSampling was carried out in June 2023 in the Eastern Ionian Sea (FAO Geographical Sub Area 20) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), onboard a fishing vessel involved in the Mediterranean International Trawl Survey (MEDITS) campaign, that conducts annual bottom trawling surveys in different areas of the Mediterranean and Black Seas (Bertrand et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Samples were gathered at 19 sites, covering a wide range of depths (41\u0026ndash;751 m) and a coastal distance range (1.5\u0026ndash;15.8 km) including both shelf and slope areas. For each haul, information about the abundance (i.e., biomass and number of individuals) of the organisms caught by the fishing net was recorded.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eeDNA was collected using a self-produced 3D-printed sampler, the \u003cem\u003emetaprobe\u003c/em\u003e (Maiello et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This hollow perforated sphere passively collects genetic material from the surrounding environment through three sterile gauze rolls placed inside it. The \u003cem\u003emetaprobe\u003c/em\u003e was placed in the trawl net codend at the start of the fishing operations and retrieved once the net had been hauled and opened on board. Using sterile gloves and forceps, the gauze rolls were recovered and each one was placed in a separate 50 mL Falcon tube containing 99% ethanol for DNA preservation. Samples were frozen directly on board and then stored at -20\u0026deg;C in the laboratory before DNA extraction. Each of the three gauze was used as a field replica. At site ST46, one gauze roll was lost, resulting in only two replicates being available for that station. In total, 56 eDNA samples were collected across all sampling sites.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eeDNA processing\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from the gauze rolls using a custom protocol. Initially, a small section (~\u0026thinsp;2 \u0026times; 2 cm) from the outer layer of the roll was cut into small pieces using sterile scissors. The pieces of gauze were placed in a 1.5 mL tube to allow the ethanol to evaporate. Each sample was then incubated at 55\u0026deg;C for 12 h in 1 mL EDTA (0.5 M pH 8) and 0.25 mg/mL Proteinase K. After lysis, samples were centrifuged, supernatant was collected and DNA was extracted with Roche Assembly Tubes silica columns (Dabney and Meyer, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A total of four extraction negatives were processed along the samples, using portions of clean gauze. DNA extracts were amplified targeting a\u0026thinsp;~\u0026thinsp;313bp fragment of the COI mitochondrial gene. Highly degenerated universal primers were used, in order to target all the metazoans (forward mICOIintF: 5\u0026prime;- GGWACWRGWTGRACWNTNTAYCCYCC-3\u0026prime; (Leray et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); reverse jgHCO2198: 5\u0026prime;- TANACYTCNGGRTGNCCRAARAAYCA-3\u0026prime; (Geller et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)). To address potential contamination linked with laboratory procedures, a positive control (\u003cem\u003ePenaeus vannamei\u003c/em\u003e, a Pacific shrimp species not found in the Mediterranean Sea) and a negative PCR control were amplified along with the DNA samples. Each forward and reverse primer was tagged with a unique 8bp index to allow sample identification during bioinformatic analysis and reduce the likelihood of cross-contamination or tag switching during sequencing. Tags differed by at least three base pairs and were preceded by 2\u0026ndash;4 degenerate bases to enhance sequence diversity. Each sample was PCR-amplified in triplicate using 20 \u0026micro;L reactions, including 10 \u0026micro;L MyFi\u0026trade; Mix (Meridian Bioscience), 0.16 \u0026micro;L Bovine Serum Albumin (20 mg/mL, Thermo Fisher Scientific), 5.84 \u0026micro;L UltraPure\u0026trade; Distilled Water (Invitrogen), 1 \u0026micro;L of each forward and reverse primer (10 \u0026micro;M, Eurofins), and 2 \u0026micro;L of template DNA. PCRs were performed under the following thermocycling conditions: polymerase activation at 95\u0026deg;C for 10 min, followed by 35 cycles of denaturation and amplification (94\u0026deg;C for 1 min, 45\u0026deg;C for 1 min, 72\u0026deg;C for 1 min), and a final elongation at 72\u0026deg;C for 5 min. PCR triplicates were pooled and visualised on a 2% agarose gel to assess the successful amplification of target sequences. PCR products were purified using Mag-Bind\u0026reg; TotalPure NGS magnetic beads (Omega Bio-tek Inc), adding a 0.8x ratio of magnetic beads to 30 \u0026micro;L of PCR product (Bronner and Quail, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Purified PCR products were then quantified with a Qubit Flex\u0026trade; 4.0 fluorometer with the Qubit\u0026trade; dsDNA HS Assay Kit (Invitrogen). Based on the DNA concentration, PCR products were normalized and pooled in equimolar concentrations for library preparation. End-repair, adapter ligation, and library PCR amplification were performed using the NEXTFLEX\u0026reg; Rapid DNA-Seq Kit 2.0 for Illumina\u0026reg; platforms (PerkinElmer) following the manufacturer\u0026rsquo;s protocol. Fragment lengths were assessed with an Agilent 4200 TapeStation and High Sensitivity D1000 ScreenTape (Agilent Technologies), and secondary products such as adaptor dimers were removed through an additional 0.8:1 ratio magnetic bead clean-up. The library was quantified using a quantitative PCR (qPCR) on a Rotor-Gene Q (Qiagen) using the NEBNext\u0026reg; Library Quant Kit for Illumina\u0026reg; (New England Biolabs) and then diluted to 4 nM according to qPCR concentration. The final library and PhiX Control were re-quantified using qPCR before sequencing. The library was sequenced at 12.5 pM with 10% PhiX control using V3 chemistry (2 x 250 bp paired-end) on an Illumina MiSeq platform. The eDNA samples analyzed in this study were sequenced alongside samples from a separate project.\u003c/p\u003e\n\u003ch3\u003eBioinformatics\u003c/h3\u003e\n\u003cp\u003eBioinformatic analyses were conducted using the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eobitools\u003c/span\u003e software (Boyer et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Read quality assessment was performed with \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003efastqc\u003c/span\u003e, and low-quality ends were trimmed using \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eobicut\u003c/span\u003e before downstream processing. Paired-end reads with a quality score exceeding 40 were merged using \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eilluminapairedend\u003c/span\u003e, while sample demultiplexing was carried out with \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003engsfilter\u003c/span\u003e, using the unique barcodes and allowing for a single nucleotide mismatch. Sequence filtering was performed using \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eobigrep\u003c/span\u003e to remove singletons and reads falling outside the expected length range (300\u0026ndash;325 bp), followed by dereplication with \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eobiuniq.\u003c/span\u003e We removed chimeras with \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003euchime\u003c/span\u003e (Edgar et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and clustered sequences into Molecular Operational Taxonomic Units (MOTUs) using \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eswarm\u003c/span\u003e (Mah\u0026eacute; et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) setting the threshold to d\u0026thinsp;=\u0026thinsp;13 (Siegenthaler et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Taxonomic assignment was performed with the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003esintax\u003c/span\u003e algorithm implemented in \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eusearch\u003c/span\u003e (Edgar, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) against the MIDORI2 reference database for COI sequences (Leray et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Contaminant removal was performed using blanks and negative controls with the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003emicrodecon\u003c/span\u003e (version 1.0.2; McKnight et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), an R package specifically designed for identifying and removing contaminants. Final refinement of the dataset consisted of removing MOTUs with a\u0026thinsp;\u0026lt;\u0026thinsp;98% identity match and reads with an occurrence below 5 per sample, in order to reduce the effects of low-abundance false positives due to tag switching and/or cross-contamination. Terrestrial taxa likely resulting from human-associated contamination (e.g., fungi, insecta, \u003cem\u003eHomo sapiens\u003c/em\u003e, \u003cem\u003eSus scrofa\u003c/em\u003e, \u003cem\u003eCanis lupus\u003c/em\u003e) were also removed from the final dataset.\u003c/p\u003e\n\u003ch3\u003eeDNA data validation\u003c/h3\u003e\n\u003cp\u003eIn the first step of the data analysis was assessed the consistency between the data obtained from eDNA and those obtained from MEDITS catches. This step is crucial for laying the foundations of the analysis and the development of subsequent models. Statistical analyses were performed to compare eDNA-based taxon detections with catch data collected by the MEDITS campaign, according to the procedure described in Maiello et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The eDNA dataset was filtered, retaining only the taxa assigned to the species level and removing algal, fungal and planktonic taxa. Cohen's Kappa coefficient was computed to assess inter-rater reliability between eDNA and fisheries catch data, with species presence-absence matrices used to evaluate agreement at the sampling site level. A Random Forest classification model was then employed to assess the predictive capacity of eDNA and catch data in distinguishing depth strata, using 100 iterations with 70% of the data allocated for training and the remaining 30% for the testing. Non-metric multidimensional scaling (nMDS) was applied using Jaccard dissimilarity to visualize differences in species assemblages detected by each of the two methods. First, only species common to both datasets (eDNA and catches) were considered, nMDS analysis and plotting were then performed taking into account common and non-common taxa to both datasets. Species occurrences were transformed into presence-absence data, and convex hulls were used to group sampling stations by depth strata. All statistical analyses and visualizations were conducted in R (v. 4.4.2) (R Core Team, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) using the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003evegan\u003c/span\u003e (Oksanen et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003erandomforest\u003c/span\u003e (Liaw and Wiener, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eggplot2\u003c/span\u003e (Wickham, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eyardstick\u003c/span\u003e (Kuhn and Vaughan, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) packages.\u003c/p\u003e\n\u003ch3\u003eSpatial analysis\u003c/h3\u003e\n\u003cp\u003eSubsequently, we gathered and collated spatial information relating the topographical and environmental features of the study area. The eDNA dataset was pre-processed to exclude taxa classified as non-marine, planktonic, or pelagic based on the ecological classification available on FishBase (Froese and Pauly, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and SeaLifeBase (Palomares and Pauly, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Taxonomic assignments were then aggregated at the order level to increase the robustness of spatial analysis. Finally, we excluded taxon orders occurring in only one sampling site to ensure sufficient data for modelling. The spatial analysis was conducted in R (v. 4.4.2) using the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003esf\u003c/span\u003e (Pebesma, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eterra\u003c/span\u003e (Hijmans, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003egstat\u003c/span\u003e (Pebesma, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) packages. A 1x1 km cell grid covering the whole sampling area was established as a spatial object and projected using WGS84. A coastline shapefile was used to calculate the shortest Euclidean distance from each sample point to the coast. Environmental variables, including depth, slope, and oceanographic data (e.g., temperature, salinity, oxygen concentration), were incorporated into the analysis. Depth data were retrieved using the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003emarmap\u003c/span\u003e (Pante and Simon-Bouhet, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) R package, which accesses the ETOPO 2022 database hosted on the NOAA website (NOAA National Centers for Environmental Information, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and were subsequently spatially linked to each sampling point. Environmental variables were downloaded from the Copernicus Marine Data Store (Clementi et al. 2023; Feudale et al. 2023) and included values for: salinity, temperature, dissolved oxygen, chlorophyll, Particulate Organic Matter (POM), nitrates and phosphates. The final values of these variables were obtained by averaging the data across individual depth layers provided by the Copernicus database. The sampling station ST35 was removed from the analyses since it was located in an area (Gulf of Corinth) not covered by the Copernicus dataset. Slope was calculated from bathymetric data using the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eterrain\u003c/span\u003e function (\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eterra\u003c/span\u003e package). Global Fishing Watch (GFW) data (2013\u0026ndash;2022) were used to assess spatial patterns of bottom trawling fishing effort (Global Fishing Watch, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The effort was measured in fishing hours, as gathered by AIS data. Inverse Distance Weighting (IDW) was applied to interpolate continuous spatial surfaces for environmental parameters and fishing effort. The IDW method was implemented using the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003egstat\u003c/span\u003e package, with a power parameter of 2 and the maximum number of neighbouring points available. All spatial datasets were resampled to a common resolution and projected into the same coordinate reference system. Spatial joins were performed to associate sample locations with environmental variables, allowing for subsequent statistical analysis.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eJoint Species Distribution Modelling\u003c/h2\u003e\u003cp\u003eTo investigate taxon-environment relationships and interspecific associations, a Joint Species Distribution Model (JSDM) was implemented using the Hierarchical Modeling of Species Communities (HMSC) framework, based on the work by Stephenson et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This approach allows for the simultaneous modelling of multiple species while accounting for environmental covariates, spatial autocorrelation, and potential biotic interactions (Ovaskainen et al. 2017), and is available in the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ehmsc\u003c/span\u003e (Tikhonov et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) R package.\u003c/p\u003e\u003cp\u003eThe JSDM consists of a probit regression model for species presence-absence data and incorporates environmental predictors, spatial random effects and a residual covariance matrix to infer species co-occurrence patterns. Specifically, environmental covariates included in the model were: \u003cem\u003ecoastal distance\u003c/em\u003e, \u003cem\u003esalinity\u003c/em\u003e, \u003cem\u003eslope\u003c/em\u003e, \u003cem\u003etemperature\u003c/em\u003e and \u003cem\u003etrawling effort\u003c/em\u003e (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The other environmental variables included in the previous steps of the analysis showed high collinearity with the selected covariates and were therefore excluded to prevent model overfitting and loss of generality. We assessed multicollinearity among environmental predictors using the Variance Inflation Factor (VIF) implemented in the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eusdm\u003c/span\u003e (Naimi, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) R package. Predictors with a VIF\u0026thinsp;\u003cb\u003e\u0026ge;\u003c/b\u003e\u0026thinsp;5 were iteratively removed, always discarding the variable with the highest VIF, until all selected variables had VIF values below this threshold. JSDM also introduces transect-level spatial random effects to account for the non-independence of observations due to the sampling design. A latent factor model was used to capture species co-occurrence patterns not explained by environmental variables, potentially indicative of biotic interactions or shared ecological preferences. Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling was employed for model fitting, ensuring robust posterior estimates. Weakly informative priors were specified to regularize the model and prevent overfitting. Multiple MCMC chains were executed, and convergence was assessed through Gelman-Rubin statistics and visual inspection of trace plots. Estimated effects of environmental variables, represented as β-coefficients, were derived from the model output. The value of each β-coefficient reflects the strength and direction of the relationship between the corresponding variable and the occurrence of VMEI taxa. To assess model performance, several validation techniques were applied: (i) the proportion of variance in taxon occurrence explained by the model was estimated through coefficient of determination (R\u0026sup2;); (ii) predictive performance was evaluated through cross-validation, whereby subsets of data were withheld to compare predicted versus observed taxon occurrences. Finally, taxon-specific prediction errors were analyzed to determine the reliability of individual taxa predictions and identify areas for potential model refinement.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBiodiversity analysis\u003c/h3\u003e\n\u003cp\u003eAn overall Richness-Weighted VME Index was calculated for the study area, taking into account the probability of occurrence estimated by the JSDM for the individual taxa orders. To investigate the relationship between the overall VME index and the biodiversity, as assessed by the eDNA detections, a series of analyses were carried out. First, sampling sites were divided into three classes (Low (0\u0026ndash;0.4), Medium (0.4\u0026ndash;0.8), and High (0.8\u0026ndash;2)) based on the corresponding VME Index value, so that each class contained the same number of sites. Using the presence/absence matrix of the eDNA dataset, taxon richness was calculated for each site. A boxplot was constructed to compare richness across VME index classes. Alpha diversity metrics were compared across VME Index classes using non-parametric Kruskal\u0026ndash;Wallis tests and pairwise Wilcoxon rank-sum tests. A general linear model was used to assess the relationships between VME index values and taxon richness. A Venn diagram was generated using the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eeulerr\u003c/span\u003e (Larsson, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) R package to visualize taxon distribution and overlap among VME Index classes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eeDNA data composition and validation\u003c/h2\u003e\n \u003cp\u003eThe eDNA dataset, after bioinformatic processing, comprised a total of 4,851,074 reads (Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). After decontamination, data filtering and non-target taxa removal, the final dataset included 274 taxa, which were identified at different taxonomic resolutions: 244 at the species, 17 at genus, 3 at family, 7 at order and 3 at class level. The most prevalent phyla, in terms of taxon richness, were Chordata (44.5%), Arthropoda (14.6%), Cnidaria (12.4%), Mollusca (10.2%), and Annelida (3.6%), while in terms of read abundance, the distribution ranks were: Chordata (94%), Mollusca (4.1%), Cnidaria (1.2%), Arthropoda (0.5%), and Echinodermata (0.1%). The composition of the eDNA and catches datasets is reported in Supplementary material (Tables S3, S4)\u003c/p\u003e\n \u003cp\u003eThe comparison between eDNA and catch data revealed a substantial level of agreement. Confusion matrices describing the output of the Random Forest classification model revealed that both MEDITS catch data and eDNA data exhibited mean values of Cohen\u0026apos;s Kappa of 0.79. (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Similar clustering patterns were observed from nMDS plots, when the analysis took into account only species common to both datasets (eDNA and catches) and common and non-common taxa to both datasets (Fig \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eA and S2B, respectively). In both nMDS plots, eDNA samples (red) and MEDITS samples (black) were closely associated, indicating a strong similarity in the species composition detected by both methods (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eVMEI taxa\u003c/h2\u003e\n \u003cp\u003eAccording to the FAO definition (FAO, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) and the list provided by GFCM (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), several VMEI taxa were present in the eDNA dataset. Specifically, 35 taxa belonging to 5 classes were identified: 1 Hexacorallia, 5 Octocorallia, 2 Crinoidea, 6 Demospongiae and 21 Hydrozoa. However, Hydrozoa were excluded from downstream analyses, as eDNA detection does not allow differentiation between the polypoid and medusoid life stages of the animal. The taxonomy referred to in this paper differs slightly from that indicated by the GFCM in its list of VMEI taxa (GFCM, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). In particular, the anthozoan subclass Octocorallia has recently undergone a major revision based on phylogenetic evidence, and is now regarded as a class; furthermore, the previous subdivision into Alcyonacea, Pennatulacea and Helioporacea has been replaced by a new subdivision into two orders only: Malacalcyonacea and Scleralcyonacea (McFadden et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAfter filtration \u0026mdash;which excluded taxonomic orders occurring at only one sampling site\u0026mdash;, 7 taxa were retained, belonging to three orders: Malacalcyonacea, Scleralcyonacea and Comatulida (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Three non-Mediterranean taxa, \u003cem\u003eAcanella arbuscula, Leptometra celtica\u003c/em\u003e and \u003cem\u003eSpinimuricea atlantica\u003c/em\u003e, were present in the original dataset. In the Mediterranean, closely related species are known to occur: \u003cem\u003eIsidella elongata, Leptometra phalangium\u003c/em\u003e and \u003cem\u003eSpinimuricea klavereni\u003c/em\u003e respectively. However, as COI reference sequences for these species are not available in GenBank, we preferred to assign these sequences to the taxonomic level compatible with the detection of Mediterranean species, labelling them as \u0026ldquo;Keratoisididae\u0026rdquo;, \u0026ldquo;\u003cem\u003eLeptometra\u003c/em\u003e sp\u0026rdquo;. and \u0026ldquo;\u003cem\u003eSpinimuricea\u003c/em\u003e sp.\u0026rdquo;. Of the VMEI taxa considered in this work, only \u003cem\u003eA. palmatum, A. mediterranea\u003c/em\u003e and Keratoisididae (\u003cem\u003eI. elongata\u003c/em\u003e) were recorded in the catches (Table S4).\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\u003eVMEI taxa retained for JSDM analysis after filtering. Taxa are categorized by phylum and order. Site occurrences and total reads are indicated.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTaxon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSites occurring\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal reads\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\u003eEchinodermata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComatulida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAntedon mediterranea\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEchinodermata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComatulida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLeptometra\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCnidaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalacalcyonacea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAlcyonium palmatum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCnidaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalacalcyonacea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSpinimuricea\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCnidaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScleralcyonacea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKeratoisididae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCnidaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScleralcyonacea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFuniculina quadrangularis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCnidaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScleralcyonacea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePennatula rubra\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3047\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\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eJoint Species Distribution Modelling\u003c/h2\u003e\n \u003cp\u003eThe Joint Species Distribution Modelling (JSDM) analysis provided key insights into how environmental drivers impact the distribution of the three VMEI orders considered: Malacalcyonacea, Scleralcyonacea and Comatulida. The model\u0026rsquo;s \u0026beta;-parameters (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) indicate both the strength and direction of these relationships, demonstrating that the three taxa are influenced differently by each environmental factor.\u003c/p\u003e\n \u003cp\u003eMalacalcyonacea displayed a pronounced positive association with temperature (\u0026beta;\u0026thinsp;=\u0026thinsp;0.97), indicating that warmer waters favour its occurrence. Additionally, this order showed a positive dependency on trawling effort (\u0026beta;\u0026thinsp;=\u0026thinsp;0.82). Conversely, negative relationships were observed with slope (\u0026beta; = -0.99), coastal distance (\u0026beta; = -0.85), and salinity (\u0026beta; = -0.74). Scleralcyonacea exhibited a strong positive dependency on trawling effort (\u0026beta;\u0026thinsp;=\u0026thinsp;1), suggesting a distribution strongly associated with areas of high fishing activity. Positive relationships were also observed with coastal distance (\u0026beta;\u0026thinsp;=\u0026thinsp;0.82) and slope (\u0026beta;\u0026thinsp;=\u0026thinsp;0.8), while negative dependencies were detected with temperature (\u0026beta; = -0.72) and salinity (\u0026beta; = -0.54). Comatulida exhibited negative relationships with all the considered environmental variables. The strongest negative associations were found with slope (\u0026beta; = -0.97) and salinity (\u0026beta; = -0.85), followed by coastal distance (\u0026beta; = -0.79), trawling effort (\u0026beta; = -0.68), and temperature (\u0026beta; = -0.57).\u003c/p\u003e\n \u003cp\u003eThe proportion of explained variance (Fig. 3) provided further insights into the relative importance of each environmental factor. Malacalcyonacea were mainly influenced by slope and temperature, which together accounted for approximately 60% of the variance. Trawling effort emerged as the primary driver for Scleralcyonacea, explaining up to 45% of the observed patterns. Comatulida showed a more balanced distribution of explained variance, with slope and random effects playing a significant role. Random effects represent a measure of residual variance, which may be due to spatial or temporal autocorrelation. This presumably highlights the effect of variables not considered in the study.\u003c/p\u003e\n \u003cp\u003eThe species distribution models generated for Malacalcyonacea, Scleralcyonacea and Comatulida indicate distinct spatial patterns of predicted occurrence across the study area (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea displays the probability of presence (PA) for each taxon, revealing high prediction areas along the western Greek coastline. Malacalcyonacea exhibit localized high-probability zones, located along the coastline from northwestern Peloponnese to the Straits of Corfu, favouring enclosed waters (inner Ionian archipelago). Notably, Scleralcyonacea show the most widespread distribution, with probability values reaching up to 1 particularly in offshore regions along the outer borders of the study area, as well as in the Gulf of Patras and the waters between the Peloponnese and the Ionian Islands. Comatulida follow a trend similar to Malacalcyonacea, but with generally lower predicted occurrence levels. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb presents the standard deviation (SD) of the predictions, illustrating the model\u0026rsquo;s uncertainty. Higher SD values, concentrated near the edges of high-prediction areas, suggest potential variability in environmental suitability or lower confidence due to limited data coverage. In contrast, core high-probability areas exhibit lower uncertainty, indicating robust model predictions in these regions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eBiodiversity\u003c/h2\u003e\n \u003cp\u003eFurther integrating the JSDM predictions, the Richness-Weighted VME Index (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) highlights key conservation priority areas by combining VMEI taxa richness with the associated probability of occurrence. The highest VME index values are concentrated in the Gulf of Patras and along the northwestern Peloponnesian coast, coinciding with regions where VMEI taxa exhibit high predicted occurrence. Additionally, elevated values are observed in the Straits of Corfu and along offshore waters surrounding the Ionian Islands, indicating potential biodiversity hotspots for VMEs. Areas with moderate index values may serve as transition zones, while lower index values along the southwestern study area suggest either limited species presence or unsuitable habitat conditions.\u003c/p\u003e\n \u003cp\u003eTaxon richness varied significantly across the three VME Index classes (Kruskal-Wallis test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0034; Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). Pairwise comparisons indicated that richness was significantly higher in High VME areas compared to both Medium (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) and Low VME classes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0016), while the difference between Low and Medium was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23). Taxon richness showed a weak but positive correlation with the VME Index (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). This trend was observed across all three VME Index classes, with the highest diversity values more frequently associated with High VME sites. The Venn diagram (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec) illustrates the distribution of unique and shared taxa among VME Index classes. A core group of 74 taxa was shared across all classes, while High VME areas hosted the highest number of unique taxa (67). These findings indicate that areas with higher VME Index values support greater and more distinct taxonomic richness.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, eDNA data collected during a scientific fishing campaign were used to detect and map VMEI taxa presence in the Eastern Ionian Sea. The use of self-produced samplers placed inside bottom-trawl nets allowed gathering more comprehensive information on the biological communities occurring at the sampling sites. Overall, a total of seven VMEI species, belonging to two phyla and three orders, were considered in the study. The JSDM revealed how the spatial distribution of each one of the three orders is primarily influenced by specific environmental factors. Taking into account these findings, it was ultimately possible to identify the areas most likely to host these species and, therefore, to be considered as VME areas. These areas were indeed shown to be associated with a higher overall biodiversity compared to those in which the predicted occurrence of VMEI taxa was low.\u003c/p\u003e\u003cp\u003eThe effectiveness and the reliability of the \u003cem\u003emetaprobes\u003c/em\u003e have already been stated in various studies, whether they have been integrated with trawling (Maiello et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Maiello et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or purse seine (Marinchel et al. unpublished data) fishing activities. In the context of the MEDITS campaign, the inclusion of a \u003cem\u003emetaprobe\u003c/em\u003e in the fishing net adds a layer of information, flanking the traditional assessment based on visual inspection of the net content. In particular, Maiello et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated how the placement of the \u003cem\u003emetaprobe\u003c/em\u003e within the trawl net allows the collection of eDNA associated not only with the contents of the net itself but also with benthic and pelagic organisms that are present in the surrounding environment and are not found in the catch. These results make the trawl-associated \u003cem\u003emetaprobe\u003c/em\u003e an effective tool for investigating a set of organisms that possess a low degree of catchability, as is the case for several VMEI taxa (Auster et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In this regard, Geange et al. (2019) have shown that catchability of VMEI taxa in trawls is taxa-dependent and generally low (below 1%). This low catchability can be particularly important for fragile species like sponges or soft corals, which are prone to fragmentation and may not be retained in trawl nets (Freese et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Chimienti et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e) reported that in a trawl survey in the Ionian Sea, despite the heavy damage caused to specimens of \u003cem\u003ePennatula rubra\u003c/em\u003e, few individuals ended up in the trawling net (0.9%).\u003c/p\u003e\u003cp\u003eTwo taxa of the order Malacalcyonacea were identified in this study: \u003cem\u003eSpinimuricea\u003c/em\u003e sp. and \u003cem\u003eAlcyonium palmatum\u003c/em\u003e. The genetic sequence for \u003cem\u003eSpinimuricea\u003c/em\u003e sp. was initially assigned, after bioinformatic procedures, to \u003cem\u003eS. atlantica\u003c/em\u003e, an Atlantic Sea gorgonian whose presence has been recently recorded in the Alboran Sea (Oca\u0026ntilde;a et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the Gulf of Lion (Digenis et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Its presence in the study area is however questionable, especially considering the scarce information available on the species distribution and its close resemblance with the congeneric \u003cem\u003eS. klavereni\u003c/em\u003e (Carpine and Grasshoff, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). \u003cem\u003eS. klavereni\u003c/em\u003e is a rare species, endemic to the Mediterranean Sea, where it occurs with a scattered distribution. Its presence is recorded in the Aegean and Adriatic Seas (Macic et al. 2021), which makes its occurrence in the study area more likely. For \u003cem\u003eS. klavereni\u003c/em\u003e there is no available COI reference sequence available, hence it is probable that our \u003cem\u003eS. atlantica\u003c/em\u003e record could correspond to its Mediterranean congeneric. \u003cem\u003eS. klavereni\u003c/em\u003e occurs in relatively high abundance in coastal areas of the Sea of Marmara (Top\u0026ccedil;u and \u0026Ouml;zt\u0026uuml;rk, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), where it forms colonies at shallow depths (20\u0026ndash;45m) in light of its opportunistic behaviour and high resistance to anthropogenic pressures that negatively affect the presence of other gorgonian species (Top\u0026ccedil;u and \u0026Ouml;zt\u0026uuml;rk, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u003cem\u003eA. palmatum\u003c/em\u003e, or red dead man\u0026rsquo;s fingers, is a Mediterranean soft coral that may occur on hard substrates or biogenic detritus (Enrichetti et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but is typical of continental shelf soft bottoms, where it acts as an engineering species contributing to the creation of structural complexity (Ambroso et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eScleralcyonacea are represented in our samples by Keratoisididae, \u003cem\u003eFuniculina quadrangularis\u003c/em\u003e and \u003cem\u003ePennatula rubra\u003c/em\u003e. \u003cem\u003eIsidella elongata\u003c/em\u003e, also known as bamboo coral, is the only Keratoisididae of the Mediterranean Sea (Vafidis et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Oceana, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), so it is safe to assume that the sequence identified by the eDNA samples belong to this taxon, also considering of the lack of COI reference sequences for this species. This species forms dense aggregations on bathyal mud up to depths of 1600 m and is typically anchored to small stones embedded into the sediment (Lauria et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Given its slow growth rate and particularly fragile structure, it is highly susceptible to bottom trawling activities (Carbonara et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which drastically damage the habitat of this species (Georges et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Of the two pennatulacean species found in our samples, the tall sea pen, \u003cem\u003eF. quadrangularis\u003c/em\u003e, lives embedded in soft mud sediments up to depths of 2000m (Greathead et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), where it can form dense aggregations representing essential habitats for several crustacean species (Fabri et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The red sea pen, \u003cem\u003eP. rubra\u003c/em\u003e, instead, is widespread on Mediterranean muddy and sandy-mud bottoms, and possesses a withdrawal behaviour which, through the closure of the polyps and the expulsion of part of the water contained within the colony, allows it to partially or completely burrow in the sediment (Chimienti et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). Pennatulaceans can form monospecific or mixed environment structuring aggregations called \u0026ldquo;sea pen fields\u0026rdquo; (Mastrototaro et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Porporato et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe order Comatulida includes two crinoid taxa: \u003cem\u003eLeptometra\u003c/em\u003e sp. and \u003cem\u003eAntedon mediterranea\u003c/em\u003e. As in the case of pennatulaceans, these benthic organisms aggregate to form the so-called \u0026ldquo;crinoid fields\u0026rdquo;, playing a role in the structuring of soft bottoms and hosting a rich associated community (Fanelli et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This is particularly true in the case of \u003cem\u003eL. phalangium\u003c/em\u003e, which typically occurs in shelf break and canyon head areas representing important areas for the juvenile stages of several species (Colloca et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). \u003cem\u003eL. phalangium\u003c/em\u003e has no COI reference sequences available; the congeneric \u003cem\u003eL. celtica\u003c/em\u003e, identified in our eDNA samples, occurs in the Atlantic Ocean, and has been reported in the Western Mediterranean Sea (Koukoras et al. 2007; Ka\u0026iuml;di et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, these observations are poorly documented in the literature which largely focuses on \u003cem\u003eL. phalangium\u003c/em\u003e when referring to the \u003cem\u003eLeptometra\u003c/em\u003e genus in the Mediterranean (Millot et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eA. mediterranea\u003c/em\u003e has a high variability of habitat preference, also occurring on abandoned fishing gear or organic substrates as an epibiont (Toma et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eJSDM estimates a different relationship with the environmental variables under consideration for the three VMEI orders, which results in a different prediction of their spatial distribution. The order Malacalcyonacea is positively influenced by the trawling effort and the seabed temperature and negatively by its slope and the coastal distance. This is probably associated with the fact that the two species representing this order are known to occur at shallow depths and close to the coastline (Top\u0026ccedil;u and \u0026Ouml;zt\u0026uuml;rk, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Enrichetti et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The relatively shallow, warm and flat seabed of the continental shelf extending between the Gulf of Patras and the island of Kefalonia, as well as in the Straits of Corfu, could therefore represent a suitable area for these species. Furthermore, the genus \u003cem\u003eSpinimuricea\u003c/em\u003e is known to be particularly resilient and tolerant to anthropogenic stresses (Top\u0026ccedil;u and \u0026Ouml;zt\u0026uuml;rk, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which is well suited to its positive association with trawling effort and its presence in areas particularly affected by fishing and maritime transport activities. More intriguing is the very strong positive association between trawling intensity and Scleralcyonacea distribution. \u003cem\u003eF. quadrangularis\u003c/em\u003e can be indeed found in areas that are exploited by fisheries since its flexibility likely allows it to temporarily lie flat under the passage of fishing gears (Lauria et al, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sbrana et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meanwhile, the withdrawal behaviour of \u003cem\u003eP. rubra\u003c/em\u003e could give this species a certain degree of resistance to bottom-contacting gears (Chimienti et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). On the other hand, Keratoisididae are heavily threatened by seafloor exploitation by fisheries and the highest concentrations of this taxon in the Mediterranean are found in refuge habitats inaccessible to fishing activities, such as underwater canyons (Mastrototaro et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A possible explanation of the positive association of scleralcyonacean species with trawling could stand in the fact that both bottom trawling fishery and the taxa belonging to this order are closely associated with soft bottoms, further strengthening the importance of trawling effort in VME conservation and protection plans. The negative effect of seafloor slope in predicting Comatulida distribution may also be related to the fact that these taxa were found in sampling sites associated with soft bottoms of the continental shelf, mainly in the Straits of Corfu. The importance of random effects in explaining the variance for the spatial distribution of this order, as well as the generally low predicted occurrence, probably depend on variables not considered in this study or on the scarcity of sites where these taxa were found. Indeed, random effects represent a measure of residual variance, which may be due to spatial or temporal autocorrelation. Overall, the areas with the highest concentration of VMEI taxa, as identified by the JSDM, are mostly located in soft bottoms located next to the Gulf of Patras and the Straits of Corfu. These enclosed and shallow waters are affected by significant anthropogenic pressure, both from fishing activities and ship traffic centred in the busy ports of Patras and Corfu. Altough the mere occurrence of VMEI taxa in these sites does not necessarily imply the presence of a structured and functional VME, we were nevertheless able to observe a correlation between the VME index predicted by our model and the species richness of the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis work aimed at enhancing the value of integrating eDNA metabarcoding-based methodologies within scientific fishing campaigns such as MEDITS. Regardless of the significant advantages, eDNA metabarcoding comes with some limitations which should be taken into account when interpreting the data. Although eDNA metabarcoding is nowadays widely used as a reliable tool for species richness assessments, raw eDNA data cannot provide solid quantitative estimates of the abundance and biomass of the species under investigation (Shelton et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Blackman et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The number of reads yielded by sequencers is influenced by various environmental and experimental factors, which prevent a direct correlation between DNA sequences and the associated biomass (Barnes et al. 2016; Kelly et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Primer choice is one of the factors that need to be taken into account. The cytochrome c oxidase subunit I (COI) primers used in this study are highly degenerated in order to amplify the DNA of all metazoans, ensuring a broad taxonomic spectrum of investigation (Porter and Hajibabei, 2020). This is particularly useful for a comprehensive approach to the identification of VMEI taxa, which cover a broad spectrum of animal phyla, from Porifera to Echinodermata. Some taxonomic groups are however known to poorly amplify with COI primers, leading to false negatives in the metabarcoding results (Elbrecht and Leese, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Collins et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Focusing on specific taxonomic groups, or even on single species, would allow the use of more selective and specific primers that are likely to yield more accurate data on species presence and even abundance (Govender et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pont et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notwithstanding all this, it must be considered that for several VMEI taxa, there are no reference sequences online, or they are only available for certain genetic regions. The rapid progress being made in eDNA metabarcoding is likely to make up for these shortcomings in the foreseeable future.\u003c/p\u003e\u003cp\u003eThe rarity of VMEI taxa, coupled with the use of non-specific primers for their identification, led to few occurrence records for these species in our eDNA data. To strengthen the JSDM model, it was therefore deemed appropriate to aggregate the VMEI presence data at the order level. This inevitably introduces a degree of generalisation in establishing the role of environmental variables in shaping the spatial distribution of VMEI. As in the case of Scleralcyonacea, VMEI orders may contain organisms with different ecology, such as gorgonians and sea pens. These limitations could be overcome by increasing sampling sites, sampling effort and generally adopting a more targeted approach towards the identification of VMEI species, even at the cost of taxonomically narrowing the field of investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, eDNA metabarcoding represents a promising, reliable and non-invasive approach for detecting and mapping VMEs and unveiling their role in shaping biodiversity across the sea floor. Our findings underscore the potential of eDNA metabarcoding to complement traditional survey methods. The implementation of an opportunistic sampling based on the association of the \u003cem\u003emetaprobe\u003c/em\u003e with pre-existing activities could represent a low-cost strategy able to provide indications of areas worthy of further investigation with more targeted approaches, such as ROV surveys or taxon-specific eDNA metabarcoding. As anthropogenic pressures continue to threaten marine biodiversity, integrating eDNA-based monitoring into conservation strategies will be essential for the establishment of solid management plans to ensure the protection of VMEs. Future research should focus on refining methodological frameworks, standardizing protocols, and expanding reference databases to maximize the accuracy and applicability of this innovative technique.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese research activities were carried out in the framework of the LETTER OF AGREEMENT between the Food and Agriculture Organization of the United Nations (‘FAO’) under the General Fisheries Commission for the Mediterranean (GFCM) project MTF/INT/943/MUL - Baby 32 \u0026amp; Baby 33 - Select Activities of the strategies towards the sustainability of fisheries and sustainable aquaculture development implemented in the Mediterranean and the Black Sea and the Department of Biology of the University of Rome “Tor Vergata”, with the support of DGMARE. The views expressed in this publication are those of the authors and do not necessarily reflect the views or policies of the Food and Agriculture Organization of the United Nations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFood and Agriculture Organization of the United Nations—General Fisheries Commission for the Mediterranean (GFCM),\u0026nbsp;MTF/INT/943/MUL\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection were performed by Archontia Chatzispyrou, Konstantinos Charalampous, Dimitrios Damalas, andCaterina Stamouli. Laboratory procedures were performed by Simone Galli, Giulia Maiello and Stefano Mariani. Data analysis was conducted by Simone Galli, Nadia Marinchel, Tommaso Russo and Alice Sbrana. Paolo Carpentieri, Dimitrios Damalas, Stefano Mariani, Tommaso Russo and Alice Sbrana supervised the work. The first draft of the manuscript was written by Simone Galli and Alice Sbrana. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAglieri G, Baillie C, Mariani S, Cattano C, Cal\u0026ograve; A, Turco G, Spatafora D, Di Franco A, Di Lorenzo M, Guidetti P, Milazzo M (2021) Environmental DNA effectively captures functional diversity of coastal fish communities. Mol Ecol 30(13):3127\u0026ndash;3139. https://doi.org/10.1111/mec.15661\u003c/li\u003e\n\u003cli\u003eAmbroso S, Gori A, Dominguez-Carri\u0026oacute; C, Gili JM, Berganzo E, Teixid\u0026oacute; N, Greenacre M, Rossi S (2013) Spatial distribution patterns of the soft corals Alcyonium acaule and Alcyonium palmatum in coastal bottoms (Cap de Creus, northwestern Mediterranean Sea). 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R package version 3.4.4. https://ggplot2.tidyverse.org\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"eDNA, biodiversity, Vulnerable Marine Ecosystems, demersal fisheries","lastPublishedDoi":"10.21203/rs.3.rs-6907089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6907089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe overexploitation of marine resources by commercial fisheries poses significant threats to marine biodiversity and ecosystem stability. Vulnerable Marine Ecosystems (VMEs) require urgent protection to mitigate the adverse impacts of fishing activities, especially deep-sea bottom trawling. Given our incomplete knowledge of the marine environment, rapid and precise localization of VMEs is a priority to protect them effectively. Traditional methods for identifying VMEs are often limited by logistical challenges, high costs, and potential sampling biases. In this study, we assess the effectiveness of environmental DNA (eDNA) metabarcoding as an innovative tool for detecting and mapping VMEs in the Mediterranean Sea. eDNA samples were gathered at 19 sampling sites during a scientific fishing campaign in the Eastern Ionian Sea. Through the amplification of the COI mitochondrial gene, we identified a total of 285 unique taxa. A total of seven VME Indicator (VMEI) taxa were detected. A Joint Species Distribution Model (JSDM), using Hierarchical Modelling of Species Communities (HMSC), was used to investigate possible relationships between VMEI taxa and environmental covariates. Predicted distribution patterns of VMEI taxa were used to calculate a richness-weighted VME index. Taxon richness was highest at sites with high VME Index values. These findings demonstrate the potential of eDNA metabarcoding to effectively map the distribution of VMEI taxa, identify key environmental drivers influencing their occurrence and assess overall ecosystem vulnerability. We argue that an integration of eDNA-based approaches with traditional fisheries surveys could significantly enhance biodiversity assessments and improve conservation strategies in data-limited marine environments.\u003c/p\u003e","manuscriptTitle":"Harnessing Environmental DNA Metabarcoding for the Detection and Mapping of Vulnerable Marine Ecosystems in the Mediterranean Sea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 05:37:20","doi":"10.21203/rs.3.rs-6907089/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"413294f8-ed72-445f-92f1-cc39afaa1ee4","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T04:54:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 05:37:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6907089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6907089","identity":"rs-6907089","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}